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+wandb
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+check_dirs := .
+
+quality:
+	black --check $(check_dirs)
+	ruff $(check_dirs)
+
+style:
+	black $(check_dirs)
+	ruff $(check_dirs) --fix
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+## Training Distil-Whisper
+
+This sub-folder contains all the scripts required to train a Distil-Whisper model in your choice of language. They are 
+slightly modified from the original scripts used to distill Whisper for English ASR (as-per the [Distil-Whisper paper](https://arxiv.org/abs/2311.00430)).
+The main difference is that these scripts are written in [PyTorch](https://pytorch.org), whereas the original scripts 
+are in [JAX](https://jax.readthedocs.io/en/latest/#)/[Flax](https://flax.readthedocs.io/en/latest/). These scripts are 
+also made to be easier to run end-to-end, whereas the original scripts require more steps and are somewhat hard-coded 
+for English ASR. Both sets of scripts achieve equivalent downstream results when the hyper-parameters are set equal.
+
+If you are interested in reproducing the original Distil-Whisper checkpoints, we refer you to the sub-folder [Flax Training](./flax/README.md).
+Otherwise, if you wish to distill Whisper on your own language/dataset, we recommend you use these scripts for ease of use
+and the configurability they provide.
+
+Reproducing the Distil-Whisper project requires four stages to be completed in successive order:
+
+1. [Pseudo-labelling](#1-pseudo-labelling)
+2. [Initialisation](#2-initialisation)
+3. [Training](#3-training)
+4. [Evaluation](#4-evaluation)
+
+This README is partitioned according to the four stages. Each section provides a minimal example for running the
+scripts used in the project. We will use a running example of distilling the Whisper model for Hindi speech recognition
+on the Common Voice dataset. Note that this dataset only contains ~20 hours of audio data. Thus, it can be run extremely
+quickly, but does not provide sufficient data to achieve optimal performance. We recommend training on upwards of 1000 
+hours of data should you want to match the performance of Whisper on high-resource languages.
+
+## Requirements
+
+The Distil-Whisper training code is written in [PyTorch](https://pytorch.org) and [Accelerate](https://huggingface.co/docs/accelerate/index). 
+It heavily leverages the Whisper implementation in [🤗 Transformers](https://github.com/huggingface/transformers) for both 
+training and inference.
+
+The instructions for installing the package are as follows:
+1. Install PyTorch from the [official instructions](https://pytorch.org/get-started/locally/), ensuring you install the correct version for your hardware and CUDA version.
+2. Fork the `distil-whisper` repository by clicking on the [fork](https://github.com/huggingface/distil-whisper/fork) button on the reopsitory's page
+3. Clone the `distil-whisper` repository and add the base repository as a remote. This will allow you to "pull" any upstream changes that are made to the base repository:
+
+```bash
+git clone https://github.com/<your GitHub handle>/distil-whisper.git
+cd distil-whisper
+git remote add upstream https://github.com/huggingface/distil-whisper.git
+```
+4. pip install the required packages from the [setup.py](./setup.py) file:
+```bash
+cd training
+pip install -e .
+cd ../..
+```
+
+5. Configure Accelerate by running the following command. Note that you should set the number of GPUs you wish to use for distillation, and also the data type (dtype) to your preferred dtype for training/inference (e.g. `bfloat16` on A100 GPUs, `float16` on V100 GPUs, etc.):
+
+```bash
+accelerate config
+```
+
+6. The last thing we need to do is link our Hugging Face account so that we can pull/push model repositories on the Hub. This will allow us to save our final distilled weights on the Hub so that we can share them with the community. Run the command:
+
+```bash
+git config --global credential.helper store
+huggingface-cli login
+```
+And then enter an authentication token from https://huggingface.co/settings/tokens. Create a new token if you do not have one already. You should make sure that this token has "write" privileges.
+
+To confirm that you have a working environment, first accept the terms of use of the Common Voice 16.1 dataset on the Hub: https://huggingface.co/datasets/mozilla-foundation/common_voice_16_1 
+
+You can run the following code cell to stream one sample of data from the Common Voice dataset, and check that you can 
+perform inference using the "tiny" Whisper model:
+
+```python
+from transformers import WhisperProcessor, WhisperForConditionalGeneration
+from datasets import load_dataset, Audio
+
+model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny", low_cpu_mem_usage=True)
+processor = WhisperProcessor.from_pretrained("openai/whisper-tiny")
+
+model.to("cuda")
+
+common_voice = load_dataset("mozilla-foundation/common_voice_16_1", "en", split="validation", streaming=True)
+common_voice = common_voice.cast_column("audio", Audio(sampling_rate=processor.feature_extractor.sampling_rate))
+
+inputs = processor(next(iter(common_voice))["audio"]["array"], sampling_rate=16000, return_tensors="pt")
+input_features = inputs.input_features
+
+generated_ids = model.generate(input_features.to("cuda"), max_new_tokens=128)
+pred_text = processor.decode(generated_ids[0], skip_special_tokens=True)
+
+print("Pred text:", pred_text)
+print("Environment set up successful?", generated_ids.shape[-1] == 20)
+```
+
+## 1. Pseudo-Labelling
+
+The python script [`run_pseudo_labelling.py`](run_pseudo_labelling.py) is a flexible inference script that can be used
+to generate pseudo-labels under a range of settings, including using both greedy and beam-search. It is also compatible
+with [🤗 Datasets](https://github.com/huggingface/datasets) *streaming mode*, allowing users to load massive audio
+datasets with **no disk space requirements**. For more information on streaming mode, the reader is referred to the 
+blog post: [A Complete Guide to Audio Datasets](https://huggingface.co/blog/audio-datasets#streaming-mode-the-silver-bullet).
+
+> As of the latest Distil-Whisper release, [`distil-large-v3`](https://huggingface.co/distil-whisper/distil-large-v3), this
+pseudo-labelling script also performs the added operation of concatenating (or packing) the audio inputs to 30-seconds. 
+Not only does this lead to a WER improvement when using sequential long-form decoding algorithm, but concatenating audios 
+to 30-seconds also improves the throughput during training, since the amount of zero-padding on the audio inputs is minimised.
+
+The following script demonstrates how to pseudo-label the Hindi split of the Common Voice 16.1 dataset with greedy sampling:
+
+```bash
+#!/usr/bin/env bash
+
+accelerate launch run_pseudo_labelling.py \
+  --model_name_or_path "openai/whisper-large-v3" \
+  --dataset_name "mozilla-foundation/common_voice_16_1" \
+  --dataset_config_name "hi" \
+  --dataset_split_name "train+validation+test" \
+  --text_column_name "sentence" \
+  --id_column_name "path" \
+  --output_dir "./common_voice_16_1_hi_pseudo_labelled" \
+  --wandb_project "distil-whisper-labelling" \
+  --per_device_eval_batch_size 64 \
+  --dtype "bfloat16" \
+  --attn_implementation "sdpa" \
+  --logging_steps 500 \
+  --max_label_length 256 \
+  --concatenate_audio \
+  --preprocessing_batch_size 500 \
+  --preprocessing_num_workers 8 \
+  --dataloader_num_workers 8 \
+  --report_to "wandb" \
+  --language "hi" \
+  --task "transcribe" \
+  --return_timestamps \
+  --streaming False \
+  --generation_num_beams 1 \
+  --push_to_hub
+```
+
+On an 80 GB A100 GPU, the following script takes approximately 5 minutes to concatenate and pre-process the 20 hours of 
+audio data, and a further 10 minutes to transcribe the pseudo-labels. The pseudo-labelled dataset corresponding to this
+script is available on the Hugging Face Hub under [sanchit-gandhi/common_voice_16_1_hi_pseudo_labelled](https://huggingface.co/datasets/sanchit-gandhi/common_voice_16_1_hi_pseudo_labelled).
+The WER of the pre-trained Whisper large-v3 model is 17.2% on the test split. We will compare the performance of our distilled model against this number.
+
+There are two noteworthy arguments that configure the dataset concatenation (or packing) process:
+1. `concatenate_audio`: whether or not to concatenate (or pack) the audios to 30-second chunks. The latest Distil-Whisper model, [`distil-large-v3`](https://huggingface.co/distil-whisper/distil-large-v3#differences-with-distil-large-v2), highlights the WER improvements obtained using the sequential long-form decoding algorithm when concatenated audios are used. Concatenating audios to 30-seconds also improves the throughput during training, since the amount of zero-padding on the audio inputs is minimised. Hence, it is highly recommended to set `--concatenate_audio=True`.
+2. `preprocessing_batch_size`: the batch size to use when concatenating (or packing) the audios. Using a larger batch size results in a greater portion of audio samples being packed to 30-seconds, at the expense of higher memory consumption. If you exceed your system's RAM when performing the concatenation operation, reduce the `preprocessing_batch_size` by a factor of 2 to 250 or even 125.
+3. `preprocessing_num_workers`: the number of multiprocessing workers to use when concatenating the audios. Using more workers will result in faster pre-processing, at the expense of higher memory consumption. Ensure you do not exceed the maximum number of CPUs on your device.
+
+In addition, the following arguments configure the inference of the Whisper model:
+1. `language`: explicitly setting the language token during inference substantially improves the generation performance of the Whisper model, since the model is forced always to predict in the given language. We recommend you set the language to the language you wish to distil the Whisper model on. The only exception is when distilling an English-only model (i.e. where the model id is appended with an `.en`, e.g. `small.en`), the language argument should be set to None, since there is no language token used during training/inference.
+2. `return_timestamps`: whether or not to predict timestamps in the pseudo-labels. Timestamp prediction is required should you want your distilled model to be able to predict timestamps at inference time (e.g. for the original OpenAI long-form transcription algorithm). However, the pseudo-labels are marginally less accurate than not using timestamps. We recommend pseudo-labelling **with** timestamps to ensure the distilled model is as general as possible.
+3. `attn_implementation`: which attention implementation to use for inference. Set to `sdpa` for [PyTorch SDPA](https://huggingface.co/docs/transformers/v4.35.2/en/perf_infer_gpu_one#bettertransformer), or `flash_attn_2` if your hardware supports Flash Attention 2 and you have the [package installed](https://github.com/Dao-AILab/flash-attention).
+4. `streaming`: whether or not to use Datasets' streaming mode. If enabled, the audio data will be streamed from the Hugging Face Hub with no disk space requirements. However, the user is then responsible for adding the pseudo-labels to the dataset script in a follow-up step (see [Using Streaming Mode](#TODO)). If set to `False`, the audio data will be downloaded and pre-processed offline. At the end of pseudo-labelling, the pseudo-labels will be automatically appended to the original dataset, meaning the dataset is ready to be used for the subsequent training step without any additional steps.
+5. `generation_num_beams`: how many beams to use while decoding. In practice, we found the distilled model to perform comparably when the data was pseudo-labelled with `generation_num_beams=1` (greedy) or `generation_num_beams>1` (beam). This is likely because the WER filter compensates for the lower quality pseudo-labels obtained using greedy search. However, using `generation_num_beams=1` gives substantially faster inference time for the pseudo-labelling step, and so we recommend this configuration.
+
+Should you have your own audio dataset, you can first [convert it](https://huggingface.co/docs/datasets/audio_dataset) to 
+Hugging Face Datasets format and push it to the Hugging Face Hub. You can then pseudo-label it using the script above, 
+replacing the `--dataset_name` with the name of your dataset on the Hub.
+
+Otherwise, you may wish to use an open-source dataset already available on the Hugging Face Hub. We provide a summary of 
+the three most popular multilingual datasets in the table below. For more details, refer to the blog post: [A Complete Guide to Audio Datasets](https://huggingface.co/blog/audio-datasets#multilingual-speech-recognition).
+
+| Dataset                                                                                       | Languages | Domain                                | Speaking Style | License   | Text Column         | ID Column    |
+|-----------------------------------------------------------------------------------------------|-----------|---------------------------------------|----------------|-----------|---------------------|--------------|
+| [Multilingual LibriSpeech](https://huggingface.co/datasets/facebook/multilingual_librispeech) | 6         | Audiobooks                            | Narrated       | CC-BY-4.0 | `"text"`            | `"id"`       |
+| [Common Voice 16](https://huggingface.co/datasets/mozilla-foundation/common_voice_16_1)       | 120       | Wikipedia text & crowd-sourced speech | Narrated       | CC0-1.0   | `"sentence"`        | `"path"`     |
+| [VoxPopuli](https://huggingface.co/datasets/facebook/voxpopuli)                               | 15        | European Parliament recordings        | Spontaneous    | CC0       | `"normalized_text"` | `"audio_id"` |
+
+To achieve *robustness* to different distributions of audio data, it is recommended to train on multiple datasets where possible.
+For example, the above three datasets all have splits for the German language. Thus, if distilling a Whisper model for German,
+it would be wise to use a combination of the three datasets during training, in order to cover at least three distinct domains
+(audiobooks, crowd-sourced speech, parliament recordings). You may wish to use a combination of open-source datasets, or 
+a combination of open-source and individually owned datasets to cover multiple distributions and domains. Moreover, if you were to train on low-resource datasets (<500 hours), you could experiment with [language mixing](#3-language-mixing) to improve robustness.
+
+## 2. Initialisation
+
+The script [`create_student_model.py`](create_student_model.py) can be used to initialise a small student model
+from a large teacher model. When initialising a student model with fewer layers than the teacher model, the student is 
+initialised by copying maximally spaced layers from the teacher, as per the [DistilBart](https://arxiv.org/abs/2010.13002)
+recommendations.
+
+First, we need to create a model repository on the Hugging Face Hub. This repository will contain all the required files 
+to reproduce the training run, alongside model weights, training logs and a README.md card. You can either create a model 
+repository directly on the Hugging Face Hub using the link: https://huggingface.co/new. Or, via the CLI, as we'll show here.
+
+Let's pick a name for our distilled model: `distil-whisper-large-v3-hi`. We can run the following command to create a repository under this name:
+
+```bash
+huggingface-cli repo create distil-whisper-large-v3-hi
+```
+
+We can now see the model on the Hub, e.g. under https://huggingface.co/sanchit-gandhi/distil-whisper-large-v3-hi
+
+Let's clone the repository so that we can place our training script and model weights inside:
+
+```bash
+git lfs install
+git clone https://huggingface.co/sanchit-gandhi/distil-whisper-large-v3-hi
+```
+
+Be sure to change the repo address to `https://huggingface.co/<your-user-name>/<your-repo-name>`
+
+We can now copy the relevant training scrips to the repository:
+```bash
+cd distil-whisper-large-v3-hi
+
+cp ../distil-whisper/training/create_student_model.py .
+cp ../distil-whisper/training/run_distillation.py .
+```
+
+The following command demonstrates how to initialise a student model from the Whisper [large-v3](https://huggingface.co/openai/whisper-large-v3) 
+checkpoint, with all 32 encoder layer and 2 decoder layers. The 2 student decoder layers are copied from teacher layers 
+1 and 32 respectively, as the maximally spaced layers:
+
+```bash
+#!/usr/bin/env bash
+
+python create_student_model.py \
+  --teacher_checkpoint "openai/whisper-large-v3" \
+  --encoder_layers 32 \
+  --decoder_layers 2 \
+  --save_dir "./distil-large-v3-init"
+```
+
+The initialised model will be saved to the sub-directory `distil-large-v3-init` in our model repository. 
+
+
+**Note:** You can leverage language transfer by setting `--teacher_checkpoint` to "distil-whisper/distil-large-v3", see [language transfer](#22-language-transfer) for more details.
+
+## 3. Training
+
+The script [`run_distillation.py`](run_distillation.py) is an end-to-end script for loading multiple
+datasets, a student model, a teacher model, and performing teacher-student distillation. It uses the loss formulation
+from the [Distil-Whisper paper](https://arxiv.org/abs/2311.00430), which is a weighted sum of the cross-entropy and 
+KL-divergence loss terms.
+
+The following command takes the Common Voice dataset that was pseudo-labelled in the first stage and trains the 
+2-layer decoder model intialised in the previous step. We pass the local path to the pseudo-labelled Common Voice dataset
+(`../common_voice_16_1_hi_pseudo_labelled`), which you can change to the path where your local pseudo-labelled dataset is 
+saved.
+
+In this example, we will combine the train and validation splits to give our training set, and evaluate on the test split 
+only. This is purely to demonstrate how to combine multiple pseudo-labelled datasets for training, rather than recommended 
+advice for defining train/validation splits. We advise that you train on the train splits of your dataset, evaluate and 
+tune hyper-parameters on the validation split, and only test the final checkpoint on the test split. Note how multiple 
+training datasets and splits can be loaded by separating the dataset arguments by `+` symbols. Thus, the script generalises 
+to any number of training datasets.
+
+```bash
+#!/usr/bin/env bash
+
+accelerate launch run_distillation.py \
+  --model_name_or_path "./distil-large-v3-init" \
+  --teacher_model_name_or_path "openai/whisper-large-v3" \
+  --train_dataset_name "../common_voice_16_1_hi_pseudo_labelled+../common_voice_16_1_hi_pseudo_labelled" \
+  --train_split_name "train+validation" \
+  --text_column_name "sentence+sentence" \
+  --train_dataset_samples "7+4" \
+  --eval_dataset_name "../common_voice_16_1_hi_pseudo_labelled" \
+  --eval_split_name "test" \
+  --eval_text_column_name "sentence" \
+  --eval_steps 1000 \
+  --save_steps 1000 \
+  --warmup_steps 50 \
+  --learning_rate 0.0001 \
+  --lr_scheduler_type "constant_with_warmup" \
+  --timestamp_probability 0.2 \
+  --condition_on_prev_probability 0.2 \
+  --language "hi" \
+  --task "transcribe" \
+  --logging_steps 25 \
+  --save_total_limit 1 \
+  --max_steps 5000 \
+  --wer_threshold 20 \
+  --per_device_train_batch_size 32 \
+  --per_device_eval_batch_size 32 \
+  --dataloader_num_workers 8 \
+  --preprocessing_num_workers 8 \
+  --ddp_timeout 7200 \
+  --dtype "bfloat16" \
+  --attn_implementation "sdpa" \
+  --output_dir "./" \
+  --do_train \
+  --do_eval \
+  --gradient_checkpointing \
+  --overwrite_output_dir \
+  --predict_with_generate \
+  --freeze_encoder \
+  --freeze_embed_positions \
+  --streaming False \
+  --push_to_hub
+
+```
+
+The above training script will take approximately 3 hours to complete on an 80 GB A100 GPU and yield a final WER of 76%.
+While the generations are starting to take form, there is still a 59% WER gap to the teacher model. This is hardly
+surprising give we only have 15 hours of un-filtered data, and closer to just 1.5 hours with data filtering. 
+As mentioned above, using upwards of 1000 hours of data and training for 10k steps will likely yield
+more competitive performance. For the [Distil-Whisper paper](https://arxiv.org/abs/2311.00430), we trained on 21k hours
+of audio data for 80k steps. We found that upwards of 13k hours of audio data was required to reach convergence on English 
+ASR (see Section 9.2 of the [paper](https://arxiv.org/abs/2311.00430)), so the more data you have, the better!
+
+Scaling to multiple GPUs using [distributed data parallelism (DDP)](https://pytorch.org/tutorials/beginner/ddp_series_theory.html)
+is trivial: simply run `accelerate config` and select the multi-GPU option, specifying the IDs of the GPUs you wish to use. The 
+above script can then be run using DDP with no code changes. 
+
+Training logs will be reported to TensorBoard and WandB, provided the relevant packages are available. An example of a 
+saved checkpoint pushed to the Hugging Face Hub can be found here: [sanchit-gandhi/distil-whisper-large-v3-hi](https://huggingface.co/sanchit-gandhi/distil-whisper-large-v3-hi).
+
+There are a few noteworthy data arguments:
+1. `train_dataset_samples`: defines the number of training samples in each dataset. Used to calculate the sampling probabilities in the dataloader. A good starting point is setting the samples to the number of hours of audio data in each split. A more refined strategy is setting it to the number of training samples in each split, however this might require downloading the dataset offline to compute these statistics.
+2. `wer_threshold`: sets the WER threshold between the normalised pseudo-labels and normalised ground truth labels. Any samples with WER > `wer_threshold` are discarded from the training data. This is beneficial to avoid training the student model on pseudo-labels where Whisper hallucinated or got the predictions grossly wrong. In our English distillation experiments, we found a WER threshold of 10% provides the optimal trade-off between ensuring high-quality transcriptions, and not filtering unnecessary amounts of training data. For multilingual distillation, the threshold should be set in accordance with the WER achieved by the pre-trained model on the test set.
+3. `streaming`: whether or not to use Datasets' streaming mode. Recommended for large datasets, where the audio data can be streamed from the Hugging Face Hub with no disk space requirements.
+4. `timestamp_probability`: the per-sample probability for retaining timestamp tokens in the labels (should they contain them). Retaining some portion of timestamp tokens in the training data is required to ensure the distilled model can predict timestamps at inference time. In our experiments, we found that training on timestamps with high-probability hurts the distilled model's transcription performance. Thus, we recommend setting this to a value below 0.5. Typically, a value of 0.2 works well, giving good transcription and timestamp performance.
+5. `condition_on_prev_probability`: the per-sample probability for conditioning on previous labels. Conditioning on previous tokens is required to ensure the distilled model can be used with the "sequential" long-form transcription algorithm at inference time. We did not experiment with this parameter, but found values around 0.2 to provide adequate performance. OpenAI pre-trained Whisper on with a 50% probability for conditioning on previous tokens. Thus, you might wish to try higher values.
+
+As well as a few noteworthy model arguments that can be configured to give optimal training performance:
+1. `freeze_encoder`: whether to freeze the entire encoder of the student model during training. Beneficial when the student encoder is copied exactly from the teacher encoder. In this case, the encoder hidden-states from the teacher model are re-used for the student model. Stopping the gradient computation through the encoder and sharing the encoder hidden-states provides a significant memory saving, and can enable up to 2x batch sizes. 
+2. `freeze_embed_positions`: whether to freeze the student model's decoder positional embeddings. Using the same embed positions as the teacher model, which is designed to handle context lengths up to 448 tokens, helps the student model retain its input id representation up to the full max input length. 
+3. `dtype`: data type (dtype) in which the model computation should be performed. Note that this only controls the dtype of the computations (forward and backward pass), and not the dtype of the parameters or optimiser states.
+4. `freeze_decoder`: whether to freeze the student model's decoder. Note that the input tokens embeddings and language modelling head will remain trainable.  
+
+And finally, a few noteworthy training arguments:
+1. `max_steps`: defines the total number of optimisation steps (forward + backward pass) during training. To reach convergence, you should use a dataset of at least 1k hours and train for a minimum of 50k steps.
+2. `lr_scheduler_stype`: defines the learning rate schedule, one of `constant_with_warmup` or `linear`. When experimenting with a training set-up or training for very few steps (< 5k), using `constant_with_warmup` is typically beneficial, since the learning rate remains high over the short training run. When performing long training runs (> 5k), using a `linear` schedule generally results in superior downstream performance of the distilled model.
+
+TODO:
+- [ ] Template for model cards
+
+## 4. Evaluation
+
+There are four types of evaluation performed in Distil-Whisper:
+1. Short form: evaluation on audio samples less than 30s in duration. Examples include typical ASR test sets, such as the LibriSpeech validation set.
+2. Sequential long form: evaluation on audio samples longer than 30s in duration using the original "sequential" long-form algorithm. Examples include entire TED talks or earnings calls.
+3. Chunked long form: evaluation on audio samples longer than 30s in duration using the Transformers "chunked" long-form algorithm.
+4. Speculative decoding: evaluation on audio samples less than 30s in duration, where a faster, distilled model is used as the assistant to a slower, teacher model. 
+
+All four forms of evaluation are performed using the script [`run_eval.py`](run_eval.py). Unlike the pseudo-labelling
+and training scripts, the evaluation script assumes that only one GPU accelerator is used. We can copy the corresponding 
+evaluation script to the model repository using the following command:
+
+```bash
+cp ../distil-whisper/training/run_eval.py .
+```
+
+Models are assessed jointly using:
+1. The *word-error rate (WER)* metric: measures the number of substitution, deletion and insertion errors relative to the total number of words. A lower WER indicates a more accurate model.
+2. The *inverse real-time factor (RTFx)* metric: measures the ratio of `audio input time : model compute time`. A higher RTFx indicates a faster model. Note that this metric is WER-dependent, meaning that it makes sense to compare two models' *RTFx* only at fixed *WER* performances. Indeed, deletions could lead to early stopping of token generation, resulting in higher *WER* and lower *RTFx*. 
+3. Token generation speed: This refers to the number of tokens generated per second. As with *RTFx*, this metric is dependent on the *WER* since token generation time is not linear. By default, this metric is calculated by averaging the total number of `generated tokens : generation time` (full forward pass of the model) when evaluating on the given test set. However, using the `--precise_tok_generation` flag will compute this metric separately for a fixed number of tokens.
+
+In all cases, it is particularly important to evaluate the final model on data that is *out-of-distribution (OOD)* with 
+the training data. Evaluating on OOD data provides insight as to how well the distilled model is likely to generalise to 
+different audio distributions at inference time. In our example, the Common Voice test set is *in-distribution (ID)* 
+with our training data, since it is taken from the same distribution as the Common Voice training set. Whereas the FLEURS 
+test set is OOD, since it is not used as part of the training set. See [Datasets](#1-datasets) section for recommendations.
+
+### Short Form
+
+The script [`run_eval.py`](run_eval.py) can be used to evaluate a trained student model over multiple short-form 
+validation sets. The following example demonstrates how to evaluate the student model trained in the previous step on 
+the Common Voice `test` set (ID) and also the FLEURS `test` set (OOD). Again, it leverages streaming mode to bypass 
+the need to download the data offline:
+
+```bash
+#!/usr/bin/env bash
+
+python run_eval.py \
+  --model_name_or_path "./" \
+  --dataset_name "../common_voice_16_1_hi_pseudo_labelled+google/fleurs" \
+  --dataset_config_name "default+hi_in" \
+  --dataset_split_name "test+test" \
+  --text_column_name "sentence+transcription" \
+  --batch_size 16 \
+  --dtype "bfloat16" \
+  --generation_max_length 256 \
+  --language "hi" \
+  --attn_implementation "sdpa" \
+  --streaming
+
+```
+
+The student model achieves an average WER of TODO% with an RTFx of TODO for a batch size of 16. We can easily adapt the above
+script to evaluate the teacher model, simply by switching the `model_name_or_path` to `openai/whisper-large-v3`, which 
+achieves an average WER of TODO% with an RTFx of TODO. Therefore, for a batch size of 16, the student model is a factor of TODO
+times faster than the teacher. The WER gap can be closed by training on more data (at least 1k hours) for more training
+steps (at least 50k).
+
+### Sequential Long Form
+
+The original Whisper paper presents a long-form transcription algorithm that sequentially transcribes 30-second segments 
+of audio and shifts the sliding window according to the timestamps predicted by the model. This style of sequential 
+inference is performed directly using the [`.generate`](https://huggingface.co/docs/transformers/model_doc/whisper#transformers.WhisperForConditionalGeneration.generate) 
+method in Transformers.
+
+The script [`run_eval.py`](run_eval.py) can be used to evaluate the trained student model on an arbitrary number of 
+long-form evaluation sets using the sequential algorithm. Since we don't have a long-form validation set for Hindi to hand, 
+in this example we'll evaluate the official Distil-Whisper model [`distil-large-v3`](https://huggingface.co/distil-whisper/distil-large-v3) 
+on the TED-LIUM validation set:
+
+```bash
+#!/usr/bin/env bash
+
+accelerate launch run_eval.py \
+  --model_name_or_path "distil-whisper/distil-large-v3" \
+  --dataset_name "distil-whisper/tedlium-long-form" \
+  --dataset_config_name "default" \
+  --dataset_split_name "validation" \
+  --text_column_name "text" \
+  --batch_size 16 \
+  --dtype "bfloat16" \
+  --generation_max_length 256 \
+  --language "en" \
+  --attn_implementation "sdpa" \
+  --streaming
+
+```
+
+### Chunked Long Form
+
+Chunked long form evaluation runs on the premise that a single long audio file can be *chunked* into smaller segments and 
+inferred in parallel. The resulting transcriptions are then joined at the boundaries to give the final text prediction. 
+A small overlap (or *stride*) is used between adjacent segments to ensure a continuous transcription across chunks.
+
+This style of chunked inference is performed using the [`pipeline`](https://huggingface.co/docs/transformers/main_classes/pipelines)
+class, which provides a wrapper around the [`.generate`](https://huggingface.co/docs/transformers/model_doc/whisper#transformers.WhisperForConditionalGeneration.generate) 
+function for long-form inference.
+
+The script [`run_eval.py`](run_eval.py) can be used to evaluate the trained student model on an arbitrary number of 
+long-form evaluation sets using the pipeline class. Again, in this example we'll evaluate distil-large-v3 on the 
+TED-LIUM validation set:
+
+```bash
+#!/usr/bin/env bash
+
+python run_eval.py \
+  --model_name_or_path "openai/whisper-large-v3" \
+  --dataset_name "distil-whisper/tedlium-long-form" \
+  --dataset_config_name "default" \
+  --dataset_split_name "validation" \
+  --text_column_name "text" \
+  --use_pipeline \
+  --chunk_length_s 25.0 \
+  --language "en" \
+  --return_timestamps \
+  --dtype "bfloat16" \
+  --streaming
+
+```
+
+The argument `chunk_length_s` controls the length of the chunked audio samples. It should be set to match the typical
+length of audio the student model was trained on. If unsure about what value of `chunk_length_s` is optimal for your case,
+it is recommended to run a *sweep* over all possible values. A template script for running a [WandB sweep](https://docs.wandb.ai/guides/sweeps) 
+can be found under [`run_chunk_length_s_sweep.yaml`](flax/long_form_transcription_scripts/run_chunk_length_s_sweep.yaml).
+
+### Speculative Decoding
+
+Speculative decoding, or assisted generation, relies on the premise that a faster, assistant model can be used to speed-up
+the generation of a slower, assistant model. Speculative decoding mathematically ensures that exactly the same outputs as 
+Whisper are obtained, while being ~2 times faster. This makes it the perfect drop-in replacement for existing Whisper 
+pipelines, since exactly the same outputs are guaranteed.
+
+Distil-Whisper checkpoints can be designed to be efficient assistant models to Whisper for speculative decoding. More precisely,
+by freezing the encoder during training, the distilled model can share the same encoder weights as Whisper during inference, since
+the encoder weights are un-changed. In doing so, only the distilled 2-layer decoder has to be loaded in addition to the 
+original Whisper model, which is approximately an 8% increase to the total parameter count, with up to 2x faster inference 
+for low batch sizes. For more details on speculative decoding, the reader is advised to refer to the following blog post:
+[Speculative Decoding for 2x Faster Whisper Inference](https://huggingface.co/blog/whisper-speculative-decoding).
+
+In the example below, we use our distilled model as an assistant to the large-v3 teacher model during inference:
+
+```bash
+#!/usr/bin/env bash
+
+python run_eval.py \
+  --model_name_or_path "openai/whisper-large-v3" \
+  --assistant_model_name_or_path "./" \
+  --dataset_name "../common_voice_16_1_hi_pseudo_labelled+google/fleurs" \
+  --dataset_config_name "default+hi_in" \
+  --dataset_split_name "test+test" \
+  --text_column_name "sentence+transcription" \
+  --batch_size 16 \
+  --dtype "bfloat16" \
+  --generation_max_length 256 \
+  --language "hi" \
+  --attn_implementation "sdpa" \
+  --streaming
+
+```
+
+We see that we achieve a WER of TODO%, the same as what we obtained with the large-v3 model, but with an RTFx of TODO, 
+a factor of TODO faster than using the large-v3 model alone. The RTFx value can be improved by training the student on 
+more data and for more training steps, since this will improve the number of predicted tokens that match the teacher 
+predictions.
+
+## Recommendations and guidelines 
+
+### 1. Datasets
+
+As explained, ideally, you should aim for ~1000 hours of audio data for training a distilled model via KD. Moreover, you should evaluate your model on out-of-distribution test sets to assess generalization capacities. With at least 1500 hours of audio data for German, Dutch, French and Spanish, 600 hours for Italian, and 300 hours for Portuguese and Polish (which can be supplemented with your own datasets), a good setup to start with is:
+- **Training datasets:** [Common Voice 17](https://huggingface.co/datasets/mozilla-foundation/common_voice_17_0) and [Multilingual Librispeech](https://huggingface.co/datasets/facebook/multilingual_librispeech). Use the `train` split for training, and the `validation` and `test` splits for in-distribution testing.
+- **Test datasets:** [VoxPopuli](https://huggingface.co/datasets/facebook/voxpopuli) and [Fleurs](https://huggingface.co/datasets/google/fleurs). Use the `validation` and `test` splits for out-of-distribution testing.
+
+### 2. Student model's decoder 
+#### 2.1 Number of Decoder Layers
+
+We recommend using a 2-layers decoder (see language transfer below).  However, you can adjust the number of decoder layers when initializing the student model to balance between inference speed and accuracy. Experimentation has revealed that the Pareto optimal points are with 2, 3, and 4-layers decoders. For indicative results, after 10,000 training steps and inference on an 80GB Nvidia H100 with a batch size of 16 and 20 tokens generation, compared to [Whiper *large-v3*](https://huggingface.co/openai/whisper-large-v3) baseline:
+
+<center>
+
+|    |  rel. token gen. speed |  ΔWER(%) |
+|----------|:-------------:|------:|
+| 2 layers |  $3.66$ | $-3.5$ |
+| 3 layers |  $3.35$ | $-2.3$ |
+| 4 layers |    $3.11$   |   $-1.8$ |
+
+</center>
+
+
+#### 2.2 Language Transfer
+
+If you opt for a 2-layers decoder, consider leveraging language transfer by initializing the student model from the [distil-large-v3 English distilled model](https://huggingface.co/distil-whisper/distil-large-v3). For French, this method has shown performance improvements of ΔWER=-1.9% (compared to a 2-layers decoder initialized from [Whiper *large-v3*](https://huggingface.co/openai/whisper-large-v3)) after 10,000 training steps.
+
+```diff
+- --teacher_checkpoint "openai/whisper-large-v3" \
++ --teacher_checkpoint "distil-whisper/distil-large-v3" \
+```
+
+### 3. Language mixing
+
+If you're working with low-resource languages (<500 hours of audio data), consider mixing your training data with a closely related language (for example, mix French and Spanish) to leverage knowledge transfer between languages. Experiments showed that mixing ~400 hours of French (which resulted in a model with poor generalization capacities) with ~500 hours of Spanish improved the model's out-of-distribution performance on French by ΔWER=-7.5%.
+
+To do this:
+1. Run [pseudo labeling](#1-pseudo-labelling) for each training dataset, setting the `--language` flag to the language of the respective dataset. In the example of mixing French and Spanish, simply modify the given [pseudo labeling](#1-pseudo-labelling) command with:
+    * pseudo labelling the French dataset
+      ```diff
+      - --dataset_config_name "hi" \
+      - --output_dir "./common_voice_16_1_hi_pseudo_labelled" \
+      - --language "hi" \
+      + --dataset_config_name "fr" \
+      + --output_dir "./common_voice_16_1_fr_pseudo_labelled" \
+      + --language "fr" \
+      ```
+    * pseudo labelling the Spanish dataset
+      ```diff
+      - --dataset_config_name "hi" \
+      - --output_dir "./common_voice_16_1_hi_pseudo_labelled" \
+      - --language "hi" \
+      + --dataset_config_name "es" \
+      + --output_dir "./common_voice_16_1_es_pseudo_labelled" \
+      + --language "es" \
+      ```
+
+2. Conduct [training](#3-training) on these pseudo-labeled datasets, using the `--language` flag set to your targeted language. Note that this flag is only used for evaluation purposes, so you set it to the targeted language. The language token used for forwarding the teacher and student model decoders is the one used and saved in pseudo labels during pseudo-labeling, ensuring it's the correct one for the considered sample. In the example of mixing French and Spanish, simply modify the given [training](#1-pseudo-labelling) command with: 
+    ```diff
+    - --train_dataset_name "../common_voice_16_1_hi_pseudo_labelled+../common_voice_16_1_hi_pseudo_labelled" \
+    - --train_split_name "train+validation" \
+    - --eval_dataset_name "../common_voice_16_1_hi_pseudo_labelled" \
+    - --eval_split_name "test" \
+    + --train_dataset_name "../common_voice_17_0_fr_pseudo_labelled+../common_voice_17_0_es_pseudo_labelled" \
+    + --train_split_name "train+train" \
+    + --eval_dataset_name "../common_voice_16_1_fr_pseudo_labelled" \
+    + --eval_split_name "validation" \
+    ```
+
+## Overview of Training Methods
+
+### 1. Fine-Tuning
+
+For fine-tuning, we take the original Whisper checkpoint and train it on one or more datasets using the standard 
+cross-entropy loss. As such, there is no involvement from the teacher checkpoint during training, and so the fine-tuned 
+model is permitted to *overfit* to the distribution of the training data we provide. This makes it appealing for "low-resource" 
+languages where the original Whisper model performs poorly, since we can boost the performance of the model on a single 
+language by *overfitting* to that distribution of data. Note that this means the fine-tuned model is prone to loosing 
+its robustness to different audio distributions, which is the trade-off with improving performance on a specified dataset.
+
+As a rule of thumb, fine-tuning is appropriate for languages where the original Whisper model performs > 20% WER, and we 
+have a relatively small quantity of training data available (< 1000 hours). With fine-tuning, we require as little as **10 hours**
+of training data to significantly boost the performance of the Whisper model. For an in-depth guide to fine-tuning Whisper,
+the reader is advised to refer to the blog post: [Fine-Tune Whisper For Multilingual ASR with 🤗 Transformers](https://huggingface.co/blog/fine-tune-whisper).
+
+### 2. Shrink and Fine-Tune
+
+Shrink and fine-tune (SFT) is a knowledge distillation (KD) technique in which we first *shrink* the teacher model to a 
+smaller student model by copying maximally spaced layers, and then *fine-tune* the student model on the cross-entropy loss 
+as described above. Typically, we retain the full encoder from the Whisper model and only shrink the decoder. Retaining 
+the entire encoder helps significantly with maintaining Whisper's robustness to different audio distributions (_c.f._ 
+Section 9.3 of the [Distil-Whisper paper](https://arxiv.org/abs/2311.00430)).
+
+We can either train the student model on a dataset of (audio, text) pairs as above. Or, we can use the pre-trained 
+Whisper model to generate *pseudo-labels* for our audio data, and train on the (audio, pseudo-label) pairs.
+
+Pseudo-labels can be used when either:
+1. The original text transcriptions are normalised (lower-cased or no punctuation): the Whisper generated pseudo-labels contain both punctuation and casing, and so can be used as a substitute for the normalised transcriptions
+2. The pre-trained Whisper model achieves < 20% WER on the languages: we then know the majority of the pseudo-labels will be accurate enough for us to train on.
+
+They are not recommended when both of the following are true:
+1. The original text is punctuated and cased
+2. The pre-trained Whisper model achieves > 20% WER on the languages: in this case, we want to overfit to the particular distribution of the language, and so train directly on the original text data
+
+To discard inaccurate pseudo-labels during training, we employ a simple WER heuristic to filter our pseudo-labelled 
+training data. We first normalise the original text and the pseudo-labelled text using the Whisper normaliser. If the 
+WER between the normalised text exceeds a 10% WER threshold, we discard the training sample. Else, we retain it for training.
+Section 9.1 of the Distil-Whisper [paper](https://arxiv.org/abs/2311.00430) demonstrates the importance of using this 
+threshold for training.
+
+### 3. KL Divergence
+
+In the KL Divergence setting, the student model is initialised by shrinking the teacher as before, and then trained to 
+match the predictions of the teacher during training. 
+
+### Summary of Methods
+
+The following table summarises the two training paradigms: fine-tuning and knowledge distillation (KD). It suggests 
+minimum values for the pre-trained WER / training data to achieve reasonable performance:
+
+| Method      | Pre-Trained WER / % | Training Data / h |
+|-------------|---------------------|-------------------|
+| Fine-tuning | > 20                | < 1000            |
+| KD          | < 20                | > 1000            |
+
+## Acknowledgements
+
+* OpenAI for the Whisper [model](https://huggingface.co/openai/whisper-large-v3) and [original codebase](https://github.com/openai/whisper)
+* Hugging Face 🤗 [Transformers](https://github.com/huggingface/transformers) for the Whisper model implementation
+* Google's [TPU Research Cloud (TRC)](https://sites.research.google/trc/about/) program for Cloud TPU v4s used to train the official Distil-Whisper models
+* The Hugging Face 🤗 cluster for enabling experimentation with the PyTorch scripts
+
+## Citation
+
+If you use this code-base, please consider citing the Distil-Whisper paper:
+
+```
+@misc{gandhi2023distilwhisper,
+      title={Distil-Whisper: Robust Knowledge Distillation via Large-Scale Pseudo Labelling}, 
+      author={Sanchit Gandhi and Patrick von Platen and Alexander M. Rush},
+      year={2023},
+      eprint={2311.00430},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```
diff --git a/added_tokens.json b/added_tokens.json
new file mode 100644
index 0000000000000000000000000000000000000000..1b33526d33aaa60d79f78ae8651dae50b730185a
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+}
diff --git a/checkpoint-5000-epoch-0/model.safetensors b/checkpoint-5000-epoch-0/model.safetensors
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--- /dev/null
+++ b/checkpoint-5000-epoch-0/model.safetensors
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 3025686376
diff --git a/checkpoint-5000-epoch-0/model_1.safetensors b/checkpoint-5000-epoch-0/model_1.safetensors
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index 0000000000000000000000000000000000000000..5346a4e7421f490b5a377a74d43f5850878a6b67
--- /dev/null
+++ b/checkpoint-5000-epoch-0/model_1.safetensors
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 4361070048
diff --git a/checkpoint-5000-epoch-0/optimizer.bin b/checkpoint-5000-epoch-0/optimizer.bin
new file mode 100644
index 0000000000000000000000000000000000000000..f2cc54dedd13385ea8c8a2f96a236f69ce3f1c18
--- /dev/null
+++ b/checkpoint-5000-epoch-0/optimizer.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 955539578
diff --git a/checkpoint-5000-epoch-0/random_states_0.pkl b/checkpoint-5000-epoch-0/random_states_0.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..0c33c3c88296f5a9821985480f52ff54b455837c
--- /dev/null
+++ b/checkpoint-5000-epoch-0/random_states_0.pkl
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 14604
diff --git a/checkpoint-5000-epoch-0/random_states_1.pkl b/checkpoint-5000-epoch-0/random_states_1.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..3430ad4247b73cc8b7a46b5bccffb4dee5484846
--- /dev/null
+++ b/checkpoint-5000-epoch-0/random_states_1.pkl
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 14604
diff --git a/checkpoint-5000-epoch-0/scheduler.bin b/checkpoint-5000-epoch-0/scheduler.bin
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index 0000000000000000000000000000000000000000..6e4438e920a510f25e65b6fd266edd0cd2fef1bc
--- /dev/null
+++ b/checkpoint-5000-epoch-0/scheduler.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:155f9eb76013e7e018690f6bb69927b931a2be52f0f4eedf79eb9dfeecc9f35d
+size 1064
diff --git a/config.json b/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..c805476141e577b2bc63717ae71d412d19877be3
--- /dev/null
+++ b/config.json
@@ -0,0 +1,50 @@
+{
+  "_name_or_path": "GalaktischeGurke/swhisper_large_8552",
+  "activation_dropout": 0.0,
+  "activation_function": "gelu",
+  "apply_spec_augment": false,
+  "architectures": [
+    "WhisperForConditionalGeneration"
+  ],
+  "attention_dropout": 0.0,
+  "begin_suppress_tokens": [
+    220,
+    50257
+  ],
+  "bos_token_id": 50257,
+  "classifier_proj_size": 256,
+  "d_model": 1280,
+  "decoder_attention_heads": 20,
+  "decoder_ffn_dim": 5120,
+  "decoder_layerdrop": 0.0,
+  "decoder_layers": 2,
+  "decoder_start_token_id": 50258,
+  "dropout": 0.0,
+  "encoder_attention_heads": 20,
+  "encoder_ffn_dim": 5120,
+  "encoder_layerdrop": 0.0,
+  "encoder_layers": 32,
+  "eos_token_id": 50257,
+  "init_std": 0.02,
+  "is_encoder_decoder": true,
+  "mask_feature_length": 10,
+  "mask_feature_min_masks": 0,
+  "mask_feature_prob": 0.0,
+  "mask_time_length": 10,
+  "mask_time_min_masks": 2,
+  "mask_time_prob": 0.05,
+  "max_length": 448,
+  "max_source_positions": 1500,
+  "max_target_positions": 448,
+  "median_filter_width": 7,
+  "model_type": "whisper",
+  "num_hidden_layers": 32,
+  "num_mel_bins": 128,
+  "pad_token_id": 50256,
+  "scale_embedding": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.41.2",
+  "use_cache": true,
+  "use_weighted_layer_sum": false,
+  "vocab_size": 51866
+}
diff --git a/core b/core
new file mode 100644
index 0000000000000000000000000000000000000000..bb7e4c3adf47655f42e3b3f6e358e0f95e60b714
--- /dev/null
+++ b/core
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 4742078464
diff --git a/create_student_model.py b/create_student_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..f10b1bce8c1408c9e291cfc12e3a1e9d7a26dc48
--- /dev/null
+++ b/create_student_model.py
@@ -0,0 +1,231 @@
+#!/usr/bin/env python
+# coding=utf-8
+# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Initialise a student Whisper model from a pre-trained teacher model for
+teacher-student distillation.
+"""
+
+import argparse
+import copy
+import logging
+
+import numpy as np
+import torch
+from transformers import GenerationConfig, WhisperForConditionalGeneration, WhisperProcessor
+
+
+logger = logging.getLogger(__name__)
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description="Initialise a student Whisper model from a teacher model, copying the relevant layer weights and adjusting the processor as necessary."
+    )
+    parser.add_argument(
+        "--teacher_checkpoint",
+        type=str,
+        required=True,
+        help="The HF Hub ID of the teacher checkpoint.",
+    )
+    parser.add_argument(
+        "--subfolder",
+        type=str,
+        default="",
+        help="In case the relevant teacher weights are located inside a subfolder of the model repo on huggingface.co, you "
+        "can specify the folder name here.",
+    )
+    parser.add_argument(
+        "--encoder_layers",
+        type=int,
+        default=None,
+        help="Number of encoder layers to use in the student model. Defaults to all layers from the teacher.",
+    )
+    parser.add_argument(
+        "--decoder_layers",
+        type=int,
+        default=2,
+        help="Number of decoder layers to use in the student model. Defaults to 2 layers.",
+    )
+    parser.add_argument(
+        "--decoder_layers_numbers",
+        type=int,
+        nargs="*",
+        help="Layers numbers of the decoder teacher to use in the student model. Defaults to None, equivalent to taking first and last layer (and equivalent to `--decoder_layers_numbers 0 -1`).",
+    )
+    parser.add_argument(
+        "--save_dir",
+        type=str,
+        required=True,
+        help="Where to save the student weights and processor.",
+    )
+    parser.add_argument(
+        "--push_to_hub",
+        type=bool,
+        required=False,
+        default=False,
+        help="Whether to push the student weights and processor to the Hub.",
+    )
+    parser.add_argument(
+        "--cache_dir",
+        type=str,
+        default=None,
+        help="Where to store the pretrained models downloaded from huggingface.co",
+    )
+
+    args = parser.parse_args()
+    return args
+
+
+def init_student_model_from_teacher(
+    teacher_checkpoint,
+    encoder_layers=None,
+    decoder_layers=2,
+    decoder_layers_numbers=None,
+    save_dir=None,
+    push_to_hub=None,
+    cache_dir=None,
+    subfolder="",
+):
+    if decoder_layers_numbers is not None and len(decoder_layers_numbers) != decoder_layers:
+        raise ValueError(
+            f"Got {len(decoder_layers_numbers)} layers number for {decoder_layers} decoder layers."
+        )
+
+    teacher_model = WhisperForConditionalGeneration.from_pretrained(
+        teacher_checkpoint,
+        cache_dir=cache_dir,
+        subfolder=subfolder,
+        low_cpu_mem_usage=True,
+    )
+    processor = WhisperProcessor.from_pretrained(teacher_checkpoint)
+    generation_config = GenerationConfig.from_pretrained(teacher_checkpoint)
+    generation_config.forced_decoder_ids = None
+
+    teacher_config = teacher_model.config
+    teacher_encoder_layers = teacher_config.encoder_layers
+    teacher_decoder_layers = teacher_config.decoder_layers
+
+    student_config = copy.deepcopy(teacher_config)
+    student_config.update(
+        {
+            "encoder_layers": encoder_layers if encoder_layers is not None else teacher_encoder_layers,
+            "decoder_layers": decoder_layers,
+        }
+    )
+
+    encoder_mapping = np.linspace(0, teacher_encoder_layers - 1, student_config.encoder_layers, dtype=int)
+    encoder_mapping[-1] = teacher_encoder_layers - 1
+    
+    encoder_map = {}
+    for student_layer, teacher_layer in enumerate(encoder_mapping):
+        encoder_map[teacher_layer] = student_layer
+
+    if decoder_layers_numbers is None:
+        decoder_mapping = np.linspace(0, teacher_decoder_layers - 1, student_config.decoder_layers, dtype=int)
+        decoder_mapping[-1] = teacher_decoder_layers - 1
+    else:
+        decoder_mapping = decoder_layers_numbers
+
+    decoder_map = {}
+    for student_layer, teacher_layer in enumerate(decoder_mapping):
+        decoder_map[teacher_layer] = student_layer
+
+    # init the student params from the teacher model
+    student_model = WhisperForConditionalGeneration(student_config)
+    missing_keys, unexpected_keys = student_model.load_state_dict(teacher_model.state_dict(), strict=False)
+    if len(missing_keys) > 0:
+        raise RuntimeError(
+            "Error(s) in loading state_dict for WhisperForConditionalGeneration. \n"
+            f"Missing key(s) in state_dict: {missing_keys}"
+        )
+    if decoder_layers == teacher_decoder_layers:
+        decoder_keys = [key for key in unexpected_keys if "model.decoder.layers" in key]
+        if len(decoder_keys) > 0:
+            raise RuntimeError(
+                "Error(s) in loading state_dict for WhisperForConditionalGeneration. \n"
+                f"Unexpected key(s) in state_dict: {decoder_keys}"
+            )
+    if encoder_layers == teacher_encoder_layers:
+        encoder_keys = [key for key in unexpected_keys if "model.encoder.layers" in key]
+        if len(encoder_keys) > 0:
+            raise RuntimeError(
+                "Error(s) in loading state_dict for WhisperForConditionalGeneration. \n"
+                f"Unexpected key(s) in state_dict: {encoder_keys}"
+            )
+        
+    for layer in range(teacher_decoder_layers):
+        if layer in decoder_map:
+            # re-introduce pre-defined layers from the teacher
+            student_model.model.decoder.layers[decoder_map[layer]].load_state_dict(
+                teacher_model.model.decoder.layers[layer].state_dict()
+            )
+
+    if encoder_layers is not None:
+        for layer in range(teacher_encoder_layers):
+            if layer in encoder_map:
+                # re-introduce pre-defined layers from the teacher
+                student_model.model.encoder.layers[encoder_map[layer]].load_state_dict(
+                    teacher_model.model.encoder.layers[layer].state_dict()
+                )
+
+    # remove the teacher params and model
+    del teacher_model
+
+    # save the converted weights and model
+    if save_dir is not None:
+        student_model.save_pretrained(save_dir)
+        # we also need to correctly save the processor and generation config
+        processor.save_pretrained(save_dir)
+        generation_config.save_pretrained(save_dir)
+
+    # check we can do a forward pass with the saved model - first load the weights and processor
+    logger.info("Checking we can load the saved model...")
+    student_model = WhisperForConditionalGeneration.from_pretrained(
+        save_dir,
+        low_cpu_mem_usage=True,
+    )
+    processor = WhisperProcessor.from_pretrained(save_dir)
+
+    # define some random inputs
+    input_features = processor(np.ones(16000), sampling_rate=16000, return_tensors="pt").input_features
+    decoder_start_token_id = student_model.config.decoder_start_token_id
+    decoder_input_ids = torch.ones((input_features.shape[0], 1), dtype=torch.long) * decoder_start_token_id
+
+    # do a forward pass - outputs will be gibberish for the initialised model so we can't check them
+    # but we make can sure the model runs as expected
+    logger.info("Checking we can run the converted model forward...")
+    _ = student_model(input_features, decoder_input_ids=decoder_input_ids).logits
+    logger.info("Conversion successful!")
+
+    if push_to_hub:
+        student_model.push_to_hub(save_dir)
+        processor.push_to_hub(save_dir)
+        generation_config.push_to_hub(save_dir)
+
+
+if __name__ == "__main__":
+    args = parse_args()
+
+    init_student_model_from_teacher(
+        teacher_checkpoint=args.teacher_checkpoint,
+        encoder_layers=args.encoder_layers,
+        decoder_layers=args.decoder_layers,
+        decoder_layers_numbers=args.decoder_layers_numbers,
+        save_dir=args.save_dir,
+        push_to_hub=args.push_to_hub,
+        cache_dir=args.cache_dir,
+        subfolder=args.subfolder,
+    )
diff --git a/distil-large-v3-init/added_tokens.json b/distil-large-v3-init/added_tokens.json
new file mode 100644
index 0000000000000000000000000000000000000000..1b33526d33aaa60d79f78ae8651dae50b730185a
--- /dev/null
+++ b/distil-large-v3-init/added_tokens.json
@@ -0,0 +1,1611 @@
+{
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+  "<|cs|>": 50283,
+  "<|cy|>": 50297,
+  "<|da|>": 50285,
+  "<|de|>": 50261,
+  "<|el|>": 50281,
+  "<|endoftext|>": 50257,
+  "<|en|>": 50259,
+  "<|es|>": 50262,
+  "<|et|>": 50307,
+  "<|eu|>": 50310,
+  "<|fa|>": 50300,
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+  "<|fr|>": 50265,
+  "<|gl|>": 50319,
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+  "<|km|>": 50323,
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+  "<|mi|>": 50295,
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+  "<|ne|>": 50313,
+  "<|nl|>": 50271,
+  "<|nn|>": 50342,
+  "<|nospeech|>": 50363,
+  "<|notimestamps|>": 50364,
+  "<|no|>": 50288,
+  "<|oc|>": 50328,
+  "<|pa|>": 50321,
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+  "<|ps|>": 50340,
+  "<|pt|>": 50267,
+  "<|ro|>": 50284,
+  "<|ru|>": 50263,
+  "<|sa|>": 50344,
+  "<|sd|>": 50332,
+  "<|si|>": 50322,
+  "<|sk|>": 50298,
+  "<|sl|>": 50305,
+  "<|sn|>": 50324,
+  "<|so|>": 50326,
+  "<|sq|>": 50317,
+  "<|sr|>": 50303,
+  "<|startoflm|>": 50361,
+  "<|startofprev|>": 50362,
+  "<|startoftranscript|>": 50258,
+  "<|su|>": 50357,
+  "<|sv|>": 50273,
+  "<|sw|>": 50318,
+  "<|ta|>": 50287,
+  "<|te|>": 50299,
+  "<|tg|>": 50331,
+  "<|th|>": 50289,
+  "<|tk|>": 50341,
+  "<|tl|>": 50348,
+  "<|transcribe|>": 50360,
+  "<|translate|>": 50359,
+  "<|tr|>": 50268,
+  "<|tt|>": 50351,
+  "<|uk|>": 50280,
+  "<|ur|>": 50290,
+  "<|uz|>": 50337,
+  "<|vi|>": 50278,
+  "<|yi|>": 50335,
+  "<|yo|>": 50325,
+  "<|yue|>": 50358,
+  "<|zh|>": 50260
+}
diff --git a/distil-large-v3-init/config.json b/distil-large-v3-init/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..c805476141e577b2bc63717ae71d412d19877be3
--- /dev/null
+++ b/distil-large-v3-init/config.json
@@ -0,0 +1,50 @@
+{
+  "_name_or_path": "GalaktischeGurke/swhisper_large_8552",
+  "activation_dropout": 0.0,
+  "activation_function": "gelu",
+  "apply_spec_augment": false,
+  "architectures": [
+    "WhisperForConditionalGeneration"
+  ],
+  "attention_dropout": 0.0,
+  "begin_suppress_tokens": [
+    220,
+    50257
+  ],
+  "bos_token_id": 50257,
+  "classifier_proj_size": 256,
+  "d_model": 1280,
+  "decoder_attention_heads": 20,
+  "decoder_ffn_dim": 5120,
+  "decoder_layerdrop": 0.0,
+  "decoder_layers": 2,
+  "decoder_start_token_id": 50258,
+  "dropout": 0.0,
+  "encoder_attention_heads": 20,
+  "encoder_ffn_dim": 5120,
+  "encoder_layerdrop": 0.0,
+  "encoder_layers": 32,
+  "eos_token_id": 50257,
+  "init_std": 0.02,
+  "is_encoder_decoder": true,
+  "mask_feature_length": 10,
+  "mask_feature_min_masks": 0,
+  "mask_feature_prob": 0.0,
+  "mask_time_length": 10,
+  "mask_time_min_masks": 2,
+  "mask_time_prob": 0.05,
+  "max_length": 448,
+  "max_source_positions": 1500,
+  "max_target_positions": 448,
+  "median_filter_width": 7,
+  "model_type": "whisper",
+  "num_hidden_layers": 32,
+  "num_mel_bins": 128,
+  "pad_token_id": 50256,
+  "scale_embedding": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.41.2",
+  "use_cache": true,
+  "use_weighted_layer_sum": false,
+  "vocab_size": 51866
+}
diff --git a/distil-large-v3-init/generation_config.json b/distil-large-v3-init/generation_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..9f4dced2a5a6e8902cd9e1d0f695f6217e02debd
--- /dev/null
+++ b/distil-large-v3-init/generation_config.json
@@ -0,0 +1,255 @@
+{
+  "alignment_heads": [
+    [
+      7,
+      0
+    ],
+    [
+      10,
+      17
+    ],
+    [
+      12,
+      18
+    ],
+    [
+      13,
+      12
+    ],
+    [
+      16,
+      1
+    ],
+    [
+      17,
+      14
+    ],
+    [
+      19,
+      11
+    ],
+    [
+      21,
+      4
+    ],
+    [
+      24,
+      1
+    ],
+    [
+      25,
+      6
+    ]
+  ],
+  "begin_suppress_tokens": [
+    220,
+    50257
+  ],
+  "bos_token_id": 50257,
+  "decoder_start_token_id": 50258,
+  "eos_token_id": 50257,
+  "is_multilingual": true,
+  "lang_to_id": {
+    "<|af|>": 50327,
+    "<|am|>": 50334,
+    "<|ar|>": 50272,
+    "<|as|>": 50350,
+    "<|az|>": 50304,
+    "<|ba|>": 50355,
+    "<|be|>": 50330,
+    "<|bg|>": 50292,
+    "<|bn|>": 50302,
+    "<|bo|>": 50347,
+    "<|br|>": 50309,
+    "<|bs|>": 50315,
+    "<|ca|>": 50270,
+    "<|cs|>": 50283,
+    "<|cy|>": 50297,
+    "<|da|>": 50285,
+    "<|de|>": 50261,
+    "<|el|>": 50281,
+    "<|en|>": 50259,
+    "<|es|>": 50262,
+    "<|et|>": 50307,
+    "<|eu|>": 50310,
+    "<|fa|>": 50300,
+    "<|fi|>": 50277,
+    "<|fo|>": 50338,
+    "<|fr|>": 50265,
+    "<|gl|>": 50319,
+    "<|gu|>": 50333,
+    "<|haw|>": 50352,
+    "<|ha|>": 50354,
+    "<|he|>": 50279,
+    "<|hi|>": 50276,
+    "<|hr|>": 50291,
+    "<|ht|>": 50339,
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+    "<|hy|>": 50312,
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+    "<|ka|>": 50329,
+    "<|kk|>": 50316,
+    "<|km|>": 50323,
+    "<|kn|>": 50306,
+    "<|ko|>": 50264,
+    "<|la|>": 50294,
+    "<|lb|>": 50345,
+    "<|ln|>": 50353,
+    "<|lo|>": 50336,
+    "<|lt|>": 50293,
+    "<|lv|>": 50301,
+    "<|mg|>": 50349,
+    "<|mi|>": 50295,
+    "<|mk|>": 50308,
+    "<|ml|>": 50296,
+    "<|mn|>": 50314,
+    "<|mr|>": 50320,
+    "<|ms|>": 50282,
+    "<|mt|>": 50343,
+    "<|my|>": 50346,
+    "<|ne|>": 50313,
+    "<|nl|>": 50271,
+    "<|nn|>": 50342,
+    "<|no|>": 50288,
+    "<|oc|>": 50328,
+    "<|pa|>": 50321,
+    "<|pl|>": 50269,
+    "<|ps|>": 50340,
+    "<|pt|>": 50267,
+    "<|ro|>": 50284,
+    "<|ru|>": 50263,
+    "<|sa|>": 50344,
+    "<|sd|>": 50332,
+    "<|si|>": 50322,
+    "<|sk|>": 50298,
+    "<|sl|>": 50305,
+    "<|sn|>": 50324,
+    "<|so|>": 50326,
+    "<|sq|>": 50317,
+    "<|sr|>": 50303,
+    "<|su|>": 50357,
+    "<|sv|>": 50273,
+    "<|sw|>": 50318,
+    "<|ta|>": 50287,
+    "<|te|>": 50299,
+    "<|tg|>": 50331,
+    "<|th|>": 50289,
+    "<|tk|>": 50341,
+    "<|tl|>": 50348,
+    "<|tr|>": 50268,
+    "<|tt|>": 50351,
+    "<|uk|>": 50280,
+    "<|ur|>": 50290,
+    "<|uz|>": 50337,
+    "<|vi|>": 50278,
+    "<|yi|>": 50335,
+    "<|yo|>": 50325,
+    "<|yue|>": 50358,
+    "<|zh|>": 50260
+  },
+  "max_initial_timestamp_index": 50,
+  "max_length": 448,
+  "no_timestamps_token_id": 50364,
+  "pad_token_id": 50257,
+  "prev_sot_token_id": 50362,
+  "return_timestamps": false,
+  "suppress_tokens": [
+    1,
+    2,
+    7,
+    8,
+    9,
+    10,
+    14,
+    25,
+    26,
+    27,
+    28,
+    29,
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+    58,
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+    12331,
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+    14157,
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+    15618,
+    16553,
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+    18956,
+    20075,
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+    22520,
+    26130,
+    26161,
+    26435,
+    28279,
+    29464,
+    31650,
+    32302,
+    32470,
+    36865,
+    42863,
+    47425,
+    49870,
+    50254,
+    50258,
+    50359,
+    50360,
+    50361,
+    50362,
+    50363
+  ],
+  "task_to_id": {
+    "transcribe": 50360,
+    "translate": 50359
+  },
+  "transformers_version": "4.41.2"
+}
diff --git a/distil-large-v3-init/merges.txt b/distil-large-v3-init/merges.txt
new file mode 100644
index 0000000000000000000000000000000000000000..6038932a2a1f09a66991b1c2adae0d14066fa29e
--- /dev/null
+++ b/distil-large-v3-init/merges.txt
@@ -0,0 +1,50001 @@
+#version: 0.2
+Ġ t
+Ġ a
+Ġt h
+i n
+e r
+Ġ w
+Ġ s
+o u
+Ġth e
+r e
+o n
+a t
+e n
+Ġ c
+i t
+i s
+Ġ b
+n d
+Ġ d
+Ġ m
+Ġ h
+Ġ o
+in g
+e s
+Ġ p
+Ġt o
+a n
+Ġ f
+o r
+l l
+Ġ I
+Ġ l
+Ġ y
+a r
+Ġ g
+Ġy ou
+e d
+Ġa nd
+Ġ in
+Ġo f
+a s
+Ġ n
+o m
+i c
+Ġth at
+u s
+e t
+v e
+a l
+o w
+l e
+Ġ is
+Ġ e
+Ġ it
+o t
+' s
+Ġb e
+i on
+Ġ T
+Ġw h
+Ġ A
+en t
+Ġ S
+Ġ re
+a y
+Ġw e
+Ġ on
+er e
+Ġh a
+u t
+a c
+i d
+i g
+o s
+k e
+v er
+i m
+Ġ Ð
+ĠT h
+a m
+a ll
+Ġf or
+e l
+c h
+r o
+Ġth is
+Ġs t
+Ġ W
+Ġ u
+a d
+ou t
+i r
+l d
+c t
+Ġ k
+i f
+Ġg o
+. .
+Ð ¾
+it h
+l y
+h t
+q u
+Ġ -
+Ġd o
+Ġ j
+Ġha ve
+Ġ B
+Ġa n
+Ġw ith
+Ġa re
+Ġ r
+Ġd e
+Ġs e
+Ġs o
+Ġ v
+s t
+i ll
+u r
+Ġl i
+Ġ M
+es t
+o d
+all y
+' t
+us t
+Ġa s
+Ġ C
+c e
+Ġm e
+Ð °
+Ð µ
+i l
+Ġ H
+Ġw as
+t er
+t h
+Ġc an
+an t
+Ġc om
+ou r
+ig ht
+Ġ Y
+at ion
+ĠA nd
+o l
+Ġs h
+Ñ Ĥ
+o p
+s e
+Ġn ot
+ĠS o
+Ġn e
+u n
+Ġa b
+Ġli ke
+Ġa t
+Ġ D
+i e
+Ġh e
+Ġc on
+Ġc h
+o re
+Ġa l
+Ġo r
+Ġ qu
+Ġ O
+om e
+r a
+u l
+Ġ N
+p p
+Ġyou r
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+Ġ P
+Ġf r
+g e
+er s
+' re
+Ð ¸
+Ġthe y
+Ġwh at
+us e
+Ġa ll
+ĠTh e
+Ġ L
+es s
+e m
+Ġk n
+Ġj ust
+ar t
+Ġp ro
+ver y
+u m
+Ġl o
+Ġ ì
+Ġm y
+o k
+Ġe x
+a b
+Ġth ere
+Ġb ut
+Ġkn ow
+Ġs u
+Ġ G
+Ñ ģ
+Ġ E
+Ġm a
+о Ð
+Ġ en
+Ġab out
+ĠI t
+is t
+Ġw or
+r i
+in d
+Ġon e
+at e
+a nd
+in k
+Ġl e
+or t
+' m
+Ġ F
+ic h
+Ñ Ģ
+id e
+Ġg et
+Ġ out
+.. .
+Ġw ill
+ã ģ
+i ve
+Ð ½
+Ġfr om
+a in
+ĠW e
+Ġu p
+p e
+re s
+c a
+Ġ R
+Ġ if
+Ġp l
+Ġd on
+ac k
+Ġ 1
+Ġ "
+Ġt r
+Ġ us
+ĠW h
+it y
+Ġ J
+ĠY ou
+Ġh ere
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+Ġs ome
+ou g
+a k
+ar d
+Ġgo ing
+Ġu n
+m ent
+Ġth ink
+Ġp e
+en d
+Ġ (
+ca use
+Ġt im
+as t
+Ã ©
+Ġ our
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+am e
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+Ġ ë
+u d
+in e
+Ġre ally
+Ġt e
+Ġse e
+c i
+Ġb y
+s o
+u re
+os e
+Ġ [
+a re
+Ġm ore
+a h
+on e
+c k
+op le
+а Ð
+Ġthe n
+Ġth ing
+Ġthe m
+v en
+ou nd
+os t
+on g
+e ct
+Ġr ight
+a g
+Ġin t
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+Ġwh en
+ou s
+p l
+Ġtim e
+Ġ im
+Ġwh o
+Ġ 2
+a p
+Ġbe cause
+h ing
+Ġn o
+ic e
+Ġlo ok
+Ġh as
+Ġw ould
+Ġh ow
+ac t
+Ġf e
+n t
+oug h
+Ġp r
+ĠB ut
+Ġs ay
+Ñ ĥ
+Ġn ow
+Ġm an
+Ġ very
+Ġwor k
+i z
+Ġ K
+i v
+it t
+Ġa r
+e p
+Ġc l
+Ġwh ich
+Ġc o
+an s
+' ve
+Ġs a
+f f
+' ll
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+Ġa ct
+Ġy e
+b er
+ac h
+a ge
+p er
+Ġal so
+f er
+Ġthe se
+Ġa d
+е Ð
+th er
+ac e
+ic k
+a ke
+re at
+i re
+u e
+Ġa g
+Ġ U
+u ch
+ion s
+r y
+0 0
+n a
+Ġd id
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+Ġha d
+Ġe very
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+an k
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+p t
+ĠTh at
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+Ġgo od
+Ġint o
+al k
+Ġbe en
+Ġa m
+Ġo ver
+u ally
+Ġ â
+ì Ŀ
+Ġu nd
+h e
+w ay
+Ġg r
+Ñ Į
+Ġd if
+Ġp er
+Ñ ı
+ĠI n
+Ġt w
+on d
+ar s
+in t
+or m
+Ġl ot
+Ġwh ere
+Ġ Ã
+Ġ V
+Ġs omet
+Ð »
+en s
+Ġg u
+Ġa c
+u g
+Ñ ĭ
+Ä ±
+Ġf irst
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+Ġh is
+itt le
+Ġim p
+Ġm o
+a v
+Ġl ittle
+ĠWh at
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+Ġ ê
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+ĠÐ ¿
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+Ã ¤
+Ġb r
+Ġth r
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+Ġ i
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+Ġ ro
+ĠS t
+Ġsomet hing
+Ġt ake
+Ġb o
+y s
+Ġsh e
+Ġt alk
+l o
+Ñ ĩ
+Ġe ven
+Ð º
+ã Ģ
+ĠÐ ½
+Ġb u
+ĠI f
+Ġd own
+ĠC h
+ad e
+ation s
+Ġ use
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+Ġof f
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+os s
+n ing
+Ã ¼
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+Ġ Ñģ
+Ġne w
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+p le
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+t ing
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+oid sha256:34de595c8d40aa1c243b11d5b6d2b4d282e3c5aaa46180b45bf90b6c33d6e924
+size 3025686376
diff --git a/distil-large-v3-init/normalizer.json b/distil-large-v3-init/normalizer.json
new file mode 100644
index 0000000000000000000000000000000000000000..dd6ae819ad738ac1a546e9f9282ef325c33b9ea0
--- /dev/null
+++ b/distil-large-v3-init/normalizer.json
@@ -0,0 +1,1742 @@
+{
+  "accessorise": "accessorize",
+  "accessorised": "accessorized",
+  "accessorises": "accessorizes",
+  "accessorising": "accessorizing",
+  "acclimatisation": "acclimatization",
+  "acclimatise": "acclimatize",
+  "acclimatised": "acclimatized",
+  "acclimatises": "acclimatizes",
+  "acclimatising": "acclimatizing",
+  "accoutrements": "accouterments",
+  "aeon": "eon",
+  "aeons": "eons",
+  "aerogramme": "aerogram",
+  "aerogrammes": "aerograms",
+  "aeroplane": "airplane",
+  "aeroplanes": "airplanes",
+  "aesthete": "esthete",
+  "aesthetes": "esthetes",
+  "aesthetic": "esthetic",
+  "aesthetically": "esthetically",
+  "aesthetics": "esthetics",
+  "aetiology": "etiology",
+  "ageing": "aging",
+  "aggrandisement": "aggrandizement",
+  "agonise": "agonize",
+  "agonised": "agonized",
+  "agonises": "agonizes",
+  "agonising": "agonizing",
+  "agonisingly": "agonizingly",
+  "almanack": "almanac",
+  "almanacks": "almanacs",
+  "aluminium": "aluminum",
+  "amortisable": "amortizable",
+  "amortisation": "amortization",
+  "amortisations": "amortizations",
+  "amortise": "amortize",
+  "amortised": "amortized",
+  "amortises": "amortizes",
+  "amortising": "amortizing",
+  "amphitheatre": "amphitheater",
+  "amphitheatres": "amphitheaters",
+  "anaemia": "anemia",
+  "anaemic": "anemic",
+  "anaesthesia": "anesthesia",
+  "anaesthetic": "anesthetic",
+  "anaesthetics": "anesthetics",
+  "anaesthetise": "anesthetize",
+  "anaesthetised": "anesthetized",
+  "anaesthetises": "anesthetizes",
+  "anaesthetising": "anesthetizing",
+  "anaesthetist": "anesthetist",
+  "anaesthetists": "anesthetists",
+  "anaesthetize": "anesthetize",
+  "anaesthetized": "anesthetized",
+  "anaesthetizes": "anesthetizes",
+  "anaesthetizing": "anesthetizing",
+  "analogue": "analog",
+  "analogues": "analogs",
+  "analyse": "analyze",
+  "analysed": "analyzed",
+  "analyses": "analyzes",
+  "analysing": "analyzing",
+  "anglicise": "anglicize",
+  "anglicised": "anglicized",
+  "anglicises": "anglicizes",
+  "anglicising": "anglicizing",
+  "annualised": "annualized",
+  "antagonise": "antagonize",
+  "antagonised": "antagonized",
+  "antagonises": "antagonizes",
+  "antagonising": "antagonizing",
+  "apologise": "apologize",
+  "apologised": "apologized",
+  "apologises": "apologizes",
+  "apologising": "apologizing",
+  "appal": "appall",
+  "appals": "appalls",
+  "appetiser": "appetizer",
+  "appetisers": "appetizers",
+  "appetising": "appetizing",
+  "appetisingly": "appetizingly",
+  "arbour": "arbor",
+  "arbours": "arbors",
+  "archaeologically": "archeologically",
+  "archaeologist": "archeologist",
+  "archaeologists": "archeologists",
+  "archaeology": "archeology</span>",
+  "archeological": "archaeological",
+  "ardour": "ardor",
+  "armour": "armor",
+  "armoured": "armored",
+  "armourer": "armorer",
+  "armourers": "armorers",
+  "armouries": "armories",
+  "armoury": "armory",
+  "artefact": "artifact",
+  "artefacts": "artifacts",
+  "authorise": "authorize",
+  "authorised": "authorized",
+  "authorises": "authorizes",
+  "authorising": "authorizing",
+  "axe": "ax",
+  "backpedalled": "backpedaled",
+  "backpedalling": "backpedaling",
+  "bannister": "banister",
+  "bannisters": "banisters",
+  "baptise": "baptize",
+  "baptised": "baptized",
+  "baptises": "baptizes",
+  "baptising": "baptizing",
+  "bastardise": "bastardize",
+  "bastardised": "bastardized",
+  "bastardises": "bastardizes",
+  "bastardising": "bastardizing",
+  "battleax": "battleaxe",
+  "baulk": "balk",
+  "baulked": "balked",
+  "baulking": "balking",
+  "baulks": "balks",
+  "bedevilled": "bedeviled",
+  "bedevilling": "bedeviling",
+  "behaviour": "behavior",
+  "behavioural": "behavioral",
+  "behaviourism": "behaviorism",
+  "behaviourist": "behaviorist",
+  "behaviourists": "behaviorists",
+  "behaviours": "behaviors",
+  "behove": "behoove",
+  "behoved": "behooved",
+  "behoves": "behooves",
+  "bejewelled": "bejeweled",
+  "belabour": "belabor",
+  "belaboured": "belabored",
+  "belabouring": "belaboring",
+  "belabours": "belabors",
+  "bevelled": "beveled",
+  "bevvies": "bevies",
+  "bevvy": "bevy",
+  "biassed": "biased",
+  "biassing": "biasing",
+  "bingeing": "binging",
+  "bougainvillaea": "bougainvillea",
+  "bougainvillaeas": "bougainvilleas",
+  "bowdlerise": "bowdlerize",
+  "bowdlerised": "bowdlerized",
+  "bowdlerises": "bowdlerizes",
+  "bowdlerising": "bowdlerizing",
+  "breathalyse": "breathalyze",
+  "breathalysed": "breathalyzed",
+  "breathalyser": "breathalyzer",
+  "breathalysers": "breathalyzers",
+  "breathalyses": "breathalyzes",
+  "breathalysing": "breathalyzing",
+  "brutalise": "brutalize",
+  "brutalised": "brutalized",
+  "brutalises": "brutalizes",
+  "brutalising": "brutalizing",
+  "busses": "buses",
+  "bussing": "busing",
+  "caesarean": "cesarean",
+  "caesareans": "cesareans",
+  "calibre": "caliber",
+  "calibres": "calibers",
+  "calliper": "caliper",
+  "callipers": "calipers",
+  "callisthenics": "calisthenics",
+  "canalise": "canalize",
+  "canalised": "canalized",
+  "canalises": "canalizes",
+  "canalising": "canalizing",
+  "cancelation": "cancellation",
+  "cancelations": "cancellations",
+  "cancelled": "canceled",
+  "cancelling": "canceling",
+  "candour": "candor",
+  "cannibalise": "cannibalize",
+  "cannibalised": "cannibalized",
+  "cannibalises": "cannibalizes",
+  "cannibalising": "cannibalizing",
+  "canonise": "canonize",
+  "canonised": "canonized",
+  "canonises": "canonizes",
+  "canonising": "canonizing",
+  "capitalise": "capitalize",
+  "capitalised": "capitalized",
+  "capitalises": "capitalizes",
+  "capitalising": "capitalizing",
+  "caramelise": "caramelize",
+  "caramelised": "caramelized",
+  "caramelises": "caramelizes",
+  "caramelising": "caramelizing",
+  "carbonise": "carbonize",
+  "carbonised": "carbonized",
+  "carbonises": "carbonizes",
+  "carbonising": "carbonizing",
+  "carolled": "caroled",
+  "carolling": "caroling",
+  "catalogue": "catalog",
+  "catalogued": "cataloged",
+  "catalogues": "catalogs",
+  "cataloguing": "cataloging",
+  "catalyse": "catalyze",
+  "catalysed": "catalyzed",
+  "catalyses": "catalyzes",
+  "catalysing": "catalyzing",
+  "categorise": "categorize",
+  "categorised": "categorized",
+  "categorises": "categorizes",
+  "categorising": "categorizing",
+  "cauterise": "cauterize",
+  "cauterised": "cauterized",
+  "cauterises": "cauterizes",
+  "cauterising": "cauterizing",
+  "cavilled": "caviled",
+  "cavilling": "caviling",
+  "centigramme": "centigram",
+  "centigrammes": "centigrams",
+  "centilitre": "centiliter",
+  "centilitres": "centiliters",
+  "centimetre": "centimeter",
+  "centimetres": "centimeters",
+  "centralise": "centralize",
+  "centralised": "centralized",
+  "centralises": "centralizes",
+  "centralising": "centralizing",
+  "centre": "center",
+  "centred": "centered",
+  "centrefold": "centerfold",
+  "centrefolds": "centerfolds",
+  "centrepiece": "centerpiece",
+  "centrepieces": "centerpieces",
+  "centres": "centers",
+  "channelled": "channeled",
+  "channelling": "channeling",
+  "characterise": "characterize",
+  "characterised": "characterized",
+  "characterises": "characterizes",
+  "characterising": "characterizing",
+  "cheque": "check",
+  "chequebook": "checkbook",
+  "chequebooks": "checkbooks",
+  "chequered": "checkered",
+  "cheques": "checks",
+  "chilli": "chili",
+  "chimaera": "chimera",
+  "chimaeras": "chimeras",
+  "chiselled": "chiseled",
+  "chiselling": "chiseling",
+  "circularise": "circularize",
+  "circularised": "circularized",
+  "circularises": "circularizes",
+  "circularising": "circularizing",
+  "civilise": "civilize",
+  "civilised": "civilized",
+  "civilises": "civilizes",
+  "civilising": "civilizing",
+  "clamour": "clamor",
+  "clamoured": "clamored",
+  "clamouring": "clamoring",
+  "clamours": "clamors",
+  "clangour": "clangor",
+  "clarinettist": "clarinetist",
+  "clarinettists": "clarinetists",
+  "collectivise": "collectivize",
+  "collectivised": "collectivized",
+  "collectivises": "collectivizes",
+  "collectivising": "collectivizing",
+  "colonisation": "colonization",
+  "colonise": "colonize",
+  "colonised": "colonized",
+  "coloniser": "colonizer",
+  "colonisers": "colonizers",
+  "colonises": "colonizes",
+  "colonising": "colonizing",
+  "colour": "color",
+  "colourant": "colorant",
+  "colourants": "colorants",
+  "coloured": "colored",
+  "coloureds": "coloreds",
+  "colourful": "colorful",
+  "colourfully": "colorfully",
+  "colouring": "coloring",
+  "colourize": "colorize",
+  "colourized": "colorized",
+  "colourizes": "colorizes",
+  "colourizing": "colorizing",
+  "colourless": "colorless",
+  "colours": "colors",
+  "commercialise": "commercialize",
+  "commercialised": "commercialized",
+  "commercialises": "commercializes",
+  "commercialising": "commercializing",
+  "compartmentalise": "compartmentalize",
+  "compartmentalised": "compartmentalized",
+  "compartmentalises": "compartmentalizes",
+  "compartmentalising": "compartmentalizing",
+  "computerise": "computerize",
+  "computerised": "computerized",
+  "computerises": "computerizes",
+  "computerising": "computerizing",
+  "conceptualise": "conceptualize",
+  "conceptualised": "conceptualized",
+  "conceptualises": "conceptualizes",
+  "conceptualising": "conceptualizing",
+  "connexion": "connection",
+  "connexions": "connections",
+  "contextualise": "contextualize",
+  "contextualised": "contextualized",
+  "contextualises": "contextualizes",
+  "contextualising": "contextualizing",
+  "cosier": "cozier",
+  "cosies": "cozies",
+  "cosiest": "coziest",
+  "cosily": "cozily",
+  "cosiness": "coziness",
+  "cosy": "cozy",
+  "councillor": "councilor",
+  "councillors": "councilors",
+  "counselled": "counseled",
+  "counselling": "counseling",
+  "counsellor": "counselor",
+  "counsellors": "counselors",
+  "crenelated": "crenellated",
+  "criminalise": "criminalize",
+  "criminalised": "criminalized",
+  "criminalises": "criminalizes",
+  "criminalising": "criminalizing",
+  "criticise": "criticize",
+  "criticised": "criticized",
+  "criticises": "criticizes",
+  "criticising": "criticizing",
+  "crueller": "crueler",
+  "cruellest": "cruelest",
+  "crystallisation": "crystallization",
+  "crystallise": "crystallize",
+  "crystallised": "crystallized",
+  "crystallises": "crystallizes",
+  "crystallising": "crystallizing",
+  "cudgelled": "cudgeled",
+  "cudgelling": "cudgeling",
+  "customise": "customize",
+  "customised": "customized",
+  "customises": "customizes",
+  "customising": "customizing",
+  "cypher": "cipher",
+  "cyphers": "ciphers",
+  "decentralisation": "decentralization",
+  "decentralise": "decentralize",
+  "decentralised": "decentralized",
+  "decentralises": "decentralizes",
+  "decentralising": "decentralizing",
+  "decriminalisation": "decriminalization",
+  "decriminalise": "decriminalize",
+  "decriminalised": "decriminalized",
+  "decriminalises": "decriminalizes",
+  "decriminalising": "decriminalizing",
+  "defence": "defense",
+  "defenceless": "defenseless",
+  "defences": "defenses",
+  "dehumanisation": "dehumanization",
+  "dehumanise": "dehumanize",
+  "dehumanised": "dehumanized",
+  "dehumanises": "dehumanizes",
+  "dehumanising": "dehumanizing",
+  "demeanour": "demeanor",
+  "demilitarisation": "demilitarization",
+  "demilitarise": "demilitarize",
+  "demilitarised": "demilitarized",
+  "demilitarises": "demilitarizes",
+  "demilitarising": "demilitarizing",
+  "demobilisation": "demobilization",
+  "demobilise": "demobilize",
+  "demobilised": "demobilized",
+  "demobilises": "demobilizes",
+  "demobilising": "demobilizing",
+  "democratisation": "democratization",
+  "democratise": "democratize",
+  "democratised": "democratized",
+  "democratises": "democratizes",
+  "democratising": "democratizing",
+  "demonise": "demonize",
+  "demonised": "demonized",
+  "demonises": "demonizes",
+  "demonising": "demonizing",
+  "demoralisation": "demoralization",
+  "demoralise": "demoralize",
+  "demoralised": "demoralized",
+  "demoralises": "demoralizes",
+  "demoralising": "demoralizing",
+  "denationalisation": "denationalization",
+  "denationalise": "denationalize",
+  "denationalised": "denationalized",
+  "denationalises": "denationalizes",
+  "denationalising": "denationalizing",
+  "deodorise": "deodorize",
+  "deodorised": "deodorized",
+  "deodorises": "deodorizes",
+  "deodorising": "deodorizing",
+  "depersonalise": "depersonalize",
+  "depersonalised": "depersonalized",
+  "depersonalises": "depersonalizes",
+  "depersonalising": "depersonalizing",
+  "deputise": "deputize",
+  "deputised": "deputized",
+  "deputises": "deputizes",
+  "deputising": "deputizing",
+  "desensitisation": "desensitization",
+  "desensitise": "desensitize",
+  "desensitised": "desensitized",
+  "desensitises": "desensitizes",
+  "desensitising": "desensitizing",
+  "destabilisation": "destabilization",
+  "destabilise": "destabilize",
+  "destabilised": "destabilized",
+  "destabilises": "destabilizes",
+  "destabilising": "destabilizing",
+  "dialled": "dialed",
+  "dialling": "dialing",
+  "dialogue": "dialog",
+  "dialogues": "dialogs",
+  "diarrhoea": "diarrhea",
+  "digitise": "digitize",
+  "digitised": "digitized",
+  "digitises": "digitizes",
+  "digitising": "digitizing",
+  "disc": "disk",
+  "discolour": "discolor",
+  "discoloured": "discolored",
+  "discolouring": "discoloring",
+  "discolours": "discolors",
+  "discs": "disks",
+  "disembowelled": "disemboweled",
+  "disembowelling": "disemboweling",
+  "disfavour": "disfavor",
+  "dishevelled": "disheveled",
+  "dishonour": "dishonor",
+  "dishonourable": "dishonorable",
+  "dishonourably": "dishonorably",
+  "dishonoured": "dishonored",
+  "dishonouring": "dishonoring",
+  "dishonours": "dishonors",
+  "disorganisation": "disorganization",
+  "disorganised": "disorganized",
+  "distil": "distill",
+  "distils": "distills",
+  "dramatisation": "dramatization",
+  "dramatisations": "dramatizations",
+  "dramatise": "dramatize",
+  "dramatised": "dramatized",
+  "dramatises": "dramatizes",
+  "dramatising": "dramatizing",
+  "draught": "draft",
+  "draughtboard": "draftboard",
+  "draughtboards": "draftboards",
+  "draughtier": "draftier",
+  "draughtiest": "draftiest",
+  "draughts": "drafts",
+  "draughtsman": "draftsman",
+  "draughtsmanship": "draftsmanship",
+  "draughtsmen": "draftsmen",
+  "draughtswoman": "draftswoman",
+  "draughtswomen": "draftswomen",
+  "draughty": "drafty",
+  "drivelled": "driveled",
+  "drivelling": "driveling",
+  "duelled": "dueled",
+  "duelling": "dueling",
+  "economise": "economize",
+  "economised": "economized",
+  "economises": "economizes",
+  "economising": "economizing",
+  "editorialise": "editorialize",
+  "editorialised": "editorialized",
+  "editorialises": "editorializes",
+  "editorialising": "editorializing",
+  "edoema": "edema",
+  "empathise": "empathize",
+  "empathised": "empathized",
+  "empathises": "empathizes",
+  "empathising": "empathizing",
+  "emphasise": "emphasize",
+  "emphasised": "emphasized",
+  "emphasises": "emphasizes",
+  "emphasising": "emphasizing",
+  "enamelled": "enameled",
+  "enamelling": "enameling",
+  "enamoured": "enamored",
+  "encyclopaedia": "encyclopedia",
+  "encyclopaedias": "encyclopedias",
+  "encyclopaedic": "encyclopedic",
+  "endeavour": "endeavor",
+  "endeavoured": "endeavored",
+  "endeavouring": "endeavoring",
+  "endeavours": "endeavors",
+  "energise": "energize",
+  "energised": "energized",
+  "energises": "energizes",
+  "energising": "energizing",
+  "enrol": "enroll",
+  "enrols": "enrolls",
+  "enthral": "enthrall",
+  "enthrals": "enthralls",
+  "epaulette": "epaulet",
+  "epaulettes": "epaulets",
+  "epicentre": "epicenter",
+  "epicentres": "epicenters",
+  "epilogue": "epilog",
+  "epilogues": "epilogs",
+  "epitomise": "epitomize",
+  "epitomised": "epitomized",
+  "epitomises": "epitomizes",
+  "epitomising": "epitomizing",
+  "equalisation": "equalization",
+  "equalise": "equalize",
+  "equalised": "equalized",
+  "equaliser": "equalizer",
+  "equalisers": "equalizers",
+  "equalises": "equalizes",
+  "equalising": "equalizing",
+  "eulogise": "eulogize",
+  "eulogised": "eulogized",
+  "eulogises": "eulogizes",
+  "eulogising": "eulogizing",
+  "evangelise": "evangelize",
+  "evangelised": "evangelized",
+  "evangelises": "evangelizes",
+  "evangelising": "evangelizing",
+  "exorcise": "exorcize",
+  "exorcised": "exorcized",
+  "exorcises": "exorcizes",
+  "exorcising": "exorcizing",
+  "extemporisation": "extemporization",
+  "extemporise": "extemporize",
+  "extemporised": "extemporized",
+  "extemporises": "extemporizes",
+  "extemporising": "extemporizing",
+  "externalisation": "externalization",
+  "externalisations": "externalizations",
+  "externalise": "externalize",
+  "externalised": "externalized",
+  "externalises": "externalizes",
+  "externalising": "externalizing",
+  "factorise": "factorize",
+  "factorised": "factorized",
+  "factorises": "factorizes",
+  "factorising": "factorizing",
+  "faecal": "fecal",
+  "faeces": "feces",
+  "familiarisation": "familiarization",
+  "familiarise": "familiarize",
+  "familiarised": "familiarized",
+  "familiarises": "familiarizes",
+  "familiarising": "familiarizing",
+  "fantasise": "fantasize",
+  "fantasised": "fantasized",
+  "fantasises": "fantasizes",
+  "fantasising": "fantasizing",
+  "favour": "favor",
+  "favourable": "favorable",
+  "favourably": "favorably",
+  "favoured": "favored",
+  "favouring": "favoring",
+  "favourite": "favorite",
+  "favourites": "favorites",
+  "favouritism": "favoritism",
+  "favours": "favors",
+  "feminise": "feminize",
+  "feminised": "feminized",
+  "feminises": "feminizes",
+  "feminising": "feminizing",
+  "fertilisation": "fertilization",
+  "fertilise": "fertilize",
+  "fertilised": "fertilized",
+  "fertiliser": "fertilizer",
+  "fertilisers": "fertilizers",
+  "fertilises": "fertilizes",
+  "fertilising": "fertilizing",
+  "fervour": "fervor",
+  "fibre": "fiber",
+  "fibreglass": "fiberglass",
+  "fibres": "fibers",
+  "fictionalisation": "fictionalization",
+  "fictionalisations": "fictionalizations",
+  "fictionalise": "fictionalize",
+  "fictionalised": "fictionalized",
+  "fictionalises": "fictionalizes",
+  "fictionalising": "fictionalizing",
+  "fillet": "filet",
+  "filleted": "fileted",
+  "filleting": "fileting",
+  "fillets": "filets",
+  "finalisation": "finalization",
+  "finalise": "finalize",
+  "finalised": "finalized",
+  "finalises": "finalizes",
+  "finalising": "finalizing",
+  "flautist": "flutist",
+  "flautists": "flutists",
+  "flavour": "flavor",
+  "flavoured": "flavored",
+  "flavouring": "flavoring",
+  "flavourings": "flavorings",
+  "flavourless": "flavorless",
+  "flavours": "flavors",
+  "flavoursome": "flavorsome",
+  "flyer / flier": "flier / flyer",
+  "foetal": "fetal",
+  "foetid": "fetid",
+  "foetus": "fetus",
+  "foetuses": "fetuses",
+  "formalisation": "formalization",
+  "formalise": "formalize",
+  "formalised": "formalized",
+  "formalises": "formalizes",
+  "formalising": "formalizing",
+  "fossilisation": "fossilization",
+  "fossilise": "fossilize",
+  "fossilised": "fossilized",
+  "fossilises": "fossilizes",
+  "fossilising": "fossilizing",
+  "fraternisation": "fraternization",
+  "fraternise": "fraternize",
+  "fraternised": "fraternized",
+  "fraternises": "fraternizes",
+  "fraternising": "fraternizing",
+  "fulfil": "fulfill",
+  "fulfilment": "fulfillment",
+  "fulfils": "fulfills",
+  "funnelled": "funneled",
+  "funnelling": "funneling",
+  "gage": "gauge",
+  "gaged": "gauged",
+  "gages": "gauges",
+  "gaging": "gauging",
+  "galvanise": "galvanize",
+  "galvanised": "galvanized",
+  "galvanises": "galvanizes",
+  "galvanising": "galvanizing",
+  "gambolled": "gamboled",
+  "gambolling": "gamboling",
+  "gaol": "jail",
+  "gaolbird": "jailbird",
+  "gaolbirds": "jailbirds",
+  "gaolbreak": "jailbreak",
+  "gaolbreaks": "jailbreaks",
+  "gaoled": "jailed",
+  "gaoler": "jailer",
+  "gaolers": "jailers",
+  "gaoling": "jailing",
+  "gaols": "jails",
+  "gasses": "gases",
+  "generalisation": "generalization",
+  "generalisations": "generalizations",
+  "generalise": "generalize",
+  "generalised": "generalized",
+  "generalises": "generalizes",
+  "generalising": "generalizing",
+  "ghettoise": "ghettoize",
+  "ghettoised": "ghettoized",
+  "ghettoises": "ghettoizes",
+  "ghettoising": "ghettoizing",
+  "gipsies": "gypsies",
+  "glamor": "glamour",
+  "glamorise": "glamorize",
+  "glamorised": "glamorized",
+  "glamorises": "glamorizes",
+  "glamorising": "glamorizing",
+  "globalisation": "globalization",
+  "globalise": "globalize",
+  "globalised": "globalized",
+  "globalises": "globalizes",
+  "globalising": "globalizing",
+  "glueing": "gluing",
+  "goitre": "goiter",
+  "goitres": "goiters",
+  "gonorrhoea": "gonorrhea",
+  "gramme": "gram",
+  "grammes": "grams",
+  "gravelled": "graveled",
+  "grey": "gray",
+  "greyed": "grayed",
+  "greying": "graying",
+  "greyish": "grayish",
+  "greyness": "grayness",
+  "greys": "grays",
+  "grovelled": "groveled",
+  "grovelling": "groveling",
+  "groyne": "groin",
+  "groynes": "groins",
+  "gruelling": "grueling",
+  "gruellingly": "gruelingly",
+  "gryphon": "griffin",
+  "gryphons": "griffins",
+  "gynaecological": "gynecological",
+  "gynaecologist": "gynecologist",
+  "gynaecologists": "gynecologists",
+  "gynaecology": "gynecology",
+  "haematological": "hematological",
+  "haematologist": "hematologist",
+  "haematologists": "hematologists",
+  "haematology": "hematology",
+  "haemoglobin": "hemoglobin",
+  "haemophilia": "hemophilia",
+  "haemophiliac": "hemophiliac",
+  "haemophiliacs": "hemophiliacs",
+  "haemorrhage": "hemorrhage",
+  "haemorrhaged": "hemorrhaged",
+  "haemorrhages": "hemorrhages",
+  "haemorrhaging": "hemorrhaging",
+  "haemorrhoids": "hemorrhoids",
+  "harbour": "harbor",
+  "harboured": "harbored",
+  "harbouring": "harboring",
+  "harbours": "harbors",
+  "harmonisation": "harmonization",
+  "harmonise": "harmonize",
+  "harmonised": "harmonized",
+  "harmonises": "harmonizes",
+  "harmonising": "harmonizing",
+  "homoeopath": "homeopath",
+  "homoeopathic": "homeopathic",
+  "homoeopaths": "homeopaths",
+  "homoeopathy": "homeopathy",
+  "homogenise": "homogenize",
+  "homogenised": "homogenized",
+  "homogenises": "homogenizes",
+  "homogenising": "homogenizing",
+  "honour": "honor",
+  "honourable": "honorable",
+  "honourably": "honorably",
+  "honoured": "honored",
+  "honouring": "honoring",
+  "honours": "honors",
+  "hospitalisation": "hospitalization",
+  "hospitalise": "hospitalize",
+  "hospitalised": "hospitalized",
+  "hospitalises": "hospitalizes",
+  "hospitalising": "hospitalizing",
+  "humanise": "humanize",
+  "humanised": "humanized",
+  "humanises": "humanizes",
+  "humanising": "humanizing",
+  "humour": "humor",
+  "humoured": "humored",
+  "humouring": "humoring",
+  "humourless": "humorless",
+  "humours": "humors",
+  "hybridise": "hybridize",
+  "hybridised": "hybridized",
+  "hybridises": "hybridizes",
+  "hybridising": "hybridizing",
+  "hypnotise": "hypnotize",
+  "hypnotised": "hypnotized",
+  "hypnotises": "hypnotizes",
+  "hypnotising": "hypnotizing",
+  "hypothesise": "hypothesize",
+  "hypothesised": "hypothesized",
+  "hypothesises": "hypothesizes",
+  "hypothesising": "hypothesizing",
+  "idealisation": "idealization",
+  "idealise": "idealize",
+  "idealised": "idealized",
+  "idealises": "idealizes",
+  "idealising": "idealizing",
+  "idolise": "idolize",
+  "idolised": "idolized",
+  "idolises": "idolizes",
+  "idolising": "idolizing",
+  "immobilisation": "immobilization",
+  "immobilise": "immobilize",
+  "immobilised": "immobilized",
+  "immobiliser": "immobilizer",
+  "immobilisers": "immobilizers",
+  "immobilises": "immobilizes",
+  "immobilising": "immobilizing",
+  "immortalise": "immortalize",
+  "immortalised": "immortalized",
+  "immortalises": "immortalizes",
+  "immortalising": "immortalizing",
+  "immunisation": "immunization",
+  "immunise": "immunize",
+  "immunised": "immunized",
+  "immunises": "immunizes",
+  "immunising": "immunizing",
+  "impanelled": "impaneled",
+  "impanelling": "impaneling",
+  "imperilled": "imperiled",
+  "imperilling": "imperiling",
+  "individualise": "individualize",
+  "individualised": "individualized",
+  "individualises": "individualizes",
+  "individualising": "individualizing",
+  "industrialise": "industrialize",
+  "industrialised": "industrialized",
+  "industrialises": "industrializes",
+  "industrialising": "industrializing",
+  "inflexion": "inflection",
+  "inflexions": "inflections",
+  "initialise": "initialize",
+  "initialised": "initialized",
+  "initialises": "initializes",
+  "initialising": "initializing",
+  "initialled": "initialed",
+  "initialling": "initialing",
+  "instal": "install",
+  "instalment": "installment",
+  "instalments": "installments",
+  "instals": "installs",
+  "instil": "instill",
+  "instils": "instills",
+  "institutionalisation": "institutionalization",
+  "institutionalise": "institutionalize",
+  "institutionalised": "institutionalized",
+  "institutionalises": "institutionalizes",
+  "institutionalising": "institutionalizing",
+  "intellectualise": "intellectualize",
+  "intellectualised": "intellectualized",
+  "intellectualises": "intellectualizes",
+  "intellectualising": "intellectualizing",
+  "internalisation": "internalization",
+  "internalise": "internalize",
+  "internalised": "internalized",
+  "internalises": "internalizes",
+  "internalising": "internalizing",
+  "internationalisation": "internationalization",
+  "internationalise": "internationalize",
+  "internationalised": "internationalized",
+  "internationalises": "internationalizes",
+  "internationalising": "internationalizing",
+  "ionisation": "ionization",
+  "ionise": "ionize",
+  "ionised": "ionized",
+  "ioniser": "ionizer",
+  "ionisers": "ionizers",
+  "ionises": "ionizes",
+  "ionising": "ionizing",
+  "italicise": "italicize",
+  "italicised": "italicized",
+  "italicises": "italicizes",
+  "italicising": "italicizing",
+  "itemise": "itemize",
+  "itemised": "itemized",
+  "itemises": "itemizes",
+  "itemising": "itemizing",
+  "jeopardise": "jeopardize",
+  "jeopardised": "jeopardized",
+  "jeopardises": "jeopardizes",
+  "jeopardising": "jeopardizing",
+  "jewelled": "jeweled",
+  "jeweller": "jeweler",
+  "jewellers": "jewelers",
+  "jewellery": "jewelry",
+  "judgement": "judgment",
+  "kilogramme": "kilogram",
+  "kilogrammes": "kilograms",
+  "kilometre": "kilometer",
+  "kilometres": "kilometers",
+  "labelled": "labeled",
+  "labelling": "labeling",
+  "labour": "labor",
+  "laboured": "labored",
+  "labourer": "laborer",
+  "labourers": "laborers",
+  "labouring": "laboring",
+  "labours": "labors",
+  "lacklustre": "lackluster",
+  "legalisation": "legalization",
+  "legalise": "legalize",
+  "legalised": "legalized",
+  "legalises": "legalizes",
+  "legalising": "legalizing",
+  "legitimise": "legitimize",
+  "legitimised": "legitimized",
+  "legitimises": "legitimizes",
+  "legitimising": "legitimizing",
+  "leukaemia": "leukemia",
+  "levelled": "leveled",
+  "leveller": "leveler",
+  "levellers": "levelers",
+  "levelling": "leveling",
+  "libelled": "libeled",
+  "libelling": "libeling",
+  "libellous": "libelous",
+  "liberalisation": "liberalization",
+  "liberalise": "liberalize",
+  "liberalised": "liberalized",
+  "liberalises": "liberalizes",
+  "liberalising": "liberalizing",
+  "licence": "license",
+  "licenced": "licensed",
+  "licences": "licenses",
+  "licencing": "licensing",
+  "likeable": "likable",
+  "lionisation": "lionization",
+  "lionise": "lionize",
+  "lionised": "lionized",
+  "lionises": "lionizes",
+  "lionising": "lionizing",
+  "liquidise": "liquidize",
+  "liquidised": "liquidized",
+  "liquidiser": "liquidizer",
+  "liquidisers": "liquidizers",
+  "liquidises": "liquidizes",
+  "liquidising": "liquidizing",
+  "litre": "liter",
+  "litres": "liters",
+  "localise": "localize",
+  "localised": "localized",
+  "localises": "localizes",
+  "localising": "localizing",
+  "louvre": "louver",
+  "louvred": "louvered",
+  "louvres": "louvers",
+  "lustre": "luster",
+  "magnetise": "magnetize",
+  "magnetised": "magnetized",
+  "magnetises": "magnetizes",
+  "magnetising": "magnetizing",
+  "manoeuvrability": "maneuverability",
+  "manoeuvrable": "maneuverable",
+  "manoeuvre": "maneuver",
+  "manoeuvred": "maneuvered",
+  "manoeuvres": "maneuvers",
+  "manoeuvring": "maneuvering",
+  "manoeuvrings": "maneuverings",
+  "marginalisation": "marginalization",
+  "marginalise": "marginalize",
+  "marginalised": "marginalized",
+  "marginalises": "marginalizes",
+  "marginalising": "marginalizing",
+  "marshalled": "marshaled",
+  "marshalling": "marshaling",
+  "marvelled": "marveled",
+  "marvelling": "marveling",
+  "marvellous": "marvelous",
+  "marvellously": "marvelously",
+  "materialisation": "materialization",
+  "materialise": "materialize",
+  "materialised": "materialized",
+  "materialises": "materializes",
+  "materialising": "materializing",
+  "maximisation": "maximization",
+  "maximise": "maximize",
+  "maximised": "maximized",
+  "maximises": "maximizes",
+  "maximising": "maximizing",
+  "meagre": "meager",
+  "mechanisation": "mechanization",
+  "mechanise": "mechanize",
+  "mechanised": "mechanized",
+  "mechanises": "mechanizes",
+  "mechanising": "mechanizing",
+  "mediaeval": "medieval",
+  "memorialise": "memorialize",
+  "memorialised": "memorialized",
+  "memorialises": "memorializes",
+  "memorialising": "memorializing",
+  "memorise": "memorize",
+  "memorised": "memorized",
+  "memorises": "memorizes",
+  "memorising": "memorizing",
+  "mesmerise": "mesmerize",
+  "mesmerised": "mesmerized",
+  "mesmerises": "mesmerizes",
+  "mesmerising": "mesmerizing",
+  "metabolise": "metabolize",
+  "metabolised": "metabolized",
+  "metabolises": "metabolizes",
+  "metabolising": "metabolizing",
+  "metre": "meter",
+  "metres": "meters",
+  "mhm": "hmm",
+  "micrometre": "micrometer",
+  "micrometres": "micrometers",
+  "militarise": "militarize",
+  "militarised": "militarized",
+  "militarises": "militarizes",
+  "militarising": "militarizing",
+  "milligramme": "milligram",
+  "milligrammes": "milligrams",
+  "millilitre": "milliliter",
+  "millilitres": "milliliters",
+  "millimetre": "millimeter",
+  "millimetres": "millimeters",
+  "miniaturisation": "miniaturization",
+  "miniaturise": "miniaturize",
+  "miniaturised": "miniaturized",
+  "miniaturises": "miniaturizes",
+  "miniaturising": "miniaturizing",
+  "minibusses": "minibuses",
+  "minimise": "minimize",
+  "minimised": "minimized",
+  "minimises": "minimizes",
+  "minimising": "minimizing",
+  "misbehaviour": "misbehavior",
+  "misdemeanour": "misdemeanor",
+  "misdemeanours": "misdemeanors",
+  "misspelt": "misspelled",
+  "mitre": "miter",
+  "mitres": "miters",
+  "mm": "hmm",
+  "mmm": "hmm",
+  "mobilisation": "mobilization",
+  "mobilise": "mobilize",
+  "mobilised": "mobilized",
+  "mobilises": "mobilizes",
+  "mobilising": "mobilizing",
+  "modelled": "modeled",
+  "modeller": "modeler",
+  "modellers": "modelers",
+  "modelling": "modeling",
+  "modernise": "modernize",
+  "modernised": "modernized",
+  "modernises": "modernizes",
+  "modernising": "modernizing",
+  "moisturise": "moisturize",
+  "moisturised": "moisturized",
+  "moisturiser": "moisturizer",
+  "moisturisers": "moisturizers",
+  "moisturises": "moisturizes",
+  "moisturising": "moisturizing",
+  "monologue": "monolog",
+  "monologues": "monologs",
+  "monopolisation": "monopolization",
+  "monopolise": "monopolize",
+  "monopolised": "monopolized",
+  "monopolises": "monopolizes",
+  "monopolising": "monopolizing",
+  "moralise": "moralize",
+  "moralised": "moralized",
+  "moralises": "moralizes",
+  "moralising": "moralizing",
+  "motorised": "motorized",
+  "mould": "mold",
+  "moulded": "molded",
+  "moulder": "molder",
+  "mouldered": "moldered",
+  "mouldering": "moldering",
+  "moulders": "molders",
+  "mouldier": "moldier",
+  "mouldiest": "moldiest",
+  "moulding": "molding",
+  "mouldings": "moldings",
+  "moulds": "molds",
+  "mouldy": "moldy",
+  "moult": "molt",
+  "moulted": "molted",
+  "moulting": "molting",
+  "moults": "molts",
+  "moustache": "mustache",
+  "moustached": "mustached",
+  "moustaches": "mustaches",
+  "moustachioed": "mustachioed",
+  "multicoloured": "multicolored",
+  "nationalisation": "nationalization",
+  "nationalisations": "nationalizations",
+  "nationalise": "nationalize",
+  "nationalised": "nationalized",
+  "nationalises": "nationalizes",
+  "nationalising": "nationalizing",
+  "naturalisation": "naturalization",
+  "naturalise": "naturalize",
+  "naturalised": "naturalized",
+  "naturalises": "naturalizes",
+  "naturalising": "naturalizing",
+  "neighbour": "neighbor",
+  "neighbourhood": "neighborhood",
+  "neighbourhoods": "neighborhoods",
+  "neighbouring": "neighboring",
+  "neighbourliness": "neighborliness",
+  "neighbourly": "neighborly",
+  "neighbours": "neighbors",
+  "neutralisation": "neutralization",
+  "neutralise": "neutralize",
+  "neutralised": "neutralized",
+  "neutralises": "neutralizes",
+  "neutralising": "neutralizing",
+  "normalisation": "normalization",
+  "normalise": "normalize",
+  "normalised": "normalized",
+  "normalises": "normalizes",
+  "normalising": "normalizing",
+  "odour": "odor",
+  "odourless": "odorless",
+  "odours": "odors",
+  "oesophagus": "esophagus",
+  "oesophaguses": "esophaguses",
+  "oestrogen": "estrogen",
+  "offence": "offense",
+  "offences": "offenses",
+  "omelette": "omelet",
+  "omelettes": "omelets",
+  "optimise": "optimize",
+  "optimised": "optimized",
+  "optimises": "optimizes",
+  "optimising": "optimizing",
+  "organisation": "organization",
+  "organisational": "organizational",
+  "organisations": "organizations",
+  "organise": "organize",
+  "organised": "organized",
+  "organiser": "organizer",
+  "organisers": "organizers",
+  "organises": "organizes",
+  "organising": "organizing",
+  "orthopaedic": "orthopedic",
+  "orthopaedics": "orthopedics",
+  "ostracise": "ostracize",
+  "ostracised": "ostracized",
+  "ostracises": "ostracizes",
+  "ostracising": "ostracizing",
+  "outmanoeuvre": "outmaneuver",
+  "outmanoeuvred": "outmaneuvered",
+  "outmanoeuvres": "outmaneuvers",
+  "outmanoeuvring": "outmaneuvering",
+  "overemphasise": "overemphasize",
+  "overemphasised": "overemphasized",
+  "overemphasises": "overemphasizes",
+  "overemphasising": "overemphasizing",
+  "oxidisation": "oxidization",
+  "oxidise": "oxidize",
+  "oxidised": "oxidized",
+  "oxidises": "oxidizes",
+  "oxidising": "oxidizing",
+  "paederast": "pederast",
+  "paederasts": "pederasts",
+  "paediatric": "pediatric",
+  "paediatrician": "pediatrician",
+  "paediatricians": "pediatricians",
+  "paediatrics": "pediatrics",
+  "paedophile": "pedophile",
+  "paedophiles": "pedophiles",
+  "paedophilia": "pedophilia",
+  "palaeolithic": "paleolithic",
+  "palaeontologist": "paleontologist",
+  "palaeontologists": "paleontologists",
+  "palaeontology": "paleontology",
+  "panelled": "paneled",
+  "panelling": "paneling",
+  "panellist": "panelist",
+  "panellists": "panelists",
+  "paralyse": "paralyze",
+  "paralysed": "paralyzed",
+  "paralyses": "paralyzes",
+  "paralysing": "paralyzing",
+  "parcelled": "parceled",
+  "parcelling": "parceling",
+  "parlour": "parlor",
+  "parlours": "parlors",
+  "particularise": "particularize",
+  "particularised": "particularized",
+  "particularises": "particularizes",
+  "particularising": "particularizing",
+  "passivisation": "passivization",
+  "passivise": "passivize",
+  "passivised": "passivized",
+  "passivises": "passivizes",
+  "passivising": "passivizing",
+  "pasteurisation": "pasteurization",
+  "pasteurise": "pasteurize",
+  "pasteurised": "pasteurized",
+  "pasteurises": "pasteurizes",
+  "pasteurising": "pasteurizing",
+  "patronise": "patronize",
+  "patronised": "patronized",
+  "patronises": "patronizes",
+  "patronising": "patronizing",
+  "patronisingly": "patronizingly",
+  "pedalled": "pedaled",
+  "pedalling": "pedaling",
+  "pedestrianisation": "pedestrianization",
+  "pedestrianise": "pedestrianize",
+  "pedestrianised": "pedestrianized",
+  "pedestrianises": "pedestrianizes",
+  "pedestrianising": "pedestrianizing",
+  "penalise": "penalize",
+  "penalised": "penalized",
+  "penalises": "penalizes",
+  "penalising": "penalizing",
+  "pencilled": "penciled",
+  "pencilling": "penciling",
+  "personalise": "personalize",
+  "personalised": "personalized",
+  "personalises": "personalizes",
+  "personalising": "personalizing",
+  "pharmacopoeia": "pharmacopeia",
+  "pharmacopoeias": "pharmacopeias",
+  "philosophise": "philosophize",
+  "philosophised": "philosophized",
+  "philosophises": "philosophizes",
+  "philosophising": "philosophizing",
+  "philtre": "filter",
+  "philtres": "filters",
+  "phoney": "phony",
+  "plagiarise": "plagiarize",
+  "plagiarised": "plagiarized",
+  "plagiarises": "plagiarizes",
+  "plagiarising": "plagiarizing",
+  "plough": "plow",
+  "ploughed": "plowed",
+  "ploughing": "plowing",
+  "ploughman": "plowman",
+  "ploughmen": "plowmen",
+  "ploughs": "plows",
+  "ploughshare": "plowshare",
+  "ploughshares": "plowshares",
+  "polarisation": "polarization",
+  "polarise": "polarize",
+  "polarised": "polarized",
+  "polarises": "polarizes",
+  "polarising": "polarizing",
+  "politicisation": "politicization",
+  "politicise": "politicize",
+  "politicised": "politicized",
+  "politicises": "politicizes",
+  "politicising": "politicizing",
+  "popularisation": "popularization",
+  "popularise": "popularize",
+  "popularised": "popularized",
+  "popularises": "popularizes",
+  "popularising": "popularizing",
+  "pouffe": "pouf",
+  "pouffes": "poufs",
+  "practise": "practice",
+  "practised": "practiced",
+  "practises": "practices",
+  "practising": "practicing",
+  "praesidium": "presidium",
+  "praesidiums": "presidiums",
+  "pressurisation": "pressurization",
+  "pressurise": "pressurize",
+  "pressurised": "pressurized",
+  "pressurises": "pressurizes",
+  "pressurising": "pressurizing",
+  "pretence": "pretense",
+  "pretences": "pretenses",
+  "primaeval": "primeval",
+  "prioritisation": "prioritization",
+  "prioritise": "prioritize",
+  "prioritised": "prioritized",
+  "prioritises": "prioritizes",
+  "prioritising": "prioritizing",
+  "privatisation": "privatization",
+  "privatisations": "privatizations",
+  "privatise": "privatize",
+  "privatised": "privatized",
+  "privatises": "privatizes",
+  "privatising": "privatizing",
+  "professionalisation": "professionalization",
+  "professionalise": "professionalize",
+  "professionalised": "professionalized",
+  "professionalises": "professionalizes",
+  "professionalising": "professionalizing",
+  "programme": "program",
+  "programmes": "programs",
+  "prologue": "prolog",
+  "prologues": "prologs",
+  "propagandise": "propagandize",
+  "propagandised": "propagandized",
+  "propagandises": "propagandizes",
+  "propagandising": "propagandizing",
+  "proselytise": "proselytize",
+  "proselytised": "proselytized",
+  "proselytiser": "proselytizer",
+  "proselytisers": "proselytizers",
+  "proselytises": "proselytizes",
+  "proselytising": "proselytizing",
+  "psychoanalyse": "psychoanalyze",
+  "psychoanalysed": "psychoanalyzed",
+  "psychoanalyses": "psychoanalyzes",
+  "psychoanalysing": "psychoanalyzing",
+  "publicise": "publicize",
+  "publicised": "publicized",
+  "publicises": "publicizes",
+  "publicising": "publicizing",
+  "pulverisation": "pulverization",
+  "pulverise": "pulverize",
+  "pulverised": "pulverized",
+  "pulverises": "pulverizes",
+  "pulverising": "pulverizing",
+  "pummelled": "pummel",
+  "pummelling": "pummeled",
+  "pyjama": "pajama",
+  "pyjamas": "pajamas",
+  "pzazz": "pizzazz",
+  "quarrelled": "quarreled",
+  "quarrelling": "quarreling",
+  "radicalise": "radicalize",
+  "radicalised": "radicalized",
+  "radicalises": "radicalizes",
+  "radicalising": "radicalizing",
+  "rancour": "rancor",
+  "randomise": "randomize",
+  "randomised": "randomized",
+  "randomises": "randomizes",
+  "randomising": "randomizing",
+  "rationalisation": "rationalization",
+  "rationalisations": "rationalizations",
+  "rationalise": "rationalize",
+  "rationalised": "rationalized",
+  "rationalises": "rationalizes",
+  "rationalising": "rationalizing",
+  "ravelled": "raveled",
+  "ravelling": "raveling",
+  "realisable": "realizable",
+  "realisation": "realization",
+  "realisations": "realizations",
+  "realise": "realize",
+  "realised": "realized",
+  "realises": "realizes",
+  "realising": "realizing",
+  "recognisable": "recognizable",
+  "recognisably": "recognizably",
+  "recognisance": "recognizance",
+  "recognise": "recognize",
+  "recognised": "recognized",
+  "recognises": "recognizes",
+  "recognising": "recognizing",
+  "reconnoitre": "reconnoiter",
+  "reconnoitred": "reconnoitered",
+  "reconnoitres": "reconnoiters",
+  "reconnoitring": "reconnoitering",
+  "refuelled": "refueled",
+  "refuelling": "refueling",
+  "regularisation": "regularization",
+  "regularise": "regularize",
+  "regularised": "regularized",
+  "regularises": "regularizes",
+  "regularising": "regularizing",
+  "remodelled": "remodeled",
+  "remodelling": "remodeling",
+  "remould": "remold",
+  "remoulded": "remolded",
+  "remoulding": "remolding",
+  "remoulds": "remolds",
+  "reorganisation": "reorganization",
+  "reorganisations": "reorganizations",
+  "reorganise": "reorganize",
+  "reorganised": "reorganized",
+  "reorganises": "reorganizes",
+  "reorganising": "reorganizing",
+  "revelled": "reveled",
+  "reveller": "reveler",
+  "revellers": "revelers",
+  "revelling": "reveling",
+  "revitalise": "revitalize",
+  "revitalised": "revitalized",
+  "revitalises": "revitalizes",
+  "revitalising": "revitalizing",
+  "revolutionise": "revolutionize",
+  "revolutionised": "revolutionized",
+  "revolutionises": "revolutionizes",
+  "revolutionising": "revolutionizing",
+  "rhapsodise": "rhapsodize",
+  "rhapsodised": "rhapsodized",
+  "rhapsodises": "rhapsodizes",
+  "rhapsodising": "rhapsodizing",
+  "rigour": "rigor",
+  "rigours": "rigors",
+  "ritualised": "ritualized",
+  "rivalled": "rivaled",
+  "rivalling": "rivaling",
+  "romanticise": "romanticize",
+  "romanticised": "romanticized",
+  "romanticises": "romanticizes",
+  "romanticising": "romanticizing",
+  "rumour": "rumor",
+  "rumoured": "rumored",
+  "rumours": "rumors",
+  "sabre": "saber",
+  "sabres": "sabers",
+  "saltpetre": "saltpeter",
+  "sanitise": "sanitize",
+  "sanitised": "sanitized",
+  "sanitises": "sanitizes",
+  "sanitising": "sanitizing",
+  "satirise": "satirize",
+  "satirised": "satirized",
+  "satirises": "satirizes",
+  "satirising": "satirizing",
+  "saviour": "savior",
+  "saviours": "saviors",
+  "savour": "savor",
+  "savoured": "savored",
+  "savouries": "savories",
+  "savouring": "savoring",
+  "savours": "savors",
+  "savoury": "savory",
+  "scandalise": "scandalize",
+  "scandalised": "scandalized",
+  "scandalises": "scandalizes",
+  "scandalising": "scandalizing",
+  "sceptic": "skeptic",
+  "sceptical": "skeptical",
+  "sceptically": "skeptically",
+  "scepticism": "skepticism",
+  "sceptics": "skeptics",
+  "sceptre": "scepter",
+  "sceptres": "scepters",
+  "scrutinise": "scrutinize",
+  "scrutinised": "scrutinized",
+  "scrutinises": "scrutinizes",
+  "scrutinising": "scrutinizing",
+  "secularisation": "secularization",
+  "secularise": "secularize",
+  "secularised": "secularized",
+  "secularises": "secularizes",
+  "secularising": "secularizing",
+  "sensationalise": "sensationalize",
+  "sensationalised": "sensationalized",
+  "sensationalises": "sensationalizes",
+  "sensationalising": "sensationalizing",
+  "sensitise": "sensitize",
+  "sensitised": "sensitized",
+  "sensitises": "sensitizes",
+  "sensitising": "sensitizing",
+  "sentimentalise": "sentimentalize",
+  "sentimentalised": "sentimentalized",
+  "sentimentalises": "sentimentalizes",
+  "sentimentalising": "sentimentalizing",
+  "sepulchre": "sepulcher",
+  "sepulchres": "sepulchers",
+  "serialisation": "serialization",
+  "serialisations": "serializations",
+  "serialise": "serialize",
+  "serialised": "serialized",
+  "serialises": "serializes",
+  "serialising": "serializing",
+  "sermonise": "sermonize",
+  "sermonised": "sermonized",
+  "sermonises": "sermonizes",
+  "sermonising": "sermonizing",
+  "sheikh": "sheik",
+  "shovelled": "shoveled",
+  "shovelling": "shoveling",
+  "shrivelled": "shriveled",
+  "shrivelling": "shriveling",
+  "signalise": "signalize",
+  "signalised": "signalized",
+  "signalises": "signalizes",
+  "signalising": "signalizing",
+  "signalled": "signaled",
+  "signalling": "signaling",
+  "smoulder": "smolder",
+  "smouldered": "smoldered",
+  "smouldering": "smoldering",
+  "smoulders": "smolders",
+  "snivelled": "sniveled",
+  "snivelling": "sniveling",
+  "snorkelled": "snorkeled",
+  "snorkelling": "snorkeling",
+  "snowplough": "snowplow",
+  "snowploughs": "snowplow",
+  "socialisation": "socialization",
+  "socialise": "socialize",
+  "socialised": "socialized",
+  "socialises": "socializes",
+  "socialising": "socializing",
+  "sodomise": "sodomize",
+  "sodomised": "sodomized",
+  "sodomises": "sodomizes",
+  "sodomising": "sodomizing",
+  "solemnise": "solemnize",
+  "solemnised": "solemnized",
+  "solemnises": "solemnizes",
+  "solemnising": "solemnizing",
+  "sombre": "somber",
+  "specialisation": "specialization",
+  "specialisations": "specializations",
+  "specialise": "specialize",
+  "specialised": "specialized",
+  "specialises": "specializes",
+  "specialising": "specializing",
+  "spectre": "specter",
+  "spectres": "specters",
+  "spiralled": "spiraled",
+  "spiralling": "spiraling",
+  "splendour": "splendor",
+  "splendours": "splendors",
+  "squirrelled": "squirreled",
+  "squirrelling": "squirreling",
+  "stabilisation": "stabilization",
+  "stabilise": "stabilize",
+  "stabilised": "stabilized",
+  "stabiliser": "stabilizer",
+  "stabilisers": "stabilizers",
+  "stabilises": "stabilizes",
+  "stabilising": "stabilizing",
+  "standardisation": "standardization",
+  "standardise": "standardize",
+  "standardised": "standardized",
+  "standardises": "standardizes",
+  "standardising": "standardizing",
+  "stencilled": "stenciled",
+  "stencilling": "stenciling",
+  "sterilisation": "sterilization",
+  "sterilisations": "sterilizations",
+  "sterilise": "sterilize",
+  "sterilised": "sterilized",
+  "steriliser": "sterilizer",
+  "sterilisers": "sterilizers",
+  "sterilises": "sterilizes",
+  "sterilising": "sterilizing",
+  "stigmatisation": "stigmatization",
+  "stigmatise": "stigmatize",
+  "stigmatised": "stigmatized",
+  "stigmatises": "stigmatizes",
+  "stigmatising": "stigmatizing",
+  "storey": "story",
+  "storeys": "stories",
+  "subsidisation": "subsidization",
+  "subsidise": "subsidize",
+  "subsidised": "subsidized",
+  "subsidiser": "subsidizer",
+  "subsidisers": "subsidizers",
+  "subsidises": "subsidizes",
+  "subsidising": "subsidizing",
+  "succour": "succor",
+  "succoured": "succored",
+  "succouring": "succoring",
+  "succours": "succors",
+  "sulphate": "sulfate",
+  "sulphates": "sulfates",
+  "sulphide": "sulfide",
+  "sulphides": "sulfides",
+  "sulphur": "sulfur",
+  "sulphurous": "sulfurous",
+  "summarise": "summarize",
+  "summarised": "summarized",
+  "summarises": "summarizes",
+  "summarising": "summarizing",
+  "swivelled": "swiveled",
+  "swivelling": "swiveling",
+  "symbolise": "symbolize",
+  "symbolised": "symbolized",
+  "symbolises": "symbolizes",
+  "symbolising": "symbolizing",
+  "sympathise": "sympathize",
+  "sympathised": "sympathized",
+  "sympathiser": "sympathizer",
+  "sympathisers": "sympathizers",
+  "sympathises": "sympathizes",
+  "sympathising": "sympathizing",
+  "synchronisation": "synchronization",
+  "synchronise": "synchronize",
+  "synchronised": "synchronized",
+  "synchronises": "synchronizes",
+  "synchronising": "synchronizing",
+  "synthesise": "synthesize",
+  "synthesised": "synthesized",
+  "synthesiser": "synthesizer",
+  "synthesisers": "synthesizers",
+  "synthesises": "synthesizes",
+  "synthesising": "synthesizing",
+  "syphon": "siphon",
+  "syphoned": "siphoned",
+  "syphoning": "siphoning",
+  "syphons": "siphons",
+  "systematisation": "systematization",
+  "systematise": "systematize",
+  "systematised": "systematized",
+  "systematises": "systematizes",
+  "systematising": "systematizing",
+  "tantalise": "tantalize",
+  "tantalised": "tantalized",
+  "tantalises": "tantalizes",
+  "tantalising": "tantalizing",
+  "tantalisingly": "tantalizingly",
+  "tasselled": "tasseled",
+  "technicolour": "technicolor",
+  "temporise": "temporize",
+  "temporised": "temporized",
+  "temporises": "temporizes",
+  "temporising": "temporizing",
+  "tenderise": "tenderize",
+  "tenderised": "tenderized",
+  "tenderises": "tenderizes",
+  "tenderising": "tenderizing",
+  "terrorise": "terrorize",
+  "terrorised": "terrorized",
+  "terrorises": "terrorizes",
+  "terrorising": "terrorizing",
+  "theatre": "theater",
+  "theatregoer": "theatergoer",
+  "theatregoers": "theatergoers",
+  "theatres": "theaters",
+  "theorise": "theorize",
+  "theorised": "theorized",
+  "theorises": "theorizes",
+  "theorising": "theorizing",
+  "tonne": "ton",
+  "tonnes": "tons",
+  "towelled": "toweled",
+  "towelling": "toweling",
+  "toxaemia": "toxemia",
+  "tranquillise": "tranquilize",
+  "tranquillised": "tranquilized",
+  "tranquilliser": "tranquilizer",
+  "tranquillisers": "tranquilizers",
+  "tranquillises": "tranquilizes",
+  "tranquillising": "tranquilizing",
+  "tranquillity": "tranquility",
+  "tranquillize": "tranquilize",
+  "tranquillized": "tranquilized",
+  "tranquillizer": "tranquilizer",
+  "tranquillizers": "tranquilizers",
+  "tranquillizes": "tranquilizes",
+  "tranquillizing": "tranquilizing",
+  "tranquilly": "tranquility",
+  "transistorised": "transistorized",
+  "traumatise": "traumatize",
+  "traumatised": "traumatized",
+  "traumatises": "traumatizes",
+  "traumatising": "traumatizing",
+  "travelled": "traveled",
+  "traveller": "traveler",
+  "travellers": "travelers",
+  "travelling": "traveling",
+  "travelog": "travelogue",
+  "travelogs": "travelogues",
+  "trialled": "trialed",
+  "trialling": "trialing",
+  "tricolour": "tricolor",
+  "tricolours": "tricolors",
+  "trivialise": "trivialize",
+  "trivialised": "trivialized",
+  "trivialises": "trivializes",
+  "trivialising": "trivializing",
+  "tumour": "tumor",
+  "tumours": "tumors",
+  "tunnelled": "tunneled",
+  "tunnelling": "tunneling",
+  "tyrannise": "tyrannize",
+  "tyrannised": "tyrannized",
+  "tyrannises": "tyrannizes",
+  "tyrannising": "tyrannizing",
+  "tyre": "tire",
+  "tyres": "tires",
+  "unauthorised": "unauthorized",
+  "uncivilised": "uncivilized",
+  "underutilised": "underutilized",
+  "unequalled": "unequaled",
+  "unfavourable": "unfavorable",
+  "unfavourably": "unfavorably",
+  "unionisation": "unionization",
+  "unionise": "unionize",
+  "unionised": "unionized",
+  "unionises": "unionizes",
+  "unionising": "unionizing",
+  "unorganised": "unorganized",
+  "unravelled": "unraveled",
+  "unravelling": "unraveling",
+  "unrecognisable": "unrecognizable",
+  "unrecognised": "unrecognized",
+  "unrivalled": "unrivaled",
+  "unsavoury": "unsavory",
+  "untrammelled": "untrammeled",
+  "urbanisation": "urbanization",
+  "urbanise": "urbanize",
+  "urbanised": "urbanized",
+  "urbanises": "urbanizes",
+  "urbanising": "urbanizing",
+  "utilisable": "utilizable",
+  "utilisation": "utilization",
+  "utilise": "utilize",
+  "utilised": "utilized",
+  "utilises": "utilizes",
+  "utilising": "utilizing",
+  "valour": "valor",
+  "vandalise": "vandalize",
+  "vandalised": "vandalized",
+  "vandalises": "vandalizes",
+  "vandalising": "vandalizing",
+  "vaporisation": "vaporization",
+  "vaporise": "vaporize",
+  "vaporised": "vaporized",
+  "vaporises": "vaporizes",
+  "vaporising": "vaporizing",
+  "vapour": "vapor",
+  "vapours": "vapors",
+  "verbalise": "verbalize",
+  "verbalised": "verbalized",
+  "verbalises": "verbalizes",
+  "verbalising": "verbalizing",
+  "victimisation": "victimization",
+  "victimise": "victimize",
+  "victimised": "victimized",
+  "victimises": "victimizes",
+  "victimising": "victimizing",
+  "videodisc": "videodisk",
+  "videodiscs": "videodisks",
+  "vigour": "vigor",
+  "visualisation": "visualization",
+  "visualisations": "visualizations",
+  "visualise": "visualize",
+  "visualised": "visualized",
+  "visualises": "visualizes",
+  "visualising": "visualizing",
+  "vocalisation": "vocalization",
+  "vocalisations": "vocalizations",
+  "vocalise": "vocalize",
+  "vocalised": "vocalized",
+  "vocalises": "vocalizes",
+  "vocalising": "vocalizing",
+  "vulcanised": "vulcanized",
+  "vulgarisation": "vulgarization",
+  "vulgarise": "vulgarize",
+  "vulgarised": "vulgarized",
+  "vulgarises": "vulgarizes",
+  "vulgarising": "vulgarizing",
+  "waggon": "wagon",
+  "waggons": "wagons",
+  "watercolour": "watercolor",
+  "watercolours": "watercolors",
+  "weaselled": "weaseled",
+  "weaselling": "weaseling",
+  "westernisation": "westernization",
+  "westernise": "westernize",
+  "westernised": "westernized",
+  "westernises": "westernizes",
+  "westernising": "westernizing",
+  "womanise": "womanize",
+  "womanised": "womanized",
+  "womaniser": "womanizer",
+  "womanisers": "womanizers",
+  "womanises": "womanizes",
+  "womanising": "womanizing",
+  "woollen": "woolen",
+  "woollens": "woolens",
+  "woollies": "woolies",
+  "woolly": "wooly",
+  "worshipped": "worshiped",
+  "worshipper": "worshiper",
+  "worshipping": "worshiping",
+  "yodelled": "yodeled",
+  "yodelling": "yodeling",
+  "yoghourt": "yogurt",
+  "yoghourts": "yogurts",
+  "yoghurt": "yogurt",
+  "yoghurts": "yogurts"
+}
diff --git a/distil-large-v3-init/preprocessor_config.json b/distil-large-v3-init/preprocessor_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..931c77a740890c46365c7ae0c9d350ba3cca908f
--- /dev/null
+++ b/distil-large-v3-init/preprocessor_config.json
@@ -0,0 +1,14 @@
+{
+  "chunk_length": 30,
+  "feature_extractor_type": "WhisperFeatureExtractor",
+  "feature_size": 128,
+  "hop_length": 160,
+  "n_fft": 400,
+  "n_samples": 480000,
+  "nb_max_frames": 3000,
+  "padding_side": "right",
+  "padding_value": 0.0,
+  "processor_class": "WhisperProcessor",
+  "return_attention_mask": false,
+  "sampling_rate": 16000
+}
diff --git a/distil-large-v3-init/special_tokens_map.json b/distil-large-v3-init/special_tokens_map.json
new file mode 100644
index 0000000000000000000000000000000000000000..312bc106291bb51bf2cc1648df070bef963a0639
--- /dev/null
+++ b/distil-large-v3-init/special_tokens_map.json
@@ -0,0 +1,139 @@
+{
+  "additional_special_tokens": [
+    "<|startoftranscript|>",
+    "<|en|>",
+    "<|zh|>",
+    "<|de|>",
+    "<|es|>",
+    "<|ru|>",
+    "<|ko|>",
+    "<|fr|>",
+    "<|ja|>",
+    "<|pt|>",
+    "<|tr|>",
+    "<|pl|>",
+    "<|ca|>",
+    "<|nl|>",
+    "<|ar|>",
+    "<|sv|>",
+    "<|it|>",
+    "<|id|>",
+    "<|hi|>",
+    "<|fi|>",
+    "<|vi|>",
+    "<|he|>",
+    "<|uk|>",
+    "<|el|>",
+    "<|ms|>",
+    "<|cs|>",
+    "<|ro|>",
+    "<|da|>",
+    "<|hu|>",
+    "<|ta|>",
+    "<|no|>",
+    "<|th|>",
+    "<|ur|>",
+    "<|hr|>",
+    "<|bg|>",
+    "<|lt|>",
+    "<|la|>",
+    "<|mi|>",
+    "<|ml|>",
+    "<|cy|>",
+    "<|sk|>",
+    "<|te|>",
+    "<|fa|>",
+    "<|lv|>",
+    "<|bn|>",
+    "<|sr|>",
+    "<|az|>",
+    "<|sl|>",
+    "<|kn|>",
+    "<|et|>",
+    "<|mk|>",
+    "<|br|>",
+    "<|eu|>",
+    "<|is|>",
+    "<|hy|>",
+    "<|ne|>",
+    "<|mn|>",
+    "<|bs|>",
+    "<|kk|>",
+    "<|sq|>",
+    "<|sw|>",
+    "<|gl|>",
+    "<|mr|>",
+    "<|pa|>",
+    "<|si|>",
+    "<|km|>",
+    "<|sn|>",
+    "<|yo|>",
+    "<|so|>",
+    "<|af|>",
+    "<|oc|>",
+    "<|ka|>",
+    "<|be|>",
+    "<|tg|>",
+    "<|sd|>",
+    "<|gu|>",
+    "<|am|>",
+    "<|yi|>",
+    "<|lo|>",
+    "<|uz|>",
+    "<|fo|>",
+    "<|ht|>",
+    "<|ps|>",
+    "<|tk|>",
+    "<|nn|>",
+    "<|mt|>",
+    "<|sa|>",
+    "<|lb|>",
+    "<|my|>",
+    "<|bo|>",
+    "<|tl|>",
+    "<|mg|>",
+    "<|as|>",
+    "<|tt|>",
+    "<|haw|>",
+    "<|ln|>",
+    "<|ha|>",
+    "<|ba|>",
+    "<|jw|>",
+    "<|su|>",
+    "<|yue|>",
+    "<|translate|>",
+    "<|transcribe|>",
+    "<|startoflm|>",
+    "<|startofprev|>",
+    "<|nospeech|>",
+    "<|notimestamps|>"
+  ],
+  "bos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}
diff --git a/distil-large-v3-init/tokenizer_config.json b/distil-large-v3-init/tokenizer_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..06ffdc8308eae6bb7bd1fdd81e94b0a881a539ab
--- /dev/null
+++ b/distil-large-v3-init/tokenizer_config.json
@@ -0,0 +1,12996 @@
+{
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "50257": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50258": {
+      "content": "<|startoftranscript|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50259": {
+      "content": "<|en|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50260": {
+      "content": "<|zh|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50261": {
+      "content": "<|de|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50262": {
+      "content": "<|es|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50263": {
+      "content": "<|ru|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50264": {
+      "content": "<|ko|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50265": {
+      "content": "<|fr|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50266": {
+      "content": "<|ja|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50267": {
+      "content": "<|pt|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50268": {
+      "content": "<|tr|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50269": {
+      "content": "<|pl|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50270": {
+      "content": "<|ca|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50271": {
+      "content": "<|nl|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50272": {
+      "content": "<|ar|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50273": {
+      "content": "<|sv|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50274": {
+      "content": "<|it|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50275": {
+      "content": "<|id|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50276": {
+      "content": "<|hi|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50277": {
+      "content": "<|fi|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50278": {
+      "content": "<|vi|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50279": {
+      "content": "<|he|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50280": {
+      "content": "<|uk|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50281": {
+      "content": "<|el|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50282": {
+      "content": "<|ms|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50283": {
+      "content": "<|cs|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50284": {
+      "content": "<|ro|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50285": {
+      "content": "<|da|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50286": {
+      "content": "<|hu|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50287": {
+      "content": "<|ta|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50288": {
+      "content": "<|no|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50289": {
+      "content": "<|th|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50290": {
+      "content": "<|ur|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50291": {
+      "content": "<|hr|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50292": {
+      "content": "<|bg|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50293": {
+      "content": "<|lt|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50294": {
+      "content": "<|la|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50295": {
+      "content": "<|mi|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50296": {
+      "content": "<|ml|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50297": {
+      "content": "<|cy|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50298": {
+      "content": "<|sk|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50299": {
+      "content": "<|te|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50300": {
+      "content": "<|fa|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50301": {
+      "content": "<|lv|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50302": {
+      "content": "<|bn|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50303": {
+      "content": "<|sr|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50304": {
+      "content": "<|az|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50305": {
+      "content": "<|sl|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50306": {
+      "content": "<|kn|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50307": {
+      "content": "<|et|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50308": {
+      "content": "<|mk|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50309": {
+      "content": "<|br|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50310": {
+      "content": "<|eu|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50311": {
+      "content": "<|is|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50312": {
+      "content": "<|hy|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50313": {
+      "content": "<|ne|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50314": {
+      "content": "<|mn|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
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+}
diff --git a/distil-large-v3-init/vocab.json b/distil-large-v3-init/vocab.json
new file mode 100644
index 0000000000000000000000000000000000000000..0f3456460629e21d559c6daa23ab6ce3644e8271
--- /dev/null
+++ b/distil-large-v3-init/vocab.json
@@ -0,0 +1,50259 @@
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diff --git a/distil_whisper.egg-info/PKG-INFO b/distil_whisper.egg-info/PKG-INFO
new file mode 100644
index 0000000000000000000000000000000000000000..6d765593b83b3a40e674f861cf9c63b84cb80a6f
--- /dev/null
+++ b/distil_whisper.egg-info/PKG-INFO
@@ -0,0 +1,655 @@
+Metadata-Version: 2.1
+Name: distil_whisper
+Version: 0.0.0
+Summary: Toolkit for distilling OpenAI's Whisper model.
+Description-Content-Type: text/markdown
+Requires-Dist: torch>=1.10
+Requires-Dist: transformers>=4.35.1
+Requires-Dist: datasets[audio]>=2.14.7
+Requires-Dist: accelerate>=0.24.1
+Requires-Dist: jiwer
+Requires-Dist: evaluate>=0.4.1
+Requires-Dist: wandb
+Requires-Dist: tensorboard
+Requires-Dist: nltk
+Provides-Extra: dev
+Requires-Dist: ruff==0.1.5; extra == "dev"
+
+## Training Distil-Whisper
+
+This sub-folder contains all the scripts required to train a Distil-Whisper model in your choice of language. They are 
+slightly modified from the original scripts used to distill Whisper for English ASR (as-per the [Distil-Whisper paper](https://arxiv.org/abs/2311.00430)).
+The main difference is that these scripts are written in [PyTorch](https://pytorch.org), whereas the original scripts 
+are in [JAX](https://jax.readthedocs.io/en/latest/#)/[Flax](https://flax.readthedocs.io/en/latest/). These scripts are 
+also made to be easier to run end-to-end, whereas the original scripts require more steps and are somewhat hard-coded 
+for English ASR. Both sets of scripts achieve equivalent downstream results when the hyper-parameters are set equal.
+
+If you are interested in reproducing the original Distil-Whisper checkpoints, we refer you to the sub-folder [Flax Training](./flax/README.md).
+Otherwise, if you wish to distill Whisper on your own language/dataset, we recommend you use these scripts for ease of use
+and the configurability they provide.
+
+Reproducing the Distil-Whisper project requires four stages to be completed in successive order:
+
+1. [Pseudo-labelling](#1-pseudo-labelling)
+2. [Initialisation](#2-initialisation)
+3. [Training](#3-training)
+4. [Evaluation](#4-evaluation)
+
+This README is partitioned according to the four stages. Each section provides a minimal example for running the
+scripts used in the project. We will use a running example of distilling the Whisper model for Hindi speech recognition
+on the Common Voice dataset. Note that this dataset only contains ~20 hours of audio data. Thus, it can be run extremely
+quickly, but does not provide sufficient data to achieve optimal performance. We recommend training on upwards of 1000 
+hours of data should you want to match the performance of Whisper on high-resource languages.
+
+## Requirements
+
+The Distil-Whisper training code is written in [PyTorch](https://pytorch.org) and [Accelerate](https://huggingface.co/docs/accelerate/index). 
+It heavily leverages the Whisper implementation in [🤗 Transformers](https://github.com/huggingface/transformers) for both 
+training and inference.
+
+The instructions for installing the package are as follows:
+1. Install PyTorch from the [official instructions](https://pytorch.org/get-started/locally/), ensuring you install the correct version for your hardware and CUDA version.
+2. Fork the `distil-whisper` repository by clicking on the [fork](https://github.com/huggingface/distil-whisper/fork) button on the reopsitory's page
+3. Clone the `distil-whisper` repository and add the base repository as a remote. This will allow you to "pull" any upstream changes that are made to the base repository:
+
+```bash
+git clone https://github.com/<your GitHub handle>/distil-whisper.git
+cd distil-whisper
+git remote add upstream https://github.com/huggingface/distil-whisper.git
+```
+4. pip install the required packages from the [setup.py](./setup.py) file:
+```bash
+cd training
+pip install -e .
+cd ../..
+```
+
+5. Configure Accelerate by running the following command. Note that you should set the number of GPUs you wish to use for distillation, and also the data type (dtype) to your preferred dtype for training/inference (e.g. `bfloat16` on A100 GPUs, `float16` on V100 GPUs, etc.):
+
+```bash
+accelerate config
+```
+
+6. The last thing we need to do is link our Hugging Face account so that we can pull/push model repositories on the Hub. This will allow us to save our final distilled weights on the Hub so that we can share them with the community. Run the command:
+
+```bash
+git config --global credential.helper store
+huggingface-cli login
+```
+And then enter an authentication token from https://huggingface.co/settings/tokens. Create a new token if you do not have one already. You should make sure that this token has "write" privileges.
+
+To confirm that you have a working environment, first accept the terms of use of the Common Voice 16.1 dataset on the Hub: https://huggingface.co/datasets/mozilla-foundation/common_voice_16_1 
+
+You can run the following code cell to stream one sample of data from the Common Voice dataset, and check that you can 
+perform inference using the "tiny" Whisper model:
+
+```python
+from transformers import WhisperProcessor, WhisperForConditionalGeneration
+from datasets import load_dataset, Audio
+
+model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny", low_cpu_mem_usage=True)
+processor = WhisperProcessor.from_pretrained("openai/whisper-tiny")
+
+model.to("cuda")
+
+common_voice = load_dataset("mozilla-foundation/common_voice_16_1", "en", split="validation", streaming=True)
+common_voice = common_voice.cast_column("audio", Audio(sampling_rate=processor.feature_extractor.sampling_rate))
+
+inputs = processor(next(iter(common_voice))["audio"]["array"], sampling_rate=16000, return_tensors="pt")
+input_features = inputs.input_features
+
+generated_ids = model.generate(input_features.to("cuda"), max_new_tokens=128)
+pred_text = processor.decode(generated_ids[0], skip_special_tokens=True)
+
+print("Pred text:", pred_text)
+print("Environment set up successful?", generated_ids.shape[-1] == 20)
+```
+
+## 1. Pseudo-Labelling
+
+The python script [`run_pseudo_labelling.py`](run_pseudo_labelling.py) is a flexible inference script that can be used
+to generate pseudo-labels under a range of settings, including using both greedy and beam-search. It is also compatible
+with [🤗 Datasets](https://github.com/huggingface/datasets) *streaming mode*, allowing users to load massive audio
+datasets with **no disk space requirements**. For more information on streaming mode, the reader is referred to the 
+blog post: [A Complete Guide to Audio Datasets](https://huggingface.co/blog/audio-datasets#streaming-mode-the-silver-bullet).
+
+> As of the latest Distil-Whisper release, [`distil-large-v3`](https://huggingface.co/distil-whisper/distil-large-v3), this
+pseudo-labelling script also performs the added operation of concatenating (or packing) the audio inputs to 30-seconds. 
+Not only does this lead to a WER improvement when using sequential long-form decoding algorithm, but concatenating audios 
+to 30-seconds also improves the throughput during training, since the amount of zero-padding on the audio inputs is minimised.
+
+The following script demonstrates how to pseudo-label the Hindi split of the Common Voice 16.1 dataset with greedy sampling:
+
+```bash
+#!/usr/bin/env bash
+
+accelerate launch run_pseudo_labelling.py \
+  --model_name_or_path "openai/whisper-large-v3" \
+  --dataset_name "mozilla-foundation/common_voice_16_1" \
+  --dataset_config_name "hi" \
+  --dataset_split_name "train+validation+test" \
+  --text_column_name "sentence" \
+  --id_column_name "path" \
+  --output_dir "./common_voice_16_1_hi_pseudo_labelled" \
+  --wandb_project "distil-whisper-labelling" \
+  --per_device_eval_batch_size 64 \
+  --dtype "bfloat16" \
+  --attn_implementation "sdpa" \
+  --logging_steps 500 \
+  --max_label_length 256 \
+  --concatenate_audio \
+  --preprocessing_batch_size 500 \
+  --preprocessing_num_workers 8 \
+  --dataloader_num_workers 8 \
+  --report_to "wandb" \
+  --language "hi" \
+  --task "transcribe" \
+  --return_timestamps \
+  --streaming False \
+  --generation_num_beams 1 \
+  --push_to_hub
+```
+
+On an 80 GB A100 GPU, the following script takes approximately 5 minutes to concatenate and pre-process the 20 hours of 
+audio data, and a further 10 minutes to transcribe the pseudo-labels. The pseudo-labelled dataset corresponding to this
+script is available on the Hugging Face Hub under [sanchit-gandhi/common_voice_16_1_hi_pseudo_labelled](https://huggingface.co/datasets/sanchit-gandhi/common_voice_16_1_hi_pseudo_labelled).
+The WER of the pre-trained Whisper large-v3 model is 17.2% on the test split. We will compare the performance of our distilled model against this number.
+
+There are two noteworthy arguments that configure the dataset concatenation (or packing) process:
+1. `concatenate_audio`: whether or not to concatenate (or pack) the audios to 30-second chunks. The latest Distil-Whisper model, [`distil-large-v3`](https://huggingface.co/distil-whisper/distil-large-v3#differences-with-distil-large-v2), highlights the WER improvements obtained using the sequential long-form decoding algorithm when concatenated audios are used. Concatenating audios to 30-seconds also improves the throughput during training, since the amount of zero-padding on the audio inputs is minimised. Hence, it is highly recommended to set `--concatenate_audio=True`.
+2. `preprocessing_batch_size`: the batch size to use when concatenating (or packing) the audios. Using a larger batch size results in a greater portion of audio samples being packed to 30-seconds, at the expense of higher memory consumption. If you exceed your system's RAM when performing the concatenation operation, reduce the `preprocessing_batch_size` by a factor of 2 to 250 or even 125.
+3. `preprocessing_num_workers`: the number of multiprocessing workers to use when concatenating the audios. Using more workers will result in faster pre-processing, at the expense of higher memory consumption. Ensure you do not exceed the maximum number of CPUs on your device.
+
+In addition, the following arguments configure the inference of the Whisper model:
+1. `language`: explicitly setting the language token during inference substantially improves the generation performance of the Whisper model, since the model is forced always to predict in the given language. We recommend you set the language to the language you wish to distil the Whisper model on. The only exception is when distilling an English-only model (i.e. where the model id is appended with an `.en`, e.g. `small.en`), the language argument should be set to None, since there is no language token used during training/inference.
+2. `return_timestamps`: whether or not to predict timestamps in the pseudo-labels. Timestamp prediction is required should you want your distilled model to be able to predict timestamps at inference time (e.g. for the original OpenAI long-form transcription algorithm). However, the pseudo-labels are marginally less accurate than not using timestamps. We recommend pseudo-labelling **with** timestamps to ensure the distilled model is as general as possible.
+3. `attn_implementation`: which attention implementation to use for inference. Set to `sdpa` for [PyTorch SDPA](https://huggingface.co/docs/transformers/v4.35.2/en/perf_infer_gpu_one#bettertransformer), or `flash_attn_2` if your hardware supports Flash Attention 2 and you have the [package installed](https://github.com/Dao-AILab/flash-attention).
+4. `streaming`: whether or not to use Datasets' streaming mode. If enabled, the audio data will be streamed from the Hugging Face Hub with no disk space requirements. However, the user is then responsible for adding the pseudo-labels to the dataset script in a follow-up step (see [Using Streaming Mode](#TODO)). If set to `False`, the audio data will be downloaded and pre-processed offline. At the end of pseudo-labelling, the pseudo-labels will be automatically appended to the original dataset, meaning the dataset is ready to be used for the subsequent training step without any additional steps.
+5. `generation_num_beams`: how many beams to use while decoding. In practice, we found the distilled model to perform comparably when the data was pseudo-labelled with `generation_num_beams=1` (greedy) or `generation_num_beams>1` (beam). This is likely because the WER filter compensates for the lower quality pseudo-labels obtained using greedy search. However, using `generation_num_beams=1` gives substantially faster inference time for the pseudo-labelling step, and so we recommend this configuration.
+
+Should you have your own audio dataset, you can first [convert it](https://huggingface.co/docs/datasets/audio_dataset) to 
+Hugging Face Datasets format and push it to the Hugging Face Hub. You can then pseudo-label it using the script above, 
+replacing the `--dataset_name` with the name of your dataset on the Hub.
+
+Otherwise, you may wish to use an open-source dataset already available on the Hugging Face Hub. We provide a summary of 
+the three most popular multilingual datasets in the table below. For more details, refer to the blog post: [A Complete Guide to Audio Datasets](https://huggingface.co/blog/audio-datasets#multilingual-speech-recognition).
+
+| Dataset                                                                                       | Languages | Domain                                | Speaking Style | License   | Text Column         | ID Column    |
+|-----------------------------------------------------------------------------------------------|-----------|---------------------------------------|----------------|-----------|---------------------|--------------|
+| [Multilingual LibriSpeech](https://huggingface.co/datasets/facebook/multilingual_librispeech) | 6         | Audiobooks                            | Narrated       | CC-BY-4.0 | `"text"`            | `"id"`       |
+| [Common Voice 16](https://huggingface.co/datasets/mozilla-foundation/common_voice_16_1)       | 120       | Wikipedia text & crowd-sourced speech | Narrated       | CC0-1.0   | `"sentence"`        | `"path"`     |
+| [VoxPopuli](https://huggingface.co/datasets/facebook/voxpopuli)                               | 15        | European Parliament recordings        | Spontaneous    | CC0       | `"normalized_text"` | `"audio_id"` |
+
+To achieve *robustness* to different distributions of audio data, it is recommended to train on multiple datasets where possible.
+For example, the above three datasets all have splits for the German language. Thus, if distilling a Whisper model for German,
+it would be wise to use a combination of the three datasets during training, in order to cover at least three distinct domains
+(audiobooks, crowd-sourced speech, parliament recordings). You may wish to use a combination of open-source datasets, or 
+a combination of open-source and individually owned datasets to cover multiple distributions and domains. Moreover, if you were to train on low-resource datasets (<500 hours), you could experiment with [language mixing](#3-language-mixing) to improve robustness.
+
+## 2. Initialisation
+
+The script [`create_student_model.py`](create_student_model.py) can be used to initialise a small student model
+from a large teacher model. When initialising a student model with fewer layers than the teacher model, the student is 
+initialised by copying maximally spaced layers from the teacher, as per the [DistilBart](https://arxiv.org/abs/2010.13002)
+recommendations.
+
+First, we need to create a model repository on the Hugging Face Hub. This repository will contain all the required files 
+to reproduce the training run, alongside model weights, training logs and a README.md card. You can either create a model 
+repository directly on the Hugging Face Hub using the link: https://huggingface.co/new. Or, via the CLI, as we'll show here.
+
+Let's pick a name for our distilled model: `distil-whisper-large-v3-hi`. We can run the following command to create a repository under this name:
+
+```bash
+huggingface-cli repo create distil-whisper-large-v3-hi
+```
+
+We can now see the model on the Hub, e.g. under https://huggingface.co/sanchit-gandhi/distil-whisper-large-v3-hi
+
+Let's clone the repository so that we can place our training script and model weights inside:
+
+```bash
+git lfs install
+git clone https://huggingface.co/sanchit-gandhi/distil-whisper-large-v3-hi
+```
+
+Be sure to change the repo address to `https://huggingface.co/<your-user-name>/<your-repo-name>`
+
+We can now copy the relevant training scrips to the repository:
+```bash
+cd distil-whisper-large-v3-hi
+
+cp ../distil-whisper/training/create_student_model.py .
+cp ../distil-whisper/training/run_distillation.py .
+```
+
+The following command demonstrates how to initialise a student model from the Whisper [large-v3](https://huggingface.co/openai/whisper-large-v3) 
+checkpoint, with all 32 encoder layer and 2 decoder layers. The 2 student decoder layers are copied from teacher layers 
+1 and 32 respectively, as the maximally spaced layers:
+
+```bash
+#!/usr/bin/env bash
+
+python create_student_model.py \
+  --teacher_checkpoint "openai/whisper-large-v3" \
+  --encoder_layers 32 \
+  --decoder_layers 2 \
+  --save_dir "./distil-large-v3-init"
+```
+
+The initialised model will be saved to the sub-directory `distil-large-v3-init` in our model repository. 
+
+
+**Note:** You can leverage language transfer by setting `--teacher_checkpoint` to "distil-whisper/distil-large-v3", see [language transfer](#22-language-transfer) for more details.
+
+## 3. Training
+
+The script [`run_distillation.py`](run_distillation.py) is an end-to-end script for loading multiple
+datasets, a student model, a teacher model, and performing teacher-student distillation. It uses the loss formulation
+from the [Distil-Whisper paper](https://arxiv.org/abs/2311.00430), which is a weighted sum of the cross-entropy and 
+KL-divergence loss terms.
+
+The following command takes the Common Voice dataset that was pseudo-labelled in the first stage and trains the 
+2-layer decoder model intialised in the previous step. We pass the local path to the pseudo-labelled Common Voice dataset
+(`../common_voice_16_1_hi_pseudo_labelled`), which you can change to the path where your local pseudo-labelled dataset is 
+saved.
+
+In this example, we will combine the train and validation splits to give our training set, and evaluate on the test split 
+only. This is purely to demonstrate how to combine multiple pseudo-labelled datasets for training, rather than recommended 
+advice for defining train/validation splits. We advise that you train on the train splits of your dataset, evaluate and 
+tune hyper-parameters on the validation split, and only test the final checkpoint on the test split. Note how multiple 
+training datasets and splits can be loaded by separating the dataset arguments by `+` symbols. Thus, the script generalises 
+to any number of training datasets.
+
+```bash
+#!/usr/bin/env bash
+
+accelerate launch run_distillation.py \
+  --model_name_or_path "./distil-large-v3-init" \
+  --teacher_model_name_or_path "openai/whisper-large-v3" \
+  --train_dataset_name "../common_voice_16_1_hi_pseudo_labelled+../common_voice_16_1_hi_pseudo_labelled" \
+  --train_split_name "train+validation" \
+  --text_column_name "sentence+sentence" \
+  --train_dataset_samples "7+4" \
+  --eval_dataset_name "../common_voice_16_1_hi_pseudo_labelled" \
+  --eval_split_name "test" \
+  --eval_text_column_name "sentence" \
+  --eval_steps 1000 \
+  --save_steps 1000 \
+  --warmup_steps 50 \
+  --learning_rate 0.0001 \
+  --lr_scheduler_type "constant_with_warmup" \
+  --timestamp_probability 0.2 \
+  --condition_on_prev_probability 0.2 \
+  --language "hi" \
+  --task "transcribe" \
+  --logging_steps 25 \
+  --save_total_limit 1 \
+  --max_steps 5000 \
+  --wer_threshold 20 \
+  --per_device_train_batch_size 32 \
+  --per_device_eval_batch_size 32 \
+  --dataloader_num_workers 8 \
+  --preprocessing_num_workers 8 \
+  --ddp_timeout 7200 \
+  --dtype "bfloat16" \
+  --attn_implementation "sdpa" \
+  --output_dir "./" \
+  --do_train \
+  --do_eval \
+  --gradient_checkpointing \
+  --overwrite_output_dir \
+  --predict_with_generate \
+  --freeze_encoder \
+  --freeze_embed_positions \
+  --streaming False \
+  --push_to_hub
+
+```
+
+The above training script will take approximately 3 hours to complete on an 80 GB A100 GPU and yield a final WER of 76%.
+While the generations are starting to take form, there is still a 59% WER gap to the teacher model. This is hardly
+surprising give we only have 15 hours of un-filtered data, and closer to just 1.5 hours with data filtering. 
+As mentioned above, using upwards of 1000 hours of data and training for 10k steps will likely yield
+more competitive performance. For the [Distil-Whisper paper](https://arxiv.org/abs/2311.00430), we trained on 21k hours
+of audio data for 80k steps. We found that upwards of 13k hours of audio data was required to reach convergence on English 
+ASR (see Section 9.2 of the [paper](https://arxiv.org/abs/2311.00430)), so the more data you have, the better!
+
+Scaling to multiple GPUs using [distributed data parallelism (DDP)](https://pytorch.org/tutorials/beginner/ddp_series_theory.html)
+is trivial: simply run `accelerate config` and select the multi-GPU option, specifying the IDs of the GPUs you wish to use. The 
+above script can then be run using DDP with no code changes. 
+
+Training logs will be reported to TensorBoard and WandB, provided the relevant packages are available. An example of a 
+saved checkpoint pushed to the Hugging Face Hub can be found here: [sanchit-gandhi/distil-whisper-large-v3-hi](https://huggingface.co/sanchit-gandhi/distil-whisper-large-v3-hi).
+
+There are a few noteworthy data arguments:
+1. `train_dataset_samples`: defines the number of training samples in each dataset. Used to calculate the sampling probabilities in the dataloader. A good starting point is setting the samples to the number of hours of audio data in each split. A more refined strategy is setting it to the number of training samples in each split, however this might require downloading the dataset offline to compute these statistics.
+2. `wer_threshold`: sets the WER threshold between the normalised pseudo-labels and normalised ground truth labels. Any samples with WER > `wer_threshold` are discarded from the training data. This is beneficial to avoid training the student model on pseudo-labels where Whisper hallucinated or got the predictions grossly wrong. In our English distillation experiments, we found a WER threshold of 10% provides the optimal trade-off between ensuring high-quality transcriptions, and not filtering unnecessary amounts of training data. For multilingual distillation, the threshold should be set in accordance with the WER achieved by the pre-trained model on the test set.
+3. `streaming`: whether or not to use Datasets' streaming mode. Recommended for large datasets, where the audio data can be streamed from the Hugging Face Hub with no disk space requirements.
+4. `timestamp_probability`: the per-sample probability for retaining timestamp tokens in the labels (should they contain them). Retaining some portion of timestamp tokens in the training data is required to ensure the distilled model can predict timestamps at inference time. In our experiments, we found that training on timestamps with high-probability hurts the distilled model's transcription performance. Thus, we recommend setting this to a value below 0.5. Typically, a value of 0.2 works well, giving good transcription and timestamp performance.
+5. `condition_on_prev_probability`: the per-sample probability for conditioning on previous labels. Conditioning on previous tokens is required to ensure the distilled model can be used with the "sequential" long-form transcription algorithm at inference time. We did not experiment with this parameter, but found values around 0.2 to provide adequate performance. OpenAI pre-trained Whisper on with a 50% probability for conditioning on previous tokens. Thus, you might wish to try higher values.
+
+As well as a few noteworthy model arguments that can be configured to give optimal training performance:
+1. `freeze_encoder`: whether to freeze the entire encoder of the student model during training. Beneficial when the student encoder is copied exactly from the teacher encoder. In this case, the encoder hidden-states from the teacher model are re-used for the student model. Stopping the gradient computation through the encoder and sharing the encoder hidden-states provides a significant memory saving, and can enable up to 2x batch sizes. 
+2. `freeze_embed_positions`: whether to freeze the student model's decoder positional embeddings. Using the same embed positions as the teacher model, which is designed to handle context lengths up to 448 tokens, helps the student model retain its input id representation up to the full max input length. 
+3. `dtype`: data type (dtype) in which the model computation should be performed. Note that this only controls the dtype of the computations (forward and backward pass), and not the dtype of the parameters or optimiser states.
+4. `freeze_decoder`: whether to freeze the student model's decoder. Note that the input tokens embeddings and language modelling head will remain trainable.  
+
+And finally, a few noteworthy training arguments:
+1. `max_steps`: defines the total number of optimisation steps (forward + backward pass) during training. To reach convergence, you should use a dataset of at least 1k hours and train for a minimum of 50k steps.
+2. `lr_scheduler_stype`: defines the learning rate schedule, one of `constant_with_warmup` or `linear`. When experimenting with a training set-up or training for very few steps (< 5k), using `constant_with_warmup` is typically beneficial, since the learning rate remains high over the short training run. When performing long training runs (> 5k), using a `linear` schedule generally results in superior downstream performance of the distilled model.
+
+TODO:
+- [ ] Template for model cards
+
+## 4. Evaluation
+
+There are four types of evaluation performed in Distil-Whisper:
+1. Short form: evaluation on audio samples less than 30s in duration. Examples include typical ASR test sets, such as the LibriSpeech validation set.
+2. Sequential long form: evaluation on audio samples longer than 30s in duration using the original "sequential" long-form algorithm. Examples include entire TED talks or earnings calls.
+3. Chunked long form: evaluation on audio samples longer than 30s in duration using the Transformers "chunked" long-form algorithm.
+4. Speculative decoding: evaluation on audio samples less than 30s in duration, where a faster, distilled model is used as the assistant to a slower, teacher model. 
+
+All four forms of evaluation are performed using the script [`run_eval.py`](run_eval.py). Unlike the pseudo-labelling
+and training scripts, the evaluation script assumes that only one GPU accelerator is used. We can copy the corresponding 
+evaluation script to the model repository using the following command:
+
+```bash
+cp ../distil-whisper/training/run_eval.py .
+```
+
+Models are assessed jointly using:
+1. The *word-error rate (WER)* metric: measures the number of substitution, deletion and insertion errors relative to the total number of words. A lower WER indicates a more accurate model.
+2. The *inverse real-time factor (RTFx)* metric: measures the ratio of `audio input time : model compute time`. A higher RTFx indicates a faster model. Note that this metric is WER-dependent, meaning that it makes sense to compare two models' *RTFx* only at fixed *WER* performances. Indeed, deletions could lead to early stopping of token generation, resulting in higher *WER* and lower *RTFx*. 
+3. Token generation speed: This refers to the number of tokens generated per second. As with *RTFx*, this metric is dependent on the *WER* since token generation time is not linear. By default, this metric is calculated by averaging the total number of `generated tokens : generation time` (full forward pass of the model) when evaluating on the given test set. However, using the `--precise_tok_generation` flag will compute this metric separately for a fixed number of tokens.
+
+In all cases, it is particularly important to evaluate the final model on data that is *out-of-distribution (OOD)* with 
+the training data. Evaluating on OOD data provides insight as to how well the distilled model is likely to generalise to 
+different audio distributions at inference time. In our example, the Common Voice test set is *in-distribution (ID)* 
+with our training data, since it is taken from the same distribution as the Common Voice training set. Whereas the FLEURS 
+test set is OOD, since it is not used as part of the training set. See [Datasets](#1-datasets) section for recommendations.
+
+### Short Form
+
+The script [`run_eval.py`](run_eval.py) can be used to evaluate a trained student model over multiple short-form 
+validation sets. The following example demonstrates how to evaluate the student model trained in the previous step on 
+the Common Voice `test` set (ID) and also the FLEURS `test` set (OOD). Again, it leverages streaming mode to bypass 
+the need to download the data offline:
+
+```bash
+#!/usr/bin/env bash
+
+python run_eval.py \
+  --model_name_or_path "./" \
+  --dataset_name "../common_voice_16_1_hi_pseudo_labelled+google/fleurs" \
+  --dataset_config_name "default+hi_in" \
+  --dataset_split_name "test+test" \
+  --text_column_name "sentence+transcription" \
+  --batch_size 16 \
+  --dtype "bfloat16" \
+  --generation_max_length 256 \
+  --language "hi" \
+  --attn_implementation "sdpa" \
+  --streaming
+
+```
+
+The student model achieves an average WER of TODO% with an RTFx of TODO for a batch size of 16. We can easily adapt the above
+script to evaluate the teacher model, simply by switching the `model_name_or_path` to `openai/whisper-large-v3`, which 
+achieves an average WER of TODO% with an RTFx of TODO. Therefore, for a batch size of 16, the student model is a factor of TODO
+times faster than the teacher. The WER gap can be closed by training on more data (at least 1k hours) for more training
+steps (at least 50k).
+
+### Sequential Long Form
+
+The original Whisper paper presents a long-form transcription algorithm that sequentially transcribes 30-second segments 
+of audio and shifts the sliding window according to the timestamps predicted by the model. This style of sequential 
+inference is performed directly using the [`.generate`](https://huggingface.co/docs/transformers/model_doc/whisper#transformers.WhisperForConditionalGeneration.generate) 
+method in Transformers.
+
+The script [`run_eval.py`](run_eval.py) can be used to evaluate the trained student model on an arbitrary number of 
+long-form evaluation sets using the sequential algorithm. Since we don't have a long-form validation set for Hindi to hand, 
+in this example we'll evaluate the official Distil-Whisper model [`distil-large-v3`](https://huggingface.co/distil-whisper/distil-large-v3) 
+on the TED-LIUM validation set:
+
+```bash
+#!/usr/bin/env bash
+
+accelerate launch run_eval.py \
+  --model_name_or_path "distil-whisper/distil-large-v3" \
+  --dataset_name "distil-whisper/tedlium-long-form" \
+  --dataset_config_name "default" \
+  --dataset_split_name "validation" \
+  --text_column_name "text" \
+  --batch_size 16 \
+  --dtype "bfloat16" \
+  --generation_max_length 256 \
+  --language "en" \
+  --attn_implementation "sdpa" \
+  --streaming
+
+```
+
+### Chunked Long Form
+
+Chunked long form evaluation runs on the premise that a single long audio file can be *chunked* into smaller segments and 
+inferred in parallel. The resulting transcriptions are then joined at the boundaries to give the final text prediction. 
+A small overlap (or *stride*) is used between adjacent segments to ensure a continuous transcription across chunks.
+
+This style of chunked inference is performed using the [`pipeline`](https://huggingface.co/docs/transformers/main_classes/pipelines)
+class, which provides a wrapper around the [`.generate`](https://huggingface.co/docs/transformers/model_doc/whisper#transformers.WhisperForConditionalGeneration.generate) 
+function for long-form inference.
+
+The script [`run_eval.py`](run_eval.py) can be used to evaluate the trained student model on an arbitrary number of 
+long-form evaluation sets using the pipeline class. Again, in this example we'll evaluate distil-large-v3 on the 
+TED-LIUM validation set:
+
+```bash
+#!/usr/bin/env bash
+
+python run_eval.py \
+  --model_name_or_path "openai/whisper-large-v3" \
+  --dataset_name "distil-whisper/tedlium-long-form" \
+  --dataset_config_name "default" \
+  --dataset_split_name "validation" \
+  --text_column_name "text" \
+  --use_pipeline \
+  --chunk_length_s 25.0 \
+  --language "en" \
+  --return_timestamps \
+  --dtype "bfloat16" \
+  --streaming
+
+```
+
+The argument `chunk_length_s` controls the length of the chunked audio samples. It should be set to match the typical
+length of audio the student model was trained on. If unsure about what value of `chunk_length_s` is optimal for your case,
+it is recommended to run a *sweep* over all possible values. A template script for running a [WandB sweep](https://docs.wandb.ai/guides/sweeps) 
+can be found under [`run_chunk_length_s_sweep.yaml`](flax/long_form_transcription_scripts/run_chunk_length_s_sweep.yaml).
+
+### Speculative Decoding
+
+Speculative decoding, or assisted generation, relies on the premise that a faster, assistant model can be used to speed-up
+the generation of a slower, assistant model. Speculative decoding mathematically ensures that exactly the same outputs as 
+Whisper are obtained, while being ~2 times faster. This makes it the perfect drop-in replacement for existing Whisper 
+pipelines, since exactly the same outputs are guaranteed.
+
+Distil-Whisper checkpoints can be designed to be efficient assistant models to Whisper for speculative decoding. More precisely,
+by freezing the encoder during training, the distilled model can share the same encoder weights as Whisper during inference, since
+the encoder weights are un-changed. In doing so, only the distilled 2-layer decoder has to be loaded in addition to the 
+original Whisper model, which is approximately an 8% increase to the total parameter count, with up to 2x faster inference 
+for low batch sizes. For more details on speculative decoding, the reader is advised to refer to the following blog post:
+[Speculative Decoding for 2x Faster Whisper Inference](https://huggingface.co/blog/whisper-speculative-decoding).
+
+In the example below, we use our distilled model as an assistant to the large-v3 teacher model during inference:
+
+```bash
+#!/usr/bin/env bash
+
+python run_eval.py \
+  --model_name_or_path "openai/whisper-large-v3" \
+  --assistant_model_name_or_path "./" \
+  --dataset_name "../common_voice_16_1_hi_pseudo_labelled+google/fleurs" \
+  --dataset_config_name "default+hi_in" \
+  --dataset_split_name "test+test" \
+  --text_column_name "sentence+transcription" \
+  --batch_size 16 \
+  --dtype "bfloat16" \
+  --generation_max_length 256 \
+  --language "hi" \
+  --attn_implementation "sdpa" \
+  --streaming
+
+```
+
+We see that we achieve a WER of TODO%, the same as what we obtained with the large-v3 model, but with an RTFx of TODO, 
+a factor of TODO faster than using the large-v3 model alone. The RTFx value can be improved by training the student on 
+more data and for more training steps, since this will improve the number of predicted tokens that match the teacher 
+predictions.
+
+## Recommendations and guidelines 
+
+### 1. Datasets
+
+As explained, ideally, you should aim for ~1000 hours of audio data for training a distilled model via KD. Moreover, you should evaluate your model on out-of-distribution test sets to assess generalization capacities. With at least 1500 hours of audio data for German, Dutch, French and Spanish, 600 hours for Italian, and 300 hours for Portuguese and Polish (which can be supplemented with your own datasets), a good setup to start with is:
+- **Training datasets:** [Common Voice 17](https://huggingface.co/datasets/mozilla-foundation/common_voice_17_0) and [Multilingual Librispeech](https://huggingface.co/datasets/facebook/multilingual_librispeech). Use the `train` split for training, and the `validation` and `test` splits for in-distribution testing.
+- **Test datasets:** [VoxPopuli](https://huggingface.co/datasets/facebook/voxpopuli) and [Fleurs](https://huggingface.co/datasets/google/fleurs). Use the `validation` and `test` splits for out-of-distribution testing.
+
+### 2. Student model's decoder 
+#### 2.1 Number of Decoder Layers
+
+We recommend using a 2-layers decoder (see language transfer below).  However, you can adjust the number of decoder layers when initializing the student model to balance between inference speed and accuracy. Experimentation has revealed that the Pareto optimal points are with 2, 3, and 4-layers decoders. For indicative results, after 10,000 training steps and inference on an 80GB Nvidia H100 with a batch size of 16 and 20 tokens generation, compared to [Whiper *large-v3*](https://huggingface.co/openai/whisper-large-v3) baseline:
+
+<center>
+
+|    |  rel. token gen. speed |  ΔWER(%) |
+|----------|:-------------:|------:|
+| 2 layers |  $3.66$ | $-3.5$ |
+| 3 layers |  $3.35$ | $-2.3$ |
+| 4 layers |    $3.11$   |   $-1.8$ |
+
+</center>
+
+
+#### 2.2 Language Transfer
+
+If you opt for a 2-layers decoder, consider leveraging language transfer by initializing the student model from the [distil-large-v3 English distilled model](https://huggingface.co/distil-whisper/distil-large-v3). For French, this method has shown performance improvements of ΔWER=-1.9% (compared to a 2-layers decoder initialized from [Whiper *large-v3*](https://huggingface.co/openai/whisper-large-v3)) after 10,000 training steps.
+
+```diff
+- --teacher_checkpoint "openai/whisper-large-v3" \
++ --teacher_checkpoint "distil-whisper/distil-large-v3" \
+```
+
+### 3. Language mixing
+
+If you're working with low-resource languages (<500 hours of audio data), consider mixing your training data with a closely related language (for example, mix French and Spanish) to leverage knowledge transfer between languages. Experiments showed that mixing ~400 hours of French (which resulted in a model with poor generalization capacities) with ~500 hours of Spanish improved the model's out-of-distribution performance on French by ΔWER=-7.5%.
+
+To do this:
+1. Run [pseudo labeling](#1-pseudo-labelling) for each training dataset, setting the `--language` flag to the language of the respective dataset. In the example of mixing French and Spanish, simply modify the given [pseudo labeling](#1-pseudo-labelling) command with:
+    * pseudo labelling the French dataset
+      ```diff
+      - --dataset_config_name "hi" \
+      - --output_dir "./common_voice_16_1_hi_pseudo_labelled" \
+      - --language "hi" \
+      + --dataset_config_name "fr" \
+      + --output_dir "./common_voice_16_1_fr_pseudo_labelled" \
+      + --language "fr" \
+      ```
+    * pseudo labelling the Spanish dataset
+      ```diff
+      - --dataset_config_name "hi" \
+      - --output_dir "./common_voice_16_1_hi_pseudo_labelled" \
+      - --language "hi" \
+      + --dataset_config_name "es" \
+      + --output_dir "./common_voice_16_1_es_pseudo_labelled" \
+      + --language "es" \
+      ```
+
+2. Conduct [training](#3-training) on these pseudo-labeled datasets, using the `--language` flag set to your targeted language. Note that this flag is only used for evaluation purposes, so you set it to the targeted language. The language token used for forwarding the teacher and student model decoders is the one used and saved in pseudo labels during pseudo-labeling, ensuring it's the correct one for the considered sample. In the example of mixing French and Spanish, simply modify the given [training](#1-pseudo-labelling) command with: 
+    ```diff
+    - --train_dataset_name "../common_voice_16_1_hi_pseudo_labelled+../common_voice_16_1_hi_pseudo_labelled" \
+    - --train_split_name "train+validation" \
+    - --eval_dataset_name "../common_voice_16_1_hi_pseudo_labelled" \
+    - --eval_split_name "test" \
+    + --train_dataset_name "../common_voice_17_0_fr_pseudo_labelled+../common_voice_17_0_es_pseudo_labelled" \
+    + --train_split_name "train+train" \
+    + --eval_dataset_name "../common_voice_16_1_fr_pseudo_labelled" \
+    + --eval_split_name "validation" \
+    ```
+
+## Overview of Training Methods
+
+### 1. Fine-Tuning
+
+For fine-tuning, we take the original Whisper checkpoint and train it on one or more datasets using the standard 
+cross-entropy loss. As such, there is no involvement from the teacher checkpoint during training, and so the fine-tuned 
+model is permitted to *overfit* to the distribution of the training data we provide. This makes it appealing for "low-resource" 
+languages where the original Whisper model performs poorly, since we can boost the performance of the model on a single 
+language by *overfitting* to that distribution of data. Note that this means the fine-tuned model is prone to loosing 
+its robustness to different audio distributions, which is the trade-off with improving performance on a specified dataset.
+
+As a rule of thumb, fine-tuning is appropriate for languages where the original Whisper model performs > 20% WER, and we 
+have a relatively small quantity of training data available (< 1000 hours). With fine-tuning, we require as little as **10 hours**
+of training data to significantly boost the performance of the Whisper model. For an in-depth guide to fine-tuning Whisper,
+the reader is advised to refer to the blog post: [Fine-Tune Whisper For Multilingual ASR with 🤗 Transformers](https://huggingface.co/blog/fine-tune-whisper).
+
+### 2. Shrink and Fine-Tune
+
+Shrink and fine-tune (SFT) is a knowledge distillation (KD) technique in which we first *shrink* the teacher model to a 
+smaller student model by copying maximally spaced layers, and then *fine-tune* the student model on the cross-entropy loss 
+as described above. Typically, we retain the full encoder from the Whisper model and only shrink the decoder. Retaining 
+the entire encoder helps significantly with maintaining Whisper's robustness to different audio distributions (_c.f._ 
+Section 9.3 of the [Distil-Whisper paper](https://arxiv.org/abs/2311.00430)).
+
+We can either train the student model on a dataset of (audio, text) pairs as above. Or, we can use the pre-trained 
+Whisper model to generate *pseudo-labels* for our audio data, and train on the (audio, pseudo-label) pairs.
+
+Pseudo-labels can be used when either:
+1. The original text transcriptions are normalised (lower-cased or no punctuation): the Whisper generated pseudo-labels contain both punctuation and casing, and so can be used as a substitute for the normalised transcriptions
+2. The pre-trained Whisper model achieves < 20% WER on the languages: we then know the majority of the pseudo-labels will be accurate enough for us to train on.
+
+They are not recommended when both of the following are true:
+1. The original text is punctuated and cased
+2. The pre-trained Whisper model achieves > 20% WER on the languages: in this case, we want to overfit to the particular distribution of the language, and so train directly on the original text data
+
+To discard inaccurate pseudo-labels during training, we employ a simple WER heuristic to filter our pseudo-labelled 
+training data. We first normalise the original text and the pseudo-labelled text using the Whisper normaliser. If the 
+WER between the normalised text exceeds a 10% WER threshold, we discard the training sample. Else, we retain it for training.
+Section 9.1 of the Distil-Whisper [paper](https://arxiv.org/abs/2311.00430) demonstrates the importance of using this 
+threshold for training.
+
+### 3. KL Divergence
+
+In the KL Divergence setting, the student model is initialised by shrinking the teacher as before, and then trained to 
+match the predictions of the teacher during training. 
+
+### Summary of Methods
+
+The following table summarises the two training paradigms: fine-tuning and knowledge distillation (KD). It suggests 
+minimum values for the pre-trained WER / training data to achieve reasonable performance:
+
+| Method      | Pre-Trained WER / % | Training Data / h |
+|-------------|---------------------|-------------------|
+| Fine-tuning | > 20                | < 1000            |
+| KD          | < 20                | > 1000            |
+
+## Acknowledgements
+
+* OpenAI for the Whisper [model](https://huggingface.co/openai/whisper-large-v3) and [original codebase](https://github.com/openai/whisper)
+* Hugging Face 🤗 [Transformers](https://github.com/huggingface/transformers) for the Whisper model implementation
+* Google's [TPU Research Cloud (TRC)](https://sites.research.google/trc/about/) program for Cloud TPU v4s used to train the official Distil-Whisper models
+* The Hugging Face 🤗 cluster for enabling experimentation with the PyTorch scripts
+
+## Citation
+
+If you use this code-base, please consider citing the Distil-Whisper paper:
+
+```
+@misc{gandhi2023distilwhisper,
+      title={Distil-Whisper: Robust Knowledge Distillation via Large-Scale Pseudo Labelling}, 
+      author={Sanchit Gandhi and Patrick von Platen and Alexander M. Rush},
+      year={2023},
+      eprint={2311.00430},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```
diff --git a/distil_whisper.egg-info/SOURCES.txt b/distil_whisper.egg-info/SOURCES.txt
new file mode 100644
index 0000000000000000000000000000000000000000..2a60003fef94628001e05287a3342034e26f1e36
--- /dev/null
+++ b/distil_whisper.egg-info/SOURCES.txt
@@ -0,0 +1,8 @@
+README.md
+pyproject.toml
+setup.py
+distil_whisper.egg-info/PKG-INFO
+distil_whisper.egg-info/SOURCES.txt
+distil_whisper.egg-info/dependency_links.txt
+distil_whisper.egg-info/requires.txt
+distil_whisper.egg-info/top_level.txt
\ No newline at end of file
diff --git a/distil_whisper.egg-info/dependency_links.txt b/distil_whisper.egg-info/dependency_links.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8b137891791fe96927ad78e64b0aad7bded08bdc
--- /dev/null
+++ b/distil_whisper.egg-info/dependency_links.txt
@@ -0,0 +1 @@
+
diff --git a/distil_whisper.egg-info/requires.txt b/distil_whisper.egg-info/requires.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e7a7a4704974bbacdb99f1dab9bf3c8261fc28d0
--- /dev/null
+++ b/distil_whisper.egg-info/requires.txt
@@ -0,0 +1,12 @@
+torch>=1.10
+transformers>=4.35.1
+datasets[audio]>=2.14.7
+accelerate>=0.24.1
+jiwer
+evaluate>=0.4.1
+wandb
+tensorboard
+nltk
+
+[dev]
+ruff==0.1.5
diff --git a/distil_whisper.egg-info/top_level.txt b/distil_whisper.egg-info/top_level.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8b137891791fe96927ad78e64b0aad7bded08bdc
--- /dev/null
+++ b/distil_whisper.egg-info/top_level.txt
@@ -0,0 +1 @@
+
diff --git a/flax/LICENSE b/flax/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..261eeb9e9f8b2b4b0d119366dda99c6fd7d35c64
--- /dev/null
+++ b/flax/LICENSE
@@ -0,0 +1,201 @@
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diff --git a/flax/Makefile b/flax/Makefile
new file mode 100644
index 0000000000000000000000000000000000000000..035241e681719b7e11f943545d578ec317462af1
--- /dev/null
+++ b/flax/Makefile
@@ -0,0 +1,9 @@
+check_dirs := .
+
+quality:
+	black --check $(check_dirs)
+	ruff $(check_dirs)
+
+style:
+	black $(check_dirs)
+	ruff $(check_dirs) --fix
diff --git a/flax/README.md b/flax/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..13a7845e344642ed5a7e4c900e0d15afc80f0aa7
--- /dev/null
+++ b/flax/README.md
@@ -0,0 +1,293 @@
+## Reproducing Distil-Whisper
+
+This sub-folder contains all the training and inference scripts to reproduce the Distil-Whisper project. Distil-Whisper
+is written in JAX to leverage the fast training and inference speed offered by TPU v4 hardware. However, it also works
+efficiently on GPU hardware without any additional code changes.
+
+Reproducing the Distil-Whisper project requires four stages to be completed in successive order:
+
+1. [Pseudo-labelling](#pseudo-labelling)
+2. [Initialisation](#initialisation)
+3. [Training](#training)
+4. [Evaluation](#evaluation)
+
+This README is partitioned according to the four stages. Each section provides a minimal example for running the
+scripts used in the project. The final scripts used to train the model are referenced in-line.
+
+It is worth noting that the experiments performed in JAX/Flax have been on English ASR only. For multilingual training code,
+the [PyTorch Training Code](../README.md) can easily be used, facilitating anyone to run Whisper distillation on a language of their choice.
+
+## Requirements
+
+Distil-Whisper is written in Python, JAX and Flax, and heavily leverages the Flax Whisper implementation in 
+[🤗 Transformers](https://github.com/huggingface/transformers). The instructions for installing the package are as follows:
+1. Install JAX from the [official instructions](https://github.com/google/jax#installation), ensuring you install the correct version for your hardware (GPU or TPU).
+2. Install the `distil_whisper` package by cloning the repository and performing an editable installation:
+
+```bash
+git clone https://github.com/huggingface/distil-whisper.git
+cd distil-whisper/training/flax
+pip install -e .
+```
+
+## Pseudo-Labelling
+
+Pseudo-labelling is the process of generating target text predictions for the input audio data using the teacher model.
+The generated text labels then replace the ground truth text labels when performing distillation. The rationale for 
+using pseudo-labels instead of ground truth labels is to circumvent the issue of inconsistent transcription formatting 
+across datasets.
+
+The python script [`run_pseudo_labelling.py`](run_pseudo_labelling.py) is a flexible inference script that can be used
+to generate pseudo-labels under a range of settings, including using both greedy and beam-search. It is also compatible
+with [🤗 Datasets](https://github.com/huggingface/datasets) *streaming mode*, allowing users to load massive audio
+datasets with **no disk space requirements**. For more information on streaming mode, the reader is referred to the 
+blog post: [A Complete Guide to Audio Datasets](https://huggingface.co/blog/audio-datasets#streaming-mode-the-silver-bullet).
+
+The following script demonstrates how to pseudo-label the [LibriSpeech 960h](https://huggingface.co/datasets/librispeech_asr)
+dataset with greedy sampling and streaming mode: 
+
+```bash
+#!/usr/bin/env bash
+
+python run_pseudo_labelling.py \
+  --model_name_or_path "openai/whisper-large-v2" \
+  --dataset_name "librispeech_asr" \
+  --dataset_config_name "all" \
+  --data_split_name "train.clean.100+train.clean.360+train.other.500" \
+  --text_column_name "text" \
+  --output_dir "./transcriptions" \
+  --per_device_eval_batch_size 16 \
+  --max_label_length 256 \
+  --dtype "bfloat16" \
+  --report_to "wandb" \
+  --dataloader_num_workers 16 \
+  --streaming \
+  --push_to_hub \
+  --generation_num_beams 1  # for greedy, set >1 for beam
+
+```
+
+The script will save the generated pseudo-labels alongside the file ids to the output directory `output_dir`. Adding the
+`--push_to_hub` argument uploads the generated pseudo-labels to the Hugging Face Hub on save.
+
+The directory [`pseudo_labelling_scripts`](pseudo_labelling_scripts) contains a collection of bash scripts for 
+pseudo-labelling all 10 audio datasets used in the project. The datasets with the Whisper generated transcriptions
+can be found on the Hugging Face Hub under the [Distil Whisper organisation](https://huggingface.co/datasets?sort=trending&search=distil-whisper%2F).
+They can be re-used should you wish to bypass the data labelling stage of the reproduction.
+
+<!--- TODO(SG): Combine PS with source audio to create dataset --->
+
+## Initialisation
+
+The script [`create_student_model.py`](create_student_model.py) can be used to initialise a small student model
+from a large teacher model. When initialising a student model with fewer layers than the teacher model, the student is 
+initialised by copying maximally spaced layers from the teacher, as per the [DistilBart](https://arxiv.org/abs/2010.13002)
+recommendations.
+
+The following command demonstrates how to initialise a student model from the [large-v2](https://huggingface.co/openai/whisper-large-v2) 
+checkpoint, with all 32 encoder layer and 2 decoder layers. The 2 student decoder layers are copied from teacher layers 
+1 and 32 respectively, as the maximally spaced layers.
+
+```bash
+#!/usr/bin/env bash
+
+python create_student_model.py \
+  --teacher_checkpoint "openai/whisper-large-v2" \
+  --encoder_layers 32 \
+  --decoder_layers 2 \
+  --save_dir "./large-32-2" \
+  --push_to_hub
+```
+
+
+## Training
+
+The script [`run_distillation.py`](run_distillation.py) is an end-to-end script for loading multiple
+datasets, a student model, a teacher model, and performing teacher-student distillation. It uses the loss formulation
+from [DistilBart](https://arxiv.org/abs/2010.13002), which is a combination of a cross-entropy, KL-divergence and 
+mean-square error (MSE) loss:
+
+https://github.com/huggingface/distil-whisper/blob/4dd831543e6c40b1159f1ec951db7f4fe0e86850/run_distillation.py#L1725
+
+The weight assigned to the MSE loss is configurable. The others are fixed to the values from the DistilBART paper.
+
+The following command takes the LibriSpeech 960h dataset that was pseudo-labelled in the first stage and trains the 
+2-layer decoder model intialised in the previous step. Note that multiple training datasets and splits can be loaded 
+by separating the dataset arguments by `+` symbols. Thus, the script generalises to any number of training datasets.
+
+```bash
+#!/usr/bin/env bash
+
+python3 run_distillation.py \
+  --model_name_or_path "./large-32-2" \
+  --teacher_model_name_or_path "openai/whisper-large-v2" \
+  --train_dataset_name "librispeech_asr+librispeech_asr+librispeech_asr" \
+  --train_dataset_config_name "all+all+all" \
+  --train_split_name "train.clean.100+train.clean.360+train.other.500" \
+  --train_dataset_samples "100+360+500" \
+  --eval_dataset_name "librispeech_asr" \
+  --eval_dataset_config_name "all" \
+  --eval_split_name "validation.clean" \
+  --eval_steps 5000 \
+  --save_steps 5000 \
+  --warmup_steps 500 \
+  --learning_rate 0.0001 \
+  --lr_scheduler_type "constant_with_warmup" \
+  --logging_steps 25 \
+  --save_total_limit 1 \
+  --max_steps 20000 \
+  --wer_threshold 10 \
+  --per_device_train_batch_size 64 \
+  --per_device_eval_batch_size 64 \
+  --dataloader_num_workers 16 \
+  --dtype "bfloat16" \
+  --output_dir "./" \
+  --do_train \
+  --do_eval \
+  --use_scan \
+  --gradient_checkpointing \
+  --overwrite_output_dir \
+  --predict_with_generate \
+  --freeze_encoder \
+  --streaming \
+  --use_auth_token \
+  --push_to_hub
+
+```
+
+The above training script will take approximately 20 hours to complete on a TPU v4-8 and yield a final WER of 2.3%.
+
+Training logs will be reported to TensorBoard and WandB, provided the relevant packages are available. An example of a 
+saved checkpoint pushed to the Hugging Face Hub can be found here: [large-32-2](https://huggingface.co/distil-whisper/large-32-2).
+
+There are a few noteworthy arguments that can be configured to give optimal training performance:
+* `train_dataset_samples`: defines the number of training samples in each dataset. Used to calculate the sampling probabilities in the dataloader. A good starting point is setting the samples to the number of hours of audio data in each split. A more refined strategy is setting it to the number of training samples in each split, however this might require downloading the dataset offline to compute these statistics.
+* `wer_threshold`: sets the WER threshold between the normalised pseudo-labels and normalised ground truth labels. Any samples with WER > `wer_threshold` are discarded from the training data. This is beneficial to avoid training the student model on pseudo-labels where Whisper hallucinated or got the predictions grossly wrong.
+* `freeze_encoder`: whether to freeze the entire encoder of the student model during training. Beneficial when the student encoder is copied exactly from the teacher encoder. In this case, the encoder hidden-states from the teacher model are re-used for the student model. Stopping the gradient computation through the encoder and sharing the encoder hidden-states provides a significant memory saving, and can enable up to 2x batch sizes. 
+* `dtype`: data type (dtype) in which the model computation should be performed. Note that this only controls the dtype of the computations (forward and backward pass), and not the dtype of the parameters or optimiser states.
+
+The Distil Whisper project extends the above script to train on a combined dataset formed from 12 open-source ASR datasets,
+totalling 22k hours and over 50k speakers. Template scripts to run training on this composite dataset can be found 
+in the directory [`distillation_scripts`](distillation_scripts).
+
+## Evaluation
+
+There are two types of evaluation performed in Distil-Whisper:
+1. Short form: evaluation on audio samples less than 30s in duration. Examples include typical ASR test sets, such as the LibriSpeech validation set.
+2. Long form: evaluation on audio samples longer than 30s in duration. Examples include entire TED talks or earnings calls.
+
+Both forms of evaluation are performed using the *word-error rate (WER)* metric.
+
+### Short Form
+
+The script [`run_eval.py`](run_eval.py) can be used to evaluate a trained student model over multiple validation sets.
+The following example demonstrates how to evaluate the student model trained in the previous step on the LibriSpeech
+`validation.clean` and `validation.other` dev sets. Again, it leverages streaming mode to bypass the need to download
+the data offline:
+
+```bash
+#!/usr/bin/env bash
+
+python run_eval.py \
+  --model_name_or_path "./large-32-2" \
+  --dataset_name "librispeech_asr+librispeech_asr" \
+  --dataset_config_name "all+all" \
+  --dataset_split_name "validation.clean+validation.other" \
+  --output_dir "./large-32-2" \
+  --per_device_eval_batch_size 64 \
+  --dtype "bfloat16" \
+  --dataloader_num_workers 16 \
+  --report_to "wandb" \
+  --streaming \
+  --predict_with_generate
+
+```
+
+### Long Form
+
+Long form evaluation runs on the premise that a single long audio file can be *chunked* into smaller segments and 
+inferred in parallel. The resulting transcriptions are then joined at the boundaries to give the final text prediction. 
+A small overlap (or *stride*) is used between adjacent segments to ensure a continuous transcription across chunks.
+
+This style of chunked inference is performed using the [`FlaxWhisperPipeline`](https://github.com/huggingface/distil-whisper/blob/6426022e3b3a0a498b4150a636b54e2e3898bf1a/distil_whisper/pipeline.py#L61)
+class, which is heavily inspired from [Whisper JAX](https://github.com/sanchit-gandhi/whisper-jax/tree/main#pipeline-usage).
+
+The script [`run_long_form_transcription.py`](run_long_form_transcription.py) can be used to evaluate the trained 
+student model on an arbitrary number of long-form evaluation sets. The following script demonstrates how to evaluate
+the example student model on two such test sets, [Earnings 21](https://huggingface.co/datasets/distil-whisper/earnings21) 
+and [Earnings 22](https://huggingface.co/datasets/distil-whisper/earnings22):
+
+```bash
+#!/usr/bin/env bash
+
+python run_long_form_transcription.py \
+  --model_name_or_path "./large-32-2" \
+  --dataset_name "distil-whisper/earnings21+distil-whisper/earnings22" \
+  --dataset_config_name "default+default" \
+  --dataset_split_name "test+test+test+test" \
+  --text_column_name "transcription+transcription" \
+  --output_dir "./large-32-2" \
+  --per_device_eval_batch_size 64 \
+  --chunk_length_s 15 \
+  --dtype "bfloat16" \
+  --report_to "wandb" \
+  --streaming
+
+```
+
+The argument `chunk_length_s` controls the length of the chunked audio samples. It should be set to match the typical
+length of audio the student model was trained on. If unsure about what value of `chunk_length_s` is optimal for your case,
+it is recommended to run a *sweep* over all possible values. A template script for running a [WandB sweep](https://docs.wandb.ai/guides/sweeps) 
+can be found under [`run_chunk_length_s_sweep.yaml`](long_form_transcription_scripts/run_chunk_length_s_sweep.yaml).
+
+### 1. Pseudo Labelling
+
+#### Greedy vs Beam
+
+We found there to be little-to-no difference in the downstream performance of the distilled model after pseudo labelling
+using either greedy or beam-search. We attribute this to the minimal difference in performance of the pre-trained Whisper
+model under greedy and beam-search decoding, giving pseudo-labelled transcriptions of similar quality. We encourage
+users to generate pseudo-labels using greedy decoding given it runs significantly faster. Beam search is only advised if 
+the pre-trained model is hallucinating significantly on the audio inputs, in which case it helps reduce the frequency and
+severity of hallucinations. If using beam search, the number of beams can be kept low: even 2 beams helps reduce the 
+amount of hallucinations significantly.
+
+#### Timestamps
+
+Whisper is trained on a timestamp prediction task as part of the pre-training set-up. Here, a fixed proportion of the 
+pre-training data includes sequence-level *timestamps* as part of the transcription labels:
+
+```bash
+<|0.00|> Hey, this is a test transcription. <|3.42|>
+```
+
+Timestamp prediction is useful for enriching the transcriptions with timing information for downstream tasks, such as
+aligning the Whisper transcription with the output of a speaker diarization system, and also reduces the frequency of 
+hallucinations.
+
+The pseudo-labelling scrip [`run_pseudo_labelling.py`](run_pseudo_labelling.py) can be extended to predict timestamp
+information in the audio data by appending the `--return_timestamps` flag to the launch command. The timestamped labelled
+data can be passed to the training script in exactly the same way as the non-timestamped version, and the pre-processing
+function will take care of encoding the timestamps and appending the required task tokens.
+
+#### Previous Context
+
+Whisper is also pre-trained on a prompting task, where the transcription for the preceding utterance is fed as context 
+to the current one:
+
+```bash
+<|startofprev|> This is the previous context from the preceding utterance.<|startoftranscript|> And this is the current utterance.<|endoftranscript|>
+```
+
+Annotating the transcriptions with previous context labels is only possible for datasets where we have consecutive files 
+and unique speaker ids, since we need to ensure segment `i` directly follows on from segment `i-1` if we use it as the 
+prompt.
+
+As per the Whisper paper, we mask out the loss over the previous context tokens. At inference time, we can replace the 
+previous context with a “prompt” to encourage the model to generate text in the style of the prompt (i.e. for specific 
+named entities, or styles of transcription)
+
+## Acknowledgements
+
+* 🤗 Hugging Face Transformers for the base Whisper implementation
+* Google's [TPU Research Cloud (TRC)](https://sites.research.google/trc/about/) programme for their generous provision of Cloud TPUs
diff --git a/flax/conversion_scripts/run_convert_distilled_train_state_to_hf.sh b/flax/conversion_scripts/run_convert_distilled_train_state_to_hf.sh
new file mode 100644
index 0000000000000000000000000000000000000000..688722f6ef56d405583169ef26e2ea0a7dcbcbc9
--- /dev/null
+++ b/flax/conversion_scripts/run_convert_distilled_train_state_to_hf.sh
@@ -0,0 +1,8 @@
+#!/usr/bin/env bash
+
+TCMALLOC_LARGE_ALLOC_REPORT_THRESHOLD=10000000000 python convert_train_state_to_hf.py \
+  --model_name_or_path "distil-whisper/large-32-2" \
+  --output_dir "./" \
+  --resume_from_checkpoint "checkpoint-15000" \
+  --cache_dir "/home/sanchitgandhi/.cache" \
+  --use_scan
diff --git a/flax/convert_train_state_to_hf.py b/flax/convert_train_state_to_hf.py
new file mode 100644
index 0000000000000000000000000000000000000000..bed16bb32d7c8c72b6bfe77eaee5d8c6092dea66
--- /dev/null
+++ b/flax/convert_train_state_to_hf.py
@@ -0,0 +1,327 @@
+#!/usr/bin/env python
+# coding=utf-8
+# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Convert a Flax training state to HF Transformers Whisper weights.
+"""
+
+import logging
+import os
+import sys
+from dataclasses import field
+from pathlib import Path
+from typing import Callable, Optional
+
+import flax
+import jax
+import jax.numpy as jnp
+import optax
+from flax import jax_utils, traverse_util
+from flax.serialization import from_bytes
+from flax.training import train_state
+from flax.training.common_utils import shard_prng_key
+from huggingface_hub import Repository, create_repo
+from optax._src import linear_algebra
+from transformers import (
+    AutoConfig,
+    HfArgumentParser,
+    Seq2SeqTrainingArguments,
+)
+from transformers.file_utils import get_full_repo_name
+from transformers.utils import check_min_version
+from transformers.utils.versions import require_version
+
+from distil_whisper import FlaxWhisperForConditionalGeneration
+
+
+# initialise JAX for multi-host set-up on TPU
+jax.distributed.initialize()
+
+# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
+check_min_version("4.27.0.dev0")
+
+require_version(
+    "datasets>=1.18.0",
+    "To fix: pip install -r examples/flax/speech-recogintion/requirements.txt",
+)
+
+logger = logging.getLogger(__name__)
+
+
+@flax.struct.dataclass
+class ModelArguments:
+    """
+    Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
+    """
+
+    model_name_or_path: str = field(
+        metadata={"help": ("Path to pretrained student model or model identifier from huggingface.co/models")}
+    )
+    config_name: Optional[str] = field(
+        default=None,
+        metadata={"help": "Pretrained config name or path if not the same as model_name"},
+    )
+    cache_dir: Optional[str] = field(
+        default=None,
+        metadata={"help": ("Where to store the pretrained models downloaded from huggingface.co")},
+    )
+    use_fast_tokenizer: bool = field(
+        default=True,
+        metadata={"help": ("Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.")},
+    )
+    model_revision: str = field(
+        default="main",
+        metadata={"help": ("The specific model version to use (can be a branch name, tag name or commit id).")},
+    )
+    use_auth_token: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Will use the token generated when running `transformers-cli login`"
+                " (necessary to use this script with private models)."
+            )
+        },
+    )
+    dtype: Optional[str] = field(
+        default="float32",
+        metadata={
+            "help": (
+                "Floating-point format in which the model weights should be initialized"
+                " and trained. Choose one of `[float32, float16, bfloat16]`."
+            )
+        },
+    )
+    load_with_scan_weights: bool = field(
+        default=False,
+        metadata={
+            "help": "Whether the pre-trained checkpoint has its weights stored in scan format. Set to True for scanned "
+            "weights, defaults to False for non-scan (unrolled) weights."
+        },
+    )
+    use_scan: bool = field(
+        default=True,
+        metadata={"help": ("Whether or not to use `scan_with_axes` over the encoder and decoder blocks.")},
+    )
+
+
+def create_learning_rate_fn(
+    num_train_steps: int, lr_scheduler_type: str, num_warmup_steps: int, learning_rate: float
+) -> Callable[[int], jnp.array]:
+    """Returns a linear warmup, linear_decay learning rate function."""
+    lr_scheduler_types = ("linear", "constant_with_warmup")
+
+    if lr_scheduler_type not in lr_scheduler_types:
+        raise ValueError(
+            f"lr_scheduler_type of type {lr_scheduler_type} not supported, choose from {lr_scheduler_types}."
+        )
+
+    warmup_fn = optax.linear_schedule(init_value=0.0, end_value=learning_rate, transition_steps=num_warmup_steps)
+    decay_fn = optax.linear_schedule(
+        init_value=learning_rate,
+        end_value=0 if lr_scheduler_type == "linear" else learning_rate,
+        transition_steps=num_train_steps - num_warmup_steps,
+    )
+    schedule_fn = optax.join_schedules(schedules=[warmup_fn, decay_fn], boundaries=[num_warmup_steps])
+    return schedule_fn
+
+
+class TrainState(train_state.TrainState):
+    dropout_rng: jnp.ndarray
+    max_grad_norm: float
+
+    def apply_gradients(self, *, grads, **kwargs):
+        """Updates `step`, `params`, `opt_state` and `**kwargs` in return value, clipping the
+        gradients by the maximum grad norm.
+
+        Note that internally this function calls `.tx.update()` followed by a call
+        to `optax.apply_updates()` to update `params` and `opt_state`.
+
+        Args:
+          grads: Gradients that have the same pytree structure as `.params`.
+          **kwargs: Additional dataclass attributes that should be `.replace()`-ed.
+
+        Returns:
+          An updated instance of `self` with `step` incremented by one, `params`
+          and `opt_state` updated by applying `grads`, and additional attributes
+          replaced as specified by `kwargs`.
+        """
+        # clip gradients by global l2 norm
+        g_norm = linear_algebra.global_norm(grads)
+        g_norm = jnp.maximum(self.max_grad_norm, g_norm)
+        grads = jax.tree_map(lambda t: (t / g_norm) * self.max_grad_norm, grads)
+
+        updates, new_opt_state = self.tx.update(grads, self.opt_state, self.params)
+        new_params = optax.apply_updates(self.params, updates)
+
+        return self.replace(
+            step=self.step + 1,
+            params=new_params,
+            opt_state=new_opt_state,
+            **kwargs,
+        )
+
+    def replicate(self):
+        return jax_utils.replicate(self).replace(dropout_rng=shard_prng_key(self.dropout_rng))
+
+    def unreplicate(self):
+        return jax_utils.unreplicate(self)
+
+
+def main():
+    # 1. Parse input arguments
+    # See all possible arguments in src/transformers/training_args.py
+    # or by passing the --help flag to this script.
+    # We now keep distinct sets of args, for a cleaner separation of concerns.
+    parser = HfArgumentParser(
+        (
+            ModelArguments,
+            Seq2SeqTrainingArguments,
+        )
+    )
+
+    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
+        # If we pass only one argument to the script and it's the path to a json file,
+        # let's parse it to get our arguments.
+        model_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
+    else:
+        model_args, training_args = parser.parse_args_into_dataclasses()
+
+    # Handle the repository creation
+    if training_args.push_to_hub:
+        if training_args.hub_model_id is None:
+            repo_name = get_full_repo_name(
+                Path(training_args.output_dir).absolute().name,
+                token=training_args.hub_token,
+            )
+        else:
+            repo_name = training_args.hub_model_id
+        create_repo(repo_name, exist_ok=True, token=training_args.hub_token)
+        repo = Repository(
+            training_args.output_dir,
+            clone_from=repo_name,
+            token=training_args.hub_token,
+        )
+
+    # 5. Load pretrained config, model and processor
+    config = AutoConfig.from_pretrained(
+        (model_args.config_name if model_args.config_name else model_args.model_name_or_path),
+        cache_dir=model_args.cache_dir,
+        revision=model_args.model_revision,
+        use_auth_token=True if model_args.use_auth_token else None,
+    )
+    student_model, student_params = FlaxWhisperForConditionalGeneration.from_pretrained(
+        model_args.model_name_or_path,
+        config=config,
+        dtype=getattr(jnp, model_args.dtype),
+        cache_dir=model_args.cache_dir,
+        revision=model_args.model_revision,
+        use_auth_token=True if model_args.use_auth_token else None,
+        _do_init=False,
+        use_scan=model_args.load_with_scan_weights,
+    )
+
+    # enable scan / gradient checkpointing if necessary in the student model
+    if model_args.use_scan:
+        student_model.enable_scan()  # to enable scan in the nn.Module
+        student_params = student_model.convert_unroll_to_scan(student_params)  # to convert the unrolled params to scan
+
+    # Initialize our student state
+    rng = jax.random.PRNGKey(training_args.seed)
+    rng, dropout_rng = jax.random.split(rng)
+
+    total_train_steps = int(training_args.max_steps)
+
+    # Create learning rate schedule
+    linear_decay_lr_schedule_fn = create_learning_rate_fn(
+        total_train_steps,
+        training_args.lr_scheduler_type,
+        training_args.warmup_steps,
+        training_args.learning_rate,
+    )
+
+    # We use Optax's "masking" functionality to not apply weight decay
+    # to bias and LayerNorm scale parameters. decay_mask_fn returns a
+    # mask boolean with the same structure as the parameters.
+    # The mask is True for parameters that should be decayed.
+    def decay_mask_fn(params):
+        flat_params = traverse_util.flatten_dict(params)
+        # find out all LayerNorm parameters
+        layer_norm_candidates = [
+            "layer_norm",
+            "self_attn_layer_norm",
+            "final_layer_norm",
+            "encoder_attn_layer_norm",
+        ]
+        layer_norm_named_params = {
+            layer[-2:]
+            for layer_norm_name in layer_norm_candidates
+            for layer in flat_params.keys()
+            if layer_norm_name in "".join(layer).lower()
+        }
+        flat_mask = {path: path[-1] != "bias" and path[-2:] not in layer_norm_named_params for path in flat_params}
+        return traverse_util.unflatten_dict(flat_mask)
+
+    # create adam optimizer
+    adamw = optax.adamw(
+        learning_rate=linear_decay_lr_schedule_fn,
+        b1=training_args.adam_beta1,
+        b2=training_args.adam_beta2,
+        eps=training_args.adam_epsilon,
+        weight_decay=training_args.weight_decay,
+        mask=decay_mask_fn,
+    )
+
+    # Setup train state
+    student_state = TrainState.create(
+        apply_fn=student_model.__call__,
+        params=student_params,
+        tx=adamw,
+        dropout_rng=dropout_rng,
+        max_grad_norm=training_args.max_grad_norm,
+    )
+
+    if training_args.resume_from_checkpoint is not None:
+        if os.path.isfile(os.path.join(training_args.resume_from_checkpoint, "train_state.msgpack")):
+            logger.info(
+                f"Checkpoint detected, resuming training at {training_args.resume_from_checkpoint}. To avoid "
+                "this behavior, omit the resume_from_checkpoint argument."
+            )
+            with Path(os.path.join(training_args.resume_from_checkpoint, "train_state.msgpack")).open("rb") as f:
+                student_state = from_bytes(student_state, f.read())
+        else:
+            logger.warning(
+                f"Checkpoint {training_args.resume_from_checkpoint} not detected, training from scratch. Ensure "
+                f"you pass the path to a folder with a valid checkpoint for your model."
+            )
+
+    cur_step = int(jax.device_get(student_state.step))
+
+    # save weights in HF Transformers format
+    if jax.process_index() == 0:
+        student_model.disable_scan()
+        student_state_params = student_model.convert_scan_to_unroll(student_state.params)
+        student_params = jax.device_get(student_state_params)
+        student_model.save_pretrained(
+            os.path.join(training_args.output_dir, f"checkpoint-{cur_step}"), params=student_params
+        )
+        if training_args.push_to_hub:
+            repo.push_to_hub(
+                commit_message=f"Saving weights of step {cur_step}",
+                blocking=False,
+            )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/flax/create_student_model.py b/flax/create_student_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..b9d5632d0d7586a3524fdc920a5211a7f1fba073
--- /dev/null
+++ b/flax/create_student_model.py
@@ -0,0 +1,226 @@
+#!/usr/bin/env python
+# coding=utf-8
+# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Initialise a student Whisper model from a pre-trained teacher model for
+teacher-student distillation.
+"""
+
+import argparse
+import copy
+import logging
+
+import jax
+import numpy as np
+from flax.core import freeze, unfreeze
+from transformers import GenerationConfig, WhisperFeatureExtractor, WhisperProcessor
+
+from distil_whisper import FlaxWhisperForConditionalGeneration
+
+
+logger = logging.getLogger(__name__)
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description="Initialise a student Whisper model from a teacher model, copying the relevant layer weights and adjusting the processor as necessary."
+    )
+    parser.add_argument(
+        "--teacher_checkpoint",
+        type=str,
+        required=True,
+        help="The HF Hub ID of the teacher checkpoint.",
+    )
+    parser.add_argument(
+        "--subfolder",
+        type=str,
+        default="",
+        help="In case the relevant teacher weights are located inside a subfolder of the model repo on huggingface.co, you "
+        "can specify the folder name here.",
+    )
+    parser.add_argument(
+        "--encoder_layers",
+        type=int,
+        default=None,
+        help="Number of encoder layers to use in the student model. Defaults to all layers from the teacher.",
+    )
+    parser.add_argument(
+        "--decoder_layers",
+        type=int,
+        default=2,
+        help="Number of decoder layers to use in the student model. Defaults to 2 layers.",
+    )
+    parser.add_argument(
+        "--max_source_positions",
+        type=int,
+        default=None,
+        help="The maximum sequence length of log-mel filter-bank features that this model might ever be used with. Can "
+        "be used to create a student model with a shorter context length than the teacher model. Defaults to the number "
+        "of source positions in the teacher model (1500).",
+    )
+    parser.add_argument(
+        "--save_dir",
+        type=str,
+        required=True,
+        help="Where to save the student weights and processor.",
+    )
+    parser.add_argument(
+        "--push_to_hub",
+        type=bool,
+        required=False,
+        default=False,
+        help="Whether to push the student weights and processor to the Hub.",
+    )
+    parser.add_argument(
+        "--cache_dir",
+        type=str,
+        default=None,
+        help="Where to store the pretrained models downloaded from huggingface.co",
+    )
+
+    args = parser.parse_args()
+    return args
+
+
+def init_student_model_from_teacher(
+    teacher_checkpoint,
+    encoder_layers=None,
+    decoder_layers=2,
+    max_source_positions=None,
+    save_dir=None,
+    push_to_hub=None,
+    cache_dir=None,
+    subfolder="",
+):
+    teacher_model, teacher_params = FlaxWhisperForConditionalGeneration.from_pretrained(
+        teacher_checkpoint,
+        _do_init=False,
+        cache_dir=cache_dir,
+        subfolder=subfolder,
+    )
+    processor = WhisperProcessor.from_pretrained(teacher_checkpoint)
+    generation_config = GenerationConfig.from_pretrained(teacher_checkpoint)
+
+    teacher_config = teacher_model.config
+    teacher_encoder_layers = teacher_config.encoder_layers
+    teacher_decoder_layers = teacher_config.decoder_layers
+
+    student_config = copy.deepcopy(teacher_config)
+    student_config.update(
+        {
+            "encoder_layers": encoder_layers if encoder_layers is not None else teacher_encoder_layers,
+            "decoder_layers": decoder_layers,
+            "max_source_positions": (
+                max_source_positions if max_source_positions is not None else student_config.max_source_positions
+            ),
+        }
+    )
+
+    encoder_mapping = np.linspace(0, teacher_encoder_layers - 1, student_config.encoder_layers, dtype=int)
+    encoder_mapping[-1] = teacher_encoder_layers - 1
+
+    encoder_map = {}
+    for student_layer, teacher_layer in enumerate(encoder_mapping):
+        encoder_map[str(teacher_layer)] = str(student_layer)
+
+    decoder_mapping = np.linspace(0, teacher_decoder_layers - 1, student_config.decoder_layers, dtype=int)
+    decoder_mapping[-1] = teacher_decoder_layers - 1
+
+    decoder_map = {}
+    for student_layer, teacher_layer in enumerate(decoder_mapping):
+        decoder_map[str(teacher_layer)] = str(student_layer)
+
+    # init the student params from the teacher model
+    student_params = unfreeze(teacher_params)
+    student_params["model"]["decoder"]["layers"] = {}
+
+    for layer in teacher_params["model"]["decoder"]["layers"]:
+        if layer in decoder_map:
+            # re-introduce pre-defined layers from the teacher
+            student_params["model"]["decoder"]["layers"][decoder_map[layer]] = teacher_params["model"]["decoder"][
+                "layers"
+            ][layer]
+
+    if encoder_layers is not None:
+        student_params["model"]["encoder"]["layers"] = {}
+        for layer in teacher_params["model"]["encoder"]["layers"]:
+            if layer in encoder_map:
+                # re-introduce pre-defined layers from the teacher
+                student_params["model"]["encoder"]["layers"][encoder_map[layer]] = teacher_params["model"]["encoder"][
+                    "layers"
+                ][layer]
+
+    if max_source_positions is not None:
+        # slice the first MAX_SOURCE_POSITIONS embedding weights
+        student_params["model"]["encoder"]["embed_positions"]["embedding"] = teacher_params["model"]["encoder"][
+            "embed_positions"
+        ]["embedding"][: student_config.max_source_positions, :]
+        # update the feature extractor to handle the new input length
+        chunk_length = int(student_config.max_source_positions * 2 / 100)
+        processor.feature_extractor = WhisperFeatureExtractor(chunk_length=chunk_length)
+
+    # remove the teacher params and model
+    del teacher_params, teacher_model
+
+    # save the converted weights and model
+    student_params = freeze(student_params)
+    student_model = FlaxWhisperForConditionalGeneration(student_config, _do_init=False)
+
+    if save_dir is not None:
+        student_model.save_pretrained(save_dir, params=student_params)
+        # we also need to correctly save the processor and generation config
+        processor.save_pretrained(save_dir)
+        generation_config.save_pretrained(save_dir)
+
+    # check we can do a forward pass with the saved model - first load the weights and processor
+    logger.info("Checking we can load the saved model...")
+    student_model, student_params = FlaxWhisperForConditionalGeneration.from_pretrained(
+        save_dir,
+        _do_init=False,
+    )
+    processor = WhisperProcessor.from_pretrained(save_dir)
+
+    # define some random inputs
+    input_features = processor(np.ones(16000), sampling_rate=16000, return_tensors="np").input_features
+    decoder_start_token_id = student_model.config.decoder_start_token_id
+    decoder_input_ids = np.ones((input_features.shape[0], 1)) * decoder_start_token_id
+
+    # do a forward pass - outputs will be gibberish for the initialised model so we can't check them
+    logger.info("Checking we can run the converted model forward...")
+    _ = student_model(input_features, decoder_input_ids=decoder_input_ids, params=student_params).logits
+    logger.info("Conversion successful!")
+
+    if push_to_hub:
+        student_model.push_to_hub(save_dir, params=student_params)
+        processor.push_to_hub(save_dir)
+        generation_config.push_to_hub(save_dir)
+
+
+if __name__ == "__main__":
+    args = parse_args()
+
+    # Set the verbosity to info of the logger - we only want one process per machine to log things on the screen
+    logger.setLevel(logging.INFO if jax.process_index() == 0 else logging.ERROR)
+
+    init_student_model_from_teacher(
+        teacher_checkpoint=args.teacher_checkpoint,
+        encoder_layers=args.encoder_layers,
+        decoder_layers=args.decoder_layers,
+        max_source_positions=args.max_source_positions,
+        save_dir=args.save_dir,
+        push_to_hub=args.push_to_hub,
+        cache_dir=args.cache_dir,
+        subfolder=args.subfolder,
+    )
diff --git a/flax/distil_whisper/__init__.py b/flax/distil_whisper/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..642aba4dadb6690f40a6eed270fb252800e5fbd8
--- /dev/null
+++ b/flax/distil_whisper/__init__.py
@@ -0,0 +1,21 @@
+# coding=utf-8
+# Copyright 2023 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+__version__ = "0.0.1"
+
+from .modeling_flax_whisper import FlaxWhisperForConditionalGeneration
+from .partitioner import PjitPartitioner
+from .pipeline import FlaxWhisperPipeline
+from .train_state import InferenceState
diff --git a/flax/distil_whisper/layers.py b/flax/distil_whisper/layers.py
new file mode 100644
index 0000000000000000000000000000000000000000..800845b280d8bd1f959ea78997201ab767d56872
--- /dev/null
+++ b/flax/distil_whisper/layers.py
@@ -0,0 +1,1338 @@
+# Copyright 2022 The T5X Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Dense attention classes and mask/weighting functions."""
+
+# pylint: disable=attribute-defined-outside-init,g-bare-generic
+
+import dataclasses
+import functools
+import operator
+from typing import Any, Callable, Iterable, List, Optional, Sequence, Tuple, Union
+
+import jax
+import jax.numpy as jnp
+import numpy as np
+from flax import linen as nn
+from flax.linen import partitioning as nn_partitioning
+from flax.linen.dtypes import promote_dtype
+from jax import lax, random
+
+
+# from flax.linen.partitioning import param_with_axes, with_sharding_constraint
+param_with_axes = nn_partitioning.param_with_axes
+with_sharding_constraint = nn_partitioning.with_sharding_constraint
+
+
+# Type annotations
+Array = jnp.ndarray
+DType = jnp.dtype
+PRNGKey = jnp.ndarray
+Shape = Iterable[int]
+Activation = Callable[..., Array]
+PrecisionLike = Union[None, str, lax.Precision, Tuple[str, str], Tuple[lax.Precision, lax.Precision]]
+DotGeneralT = Callable[..., Array]
+ConvGeneralDilatedT = Callable[..., Array]
+PaddingLike = Union[str, int, Sequence[Union[int, Tuple[int, int]]]]
+LaxPadding = Union[str, Sequence[Tuple[int, int]]]
+
+# Parameter initializers.
+Initializer = Callable[[PRNGKey, Shape, DType], Array]
+InitializerAxis = Union[int, Tuple[int, ...]]
+NdInitializer = Callable[[PRNGKey, Shape, DType, InitializerAxis, InitializerAxis], Array]
+
+default_embed_init = nn.initializers.variance_scaling(1.0, "fan_in", "normal", out_axis=0)
+
+
+# ------------------------------------------------------------------------------
+# Temporary inlined JAX N-d initializer code
+# TODO(levskaya): remove once new JAX release is out.
+# ------------------------------------------------------------------------------
+def _compute_fans(shape: jax.core.NamedShape, in_axis=-2, out_axis=-1):
+    """Inlined JAX `nn.initializer._compute_fans`."""
+    if isinstance(in_axis, int):
+        in_size = shape[in_axis]
+    else:
+        in_size = int(np.prod([shape[i] for i in in_axis]))
+    if isinstance(out_axis, int):
+        out_size = shape[out_axis]
+    else:
+        out_size = int(np.prod([shape[i] for i in out_axis]))
+    receptive_field_size = shape.total / in_size / out_size
+    fan_in = in_size * receptive_field_size
+    fan_out = out_size * receptive_field_size
+    return fan_in, fan_out
+
+
+def variance_scaling(scale, mode, distribution, in_axis=-2, out_axis=-1, dtype=jnp.float_):
+    """Inlined JAX `nn.initializer.variance_scaling`."""
+
+    def init(key, shape, dtype=dtype):
+        return jnp.zeros(shape, dtype=dtype)
+        dtype = jax.dtypes.canonicalize_dtype(dtype)
+        shape = jax.core.as_named_shape(shape)
+        fan_in, fan_out = _compute_fans(shape, in_axis, out_axis)
+        if mode == "fan_in":
+            denominator = fan_in
+        elif mode == "fan_out":
+            denominator = fan_out
+        elif mode == "fan_avg":
+            denominator = (fan_in + fan_out) / 2
+        else:
+            raise ValueError("invalid mode for variance scaling initializer: {}".format(mode))
+        variance = jnp.array(scale / denominator, dtype=dtype)
+
+        if distribution == "truncated_normal":
+            # constant is stddev of standard normal truncated to (-2, 2)
+            stddev = jnp.sqrt(variance) / jnp.array(0.87962566103423978, dtype)
+            return random.truncated_normal(key, -2, 2, shape, dtype) * stddev
+        elif distribution == "normal":
+            return random.normal(key, shape, dtype) * jnp.sqrt(variance)
+        elif distribution == "uniform":
+            return random.uniform(key, shape, dtype, -1) * jnp.sqrt(3 * variance)
+        else:
+            raise ValueError("invalid distribution for variance scaling initializer: {}".format(distribution))
+
+    return init
+
+
+# ------------------------------------------------------------------------------
+
+
+def nd_dense_init(scale, mode, distribution):
+    """Initializer with in_axis, out_axis set at call time."""
+
+    def init_fn(key, shape, dtype, in_axis, out_axis):
+        fn = variance_scaling(scale, mode, distribution, in_axis, out_axis)
+        return fn(key, shape, dtype)
+
+    return init_fn
+
+
+def dot_product_attention(
+    query: Array,
+    key: Array,
+    value: Array,
+    bias: Optional[Array] = None,
+    dropout_rng: Optional[PRNGKey] = None,
+    dropout_rate: float = 0.0,
+    deterministic: bool = False,
+    dtype: DType = jnp.float32,
+    float32_logits: bool = False,
+):
+    """Computes dot-product attention given query, key, and value.
+
+    This is the core function for applying attention based on
+    https://arxiv.org/abs/1706.03762. It calculates the attention weights given
+    query and key and combines the values using the attention weights.
+
+    Args:
+      query: queries for calculating attention with shape of `[batch, q_length,
+        num_heads, qk_depth_per_head]`.
+      key: keys for calculating attention with shape of `[batch, kv_length,
+        num_heads, qk_depth_per_head]`.
+      value: values to be used in attention with shape of `[batch, kv_length,
+        num_heads, v_depth_per_head]`.
+      bias: bias for the attention weights. This should be broadcastable to the
+        shape `[batch, num_heads, q_length, kv_length]` This can be used for
+        incorporating causal masks, padding masks, proximity bias, etc.
+      dropout_rng: JAX PRNGKey: to be used for dropout
+      dropout_rate: dropout rate
+      deterministic: bool, deterministic or not (to apply dropout)
+      dtype: the dtype of the computation (default: float32)
+      float32_logits: bool, if True then compute logits in float32 to avoid
+        numerical issues with bfloat16.
+
+    Returns:
+      Output of shape `[batch, length, num_heads, v_depth_per_head]`.
+    """
+    assert key.ndim == query.ndim == value.ndim, "q, k, v must have same rank."
+    assert query.shape[:-3] == key.shape[:-3] == value.shape[:-3], "q, k, v batch dims must match."
+    assert query.shape[-2] == key.shape[-2] == value.shape[-2], "q, k, v num_heads must match."
+    assert key.shape[-3] == value.shape[-3], "k, v lengths must match."
+    assert query.shape[-1] == key.shape[-1], "q, k depths must match."
+
+    # Casting logits and softmax computation for float32 for model stability.
+    if float32_logits:
+        query = query.astype(jnp.float32)
+        key = key.astype(jnp.float32)
+
+    # `attn_weights`: [batch, num_heads, q_length, kv_length]
+    attn_weights = jnp.einsum("bqhd,bkhd->bhqk", query, key)
+
+    # Apply attention bias: masking, dropout, proximity bias, etc.
+    if bias is not None:
+        attn_weights = attn_weights + bias.astype(attn_weights.dtype)
+
+    # Normalize the attention weights across `kv_length` dimension.
+    attn_weights = jax.nn.softmax(attn_weights).astype(dtype)
+
+    # Apply attention dropout.
+    if not deterministic and dropout_rate > 0.0:
+        keep_prob = 1.0 - dropout_rate
+        # T5 broadcasts along the "length" dim, but unclear which one that
+        # corresponds to in positional dimensions here, assuming query dim.
+        dropout_shape = list(attn_weights.shape)
+        dropout_shape[-2] = 1
+        keep = random.bernoulli(dropout_rng, keep_prob, dropout_shape)
+        keep = jnp.broadcast_to(keep, attn_weights.shape)
+        multiplier = keep.astype(attn_weights.dtype) / jnp.asarray(keep_prob, dtype=dtype)
+        attn_weights = attn_weights * multiplier
+
+    # Take the linear combination of `value`.
+    return jnp.einsum("bhqk,bkhd->bqhd", attn_weights, value)
+
+
+dynamic_vector_slice_in_dim = jax.vmap(lax.dynamic_slice_in_dim, in_axes=(None, 0, None, None))
+
+
+class MultiHeadDotProductAttention(nn.Module):
+    """Multi-head dot-product attention.
+
+    Attributes:
+      num_heads: number of attention heads. Features (i.e. inputs_q.shape[-1])
+        should be divisible by the number of heads.
+      head_dim: dimension of each head.
+      dtype: the dtype of the computation.
+      dropout_rate: dropout rate
+      kernel_init: initializer for the kernel of the Dense layers.
+      float32_logits: bool, if True then compute logits in float32 to avoid
+        numerical issues with bfloat16.
+    """
+
+    num_heads: int
+    head_dim: int
+    dtype: DType = jnp.float32
+    dropout_rate: float = 0.0
+    kernel_init: NdInitializer = nd_dense_init(1.0, "fan_in", "normal")
+    float32_logits: bool = False  # computes logits in float32 for stability.
+
+    @nn.compact
+    def __call__(
+        self,
+        inputs_q: Array,
+        inputs_kv: Array,
+        mask: Optional[Array] = None,
+        bias: Optional[Array] = None,
+        *,
+        decode: bool = False,
+        deterministic: bool = False,
+    ) -> Array:
+        """Applies multi-head dot product attention on the input data.
+
+        Projects the inputs into multi-headed query, key, and value vectors,
+        applies dot-product attention and project the results to an output vector.
+
+        There are two modes: decoding and non-decoding (e.g., training). The mode is
+        determined by `decode` argument. For decoding, this method is called twice,
+        first to initialize the cache and then for an actual decoding process. The
+        two calls are differentiated by the presence of 'cached_key' in the variable
+        dict. In the cache initialization stage, the cache variables are initialized
+        as zeros and will be filled in the subsequent decoding process.
+
+        In the cache initialization call, `inputs_q` has a shape [batch, length,
+        q_features] and `inputs_kv`: [batch, length, kv_features]. During the
+        incremental decoding stage, query, key and value all have the shape [batch,
+        1, qkv_features] corresponding to a single step.
+
+        Args:
+          inputs_q: input queries of shape `[batch, q_length, q_features]`.
+          inputs_kv: key/values of shape `[batch, kv_length, kv_features]`.
+          mask: attention mask of shape `[batch, num_heads, q_length, kv_length]`.
+          bias: attention bias of shape `[batch, num_heads, q_length, kv_length]`.
+          decode: Whether to prepare and use an autoregressive cache.
+          deterministic: Disables dropout if set to True.
+
+        Returns:
+          output of shape `[batch, length, q_features]`.
+        """
+        projection = functools.partial(
+            DenseGeneral,
+            axis=-1,
+            features=(self.num_heads, self.head_dim),
+            kernel_axes=("embed", "heads", "kv"),
+            dtype=self.dtype,
+        )
+
+        # NOTE: T5 does not explicitly rescale the attention logits by
+        #       1/sqrt(depth_kq)!  This is folded into the initializers of the
+        #       linear transformations, which is equivalent under Adafactor.
+        depth_scaling = jnp.sqrt(self.head_dim).astype(self.dtype)
+
+        def query_init(*args):
+            return self.kernel_init(*args) / depth_scaling
+
+        # Project inputs_q to multi-headed q/k/v
+        # dimensions are then [batch, length, num_heads, head_dim]
+        query = projection(kernel_init=query_init, name="query")(inputs_q)
+        key = projection(kernel_init=self.kernel_init, name="key")(inputs_kv)
+        value = projection(kernel_init=self.kernel_init, name="value")(inputs_kv)
+
+        query = with_sharding_constraint(query, ("batch", "length", "heads", "kv"))
+        key = with_sharding_constraint(key, ("batch", "length", "heads", "kv"))
+        value = with_sharding_constraint(value, ("batch", "length", "heads", "kv"))
+
+        if decode:
+            # Detect if we're initializing by absence of existing cache data.
+            is_initialized = self.has_variable("cache", "cached_key")
+
+            # The key and value have dimension [batch, length, num_heads, head_dim],
+            # but we cache them as [batch, num_heads, head_dim, length] as a TPU
+            # fusion optimization. This also enables the "scatter via one-hot
+            # broadcast" trick, which means we do a one-hot broadcast instead of a
+            # scatter/gather operations, resulting in a 3-4x speedup in practice.
+            def swap_dims(x):
+                return x[:-3] + tuple(x[i] for i in [-2, -1, -3])
+
+            cached_key = self.variable("cache", "cached_key", jnp.zeros, swap_dims(key.shape), key.dtype)
+            cached_value = self.variable("cache", "cached_value", jnp.zeros, swap_dims(value.shape), value.dtype)
+            cache_index = self.variable("cache", "cache_index", lambda: jnp.array(0, dtype=jnp.int32))
+            if is_initialized:
+                batch, num_heads, head_dim, length = cached_key.value.shape
+                # During fast autoregressive decoding, we feed one position at a time,
+                # and cache the keys and values step by step.
+                # Sanity shape check of cached key against input query.
+                expected_shape = (batch, 1, num_heads, head_dim)
+                if expected_shape != query.shape:
+                    raise ValueError(
+                        "Autoregressive cache shape error, "
+                        "expected query shape %s instead got %s." % (expected_shape, query.shape)
+                    )
+
+                # Create a OHE of the current index. NOTE: the index is increased below.
+                cur_index = cache_index.value
+                one_hot_indices = jax.nn.one_hot(cur_index, length, dtype=key.dtype)
+                # In order to update the key, value caches with the current key and
+                # value, we move the length axis to the back, similar to what we did for
+                # the cached ones above.
+                # Note these are currently the key and value of a single position, since
+                # we feed one position at a time.
+                one_token_key = jnp.moveaxis(key, -3, -1)
+                one_token_value = jnp.moveaxis(value, -3, -1)
+                # Update key, value caches with our new 1d spatial slices.
+                # We implement an efficient scatter into the cache via one-hot
+                # broadcast and addition.
+                key = cached_key.value + one_token_key * one_hot_indices
+                value = cached_value.value + one_token_value * one_hot_indices
+                cached_key.value = key
+                cached_value.value = value
+                cache_index.value = cache_index.value + 1
+                # Move the keys and values back to their original shapes.
+                key = jnp.moveaxis(key, -1, -3)
+                value = jnp.moveaxis(value, -1, -3)
+
+                # Causal mask for cached decoder self-attention: our single query
+                # position should only attend to those key positions that have already
+                # been generated and cached, not the remaining zero elements.
+                mask = combine_masks(
+                    mask,
+                    jnp.broadcast_to(
+                        jnp.arange(length) <= cur_index,
+                        # (1, 1, length) represent (head dim, query length, key length)
+                        # query length is 1 because during decoding we deal with one
+                        # index.
+                        # The same mask is applied to all batch elements and heads.
+                        (batch, 1, 1, length),
+                    ),
+                )
+
+                # Grab the correct relative attention bias during decoding. This is
+                # only required during single step decoding.
+                if bias is not None:
+                    # The bias is a full attention matrix, but during decoding we only
+                    # have to take a slice of it.
+                    # This is equivalent to bias[..., cur_index:cur_index+1, :].
+                    bias = dynamic_vector_slice_in_dim(jnp.squeeze(bias, axis=0), jnp.reshape(cur_index, (-1)), 1, -2)
+
+        # Convert the boolean attention mask to an attention bias.
+        if mask is not None:
+            # attention mask in the form of attention bias
+            attention_bias = lax.select(
+                mask > 0,
+                jnp.full(mask.shape, 0.0).astype(self.dtype),
+                jnp.full(mask.shape, -1e10).astype(self.dtype),
+            )
+        else:
+            attention_bias = None
+
+        # Add provided bias term (e.g. relative position embedding).
+        if bias is not None:
+            attention_bias = combine_biases(attention_bias, bias)
+
+        dropout_rng = None
+        if not deterministic and self.dropout_rate > 0.0:
+            dropout_rng = self.make_rng("dropout")
+
+        # Apply attention.
+        x = dot_product_attention(
+            query,
+            key,
+            value,
+            bias=attention_bias,
+            dropout_rng=dropout_rng,
+            dropout_rate=self.dropout_rate,
+            deterministic=deterministic,
+            dtype=self.dtype,
+            float32_logits=self.float32_logits,
+        )
+
+        # Back to the original inputs dimensions.
+        out = DenseGeneral(
+            features=inputs_q.shape[-1],  # output dim is set to the input dim.
+            axis=(-2, -1),
+            kernel_init=self.kernel_init,
+            kernel_axes=("heads", "kv", "embed"),
+            dtype=self.dtype,
+            name="out",
+        )(x)
+        return out
+
+
+def _normalize_axes(axes: Iterable[int], ndim: int) -> Tuple[int]:
+    # A tuple by convention. len(axes_tuple) then also gives the rank efficiently.
+    return tuple([ax if ax >= 0 else ndim + ax for ax in axes])
+
+
+def _canonicalize_tuple(x):
+    if isinstance(x, Iterable):
+        return tuple(x)
+    else:
+        return (x,)
+
+
+# ------------------------------------------------------------------------------
+# DenseGeneral for attention layers.
+# ------------------------------------------------------------------------------
+class DenseGeneral(nn.Module):
+    """A linear transformation (without bias) with flexible axes.
+
+    Attributes:
+      features: tuple with numbers of output features.
+      axis: tuple with axes to apply the transformation on.
+      dtype: the dtype of the computation (default: float32).
+      kernel_init: initializer function for the weight matrix.
+    """
+
+    features: Union[Iterable[int], int]
+    axis: Union[Iterable[int], int] = -1
+    dtype: DType = jnp.float32
+    params_dtype: DType = jnp.float32
+    kernel_init: NdInitializer = nd_dense_init(1.0, "fan_in", "normal")
+    kernel_axes: Tuple[str, ...] = ()
+    use_bias: bool = True
+    bias_init: Any = nn.initializers.zeros
+
+    @nn.compact
+    def __call__(self, inputs: Array) -> Array:
+        """Applies a linear transformation to the inputs along multiple dimensions.
+
+        Args:
+          inputs: The nd-array to be transformed.
+
+        Returns:
+          The transformed input.
+        """
+        features = _canonicalize_tuple(self.features)
+        axis = _canonicalize_tuple(self.axis)
+
+        inputs = jnp.asarray(inputs, self.dtype)
+        axis = _normalize_axes(axis, inputs.ndim)
+
+        kernel_shape = tuple([inputs.shape[ax] for ax in axis]) + features
+        kernel_in_axis = np.arange(len(axis))
+        kernel_out_axis = np.arange(len(axis), len(axis) + len(features))
+        kernel = param_with_axes(
+            "kernel",
+            self.kernel_init,
+            kernel_shape,
+            self.params_dtype,
+            kernel_in_axis,
+            kernel_out_axis,
+            axes=self.kernel_axes,
+        )
+        if self.use_bias:
+            bias = param_with_axes(
+                "bias",
+                self.bias_init,
+                features,
+                self.params_dtype,
+                axes=(self.kernel_axes[-1],),
+            )
+        kernel = jnp.asarray(kernel, self.dtype)
+
+        contract_ind = tuple(range(0, len(axis)))
+        y = lax.dot_general(inputs, kernel, ((axis, contract_ind), ((), ())))
+        if self.use_bias:
+            bias = jnp.asarray(bias, self.dtype)
+            # y += jnp.reshape(bias, (1,) * (y.ndim - 1) + (-1,))
+            y += jnp.reshape(bias, (1,) * (len(features) - y.ndim) + bias.shape[:])
+        return y
+
+
+def _convert_to_activation_function(fn_or_string: Union[str, Callable]) -> Callable:
+    """Convert a string to an activation function."""
+    if fn_or_string == "linear":
+        return lambda x: x
+    elif isinstance(fn_or_string, str):
+        return getattr(nn, fn_or_string)
+    elif callable(fn_or_string):
+        return fn_or_string
+    else:
+        raise ValueError("don't know how to convert %s to an activation function" % (fn_or_string,))
+
+
+class MlpBlock(nn.Module):
+    """Transformer MLP / feed-forward block.
+
+    Attributes:
+      intermediate_dim: Shared dimension of hidden layers.
+      activations: Type of activations for each layer.  Each element is either
+        'linear', a string function name in flax.linen, or a function.
+      kernel_init: Kernel function, passed to the dense layers.
+      deterministic: Whether the dropout layers should be deterministic.
+      intermediate_dropout_rate: Dropout rate used after the intermediate layers.
+      dtype: Type for the dense layer.
+    """
+
+    intermediate_dim: int = 2048
+    activations: Sequence[Union[str, Callable]] = ("relu",)
+    kernel_init: NdInitializer = nd_dense_init(1.0, "fan_in", "truncated_normal")
+    intermediate_dropout_rate: float = 0.1
+    dtype: Any = jnp.float32
+
+    @nn.compact
+    def __call__(self, inputs, decode: bool = False, deterministic: bool = False):
+        """Applies Transformer MlpBlock module."""
+        # Iterate over specified MLP input activation functions.
+        # e.g. ('relu',) or ('gelu', 'linear') for gated-gelu.
+        activations = []
+        for idx, act_fn in enumerate(self.activations):
+            dense_name = "wi" if len(self.activations) == 1 else f"wi_{idx}"
+            x = DenseGeneral(
+                self.intermediate_dim,
+                dtype=self.dtype,
+                kernel_init=self.kernel_init,
+                kernel_axes=("embed", "mlp"),
+                name=dense_name,
+            )(inputs)
+            x = _convert_to_activation_function(act_fn)(x)
+            activations.append(x)
+
+        # Take elementwise product of above intermediate activations.
+        x = functools.reduce(operator.mul, activations)
+        # Apply dropout and final dense output projection.
+        x = nn.Dropout(rate=self.intermediate_dropout_rate, broadcast_dims=(-2,))(
+            x, deterministic=deterministic
+        )  # Broadcast along length.
+        x = with_sharding_constraint(x, ("batch", "length", "mlp"))
+        output = DenseGeneral(
+            inputs.shape[-1],
+            dtype=self.dtype,
+            kernel_init=self.kernel_init,
+            kernel_axes=("mlp", "embed"),
+            name="wo",
+        )(x)
+        return output
+
+
+class Embed(nn.Module):
+    """A parameterized function from integers [0, n) to d-dimensional vectors.
+
+    Attributes:
+      num_embeddings: number of embeddings.
+      features: number of feature dimensions for each embedding.
+      dtype: the dtype of the embedding vectors (default: float32).
+      embedding_init: embedding initializer.
+      one_hot: performs the gather with a one-hot contraction rather than a true
+        gather. This is currently needed for SPMD partitioning.
+    """
+
+    num_embeddings: int
+    features: int
+    cast_input_dtype: Optional[DType] = None
+    dtype: DType = jnp.float32
+    params_dtype: DType = jnp.float32
+    attend_dtype: Optional[DType] = None
+    embedding_init: Initializer = default_embed_init
+    one_hot: bool = True
+    embedding: Array = dataclasses.field(init=False)
+
+    def setup(self):
+        self.embedding = param_with_axes(
+            "embedding",
+            self.embedding_init,
+            (self.num_embeddings, self.features),
+            self.params_dtype,
+            axes=("vocab", "embed"),
+        )
+
+    def __call__(self, inputs: Array) -> Array:
+        """Embeds the inputs along the last dimension.
+
+        Args:
+          inputs: input data, all dimensions are considered batch dimensions.
+
+        Returns:
+          Output which is embedded input data.  The output shape follows the input,
+          with an additional `features` dimension appended.
+        """
+        if self.cast_input_dtype:
+            inputs = inputs.astype(self.cast_input_dtype)
+        if not jnp.issubdtype(inputs.dtype, jnp.integer):
+            raise ValueError("Input type must be an integer or unsigned integer.")
+        if self.one_hot:
+            iota = lax.iota(jnp.int32, self.num_embeddings)
+            one_hot = jnp.array(inputs[..., jnp.newaxis] == iota, dtype=self.dtype)
+            output = jnp.dot(one_hot, jnp.asarray(self.embedding, self.dtype))
+        else:
+            output = jnp.asarray(self.embedding, self.dtype)[inputs]
+            output = with_sharding_constraint(output, ("batch", "length", "embed"))
+        return output
+
+    def attend(self, query: Array) -> Array:
+        """Attend over the embedding using a query array.
+
+        Args:
+          query: array with last dimension equal the feature depth `features` of the
+            embedding.
+
+        Returns:
+          An array with final dim `num_embeddings` corresponding to the batched
+          inner-product of the array of query vectors against each embedding.
+          Commonly used for weight-sharing between embeddings and logit transform
+          in NLP models.
+        """
+        dtype = self.attend_dtype if self.attend_dtype is not None else self.dtype
+        return jnp.dot(query, jnp.asarray(self.embedding, dtype).T)
+
+
+class RelativePositionBiases(nn.Module):
+    """Adds T5-style relative positional embeddings to the attention logits.
+
+    Attributes:
+      num_buckets: Number of buckets to bucket distances between key and query
+        positions into.
+      max_distance: Maximum distance before everything is lumped into the last
+        distance bucket.
+      num_heads: Number of heads in the attention layer. Each head will get a
+        different relative position weighting.
+      dtype: Type of arrays through this module.
+      embedding_init: initializer for relative embedding table.
+    """
+
+    num_buckets: int
+    max_distance: int
+    num_heads: int
+    dtype: Any
+    embedding_init: Callable[..., Array] = nn.linear.default_embed_init
+
+    @staticmethod
+    def _relative_position_bucket(relative_position, bidirectional=True, num_buckets=32, max_distance=128):
+        """Translate relative position to a bucket number for relative attention.
+
+        The relative position is defined as memory_position - query_position, i.e.
+        the distance in tokens from the attending position to the attended-to
+        position.  If bidirectional=False, then positive relative positions are
+        invalid.
+        We use smaller buckets for small absolute relative_position and larger
+        buckets for larger absolute relative_positions.  All relative
+        positions >=max_distance  map to the same bucket.  All relative
+        positions <=-max_distance map to the same bucket.  This should allow for
+        more graceful generalization to longer sequences than the model has been
+        trained on.
+
+        Args:
+          relative_position: an int32 array
+          bidirectional: a boolean - whether the attention is bidirectional
+          num_buckets: an integer
+          max_distance: an integer
+
+        Returns:
+          a Tensor with the same shape as relative_position, containing int32
+            values in the range [0, num_buckets)
+        """
+        ret = 0
+        n = -relative_position
+        if bidirectional:
+            num_buckets //= 2
+            ret += (n < 0).astype(np.int32) * num_buckets
+            n = np.abs(n)
+        else:
+            n = np.maximum(n, 0)
+        # now n is in the range [0, inf)
+        max_exact = num_buckets // 2
+        is_small = n < max_exact
+        val_if_large = max_exact + (
+            np.log(n.astype(np.float32) / max_exact + np.finfo(np.float32).eps)
+            / np.log(max_distance / max_exact)
+            * (num_buckets - max_exact)
+        ).astype(np.int32)
+        val_if_large = np.minimum(val_if_large, num_buckets - 1)
+        ret += np.where(is_small, n, val_if_large)
+        return ret
+
+    @nn.compact
+    def __call__(self, qlen, klen, bidirectional=True):
+        """Produce relative position embedding attention biases.
+
+        Args:
+          qlen: attention query length.
+          klen: attention key length.
+          bidirectional: whether to allow positive memory-query relative position
+            embeddings.
+
+        Returns:
+          output: `(1, len, q_len, k_len)` attention bias
+        """
+        # TODO(levskaya): should we be computing this w. numpy as a program
+        # constant?
+        context_position = np.arange(qlen, dtype=jnp.int32)[:, None]
+        memory_position = np.arange(klen, dtype=jnp.int32)[None, :]
+        relative_position = memory_position - context_position  # shape (qlen, klen)
+        rp_bucket = self._relative_position_bucket(
+            relative_position,
+            bidirectional=bidirectional,
+            num_buckets=self.num_buckets,
+            max_distance=self.max_distance,
+        )
+        relative_attention_bias = param_with_axes(
+            "rel_embedding",
+            self.embedding_init,
+            (self.num_heads, self.num_buckets),
+            jnp.float32,
+            axes=("heads", "relpos_buckets"),
+        )
+
+        relative_attention_bias = jnp.asarray(relative_attention_bias, self.dtype)
+        # Instead of using a slow gather, we create a leading-dimension one-hot
+        # array from rp_bucket and use it to perform the gather-equivalent via a
+        # contraction, i.e.:
+        # (num_head, num_buckets) x (num_buckets one-hot, qlen, klen).
+        # This is equivalent to relative_attention_bias[:, rp_bucket]
+        bcast_iota = lax.broadcasted_iota(jnp.int32, (self.num_buckets, 1, 1), 0)
+        rp_bucket_one_hot = jnp.array(rp_bucket[jnp.newaxis, ...] == bcast_iota, dtype=self.dtype)
+        # --> shape (qlen, klen, num_heads)
+        values = lax.dot_general(
+            relative_attention_bias,
+            rp_bucket_one_hot,
+            (((1,), (0,)), ((), ())),  # rhs, lhs contracting dims
+        )  # no batched dims
+        # Add a singleton batch dimension.
+        # --> shape (1, num_heads, qlen, klen)
+        return values[jnp.newaxis, ...]
+
+
+# ------------------------------------------------------------------------------
+# T5 Layernorm - no subtraction of mean or bias.
+# ------------------------------------------------------------------------------
+# class LayerNorm(nn.Module):
+#   """T5 Layer normalization operating on the last axis of the input data."""
+#   epsilon: float = 1e-6
+#   dtype: Any = jnp.float32
+#   scale_init: Initializer = nn.initializers.ones
+
+#   @nn.compact
+#   def __call__(self, x: jnp.ndarray) -> jnp.ndarray:
+#     """Applies layer normalization on the input."""
+#     x = jnp.asarray(x, jnp.float32)
+#     features = x.shape[-1]
+#     mean2 = jnp.mean(lax.square(x), axis=-1, keepdims=True)
+#     y = jnp.asarray(x * lax.rsqrt(mean2 + self.epsilon), self.dtype)
+#     scale = param_with_axes(
+#         'scale', self.scale_init, (features,), jnp.float32, axes=('embed',))
+
+#     scale = jnp.asarray(scale, self.dtype)
+#     return y * scale
+
+
+class LayerNorm(nn.Module):
+    """Layer normalization (https://arxiv.org/abs/1607.06450).
+    Operates on the last axis of the input data.
+    It normalizes the activations of the layer for each given example in a
+    batch independently, rather than across a batch like Batch Normalization.
+    i.e. applies a transformation that maintains the mean activation within
+    each example close to 0 and the activation standard deviation close to 1.
+    Attributes:
+      epsilon: A small float added to variance to avoid dividing by zero.
+      dtype: the dtype of the computation (default: float32).
+      use_bias:  If True, bias (beta) is added.
+      use_scale: If True, multiply by scale (gamma). When the next layer is linear
+        (also e.g. nn.relu), this can be disabled since the scaling will be done
+        by the next layer.
+      bias_init: Initializer for bias, by default, zero.
+      scale_init: Initializer for scale, by default, one.
+    """
+
+    epsilon: float = 1e-6
+    dtype: Any = jnp.float32
+    params_dtype: DType = jnp.float32
+    use_bias: bool = True
+    use_scale: bool = True
+    bias_init: Callable[[PRNGKey, Shape, Any], Array] = nn.initializers.zeros
+    scale_init: Callable[[PRNGKey, Shape, Any], Array] = nn.initializers.ones
+
+    @nn.compact
+    def __call__(self, x):
+        """Applies layer normalization on the input.
+        Args:
+          x: the inputs
+        Returns:
+          Normalized inputs (the same shape as inputs).
+        """
+        x = jnp.asarray(x, jnp.float32)
+        features = x.shape[-1]
+        mean = jnp.mean(x, axis=-1, keepdims=True)
+        mean2 = jnp.mean(lax.square(x), axis=-1, keepdims=True)
+        var = mean2 - lax.square(mean)
+        mul = lax.rsqrt(var + self.epsilon)
+        if self.use_scale:
+            scale = param_with_axes(
+                "scale",
+                self.scale_init,
+                (features,),
+                self.params_dtype,
+                axes=("embed",),
+            )
+            mul = mul * jnp.asarray(scale, self.dtype)
+        y = (x - mean) * mul
+        if self.use_bias:
+            bias = param_with_axes("bias", self.bias_init, (features,), self.params_dtype, axes=("embed",))
+            y = y + jnp.asarray(bias, self.dtype)
+        return jnp.asarray(y, self.dtype)
+
+
+# ------------------------------------------------------------------------------
+# Mask-making utility functions.
+# ------------------------------------------------------------------------------
+def make_attention_mask(
+    query_input: Array,
+    key_input: Array,
+    pairwise_fn: Callable = jnp.multiply,
+    extra_batch_dims: int = 0,
+    dtype: DType = jnp.float32,
+) -> Array:
+    """Mask-making helper for attention weights.
+
+    In case of 1d inputs (i.e., `[batch, len_q]`, `[batch, len_kv]`, the
+    attention weights will be `[batch, heads, len_q, len_kv]` and this
+    function will produce `[batch, 1, len_q, len_kv]`.
+
+    Args:
+      query_input: a batched, flat input of query_length size
+      key_input: a batched, flat input of key_length size
+      pairwise_fn: broadcasting elementwise comparison function
+      extra_batch_dims: number of extra batch dims to add singleton axes for, none
+        by default
+      dtype: mask return dtype
+
+    Returns:
+      A `[batch, 1, len_q, len_kv]` shaped mask for 1d attention.
+    """
+    # [batch, len_q, len_kv]
+    mask = pairwise_fn(
+        # [batch, len_q] -> [batch, len_q, 1]
+        jnp.expand_dims(query_input, axis=-1),
+        # [batch, len_q] -> [batch, 1, len_kv]
+        jnp.expand_dims(key_input, axis=-2),
+    )
+
+    # [batch, 1, len_q, len_kv]. This creates the head dim.
+    mask = jnp.expand_dims(mask, axis=-3)
+    mask = jnp.expand_dims(mask, axis=tuple(range(extra_batch_dims)))
+    return mask.astype(dtype)
+
+
+def make_causal_mask(x: Array, extra_batch_dims: int = 0, dtype: DType = jnp.float32) -> Array:
+    """Make a causal mask for self-attention.
+
+    In case of 1d inputs (i.e., `[batch, len]`, the self-attention weights
+    will be `[batch, heads, len, len]` and this function will produce a
+    causal mask of shape `[batch, 1, len, len]`.
+
+    Note that a causal mask does not depend on the values of x; it only depends on
+    the shape. If x has padding elements, they will not be treated in a special
+    manner.
+
+    Args:
+      x: input array of shape `[batch, len]`
+      extra_batch_dims: number of batch dims to add singleton axes for, none by
+        default
+      dtype: mask return dtype
+
+    Returns:
+      A `[batch, 1, len, len]` shaped causal mask for 1d attention.
+    """
+    idxs = jnp.broadcast_to(jnp.arange(x.shape[-1], dtype=jnp.int32), x.shape)
+    return make_attention_mask(idxs, idxs, jnp.greater_equal, extra_batch_dims=extra_batch_dims, dtype=dtype)
+
+
+def combine_masks(*masks: Optional[Array], dtype: DType = jnp.float32):
+    """Combine attention masks.
+
+    Args:
+      *masks: set of attention mask arguments to combine, some can be None.
+      dtype: final mask dtype
+
+    Returns:
+      Combined mask, reduced by logical and, returns None if no masks given.
+    """
+    masks = [m for m in masks if m is not None]
+    if not masks:
+        return None
+    assert all(
+        (x.ndim == masks[0].ndim for x in masks)
+    ), f"masks must have same rank: {tuple((x.ndim for x in masks))}"
+    mask, *other_masks = masks
+    for other_mask in other_masks:
+        mask = jnp.logical_and(mask, other_mask)
+    return mask.astype(dtype)
+
+
+def combine_biases(*masks: Optional[Array]):
+    """Combine attention biases.
+
+    Args:
+      *masks: set of attention bias arguments to combine, some can be None.
+
+    Returns:
+      Combined mask, reduced by summation, returns None if no masks given.
+    """
+    masks = [m for m in masks if m is not None]
+    if not masks:
+        return None
+    assert all(
+        (x.ndim == masks[0].ndim for x in masks)
+    ), f"masks must have same rank: {tuple((x.ndim for x in masks))}"
+    mask, *other_masks = masks
+    for other_mask in other_masks:
+        mask = mask + other_mask
+    return mask
+
+
+def make_decoder_mask(
+    decoder_target_tokens: Array,
+    dtype: DType,
+    decoder_causal_attention: Optional[Array] = None,
+    decoder_segment_ids: Optional[Array] = None,
+) -> Array:
+    """Compute the self-attention mask for a decoder.
+
+    Decoder mask is formed by combining a causal mask, a padding mask and an
+    optional packing mask. If decoder_causal_attention is passed, it makes the
+    masking non-causal for positions that have value of 1.
+
+    A prefix LM is applied to a dataset which has a notion of "inputs" and
+    "targets", e.g., a machine translation task. The inputs and targets are
+    concatenated to form a new target. `decoder_target_tokens` is the concatenated
+    decoder output tokens.
+
+    The "inputs" portion of the concatenated sequence can attend to other "inputs"
+    tokens even for those at a later time steps. In order to control this
+    behavior, `decoder_causal_attention` is necessary. This is a binary mask with
+    a value of 1 indicating that the position belonged to "inputs" portion of the
+    original dataset.
+
+    Example:
+
+      Suppose we have a dataset with two examples.
+
+      ds = [{"inputs": [6, 7], "targets": [8]},
+            {"inputs": [3, 4], "targets": [5]}]
+
+      After the data preprocessing with packing, the two examples are packed into
+      one example with the following three fields (some fields are skipped for
+      simplicity).
+
+         decoder_target_tokens = [[6, 7, 8, 3, 4, 5, 0]]
+           decoder_segment_ids = [[1, 1, 1, 2, 2, 2, 0]]
+      decoder_causal_attention = [[1, 1, 0, 1, 1, 0, 0]]
+
+      where each array has [batch, length] shape with batch size being 1. Then,
+      this function computes the following mask.
+
+                        mask = [[[[1, 1, 0, 0, 0, 0, 0],
+                                  [1, 1, 0, 0, 0, 0, 0],
+                                  [1, 1, 1, 0, 0, 0, 0],
+                                  [0, 0, 0, 1, 1, 0, 0],
+                                  [0, 0, 0, 1, 1, 0, 0],
+                                  [0, 0, 0, 1, 1, 1, 0],
+                                  [0, 0, 0, 0, 0, 0, 0]]]]
+
+      mask[b, 1, :, :] represents the mask for the example `b` in the batch.
+      Because mask is for a self-attention layer, the mask's shape is a square of
+      shape [query length, key length].
+
+      mask[b, 1, i, j] = 1 means that the query token at position i can attend to
+      the key token at position j.
+
+    Args:
+      decoder_target_tokens: decoder output tokens. [batch, length]
+      dtype: dtype of the output mask.
+      decoder_causal_attention: a binary mask indicating which position should
+        only attend to earlier positions in the sequence. Others will attend
+        bidirectionally. [batch, length]
+      decoder_segment_ids: decoder segmentation info for packed examples. [batch,
+        length]
+
+    Returns:
+      the combined decoder mask.
+    """
+    masks = []
+    # The same mask is applied to all attention heads. So the head dimension is 1,
+    # i.e., the mask will be broadcast along the heads dim.
+    # [batch, 1, length, length]
+    causal_mask = make_causal_mask(decoder_target_tokens, dtype=dtype)
+
+    # Positions with value 1 in `decoder_causal_attneition` can attend
+    # bidirectionally.
+    if decoder_causal_attention is not None:
+        # [batch, 1, length, length]
+        inputs_mask = make_attention_mask(
+            decoder_causal_attention,
+            decoder_causal_attention,
+            jnp.logical_and,
+            dtype=dtype,
+        )
+        masks.append(jnp.logical_or(causal_mask, inputs_mask).astype(dtype))
+    else:
+        masks.append(causal_mask)
+
+    # Padding mask.
+    masks.append(make_attention_mask(decoder_target_tokens > 0, decoder_target_tokens > 0, dtype=dtype))
+
+    # Packing mask
+    if decoder_segment_ids is not None:
+        masks.append(make_attention_mask(decoder_segment_ids, decoder_segment_ids, jnp.equal, dtype=dtype))
+
+    return combine_masks(*masks, dtype=dtype)
+
+
+def canonicalize_padding(padding: PaddingLike, rank: int) -> LaxPadding:
+    """ "Canonicalizes conv padding to a jax.lax supported format."""
+    if isinstance(padding, str):
+        return padding
+    if isinstance(padding, int):
+        return [(padding, padding)] * rank
+    if isinstance(padding, Sequence) and len(padding) == rank:
+        new_pad = []
+        for p in padding:
+            if isinstance(p, int):
+                new_pad.append((p, p))
+            elif isinstance(p, tuple) and len(p) == 2:
+                new_pad.append(p)
+            else:
+                break
+        if len(new_pad) == rank:
+            return new_pad
+    raise ValueError(
+        f"Invalid padding format: {padding}, should be str, int,"
+        f" or a sequence of len {rank} where each element is an"
+        " int or pair of ints."
+    )
+
+
+def _conv_dimension_numbers(input_shape):
+    """Computes the dimension numbers based on the input shape."""
+    ndim = len(input_shape)
+    lhs_spec = (0, ndim - 1) + tuple(range(1, ndim - 1))
+    rhs_spec = (ndim - 1, ndim - 2) + tuple(range(0, ndim - 2))
+    out_spec = lhs_spec
+    return lax.ConvDimensionNumbers(lhs_spec, rhs_spec, out_spec)
+
+
+class _Conv(nn.Module):
+    """Convolution Module wrapping `lax.conv_general_dilated[_local]`.
+
+    Attributes:
+      features: number of convolution filters.
+      kernel_size: shape of the convolutional kernel. For 1D convolution,
+        the kernel size can be passed as an integer. For all other cases, it must
+        be a sequence of integers.
+      strides: an integer or a sequence of `n` integers, representing the
+        inter-window strides (default: 1).
+      padding: either the string `'SAME'`, the string `'VALID'`, the string
+        `'CIRCULAR'` (periodic boundary conditions), or a sequence of `n` `(low,
+        high)` integer pairs that give the padding to apply before and after each
+        spatial dimension. A single int is interpeted as applying the same padding
+        in all dims and passign a single int in a sequence causes the same padding
+        to be used on both sides. `'CAUSAL'` padding for a 1D convolution will
+        left-pad the convolution axis, resulting in same-sized output.
+      input_dilation: an integer or a sequence of `n` integers, giving the
+        dilation factor to apply in each spatial dimension of `inputs`
+        (default: 1). Convolution with input dilation `d` is equivalent to
+        transposed convolution with stride `d`.
+      kernel_dilation: an integer or a sequence of `n` integers, giving the
+        dilation factor to apply in each spatial dimension of the convolution
+        kernel (default: 1). Convolution with kernel dilation
+        is also known as 'atrous convolution'.
+      feature_group_count: integer, default 1. If specified divides the input
+        features into groups.
+      use_bias: whether to add a bias to the output (default: True).
+      mask: Optional mask for the weights during masked convolution. The mask must
+            be the same shape as the convolution weight matrix.
+      dtype: the dtype of the computation (default: infer from input and params).
+      params_dtype: the dtype passed to parameter initializers (default: float32).
+      precision: numerical precision of the computation see `jax.lax.Precision`
+        for details.
+      kernel_init: initializer for the convolutional kernel.
+      bias_init: initializer for the bias.
+    """
+
+    features: int
+    kernel_size: Sequence[int]
+    strides: Union[None, int, Sequence[int]] = 1
+    padding: PaddingLike = "SAME"
+    input_dilation: Union[None, int, Sequence[int]] = 1
+    kernel_dilation: Union[None, int, Sequence[int]] = 1
+    feature_group_count: int = 1
+    use_bias: bool = True
+    mask: Optional[Array] = None
+    dtype: Optional[DType] = None
+    params_dtype: DType = jnp.float32
+    precision: PrecisionLike = None
+    kernel_init: Callable[[PRNGKey, Shape, DType], Array] = nn.initializers.lecun_normal()
+    bias_init: Callable[[PRNGKey, Shape, DType], Array] = nn.initializers.zeros
+    conv_general_dilated: ConvGeneralDilatedT = lax.conv_general_dilated
+    kernel_axes: Tuple[str, ...] = ()
+
+    @property
+    def shared_weights(self) -> bool:  # type: ignore
+        """Defines whether weights are shared or not between different pixels.
+
+        Returns:
+          `True` to use shared weights in convolution (regular convolution).
+          `False` to use different weights at different pixels, a.k.a.
+          "locally connected layer", "unshared convolution", or "local convolution".
+
+        """
+        ...
+
+    @nn.compact
+    def __call__(self, inputs: Array) -> Array:
+        """Applies a (potentially unshared) convolution to the inputs.
+
+        Args:
+          inputs: input data with dimensions (*batch_dims, spatial_dims...,
+            features). This is the channels-last convention, i.e. NHWC for a 2d
+            convolution and NDHWC for a 3D convolution. Note: this is different from
+            the input convention used by `lax.conv_general_dilated`, which puts the
+            spatial dimensions last.
+            Note: If the input has more than 1 batch dimension, all batch dimensions
+            are flattened into a single dimension for the convolution and restored
+            before returning.  In some cases directly vmap'ing the layer may yield
+            better performance than this default flattening approach.  If the input
+            lacks a batch dimension it will be added for the convolution and removed
+            n return, an allowance made to enable writing single-example code.
+
+        Returns:
+          The convolved data.
+        """
+
+        if isinstance(self.kernel_size, int):
+            raise TypeError(
+                "Expected Conv kernel_size to be a"
+                " tuple/list of integers (eg.: [3, 3]) but got"
+                f" {self.kernel_size}."
+            )
+        else:
+            kernel_size = tuple(self.kernel_size)
+
+        def maybe_broadcast(x: Optional[Union[int, Sequence[int]]]) -> Tuple[int, ...]:
+            if x is None:
+                # backward compatibility with using None as sentinel for
+                # broadcast 1
+                x = 1
+            if isinstance(x, int):
+                return (x,) * len(kernel_size)
+            return tuple(x)
+
+        # Combine all input batch dimensions into a single leading batch axis.
+        num_batch_dimensions = inputs.ndim - (len(kernel_size) + 1)
+        if num_batch_dimensions != 1:
+            input_batch_shape = inputs.shape[:num_batch_dimensions]
+            total_batch_size = int(np.prod(input_batch_shape))
+            flat_input_shape = (total_batch_size,) + inputs.shape[num_batch_dimensions:]
+            inputs = jnp.reshape(inputs, flat_input_shape)
+
+        # self.strides or (1,) * (inputs.ndim - 2)
+        strides = maybe_broadcast(self.strides)
+        input_dilation = maybe_broadcast(self.input_dilation)
+        kernel_dilation = maybe_broadcast(self.kernel_dilation)
+
+        padding_lax = canonicalize_padding(self.padding, len(kernel_size))
+        if padding_lax == "CIRCULAR":
+            kernel_size_dilated = [(k - 1) * d + 1 for k, d in zip(kernel_size, kernel_dilation)]
+            zero_pad: List[Tuple[int, int]] = [(0, 0)]
+            pads = zero_pad + [((k - 1) // 2, k // 2) for k in kernel_size_dilated] + [(0, 0)]
+            inputs = jnp.pad(inputs, pads, mode="wrap")
+            padding_lax = "VALID"
+        elif padding_lax == "CAUSAL":
+            if len(kernel_size) != 1:
+                raise ValueError("Causal padding is only implemented for 1D convolutions.")
+            left_pad = kernel_dilation[0] * (kernel_size[0] - 1)
+            pads = [(0, 0), (left_pad, 0), (0, 0)]
+            inputs = jnp.pad(inputs, pads)
+            padding_lax = "VALID"
+
+        dimension_numbers = _conv_dimension_numbers(inputs.shape)
+        in_features = jnp.shape(inputs)[-1]
+
+        if self.shared_weights:
+            # One shared convolutional kernel for all pixels in the output.
+            assert in_features % self.feature_group_count == 0
+            kernel_shape = kernel_size + (
+                in_features // self.feature_group_count,
+                self.features,
+            )
+
+        else:
+            if self.feature_group_count != 1:
+                raise NotImplementedError(
+                    "`lax.conv_general_dilated_local` does not support "
+                    f"`feature_group_count != 1`, got `{self.feature_group_count}`."
+                )
+
+            # Need to know the spatial output shape of a standard convolution to
+            # create the unshared convolution kernel.
+            conv_output_shape = jax.eval_shape(
+                lambda lhs, rhs: self.conv_general_dilated(  # pylint: disable=g-long-lambda
+                    lhs=lhs,
+                    rhs=rhs,
+                    window_strides=strides,
+                    padding=padding_lax,
+                    dimension_numbers=dimension_numbers,
+                    lhs_dilation=input_dilation,
+                    rhs_dilation=kernel_dilation,
+                ),
+                inputs,
+                jax.ShapedArray(kernel_size + (in_features, self.features), inputs.dtype),
+            ).shape
+
+            # One (unshared) convolutional kernel per each pixel in the output.
+            kernel_shape = conv_output_shape[1:-1] + (
+                np.prod(kernel_size) * in_features,
+                self.features,
+            )
+
+        if self.mask is not None and self.mask.shape != kernel_shape:
+            raise ValueError(
+                "Mask needs to have the same shape as weights. " f"Shapes are: {self.mask.shape}, {kernel_shape}"
+            )
+
+        kernel = param_with_axes(
+            "kernel",
+            self.kernel_init,
+            kernel_shape,
+            self.params_dtype,
+            axes=self.kernel_axes,
+        )
+
+        if self.mask is not None:
+            kernel *= self.mask
+
+        if self.use_bias:
+            if self.shared_weights:
+                # One bias weight per output channel, shared between pixels.
+                bias_shape = (self.features,)
+            else:
+                # One bias weight per output entry, unshared betwen pixels.
+                bias_shape = conv_output_shape[1:]
+
+            bias = param_with_axes(
+                "bias",
+                self.bias_init,
+                bias_shape,
+                self.params_dtype,
+                axes=(self.kernel_axes[-1],),
+            )
+        else:
+            bias = None
+
+        inputs, kernel, bias = promote_dtype(inputs, kernel, bias, dtype=self.dtype)
+        if self.shared_weights:
+            y = self.conv_general_dilated(
+                inputs,
+                kernel,
+                strides,
+                padding_lax,
+                lhs_dilation=input_dilation,
+                rhs_dilation=kernel_dilation,
+                dimension_numbers=dimension_numbers,
+                feature_group_count=self.feature_group_count,
+                precision=self.precision,
+            )
+        else:
+            y = lax.conv_general_dilated_local(
+                lhs=inputs,
+                rhs=kernel,
+                window_strides=strides,
+                padding=padding_lax,
+                filter_shape=kernel_size,
+                lhs_dilation=input_dilation,
+                rhs_dilation=kernel_dilation,
+                dimension_numbers=dimension_numbers,
+                precision=self.precision,
+            )
+
+        if self.use_bias:
+            bias = bias.reshape((1,) * (y.ndim - bias.ndim) + bias.shape)
+            y += bias
+
+        if num_batch_dimensions != 1:
+            output_shape = input_batch_shape + y.shape[1:]
+            y = jnp.reshape(y, output_shape)
+        return y
+
+
+class Conv(_Conv):
+    """Convolution Module wrapping `lax.conv_general_dilated`.
+
+    Attributes:
+      features: number of convolution filters.
+      kernel_size: shape of the convolutional kernel. For 1D convolution,
+        the kernel size can be passed as an integer. For all other cases, it must
+        be a sequence of integers.
+      strides: an integer or a sequence of `n` integers, representing the
+        inter-window strides (default: 1).
+      padding: either the string `'SAME'`, the string `'VALID'`, the string
+        `'CIRCULAR'` (periodic boundary conditions), or a sequence of `n` `(low,
+        high)` integer pairs that give the padding to apply before and after each
+        spatial dimension. A single int is interpeted as applying the same padding
+        in all dims and passign a single int in a sequence causes the same padding
+        to be used on both sides. `'CAUSAL'` padding for a 1D convolution will
+        left-pad the convolution axis, resulting in same-sized output.
+      input_dilation: an integer or a sequence of `n` integers, giving the
+        dilation factor to apply in each spatial dimension of `inputs`
+        (default: 1). Convolution with input dilation `d` is equivalent to
+        transposed convolution with stride `d`.
+      kernel_dilation: an integer or a sequence of `n` integers, giving the
+        dilation factor to apply in each spatial dimension of the convolution
+        kernel (default: 1). Convolution with kernel dilation
+        is also known as 'atrous convolution'.
+      feature_group_count: integer, default 1. If specified divides the input
+        features into groups.
+      use_bias: whether to add a bias to the output (default: True).
+      mask: Optional mask for the weights during masked convolution. The mask must
+            be the same shape as the convolution weight matrix.
+      dtype: the dtype of the computation (default: infer from input and params).
+      params_dtype: the dtype passed to parameter initializers (default: float32).
+      precision: numerical precision of the computation see `jax.lax.Precision`
+        for details.
+      kernel_init: initializer for the convolutional kernel.
+      bias_init: initializer for the bias.
+    """
+
+    @property
+    def shared_weights(self) -> bool:
+        return True
diff --git a/flax/distil_whisper/modeling_flax_whisper.py b/flax/distil_whisper/modeling_flax_whisper.py
new file mode 100644
index 0000000000000000000000000000000000000000..da6a634e7f7bbb9598c75c59ebf118680c9dc495
--- /dev/null
+++ b/flax/distil_whisper/modeling_flax_whisper.py
@@ -0,0 +1,2135 @@
+# coding=utf-8
+# Copyright 2023 The OpenAI Authors and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" Flax whisper model."""
+
+import random
+from functools import partial
+from typing import Dict, Optional, Tuple, Union
+
+import flax.linen as nn
+import jax
+import jax.numpy as jnp
+from flax.core.frozen_dict import FrozenDict, freeze, unfreeze
+from flax.linen import combine_masks, make_causal_mask
+from flax.linen.attention import dot_product_attention_weights
+from flax.linen.partitioning import remat, scan_with_axes
+from flax.traverse_util import flatten_dict, unflatten_dict
+from jax import lax
+from jax.random import PRNGKey
+from transformers import WhisperConfig
+from transformers.generation.flax_logits_process import (
+    FlaxLogitsProcessor,
+    FlaxLogitsProcessorList,
+    FlaxWhisperTimeStampLogitsProcessor,
+)
+from transformers.modeling_flax_outputs import (
+    FlaxBaseModelOutput,
+    FlaxBaseModelOutputWithPastAndCrossAttentions,
+    FlaxCausalLMOutputWithCrossAttentions,
+    FlaxSeq2SeqLMOutput,
+    FlaxSeq2SeqModelOutput,
+)
+from transformers.modeling_flax_utils import (
+    ACT2FN,
+    FlaxPreTrainedModel,
+    append_call_sample_docstring,
+    append_replace_return_docstrings,
+    overwrite_call_docstring,
+)
+from transformers.utils import (
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+    logging,
+    replace_return_docstrings,
+)
+
+from .layers import Conv, DenseGeneral, Embed, LayerNorm, with_sharding_constraint
+
+
+logger = logging.get_logger(__name__)
+
+
+_CHECKPOINT_FOR_DOC = "openai/whisper-tiny"
+_CONFIG_FOR_DOC = "WhisperConfig"
+
+
+WHISPER_START_DOCSTRING = r"""
+    This model inherits from [`FlaxPreTrainedModel`]. Check the superclass documentation for the generic methods the
+    library implements for all its models (such as downloading or saving, resizing the input embeddings, pruning heads
+    etc.) This model is also a Flax Linen
+    [flax.nn.Module](https://flax.readthedocs.io/en/latest/_autosummary/flax.nn.module.html) subclass. Use it as a
+    regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior.
+    Finally, this model supports inherent JAX features such as:
+    - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)
+    - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)
+    - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)
+    - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)
+
+    Parameters:
+        config ([`WhisperConfig`]): Model configuration class with all the parameters of the model.
+            Initializing with a config file does not load the weights associated with the model, only the
+            configuration. Check out the [`~FlaxPreTrainedModel.from_pretrained`] method to load the model weights.
+        dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`):
+            The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and
+            `jax.numpy.bfloat16` (on TPUs). This can be used to enable mixed-precision training or half-precision
+            inference on GPUs or TPUs. If specified all the computation will be performed with the given `dtype`.
+            **Note that this only specifies the dtype of the computation and does not influence the dtype of model
+            parameters.** If you wish to change the dtype of the model parameters, see [`~FlaxPreTrainedModel.to_fp16`]
+            and [`~FlaxPreTrainedModel.to_bf16`].
+"""
+
+WHISPER_INPUTS_DOCSTRING = r"""
+    Args:
+        input_features (`numpy.ndarray` of shape `(batch_size, feature_size, sequence_length)`):
+            Float values mel features extracted from the raw speech waveform. Raw speech waveform can be obtained by
+            loading a `.flac` or `.wav` audio file into an array of type `List[float]` or a `numpy.ndarray`, *e.g.* via
+            the soundfile library (`pip install soundfile`). To prepare the array into `input_features`, the
+            [`WhisperFeatureExtractor`] should be used for extracting the features, padding and conversion into a
+            tensor of type `numpy.ndarray`. See [`~WhisperFeatureExtractor.__call__`]
+        attention_mask (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
+            Whisper does not support masking of the `input_features`, this argument is preserved for compatibility, but
+            is not used. By default the silence in the input log mel spectrogram are ignored.
+        decoder_input_ids (`numpy.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*):
+            Indices of decoder input sequence tokens in the vocabulary. Indices can be obtained using
+            [`WhisperTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details.
+            [What are decoder input IDs?](../glossary#decoder-input-ids) Whisper uses the `decoder_start_token_id` as
+            the starting token for `decoder_input_ids` generation.
+        decoder_attention_mask (`numpy.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*):
+            Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
+            be used by default. If you want to change padding behavior, you should modify to your needs. See diagram 1
+            in [the paper](https://arxiv.org/abs/1910.13461) for more information on the default strategy.
+        position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
+            Whisper does not use `position_ids` in the encoder as `input_features` is always the same size and doesn't
+            use masking, but this argument is preserved for compatibility. By default the silence in the input log mel
+            spectrogram are ignored.
+        decoder_position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
+            Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the
+            range `[0, config.max_position_embeddings - 1]`.
+        output_attentions (`bool`, *optional*):
+            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+            tensors for more detail.
+        output_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+            more detail.
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+WHISPER_ENCODE_INPUTS_DOCSTRING = r"""
+    Args:
+        input_features (`numpy.ndarray` of shape `(batch_size, feature_size, sequence_length)`):
+            Float values mel features extracted from the raw speech waveform. Raw speech waveform can be obtained by
+            loading a `.flac` or `.wav` audio file into an array of type `List[float]` or a `numpy.ndarray`, *e.g.* via
+            the soundfile library (`pip install soundfile`). To prepare the array into `input_features`, the
+            [`WhisperFeatureExtractor`] should be used for extracting the mel features, padding and conversion into a
+            tensor of type `numpy.ndarray`. See [`~WhisperFeatureExtractor.__call__`].
+        attention_mask (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
+            Whisper does not support masking of the `input_features`, this argument is preserved for compatibility, but
+            is not used. By default the silence in the input log mel spectrogram are ignored.
+        output_attentions (`bool`, *optional*):
+            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+            tensors for more detail.
+        output_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+            more detail.
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+WHISPER_DECODE_INPUTS_DOCSTRING = r"""
+    Args:
+        decoder_input_ids (`numpy.ndarray` of shape `(batch_size, target_sequence_length)`):
+            Indices of decoder input sequence tokens in the vocabulary. Indices can be obtained using
+            [`WhisperTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details.
+            [What are decoder input IDs?](../glossary#decoder-input-ids)
+        encoder_outputs (`tuple(tuple(numpy.ndarray)`):
+            Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`)
+            `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of
+            hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
+        encoder_attention_mask (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
+           Whisper does not support masking of the `input_features`, this argument is preserved for compatibility,
+            but it is not used. By default the silence in the input log mel spectrogram are ignored.
+        decoder_attention_mask (`numpy.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*):
+            Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
+            be used by default. If you want to change padding behavior, you should modify to your needs. See diagram 1
+            in [the paper](https://arxiv.org/abs/1910.13461) for more information on the default strategy.
+        decoder_position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
+            Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the
+            range `[0, config.max_position_embeddings - 1]`.
+        past_key_values (`Dict[str, numpy.ndarray]`, *optional*, returned by `init_cache` or when passing previous `past_key_values`):
+            Dictionary of pre-computed hidden-states (key and values in the attention blocks) that can be used for fast
+            auto-regressive decoding. Pre-computed key and value hidden-states are of shape *[batch_size, max_length]*.
+        output_attentions (`bool`, *optional*):
+            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+            tensors for more detail.
+        output_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+            more detail.
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+
+class FlaxStaticForceTokensLogitsProcessor(FlaxLogitsProcessor):
+    r"""
+    [`FlaxLogitsProcessor`] that takes a list of pairs of integers which indicates a mapping from generation indices to
+    token indices that will be forced before sampling. The processor will set their log probs to 0 and all other tokens
+    to `-inf` so that they are sampled at their corresponding index. This is a static version of the `transformers` logit
+    processor [`FlaxForceTokensLogitsProcessor`] that is compatible with sharded forced tokens.
+
+    Args:
+        force_token_map (`list`):
+            Map giving token ids and indices where they will be forced to be sampled.
+    """
+
+    def __init__(self, force_token_map):
+        # The generic `transformers` logit processor builds `force_token_array` as a dictionary - this is not a valid
+        # JAX type, and so we switch to using a JAX array instead
+        force_token_map = jnp.array(force_token_map)
+        # Converts the array of format [[index, token]] containing the tokens to be forced to an array, where the
+        # index of the array corresponds to the index of the token to be forced. For XLA compatibility,
+        # indexes without forced tokens will have a negative value. Note that the last token we ever need to force in
+        # Whisper is at position 3, so we only construct an array up to this index. The native version constructs a tensor
+        # dynamically according to the length of the `force_token_map`. Array shapes need to be concrete for XLA compatibility,
+        # so this is not permitted here.
+        force_token_array = jnp.ones(3, dtype=jnp.int32) * -1
+        for index, token in force_token_map:
+            force_token_array = force_token_array.at[index].set(token)
+        self.force_token_array = jnp.int32(force_token_array)
+
+    def __call__(self, input_ids: jnp.ndarray, scores: jnp.ndarray, cur_len: int) -> jnp.ndarray:
+        def _force_token(generation_idx):
+            batch_size = scores.shape[0]
+            current_token = self.force_token_array[generation_idx]
+
+            new_scores = jnp.ones_like(scores, dtype=scores.dtype) * -float("inf")
+            updates = jnp.zeros((batch_size, 1), dtype=scores.dtype)
+            new_scores = lax.dynamic_update_slice(new_scores, updates, (0, current_token))
+            return new_scores
+
+        scores = lax.cond(
+            cur_len >= self.force_token_array.shape[0],
+            # If the current length is geq than the length of force_token_array, the processor does nothing.
+            lambda: scores,
+            # Otherwise, it may force a certain token.
+            lambda: lax.cond(
+                self.force_token_array[cur_len] >= 0,
+                # Only valid (positive) tokens are forced
+                lambda: _force_token(cur_len),
+                # Otherwise, the processor does nothing.
+                lambda: scores,
+            ),
+        )
+        return scores
+
+
+class FlaxWhisperAttention(nn.Module):
+    config: WhisperConfig
+    embed_dim: int
+    num_heads: int
+    dropout: float = 0.0
+    causal: bool = False
+    bias: bool = True
+    dtype: jnp.dtype = jnp.float32
+    params_dtype: jnp.dtype = jnp.float32
+
+    def setup(self) -> None:
+        self.head_dim = self.embed_dim // self.num_heads
+        if self.head_dim * self.num_heads != self.embed_dim:
+            raise ValueError(
+                "embed_dim must be divisible by num_heads (got `embed_dim`:"
+                f" {self.embed_dim} and `num_heads`: {self.num_heads})."
+            )
+
+        dense = partial(
+            DenseGeneral,
+            self.embed_dim,
+            axis=-1,
+            dtype=self.dtype,
+            params_dtype=self.params_dtype,
+            kernel_axes=("embed", "joined_kv"),
+        )
+
+        self.q_proj = dense(use_bias=self.bias)
+        self.k_proj = dense(use_bias=False)
+        self.v_proj = dense(use_bias=self.bias)
+
+        self.out_proj = DenseGeneral(
+            self.embed_dim,
+            axis=-1,
+            dtype=self.dtype,
+            params_dtype=self.params_dtype,
+            kernel_axes=("joined_kv", "embed"),
+            use_bias=self.bias,
+        )
+
+        if self.causal:
+            self.causal_mask = make_causal_mask(
+                jnp.ones((1, self.config.max_target_positions), dtype="bool"),
+                dtype="bool",
+            )
+
+    def __call__(
+        self,
+        hidden_states: jnp.ndarray,
+        key_value_states: Optional[jnp.ndarray] = None,
+        attention_mask: Optional[jnp.ndarray] = None,
+        init_cache: bool = False,
+        deterministic: bool = True,
+    ) -> Tuple[jnp.ndarray]:
+        is_cross_attention = key_value_states is not None
+        batch_size = hidden_states.shape[0]
+
+        query_states = self.q_proj(hidden_states)
+
+        if is_cross_attention:
+            key_states = self.k_proj(key_value_states)
+            value_states = self.v_proj(key_value_states)
+        else:
+            key_states = self.k_proj(hidden_states)
+            value_states = self.v_proj(hidden_states)
+
+        query_states = self._split_heads(query_states)
+        key_states = self._split_heads(key_states)
+        value_states = self._split_heads(value_states)
+
+        query_states = with_sharding_constraint(query_states, ("batch", "length", "heads", "kv"))
+        key_states = with_sharding_constraint(key_states, ("batch", "length", "heads", "kv"))
+        value_states = with_sharding_constraint(value_states, ("batch", "length", "heads", "kv"))
+
+        if self.causal:
+            query_length, key_length = query_states.shape[1], key_states.shape[1]
+            if self.has_variable("cache", "cached_key"):
+                mask_shift = self.variables["cache"]["cache_index"]
+                # max_length of cached_key is last dim
+                max_decoder_length = self.variables["cache"]["cached_key"].shape[-1]
+                causal_mask = lax.dynamic_slice(
+                    self.causal_mask,
+                    (0, 0, mask_shift, 0),
+                    (1, 1, query_length, max_decoder_length),
+                )
+            else:
+                causal_mask = self.causal_mask[:, :, :query_length, :key_length]
+            causal_mask = jnp.broadcast_to(causal_mask, (batch_size,) + causal_mask.shape[1:])
+
+        # combine masks if needed
+        if attention_mask is not None and self.causal:
+            attention_mask = jnp.broadcast_to(jnp.expand_dims(attention_mask, axis=(-3, -2)), causal_mask.shape)
+            attention_mask = combine_masks(attention_mask, causal_mask)
+        elif self.causal:
+            attention_mask = causal_mask
+        elif attention_mask is not None:
+            attention_mask = jnp.expand_dims(attention_mask, axis=(-3, -2))
+
+        # During fast autoregressive decoding, we feed one position at a time,
+        # and cache the keys and values step by step.
+
+        if self.causal and (self.has_variable("cache", "cached_key") or init_cache):
+            key_states, value_states, attention_mask = self._concatenate_to_cache(
+                key_states, value_states, query_states, attention_mask
+            )
+
+        # Convert the boolean attention mask to an attention bias.
+        if attention_mask is not None:
+            # attention mask in the form of attention bias
+            attention_bias = lax.select(
+                attention_mask > 0,
+                jnp.full(attention_mask.shape, 0.0).astype(self.dtype),
+                jnp.full(attention_mask.shape, jnp.finfo(self.dtype).min).astype(self.dtype),
+            )
+        else:
+            attention_bias = None
+
+        dropout_rng = None
+        if not deterministic and self.dropout > 0.0:
+            dropout_rng = self.make_rng("dropout")
+
+        attn_weights = dot_product_attention_weights(
+            query_states,
+            key_states,
+            bias=attention_bias,
+            dropout_rng=dropout_rng,
+            dropout_rate=self.dropout,
+            broadcast_dropout=True,
+            deterministic=deterministic,
+            dtype=self.dtype,
+            precision=None,
+        )
+
+        attn_output = jnp.einsum("...hqk,...khd->...qhd", attn_weights, value_states)
+        attn_output = self._merge_heads(attn_output)
+        attn_output = self.out_proj(attn_output)
+
+        return attn_output, attn_weights
+
+    def _split_heads(self, hidden_state) -> jnp.ndarray:
+        return hidden_state.reshape(hidden_state.shape[:2] + (self.num_heads, self.head_dim))
+
+    def _merge_heads(self, hidden_state) -> jnp.ndarray:
+        return hidden_state.reshape(hidden_state.shape[:2] + (self.embed_dim,))
+
+    @nn.compact
+    def _concatenate_to_cache(self, key, value, query, attention_mask):
+        # The following code is largely copied from: https://github.com/google-research/t5x/blob/63d9addf628c6d8c547a407a32095fcb527bb20b/t5x/examples/scalable_t5/layers.py#L280-L284
+        is_initialized = self.has_variable("cache", "cached_key")
+
+        # The key and value have dimension [batch_size, seq_length, num_heads, head_dim],
+        # but we cache them as [batch_size, num_heads, head_dim, seq_length] as a TPU
+        # fusion optimization. This also enables the "scatter via one-hot
+        # broadcast" trick, which means we do a one-hot broadcast instead of a
+        # scatter/gather operations, resulting in a 3-4x speedup in practice.
+        def swap_dims(x):
+            return x[:-3] + tuple(x[i] for i in [-2, -1, -3])
+
+        cached_key = self.variable("cache", "cached_key", jnp.zeros, swap_dims(key.shape), key.dtype)
+        cached_value = self.variable("cache", "cached_value", jnp.zeros, swap_dims(value.shape), value.dtype)
+        cache_index = self.variable("cache", "cache_index", lambda: jnp.array(0, dtype=jnp.int32))
+
+        if is_initialized:
+            batch_size, num_heads, head_dim, seq_length = cached_key.value.shape
+            # During fast autoregressive decoding, we feed one position at a time,
+            # and cache the keys and values step by step.
+            # Sanity shape check of cached key against input query.
+            num_updated_cache_vectors = query.shape[1]
+            expected_shape = (batch_size, 1, num_heads, head_dim)
+            if num_updated_cache_vectors == 1 and expected_shape != query.shape:
+                raise ValueError(
+                    "Autoregressive cache shape error, expected query shape"
+                    f" {expected_shape} instead got {query.shape}"
+                )
+
+            # Create a OHE of the current index. NOTE: the index is increased below.
+            cur_index = cache_index.value
+
+            # In order to update the key, value caches with the current key and
+            # value, we move the seq_length axis to the back, similar to what we did for
+            # the cached ones above.
+            # Note these are currently the key and value of a single position, since
+            # we feed one position at a time.
+            one_token_key = jnp.moveaxis(key, -3, -1)
+            one_token_value = jnp.moveaxis(value, -3, -1)
+
+            # Update key, value caches with our new 1d spatial slices.
+            # We implement an efficient scatter into the cache via one-hot
+            # broadcast and addition.
+            if num_updated_cache_vectors > 1:
+                indices = jnp.eye(num_updated_cache_vectors, seq_length)[None, None]
+                key = cached_key.value + jnp.matmul(one_token_key, indices)
+                value = cached_value.value + jnp.matmul(one_token_value, indices)
+            else:
+                one_hot_indices = jax.nn.one_hot(cur_index, seq_length, dtype=key.dtype)
+                key = cached_key.value + one_token_key * one_hot_indices
+                value = cached_value.value + one_token_value * one_hot_indices
+
+            cached_key.value = key
+            cached_value.value = value
+            cache_index.value = cache_index.value + num_updated_cache_vectors
+
+            # Move the keys and values back to their original shapes.
+            key = jnp.moveaxis(key, -1, -3)
+            value = jnp.moveaxis(value, -1, -3)
+
+            # causal mask for cached decoder self-attention: our single query position should only
+            # attend to those key positions that have already been generated and cached, not the
+            # remaining zero elements.
+            pad_mask = jnp.broadcast_to(
+                jnp.arange(seq_length) < cur_index + num_updated_cache_vectors,
+                (batch_size,) + (1, num_updated_cache_vectors, seq_length),
+            )
+            attention_mask = combine_masks(pad_mask, attention_mask)
+
+        return key, value, attention_mask
+
+
+class FlaxWhisperEncoderLayer(nn.Module):
+    config: WhisperConfig
+    dtype: jnp.dtype = jnp.float32
+    params_dtype: jnp.dtype = jnp.float32
+    use_scan: bool = False
+
+    def setup(self) -> None:
+        self.embed_dim = self.config.d_model
+        self.self_attn = FlaxWhisperAttention(
+            config=self.config,
+            embed_dim=self.embed_dim,
+            num_heads=self.config.encoder_attention_heads,
+            dropout=self.config.attention_dropout,
+            dtype=self.dtype,
+            params_dtype=self.params_dtype,
+        )
+        self.self_attn_layer_norm = LayerNorm(dtype=self.dtype, epsilon=1e-05, params_dtype=self.params_dtype)
+        self.dropout_layer = nn.Dropout(rate=self.config.dropout)
+        self.activation_fn = ACT2FN[self.config.activation_function]
+        self.activation_dropout_layer = nn.Dropout(rate=self.config.activation_dropout)
+        self.fc1 = DenseGeneral(
+            self.config.encoder_ffn_dim,
+            dtype=self.dtype,
+            params_dtype=self.params_dtype,
+            kernel_axes=("embed", "mlp"),
+        )
+        self.fc2 = DenseGeneral(
+            self.embed_dim,
+            dtype=self.dtype,
+            params_dtype=self.params_dtype,
+            kernel_axes=("mlp", "embed"),
+        )
+        self.final_layer_norm = LayerNorm(dtype=self.dtype, epsilon=1e-05, params_dtype=self.params_dtype)
+
+    def __call__(
+        self,
+        hidden_states: jnp.ndarray,
+        attention_mask: jnp.ndarray,
+        output_attentions: bool = True,
+        deterministic: bool = True,
+        all_hidden_states=None,  # only used when `use_scan=True` -> we have to fetch the hidden states from within the layer
+    ) -> Tuple[jnp.ndarray]:
+        if self.use_scan:
+            hidden_states = hidden_states[0]
+
+        hidden_states = with_sharding_constraint(hidden_states, ("batch", "length", "embed"))
+
+        residual = hidden_states
+
+        layernorm_output = self.self_attn_layer_norm(hidden_states)
+        layernorm_output = with_sharding_constraint(layernorm_output, ("batch", "length", "embed"))
+
+        attn_output, attn_weights = self.self_attn(hidden_states=layernorm_output, attention_mask=attention_mask)
+        attn_output = self.dropout_layer(attn_output, deterministic=deterministic)
+        attn_output = residual + attn_output
+        attn_output = with_sharding_constraint(attn_output, ("batch", "length", "embed"))
+
+        residual = attn_output
+
+        post_layer_norm = self.final_layer_norm(attn_output)
+        post_layer_norm = with_sharding_constraint(post_layer_norm, ("batch", "length", "embed"))
+
+        fc1_output = self.activation_fn(self.fc1(post_layer_norm))
+        fc1_output = self.activation_dropout_layer(fc1_output, deterministic=deterministic)
+        fc1_output = with_sharding_constraint(fc1_output, ("batch", "length", "mlp"))
+
+        hidden_states = self.fc2(fc1_output)
+        hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
+        hidden_states = residual + hidden_states
+        hidden_states = with_sharding_constraint(hidden_states, ("batch", "length", "embed"))
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (attn_weights,)
+
+        if self.use_scan:
+            if all_hidden_states is not None:
+                all_hidden_states = all_hidden_states + (hidden_states,)
+            outputs = (
+                outputs,
+                all_hidden_states,
+            )
+
+        return outputs
+
+
+class FlaxWhisperEncoderLayerCollection(nn.Module):
+    config: WhisperConfig
+    dtype: jnp.dtype = jnp.float32  # the dtype of the computation
+    params_dtype: jnp.dtype = jnp.float32
+    use_scan: bool = False
+    gradient_checkpointing: bool = False
+
+    @nn.compact
+    def __call__(
+        self,
+        hidden_states,
+        attention_mask,
+        deterministic: bool = True,
+        output_attentions: bool = False,
+        output_hidden_states: bool = False,
+        return_dict: bool = True,
+    ):
+        all_attentions = () if output_attentions else None
+        all_hidden_states = () if output_hidden_states else None
+
+        FlaxWhisperEncoderCheckpointLayer = (
+            remat(
+                FlaxWhisperEncoderLayer,
+                static_argnums=(2, 3),
+                prevent_cse=not self.use_scan,
+            )
+            if self.gradient_checkpointing
+            else FlaxWhisperEncoderLayer
+        )
+
+        if self.use_scan:
+            if output_attentions:
+                raise ValueError("Cannot use `scan` with `output_attentions` set to True")
+
+            # nicest behaviour for scan is to let the compiler figure out the correct shapes for the hidden states
+            # so we'll just pass an empty tuple as the carry initializer and hold on to the first hidden states for later
+            input_hidden_states = hidden_states
+            hidden_states = (hidden_states,)
+
+            hidden_states, all_hidden_states = scan_with_axes(
+                FlaxWhisperEncoderCheckpointLayer,
+                variable_axes={"params": 0, "cache": 0},
+                split_rngs={"params": True, "dropout": True},
+                in_axes=(
+                    nn.broadcast,
+                    nn.broadcast,
+                    nn.broadcast,
+                    nn.broadcast,
+                ),
+                variable_carry="all_hidden_states",
+                length=self.config.encoder_layers,
+            )(
+                self.config,
+                dtype=self.dtype,
+                params_dtype=self.params_dtype,
+                use_scan=True,
+                name="FlaxEncoderScanLayers",
+            )(
+                hidden_states,
+                attention_mask,
+                output_attentions,
+                deterministic,
+                all_hidden_states,  # tuple intializer (or None if not using output_hidden_states)
+            )
+
+            # remove the scan dimension
+            hidden_states = hidden_states[0]
+
+            if output_hidden_states:
+                # if we're using scan we'll surely be training -> return hidden states as a tensor rather than tuple
+                all_hidden_states = jnp.vstack([input_hidden_states[None, ...], all_hidden_states[0]])
+
+        else:
+            for layer_idx in range(self.config.encoder_layers):
+                if output_hidden_states:
+                    all_hidden_states = all_hidden_states + (hidden_states,)
+                # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
+                dropout_probability = random.uniform(0, 1)
+                if not deterministic and (dropout_probability < self.config.encoder_layerdrop):  # skip the layer
+                    layer_outputs = (None, None)
+                else:
+                    layer_outputs = FlaxWhisperEncoderCheckpointLayer(
+                        self.config,
+                        dtype=self.dtype,
+                        params_dtype=self.params_dtype,
+                        name=str(layer_idx),
+                    )(
+                        hidden_states,
+                        attention_mask,
+                        output_attentions,
+                        deterministic,
+                    )
+                hidden_states = layer_outputs[0]
+                if output_attentions:
+                    all_attentions = all_attentions + (layer_outputs[1],)
+
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+        outputs = (hidden_states, all_hidden_states, all_attentions)
+
+        if not return_dict:
+            return tuple(v for v in outputs if v is not None)
+
+        return FlaxBaseModelOutput(
+            last_hidden_state=hidden_states,
+            hidden_states=all_hidden_states,
+            attentions=all_attentions,
+        )
+
+
+class FlaxWhisperDecoderLayer(nn.Module):
+    config: WhisperConfig
+    dtype: jnp.dtype = jnp.float32
+    params_dtype: jnp.dtype = jnp.float32
+    use_scan: bool = False
+
+    def setup(self) -> None:
+        self.embed_dim = self.config.d_model
+        self.self_attn = FlaxWhisperAttention(
+            config=self.config,
+            embed_dim=self.embed_dim,
+            num_heads=self.config.decoder_attention_heads,
+            dropout=self.config.attention_dropout,
+            causal=True,
+            dtype=self.dtype,
+            params_dtype=self.params_dtype,
+        )
+        self.dropout_layer = nn.Dropout(rate=self.config.dropout)
+        self.activation_fn = ACT2FN[self.config.activation_function]
+        self.activation_dropout_layer = nn.Dropout(rate=self.config.activation_dropout)
+
+        self.self_attn_layer_norm = LayerNorm(dtype=self.dtype, epsilon=1e-05, params_dtype=self.params_dtype)
+        self.encoder_attn = FlaxWhisperAttention(
+            config=self.config,
+            embed_dim=self.embed_dim,
+            num_heads=self.config.decoder_attention_heads,
+            dropout=self.config.attention_dropout,
+            dtype=self.dtype,
+            params_dtype=self.params_dtype,
+        )
+        self.encoder_attn_layer_norm = LayerNorm(dtype=self.dtype, epsilon=1e-05, params_dtype=self.params_dtype)
+        self.fc1 = DenseGeneral(
+            self.config.decoder_ffn_dim,
+            dtype=self.dtype,
+            params_dtype=self.params_dtype,
+            kernel_axes=("embed", "mlp"),
+        )
+        self.fc2 = DenseGeneral(
+            self.embed_dim,
+            dtype=self.dtype,
+            params_dtype=self.params_dtype,
+            kernel_axes=("mlp", "embed"),
+        )
+        self.final_layer_norm = LayerNorm(dtype=self.dtype, epsilon=1e-05, params_dtype=self.params_dtype)
+
+    def __call__(
+        self,
+        hidden_states: jnp.ndarray,
+        attention_mask: jnp.ndarray,
+        encoder_hidden_states: Optional[jnp.ndarray] = None,
+        encoder_attention_mask: Optional[jnp.ndarray] = None,
+        init_cache: bool = False,
+        output_attentions: bool = True,
+        deterministic: bool = True,
+        all_hidden_states=None,  # only used when `use_scan=True` -> we have to fetch the hidden states from within the layer
+    ) -> Tuple[jnp.ndarray]:
+        if self.use_scan:
+            hidden_states = hidden_states[0]
+
+        hidden_states = with_sharding_constraint(hidden_states, ("batch", "length", "embed"))
+
+        residual = hidden_states
+
+        layer_norm_output = self.self_attn_layer_norm(hidden_states)
+        layer_norm_output = with_sharding_constraint(layer_norm_output, ("batch", "length", "embed"))
+
+        # Self Attention
+        self_attn_output, self_attn_weights = self.self_attn(
+            hidden_states=layer_norm_output,
+            attention_mask=attention_mask,
+            init_cache=init_cache,
+        )
+        self_attn_output = self.dropout_layer(self_attn_output, deterministic=deterministic)
+        self_attn_output = residual + self_attn_output
+        self_attn_output = with_sharding_constraint(self_attn_output, ("batch", "length", "embed"))
+
+        # Cross-Attention Block
+        cross_attn_weights = None
+        if encoder_hidden_states is not None:
+            residual = self_attn_output
+
+            encoder_layer_norm_output = self.encoder_attn_layer_norm(self_attn_output)
+            encoder_layer_norm_output = with_sharding_constraint(
+                encoder_layer_norm_output, ("batch", "length", "embed")
+            )
+
+            cross_attn_output, cross_attn_weights = self.encoder_attn(
+                hidden_states=encoder_layer_norm_output,
+                key_value_states=encoder_hidden_states,
+                attention_mask=encoder_attention_mask,
+            )
+            cross_attn_output = self.dropout_layer(cross_attn_output, deterministic=deterministic)
+            cross_attn_output = residual + cross_attn_output
+            cross_attn_output = with_sharding_constraint(cross_attn_output, ("batch", "length", "embed"))
+
+        # Fully Connected
+        residual = cross_attn_output
+
+        post_layer_norm = self.final_layer_norm(cross_attn_output)
+        post_layer_norm = with_sharding_constraint(post_layer_norm, ("batch", "length", "embed"))
+
+        fc1_output = self.activation_fn(self.fc1(post_layer_norm))
+        fc1_output = self.activation_dropout_layer(fc1_output, deterministic=deterministic)
+        fc1_output = with_sharding_constraint(fc1_output, ("batch", "length", "mlp"))
+
+        hidden_states = self.fc2(fc1_output)
+        hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
+        hidden_states = residual + hidden_states
+        hidden_states = with_sharding_constraint(hidden_states, ("batch", "length", "embed"))
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (self_attn_weights, cross_attn_weights)
+
+        if self.use_scan:
+            if all_hidden_states is not None:
+                all_hidden_states = all_hidden_states + (hidden_states,)
+            outputs = (
+                outputs,
+                all_hidden_states,
+            )
+
+        return outputs
+
+
+class FlaxWhisperDecoderLayerCollection(nn.Module):
+    config: WhisperConfig
+    dtype: jnp.dtype = jnp.float32  # the dtype of the computation
+    params_dtype: jnp.dtype = jnp.float32
+    use_scan: bool = False
+    gradient_checkpointing: bool = False
+
+    @nn.compact
+    def __call__(
+        self,
+        hidden_states,
+        attention_mask,
+        encoder_hidden_states: Optional[jnp.ndarray] = None,
+        encoder_attention_mask: Optional[jnp.ndarray] = None,
+        deterministic: bool = True,
+        init_cache: bool = False,
+        output_attentions: bool = False,
+        output_hidden_states: bool = False,
+        return_dict: bool = True,
+    ):
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+        all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None
+
+        FlaxWhisperDecoderCheckpointLayer = (
+            remat(
+                FlaxWhisperDecoderLayer,
+                static_argnums=(4, 5, 6),
+                prevent_cse=not self.use_scan,
+            )
+            if self.gradient_checkpointing
+            else FlaxWhisperDecoderLayer
+        )
+
+        if self.use_scan:
+            if output_attentions:
+                raise ValueError("Cannot use `scan` with `output_attentions` set to True")
+
+            input_hidden_states = hidden_states
+            hidden_states = (hidden_states,)
+
+            hidden_states, all_hidden_states = scan_with_axes(
+                FlaxWhisperDecoderCheckpointLayer,
+                variable_axes={"params": 0, "cache": 0},
+                split_rngs={"params": True, "dropout": True},
+                in_axes=(
+                    nn.broadcast,
+                    nn.broadcast,
+                    nn.broadcast,
+                    nn.broadcast,
+                    nn.broadcast,
+                    nn.broadcast,
+                    nn.broadcast,
+                ),
+                variable_carry="all_hidden_states",
+                length=self.config.decoder_layers,
+            )(
+                self.config,
+                dtype=self.dtype,
+                params_dtype=self.params_dtype,
+                use_scan=True,
+                name="FlaxDecoderScanLayers",
+            )(
+                hidden_states,
+                attention_mask,
+                encoder_hidden_states,
+                encoder_attention_mask,
+                init_cache,
+                output_attentions,
+                deterministic,
+                all_hidden_states,
+            )
+            hidden_states = hidden_states[0]
+
+            if output_hidden_states:
+                # if we're using scan we'll surely be training -> return hidden states as a tensor rather than tuple
+                all_hidden_states = jnp.vstack([input_hidden_states[None, ...], all_hidden_states[0]])
+
+        else:
+            for layer_idx in range(self.config.decoder_layers):
+                if output_hidden_states:
+                    all_hidden_states += (hidden_states,)
+                    # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
+                dropout_probability = random.uniform(0, 1)
+                if not deterministic and (dropout_probability < self.config.decoder_layerdrop):
+                    layer_outputs = (None, None, None)
+                else:
+                    layer_outputs = FlaxWhisperDecoderCheckpointLayer(
+                        self.config,
+                        dtype=self.dtype,
+                        params_dtype=self.params_dtype,
+                        name=str(layer_idx),
+                    )(
+                        hidden_states,
+                        attention_mask,
+                        encoder_hidden_states,
+                        encoder_attention_mask,
+                        init_cache,
+                        output_attentions,
+                        deterministic,
+                    )
+
+                hidden_states = layer_outputs[0]
+                if output_attentions:
+                    all_self_attns += (layer_outputs[1],)
+
+                    if encoder_hidden_states is not None:
+                        all_cross_attentions += (layer_outputs[2],)
+
+            # add hidden states from the last decoder layer
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+        outputs = [
+            hidden_states,
+            all_hidden_states,
+            all_self_attns,
+            all_cross_attentions,
+        ]
+
+        if not return_dict:
+            return tuple(v for v in outputs if v is not None)
+
+        return FlaxBaseModelOutputWithPastAndCrossAttentions(
+            last_hidden_state=hidden_states,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+            cross_attentions=all_cross_attentions,
+        )
+
+
+class FlaxWhisperEncoder(nn.Module):
+    config: WhisperConfig
+    dtype: jnp.dtype = jnp.float32
+    params_dtype: jnp.dtype = jnp.float32
+    use_scan: bool = False
+    gradient_checkpointing: bool = False
+
+    def setup(self) -> None:
+        self.conv1 = Conv(
+            self.config.d_model,
+            kernel_size=(3,),
+            padding=1,
+            dtype=self.dtype,
+            params_dtype=self.params_dtype,
+            kernel_axes=("channels", "num_mel", "embed"),
+        )
+        self.conv2 = Conv(
+            self.config.d_model,
+            kernel_size=(3,),
+            strides=2,
+            padding=1,
+            dtype=self.dtype,
+            params_dtype=self.params_dtype,
+            kernel_axes=("channels", "embed", "num_mel"),
+        )
+
+        self.dropout_layer = nn.Dropout(rate=self.config.dropout)
+
+        self.layers = FlaxWhisperEncoderLayerCollection(
+            self.config,
+            dtype=self.dtype,
+            params_dtype=self.params_dtype,
+            use_scan=self.use_scan,
+            gradient_checkpointing=self.gradient_checkpointing,
+        )
+        self.embed_positions = Embed(
+            self.config.max_source_positions,
+            self.config.d_model,
+            dtype=self.dtype,
+            params_dtype=self.params_dtype,
+        )
+
+        self.layer_norm = LayerNorm(dtype=self.dtype, epsilon=1e-05, params_dtype=self.params_dtype)
+
+    def __call__(
+        self,
+        input_features: jnp.ndarray,
+        output_attentions: bool = False,
+        output_hidden_states: bool = False,
+        return_dict: bool = True,
+        deterministic: bool = True,
+    ) -> Tuple[jnp.ndarray]:
+        if input_features.shape[1:] != (
+            self.config.num_mel_bins,
+            self.config.max_source_positions * 2,
+        ):
+            raise ValueError(
+                "input_features.shape[1:], must be equal to (self.config.num_mel_bins,"
+                " self.config.max_source_positions * 2) (got"
+                f" {input_features.shape[1:]}, but should be"
+                f" ({self.config.num_mel_bins},"
+                f" {self.config.max_source_positions * 2}))"
+            )
+
+        input_features = input_features.transpose(0, 2, 1)
+        hidden_states = jax.nn.gelu(self.conv1(input_features), approximate=False)
+        hidden_states = with_sharding_constraint(hidden_states, ("batch", "embed", "num_mel"))
+        hidden_states = jax.nn.gelu(self.conv2(hidden_states), approximate=False)
+        hidden_states = with_sharding_constraint(hidden_states, ("batch", "length", "embed"))
+
+        embed_positions = self.embed_positions(jnp.arange(self.config.max_source_positions))
+        # sinusoidal positional embeddings should not be trained
+        embed_positions = jax.lax.stop_gradient(embed_positions)
+        hidden_states = hidden_states + embed_positions
+
+        hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
+
+        outputs = self.layers(
+            hidden_states,
+            attention_mask=None,
+            deterministic=deterministic,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        last_hidden_states = outputs[0]
+        last_hidden_states = self.layer_norm(last_hidden_states)
+
+        # update the last element in `hidden_states` after applying `layernorm` above
+        hidden_states = None
+        if output_hidden_states:
+            hidden_states = outputs[1]
+            if self.use_scan:
+                hidden_states = jnp.vstack([hidden_states[:-1], last_hidden_states[None, ...]])
+            else:
+                hidden_states = hidden_states[:-1] + (last_hidden_states,)
+
+        if not return_dict:
+            outputs = (last_hidden_states, hidden_states) + (outputs[2:] if output_hidden_states else outputs[1:])
+            return tuple(v for v in outputs if v is not None)
+
+        return FlaxBaseModelOutput(
+            last_hidden_state=last_hidden_states,
+            hidden_states=hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+class FlaxWhisperDecoder(nn.Module):
+    config: WhisperConfig
+    dtype: jnp.dtype = jnp.float32
+    params_dtype: jnp.dtype = jnp.float32
+    use_scan: bool = False
+    gradient_checkpointing: bool = False
+
+    def setup(self) -> None:
+        self.embed_tokens = Embed(
+            self.config.vocab_size,
+            self.config.d_model,
+            dtype=self.dtype,
+            params_dtype=self.params_dtype,
+        )
+        self.embed_positions = Embed(
+            self.config.max_target_positions,
+            self.config.d_model,
+            dtype=self.dtype,
+            params_dtype=self.params_dtype,
+        )
+
+        self.layers = FlaxWhisperDecoderLayerCollection(
+            self.config,
+            dtype=self.dtype,
+            params_dtype=self.params_dtype,
+            use_scan=self.use_scan,
+            gradient_checkpointing=self.gradient_checkpointing,
+        )
+
+        self.dropout_layer = nn.Dropout(rate=self.config.dropout)
+
+        self.layer_norm = LayerNorm(dtype=self.dtype, epsilon=1e-5, params_dtype=self.params_dtype)
+
+    def __call__(
+        self,
+        input_ids: jnp.ndarray,
+        attention_mask: jnp.ndarray,
+        position_ids: jnp.ndarray,
+        encoder_hidden_states: Optional[jnp.ndarray] = None,
+        init_cache: bool = False,
+        output_attentions: bool = False,
+        output_hidden_states: bool = False,
+        return_dict: bool = True,
+        deterministic: bool = True,
+    ) -> Tuple[jnp.ndarray]:
+        input_embeds = self.embed_tokens(input_ids)
+        position_embeds = self.embed_positions(position_ids)
+
+        hidden_states = input_embeds + position_embeds
+        hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
+
+        outputs = self.layers(
+            hidden_states,
+            attention_mask=attention_mask,
+            encoder_hidden_states=encoder_hidden_states,
+            deterministic=deterministic,
+            init_cache=init_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        last_hidden_states = outputs[0]
+        last_hidden_states = self.layer_norm(last_hidden_states)
+
+        # update the last element in `hidden_states` after applying `layernorm` above
+        hidden_states = None
+        if output_hidden_states:
+            hidden_states = outputs[1]
+            if self.use_scan:
+                hidden_states = jnp.vstack([hidden_states[:-1], last_hidden_states[None, ...]])
+            else:
+                hidden_states = hidden_states[:-1] + (last_hidden_states,)
+
+        if not return_dict:
+            outputs = (last_hidden_states, hidden_states) + (outputs[2:] if output_hidden_states else outputs[1:])
+            return tuple(v for v in outputs if v is not None)
+
+        return FlaxBaseModelOutputWithPastAndCrossAttentions(
+            last_hidden_state=last_hidden_states,
+            hidden_states=hidden_states,
+            attentions=outputs.attentions,
+            cross_attentions=outputs.cross_attentions,
+        )
+
+
+class FlaxWhisperModule(nn.Module):
+    config: WhisperConfig
+    dtype: jnp.dtype = jnp.float32
+    params_dtype: jnp.dtype = jnp.float32
+    use_scan: bool = False
+    gradient_checkpointing: bool = False
+
+    def setup(self) -> None:
+        self.encoder = FlaxWhisperEncoder(
+            self.config,
+            dtype=self.dtype,
+            params_dtype=self.params_dtype,
+            use_scan=self.use_scan,
+            gradient_checkpointing=self.gradient_checkpointing,
+        )
+        self.decoder = FlaxWhisperDecoder(
+            self.config,
+            dtype=self.dtype,
+            params_dtype=self.params_dtype,
+            use_scan=self.use_scan,
+            gradient_checkpointing=self.gradient_checkpointing,
+        )
+
+    def __call__(
+        self,
+        input_features: jnp.ndarray,
+        decoder_input_ids: jnp.ndarray,
+        decoder_attention_mask: jnp.ndarray,
+        decoder_position_ids: jnp.ndarray,
+        output_attentions: bool = False,
+        output_hidden_states: bool = False,
+        freeze_encoder: bool = False,
+        return_dict: bool = True,
+        deterministic: bool = True,
+    ):
+        encoder_outputs = self.encoder(
+            input_features,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            deterministic=deterministic,
+        )
+
+        encoder_hidden_states = encoder_outputs[0]
+
+        if freeze_encoder:
+            encoder_hidden_states = jax.lax.stop_gradient(encoder_hidden_states)
+
+        decoder_outputs = self.decoder(
+            input_ids=decoder_input_ids,
+            attention_mask=decoder_attention_mask,
+            position_ids=decoder_position_ids,
+            encoder_hidden_states=encoder_hidden_states,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            deterministic=deterministic,
+        )
+
+        if not return_dict:
+            return decoder_outputs + encoder_outputs
+
+        return FlaxSeq2SeqModelOutput(
+            last_hidden_state=decoder_outputs.last_hidden_state,
+            decoder_hidden_states=decoder_outputs.hidden_states,
+            decoder_attentions=decoder_outputs.attentions,
+            cross_attentions=decoder_outputs.cross_attentions,
+            encoder_last_hidden_state=encoder_outputs.last_hidden_state,
+            encoder_hidden_states=encoder_outputs.hidden_states,
+            encoder_attentions=encoder_outputs.attentions,
+        )
+
+    def _get_encoder_module(self):
+        return self.encoder
+
+    def _get_decoder_module(self):
+        return self.decoder
+
+
+class FlaxWhisperPreTrainedModel(FlaxPreTrainedModel):
+    config_class = WhisperConfig
+    base_model_prefix: str = "model"
+    main_input_name = "input_features"
+    module_class: nn.Module = None
+
+    def __init__(
+        self,
+        config: WhisperConfig,
+        input_shape: Tuple[int, int, int] = None,
+        seed: int = 0,
+        dtype: jnp.dtype = jnp.float32,
+        params_dtype: jnp.dtype = jnp.float32,
+        _do_init: bool = True,
+        # Can only use_scan=True in init if loading scanned weights -> need to handle use_scan=True and unrolled weights
+        use_scan: bool = False,
+        gradient_checkpointing: bool = False,
+        **kwargs,
+    ):
+        self.use_scan = use_scan
+        self.gradient_checkpointing = gradient_checkpointing
+
+        module = self.module_class(
+            config=config,
+            dtype=dtype,
+            params_dtype=params_dtype,
+            use_scan=use_scan,
+            gradient_checkpointing=gradient_checkpointing,
+            **kwargs,
+        )
+
+        if input_shape is None:
+            input_shape = (1, config.num_mel_bins, 2 * config.max_source_positions)
+
+        super().__init__(
+            config,
+            module,
+            input_shape=input_shape,
+            seed=seed,
+            dtype=dtype,
+            _do_init=_do_init,
+        )
+
+    def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict:
+        # init input tensors
+        input_features = jnp.zeros(input_shape, dtype="f4")
+        input_features = input_features.at[(..., -1)].set(self.config.eos_token_id)
+
+        decoder_input_ids = jnp.zeros((input_shape[0], 1), dtype="i4")
+        decoder_attention_mask = jnp.ones_like(decoder_input_ids)
+
+        batch_size, sequence_length = decoder_input_ids.shape
+        decoder_position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length))
+
+        params_rng, dropout_rng = jax.random.split(rng)
+        rngs = {"params": params_rng, "dropout": dropout_rng}
+
+        random_params = self.module.init(
+            rngs,
+            input_features=input_features,
+            decoder_input_ids=decoder_input_ids,
+            decoder_attention_mask=decoder_attention_mask,
+            decoder_position_ids=decoder_position_ids,
+        )["params"]
+
+        if params is not None:
+            random_params = flatten_dict(unfreeze(random_params))
+            params = flatten_dict(unfreeze(params))
+            for missing_key in self._missing_keys:
+                params[missing_key] = random_params[missing_key]
+            self._missing_keys = set()
+            return freeze(unflatten_dict(params))
+        else:
+            return random_params
+
+    def enable_gradient_checkpointing(self):
+        self.gradient_checkpointing = True
+        self._module = self.module_class(
+            config=self.config,
+            dtype=self.dtype,
+            use_scan=self.use_scan,
+            gradient_checkpointing=self.gradient_checkpointing,
+        )
+
+    def enable_scan(self):
+        self.use_scan = True
+        self._module = self.module_class(
+            config=self.config,
+            dtype=self.dtype,
+            use_scan=self.use_scan,
+            gradient_checkpointing=self.gradient_checkpointing,
+        )
+        init_fn = partial(self.init_weights, input_shape=self.input_shape)
+        params_shape_tree = jax.eval_shape(init_fn, self.key)
+
+        # get the shape of the parameters
+        self._params_shape_tree = params_shape_tree
+
+        # save required_params as set
+        self._required_params = set(flatten_dict(unfreeze(params_shape_tree)).keys())
+
+        # initialize the parameters
+        if self._is_initialized:
+            self.params = self.convert_unroll_to_scan(self.params)
+
+    def disable_scan(self):
+        self.use_scan = False
+        self._module = self.module_class(
+            config=self.config,
+            dtype=self.dtype,
+            use_scan=self.use_scan,
+            gradient_checkpointing=self.gradient_checkpointing,
+        )
+        init_fn = partial(self.init_weights, input_shape=self.input_shape)
+        params_shape_tree = jax.eval_shape(init_fn, self.key)
+
+        # get the shape of the parameters
+        self._params_shape_tree = params_shape_tree
+
+        # save required_params as set
+        self._required_params = set(flatten_dict(unfreeze(params_shape_tree)).keys())
+
+        # initialize the parameters
+        if self._is_initialized:
+            self.params = self.convert_scan_to_unroll(self.params)
+
+    def convert_unroll_to_scan(self, params: Union[Dict, FrozenDict]):
+        r"""
+        Convert a `PyTree` of unrolled model parameters to a scanned block of model parameters. This method can be used
+        to explicitly convert the model parameters to scanned format. This returns a new `params` tree and does not
+        convert the `params` in place.
+
+        To illustrate the workings of this method, take the Flax BERT model. The unrolled structure for the query
+        projection params is as follows:
+            ('bert', 'encoder', 'layer', '0', 'self_attn', 'q_proj') ('bert', 'encoder', 'layer', '1', 'self_attn',
+            'q_proj') ... ('bert', 'encoder', 'layer', '23', 'self_attn', 'q_proj')
+        This method takes each of the `q_proj` matrices for layers (0, ..., 23) and stacks them into a single 'super'
+        matrix, giving a *single* block of weights for all 24 layers compatible with the scanned model:
+            ('bert', 'encoder', 'layer', 'ScanLayers', 'self_attn', 'q_proj')
+
+        When enabling scan with _do_init=True (default), this method will be called automatically under the hood. With
+        _do_init=False, it will have to be called explicitly (see example below).
+
+        Arguments:
+            params (`Union[Dict, FrozenDict]`):
+                A `PyTree` of model parameters.
+
+        Examples:
+
+        ```python
+        >>> from distil_whisper import FlaxWhisperForConditionalGeneration
+
+        >>> # Download model and configuration from huggingface.co
+        >>> model, params = FlaxWhisperModel.from_pretrained("openai/whisper-tiny.en", _do_init=False)
+        >>> # By default, the model params will be in unrolled format. To illustrate the use of this method,
+        >>> # we'll first convert to scan format and then back to unrolled
+        >>> model.enable_scan()
+        >>> params = model.convert_unroll_to_scan(params)
+        >>> # now convert back to unrolled
+        >>> model.disable_scan()
+        >>> params = model.convert_scan_to_unroll(params)
+        ```"""
+        if isinstance(params, FrozenDict):
+            params = unfreeze(params)
+
+        params = flatten_dict(params, sep="/")
+        keys = list(params.keys())
+
+        for k in keys:
+            # Identify all "unrolled" layers formed as part of the FlaxBertLayerCollection
+            # These params contain the identifier `layer` in their key
+            if "layers/0" in k:
+                if "decoder" in k:
+                    block_prefix = "Decoder"
+                    num_hidden_layers = self.config.decoder_layers
+                else:
+                    block_prefix = "Encoder"
+                    num_hidden_layers = self.config.encoder_layers
+
+                # Squash the keys for the N unrolled layers into one single key:
+                # (layer/0, ..., layer/N) -> layer/FlaxScanLayers
+                scan_key = k.replace("0", f"Flax{block_prefix}ScanLayers")
+                stacked_params = []
+
+                # Iterate over the unrolled layers (1,...,N)
+                for i in range(num_hidden_layers):
+                    # Stack the params for the N layers into one super block
+                    # and remove the unrolled layer params on the fly
+                    # -> no memory overhead for conversion!
+                    unrolled_layer = params.pop(k.replace("0", str(i)))
+                    stacked_params.append(unrolled_layer)
+
+                params[scan_key] = jnp.stack(stacked_params)
+
+        # Finally, unflatten the dict to restore the nested pytree structure
+        params = unflatten_dict(params, sep="/")
+        return params
+
+    def convert_scan_to_unroll(self, params: Union[Dict, FrozenDict]):
+        r"""
+        Convert a `PyTree` of scanned model parameters to an unrolled stack of model parameters. This method can be
+        used to explicitly convert the model parameters to unrolled format. This returns a new `params` tree and does
+        not convert the `params` in place.
+
+        To illustrate the workings of this method, take the Flax BERT model. The scanned structure for the query
+        projection (`q_proj`) params is a single, stacked matrix of parameters over all N layers:
+            ('bert', 'encoder', 'layer', 'FlaxScanLayers', 'self_attn', 'q_proj')
+
+        This method slices each layer of the `q_proj` scanned matrix into single, standalone layers, and replaces the
+        scanned matrix of parameteres on the fly:
+            ('bert', 'encoder', 'layer', '0', 'self_attn', 'q_proj') ('bert', 'encoder', 'layer', '1', 'self_attn',
+            'q_proj') ... ('bert', 'encoder', 'layer', 'N', 'self_attn', 'q_proj')
+
+        When enabling scan with _do_init=True (default), this method will be called automatically under the hood. With
+        _do_init=False, it will have to be called explicitly (see example below).
+
+        Arguments:
+            params (`Union[Dict, FrozenDict]`):
+                A `PyTree` of model parameters.
+
+        Examples:
+
+        ```python
+        >>> from distil_whisper import FlaxWhisperForConditionalGeneration
+
+        >>> # Download model and configuration from huggingface.co
+        >>> model, params = FlaxWhisperModel.from_pretrained("openai/whisper-tiny.en", _do_init=False)
+        >>> # By default, the model params will be in unrolled format. To illustrate the use of this method,
+        >>> # we'll first convert to scan format and then back to unrolled
+        >>> model.enable_scan()
+        >>> params = model.convert_unroll_to_scan(params)
+        >>> # now convert back to unrolled
+        >>> model.disable_scan()
+        >>> params = model.convert_scan_to_unroll(params)
+        ```"""
+
+        if isinstance(params, FrozenDict):
+            params = unfreeze(params)
+
+        params = flatten_dict(params, sep="/")
+        keys = list(params.keys())
+
+        for k in keys:
+            # Identify all "scan" layers formed as part of the FlaxBertLayerCollection
+            # These params contain the identifier `FlaxScanLayers` in their key
+            if "FlaxEncoderScanLayers" in k:
+                # Remove the scan layer from the PyTree of params
+                scan_layer = params.pop(k)
+
+                # Unroll the key for the stacked scan matrix into N separate keys, indexed by layer number
+                # layer/FlaxScanLayers -> (layer/0, ..., layer/N)
+                for i in range(self.config.encoder_layers):
+                    # Unstack the params for the i-th scan layer to unrolled
+                    # and remove corresponding scan params on the fly
+                    # -> no memory overhead for conversion!
+                    unrolled_key = k.replace("FlaxEncoderScanLayers", str(i))
+                    params[unrolled_key], scan_layer = scan_layer[0], scan_layer[1:]
+
+            elif "FlaxDecoderScanLayers" in k:
+                # Remove the scan layer from the PyTree of params
+                scan_layer = params.pop(k)
+
+                # Unroll the key for the stacked scan matrix into N separate keys, indexed by layer number
+                # layer/FlaxScanLayers -> (layer/0, ..., layer/N)
+                for i in range(self.config.decoder_layers):
+                    # Unstack the params for the i-th scan layer to unrolled
+                    # and remove corresponding scan params on the fly
+                    # -> no memory overhead for conversion!
+                    unrolled_key = k.replace("FlaxDecoderScanLayers", str(i))
+                    params[unrolled_key], scan_layer = scan_layer[0], scan_layer[1:]
+
+        params = unflatten_dict(params, sep="/")
+        return params
+
+    # Copied from transformers.models.whisper.modeling_flax_whisper.FlaxWhisperPreTrainedModel.init_cache
+    def init_cache(self, batch_size, max_length, encoder_outputs):
+        r"""
+        Args:
+            batch_size (`int`):
+                batch_size used for fast auto-regressive decoding. Defines the batch size of the initialized cache.
+            max_length (`int`):
+                maximum possible length for auto-regressive decoding. Defines the sequence length of the initialized
+                cache.
+            encoder_outputs (`Union[FlaxBaseModelOutput, tuple(tuple(jnp.ndarray)]`):
+                `encoder_outputs` consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*:
+                `attentions`). `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*)
+                is a sequence of hidden-states at the output of the last layer of the encoder. Used in the
+                cross-attention of the decoder.
+        """
+        # init input variables to retrieve cache
+        decoder_input_ids = jnp.ones((batch_size, max_length), dtype="i4")
+        decoder_attention_mask = jnp.ones_like(decoder_input_ids)
+        decoder_position_ids = jnp.broadcast_to(
+            jnp.arange(jnp.atleast_2d(decoder_input_ids).shape[-1]),
+            decoder_input_ids.shape,
+        )
+
+        def _decoder_forward(
+            module,
+            decoder_input_ids,
+            decoder_attention_mask,
+            decoder_position_ids,
+            **kwargs,
+        ):
+            decoder_module = module._get_decoder_module()
+            return decoder_module(
+                decoder_input_ids,
+                decoder_attention_mask,
+                decoder_position_ids,
+                **kwargs,
+            )
+
+        init_variables = self.module.init(
+            jax.random.PRNGKey(0),
+            decoder_input_ids=decoder_input_ids,
+            decoder_attention_mask=decoder_attention_mask,
+            decoder_position_ids=decoder_position_ids,
+            encoder_hidden_states=encoder_outputs[0],
+            init_cache=True,
+            method=_decoder_forward,  # we only need to call the decoder to init the cache
+        )
+        return unfreeze(init_variables["cache"])
+
+    @add_start_docstrings(WHISPER_ENCODE_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=FlaxBaseModelOutput, config_class=WhisperConfig)
+    def encode(
+        self,
+        input_features: jnp.ndarray,
+        attention_mask: Optional[jnp.ndarray] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        train: bool = False,
+        params: dict = None,
+        dropout_rng: PRNGKey = None,
+        **kwargs,
+    ):
+        r"""
+        Returns:
+
+        Example:
+
+        ```python
+        >>> from transformers import WhisperProcessor, FlaxWhisperForConditionalGeneration
+        >>> from datasets import load_dataset
+
+        >>> processor = WhisperProcessor.from_pretrained("openai/whisper-tiny.en")
+        >>> model = FlaxWhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en", from_pt=True)
+        >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
+        >>> inputs = processor(ds[0]["audio"]["array"], return_tensors="np")
+        >>> input_features = inputs.input_features
+        >>> encoder_outputs = model.encode(input_features=input_features)
+        ```"""
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.return_dict
+
+        # Handle any PRNG if needed
+        rngs = {}
+        if dropout_rng is not None:
+            rngs["dropout"] = dropout_rng
+
+        def _encoder_forward(module, input_features, **kwargs):
+            encode_module = module._get_encoder_module()
+            return encode_module(input_features, **kwargs)
+
+        return self.module.apply(
+            {"params": params or self.params},
+            input_features=jnp.array(input_features, dtype="f4"),
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            deterministic=not train,
+            rngs=rngs,
+            method=_encoder_forward,
+        )
+
+    @add_start_docstrings(WHISPER_DECODE_INPUTS_DOCSTRING)
+    @replace_return_docstrings(
+        output_type=FlaxBaseModelOutputWithPastAndCrossAttentions,
+        config_class=WhisperConfig,
+    )
+    def decode(
+        self,
+        decoder_input_ids,
+        encoder_outputs,
+        encoder_attention_mask: Optional[jnp.ndarray] = None,
+        decoder_attention_mask: Optional[jnp.ndarray] = None,
+        decoder_position_ids: Optional[jnp.ndarray] = None,
+        past_key_values: dict = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        train: bool = False,
+        params: dict = None,
+        dropout_rng: PRNGKey = None,
+    ):
+        r"""
+        Returns:
+
+        Example:
+
+        ```python
+        >>> from transformers import WhisperProcessor, FlaxWhisperForConditionalGeneration
+        >>> from datasets import load_dataset
+
+        >>> processor = WhisperProcessor.from_pretrained("openai/whisper-tiny.en")
+        >>> model = FlaxWhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en", from_pt=True)
+        >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
+        >>> inputs = processor(ds[0]["audio"]["array"], return_tensors="np")
+        >>> input_features = inputs.input_features
+        >>> encoder_outputs = model.encode(input_features=input_features)
+        >>> decoder_start_token_id = model.config.decoder_start_token_id
+
+        >>> decoder_input_ids = jnp.ones((inputs.input_ids.shape[0], 1), dtype="i4") * decoder_start_token_id
+
+        >>> outputs = model.decode(decoder_input_ids, encoder_outputs)
+        >>> last_decoder_hidden_states = outputs.last_hidden_state
+        ```"""
+
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.return_dict
+
+        encoder_hidden_states = encoder_outputs[0]
+
+        batch_size, sequence_length = decoder_input_ids.shape
+        if decoder_position_ids is None:
+            if past_key_values is not None:
+                raise ValueError("Make sure to provide `decoder_position_ids` when passing `past_key_values`.")
+
+            if decoder_attention_mask is not None:
+                decoder_position_ids = (decoder_attention_mask.cumsum(-1) * decoder_attention_mask) - 1
+            else:
+                decoder_position_ids = jnp.broadcast_to(
+                    jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)
+                )
+
+        if decoder_attention_mask is None:
+            decoder_attention_mask = jnp.ones((batch_size, sequence_length))
+
+        # Handle any PRNG if needed
+        rngs = {}
+        if dropout_rng is not None:
+            rngs["dropout"] = dropout_rng
+
+        inputs = {"params": params or self.params}
+
+        # if past_key_values are passed then cache is already initialized a private flag init_cache has to be
+        # passed down to ensure cache is used. It has to be made sure that cache is marked as mutable so that
+        # it can be changed by FlaxWhisperAttention module
+        if past_key_values:
+            inputs["cache"] = past_key_values
+            mutable = ["cache"]
+        else:
+            mutable = False
+
+        def _decoder_forward(
+            module,
+            decoder_input_ids,
+            decoder_attention_mask,
+            decoder_position_ids,
+            **kwargs,
+        ):
+            decoder_module = module._get_decoder_module()
+            return decoder_module(
+                input_ids=decoder_input_ids,
+                attention_mask=decoder_attention_mask,
+                position_ids=decoder_position_ids,
+                **kwargs,
+            )
+
+        outputs = self.module.apply(
+            inputs,
+            decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"),
+            decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"),
+            decoder_position_ids=jnp.array(decoder_position_ids, dtype="i4"),
+            encoder_hidden_states=encoder_hidden_states,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            deterministic=not train,
+            rngs=rngs,
+            mutable=mutable,
+            method=_decoder_forward,
+        )
+
+        # add updated cache to model output
+        if past_key_values is not None and return_dict:
+            outputs, past = outputs
+            outputs["past_key_values"] = unfreeze(past["cache"])
+            return outputs
+        elif past_key_values is not None and not return_dict:
+            outputs, past = outputs
+            outputs = outputs[:1] + (unfreeze(past["cache"]),) + outputs[1:]
+
+        return outputs
+
+    @add_start_docstrings_to_model_forward(WHISPER_INPUTS_DOCSTRING)
+    def __call__(
+        self,
+        input_features: jnp.ndarray,
+        decoder_input_ids: jnp.ndarray,
+        attention_mask: Optional[jnp.ndarray] = None,
+        decoder_attention_mask: Optional[jnp.ndarray] = None,
+        position_ids: Optional[jnp.ndarray] = None,
+        decoder_position_ids: Optional[jnp.ndarray] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        freeze_encoder: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        train: bool = False,
+        params: dict = None,
+        dropout_rng: PRNGKey = None,
+    ):
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.return_dict
+
+        # prepare decoder inputs
+        if decoder_position_ids is None:
+            if decoder_attention_mask is not None:
+                decoder_position_ids = (decoder_attention_mask.cumsum(-1) * decoder_attention_mask) - 1
+            else:
+                batch_size, sequence_length = decoder_input_ids.shape
+                decoder_position_ids = jnp.broadcast_to(
+                    jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)
+                )
+        if decoder_attention_mask is None:
+            decoder_attention_mask = jnp.ones_like(decoder_input_ids)
+
+        # Handle any PRNG if needed
+        rngs = {"dropout": dropout_rng} if dropout_rng is not None else {}
+
+        return self.module.apply(
+            {"params": params or self.params},
+            input_features=jnp.array(input_features, dtype="f4"),
+            decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"),
+            decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"),
+            decoder_position_ids=jnp.array(decoder_position_ids, dtype="i4"),
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            freeze_encoder=freeze_encoder,
+            return_dict=return_dict,
+            deterministic=not train,
+            rngs=rngs,
+        )
+
+
+@add_start_docstrings(
+    ("The bare Whisper Model transformer outputting raw hidden-states without any specific head on top."),
+    WHISPER_START_DOCSTRING,
+)
+class FlaxWhisperModel(FlaxWhisperPreTrainedModel):
+    config: WhisperConfig
+    dtype: jnp.dtype = jnp.float32  # the dtype of the computation
+    params_dtype: jnp.dtype = jnp.float32
+    module_class = FlaxWhisperModule
+
+
+append_call_sample_docstring(FlaxWhisperModel, _CHECKPOINT_FOR_DOC, FlaxSeq2SeqModelOutput, _CONFIG_FOR_DOC)
+
+
+class FlaxWhisperForConditionalGenerationModule(nn.Module):
+    config: WhisperConfig
+    dtype: jnp.dtype = jnp.float32
+    params_dtype: jnp.dtype = jnp.float32
+    use_scan: bool = False
+    gradient_checkpointing: bool = False
+
+    def setup(self) -> None:
+        self.model = FlaxWhisperModule(
+            config=self.config,
+            dtype=self.dtype,
+            params_dtype=self.params_dtype,
+            use_scan=self.use_scan,
+            gradient_checkpointing=self.gradient_checkpointing,
+        )
+        self.lm_head = DenseGeneral(
+            self.config.vocab_size,
+            use_bias=False,
+            dtype=self.dtype,
+            params_dtype=self.params_dtype,
+            kernel_axes=("embed", "vocab"),
+        )
+
+    def _get_encoder_module(self):
+        return self.model.encoder
+
+    def _get_decoder_module(self):
+        return self.model.decoder
+
+    def __call__(
+        self,
+        input_features,
+        decoder_input_ids,
+        decoder_attention_mask: jnp.ndarray = None,
+        decoder_position_ids: jnp.ndarray = None,
+        position_ids: jnp.ndarray = None,
+        attention_mask: jnp.ndarray = None,
+        output_attentions: bool = False,
+        output_hidden_states: bool = False,
+        freeze_encoder: bool = False,
+        return_dict: bool = True,
+        deterministic: bool = True,
+    ):
+        outputs = self.model(
+            input_features=input_features,
+            decoder_input_ids=decoder_input_ids,
+            decoder_attention_mask=decoder_attention_mask,
+            decoder_position_ids=decoder_position_ids,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            freeze_encoder=freeze_encoder,
+            return_dict=return_dict,
+            deterministic=deterministic,
+        )
+
+        hidden_states = outputs[0]
+
+        if self.config.tie_word_embeddings:
+            shared_embedding = self.model.decoder.embed_tokens.variables["params"]["embedding"]
+            lm_logits = self.lm_head.apply({"params": {"kernel": shared_embedding.T}}, hidden_states)
+        else:
+            lm_logits = self.lm_head(hidden_states)
+
+        if not return_dict:
+            output = (lm_logits,) + outputs[1:]
+            return output
+
+        return FlaxSeq2SeqLMOutput(
+            logits=lm_logits,
+            decoder_hidden_states=outputs.decoder_hidden_states,
+            decoder_attentions=outputs.decoder_attentions,
+            cross_attentions=outputs.cross_attentions,
+            encoder_last_hidden_state=outputs.encoder_last_hidden_state,
+            encoder_hidden_states=outputs.encoder_hidden_states,
+            encoder_attentions=outputs.encoder_attentions,
+        )
+
+
+@add_start_docstrings("The Whisper Model with a language modeling head.", WHISPER_START_DOCSTRING)
+class FlaxWhisperForConditionalGeneration(FlaxWhisperPreTrainedModel):
+    module_class = FlaxWhisperForConditionalGenerationModule
+
+    @add_start_docstrings(WHISPER_DECODE_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=FlaxCausalLMOutputWithCrossAttentions, config_class=WhisperConfig)
+    def decode(
+        self,
+        decoder_input_ids,
+        encoder_outputs,
+        encoder_attention_mask: Optional[jnp.ndarray] = None,
+        decoder_attention_mask: Optional[jnp.ndarray] = None,
+        decoder_position_ids: Optional[jnp.ndarray] = None,
+        past_key_values: dict = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        train: bool = False,
+        params: dict = None,
+        dropout_rng: PRNGKey = None,
+    ):
+        r"""
+        Returns:
+
+        Example:
+
+        ```python
+        >>> from transformers import WhisperProcessor, FlaxWhisperForConditionalGeneration
+        >>> from datasets import load_dataset
+
+        >>> processor = WhisperProcessor.from_pretrained("openai/whisper-tiny.en")
+        >>> model = FlaxWhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en", from_pt=True)
+        >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
+        >>> inputs = processor(ds[0]["audio"]["array"], return_tensors="np")
+        >>> input_features = inputs.input_features
+        >>> encoder_outputs = model.encode(input_features=input_features)
+        >>> decoder_start_token_id = model.config.decoder_start_token_id
+
+        >>> decoder_input_ids = jnp.ones((inputs.input_ids.shape[0], 1), dtype="i4") * decoder_start_token_id
+
+        >>> outputs = model.decode(decoder_input_ids, encoder_outputs)
+        >>> last_decoder_hidden_states = outputs.last_hidden_state
+        ```"""
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.return_dict
+
+        encoder_hidden_states = encoder_outputs[0]
+
+        batch_size, sequence_length = decoder_input_ids.shape
+        if decoder_position_ids is None:
+            if past_key_values is not None:
+                raise ValueError("Make sure to provide `decoder_position_ids` when passing `past_key_values`.")
+
+            if decoder_attention_mask is not None:
+                decoder_position_ids = (decoder_attention_mask.cumsum(-1) * decoder_attention_mask) - 1
+            else:
+                decoder_position_ids = jnp.broadcast_to(
+                    jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)
+                )
+        if decoder_attention_mask is None:
+            decoder_attention_mask = jnp.ones((batch_size, sequence_length), dtype="i4")
+
+        # Handle any PRNG if needed
+        rngs = {}
+        if dropout_rng is not None:
+            rngs["dropout"] = dropout_rng
+
+        inputs = {"params": params or self.params}
+
+        # if past_key_values are passed then cache is already initialized a private flag init_cache has to be
+        # passed down to ensure cache is used. It has to be made sure that cache is marked as mutable so that
+        # it can be changed by FlaxWhisperAttention module
+        if past_key_values:
+            inputs["cache"] = past_key_values
+            mutable = ["cache"]
+        else:
+            mutable = False
+
+        def _decoder_forward(
+            module,
+            decoder_input_ids,
+            decoder_attention_mask,
+            decoder_position_ids,
+            **kwargs,
+        ):
+            decoder_module = module._get_decoder_module()
+            outputs = decoder_module(
+                input_ids=decoder_input_ids,
+                attention_mask=decoder_attention_mask,
+                position_ids=decoder_position_ids,
+                **kwargs,
+            )
+            hidden_states = outputs[0]
+
+            if self.config.tie_word_embeddings:
+                shared_embedding = module.model.decoder.embed_tokens.variables["params"]["embedding"]
+                lm_logits = module.lm_head.apply({"params": {"kernel": shared_embedding.T}}, hidden_states)
+            else:
+                lm_logits = module.lm_head(hidden_states)
+
+            return lm_logits, outputs
+
+        outputs = self.module.apply(
+            inputs,
+            decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"),
+            decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"),
+            decoder_position_ids=jnp.array(decoder_position_ids, dtype="i4"),
+            encoder_hidden_states=encoder_hidden_states,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            deterministic=not train,
+            rngs=rngs,
+            mutable=mutable,
+            method=_decoder_forward,
+        )
+
+        if past_key_values is None:
+            lm_logits, decoder_outputs = outputs
+        else:
+            (lm_logits, decoder_outputs), past = outputs
+
+        if return_dict:
+            outputs = FlaxCausalLMOutputWithCrossAttentions(
+                logits=lm_logits,
+                hidden_states=decoder_outputs.hidden_states,
+                attentions=decoder_outputs.attentions,
+                cross_attentions=decoder_outputs.cross_attentions,
+            )
+        else:
+            outputs = (lm_logits,) + decoder_outputs[1:]
+
+        # add updated cache to model output
+        if past_key_values is not None and return_dict:
+            outputs["past_key_values"] = unfreeze(past["cache"])
+            return outputs
+        elif past_key_values is not None and not return_dict:
+            outputs = outputs[:1] + (unfreeze(past["cache"]),) + outputs[1:]
+
+        return outputs
+
+    def generate(
+        self,
+        input_features,
+        generation_config=None,
+        logits_processor=None,
+        return_timestamps=None,
+        task=None,
+        language=None,
+        is_multilingual=None,
+        **kwargs,
+    ):
+        if generation_config is None:
+            generation_config = self.generation_config
+
+        if return_timestamps is not None:
+            generation_config.return_timestamps = return_timestamps
+
+        if task is not None:
+            generation_config.task = task
+
+        if is_multilingual is not None:
+            generation_config.is_multilingual = is_multilingual
+
+        if language is not None:
+            generation_config.language = language
+
+        if kwargs is not None and "decoder_input_ids" in kwargs:
+            decoder_input_length = len(kwargs["decoder_input_ids"])
+        else:
+            decoder_input_length = 1
+
+        forced_decoder_ids = []
+
+        if hasattr(generation_config, "is_multilingual") and generation_config.is_multilingual:
+            if hasattr(generation_config, "language"):
+                forced_decoder_ids.append((1, generation_config.lang_to_id[generation_config.language]))
+            else:
+                forced_decoder_ids.append((1, None))
+
+            if hasattr(generation_config, "task"):
+                forced_decoder_ids.append((2, generation_config.task_to_id[generation_config.task]))
+            else:
+                forced_decoder_ids.append((2, generation_config.task_to_id["transcribe"]))
+
+        if (
+            hasattr(generation_config, "return_timestamps") and generation_config.return_timestamps
+        ) or return_timestamps:
+            logits_processor = [
+                FlaxWhisperTimeStampLogitsProcessor(generation_config, self.config, decoder_input_length)
+            ]
+        else:
+            if forced_decoder_ids and forced_decoder_ids[-1][0] != generation_config.no_timestamps_token_id:
+                idx = forced_decoder_ids[-1][0] + 1 if forced_decoder_ids else 1
+                forced_decoder_ids.append((idx, generation_config.no_timestamps_token_id))
+
+        if len(forced_decoder_ids) > 0:
+            generation_config.forced_decoder_ids = forced_decoder_ids
+
+        return super().generate(
+            input_features,
+            generation_config,
+            logits_processor=logits_processor,
+            **kwargs,
+        )
+
+    def pipeline_generate(
+        self,
+        input_features,
+        forced_decoder_ids,
+        return_timestamps=False,
+        generation_config=None,
+        **kwargs,
+    ):
+        if generation_config is None:
+            generation_config = self.generation_config
+
+        # override the generation config forced decoder ids in preference of the ones we have set
+        generation_config.forced_decoder_ids = None
+
+        logits_processor = FlaxLogitsProcessorList()
+        logits_processor.append(FlaxStaticForceTokensLogitsProcessor(forced_decoder_ids))
+
+        if hasattr(generation_config, "return_timestamps") and return_timestamps:
+            logits_processor.append(FlaxWhisperTimeStampLogitsProcessor(generation_config, self.config, 1))
+
+        return super().generate(
+            input_features,
+            generation_config,
+            logits_processor=logits_processor,
+            **kwargs,
+        )
+
+    def prepare_inputs_for_generation(
+        self,
+        decoder_input_ids,
+        max_length,
+        attention_mask: Optional[jax.Array] = None,
+        decoder_attention_mask: Optional[jax.Array] = None,
+        encoder_outputs=None,
+        **kwargs,
+    ):
+        # initializing the cache
+        batch_size, seq_length = decoder_input_ids.shape
+
+        past_key_values = self.init_cache(batch_size, max_length, encoder_outputs)
+        # Note that usually one would have to put 0's in the attention_mask for x > input_ids.shape[-1] and x < cache_length.
+        # But since the decoder uses a causal mask, those positions are masked anyways.
+        # Thus we can create a single static attention_mask here, which is more efficient for compilation
+        extended_attention_mask = jnp.ones((batch_size, max_length), dtype="i4")
+        if decoder_attention_mask is not None:
+            position_ids = decoder_attention_mask.cumsum(-1) - 1
+            extended_attention_mask = lax.dynamic_update_slice(extended_attention_mask, decoder_attention_mask, (0, 0))
+        else:
+            position_ids = jnp.broadcast_to(jnp.arange(seq_length, dtype="i4")[None, :], (batch_size, seq_length))
+
+        return {
+            "past_key_values": past_key_values,
+            "encoder_outputs": encoder_outputs,
+            "encoder_attention_mask": attention_mask,
+            "decoder_attention_mask": extended_attention_mask,
+            "decoder_position_ids": position_ids,
+        }
+
+    def update_inputs_for_generation(self, model_outputs, model_kwargs):
+        model_kwargs["past_key_values"] = model_outputs.past_key_values
+        model_kwargs["decoder_position_ids"] = model_kwargs["decoder_position_ids"][:, -1:] + 1
+        return model_kwargs
+
+
+FLAX_WHISPER_CONDITIONAL_GENERATION_DOCSTRING = r"""
+    Returns:
+
+    Transcription example:
+
+    ```python
+    >>> from transformers import WhisperProcessor, FlaxWhisperForConditionalGeneration
+    >>> from datasets import load_dataset
+
+    >>> processor = WhisperProcessor.from_pretrained("openai/whisper-tiny.en")
+    >>> model = FlaxWhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en", from_pt=True)
+    >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
+    >>> inputs = processor(ds[0]["audio"]["array"], return_tensors="np")
+    >>> input_features = inputs.input_features
+    >>> generated_ids = model.generate(input_ids=input_features)
+    >>> transcription = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+    >>> transcription
+    ' Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel.'
+    ```
+"""
+
+overwrite_call_docstring(
+    FlaxWhisperForConditionalGeneration,
+    WHISPER_INPUTS_DOCSTRING + FLAX_WHISPER_CONDITIONAL_GENERATION_DOCSTRING,
+)
+append_replace_return_docstrings(
+    FlaxWhisperForConditionalGeneration,
+    output_type=FlaxSeq2SeqLMOutput,
+    config_class=_CONFIG_FOR_DOC,
+)
diff --git a/flax/distil_whisper/partitioner.py b/flax/distil_whisper/partitioner.py
new file mode 100644
index 0000000000000000000000000000000000000000..9e02437b59d3b6f36e9df9a1bf37dcb1b4e59ceb
--- /dev/null
+++ b/flax/distil_whisper/partitioner.py
@@ -0,0 +1,965 @@
+# Copyright 2022 The T5X Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Utilities for partitioning."""
+
+import abc
+import collections
+import dataclasses
+import typing
+from typing import Any, Callable, Optional, Sequence, Tuple, Union
+
+import cached_property
+import jax
+import numpy as np
+from absl import logging
+from flax import traverse_util
+from flax.linen import partitioning as flax_partitioning
+from jax import numpy as jnp
+from jax import random
+from jax.experimental import multihost_utils
+from jax.experimental.mesh_utils import create_hybrid_device_mesh
+from jax.experimental.pjit import pjit as jax_pjit
+from jax.sharding import Mesh, PartitionSpec
+
+
+JaxDevice = Any
+TpuMesh = Tuple[int, int, int, int]  # (x, y, z, num_cores).
+OtherMesh = Tuple[int, int]
+HardwareMesh = Union[TpuMesh, OtherMesh]
+PyTreeDef = type(jax.tree_util.tree_structure(None))
+TrainState = Any
+LogicalAxisRules = Sequence[Tuple[str, Optional[str]]]
+
+if typing.TYPE_CHECKING:  # See b/163639353
+    cached_property = property  # pylint: disable=invalid-name
+else:
+    cached_property = cached_property.cached_property
+
+
+class AxisNames(tuple):
+    """Tuple of strings specifying name for each axis.
+
+    We create a separate class for this so JAX's pytree utilities can distinguish
+    it from a tuple that should be treated as a pytree, instead treating it as a
+    leaf.
+    """
+
+    def __new__(cls, *names):
+        return tuple.__new__(AxisNames, names)
+
+    def __repr__(self):
+        return "AxisNames%s" % tuple.__repr__(self)
+
+
+# pjit wrappers for cpu fallback.
+# ----------------------------------------------------------------------------
+# TODO(levskaya): This function is now no different than jax_pjit, but callers
+# currently depend on `backend` argument
+def pjit(
+    fun: Callable,  # pylint: disable=g-bare-generic
+    in_axis_resources,
+    out_axis_resources,
+    static_argnums: Union[int, Sequence[int]] = (),
+    donate_argnums: Union[int, Sequence[int]] = (),
+    backend: Optional[str] = None,
+):
+    """Wrapper for pjit."""
+    del backend
+    return jax_pjit(
+        fun,
+        in_axis_resources,
+        out_axis_resources,
+        static_argnums=static_argnums,
+        donate_argnums=donate_argnums,
+    )
+
+
+# pjit wrappers for cpu fallback.
+# -----------------------------------------------------------------------------
+# TODO(levskaya): upstream this fallback behavior to jax pjit.
+def pjit_with_cpu_fallback(
+    fun: Callable,  # pylint: disable=g-bare-generic
+    in_axis_resources,
+    out_axis_resources,
+    static_argnums: Union[int, Sequence[int]] = (),
+    donate_argnums: Union[int, Sequence[int]] = (),
+    backend: Optional[str] = None,
+):
+    """Wrapper for pjit that calls normal jit on cpu."""
+    if jax.devices(backend)[0].platform == "cpu":
+        return jax.jit(fun, static_argnums=static_argnums, donate_argnums=donate_argnums)
+    else:
+        return jax_pjit(
+            fun,
+            in_axis_resources,
+            out_axis_resources,
+            static_argnums=static_argnums,
+            donate_argnums=donate_argnums,
+        )
+
+
+def with_sharding_constraint(x, axis_resources):
+    """Wrapper for pjit with_sharding_constraint, no-op on cpu or outside pjit."""
+    if jax.devices()[0].platform == "cpu" or not global_mesh_defined():
+        return x
+    else:
+        return jax.experimental.pjit.with_sharding_constraint(x, axis_resources)
+
+
+# pjit Mesh creation functions.
+# -----------------------------------------------------------------------------
+def bounds_from_last_device(last_device: JaxDevice) -> HardwareMesh:
+    """Get the bound from the given last device."""
+    # Must be passed the device at the highest-coordinate corner of the
+    # relevant mesh, which is a requirement we know is satisfied by the last
+    # device in jax.devices().
+    if hasattr(last_device, "coords"):
+        x, y, z = last_device.coords
+        return x + 1, y + 1, z + 1, last_device.core_on_chip + 1
+    else:
+        # On non-TPU platforms, the "mesh" is hosts x devices per host in order
+        # to take advantage of faster within-host interconnect.
+        return jax.host_count(), jax.local_device_count()
+
+
+def get_coords(device: JaxDevice) -> HardwareMesh:
+    """Returns the coordinates of the given device."""
+    if hasattr(device, "coords"):
+        return (*device.coords, device.core_on_chip)
+    return (device.process_index, device.id % jax.local_device_count())
+
+
+def global_mesh_defined():
+    """Checks if global xmap/pjit mesh resource environment is defined."""
+    maps_env = jax.experimental.maps.thread_resources.env
+    return maps_env.physical_mesh.devices.shape != ()  # pylint: disable=g-explicit-bool-comparison
+
+
+def get_mesh(
+    model_parallel_submesh: HardwareMesh,
+    input_devices: Sequence[JaxDevice] = (),
+    input_local_devices: Sequence[JaxDevice] = (),
+    tile_by_host_if_needed: bool = True,
+    backend: Optional[str] = None,
+) -> Mesh:
+    """Construct an xmap/pjit Mesh for the given model-parallel submesh.
+
+    The resulting mesh has two resource axes: 'model', with the provided submesh
+    shape, and 'data', which covers the rest of the mesh.
+
+    Args:
+      model_parallel_submesh: a HardwareMesh spec, namely (x,y,z,core) on TPU for
+        a single model-parallel replica's "tile" in the physical device mesh. The
+        first three elements (`x`, `y`, and `z`) should be factors of the pod
+        slice; e.g., if you are using df_4x8, then `x` should be a factor of 4
+        (one of 1, 2, 4), `y` should be a factor of 8 (one of 1, 2, 4, 8), and `z`
+        must be 1, because TPU v3 slices are only 2D. `z` can be >1 for TPU v4
+        (and maybe later TPUs) that allow 3D slices. `core` is the number of cores
+        to use from each TPU node. As communication is usually fastest inside the
+        same node, if you need a tile of more than 1 core, then
+        you should first increase `core`: e.g., for TPU v3, (1,1,1,2) is better
+          than (2,1,1,1). To pick a good spec, try a few possible values until you
+          get high TPU utilization.
+      input_devices: the devices to use, will use jax.devices() if this is not
+        set.
+      input_local_devices: the local devices to use, will use jax.local_devices()
+        if this is not set.
+      tile_by_host_if_needed: JAX currently requires that the parts of any sharded
+        array that are located on one host's local devices form a single
+        contiguous slice. A best effort will be made to achieve this without
+        "tiling" the device assignment over hosts (which can reduce XLA collective
+        performance). If this flag is True, then the device assignment will be
+        tiled over hosts if necessary to satisfy this constraint and create a
+        buildable mesh; if false, mesh construction will fail instead.
+      backend: get devices from the pinned backend, if specified. This is
+        useful for explicitly specifying the devices other than relying on
+        jax_platform_name.
+
+    Returns:
+      A xmap / pjit Mesh containing the virtual device mesh with data, model axes.
+    """
+    input_devices = input_devices or jax.devices(backend)
+    input_local_devices = input_local_devices or jax.local_devices(0, backend)
+    # Sort input_devices based on coords, as backends might not return devices
+    # in order.
+    last_device = sorted(input_devices, key=get_coords)[-1]
+    last_input_local_devices = sorted(input_local_devices, key=get_coords)[-1]
+    logging.info(
+        "last device coords : %r\nlast local device coords: %r",
+        get_coords(last_device),
+        get_coords(last_input_local_devices),
+    )
+    global_hardware_mesh = bounds_from_last_device(last_device)
+    mesh_ndim = len(global_hardware_mesh)
+    local_hardware_mesh = bounds_from_last_device(last_input_local_devices)
+    mesh_err = (
+        f"each dimension of the model parallel submesh {model_parallel_submesh} "
+        "must be a factor of the corresponding dimension of the global device "
+        f"mesh {global_hardware_mesh}"
+    )
+    assert not any(g % m for g, m in zip(global_hardware_mesh, model_parallel_submesh)), mesh_err
+    assert not any(g % l for g, l in zip(global_hardware_mesh, local_hardware_mesh))
+    devices = np.empty(global_hardware_mesh, dtype=object)
+    for device in input_devices:
+        device_coords = get_coords(device)
+        devices[device_coords] = device
+    tile_by_host = tile_by_host_if_needed
+    if len(global_hardware_mesh) == 4:
+        # enable contiguous local chunks without host tiling by making Z major
+        global_hardware_mesh = typing.cast(Tuple[int, int, int, int], global_hardware_mesh)
+        model_parallel_submesh = typing.cast(Tuple[int, int, int, int], model_parallel_submesh)
+        gx, gy, gz, gc = global_hardware_mesh
+        mx, my, mz, mc = model_parallel_submesh
+        if (mx == gx > 1 and my == mz == 1) or (mx == 1 and my == gy > 1 and mz == gz > 1):
+            logging.info("ensuring YZ plane has a Z-major device order")
+            # YZ should be ZY
+            assert mc == gc, (mc, gc)
+            global_hardware_mesh = gx, gz, gy, gc
+            model_parallel_submesh = mx, mz, my, mc
+            devices = devices.swapaxes(1, 2)
+            tile_by_host = False
+        if (my == gy > 1 and mx == mz == 1) or (my == 1 and mx == gx > 1 and mz == gz > 1):
+            logging.info("ensuring XZ plane has a Z-major device order")
+            # XZ should be ZX
+            assert mc == gc, (mc, gc)
+            global_hardware_mesh = gz, gy, gx, gc
+            model_parallel_submesh = mz, my, mx, mc
+            devices = devices.swapaxes(0, 2)
+            tile_by_host = False
+    if tile_by_host:
+        logging.warning(
+            "Tiling device assignment mesh by hosts, which may lead to "
+            "reduced XLA collective performance. To avoid this, modify "
+            "the model parallel submesh or run with more tasks per host."
+        )
+        tile_err = (
+            "to tile the mesh by hosts, each dimension of the model parallel "
+            "submesh must be either a factor or a multiple of the corresponding "
+            "dimension of the per-host submesh"
+        )
+
+        def dh_dd_mh_md(g: int, m: int, l: int) -> Tuple[int, int, int, int]:
+            """Split a global mesh dimension into four tiling components.
+
+            Args:
+              g: global mesh bounds dimension size
+              m: model-parallel submesh bounds dimension size
+              l: local submesh bounds dimension size
+
+            Returns:
+              The resulting tuple divides the dimension into the hosts component of
+              the data-parallel submesh, the devices component of the data-parallel
+              submesh, the hosts component of the model-parallel submesh, and the
+              devices component of the model-parallel submesh.
+            """
+            d = g // m
+            if m >= l:
+                assert not m % l, tile_err
+                return (d, 1, m // l, l)
+            else:
+                assert not l % m, tile_err
+                return (d // (l // m), l // m, 1, m)
+
+        # e.g. [(x_data_hosts, x_data_devs, x_model_hosts, x_model_devs), ...]
+        dh_dd_mh_md_tups = map(
+            dh_dd_mh_md,
+            global_hardware_mesh,
+            model_parallel_submesh,
+            local_hardware_mesh,
+        )
+        # reshape to e.g. (x_dh, x_dd, x_mh, x_md, y_dh, ...)
+        devices = devices.reshape(*(s for t in dh_dd_mh_md_tups for s in t))  # pylint: disable=g-complex-comprehension
+        # TODO(jekbradbury): reorder local subgroups for ring locality
+        # Transpose to [data_host], [data_device], [model_host], [model_device]
+        # block ordering e.g. (x_dh, y_dh, ..., x_dd, y_dd, ...)
+        devices = devices.transpose(
+            *(4 * i for i in range(mesh_ndim)),
+            *(4 * i + 1 for i in range(mesh_ndim)),
+            *(4 * i + 2 for i in range(mesh_ndim)),
+            *(4 * i + 3 for i in range(mesh_ndim)),
+        )
+    else:
+        # e.g. [(x_data, x_model), (y_data, y_model), ...]
+        model_data_tups = [(g // m, m) for g, m in zip(global_hardware_mesh, model_parallel_submesh)]
+        # reshape to e.g. (x_data, x_model, y_data, y_model...)
+        devices = devices.reshape(*(s for t in model_data_tups for s in t))  # pylint: disable=g-complex-comprehension
+        # TODO(jekbradbury): reorder small subgroups for ring locality
+        # transpose to e.g. (x_data, y_data, ..., x_model, ...)
+        devices = devices.transpose(*(2 * i for i in range(mesh_ndim)), *(2 * i + 1 for i in range(mesh_ndim)))
+    # reshape to (data, model)
+    devices = devices.reshape(-1, np.prod(model_parallel_submesh))
+    global_mesh = Mesh(devices, ["data", "model"])
+    logging.info("global_mesh axis_names: %s", global_mesh.axis_names)
+    logging.info("global_mesh devices: %s", global_mesh.devices)
+    logging.info("global_mesh devices shape: %s", global_mesh.devices.shape)
+    return global_mesh
+
+
+def get_cpu_mesh() -> Mesh:
+    """Trivial mesh for CPU Testing."""
+    devices = np.empty((jax.host_count(), jax.local_device_count()), dtype=object)
+    for device in jax.devices():
+        devices[device.process_index, device.id % jax.local_device_count()] = device
+    return Mesh(devices, ["data", "model"])
+
+
+def get_gpu_mesh(num_partitions: int) -> Mesh:
+    """Mesh for GPUs that preferentially places 'model' on NVLink."""
+    nvlink_size = jax.local_device_count()
+    dcn_size = jax.process_count()
+    nvlink_mp = min(num_partitions, nvlink_size)
+    nvlink_dp, extra1 = divmod(nvlink_size, nvlink_mp)
+    dcn_mp, extra2 = divmod(num_partitions, nvlink_mp)
+    assert not (
+        extra1 or extra2
+    ), "number of partitions on GPU must be a factor or multiple of the number of local devices"
+    dcn_dp = dcn_size // dcn_mp
+
+    devices = create_hybrid_device_mesh(
+        mesh_shape=[nvlink_dp, nvlink_mp],
+        dcn_mesh_shape=[dcn_dp, dcn_mp],
+        process_is_granule=True,
+    )
+
+    global_mesh = Mesh(devices, ["data", "model"])
+    logging.info("global_mesh axis_names: %s", global_mesh.axis_names)
+    logging.info("global_mesh devices: %s", global_mesh.devices)
+    return global_mesh
+
+
+def default_mesh(
+    num_partitions: int,
+    model_parallel_submesh: Optional[HardwareMesh] = None,
+    backend: Optional[str] = None,
+) -> Mesh:
+    """Attempt to return a default mesh for simple cases.
+
+    Args:
+      num_partitions: number of partitions to use, will be ignored if
+        model_parallel_submesh is provided.
+      model_parallel_submesh: 4-tuple that specifies the x,y,z,c submesh to use as
+        the model-parallel device tile.
+      backend: get devices from the pinned backend, if specified. This is useful
+        for explicitly specifying the devices other than relying on
+        jax_platform_name.
+
+    Returns:
+      xmap/pjit 2D Mesh with 'data', 'model' mesh axes.
+    """
+    last_device = jax.devices(backend)[-1]
+    platform = last_device.platform
+    device_kind = last_device.device_kind
+    bounds = bounds_from_last_device(last_device)
+
+    if model_parallel_submesh:
+        return get_mesh(model_parallel_submesh, backend=backend)
+
+    if platform == "cpu":
+        return get_cpu_mesh()
+    elif platform == "gpu":
+        return get_gpu_mesh(num_partitions)
+
+    mps = None
+    if device_kind in ("TPU v2", "TPU v3"):
+        if num_partitions == 1:
+            mps = (1, 1, 1, 1)
+        elif num_partitions == 2:
+            mps = (1, 1, 1, 2)
+        elif num_partitions == 4:
+            mps = (2, 1, 1, 2)
+        elif num_partitions == 8:
+            mps = (2, 2, 1, 2)
+        elif num_partitions == 16:
+            mps = (4, 2, 1, 2)
+    # assume the use of megacore on TPU v4
+    elif (device_kind == "TPU v4" or device_kind == "TPU v4 lite") and bounds[3] == 1:
+        if num_partitions == 1:
+            mps = (1, 1, 1, 1)
+        elif num_partitions == 2:
+            mps = (1, 2, 1, 1)
+        elif num_partitions == 4:
+            if bounds[0] >= 4:
+                mps = (4, 1, 1, 1)
+            else:
+                mps = (2, 2, 1, 1)
+        elif num_partitions == 8:
+            if bounds[2] >= 8:
+                mps = (1, 1, 8, 1)
+            else:
+                mps = (4, 2, 1, 1)
+        elif num_partitions == 16:
+            if bounds[2] >= 16:
+                mps = (1, 1, 16, 1)
+            elif bounds[0] >= 8:
+                mps = (8, 2, 1, 1)
+            elif bounds[0] >= 4:
+                mps = (4, 4, 1, 1)
+            else:
+                mps = (2, 2, 4, 1)
+
+    if mps is None:
+        raise ValueError(
+            "No default mesh for this configuration: specify " "config.model_parallel_submesh explicitly."
+        )
+    return get_mesh(mps, backend=backend)
+
+
+# Data chunking helper.
+# -----------------------------------------------------------------------------
+@dataclasses.dataclass
+class LocalChunkInfo:
+    # The logical slice of an array located on this host's local devices.
+    slice: Tuple[slice, ...]
+    # A unique index for this host/local chunk among chunks with the same slice.
+    replica_id: int
+
+
+class LocalChunker:
+    """Utility class to aid chunking of sharded arrays in multihost settings."""
+
+    def __init__(self, global_mesh: Mesh):
+        self.global_mesh = global_mesh
+        local_mesh = global_mesh.local_mesh
+        first_local_device = local_mesh.devices.reshape(-1)[0]
+        host_location = collections.OrderedDict(
+            zip(
+                global_mesh.shape.keys(),
+                list(zip(*np.nonzero(global_mesh.devices == first_local_device)))[0],
+            )
+        )
+        self.num_chunks = collections.OrderedDict()
+        self.chunk_ids = collections.OrderedDict()
+        self.mesh_axes = list(global_mesh.shape.keys())
+        for mesh_axis in self.mesh_axes:
+            num_devices_per_chunk = local_mesh.shape[mesh_axis]
+            self.num_chunks[mesh_axis] = global_mesh.shape[mesh_axis] // num_devices_per_chunk
+            self.chunk_ids[mesh_axis] = host_location[mesh_axis] // num_devices_per_chunk
+
+    def get_local_chunk_info(
+        self, global_shape: Tuple[int, ...], mesh_axes: Sequence[Optional[str]]
+    ) -> LocalChunkInfo:
+        """Get the local chunk info for a given array shape and sharded axes.
+
+        Args:
+          global_shape: the global, unsharded shape of the array to chunk.
+          mesh_axes: a sequence of names (or None) of equal rank to `global_shape`
+            that specifies which mesh dimensions the array is sharded along.
+
+        Returns:
+          LocalChunkInfo containing the logical slices of the array found on this
+          host's local devices, as well as the replica index for this chunk among
+          chunks with the same slice. The latter is used to determine which
+          host should write this chunk during checkpointing.
+        """
+        local_slice = [slice(None) for dim in global_shape]
+        sharded_mesh_axes = set()
+        for i, (mesh_axis, size) in enumerate(zip(mesh_axes, global_shape)):
+            if not mesh_axis:
+                continue
+            sharded_mesh_axes.add(mesh_axis)
+            if not isinstance(mesh_axis, str):
+                raise NotImplementedError("TODO(jekbradbury)")
+            chunk_id = self.chunk_ids[mesh_axis]
+            chunk_size = size // self.num_chunks[mesh_axis]
+            local_slice[i] = slice(chunk_id * chunk_size, (chunk_id + 1) * chunk_size)
+
+        replicated_mesh_axes = [mesh_axis for mesh_axis in self.mesh_axes if mesh_axis not in sharded_mesh_axes]
+        replica_id = 0
+        for mesh_axis in replicated_mesh_axes:
+            chunk_id = self.chunk_ids[mesh_axis]
+            replica_id = replica_id * self.num_chunks[mesh_axis] + chunk_id
+
+        return LocalChunkInfo(tuple(local_slice), replica_id)
+
+
+def standard_logical_axis_rules(
+    activation_partitioning_dims: int = 1,
+    parameter_partitioning_dims: int = 1,
+    additional_rules: Optional[LogicalAxisRules] = None,
+) -> LogicalAxisRules:
+    """Default sharding rules for T5X model in terms of logical axis names.
+
+    Args:
+      activation_partitioning_dims: enables 2-D activation sharding when set to 2.
+      parameter_partitioning_dims: enables 2-D parameter sharding when set to 2.
+      additional_rules: additional rules (a sequence of tuples) that will be
+        appended to the standard rules.
+
+    Returns:
+      Sequence of logical axis rules
+    """
+    logging.info(
+        "`activation_partitioning_dims` = %d, `parameter_partitioning_dims` = %d",
+        activation_partitioning_dims,
+        parameter_partitioning_dims,
+    )
+
+    if activation_partitioning_dims == 1 and parameter_partitioning_dims == 1:
+        rules = [
+            ("batch", "data"),
+            ("vocab", "model"),
+            ("embed", None),
+            ("mlp", "model"),
+            ("heads", "model"),
+            ("kv", None),
+            ("joined_kv", "model"),  # joined heads+kv dim in 2D attn param layouts
+        ]
+    elif activation_partitioning_dims == 2 and parameter_partitioning_dims == 1:
+        rules = [
+            ("batch", "data"),
+            ("vocab", "model"),
+            ("mlp", "model"),
+            ("heads", "model"),
+            ("kv", None),
+            ("joined_kv", "model"),
+            ("embed", "model"),
+        ]
+    elif activation_partitioning_dims == 1 and parameter_partitioning_dims == 2:
+        rules = [
+            ("batch", "data"),
+            ("vocab", "model"),
+            ("mlp", "model"),
+            ("heads", "model"),
+            ("kv", None),
+            ("joined_kv", "model"),
+            ("embed", "data"),
+        ]
+    elif activation_partitioning_dims == 2 and parameter_partitioning_dims == 2:
+        rules = [
+            ("batch", "data"),
+            ("vocab", "model"),
+            ("mlp", "model"),
+            ("heads", "model"),
+            ("kv", None),
+            ("joined_kv", "model"),
+            ("embed", "model"),
+            ("embed", "data"),
+        ]
+    else:
+        raise ValueError(
+            f"`activation_partitioning_dims` = {activation_partitioning_dims} "
+            f"`parameter_partitioning_dims` = {parameter_partitioning_dims} "
+            "is not supported."
+        )
+
+    # Add the common rules for the replicated logical axes names.
+    replicated_rules = [
+        ("relpos_buckets", None),
+        ("abspos_buckets", None),
+        ("length", None),
+        ("layers", None),
+        ("stack", None),
+        ("mlp_activations", None),
+    ]
+    rules.extend(replicated_rules)
+
+    if additional_rules:
+        rules.extend(additional_rules)
+
+    return rules
+
+
+# NB: This needs to be top-level for the jax compilation cache.
+def _id_fn(x, ix):
+    """Identity function for copying parameters to the devices, sharded."""
+    # A pure identity such as `lambda x, *: x` can get optimized away, so we
+    # include a random.split as a cheap function that cannot be optimized away.
+    y = random.split(random.PRNGKey(jnp.array(ix, dtype=jnp.uint32)))
+    return x, y
+
+
+@dataclasses.dataclass
+class DataLayout:
+    """Represents data layout for the partitioned model."""
+
+    batch_size: int
+    shard_id: int
+    num_shards: int
+    is_first_host_in_replica_set: bool
+
+
+PartitionedCallable = Callable[..., Any]
+CompiledPartitionedCallable = Callable[..., Any]
+
+
+class BasePartitioner(metaclass=abc.ABCMeta):
+    """Interface for partitioning computations across hardware devices."""
+
+    def __init__(
+        self,
+        num_partitions: Optional[int] = None,
+        model_parallel_submesh: Optional[HardwareMesh] = None,
+        params_on_devices: bool = True,
+        backend: Optional[str] = None,
+    ):
+        """Configures the partitioner.
+
+        Args:
+          num_partitions: the number of partitions to use. Ignored if
+            `model_parallel_submesh` is provided.
+          model_parallel_submesh: 4-tuple that specifies the x,y,z,c submesh to use
+            as the model-parallel device tile. This submesh is used for the larger
+            of the two parameter dimensions, and, if 2-D activation sharding is
+            enabled, for the model dimension of activations. The rest of the mesh is
+            used for data parallelism and, if 2-D parameter sharding is enabled, the
+            other parameter dimension.
+          params_on_devices: whether to keep the params on devices, if False -
+            params stay in the host memory. Note that some partitioners might ignore
+            this setting, for example if they don't support storing all params on
+            device memory.
+          backend: get devices from the pinned backend, if specified. This is useful
+            for explicitly specifying the devices other than relying on
+            jax_platform_name.
+        """
+
+        if not num_partitions and not model_parallel_submesh:
+            raise ValueError("At least one of `num_partitions` or " "`model_parallel_submesh` must be set.")
+
+        if model_parallel_submesh is not None and len(model_parallel_submesh) != 4:
+            logging.error(
+                (
+                    "`model_parallel_submesh` must be either None or a 4-tuple. Got"
+                    " `model_parallel_submesh`=%s. A ValueError will be raised"
+                    " beginning March 1, 2022."
+                ),
+                model_parallel_submesh,
+            )
+
+        if bool(num_partitions) and bool(model_parallel_submesh):
+            logging.error(
+                (
+                    "At most one of `num_partitions` or `model_parallel_submesh` can be"
+                    " set. Got `num_partitions=%s` and `model_parallel_submesh`=%s. A"
+                    " ValueError will be raised beginning March 21, 2022."
+                ),
+                num_partitions,
+                model_parallel_submesh,
+            )
+
+        self._num_partitions = num_partitions
+        self._model_parallel_submesh = model_parallel_submesh
+        self._params_on_devices = params_on_devices
+        self._data_axis = "data"
+        self._backend = backend
+
+    @property
+    def mesh(self) -> Mesh:
+        raise NotImplementedError
+
+    @property
+    def data_partition_spec(self) -> PartitionSpec:
+        return PartitionSpec(self._data_axis)
+
+    def get_data_layout(self, batch_size: Optional[int] = None, host_index: Optional[int] = None) -> DataLayout:
+        """Returns filled `DataLayout` based on the partitioned model layout.
+
+        Args:
+          batch_size: if set, indicates the requested batch size. The exception will
+            be raised if this batch size is not compatible with the layout. If not
+            set, the batch size is inferred from the layout.
+          host_index: indicates the host index to use for the calculations, if not
+            set - use JAX-provided one. Should be in [0, num_hosts) interval and the
+            order should match the order of corresponding CPU devices in
+            `jax.devices()`.
+
+        Returns:
+          Filled `DataLayout` structure.
+        """
+        if host_index is not None:
+            raise NotImplementedError("Explicit host_index is not yet implemented.")
+        if self._data_axis is None:
+            return DataLayout(
+                batch_size=batch_size,
+                shard_id=0,
+                num_shards=1,
+                is_first_host_in_replica_set=(jax.process_index() == 0),
+            )
+        mesh_size = self._local_chunker.global_mesh.shape[self._data_axis]
+        batch_size = batch_size or mesh_size
+        if batch_size % mesh_size:
+            raise ValueError(
+                f"Batch size ({batch_size}) must be divisible by corresponding " f"mesh size ({mesh_size})."
+            )
+        num_shards = self._local_chunker.num_chunks[self._data_axis]
+        if batch_size % num_shards:
+            raise ValueError(f"Batch size ({batch_size}) must be divisible by number of " f"replicas ({num_shards}).")
+        replica_id = self._local_chunker.get_local_chunk_info((batch_size,), [self._data_axis]).replica_id
+        return DataLayout(
+            batch_size=int(batch_size),
+            shard_id=int(self._local_chunker.chunk_ids[self._data_axis]),
+            num_shards=int(num_shards),
+            is_first_host_in_replica_set=(replica_id == 0),
+        )
+
+    def get_local_chunk_info(
+        self, global_shape: Tuple[int, ...], mesh_axes: Sequence[Optional[str]]
+    ) -> LocalChunkInfo:
+        """Returns the local chunk info for a given array shape and sharded axes."""
+        return self._local_chunker.get_local_chunk_info(global_shape, mesh_axes)
+
+    @property
+    def params_on_devices(self):
+        return self._params_on_devices
+
+    def move_params_to_devices(self, train_state: TrainState, train_state_axes: TrainState) -> TrainState:
+        """Moves the optimizer parameters to devices."""
+        p_id_fn = self.partition(
+            _id_fn,
+            in_axis_resources=(train_state_axes, None),
+            out_axis_resources=(train_state_axes, None),
+            donate_argnums=(0,),
+        )
+        if jax.config.jax_array and jax.process_count() > 1:
+            train_state = multihost_utils.host_local_array_to_global_array(train_state, self.mesh, train_state_axes)
+        train_state, _ = p_id_fn(train_state, jnp.ones((), dtype=jnp.uint32))
+        return train_state
+
+    @property
+    @abc.abstractmethod
+    def _local_chunker(self):
+        """Returns the chunker that matches the parameters of this partitioner."""
+        raise NotImplementedError
+
+    def get_logical_axes(self, train_state: TrainState) -> TrainState:
+        """Returns a copy of TrainState with Optional[AxisNames] as leaves."""
+        # By default, return None for the logical axes.
+        return train_state.restore_state(jax.tree_map(lambda x: None, train_state.state_dict()))
+
+    def get_mesh_axes(self, train_state: TrainState) -> TrainState:
+        """Returns a copy of TrainState with Optional[PartitionSpecs] as leaves."""
+        raise NotImplementedError
+
+    @abc.abstractmethod
+    def partition(
+        self,
+        fn: Callable,  # pylint: disable=g-bare-generic
+        in_axis_resources,
+        out_axis_resources,
+        static_argnums: Union[int, Sequence[int]] = (),
+        donate_argnums: Union[int, Sequence[int]] = (),
+    ) -> PartitionedCallable:
+        """Partitions the computation using partitioner-specific implementation.
+
+        Args:
+          fn: the function to partition.
+          in_axis_resources: Pytree of structure matching that of arguments to `fn`,
+            with all actual arguments replaced by resource assignment
+            specifications. It is also valid to specify a pytree prefix (e.g. one
+            value in place of a whole subtree), in which case the leaves get
+            broadcast to all values in that subtree.
+            The valid resource assignment specifications are:
+              `None`: in which case the value will be replicated on all devices
+              `PartitionSpec`: a tuple of length at most equal to the rank of the
+                partitioned value. Each element can be a `None`, a mesh axis or a
+                tuple of mesh axes, and specifies the set of resources assigned to
+                partition the value's dimension matching its position in the spec.
+          out_axis_resources: Like `in_axis_resources`, but specifies resource
+            assignment for function outputs.
+          static_argnums: an optional int or collection of ints that specify which
+            positional arguments to treat as static (compile-time constant) in the
+            partitioned function.
+          donate_argnums: an optional int or collection of ints that specify which
+            argument buffers are "donated" to the computation. It is safe to donate
+            argument buffers if you no longer need them once the computation has
+            finished.
+
+        Returns:
+          A partitioned version of the input function.
+        """
+        raise NotImplementedError
+
+    @abc.abstractmethod
+    def compile(self, partitioned_fn: PartitionedCallable, *args) -> CompiledPartitionedCallable:
+        """Compiles and returns the partitioned function, or the original.
+
+        Args:
+          partitioned_fn: The partitioned function.
+          *args: Sample arguments to the partitioned function matching the input
+            shapes that will be passed to the compiled function.
+
+        Returns:
+          The compiled function, or the original if this partitioner does not
+          support compilation.
+        """
+        raise NotImplementedError
+
+
+class PjittedFnWithContext(PartitionedCallable):
+    """Wraps pjitted function to apply the appropriate contexts."""
+
+    def __init__(
+        self,
+        pjitted_fn,
+        partition_mesh: Mesh,
+        logical_axis_rules: flax_partitioning.LogicalRules = (),
+    ):
+        self._pjitted_fn = pjitted_fn
+        self._mesh = partition_mesh
+        self._logical_axis_rules = logical_axis_rules
+
+    def __call__(self, *args):
+        with Mesh(self._mesh.devices, self._mesh.axis_names), flax_partitioning.axis_rules(self._logical_axis_rules):
+            return self._pjitted_fn(*args)
+
+    def lower(self, *args):
+        with Mesh(self._mesh.devices, self._mesh.axis_names), flax_partitioning.axis_rules(self._logical_axis_rules):
+            return self._pjitted_fn.lower(*args)
+
+
+class BasePjitPartitioner(BasePartitioner):
+    """Partitioner that uses T5X version of jax.pjit."""
+
+    @cached_property
+    def _local_chunker(self) -> LocalChunker:
+        return LocalChunker(self.mesh)
+
+    @cached_property
+    def mesh(self) -> Mesh:
+        return default_mesh(self._num_partitions, self._model_parallel_submesh, self._backend)
+
+    def partition(
+        self,
+        fn: Callable,  # pylint: disable=g-bare-generic
+        in_axis_resources,
+        out_axis_resources,
+        static_argnums: Union[int, Sequence[int]] = (),
+        donate_argnums: Union[int, Sequence[int]] = (),
+    ) -> PjittedFnWithContext:
+        pjitted = pjit(
+            fn,
+            in_axis_resources=in_axis_resources,
+            out_axis_resources=out_axis_resources,
+            static_argnums=static_argnums,
+            donate_argnums=donate_argnums,
+            backend=self._backend,
+        )
+
+        return PjittedFnWithContext(pjitted, self.mesh)
+
+    def compile(self, partitioned_fn: PjittedFnWithContext, *args) -> CompiledPartitionedCallable:
+        return partitioned_fn.lower(*args).compile()
+
+
+class PjitPartitioner(BasePjitPartitioner):
+    """Partitioner that uses named axes and jax.pjit."""
+
+    def __init__(
+        self,
+        num_partitions: Optional[int] = None,
+        model_parallel_submesh: Optional[HardwareMesh] = None,
+        params_on_devices: bool = True,
+        backend: Optional[str] = None,
+        logical_axis_rules: Optional[LogicalAxisRules] = None,
+        use_cpu_pjit: Optional[bool] = False,
+    ):
+        """PjitPartitioner constructor.
+
+        See https://github.com/google-research/text-to-text-transfer-transformer/blob/main/README.mdx/usage/partitioning for details.
+
+        Args:
+          num_partitions: an integer that specifies the size of the model parallel
+            submesh to be automatically selected for the current topology. See
+            `model_parallel_submesh` for details on how this submesh is used.
+            Mutually exlusive with `model_parallel_submesh`.
+          model_parallel_submesh: is a 4-tuple that specifies the `(x, y, z, c)`
+            submesh model-parallel device tile, an axis of accelerator parallelism
+            orthogonal to data parallelism. Array axes in a model's parameters or
+            activations can be sharded over this submesh using axis rules (see
+            `logical_axis_rules`) that map them to 'model'. The effective number of
+            model sub-partitions is equal to `np.prod(model_parallel_submesh)` and
+            must evenly divide the total number of devices (i.e.,
+            `jax.device_count() % np.prod(model_parallel_submesh) == 0`). The rest
+            of the TPU mesh is the data parallel submesh, providing
+            `jax.device_count() // np.prod(model_parallel_submesh)` partitions. It
+            is used for data (batch) parallelism and to shard other array axes that
+            are mapped to 'data'. This argument is mutually exclusive with
+            `num_partitions`.
+          params_on_devices: whether to keep the params on devices, if False -
+            params stay in the host memory. Note that some partitioners might ignore
+            this setting, for example if they don't support storing all params on
+            device memory.
+          backend: get devices from the pinned backend, if specified. This is
+            useful for explicitly specifying the devices other than relying on
+            jax_platform_name.
+          logical_axis_rules: a priority-ordered sequence of KV tuples that maps
+            logical axis names to either `None` (not sharded), 'model' (to shard
+            across the model-parallel submesh), or 'data' (to shard across the
+            data-parallel submesh).
+          use_cpu_pjit: enables wrapper function for pjit which just jits the
+            function if using CPU backend.
+        """
+        super().__init__(
+            num_partitions=num_partitions,
+            model_parallel_submesh=model_parallel_submesh,
+            params_on_devices=params_on_devices,
+            backend=backend,
+        )
+        if logical_axis_rules is None:
+            logical_axis_rules = standard_logical_axis_rules()
+        self._logical_axis_rules = tuple(logical_axis_rules)
+        (self._data_axis,) = flax_partitioning.logical_to_mesh_axes(["batch"], logical_axis_rules)
+        self._use_cpu_pjit = use_cpu_pjit
+
+    def partition(
+        self,
+        fn: Callable,  # pylint: disable=g-bare-generic
+        in_axis_resources,
+        out_axis_resources,
+        static_argnums: Union[int, Sequence[int]] = (),
+        donate_argnums: Union[int, Sequence[int]] = (),
+    ) -> PjittedFnWithContext:
+        """Partitions the function using jax.pjit."""
+        if self._use_cpu_pjit:
+            pjit_fn = pjit_with_cpu_fallback
+        else:
+            pjit_fn = pjit
+        pjitted = pjit_fn(
+            fn,
+            in_axis_resources=in_axis_resources,
+            out_axis_resources=out_axis_resources,
+            static_argnums=static_argnums,
+            donate_argnums=donate_argnums,
+            backend=self._backend,
+        )
+
+        return PjittedFnWithContext(pjitted, self.mesh, self._logical_axis_rules)
+
+    @property
+    def logical_axis_rules(self):
+        """Returns the logical axis rules."""
+        return self._logical_axis_rules
+
+    def get_logical_axes(self, train_state: TrainState) -> TrainState:
+        """Returns a copy of TrainState with Optional[AxisNames] as leaves."""
+        return train_state.as_logical_axes()
+
+    def get_mesh_axes(self, train_state: TrainState) -> TrainState:
+        """Returns a copy of TrainState with Optional[PartitionSpecs] as leaves."""
+        logical_axes = self.get_logical_axes(train_state)
+
+        def _logical_to_mesh_axes(param_name, logical_axes):
+            if logical_axes is None:
+                return None
+            elif logical_axes is traverse_util.empty_node:
+                return traverse_util.empty_node
+            try:
+                return flax_partitioning.logical_to_mesh_axes(logical_axes, self._logical_axis_rules)
+            except ValueError as e:
+                raise ValueError(f"Failed to map logical axes for {param_name}") from e
+
+        flat_logical_axes = traverse_util.flatten_dict(logical_axes.state_dict(), keep_empty_nodes=True, sep="/")
+        flat_mesh_axes = {k: _logical_to_mesh_axes(k, v) for k, v in flat_logical_axes.items()}
+
+        return logical_axes.restore_state(traverse_util.unflatten_dict(flat_mesh_axes, sep="/"))
diff --git a/flax/distil_whisper/pipeline.py b/flax/distil_whisper/pipeline.py
new file mode 100644
index 0000000000000000000000000000000000000000..f00d0504ef119a19bb128d3b7216d1f39a1b6773
--- /dev/null
+++ b/flax/distil_whisper/pipeline.py
@@ -0,0 +1,527 @@
+# coding=utf-8
+# Copyright 2023 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Whisper JAX pipeline compatible with Distil Whisper checkpoints. Copied from https://github.com/sanchit-gandhi/whisper-jax/blob/main/whisper_jax/pipeline.py"""
+
+import math
+
+import jax
+import jax.numpy as jnp
+import numpy as np
+import requests
+import torch
+from flax import jax_utils
+from flax.core.frozen_dict import freeze
+from flax.training.common_utils import shard
+from transformers import WhisperFeatureExtractor, WhisperTokenizerFast
+from transformers.models.whisper.tokenization_whisper import TO_LANGUAGE_CODE
+from transformers.pipelines.audio_utils import ffmpeg_read
+from transformers.utils import logging
+
+from .modeling_flax_whisper import FlaxWhisperForConditionalGeneration
+
+
+logger = logging.get_logger(__name__)
+
+
+class FlaxWhisperFeatureExtractor(WhisperFeatureExtractor):
+    def _np_extract_fbank_features(self, waveform: np.array) -> np.ndarray:
+        """
+        Compute the log-mel spectrogram of the provided audio using torch filters. Using the torch implementation
+        computes stft filter banks approx 5x faster than its numpy counterpart, which is the native implementation
+        in transformers, and matches to within 1e-5 abs tolerance.
+        """
+        waveform = torch.from_numpy(waveform).type(torch.float32)
+
+        window = torch.hann_window(self.n_fft)
+        stft = torch.stft(waveform, self.n_fft, self.hop_length, window=window, return_complex=True)
+        magnitudes = stft[..., :-1].abs() ** 2
+
+        mel_filters = torch.from_numpy(self.mel_filters).type(torch.float32)
+        mel_spec = mel_filters.T @ magnitudes
+
+        log_spec = torch.clamp(mel_spec, min=1e-10).log10()
+        log_spec = torch.maximum(log_spec, log_spec.max() - 8.0)
+        log_spec = (log_spec + 4.0) / 4.0
+        return log_spec.numpy()
+
+
+class FlaxWhisperPipeline:
+    def __init__(
+        self,
+        checkpoint="openai/whisper-large-v2",
+        dtype=jnp.float32,
+        batch_size=None,
+        max_length=None,
+        **kwargs,
+    ):
+        """
+        Args
+            checkpoint (`str`, *optional*, defaults to `"openai/whisper-large-v2"):
+                The Whisper checkpoint to use with the pipeline. Must be an available checkpoint on the Hugging Face Hub
+                with Flax weights.
+            dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`):
+                The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and
+                `jax.numpy.bfloat16` (on TPUs). This can be used to enable half-precision inference on GPUs or TPUs.
+                If specified all the computation will be performed with the given `dtype`. **Note that this only
+                specifies the dtype of the computation and does not influence the dtype of model parameters.**
+            batch_size (`int`, *optional*, defaults to the minimum per-device batch size, i.e. `jax.local_device_count()`):
+                The batch size to be used in chunking transcription. Beneficial for transcribing long audio files. Passing
+                a batch size in the `__init__` method will be superseded by any batch size passed to the `__call__` method.
+            max_length (`int`, *optional*):
+                The maximum numbers of tokens to generate. Defaults to `model.config.max_length`.
+        """
+        self.checkpoint = checkpoint
+        self.dtype = dtype
+
+        self.feature_extractor = FlaxWhisperFeatureExtractor.from_pretrained(self.checkpoint)
+        self.tokenizer = WhisperTokenizerFast.from_pretrained(self.checkpoint)
+
+        self.model, self.params = FlaxWhisperForConditionalGeneration.from_pretrained(
+            self.checkpoint,
+            _do_init=False,
+            dtype=self.dtype,
+            **kwargs,
+        )
+
+        self.max_length = max_length if max_length is not None else self.model.generation_config.max_length
+        self.min_batch_size = jax.local_device_count()
+        self.batch_size = (
+            batch_size if batch_size is not None else self.min_batch_size
+        )  # we need a minimum of 1 batch per-device
+
+        def generate(
+            params,
+            input_features,
+            forced_decoder_ids,
+            return_timestamps,
+            num_beams,
+            length_penalty,
+            do_sample,
+            top_k,
+            temperature,
+        ):
+            output_ids = self.model.pipeline_generate(
+                input_features,
+                params=params,
+                forced_decoder_ids=forced_decoder_ids,
+                return_timestamps=return_timestamps,
+                max_length=self.max_length,
+                num_beams=num_beams,
+                length_penalty=length_penalty,
+                do_sample=do_sample,
+                top_k=top_k,
+                temperature=temperature,
+            )
+            return output_ids
+
+        self.params = jax_utils.replicate(self.params)
+        self.p_generate = jax.pmap(
+            generate,
+            "input_features",
+            in_axes=(0, 0, None, None, None, None, None, None, None),
+            static_broadcasted_argnums=(
+                3,
+                4,
+                5,
+                6,
+                7,
+                8,
+            ),
+        )
+
+    def generate(
+        self,
+        input_features,
+        language=None,
+        task=None,
+        return_timestamps=False,
+        num_beams=1,
+        length_penalty=1.0,
+        do_sample=False,
+        top_k=50,
+        temperature=1.0,
+    ):
+        forced_decoder_ids = self.get_forced_decoder_ids(
+            language=language, task=task, return_timestamps=return_timestamps
+        )
+        # if we're using pmap we need to manually replicate the input data across devices and gather the output tokens
+        output_ids = self.p_generate(
+            freeze(self.params),
+            shard(input_features),
+            forced_decoder_ids,
+            return_timestamps,
+            num_beams,
+            length_penalty,
+            do_sample,
+            top_k,
+            temperature,
+        ).sequences
+        output_ids = jax.device_get(output_ids.reshape(-1, self.max_length))
+        return output_ids
+
+    def get_forced_decoder_ids(self, generation_config=None, task=None, language=None, return_timestamps=False):
+        if generation_config is None:
+            generation_config = self.model.generation_config
+
+        if hasattr(generation_config, "is_multilingual"):
+            is_multilingual = generation_config.is_multilingual
+        else:
+            is_multilingual = None
+
+        forced_decoder_ids = []
+
+        if is_multilingual:
+            if language is not None:
+                language = language.lower()
+                if language in generation_config.lang_to_id.keys():
+                    language_token = language
+                elif language in TO_LANGUAGE_CODE.values():
+                    language_token = f"<|{language}|>"
+                elif language in TO_LANGUAGE_CODE.keys():
+                    language_token = f"<|{TO_LANGUAGE_CODE[language]}|>"
+                else:
+                    if len(language) == 2:
+                        # ISO 639-1 language code
+                        acceptable_languages = list(TO_LANGUAGE_CODE.values())
+                    elif "<" in language or "|" in language or ">" in language:
+                        # generation config language code
+                        acceptable_languages = list(generation_config.lang_to_id.keys())
+                    else:
+                        # language passed as a string
+                        acceptable_languages = list(TO_LANGUAGE_CODE.keys())
+                    raise ValueError(
+                        f"Unsupported language: {language}. Language should be one of:" f" {acceptable_languages}."
+                    )
+                forced_decoder_ids.append((1, generation_config.lang_to_id[language_token]))
+
+            if task is not None:
+                forced_decoder_ids.append((2, generation_config.task_to_id[task]))
+            else:
+                forced_decoder_ids.append((2, generation_config.task_to_id["transcribe"]))
+
+        if not return_timestamps:
+            if forced_decoder_ids and forced_decoder_ids[-1][0] != generation_config.no_timestamps_token_id:
+                idx = forced_decoder_ids[-1][0] + 1 if forced_decoder_ids else 1
+                forced_decoder_ids.append((idx, generation_config.no_timestamps_token_id))
+            else:
+                forced_decoder_ids.append((1, generation_config.no_timestamps_token_id))
+
+        return forced_decoder_ids
+
+    def chunk_iter_with_batch(self, inputs, chunk_len, stride_left, stride_right, batch_size):
+        inputs_len = inputs.shape[0]
+        step = chunk_len - stride_left - stride_right
+
+        all_chunk_start_idx = np.arange(0, inputs_len, step)
+        num_samples = len(all_chunk_start_idx)
+
+        num_batches = math.ceil(num_samples / batch_size)
+        batch_idx = np.array_split(np.arange(num_samples), num_batches)
+
+        for idx in batch_idx:
+            chunk_start_idx = all_chunk_start_idx[idx]
+
+            chunk_end_idx = chunk_start_idx + chunk_len
+
+            chunks = [inputs[chunk_start:chunk_end] for chunk_start, chunk_end in zip(chunk_start_idx, chunk_end_idx)]
+            processed = self.feature_extractor(
+                chunks, sampling_rate=self.feature_extractor.sampling_rate, return_tensors="np"
+            )
+
+            _stride_left = np.where(chunk_start_idx == 0, 0, stride_left)
+            is_last = np.where(stride_right > 0, chunk_end_idx > inputs_len, chunk_end_idx >= inputs_len)
+            _stride_right = np.where(is_last, 0, stride_right)
+
+            chunk_lens = [chunk.shape[0] for chunk in chunks]
+            strides = [
+                (chunk_l, _stride_l, _stride_r)
+                for chunk_l, _stride_l, _stride_r in zip(chunk_lens, _stride_left, _stride_right)
+            ]
+
+            yield {"stride": strides, **processed}
+
+    def preprocess_batch(self, inputs, chunk_length_s=30.0, stride_length_s=None, batch_size=None):
+        if isinstance(inputs, np.ndarray):
+            logger.warning(
+                "Numpy array passed as input - no sampling rate checks will be performed."
+                "It is strongly recommended to pass the input as a dictionary with an 'array' key "
+                "containing the numpy array representing the audio, and a 'sampling_rate' key "
+                "containing the sampling rate associated with the audio array."
+                "Failing to do so can result in silent errors that might be hard to debug."
+            )
+
+        if isinstance(inputs, str):
+            if inputs.startswith("http://") or inputs.startswith("https://"):
+                # We need to actually check for a real protocol, otherwise it's impossible to use a local file
+                # like http_huggingface_co.png
+                inputs = requests.get(inputs).content
+            else:
+                with open(inputs, "rb") as f:
+                    inputs = f.read()
+
+        if isinstance(inputs, bytes):
+            inputs = ffmpeg_read(inputs, self.feature_extractor.sampling_rate)
+
+        stride = None
+        if isinstance(inputs, dict):
+            stride = inputs.get("stride", None)
+            # Accepting `"array"` which is the key defined in `datasets` for
+            # better integration
+            if not ("sampling_rate" in inputs and "array" in inputs):
+                raise ValueError(
+                    "When passing a dictionary to FlaxWhisperPipline, the dict needs to contain an 'array' key "
+                    "containing the numpy array representing the audio, and a 'sampling_rate' key "
+                    "containing the sampling rate associated with the audio array."
+                )
+
+            in_sampling_rate = inputs.get("sampling_rate")
+            inputs = inputs.get("array", None)
+
+            if in_sampling_rate != self.feature_extractor.sampling_rate:
+                try:
+                    import librosa
+                except ImportError as err:
+                    raise ImportError(
+                        "To support resampling audio files, please install 'librosa' and 'soundfile'."
+                    ) from err
+
+                inputs = librosa.resample(
+                    inputs, orig_sr=in_sampling_rate, target_sr=self.feature_extractor.sampling_rate
+                )
+                ratio = self.feature_extractor.sampling_rate / in_sampling_rate
+            else:
+                ratio = 1
+
+        if not isinstance(inputs, np.ndarray):
+            raise ValueError(f"We expect a numpy ndarray as input, got `{type(inputs)}`")
+        if len(inputs.shape) != 1:
+            raise ValueError("We expect a single channel audio input for AutomaticSpeechRecognitionPipeline")
+
+        if stride is not None:
+            if stride[0] + stride[1] > inputs.shape[0]:
+                raise ValueError("Stride is too large for input")
+
+            # Stride needs to get the chunk length here, it's going to get
+            # swallowed by the `feature_extractor` later, and then batching
+            # can add extra data in the inputs, so we need to keep track
+            # of the original length in the stride so we can cut properly.
+            stride = (inputs.shape[0], int(round(stride[0] * ratio)), int(round(stride[1] * ratio)))
+
+        if chunk_length_s:
+            if stride_length_s is None:
+                stride_length_s = chunk_length_s / 6
+
+            if isinstance(stride_length_s, (int, float)):
+                stride_length_s = [stride_length_s, stride_length_s]
+
+            chunk_len = round(chunk_length_s * self.feature_extractor.sampling_rate)
+            stride_left = round(stride_length_s[0] * self.feature_extractor.sampling_rate)
+            stride_right = round(stride_length_s[1] * self.feature_extractor.sampling_rate)
+
+            if chunk_len < stride_left + stride_right:
+                raise ValueError("Chunk length must be superior to stride length")
+
+            for item in self.chunk_iter_with_batch(
+                inputs,
+                chunk_len,
+                stride_left,
+                stride_right,
+                batch_size,
+            ):
+                yield item
+        else:
+            processed = self.feature_extractor(
+                inputs, sampling_rate=self.feature_extractor.sampling_rate, return_tensors="np"
+            )
+            if stride is not None:
+                processed["stride"] = stride
+            yield processed
+
+    def postprocess(self, model_outputs, return_timestamps=None, return_language=None):
+        # unpack the outputs from list(dict(list)) to list(dict)
+        model_outputs = [dict(zip(output, t)) for output in model_outputs for t in zip(*output.values())]
+
+        time_precision = self.feature_extractor.chunk_length / self.model.config.max_source_positions
+        # Send the chunking back to seconds, it's easier to handle in whisper
+        sampling_rate = self.feature_extractor.sampling_rate
+        for output in model_outputs:
+            if "stride" in output:
+                chunk_len, stride_left, stride_right = output["stride"]
+                # Go back in seconds
+                chunk_len /= sampling_rate
+                stride_left /= sampling_rate
+                stride_right /= sampling_rate
+                output["stride"] = chunk_len, stride_left, stride_right
+
+        text, optional = self.tokenizer._decode_asr(
+            model_outputs,
+            return_timestamps=return_timestamps,
+            return_language=return_language,
+            time_precision=time_precision,
+        )
+        return {"text": text, **optional}
+
+    def forward(
+        self,
+        model_inputs,
+        batch_size=None,
+        language=None,
+        task=None,
+        return_timestamps=False,
+        num_beams=1,
+        length_penalty=1.0,
+        do_sample=False,
+        top_k=50,
+        temperature=1.0,
+    ):
+        # We need to keep track of some additional input arguments for post-processing so need to forward these on after running generation
+        input_features = model_inputs.pop("input_features")
+        input_batch_size = input_features.shape[0]
+
+        if input_batch_size != batch_size:
+            padding = np.zeros([batch_size - input_batch_size, *input_features.shape[1:]], input_features.dtype)
+            input_features = np.concatenate([input_features, padding])
+
+        pred_ids = self.generate(
+            input_features,
+            language=language,
+            task=task,
+            return_timestamps=return_timestamps,
+            num_beams=num_beams,
+            length_penalty=length_penalty,
+            do_sample=do_sample,
+            top_k=top_k,
+            temperature=temperature,
+        )[:input_batch_size]
+
+        # tokenizer's decode method expects an extra dim - we insert it here for convenience
+        out = {"tokens": pred_ids[:, None, :]}
+
+        stride = model_inputs.pop("stride", None)
+        if stride is not None:
+            out["stride"] = stride
+
+        return out
+
+    def __call__(
+        self,
+        inputs,
+        chunk_length_s=30.0,
+        stride_length_s=None,
+        batch_size=None,
+        language=None,
+        task=None,
+        return_timestamps=None,
+        num_beams=1,
+        length_penalty=1.0,
+        do_sample=False,
+        top_k=50,
+        temperature=1.0,
+    ):
+        """
+        Transcribe an audio input sequence to a text transcription, optionally with timestamps.
+
+        Args:
+            inputs (`np.ndarray` or `bytes` or `str` or `dict`):
+                The inputs is either:
+                    - `str` that is the filename of the audio file, the file will be read at the correct sampling rate
+                      to get the waveform using *ffmpeg*. This requires *ffmpeg* to be installed on the system.
+                    - `bytes` is the byte content of an audio file and is interpreted by *ffmpeg* in the
+                      same way.
+                    - (`np.ndarray` of shape (n, ) of type `np.float32` or `np.float64`)
+                        Raw audio assumed to be at the correct sampling rate (16kHz). Note that no further sampling
+                        rate check will be done.
+                    - `dict` form can be used to pass raw audio sampled at arbitrary `sampling_rate` and let this
+                      pipeline do the resampling. The dict must be in the format `{"sampling_rate": int, "array":
+                      np.array}`. Optionally an additional argument `"stride": (left: int, right: int)` can be used to
+                       ask the pipeline to treat the first `left` samples and last `right` samples to be ignored in
+                       decoding (but used at inference to provide more context to the model). In general, this additional
+                       stride argument is not required.
+            chunk_length_s (`float`, *optional*, defaults to 30.0):
+                The input length for each chunk. If `chunk_length_s = 0` then chunking is disabled. By default, the chunk
+                length is set 30.0s, equal to Whisper's context window.
+            stride_length_s (`float`, *optional*, defaults to `chunk_length_s / 6`):
+                The length of stride on the left and right of each chunk. Used only with `chunk_length_s > 0`. This enables
+                the model to *see* more context and infer letters better than without this context but the pipeline
+                discards the stride bits at the end to make the final reconstitution as perfect as possible.
+
+                <Tip>
+
+                For more information on how to effectively use `stride_length_s`, refer to the [ASR chunking
+                blog post](https://huggingface.co/blog/asr-chunking).
+
+                </Tip>
+            batch_size (`int`, *optional*, defaults to the minimum per-device batch size, i.e. `jax.local_device_count()`):
+                The batch size to be used in chunking transcription. Beneficial for transcribing long audio files. Passing
+                a batch size in the `__call__` method will supersede any batch size passed to the `__init__`.
+            task (`str`, *optional*):
+                Task to use for generation, either `"transcribe"` or `"translate"`. Defaults to `"transcribe"`.
+            language (`str`, *optional*):
+                Language token to use for generation, can be either in the form of `"<|en|>"`, `"en"` or `"english"`.
+                Defaults to `None`, meaning the language is automatically inferred from the audio input.
+            return_timestamps (*optional*, `bool`):
+                Whether to return timestamps in the prediction. Defaults to False. If set to true, the pipeline
+                will return two keys in the output dictionary: `"text"` containing the text transcription, and `"chunks"`
+                containing the transcription segments chunked by their utterance-level timestamps.
+            length_penalty (*optional*, `float`):
+                Exponential penalty to the length that is used with beam-based generation. It is applied as an
+                exponent to the sequence length, which in turn is used to divide the score of the sequence. Since
+                the score is the log likelihood of the sequence (i.e. negative), length_penalty > 1.0 promotes
+                longer sequences, while length_penalty < 1.0 encourages shorter sequences.
+            do_sample (*optional*, `bool`):
+                Whether or not to use sampling ; use greedy decoding otherwise.
+            top_k (*optional*, `int`):
+                The number of the highest probability vocabulary tokens to keep for top-k-filtering.
+            temperature (*optional*, `float`):
+                The value used to modulate the next token probabilities if sampling.
+
+        Return:
+            `Dict`: A dictionary with the following keys:
+                - **text** (`str` ) -- The recognised text.
+                - **chunks** (*optional(, `List[Dict]`)
+                    When using `return_timestamps`, the `chunks` will become a list containing all the various text
+                    chunks identified by the model, *e.g.* `[{"text": "hi ", "timestamps": (0.5,0.9), {"text":
+                    "there", "timestamps": (1.0, 1.5)}]`. The original full text can roughly be recovered by doing
+                    `"".join(chunk["text"] for chunk in output["chunks"])`.
+        """
+        batch_size = batch_size if batch_size is not None else self.batch_size
+        if batch_size % self.min_batch_size != 0:
+            raise ValueError(
+                f"Batch size must be a multiple of the number of JAX devices, but got batch size {batch_size} and num devices {self.min_batch_size}."
+            )
+
+        dataloader = self.preprocess_batch(
+            inputs, chunk_length_s=chunk_length_s, stride_length_s=stride_length_s, batch_size=batch_size
+        )
+        model_outputs = []
+        # iterate over our chunked audio samples
+        for batch in dataloader:
+            model_outputs.append(
+                self.forward(
+                    batch,
+                    batch_size=batch_size,
+                    language=language,
+                    task=task,
+                    return_timestamps=return_timestamps,
+                    num_beams=num_beams,
+                    length_penalty=length_penalty,
+                    do_sample=do_sample,
+                    top_k=top_k,
+                    temperature=temperature,
+                )
+            )
+        post_processed = self.postprocess(model_outputs, return_timestamps=return_timestamps)
+        return post_processed
diff --git a/flax/distil_whisper/train_state.py b/flax/distil_whisper/train_state.py
new file mode 100644
index 0000000000000000000000000000000000000000..a049933681bf247538ec61e12d469352201bc4cc
--- /dev/null
+++ b/flax/distil_whisper/train_state.py
@@ -0,0 +1,118 @@
+from typing import Any, Mapping, MutableMapping, Optional, Tuple
+
+import flax.core
+import flax.serialization
+import flax.struct
+import jax.numpy as jnp
+from flax import traverse_util
+from flax.core import scope as flax_scope
+from flax.linen import partitioning as flax_partitioning
+
+
+EMPTY_DICT = flax.core.freeze({})
+FrozenDict = flax_scope.FrozenDict
+FrozenVariableDict = flax_scope.FrozenVariableDict
+MutableVariableDict = flax_scope.MutableVariableDict
+VariableDict = flax_scope.VariableDict
+
+
+def _validate_params_axes(params_axes, params):
+    axis_names = flax_partitioning.get_axis_names(params_axes)
+    missing_params_axes = set(traverse_util.flatten_dict(params, sep="/")) - set(
+        traverse_util.flatten_dict(axis_names, sep="/")
+    )
+    if missing_params_axes:
+        raise ValueError(f"Missing axis names for parameters: {missing_params_axes}")
+
+
+def _split_variables_and_axes(
+    variables_and_axes: FrozenVariableDict,
+) -> Tuple[FrozenVariableDict, FrozenVariableDict]:
+    """Splits `variables_and_axes` into two separate dicts with the same keys."""
+    # For each `key`, `key_axes` (if any) are its axes in `variables_and_axes`.
+    variables = {}
+    axes = {}
+    for k, v in variables_and_axes.items():
+        if k.endswith("_axes"):
+            axes[k[:-5]] = v  # k without "_axes".
+            _validate_params_axes(v, variables_and_axes[k[:-5]])  # k without "_axes".
+        else:
+            variables[k] = v
+    return flax.core.freeze(variables), flax.core.freeze(axes)
+
+
+class InferenceState(flax.struct.PyTreeNode):
+    """State compatible with FlaxOptimTrainState without optimizer state."""
+
+    step: jnp.ndarray
+    params: flax_scope.FrozenVariableDict
+    params_axes: Optional[flax_scope.FrozenVariableDict] = None
+    flax_mutables: flax_scope.FrozenDict = EMPTY_DICT
+    flax_mutables_axes: Optional[flax_scope.FrozenVariableDict] = None
+
+    @classmethod
+    def create(cls, model_variables: FrozenVariableDict) -> "InferenceState":
+        other_variables, params = model_variables.pop("params")
+        if "params_axes" in other_variables:
+            other_variables, params_axes = other_variables.pop("params_axes")
+            _validate_params_axes(params_axes, params)
+        else:
+            params_axes = None
+
+        # Split other_variables into mutables and their corresponding axes.
+        flax_mutables, flax_mutables_axes = _split_variables_and_axes(other_variables)
+        flax_mutables_axes = flax_mutables_axes or None
+        return InferenceState(
+            step=jnp.array(0),
+            params=params,
+            params_axes=params_axes,
+            flax_mutables=flax_mutables,
+            flax_mutables_axes=flax_mutables_axes,
+        )
+
+    @property
+    def param_states(self) -> FrozenVariableDict:
+        """The optimizer states of the parameters as a PyTree."""
+        raise NotImplementedError("InferenceState has no optimizer states.")
+
+    def apply_gradient(self, *args, **kwargs) -> "InferenceState":
+        raise NotImplementedError("InferenceState does not support `apply_gradient`.")
+
+    def state_dict(self) -> MutableMapping[str, Any]:
+        state_dict = {
+            "target": flax.core.unfreeze(self.params),
+            "state": {"step": self.step},
+        }
+        if self.flax_mutables:
+            state_dict["flax_mutables"] = flax.core.unfreeze(self.flax_mutables)
+        return state_dict
+
+    def replace_step(self, step: jnp.ndarray) -> "InferenceState":
+        return self.replace(step=step)
+
+    def replace_params(self, params: FrozenVariableDict) -> "InferenceState":
+        return self.replace(params=params)
+
+    def replace_flax_mutables(self, flax_mutables: FrozenDict) -> "InferenceState":
+        return self.replace(flax_mutables=flax_mutables)
+
+    def restore_state(self, state_dict: Mapping[str, Any]) -> "InferenceState":
+        return self.replace(
+            params=flax.core.freeze(state_dict["target"]),
+            step=state_dict["state"]["step"],
+            flax_mutables=(
+                flax.core.freeze(state_dict["flax_mutables"]) if "flax_mutables" in state_dict else EMPTY_DICT
+            ),
+        )
+
+    def as_logical_axes(self) -> "InferenceState":
+        # Set step to None so that when the logical axes are processed by the
+        # flax.partitioning.logical_to_mesh_axes function, it will be skipped
+        # because jax.tree_map will short circut and never call the function on the
+        # step.
+        flax_mutables_axes = self.flax_mutables_axes or EMPTY_DICT
+        return InferenceState(
+            step=None,
+            params=flax_partitioning.get_axis_names(self.params_axes),
+            flax_mutables=flax_partitioning.get_axis_names(flax_mutables_axes),
+        )
diff --git a/flax/distillation_scripts/run_32_2_pt.sh b/flax/distillation_scripts/run_32_2_pt.sh
new file mode 100644
index 0000000000000000000000000000000000000000..1e93d75afe65a83c7d2ea5d2cd580704e61f71cf
--- /dev/null
+++ b/flax/distillation_scripts/run_32_2_pt.sh
@@ -0,0 +1,38 @@
+#!/bin/bash
+
+accelerate launch --multi_gpu --mixed_precision=bf16 --num_processes=2 run_distillation_pt.py \
+    --model_name_or_path distil-whisper/large-32-2 \
+    --teacher_model_name_or_path openai/whisper-large-v2 \
+    --train_dataset_config_name all+all+all+l \
+    --train_dataset_samples 2.9+10.4+14.9+226.6 \
+    --train_dataset_name librispeech_asr+librispeech_asr+librispeech_asr+gigaspeech-l \
+    --train_split_name train.clean.100+train.clean.360+train.other.500+train \
+    --eval_dataset_name librispeech_asr+librispeech_asr+gigaspeech-l \
+    --eval_dataset_config_name all+all+l \
+    --eval_split_name validation.clean+validation.other+validation \
+    --eval_text_column_name text+text+text \
+    --eval_steps 2500 \
+    --save_steps 2500 \
+    --warmup_steps 50 \
+    --learning_rate 0.0001 \
+    --lr_scheduler_type constant_with_warmup \
+    --logging_steps 25 \
+    --save_total_limit 1 \
+    --max_steps 10000 \
+    --wer_threshold 10 \
+    --per_device_train_batch_size 64 \
+    --gradient_accumulation_steps 2 \
+    --per_device_eval_batch_size 64 \
+    --dataloader_num_workers 16 \
+    --cache_dir /fsx/sanchit/cache \
+    --dataset_cache_dir /fsx/sanchit/cache \
+    --dtype bfloat16 \
+    --output_dir ./ \
+    --wandb_project distil-whisper-training \
+    --do_train \
+    --do_eval \
+    --gradient_checkpointing \
+    --overwrite_output_dir \
+    --predict_with_generate \
+    --freeze_encoder \
+    --streaming
diff --git a/flax/distillation_scripts/run_bs_sweep.yaml b/flax/distillation_scripts/run_bs_sweep.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..3c42f6fa91a49486c0984ff414731a06e2b4bc97
--- /dev/null
+++ b/flax/distillation_scripts/run_bs_sweep.yaml
@@ -0,0 +1,67 @@
+command:
+  - python3
+  - ${program}
+  - --do_train
+  - --use_scan
+  - --gradient_checkpointing
+  - --overwrite_output_dir
+  - --predict_with_generate
+  - --freeze_encoder
+  - --streaming
+  - --use_auth_token
+  - --compilation_cache
+  - ${args}
+method: grid
+metric:
+  goal: minimize
+  name: train/loss
+parameters:
+  model_name_or_path:
+    value: distil-whisper/large-32-2
+  teacher_model_name_or_path:
+    value: openai/whisper-large-v2
+  train_dataset_name:
+    value: librispeech_asr
+  train_dataset_config_name:
+    value: all
+  train_split_name:
+    value: train.other.500
+  train_dataset_samples:
+    value: 100
+  cache_dir:
+    value: /fsx/sanchitgandhi/cache
+  dataset_cache_dir:
+    value: /fsx/sanchitgandhi/cache
+  output_dir:
+    value: ./
+  per_device_train_batch_size:
+    values:
+      - 128
+      - 256
+      - 512
+  precision:
+    values:
+      - "full_mixed"
+      - "half_mixed"
+  dtype:
+    value: bfloat16
+  do_eval:
+    value: false
+  learning_rate:
+    value: 3e-4
+  lr_scheduler_type:
+    value: constant_with_warmup
+  warmup_steps:
+    value: 30
+  max_steps:
+    value: 30
+  save_steps:
+    value: 51  # don't save checkpoints during sweep
+  dataloader_num_workers:
+    value: 48
+  logging_steps:
+    value: 5
+  wer_threshold:
+    value: 100
+program: run_distillation.py
+project: distil-whisper-sweeps
diff --git a/flax/distillation_scripts/run_dataset_sweep.yaml b/flax/distillation_scripts/run_dataset_sweep.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..6d10f562da275b3bf75310926eafe804aeafa597
--- /dev/null
+++ b/flax/distillation_scripts/run_dataset_sweep.yaml
@@ -0,0 +1,77 @@
+command:
+  - python3
+  - ${program}
+  - --do_train
+  - --do_eval
+  - --use_scan
+  - --gradient_checkpointing
+  - --overwrite_output_dir
+  - --predict_with_generate
+  - --freeze_encoder
+  - --streaming
+  - --use_auth_token
+  - ${args}
+method: grid
+metric:
+  goal: minimize
+  name: gigaspeech-l/validation/wer
+parameters:
+  model_name_or_path:
+    value: distil-whisper/large-32-2
+  teacher_model_name_or_path:
+    value: openai/whisper-large-v2
+  max_train_samples:
+    values:
+      - 109876
+      - 219752
+      - 439504
+      - 879008
+      - 1758015
+      - 3516030
+      - 7032061
+  train_dataset_name:
+    value: librispeech_asr-timestamped+librispeech_asr-timestamped+librispeech_asr-timestamped+common_voice_13_0-timestamped+voxpopuli-timestamped+ami-ihm-timestamped+ami-sdm-timestamped+peoples_speech-clean-timestamped+tedlium-timestamped+switchboard-data+gigaspeech-l-timestamped+librispeech_asr-prompted+librispeech_asr-prompted+librispeech_asr-prompted+tedlium-prompted
+  train_dataset_config_name:
+    value: all+all+all+en+en+ihm+sdm+clean+release3+all+l+all+all+all+release3
+  train_split_name:
+    value: train.clean.100+train.clean.360+train.other.500+train+train+train+train+train+train+train+train+train.clean.100+train.clean.360+train.other.500+train
+  train_dataset_samples:
+    value: 2.9+10.4+14.9+89+18.2+10.9+10.9+288+26.8+371.2+226.6+2.9+10.4+14.9+26.8
+  eval_dataset_name:
+    value: librispeech_asr+librispeech_asr+common_voice_13_0+voxpopuli+ami-ihm+ami-sdm+peoples_speech-clean+tedlium+switchboard-data+gigaspeech-l+spgispeech+chime4+google/fleurs
+  eval_dataset_config_name:
+    value: all+all+en+en+ihm+sdm+clean+release3+all+l+L+1-channel+en_us
+  eval_split_name:
+    value: validation.clean+validation.other+validation+validation+validation+validation+validation+validation+validation+validation+validation+validation+validation
+  eval_text_column_name:
+    value: text+text+text+text+text+text+text+text+text+text+text+text+transcription
+  cache_dir:
+    value: /home/sanchitgandhi/.cache
+  dataset_cache_dir:
+    value: /home/sanchitgandhi/.cache
+  output_dir:
+    value: ./
+  per_device_train_batch_size:
+    value: 64
+  per_device_eval_batch_size:
+    value: 64
+  dtype:
+    value: bfloat16
+  learning_rate:
+    value: 1e-4
+  lr_scheduler_type:
+    value: constant_with_warmup
+  warmup_steps:
+    value: 50
+  max_steps:
+    value: 10000
+  save_steps:
+    value: 10001  # don't save checkpoints during sweep
+  dataloader_num_workers:
+    value: 48
+  logging_steps:
+    value: 25
+  wer_threshold:
+    value: 10
+program: run_distillation.py
+project: distil-whisper-sweeps
diff --git a/flax/distillation_scripts/run_decoder_sweep.yaml b/flax/distillation_scripts/run_decoder_sweep.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..4fd916676456ced2707fa12be5209e8235fff5c5
--- /dev/null
+++ b/flax/distillation_scripts/run_decoder_sweep.yaml
@@ -0,0 +1,72 @@
+command:
+  - python3
+  - ${program}
+  - --do_train
+  - --do_eval
+  - --use_scan
+  - --gradient_checkpointing
+  - --overwrite_output_dir
+  - --predict_with_generate
+  - --freeze_encoder
+  - --streaming
+  - --use_auth_token
+  - ${args}
+method: grid
+metric:
+  goal: minimize
+  name: gigaspeech-l/validation/wer
+parameters:
+  model_name_or_path:
+    values:
+     - distil-whisper/large-32-16
+     - distil-whisper/large-32-8
+     - distil-whisper/large-32-4
+     - distil-whisper/large-32-2
+  teacher_model_name_or_path:
+    value: openai/whisper-large-v2
+  train_dataset_name:
+    value: librispeech_asr-timestamped+librispeech_asr-timestamped+librispeech_asr-timestamped+common_voice_13_0-timestamped+voxpopuli-timestamped+ami-ihm-timestamped+ami-sdm-timestamped+peoples_speech-clean-timestamped+tedlium-timestamped+switchboard-data+gigaspeech-l-timestamped+librispeech_asr-prompted+librispeech_asr-prompted+librispeech_asr-prompted+tedlium-prompted
+  train_dataset_config_name:
+    value: all+all+all+en+en+ihm+sdm+clean+release3+all+l+all+all+all+release3
+  train_split_name:
+    value: train.clean.100+train.clean.360+train.other.500+train+train+train+train+train+train+train+train+train.clean.100+train.clean.360+train.other.500+train
+  train_dataset_samples:
+    value: 2.9+10.4+14.9+89+18.2+10.9+10.9+288+26.8+371.2+226.6+2.9+10.4+14.9+26.8
+  eval_dataset_name:
+    value: librispeech_asr+librispeech_asr+common_voice_13_0+voxpopuli+ami-ihm+ami-sdm+peoples_speech-clean+tedlium+switchboard-data+gigaspeech-l+spgispeech+chime4+google/fleurs
+  eval_dataset_config_name:
+    value: all+all+en+en+ihm+sdm+clean+release3+all+l+L+1-channel+en_us
+  eval_split_name:
+    value: validation.clean+validation.other+validation+validation+validation+validation+validation+validation+validation+validation+validation+validation+validation
+  eval_text_column_name:
+    value: text+text+text+text+text+text+text+text+text+text+text+text+transcription
+  cache_dir:
+    value: /home/sanchitgandhi/.cache
+  dataset_cache_dir:
+    value: /home/sanchitgandhi/.cache
+  output_dir:
+    value: ./
+  per_device_train_batch_size:
+    value: 64
+  per_device_eval_batch_size:
+    value: 64
+  dtype:
+    value: bfloat16
+  learning_rate:
+    value: 1e-4
+  lr_scheduler_type:
+    value: constant_with_warmup
+  warmup_steps:
+    value: 50
+  max_steps:
+    value: 10000
+  save_steps:
+    value: 10001  # don't save checkpoints during sweep
+  dataloader_num_workers:
+    value: 48
+  logging_steps:
+    value: 25
+  wer_threshold:
+    value: 10
+program: run_distillation.py
+project: distil-whisper-sweeps
diff --git a/flax/distillation_scripts/run_distillation_12_2_timestamped.sh b/flax/distillation_scripts/run_distillation_12_2_timestamped.sh
new file mode 100644
index 0000000000000000000000000000000000000000..6c86711dee002b6566bfa593c0ee30e85d480260
--- /dev/null
+++ b/flax/distillation_scripts/run_distillation_12_2_timestamped.sh
@@ -0,0 +1,42 @@
+#!/usr/bin/env bash
+
+TCMALLOC_LARGE_ALLOC_REPORT_THRESHOLD=10000000000 python3 run_distillation.py \
+  --model_name_or_path "distil-whisper/small-12-2" \
+  --teacher_model_name_or_path "openai/whisper-medium.en" \
+  --train_dataset_config_name "all+all+all+en+en+ihm+sdm+clean+release3+all+l+all+all+all+release3" \
+  --train_dataset_samples "2.9+10.4+14.9+89+18.2+10.9+10.9+288+26.8+371.2+226.6+2.9+10.4+14.9+26.8" \
+  --train_dataset_name "librispeech_asr-timestamped+librispeech_asr-timestamped+librispeech_asr-timestamped+common_voice_13_0-timestamped+voxpopuli-timestamped+ami-ihm-timestamped+ami-sdm-timestamped+peoples_speech-clean-timestamped+tedlium-timestamped+switchboard-data+gigaspeech-l-timestamped+librispeech_asr-prompted+librispeech_asr-prompted+librispeech_asr-prompted+tedlium-prompted" \
+  --train_split_name "train.clean.100+train.clean.360+train.other.500+train+train+train+train+train+train+train+train+train.clean.100+train.clean.360+train.other.500+train" \
+  --eval_dataset_name "distil-whisper/gigaspeech-l+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset" \
+  --eval_dataset_config_name "l+librispeech+librispeech+common_voice+common_voice+voxpopuli+voxpopuli+tedlium+tedlium+spgispeech+spgispeech+ami+ami" \
+  --eval_split_name "validation+clean+other+clean+other+clean+other+clean+other+clean+other+clean+other" \
+  --eval_text_column_name "text+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript" \
+  --eval_steps 5000 \
+  --save_steps 5000 \
+  --warmup_steps 500 \
+  --learning_rate 0.0001 \
+  --logging_steps 25 \
+  --save_total_limit 1 \
+  --max_steps 80000 \
+  --wer_threshold 10 \
+  --per_device_train_batch_size 64 \
+  --per_device_eval_batch_size 64 \
+  --dtype "bfloat16" \
+  --dataloader_num_workers 16 \
+  --cache_dir "/home/sanchitgandhi/.cache" \
+  --dataset_cache_dir "/home/sanchitgandhi/.cache" \
+  --output_dir "./" \
+  --timestamp_probability 0.2 \
+  --wandb_name "small-12-2-tpu-timestamped-prob-0.2" \
+  --wandb_dir "/home/sanchitgandhi/.cache" \
+  --wandb_project "distil-whisper" \
+  --do_train \
+  --do_eval \
+  --use_scan \
+  --gradient_checkpointing \
+  --overwrite_output_dir \
+  --predict_with_generate \
+  --freeze_encoder \
+  --streaming \
+  --use_auth_token \
+  --push_to_hub
diff --git a/flax/distillation_scripts/run_distillation_15s_context.sh b/flax/distillation_scripts/run_distillation_15s_context.sh
new file mode 100644
index 0000000000000000000000000000000000000000..91a05e1f076a1bd77808a253a8086798098875d5
--- /dev/null
+++ b/flax/distillation_scripts/run_distillation_15s_context.sh
@@ -0,0 +1,43 @@
+#!/usr/bin/env bash
+
+TCMALLOC_LARGE_ALLOC_REPORT_THRESHOLD=10000000000 python3 run_distillation.py \
+  --model_name_or_path "distil-whisper/large-32-2-15s-context" \
+  --teacher_model_name_or_path "openai/whisper-large-v2" \
+  --feature_extractor_name "openai/whisper-large-v2" \
+  --train_dataset_config_name "all+all+all+en+en+ihm+sdm+clean+release3+all+l+L" \
+  --train_dataset_samples "100+360+500+2300+450+90+90+12000+450+3600+2500+5000" \
+  --train_dataset_name "librispeech_asr+librispeech_asr+librispeech_asr+common_voice_13_0+voxpopuli+ami-ihm+ami-sdm+peoples_speech-clean+tedlium+switchboard-data+gigaspeech-l+spgispeech" \
+  --train_split_name "train.clean.100+train.clean.360+train.other.500+train+train+train+train+train+train+train+train+train" \
+  --eval_dataset_name "distil-whisper/gigaspeech-l+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset" \
+  --eval_dataset_config_name "l+librispeech+librispeech+common_voice+common_voice+voxpopuli+voxpopuli+tedlium+tedlium+spgispeech+spgispeech+ami+ami" \
+  --eval_split_name "validation+clean+other+clean+other+clean+other+clean+other+clean+other+clean+other" \
+  --eval_text_column_name "text+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript" \
+  --eval_steps 5000 \
+  --save_steps 5000 \
+  --warmup_steps 500 \
+  --learning_rate 0.0001 \
+  --lr_scheduler_type "linear" \
+  --logging_steps 25 \
+  --save_total_limit 1 \
+  --max_steps 80000 \
+  --wer_threshold 10 \
+  --per_device_train_batch_size 64 \
+  --per_device_eval_batch_size 64 \
+  --max_duration_in_seconds 15 \
+  --dataloader_num_workers 16 \
+  --cache_dir "/home/sanchitgandhi/.cache" \
+  --dataset_cache_dir "/home/sanchitgandhi/.cache" \
+  --dtype "bfloat16" \
+  --output_dir "./" \
+  --wandb_name "large-32-2-ts-28k-wer-10-context-15s" \
+  --wandb_dir "/home/sanchitgandhi/.cache" \
+  --wandb_project "distil-whisper" \
+  --do_train \
+  --do_eval \
+  --use_scan \
+  --gradient_checkpointing \
+  --overwrite_output_dir \
+  --predict_with_generate \
+  --streaming \
+  --use_auth_token \
+  --push_to_hub
diff --git a/flax/distillation_scripts/run_distillation_16_2.sh b/flax/distillation_scripts/run_distillation_16_2.sh
new file mode 100644
index 0000000000000000000000000000000000000000..2ae8c27a5a46194303bc5b45c075a1e6dbe0c39b
--- /dev/null
+++ b/flax/distillation_scripts/run_distillation_16_2.sh
@@ -0,0 +1,41 @@
+#!/usr/bin/env bash
+
+TCMALLOC_LARGE_ALLOC_REPORT_THRESHOLD=10000000000 python3 run_distillation.py \
+  --model_name_or_path "distil-whisper/large-16-2" \
+  --teacher_model_name_or_path "openai/whisper-large-v2" \
+  --train_dataset_config_name "all+all+all+en+en+ihm+sdm+clean+release3+all+l+L" \
+  --train_dataset_samples "100+360+500+2300+450+90+90+12000+450+3600+2500+5000" \
+  --train_dataset_name "librispeech_asr+librispeech_asr+librispeech_asr+common_voice_13_0+voxpopuli+ami-ihm+ami-sdm+peoples_speech-clean+tedlium+switchboard-data+gigaspeech-l+spgispeech" \
+  --train_split_name "train.clean.100+train.clean.360+train.other.500+train+train+train+train+train+train+train+train+train" \
+  --eval_dataset_name "distil-whisper/gigaspeech-l+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset" \
+  --eval_dataset_config_name "l+librispeech+librispeech+common_voice+common_voice+voxpopuli+voxpopuli+tedlium+tedlium+spgispeech+spgispeech+ami+ami" \
+  --eval_split_name "validation+clean+other+clean+other+clean+other+clean+other+clean+other+clean+other" \
+  --eval_text_column_name "text+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript" \
+  --eval_steps 5000 \
+  --save_steps 5000 \
+  --warmup_steps 500 \
+  --learning_rate 0.0001 \
+  --lr_scheduler_type "linear" \
+  --logging_steps 25 \
+  --save_total_limit 1 \
+  --max_steps 80000 \
+  --wer_threshold 10 \
+  --per_device_eval_batch_size 64 \
+  --per_device_train_batch_size 64 \
+  --dataloader_num_workers 16 \
+  --cache_dir "/home/sanchitgandhi/.cache" \
+  --dataset_cache_dir "/home/sanchitgandhi/.cache" \
+  --dtype "bfloat16" \
+  --output_dir "./" \
+  --wandb_name "large-16-2-ts-28k-wer-10" \
+  --wandb_dir "/home/sanchitgandhi/.cache" \
+  --wandb_project "distil-whisper" \
+  --do_train \
+  --do_eval \
+  --use_scan \
+  --gradient_checkpointing \
+  --overwrite_output_dir \
+  --predict_with_generate \
+  --streaming \
+  --use_auth_token \
+  --push_to_hub
diff --git a/flax/distillation_scripts/run_distillation_24_2.sh b/flax/distillation_scripts/run_distillation_24_2.sh
new file mode 100644
index 0000000000000000000000000000000000000000..cc4661fc7eb17ce56e979ce544a8a34b122a345c
--- /dev/null
+++ b/flax/distillation_scripts/run_distillation_24_2.sh
@@ -0,0 +1,42 @@
+#!/usr/bin/env bash
+
+TCMALLOC_LARGE_ALLOC_REPORT_THRESHOLD=10000000000 python3 run_distillation.py \
+  --model_name_or_path "distil-whisper/medium-24-2" \
+  --teacher_model_name_or_path "openai/whisper-medium.en" \
+  --train_dataset_config_name "all+all+all+en+en+ihm+sdm+clean+release3+all+l+L" \
+  --train_dataset_samples "100+360+500+2300+450+90+90+12000+450+3600+2500+5000" \
+  --train_dataset_name "librispeech_asr+librispeech_asr+librispeech_asr+common_voice_13_0+voxpopuli+ami-ihm+ami-sdm+peoples_speech-clean+tedlium+switchboard-data+gigaspeech-l+spgispeech" \
+  --train_split_name "train.clean.100+train.clean.360+train.other.500+train+train+train+train+train+train+train+train+train" \
+  --eval_dataset_name "distil-whisper/gigaspeech-l+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset" \
+  --eval_dataset_config_name "l+librispeech+librispeech+common_voice+common_voice+voxpopuli+voxpopuli+tedlium+tedlium+spgispeech+spgispeech+ami+ami" \
+  --eval_split_name "validation+clean+other+clean+other+clean+other+clean+other+clean+other+clean+other" \
+  --eval_text_column_name "text+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript" \
+  --eval_steps 5000 \
+  --save_steps 5000 \
+  --warmup_steps 500 \
+  --learning_rate 0.0001 \
+  --lr_scheduler_type "linear" \
+  --logging_steps 25 \
+  --save_total_limit 1 \
+  --max_steps 80000 \
+  --wer_threshold 10 \
+  --per_device_eval_batch_size 64 \
+  --per_device_train_batch_size 64 \
+  --dataloader_num_workers 16 \
+  --cache_dir "/home/sanchitgandhi/.cache" \
+  --dataset_cache_dir "/home/sanchitgandhi/.cache" \
+  --dtype "bfloat16" \
+  --output_dir "./" \
+  --wandb_name "medium-24-2-ts-freeze-28k-wer-10" \
+  --wandb_dir "/home/sanchitgandhi/.cache" \
+  --wandb_project "distil-whisper" \
+  --do_train \
+  --do_eval \
+  --use_scan \
+  --gradient_checkpointing \
+  --overwrite_output_dir \
+  --predict_with_generate \
+  --streaming \
+  --freeze_encoder \
+  --use_auth_token \
+  --push_to_hub
diff --git a/flax/distillation_scripts/run_distillation_24_2_timestamped.sh b/flax/distillation_scripts/run_distillation_24_2_timestamped.sh
new file mode 100644
index 0000000000000000000000000000000000000000..aedbfd42c2a2ee687c7bfd32f0468ad0850f5316
--- /dev/null
+++ b/flax/distillation_scripts/run_distillation_24_2_timestamped.sh
@@ -0,0 +1,42 @@
+#!/usr/bin/env bash
+
+TCMALLOC_LARGE_ALLOC_REPORT_THRESHOLD=10000000000 python3 run_distillation.py \
+  --model_name_or_path "distil-whisper/medium-24-2" \
+  --teacher_model_name_or_path "openai/whisper-medium.en" \
+  --train_dataset_config_name "all+all+all+en+en+ihm+sdm+clean+release3+all+l+all+all+all+release3" \
+  --train_dataset_samples "2.9+10.4+14.9+89+18.2+10.9+10.9+288+26.8+371.2+226.6+2.9+10.4+14.9+26.8" \
+  --train_dataset_name "librispeech_asr-timestamped+librispeech_asr-timestamped+librispeech_asr-timestamped+common_voice_13_0-timestamped+voxpopuli-timestamped+ami-ihm-timestamped+ami-sdm-timestamped+peoples_speech-clean-timestamped+tedlium-timestamped+switchboard-data+gigaspeech-l-timestamped+librispeech_asr-prompted+librispeech_asr-prompted+librispeech_asr-prompted+tedlium-prompted" \
+  --train_split_name "train.clean.100+train.clean.360+train.other.500+train+train+train+train+train+train+train+train+train.clean.100+train.clean.360+train.other.500+train" \
+  --eval_dataset_name "distil-whisper/gigaspeech-l+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset" \
+  --eval_dataset_config_name "l+librispeech+librispeech+common_voice+common_voice+voxpopuli+voxpopuli+tedlium+tedlium+spgispeech+spgispeech+ami+ami" \
+  --eval_split_name "validation+clean+other+clean+other+clean+other+clean+other+clean+other+clean+other" \
+  --eval_text_column_name "text+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript" \
+  --eval_steps 5000 \
+  --save_steps 5000 \
+  --warmup_steps 500 \
+  --learning_rate 0.0001 \
+  --logging_steps 25 \
+  --save_total_limit 1 \
+  --max_steps 80000 \
+  --wer_threshold 10 \
+  --per_device_train_batch_size 64 \
+  --per_device_eval_batch_size 64 \
+  --dtype "bfloat16" \
+  --dataloader_num_workers 16 \
+  --cache_dir "/home/sanchitgandhi/.cache" \
+  --dataset_cache_dir "/home/sanchitgandhi/.cache" \
+  --output_dir "./" \
+  --timestamp_probability 0.2 \
+  --wandb_name "medium-24-2-tpu-timestamped-prob-0.2" \
+  --wandb_dir "/home/sanchitgandhi/.cache" \
+  --wandb_project "distil-whisper" \
+  --do_train \
+  --do_eval \
+  --use_scan \
+  --gradient_checkpointing \
+  --overwrite_output_dir \
+  --predict_with_generate \
+  --freeze_encoder \
+  --streaming \
+  --use_auth_token \
+  --push_to_hub
diff --git a/flax/distillation_scripts/run_distillation_32_2.sh b/flax/distillation_scripts/run_distillation_32_2.sh
new file mode 100644
index 0000000000000000000000000000000000000000..30fb3d25cf783676f891ced6b2441596a65271fc
--- /dev/null
+++ b/flax/distillation_scripts/run_distillation_32_2.sh
@@ -0,0 +1,42 @@
+#!/usr/bin/env bash
+
+TCMALLOC_LARGE_ALLOC_REPORT_THRESHOLD=10000000000 python3 run_distillation.py \
+  --model_name_or_path "distil-whisper/large-32-2" \
+  --teacher_model_name_or_path "openai/whisper-large-v2" \
+  --train_dataset_config_name "all+all+all+l" \
+  --train_dataset_samples "100+360+500+2500" \
+  --train_dataset_name "librispeech_asr-token-ids+librispeech_asr-token-ids+librispeech_asr-token-ids+gigaspeech-l-token-ids" \
+  --train_split_name "train.clean.100+train.clean.360+train.other.500+train" \
+  --eval_dataset_name "librispeech_asr+librispeech_asr+gigaspeech-l" \
+  --eval_dataset_config_name "all+all+l" \
+  --eval_split_name "validation.clean+validation.other+validation" \
+  --eval_text_column_name "text+text+text" \
+  --eval_steps 5000 \
+  --save_steps 5000 \
+  --warmup_steps 50 \
+  --learning_rate 0.0001 \
+  --lr_scheduler_type "constant_with_warmup" \
+  --logging_steps 25 \
+  --save_total_limit 1 \
+  --max_steps 10000 \
+  --wer_threshold 10 \
+  --per_device_train_batch_size 64 \
+  --per_device_eval_batch_size 64 \
+  --dataloader_num_workers 16 \
+  --cache_dir "/home/sanchitgandhi/.cache" \
+  --dataset_cache_dir "/home/sanchitgandhi/.cache" \
+  --dtype "bfloat16" \
+  --output_dir "./" \
+  --wandb_name "large-32-2-ls-gs-token-ids" \
+  --wandb_dir "/home/sanchitgandhi/.cache" \
+  --wandb_project "distil-whisper" \
+  --do_train \
+  --do_eval \
+  --use_scan \
+  --gradient_checkpointing \
+  --overwrite_output_dir \
+  --predict_with_generate \
+  --freeze_encoder \
+  --streaming \
+  --use_auth_token \
+  --push_to_hub
diff --git a/flax/distillation_scripts/run_distillation_32_2_by_samples.sh b/flax/distillation_scripts/run_distillation_32_2_by_samples.sh
new file mode 100644
index 0000000000000000000000000000000000000000..840f2b4c8c6bebe447367787896e298337507a03
--- /dev/null
+++ b/flax/distillation_scripts/run_distillation_32_2_by_samples.sh
@@ -0,0 +1,42 @@
+#!/usr/bin/env bash
+
+TCMALLOC_LARGE_ALLOC_REPORT_THRESHOLD=10000000000 python3 run_distillation.py \
+  --model_name_or_path "distil-whisper/large-32-2" \
+  --teacher_model_name_or_path "openai/whisper-large-v2" \
+  --train_dataset_config_name "all+all+all+en+en+ihm+sdm+clean+release3+all+l+L" \
+  --train_dataset_samples "2.9+10.4+14.9+89+18.2+10.9+10.9+288+26.8+371.2+226.6+192.7" \
+  --train_dataset_name "librispeech_asr+librispeech_asr+librispeech_asr+common_voice_13_0+voxpopuli+ami-ihm+ami-sdm+peoples_speech-clean+tedlium+switchboard-data+gigaspeech-l+spgispeech" \
+  --train_split_name "train.clean.100+train.clean.360+train.other.500+train+train+train+train+train+train+train+train+train" \
+  --eval_dataset_name "distil-whisper/gigaspeech-l+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset" \
+  --eval_dataset_config_name "l+librispeech+librispeech+common_voice+common_voice+voxpopuli+voxpopuli+tedlium+tedlium+spgispeech+spgispeech+ami+ami" \
+  --eval_split_name "validation+clean+other+clean+other+clean+other+clean+other+clean+other+clean+other" \
+  --eval_text_column_name "text+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript" \
+  --eval_steps 5000 \
+  --save_steps 5000 \
+  --warmup_steps 500 \
+  --learning_rate 0.0001 \
+  --lr_scheduler_type "linear" \
+  --logging_steps 25 \
+  --save_total_limit 1 \
+  --max_steps 80000 \
+  --wer_threshold 10 \
+  --per_device_train_batch_size 64 \
+  --per_device_eval_batch_size 64 \
+  --dataloader_num_workers 16 \
+  --cache_dir "/home/sanchitgandhi/.cache" \
+  --dataset_cache_dir "/home/sanchitgandhi/.cache" \
+  --dtype "bfloat16" \
+  --output_dir "./" \
+  --wandb_name "large-32-2-ts-freeze-28k-wer-10-probs-by-samples" \
+  --wandb_dir "/home/sanchitgandhi/.cache" \
+  --wandb_project "distil-whisper" \
+  --do_train \
+  --do_eval \
+  --use_scan \
+  --gradient_checkpointing \
+  --overwrite_output_dir \
+  --predict_with_generate \
+  --freeze_encoder \
+  --streaming \
+  --use_auth_token \
+  --push_to_hub
diff --git a/flax/distillation_scripts/run_distillation_32_2_gpu.sh b/flax/distillation_scripts/run_distillation_32_2_gpu.sh
new file mode 100644
index 0000000000000000000000000000000000000000..919164cb8ea150e28f25ffc8605c5725e60658e8
--- /dev/null
+++ b/flax/distillation_scripts/run_distillation_32_2_gpu.sh
@@ -0,0 +1,43 @@
+#!/usr/bin/env bash
+
+TCMALLOC_LARGE_ALLOC_REPORT_THRESHOLD=10000000000 python3 run_distillation.py \
+  --model_name_or_path "distil-whisper/large-32-2" \
+  --teacher_model_name_or_path "openai/whisper-large-v2" \
+  --train_dataset_config_name "all+all+all+en+en+ihm+sdm+clean+all+L" \
+  --train_dataset_samples "2.9+10.4+14.9+89+18.2+10.9+10.9+288+371.2+192.7" \
+  --train_dataset_name "librispeech_asr+librispeech_asr+librispeech_asr+common_voice_13_0+voxpopuli+ami-ihm+ami-sdm+peoples_speech-clean+switchboard-data+spgispeech" \
+  --train_split_name "train.clean.100+train.clean.360+train.other.500+train+train+train+train+train+train+train" \
+  --eval_dataset_name "distil-whisper/gigaspeech-l+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset" \
+  --eval_dataset_config_name "l+librispeech+librispeech+common_voice+common_voice+voxpopuli+voxpopuli+tedlium+tedlium+spgispeech+spgispeech+ami+ami" \
+  --eval_split_name "validation+clean+other+clean+other+clean+other+clean+other+clean+other+clean+other" \
+  --eval_text_column_name "text+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript" \
+  --eval_steps 1250 \
+  --save_steps 1250 \
+  --warmup_steps 250 \
+  --learning_rate 0.0001 \
+  --lr_scheduler_type "constant_with_warmup" \
+  --logging_steps 25 \
+  --save_total_limit 1 \
+  --max_steps 20000 \
+  --wer_threshold 10 \
+  --per_device_train_batch_size 128 \
+  --per_device_eval_batch_size 128 \
+  --dtype "bfloat16" \
+  --precision "full_mixed" \
+  --dataloader_num_workers 16 \
+  --cache_dir "/fsx/sanchit/.cache" \
+  --dataset_cache_dir "/fsx/sanchit/.cache" \
+  --output_dir "./" \
+  --wandb_name "large-32-2-gpu-flat-lr" \
+  --wandb_dir "/fsx/sanchit/.cache" \
+  --wandb_project "distil-whisper" \
+  --do_train \
+  --do_eval \
+  --use_scan \
+  --gradient_checkpointing \
+  --overwrite_output_dir \
+  --predict_with_generate \
+  --freeze_encoder \
+  --streaming \
+  --use_auth_token \
+  --push_to_hub
diff --git a/flax/distillation_scripts/run_distillation_32_2_timestamped.sh b/flax/distillation_scripts/run_distillation_32_2_timestamped.sh
new file mode 100644
index 0000000000000000000000000000000000000000..ae17c7387a273af33bd1ae2b7961862afc369832
--- /dev/null
+++ b/flax/distillation_scripts/run_distillation_32_2_timestamped.sh
@@ -0,0 +1,41 @@
+#!/usr/bin/env bash
+
+TCMALLOC_LARGE_ALLOC_REPORT_THRESHOLD=10000000000 python3 run_distillation.py \
+  --model_name_or_path "distil-whisper/large-32-2" \
+  --teacher_model_name_or_path "openai/whisper-large-v2" \
+  --train_dataset_config_name "all+all+all+en+en+ihm+sdm+clean+release3+all+l+all+all+all+release3" \
+  --train_dataset_samples "2.9+10.4+14.9+89+18.2+10.9+10.9+288+26.8+371.2+226.6+2.9+10.4+14.9+26.8" \
+  --train_dataset_name "librispeech_asr-timestamped+librispeech_asr-timestamped+librispeech_asr-timestamped+common_voice_13_0-timestamped+voxpopuli-timestamped+ami-ihm-timestamped+ami-sdm-timestamped+peoples_speech-clean-timestamped+tedlium-timestamped+switchboard-data+gigaspeech-l-timestamped+librispeech_asr-prompted+librispeech_asr-prompted+librispeech_asr-prompted+tedlium-prompted" \
+  --train_split_name "train.clean.100+train.clean.360+train.other.500+train+train+train+train+train+train+train+train+train.clean.100+train.clean.360+train.other.500+train" \
+  --eval_dataset_name "distil-whisper/gigaspeech-l+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset" \
+  --eval_dataset_config_name "l+librispeech+librispeech+common_voice+common_voice+voxpopuli+voxpopuli+tedlium+tedlium+spgispeech+spgispeech+ami+ami" \
+  --eval_split_name "validation+clean+other+clean+other+clean+other+clean+other+clean+other+clean+other" \
+  --eval_text_column_name "text+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript" \
+  --eval_steps 5000 \
+  --save_steps 5000 \
+  --warmup_steps 500 \
+  --learning_rate 0.0001 \
+  --logging_steps 25 \
+  --save_total_limit 1 \
+  --max_steps 80000 \
+  --wer_threshold 10 \
+  --per_device_train_batch_size 64 \
+  --per_device_eval_batch_size 64 \
+  --dtype "bfloat16" \
+  --dataloader_num_workers 16 \
+  --cache_dir "/home/sanchitgandhi/.cache" \
+  --dataset_cache_dir "/home/sanchitgandhi/.cache" \
+  --output_dir "./" \
+  --wandb_name "large-32-2-tpu-timestamped" \
+  --wandb_dir "/home/sanchitgandhi/.cache" \
+  --wandb_project "distil-whisper" \
+  --do_train \
+  --do_eval \
+  --use_scan \
+  --gradient_checkpointing \
+  --overwrite_output_dir \
+  --predict_with_generate \
+  --freeze_encoder \
+  --streaming \
+  --use_auth_token \
+  --push_to_hub
diff --git a/flax/distillation_scripts/run_distillation_large_32_2_gpu_timestamped.sh b/flax/distillation_scripts/run_distillation_large_32_2_gpu_timestamped.sh
new file mode 100644
index 0000000000000000000000000000000000000000..08ae4fb93b0b10c8e37b2d18bd30068a53e5757a
--- /dev/null
+++ b/flax/distillation_scripts/run_distillation_large_32_2_gpu_timestamped.sh
@@ -0,0 +1,41 @@
+#!/usr/bin/env bash
+
+TCMALLOC_LARGE_ALLOC_REPORT_THRESHOLD=10000000000 python3 run_distillation.py \
+  --model_name_or_path "distil-whisper/large-32-2" \
+  --teacher_model_name_or_path "openai/whisper-large-v2" \
+  --train_dataset_config_name "all+all+all+en+en+ihm+sdm+clean+release3+all+l+all+all+all+release3" \
+  --train_dataset_samples "2.9+10.4+14.9+89+18.2+10.9+10.9+288+26.8+371.2+226.6+2.9+10.4+14.9+26.8" \
+  --train_dataset_name "librispeech_asr-timestamped+librispeech_asr-timestamped+librispeech_asr-timestamped+common_voice_13_0-timestamped+voxpopuli-timestamped+ami-ihm-timestamped+ami-sdm-timestamped+peoples_speech-clean-timestamped+tedlium-timestamped+switchboard-data+gigaspeech-l-timestamped+librispeech_asr-prompted+librispeech_asr-prompted+librispeech_asr-prompted+tedlium-prompted" \
+  --train_split_name "train.clean.100+train.clean.360+train.other.500+train+train+train+train+train+train+train+train+train.clean.100+train.clean.360+train.other.500+train" \
+  --eval_dataset_name "distil-whisper/gigaspeech-l+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset+esb/diagnostic-dataset" \
+  --eval_dataset_config_name "l+librispeech+librispeech+common_voice+common_voice+voxpopuli+voxpopuli+tedlium+tedlium+spgispeech+spgispeech+ami+ami" \
+  --eval_split_name "validation+clean+other+clean+other+clean+other+clean+other+clean+other+clean+other" \
+  --eval_text_column_name "text+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript+ortho_transcript" \
+  --eval_steps 5000 \
+  --save_steps 5000 \
+  --warmup_steps 500 \
+  --learning_rate 0.0001 \
+  --logging_steps 25 \
+  --save_total_limit 1 \
+  --max_steps 80000 \
+  --wer_threshold 10 \
+  --per_device_train_batch_size 64 \
+  --per_device_eval_batch_size 64 \
+  --dtype "bfloat16" \
+  --dataloader_num_workers 16 \
+  --cache_dir "/fsx/sanchit/.cache" \
+  --dataset_cache_dir "/fsx/sanchit/.cache" \
+  --output_dir "./" \
+  --wandb_name "large-32-2-gpu-timestamped" \
+  --wandb_dir "/fsx/sanchit/.cache" \
+  --wandb_project "distil-whisper" \
+  --do_train \
+  --do_eval \
+  --use_scan \
+  --gradient_checkpointing \
+  --overwrite_output_dir \
+  --predict_with_generate \
+  --freeze_encoder \
+  --streaming \
+  --use_auth_token \
+  --push_to_hub
diff --git a/flax/distillation_scripts/run_distillation_objective.yaml b/flax/distillation_scripts/run_distillation_objective.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..feb340ad04c98fdbbd132eaacb4a150d5e6ea681
--- /dev/null
+++ b/flax/distillation_scripts/run_distillation_objective.yaml
@@ -0,0 +1,72 @@
+command:
+  - python3
+  - ${program}
+  - --do_train
+  - --do_eval
+  - --use_scan
+  - --gradient_checkpointing
+  - --overwrite_output_dir
+  - --predict_with_generate
+  - --freeze_encoder
+  - --streaming
+  - --use_auth_token
+  - ${args}
+method: grid
+metric:
+  goal: minimize
+  name: gigaspeech-l/validation/wer
+parameters:
+  model_name_or_path:
+    value: distil-whisper/large-32-2
+  teacher_model_name_or_path:
+    value: openai/whisper-large-v2
+  train_dataset_name:
+    value: librispeech_asr-timestamped+librispeech_asr-timestamped+librispeech_asr-timestamped+common_voice_13_0-timestamped+voxpopuli-timestamped+ami-ihm-timestamped+ami-sdm-timestamped+peoples_speech-clean-timestamped+tedlium-timestamped+switchboard-data+gigaspeech-l-timestamped+librispeech_asr-prompted+librispeech_asr-prompted+librispeech_asr-prompted+tedlium-prompted
+  train_dataset_config_name:
+    value: all+all+all+en+en+ihm+sdm+clean+release3+all+l+all+all+all+release3
+  train_split_name:
+    value: train.clean.100+train.clean.360+train.other.500+train+train+train+train+train+train+train+train+train.clean.100+train.clean.360+train.other.500+train
+  train_dataset_samples:
+    value: 2.9+10.4+14.9+89+18.2+10.9+10.9+288+26.8+371.2+226.6+2.9+10.4+14.9+26.8
+  eval_dataset_name:
+    value: librispeech_asr+librispeech_asr+common_voice_13_0+voxpopuli+ami-ihm+ami-sdm+peoples_speech-clean+tedlium+switchboard-data+gigaspeech-l+spgispeech+chime4+google/fleurs
+  eval_dataset_config_name:
+    value: all+all+en+en+ihm+sdm+clean+release3+all+l+L+1-channel+en_us
+  eval_split_name:
+    value: validation.clean+validation.other+validation+validation+validation+validation+validation+validation+validation+validation+validation+validation+validation
+  eval_text_column_name:
+    value: text+text+text+text+text+text+text+text+text+text+text+text+transcription
+  cache_dir:
+    value: /home/sanchitgandhi/.cache
+  dataset_cache_dir:
+    value: /home/sanchitgandhi/.cache
+  output_dir:
+    value: ./
+  per_device_train_batch_size:
+    value: 64
+  per_device_eval_batch_size:
+    value: 64
+  dtype:
+    value: bfloat16
+  learning_rate:
+    value: 1e-4
+  lr_scheduler_type:
+    value: constant_with_warmup
+  warmup_steps:
+    value: 50
+  max_steps:
+    value: 10000
+  save_steps:
+    value: 10001  # don't save checkpoints during sweep
+  dataloader_num_workers:
+    value: 48
+  logging_steps:
+    value: 25
+  wer_threshold:
+    value: 10
+  kl_weight:
+    values:
+      - 0.0
+      - 1.0
+program: run_distillation.py
+project: distil-whisper-sweeps
diff --git a/flax/distillation_scripts/run_dropout_sweep.yaml b/flax/distillation_scripts/run_dropout_sweep.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..7bae2ed823c884821953b2537aebbeb46030e912
--- /dev/null
+++ b/flax/distillation_scripts/run_dropout_sweep.yaml
@@ -0,0 +1,84 @@
+command:
+  - python3
+  - ${program}
+  - --do_train
+  - --do_eval
+  - --use_scan
+  - --gradient_checkpointing
+  - --overwrite_output_dir
+  - --predict_with_generate
+  - --streaming
+  - --use_auth_token
+  - ${args}
+method: random
+metric:
+  goal: minimize
+  name: eval/wer
+parameters:
+  model_name_or_path:
+    value: distil-whisper/large-32-2
+  teacher_model_name_or_path:
+    value: openai/whisper-large-v2
+  train_dataset_name:
+    value: librispeech_asr+librispeech_asr+librispeech_asr+common_voice_13_0+voxpopuli+ami-ihm+ami-sdm+peoples_speech-clean+tedlium+switchboard-data+gigaspeech-l+spgispeech
+  train_dataset_config_name:
+    value: all+all+all+en+en+ihm+sdm+clean+release3+all+l+L
+  train_split_name:
+    value: train.clean.100+train.clean.360+train.other.500+train+train+train+train+train+train+train+train+train
+  train_dataset_samples:
+    value: 100+360+500+2300+450+90+90+12000+450+3600+2500+5000
+  eval_dataset_name:
+    value: "distil-whisper/gigaspeech-l"
+  eval_dataset_config_name:
+    value: "l"
+  cache_dir:
+    value: /home/sanchitgandhi/cache
+  dataset_cache_dir:
+    value: /home/sanchitgandhi/cache
+  output_dir:
+    value: ./
+  per_device_train_batch_size:
+    value: 32
+  per_device_eval_batch_size:
+    value: 64
+  dtype:
+    value: bfloat16
+  learning_rate:
+    value: 1e-4
+  lr_scheduler_type:
+    value: constant_with_warmup
+  warmup_steps:
+    value: 50
+  max_steps:
+    value: 1000
+  eval_steps:
+    value: 1000
+  save_steps:
+    value: 1000
+  dataloader_num_workers:
+    value: 16
+  logging_steps:
+    value: 5
+  wer_threshold:
+    value: 10
+  activation_dropout:
+    values:
+      - 0
+      - 0.05
+      - 0.1
+  attention_dropout:
+    values:
+      - 0
+      - 0.05
+      - 0.1
+  dropout:
+    values:
+      - 0
+      - 0.05
+      - 0.1
+  freeze_encoder:
+    values:
+      - true
+      - false
+program: run_distillation.py
+project: distil-whisper-sweeps
diff --git a/flax/distillation_scripts/run_librispeech.sh b/flax/distillation_scripts/run_librispeech.sh
new file mode 100644
index 0000000000000000000000000000000000000000..640a7aea40b6ed572adac0e92201522e35dd6d2d
--- /dev/null
+++ b/flax/distillation_scripts/run_librispeech.sh
@@ -0,0 +1,33 @@
+#!/usr/bin/env bash
+
+TCMALLOC_LARGE_ALLOC_REPORT_THRESHOLD=10000000000 python run_distillation.py \
+  --model_name_or_path "distil-whisper/large-32-2" \
+  --teacher_model_name_or_path "openai/whisper-large-v2" \
+  --dataset_name "distil-whisper/librispeech_asr" \
+  --dataset_config_name "all" \
+  --train_split_name "train.clean.100+train.clean.360+train.other.500" \
+  --eval_split_name "validation.clean" \
+  --text_column_name "whisper_transcript" \
+  --cache_dir "/home/sanchitgandhi/cache" \
+  --dataset_cache_dir "/home/sanchitgandhi/cache" \
+  --output_dir "./" \
+  --wandb_name "large-32-2-ts-librispeech" \
+  --wandb_dir "/home/sanchitgandhi/.cache" \
+  --wandb_project "distil-whisper-librispeech" \
+  --per_device_train_batch_size 32 \
+  --per_device_eval_batch_size 16 \
+  --dtype "bfloat16" \
+  --learning_rate 1e-4 \
+  --warmup_steps 500 \
+  --temperature 2.0 \
+  --do_train \
+  --do_eval \
+  --num_train_epochs 10 \
+  --preprocessing_num_workers 16 \
+  --dataloader_num_workers 8 \
+  --logging_steps 25 \
+  --use_scan \
+  --gradient_checkpointing \
+  --overwrite_output_dir \
+  --predict_with_generate \
+  --push_to_hub
diff --git a/flax/distillation_scripts/run_librispeech_dummy_pt.sh b/flax/distillation_scripts/run_librispeech_dummy_pt.sh
new file mode 100644
index 0000000000000000000000000000000000000000..171882c5b0a51455c416f68aea9aee541b52ce65
--- /dev/null
+++ b/flax/distillation_scripts/run_librispeech_dummy_pt.sh
@@ -0,0 +1,35 @@
+#!/usr/bin/env bash
+
+accelerate launch --mixed_precision=bf16 --num_processes=1 run_distillation_pt.py \
+  --model_name_or_path "distil-whisper/tiny-random-whisper-2-1" \
+  --teacher_model_name_or_path "distil-whisper/tiny-random-whisper" \
+  --train_dataset_name "distil-whisper/librispeech_asr_dummy" \
+  --train_dataset_config_name "clean" \
+  --train_dataset_samples "100" \
+  --train_split_name "validation" \
+  --eval_dataset_name "distil-whisper/librispeech_asr_dummy" \
+  --eval_dataset_config_name "clean" \
+  --eval_split_name "validation" \
+  --eval_text_column_name "text" \
+  --cache_dir "/home/sanchit/.cache" \
+  --dataset_cache_dir "/home/sanchit/.cache" \
+  --wandb_project "distil-whisper-debug" \
+  --output_dir "./" \
+  --do_train \
+  --do_eval \
+  --learning_rate 1e-4 \
+  --warmup_steps 25 \
+  --per_device_train_batch_size 8 \
+  --per_device_eval_batch_size 8 \
+  --gradient_checkpointing \
+  --max_steps 100 \
+  --eval_steps 50 \
+  --save_steps 50 \
+  --dataloader_num_workers 14 \
+  --wer_threshold 10 \
+  --logging_steps 5 \
+  --overwrite_output_dir \
+  --dtype bfloat16 \
+  --predict_with_generate \
+  --freeze_encoder \
+  --streaming False
diff --git a/flax/distillation_scripts/run_librispeech_streaming_dummy.sh b/flax/distillation_scripts/run_librispeech_streaming_dummy.sh
new file mode 100644
index 0000000000000000000000000000000000000000..85a261352e1a743d6c6698c206c68fbbbfed679e
--- /dev/null
+++ b/flax/distillation_scripts/run_librispeech_streaming_dummy.sh
@@ -0,0 +1,37 @@
+#!/usr/bin/env bash
+
+python run_distillation.py \
+  --model_name_or_path "distil-whisper/tiny-random-whisper-2-1" \
+  --teacher_model_name_or_path "distil-whisper/tiny-random-whisper" \
+  --train_dataset_name "distil-whisper/librispeech_asr+distil-whisper/librispeech_asr-timestamped" \
+  --train_dataset_config_name "all+all" \
+  --train_dataset_samples "100+360" \
+  --train_split_name "train.clean.100+train.clean.360" \
+  --eval_dataset_name "distil-whisper/gigaspeech-l+esb/diagnostic-dataset" \
+  --eval_dataset_config_name "l+librispeech" \
+  --eval_split_name "validation+clean" \
+  --eval_text_column_name "text+ortho_transcript" \
+  --max_train_samples 1024 \
+  --max_eval_samples 32 \
+  --cache_dir "/home/sanchitgandhi/.cache" \
+  --dataset_cache_dir "/home/sanchitgandhi/.cache" \
+  --wandb_dir "/home/sanchitgandhi/.cache" \
+  --output_dir "./" \
+  --do_train \
+  --do_eval \
+  --per_device_train_batch_size 2 \
+  --per_device_eval_batch_size 2 \
+  --max_steps 10 \
+  --eval_steps 5 \
+  --dataloader_num_workers 14 \
+  --save_steps 5 \
+  --wer_threshold 10 \
+  --wandb_project "distil-whisper-debug" \
+  --logging_steps 1 \
+  --use_scan \
+  --gradient_checkpointing \
+  --overwrite_output_dir \
+  --predict_with_generate \
+  --return_timestamps \
+  --timestamp_probability 1 \
+  --freeze_encoder
diff --git a/flax/distillation_scripts/run_lr_sweep.yaml b/flax/distillation_scripts/run_lr_sweep.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..fadbb1f42cf03941a4eda4a9499f41ecac8513a4
--- /dev/null
+++ b/flax/distillation_scripts/run_lr_sweep.yaml
@@ -0,0 +1,73 @@
+command:
+  - python3
+  - ${program}
+  - --do_train
+  - --do_eval
+  - --use_scan
+  - --gradient_checkpointing
+  - --overwrite_output_dir
+  - --predict_with_generate
+  - --freeze_encoder
+  - --streaming
+  - --use_auth_token
+  - --compilation_cache
+  - --load_with_scan_weights  # checkpoint is saved with scan weights
+  - ${args}
+method: grid
+metric:
+  goal: minimize
+  name: eval/wer
+parameters:
+  model_name_or_path:
+    value: distil-whisper/large-32-2-ts-freeze-librispeech  # resume from a partially trained checkpoint
+  teacher_model_name_or_path:
+    value: openai/whisper-large-v2
+  train_dataset_name:
+    value: librispeech_asr+librispeech_asr+librispeech_asr+common_voice_13_0+voxpopuli+ami-ihm+ami-sdm+peoples_speech-clean+tedlium+switchboard-data+gigaspeech-l+spgispeech
+  train_dataset_config_name:
+    value: all+all+all+en+en+ihm+sdm+clean+release3+all+l+L
+  train_split_name:
+    value: train.clean.100+train.clean.360+train.other.500+train+train+train+train+train+train+train+train+train
+  train_dataset_samples:
+    value: 100+360+500+2300+450+90+90+12000+450+3600+2500+5000
+  eval_dataset_name:
+    value: "distil-whisper/gigaspeech-l"
+  eval_dataset_config_name:
+    value: "l"
+  cache_dir:
+    value: /fsx/sanchit/cache
+  dataset_cache_dir:
+    value: /fsx/sanchit/cache
+  output_dir:
+    value: ./
+  per_device_train_batch_size:
+    value: 128
+  per_device_eval_batch_size:
+    value: 128
+  dtype:
+    value: bfloat16
+  learning_rate:
+    values:
+      - 1e-3
+      - 3e-4
+      - 1e-4
+      - 3e-5
+      - 1e-5
+  lr_scheduler_type:
+    value: constant_with_warmup
+  warmup_steps:
+    value: 50
+  max_steps:
+    value: 500
+  eval_steps:
+    value: 500
+  save_steps:
+    value: 501  # don't save checkpoints during sweep
+  dataloader_num_workers:
+    value: 16
+  logging_steps:
+    value: 5
+  wer_threshold:
+    value: 10
+program: run_distillation.py
+project: distil-whisper-sweeps
diff --git a/flax/distillation_scripts/run_mse_sweep.yaml b/flax/distillation_scripts/run_mse_sweep.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..feb75c56f5d2ad22a7f0885beeac54a0b04b9b6b
--- /dev/null
+++ b/flax/distillation_scripts/run_mse_sweep.yaml
@@ -0,0 +1,71 @@
+command:
+  - python3
+  - ${program}
+  - --do_train
+  - --do_eval
+  - --gradient_checkpointing
+  - --overwrite_output_dir
+  - --predict_with_generate
+  - --streaming
+  - --use_auth_token
+  - --use_scan
+  - ${args}
+method: grid
+metric:
+  goal: minimize
+  name: eval/wer
+parameters:
+  model_name_or_path:
+    value: distil-whisper/large-16-2
+  teacher_model_name_or_path:
+    value: openai/whisper-large-v2
+  train_dataset_name:
+    value: librispeech_asr+librispeech_asr+librispeech_asr+common_voice_13_0+voxpopuli+ami-ihm+ami-sdm+peoples_speech-clean+tedlium+switchboard-data+gigaspeech-l+spgispeech
+  train_dataset_config_name:
+    value: all+all+all+en+en+ihm+sdm+clean+release3+all+l+L
+  train_split_name:
+    value: train.clean.100+train.clean.360+train.other.500+train+train+train+train+train+train+train+train+train
+  train_dataset_samples:
+    value: 100+360+500+2300+450+90+90+12000+450+3600+2500+5000
+  eval_dataset_name:
+    value: "distil-whisper/gigaspeech-l"
+  eval_dataset_config_name:
+    value: "l"
+  cache_dir:
+    value: /home/sanchitgandhi/cache
+  dataset_cache_dir:
+    value: /home/sanchitgandhi/cache
+  output_dir:
+    value: ./
+  per_device_train_batch_size:
+    value: 32
+  per_device_eval_batch_size:
+    value: 64
+  dtype:
+    value: bfloat16
+  learning_rate:
+    value: 0.0001
+  lr_scheduler_type:
+    value: constant_with_warmup
+  warmup_steps:
+    value: 50
+  max_steps:
+    value: 2500
+  eval_steps:
+    value: 2500
+  save_steps:
+    value: 2001  # don't save checkpoints during sweep
+  dataloader_num_workers:
+    value: 16
+  logging_steps:
+    value: 5
+  wer_threshold:
+    value: 10
+  mse_weight:
+    values:
+      - 0.0
+      - 0.3
+      - 1
+      - 3
+program: run_distillation.py
+project: distil-whisper-sweeps
diff --git a/flax/distillation_scripts/run_timestamp_sweep.yaml b/flax/distillation_scripts/run_timestamp_sweep.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..67fd45b102f2e314ddad3f7e0692b77540a09f1b
--- /dev/null
+++ b/flax/distillation_scripts/run_timestamp_sweep.yaml
@@ -0,0 +1,76 @@
+command:
+  - python3
+  - ${program}
+  - --do_train
+  - --do_eval
+  - --use_scan
+  - --gradient_checkpointing
+  - --overwrite_output_dir
+  - --predict_with_generate
+  - --freeze_encoder
+  - --streaming
+  - --use_auth_token
+  - --compilation_cache
+  - --return_timestamps
+  - ${args}
+method: grid
+metric:
+  goal: minimize
+  name: eval/wer
+parameters:
+  model_name_or_path:
+    value: distil-whisper/large-32-2
+  teacher_model_name_or_path:
+    value: openai/whisper-large-v2
+  train_dataset_name:
+    value: librispeech_asr-timestamped+librispeech_asr-timestamped+librispeech_asr-timestamped+common_voice_13_0-timestamped+voxpopuli-timestamped+ami-ihm-timestamped+ami-sdm-timestamped+peoples_speech-clean-timestamped+tedlium-timestamped+switchboard-data+gigaspeech-l-timestamped+spgispeech-timestamped
+  train_dataset_config_name:
+    value: all+all+all+en+en+ihm+sdm+clean+release3+all+l+L
+  train_split_name:
+    value: train.clean.100+train.clean.360+train.other.500+train+train+train+train+train+train+train+train+train
+  train_dataset_samples:
+    value: 2.9+10.4+14.9+89+18.2+10.9+10.9+288+26.8+371.2+226.6+192.7
+  timestamp_probability:
+    values:
+      - 0.0
+      - 0.2
+      - 0.4
+      - 0.6
+      - 0.8
+      - 1.0
+  round_timestamps:
+    values:
+      - True
+      - False
+  eval_dataset_name:
+    value: "distil-whisper/gigaspeech-l"
+  eval_dataset_config_name:
+    value: "l"
+  cache_dir:
+    value: /home/sanchitgandhi/.cache
+  dataset_cache_dir:
+    value: /home/sanchitgandhi/.cache
+  output_dir:
+    value: ./
+  per_device_train_batch_size:
+    value: 64
+  dtype:
+    value: bfloat16
+  learning_rate:
+    value: 1e-4
+  lr_scheduler_type:
+    value: constant_with_warmup
+  warmup_steps:
+    value: 50
+  max_steps:
+    value: 2500
+  save_steps:
+    value: 2501  # don't save checkpoints during sweep
+  dataloader_num_workers:
+    value: 48
+  logging_steps:
+    value: 25
+  wer_threshold:
+    value: 10
+program: run_distillation.py
+project: distil-whisper-sweeps
diff --git a/flax/distillation_scripts/run_wer_sweep.yaml b/flax/distillation_scripts/run_wer_sweep.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..fc78e6fa82e5e6076470fdc214d3752ba41a9797
--- /dev/null
+++ b/flax/distillation_scripts/run_wer_sweep.yaml
@@ -0,0 +1,73 @@
+command:
+  - python3
+  - ${program}
+  - --do_train
+  - --do_eval
+  - --use_scan
+  - --gradient_checkpointing
+  - --overwrite_output_dir
+  - --predict_with_generate
+  - --freeze_encoder
+  - --streaming
+  - --use_auth_token
+  - ${args}
+method: grid
+metric:
+  goal: minimize
+  name: gigaspeech-l/validation/wer
+parameters:
+  model_name_or_path:
+    value: distil-whisper/large-32-2
+  teacher_model_name_or_path:
+    value: openai/whisper-large-v2
+  train_dataset_name:
+    value: librispeech_asr-timestamped+librispeech_asr-timestamped+librispeech_asr-timestamped+common_voice_13_0-timestamped+voxpopuli-timestamped+ami-ihm-timestamped+ami-sdm-timestamped+peoples_speech-clean-timestamped+tedlium-timestamped+switchboard-data+gigaspeech-l-timestamped+librispeech_asr-prompted+librispeech_asr-prompted+librispeech_asr-prompted+tedlium-prompted
+  train_dataset_config_name:
+    value: all+all+all+en+en+ihm+sdm+clean+release3+all+l+all+all+all+release3
+  train_split_name:
+    value: train.clean.100+train.clean.360+train.other.500+train+train+train+train+train+train+train+train+train.clean.100+train.clean.360+train.other.500+train
+  train_dataset_samples:
+    value: 2.9+10.4+14.9+89+18.2+10.9+10.9+288+26.8+371.2+226.6+2.9+10.4+14.9+26.8
+  eval_dataset_name:
+    value: librispeech_asr+librispeech_asr+common_voice_13_0+voxpopuli+ami-ihm+ami-sdm+peoples_speech-clean+tedlium+switchboard-data+gigaspeech-l+spgispeech+chime4+google/fleurs
+  eval_dataset_config_name:
+    value: all+all+en+en+ihm+sdm+clean+release3+all+l+L+1-channel+en_us
+  eval_split_name:
+    value: validation.clean+validation.other+validation+validation+validation+validation+validation+validation+validation+validation+validation+validation+validation
+  eval_text_column_name:
+    value: text+text+text+text+text+text+text+text+text+text+text+text+transcription
+  cache_dir:
+    value: /home/sanchitgandhi/.cache
+  dataset_cache_dir:
+    value: /home/sanchitgandhi/.cache
+  output_dir:
+    value: ./
+  per_device_train_batch_size:
+    value: 64
+  per_device_eval_batch_size:
+    value: 64
+  dtype:
+    value: bfloat16
+  learning_rate:
+    value: 1e-4
+  lr_scheduler_type:
+    value: constant_with_warmup
+  warmup_steps:
+    value: 50
+  max_steps:
+    value: 10000
+  save_steps:
+    value: 10001  # don't save checkpoints during sweep
+  dataloader_num_workers:
+    value: 48
+  logging_steps:
+    value: 25
+  wer_threshold:
+    values:
+      - 100
+      - 20
+      - 15
+      - 10
+      - 5
+program: run_distillation.py
+project: distil-whisper-sweeps
diff --git a/flax/evaluation_scripts/run_baselines.sh b/flax/evaluation_scripts/run_baselines.sh
new file mode 100644
index 0000000000000000000000000000000000000000..9ab00ff2fe586bda8695bb5c7ee386ff870150d8
--- /dev/null
+++ b/flax/evaluation_scripts/run_baselines.sh
@@ -0,0 +1,96 @@
+#!/usr/bin/env bash
+
+python run_eval.py \
+  --model_name_or_path "openai/whisper-tiny.en" \
+  --dataset_name "librispeech_asr+librispeech_asr+common_voice_13_0+voxpopuli+ami-ihm+ami-sdm+peoples_speech-clean+tedlium+switchboard-data+gigaspeech-l+spgispeech+chime4+google/fleurs+sanchit-gandhi/earnings22_split_resampled" \
+  --dataset_config_name "all+all+en+en+ihm+sdm+clean+release3+all+l+L+1-channel+en_us+default" \
+  --dataset_split_name "validation.clean+validation.other+validation+validation+validation+validation+validation+validation+validation+validation+validation+validation+validation+validation" \
+  --text_column_name "text+text+text+text+text+text+text+text+text+text+text+text+transcription+sentence" \
+  --cache_dir "/home/sanchitgandhi/.cache" \
+  --dataset_cache_dir "/home/sanchitgandhi/.cache" \
+  --output_dir "./" \
+  --wandb_dir "/home/sanchitgandhi/.cache" \
+  --wandb_project "distil-whisper-eval" \
+  --wandb_name "tiny.en" \
+  --per_device_eval_batch_size 32 \
+  --dtype "bfloat16" \
+  --dataloader_num_workers 0 \
+  --report_to "wandb" \
+  --streaming \
+  --predict_with_generate
+
+python run_eval.py \
+  --model_name_or_path "openai/whisper-base.en" \
+  --dataset_name "librispeech_asr+librispeech_asr+common_voice_13_0+voxpopuli+ami-ihm+ami-sdm+peoples_speech-clean+tedlium+switchboard-data+gigaspeech-l+spgispeech+chime4+google/fleurs+sanchit-gandhi/earnings22_split_resampled" \
+  --dataset_config_name "all+all+en+en+ihm+sdm+clean+release3+all+l+L+1-channel+en_us+default" \
+  --dataset_split_name "validation.clean+validation.other+validation+validation+validation+validation+validation+validation+validation+validation+validation+validation+validation+validation" \
+  --text_column_name "text+text+text+text+text+text+text+text+text+text+text+text+transcription+sentence" \
+  --cache_dir "/home/sanchitgandhi/.cache" \
+  --dataset_cache_dir "/home/sanchitgandhi/.cache" \
+  --output_dir "./" \
+  --wandb_dir "/home/sanchitgandhi/.cache" \
+  --wandb_project "distil-whisper-eval" \
+  --wandb_name "base.en" \
+  --per_device_eval_batch_size 32 \
+  --dtype "bfloat16" \
+  --dataloader_num_workers 0 \
+  --report_to "wandb" \
+  --streaming \
+  --predict_with_generate
+
+python run_eval.py \
+  --model_name_or_path "openai/whisper-small.en" \
+  --dataset_name "librispeech_asr+librispeech_asr+common_voice_13_0+voxpopuli+ami-ihm+ami-sdm+peoples_speech-clean+tedlium+switchboard-data+gigaspeech-l+spgispeech+chime4+google/fleurs+sanchit-gandhi/earnings22_split_resampled" \
+  --dataset_config_name "all+all+en+en+ihm+sdm+clean+release3+all+l+L+1-channel+en_us+default" \
+  --dataset_split_name "validation.clean+validation.other+validation+validation+validation+validation+validation+validation+validation+validation+validation+validation+validation+validation" \
+  --text_column_name "text+text+text+text+text+text+text+text+text+text+text+text+transcription+sentence" \
+  --cache_dir "/home/sanchitgandhi/.cache" \
+  --dataset_cache_dir "/home/sanchitgandhi/.cache" \
+  --output_dir "./" \
+  --wandb_dir "/home/sanchitgandhi/.cache" \
+  --wandb_project "distil-whisper-eval" \
+  --wandb_name "small.en" \
+  --per_device_eval_batch_size 32 \
+  --dtype "bfloat16" \
+  --dataloader_num_workers 0 \
+  --report_to "wandb" \
+  --streaming \
+  --predict_with_generate
+
+python run_eval.py \
+  --model_name_or_path "openai/whisper-medium.en" \
+  --dataset_name "librispeech_asr+librispeech_asr+common_voice_13_0+voxpopuli+ami-ihm+ami-sdm+peoples_speech-clean+tedlium+switchboard-data+gigaspeech-l+spgispeech+chime4+google/fleurs+sanchit-gandhi/earnings22_split_resampled" \
+  --dataset_config_name "all+all+en+en+ihm+sdm+clean+release3+all+l+L+1-channel+en_us+default" \
+  --dataset_split_name "validation.clean+validation.other+validation+validation+validation+validation+validation+validation+validation+validation+validation+validation+validation+validation" \
+  --text_column_name "text+text+text+text+text+text+text+text+text+text+text+text+transcription+sentence" \
+  --cache_dir "/home/sanchitgandhi/.cache" \
+  --dataset_cache_dir "/home/sanchitgandhi/.cache" \
+  --output_dir "./" \
+  --wandb_dir "/home/sanchitgandhi/.cache" \
+  --wandb_project "distil-whisper-eval" \
+  --wandb_name "medium.en" \
+  --per_device_eval_batch_size 32 \
+  --dtype "bfloat16" \
+  --dataloader_num_workers 0 \
+  --report_to "wandb" \
+  --streaming \
+  --predict_with_generate
+
+python run_eval.py \
+  --model_name_or_path "openai/whisper-large-v2" \
+  --dataset_name "librispeech_asr+librispeech_asr+common_voice_13_0+voxpopuli+ami-ihm+ami-sdm+peoples_speech-clean+tedlium+switchboard-data+gigaspeech-l+spgispeech+chime4+google/fleurs+sanchit-gandhi/earnings22_split_resampled" \
+  --dataset_config_name "all+all+en+en+ihm+sdm+clean+release3+all+l+L+1-channel+en_us+default" \
+  --dataset_split_name "validation.clean+validation.other+validation+validation+validation+validation+validation+validation+validation+validation+validation+validation+validation+validation" \
+  --text_column_name "text+text+text+text+text+text+text+text+text+text+text+text+transcription+sentence" \
+  --cache_dir "/home/sanchitgandhi/.cache" \
+  --dataset_cache_dir "/home/sanchitgandhi/.cache" \
+  --output_dir "./" \
+  --wandb_dir "/home/sanchitgandhi/.cache" \
+  --wandb_project "distil-whisper-eval" \
+  --wandb_name "large-v2" \
+  --per_device_eval_batch_size 16 \
+  --dtype "bfloat16" \
+  --dataloader_num_workers 0 \
+  --report_to "wandb" \
+  --streaming \
+  --predict_with_generate
\ No newline at end of file
diff --git a/flax/evaluation_scripts/run_distilled.sh b/flax/evaluation_scripts/run_distilled.sh
new file mode 100644
index 0000000000000000000000000000000000000000..d5b2ff4573744726b351db6cdc3146017b5a287f
--- /dev/null
+++ b/flax/evaluation_scripts/run_distilled.sh
@@ -0,0 +1,21 @@
+#!/usr/bin/env bash
+
+python run_eval.py \
+  --model_name_or_path "sanchit-gandhi/large-32-2-ts-freeze-28k-wer-10" \
+  --subfolder "checkpoint-15000" \
+  --dataset_name "librispeech_asr+librispeech_asr+common_voice_13_0+voxpopuli+ami-ihm+ami-sdm+peoples_speech-clean+tedlium+switchboard-data+gigaspeech-l+spgispeech+chime4+google/fleurs+sanchit-gandhi/earnings22_split_resampled" \
+  --dataset_config_name "all+all+en+en+ihm+sdm+clean+release3+all+l+L+1-channel+en_us+default" \
+  --dataset_split_name "validation.clean+validation.other+validation+validation+validation+validation+validation+validation+validation+validation+validation+validation+validation+validation" \
+  --text_column_name "text+text+text+text+text+text+text+text+text+text+text+text+transcription+sentence" \
+  --cache_dir "/home/sanchitgandhi/.cache" \
+  --dataset_cache_dir "/home/sanchitgandhi/.cache" \
+  --output_dir "./" \
+  --wandb_dir "/home/sanchitgandhi/.cache" \
+  --wandb_project "distil-whisper-eval" \
+  --wandb_name "large-32-2-ts-freeze-28k-wer-10-30k-steps" \
+  --per_device_eval_batch_size 64 \
+  --dtype "bfloat16" \
+  --dataloader_num_workers 0 \
+  --report_to "wandb" \
+  --streaming \
+  --predict_with_generate
diff --git a/flax/evaluation_scripts/run_distilled_16_2.sh b/flax/evaluation_scripts/run_distilled_16_2.sh
new file mode 100644
index 0000000000000000000000000000000000000000..661b4a6666e940eeb4cbc07fdb4324ee79e50700
--- /dev/null
+++ b/flax/evaluation_scripts/run_distilled_16_2.sh
@@ -0,0 +1,21 @@
+#!/usr/bin/env bash
+
+python run_eval.py \
+  --model_name_or_path "sanchit-gandhi/large-16-2-ts-28k-wer-10" \
+  --subfolder "checkpoint-10000" \
+  --dataset_name "librispeech_asr+librispeech_asr+common_voice_13_0+voxpopuli+ami-ihm+ami-sdm+peoples_speech-clean+tedlium+switchboard-data+gigaspeech-l+spgispeech+chime4+google/fleurs" \
+  --dataset_config_name "all+all+en+en+ihm+sdm+clean+release3+all+l+L+1-channel+en_us" \
+  --dataset_split_name "validation.clean+validation.other+validation+validation+validation+validation+validation+validation+validation+validation+validation+validation+validation" \
+  --text_column_name "text+text+text+text+text+text+text+text+text+text+text+text+transcription" \
+  --cache_dir "/home/sanchitgandhi/.cache" \
+  --dataset_cache_dir "/home/sanchitgandhi/.cache" \
+  --output_dir "./" \
+  --wandb_dir "/home/sanchitgandhi/.cache" \
+  --wandb_project "distil-whisper-eval" \
+  --wandb_name "large-16-2-eval" \
+  --per_device_eval_batch_size 64 \
+  --dtype "bfloat16" \
+  --dataloader_num_workers 0 \
+  --report_to "wandb" \
+  --streaming \
+  --predict_with_generate
diff --git a/flax/evaluation_scripts/run_librispeech_eval_dummy.sh b/flax/evaluation_scripts/run_librispeech_eval_dummy.sh
new file mode 100644
index 0000000000000000000000000000000000000000..e3dbf8027d3d9a665ec0b8f4602bcdf08d4e9c09
--- /dev/null
+++ b/flax/evaluation_scripts/run_librispeech_eval_dummy.sh
@@ -0,0 +1,22 @@
+#!/usr/bin/env bash
+
+python run_eval.py \
+  --model_name_or_path "openai/whisper-large-v2" \
+  --dataset_name "gigaspeech-l+gigaspeech-l" \
+  --dataset_config_name "l+l" \
+  --dataset_split_name "train+validation" \
+  --text_column_name "text" \
+  --cache_dir "/home/sanchitgandhi/.cache" \
+  --dataset_cache_dir "/home/sanchitgandhi/.cache" \
+  --output_dir "./" \
+  --wandb_dir "/home/sanchitgandhi/.cache" \
+  --wandb_project "distil-whisper-label" \
+  --wandb_name "whisper-large-v2-gigaspeech-l-with-audio" \
+  --per_device_eval_batch_size 64 \
+  --dtype "bfloat16" \
+  --dataloader_num_workers 0 \
+  --report_to "wandb" \
+  --streaming \
+  --max_eval_samples 1024 \
+  --predict_with_generate \
+  --log_audio
diff --git a/flax/evaluation_scripts/test/run_baselines.sh b/flax/evaluation_scripts/test/run_baselines.sh
new file mode 100644
index 0000000000000000000000000000000000000000..91b193aa824b3ebdd04841770064ed067a6dab7d
--- /dev/null
+++ b/flax/evaluation_scripts/test/run_baselines.sh
@@ -0,0 +1,101 @@
+#!/usr/bin/env bash
+
+DATASET_NAMES="librispeech_asr+librispeech_asr+common_voice_13_0+voxpopuli+ami-ihm+ami-sdm+peoples_speech-clean+tedlium+switchboard-data+switchboard-data+gigaspeech-l+spgispeech+chime4+google/fleurs+earnings22"
+DATASET_CONFIG_NAMES="all+all+en+en+ihm+sdm+clean+release3+all+all+l+L+1-channel+en_us+chunked"
+DATASET_SPLIT_NAMES="test.clean+test.other+test+test+test+test+test+test+test.switchboard+test.callhome+test+test+test+test+test"
+TEXT_COLUMN_NAMES="text+text+text+text+text+text+text+text+text+text+text+text+text+transcription+transcription"
+
+python run_eval.py \
+  --model_name_or_path "openai/whisper-tiny.en" \
+  --dataset_name $DATASET_NAMES \
+  --dataset_config_name $DATASET_CONFIG_NAMES \
+  --dataset_split_name $DATASET_SPLIT_NAMES \
+  --text_column_name $TEXT_COLUMN_NAMES \
+  --cache_dir "/home/sanchitgandhi/.cache" \
+  --dataset_cache_dir "/home/sanchitgandhi/.cache" \
+  --output_dir "./" \
+  --wandb_dir "/home/sanchitgandhi/.cache" \
+  --wandb_project "distil-whisper-test" \
+  --wandb_name "tiny.en" \
+  --per_device_eval_batch_size 32 \
+  --dtype "bfloat16" \
+  --dataloader_num_workers 0 \
+  --report_to "wandb" \
+  --streaming \
+  --predict_with_generate
+
+python run_eval.py \
+  --model_name_or_path "openai/whisper-base.en" \
+  --dataset_name $DATASET_NAMES \
+  --dataset_config_name $DATASET_CONFIG_NAMES \
+  --dataset_split_name $DATASET_SPLIT_NAMES \
+  --text_column_name $TEXT_COLUMN_NAMES \
+  --cache_dir "/home/sanchitgandhi/.cache" \
+  --dataset_cache_dir "/home/sanchitgandhi/.cache" \
+  --output_dir "./" \
+  --wandb_dir "/home/sanchitgandhi/.cache" \
+  --wandb_project "distil-whisper-test" \
+  --wandb_name "base.en" \
+  --per_device_eval_batch_size 32 \
+  --dtype "bfloat16" \
+  --dataloader_num_workers 0 \
+  --report_to "wandb" \
+  --streaming \
+  --predict_with_generate
+
+python run_eval.py \
+  --model_name_or_path "openai/whisper-small.en" \
+  --dataset_name $DATASET_NAMES \
+  --dataset_config_name $DATASET_CONFIG_NAMES \
+  --dataset_split_name $DATASET_SPLIT_NAMES \
+  --text_column_name $TEXT_COLUMN_NAMES \
+  --cache_dir "/home/sanchitgandhi/.cache" \
+  --dataset_cache_dir "/home/sanchitgandhi/.cache" \
+  --output_dir "./" \
+  --wandb_dir "/home/sanchitgandhi/.cache" \
+  --wandb_project "distil-whisper-test" \
+  --wandb_name "small.en" \
+  --per_device_eval_batch_size 32 \
+  --dtype "bfloat16" \
+  --dataloader_num_workers 0 \
+  --report_to "wandb" \
+  --streaming \
+  --predict_with_generate
+
+python run_eval.py \
+  --model_name_or_path "openai/whisper-medium.en" \
+  --dataset_name $DATASET_NAMES \
+  --dataset_config_name $DATASET_CONFIG_NAMES \
+  --dataset_split_name $DATASET_SPLIT_NAMES \
+  --text_column_name $TEXT_COLUMN_NAMES \
+  --cache_dir "/home/sanchitgandhi/.cache" \
+  --dataset_cache_dir "/home/sanchitgandhi/.cache" \
+  --output_dir "./" \
+  --wandb_dir "/home/sanchitgandhi/.cache" \
+  --wandb_project "distil-whisper-test" \
+  --wandb_name "medium.en" \
+  --per_device_eval_batch_size 32 \
+  --dtype "bfloat16" \
+  --dataloader_num_workers 0 \
+  --report_to "wandb" \
+  --streaming \
+  --predict_with_generate
+
+python run_eval.py \
+  --model_name_or_path "openai/whisper-large-v2" \
+  --dataset_name $DATASET_NAMES \
+  --dataset_config_name $DATASET_CONFIG_NAMES \
+  --dataset_split_name $DATASET_SPLIT_NAMES \
+  --text_column_name $TEXT_COLUMN_NAMES \
+  --cache_dir "/home/sanchitgandhi/.cache" \
+  --dataset_cache_dir "/home/sanchitgandhi/.cache" \
+  --output_dir "./" \
+  --wandb_dir "/home/sanchitgandhi/.cache" \
+  --wandb_project "distil-whisper-test" \
+  --wandb_name "large-v2" \
+  --per_device_eval_batch_size 16 \
+  --dtype "bfloat16" \
+  --dataloader_num_workers 0 \
+  --report_to "wandb" \
+  --streaming \
+  --predict_with_generate
diff --git a/flax/evaluation_scripts/test/run_baselines_pt.sh b/flax/evaluation_scripts/test/run_baselines_pt.sh
new file mode 100644
index 0000000000000000000000000000000000000000..7e81fc922e54b1ddc99250175b961640ea7866b7
--- /dev/null
+++ b/flax/evaluation_scripts/test/run_baselines_pt.sh
@@ -0,0 +1,22 @@
+#!/usr/bin/env bash
+
+DATASET_NAMES="librispeech_asr+librispeech_asr+common_voice_13_0+voxpopuli+ami-ihm+ami-sdm+peoples_speech-clean+tedlium+switchboard-data+switchboard-data+gigaspeech-l+spgispeech+chime4+google/fleurs+earnings22"
+DATASET_CONFIG_NAMES="all+all+en+en+ihm+sdm+clean+release3+all+all+l+L+1-channel+en_us+chunked"
+DATASET_SPLIT_NAMES="test.clean+test.other+test+test+test+test+test+test+test.switchboard+test.callhome+test+test+test+test+test"
+TEXT_COLUMN_NAMES="text+text+text+text+text+text+text+text+text+text+text+text+text+transcription+transcription"
+
+python run_pt_long_form_transcription.py \
+  --model_name_or_path "facebook/wav2vec2-large-960h" \
+  --wandb_name "facebook/wav2vec2-large-960h" \
+  --dataset_name $DATASET_NAMES \
+  --dataset_config_name $DATASET_CONFIG_NAMES \
+  --dataset_split_name $DATASET_SPLIT_NAMES \
+  --text_column_name $TEXT_COLUMN_NAMES \
+  --output_dir "./" \
+  --wandb_project "distil-whisper-test" \
+  --per_device_eval_batch_size 32 \
+  --dtype "float16" \
+  --dataloader_num_workers 0 \
+  --report_to "wandb" \
+  --streaming \
+  --predict_with_generate
diff --git a/flax/evaluation_scripts/test/run_distilled.sh b/flax/evaluation_scripts/test/run_distilled.sh
new file mode 100644
index 0000000000000000000000000000000000000000..2a1b234520bbc335c08eb0cc58aaaa8bdc8b5a49
--- /dev/null
+++ b/flax/evaluation_scripts/test/run_distilled.sh
@@ -0,0 +1,46 @@
+#!/usr/bin/env bash
+
+DATASET_NAMES="librispeech_asr+librispeech_asr+common_voice_13_0+voxpopuli+ami-ihm+ami-sdm+peoples_speech-clean+tedlium+switchboard-data+switchboard-data+gigaspeech-l+spgispeech+chime4+google/fleurs+earnings22"
+DATASET_CONFIG_NAMES="all+all+en+en+ihm+sdm+clean+release3+all+all+l+L+1-channel+en_us+chunked"
+DATASET_SPLIT_NAMES="test.clean+test.other+test+test+test+test+test+test+test.switchboard+test.callhome+test+test+test+test+test"
+TEXT_COLUMN_NAMES="text+text+text+text+text+text+text+text+text+text+text+text+text+transcription+transcription"
+
+python run_eval.py \
+  --model_name_or_path "sanchit-gandhi/large-32-2-tpu-timestamped-resumed" \
+  --wandb_name "large-32-2-tpu-timestamped" \
+  --dataset_name $DATASET_NAMES \
+  --dataset_config_name $DATASET_CONFIG_NAMES \
+  --dataset_split_name $DATASET_SPLIT_NAMES \
+  --text_column_name $TEXT_COLUMN_NAMES \
+  --cache_dir "/home/sanchitgandhi/.cache" \
+  --dataset_cache_dir "/home/sanchitgandhi/.cache" \
+  --output_dir "./" \
+  --wandb_dir "/home/sanchitgandhi/.cache" \
+  --wandb_project "distil-whisper-test" \
+  --per_device_eval_batch_size 32 \
+  --dtype "bfloat16" \
+  --dataloader_num_workers 0 \
+  --report_to "wandb" \
+  --streaming \
+  --predict_with_generate
+
+
+python run_eval.py \
+  --model_name_or_path "sanchit-gandhi/medium-24-2-tpu-timestamped-prob-0.2" \
+  --subfolder "checkpoint-45000" \
+  --wandb_name "medium-24-2-tpu-timestamped-prob-0.2" \
+  --dataset_name $DATASET_NAMES \
+  --dataset_config_name $DATASET_CONFIG_NAMES \
+  --dataset_split_name $DATASET_SPLIT_NAMES \
+  --text_column_name $TEXT_COLUMN_NAMES \
+  --cache_dir "/home/sanchitgandhi/.cache" \
+  --dataset_cache_dir "/home/sanchitgandhi/.cache" \
+  --output_dir "./" \
+  --wandb_dir "/home/sanchitgandhi/.cache" \
+  --wandb_project "distil-whisper-test" \
+  --per_device_eval_batch_size 32 \
+  --dtype "bfloat16" \
+  --dataloader_num_workers 0 \
+  --report_to "wandb" \
+  --streaming \
+  --predict_with_generate
diff --git a/flax/finetuning_scripts/run_librispeech.sh b/flax/finetuning_scripts/run_librispeech.sh
new file mode 100644
index 0000000000000000000000000000000000000000..a81cd3fd583a9566879789415a403dceaaab1f69
--- /dev/null
+++ b/flax/finetuning_scripts/run_librispeech.sh
@@ -0,0 +1,31 @@
+#!/usr/bin/env bash
+
+python run_finetuning.py \
+  --model_name_or_path "distil-whisper/large-32-2" \
+  --dataset_name "distil-whisper/librispeech_asr" \
+  --dataset_config_name "all" \
+  --train_split_name "train.clean.100+train.clean.360+train.other.500" \
+  --eval_split_name "validation.clean" \
+  --text_column_name "whisper_transcript" \
+  --cache_dir "/home/sanchitgandhi/cache" \
+  --dataset_cache_dir "/home/sanchitgandhi/cache" \
+  --output_dir "./" \
+  --wandb_name "large-32-2-pl-librispeech" \
+  --wandb_dir "/home/sanchitgandhi/.cache" \
+  --wandb_project "distil-whisper-librispeech" \
+  --per_device_train_batch_size 32 \
+  --per_device_eval_batch_size 16 \
+  --dtype "bfloat16" \
+  --learning_rate 1e-4 \
+  --warmup_steps 500 \
+  --do_train \
+  --do_eval \
+  --num_train_epochs 10 \
+  --preprocessing_num_workers 16 \
+  --dataloader_num_workers 8 \
+  --logging_steps 25 \
+  --use_scan \
+  --gradient_checkpointing \
+  --overwrite_output_dir \
+  --predict_with_generate \
+  --push_to_hub
diff --git a/flax/finetuning_scripts/run_librispeech_dummy.sh b/flax/finetuning_scripts/run_librispeech_dummy.sh
new file mode 100644
index 0000000000000000000000000000000000000000..6a3b9e73b051c1eee0b94af8161c8f45167622f3
--- /dev/null
+++ b/flax/finetuning_scripts/run_librispeech_dummy.sh
@@ -0,0 +1,27 @@
+#!/usr/bin/env bash
+
+python run_finetuning.py \
+  --model_name_or_path "distil-whisper/tiny-random-whisper" \
+  --dataset_name "distil-whisper/librispeech_asr" \
+  --dataset_config_name "all" \
+  --train_split_name "train.clean.100[:1024]" \
+  --eval_split_name "validation.clean[:1024]" \
+  --cache_dir "/home/sanchitgandhi/cache" \
+  --dataset_cache_dir "/home/sanchitgandhi/cache" \
+  --wandb_dir "/home/sanchitgandhi/.cache" \
+  --text_column_name "text" \
+  --output_dir "./" \
+  --do_train \
+  --do_eval \
+  --per_device_train_batch_size 8 \
+  --per_device_eval_batch_size 4 \
+  --dtype "bfloat16" \
+  --num_train_epochs 2 \
+  --dataloader_num_workers 16 \
+  --freeze_encoder \
+  --wandb_project "distil-whisper-debug" \
+  --logging_steps 2 \
+  --use_scan \
+  --gradient_checkpointing \
+  --overwrite_output_dir \
+  --predict_with_generate
diff --git a/flax/finetuning_scripts/run_librispeech_eval.sh b/flax/finetuning_scripts/run_librispeech_eval.sh
new file mode 100644
index 0000000000000000000000000000000000000000..116696c9ba72531a1966d93b26ecdcdce1c7986c
--- /dev/null
+++ b/flax/finetuning_scripts/run_librispeech_eval.sh
@@ -0,0 +1,22 @@
+#!/usr/bin/env bash
+
+python run_eval.py \
+  --model_name_or_path "./" \
+  --dataset_name "distil-whisper/librispeech_asr" \
+  --dataset_config_name "all" \
+  --test_split_name "validation.clean+validation.other+test.clean+test.other" \
+  --text_column_name "text" \
+  --cache_dir "/home/sanchitgandhi/cache" \
+  --dataset_cache_dir "/home/sanchitgandhi/cache" \
+  --output_dir "./" \
+  --wandb_name "large-32-2-pl-freeze-librispeech-eval" \
+  --wandb_dir "/home/sanchitgandhi/.cache" \
+  --wandb_project "distil-whisper-librispeech" \
+  --per_device_eval_batch_size 128 \
+  --dtype "bfloat16" \
+  --do_predict \
+  --preprocessing_num_workers 16 \
+  --dataloader_num_workers 8 \
+  --load_with_scan \
+  --predict_with_generate \
+  --report_to "wandb"
diff --git a/flax/finetuning_scripts/run_librispeech_eval_dummy.sh b/flax/finetuning_scripts/run_librispeech_eval_dummy.sh
new file mode 100644
index 0000000000000000000000000000000000000000..7e980e0f4b50a6202831d65ad9c96bed2b5acae9
--- /dev/null
+++ b/flax/finetuning_scripts/run_librispeech_eval_dummy.sh
@@ -0,0 +1,21 @@
+#!/usr/bin/env bash
+
+python run_eval.py \
+  --model_name_or_path "./" \
+  --dataset_name "distil-whisper/librispeech_asr" \
+  --dataset_config_name "all" \
+  --test_split_name "validation.clean[:32]+validation.other[:32]" \
+  --text_column_name "text" \
+  --cache_dir "/home/sanchitgandhi/cache" \
+  --dataset_cache_dir "/home/sanchitgandhi/cache" \
+  --output_dir "./" \
+  --wandb_dir "/home/sanchitgandhi/.cache" \
+  --wandb_project "distil-whisper-debug" \
+  --per_device_eval_batch_size 4 \
+  --dtype "bfloat16" \
+  --do_predict \
+  --preprocessing_num_workers 16 \
+  --dataloader_num_workers 8 \
+  --load_with_scan \
+  --predict_with_generate \
+  --report_to "wandb"
diff --git a/flax/finetuning_scripts/run_librispeech_sweep.yaml b/flax/finetuning_scripts/run_librispeech_sweep.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..07634a8db6da104a37b2082be38dcf58ebf8cee4
--- /dev/null
+++ b/flax/finetuning_scripts/run_librispeech_sweep.yaml
@@ -0,0 +1,60 @@
+command:
+  - python3
+  - ${program}
+  - --do_train
+  - --do_eval
+  - --use_scan
+  - --gradient_checkpointing
+  - --overwrite_output_dir
+  - --predict_with_generate
+  - ${args}
+method: random
+metric:
+  goal: minimize
+  name: eval/wer
+parameters:
+  model_name_or_path:
+    value: distil-whisper/large-32-2
+  dataset_name:
+    value: distil-whisper/librispeech_asr
+  dataset_config_name:
+    value: all
+  train_split_name:
+    value: train.clean.100+train.clean.360+train.other.500
+  eval_split_name:
+    value: validation.clean
+  text_column_name:
+    value: whisper_transcript
+  cache_dir:
+    value: /home/sanchitgandhi/cache
+  dataset_cache_dir:
+    value: /home/sanchitgandhi/cache
+  output_dir:
+    value: ./
+  per_device_train_batch_size:
+    value: 32
+  per_device_eval_batch_size:
+    value: 16
+  dtype:
+    value: bfloat16
+  learning_rate:
+    distribution: log_uniform
+    max: -6.91
+    min: -11.51
+  warmup_steps:
+    value 500
+  num_train_epochs:
+    value: 1
+  preprocessing_num_workers:
+    value: 16
+  dataloader_num_workers:
+    value: 16
+  logging_steps:
+    value: 25
+  freeze_encoder:
+    values:
+      - True
+      - False
+
+program: run_finetuning.py
+project: distil-whisper
diff --git a/flax/initialisation_scripts/run_large_32_2_init.sh b/flax/initialisation_scripts/run_large_32_2_init.sh
new file mode 100644
index 0000000000000000000000000000000000000000..7e7ab99e86ca33517a049ea7b9c8865b1afa1797
--- /dev/null
+++ b/flax/initialisation_scripts/run_large_32_2_init.sh
@@ -0,0 +1,6 @@
+#!/usr/bin/env bash
+
+TCMALLOC_LARGE_ALLOC_REPORT_THRESHOLD=10000000000 python create_student_model.py \
+  --teacher_checkpoint "openai/whisper-large-v2" \
+  --decoder_layers 2 \
+  --save_dir "./"
\ No newline at end of file
diff --git a/flax/initialisation_scripts/run_medium_24_2_init.sh b/flax/initialisation_scripts/run_medium_24_2_init.sh
new file mode 100644
index 0000000000000000000000000000000000000000..6ee9f533ab090a24933ec1340c9a930a4a788e91
--- /dev/null
+++ b/flax/initialisation_scripts/run_medium_24_2_init.sh
@@ -0,0 +1,6 @@
+#!/usr/bin/env bash
+
+TCMALLOC_LARGE_ALLOC_REPORT_THRESHOLD=10000000000 python create_student_model.py \
+  --teacher_checkpoint "openai/whisper-medium.en" \
+  --decoder_layers 2 \
+  --save_dir "./"
diff --git a/flax/initialisation_scripts/run_small_12_2_init.sh b/flax/initialisation_scripts/run_small_12_2_init.sh
new file mode 100644
index 0000000000000000000000000000000000000000..57b4bd5b6d183170d653a4af15223762e8c15f55
--- /dev/null
+++ b/flax/initialisation_scripts/run_small_12_2_init.sh
@@ -0,0 +1,6 @@
+#!/usr/bin/env bash
+
+TCMALLOC_LARGE_ALLOC_REPORT_THRESHOLD=10000000000 python create_student_model.py \
+  --teacher_checkpoint "openai/whisper-small.en" \
+  --decoder_layers 2 \
+  --save_dir "./"
diff --git a/flax/initialisation_scripts/run_tiny_2_1_init.sh b/flax/initialisation_scripts/run_tiny_2_1_init.sh
new file mode 100644
index 0000000000000000000000000000000000000000..a1c3d70f7cbc300b6fcc52eaf56b139bf5a896b1
--- /dev/null
+++ b/flax/initialisation_scripts/run_tiny_2_1_init.sh
@@ -0,0 +1,6 @@
+#!/usr/bin/env bash
+
+TCMALLOC_LARGE_ALLOC_REPORT_THRESHOLD=10000000000 python create_student_model.py \
+  --teacher_checkpoint "distil-whisper/tiny-random-whisper" \
+  --decoder_layers 1 \
+  --save_dir "./"
diff --git a/flax/initialisation_scripts/run_tiny_2_1_init_pt.sh b/flax/initialisation_scripts/run_tiny_2_1_init_pt.sh
new file mode 100644
index 0000000000000000000000000000000000000000..7f850d96ab77d7a2b385380f3e724db19af2617f
--- /dev/null
+++ b/flax/initialisation_scripts/run_tiny_2_1_init_pt.sh
@@ -0,0 +1,6 @@
+#!/usr/bin/env bash
+
+python create_student_model_pt.py \
+  --teacher_checkpoint "distil-whisper/tiny-random-whisper" \
+  --decoder_layers 1 \
+  --save_dir "./"
diff --git a/flax/latency_scripts/run_speculative.sh b/flax/latency_scripts/run_speculative.sh
new file mode 100644
index 0000000000000000000000000000000000000000..d25389b5b8c4b8075aa59c1c369debf3edb63ea8
--- /dev/null
+++ b/flax/latency_scripts/run_speculative.sh
@@ -0,0 +1,30 @@
+#!/usr/bin/env bash
+# batch_sizes=(1 4)
+batch_sizes=(1)
+names=("openai/whisper-large-v2" "openai/whisper-large-v2" "openai/whisper-medium.en" "openai/whisper-medium.en")
+assistant_names=("patrickvonplaten/whisper-large-v2-32-2" "openai/whisper-small" "patrickvonplaten/whisper-medium-24-2" "openai/whisper-base.en")
+
+# --assistant_model_name_or_path "patrickvonplaten/whisper-large-v2-32-2" \
+# --use_pipeline \
+
+# Double loop
+
+for (( i=0; i<${#names[*]}; ++i)); do
+  name=${names[$i]}
+  assistant_name=${assistant_names[$i]}
+
+  for batch_size in "${batch_sizes[@]}"; do
+      CUDA_VISIBLE_DEVICES="0" python ./run_speed_pt.py \
+        --dataset_name "distil-whisper/chime4+distil-whisper/earnings22+google/fleurs+kensho/spgispeech" \
+        --wandb_name "FP16-RTX-4090-bsz${batch_size}-${name}-${assistant_name}" \
+        --model_name_or_path ${name} \
+        --wandb_project "distil-whisper-speed-bench-check-spec-dec-final" \
+        --dataset_config_name "1-channel+chunked+en_us+test" \
+        --dataset_split_name "test+test+test+test" \
+        --text_column_name "text+transcription+transcription+transcript" \
+        --attn_type "flash2" \
+        --assistant_model_name_or_path ${assistant_name} \
+        --samples_per_dataset "10" \
+        --batch_size ${batch_size}
+    done
+done
diff --git a/flax/latency_scripts/run_speed.sh b/flax/latency_scripts/run_speed.sh
new file mode 100644
index 0000000000000000000000000000000000000000..2273a817698ca23e2a4127735fcbc9cde6e29c27
--- /dev/null
+++ b/flax/latency_scripts/run_speed.sh
@@ -0,0 +1,21 @@
+#!/usr/bin/env bash
+# --assistant_model_name_or_path "patrickvonplaten/whisper-large-v2-32-2" \
+# --attn_type "flash2" \
+names=("openai/whisper-large-v2" "openai/whisper-medium.en" "openai/whisper-small.en" "openai/whisper-base.en" "openai/whisper-tiny.en" "patrickvonplaten/whisper-large-v2-32-2" "patrickvonplaten/whisper-medium-24-2")
+batch_sizes=(1 4 16)
+
+# Double loop
+for name in "${names[@]}"; do
+  for batch_size in "${batch_sizes[@]}"; do
+      CUDA_VISIBLE_DEVICES="1" python ./run_speed_pt.py \
+        --dataset_name "google/fleurs+distil-whisper/chime4+distil-whisper/earnings22+kensho/spgispeech" \
+        --wandb_name "A100-bsz${batch_size}-${name}" \
+        --model_name_or_path ${name} \
+        --wandb_project "distil-whisper-speed-bench-256-no-timestamps" \
+        --dataset_config_name "en_us+1-channel+chunked+test" \
+        --dataset_split_nam "test+test+test+test" \
+        --text_column_name "transcription+text+transcription+transcript" \
+	--samples_per_dataset "256" \
+        --batch_size ${batch_size}
+    done
+done
diff --git a/flax/latency_scripts/run_speed_longform.sh b/flax/latency_scripts/run_speed_longform.sh
new file mode 100644
index 0000000000000000000000000000000000000000..ed7fe5d7f5c9ac27d555033bdd343e891f60e94e
--- /dev/null
+++ b/flax/latency_scripts/run_speed_longform.sh
@@ -0,0 +1,30 @@
+#!/usr/bin/env bash
+names=("openai/whisper-large-v2" "openai/whisper-medium.en" "openai/whisper-small.en" "openai/whisper-base.en" "openai/whisper-tiny.en" "patrickvonplaten/whisper-large-v2-32-2" "patrickvonplaten/whisper-medium-24-2")
+
+# chunk_lengths=("15.0" "30.0")
+# --assistant_model_name_or_path "patrickvonplaten/whisper-large-v2-32-2" \
+# --attn_type "flash" \
+
+# Double loop
+for name in "${names[@]}"; do
+  if [[ ${name:0:6} == "openai" ]]; then
+    chunk_length_s=30.0
+  else
+    chunk_length_s=15.0
+  fi
+
+  CUDA_VISIBLE_DEVICES="1" python ./run_speed_pt.py \
+    --dataset_name "distil-whisper/earnings21+distil-whisper/earnings22+distil-whisper/meanwhile+distil-whisper/rev16" \
+    --wandb_name "T4-${name}-Longform" \
+    --model_name_or_path ${name} \
+    --wandb_project "distil-whisper-speed-bench-long-form-32" \
+    --dataset_config_name "full+full+default+whisper_subset" \
+    --dataset_split_name "test+test+test+test" \
+    --text_column_name "transcription+transcription+text+transcription" \
+    --chunk_length_s "$chunk_length_s" \
+    --use_pipeline \
+    --return_timestamps \
+    --max_label_length "1000000" \
+    --samples_per_dataset "32" \
+    --batch_size "1"
+done
diff --git a/flax/latency_scripts/run_trial.sh b/flax/latency_scripts/run_trial.sh
new file mode 100644
index 0000000000000000000000000000000000000000..d7fafd6c98557e3abaa0300d9975494060960d1e
--- /dev/null
+++ b/flax/latency_scripts/run_trial.sh
@@ -0,0 +1,10 @@
+#!/usr/bin/env bash
+CUDA_VISIBLE_DEVICES="0" python ./run_speed_pt.py \
+  --dataset_name "distil-whisper/earnings22" \
+  --wandb_name "[Earnings] RTX 4090 - large-v2-32-2" \
+  --model_name_or_path "patrickvonplaten/whisper-large-v2-32-2" \
+  --wandb_project "distil-whisper-speed-benchmark" \
+  --dataset_config_name "chunked" \
+  --dataset_split_nam "test" \
+  --text_column_name "transcription" \
+  --batch_size 1
diff --git a/flax/long_form_transcription_scripts/run_chunk_length_s_sweep.yaml b/flax/long_form_transcription_scripts/run_chunk_length_s_sweep.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..c1ff93d231916a3b06338ea5f7d71d3f943a7690
--- /dev/null
+++ b/flax/long_form_transcription_scripts/run_chunk_length_s_sweep.yaml
@@ -0,0 +1,47 @@
+command:
+  - python3
+  - ${program}
+  - --streaming
+  - ${args}
+method: grid
+metric:
+  goal: minimize
+  name: tedlium-long-form/validation/wer
+parameters:
+  model_name_or_path:
+    value: sanchit-gandhi/large-32-2-ts-freeze-28k-wer-10
+  subfolder:
+    value: checkpoint-15000
+  dataset_name:
+    value: distil-whisper/tedlium-long-form
+  dataset_config_name:
+    value: all
+  dataset_split_name:
+    value: validation
+  cache_dir:
+    value: /home/sanchitgandhi/.cache
+  dataset_cache_dir:
+    value: /home/sanchitgandhi/.cache
+  compilation_cache:
+    value: /home/sanchitgandhi/.cache
+  output_dir:
+    value: ./
+  wandb_dir:
+    value: /home/sanchitgandhi/.cache
+  per_device_eval_batch_size:
+    value: 8
+  dtype:
+    value: bfloat16
+  report_to:
+    value: wandb
+  chunk_length_s:
+    values:
+      - 10
+      - 15
+      - 20
+      - 25
+      - 30
+  generation_max_length:
+    value: 128
+program: run_long_form_transcription.py
+project: distil-whisper-long-form
\ No newline at end of file
diff --git a/flax/long_form_transcription_scripts/run_eval_with_pipeline.sh b/flax/long_form_transcription_scripts/run_eval_with_pipeline.sh
new file mode 100644
index 0000000000000000000000000000000000000000..4db30b3ce86bfc8a2da40e99fe894cc8f415086b
--- /dev/null
+++ b/flax/long_form_transcription_scripts/run_eval_with_pipeline.sh
@@ -0,0 +1,24 @@
+#!/usr/bin/env bash
+
+DATASET_NAMES="librispeech_asr+librispeech_asr+common_voice_13_0+voxpopuli+ami-ihm+ami-sdm+peoples_speech-clean+tedlium+switchboard-data+gigaspeech-l+spgispeech+chime4+google/fleurs+sanchit-gandhi/earnings22_split_resampled"
+DATASET_CONFIG_NAMES="all+all+en+en+ihm+sdm+clean+release3+all+l+L+1-channel+en_us+default"
+DATASET_SPLIT_NAMES="validation.clean+validation.other+validation+validation+validation+validation+validation+validation+validation+validation+validation+validation+validation+validation"
+TEXT_COLUMN_NAMES="text+text+text+text+text+text+text+text+text+text+text+text+transcription+sentence"
+
+python run_long_form_transcription.py \
+  --model_name_or_path "sanchit-gandhi/large-32-2-ts-28k-wer-10-converted-context-20s" \
+  --dataset_name $DATASET_NAMES \
+  --dataset_config_name $DATASET_CONFIG_NAMES \
+  --dataset_split_name  $DATASET_SPLIT_NAMES \
+  --text_column_name $TEXT_COLUMN_NAMES \
+  --cache_dir "/home/sanchitgandhi/.cache" \
+  --dataset_cache_dir "/home/sanchitgandhi/.cache" \
+  --output_dir "./" \
+  --wandb_dir "/home/sanchitgandhi/.cache" \
+  --wandb_project "distil-whisper-eval" \
+  --wandb_name "large-32-2-ts-freeze-28k-wer-10-30k-steps-chunk-length-15-context-20" \
+  --per_device_eval_batch_size 1 \
+  --chunk_length_s 15 \
+  --dtype "bfloat16" \
+  --report_to "wandb" \
+  --streaming
diff --git a/flax/long_form_transcription_scripts/run_length_penalty_sweep.yaml b/flax/long_form_transcription_scripts/run_length_penalty_sweep.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..18f6ad7f67a8a7c6c020ac9096a91e5343deb113
--- /dev/null
+++ b/flax/long_form_transcription_scripts/run_length_penalty_sweep.yaml
@@ -0,0 +1,47 @@
+command:
+  - python3
+  - ${program}
+  - --streaming
+  - ${args}
+method: grid
+metric:
+  goal: minimize
+  name: tedlium-long-form/validation/wer
+parameters:
+  model_name_or_path:
+    value: sanchit-gandhi/large-32-2-ts-freeze-28k-wer-10
+  subfolder:
+    value: checkpoint-15000
+  dataset_name: 
+    value: distil-whisper/tedlium-long-form
+  dataset_config_name: 
+    value: all
+  dataset_split_name: 
+    value: validation
+  cache_dir: 
+    value: /home/sanchitgandhi/.cache
+  dataset_cache_dir: 
+    value: /home/sanchitgandhi/.cache
+  output_dir: 
+    value: ./
+  wandb_dir: 
+    value: /home/sanchitgandhi/.cache
+  per_device_eval_batch_size:
+    value: 32
+  dtype: 
+    value: bfloat16
+  report_to:
+    value: wandb
+  generation_num_beams:
+    value: 5
+  generation_max_length:
+    value: 256
+  length_penalty:
+    values:
+      - 0.6
+      - 0.8
+      - 1.0
+      - 1.2
+      - 1.4
+program: run_long_form_transcription.py
+project: distil-whisper-long-form
\ No newline at end of file
diff --git a/flax/long_form_transcription_scripts/run_tedlium_long_form.sh b/flax/long_form_transcription_scripts/run_tedlium_long_form.sh
new file mode 100644
index 0000000000000000000000000000000000000000..53a7152a764c3531b224cee30267cf0a352dadf0
--- /dev/null
+++ b/flax/long_form_transcription_scripts/run_tedlium_long_form.sh
@@ -0,0 +1,19 @@
+#!/usr/bin/env bash
+
+python run_long_form_transcription.py \
+  --model_name_or_path "sanchit-gandhi/large-32-2-ts-freeze-28k-wer-10" \
+  --subfolder "checkpoint-15000" \
+  --dataset_name "distil-whisper/tedlium-long-form" \
+  --dataset_config_name "all" \
+  --dataset_split_name "validation" \
+  --cache_dir "/home/sanchitgandhi/.cache" \
+  --dataset_cache_dir "/home/sanchitgandhi/.cache" \
+  --output_dir "./" \
+  --wandb_dir "/home/sanchitgandhi/.cache" \
+  --wandb_project "distil-whisper-long-form" \
+  --wandb_name "large-32-2-ts-freeze-28k-wer-10-30k-steps" \
+  --per_device_eval_batch_size 32 \
+  --chunk_length_s 20 \
+  --dtype "bfloat16" \
+  --report_to "wandb" \
+  --streaming
diff --git a/flax/long_form_transcription_scripts/run_tedlium_long_form_dummy.sh b/flax/long_form_transcription_scripts/run_tedlium_long_form_dummy.sh
new file mode 100644
index 0000000000000000000000000000000000000000..ddf854b8d6d402f042357dc0511509febc757400
--- /dev/null
+++ b/flax/long_form_transcription_scripts/run_tedlium_long_form_dummy.sh
@@ -0,0 +1,18 @@
+#!/usr/bin/env bash
+
+python run_long_form_transcription.py \
+  --model_name_or_path "openai/whisper-tiny" \
+  --dataset_name "distil-whisper/tedlium-long-form" \
+  --dataset_config_name "all" \
+  --dataset_split_name "validation" \
+  --cache_dir "/home/sanchitgandhi/.cache" \
+  --dataset_cache_dir "/home/sanchitgandhi/.cache" \
+  --output_dir "./" \
+  --wandb_dir "/home/sanchitgandhi/.cache" \
+  --wandb_project "distil-whisper-debug" \
+  --wandb_name "whisper-tiny-tedlium-long-form" \
+  --per_device_eval_batch_size 64 \
+  --max_eval_samples 1 \
+  --dtype "bfloat16" \
+  --report_to "wandb" \
+  --streaming
diff --git a/flax/long_form_transcription_scripts/run_tedlium_long_form_timestamps.sh b/flax/long_form_transcription_scripts/run_tedlium_long_form_timestamps.sh
new file mode 100644
index 0000000000000000000000000000000000000000..314c566928a5751090e85059a33dba239bef1cd2
--- /dev/null
+++ b/flax/long_form_transcription_scripts/run_tedlium_long_form_timestamps.sh
@@ -0,0 +1,18 @@
+#!/usr/bin/env bash
+
+python run_long_form_transcription.py \
+  --model_name_or_path "sanchit-gandhi/large-32-2-ts-freeze-28k-wer-10-v4-8-10k-steps" \
+  --dataset_name "distil-whisper/tedlium-long-form+distil-whisper/tedlium-long-form" \
+  --dataset_config_name "all+all" \
+  --dataset_split_name "validation+test" \
+  --cache_dir "/home/sanchitgandhi/.cache" \
+  --dataset_cache_dir "/home/sanchitgandhi/.cache" \
+  --output_dir "./" \
+  --wandb_dir "/home/sanchitgandhi/.cache" \
+  --wandb_project "distil-whisper-long-form" \
+  --wandb_name "large-32-2-ts-freeze-28k-wer-10-v4-8-10k-steps-tedlium-timestamps" \
+  --per_device_eval_batch_size 32 \
+  --dtype "bfloat16" \
+  --report_to "wandb" \
+  --streaming \
+  --return_timestamps
\ No newline at end of file
diff --git a/flax/long_form_transcription_scripts/run_top_k_temperature_sweep.yaml b/flax/long_form_transcription_scripts/run_top_k_temperature_sweep.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..bb2c1a72028d2c5c9a0a038824885a72203e8701
--- /dev/null
+++ b/flax/long_form_transcription_scripts/run_top_k_temperature_sweep.yaml
@@ -0,0 +1,51 @@
+command:
+  - python3
+  - ${program}
+  - --streaming
+  - --do_sample
+  - ${args}
+method: grid
+metric:
+  goal: minimize
+  name: tedlium-long-form/validation/wer
+parameters:
+  model_name_or_path:
+    value: sanchit-gandhi/large-32-2-ts-freeze-28k-wer-10
+  subfolder:
+    value: checkpoint-15000
+  dataset_name:
+    value: distil-whisper/tedlium-long-form
+  dataset_config_name:
+    value: all
+  dataset_split_name:
+    value: validation
+  cache_dir:
+    value: /home/sanchitgandhi/.cache
+  dataset_cache_dir:
+    value: /home/sanchitgandhi/.cache
+  output_dir:
+    value: ./
+  wandb_dir:
+    value: /home/sanchitgandhi/.cache
+  per_device_eval_batch_size:
+    value: 32
+  dtype:
+    value: bfloat16
+  report_to:
+    value: wandb
+  generation_num_beams:
+    value: 1
+  generation_max_length:
+    value: 256
+  temperature:
+    values:
+      - 0.2
+      - 0.4
+      - 0.6
+      - 0.8
+      - 1.0
+      - 1.2
+  chunk_length_s:
+    value: 20
+program: run_long_form_transcription.py
+project: distil-whisper-long-form
\ No newline at end of file
diff --git a/flax/long_form_transcription_scripts/test/run_baselines.sh b/flax/long_form_transcription_scripts/test/run_baselines.sh
new file mode 100644
index 0000000000000000000000000000000000000000..d1a1b586cbce5d45877dfd1099d854a16211e317
--- /dev/null
+++ b/flax/long_form_transcription_scripts/test/run_baselines.sh
@@ -0,0 +1,106 @@
+#!/usr/bin/env bash
+
+DATASET_NAMES="distil-whisper/tedlium-long-form+distil-whisper/earnings21+distil-whisper/earnings22+distil-whisper/meanwhile+distil-whisper/rev16"
+DATASET_CONFIG_NAMES="all+full+full+default+whisper_subset"
+DATASET_SPLIT_NAMES="test+test+test+test+test"
+TEXT_COLUMN_NAMES="text+transcription+transcription+text+transcription"
+
+python run_long_form_transcription.py \
+  --model_name_or_path "openai/whisper-tiny.en" \
+  --dataset_name $DATASET_NAMES \
+  --dataset_config_name $DATASET_CONFIG_NAMES \
+  --dataset_split_name  $DATASET_SPLIT_NAMES \
+  --text_column_name $TEXT_COLUMN_NAMES \
+  --cache_dir "/home/sanchitgandhi/.cache" \
+  --dataset_cache_dir "/home/sanchitgandhi/.cache" \
+  --output_dir "./" \
+  --wandb_dir "/home/sanchitgandhi/.cache" \
+  --wandb_project "distil-whisper-long-form-test" \
+  --wandb_name "tiny.en" \
+  --per_device_eval_batch_size 16 \
+  --chunk_length_s 30 \
+  --generation_max_length 128 \
+  --dtype "bfloat16" \
+  --report_to "wandb" \
+  --streaming \
+  --return_timestamps
+
+python run_long_form_transcription.py \
+  --model_name_or_path "openai/whisper-base.en" \
+  --dataset_name $DATASET_NAMES \
+  --dataset_config_name $DATASET_CONFIG_NAMES \
+  --dataset_split_name  $DATASET_SPLIT_NAMES \
+  --text_column_name $TEXT_COLUMN_NAMES \
+  --cache_dir "/home/sanchitgandhi/.cache" \
+  --dataset_cache_dir "/home/sanchitgandhi/.cache" \
+  --output_dir "./" \
+  --wandb_dir "/home/sanchitgandhi/.cache" \
+  --wandb_project "distil-whisper-long-form-test" \
+  --wandb_name "base.en" \
+  --per_device_eval_batch_size 16 \
+  --chunk_length_s 30 \
+  --generation_max_length 128 \
+  --dtype "bfloat16" \
+  --report_to "wandb" \
+  --streaming \
+  --return_timestamps
+
+python run_long_form_transcription.py \
+  --model_name_or_path "openai/whisper-small.en" \
+  --dataset_name $DATASET_NAMES \
+  --dataset_config_name $DATASET_CONFIG_NAMES \
+  --dataset_split_name  $DATASET_SPLIT_NAMES \
+  --text_column_name $TEXT_COLUMN_NAMES \
+  --cache_dir "/home/sanchitgandhi/.cache" \
+  --dataset_cache_dir "/home/sanchitgandhi/.cache" \
+  --output_dir "./" \
+  --wandb_dir "/home/sanchitgandhi/.cache" \
+  --wandb_project "distil-whisper-long-form-test" \
+  --wandb_name "small.en" \
+  --per_device_eval_batch_size 16 \
+  --chunk_length_s 30 \
+  --generation_max_length 128 \
+  --dtype "bfloat16" \
+  --report_to "wandb" \
+  --streaming \
+  --return_timestamps
+
+python run_long_form_transcription.py \
+  --model_name_or_path "openai/whisper-medium.en" \
+  --dataset_name $DATASET_NAMES \
+  --dataset_config_name $DATASET_CONFIG_NAMES \
+  --dataset_split_name  $DATASET_SPLIT_NAMES \
+  --text_column_name $TEXT_COLUMN_NAMES \
+  --cache_dir "/home/sanchitgandhi/.cache" \
+  --dataset_cache_dir "/home/sanchitgandhi/.cache" \
+  --output_dir "./" \
+  --wandb_dir "/home/sanchitgandhi/.cache" \
+  --wandb_project "distil-whisper-long-form-test" \
+  --wandb_name "medium.en" \
+  --per_device_eval_batch_size 16 \
+  --chunk_length_s 30 \
+  --generation_max_length 128 \
+  --dtype "bfloat16" \
+  --report_to "wandb" \
+  --streaming \
+  --return_timestamps
+
+python run_long_form_transcription.py \
+  --model_name_or_path "openai/whisper-large-v2" \
+  --dataset_name $DATASET_NAMES \
+  --dataset_config_name $DATASET_CONFIG_NAMES \
+  --dataset_split_name  $DATASET_SPLIT_NAMES \
+  --text_column_name $TEXT_COLUMN_NAMES \
+  --cache_dir "/home/sanchitgandhi/.cache" \
+  --dataset_cache_dir "/home/sanchitgandhi/.cache" \
+  --output_dir "./" \
+  --wandb_dir "/home/sanchitgandhi/.cache" \
+  --wandb_project "distil-whisper-long-form-test" \
+  --wandb_name "large-v2" \
+  --per_device_eval_batch_size 16 \
+  --chunk_length_s 30 \
+  --generation_max_length 128 \
+  --dtype "bfloat16" \
+  --report_to "wandb" \
+  --streaming \
+  --return_timestamps
diff --git a/flax/long_form_transcription_scripts/test/run_baselines_pt.sh b/flax/long_form_transcription_scripts/test/run_baselines_pt.sh
new file mode 100644
index 0000000000000000000000000000000000000000..84776311005296b455b5ce612f17dafee4d4ba2f
--- /dev/null
+++ b/flax/long_form_transcription_scripts/test/run_baselines_pt.sh
@@ -0,0 +1,21 @@
+#!/usr/bin/env bash
+
+DATASET_NAMES="distil-whisper/tedlium-long-form+distil-whisper/earnings21+distil-whisper/earnings22+distil-whisper/meanwhile+distil-whisper/rev16"
+DATASET_CONFIG_NAMES="all+full+full+default+whisper_subset"
+DATASET_SPLIT_NAMES="test+test+test+test+test"
+TEXT_COLUMN_NAMES="text+transcription+transcription+text+transcription"
+
+python run_pt_long_form_transcription.py \
+  --model_name_or_path "facebook/wav2vec2-large-960h" \
+  --dataset_name $DATASET_NAMES \
+  --dataset_config_name $DATASET_CONFIG_NAMES \
+  --dataset_split_name  $DATASET_SPLIT_NAMES \
+  --text_column_name $TEXT_COLUMN_NAMES \
+  --output_dir "./" \
+  --wandb_project "distil-whisper-long-form-test" \
+  --wandb_name "wav2vec2-large-960h" \
+  --per_device_eval_batch_size 32 \
+  --chunk_length_s 20 \
+  --dtype "float16" \
+  --report_to "wandb" \
+  --streaming
diff --git a/flax/long_form_transcription_scripts/test/run_distilled.sh b/flax/long_form_transcription_scripts/test/run_distilled.sh
new file mode 100644
index 0000000000000000000000000000000000000000..8d1b879d4f4f2abba06257d518d1eaced6bca4f2
--- /dev/null
+++ b/flax/long_form_transcription_scripts/test/run_distilled.sh
@@ -0,0 +1,45 @@
+#!/usr/bin/env bash
+
+DATASET_NAMES="distil-whisper/tedlium-long-form+distil-whisper/earnings21+distil-whisper/earnings22+distil-whisper/meanwhile+distil-whisper/rev16"
+DATASET_CONFIG_NAMES="all+full+full+default+whisper_subset"
+DATASET_SPLIT_NAMES="test+test+test+test+test"
+TEXT_COLUMN_NAMES="text+transcription+transcription+text+transcription"
+
+python run_long_form_transcription.py \
+  --model_name_or_path "sanchit-gandhi/large-32-2-tpu-timestamped-resumed" \
+  --dataset_name $DATASET_NAMES \
+  --dataset_config_name $DATASET_CONFIG_NAMES \
+  --dataset_split_name  $DATASET_SPLIT_NAMES \
+  --text_column_name $TEXT_COLUMN_NAMES \
+  --cache_dir "/home/sanchitgandhi/.cache" \
+  --dataset_cache_dir "/home/sanchitgandhi/.cache" \
+  --output_dir "./" \
+  --wandb_dir "/home/sanchitgandhi/.cache" \
+  --wandb_project "distil-whisper-long-form-test" \
+  --wandb_name "large-32-2" \
+  --per_device_eval_batch_size 16 \
+  --chunk_length_s 15 \
+  --generation_max_length 128 \
+  --dtype "bfloat16" \
+  --report_to "wandb" \
+  --streaming
+
+python run_long_form_transcription.py \
+  --model_name_or_path "sanchit-gandhi/medium-24-2-tpu-timestamped-prob-0.2" \
+  --subfolder "checkpoint-45000" \
+  --dataset_name $DATASET_NAMES \
+  --dataset_config_name $DATASET_CONFIG_NAMES \
+  --dataset_split_name  $DATASET_SPLIT_NAMES \
+  --text_column_name $TEXT_COLUMN_NAMES \
+  --cache_dir "/home/sanchitgandhi/.cache" \
+  --dataset_cache_dir "/home/sanchitgandhi/.cache" \
+  --output_dir "./" \
+  --wandb_dir "/home/sanchitgandhi/.cache" \
+  --wandb_project "distil-whisper-long-form-test" \
+  --wandb_name "medium-24-2" \
+  --per_device_eval_batch_size 16 \
+  --chunk_length_s 20 \
+  --generation_max_length 128 \
+  --dtype "bfloat16" \
+  --report_to "wandb" \
+  --streaming
diff --git a/flax/noise_evaluation_scripts/run_baselines.sh b/flax/noise_evaluation_scripts/run_baselines.sh
new file mode 100644
index 0000000000000000000000000000000000000000..eb9a2e507ab102fb5a9db8e6c6c8947ce96aae32
--- /dev/null
+++ b/flax/noise_evaluation_scripts/run_baselines.sh
@@ -0,0 +1,98 @@
+#!/usr/bin/env bash
+
+DATASET_NAME="librispeech_asr-noise+librispeech_asr-noise+librispeech_asr-noise+librispeech_asr-noise+librispeech_asr-noise+librispeech_asr-noise+librispeech_asr-noise+librispeech_asr-noise+librispeech_asr-noise+librispeech_asr-noise+librispeech_asr-noise"
+DATASET_CONFIG_NAME=("validation-white-noise" "validation-pub-noise")
+DATASET_SPLIT_NAME="40+35+30+25+20+15+10+5+0+minus5+minus10"
+
+for i in "${!DATASET_CONFIG_NAME[@]}"; do
+  python run_eval.py \
+    --model_name_or_path "openai/whisper-tiny.en" \
+    --dataset_name $DATASET_NAME \
+    --dataset_config_name "${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}" \
+    --dataset_split_name $DATASET_SPLIT_NAME \
+    --cache_dir "/home/sanchitgandhi/cache" \
+    --dataset_cache_dir "/home/sanchitgandhi/cache" \
+    --output_dir "./" \
+    --wandb_dir "/home/sanchitgandhi/cache" \
+    --wandb_project "distil-whisper-noise-eval" \
+    --wandb_name "tiny.en-${DATASET_CONFIG_NAME[i]}" \
+    --per_device_eval_batch_size 64 \
+    --dtype "bfloat16" \
+    --dataloader_num_workers 16 \
+    --report_to "wandb" \
+    --streaming \
+    --predict_with_generate
+
+  python run_eval.py \
+    --model_name_or_path "openai/whisper-base.en" \
+    --dataset_name $DATASET_NAME \
+    --dataset_config_name "${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}" \
+    --dataset_split_name $DATASET_SPLIT_NAME \
+    --cache_dir "/home/sanchitgandhi/cache" \
+    --dataset_cache_dir "/home/sanchitgandhi/cache" \
+    --output_dir "./" \
+    --wandb_dir "/home/sanchitgandhi/cache" \
+    --wandb_project "distil-whisper-noise-eval" \
+    --wandb_name "base.en-${DATASET_CONFIG_NAME[i]}" \
+    --per_device_eval_batch_size 64 \
+    --dtype "bfloat16" \
+    --dataloader_num_workers 16 \
+    --report_to "wandb" \
+    --streaming \
+    --predict_with_generate
+
+  python run_eval.py \
+    --model_name_or_path "openai/whisper-small.en" \
+    --dataset_name $DATASET_NAME \
+    --dataset_config_name "${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}" \
+    --dataset_split_name $DATASET_SPLIT_NAME \
+    --cache_dir "/home/sanchitgandhi/cache" \
+    --dataset_cache_dir "/home/sanchitgandhi/cache" \
+    --output_dir "./" \
+    --wandb_dir "/home/sanchitgandhi/cache" \
+    --wandb_project "distil-whisper-noise-eval" \
+    --wandb_name "small.en-${DATASET_CONFIG_NAME[i]}" \
+    --per_device_eval_batch_size 64 \
+    --dtype "bfloat16" \
+    --dataloader_num_workers 16 \
+    --report_to "wandb" \
+    --streaming \
+    --predict_with_generate
+
+  python run_eval.py \
+    --model_name_or_path "openai/whisper-medium.en" \
+    --dataset_name $DATASET_NAME \
+    --dataset_config_name "${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}" \
+    --dataset_split_name $DATASET_SPLIT_NAME \
+    --cache_dir "/home/sanchitgandhi/cache" \
+    --dataset_cache_dir "/home/sanchitgandhi/cache" \
+    --output_dir "./" \
+    --wandb_dir "/home/sanchitgandhi/cache" \
+    --wandb_project "distil-whisper-noise-eval" \
+    --wandb_name "medium.en-${DATASET_CONFIG_NAME[i]}" \
+    --per_device_eval_batch_size 64 \
+    --dtype "bfloat16" \
+    --dataloader_num_workers 16 \
+    --report_to "wandb" \
+    --streaming \
+    --predict_with_generate
+
+  python run_eval.py \
+    --model_name_or_path "openai/whisper-large-v2" \
+    --dataset_name $DATASET_NAME \
+    --dataset_config_name "${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}" \
+    --dataset_split_name $DATASET_SPLIT_NAME \
+    --cache_dir "/home/sanchitgandhi/cache" \
+    --dataset_cache_dir "/home/sanchitgandhi/cache" \
+    --output_dir "./" \
+    --wandb_dir "/home/sanchitgandhi/cache" \
+    --wandb_project "distil-whisper-noise-eval" \
+    --wandb_name "large-v2-${DATASET_CONFIG_NAME[i]}" \
+    --per_device_eval_batch_size 32 \
+    --dtype "bfloat16" \
+    --dataloader_num_workers 16 \
+    --report_to "wandb" \
+    --streaming \
+    --predict_with_generate
+
+done
\ No newline at end of file
diff --git a/flax/noise_evaluation_scripts/run_baselines_pt.sh b/flax/noise_evaluation_scripts/run_baselines_pt.sh
new file mode 100644
index 0000000000000000000000000000000000000000..cc49dc577c58fba90d17abb916ed46971d30bc87
--- /dev/null
+++ b/flax/noise_evaluation_scripts/run_baselines_pt.sh
@@ -0,0 +1,27 @@
+#!/usr/bin/env bash
+
+MODEL_IDs=("facebook/wav2vec2-base-960h" "facebook/wav2vec2-large-960h" "facebook/wav2vec2-large-960h-lv60-self" "facebook/wav2vec2-large-robust-ft-libri-960h" "facebook/wav2vec2-conformer-rel-pos-large-960h-ft" "facebook/wav2vec2-conformer-rope-large-960h-ft" "facebook/hubert-large-ls960-ft" "facebook/hubert-xlarge-ls960-ft" "facebook/mms-1b-all" "facebook/mms-1b-fl102" "facebook/data2vec-audio-large-960h" "facebook/data2vec-audio-base-960h")
+DATASET_NAME="librispeech_asr-noise+librispeech_asr-noise+librispeech_asr-noise+librispeech_asr-noise+librispeech_asr-noise+librispeech_asr-noise+librispeech_asr-noise+librispeech_asr-noise+librispeech_asr-noise+librispeech_asr-noise+librispeech_asr-noise"
+DATASET_CONFIG_NAME=("test-white-noise" "test-pub-noise")
+DATASET_SPLIT_NAME="40+35+30+25+20+15+10+5+0+minus5+minus10"
+
+for i in "${!MODEL_IDs[@]}"; do
+  for j in "${!DATASET_CONFIG_NAME[@]}"; do
+    python run_pt_long_form_transcription.py \
+        --model_name_or_path "${MODEL_IDs[i]}" \
+        --dataset_name $DATASET_NAME \
+        --dataset_config_name "${DATASET_CONFIG_NAME[j]}+${DATASET_CONFIG_NAME[j]}+${DATASET_CONFIG_NAME[j]}+${DATASET_CONFIG_NAME[j]}+${DATASET_CONFIG_NAME[j]}+${DATASET_CONFIG_NAME[j]}+${DATASET_CONFIG_NAME[j]}+${DATASET_CONFIG_NAME[j]}+${DATASET_CONFIG_NAME[j]}+${DATASET_CONFIG_NAME[j]}+${DATASET_CONFIG_NAME[j]}" \
+        --dataset_split_name $DATASET_SPLIT_NAME \
+        --cache_dir "/home/sanchit/.cache" \
+        --dataset_cache_dir "/home/sanchit/.cache" \
+        --output_dir "./" \
+        --wandb_dir "/home/sanchit/.cache" \
+        --wandb_project "distil-whisper-noise-test" \
+        --wandb_name "${MODEL_IDs[i]}-${DATASET_CONFIG_NAME[j]}" \
+        --per_device_eval_batch_size 16 \
+        --dtype "float16" \
+        --report_to "wandb" \
+        --streaming \
+        --predict_with_generate
+  done
+done
diff --git a/flax/noise_evaluation_scripts/run_distilled.sh b/flax/noise_evaluation_scripts/run_distilled.sh
new file mode 100644
index 0000000000000000000000000000000000000000..123971f50432ec2f00e10d24b869fcd9e36f7c6f
--- /dev/null
+++ b/flax/noise_evaluation_scripts/run_distilled.sh
@@ -0,0 +1,25 @@
+#!/usr/bin/env bash
+
+DATASET_NAME="librispeech_asr-noise+librispeech_asr-noise+librispeech_asr-noise+librispeech_asr-noise+librispeech_asr-noise+librispeech_asr-noise+librispeech_asr-noise+librispeech_asr-noise+librispeech_asr-noise+librispeech_asr-noise+librispeech_asr-noise"
+DATASET_CONFIG_NAME=("validation-white-noise" "validation-pub-noise")
+DATASET_SPLIT_NAME="40+35+30+25+20+15+10+5+0+minus5+minus10"
+
+for i in "${!DATASET_CONFIG_NAME[@]}"; do
+  python run_eval.py \
+    --model_name_or_path "sanchit-gandhi/large-32-2-gpu-flat-lr" \
+    --dataset_name $DATASET_NAME \
+    --dataset_config_name "${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}" \
+    --dataset_split_name $DATASET_SPLIT_NAME \
+    --cache_dir "/home/sanchitgandhi/cache" \
+    --dataset_cache_dir "/home/sanchitgandhi/cache" \
+    --output_dir "./" \
+    --wandb_dir "/home/sanchitgandhi/cache" \
+    --wandb_project "distil-whisper-noise-eval" \
+    --wandb_name "large-32-2-gpu-flat-lr-${DATASET_CONFIG_NAME[i]}" \
+    --per_device_eval_batch_size 64 \
+    --dtype "bfloat16" \
+    --dataloader_num_workers 16 \
+    --report_to "wandb" \
+    --streaming \
+    --predict_with_generate
+done
diff --git a/flax/noise_evaluation_scripts/test/run_baselines.sh b/flax/noise_evaluation_scripts/test/run_baselines.sh
new file mode 100644
index 0000000000000000000000000000000000000000..427a25e3a704c6a37284608646a076755e7405a5
--- /dev/null
+++ b/flax/noise_evaluation_scripts/test/run_baselines.sh
@@ -0,0 +1,97 @@
+#!/usr/bin/env bash
+
+DATASET_NAME="librispeech_asr-noise+librispeech_asr-noise+librispeech_asr-noise+librispeech_asr-noise+librispeech_asr-noise+librispeech_asr-noise+librispeech_asr-noise+librispeech_asr-noise+librispeech_asr-noise+librispeech_asr-noise+librispeech_asr-noise"
+DATASET_CONFIG_NAME=("test-white-noise" "test-pub-noise")
+DATASET_SPLIT_NAME="40+35+30+25+20+15+10+5+0+minus5+minus10"
+
+for i in "${!DATASET_CONFIG_NAME[@]}"; do
+  python run_eval.py \
+    --model_name_or_path "openai/whisper-tiny.en" \
+    --dataset_name $DATASET_NAME \
+    --dataset_config_name "${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}" \
+    --dataset_split_name $DATASET_SPLIT_NAME \
+    --cache_dir "/home/sanchitgandhi/cache" \
+    --dataset_cache_dir "/home/sanchitgandhi/cache" \
+    --output_dir "./" \
+    --wandb_dir "/home/sanchitgandhi/cache" \
+    --wandb_project "distil-whisper-noise-test" \
+    --wandb_name "tiny.en-${DATASET_CONFIG_NAME[i]}" \
+    --per_device_eval_batch_size 32 \
+    --dtype "bfloat16" \
+    --dataloader_num_workers 16 \
+    --report_to "wandb" \
+    --streaming \
+    --predict_with_generate
+
+  python run_eval.py \
+    --model_name_or_path "openai/whisper-base.en" \
+    --dataset_name $DATASET_NAME \
+    --dataset_config_name "${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}" \
+    --dataset_split_name $DATASET_SPLIT_NAME \
+    --cache_dir "/home/sanchitgandhi/cache" \
+    --dataset_cache_dir "/home/sanchitgandhi/cache" \
+    --output_dir "./" \
+    --wandb_dir "/home/sanchitgandhi/cache" \
+    --wandb_project "distil-whisper-noise-test" \
+    --wandb_name "base.en-${DATASET_CONFIG_NAME[i]}" \
+    --per_device_eval_batch_size 32 \
+    --dtype "bfloat16" \
+    --dataloader_num_workers 16 \
+    --report_to "wandb" \
+    --streaming \
+    --predict_with_generate
+
+  python run_eval.py \
+    --model_name_or_path "openai/whisper-small.en" \
+    --dataset_name $DATASET_NAME \
+    --dataset_config_name "${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}" \
+    --dataset_split_name $DATASET_SPLIT_NAME \
+    --cache_dir "/home/sanchitgandhi/cache" \
+    --dataset_cache_dir "/home/sanchitgandhi/cache" \
+    --output_dir "./" \
+    --wandb_dir "/home/sanchitgandhi/cache" \
+    --wandb_project "distil-whisper-noise-test" \
+    --wandb_name "small.en-${DATASET_CONFIG_NAME[i]}" \
+    --per_device_eval_batch_size 32 \
+    --dtype "bfloat16" \
+    --dataloader_num_workers 16 \
+    --report_to "wandb" \
+    --streaming \
+    --predict_with_generate
+
+  python run_eval.py \
+    --model_name_or_path "openai/whisper-medium.en" \
+    --dataset_name $DATASET_NAME \
+    --dataset_config_name "${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}" \
+    --dataset_split_name $DATASET_SPLIT_NAME \
+    --cache_dir "/home/sanchitgandhi/cache" \
+    --dataset_cache_dir "/home/sanchitgandhi/cache" \
+    --output_dir "./" \
+    --wandb_dir "/home/sanchitgandhi/cache" \
+    --wandb_project "distil-whisper-noise-test" \
+    --wandb_name "medium.en-${DATASET_CONFIG_NAME[i]}" \
+    --per_device_eval_batch_size 32 \
+    --dtype "bfloat16" \
+    --dataloader_num_workers 16 \
+    --report_to "wandb" \
+    --streaming \
+    --predict_with_generate
+
+  python run_eval.py \
+    --model_name_or_path "openai/whisper-large-v2" \
+    --dataset_name $DATASET_NAME \
+    --dataset_config_name "${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}" \
+    --dataset_split_name $DATASET_SPLIT_NAME \
+    --cache_dir "/home/sanchitgandhi/cache" \
+    --dataset_cache_dir "/home/sanchitgandhi/cache" \
+    --output_dir "./" \
+    --wandb_dir "/home/sanchitgandhi/cache" \
+    --wandb_project "distil-whisper-noise-test" \
+    --wandb_name "large-v2-${DATASET_CONFIG_NAME[i]}" \
+    --per_device_eval_batch_size 16 \
+    --dtype "bfloat16" \
+    --dataloader_num_workers 16 \
+    --report_to "wandb" \
+    --streaming \
+    --predict_with_generate
+done
\ No newline at end of file
diff --git a/flax/noise_evaluation_scripts/test/run_distilled.sh b/flax/noise_evaluation_scripts/test/run_distilled.sh
new file mode 100644
index 0000000000000000000000000000000000000000..b7ede30a91db9dc1d8ff33e51d4772259741ad7e
--- /dev/null
+++ b/flax/noise_evaluation_scripts/test/run_distilled.sh
@@ -0,0 +1,44 @@
+#!/usr/bin/env bash
+
+DATASET_NAME="librispeech_asr-noise+librispeech_asr-noise+librispeech_asr-noise+librispeech_asr-noise+librispeech_asr-noise+librispeech_asr-noise+librispeech_asr-noise+librispeech_asr-noise+librispeech_asr-noise+librispeech_asr-noise+librispeech_asr-noise"
+DATASET_CONFIG_NAME=("test-white-noise" "test-pub-noise")
+DATASET_SPLIT_NAME="40+35+30+25+20+15+10+5+0+minus5+minus10"
+
+for i in "${!DATASET_CONFIG_NAME[@]}"; do
+  python run_eval.py \
+    --model_name_or_path "sanchit-gandhi/large-32-2-tpu-timestamped-resumed" \
+    --dataset_name $DATASET_NAME \
+    --dataset_config_name "${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}" \
+    --dataset_split_name $DATASET_SPLIT_NAME \
+    --cache_dir "/home/sanchitgandhi/cache" \
+    --dataset_cache_dir "/home/sanchitgandhi/cache" \
+    --output_dir "./" \
+    --wandb_dir "/home/sanchitgandhi/cache" \
+    --wandb_project "distil-whisper-noise-test" \
+    --wandb_name "large-32-2-tpu-timestamped-${DATASET_CONFIG_NAME[i]}" \
+    --per_device_eval_batch_size 64 \
+    --dtype "bfloat16" \
+    --dataloader_num_workers 16 \
+    --report_to "wandb" \
+    --streaming \
+    --predict_with_generate
+    
+    python run_eval.py \
+    --model_name_or_path "sanchit-gandhi/medium-24-2-tpu-timestamped-prob-0.2" \
+    --subfolder "checkpoint-45000" \
+    --dataset_name $DATASET_NAME \
+    --dataset_config_name "${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}+${DATASET_CONFIG_NAME[i]}" \
+    --dataset_split_name $DATASET_SPLIT_NAME \
+    --cache_dir "/home/sanchitgandhi/cache" \
+    --dataset_cache_dir "/home/sanchitgandhi/cache" \
+    --output_dir "./" \
+    --wandb_dir "/home/sanchitgandhi/cache" \
+    --wandb_project "distil-whisper-noise-test" \
+    --wandb_name "medium-24-2-tpu-timestamped-prob-0.2-${DATASET_CONFIG_NAME[i]}" \
+    --per_device_eval_batch_size 64 \
+    --dtype "bfloat16" \
+    --dataloader_num_workers 16 \
+    --report_to "wandb" \
+    --streaming \
+    --predict_with_generate
+done
diff --git a/flax/pseudo_labelling_scripts/run_librispeech_pseudo_labelling.sh b/flax/pseudo_labelling_scripts/run_librispeech_pseudo_labelling.sh
new file mode 100644
index 0000000000000000000000000000000000000000..8f6d14a15412cc7a3e1825ab03595e22ddf54c32
--- /dev/null
+++ b/flax/pseudo_labelling_scripts/run_librispeech_pseudo_labelling.sh
@@ -0,0 +1,22 @@
+#!/usr/bin/env bash
+
+python run_pseudo_labelling.py \
+  --model_name_or_path "openai/whisper-large-v2" \
+  --dataset_name "sanchit-gandhi/librispeech_asr_clean" \
+  --dataset_config_name "clean" \
+  --data_split_name "train.100" \
+  --text_column_name "text" \
+  --cache_dir "/home/sanchitgandhi/cache" \
+  --dataset_cache_dir "/home/sanchitgandhi/cache" \
+  --output_dir "./transcriptions-streaming" \
+  --wandb_dir "/home/sanchitgandhi/.cache" \
+  --wandb_project "distil-whisper-debug" \
+  --wandb_name "whisper-large-v2-beam-libri-train.clean.100" \
+  --per_device_eval_batch_size 16 \
+  --max_label_length 256 \
+  --dtype "bfloat16" \
+  --preprocessing_num_workers 16 \
+  --report_to "wandb" \
+  --dataloader_num_workers 16 \
+  --streaming False \
+  --generation_num_beams 1
diff --git a/flax/pseudo_labelling_scripts/run_librispeech_pseudo_labelling_dummy.sh b/flax/pseudo_labelling_scripts/run_librispeech_pseudo_labelling_dummy.sh
new file mode 100644
index 0000000000000000000000000000000000000000..e96b9cfbc57ef322aed0dcde1bbd7794050a8047
--- /dev/null
+++ b/flax/pseudo_labelling_scripts/run_librispeech_pseudo_labelling_dummy.sh
@@ -0,0 +1,23 @@
+#!/usr/bin/env bash
+
+python run_pseudo_labelling.py \
+  --model_name_or_path "openai/whisper-tiny" \
+  --dataset_name "distil-whisper/librispeech_asr" \
+  --dataset_config_name "all" \
+  --data_split_name "validation.clean+validation.other" \
+  --text_column_name "text" \
+  --cache_dir "/home/sanchitgandhi/.cache" \
+  --dataset_cache_dir "/home/sanchitgandhi/.cache" \
+  --output_dir "./transcriptions-streaming" \
+  --wandb_dir "/home/sanchitgandhi/.cache" \
+  --wandb_project "distil-whisper-debug" \
+  --per_device_eval_batch_size 1 \
+  --dtype "bfloat16" \
+  --dataloader_num_workers 16 \
+  --logging_steps 2 \
+  --report_to "wandb" \
+  --streaming \
+  --max_samples_per_split 256 \
+  --max_label_length 256 \
+  --return_timestamps \
+  --decode_token_ids False
diff --git a/flax/pseudo_labelling_scripts/run_pseudo_labelling.sh b/flax/pseudo_labelling_scripts/run_pseudo_labelling.sh
new file mode 100644
index 0000000000000000000000000000000000000000..e15ead92ee593926d065b4b65187a6693abaf326
--- /dev/null
+++ b/flax/pseudo_labelling_scripts/run_pseudo_labelling.sh
@@ -0,0 +1,59 @@
+#!/usr/bin/env bash
+
+MODEL_NAME="openai/whisper-large-v3"
+CACHE_DIR="/home/sanchitgandhi/.cache"
+OUTPUT_DIR="./transcriptions-streaming"
+WANDB_DIR="/home/sanchitgandhi/.cache"
+WANDB_PROJECT="distil-whisper-label"
+BATCH_SIZE=64
+NUM_BEAMS=1
+MAX_LABEL_LENGTH=256
+LOGGING_STEPS=500
+NUM_WORKERS=64
+RETURN_TIMESTAMPS=False
+
+python run_pseudo_labelling.py \
+  --model_name_or_path $MODEL_NAME \
+  --dataset_name "distil-whisper/librispeech_asr" \
+  --dataset_config_name "all" \
+  --data_split_name "train.other.500+validation.clean+validation.other+test.clean+test.other" \
+  --wandb_name "whisper-large-v2-librispeech_asr" \
+  --cache_dir $CACHE_DIR \
+  --dataset_cache_dir $CACHE_DIR \
+  --output_dir $OUTPUT_DIR \
+  --wandb_dir $WANDB_DIR \
+  --wandb_project $WANDB_PROJECT \
+  --per_device_eval_batch_size $BATCH_SIZE \
+  --generation_num_beams $NUM_BEAMS \
+  --max_label_length $MAX_LABEL_LENGTH \
+  --logging_steps $LOGGING_STEPS \
+  --dataloader_num_workers $NUM_WORKERS \
+  --dtype "bfloat16" \
+  --report_to "wandb" \
+  --streaming True \
+  --push_to_hub \
+  --return_timestamps $RETURN_TIMESTAMPS \
+  --compilation_cache $CACHE_DIR
+
+python run_pseudo_labelling.py \
+  --model_name_or_path $MODEL_NAME \
+  --dataset_name "distil-whisper/peoples_speech-clean" \
+  --dataset_config_name "clean" \
+  --data_split_name "train+validation+test" \
+  --wandb_name "whisper-large-v2-peoples_speech-clean" \
+  --cache_dir $CACHE_DIR \
+  --dataset_cache_dir $CACHE_DIR \
+  --output_dir $OUTPUT_DIR \
+  --wandb_dir $WANDB_DIR \
+  --wandb_project $WANDB_PROJECT \
+  --per_device_eval_batch_size $BATCH_SIZE \
+  --generation_num_beams $NUM_BEAMS \
+  --max_label_length $MAX_LABEL_LENGTH \
+  --logging_steps $LOGGING_STEPS \
+  --dataloader_num_workers $NUM_WORKERS \
+  --dtype "bfloat16" \
+  --report_to "wandb" \
+  --streaming True \
+  --push_to_hub \
+  --return_timestamps $RETURN_TIMESTAMPS \
+  --compilation_cache $CACHE_DIR
diff --git a/flax/pseudo_labelling_scripts/run_pseudo_labelling_2.sh b/flax/pseudo_labelling_scripts/run_pseudo_labelling_2.sh
new file mode 100644
index 0000000000000000000000000000000000000000..2f83ed36dc23301c4151217e42d841e10e6d2294
--- /dev/null
+++ b/flax/pseudo_labelling_scripts/run_pseudo_labelling_2.sh
@@ -0,0 +1,42 @@
+#!/usr/bin/env bash
+
+MODEL_NAME="openai/whisper-large-v3"
+CACHE_DIR="/home/sanchitgandhi/.cache"
+OUTPUT_DIR="./transcriptions-streaming"
+WANDB_DIR="/home/sanchitgandhi/.cache"
+WANDB_PROJECT="distil-whisper-label"
+SPLITS="train+validation+test"
+BATCH_SIZE=64
+NUM_BEAMS=1
+MAX_LABEL_LENGTH=256
+LOGGING_STEPS=500
+NUM_WORKERS=64
+RETURN_TIMESTAMPS=False
+
+DATASET_NAMES=("distil-whisper/common_voice_13_0" "distil-whisper/voxpopuli" "distil-whisper/tedlium" "distil-whisper/ami-ihm" "distil-whisper/ami-sdm" "distil-whisper/spgispeech" "distil-whisper/gigaspeech-l")
+CONFIGS=("en" "en" "release3" "ihm" "sdm" "L" "l")
+
+for i in "${!DATASET_NAMES[@]}"; do
+  python run_pseudo_labelling.py \
+  --model_name_or_path $MODEL_NAME \
+  --dataset_name "${DATASET_NAMES[i]}" \
+  --dataset_config_name "${CONFIGS[i]}" \
+  --data_split_name "$SPLITS" \
+  --wandb_name "whisper-large-v2-${DATASET_NAMES[i]}" \
+  --cache_dir $CACHE_DIR \
+  --dataset_cache_dir $CACHE_DIR \
+  --output_dir $OUTPUT_DIR \
+  --wandb_dir $WANDB_DIR \
+  --wandb_project $WANDB_PROJECT \
+  --per_device_eval_batch_size $BATCH_SIZE \
+  --generation_num_beams $NUM_BEAMS \
+  --max_label_length $MAX_LABEL_LENGTH \
+  --logging_steps $LOGGING_STEPS \
+  --dataloader_num_workers $NUM_WORKERS \
+  --dtype "bfloat16" \
+  --report_to "wandb" \
+  --streaming True \
+  --push_to_hub \
+  --return_timestamps $RETURN_TIMESTAMPS \
+  --compilation_cache $CACHE_DIR
+done
diff --git a/flax/pseudo_labelling_scripts/run_pseudo_labelling_dummy_pt.sh b/flax/pseudo_labelling_scripts/run_pseudo_labelling_dummy_pt.sh
new file mode 100644
index 0000000000000000000000000000000000000000..e7891bc761de5d53816a6baa4e25cbc6bcaf16ac
--- /dev/null
+++ b/flax/pseudo_labelling_scripts/run_pseudo_labelling_dummy_pt.sh
@@ -0,0 +1,22 @@
+#!/usr/bin/env bash
+
+accelerate launch --mixed_precision=bf16 --num_processes=1 run_pseudo_labelling_pt.py \
+  --model_name_or_path "openai/whisper-tiny" \
+  --dataset_name "distil-whisper/librispeech_asr" \
+  --dataset_config_name "all" \
+  --data_split_name "validation.clean+validation.other" \
+  --text_column_name "text" \
+  --cache_dir "/home/sanchit/.cache" \
+  --dataset_cache_dir "/home/sanchit/.cache" \
+  --output_dir "./transcriptions-streaming" \
+  --wandb_project "distil-whisper-debug" \
+  --per_device_eval_batch_size 8 \
+  --dtype "bfloat16" \
+  --dataloader_num_workers 16 \
+  --logging_steps 2 \
+  --report_to "wandb" \
+  --streaming \
+  --max_samples_per_split 256 \
+  --max_label_length 256 \
+  --return_timestamps \
+  --decode_token_ids False
diff --git a/flax/pseudo_labelling_scripts/run_pseudo_labelling_token_ids.sh b/flax/pseudo_labelling_scripts/run_pseudo_labelling_token_ids.sh
new file mode 100644
index 0000000000000000000000000000000000000000..fe046ab90b856a5a8c9d4a1eb8fd19efb9811fed
--- /dev/null
+++ b/flax/pseudo_labelling_scripts/run_pseudo_labelling_token_ids.sh
@@ -0,0 +1,62 @@
+#!/usr/bin/env bash
+
+MODEL_NAME="openai/whisper-large-v3"
+CACHE_DIR="/home/sanchitgandhi/.cache"
+OUTPUT_DIR="./transcriptions-streaming"
+WANDB_DIR="/home/sanchitgandhi/.cache"
+WANDB_PROJECT="distil-whisper-label"
+BATCH_SIZE=16
+NUM_BEAMS=1
+MAX_LABEL_LENGTH=256
+LOGGING_STEPS=500
+NUM_WORKERS=64
+RETURN_TIMESTAMPS=False
+DECODE_TOKEN_IDS=False
+
+python run_pseudo_labelling.py \
+  --model_name_or_path $MODEL_NAME \
+  --dataset_name "distil-whisper/librispeech_asr" \
+  --dataset_config_name "all" \
+  --data_split_name "train.other.500+validation.clean+validation.other+test.clean+test.other" \
+  --wandb_name "whisper-large-v2-librispeech_asr-token-ids" \
+  --cache_dir $CACHE_DIR \
+  --dataset_cache_dir $CACHE_DIR \
+  --output_dir $OUTPUT_DIR \
+  --wandb_dir $WANDB_DIR \
+  --wandb_project $WANDB_PROJECT \
+  --per_device_eval_batch_size $BATCH_SIZE \
+  --generation_num_beams $NUM_BEAMS \
+  --max_label_length $MAX_LABEL_LENGTH \
+  --logging_steps $LOGGING_STEPS \
+  --dataloader_num_workers $NUM_WORKERS \
+  --dtype "bfloat16" \
+  --report_to "wandb" \
+  --streaming True \
+  --push_to_hub \
+  --return_timestamps $RETURN_TIMESTAMPS \
+  --compilation_cache $CACHE_DIR \
+  --decode_token_ids $DECODE_TOKEN_IDS
+
+python run_pseudo_labelling.py \
+  --model_name_or_path $MODEL_NAME \
+  --dataset_name "distil-whisper/peoples_speech-clean" \
+  --dataset_config_name "clean" \
+  --data_split_name "train+validation+test" \
+  --wandb_name "whisper-large-v2-peoples_speech-clean-token-ids" \
+  --cache_dir $CACHE_DIR \
+  --dataset_cache_dir $CACHE_DIR \
+  --output_dir $OUTPUT_DIR \
+  --wandb_dir $WANDB_DIR \
+  --wandb_project $WANDB_PROJECT \
+  --per_device_eval_batch_size $BATCH_SIZE \
+  --generation_num_beams $NUM_BEAMS \
+  --max_label_length $MAX_LABEL_LENGTH \
+  --logging_steps $LOGGING_STEPS \
+  --dataloader_num_workers $NUM_WORKERS \
+  --dtype "bfloat16" \
+  --report_to "wandb" \
+  --streaming True \
+  --push_to_hub \
+  --return_timestamps $RETURN_TIMESTAMPS \
+  --compilation_cache $CACHE_DIR \
+  --decode_token_ids $DECODE_TOKEN_IDS
diff --git a/flax/pseudo_labelling_scripts/run_pseudo_labelling_token_ids_2.sh b/flax/pseudo_labelling_scripts/run_pseudo_labelling_token_ids_2.sh
new file mode 100644
index 0000000000000000000000000000000000000000..9fcc4c0928bb12ad5c0d4befebe7a7932532a47e
--- /dev/null
+++ b/flax/pseudo_labelling_scripts/run_pseudo_labelling_token_ids_2.sh
@@ -0,0 +1,44 @@
+#!/usr/bin/env bash
+
+MODEL_NAME="openai/whisper-large-v3"
+CACHE_DIR="/home/sanchitgandhi/.cache"
+OUTPUT_DIR="./transcriptions-streaming"
+WANDB_DIR="/home/sanchitgandhi/.cache"
+WANDB_PROJECT="distil-whisper-label"
+SPLITS="train+validation+test"
+BATCH_SIZE=16
+NUM_BEAMS=1
+MAX_LABEL_LENGTH=256
+LOGGING_STEPS=500
+NUM_WORKERS=64
+RETURN_TIMESTAMPS=False
+DECODE_TOKEN_IDS=False
+
+DATASET_NAMES=("distil-whisper/common_voice_13_0" "distil-whisper/voxpopuli" "distil-whisper/tedlium" "distil-whisper/ami-ihm" "distil-whisper/ami-sdm" "distil-whisper/spgispeech" "distil-whisper/gigaspeech-l")
+CONFIGS=("en" "en" "release3" "ihm" "sdm" "L" "l")
+
+for i in "${!DATASET_NAMES[@]}"; do
+  python run_pseudo_labelling.py \
+  --model_name_or_path $MODEL_NAME \
+  --dataset_name "${DATASET_NAMES[i]}" \
+  --dataset_config_name "${CONFIGS[i]}" \
+  --data_split_name "$SPLITS" \
+  --wandb_name "whisper-large-v2-${DATASET_NAMES[i]}-token-ids" \
+  --cache_dir $CACHE_DIR \
+  --dataset_cache_dir $CACHE_DIR \
+  --output_dir $OUTPUT_DIR \
+  --wandb_dir $WANDB_DIR \
+  --wandb_project $WANDB_PROJECT \
+  --per_device_eval_batch_size $BATCH_SIZE \
+  --generation_num_beams $NUM_BEAMS \
+  --max_label_length $MAX_LABEL_LENGTH \
+  --logging_steps $LOGGING_STEPS \
+  --dataloader_num_workers $NUM_WORKERS \
+  --dtype "bfloat16" \
+  --report_to "wandb" \
+  --streaming True \
+  --push_to_hub \
+  --return_timestamps $RETURN_TIMESTAMPS \
+  --compilation_cache $CACHE_DIR \
+  --decode_token_ids $DECODE_TOKEN_IDS
+done
diff --git a/flax/pyproject.toml b/flax/pyproject.toml
new file mode 100644
index 0000000000000000000000000000000000000000..776884b3ff48d48581b82b4d76fd4233700ec271
--- /dev/null
+++ b/flax/pyproject.toml
@@ -0,0 +1,17 @@
+[tool.black]
+line-length = 119
+target-version = ['py37']
+
+[tool.ruff]
+# Never enforce `E501` (line length violations).
+ignore = ["C901", "E501", "E741", "W605"]
+select = ["C", "E", "F", "I", "W"]
+line-length = 119
+
+# Ignore import violations in all `__init__.py` files.
+[tool.ruff.per-file-ignores]
+"__init__.py" = ["E402", "F401", "F403", "F811"]
+
+[tool.ruff.isort]
+lines-after-imports = 2
+known-first-party = ["distil_whisper"]
\ No newline at end of file
diff --git a/flax/requirements.txt b/flax/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..13f585fce0ae73a651e18731e842497354af656c
--- /dev/null
+++ b/flax/requirements.txt
@@ -0,0 +1,5 @@
+torch>=1.7
+transformers
+datasets[audio]
+jiwer
+evaluate>=0.3.0
diff --git a/flax/run_distillation.py b/flax/run_distillation.py
new file mode 100644
index 0000000000000000000000000000000000000000..dca7a4b41c2eb686c20f220ee04cb149162e558f
--- /dev/null
+++ b/flax/run_distillation.py
@@ -0,0 +1,2137 @@
+#!/usr/bin/env python
+# coding=utf-8
+# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Training the Whisper model for sequence to sequence speech recognition via teacher-student distillation.
+"""
+# You can also adapt this script for your own distillation tasks. Pointers for this are left as comments.
+
+import logging
+import os
+import re
+import shutil
+import string
+import sys
+import time
+from dataclasses import dataclass, field
+from functools import partial
+from pathlib import Path
+from typing import Any, Callable, Dict, List, Optional, Union
+
+import datasets
+import evaluate
+import flax
+import jax
+import jax.numpy as jnp
+import numpy as np
+import optax
+import torch
+import transformers
+from datasets import (
+    DatasetDict,
+    IterableDataset,
+    IterableDatasetDict,
+    concatenate_datasets,
+    interleave_datasets,
+    load_dataset,
+)
+from flax import jax_utils, traverse_util
+from flax.jax_utils import pad_shard_unpad, unreplicate
+from flax.serialization import from_bytes, to_bytes
+from flax.training import train_state
+from flax.training.common_utils import get_metrics, onehot, shard, shard_prng_key
+from huggingface_hub import Repository, create_repo
+from jax.experimental.compilation_cache import compilation_cache as cc
+from optax._src import linear_algebra
+from torch.utils.data import DataLoader
+from torchdata.datapipes.iter import IterableWrapper
+from tqdm import tqdm
+from transformers import (
+    AddedToken,
+    HfArgumentParser,
+    Seq2SeqTrainingArguments,
+    WhisperConfig,
+    WhisperFeatureExtractor,
+    WhisperProcessor,
+    WhisperTokenizerFast,
+    is_tensorboard_available,
+    is_wandb_available,
+    set_seed,
+)
+from transformers.file_utils import get_full_repo_name
+from transformers.modeling_flax_outputs import FlaxBaseModelOutput
+from transformers.models.whisper.english_normalizer import EnglishTextNormalizer
+from transformers.utils import check_min_version, send_example_telemetry
+from transformers.utils.versions import require_version
+
+from distil_whisper import FlaxWhisperForConditionalGeneration
+
+
+# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
+check_min_version("4.27.0.dev0")
+
+require_version(
+    "datasets>=1.18.0",
+    "To fix: pip install -r examples/flax/speech-recogintion/requirements.txt",
+)
+
+logger = logging.getLogger(__name__)
+
+
+@flax.struct.dataclass
+class ModelArguments:
+    """
+    Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
+    """
+
+    model_name_or_path: str = field(
+        metadata={"help": ("Path to pretrained student model or model identifier from huggingface.co/models")}
+    )
+    teacher_model_name_or_path: str = field(
+        metadata={"help": ("Path to pretrained teacher model or model identifier from huggingface.co/models")}
+    )
+    config_name: Optional[str] = field(
+        default=None,
+        metadata={"help": "Pretrained config name or path if not the same as model_name"},
+    )
+    tokenizer_name: Optional[str] = field(
+        default=None,
+        metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"},
+    )
+    feature_extractor_name: Optional[str] = field(
+        default=None,
+        metadata={"help": "feature extractor name or path if not the same as model_name"},
+    )
+    cache_dir: Optional[str] = field(
+        default=None,
+        metadata={"help": ("Where to store the pretrained models downloaded from huggingface.co")},
+    )
+    use_fast_tokenizer: bool = field(
+        default=True,
+        metadata={"help": ("Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.")},
+    )
+    model_revision: str = field(
+        default="main",
+        metadata={"help": ("The specific model version to use (can be a branch name, tag name or commit id).")},
+    )
+    subfolder: str = field(
+        default="",
+        metadata={
+            "help": "In case the relevant files are located inside a subfolder of the model repo on huggingface.co, you can"
+            "specify the folder name here."
+        },
+    )
+    use_auth_token: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Will use the token generated when running `transformers-cli login`"
+                " (necessary to use this script with private models)."
+            )
+        },
+    )
+    dtype: Optional[str] = field(
+        default="float32",
+        metadata={
+            "help": (
+                "Floating-point format in which the model weights should be initialized"
+                " and trained. Choose one of `[float32, float16, bfloat16]`."
+            )
+        },
+    )
+    load_with_scan_weights: bool = field(
+        default=False,
+        metadata={
+            "help": "Whether the pre-trained checkpoint has its weights stored in scan format. Set to True for scanned "
+            "weights, defaults to False for non-scan (unrolled) weights."
+        },
+    )
+    activation_dropout: float = field(
+        default=0.0,
+        metadata={"help": "The dropout ratio for activations inside the fully connected layer."},
+    )
+    attention_dropout: float = field(
+        default=0.0,
+        metadata={"help": "The dropout ratio for the attention probabilities."},
+    )
+    dropout: float = field(
+        default=0.0,
+        metadata={
+            "help": "The dropout probability for all fully connected layers in the embeddings, encoder, and pooler."
+        },
+    )
+
+
+@flax.struct.dataclass
+class DataTrainingArguments:
+    """
+    Arguments pertaining to what data we are going to input our model for training and eval.
+    """
+
+    train_dataset_name: str = field(
+        default=None,
+        metadata={
+            "help": "The name of the training dataset to use (via the datasets library). Load and combine "
+            "multiple datasets by separating dataset ids by a '+' symbol. For example, to load and combine "
+            " librispeech and common voice, set `train_dataset_name='librispeech_asr+common_voice'`."
+        },
+    )
+    train_dataset_config_name: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": "The configuration name of the training dataset to use (via the datasets library). Load and combine "
+            "multiple datasets by separating dataset configs by a '+' symbol."
+        },
+    )
+    train_dataset_samples: str = field(
+        default=None,
+        metadata={
+            "help": "Number of samples in the training data. Load and combine "
+            "multiple datasets by separating dataset samples by a '+' symbol."
+        },
+    )
+    eval_dataset_name: str = field(
+        default=None,
+        metadata={
+            "help": "The name of the evaluation dataset to use (via the datasets library). Defaults to the training dataset name if unspecified."
+        },
+    )
+    eval_dataset_config_name: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": "The configuration name of the evaluation dataset to use (via the datasets library). Defaults to the training dataset config name if unspecified"
+        },
+    )
+    dataset_cache_dir: Optional[str] = field(
+        default=None,
+        metadata={"help": "Path to cache directory for saving and loading datasets"},
+    )
+    overwrite_cache: bool = field(
+        default=False,
+        metadata={"help": "Overwrite the cached training and evaluation sets"},
+    )
+    preprocessing_num_workers: Optional[int] = field(
+        default=None,
+        metadata={"help": "The number of processes to use for the preprocessing."},
+    )
+    max_train_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": (
+                "For debugging purposes or quicker training, truncate the number of"
+                " training examples to this value if set."
+            )
+        },
+    )
+    max_eval_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": (
+                "For debugging purposes or quicker training, truncate the number of"
+                " evaluation examples to this value if set."
+            )
+        },
+    )
+    audio_column_name: str = field(
+        default="audio",
+        metadata={"help": ("The name of the dataset column containing the audio data. Defaults to 'audio'")},
+    )
+    train_text_column_name: str = field(
+        default="whisper_transcript",
+        metadata={
+            "help": (
+                "The name of the dataset column containing the text data. Defaults to"
+                " 'whisper_transcript'which is the pseudo-labelled Whisper"
+                " transcription data."
+            )
+        },
+    )
+    eval_text_column_name: str = field(
+        default="text",
+        metadata={
+            "help": (
+                "The name of the dataset column containing the text data. Defaults to"
+                " 'text', which is the original text data"
+            )
+        },
+    )
+    max_duration_in_seconds: float = field(
+        default=30.0,
+        metadata={"help": ("Filter audio files that are longer than `max_duration_in_seconds` seconds")},
+    )
+    min_duration_in_seconds: float = field(
+        default=0.0,
+        metadata={"help": ("Filter audio files that are shorter than `min_duration_in_seconds` seconds")},
+    )
+    max_label_length: int = field(
+        default=128,
+        metadata={"help": "Truncate transcriptions that are longer `max_label_length` tokens."},
+    )
+    pad_target_to_multiple_of: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": (
+                "If set will pad the target sequence to a multiple of the provided"
+                " value. This is important to avoid triggering recompilations on TPU."
+                " If unspecified, will default to padding the targets to max length."
+            )
+        },
+    )
+    preprocessing_only: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether to only do data preprocessing and skip training. This is"
+                " especially useful when data preprocessing errors out in distributed"
+                " training due to timeout. In this case, one should run the"
+                " preprocessing in a non-distributed setup with"
+                " `preprocessing_only=True` so that the cached datasets can"
+                " consequently be loaded in distributed training"
+            )
+        },
+    )
+    train_split_name: str = field(
+        default="train",
+        metadata={
+            "help": ("The name of the training data set split to use (via the datasets library). Defaults to 'train'")
+        },
+    )
+    eval_split_name: str = field(
+        default="validation",
+        metadata={
+            "help": (
+                "The name of the evaluation data set split to use (via the datasets"
+                " library). Defaults to 'validation'"
+            )
+        },
+    )
+    wandb_project: str = field(
+        default="distil-whisper",
+        metadata={"help": "The name of the wandb project."},
+    )
+    wandb_name: str = field(
+        default=None,
+        metadata={"help": "The name of the wandb run."},
+    )
+    wandb_job_type: str = field(
+        default="distil-whisper",
+        metadata={"help": "The name of the wandb job type."},
+    )
+    wandb_dir: str = field(
+        default=None,
+        metadata={"help": "The absolute path to save the wandb logs."},
+    )
+    save_code_to_wandb: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether to save main script to wandb. This is valuable for improving"
+                " experiment reproducibility and to diff code across experiments in"
+                " the UI."
+            )
+        },
+    )
+    streaming: bool = field(
+        default=True,
+        metadata={"help": "Whether to use Datasets' streaming mode to load and the data."},
+    )
+    wer_threshold: float = field(
+        default=None,
+        metadata={
+            "help": "Filter training data with Whisper transcriptions that have greater than `wer_threshold` "
+            "WER with the normalised transcriptions."
+        },
+    )
+    prefetch_size: int = field(
+        default=0,
+        metadata={"help": "Number of samples to pre-fetch if using an iterable dataset."},
+    )
+    timestamp_probability: float = field(
+        default=0.5, metadata={"help": "Probability for training on timestamped tokens if the data contains it."}
+    )
+    return_timestamps: bool = field(
+        default=False, metadata={"help": "Whether or not to predict timestamps in the generation step."}
+    )
+    round_timestamps: bool = field(
+        default=False,
+        metadata={
+            "help": "Whether or not to round the timestamp tokens to the nearest tenth of a second."
+            "By default, Whisper predicts timestamps to the nearest hundredth of a second."
+            "Reducing the timestamp precision to one tenth of a second simplifies the timestamp"
+            "prediction task, at the expense of timestamp granularity."
+        },
+    )
+
+
+@dataclass
+class FlaxSeq2SeqTrainingArguments(Seq2SeqTrainingArguments):
+    use_scan: Optional[bool] = field(
+        default=True,
+        metadata={
+            "help": (
+                "Whether or not to use `scan_with_axes` over the encoder and decoder blocks. Using scan results "
+                "in faster compile times and more efficient memory use during training, since all of the layers "
+                "in the encoder/decoder are stacked, and we perform a lax.scan over the stacked block to index "
+                "each layer. However, it results in slower inference time due to the overhead of stacking the "
+                "layers this way. Thus, we **always** default to disabling scan for the inference step."
+            )
+        },
+    )
+    freeze_encoder: Optional[bool] = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether to freeze the entire encoder model. Only recommended when the entire encoder has been "
+                "copied from the teacher model."
+            )
+        },
+    )
+    temperature: Optional[float] = field(
+        default=2.0, metadata={"help": "Temperature to anneal the logits when computing the softmax."}
+    )
+    kl_weight: Optional[float] = field(
+        default=1.0,
+        metadata={
+            "help": (
+                "Weighting assigned to the MSE loss in the KD formulation. MSE loss is "
+                "computed between the teacher-student hidden states and attentions."
+            )
+        },
+    )
+    mse_weight: Optional[float] = field(
+        default=0.0,
+        metadata={
+            "help": (
+                "Weighting assigned to the MSE loss in the KD formulation. MSE loss is "
+                "computed between the teacher-student hidden states and attentions."
+            )
+        },
+    )
+    precision: Optional[str] = field(
+        default="half_mixed",
+        metadata={
+            "help": (
+                "Precision with which run training, Can be one of `full`, `half_mixed` or `full_mixed`, the latter two"
+                "of which enable *mixed-precision* training. **Note that this only specifies the dtype of the computation "
+                "and optimizer state. It does not influence the dtype of model parameters.** An explanation of the three "
+                "settings is provided below:"
+                "   1. Full precision: forward pass, backward pass and optimiser states all in float32."
+                "   2. Half mixed precision: forward pass in bfloat16, backward pass and optimiser states in float32. This "
+                "   corresponds to setting the dtype argument to bfloat16 when instantiating the model."
+                "   3. Full mixed precision: forward pass, backward pass and optimiser states all in bfloat16. The dtype "
+                "   argument is set to bfloat16 for the forward pass, and the gradients computed with respect to the bfloat16 "
+                "   parameters in the backward pass (giving bfloat16 gradients). The new optimiser states and parameter "
+                "   updates are computed in float32 by upcasting the bfloat16 gradients and optimiser states to float32 "
+                "   prior to the optimiser update step. The optimiser states are returned in float32 (but not saved to "
+                "   memory) and then downcasted to bfloat16 (saved to memory) for the subsequent train step."
+                "For further details, refer to https://github.com/deepmind/optax/discussions/336"
+            )
+        },
+    )
+    compilation_cache: Optional[bool] = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether to enable the JAX (experimental) compilation cache. The compilation step is *cached* the "
+                "first time it is run. Successive compilation steps for the same function utilise the cache to reduce"
+                "the compilation time."
+            )
+        },
+    )
+    save_train_state: Optional[bool] = field(
+        default=False,
+        metadata={
+            "help": "Whether or not to save the Flax Train State on each `save_steps` steps. Required if you intend"
+            "to resume training from partial training runs. If False, only the model weights will be saved."
+            "If True, both the model weights and Flax Train state will be saved."
+        },
+    )
+
+
+def shift_tokens_right(label_ids: np.array, decoder_start_token_id: int) -> np.ndarray:
+    """
+    Shift label ids one token to the right.
+    """
+    shifted_label_ids = np.zeros_like(label_ids)
+    shifted_label_ids[:, 1:] = label_ids[:, :-1]
+    shifted_label_ids[:, 0] = decoder_start_token_id
+
+    return shifted_label_ids
+
+
+@flax.struct.dataclass
+class FlaxDataCollatorSpeechSeq2SeqWithPadding:
+    """
+    Data collator that will dynamically pad the inputs received.
+    Args:
+        processor ([`Wav2Vec2Processor`])
+            The processor used for proccessing the data.
+        decoder_start_token_id (:obj: `int`)
+            The start-of-sequence token id of the decoder.
+        decoder_prev_token_id (:obj: `int`)
+            The start-of-prompt token id of the decoder
+        input_padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`, `optional`, defaults to :obj:`True`):
+            Select a strategy to pad the returned input sequences (according to the model's padding side and padding index)
+            among:
+            * :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
+              sequence if provided).
+            * :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the
+              maximum acceptable input length for the model if that argument is not provided.
+            * :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of
+              different lengths).
+        target_padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`, `optional`, defaults to :obj:`True`):
+            Select a strategy to pad the returned target sequences (according to the model's padding side and padding index).
+            See above for details.
+        max_target_length (:obj:`int`, `optional`):
+            Maximum length of the ``labels`` of the returned list and optionally padding length (see above).
+    """
+
+    processor: Any
+    decoder_start_token_id: int
+    decoder_prev_token_id: int
+    input_padding: Union[bool, str] = "max_length"
+    target_padding: Union[bool, str] = "max_length"
+    max_target_length: Optional[int] = None
+
+    def __call__(self, features: List[Dict[str, Union[List[int], np.ndarray]]]) -> Dict[str, np.ndarray]:
+        # split inputs and labels since they have to be of different lengths and need
+        # different padding methods
+        model_input_name = self.processor.model_input_names[0]
+
+        # dataloader returns a list of features which we convert to a dict
+        input_features = {model_input_name: [feature[model_input_name] for feature in features]}
+        label_features = {"input_ids": [feature["labels"] for feature in features]}
+
+        # reformat list to dict and set to pytorch format
+        batch = self.processor.feature_extractor.pad(
+            input_features,
+            padding=self.input_padding,
+            return_tensors="np",
+        )
+
+        labels_batch = self.processor.tokenizer.pad(
+            label_features,
+            max_length=self.max_target_length,
+            padding=self.target_padding,
+            return_tensors="np",
+        )
+
+        # if bos token is appended in previous tokenization step,
+        # cut bos token here as it's append later anyways
+        labels = labels_batch["input_ids"]
+        if set(np.unique(labels[:, 0])).issubset({self.decoder_start_token_id, self.decoder_prev_token_id}):
+            decoder_input_ids = labels[:, :-1]
+            labels = labels[:, 1:]
+            labels_batch.attention_mask = labels_batch.attention_mask[:, 1:]
+        else:
+            decoder_input_ids = shift_tokens_right(labels, self.decoder_start_token_id)
+
+        # replace padding with -100 to ignore correctly when computing the loss
+        labels = np.ma.array(labels, mask=np.not_equal(labels_batch.attention_mask, 1))
+        labels = labels.filled(fill_value=-100)
+
+        # replace initial prompt tokens with -100 to ignore correctly when computing the loss
+        bos_index = np.argmax(labels == self.decoder_start_token_id, axis=1)
+        prompt_mask = np.arange(labels.shape[1]) < bos_index[:, None]
+        labels = np.where(prompt_mask, -100, labels)
+
+        batch["labels"] = labels
+        batch["decoder_input_ids"] = decoder_input_ids
+
+        return batch
+
+
+def get_data_loader(
+    seed: int,
+    dataset: IterableDataset,
+    batch_size: int,
+    data_collator: FlaxDataCollatorSpeechSeq2SeqWithPadding,
+    shuffle: bool = True,
+    drop_last: bool = True,
+    dataloader_num_workers: int = 0,
+    skip_batches: int = 0,
+    pin_memory: bool = True,
+    prefetch_size: int = 0,
+) -> DataLoader:
+    """
+    Returns batches of size `batch_size` from `dataset`. If `drop_last` is set to `False`, the final batch may be incomplete,
+    and range in size from 1 to `batch_size`. Shuffle batches if `shuffle` is `True`.
+
+    Args:
+        seed (int): Numpy seed for generating pseudo random numbers. Used if shuffling the dataset.
+        dataset (IterableDataset): streaming dataset from which to load the data.
+        batch_size (int): how many samples per batch to load.
+        data_collator (FlaxDataCollatorSpeechSeq2SeqWithPadding, optional): merges a list of samples to form a
+            mini-batch of Tensor(s).  Used when using batched loading from a map-style dataset.
+        shuffle (bool, optional): set to `True` to have the batches reshuffled.
+        drop_last (bool, optional): set to ``True`` to drop the last incomplete batch,
+            if the dataset size is not divisible by the batch size. If ``False`` and
+            the size of dataset is not divisible by the batch size, then the last batch
+            will be smaller. (default: ``False``)
+        dataloader_num_workers (int, optional): how many subprocesses to use for data
+            loading. ``0`` means that the data will be loaded in the main process.
+            (default: ``0``)
+        skip_batches (int, optional): Efficiently skip the first `skip_batches`.
+        pin_memory (bool, optional): If ``True``, the data loader will copy Tensors
+            into device/CUDA pinned memory before returning them.  If your data elements
+            are a custom type, or your :attr:`collate_fn` returns a batch that is a custom type,
+            see the example below.
+
+    """
+    if shuffle:
+        dataset = dataset.shuffle(seed)
+
+    if skip_batches > 0:
+        dataset = dataset.skip(skip_batches * batch_size)
+
+    if prefetch_size > 0:
+        dataset = IterableWrapper(dataset)
+        dataset = dataset.prefetch(prefetch_size)
+
+    data_loader = DataLoader(
+        dataset,
+        batch_size=batch_size,
+        drop_last=drop_last,
+        pin_memory=pin_memory,
+        collate_fn=data_collator,
+        num_workers=dataloader_num_workers,
+    )
+
+    return data_loader
+
+
+def sorted_checkpoints(output_dir=None, checkpoint_prefix="checkpoint", use_mtime=False) -> List[str]:
+    ordering_and_checkpoint_path = []
+
+    glob_checkpoints = [str(x) for x in Path(output_dir).glob(f"{checkpoint_prefix}-*") if os.path.isdir(x)]
+
+    for path in glob_checkpoints:
+        if use_mtime:
+            ordering_and_checkpoint_path.append((os.path.getmtime(path), path))
+        else:
+            regex_match = re.match(f".*{checkpoint_prefix}-([0-9]+)", path)
+            if regex_match is not None and regex_match.groups() is not None:
+                ordering_and_checkpoint_path.append((int(regex_match.groups()[0]), path))
+
+    checkpoints_sorted = sorted(ordering_and_checkpoint_path)
+    checkpoints_sorted = [checkpoint[1] for checkpoint in checkpoints_sorted]
+    return checkpoints_sorted
+
+
+def rotate_checkpoints(
+    save_total_limit=None, use_mtime=False, output_dir=None, checkpoint_prefix="checkpoint"
+) -> None:
+    if save_total_limit is None or save_total_limit <= 0:
+        return
+
+    # Check if we should delete older checkpoint(s)
+    checkpoints_sorted = sorted_checkpoints(
+        use_mtime=use_mtime, output_dir=output_dir, checkpoint_prefix=checkpoint_prefix
+    )
+    if len(checkpoints_sorted) <= save_total_limit:
+        return
+
+    number_of_checkpoints_to_delete = max(0, len(checkpoints_sorted) - save_total_limit)
+    checkpoints_to_be_deleted = checkpoints_sorted[:number_of_checkpoints_to_delete]
+    for checkpoint in checkpoints_to_be_deleted:
+        logger.info(f"Deleting older checkpoint [{checkpoint}] due to args.save_total_limit")
+        shutil.rmtree(checkpoint, ignore_errors=True)
+
+
+def to_fp32(t):
+    return jax.tree_map(lambda x: x.astype(jnp.float32) if x.dtype == jnp.bfloat16 else x, t)
+
+
+def to_bf16(t):
+    return jax.tree_map(lambda x: x.astype(jnp.bfloat16) if x.dtype == jnp.float32 else x, t)
+
+
+class TrainState(train_state.TrainState):
+    dropout_rng: jnp.ndarray
+    max_grad_norm: float
+
+    def apply_gradients(self, *, grads, to_dtype: to_fp32, **kwargs):
+        """Updates `step`, `params`, `opt_state` and `**kwargs` in return value, clipping the
+        gradients by the maximum grad norm.
+
+        Note that internally this function calls `.tx.update()` followed by a call
+        to `optax.apply_updates()` to update `params` and `opt_state`.
+
+        Args:
+          grads: Gradients that have the same pytree structure as `.params`.
+          **kwargs: Additional dataclass attributes that should be `.replace()`-ed.
+
+        Returns:
+          An updated instance of `self` with `step` incremented by one, `params`
+          and `opt_state` updated by applying `grads`, and additional attributes
+          replaced as specified by `kwargs`.
+        """
+        # clip gradients by global l2 norm
+        casted_max_grad_norm = to_dtype(self.max_grad_norm)
+        g_norm = linear_algebra.global_norm(grads)
+        g_norm = jnp.maximum(casted_max_grad_norm, g_norm)
+        grads = jax.tree_map(lambda t: (t / g_norm) * casted_max_grad_norm, grads)
+
+        # perform update step in fp32 and subsequently downcast optimizer states if mixed precision training
+        # grads and opt_state in bf16 (need to upcast), params in fp32 (leave as is)
+        updates, new_opt_state = self.tx.update(to_fp32(grads), to_fp32(self.opt_state), self.params)
+
+        new_params = optax.apply_updates(self.params, updates)
+
+        return self.replace(
+            step=self.step + 1,
+            params=new_params,
+            opt_state=to_dtype(new_opt_state),
+            **kwargs,
+        )
+
+    @classmethod
+    def create(cls, *, apply_fn, params, tx, to_dtype: to_fp32, **kwargs):
+        """Creates a new instance with `step=0` and initialized `opt_state`."""
+        # downcast optimizer state to bf16 if mixed-precision training
+        opt_state = tx.init(to_dtype(params))
+        return cls(
+            step=0,
+            apply_fn=apply_fn,
+            params=params,
+            tx=tx,
+            opt_state=opt_state,
+            **kwargs,
+        )
+
+    def replicate(self):
+        return jax_utils.replicate(self).replace(dropout_rng=shard_prng_key(self.dropout_rng))
+
+    def unreplicate(self):
+        return jax_utils.unreplicate(self)
+
+    def save_state(self, output_dir, save_total_limit=None, checkpoint_prefix="checkpoint"):
+        step = int(jax.device_get(unreplicate(self.step)))
+        serialized_state = to_bytes(self.unreplicate())
+
+        output_file = Path(os.path.join(output_dir, f"{checkpoint_prefix}-{step}", "train_state.msgpack"))
+        output_file.parent.mkdir(exist_ok=True, parents=True)
+
+        with output_file.open("wb") as f:
+            f.write(serialized_state)
+
+        logger.info(f"Flax train state saved in {output_file}")
+        rotate_checkpoints(
+            save_total_limit=save_total_limit, output_dir=output_dir, checkpoint_prefix=checkpoint_prefix
+        )
+
+
+def save_hf_weights(
+    student_state: TrainState,
+    student_model: FlaxWhisperForConditionalGeneration,
+    processor: WhisperProcessor,
+    output_dir: str,
+    cur_step: int,
+    total_train_steps: int,
+    use_scan: bool = True,
+    checkpoint_prefix: str = "checkpoint",
+) -> None:
+    # always disable scan in the params / model so that we can load from PyTorch directly - this is a no-op if we're not using scan for training
+    student_state_params = unreplicate(student_state.params)
+    student_state_params = student_model.convert_scan_to_unroll(student_state_params)
+    student_params = jax.device_get(student_state_params)
+    student_model.disable_scan()
+
+    if cur_step != total_train_steps:
+        output_dir = os.path.join(output_dir, f"{checkpoint_prefix}-{cur_step}")
+        os.makedirs(output_dir, exist_ok=True)
+
+    student_model.save_pretrained(output_dir, params=student_params)
+    processor.save_pretrained(output_dir)
+
+    # re-enable scan only if required for training
+    if use_scan:
+        student_model.enable_scan()
+
+
+def write_train_metric(summary_writer, train_metrics, train_time, step, logging_steps):
+    summary_writer.scalar("train/time", train_time, step)
+
+    train_metrics = get_metrics(train_metrics)
+    for key, vals in train_metrics.items():
+        steps_arr = np.arange(0, step, logging_steps)[-len(vals) :]
+        tag = f"train/{key}"
+        for i, val in enumerate(vals):
+            summary_writer.scalar(tag, val, steps_arr[i])
+
+
+def write_eval_metric(summary_writer, eval_metrics, step, prefix="eval"):
+    for metric_name, value in eval_metrics.items():
+        summary_writer.scalar(f"{prefix}/{metric_name}", value, step)
+
+
+def write_wandb_metric(wandb_logger, metrics, train_time, step, epoch, prefix="train"):
+    log_metrics = {}
+    for k, v in metrics.items():
+        log_metrics[f"{prefix}/{k}"] = v
+    log_metrics[f"{prefix}/time"] = train_time
+    log_metrics[f"{prefix}/epoch"] = epoch
+    wandb_logger.log(log_metrics, step)
+
+
+def write_wandb_pred(
+    wandb_logger, pred_str, label_str, norm_pred_str, norm_label_str, cur_step, prefix="eval", num_lines=200000
+):
+    # pretty name for current step: step 50000 -> step 50k
+    cur_step_pretty = f"{int(cur_step // 1000)}k" if cur_step > 1000 else cur_step
+    # convert str data to a wandb compatible format
+    str_data = [[label_str[i], pred_str[i], norm_label_str[i], norm_pred_str[i]] for i in range(len(pred_str))]
+    # log as a table with the appropriate headers
+    wandb_logger.log(
+        {
+            f"predictions/{prefix.replace('/', '-')}-step-{cur_step_pretty}": wandb_logger.Table(
+                columns=["Target", "Pred", "Norm Target", "Norm Pred"], data=str_data[:num_lines]
+            )
+        },
+        cur_step,
+    )
+    # log incorrect normalised predictions
+    str_data = np.asarray(str_data)
+    str_data_incorrect = str_data[str_data[:, -2] != str_data[:, -1]]
+    # log as a table with the appropriate headers
+    wandb_logger.log(
+        {
+            f"incorrect_predictions/{prefix.replace('/', '-')}-step-{cur_step_pretty}": wandb_logger.Table(
+                columns=["Target", "Pred", "Norm Target", "Norm Pred"], data=str_data_incorrect[:num_lines]
+            )
+        },
+        cur_step,
+    )
+
+
+def create_learning_rate_fn(
+    num_train_steps: int, lr_scheduler_type: str, num_warmup_steps: int, learning_rate: float
+) -> Callable[[int], jnp.array]:
+    """Returns a linear warmup, linear_decay learning rate function."""
+    lr_scheduler_types = ("linear", "constant_with_warmup")
+
+    if lr_scheduler_type not in lr_scheduler_types:
+        raise ValueError(
+            f"lr_scheduler_type of type {lr_scheduler_type} not supported, choose from {lr_scheduler_types}."
+        )
+
+    warmup_fn = optax.linear_schedule(init_value=0.0, end_value=learning_rate, transition_steps=num_warmup_steps)
+    decay_fn = optax.linear_schedule(
+        init_value=learning_rate,
+        end_value=0 if lr_scheduler_type == "linear" else learning_rate,
+        transition_steps=num_train_steps - num_warmup_steps,
+    )
+    schedule_fn = optax.join_schedules(schedules=[warmup_fn, decay_fn], boundaries=[num_warmup_steps])
+    return schedule_fn
+
+
+def convert_dataset_str_to_list(
+    dataset_names,
+    dataset_config_names,
+    splits=None,
+    text_column_names=None,
+    dataset_samples=None,
+    default_split="train",
+):
+    if isinstance(dataset_names, str):
+        dataset_names = dataset_names.split("+")
+
+        # we assume that all the datasets we're using derive from the distil-whisper org on the Hub - prepend the org name if necessary
+        for i in range(len(dataset_names)):
+            ds_name = dataset_names[i]
+            dataset_names[i] = f"distil-whisper/{ds_name}" if "/" not in ds_name else ds_name
+
+        dataset_config_names = dataset_config_names.split("+")
+        splits = splits.split("+") if splits is not None else None
+        text_column_names = text_column_names.split("+") if text_column_names is not None else None
+        dataset_samples = dataset_samples.split("+") if dataset_samples is not None else None
+
+    # basic checks to ensure we've got the right number of datasets/configs/splits/columns/probs
+    if len(dataset_names) != len(dataset_config_names):
+        raise ValueError(
+            f"Ensure one config is passed for each dataset, got {len(dataset_names)} datasets and"
+            f" {len(dataset_config_names)} configs."
+        )
+
+    if splits is not None and len(splits) != len(dataset_names):
+        raise ValueError(
+            f"Ensure one split is passed for each dataset, got {len(dataset_names)} datasets and {len(splits)} splits."
+        )
+
+    if text_column_names is not None and len(text_column_names) != len(dataset_names):
+        raise ValueError(
+            f"Ensure one text column name is passed for each dataset, got {len(dataset_names)} datasets and"
+            f" {len(text_column_names)} text column names."
+        )
+
+    if dataset_samples is not None:
+        if len(dataset_samples) != len(dataset_names):
+            raise ValueError(
+                f"Ensure one sample is passed for each dataset, got {len(dataset_names)} datasets and "
+                f"{len(dataset_samples)} samples."
+            )
+        dataset_samples = [float(ds_sample) for ds_sample in dataset_samples]
+    else:
+        dataset_samples = [None] * len(dataset_names)
+
+    text_column_names = (
+        text_column_names if text_column_names is not None else ["text" for _ in range(len(dataset_names))]
+    )
+    splits = splits if splits is not None else [default_split for _ in range(len(dataset_names))]
+
+    dataset_names_dict = []
+    for i, ds_name in enumerate(dataset_names):
+        dataset_names_dict.append(
+            {
+                "name": ds_name,
+                "config": dataset_config_names[i],
+                "split": splits[i],
+                "text_column_name": text_column_names[i],
+                "samples": dataset_samples[i],
+            }
+        )
+    return dataset_names_dict
+
+
+def load_multiple_datasets(
+    dataset_names: Union[List, str],
+    dataset_config_names: Union[List, str],
+    splits: Optional[Union[List, str]] = None,
+    text_column_names: Optional[List] = None,
+    sampling_rate: Optional[int] = 16000,
+    stopping_strategy: Optional[str] = "first_exhausted",
+    dataset_samples: Optional[Union[List, np.array]] = None,
+    streaming: bool = True,
+    seed: int = None,
+    **kwargs,
+) -> IterableDataset:
+    dataset_names_dict = convert_dataset_str_to_list(
+        dataset_names, dataset_config_names, splits, text_column_names, dataset_samples
+    )
+
+    if dataset_samples is not None:
+        dataset_samples = [ds_dict["samples"] for ds_dict in dataset_names_dict]
+        probabilities = np.array(dataset_samples) / np.sum(dataset_samples)
+    else:
+        probabilities = None
+
+    if len(dataset_names_dict) == 1:
+        dataset_dict = dataset_names_dict[0]
+        # we have a single dataset so just return it as is
+        return load_dataset(
+            dataset_dict["name"],
+            dataset_dict["config"],
+            split=dataset_dict["split"],
+            streaming=streaming,
+            **kwargs,
+        )
+
+    all_datasets = []
+    # iterate over the datasets we want to interleave
+    for dataset_dict in tqdm(dataset_names_dict, desc="Combining datasets..."):
+        dataset = load_dataset(
+            dataset_dict["name"],
+            dataset_dict["config"],
+            split=dataset_dict["split"],
+            streaming=streaming,
+            **kwargs,
+        )
+        # resample to specified sampling rate
+        dataset = dataset.cast_column("audio", datasets.features.Audio(sampling_rate))
+        dataset = dataset.remove_columns(
+            set(dataset.features.keys()) - {"audio", dataset_dict["text_column_name"], "whisper_transcript"}
+        )
+        all_datasets.append(dataset)
+
+    if streaming:
+        interleaved_dataset = interleave_datasets(
+            all_datasets,
+            stopping_strategy=stopping_strategy,
+            probabilities=probabilities,
+            seed=seed,
+        )
+    else:
+        interleaved_dataset = concatenate_datasets(all_datasets)
+
+    return interleaved_dataset
+
+
+def get_layers_to_supervise(student_layers: int, teacher_layers: int) -> dict:
+    """Helper function to map the student layer i to the teacher layer j whose output we'd like them to emulate. Used
+    for MSE loss terms in distillation (hidden-states and activations). Student layers are paired with teacher layers
+    in equal increments, e.g. for a 12-layer model distilled to a 3-layer model, student layer 0 emulates teacher layer
+    3 (such that it behaves like the first 4 teacher layers), student layer 1 emulates teacher layer 7, and student layer
+    2 emulates teacher layer 11. This mapping is summarised by the dictionary: {0: 3, 1: 7, 2: 11}, which is precisely
+    the output of this function for the arguments (student_layers=3, teacher_layers=12)."""
+    layer_intervals = np.linspace(teacher_layers // student_layers - 1, teacher_layers - 1, student_layers, dtype=int)
+    layer_intervals[-1] = teacher_layers - 1
+    layer_map = {}
+
+    for student_layer, teacher_layer in enumerate(layer_intervals):
+        layer_map[student_layer] = teacher_layer
+
+    return layer_map
+
+
+class FlaxWhisperFeatureExtractor(WhisperFeatureExtractor):
+    def _np_extract_fbank_features(self, waveform: np.array) -> np.ndarray:
+        """
+        Compute the log-mel spectrogram of the provided audio using torch filters. Using the torch implementation
+        computes stft filter banks approx 5x faster than its numpy counterpart, which is the native implementation
+        in transformers, and matches to within 1e-5 abs tolerance.
+        """
+        waveform = torch.from_numpy(waveform).type(torch.float32)
+
+        window = torch.hann_window(self.n_fft)
+        stft = torch.stft(waveform, self.n_fft, self.hop_length, window=window, return_complex=True)
+        magnitudes = stft[..., :-1].abs() ** 2
+
+        mel_filters = torch.from_numpy(self.mel_filters).type(torch.float32)
+        mel_spec = mel_filters.T @ magnitudes
+
+        log_spec = torch.clamp(mel_spec, min=1e-10).log10()
+        log_spec = torch.maximum(log_spec, log_spec.max() - 8.0)
+        log_spec = (log_spec + 4.0) / 4.0
+        return log_spec.numpy()
+
+
+def main():
+    # 1. Parse input arguments
+    # See all possible arguments in src/transformers/training_args.py
+    # or by passing the --help flag to this script.
+    # We now keep distinct sets of args, for a cleaner separation of concerns.
+    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, FlaxSeq2SeqTrainingArguments))
+
+    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
+        # If we pass only one argument to the script and it's the path to a json file,
+        # let's parse it to get our arguments.
+        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
+    else:
+        model_args, data_args, training_args = parser.parse_args_into_dataclasses()
+
+    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
+    # information sent is the one passed as arguments along with your JAX/Flax versions.
+    send_example_telemetry("run_flax_speech_recognition_seq2seq", model_args, data_args, framework="flax")
+
+    # 2. Define remote logging - do this early so that we get the full traceback on our remote logs
+    # Enable tensorboard only on the master node
+    has_tensorboard = is_tensorboard_available()
+    if has_tensorboard:
+        if jax.process_index() == 0:
+            try:
+                from flax.metrics.tensorboard import SummaryWriter
+
+                summary_writer = SummaryWriter(log_dir=os.path.join(Path(training_args.output_dir), "runs"))
+            except ImportError as ie:
+                has_tensorboard = False
+                logger.warning(
+                    "Unable to display metrics through TensorBoard because some package" f" are not installed: {ie}"
+                )
+    else:
+        logger.warning(
+            "Unable to display metrics through TensorBoard because the package is not"
+            " installed: Please run `pip install tensorboard` to enable."
+        )
+
+    # Enable wandb only on the master node
+    has_wandb = is_wandb_available()
+    if has_wandb:
+        import wandb as wandb_logger
+
+        # Set up wandb run
+        if jax.process_index() == 0:
+            wandb_logger.init(
+                project=data_args.wandb_project,
+                name=data_args.wandb_name,
+                job_type=data_args.wandb_job_type,
+                dir=data_args.wandb_dir,
+                save_code=data_args.save_code_to_wandb,
+            )
+    else:
+        logger.warning("Wandb logging requires wandb to be installed. Run `pip install wandb` to enable.")
+
+    # 3. Setup local logging
+    # Make one log on every process with the configuration for debugging.
+    logging.basicConfig(
+        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+        datefmt="%m/%d/%Y %H:%M:%S",
+        handlers=[logging.StreamHandler(sys.stdout)],
+    )
+    # Set the verbosity to info of the Transformers logger.
+    # We only want one process per machine to log things on the screen.
+    logger.setLevel(logging.INFO if jax.process_index() == 0 else logging.ERROR)
+    if jax.process_index() == 0:
+        datasets.utils.logging.set_verbosity_warning()
+        transformers.utils.logging.set_verbosity_info()
+    else:
+        datasets.utils.logging.set_verbosity_error()
+        transformers.utils.logging.set_verbosity_error()
+
+    logger.info("Training/evaluation parameters %s", training_args)
+
+    # Check the output dir is valid
+    if (
+        os.path.exists(training_args.output_dir)
+        and os.listdir(training_args.output_dir)
+        and training_args.do_train
+        and not training_args.overwrite_output_dir
+    ):
+        raise ValueError(
+            f"Output directory ({training_args.output_dir}) already exists and is not"
+            " empty. Use `--overwrite_output_dir` to overcome."
+        )
+
+    # 4. Handle the repository creation
+    if training_args.push_to_hub:
+        if training_args.hub_model_id is None:
+            repo_name = get_full_repo_name(
+                Path(training_args.output_dir).absolute().name,
+                token=training_args.hub_token,
+            )
+        else:
+            repo_name = training_args.hub_model_id
+        create_repo(repo_name, exist_ok=True, token=training_args.hub_token)
+        repo = Repository(
+            training_args.output_dir,
+            clone_from=repo_name,
+            token=training_args.hub_token,
+        )
+
+    if training_args.compilation_cache:
+        cc.initialize_cache(os.path.join(model_args.cache_dir, "jax_cache"))
+
+    # 5. Load dataset
+    raw_datasets = IterableDatasetDict() if data_args.streaming else DatasetDict()
+
+    # set seed for determinism
+    set_seed(training_args.seed)
+
+    if training_args.do_train:
+        raw_datasets["train"] = load_multiple_datasets(
+            data_args.train_dataset_name,
+            data_args.train_dataset_config_name,
+            splits=data_args.train_split_name,
+            streaming=data_args.streaming,
+            dataset_samples=data_args.train_dataset_samples,
+            seed=training_args.seed,
+            cache_dir=data_args.dataset_cache_dir,
+            token=True if model_args.use_auth_token else None,
+        )
+
+    if training_args.do_eval:
+        dataset_names_dict = convert_dataset_str_to_list(
+            data_args.eval_dataset_name if data_args.eval_dataset_name else data_args.train_dataset_name,
+            (
+                data_args.eval_dataset_config_name
+                if data_args.eval_dataset_config_name
+                else data_args.train_dataset_config_name
+            ),
+            splits=data_args.eval_split_name,
+            text_column_names=data_args.eval_text_column_name,
+        )
+        all_eval_splits = []
+        if len(dataset_names_dict) == 1:
+            # load a single eval set
+            dataset_dict = dataset_names_dict[0]
+            all_eval_splits.append("eval")
+            raw_datasets["eval"] = load_dataset(
+                dataset_dict["name"],
+                dataset_dict["config"],
+                split=dataset_dict["split"],
+                cache_dir=data_args.dataset_cache_dir,
+                token=True if model_args.use_auth_token else None,
+                streaming=data_args.streaming,
+            )
+        else:
+            # load multiple eval sets
+            for dataset_dict in dataset_names_dict:
+                if dataset_dict["name"] == "esb/diagnostic-dataset":
+                    # for the ESB diagnostic dataset, the dataset name is effectively the config
+                    pretty_name = f"{dataset_dict['config']}-diagnostic/{dataset_dict['split']}"
+                else:
+                    pretty_name = f"{dataset_dict['name'].split('/')[-1]}/{dataset_dict['split'].replace('.', '-')}"
+                all_eval_splits.append(pretty_name)
+                raw_datasets[pretty_name] = load_dataset(
+                    dataset_dict["name"],
+                    dataset_dict["config"],
+                    split=dataset_dict["split"],
+                    cache_dir=data_args.dataset_cache_dir,
+                    token=True if model_args.use_auth_token else None,
+                    streaming=data_args.streaming,
+                )
+                features = raw_datasets[pretty_name].features.keys()
+                if "text" not in features:
+                    raw_datasets[pretty_name] = raw_datasets[pretty_name].rename_column(
+                        dataset_dict["text_column_name"], "text"
+                    )
+                raw_datasets[pretty_name] = raw_datasets[pretty_name].remove_columns(
+                    set(raw_datasets[pretty_name].features.keys()) - {"audio", "text"}
+                )
+
+    if not training_args.do_train and not training_args.do_eval:
+        raise ValueError(
+            "Cannot not train and not do evaluation. At least one of training or evaluation has to be performed."
+        )
+
+    raw_datasets_train_features = list(raw_datasets["train"].features.keys())
+
+    if data_args.audio_column_name not in raw_datasets_train_features:
+        raise ValueError(
+            f"--audio_column_name '{data_args.audio_column_name}' not found in dataset"
+            f" '{data_args.dataset_name}'. Make sure to set `--audio_column_name` to"
+            " the correct audio column - one of"
+            f" {', '.join(raw_datasets_train_features)}."
+        )
+
+    if data_args.train_text_column_name not in raw_datasets_train_features:
+        raise ValueError(
+            f"--train_text_column_name {data_args.train_text_column_name} not found in dataset"
+            f" '{data_args.dataset_name}'. Make sure to set `--train_text_column_name` to the"
+            " correct text column - one of"
+            f" {', '.join(raw_datasets_train_features)}."
+        )
+
+    # 6. Load pretrained model, tokenizer, and feature extractor
+    config = WhisperConfig.from_pretrained(
+        (model_args.config_name if model_args.config_name else model_args.model_name_or_path),
+        cache_dir=model_args.cache_dir,
+        revision=model_args.model_revision,
+        token=True if model_args.use_auth_token else None,
+    )
+    feature_extractor = FlaxWhisperFeatureExtractor.from_pretrained(
+        (model_args.feature_extractor_name if model_args.feature_extractor_name else model_args.model_name_or_path),
+        cache_dir=model_args.cache_dir,
+        revision=model_args.model_revision,
+        token=True if model_args.use_auth_token else None,
+    )
+    tokenizer = WhisperTokenizerFast.from_pretrained(
+        (model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path),
+        cache_dir=model_args.cache_dir,
+        use_fast=model_args.use_fast_tokenizer,
+        revision=model_args.model_revision,
+        token=True if model_args.use_auth_token else None,
+    )
+
+    # override timestamp tokens until tokenizer issues are fixed in transformers
+    timestamps = [AddedToken("<|%.2f|>" % (i * 0.02), lstrip=False, rstrip=False) for i in range(1500 + 1)]
+    tokenizer.add_tokens(timestamps)
+
+    config.update(
+        {
+            "activation_dropout": model_args.activation_dropout,
+            "attention_dropout": model_args.attention_dropout,
+            "dropout": model_args.dropout,
+        }
+    )
+
+    if training_args.precision == "full_mixed":
+        # forward pass, backward pass and optimiser states in bf16
+        dtype = jnp.bfloat16
+        to_dtype = to_bf16
+    elif training_args.precision == "half_mixed" or model_args.dtype == "bfloat16":
+        # forward pass in bf16, backward pass and optimiser states in fp32
+        dtype = jnp.bfloat16
+        to_dtype = to_fp32
+    else:
+        if training_args.precision != "full":
+            raise ValueError(
+                f"`precision` should be one of: `full`, `half_mixed` or `full_mixed`, got {training_args.precision}"
+            )
+        # forward pass, backward pass and optimiser states in fp32
+        dtype = jnp.float32
+        to_dtype = to_fp32
+
+    student_model, student_params = FlaxWhisperForConditionalGeneration.from_pretrained(
+        model_args.model_name_or_path,
+        config=config,
+        dtype=dtype,
+        cache_dir=model_args.cache_dir,
+        revision=model_args.model_revision,
+        subfolder=model_args.subfolder,
+        token=True if model_args.use_auth_token else None,
+        _do_init=False,
+        use_scan=model_args.load_with_scan_weights,
+    )
+
+    teacher_model, teacher_params = FlaxWhisperForConditionalGeneration.from_pretrained(
+        model_args.teacher_model_name_or_path,
+        # config=config,
+        dtype=dtype,
+        cache_dir=model_args.cache_dir,
+        # revision=model_args.model_revision,
+        token=True if model_args.use_auth_token else None,
+        _do_init=False,
+    )
+
+    if student_model.config.decoder_start_token_id is None or teacher_model.config.decoder_start_token_id is None:
+        raise ValueError(
+            f"Make sure that `config.decoder_start_token_id` is correctly defined for both the "
+            f"student and teacher model. Got {student_model.config.decoder_start_token_id} for the "
+            f"student and {teacher_model.config.decoder_start_token_id} for the teacher."
+        )
+
+    # enable scan / gradient checkpointing if necessary
+    if training_args.use_scan:
+        student_model.enable_scan()  # to enable scan in the nn.Module
+        student_params = student_model.convert_unroll_to_scan(student_params)  # to convert the unrolled params to scan
+
+        teacher_model.enable_scan()  # faster compile time (even though we don't train the teacher)
+        teacher_params = teacher_model.convert_unroll_to_scan(teacher_params)
+
+    if training_args.gradient_checkpointing:
+        student_model.enable_gradient_checkpointing()  # to enable checkpointing in the nn.Module, there is no change to the params structure
+        teacher_model.enable_gradient_checkpointing()
+
+    if hasattr(teacher_model.generation_config, "is_multilingual") and teacher_model.generation_config.is_multilingual:
+        # We need to set the language and task ids for previously multilingual checkpoints - for now we hardcode this to English
+        tokenizer.set_prefix_tokens(language="English", task="transcribe", predict_timestamps=False)
+        student_model.generation_config.update(
+            **{
+                "language": "<|en|>",
+                "task": "transcribe",
+            }
+        )
+
+    # 7. Resample speech dataset: `datasets` takes care of automatically loading and resampling the audio,
+    # so we just need to set the correct target sampling rate.
+    raw_datasets = raw_datasets.cast_column(
+        data_args.audio_column_name,
+        datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate),
+    )
+
+    # 8. Preprocessing the datasets.
+    # We need to read the audio files as arrays and tokenize the targets.
+    max_input_length = int(data_args.max_duration_in_seconds * feature_extractor.sampling_rate)
+    min_input_length = int(data_args.min_duration_in_seconds * feature_extractor.sampling_rate)
+    max_label_length = (
+        data_args.max_label_length if data_args.max_label_length is not None else student_model.config.max_length
+    )
+    audio_column_name = data_args.audio_column_name
+    num_workers = data_args.preprocessing_num_workers
+    dataloader_num_workers = training_args.dataloader_num_workers
+    dataloader_prefetch_size = data_args.prefetch_size
+    train_text_column_name = data_args.train_text_column_name
+    eval_text_column_name = "text"
+    model_input_name = feature_extractor.model_input_names[0]
+    normalizer = EnglishTextNormalizer(tokenizer.english_spelling_normalizer)
+    wer_threshold = data_args.wer_threshold
+    round_timestamps = data_args.round_timestamps
+
+    if training_args.do_train and data_args.max_train_samples is not None:
+        raw_datasets["train"] = (
+            raw_datasets["train"].take(data_args.max_train_samples)
+            if data_args.streaming
+            else raw_datasets["train"].select(range(data_args.max_train_samples))
+        )
+
+    if training_args.do_eval and data_args.max_eval_samples is not None:
+        for eval_split in all_eval_splits:
+            raw_datasets[eval_split] = (
+                raw_datasets[eval_split].take(data_args.max_eval_samples)
+                if data_args.streaming
+                else raw_datasets[eval_split].select(range(data_args.max_eval_samples))
+            )
+
+    def is_wer_in_range(ground_truth, whisper_transcript):
+        norm_ground_truth = normalizer(ground_truth)
+        if len(norm_ground_truth) > 0 and whisper_transcript is not None:
+            norm_whisper_transcript = normalizer(whisper_transcript)
+            wer = 100 * metric.compute(predictions=[norm_whisper_transcript], references=[norm_ground_truth])
+            return wer < wer_threshold
+        else:
+            # filter automatically since we can't know the WER
+            return False
+
+    filter_by_wer_threshold = partial(
+        raw_datasets["train"].filter,
+        function=is_wer_in_range,
+        input_columns=[eval_text_column_name, train_text_column_name],
+    )
+
+    if wer_threshold is not None:
+        raw_datasets["train"] = (
+            filter_by_wer_threshold(num_proc=num_workers, desc="filtering train dataset by wer")
+            if not data_args.streaming
+            else filter_by_wer_threshold()
+        )
+
+    def has_timestamp_tokens(input_str):
+        """
+        Identify whether the input string contains timestamp tokens, of the form <|0.00|>, by searching for
+        pairs of left and right-angle brackets.
+        """
+        return bool(re.search("\<[^\>]*\>", input_str))
+
+    def round_timestamp_tokens(input_str: str, ndigits: int = 1):
+        timestamps = re.findall("\<[^\>]*\>", input_str, re.DOTALL)
+        for token in timestamps:
+            # extract time digits from timestamp token, e.g. <|6.24|> to 6.24
+            time_digit = token[2:-2]
+            # round to specified number of digits, e.g. 6.24 to 6.2
+            time_digit = round(float(time_digit), ndigits=ndigits)
+            # replace in original string with the same precision, e.g. <|6.24|> to <|6.20|>
+            input_str = input_str.replace(token, "<|{:.2f}|>".format(time_digit))
+        return input_str
+
+    def prepare_train_dataset(batch):
+        # process audio input
+        sample = batch[audio_column_name]
+        inputs = feature_extractor(sample["array"], sampling_rate=sample["sampling_rate"])
+        batch[model_input_name] = inputs.get(model_input_name)[0]
+        batch["input_length"] = len(sample["array"])
+
+        # process text targets
+        input_str = batch[train_text_column_name]
+
+        # prompt & timestamp processing: for now, we only do one or the other
+        if input_str.startswith("<|startoftranscript|>") or input_str.startswith("<|startofprev|>"):
+            # prompted target text already has special ids added, so don't add them here
+            batch["labels"] = tokenizer(input_str, add_special_tokens=False).input_ids
+            return batch
+
+        has_timestamps = has_timestamp_tokens(input_str)
+
+        if has_timestamps:
+            predict_timestamps = bool(np.random.binomial(1, data_args.timestamp_probability))
+            if not predict_timestamps:
+                # filter timestamp token ids if not part of the prediction task
+                input_str = tokenizer._filter_timestamp_ids(input_str)
+            elif round_timestamps:
+                input_str = round_timestamp_tokens(input_str)
+        else:
+            predict_timestamps = False
+
+        tokenizer.set_prefix_tokens(language="English", task="transcribe", predict_timestamps=predict_timestamps)
+        input_ids = tokenizer(input_str).input_ids
+        batch["labels"] = input_ids
+        return batch
+
+    def prepare_eval_dataset(batch):
+        # process audio
+        sample = batch[audio_column_name]
+        inputs = feature_extractor(sample["array"], sampling_rate=sample["sampling_rate"])
+        # process audio length
+        batch[model_input_name] = inputs.get(model_input_name)[0]
+        batch["input_length"] = len(sample["array"])
+
+        # process targets
+        input_str = batch[eval_text_column_name]
+        batch["labels"] = tokenizer(input_str).input_ids
+        return batch
+
+    vectorized_datasets = IterableDatasetDict() if data_args.streaming else DatasetDict()
+    if training_args.do_train:
+        map_fn_train = partial(
+            raw_datasets["train"].map, function=prepare_train_dataset, remove_columns=raw_datasets_train_features
+        )
+        vectorized_datasets["train"] = (
+            map_fn_train(num_proc=num_workers, desc="preprocess train dataset")
+            if not data_args.streaming
+            else map_fn_train()
+        )
+    if training_args.do_eval:
+        for eval_split in all_eval_splits:
+            raw_datasets_eval_features = list(raw_datasets[eval_split].features.keys())
+            map_fn_eval = partial(
+                raw_datasets[eval_split].map, function=prepare_eval_dataset, remove_columns=raw_datasets_eval_features
+            )
+            vectorized_datasets[eval_split] = (
+                map_fn_eval(num_proc=num_workers, desc="preprocess eval dataset")
+                if not data_args.streaming
+                else map_fn_eval()
+            )
+
+    # filter training data with inputs longer than max_input_length
+    def is_audio_in_length_range(length):
+        return min_input_length < length < max_input_length
+
+    filter_by_audio_fn = partial(
+        vectorized_datasets.filter, function=is_audio_in_length_range, input_columns=["input_length"]
+    )
+    vectorized_datasets = (
+        filter_by_audio_fn(num_proc=num_workers, desc="filtering train dataset by audio length")
+        if not data_args.streaming
+        else filter_by_audio_fn()
+    )
+
+    # filter training data with labels longer than max_label_length
+    def is_labels_in_length_range(labels):
+        return 0 < len(labels) < max_label_length
+
+    filter_by_labels_fn = partial(
+        vectorized_datasets.filter, function=is_labels_in_length_range, input_columns=["labels"]
+    )
+    vectorized_datasets = (
+        filter_by_labels_fn(num_proc=num_workers, desc="filtering train dataset")
+        if not data_args.streaming
+        else filter_by_labels_fn()
+    )
+
+    # for large datasets it is advised to run the preprocessing on a
+    # single machine first with `args.preprocessing_only` since there will mostly likely
+    # be a timeout when running the script in distributed mode.
+    # In a second step `args.preprocessing_only` can then be set to `False` to load the
+    # cached dataset
+    if data_args.preprocessing_only:
+        cache = {k: v.cache_files for k, v in vectorized_datasets.items()}
+        logger.info(f"Data preprocessing finished. Files cached at {cache}.")
+        return
+
+    # 8. Load Metric
+    metric = evaluate.load("wer")
+    # convention is that we space all punctuation *except* apostrophes
+    all_punctuation = list(string.punctuation.replace("'", ""))
+    return_timestamps = data_args.return_timestamps if data_args.timestamp_probability > 0 else False
+
+    def compute_metrics(preds, labels):
+        # replace padded labels by the padding token
+        for idx in range(len(labels)):
+            labels[idx][labels[idx] == -100] = tokenizer.pad_token_id
+
+        pred_str = tokenizer.batch_decode(preds, skip_special_tokens=True, decode_with_timestamps=return_timestamps)
+        # we do not want to group tokens when computing the metrics
+        label_str = tokenizer.batch_decode(labels, skip_special_tokens=True)
+
+        # space punctuation for orthographic WER (c.f. ESB paper https://arxiv.org/abs/2210.13352)
+        spaced_pred_str = [
+            pred_str[i].replace(punctuation, f" {punctuation} ")
+            for punctuation in all_punctuation
+            for i in range(len(pred_str))
+        ]
+        spaced_label_str = [
+            label_str[i].replace(punctuation, f" {punctuation} ")
+            for punctuation in all_punctuation
+            for i in range(len(label_str))
+        ]
+        wer_ortho = 100 * metric.compute(predictions=spaced_pred_str, references=spaced_label_str)
+
+        # normalize everything and re-compute the WER
+        norm_pred_str = [normalizer(pred) for pred in pred_str]
+        norm_label_str = [normalizer(label) for label in label_str]
+        # for logging, we need the pred/labels to match the norm_pred/norm_labels, so discard any filtered samples here
+        pred_str = [pred_str[i] for i in range(len(norm_pred_str)) if len(norm_label_str[i]) > 0]
+        label_str = [label_str[i] for i in range(len(norm_label_str)) if len(norm_label_str[i]) > 0]
+        # filtering step to only evaluate the samples that correspond to non-zero normalized references:
+        norm_pred_str = [norm_pred_str[i] for i in range(len(norm_pred_str)) if len(norm_label_str[i]) > 0]
+        norm_label_str = [norm_label_str[i] for i in range(len(norm_label_str)) if len(norm_label_str[i]) > 0]
+
+        wer = 100 * metric.compute(predictions=norm_pred_str, references=norm_label_str)
+
+        return {"wer": wer, "wer_ortho": wer_ortho}, pred_str, label_str, norm_pred_str, norm_label_str
+
+    # 9. Save feature extractor, tokenizer, config and generation config
+    feature_extractor.save_pretrained(training_args.output_dir)
+    tokenizer.save_pretrained(training_args.output_dir)
+    config.save_pretrained(training_args.output_dir)
+    student_model.generation_config.save_pretrained(
+        training_args.output_dir
+    )  # generation config stays bound to model to make it easy to jit
+
+    processor = WhisperProcessor.from_pretrained(training_args.output_dir)
+
+    data_collator = FlaxDataCollatorSpeechSeq2SeqWithPadding(
+        processor=processor,
+        decoder_start_token_id=student_model.config.decoder_start_token_id,  # <|startoftranscript|>
+        decoder_prev_token_id=tokenizer.all_special_ids[-3],  # <|startofprev|>
+        input_padding="longest",
+        target_padding="max_length",
+        max_target_length=max_label_length,
+    )
+
+    # Initialize our training
+    rng = jax.random.PRNGKey(training_args.seed)
+    rng, dropout_rng = jax.random.split(rng)
+
+    # Store some constants
+    train_batch_size = int(training_args.per_device_train_batch_size) * jax.device_count()
+    gradient_accumulation_steps = int(training_args.gradient_accumulation_steps)
+    per_device_eval_batch_size = int(training_args.per_device_eval_batch_size)
+    eval_batch_size = per_device_eval_batch_size * jax.device_count()
+
+    if not data_args.streaming and training_args.max_steps < 0:
+        num_epochs = int(training_args.num_train_epochs)
+        steps_per_epoch = len(vectorized_datasets["train"]) // train_batch_size
+        total_train_steps = steps_per_epoch * num_epochs
+    elif training_args.max_steps > 0:
+        logger.info("max_steps is given, it will override any value given in num_train_epochs")
+        total_train_steps = int(training_args.max_steps)
+        # Setting a very large number of epochs so we go as many times as necessary over the iterator.
+        num_epochs = sys.maxsize
+        steps_per_epoch = total_train_steps
+    else:
+        raise ValueError("max_steps must be specified when training with a streaming (iterable) dataset")
+
+    if training_args.eval_steps is None:
+        logger.info(
+            f"eval_steps is not set, evaluating at the end of {'each epoch' if not data_args.streaming else 'training'}"
+        )
+        eval_steps = steps_per_epoch
+    else:
+        eval_steps = training_args.eval_steps
+
+    # Create learning rate schedule
+    linear_decay_lr_schedule_fn = create_learning_rate_fn(
+        total_train_steps * gradient_accumulation_steps,
+        training_args.lr_scheduler_type,
+        training_args.warmup_steps * gradient_accumulation_steps,
+        training_args.learning_rate,
+    )
+
+    # We use Optax's "masking" functionality to not apply weight decay
+    # to bias and LayerNorm scale parameters. decay_mask_fn returns a
+    # mask boolean with the same structure as the parameters.
+    # The mask is True for parameters that should be decayed.
+    def decay_mask_fn(params):
+        flat_params = traverse_util.flatten_dict(params)
+        # find out all LayerNorm parameters
+        layer_norm_candidates = [
+            "layer_norm",
+            "self_attn_layer_norm",
+            "final_layer_norm",
+            "encoder_attn_layer_norm",
+        ]
+        layer_norm_named_params = {
+            layer[-2:]
+            for layer_norm_name in layer_norm_candidates
+            for layer in flat_params.keys()
+            if layer_norm_name in "".join(layer).lower()
+        }
+        flat_mask = {path: path[-1] != "bias" and path[-2:] not in layer_norm_named_params for path in flat_params}
+        return traverse_util.unflatten_dict(flat_mask)
+
+    # create adam optimizer
+    adamw = optax.adamw(
+        learning_rate=linear_decay_lr_schedule_fn,
+        b1=training_args.adam_beta1,
+        b2=training_args.adam_beta2,
+        eps=training_args.adam_epsilon,
+        weight_decay=training_args.weight_decay,
+        mask=decay_mask_fn,
+    )
+
+    if gradient_accumulation_steps > 1:
+        # accumulate gradients and apply once every k steps
+        adamw = optax.MultiSteps(adamw, every_k_schedule=gradient_accumulation_steps)
+
+    share_hidden_states = training_args.freeze_encoder and student_model.config.d_model == teacher_model.config.d_model
+    encoder_layer_mapping = get_layers_to_supervise(
+        student_model.config.encoder_layers, teacher_model.config.encoder_layers
+    )
+    decoder_layer_mapping = get_layers_to_supervise(
+        student_model.config.decoder_layers, teacher_model.config.decoder_layers
+    )
+
+    # Setup train state
+    student_state = TrainState.create(
+        apply_fn=student_model.decode if share_hidden_states else student_model.__call__,
+        params=student_params,
+        tx=adamw,
+        to_dtype=to_dtype,
+        dropout_rng=dropout_rng,
+        max_grad_norm=training_args.max_grad_norm,
+    )
+
+    if training_args.resume_from_checkpoint is not None:
+        if os.path.isfile(os.path.join(training_args.resume_from_checkpoint, "train_state.msgpack")):
+            logger.info(
+                f"Checkpoint detected, resuming training at {training_args.resume_from_checkpoint}. To avoid "
+                "this behavior, omit the resume_from_checkpoint argument."
+            )
+            with Path(os.path.join(training_args.resume_from_checkpoint, "train_state.msgpack")).open("rb") as f:
+                student_state = from_bytes(student_state, f.read())
+        else:
+            logger.warning(
+                f"Checkpoint {training_args.resume_from_checkpoint} not detected, training from scratch. Ensure "
+                f"you pass the path to a folder with a valid checkpoint for your model."
+            )
+
+    def cross_entropy_loss(logits, labels):
+        vocab_size = logits.shape[-1]
+        # optax onehot always returns a float32 device array, need to downcast if performing mixed precision training
+        onehot_targets = to_dtype(onehot(labels, vocab_size))
+        loss = optax.softmax_cross_entropy(logits, onehot_targets)
+        # ignore padded tokens from loss, i.e. where labels are not set to -100
+        padding = labels >= 0
+        loss = loss * padding
+        loss = loss.sum()
+        num_labels = padding.sum()
+        return loss, num_labels
+
+    # temperature smoothed kl-divergence
+    def kl_divergence(target_distribution, log_predicted_distribution, labels, eps=1e-20):
+        divergence = -target_distribution * (log_predicted_distribution - jnp.log(target_distribution + eps))
+        # ignore padded tokens from divergence, i.e. where labels are not set to -100
+        padding_mask = labels >= 0
+        padding_mask = jnp.expand_dims(padding_mask, axis=-1)
+        divergence = (divergence * padding_mask).sum()
+        return to_dtype(divergence)  # respect the dtype of the backprop
+
+    def mean_square_error_loss(student_outputs, teacher_outputs):
+        mse = dtype(0.0)
+
+        # tie encoder embeddings
+        mse += jnp.mean(
+            jnp.square(teacher_outputs.encoder_hidden_states[0] - student_outputs.encoder_hidden_states[0])
+        )
+
+        for student_layer_id, teacher_layer_id in encoder_layer_mapping.items():
+            # offset the hidden-state layer ids by 1 to account for the extra embedding hidden-state
+            student_hidden_state = student_outputs.encoder_hidden_states[student_layer_id + 1]
+            teacher_hidden_state = teacher_outputs.encoder_hidden_states[teacher_layer_id + 1]
+            mse += jnp.mean(jnp.square(teacher_hidden_state - student_hidden_state))
+
+            # student_attention = student_outputs.encoder_attentions[student_layer_id]
+            # teacher_attention = teacher_outputs.encoder_attentions[teacher_layer_id]
+            # mse += jnp.mean(jnp.square(student_attention - teacher_attention))
+
+        # tie decoder embeddings
+        mse += jnp.mean(
+            jnp.square(teacher_outputs.decoder_hidden_states[0] - student_outputs.decoder_hidden_states[0])
+        )
+
+        for student_layer_id, teacher_layer_id in decoder_layer_mapping.items():
+            # offset the hidden-state layer ids by 1 to account for the extra embedding hidden-state
+            student_hidden_state = student_outputs.decoder_hidden_states[student_layer_id + 1]
+            teacher_hidden_state = teacher_outputs.decoder_hidden_states[teacher_layer_id + 1]
+            mse += jnp.mean(jnp.square(teacher_hidden_state - student_hidden_state))
+
+            # student_attention = student_outputs.decoder_attentions[student_layer_id]
+            # teacher_attention = teacher_outputs.decoder_attentions[teacher_layer_id]
+            # mse += jnp.mean(jnp.square(student_attention - teacher_attention))
+
+            # student_cross_attention = student_outputs.cross_attentions[student_layer_id]
+            # teacher_cross_attention = teacher_outputs.cross_attentions[teacher_layer_id]
+            # mse += jnp.mean(jnp.square(student_cross_attention - teacher_cross_attention))
+
+        return to_dtype(mse)  # respect the dtype of the backprop
+
+    # Define gradient update step fn
+    def train_step(
+        student_state,
+        teacher_params,
+        batch,
+        freeze_encoder,
+        share_hidden_states,
+        temperature=2.0,
+    ):
+        dropout_rng, new_dropout_rng = jax.random.split(student_state.dropout_rng)
+
+        def compute_loss(student_params):
+            labels = batch.pop("labels")
+            output_hidden_states = not share_hidden_states and training_args.mse_weight > 0.0
+
+            teacher_outputs = teacher_model(
+                **batch,
+                params=teacher_params,
+                freeze_encoder=True,
+                output_hidden_states=output_hidden_states,
+                train=False,
+            )
+
+            if share_hidden_states:
+                # if the student and teacher share the same frozen encoder then we don't have to recompute the
+                # encoder hidden-states for the student model, we can just re-use from the teacher
+                encoder_hidden_states = jax.lax.stop_gradient(teacher_outputs.encoder_last_hidden_state)
+                encoder_outputs = FlaxBaseModelOutput(last_hidden_state=encoder_hidden_states)
+
+                student_outputs = student_state.apply_fn(
+                    decoder_input_ids=batch["decoder_input_ids"],
+                    encoder_outputs=encoder_outputs,
+                    params=student_params,
+                    dropout_rng=dropout_rng,
+                    train=True,
+                )
+            else:
+                # do the full forward pass for the student model (encoder + decoder)
+                student_outputs = student_state.apply_fn(
+                    **batch,
+                    params=student_params,
+                    dropout_rng=dropout_rng,
+                    freeze_encoder=freeze_encoder,
+                    output_hidden_states=output_hidden_states,
+                    train=True,
+                )
+
+            # CE (data) loss
+            ce_loss, num_labels = cross_entropy_loss(student_outputs.logits, labels)
+
+            # rescale by temperature to ensure gradients scale correctly
+            teacher_distribution = jax.nn.softmax(teacher_outputs.logits / temperature, axis=-1)
+            # ensure no information flow backwards through teacher
+            teacher_distribution = jax.lax.stop_gradient(teacher_distribution)
+            # log softmax of student predictions for numerical stability
+            student_distribution = jax.nn.log_softmax(student_outputs.logits / temperature, axis=-1)
+            # KL-divergence loss (scaled by temperature)
+            kl_loss = kl_divergence(teacher_distribution, student_distribution, labels) * temperature**2
+
+            # MSE loss between enc-dec hidden-states and attentions
+            mse_loss = (
+                mean_square_error_loss(student_outputs, teacher_outputs)
+                if output_hidden_states
+                else jnp.zeros_like(kl_loss)
+            )
+
+            # use DistilBart formulation - only tune the MSE weight and take remaining HPs from DistilBERT
+            ce_weight = 0.8 if training_args.kl_weight > 0 else 1.0
+            loss = ce_weight * ce_loss + training_args.kl_weight * kl_loss + training_args.mse_weight * mse_loss
+
+            return loss, (
+                ce_loss,
+                kl_loss,
+                mse_loss,
+                num_labels,
+            )
+
+        grad_fn = jax.value_and_grad(compute_loss, has_aux=True)
+        (loss, (ce_loss, kl_loss, mse_loss, num_labels)), grad = grad_fn(to_dtype(student_state.params))
+
+        # true loss = total loss / total samples
+        loss = jax.lax.psum(loss, "batch")
+        num_labels = jax.lax.psum(num_labels, "batch")
+        loss = jax.tree_util.tree_map(lambda x: x / num_labels, loss)
+
+        # true grad = total grad / total samples
+        grad = jax.lax.psum(grad, "batch")
+        grad = jax.tree_util.tree_map(lambda x: x / num_labels, grad)
+        new_state = student_state.apply_gradients(grads=grad, dropout_rng=new_dropout_rng, to_dtype=to_dtype)
+
+        # CE/KL/MSE losses for logging
+        ce_loss = jax.lax.psum(ce_loss, "batch")
+        ce_loss = jax.tree_util.tree_map(lambda x: x / num_labels, ce_loss)
+
+        kl_loss = jax.lax.psum(kl_loss, "batch")
+        kl_loss = jax.tree_util.tree_map(lambda x: x / num_labels, kl_loss)
+
+        mse_loss = jax.lax.psum(mse_loss, "batch")
+        mse_loss = jax.tree_util.tree_map(lambda x: x / num_labels, mse_loss)
+
+        metrics = {
+            "loss": loss,
+            "learning_rate": linear_decay_lr_schedule_fn(student_state.step),
+            "ce_loss": ce_loss,
+            "kl_loss": kl_loss,
+            "mse_loss": mse_loss,
+        }
+        return new_state, metrics
+
+    # Define eval fn
+    def eval_step(student_params, teacher_params, batch):
+        labels = batch.pop("labels")
+        output_hidden_states = not share_hidden_states and training_args.mse_weight > 0
+
+        student_outputs = student_model(
+            **batch,
+            params=student_params,
+            output_hidden_states=output_hidden_states,
+            train=False,
+        )
+        student_distribution = jax.nn.log_softmax(student_outputs.logits, axis=-1)
+        ce_loss, num_labels = cross_entropy_loss(student_outputs.logits, labels)
+
+        teacher_outputs = teacher_model(
+            **batch,
+            params=teacher_params,
+            output_hidden_states=output_hidden_states,
+            train=False,
+        )
+        teacher_distribution = jax.nn.softmax(teacher_outputs.logits, axis=-1)
+        # temperature is always 1 for eval
+        kl_loss = kl_divergence(teacher_distribution, student_distribution, labels)
+
+        mse_loss = (
+            mean_square_error_loss(student_outputs, teacher_outputs)
+            if output_hidden_states
+            else jnp.zeros_like(kl_loss)
+        )
+
+        ce_weight = 0.8 if training_args.kl_weight > 0 else 1.0
+        loss = ce_weight * ce_loss + training_args.kl_weight * kl_loss + training_args.mse_weight * mse_loss
+        # true loss = total loss / total samples
+        loss = jax.lax.psum(loss, "batch")
+        num_labels = jax.lax.psum(num_labels, "batch")
+        loss = jax.tree_util.tree_map(lambda x: x / num_labels, loss)
+
+        # CE/KL/MSE losses for logging
+        ce_loss = jax.lax.psum(ce_loss, "batch")
+        ce_loss = jax.tree_util.tree_map(lambda x: x / num_labels, ce_loss)
+
+        kl_loss = jax.lax.psum(kl_loss, "batch")
+        kl_loss = jax.tree_util.tree_map(lambda x: x / num_labels, kl_loss)
+
+        mse_loss = jax.lax.psum(mse_loss, "batch")
+        mse_loss = jax.tree_util.tree_map(lambda x: x / num_labels, mse_loss)
+
+        metrics = {"loss": loss, "ce_loss": ce_loss, "kl_loss": kl_loss, "mse_loss": mse_loss}
+        return metrics
+
+    # Define generation function
+    num_beams = (
+        training_args.generation_num_beams
+        if training_args.generation_num_beams is not None
+        else student_model.config.num_beams
+    )
+
+    # forcing the language and task tokens helps the model in its generations
+    gen_kwargs = {
+        "max_length": max_label_length,
+        "num_beams": num_beams,
+        "language": "<|en|>",
+        "task": "transcribe",
+        "return_timestamps": return_timestamps,
+    }
+
+    def generate_step(student_params, batch):
+        output_ids = student_model.generate(
+            batch[model_input_name],
+            attention_mask=batch.get("attention_mask"),
+            params=student_params,
+            **gen_kwargs,
+        )
+        return output_ids.sequences
+
+    # Replicate the train state on each device
+    student_state = student_state.replicate()
+
+    # Replicate the teacher params on each device
+    teacher_params = jax_utils.replicate(teacher_params)
+
+    # Create parallel version of the train and eval step
+    p_train_step = jax.pmap(
+        train_step,
+        "batch",
+        in_axes=(0, 0, 0, None, None, None),
+        donate_argnums=(0,),
+        static_broadcasted_argnums=(
+            3,
+            4,
+        ),
+    )
+    p_eval_step = jax.pmap(eval_step, "batch")
+    p_generate_step = jax.pmap(generate_step, "batch")
+
+    logger.info("***** Running training *****")
+    logger.info(f"  Num examples = {total_train_steps * train_batch_size * gradient_accumulation_steps}")
+    logger.info("  Instantaneous batch size per device =" f" {training_args.per_device_train_batch_size}")
+    logger.info("  Gradient accumulation steps =" f" {gradient_accumulation_steps}")
+    logger.info(
+        f"  Total train batch size (w. parallel & distributed) = {train_batch_size * gradient_accumulation_steps}"
+    )
+    logger.info(f"  Total optimization steps = {total_train_steps}")
+
+    # ======================== Training ================================
+    train_time = 0
+    train_start = time.time()
+    train_metrics = []
+    batches_to_skip = jax.device_get(unreplicate(student_state.step))
+    cur_step = int(batches_to_skip)  # will be zero if starting from scratch
+    epochs_trained = batches_to_skip // steps_per_epoch
+    steps_trained_progress_bar = tqdm(range(total_train_steps), desc="Train steps ... ", position=0)
+    steps_trained_progress_bar.update(batches_to_skip)
+    continue_training = True
+    minibatch_steps = 0
+
+    if batches_to_skip > 0:
+        logger.info("  Continuing training from checkpoint, will skip to saved global_step")
+        logger.info(f"  Continuing training from epoch {epochs_trained}")
+        logger.info(f"  Continuing training from global step {batches_to_skip}")
+
+    # Generate a training data loader by shuffling sampling indices from the train dataset
+    train_loader = get_data_loader(
+        training_args.seed,
+        vectorized_datasets["train"],
+        batch_size=train_batch_size,
+        data_collator=data_collator,
+        dataloader_num_workers=dataloader_num_workers,
+        skip_batches=batches_to_skip,
+        prefetch_size=dataloader_prefetch_size,
+    )
+
+    for epoch in range(epochs_trained, num_epochs):
+        if hasattr(train_loader, "dataset") and isinstance(train_loader.dataset, IterableDataset):
+            train_loader.dataset.set_epoch(epoch)
+
+        for batch in train_loader:
+            minibatch_steps += 1
+            update_step = minibatch_steps == gradient_accumulation_steps
+
+            if update_step:
+                steps_trained_progress_bar.update(1)
+                cur_step += 1
+                minibatch_steps = 0
+
+            batch = shard(batch.data)
+            student_state, train_metric = p_train_step(
+                student_state,
+                teacher_params,
+                batch,
+                training_args.freeze_encoder,
+                share_hidden_states,
+                training_args.temperature,
+            )
+
+            if cur_step % training_args.logging_steps == 0 and update_step:
+                train_metrics.append(train_metric)
+                train_metric_to_write = unreplicate(train_metric)
+                steps_trained_progress_bar.write(
+                    f"Step... ({cur_step} / {total_train_steps} | Loss:"
+                    f" {train_metric_to_write['loss']}, Learning Rate:"
+                    f" {train_metric_to_write['learning_rate']})"
+                )
+                if has_wandb and jax.process_index() == 0:
+                    write_wandb_metric(
+                        wandb_logger,
+                        train_metric_to_write,
+                        train_time + time.time() - train_start,
+                        cur_step,
+                        epoch,
+                        prefix="train",
+                    )
+
+            # save checkpoint and weights after each save_steps and at the end of training
+            if (cur_step % training_args.save_steps == 0 and update_step) or cur_step == total_train_steps:
+                if jax.process_index() == 0:
+                    save_hf_weights(
+                        student_state,
+                        student_model,
+                        processor,
+                        training_args.output_dir,
+                        cur_step,
+                        total_train_steps,
+                        use_scan=training_args.use_scan,
+                    )
+                    if training_args.save_train_state:
+                        student_state.save_state(
+                            training_args.output_dir, save_total_limit=training_args.save_total_limit
+                        )
+                    if training_args.push_to_hub:
+                        repo.push_to_hub(
+                            commit_message=f"Saving train state of step {cur_step}",
+                            blocking=False,
+                        )
+
+            if training_args.do_eval and (
+                (cur_step % eval_steps == 0 and update_step) or cur_step == total_train_steps
+            ):
+                train_time += time.time() - train_start
+                # ======================== Evaluating ==============================
+                for eval_split in all_eval_splits:
+                    eval_metrics = []
+                    eval_preds = []
+                    eval_labels = []
+                    eval_start = time.time()
+
+                    eval_loader = get_data_loader(
+                        training_args.seed,
+                        vectorized_datasets[eval_split],
+                        batch_size=eval_batch_size,
+                        data_collator=data_collator,
+                        shuffle=False,
+                        drop_last=False,
+                        dataloader_num_workers=dataloader_num_workers,
+                    )
+                    for batch in tqdm(eval_loader, desc=f"Evaluating {eval_split}...", position=2):
+                        # Model forward
+                        labels = batch["labels"]
+
+                        metrics = pad_shard_unpad(
+                            p_eval_step,
+                            static_argnums=(
+                                0,
+                                1,
+                            ),
+                            static_return=True,
+                        )(
+                            student_state.params,
+                            teacher_params,
+                            batch.data,
+                            min_device_batch=per_device_eval_batch_size,
+                        )
+                        eval_metrics.append(metrics)
+
+                        # generation
+                        if training_args.predict_with_generate:
+                            generated_ids = pad_shard_unpad(p_generate_step)(
+                                student_state.params, batch.data, min_device_batch=per_device_eval_batch_size
+                            )
+                            eval_preds.extend(jax.device_get(generated_ids.reshape(-1, gen_kwargs["max_length"])))
+                            eval_labels.extend(labels)
+
+                    eval_time = time.time() - eval_start
+
+                    # normalize eval metrics
+                    eval_metrics = get_metrics(eval_metrics)
+                    eval_metrics = jax.tree_util.tree_map(jnp.mean, eval_metrics)
+
+                    # compute WER metric
+                    wer_desc = ""
+                    if training_args.predict_with_generate:
+                        wer_metric, pred_str, label_str, norm_pred_str, norm_label_str = compute_metrics(
+                            eval_preds, eval_labels
+                        )
+                        eval_metrics.update(wer_metric)
+                        wer_desc = " ".join([f"Eval {key}: {value} |" for key, value in wer_metric.items()])
+
+                    # Print metrics and update progress bar
+                    steps_trained_progress_bar.write(
+                        f"Eval results for step ({cur_step} / {total_train_steps} | Eval Loss: {eval_metrics['loss']} |"
+                        f" {wer_desc})"
+                    )
+
+                    if has_tensorboard and jax.process_index() == 0:
+                        write_eval_metric(
+                            summary_writer,
+                            eval_metrics,
+                            cur_step,
+                            prefix=eval_split,
+                        )
+
+                    if has_wandb and jax.process_index() == 0:
+                        write_wandb_metric(wandb_logger, eval_metrics, eval_time, cur_step, epoch, prefix=eval_split)
+                        if training_args.predict_with_generate:
+                            write_wandb_pred(
+                                wandb_logger,
+                                pred_str,
+                                label_str,
+                                norm_pred_str,
+                                norm_label_str,
+                                cur_step,
+                                prefix=eval_split,
+                            )
+
+                if has_tensorboard and jax.process_index() == 0:
+                    # we'll only log to tensorboard every eval steps
+                    write_train_metric(
+                        summary_writer,
+                        train_metrics,
+                        train_time,
+                        cur_step,
+                        training_args.logging_steps,
+                    )
+
+                # flush the train metrics
+                train_start = time.time()
+                train_metrics = []
+
+            # break condition
+            if cur_step == total_train_steps:
+                continue_training = False
+                break
+
+        if not continue_training:
+            break
+
+
+if __name__ == "__main__":
+    main()
diff --git a/flax/run_eval.py b/flax/run_eval.py
new file mode 100644
index 0000000000000000000000000000000000000000..3f2c9869d94bff2917f8c3dc32ab226b1728ad78
--- /dev/null
+++ b/flax/run_eval.py
@@ -0,0 +1,978 @@
+#!/usr/bin/env python
+# coding=utf-8
+# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Evaluating a Whisper model on one or more evaluation datasets.
+"""
+# You can also adapt this script for your own speech recognition validation. Pointers for this are left as comments.
+
+import logging
+import os
+import string
+import sys
+import time
+from dataclasses import field
+from functools import partial
+from pathlib import Path
+from typing import Any, Dict, List, Optional, Union
+
+import datasets
+import evaluate
+import flax
+import jax
+import jax.numpy as jnp
+import numpy as np
+import optax
+import torch
+import transformers
+from datasets import Dataset, DatasetDict, IterableDatasetDict, load_dataset
+from flax import jax_utils
+from flax.jax_utils import pad_shard_unpad
+from flax.training.common_utils import get_metrics, onehot
+from torch.utils.data import DataLoader
+from tqdm import tqdm
+from transformers import (
+    HfArgumentParser,
+    Seq2SeqTrainingArguments,
+    WhisperConfig,
+    WhisperFeatureExtractor,
+    WhisperProcessor,
+    WhisperTokenizerFast,
+    is_tensorboard_available,
+    is_wandb_available,
+)
+from transformers.models.whisper.english_normalizer import EnglishTextNormalizer
+from transformers.utils import check_min_version, send_example_telemetry
+from transformers.utils.versions import require_version
+
+from distil_whisper import FlaxWhisperForConditionalGeneration
+
+
+# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
+check_min_version("4.27.0.dev0")
+
+require_version(
+    "datasets>=1.18.0",
+    "To fix: pip install -r examples/flax/speech-recogintion/requirements.txt",
+)
+
+logger = logging.getLogger(__name__)
+
+
+@flax.struct.dataclass
+class ModelArguments:
+    """
+    Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
+    """
+
+    model_name_or_path: str = field(
+        metadata={"help": ("Path to pretrained model or model identifier from huggingface.co/models")}
+    )
+    config_name: Optional[str] = field(
+        default=None,
+        metadata={"help": "Pretrained config name or path if not the same as model_name"},
+    )
+    tokenizer_name: Optional[str] = field(
+        default=None,
+        metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"},
+    )
+    feature_extractor_name: Optional[str] = field(
+        default=None,
+        metadata={"help": "feature extractor name or path if not the same as model_name"},
+    )
+    processor_name: Optional[str] = field(
+        default=None,
+        metadata={"help": "processor name or path if not the same as model_name"},
+    )
+    cache_dir: Optional[str] = field(
+        default=None,
+        metadata={"help": ("Where to store the pretrained models downloaded from huggingface.co")},
+    )
+    use_fast_tokenizer: bool = field(
+        default=True,
+        metadata={"help": ("Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.")},
+    )
+    model_revision: str = field(
+        default="main",
+        metadata={"help": ("The specific model version to use (can be a branch name, tag name or commit id).")},
+    )
+    subfolder: str = field(
+        default="",
+        metadata={
+            "help": "In case the relevant files are located inside a subfolder of the model repo on huggingface.co, you can"
+            "specify the folder name here."
+        },
+    )
+    use_auth_token: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Will use the token generated when running `transformers-cli login`"
+                " (necessary to use this script with private models)."
+            )
+        },
+    )
+    dtype: Optional[str] = field(
+        default="float32",
+        metadata={
+            "help": (
+                "Floating-point format in which the model weights should be initialized"
+                " and trained. Choose one of `[float32, float16, bfloat16]`."
+            )
+        },
+    )
+    load_with_scan: Optional[bool] = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether to load the model with scan enabled. Required when the model was saved with scan enabled"
+            )
+        },
+    )
+    return_timestamps: bool = field(
+        default=False, metadata={"help": "Whether or not to predict timestamps in the generation step."}
+    )
+
+
+@flax.struct.dataclass
+class DataTrainingArguments:
+    """
+    Arguments pertaining to what data we are going to input our model for training and eval.
+    """
+
+    dataset_name: str = field(
+        default=None,
+        metadata={
+            "help": "The name of the dataset to use (via the datasets library). Load and combine "
+            "multiple datasets by separating dataset hours by a '+' symbol."
+        },
+    )
+    dataset_config_name: Optional[str] = field(
+        default=None,
+        metadata={"help": "The configuration name of the dataset to use (via the datasets library)."},
+    )
+    dataset_split_name: Optional[str] = field(
+        default=None,
+        metadata={"help": "The split name of the dataset to use (via the datasets library)."},
+    )
+    dataset_cache_dir: Optional[str] = field(
+        default=None,
+        metadata={"help": "Path to cache directory for saving and loading datasets"},
+    )
+    overwrite_cache: bool = field(
+        default=False,
+        metadata={"help": "Overwrite the cached training and evaluation sets"},
+    )
+    preprocessing_num_workers: Optional[int] = field(
+        default=None,
+        metadata={"help": "The number of processes to use for the preprocessing."},
+    )
+    audio_column_name: str = field(
+        default="audio",
+        metadata={"help": "The name of the dataset column containing the audio data. Defaults to 'audio'"},
+    )
+    text_column_name: str = field(
+        default=None,
+        metadata={"help": "The name of the dataset column containing the text data. Defaults to `text`."},
+    )
+    max_duration_in_seconds: float = field(
+        default=30.0,
+        metadata={"help": "Filter audio files that are longer than `max_duration_in_seconds` seconds"},
+    )
+    min_duration_in_seconds: float = field(
+        default=0.0,
+        metadata={"help": "Filter audio files that are shorter than `min_duration_in_seconds` seconds"},
+    )
+    max_label_length: int = field(
+        default=128,
+        metadata={"help": "Truncate transcriptions that are longer `max_label_length` tokens."},
+    )
+    pad_target_to_multiple_of: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": (
+                "If set will pad the target sequence to a multiple of the provided"
+                " value. This is important to avoid triggering recompilations on TPU."
+                " If unspecified, will default to padding the targets to max length."
+            )
+        },
+    )
+    preprocessing_only: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether to only do data preprocessing and skip training. This is"
+                " especially useful when data preprocessing errors out in distributed"
+                " training due to timeout. In this case, one should run the"
+                " preprocessing in a non-distributed setup with"
+                " `preprocessing_only=True` so that the cached datasets can"
+                " consequently be loaded in distributed training"
+            )
+        },
+    )
+    wandb_project: str = field(
+        default="distil-whisper",
+        metadata={"help": "The name of the wandb project."},
+    )
+    wandb_name: str = field(
+        default=None,
+        metadata={"help": "The name of the wandb run."},
+    )
+    wandb_job_type: str = field(
+        default="distil-whisper",
+        metadata={"help": "The name of the wandb job type."},
+    )
+    wandb_dir: str = field(
+        default=None,
+        metadata={"help": "The absolute path to save the wandb logs."},
+    )
+    save_code_to_wandb: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether to save main script to wandb. This is valuable for improving"
+                " experiment reproducibility and to diff code across experiments in"
+                " the UI."
+            )
+        },
+    )
+    streaming: bool = field(
+        default=True,
+        metadata={"help": "Whether to use Datasets' streaming mode to load and the data."},
+    )
+    max_eval_samples: Optional[int] = field(
+        default=None,
+        metadata={"help": "For debugging purposes, truncate the number of eval examples to this value if set."},
+    )
+    log_audio: Optional[bool] = field(
+        default=False,
+        metadata={"help": "For debugging purposes, record the audio samples as well as the ground truths / preds."},
+    )
+
+
+def shift_tokens_right(label_ids: np.array, decoder_start_token_id: int) -> np.ndarray:
+    """
+    Shift label ids one token to the right.
+    """
+    shifted_label_ids = np.zeros_like(label_ids)
+    shifted_label_ids[:, 1:] = label_ids[:, :-1]
+    shifted_label_ids[:, 0] = decoder_start_token_id
+
+    return shifted_label_ids
+
+
+@flax.struct.dataclass
+class FlaxDataCollatorSpeechSeq2SeqWithPadding:
+    """
+    Data collator that will dynamically pad the inputs received.
+    Args:
+        processor ([`Wav2Vec2Processor`])
+            The processor used for proccessing the data.
+        decoder_start_token_id (:obj: `int`)
+            The begin-of-sentence of the decoder.
+        input_padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`, `optional`, defaults to :obj:`True`):
+            Select a strategy to pad the returned input sequences (according to the model's padding side and padding index)
+            among:
+            * :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
+              sequence if provided).
+            * :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the
+              maximum acceptable input length for the model if that argument is not provided.
+            * :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of
+              different lengths).
+        target_padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`, `optional`, defaults to :obj:`True`):
+            Select a strategy to pad the returned target sequences (according to the model's padding side and padding index).
+            See above for details.
+        max_target_length (:obj:`int`, `optional`):
+            Maximum length of the ``labels`` of the returned list and optionally padding length (see above).
+        log_audio (:obj:`bool`):
+            Whether we're logging audio samples as part of our eval. If so, will forward on the audio samples to the batch.
+         audio_column_name (:obj:`str`):
+            Name of the audio column in the dataset. Only relevant if logging audio samples.
+    """
+
+    processor: Any
+    decoder_start_token_id: int
+    input_padding: Union[bool, str] = "max_length"
+    target_padding: Union[bool, str] = "max_length"
+    max_target_length: Optional[int] = None
+    log_audio: Optional[bool] = False
+    audio_column_name: Optional[str] = "audio"
+
+    def __call__(self, features: List[Dict[str, Union[List[int], np.ndarray]]]) -> Dict[str, np.ndarray]:
+        # split inputs and labels since they have to be of different lengths and need
+        # different padding methods
+        model_input_name = self.processor.model_input_names[0]
+
+        # dataloader returns a list of features which we convert to a dict
+        input_features = {model_input_name: [feature[model_input_name] for feature in features]}
+        label_features = {"input_ids": [feature["labels"] for feature in features]}
+
+        # reformat list to dict and set to pytorch format
+        batch = self.processor.feature_extractor.pad(
+            input_features,
+            padding=self.input_padding,
+            return_tensors="np",
+        )
+
+        labels_batch = self.processor.tokenizer.pad(
+            label_features,
+            max_length=self.max_target_length,
+            padding=self.target_padding,
+            return_tensors="np",
+        )
+
+        # if bos token is appended in previous tokenization step,
+        # cut bos token here as it's append later anyways
+        labels = labels_batch["input_ids"]
+        if (labels[:, 0] == self.decoder_start_token_id).all().item():
+            labels = labels[:, 1:]
+            labels_batch.attention_mask = labels_batch.attention_mask[:, 1:]
+
+        decoder_input_ids = shift_tokens_right(labels, self.decoder_start_token_id)
+
+        # replace padding with -100 to ignore correctly when computing the loss
+        labels = np.ma.array(labels, mask=np.not_equal(labels_batch.attention_mask, 1))
+        labels = labels.filled(fill_value=-100)
+
+        batch["labels"] = labels
+        batch["decoder_input_ids"] = decoder_input_ids
+
+        if self.log_audio:
+            audio_samples = [feature[self.audio_column_name] for feature in features]
+            batch["audio"] = audio_samples
+
+        return batch
+
+
+def get_data_loader(
+    dataset: Dataset,
+    batch_size: int,
+    data_collator: FlaxDataCollatorSpeechSeq2SeqWithPadding,
+    dataloader_num_workers: int = 0,
+    pin_memory: bool = True,
+) -> DataLoader:
+    """
+    Returns batches of size `batch_size` from `dataset`. If `drop_last` is set to `False`, the final batch may be incomplete,
+    and range in size from 1 to `batch_size`. Shuffle batches if `shuffle` is `True`.
+
+    Args:
+        dataset (Dataset): dataset from which to load the data.
+        batch_size (int): how many samples per batch to load.
+        data_collator (FlaxDataCollatorSpeechSeq2SeqWithPadding, optional): merges a list of samples to form a
+            mini-batch of Tensor(s).  Used when using batched loading from a map-style dataset.
+        dataloader_num_workers (int, optional): how many subprocesses to use for data
+            loading. ``0`` means that the data will be loaded in the main process.
+            (default: ``0``)
+        pin_memory (bool, optional): If ``True``, the data loader will copy Tensors
+            into device/CUDA pinned memory before returning them.  If your data elements
+            are a custom type, or your :attr:`collate_fn` returns a batch that is a custom type,
+            see the example below.
+    """
+
+    data_loader = DataLoader(
+        dataset,
+        batch_size=batch_size,
+        drop_last=False,
+        pin_memory=pin_memory,
+        collate_fn=data_collator,
+        num_workers=dataloader_num_workers,
+    )
+
+    return data_loader
+
+
+def write_metric(summary_writer, eval_metrics, step, prefix="eval"):
+    for metric_name, value in eval_metrics.items():
+        summary_writer.scalar(f"{prefix}/{metric_name}", value, step)
+
+
+def write_wandb_metric(wandb_logger, metrics, train_time, prefix):
+    log_metrics = {}
+    for k, v in metrics.items():
+        log_metrics[f"{prefix}/{k}"] = v
+    log_metrics[f"{prefix}/time"] = train_time
+    wandb_logger.log(log_metrics)  # TODO(SG): bug with wandb means we can't log the step count
+
+
+def convert_audio_to_wandb(wandb_logger, audio):
+    return wandb_logger.Audio(audio["array"][:, np.newaxis], sample_rate=audio["sampling_rate"])
+
+
+def write_wandb_pred(
+    wandb_logger,
+    eval_audios,
+    pred_str,
+    label_str,
+    norm_pred_str,
+    norm_label_str,
+    prefix="eval",
+    num_lines=200000,
+):
+    columns = ["Target", "Pred", "Norm Target", "Norm Pred"]
+    # convert str data to a wandb compatible format
+    str_data = [[label_str[i], pred_str[i], norm_label_str[i], norm_pred_str[i]] for i in range(len(pred_str))]
+
+    if len(eval_audios) > 0:
+        columns.insert(0, "Audio")
+        str_data = [
+            [
+                convert_audio_to_wandb(wandb_logger, eval_audios[i]),
+                *str_data[i],
+            ]
+            for i in range(len(pred_str))
+        ]
+
+    # log as a table with the appropriate headers
+    wandb_logger.log(
+        {f"{prefix}/all_predictions": wandb_logger.Table(columns=columns, data=str_data[:num_lines])},
+    )
+    # log incorrect normalised predictions
+    str_data = np.asarray(str_data)
+    str_data_incorrect = str_data[str_data[:, -2] != str_data[:, -1]]
+    # log as a table with the appropriate headers
+    wandb_logger.log(
+        {f"{prefix}/incorrect_predictions": wandb_logger.Table(columns=columns, data=str_data_incorrect[:num_lines])},
+    )
+
+
+def convert_dataset_str_to_list(
+    dataset_names, dataset_config_names, splits=None, text_column_names=None, dataset_hours=None, default_split="train"
+):
+    if isinstance(dataset_names, str):
+        dataset_names = dataset_names.split("+")
+
+        # we assume that all the datasets we're using derive from the distil-whisper org on the Hub - prepend the org name if necessary
+        for i in range(len(dataset_names)):
+            ds_name = dataset_names[i]
+            dataset_names[i] = f"distil-whisper/{ds_name}" if "/" not in ds_name else ds_name
+
+        dataset_config_names = dataset_config_names.split("+")
+        splits = splits.split("+") if splits is not None else None
+        text_column_names = text_column_names.split("+") if text_column_names is not None else None
+        dataset_hours = dataset_hours.split("+") if dataset_hours is not None else None
+
+    # basic checks to ensure we've got the right number of datasets/configs/splits/columns/probs
+    if len(dataset_names) != len(dataset_config_names):
+        raise ValueError(
+            f"Ensure one config is passed for each dataset, got {len(dataset_names)} datasets and"
+            f" {len(dataset_config_names)} configs."
+        )
+
+    if splits is not None and len(splits) != len(dataset_names):
+        raise ValueError(
+            f"Ensure one split is passed for each dataset, got {len(dataset_names)} datasets and {len(splits)} splits."
+        )
+
+    if text_column_names is not None and len(text_column_names) != len(dataset_names):
+        raise ValueError(
+            f"Ensure one text column name is passed for each dataset, got {len(dataset_names)} datasets and"
+            f" {len(text_column_names)} text column names."
+        )
+
+    if dataset_hours is not None:
+        if len(dataset_hours) != len(dataset_names):
+            raise ValueError(
+                f"Ensure one probability is passed for each dataset, got {len(dataset_names)} datasets and "
+                f"{len(dataset_hours)} hours."
+            )
+        dataset_hours = [float(ds_hours) for ds_hours in dataset_hours]
+    else:
+        dataset_hours = [None] * len(dataset_names)
+
+    text_column_names = (
+        text_column_names if text_column_names is not None else ["text" for _ in range(len(dataset_names))]
+    )
+    splits = splits if splits is not None else [default_split for _ in range(len(dataset_names))]
+
+    dataset_names_dict = []
+    for i, ds_name in enumerate(dataset_names):
+        dataset_names_dict.append(
+            {
+                "name": ds_name,
+                "config": dataset_config_names[i],
+                "split": splits[i],
+                "text_column_name": text_column_names[i],
+                "hours": dataset_hours[i],
+            }
+        )
+    return dataset_names_dict
+
+
+class FlaxWhisperFeatureExtractor(WhisperFeatureExtractor):
+    def _np_extract_fbank_features(self, waveform: np.array) -> np.ndarray:
+        """
+        Compute the log-mel spectrogram of the provided audio using torch filters. Using the torch implementation
+        computes stft filter banks approx 5x faster than its numpy counterpart, which is the native implementation
+        in transformers, and matches to within 1e-5 abs tolerance.
+        """
+        waveform = torch.from_numpy(waveform).type(torch.float32)
+
+        window = torch.hann_window(self.n_fft)
+        stft = torch.stft(waveform, self.n_fft, self.hop_length, window=window, return_complex=True)
+        magnitudes = stft[..., :-1].abs() ** 2
+
+        mel_filters = torch.from_numpy(self.mel_filters).type(torch.float32)
+        mel_spec = mel_filters.T @ magnitudes
+
+        log_spec = torch.clamp(mel_spec, min=1e-10).log10()
+        log_spec = torch.maximum(log_spec, log_spec.max() - 8.0)
+        log_spec = (log_spec + 4.0) / 4.0
+        return log_spec.numpy()
+
+
+def main():
+    # 1. Parse input arguments
+    # See all possible arguments in src/transformers/training_args.py
+    # or by passing the --help flag to this script.
+    # We now keep distinct sets of args, for a cleaner separation of concerns.
+    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, Seq2SeqTrainingArguments))
+
+    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
+        # If we pass only one argument to the script and it's the path to a json file,
+        # let's parse it to get our arguments.
+        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
+    else:
+        model_args, data_args, training_args = parser.parse_args_into_dataclasses()
+
+    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
+    # information sent is the one passed as arguments along with your JAX/Flax versions.
+    send_example_telemetry("run_flax_speech_recognition_seq2seq", model_args, data_args, framework="flax")
+
+    # 2. Setup logging
+    # Make one log on every process with the configuration for debugging.
+    logging.basicConfig(
+        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+        datefmt="%m/%d/%Y %H:%M:%S",
+        handlers=[logging.StreamHandler(sys.stdout)],
+    )
+    # Set the verbosity to info of the Transformers logger.
+    # We only want one process per machine to log things on the screen.
+    logger.setLevel(logging.INFO if jax.process_index() == 0 else logging.ERROR)
+    if jax.process_index() == 0:
+        datasets.utils.logging.set_verbosity_warning()
+        transformers.utils.logging.set_verbosity_info()
+    else:
+        datasets.utils.logging.set_verbosity_error()
+        transformers.utils.logging.set_verbosity_error()
+
+    logger.info("Evaluation parameters %s", training_args)
+
+    # Enable tensorboard only on the master node
+    has_tensorboard = is_tensorboard_available()
+    if "tensorboard" in training_args.report_to:
+        if has_tensorboard and jax.process_index() == 0:
+            try:
+                from flax.metrics.tensorboard import SummaryWriter
+
+                summary_writer = SummaryWriter(log_dir=Path(training_args.output_dir))
+            except ImportError as ie:
+                has_tensorboard = False
+                logger.warning(
+                    "Unable to display metrics through TensorBoard because some" f" package are not installed: {ie}"
+                )
+        else:
+            logger.warning(
+                "Unable to display metrics through TensorBoard because the package is"
+                " not installed: Please run `pip install tensorboard` to enable."
+            )
+
+    # Enable wandb only on the master node
+    has_wandb = is_wandb_available()
+    if "wandb" in training_args.report_to:
+        if has_wandb and jax.process_index() == 0:
+            import wandb as wandb_logger
+
+            # Set up wandb run
+            wandb_logger.init(
+                project=data_args.wandb_project,
+                name=data_args.wandb_name,
+                job_type=data_args.wandb_job_type,
+                dir=data_args.wandb_dir,
+                save_code=data_args.save_code_to_wandb,
+            )
+        else:
+            logger.warning("Wandb logging requires wandb to be installed. Run `pip install wandb` to enable.")
+
+    # 3. Load dataset
+    raw_datasets = IterableDatasetDict() if data_args.streaming else DatasetDict()
+
+    # Convert lists of dataset names/configs/splits to a dict
+    # names: "librispeech_asr+gigaspeech", configs: "all+l", splits: "validation.clean+validation"
+    # -> [{"name: "librispeech_asr": "config": "all", "split": "validation.clean"}, {"name: "gigaspeech": "config": "l", "split": "validation"}
+    dataset_names_dict = convert_dataset_str_to_list(
+        data_args.dataset_name,
+        data_args.dataset_config_name,
+        splits=data_args.dataset_split_name,
+        text_column_names=data_args.text_column_name,
+    )
+
+    if len(dataset_names_dict) == 1:
+        # load a single eval set
+        dataset_dict = dataset_names_dict[0]
+        raw_datasets["eval"] = load_dataset(
+            dataset_dict["name"],
+            dataset_dict["config"],
+            split=dataset_dict["split"],
+            cache_dir=data_args.dataset_cache_dir,
+            use_auth_token=True if model_args.use_auth_token else None,
+            streaming=data_args.streaming,
+        )
+        if dataset_dict["text_column_name"] not in list(raw_datasets["eval"].features.keys()):
+            raise ValueError(
+                f"--text column name {dataset_dict['text_column_name']} not found in the evaluation "
+                f"dataset {dataset_dict['name']}. Ensure `text_column_name` is set to the correct column "
+                f"for the target text. Should be one of {' '.join(list(raw_datasets['eval'].features.keys()))}"
+            )
+        if dataset_dict["text_column_name"] != "text":
+            raw_datasets["eval"] = raw_datasets["eval"].rename_column(dataset_dict["text_column_name"], "text")
+    else:
+        # load multiple eval sets
+        for dataset_dict in tqdm(dataset_names_dict, desc="Loading datasets..."):
+            # Clean-up the dataset name for pretty logging
+            # ("distil-whisper/librispeech_asr", "validation.clean") -> "librispeech_asr/validation-clean"
+            pretty_name = f"{dataset_dict['name'].split('/')[-1]}/{dataset_dict['split'].replace('.', '-')}"
+            raw_datasets[pretty_name] = load_dataset(
+                dataset_dict["name"],
+                dataset_dict["config"],
+                split=dataset_dict["split"],
+                cache_dir=data_args.dataset_cache_dir,
+                use_auth_token=True if model_args.use_auth_token else None,
+                streaming=data_args.streaming,
+            )
+            if dataset_dict["text_column_name"] not in list(raw_datasets[pretty_name].features.keys()):
+                raise ValueError(
+                    f"`--text_column_name` {dataset_dict['text_column_name']} not found in the evaluation "
+                    f"dataset {dataset_dict['name']}. Ensure `text_column_name` is set to the correct column "
+                    f"for the target text. Should be one of {' '.join(list(raw_datasets[pretty_name].features.keys()))}"
+                )
+            if dataset_dict["text_column_name"] != "text":
+                raw_datasets[pretty_name] = raw_datasets[pretty_name].rename_column(
+                    dataset_dict["text_column_name"], "text"
+                )
+
+    # 5. Load pretrained model, tokenizer, and feature extractor
+    config = WhisperConfig.from_pretrained(
+        (model_args.config_name if model_args.config_name else model_args.model_name_or_path),
+        cache_dir=model_args.cache_dir,
+        revision=model_args.model_revision,
+        use_auth_token=True if model_args.use_auth_token else None,
+    )
+    feature_extractor = FlaxWhisperFeatureExtractor.from_pretrained(
+        (model_args.feature_extractor_name if model_args.feature_extractor_name else model_args.model_name_or_path),
+        cache_dir=model_args.cache_dir,
+        revision=model_args.model_revision,
+        use_auth_token=True if model_args.use_auth_token else None,
+    )
+    tokenizer = WhisperTokenizerFast.from_pretrained(
+        (model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path),
+        cache_dir=model_args.cache_dir,
+        use_fast=model_args.use_fast_tokenizer,
+        revision=model_args.model_revision,
+        use_auth_token=True if model_args.use_auth_token else None,
+    )
+    processor = WhisperProcessor.from_pretrained(
+        (model_args.processor_name if model_args.processor_name else model_args.model_name_or_path),
+        cache_dir=model_args.cache_dir,
+        revision=model_args.model_revision,
+        use_auth_token=True if model_args.use_auth_token else None,
+    )
+
+    model, params = FlaxWhisperForConditionalGeneration.from_pretrained(
+        model_args.model_name_or_path,
+        config=config,
+        dtype=getattr(jnp, model_args.dtype),
+        cache_dir=model_args.cache_dir,
+        revision=model_args.model_revision,
+        use_auth_token=True if model_args.use_auth_token else None,
+        _do_init=False,
+        subfolder=model_args.subfolder,
+        # use_scan=model_args.load_with_scan,  # Model might have (erroneously) been saved with scan still enabled
+    )
+
+    if model.config.decoder_start_token_id is None:
+        raise ValueError("Make sure that `config.decoder_start_token_id` is correctly defined")
+
+    # disable scan if necessary (makes the inference step faster)
+    if model_args.load_with_scan:
+        model.disable_scan()  # to disable scan in the nn.Module
+        params = model.convert_scan_to_unroll(params)  # to convert the scan params to unrolled
+
+    # 6. Resample speech dataset: `datasets` takes care of automatically loading and resampling the audio,
+    # so we just need to set the correct target sampling rate.
+    raw_datasets = raw_datasets.cast_column(
+        data_args.audio_column_name,
+        datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate),
+    )
+
+    # 7. Preprocessing the datasets.
+    # We need to read the audio files as arrays and tokenize the targets.
+    max_label_length = (
+        data_args.max_label_length if data_args.max_label_length is not None else model.config.max_length
+    )
+    audio_column_name = data_args.audio_column_name
+    num_workers = data_args.preprocessing_num_workers
+    dataloader_num_workers = training_args.dataloader_num_workers
+    model_input_name = feature_extractor.model_input_names[0]
+    normalizer = EnglishTextNormalizer(tokenizer.english_spelling_normalizer)
+
+    if data_args.max_eval_samples is not None:
+        for split in raw_datasets:
+            raw_datasets[split] = (
+                raw_datasets[split].take(data_args.max_eval_samples)
+                if data_args.streaming
+                else raw_datasets[split].select(range(data_args.max_eval_samples))
+            )
+
+    def prepare_dataset(batch):
+        # process audio
+        sample = batch[audio_column_name]
+        inputs = feature_extractor(sample["array"], sampling_rate=sample["sampling_rate"])
+        # process audio length
+        batch[model_input_name] = inputs.get(model_input_name)[0]
+
+        # process targets
+        input_str = batch["text"]
+        batch["labels"] = tokenizer(input_str, max_length=max_label_length, truncation=True).input_ids
+        return batch
+
+    vectorized_datasets = IterableDatasetDict() if data_args.streaming else DatasetDict()
+
+    for split in raw_datasets:
+        raw_datasets_features = list(raw_datasets[split].features.keys())
+        if data_args.log_audio:
+            # if logging audio samples preserve the audio column when mapping the dataset
+            raw_datasets_features.remove(audio_column_name)
+
+        map_fn = partial(
+            raw_datasets[split].map,
+            function=prepare_dataset,
+            remove_columns=raw_datasets_features,
+        )
+
+        vectorized_datasets[split] = (
+            map_fn(num_proc=num_workers, desc="preprocess eval dataset")
+            if not data_args.streaming
+            else map_fn()  # In streaming, we can't run multiproc - errors out if we try to
+        )
+
+    # for large datasets it is advised to run the preprocessing on a
+    # single machine first with `args.preprocessing_only` since there will mostly likely
+    # be a timeout when running the script in distributed mode.
+    # In a second step `args.preprocessing_only` can then be set to `False` to load the
+    # cached dataset
+    if data_args.preprocessing_only:
+        cache = {k: v.cache_files for k, v in vectorized_datasets.items()}
+        logger.info(f"Data preprocessing finished. Files cached at {cache}.")
+        return
+
+    # 8. Load Metric
+    metric = evaluate.load("wer")
+    # convention is that we space all punctuation *except* apostrophes
+    all_punctuation = list(string.punctuation.replace("'", ""))
+    return_timestamps = model_args.return_timestamps
+
+    def compute_metrics(preds, labels):
+        # replace padded labels by the padding token
+        for idx in range(len(labels)):
+            labels[idx][labels[idx] == -100] = tokenizer.pad_token_id
+
+        pred_str = tokenizer.batch_decode(preds, skip_special_tokens=True, decode_with_timestamps=return_timestamps)
+        # we do not want to group tokens when computing the metrics
+        label_str = tokenizer.batch_decode(labels, skip_special_tokens=True)
+
+        # space punctuation for orthographic WER (c.f. ESB paper https://arxiv.org/abs/2210.13352)
+        spaced_pred_str = [
+            pred_str[i].replace(punctuation, f" {punctuation} ")
+            for punctuation in all_punctuation
+            for i in range(len(pred_str))
+        ]
+        spaced_label_str = [
+            label_str[i].replace(punctuation, f" {punctuation} ")
+            for punctuation in all_punctuation
+            for i in range(len(label_str))
+        ]
+        wer_ortho = 100 * metric.compute(predictions=spaced_pred_str, references=spaced_label_str)
+
+        # normalize everything and re-compute the WER
+        norm_pred_str = [normalizer(pred) for pred in pred_str]
+        norm_label_str = [normalizer(label) for label in label_str]
+        # for logging, we need the pred/labels to match the norm_pred/norm_labels, so discard any filtered samples here
+        pred_str = [pred_str[i] for i in range(len(norm_pred_str)) if len(norm_label_str[i]) > 0]
+        label_str = [label_str[i] for i in range(len(norm_label_str)) if len(norm_label_str[i]) > 0]
+        # filtering step to only evaluate the samples that correspond to non-zero normalized references:
+        norm_pred_str = [norm_pred_str[i] for i in range(len(norm_pred_str)) if len(norm_label_str[i]) > 0]
+        norm_label_str = [norm_label_str[i] for i in range(len(norm_label_str)) if len(norm_label_str[i]) > 0]
+
+        wer = 100 * metric.compute(predictions=norm_pred_str, references=norm_label_str)
+
+        return {"wer": wer, "wer_ortho": wer_ortho}, pred_str, label_str, norm_pred_str, norm_label_str
+
+    data_collator = FlaxDataCollatorSpeechSeq2SeqWithPadding(
+        processor=processor,
+        decoder_start_token_id=model.config.decoder_start_token_id,
+        input_padding="longest",
+        target_padding="max_length",
+        max_target_length=max_label_length,
+        log_audio=data_args.log_audio,
+    )
+
+    # Store some constants
+    per_device_eval_batch_size = int(training_args.per_device_eval_batch_size)
+    eval_batch_size = per_device_eval_batch_size * jax.device_count()
+
+    # label smoothed cross entropy
+    def loss_fn(logits, labels, label_smoothing_factor=0.0):
+        """
+        The label smoothing implementation is adapted from Flax's official example:
+        https://github.com/google/flax/blob/87a211135c6a377c8f29048a1cac3840e38b9da4/examples/wmt/train.py#L104
+        """
+        vocab_size = logits.shape[-1]
+        confidence = 1.0 - label_smoothing_factor
+        low_confidence = (1.0 - confidence) / (vocab_size - 1)
+        normalizing_constant = -(
+            confidence * jnp.log(confidence) + (vocab_size - 1) * low_confidence * jnp.log(low_confidence + 1e-20)
+        )
+        soft_labels = onehot(labels, vocab_size, on_value=confidence, off_value=low_confidence)
+
+        loss = optax.softmax_cross_entropy(logits, soft_labels)
+        loss = loss - normalizing_constant
+
+        # ignore padded tokens from loss, i.e. where labels are not set to -100
+        padding_mask = labels >= 0
+        loss = loss * padding_mask
+        loss = loss.sum()
+        num_labels = padding_mask.sum()
+        return loss, num_labels
+
+    # Define eval fn
+    def eval_step(params, batch, label_smoothing_factor=0.0):
+        labels = batch.pop("labels")
+        logits = model(**batch, params=params, freeze_encoder=True, train=False)[0]
+
+        loss, num_labels = loss_fn(logits, labels, label_smoothing_factor)
+        num_labels = jax.lax.psum(num_labels, "batch")
+
+        # true loss = total loss / total samples
+        loss = jax.lax.psum(loss, "batch")
+        loss = jax.tree_util.tree_map(lambda x: x / num_labels, loss)
+
+        metrics = {"loss": loss}
+        return metrics
+
+    # Define generation function
+    num_beams = (
+        training_args.generation_num_beams
+        if training_args.generation_num_beams is not None
+        else model.config.num_beams
+    )
+
+    # forcing the language and task tokens helps the flax teacher model in its generations
+    gen_kwargs = {
+        "max_length": max_label_length,
+        "num_beams": num_beams,
+        "language": "<|en|>",
+        "task": "transcribe",
+        "return_timestamps": return_timestamps,
+    }
+
+    def generate_step(params, batch):
+        output_ids = model.generate(
+            batch[model_input_name],
+            attention_mask=batch.get("attention_mask"),
+            params=params,
+            freeze_encoder=True,
+            **gen_kwargs,
+        )
+        return output_ids.sequences
+
+    # Create parallel version of the eval and generate step
+    p_eval_step = jax.pmap(
+        partial(eval_step, label_smoothing_factor=training_args.label_smoothing_factor),
+        "batch",
+    )
+    p_generate_step = jax.pmap(generate_step, "batch")
+
+    # Replicate params on each device
+    params = jax_utils.replicate(params)
+
+    def eval_step(split="eval"):
+        # ======================== Evaluating ==============================
+        eval_metrics = []
+        eval_preds = []
+        eval_labels = []
+        eval_audios = []
+        eval_start = time.time()
+
+        eval_loader = get_data_loader(
+            vectorized_datasets[split],
+            batch_size=eval_batch_size,
+            data_collator=data_collator,
+            dataloader_num_workers=dataloader_num_workers,
+        )
+        for batch in tqdm(eval_loader, desc=f"Evaluating {split}..."):
+            # Model forward
+            labels = batch["labels"]
+            if data_args.log_audio:
+                eval_audios.extend(batch.pop("audio"))
+
+            metrics = pad_shard_unpad(p_eval_step, static_return=True)(
+                params, batch.data, min_device_batch=per_device_eval_batch_size
+            )
+            eval_metrics.append(metrics)
+
+            # generation
+            if training_args.predict_with_generate:
+                generated_ids = pad_shard_unpad(p_generate_step)(
+                    params, batch.data, min_device_batch=per_device_eval_batch_size
+                )
+                eval_preds.extend(jax.device_get(generated_ids.reshape(-1, gen_kwargs["max_length"])))
+                eval_labels.extend(labels)
+
+        eval_time = time.time() - eval_start
+
+        # normalize eval metrics
+        eval_metrics = get_metrics(eval_metrics)
+        eval_metrics = jax.tree_util.tree_map(jnp.mean, eval_metrics)
+
+        # compute WER metric
+        wer_desc = ""
+        if training_args.predict_with_generate:
+            wer_metric, pred_str, label_str, norm_pred_str, norm_label_str = compute_metrics(eval_preds, eval_labels)
+            eval_metrics.update(wer_metric)
+            wer_desc = " ".join([f"Eval {key}: {value} |" for key, value in wer_metric.items()])
+
+        # Print metrics
+        logger.info(f"Eval Loss: {eval_metrics['loss']} | {wer_desc})")
+
+        # Save metrics
+        if has_tensorboard and jax.process_index() == 0 and "tensorboard" in training_args.report_to:
+            write_metric(summary_writer, eval_metrics, model_args.step, prefix=split)
+
+        if has_wandb and jax.process_index() == 0 and "wandb" in training_args.report_to:
+            write_wandb_metric(wandb_logger, eval_metrics, eval_time, prefix=split)
+            if training_args.predict_with_generate:
+                write_wandb_pred(
+                    wandb_logger, eval_audios, pred_str, label_str, norm_pred_str, norm_label_str, prefix=split
+                )
+
+    logger.info("***** Running Eval *****")
+    logger.info("  Instantaneous batch size per device =" f" {training_args.per_device_eval_batch_size}")
+    logger.info(f"  Total eval batch size (w. parallel & distributed) = {eval_batch_size}")
+    for split in vectorized_datasets:
+        eval_step(split=split)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/flax/run_finetuning.py b/flax/run_finetuning.py
new file mode 100644
index 0000000000000000000000000000000000000000..4fc2c2c51a8b2ba86bdd9d4d6e04a408c0e9ec9d
--- /dev/null
+++ b/flax/run_finetuning.py
@@ -0,0 +1,1122 @@
+#!/usr/bin/env python
+# coding=utf-8
+# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Fine-tuning the Whisper model for sequence to sequence speech recognition.
+"""
+# You can also adapt this script for your own speech recognition task. Pointers for this are left as comments.
+
+import logging
+import os
+import string
+import sys
+import time
+from dataclasses import dataclass, field
+from functools import partial
+from pathlib import Path
+from typing import Any, Callable, Dict, List, Optional, Union
+
+import datasets
+import evaluate
+import flax
+import jax
+import jax.numpy as jnp
+import numpy as np
+import optax
+import transformers
+from datasets import Dataset, DatasetDict, load_dataset
+from flax import jax_utils, traverse_util
+from flax.jax_utils import pad_shard_unpad, unreplicate
+from flax.training import train_state
+from flax.training.common_utils import get_metrics, onehot, shard, shard_prng_key
+from huggingface_hub import Repository, create_repo
+from torch.utils.data import DataLoader
+from tqdm import tqdm
+from transformers import (
+    AutoConfig,
+    AutoFeatureExtractor,
+    AutoProcessor,
+    AutoTokenizer,
+    HfArgumentParser,
+    Seq2SeqTrainingArguments,
+    is_tensorboard_available,
+    is_wandb_available,
+)
+from transformers.file_utils import get_full_repo_name
+from transformers.models.whisper.english_normalizer import EnglishTextNormalizer
+from transformers.utils import check_min_version, send_example_telemetry
+from transformers.utils.versions import require_version
+
+from distil_whisper import FlaxWhisperForConditionalGeneration
+
+
+# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
+check_min_version("4.27.0.dev0")
+
+require_version(
+    "datasets>=1.18.0",
+    "To fix: pip install -r examples/flax/speech-recogintion/requirements.txt",
+)
+
+logger = logging.getLogger(__name__)
+
+
+@flax.struct.dataclass
+class ModelArguments:
+    """
+    Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
+    """
+
+    model_name_or_path: str = field(
+        metadata={"help": ("Path to pretrained model or model identifier from huggingface.co/models")}
+    )
+    config_name: Optional[str] = field(
+        default=None,
+        metadata={"help": "Pretrained config name or path if not the same as model_name"},
+    )
+    tokenizer_name: Optional[str] = field(
+        default=None,
+        metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"},
+    )
+    feature_extractor_name: Optional[str] = field(
+        default=None,
+        metadata={"help": "feature extractor name or path if not the same as model_name"},
+    )
+    cache_dir: Optional[str] = field(
+        default=None,
+        metadata={"help": ("Where to store the pretrained models downloaded from huggingface.co")},
+    )
+    use_fast_tokenizer: bool = field(
+        default=True,
+        metadata={"help": ("Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.")},
+    )
+    model_revision: str = field(
+        default="main",
+        metadata={"help": ("The specific model version to use (can be a branch name, tag name or commit id).")},
+    )
+    use_auth_token: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Will use the token generated when running `transformers-cli login`"
+                " (necessary to use this script with private models)."
+            )
+        },
+    )
+    dtype: Optional[str] = field(
+        default="float32",
+        metadata={
+            "help": (
+                "Floating-point format in which the model weights should be initialized"
+                " and trained. Choose one of `[float32, float16, bfloat16]`."
+            )
+        },
+    )
+    activation_dropout: float = field(
+        default=0.0,
+        metadata={"help": "The dropout ratio for activations inside the fully connected layer."},
+    )
+    attention_dropout: float = field(
+        default=0.0,
+        metadata={"help": "The dropout ratio for the attention probabilities."},
+    )
+    dropout: float = field(
+        default=0.0,
+        metadata={
+            "help": "The dropout probability for all fully connected layers in the embeddings, encoder, and pooler."
+        },
+    )
+
+
+@flax.struct.dataclass
+class DataTrainingArguments:
+    """
+    Arguments pertaining to what data we are going to input our model for training and eval.
+    """
+
+    dataset_name: str = field(
+        default=None,
+        metadata={"help": "The name of the dataset to use (via the datasets library)."},
+    )
+    dataset_config_name: Optional[str] = field(
+        default=None,
+        metadata={"help": ("The configuration name of the dataset to use (via the datasets library).")},
+    )
+    dataset_cache_dir: Optional[str] = field(
+        default=None,
+        metadata={"help": "Path to cache directory for saving and loading datasets"},
+    )
+    overwrite_cache: bool = field(
+        default=False,
+        metadata={"help": "Overwrite the cached training and evaluation sets"},
+    )
+    preprocessing_num_workers: Optional[int] = field(
+        default=None,
+        metadata={"help": "The number of processes to use for the preprocessing."},
+    )
+    max_train_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": (
+                "For debugging purposes or quicker training, truncate the number of"
+                " training examples to this value if set."
+            )
+        },
+    )
+    max_eval_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": (
+                "For debugging purposes or quicker training, truncate the number of"
+                " evaluation examples to this value if set."
+            )
+        },
+    )
+    audio_column_name: str = field(
+        default="audio",
+        metadata={"help": ("The name of the dataset column containing the audio data. Defaults to 'audio'")},
+    )
+    text_column_name: str = field(
+        default="whisper_transcript",
+        metadata={
+            "help": (
+                "The name of the dataset column containing the text data. Defaults to"
+                " 'whisper_transcript'which is the pseudo-labelled Whisper"
+                " transcription data."
+            )
+        },
+    )
+    max_duration_in_seconds: float = field(
+        default=30.0,
+        metadata={"help": ("Filter audio files that are longer than `max_duration_in_seconds` seconds")},
+    )
+    min_duration_in_seconds: float = field(
+        default=0.0,
+        metadata={"help": ("Filter audio files that are shorter than `min_duration_in_seconds` seconds")},
+    )
+    max_label_length: int = field(
+        default=128,
+        metadata={"help": "Truncate transcriptions that are longer `max_label_length` tokens."},
+    )
+    pad_target_to_multiple_of: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": (
+                "If set will pad the target sequence to a multiple of the provided"
+                " value. This is important to avoid triggering recompilations on TPU."
+                " If unspecified, will default to padding the targets to max length."
+            )
+        },
+    )
+    preprocessing_only: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether to only do data preprocessing and skip training. This is"
+                " especially useful when data preprocessing errors out in distributed"
+                " training due to timeout. In this case, one should run the"
+                " preprocessing in a non-distributed setup with"
+                " `preprocessing_only=True` so that the cached datasets can"
+                " consequently be loaded in distributed training"
+            )
+        },
+    )
+    train_split_name: str = field(
+        default="train",
+        metadata={
+            "help": ("The name of the training data set split to use (via the datasets library). Defaults to 'train'")
+        },
+    )
+    eval_split_name: str = field(
+        default="validation",
+        metadata={
+            "help": (
+                "The name of the evaluation data set split to use (via the datasets"
+                " library). Defaults to 'validation'"
+            )
+        },
+    )
+    wandb_project: str = field(
+        default="distil-whisper",
+        metadata={"help": "The name of the wandb project."},
+    )
+    wandb_name: str = field(
+        default=None,
+        metadata={"help": "The name of the wandb run."},
+    )
+    wandb_job_type: str = field(
+        default="distil-whisper",
+        metadata={"help": "The name of the wandb job type."},
+    )
+    wandb_dir: str = field(
+        default=None,
+        metadata={"help": "The absolute path to save the wandb logs."},
+    )
+    save_code_to_wandb: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether to save main script to wandb. This is valuable for improving"
+                " experimentreproducibility and to diff code across experiments in"
+                " the UI."
+            )
+        },
+    )
+
+
+@dataclass
+class FlaxSeq2SeqTrainingArguments(Seq2SeqTrainingArguments):
+    use_scan: Optional[bool] = field(
+        default=True,
+        metadata={
+            "help": (
+                "Whether or not to use `scan_with_axes` over the encoder and decoder"
+                " blocks. Using scan results in faster compile times and more efficient"
+                " memory use during training, since all of the layers in the"
+                " encoder/decoder are stacked, and we perform a lax.scan over the"
+                " stacked block to index each layer. However, it results in slower"
+                " inference time due to the overhead of stacking the layers this way."
+                " Thus, we always default to disabling scan for the inference step."
+            )
+        },
+    )
+    freeze_encoder: Optional[bool] = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether to freeze the entire encoder model. Only recommended when the"
+                " entire encoder has been copiedfrom the teacher model."
+            )
+        },
+    )
+
+
+def shift_tokens_right(label_ids: np.array, decoder_start_token_id: int) -> np.ndarray:
+    """
+    Shift label ids one token to the right.
+    """
+    shifted_label_ids = np.zeros_like(label_ids)
+    shifted_label_ids[:, 1:] = label_ids[:, :-1]
+    shifted_label_ids[:, 0] = decoder_start_token_id
+
+    return shifted_label_ids
+
+
+@flax.struct.dataclass
+class FlaxDataCollatorSpeechSeq2SeqWithPadding:
+    """
+    Data collator that will dynamically pad the inputs received.
+    Args:
+        processor ([`Wav2Vec2Processor`])
+            The processor used for proccessing the data.
+        decoder_start_token_id (:obj: `int`)
+            The begin-of-sentence of the decoder.
+        input_padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`, `optional`, defaults to :obj:`True`):
+            Select a strategy to pad the returned input sequences (according to the model's padding side and padding index)
+            among:
+            * :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
+              sequence if provided).
+            * :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the
+              maximum acceptable input length for the model if that argument is not provided.
+            * :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of
+              different lengths).
+        target_padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`, `optional`, defaults to :obj:`True`):
+            Select a strategy to pad the returned target sequences (according to the model's padding side and padding index).
+            See above for details.
+        max_target_length (:obj:`int`, `optional`):
+            Maximum length of the ``labels`` of the returned list and optionally padding length (see above).
+    """
+
+    processor: Any
+    decoder_start_token_id: int
+    input_padding: Union[bool, str] = "max_length"
+    target_padding: Union[bool, str] = "max_length"
+    max_target_length: Optional[int] = None
+
+    def __call__(self, features: List[Dict[str, Union[List[int], np.ndarray]]]) -> Dict[str, np.ndarray]:
+        # split inputs and labels since they have to be of different lengths and need
+        # different padding methods
+        model_input_name = self.processor.model_input_names[0]
+
+        # dataloader returns a list of features which we convert to a dict
+        input_features = {model_input_name: [feature[model_input_name] for feature in features]}
+        label_features = {"input_ids": [feature["labels"] for feature in features]}
+
+        # reformat list to dict and set to pytorch format
+        batch = self.processor.feature_extractor.pad(
+            input_features,
+            padding=self.input_padding,
+            return_tensors="np",
+        )
+
+        labels_batch = self.processor.tokenizer.pad(
+            label_features,
+            max_length=self.max_target_length,
+            padding=self.target_padding,
+            return_tensors="np",
+        )
+
+        # if bos token is appended in previous tokenization step,
+        # cut bos token here as it's append later anyways
+        labels = labels_batch["input_ids"]
+        if (labels[:, 0] == self.decoder_start_token_id).all().item():
+            labels = labels[:, 1:]
+            labels_batch.attention_mask = labels_batch.attention_mask[:, 1:]
+
+        decoder_input_ids = shift_tokens_right(labels, self.decoder_start_token_id)
+
+        # replace padding with -100 to ignore correctly when computing the loss
+        labels = np.ma.array(labels, mask=np.not_equal(labels_batch.attention_mask, 1))
+        labels = labels.filled(fill_value=-100)
+
+        batch["labels"] = labels
+        batch["decoder_input_ids"] = decoder_input_ids
+
+        return batch
+
+
+def get_data_loader(
+    rng: jax.random.PRNGKey,
+    dataset: Dataset,
+    batch_size: int,
+    data_collator: FlaxDataCollatorSpeechSeq2SeqWithPadding,
+    shuffle: bool = True,
+    drop_last: bool = True,
+    dataloader_num_workers: int = 0,
+    pin_memory: bool = True,
+) -> DataLoader:
+    """
+    Returns batches of size `batch_size` from `dataset`. If `drop_last` is set to `False`, the final batch may be incomplete,
+    and range in size from 1 to `batch_size`. Shuffle batches if `shuffle` is `True`.
+
+    Args:
+        rng (List(int)): JAX rng for generating pseudo random numbers. Used if shuffling the dataset.
+        dataset (Dataset): dataset from which to load the data.
+        batch_size (int): how many samples per batch to load.
+        data_collator (FlaxDataCollatorSpeechSeq2SeqWithPadding, optional): merges a list of samples to form a
+            mini-batch of Tensor(s).  Used when using batched loading from a map-style dataset.
+        shuffle (bool, optional): set to `True` to have the batches reshuffled.
+        drop_last (bool, optional): set to ``True`` to drop the last incomplete batch,
+            if the dataset size is not divisible by the batch size. If ``False`` and
+            the size of dataset is not divisible by the batch size, then the last batch
+            will be smaller. (default: ``False``)
+        dataloader_num_workers (int, optional): how many subprocesses to use for data
+            loading. ``0`` means that the data will be loaded in the main process.
+            (default: ``0``)
+        pin_memory (bool, optional): If ``True``, the data loader will copy Tensors
+            into device/CUDA pinned memory before returning them.  If your data elements
+            are a custom type, or your :attr:`collate_fn` returns a batch that is a custom type,
+            see the example below.
+
+    """
+    if shuffle:
+        batch_idx = jax.random.permutation(rng, len(dataset))
+        batch_idx = np.asarray(batch_idx)
+        dataset = dataset.select(batch_idx)
+
+    data_loader = DataLoader(
+        dataset,
+        batch_size=batch_size,
+        drop_last=drop_last,
+        pin_memory=pin_memory,
+        collate_fn=data_collator,
+        num_workers=dataloader_num_workers,
+    )
+
+    return data_loader
+
+
+class TrainState(train_state.TrainState):
+    dropout_rng: jnp.ndarray
+
+    def replicate(self):
+        return jax_utils.replicate(self).replace(dropout_rng=shard_prng_key(self.dropout_rng))
+
+
+def write_metric(summary_writer, train_metrics, eval_metrics, train_time, step, logging_steps):
+    summary_writer.scalar("train/time", train_time, step)
+
+    train_metrics = get_metrics(train_metrics)
+    for key, vals in train_metrics.items():
+        steps_arr = np.arange(0, step, logging_steps)[-len(vals) :]
+        tag = f"train/{key}"
+        for i, val in enumerate(vals):
+            summary_writer.scalar(tag, val, steps_arr[i])
+
+    for metric_name, value in eval_metrics.items():
+        summary_writer.scalar(f"eval/{metric_name}", value, step)
+
+
+def write_wandb_metric(wandb_logger, metrics, train_time, step, prefix):
+    log_metrics = {}
+    for k, v in metrics.items():
+        log_metrics[f"{prefix}/{k}"] = v
+    log_metrics[f"{prefix}/time"] = train_time
+    wandb_logger.log(log_metrics, step)
+
+
+def write_wandb_pred(wandb_logger, pred_str, label_str, prefix="eval", num_lines=100):
+    # convert str data to a wandb compatible format
+    if num_lines < len(pred_str):
+        str_data = [[label_str[i], pred_str[i]] for i in range(num_lines)]
+    else:
+        str_data = [[label_str[i], pred_str[i]] for i in range(len(pred_str))]
+    # log as a table with the appropriate headers
+    wandb_logger.log(
+        {f"{prefix}/predictions": wandb_logger.Table(columns=["label_str", "pred_str"], data=str_data)},
+    )
+
+
+def create_learning_rate_fn(
+    num_train_steps: int, num_warmup_steps: int, learning_rate: float
+) -> Callable[[int], jnp.array]:
+    """Returns a linear warmup, linear_decay learning rate function."""
+    warmup_fn = optax.linear_schedule(init_value=0.0, end_value=learning_rate, transition_steps=num_warmup_steps)
+    decay_fn = optax.linear_schedule(
+        init_value=learning_rate,
+        end_value=0,
+        transition_steps=num_train_steps - num_warmup_steps,
+    )
+    schedule_fn = optax.join_schedules(schedules=[warmup_fn, decay_fn], boundaries=[num_warmup_steps])
+    return schedule_fn
+
+
+def main():
+    # 1. Parse input arguments
+    # See all possible arguments in src/transformers/training_args.py
+    # or by passing the --help flag to this script.
+    # We now keep distinct sets of args, for a cleaner separation of concerns.
+    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, FlaxSeq2SeqTrainingArguments))
+
+    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
+        # If we pass only one argument to the script and it's the path to a json file,
+        # let's parse it to get our arguments.
+        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
+    else:
+        model_args, data_args, training_args = parser.parse_args_into_dataclasses()
+
+    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
+    # information sent is the one passed as arguments along with your JAX/Flax versions.
+    send_example_telemetry("run_flax_speech_recognition_seq2seq", model_args, data_args, framework="flax")
+
+    # 2. Setup logging
+    # Make one log on every process with the configuration for debugging.
+    logging.basicConfig(
+        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+        datefmt="%m/%d/%Y %H:%M:%S",
+        handlers=[logging.StreamHandler(sys.stdout)],
+    )
+    # Set the verbosity to info of the Transformers logger.
+    # We only want one process per machine to log things on the screen.
+    logger.setLevel(logging.INFO if jax.process_index() == 0 else logging.ERROR)
+    if jax.process_index() == 0:
+        datasets.utils.logging.set_verbosity_warning()
+        transformers.utils.logging.set_verbosity_info()
+    else:
+        datasets.utils.logging.set_verbosity_error()
+        transformers.utils.logging.set_verbosity_error()
+
+    logger.info("Training/evaluation parameters %s", training_args)
+
+    # Check the output dir is valid
+    if (
+        os.path.exists(training_args.output_dir)
+        and os.listdir(training_args.output_dir)
+        and training_args.do_train
+        and not training_args.overwrite_output_dir
+    ):
+        raise ValueError(
+            f"Output directory ({training_args.output_dir}) already exists and is not"
+            " empty.Use `--overwrite_output_dir` to overcome."
+        )
+
+    # Handle the repository creation
+    if training_args.push_to_hub:
+        if training_args.hub_model_id is None:
+            repo_name = get_full_repo_name(
+                Path(training_args.output_dir).absolute().name,
+                token=training_args.hub_token,
+            )
+        else:
+            repo_name = training_args.hub_model_id
+        create_repo(repo_name, exist_ok=True, token=training_args.hub_token)
+        repo = Repository(
+            training_args.output_dir,
+            clone_from=repo_name,
+            token=training_args.hub_token,
+        )
+
+    # 3. Load dataset
+    raw_datasets = DatasetDict()
+
+    if training_args.do_train:
+        raw_datasets["train"] = load_dataset(
+            data_args.dataset_name,
+            data_args.dataset_config_name,
+            split=data_args.train_split_name,
+            cache_dir=data_args.dataset_cache_dir,
+            use_auth_token=True if model_args.use_auth_token else None,
+            num_proc=data_args.preprocessing_num_workers,
+        )
+
+    if training_args.do_eval:
+        raw_datasets["eval"] = load_dataset(
+            data_args.dataset_name,
+            data_args.dataset_config_name,
+            split=data_args.eval_split_name,
+            cache_dir=data_args.dataset_cache_dir,
+            use_auth_token=True if model_args.use_auth_token else None,
+            num_proc=data_args.preprocessing_num_workers,
+        )
+
+    if not training_args.do_train and not training_args.do_eval:
+        raise ValueError(
+            "Cannot not train and not do evaluation. At least one of training or evaluation has to be performed."
+        )
+
+    if data_args.audio_column_name not in next(iter(raw_datasets.values())).column_names:
+        raise ValueError(
+            f"--audio_column_name '{data_args.audio_column_name}' not found in dataset"
+            f" '{data_args.dataset_name}'. Make sure to set `--audio_column_name` to"
+            " the correct audio column - one of"
+            f" {', '.join(next(iter(raw_datasets.values())).column_names)}."
+        )
+
+    if data_args.text_column_name not in next(iter(raw_datasets.values())).column_names:
+        raise ValueError(
+            f"--text_column_name {data_args.text_column_name} not found in dataset"
+            f" '{data_args.dataset_name}'. Make sure to set `--text_column_name` to the"
+            " correct text column - one of"
+            f" {', '.join(next(iter(raw_datasets.values())).column_names)}."
+        )
+
+    # 5. Load pretrained model, tokenizer, and feature extractor
+    config = AutoConfig.from_pretrained(
+        (model_args.config_name if model_args.config_name else model_args.model_name_or_path),
+        cache_dir=model_args.cache_dir,
+        revision=model_args.model_revision,
+        use_auth_token=True if model_args.use_auth_token else None,
+    )
+    feature_extractor = AutoFeatureExtractor.from_pretrained(
+        (model_args.feature_extractor_name if model_args.feature_extractor_name else model_args.model_name_or_path),
+        cache_dir=model_args.cache_dir,
+        revision=model_args.model_revision,
+        use_auth_token=True if model_args.use_auth_token else None,
+    )
+    tokenizer = AutoTokenizer.from_pretrained(
+        (model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path),
+        cache_dir=model_args.cache_dir,
+        use_fast=model_args.use_fast_tokenizer,
+        revision=model_args.model_revision,
+        use_auth_token=True if model_args.use_auth_token else None,
+    )
+
+    config.update(
+        {
+            "activation_dropout": model_args.activation_dropout,
+            "attention_dropout": model_args.attention_dropout,
+            "dropout": model_args.dropout,
+        }
+    )
+
+    model, params = FlaxWhisperForConditionalGeneration.from_pretrained(
+        model_args.model_name_or_path,
+        config=config,
+        dtype=getattr(jnp, model_args.dtype),
+        cache_dir=model_args.cache_dir,
+        revision=model_args.model_revision,
+        use_auth_token=True if model_args.use_auth_token else None,
+        _do_init=False,
+    )
+
+    if model.config.decoder_start_token_id is None:
+        raise ValueError("Make sure that `config.decoder_start_token_id` is correctly defined")
+
+    # enable scan / gradient checkpointing if necessary
+    if training_args.use_scan:
+        model.enable_scan()  # to enable scan in the nn.Module
+        params = model.convert_unroll_to_scan(params)  # to convert the unrolled params to scan
+
+    if training_args.gradient_checkpointing:
+        model.enable_gradient_checkpointing()  # to enable checkpointing in the nn.Module, there is no change to the params structure
+
+    if hasattr(model.generation_config, "is_multilingual") and model.generation_config.is_multilingual:
+        # We need to set the language and task ids for previously multilingual checkpoints
+        tokenizer.set_prefix_tokens(language="English", task="transcribe", predict_timestamps=False)
+        model.generation_config.forced_decoder_ids = tokenizer.get_decoder_prompt_ids(
+            language="English", task="transcribe", no_timestamps=True
+        )
+
+    # 6. Resample speech dataset: `datasets` takes care of automatically loading and resampling the audio,
+    # so we just need to set the correct target sampling rate.
+    raw_datasets = raw_datasets.cast_column(
+        data_args.audio_column_name,
+        datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate),
+    )
+
+    # 7. Preprocessing the datasets.
+    # We need to read the audio files as arrays and tokenize the targets.
+    max_input_length = int(data_args.max_duration_in_seconds * feature_extractor.sampling_rate)
+    min_input_length = int(data_args.min_duration_in_seconds * feature_extractor.sampling_rate)
+    max_label_length = (
+        data_args.max_label_length if data_args.max_label_length is not None else model.config.max_length
+    )
+    audio_column_name = data_args.audio_column_name
+    num_workers = data_args.preprocessing_num_workers
+    dataloader_num_workers = training_args.dataloader_num_workers
+    text_column_name = data_args.text_column_name
+    model_input_name = feature_extractor.model_input_names[0]
+    normalizer = EnglishTextNormalizer(tokenizer.english_spelling_normalizer)
+
+    if training_args.do_train and data_args.max_train_samples is not None:
+        raw_datasets["train"] = raw_datasets["train"].select(range(data_args.max_train_samples))
+
+    if training_args.do_eval and data_args.max_eval_samples is not None:
+        raw_datasets["eval"] = raw_datasets["eval"].select(range(data_args.max_eval_samples))
+
+    def prepare_dataset(batch):
+        # process audio
+        sample = batch[audio_column_name]
+        inputs = feature_extractor(sample["array"], sampling_rate=sample["sampling_rate"])
+        # process audio length
+        batch[model_input_name] = inputs.get(model_input_name)[0]
+        batch["input_length"] = len(sample["array"])
+
+        # process targets
+        input_str = " " + batch[text_column_name].lower()
+        batch["labels"] = tokenizer(input_str).input_ids
+        return batch
+
+    vectorized_datasets = raw_datasets.map(
+        prepare_dataset,
+        remove_columns=next(iter(raw_datasets.values())).column_names,
+        num_proc=num_workers,
+        desc="preprocess train dataset",
+    )
+
+    # filter training data with inputs longer than max_input_length
+    def is_audio_in_length_range(length):
+        return min_input_length < length < max_input_length
+
+    vectorized_datasets = vectorized_datasets.filter(
+        is_audio_in_length_range,
+        num_proc=num_workers,
+        input_columns=["input_length"],
+    )
+
+    # filter training data with labels longer than max_label_length
+    def is_labels_in_length_range(labels):
+        return 0 < len(labels) < max_label_length
+
+    vectorized_datasets = vectorized_datasets.filter(
+        is_labels_in_length_range,
+        num_proc=num_workers,
+        input_columns=["labels"],
+    )
+
+    # for large datasets it is advised to run the preprocessing on a
+    # single machine first with `args.preprocessing_only` since there will mostly likely
+    # be a timeout when running the script in distributed mode.
+    # In a second step `args.preprocessing_only` can then be set to `False` to load the
+    # cached dataset
+    if data_args.preprocessing_only:
+        cache = {k: v.cache_files for k, v in vectorized_datasets.items()}
+        logger.info(f"Data preprocessing finished. Files cached at {cache}.")
+        return
+
+    # 8. Load Metric
+    metric = evaluate.load("wer")
+    all_punctuation = list(string.punctuation.replace("'", ""))
+
+    def compute_metrics(preds, labels):
+        # replace padded labels by the padding token
+        for idx in range(len(labels)):
+            labels[idx][labels[idx] == -100] = tokenizer.pad_token_id
+
+        pred_str = tokenizer.batch_decode(preds, skip_special_tokens=True)
+        # we do not want to group tokens when computing the metrics
+        label_str = tokenizer.batch_decode(labels, skip_special_tokens=True)
+
+        # space punctuation for orthographic WER (c.f. ESB paper https://arxiv.org/abs/2210.13352)
+        spaced_pred_str = [
+            pred_str[i].replace(punctuation, "") for punctuation in all_punctuation for i in range(len(pred_str))
+        ]
+        spaced_label_str = [
+            label_str[i].replace(punctuation, "") for punctuation in all_punctuation for i in range(len(label_str))
+        ]
+        wer_ortho = 100 * metric.compute(predictions=spaced_pred_str, references=spaced_label_str)
+
+        # normalize everything and re-compute the WER
+        norm_pred_str = [normalizer(pred) for pred in pred_str]
+        norm_label_str = [normalizer(label) for label in label_str]
+        # filtering step to only evaluate the samples that correspond to non-zero normalized references:
+        norm_pred_str = [norm_pred_str[i] for i in range(len(norm_pred_str)) if len(norm_label_str[i]) > 0]
+        norm_label_str = [norm_label_str[i] for i in range(len(norm_label_str)) if len(norm_label_str[i]) > 0]
+
+        wer = 100 * metric.compute(predictions=norm_pred_str, references=norm_label_str)
+
+        return {"wer": wer, "wer_ortho": wer_ortho}, pred_str, label_str
+
+    # 9. Save feature extractor, tokenizer, config and generation config
+    feature_extractor.save_pretrained(training_args.output_dir)
+    tokenizer.save_pretrained(training_args.output_dir)
+    config.save_pretrained(training_args.output_dir)
+    model.generation_config.save_pretrained(
+        training_args.output_dir
+    )  # generation config stays bound to model to make it easy to jit
+
+    processor = AutoProcessor.from_pretrained(training_args.output_dir)
+
+    data_collator = FlaxDataCollatorSpeechSeq2SeqWithPadding(
+        processor=processor,
+        decoder_start_token_id=model.config.decoder_start_token_id,
+        input_padding="longest",
+        target_padding="max_length",
+        max_target_length=max_label_length,
+    )
+
+    # Enable tensorboard only on the master node
+    has_tensorboard = is_tensorboard_available()
+    if has_tensorboard and jax.process_index() == 0:
+        try:
+            from flax.metrics.tensorboard import SummaryWriter
+
+            summary_writer = SummaryWriter(log_dir=Path(training_args.output_dir))
+        except ImportError as ie:
+            has_tensorboard = False
+            logger.warning(
+                "Unable to display metrics through TensorBoard because some package" f" are not installed: {ie}"
+            )
+    else:
+        logger.warning(
+            "Unable to display metrics through TensorBoard because the package is not"
+            " installed: Please run `pip install tensorboard` to enable."
+        )
+
+    # Enable wandb only on the master node
+    has_wandb = is_wandb_available()
+    if has_wandb:
+        import wandb as wandb_logger
+
+        # Set up wandb run
+        if jax.process_index() == 0:
+            wandb_logger.init(
+                project=data_args.wandb_project,
+                name=data_args.wandb_name,
+                job_type=data_args.wandb_job_type,
+                dir=data_args.wandb_dir,
+                save_code=data_args.save_code_to_wandb,
+            )
+    else:
+        logger.warning("Wandb logging requires wandb to be installed. Run `pip install wandb` to enable.")
+
+    # Initialize our training
+    rng = jax.random.PRNGKey(training_args.seed)
+    rng, dropout_rng = jax.random.split(rng)
+
+    # Store some constant
+    num_epochs = int(training_args.num_train_epochs)
+    train_batch_size = int(training_args.per_device_train_batch_size) * jax.device_count()
+    per_device_eval_batch_size = int(training_args.per_device_eval_batch_size)
+    eval_batch_size = per_device_eval_batch_size * jax.device_count()
+    steps_per_epoch = len(vectorized_datasets["train"]) // train_batch_size
+    total_train_steps = steps_per_epoch * num_epochs
+
+    # Create learning rate schedule
+    linear_decay_lr_schedule_fn = create_learning_rate_fn(
+        total_train_steps,
+        training_args.warmup_steps,
+        training_args.learning_rate,
+    )
+
+    # We use Optax's "masking" functionality to not apply weight decay
+    # to bias and LayerNorm scale parameters. decay_mask_fn returns a
+    # mask boolean with the same structure as the parameters.
+    # The mask is True for parameters that should be decayed.
+    def decay_mask_fn(params):
+        flat_params = traverse_util.flatten_dict(params)
+        # find out all LayerNorm parameters
+        layer_norm_candidates = [
+            "layer_norm",
+            "self_attn_layer_norm",
+            "final_layer_norm",
+            "encoder_attn_layer_norm",
+        ]
+        layer_norm_named_params = {
+            layer[-2:]
+            for layer_norm_name in layer_norm_candidates
+            for layer in flat_params.keys()
+            if layer_norm_name in "".join(layer).lower()
+        }
+        flat_mask = {path: path[-1] != "bias" and path[-2:] not in layer_norm_named_params for path in flat_params}
+        return traverse_util.unflatten_dict(flat_mask)
+
+    # create adam optimizer
+    adamw = optax.adamw(
+        learning_rate=linear_decay_lr_schedule_fn,
+        b1=training_args.adam_beta1,
+        b2=training_args.adam_beta2,
+        eps=training_args.adam_epsilon,
+        weight_decay=training_args.weight_decay,
+        mask=decay_mask_fn,
+    )
+
+    # Setup train state
+    state = TrainState.create(apply_fn=model.__call__, params=params, tx=adamw, dropout_rng=dropout_rng)
+
+    # label smoothed cross entropy
+    def loss_fn(logits, labels, label_smoothing_factor=0.0):
+        """
+        The label smoothing implementation is adapted from Flax's official example:
+        https://github.com/google/flax/blob/87a211135c6a377c8f29048a1cac3840e38b9da4/examples/wmt/train.py#L104
+        """
+        vocab_size = logits.shape[-1]
+        confidence = 1.0 - label_smoothing_factor
+        low_confidence = (1.0 - confidence) / (vocab_size - 1)
+        normalizing_constant = -(
+            confidence * jnp.log(confidence) + (vocab_size - 1) * low_confidence * jnp.log(low_confidence + 1e-20)
+        )
+        soft_labels = onehot(labels, vocab_size, on_value=confidence, off_value=low_confidence)
+
+        loss = optax.softmax_cross_entropy(logits, soft_labels)
+        loss = loss - normalizing_constant
+
+        # ignore padded tokens from loss, i.e. where labels are not set to -100
+        padding_mask = labels >= 0
+        loss = loss * padding_mask
+        loss = loss.sum()
+        num_labels = padding_mask.sum()
+        return loss, num_labels
+
+    # Define gradient update step fn
+    def train_step(state, batch, freeze_encoder, label_smoothing_factor=0.0):
+        dropout_rng, new_dropout_rng = jax.random.split(state.dropout_rng)
+
+        def compute_loss(params):
+            labels = batch.pop("labels")
+            logits = state.apply_fn(
+                **batch,
+                params=params,
+                dropout_rng=dropout_rng,
+                freeze_encoder=freeze_encoder,
+                train=True,
+            )[0]
+            loss, num_labels = loss_fn(logits, labels, label_smoothing_factor)
+            return loss, num_labels
+
+        grad_fn = jax.value_and_grad(compute_loss, has_aux=True)
+        (loss, num_labels), grad = grad_fn(state.params)
+        num_labels = jax.lax.psum(num_labels, "batch")
+
+        # true loss = total loss / total samples
+        loss = jax.lax.psum(loss, "batch")
+        loss = jax.tree_util.tree_map(lambda x: x / num_labels, loss)
+
+        # true grad = total grad / total samples
+        grad = jax.lax.psum(grad, "batch")
+        grad = jax.tree_util.tree_map(lambda x: x / num_labels, grad)
+        new_state = state.apply_gradients(grads=grad, dropout_rng=new_dropout_rng)
+
+        metrics = {
+            "loss": loss,
+            "learning_rate": linear_decay_lr_schedule_fn(state.step),
+        }
+        return new_state, metrics
+
+    # Define eval fn
+    def eval_step(params, batch, label_smoothing_factor=0.0):
+        labels = batch.pop("labels")
+        logits = model(**batch, params=params, train=False)[0]
+
+        loss, num_labels = loss_fn(logits, labels, label_smoothing_factor)
+        num_labels = jax.lax.psum(num_labels, "batch")
+
+        # true loss = total loss / total samples
+        loss = jax.lax.psum(loss, "batch")
+        loss = jax.tree_util.tree_map(lambda x: x / num_labels, loss)
+
+        metrics = {"loss": loss}
+        return metrics
+
+    # Define generation function
+    num_beams = (
+        training_args.generation_num_beams
+        if training_args.generation_num_beams is not None
+        else model.config.num_beams
+    )
+    gen_kwargs = {"max_length": max_label_length, "num_beams": num_beams}
+
+    def generate_step(params, batch):
+        output_ids = model.generate(
+            batch[model_input_name],
+            attention_mask=batch.get("attention_mask"),
+            params=params,
+            **gen_kwargs,
+        )
+        return output_ids.sequences
+
+    # Create parallel version of the train and eval step
+    p_train_step = jax.pmap(
+        partial(train_step, label_smoothing_factor=training_args.label_smoothing_factor),
+        "batch",
+        donate_argnums=(0,),
+        static_broadcasted_argnums=(2,),
+    )
+    p_eval_step = jax.pmap(
+        partial(eval_step, label_smoothing_factor=training_args.label_smoothing_factor),
+        "batch",
+    )
+    p_generate_step = jax.pmap(generate_step, "batch")
+
+    # Replicate the train state on each device
+    state = state.replicate()
+
+    logger.info("***** Running training *****")
+    logger.info(f"  Num examples = {len(vectorized_datasets['train'])}")
+    logger.info(f"  Num Epochs = {num_epochs}")
+    logger.info("  Instantaneous batch size per device =" f" {training_args.per_device_train_batch_size}")
+    logger.info(f"  Total train batch size (w. parallel & distributed) = {train_batch_size}")
+    logger.info(f"  Total optimization steps = {total_train_steps}")
+
+    train_time = 0
+    epochs = tqdm(range(num_epochs), desc=f"Epoch ... (1/{num_epochs})", position=0)
+    for epoch in epochs:
+        # ======================== Training ================================
+        train_start = time.time()
+
+        # Create sampling rng
+        rng, input_rng = jax.random.split(rng)
+        train_metrics = []
+
+        # Generate an epoch by shuffling sampling indices from the train dataset
+        train_loader = get_data_loader(
+            input_rng,
+            vectorized_datasets["train"],
+            batch_size=train_batch_size,
+            data_collator=data_collator,
+            dataloader_num_workers=dataloader_num_workers,
+        )
+        # train
+        for step, batch in enumerate(tqdm(train_loader, desc="Training...", position=1), 1):
+            batch = shard(batch.data)
+            state, train_metric = p_train_step(state, batch, training_args.freeze_encoder)
+
+            cur_step = epoch * steps_per_epoch + step
+            if cur_step % training_args.logging_steps == 0:
+                train_metrics.append(train_metric)
+                train_metric_to_write = unreplicate(train_metric)
+                epochs.write(
+                    f"Step... ({cur_step} / {total_train_steps} | Loss:"
+                    f" {train_metric_to_write['loss']}, Learning Rate:"
+                    f" {train_metric_to_write['learning_rate']})"
+                )
+                if has_wandb and jax.process_index() == 0:
+                    write_wandb_metric(
+                        wandb_logger,
+                        train_metric_to_write,
+                        train_time + time.time() - train_start,
+                        cur_step,
+                        "train",
+                    )
+
+        train_time += time.time() - train_start
+
+        train_metric = unreplicate(train_metric)
+
+        epochs.write(
+            f"Epoch... ({epoch + 1}/{num_epochs} | Loss: {train_metric['loss']},"
+            f" Learning Rate: {train_metric['learning_rate']})"
+        )
+
+        # ======================== Evaluating ==============================
+        eval_metrics = []
+        eval_preds = []
+        eval_labels = []
+        eval_start = time.time()
+
+        eval_loader = get_data_loader(
+            input_rng,
+            vectorized_datasets["eval"],
+            batch_size=eval_batch_size,
+            data_collator=data_collator,
+            shuffle=False,
+            drop_last=False,
+            dataloader_num_workers=dataloader_num_workers,
+        )
+        for batch in tqdm(eval_loader, desc="Evaluating...", position=2):
+            # Model forward
+            labels = batch["labels"]
+
+            metrics = pad_shard_unpad(p_eval_step, static_return=True)(
+                state.params, batch.data, min_device_batch=per_device_eval_batch_size
+            )
+            eval_metrics.append(metrics)
+
+            # generation
+            if training_args.predict_with_generate:
+                generated_ids = pad_shard_unpad(p_generate_step)(
+                    state.params, batch.data, min_device_batch=per_device_eval_batch_size
+                )
+                eval_preds.extend(jax.device_get(generated_ids.reshape(-1, gen_kwargs["max_length"])))
+                eval_labels.extend(labels)
+
+        eval_time = time.time() - eval_start
+
+        # normalize eval metrics
+        eval_metrics = get_metrics(eval_metrics)
+        eval_metrics = jax.tree_util.tree_map(jnp.mean, eval_metrics)
+
+        # compute WER metric
+        wer_desc = ""
+        if training_args.predict_with_generate:
+            wer_metric, pred_str, label_str = compute_metrics(eval_preds, eval_labels)
+            eval_metrics.update(wer_metric)
+            wer_desc = " ".join([f"Eval {key}: {value} |" for key, value in wer_metric.items()])
+
+        # Print metrics and update progress bar
+        desc = f"Epoch... ({epoch + 1}/{num_epochs} | Eval Loss: {eval_metrics['loss']} |" f" {wer_desc})"
+        epochs.write(desc)
+        epochs.desc = desc
+
+        # Save metrics
+        if has_tensorboard and jax.process_index() == 0:
+            write_metric(
+                summary_writer,
+                train_metrics,
+                eval_metrics,
+                train_time,
+                cur_step,
+                training_args.logging_steps,
+            )
+
+        if has_wandb and jax.process_index() == 0:
+            write_wandb_metric(wandb_logger, eval_metrics, eval_time, cur_step, "eval")
+            if training_args.predict_with_generate:
+                write_wandb_pred(wandb_logger, pred_str, label_str)
+
+        # save checkpoint after each epoch and push checkpoint to the hub
+        if jax.process_index() == 0:
+            params = jax.device_get(jax.tree_util.tree_map(lambda x: x[0], state.params))
+            model.save_pretrained(training_args.output_dir, params=params)
+            tokenizer.save_pretrained(training_args.output_dir)
+            if training_args.push_to_hub:
+                repo.push_to_hub(
+                    commit_message=f"Saving weights and logs of epoch {epoch + 1}",
+                    blocking=False,
+                )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/flax/run_long_form_transcription.py b/flax/run_long_form_transcription.py
new file mode 100644
index 0000000000000000000000000000000000000000..4ea91f15856b96ae22d562412a161f33eb3489d3
--- /dev/null
+++ b/flax/run_long_form_transcription.py
@@ -0,0 +1,653 @@
+#!/usr/bin/env python
+# coding=utf-8
+# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Evaluating a Whisper model on one or more long-form evaluation datasets.
+"""
+# You can also adapt this script for your own speech recognition validation. Pointers for this are left as comments.
+
+import logging
+import os
+import sys
+import time
+from dataclasses import field
+from pathlib import Path
+from typing import Optional
+
+import datasets
+import flax
+import jax
+import jax.numpy as jnp
+import numpy as np
+import transformers
+from datasets import DatasetDict, IterableDatasetDict, load_dataset
+from jax.experimental.compilation_cache import compilation_cache as cc
+from jiwer import process_words, wer_default
+from nltk import ngrams
+from tqdm import tqdm
+from transformers import (
+    HfArgumentParser,
+    Seq2SeqTrainingArguments,
+    is_tensorboard_available,
+    is_wandb_available,
+)
+from transformers.models.whisper.english_normalizer import EnglishTextNormalizer
+from transformers.utils import check_min_version, send_example_telemetry
+from transformers.utils.versions import require_version
+
+from distil_whisper import FlaxWhisperPipeline
+
+
+# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
+check_min_version("4.27.0.dev0")
+
+require_version(
+    "datasets>=1.18.0",
+    "To fix: pip install -r examples/flax/speech-recogintion/requirements.txt",
+)
+
+logger = logging.getLogger(__name__)
+
+
+@flax.struct.dataclass
+class ModelArguments:
+    """
+    Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
+    """
+
+    model_name_or_path: str = field(
+        metadata={"help": ("Path to pretrained model or model identifier from huggingface.co/models")}
+    )
+    cache_dir: Optional[str] = field(
+        default=None,
+        metadata={"help": ("Where to store the pretrained models downloaded from huggingface.co")},
+    )
+    model_revision: str = field(
+        default="main",
+        metadata={"help": ("The specific model version to use (can be a branch name, tag name or commit id).")},
+    )
+    subfolder: str = field(
+        default="",
+        metadata={
+            "help": "In case the relevant files are located inside a subfolder of the model repo on huggingface.co, you can"
+            "specify the folder name here."
+        },
+    )
+    use_auth_token: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Will use the token generated when running `transformers-cli login`"
+                " (necessary to use this script with private models)."
+            )
+        },
+    )
+    dtype: Optional[str] = field(
+        default="float32",
+        metadata={
+            "help": (
+                "Floating-point format in which the model weights should be initialized"
+                " and trained. Choose one of `[float32, float16, bfloat16]`."
+            )
+        },
+    )
+    load_with_scan: Optional[bool] = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether to load the model with scan enabled. Required when the model was saved with scan enabled"
+            )
+        },
+    )
+    return_timestamps: Optional[bool] = field(
+        default=False,
+        metadata={
+            "help": "Whether to predict timestamps (alongside the text predictions). Timestamp predictions "
+            "are discarded at the end of inference, but may assist in the model in reducing hallucinations."
+        },
+    )
+    length_penalty: Optional[float] = field(
+        default=1.0,
+        metadata={
+            "help": (
+                "Exponential penalty to the length that is used with beam-based generation. It is applied as an "
+                "exponent to the sequence length, which in turn is used to divide the score of the sequence. Since "
+                "the score is the log likelihood of the sequence (i.e. negative), length_penalty > 1.0 promotes "
+                "longer sequences, while length_penalty < 1.0 encourages shorter sequences."
+            )
+        },
+    )
+    do_sample: Optional[bool] = field(
+        default=False,
+        metadata={"help": "Whether or not to use sampling ; use greedy decoding otherwise."},
+    )
+    top_k: Optional[int] = field(
+        default=50,
+        metadata={"help": "The number of the highest probability vocabulary tokens to keep for top-k-filtering."},
+    )
+    temperature: Optional[float] = field(
+        default=1.0,
+        metadata={"help": "The value used to modulate the next token probabilities if sampling."},
+    )
+    chunk_length_s: Optional[float] = field(
+        default=30.0,
+        metadata={
+            "help": "The input length for each chunk. By default, the chunk"
+            "length is set 30.0s, equal to Whisper's context window."
+        },
+    )
+    compilation_cache: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": (
+                "Whether to enable the JAX (experimental) compilation cache. The compilation step is *cached* the "
+                "first time it is run. Successive compilation steps for the same function utilise the cache to reduce"
+                "the compilation time."
+            )
+        },
+    )
+
+
+@flax.struct.dataclass
+class DataTrainingArguments:
+    """
+    Arguments pertaining to what data we are going to input our model for training and eval.
+    """
+
+    dataset_name: str = field(
+        default=None,
+        metadata={
+            "help": "The name of the dataset to use (via the datasets library). Load and combine "
+            "multiple datasets by separating dataset hours by a '+' symbol."
+        },
+    )
+    dataset_config_name: Optional[str] = field(
+        default=None,
+        metadata={"help": "The configuration name of the dataset to use (via the datasets library)."},
+    )
+    dataset_split_name: Optional[str] = field(
+        default=None,
+        metadata={"help": "The split name of the dataset to use (via the datasets library)."},
+    )
+    dataset_cache_dir: Optional[str] = field(
+        default=None,
+        metadata={"help": "Path to cache directory for saving and loading datasets"},
+    )
+    overwrite_cache: bool = field(
+        default=False,
+        metadata={"help": "Overwrite the cached training and evaluation sets"},
+    )
+    audio_column_name: str = field(
+        default="audio",
+        metadata={"help": "The name of the dataset column containing the audio data. Defaults to 'audio'"},
+    )
+    text_column_name: str = field(
+        default=None,
+        metadata={"help": "The name of the dataset column containing the text data. Defaults to 'text'."},
+    )
+    max_label_length: int = field(
+        default=256,
+        metadata={"help": "Truncate transcriptions that are longer `max_label_length` tokens."},
+    )
+    wandb_project: str = field(
+        default="distil-whisper",
+        metadata={"help": "The name of the wandb project."},
+    )
+    wandb_name: str = field(
+        default=None,
+        metadata={"help": "The name of the wandb run."},
+    )
+    wandb_job_type: str = field(
+        default="distil-whisper",
+        metadata={"help": "The name of the wandb job type."},
+    )
+    wandb_dir: str = field(
+        default=None,
+        metadata={"help": "The absolute path to save the wandb logs."},
+    )
+    save_code_to_wandb: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether to save main script to wandb. This is valuable for improving"
+                " experiment reproducibility and to diff code across experiments in"
+                " the UI."
+            )
+        },
+    )
+    streaming: bool = field(
+        default=True,
+        metadata={"help": "Whether to use Datasets' streaming mode to load and the data."},
+    )
+    max_eval_samples: Optional[int] = field(
+        default=None,
+        metadata={"help": "For debugging purposes, truncate the number of eval examples to this value if set."},
+    )
+    log_audio: Optional[bool] = field(
+        default=False,
+        metadata={"help": "For debugging purposes, record the audio samples as well as the ground truths / preds."},
+    )
+    log_predictions: Optional[bool] = field(
+        default=True,
+        metadata={"help": "Whether or not to log the ground truths / pred text to the wandb logger."},
+    )
+    ngram_degree: Optional[int] = field(
+        default=5, metadata={"help": "Degree of n-grams used when computing duplicate n-grams in the predicted text."}
+    )
+
+
+def write_metric(summary_writer, eval_metrics, prefix="eval"):
+    for metric_name, value in eval_metrics.items():
+        summary_writer.scalar(f"{prefix}/{metric_name}", value, 0)
+
+
+def write_wandb_metric(wandb_logger, metrics, train_time, prefix):
+    log_metrics = {}
+    for k, v in metrics.items():
+        log_metrics[f"{prefix}/{k}"] = v
+    log_metrics[f"{prefix}/time"] = train_time
+    wandb_logger.log(log_metrics)
+
+
+def convert_audio_to_wandb(wandb_logger, audio):
+    return wandb_logger.Audio(audio["array"][:, np.newaxis], sample_rate=audio["sampling_rate"])
+
+
+def write_wandb_pred(
+    wandb_logger,
+    eval_audios,
+    pred_str,
+    label_str,
+    norm_pred_str,
+    norm_label_str,
+    prefix="eval",
+):
+    columns = ["Target", "Pred", "Norm Target", "Norm Pred"]
+    # convert str data to a wandb compatible format
+    str_data = [[label_str[i], pred_str[i], norm_label_str[i], norm_pred_str[i]] for i in range(len(pred_str))]
+
+    if len(eval_audios) > 0:
+        columns.insert(0, "Audio")
+        str_data = [
+            [
+                convert_audio_to_wandb(wandb_logger, eval_audios[i]),
+                *str_data[i],
+            ]
+            for i in range(len(pred_str))
+        ]
+
+    # log as a table with the appropriate headers
+    wandb_logger.log(
+        {f"{prefix}/predictions": wandb_logger.Table(columns=columns, data=str_data)},
+    )
+
+
+def convert_dataset_str_to_list(
+    dataset_names, dataset_config_names, splits=None, text_column_names=None, dataset_hours=None, default_split="train"
+):
+    if isinstance(dataset_names, str):
+        dataset_names = dataset_names.split("+")
+
+        # we assume that all the datasets we're using derive from the distil-whisper org on the Hub - prepend the org name if necessary
+        for i in range(len(dataset_names)):
+            ds_name = dataset_names[i]
+            dataset_names[i] = f"distil-whisper/{ds_name}" if "/" not in ds_name else ds_name
+
+        dataset_config_names = dataset_config_names.split("+")
+        splits = splits.split("+") if splits is not None else None
+        text_column_names = text_column_names.split("+") if text_column_names is not None else None
+        dataset_hours = dataset_hours.split("+") if dataset_hours is not None else None
+
+    # basic checks to ensure we've got the right number of datasets/configs/splits/columns/probs
+    if len(dataset_names) != len(dataset_config_names):
+        raise ValueError(
+            f"Ensure one config is passed for each dataset, got {len(dataset_names)} datasets and"
+            f" {len(dataset_config_names)} configs."
+        )
+
+    if splits is not None and len(splits) != len(dataset_names):
+        raise ValueError(
+            f"Ensure one split is passed for each dataset, got {len(dataset_names)} datasets and {len(splits)} splits."
+        )
+
+    if text_column_names is not None and len(text_column_names) != len(dataset_names):
+        raise ValueError(
+            f"Ensure one text column name is passed for each dataset, got {len(dataset_names)} datasets and"
+            f" {len(text_column_names)} text column names."
+        )
+
+    if dataset_hours is not None:
+        if len(dataset_hours) != len(dataset_names):
+            raise ValueError(
+                f"Ensure one probability is passed for each dataset, got {len(dataset_names)} datasets and "
+                f"{len(dataset_hours)} hours."
+            )
+        dataset_hours = [float(ds_hours) for ds_hours in dataset_hours]
+    else:
+        dataset_hours = [None] * len(dataset_names)
+
+    text_column_names = (
+        text_column_names if text_column_names is not None else ["text" for _ in range(len(dataset_names))]
+    )
+    splits = splits if splits is not None else [default_split for _ in range(len(dataset_names))]
+
+    dataset_names_dict = []
+    for i, ds_name in enumerate(dataset_names):
+        dataset_names_dict.append(
+            {
+                "name": ds_name,
+                "config": dataset_config_names[i],
+                "split": splits[i],
+                "text_column_name": text_column_names[i],
+                "hours": dataset_hours[i],
+            }
+        )
+    return dataset_names_dict
+
+
+def main():
+    # 1. Parse input arguments
+    # See all possible arguments in src/transformers/training_args.py
+    # or by passing the --help flag to this script.
+    # We now keep distinct sets of args, for a cleaner separation of concerns.
+    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, Seq2SeqTrainingArguments))
+
+    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
+        # If we pass only one argument to the script and it's the path to a json file,
+        # let's parse it to get our arguments.
+        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
+    else:
+        model_args, data_args, training_args = parser.parse_args_into_dataclasses()
+
+    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
+    # information sent is the one passed as arguments along with your JAX/Flax versions.
+    send_example_telemetry("run_flax_speech_recognition_seq2seq", model_args, data_args, framework="flax")
+
+    # Enable tensorboard only on the master node
+    has_tensorboard = is_tensorboard_available()
+    if "tensorboard" in training_args.report_to:
+        if has_tensorboard and jax.process_index() == 0:
+            try:
+                from flax.metrics.tensorboard import SummaryWriter
+
+                summary_writer = SummaryWriter(log_dir=Path(os.path.join(training_args.output_dir, "runs")))
+            except ImportError as ie:
+                has_tensorboard = False
+                logger.warning(
+                    f"Unable to display metrics through TensorBoard because some packages are not installed: {ie}"
+                )
+        else:
+            logger.warning(
+                "Unable to display metrics through TensorBoard because the package is"
+                " not installed: Please run `pip install tensorboard` to enable."
+            )
+
+    # Enable wandb only on the master node
+    has_wandb = is_wandb_available()
+    if "wandb" in training_args.report_to:
+        if has_wandb and jax.process_index() == 0:
+            import wandb as wandb_logger
+
+            # Set up wandb run
+            wandb_logger.init(
+                project=data_args.wandb_project,
+                name=data_args.wandb_name,
+                job_type=data_args.wandb_job_type,
+                dir=data_args.wandb_dir,
+                save_code=data_args.save_code_to_wandb,
+            )
+        else:
+            logger.warning("Wandb logging requires wandb to be installed. Run `pip install wandb` to enable.")
+
+    # 2. Setup logging
+    # Make one log on every process with the configuration for debugging.
+    logging.basicConfig(
+        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+        datefmt="%m/%d/%Y %H:%M:%S",
+        handlers=[logging.StreamHandler(sys.stdout)],
+    )
+    # Set the verbosity to info of the Transformers logger.
+    # We only want one process per machine to log things on the screen.
+    logger.setLevel(logging.INFO if jax.process_index() == 0 else logging.ERROR)
+    if jax.process_index() == 0:
+        datasets.utils.logging.set_verbosity_warning()
+        transformers.utils.logging.set_verbosity_info()
+    else:
+        datasets.utils.logging.set_verbosity_error()
+        transformers.utils.logging.set_verbosity_error()
+
+    logger.info("Evaluation parameters %s", training_args)
+
+    if model_args.compilation_cache:
+        cc.initialize_cache(os.path.join(model_args.cache_dir, "jax_cache"))
+
+    # 3. Load dataset
+    raw_datasets = IterableDatasetDict() if data_args.streaming else DatasetDict()
+
+    # Convert lists of dataset names/configs/splits to a dict
+    # names: "librispeech_asr+gigaspeech", configs: "all+l", splits: "validation.clean+validation"
+    # -> [{"name: "librispeech_asr": "config": "all", "split": "validation.clean"}, {"name: "gigaspeech": "config": "l", "split": "validation"}
+    dataset_names_dict = convert_dataset_str_to_list(
+        data_args.dataset_name,
+        data_args.dataset_config_name,
+        splits=data_args.dataset_split_name,
+        text_column_names=data_args.text_column_name,
+    )
+
+    # load multiple eval sets
+    for dataset_dict in dataset_names_dict:
+        # Clean-up the dataset name for pretty logging
+        # ("distil-whisper/librispeech_asr", "validation.clean") -> "librispeech_asr/validation-clean"
+        pretty_name = f"{dataset_dict['name'].split('/')[-1]}/{dataset_dict['split'].replace('.', '-')}"
+        raw_datasets[pretty_name] = load_dataset(
+            dataset_dict["name"],
+            dataset_dict["config"],
+            split=dataset_dict["split"],
+            cache_dir=data_args.dataset_cache_dir,
+            use_auth_token=True if model_args.use_auth_token else None,
+            streaming=data_args.streaming,
+        )
+        if dataset_dict["text_column_name"] not in list(raw_datasets[pretty_name].features.keys()):
+            raise ValueError(
+                f"--text column name {dataset_dict['text_column_name']} not found in the evaluation "
+                f"dataset {dataset_dict['name']}. Ensure `text_column_name` is set to the correct column "
+                f"for the target text. Should be one of {' '.join(list(raw_datasets[pretty_name].features.keys()))}"
+            )
+        if dataset_dict["text_column_name"] != "text":
+            raw_datasets[pretty_name] = raw_datasets[pretty_name].rename_column(
+                dataset_dict["text_column_name"], "text"
+            )
+
+    # Streaming mode robust way of obtaining the features
+    raw_datasets_features = list(next(iter(raw_datasets.values())).features.keys())
+    audio_column_name = data_args.audio_column_name
+
+    if audio_column_name not in raw_datasets_features:
+        raise ValueError(
+            f"--audio_column_name '{audio_column_name}' not found in dataset"
+            f" '{data_args.dataset_name}'. Make sure to set `--audio_column_name` to"
+            " the correct audio column - one of"
+            f" {', '.join(raw_datasets_features)}."
+        )
+
+    for split in raw_datasets:
+        raw_datasets[split] = raw_datasets[split].remove_columns(
+            set(raw_datasets[split].features.keys()) - {audio_column_name, "text"}
+        )
+
+    # 5. Load pretrained model, tokenizer, and feature extractor
+    pipeline = FlaxWhisperPipeline(
+        model_args.model_name_or_path,
+        dtype=getattr(jnp, model_args.dtype),
+        max_length=training_args.generation_max_length,
+        cache_dir=model_args.cache_dir,
+        revision=model_args.model_revision,
+        use_auth_token=True if model_args.use_auth_token else None,
+        subfolder=model_args.subfolder,
+        # use_scan=model_args.load_with_scan,  # Model might have (erroneously) been saved with scan still enabled
+    )
+
+    if pipeline.model.config.decoder_start_token_id is None:
+        raise ValueError("Make sure that `config.decoder_start_token_id` is correctly defined")
+
+    # disable scan if necessary (makes the inference step faster)
+    if model_args.load_with_scan:
+        pipeline.model.disable_scan()  # to disable scan in the nn.Module
+        pipeline.params = pipeline.model.convert_scan_to_unroll(
+            pipeline.params
+        )  # to convert the scan params to unrolled
+
+    # 6. Possibly evaluate on a subset of data
+    if data_args.max_eval_samples is not None:
+        for split in raw_datasets:
+            raw_datasets[split] = (
+                raw_datasets[split].take(data_args.max_eval_samples)
+                if data_args.streaming
+                else raw_datasets[split].select(range(data_args.max_eval_samples))
+            )
+
+    # 8. Compute WER Metrics
+    normalizer = EnglishTextNormalizer(pipeline.tokenizer.english_spelling_normalizer)
+
+    def compute_metrics(pred_str, label_str, ngram_degree=5):
+        # normalize everything and compute the WER
+        norm_pred_str = [normalizer(pred).replace(".", "") for pred in pred_str]
+        norm_label_str = [normalizer(label) for label in label_str]
+        # for logging, we need the pred/labels to match the norm_pred/norm_labels, so discard any filtered samples here
+        pred_str = [pred_str[i] for i in range(len(norm_pred_str)) if len(norm_label_str[i]) > 0]
+        label_str = [label_str[i] for i in range(len(norm_label_str)) if len(norm_label_str[i]) > 0]
+        # filtering step to only evaluate the samples that correspond to non-zero normalized references:
+        norm_pred_str = [norm_pred_str[i] for i in range(len(norm_pred_str)) if len(norm_label_str[i]) > 0]
+        norm_label_str = [norm_label_str[i] for i in range(len(norm_label_str)) if len(norm_label_str[i]) > 0]
+
+        wer_output = process_words(norm_label_str, norm_pred_str, wer_default, wer_default)
+        wer_norm = 100 * wer_output.wer
+        ier_norm = 100 * wer_output.insertions / sum([len(ref) for ref in wer_output.references])
+        ser_norm = 100 * wer_output.substitutions / sum([len(ref) for ref in wer_output.references])
+        der_norm = 100 * wer_output.deletions / sum([len(ref) for ref in wer_output.references])
+
+        all_ngrams = list(ngrams(" ".join(norm_pred_str).split(), ngram_degree))
+        repeated_ngrams = len(all_ngrams) - len(set(all_ngrams))
+
+        return (
+            {"wer": wer_norm, "ier": ier_norm, "ser": ser_norm, "der": der_norm, "repeated_ngrams": repeated_ngrams},
+            pred_str,
+            label_str,
+            norm_pred_str,
+            norm_label_str,
+        )
+
+    # Store some constants
+    per_device_eval_batch_size = int(training_args.per_device_eval_batch_size)
+    eval_batch_size = per_device_eval_batch_size * jax.device_count()
+    num_beams = (
+        training_args.generation_num_beams
+        if training_args.generation_num_beams is not None
+        else pipeline.model.config.num_beams
+    )
+
+    generation_config = pipeline.model.generation_config
+    if hasattr(generation_config, "is_multilingual") and generation_config.is_multilingual:
+        # We need to set the language and task ids for previously multilingual checkpoints - for now we hardcode this to English
+        language = "English"
+        task = "transcribe"
+    else:
+        language = None
+        task = None
+
+    # pre-compile the model so that we don't count it in our eval
+    logger.info("Pre-compiling the generate call...")
+    random_inputs = {"input_features": np.ones((eval_batch_size, 80, 2 * pipeline.model.config.max_source_positions))}
+    pipeline.forward(
+        random_inputs,
+        batch_size=eval_batch_size,
+        language=language,
+        task=task,
+        return_timestamps=model_args.return_timestamps,
+        num_beams=num_beams,
+        length_penalty=model_args.length_penalty,
+        do_sample=model_args.do_sample,
+        top_k=model_args.top_k,
+        temperature=model_args.temperature,
+    )
+
+    def eval_step(split="eval"):
+        # ======================== Evaluating ==============================
+        eval_preds = []
+        eval_labels = []
+        eval_audios = []
+        eval_start = time.time()
+
+        for sample in tqdm(raw_datasets[split], desc=f"Evaluating {split}..."):
+            # Model forward
+            label_str = sample["text"]
+            if data_args.log_audio:
+                eval_audios.append(sample["audio"])
+
+            pred_str = pipeline(
+                sample["audio"],
+                batch_size=eval_batch_size,
+                language=language,
+                task=task,
+                chunk_length_s=model_args.chunk_length_s,
+                return_timestamps=model_args.return_timestamps,
+                num_beams=num_beams,
+                length_penalty=model_args.length_penalty,
+                do_sample=model_args.do_sample,
+                top_k=model_args.top_k,
+                temperature=model_args.temperature,
+            )
+            eval_preds.append(pred_str["text"])
+            eval_labels.append(label_str)
+
+        eval_time = time.time() - eval_start
+
+        wer_metric, pred_str, label_str, norm_pred_str, norm_label_str = compute_metrics(
+            eval_preds, eval_labels, ngram_degree=data_args.ngram_degree
+        )
+        wer_desc = " ".join([f"{split} {key}: {value} |" for key, value in wer_metric.items()])
+
+        # Print metrics to stdout
+        logger.info(wer_desc)
+
+        # Save metrics to tensorboard
+        if has_tensorboard and jax.process_index() == 0 and "tensorboard" in training_args.report_to:
+            write_metric(summary_writer, wer_metric, prefix=split)
+
+        # Save metrics to wandb
+        if has_wandb and jax.process_index() == 0 and "wandb" in training_args.report_to:
+            write_wandb_metric(wandb_logger, wer_metric, eval_time, prefix=split)
+            if data_args.log_predictions:
+                write_wandb_pred(
+                    wandb_logger, eval_audios, pred_str, label_str, norm_pred_str, norm_label_str, prefix=split
+                )
+
+    logger.info("***** Running Eval *****")
+    logger.info("  Instantaneous batch size per device =" f" {training_args.per_device_eval_batch_size}")
+    logger.info(f"  Total eval batch size (w. parallel & distributed) = {eval_batch_size}")
+    logger.info(f"  Beam size = {num_beams}")
+    if num_beams > 1:
+        logger.info(f"  Length penalty size = {model_args.length_penalty}")
+    logger.info(f"  Do sample = {model_args.do_sample}")
+    if model_args.do_sample:
+        logger.info(f"  Top k = {model_args.top_k}")
+        logger.info(f"  Temperature = {model_args.temperature}")
+
+    for split in raw_datasets:
+        eval_step(split=split)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/flax/run_orig_longform.sh b/flax/run_orig_longform.sh
new file mode 100644
index 0000000000000000000000000000000000000000..0451b29ebc1304738858b95b174c0dc67d9c38d7
--- /dev/null
+++ b/flax/run_orig_longform.sh
@@ -0,0 +1,25 @@
+#!/usr/bin/env bash
+names=("openai/whisper-large-v2" "openai/whisper-medium.en" "openai/whisper-small.en" "openai/whisper-base.en" "openai/whisper-tiny.en")
+names=("openai/whisper-small.en" "openai/whisper-base.en" "openai/whisper-tiny.en")
+# names=("patrickvonplaten/whisper-large-v2-32-2" "patrickvonplaten/whisper-medium-24-2")
+
+# chunk_lengths=("15.0" "30.0")
+# --return_timestamps \
+# --assistant_model_name_or_path "patrickvonplaten/whisper-large-v2-32-2" \
+# --attn_type "flash2"  \
+
+# Double loop
+for name in "${names[@]}"; do
+  CUDA_VISIBLE_DEVICES="1" python ./run_speed_pt.py \
+    --dataset_name "distil-whisper/earnings21+distil-whisper/earnings22+distil-whisper/meanwhile+distil-whisper/rev16" \
+    --wandb_name "A100-${name}-Longform-Orig" \
+    --model_name_or_path ${name} \
+    --wandb_project "distil-whisper-speed-bench-long-form-orig-32" \
+    --dataset_config_name "full+full+default+whisper_subset" \
+    --dataset_split_name "test+test+test+test" \
+    --text_column_name "transcription+transcription+text+transcription" \
+    --use_orig_whisper \
+    --max_label_length "1000000" \
+    --samples_per_dataset "32" \
+    --batch_size "1"
+done
diff --git a/flax/run_pseudo_labelling_pt.py b/flax/run_pseudo_labelling_pt.py
new file mode 100644
index 0000000000000000000000000000000000000000..78e8bc4d939da5da536617e0852f62b4c93f238d
--- /dev/null
+++ b/flax/run_pseudo_labelling_pt.py
@@ -0,0 +1,880 @@
+#!/usr/bin/env python
+# coding=utf-8
+# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Pseudo-labelling audio data using the Whisper model in preparation for distillation.
+"""
+import csv
+
+# You can also adapt this script for your own pseudo-labelling tasks. Pointers for this are left as comments.
+import logging
+import os
+import string
+import sys
+import time
+from dataclasses import dataclass, field
+from datetime import timedelta
+from pathlib import Path
+from typing import Any, Dict, List, Optional, Union
+
+import datasets
+import evaluate
+import numpy as np
+import torch
+import transformers
+from accelerate import Accelerator, InitProcessGroupKwargs
+from accelerate.logging import get_logger
+from datasets import (
+    DatasetDict,
+    IterableDatasetDict,
+    load_dataset,
+)
+from huggingface_hub import HfFolder, Repository, create_repo, get_full_repo_name
+from torch.utils.data import DataLoader
+from tqdm import tqdm
+from transformers import (
+    HfArgumentParser,
+    Seq2SeqTrainingArguments,
+    WhisperConfig,
+    WhisperFeatureExtractor,
+    WhisperForConditionalGeneration,
+    WhisperProcessor,
+    WhisperTokenizerFast,
+)
+from transformers.models.whisper.english_normalizer import EnglishTextNormalizer
+from transformers.utils import check_min_version
+from transformers.utils.versions import require_version
+
+
+# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
+check_min_version("4.34.0.dev0")
+
+require_version("datasets>=2.14.6", "To fix: `pip install --upgrade datasets`")
+
+logger = get_logger(__name__)
+
+
+@dataclass
+class ModelArguments:
+    """
+    Arguments pertaining to which model/config/tokenizer we are going to distill from.
+    """
+
+    model_name_or_path: str = field(
+        metadata={"help": "Path to pretrained Whisper model or model identifier from huggingface.co/models"}
+    )
+    config_name: Optional[str] = field(
+        default=None,
+        metadata={"help": "Pretrained config name or path if not the same as model_name"},
+    )
+    tokenizer_name: Optional[str] = field(
+        default=None,
+        metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"},
+    )
+    feature_extractor_name: Optional[str] = field(
+        default=None,
+        metadata={"help": "feature extractor name or path if not the same as model_name"},
+    )
+    processor_name: Optional[str] = field(
+        default=None,
+        metadata={"help": "processor name or path if not the same as model_name"},
+    )
+    cache_dir: Optional[str] = field(
+        default=None,
+        metadata={"help": "Where to store the pretrained models downloaded from huggingface.co"},
+    )
+    use_fast_tokenizer: bool = field(
+        default=True,
+        metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
+    )
+    model_revision: str = field(
+        default="main",
+        metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
+    )
+    subfolder: str = field(
+        default="",
+        metadata={
+            "help": "In case the relevant files are located inside a subfolder of the model repo on huggingface.co, you can"
+            "specify the folder name here."
+        },
+    )
+    token: str = field(
+        default=None,
+        metadata={
+            "help": (
+                "The token to use as HTTP bearer authorization for remote files. If not specified, will use the token "
+                "generated when running `huggingface-cli login` (stored in `~/.huggingface`)."
+            )
+        },
+    )
+    dtype: Optional[str] = field(
+        default="float32",
+        metadata={
+            "help": (
+                "The data type (dtype) in which to load the model weights. One of `float32` (full-precision), "
+                "`float16` or `bfloat16` (both half-precision)."
+            )
+        },
+    )
+    attn_type: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": (
+                "Which attention implementation to use in the encoder and decoder attention layers. Can be one of:\n"
+                "1. `None`: default Transformers attention implementation."
+                "2. `flash_attn`: Flash Attention through PyTorch SDPA. Requires `torch>=2.0` and `optimum` to be installed. Recommended for hardware where Flash Attention 2 is not supported, e.g. Turing GPUs, (T4, RTX 2080)"
+                "3. `flash_attn_2`: Flash Attention 2 through the Flash Attention package https://github.com/Dao-AILab/flash-attention. **Always** recommended on supported hardware (Ampere, Ada, or Hopper GPUs, e.g., A100, RTX 3090, RTX 4090, H100)"
+            )
+        },
+    )
+    compile_encoder: Optional[bool] = field(
+        default=True,
+        metadata={
+            "help": "Whether or not to enable torch compile in the encoder module. Requires `torch>=2.0` to be installed."
+        },
+    )
+
+
+@dataclass
+class DataTrainingArguments:
+    """
+    Arguments pertaining to what data we are going to input our model for training and eval.
+    """
+
+    dataset_name: str = field(
+        default=None,
+        metadata={"help": "The name of the dataset to use (via the datasets library)."},
+    )
+    dataset_config_name: Optional[str] = field(
+        default=None,
+        metadata={"help": "The configuration name of the dataset to use (via the datasets library)."},
+    )
+    dataset_cache_dir: Optional[str] = field(
+        default=None,
+        metadata={"help": "Path to cache directory for saving and loading datasets"},
+    )
+    overwrite_cache: bool = field(
+        default=False,
+        metadata={"help": "Overwrite the cached training and evaluation sets"},
+    )
+    preprocessing_num_workers: Optional[int] = field(
+        default=None,
+        metadata={"help": "The number of processes to use for the preprocessing."},
+    )
+    audio_column_name: str = field(
+        default="audio",
+        metadata={"help": "The name of the dataset column containing the audio data. Defaults to 'audio'"},
+    )
+    text_column_name: str = field(
+        default="text",
+        metadata={"help": "The name of the dataset column containing the text data. Defaults to 'text'."},
+    )
+    id_column_name: str = field(
+        default="id",
+        metadata={"help": "The name of the dataset column containing the id data. Defaults to 'id'"},
+    )
+    max_label_length: int = field(
+        default=128,
+        metadata={"help": "Truncate transcriptions that are longer `max_label_length` tokens."},
+    )
+    preprocessing_only: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether to only do data preprocessing and skip training. This is"
+                " especially useful when data preprocessing errors out in distributed"
+                " training due to timeout. In this case, one should run the"
+                " preprocessing in a non-distributed setup with"
+                " `preprocessing_only=True` so that the cached datasets can"
+                " consequently be loaded in distributed training"
+            )
+        },
+    )
+    data_split_name: str = field(
+        default="train+validation+test",
+        metadata={
+            "help": (
+                "The name of the data set splits to use (via the datasets library)."
+                " Defaults to 'train+validation+test'. Multiple splits can be passed by splitting a"
+                " list through the '+' character, e.g. 'train+validation' will"
+                " pseudo-label both the 'train' and 'validation' splits sequentially."
+            )
+        },
+    )
+    wandb_project: str = field(
+        default="distil-whisper",
+        metadata={"help": "The name of the wandb project."},
+    )
+    streaming: bool = field(
+        default=False,
+        metadata={"help": "Whether to use dataset's streaming mode to load and pre-process the data."},
+    )
+    max_samples_per_split: Optional[int] = field(
+        default=None,
+        metadata={"help": "For debugging purposes, truncate the number of examples per split to this value if set."},
+    )
+    return_timestamps: bool = field(
+        default=False,
+        metadata={
+            "help": "Whether to return the timestamps with the text. This enables the `FlaxWhisperTimestampsLogitsProcessor`."
+        },
+    )
+    language: str = field(
+        default=None,
+        metadata={
+            "help": (
+                "Language for multilingual distillation. This argument should be set for multilingual distillation "
+                "only. For English speech recognition, it should be left as `None`."
+            )
+        },
+    )
+    task: str = field(
+        default="transcribe",
+        metadata={
+            "help": "Task, either `transcribe` for speech recognition or `translate` for speech translation."
+            "This argument should be set for multilingual distillation only. For English speech recognition, it should be left as `None`."
+        },
+    )
+    decode_token_ids: bool = field(
+        default=True,
+        metadata={"help": "Whether or not to decode the predicted token ids to text transcriptions."},
+    )
+    private_dataset: bool = field(
+        default=False,
+        metadata={"help": "Whether or not to create a private dataset for the pseudo-labelled data."},
+    )
+
+
+def shift_tokens_right(label_ids: np.array, decoder_start_token_id: int) -> np.ndarray:
+    """
+    Shift label ids one token to the right.
+    """
+    shifted_label_ids = np.zeros_like(label_ids)
+    shifted_label_ids[:, 1:] = label_ids[:, :-1]
+    shifted_label_ids[:, 0] = decoder_start_token_id
+
+    return shifted_label_ids
+
+
+@dataclass
+class DataCollatorSpeechSeq2SeqWithPadding:
+    """
+    Data collator that will dynamically pad the inputs received.
+    Args:
+        processor ([`Wav2Vec2Processor`])
+            The processor used for proccessing the data.
+        decoder_start_token_id (:obj: `int`)
+            The start-of-sequence token id of the decoder.
+        decoder_prev_token_id (:obj: `int`)
+            The start-of-prompt token id of the decoder
+        input_padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`, `optional`, defaults to :obj:`True`):
+            Select a strategy to pad the returned input sequences (according to the model's padding side and padding index)
+            among:
+            * :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
+              sequence if provided).
+            * :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the
+              maximum acceptable input length for the model if that argument is not provided.
+            * :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of
+              different lengths).
+        target_padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`, `optional`, defaults to :obj:`True`):
+            Select a strategy to pad the returned target sequences (according to the model's padding side and padding index).
+            See above for details.
+        max_target_length (:obj:`int`, `optional`):
+            Maximum length of the ``labels`` of the returned list and optionally padding length (see above).
+    """
+
+    processor: Any
+    decoder_start_token_id: int
+    decoder_prev_token_id: int
+    input_padding: Union[bool, str] = "max_length"
+    target_padding: Union[bool, str] = "max_length"
+    max_target_length: Optional[int] = None
+
+    def __call__(self, features: List[Dict[str, Union[List[int], np.ndarray]]]) -> Dict[str, np.ndarray]:
+        # split inputs and labels since they have to be of different lengths and need
+        # different padding methods
+        model_input_name = self.processor.model_input_names[0]
+
+        # dataloader returns a list of features which we convert to a dict
+        input_features = {model_input_name: [feature[model_input_name] for feature in features]}
+        label_features = {"input_ids": [feature["labels"] for feature in features]}
+        file_ids = {"input_ids": [feature["file_id"] for feature in features]}
+
+        # reformat list to dict and set to pytorch format
+        batch = self.processor.feature_extractor.pad(
+            input_features,
+            padding=self.input_padding,
+            return_tensors="pt",
+        )
+
+        labels_batch = self.processor.tokenizer.pad(
+            label_features,
+            max_length=self.max_target_length,
+            padding=self.target_padding,
+            return_tensors="pt",
+        )
+
+        file_ids_batch = self.processor.tokenizer.pad(
+            file_ids,
+            max_length=self.max_target_length,
+            padding=self.target_padding,
+            return_tensors="pt",
+        )
+
+        # replace padding with -100 to ignore correctly when computing the loss
+        labels = labels_batch["input_ids"].masked_fill(labels_batch.attention_mask.ne(1), -100)
+
+        # if bos token is appended in previous tokenization step,
+        # cut bos token here as it's append later anyways
+        if set(torch.unique(labels[:, 0])).issubset({self.decoder_start_token_id, self.decoder_prev_token_id}):
+            labels = labels[:, 1:]
+
+        # replace initial prompt tokens with -100 to ignore correctly when computing the loss
+        bos_index = torch.argmax((labels == self.decoder_start_token_id).long(), dim=1)
+        prompt_mask = torch.arange(labels.shape[1]) < bos_index[:, None]
+        labels = torch.where(prompt_mask, -100, labels)
+
+        batch["labels"] = labels
+        batch["file_ids"] = file_ids_batch["input_ids"]
+
+        return batch
+
+
+def log_metric(
+    accelerator,
+    metrics: Dict,
+    train_time: float,
+    prefix: str = "eval",
+):
+    """Helper function to log all evaluation metrics with the correct prefixes and styling."""
+    log_metrics = {}
+    for k, v in metrics.items():
+        log_metrics[f"{prefix}/{k}"] = v
+    log_metrics[f"{prefix}/time"] = train_time
+    accelerator.log(log_metrics)
+
+
+def log_pred(
+    accelerator,
+    pred_str: List[str],
+    label_str: List[str],
+    norm_pred_str: List[str],
+    norm_label_str: List[str],
+    prefix: str = "eval",
+    num_lines: int = 200000,
+):
+    """Helper function to log target/predicted transcriptions to weights and biases (wandb)."""
+    if accelerator.is_main_process:
+        wandb_tracker = accelerator.get_tracker("wandb")
+        # pretty name for split
+        prefix = prefix.replace("/", "-")
+
+        # convert str data to a wandb compatible format
+        str_data = [[label_str[i], pred_str[i], norm_label_str[i], norm_pred_str[i]] for i in range(len(pred_str))]
+        # log as a table with the appropriate headers
+        wandb_tracker.log_table(
+            table_name=f"{prefix}/all_predictions",
+            columns=["Target", "Pred", "Norm Target", "Norm Pred"],
+            data=str_data[:num_lines],
+        )
+
+        # log incorrect normalised predictions
+        str_data = np.asarray(str_data)
+        str_data_incorrect = str_data[str_data[:, -2] != str_data[:, -1]]
+        # log as a table with the appropriate headers
+        wandb_tracker.log_table(
+            table_name=f"{prefix}/incorrect_predictions",
+            columns=["Target", "Pred", "Norm Target", "Norm Pred"],
+            data=str_data_incorrect[:num_lines],
+        )
+
+
+def main():
+    # 1. Parse input arguments
+    # We keep distinct sets of args, for cleaner separation of model/data/training related args
+    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, Seq2SeqTrainingArguments))
+
+    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
+        # If we pass only one argument to the script and it's the path to a json file,
+        # let's parse it to get our arguments.
+        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
+    else:
+        model_args, data_args, training_args = parser.parse_args_into_dataclasses()
+
+    # 2. Initialize the accelerator
+    # We will let the accelerator handle device placement for us in this example
+    # We simply have to specify the training precision and any trackers being used
+    # We'll use the same dtype arguments as our JAX/Flax training script and convert
+    # it to accelerate format
+    if model_args.dtype == "float16":
+        mixed_precision = "fp16"
+        torch_dtype = torch.float16
+    elif model_args.dtype == "bfloat16":
+        mixed_precision = "bf16"
+        torch_dtype = torch.bfloat16
+    else:
+        mixed_precision = "no"
+        torch_dtype = torch.float32
+
+    kwargs = InitProcessGroupKwargs(timeout=timedelta(seconds=7200))
+
+    accelerator = Accelerator(
+        gradient_accumulation_steps=training_args.gradient_accumulation_steps,
+        mixed_precision=mixed_precision,
+        log_with=training_args.report_to,
+        project_dir=training_args.output_dir,
+        kwargs_handlers=[kwargs],
+    )
+
+    accelerator.init_trackers(project_name=data_args.wandb_project)
+
+    # 3. Set-up basic logging
+    # Create one log on every process with the configuration for debugging
+    logging.basicConfig(
+        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+        datefmt="%m/%d/%Y %H:%M:%S",
+        level=logging.INFO,
+    )
+    # Log a small summary on each proces
+    logger.warning(
+        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, "
+        f"distributed training: {training_args.parallel_mode.value == 'distributed'}, 16-bits training: {training_args.fp16}"
+    )
+
+    # Set the verbosity to info of the Transformers logger (on main process only)
+    if accelerator.is_local_main_process:
+        datasets.utils.logging.set_verbosity_warning()
+        transformers.utils.logging.set_verbosity_info()
+    else:
+        datasets.utils.logging.set_verbosity_error()
+        transformers.utils.logging.set_verbosity_error()
+    logger.info("Training/evaluation parameters %s", training_args)
+
+    # 3. Load dataset
+    raw_datasets = IterableDatasetDict() if data_args.streaming else DatasetDict()
+    token = model_args.token if model_args.token is not None else HfFolder().get_token()
+
+    data_splits = data_args.data_split_name.split("+")
+    for split in data_splits:
+        if data_args.streaming:
+            raw_datasets[split] = load_dataset(
+                data_args.dataset_name,
+                data_args.dataset_config_name,
+                split=split,
+                cache_dir=data_args.dataset_cache_dir,
+                token=token,
+                streaming=True,
+            )
+        else:
+            raw_datasets[split] = load_dataset(
+                data_args.dataset_name,
+                data_args.dataset_config_name,
+                split=split,
+                cache_dir=data_args.dataset_cache_dir,
+                token=token,
+                streaming=False,
+                num_proc=data_args.preprocessing_num_workers,
+            )
+
+    if data_args.audio_column_name not in next(iter(raw_datasets.values())).column_names:
+        raise ValueError(
+            f"--audio_column_name '{data_args.audio_column_name}' not found in dataset"
+            f" '{data_args.dataset_name}'. Make sure to set `--audio_column_name` to"
+            " the correct audio column - one of"
+            f" {', '.join(next(iter(raw_datasets.values())).column_names)}."
+        )
+
+    if data_args.text_column_name not in next(iter(raw_datasets.values())).column_names:
+        raise ValueError(
+            f"--text_column_name {data_args.text_column_name} not found in dataset"
+            f" '{data_args.dataset_name}'. Make sure to set `--text_column_name` to the"
+            " correct text column - one of"
+            f" {', '.join(next(iter(raw_datasets.values())).column_names)}."
+        )
+
+    # 7. Load pretrained model, tokenizer, and feature extractor
+    config = WhisperConfig.from_pretrained(
+        (model_args.config_name if model_args.config_name else model_args.model_name_or_path),
+        cache_dir=model_args.cache_dir,
+        revision=model_args.model_revision,
+        token=token,
+    )
+    feature_extractor = WhisperFeatureExtractor.from_pretrained(
+        (model_args.feature_extractor_name if model_args.feature_extractor_name else model_args.model_name_or_path),
+        cache_dir=model_args.cache_dir,
+        revision=model_args.model_revision,
+        token=token,
+    )
+    tokenizer = WhisperTokenizerFast.from_pretrained(
+        (model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path),
+        cache_dir=model_args.cache_dir,
+        use_fast=model_args.use_fast_tokenizer,
+        revision=model_args.model_revision,
+        token=token,
+    )
+    processor = WhisperProcessor.from_pretrained(
+        (model_args.processor_name if model_args.processor_name else model_args.model_name_or_path),
+        cache_dir=model_args.cache_dir,
+        revision=model_args.model_revision,
+        token=token,
+    )
+    model = WhisperForConditionalGeneration.from_pretrained(
+        model_args.model_name_or_path,
+        config=config,
+        cache_dir=model_args.cache_dir,
+        revision=model_args.model_revision,
+        subfolder=model_args.subfolder,
+        token=token,
+        low_cpu_mem_usage=True,
+        torch_dtype=torch_dtype,
+        use_flash_attention_2=model_args.attn_type == "flash_attn_2",
+    )
+
+    if model_args.attn_type == "flash_attn":
+        model = model.to_bettertransformer()
+    elif model_args.attn_type not in [None, "flash_attn", "flash_attn_2"]:
+        raise ValueError(
+            f"Argument `attn_type` is set to {model_args.attn_type}. Should be one of:"
+            "1. `None`: default Transformers attention implementation."
+            "2. `flash_attn`: Flash Attention through PyTorch SDPA. Requires `torch>=2.0` and `optimum` to be installed. Recommended for hardware where Flash Attention 2 is not supported, e.g. Turing GPUs, (T4, RTX 2080)."
+            "3. `flash_attn_2`: Flash Attention 2 through the Flash Attention package https://github.com/Dao-AILab/flash-attention. **Always** recommended on supported hardware (Ampere, Ada, or Hopper GPUs, e.g., A100, RTX 3090, RTX 4090, H100)."
+        )
+
+    if model_args.compile_encoder:
+        model.model.encoder.forward = torch.compile(
+            model.model.encoder.forward, mode="reduce-overhead", fullgraph=True
+        )
+
+    model.eval()
+
+    if model.config.decoder_start_token_id is None:
+        raise ValueError("Make sure that `config.decoder_start_token_id` is correctly defined")
+
+    return_timestamps = data_args.return_timestamps
+    if hasattr(model.generation_config, "is_multilingual") and model.generation_config.is_multilingual:
+        # We need to set the language and task ids for multilingual checkpoints
+        tokenizer.set_prefix_tokens(
+            language=data_args.language, task=data_args.task, predict_timestamps=return_timestamps
+        )
+    elif data_args.language is not None:
+        raise ValueError(
+            "Setting language token for an English-only checkpoint is not permitted. The language argument should "
+            "only be set for multilingual checkpoints."
+        )
+
+    # 6. Resample speech dataset: `datasets` takes care of automatically loading and resampling the audio,
+    # so we just need to set the correct target sampling rate.
+    raw_datasets = raw_datasets.cast_column(
+        data_args.audio_column_name,
+        datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate),
+    )
+
+    # 7. Preprocessing the datasets.
+    # We need to read the audio files as arrays and tokenize the targets.
+    max_label_length = (
+        data_args.max_label_length if data_args.max_label_length is not None else model.config.max_length
+    )
+    audio_column_name = data_args.audio_column_name
+    num_workers = data_args.preprocessing_num_workers
+    dataloader_num_workers = training_args.dataloader_num_workers
+    text_column_name = data_args.text_column_name
+    model_input_name = feature_extractor.model_input_names[0]
+    id_column_name = data_args.id_column_name
+    normalizer = EnglishTextNormalizer(tokenizer.english_spelling_normalizer)
+
+    if data_args.max_samples_per_split is not None:
+        for split in data_splits:
+            raw_datasets[split] = (
+                raw_datasets[split].take(data_args.max_samples_per_split)
+                if data_args.streaming
+                else raw_datasets[split].select(range(data_args.max_samples_per_split))
+            )
+
+    def prepare_dataset(batch):
+        # process audio
+        sample = batch[audio_column_name]
+        inputs = feature_extractor(sample["array"], sampling_rate=sample["sampling_rate"])
+        # process audio length
+        batch[model_input_name] = inputs.get(model_input_name)[0]
+
+        # process targets
+        input_str = batch[text_column_name]
+        batch["labels"] = tokenizer(input_str, max_length=max_label_length, truncation=True).input_ids
+
+        # record the id of the sample as token ids
+        batch["file_id"] = tokenizer(batch[id_column_name], add_special_tokens=False).input_ids
+        return batch
+
+    raw_datasets_features = list(next(iter(raw_datasets.values())).features.keys())
+    if data_args.streaming:
+        vectorized_datasets = raw_datasets.map(prepare_dataset, remove_columns=raw_datasets_features)
+    else:
+        vectorized_datasets = raw_datasets.map(
+            prepare_dataset,
+            remove_columns=raw_datasets_features,
+            num_proc=num_workers,
+            desc="preprocess dataset",
+        )
+
+    # for large datasets it is advised to run the preprocessing on a
+    # single machine first with `args.preprocessing_only` since there will mostly likely
+    # be a timeout when running the script in distributed mode.
+    # In a second step `args.preprocessing_only` can then be set to `False` to load the
+    # cached dataset
+    if data_args.preprocessing_only:
+        cache = {k: v.cache_files for k, v in vectorized_datasets.items()}
+        logger.info(f"Data preprocessing finished. Files cached at {cache}.")
+        return
+
+    if data_args.streaming and dataloader_num_workers > 0:
+        logger.warning(
+            "Using multiple dataloader num workers with streaming mode will result in different shards of "
+            "data being transcribed in parallel. This is not advised if you want to preserve the order of the "
+            "audio-text data."
+        )
+
+    # Handle the repository creation
+    output_dir = training_args.output_dir
+    if training_args.push_to_hub:
+        if training_args.hub_model_id is None:
+            repo_name = get_full_repo_name(
+                Path(output_dir).absolute().name,
+                token=token,
+            )
+        else:
+            repo_name = training_args.hub_model_id
+        create_repo(repo_name, exist_ok=True, token=token, repo_type="dataset", private=data_args.private_dataset)
+        repo = Repository(
+            output_dir,
+            clone_from=repo_name,
+            token=token,
+            repo_type="dataset",
+        )
+        # Ensure large txt files can be pushed to the Hub with git-lfs
+        with open(os.path.join(output_dir, ".gitattributes"), "r+") as f:
+            git_lfs_extensions = f.read()
+            if "*.csv" not in git_lfs_extensions:
+                f.write("*.csv filter=lfs diff=lfs merge=lfs -text")
+    else:
+        # this is where we'll save our transcriptions
+        if not os.path.exists(output_dir):
+            os.makedirs(output_dir)
+
+    # 8. Load Metric
+    metric = evaluate.load("wer")
+    # convention is that we space all punctuation *except* apostrophes
+    all_punctuation = list(string.punctuation.replace("'", ""))
+
+    def compute_metrics(preds, labels, file_ids):
+        # replace padded labels by the padding token
+        for idx in range(len(labels)):
+            labels[idx][labels[idx] == -100] = tokenizer.pad_token_id
+
+        pred_str = tokenizer.batch_decode(preds, skip_special_tokens=True, decode_with_timestamps=return_timestamps)
+        # we do not want to group tokens when computing the metrics
+        label_str = tokenizer.batch_decode(labels, skip_special_tokens=True)
+
+        # space punctuation for orthographic WER (c.f. ESB paper https://arxiv.org/abs/2210.13352)
+        spaced_pred_str = [
+            pred_str[i].replace(punctuation, f" {punctuation} ")
+            for punctuation in all_punctuation
+            for i in range(len(pred_str))
+        ]
+        spaced_label_str = [
+            label_str[i].replace(punctuation, f" {punctuation} ")
+            for punctuation in all_punctuation
+            for i in range(len(label_str))
+        ]
+        wer_ortho = 100 * metric.compute(predictions=spaced_pred_str, references=spaced_label_str)
+
+        # normalize everything and re-compute the WER
+        norm_pred_str = [normalizer(pred) for pred in pred_str]
+        norm_label_str = [normalizer(label) for label in label_str]
+        # for logging, we need the pred/labels to match the norm_pred/norm_labels, so discard any filtered samples here
+        pred_str = [pred_str[i] for i in range(len(norm_pred_str)) if len(norm_label_str[i]) > 0]
+        label_str = [label_str[i] for i in range(len(norm_label_str)) if len(norm_label_str[i]) > 0]
+        file_ids = [file_ids[i] for i in range(len(file_ids)) if len(norm_label_str[i]) > 0]
+        # filtering step to only evaluate the samples that correspond to non-zero normalized references:
+        norm_pred_str = [norm_pred_str[i] for i in range(len(norm_pred_str)) if len(norm_label_str[i]) > 0]
+        norm_label_str = [norm_label_str[i] for i in range(len(norm_label_str)) if len(norm_label_str[i]) > 0]
+
+        wer = 100 * metric.compute(predictions=norm_pred_str, references=norm_label_str)
+
+        return {"wer": wer, "wer_ortho": wer_ortho}, pred_str, label_str, norm_pred_str, norm_label_str, file_ids
+
+    # 12. Define Training Schedule
+    per_device_eval_batch_size = int(training_args.per_device_eval_batch_size)
+
+    data_collator = DataCollatorSpeechSeq2SeqWithPadding(
+        processor=processor,
+        decoder_start_token_id=model.config.decoder_start_token_id,  # <|startoftranscript|>
+        decoder_prev_token_id=tokenizer.all_special_ids[-3],  # <|startofprev|>
+        input_padding="longest",
+        target_padding="max_length",
+        max_target_length=max_label_length,
+    )
+
+    # 14. Define generation arguments - we need to do this before we wrap the models in DDP
+    # so that we can still access the configs
+    num_beams = (
+        training_args.generation_num_beams
+        if training_args.generation_num_beams is not None
+        else getattr(model.generation_config, "num_beams", 1)
+    )
+
+    gen_kwargs = {
+        "max_length": max_label_length,
+        "num_beams": num_beams,
+        "return_timestamps": return_timestamps,
+    }
+    if hasattr(model.generation_config, "is_multilingual") and model.generation_config.is_multilingual:
+        # forcing the language and task tokens helps multilingual models in their generations
+        gen_kwargs.update(
+            {
+                "language": data_args.language,
+                "task": data_args.task,
+            }
+        )
+
+    # 15. Prepare everything with accelerate
+    model = accelerator.prepare(model)
+
+    def eval_step_with_save(split="eval"):
+        # ======================== Evaluating ==============================
+        eval_preds = []
+        eval_labels = []
+        eval_ids = []
+        eval_start = time.time()
+
+        eval_loader = DataLoader(
+            vectorized_datasets[split],
+            batch_size=per_device_eval_batch_size,
+            collate_fn=data_collator,
+            num_workers=dataloader_num_workers,
+            pin_memory=True,
+        )
+
+        eval_loader = accelerator.prepare(eval_loader)
+        batches = tqdm(eval_loader, desc=f"Evaluating {split}...", disable=not accelerator.is_local_main_process)
+
+        # make the split name pretty for librispeech etc
+        split = split.replace(".", "-").split("/")[-1]
+        output_csv = os.path.join(output_dir, f"{split}-transcription.csv")
+
+        for step, batch in enumerate(batches):
+            file_ids = batch.pop("file_ids")
+            # Generate predictions and pad to max generated length
+            generated_ids = model.module.generate(batch["input_features"].to(dtype=torch_dtype), **gen_kwargs)
+            generated_ids = accelerator.pad_across_processes(generated_ids, dim=1, pad_index=tokenizer.pad_token_id)
+            # Gather all predictions and targets
+            file_ids, generated_ids, labels = accelerator.gather_for_metrics(
+                (file_ids, generated_ids, batch["labels"])
+            )
+            eval_preds.extend(generated_ids.cpu().numpy())
+            eval_labels.extend(labels.cpu().numpy())
+            file_ids = tokenizer.batch_decode(file_ids, skip_special_tokens=True)
+            eval_ids.extend(file_ids)
+
+            if step % training_args.logging_steps == 0 and step > 0:
+                batches.write(f"Saving transcriptions for split {split} step {step}")
+                accelerator.wait_for_everyone()
+                if data_args.decode_token_ids:
+                    eval_preds = tokenizer.batch_decode(
+                        eval_preds, skip_special_tokens=True, decode_with_timestamps=return_timestamps
+                    )
+                csv_data = [[eval_ids[i], eval_preds[i]] for i in range(len(eval_preds))]
+
+                with open(output_csv, "w", encoding="UTF8", newline="") as f:
+                    writer = csv.writer(f)
+                    # write multiple rows
+                    writer.writerow(["file_id", "whisper_transcript"])
+                    writer.writerows(csv_data)
+
+                if training_args.push_to_hub and accelerator.is_main_process:
+                    repo.push_to_hub(
+                        commit_message=f"Saving transcriptions for split {split} step {step}.",
+                        blocking=False,
+                    )
+
+        accelerator.wait_for_everyone()
+        eval_time = time.time() - eval_start
+
+        # compute WER metric for eval sets
+        wer_desc = ""
+        if "validation" in split or "test" in split:
+            wer_metric, pred_str, label_str, norm_pred_str, norm_label_str, eval_ids = compute_metrics(
+                eval_preds, eval_labels, eval_ids
+            )
+            wer_desc = " ".join([f"Eval {key}: {value} |" for key, value in wer_metric.items()])
+            # Save metrics + predictions
+            log_metric(
+                accelerator,
+                metrics=wer_metric,
+                train_time=eval_time,
+                prefix=split,
+            )
+            log_pred(
+                accelerator,
+                pred_str,
+                label_str,
+                norm_pred_str,
+                norm_label_str,
+                prefix=split,
+            )
+            if data_args.decode_token_ids:
+                eval_preds = pred_str
+        elif data_args.decode_token_ids:
+            eval_preds = tokenizer.batch_decode(
+                eval_preds, skip_special_tokens=True, decode_with_timestamps=return_timestamps
+            )
+
+        batches.write(f"Saving final transcriptions for split {split}.")
+        csv_data = [[eval_ids[i], eval_preds[i]] for i in range(len(eval_preds))]
+        with open(output_csv, "w", encoding="UTF8", newline="") as f:
+            writer = csv.writer(f)
+            # write multiple rows
+            writer.writerow(["file_id", "whisper_transcript"])
+            writer.writerows(csv_data)
+
+        # Print metrics
+        logger.info(wer_desc)
+
+        if not data_args.streaming:
+            raw_datasets[split] = raw_datasets[split].add_column("whisper_transcript", eval_preds)
+
+    logger.info("***** Running Labelling *****")
+    logger.info("  Instantaneous batch size per device =" f" {training_args.per_device_eval_batch_size}")
+    logger.info(
+        f"  Total eval batch size (w. parallel & distributed) = {training_args.per_device_eval_batch_size * accelerator.num_processes}"
+    )
+    logger.info(f"  Predict labels with timestamps = {return_timestamps}")
+    logger.info(f"  Decode labels to transcriptions = {data_args.decode_token_ids}")
+    for split in data_splits:
+        eval_step_with_save(split=split)
+        accelerator.wait_for_everyone()
+        if training_args.push_to_hub and accelerator.is_main_process:
+            repo.push_to_hub(
+                commit_message=f"Saving final transcriptions for split {split.replace('.', '-').split('/')[-1]}",
+                blocking=False,
+            )
+    if not data_args.streaming and accelerator.is_main_process:
+        raw_datasets.save_to_disk(output_dir, num_proc=num_workers)
+        if training_args.push_to_hub:
+            raw_datasets.push_to_hub(repo_name, config_name=data_args.dataset_config_name)
+    accelerator.end_training()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/flax/run_pt_long_form_transcription.py b/flax/run_pt_long_form_transcription.py
new file mode 100644
index 0000000000000000000000000000000000000000..f2b8aac809bc44ca757bc6133db9d642bea4fd0a
--- /dev/null
+++ b/flax/run_pt_long_form_transcription.py
@@ -0,0 +1,597 @@
+#!/usr/bin/env python
+# coding=utf-8
+# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Evaluating a Whisper model on one or more long-form evaluation datasets.
+"""
+# You can also adapt this script for your own speech recognition validation. Pointers for this are left as comments.
+
+import logging
+import os
+import sys
+import time
+from dataclasses import dataclass, field
+from typing import Optional
+
+import datasets
+import numpy as np
+import torch
+import transformers
+from datasets import DatasetDict, IterableDatasetDict, load_dataset
+from jiwer import process_words, wer_default
+from nltk import ngrams
+from tqdm import tqdm
+from transformers import (
+    HfArgumentParser,
+    Seq2SeqTrainingArguments,
+    WhisperTokenizer,
+    is_tensorboard_available,
+    is_wandb_available,
+    pipeline,
+)
+from transformers.models.whisper.english_normalizer import EnglishTextNormalizer
+from transformers.utils import check_min_version
+from transformers.utils.versions import require_version
+
+
+# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
+check_min_version("4.27.0.dev0")
+
+require_version(
+    "datasets>=1.18.0",
+    "To fix: update `datasets` to the latest version: `pip install --upgrade datasets[audio]`",
+)
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class ModelArguments:
+    """
+    Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
+    """
+
+    model_name_or_path: str = field(
+        metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
+    )
+    cache_dir: Optional[str] = field(
+        default=None,
+        metadata={"help": "Where to store the pretrained models downloaded from huggingface.co"},
+    )
+    model_revision: str = field(
+        default="main",
+        metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
+    )
+    subfolder: str = field(
+        default="",
+        metadata={
+            "help": "In case the relevant files are located inside a subfolder of the model repo on huggingface.co, you can"
+            "specify the folder name here."
+        },
+    )
+    use_auth_token: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Will use the token generated when running `transformers-cli login`"
+                " (necessary to use this script with private models)."
+            )
+        },
+    )
+    dtype: Optional[str] = field(
+        default="float32",
+        metadata={
+            "help": (
+                "Floating-point format in which the model weights should be initialized"
+                " and evaluated. Choose one of `[float32, float16, bfloat16]`."
+            )
+        },
+    )
+    return_timestamps: Optional[bool] = field(
+        default=False,
+        metadata={
+            "help": "Whether to predict timestamps (alongside the text predictions). Timestamp predictions "
+            "are discarded at the end of inference, but may assist in the model in reducing hallucinations."
+        },
+    )
+    length_penalty: Optional[float] = field(
+        default=1.0,
+        metadata={
+            "help": (
+                "Exponential penalty to the length that is used with beam-based generation. It is applied as an "
+                "exponent to the sequence length, which in turn is used to divide the score of the sequence. Since "
+                "the score is the log likelihood of the sequence (i.e. negative), length_penalty > 1.0 promotes "
+                "longer sequences, while length_penalty < 1.0 encourages shorter sequences."
+            )
+        },
+    )
+    do_sample: Optional[bool] = field(
+        default=False,
+        metadata={"help": "Whether or not to use sampling ; use greedy decoding otherwise."},
+    )
+    top_k: Optional[int] = field(
+        default=50,
+        metadata={"help": "The number of the highest probability vocabulary tokens to keep for top-k-filtering."},
+    )
+    temperature: Optional[float] = field(
+        default=1.0,
+        metadata={"help": "The value used to modulate the next token probabilities if sampling."},
+    )
+    chunk_length_s: Optional[float] = field(
+        default=0,
+        metadata={
+            "help": "The input length for each chunk. By default, the chunk length is set to 0, which means no chunking."
+        },
+    )
+
+
+@dataclass
+class DataTrainingArguments:
+    """
+    Arguments pertaining to what data we are going to input our model for training and eval.
+    """
+
+    dataset_name: str = field(
+        default=None,
+        metadata={
+            "help": "The name of the dataset to use (via the datasets library). Load and combine "
+            "multiple datasets by separating dataset hours by a '+' symbol."
+        },
+    )
+    dataset_config_name: Optional[str] = field(
+        default=None,
+        metadata={"help": "The configuration name of the dataset to use (via the datasets library)."},
+    )
+    dataset_split_name: Optional[str] = field(
+        default=None,
+        metadata={"help": "The split name of the dataset to use (via the datasets library)."},
+    )
+    dataset_cache_dir: Optional[str] = field(
+        default=None,
+        metadata={"help": "Path to cache directory for saving and loading datasets"},
+    )
+    overwrite_cache: bool = field(
+        default=False,
+        metadata={"help": "Overwrite the cached training and evaluation sets"},
+    )
+    audio_column_name: str = field(
+        default="audio",
+        metadata={"help": "The name of the dataset column containing the audio data. Defaults to 'audio'"},
+    )
+    text_column_name: str = field(
+        default=None,
+        metadata={"help": "The name of the dataset column containing the text data. Defaults to 'text'."},
+    )
+    max_label_length: int = field(
+        default=256,
+        metadata={"help": "Truncate transcriptions that are longer `max_label_length` tokens."},
+    )
+    wandb_project: str = field(
+        default="distil-whisper",
+        metadata={"help": "The name of the wandb project."},
+    )
+    wandb_name: str = field(
+        default=None,
+        metadata={"help": "The name of the wandb run."},
+    )
+    wandb_job_type: str = field(
+        default="distil-whisper",
+        metadata={"help": "The name of the wandb job type."},
+    )
+    wandb_dir: str = field(
+        default=None,
+        metadata={"help": "The absolute path to save the wandb logs."},
+    )
+    save_code_to_wandb: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether to save main script to wandb. This is valuable for improving"
+                " experiment reproducibility and to diff code across experiments in"
+                " the UI."
+            )
+        },
+    )
+    streaming: bool = field(
+        default=True,
+        metadata={"help": "Whether to use Datasets' streaming mode to load and the data."},
+    )
+    max_eval_samples: Optional[int] = field(
+        default=None,
+        metadata={"help": "For debugging purposes, truncate the number of eval examples to this value if set."},
+    )
+    log_audio: Optional[bool] = field(
+        default=False,
+        metadata={"help": "For debugging purposes, record the audio samples as well as the ground truths / preds."},
+    )
+    log_predictions: Optional[bool] = field(
+        default=True,
+        metadata={"help": "Whether or not to log the ground truths / pred text to the wandb logger."},
+    )
+    ngram_degree: Optional[int] = field(
+        default=5, metadata={"help": "Degree of n-grams used when computing duplicate n-grams in the predicted text."}
+    )
+
+
+def write_metric(summary_writer, eval_metrics, prefix="eval"):
+    for metric_name, value in eval_metrics.items():
+        summary_writer.add_scalar(f"{prefix}/{metric_name}", value, 0)
+
+
+def write_wandb_metric(wandb_logger, metrics, train_time, prefix):
+    log_metrics = {}
+    for k, v in metrics.items():
+        log_metrics[f"{prefix}/{k}"] = v
+    log_metrics[f"{prefix}/time"] = train_time
+    wandb_logger.log(log_metrics)
+
+
+def convert_audio_to_wandb(wandb_logger, audio):
+    return wandb_logger.Audio(audio["array"][:, np.newaxis], sample_rate=audio["sampling_rate"])
+
+
+def write_wandb_pred(
+    wandb_logger,
+    eval_audios,
+    pred_str,
+    label_str,
+    norm_pred_str,
+    norm_label_str,
+    prefix="eval",
+):
+    columns = ["Target", "Pred", "Norm Target", "Norm Pred"]
+    # convert str data to a wandb compatible format
+    str_data = [[label_str[i], pred_str[i], norm_label_str[i], norm_pred_str[i]] for i in range(len(pred_str))]
+
+    if len(eval_audios) > 0:
+        columns.insert(0, "Audio")
+        str_data = [
+            [
+                convert_audio_to_wandb(wandb_logger, eval_audios[i]),
+                *str_data[i],
+            ]
+            for i in range(len(pred_str))
+        ]
+
+    # log as a table with the appropriate headers
+    wandb_logger.log(
+        {f"{prefix}/predictions": wandb_logger.Table(columns=columns, data=str_data)},
+    )
+
+
+def convert_dataset_str_to_list(
+    dataset_names, dataset_config_names, splits=None, text_column_names=None, dataset_hours=None, default_split="train"
+):
+    if isinstance(dataset_names, str):
+        dataset_names = dataset_names.split("+")
+
+        # we assume that all the datasets we're using derive from the distil-whisper org on the Hub - prepend the org name if necessary
+        for i in range(len(dataset_names)):
+            ds_name = dataset_names[i]
+            dataset_names[i] = f"distil-whisper/{ds_name}" if "/" not in ds_name else ds_name
+
+        dataset_config_names = dataset_config_names.split("+")
+        splits = splits.split("+") if splits is not None else None
+        text_column_names = text_column_names.split("+") if text_column_names is not None else None
+        dataset_hours = dataset_hours.split("+") if dataset_hours is not None else None
+
+    # basic checks to ensure we've got the right number of datasets/configs/splits/columns/probs
+    if len(dataset_names) != len(dataset_config_names):
+        raise ValueError(
+            f"Ensure one config is passed for each dataset, got {len(dataset_names)} datasets and"
+            f" {len(dataset_config_names)} configs."
+        )
+
+    if splits is not None and len(splits) != len(dataset_names):
+        raise ValueError(
+            f"Ensure one split is passed for each dataset, got {len(dataset_names)} datasets and {len(splits)} splits."
+        )
+
+    if text_column_names is not None and len(text_column_names) != len(dataset_names):
+        raise ValueError(
+            f"Ensure one text column name is passed for each dataset, got {len(dataset_names)} datasets and"
+            f" {len(text_column_names)} text column names."
+        )
+
+    if dataset_hours is not None:
+        if len(dataset_hours) != len(dataset_names):
+            raise ValueError(
+                f"Ensure one probability is passed for each dataset, got {len(dataset_names)} datasets and "
+                f"{len(dataset_hours)} hours."
+            )
+        dataset_hours = [float(ds_hours) for ds_hours in dataset_hours]
+    else:
+        dataset_hours = [None] * len(dataset_names)
+
+    text_column_names = (
+        text_column_names if text_column_names is not None else ["text" for _ in range(len(dataset_names))]
+    )
+    splits = splits if splits is not None else [default_split for _ in range(len(dataset_names))]
+
+    dataset_names_dict = []
+    for i, ds_name in enumerate(dataset_names):
+        dataset_names_dict.append(
+            {
+                "name": ds_name,
+                "config": dataset_config_names[i],
+                "split": splits[i],
+                "text_column_name": text_column_names[i],
+                "hours": dataset_hours[i],
+            }
+        )
+    return dataset_names_dict
+
+
+def data(dataset, text_column_name="text", log_audio=False):
+    for item in dataset:
+        yield {**item["audio"], "reference": item[text_column_name], "audio": item["audio"] if log_audio else None}
+
+
+def main():
+    # 1. Parse input arguments
+    # See all possible arguments in src/transformers/training_args.py
+    # or by passing the --help flag to this script.
+    # We now keep distinct sets of args, for a cleaner separation of concerns.
+    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, Seq2SeqTrainingArguments))
+
+    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
+        # If we pass only one argument to the script and it's the path to a json file,
+        # let's parse it to get our arguments.
+        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
+    else:
+        model_args, data_args, training_args = parser.parse_args_into_dataclasses()
+
+    # Enable tensorboard only on the master node
+    has_tensorboard = is_tensorboard_available()
+    if "tensorboard" in training_args.report_to:
+        if has_tensorboard:
+            try:
+                from torch.utils.tensorboard import SummaryWriter
+
+                summary_writer = SummaryWriter(log_dir=os.path.join(training_args.output_dir, "runs"))
+            except ImportError as ie:
+                has_tensorboard = False
+                logger.warning(
+                    "Unable to display metrics through TensorBoard because some" f" package are not installed: {ie}"
+                )
+        else:
+            logger.warning(
+                "Unable to display metrics through TensorBoard because the package is"
+                " not installed: Please run `pip install tensorboard` to enable."
+            )
+
+    # Enable wandb only on the master node
+    has_wandb = is_wandb_available()
+    if "wandb" in training_args.report_to:
+        if has_wandb:
+            import wandb as wandb_logger
+
+            # Set up wandb run
+            wandb_logger.init(
+                project=data_args.wandb_project,
+                name=data_args.wandb_name,
+                job_type=data_args.wandb_job_type,
+                dir=data_args.wandb_dir,
+                save_code=data_args.save_code_to_wandb,
+            )
+        else:
+            logger.warning("Wandb logging requires wandb to be installed. Run `pip install wandb` to enable.")
+
+    # 2. Setup logging
+    # Make one log on every process with the configuration for debugging.
+    logging.basicConfig(
+        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+        datefmt="%m/%d/%Y %H:%M:%S",
+        handlers=[logging.StreamHandler(sys.stdout)],
+    )
+    # Set the verbosity to info of the Transformers logger.
+    # We only want one process per machine to log things on the screen.
+    logger.setLevel(logging.INFO)
+    datasets.utils.logging.set_verbosity_warning()
+    transformers.utils.logging.set_verbosity_info()
+
+    logger.info("Evaluation parameters %s", training_args)
+
+    # 3. Load dataset
+    raw_datasets = IterableDatasetDict() if data_args.streaming else DatasetDict()
+
+    # Convert lists of dataset names/configs/splits to a dict
+    # names: "librispeech_asr+gigaspeech", configs: "all+l", splits: "validation.clean+validation"
+    # -> [{"name: "librispeech_asr": "config": "all", "split": "validation.clean"}, {"name: "gigaspeech": "config": "l", "split": "validation"}
+    dataset_names_dict = convert_dataset_str_to_list(
+        data_args.dataset_name,
+        data_args.dataset_config_name,
+        splits=data_args.dataset_split_name,
+        text_column_names=data_args.text_column_name,
+    )
+
+    # load multiple eval sets
+    for dataset_dict in dataset_names_dict:
+        # Clean-up the dataset name for pretty logging
+        # ("distil-whisper/librispeech_asr", "validation.clean") -> "librispeech_asr/validation-clean"
+        pretty_name = f"{dataset_dict['name'].split('/')[-1]}/{dataset_dict['split'].replace('.', '-')}"
+        raw_datasets[pretty_name] = load_dataset(
+            dataset_dict["name"],
+            dataset_dict["config"],
+            split=dataset_dict["split"],
+            cache_dir=data_args.dataset_cache_dir,
+            use_auth_token=True if model_args.use_auth_token else None,
+            streaming=data_args.streaming,
+        )
+        if dataset_dict["text_column_name"] not in list(raw_datasets[pretty_name].features.keys()):
+            raise ValueError(
+                f"--text column name {dataset_dict['text_column_name']} not found in the evaluation "
+                f"dataset {dataset_dict['name']}. Ensure `text_column_name` is set to the correct column "
+                f"for the target text. Should be one of {' '.join(list(raw_datasets[pretty_name].features.keys()))}"
+            )
+        if dataset_dict["text_column_name"] != "text":
+            raw_datasets[pretty_name] = raw_datasets[pretty_name].rename_column(
+                dataset_dict["text_column_name"], "text"
+            )
+
+    # Streaming mode robust way of obtaining the features
+    raw_datasets_features = list(next(iter(raw_datasets.values())).features.keys())
+    audio_column_name = data_args.audio_column_name
+
+    if audio_column_name not in raw_datasets_features:
+        raise ValueError(
+            f"--audio_column_name '{audio_column_name}' not found in dataset"
+            f" '{data_args.dataset_name}'. Make sure to set `--audio_column_name` to"
+            " the correct audio column - one of"
+            f" {', '.join(raw_datasets_features)}."
+        )
+
+    for split in raw_datasets:
+        raw_datasets[split] = raw_datasets[split].remove_columns(
+            set(raw_datasets[split].features.keys()) - {audio_column_name, "text"}
+        )
+
+    if data_args.max_eval_samples is not None:
+        for split in raw_datasets:
+            raw_datasets[split] = (
+                raw_datasets[split].take(data_args.max_eval_samples)
+                if data_args.streaming
+                else raw_datasets[split].select(range(data_args.max_eval_samples))
+            )
+
+    # Store some constants
+    per_device_eval_batch_size = int(training_args.per_device_eval_batch_size)
+    num_beams = training_args.generation_num_beams if training_args.generation_num_beams is not None else 1
+
+    model_kwargs = {
+        "cache_dir": model_args.cache_dir,
+        "use_auth_token": True if model_args.use_auth_token else None,
+        "subfolder": model_args.subfolder,
+    }
+
+    # 5. Load pretrained model, tokenizer, and feature extractor
+    pipe = pipeline(
+        "automatic-speech-recognition",
+        model_args.model_name_or_path,
+        torch_dtype=getattr(torch, model_args.dtype),
+        model_kwargs=model_kwargs,
+        max_new_tokens=training_args.generation_max_length,
+        batch_size=per_device_eval_batch_size,
+        chunk_length_s=model_args.chunk_length_s,
+        return_timestamps=model_args.return_timestamps,
+        device="cuda:0" if torch.cuda.is_available() else "cpu",
+    )
+
+    if pipe.model.can_generate():
+        if pipe.model.config.decoder_start_token_id is None:
+            raise ValueError("Make sure that `config.decoder_start_token_id` is correctly defined")
+        generate_kwargs = {
+            "num_beams": num_beams,
+            "length_penalty": model_args.length_penalty,
+            "do_sample": model_args.do_sample,
+            "top_k": model_args.top_k,
+            "temperature": model_args.temperature,
+        }
+        if hasattr(pipe.model.generation_config, "is_multilingual") and pipe.model.generation_config.is_multilingual:
+            generate_kwargs = generate_kwargs.update({"langauge": "English", "task": "transcribe"})
+    else:
+        generate_kwargs = None
+
+    # 8. Load Metric
+    whisper_tokenizer = WhisperTokenizer.from_pretrained("openai/whisper-tiny.en")
+    normalizer = EnglishTextNormalizer(whisper_tokenizer.english_spelling_normalizer)
+
+    def compute_metrics(pred_str, label_str, ngram_degree=5):
+        # normalize everything and re-compute the WER
+        norm_pred_str = [normalizer(pred) for pred in pred_str]
+        norm_label_str = [normalizer(label) for label in label_str]
+        # for logging, we need the pred/labels to match the norm_pred/norm_labels, so discard any filtered samples here
+        pred_str = [pred_str[i] for i in range(len(norm_pred_str)) if len(norm_label_str[i]) > 0]
+        label_str = [label_str[i] for i in range(len(norm_label_str)) if len(norm_label_str[i]) > 0]
+        # filtering step to only evaluate the samples that correspond to non-zero normalized references:
+        norm_pred_str = [norm_pred_str[i] for i in range(len(norm_pred_str)) if len(norm_label_str[i]) > 0]
+        norm_label_str = [norm_label_str[i] for i in range(len(norm_label_str)) if len(norm_label_str[i]) > 0]
+
+        wer_output = process_words(norm_label_str, norm_pred_str, wer_default, wer_default)
+        wer_norm = 100 * wer_output.wer
+        ier_norm = 100 * wer_output.insertions / sum([len(ref) for ref in wer_output.references])
+        ser_norm = 100 * wer_output.substitutions / sum([len(ref) for ref in wer_output.references])
+        der_norm = 100 * wer_output.deletions / sum([len(ref) for ref in wer_output.references])
+
+        all_ngrams = list(ngrams(" ".join(norm_pred_str).split(), ngram_degree))
+        repeated_ngrams = len(all_ngrams) - len(set(all_ngrams))
+
+        return (
+            {"wer": wer_norm, "ier": ier_norm, "ser": ser_norm, "der": der_norm, "repeated_ngrams": repeated_ngrams},
+            pred_str,
+            label_str,
+            norm_pred_str,
+            norm_label_str,
+        )
+
+    def eval_step(split="eval"):
+        # ======================== Evaluating ==============================
+        eval_preds = []
+        eval_labels = []
+        eval_audios = []
+        eval_start = time.time()
+
+        for sample in tqdm(
+            pipe(
+                data(raw_datasets[split], log_audio=data_args.log_audio),
+                generate_kwargs=generate_kwargs,
+            ),
+            desc=f"Evaluating {split}...",
+        ):
+            eval_preds.append(sample["text"])
+            eval_labels.append(sample["reference"][0])
+            if data_args.log_audio:
+                eval_audios.append(sample["audio"][0])
+
+        eval_time = time.time() - eval_start
+
+        wer_metric, pred_str, label_str, norm_pred_str, norm_label_str = compute_metrics(
+            eval_preds, eval_labels, data_args.ngram_degree
+        )
+        wer_desc = " ".join([f"{split} {key}: {value} |" for key, value in wer_metric.items()])
+
+        # Print metrics to stdout
+        logger.info(wer_desc)
+
+        # Save metrics to tensorboard
+        if has_tensorboard and "tensorboard" in training_args.report_to:
+            write_metric(summary_writer, wer_metric, prefix=split)
+
+        # Save metrics to wandb
+        if has_wandb and "wandb" in training_args.report_to:
+            write_wandb_metric(wandb_logger, wer_metric, eval_time, prefix=split)
+            if data_args.log_predictions:
+                write_wandb_pred(
+                    wandb_logger, eval_audios, pred_str, label_str, norm_pred_str, norm_label_str, prefix=split
+                )
+
+    logger.info("***** Running Eval *****")
+    logger.info("  Instantaneous batch size per device =" f" {training_args.per_device_eval_batch_size}")
+    logger.info(f"  Total eval batch size (w. parallel & distributed) = {training_args.per_device_eval_batch_size}")
+    if pipe.model.can_generate():
+        logger.info(f"  Beam size = {num_beams}")
+        if num_beams > 1:
+            logger.info(f"  Length penalty size = {model_args.length_penalty}")
+        logger.info(f"  Do sample = {model_args.do_sample}")
+        if model_args.do_sample:
+            logger.info(f"  Top k = {model_args.top_k}")
+            logger.info(f"  Temperature = {model_args.temperature}")
+
+    for split in raw_datasets:
+        eval_step(split=split)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/flax/run_speculative_decoding.py b/flax/run_speculative_decoding.py
new file mode 100644
index 0000000000000000000000000000000000000000..39cdccb2369fa15a1d910b1dd4a5a0c628619e88
--- /dev/null
+++ b/flax/run_speculative_decoding.py
@@ -0,0 +1,122 @@
+#!/usr/bin/env python3
+# make sure to use branch: https://github.com/huggingface/transformers/pull/26701
+import copy
+import time
+
+import torch
+from datasets import load_dataset
+from transformers import (
+    AutoProcessor,
+    WhisperForConditionalGeneration,
+)
+
+
+DEVICE = "cuda"
+DTYPE = torch.float16
+SAMPLING_RATE = 16_000
+BATCH_SIZE = 1
+USE_FLASH_ATTN_2 = True
+
+# TO DEBUG
+GAMMAS = [5, 7, 6, 5, 4, 3, 5]
+COUNT = 0
+
+# local loading is faster
+teacher = WhisperForConditionalGeneration.from_pretrained(
+    "/home/patrick/distil_whisper/",
+    torch_dtype=DTYPE,
+    variant="fp16",
+    low_cpu_mem_usage=True,
+    use_flash_attention_2=USE_FLASH_ATTN_2,
+)
+student = WhisperForConditionalGeneration.from_pretrained(
+    "/home/patrick/distil_whisper_student/",
+    torch_dtype=DTYPE,
+    variant="fp16",
+    low_cpu_mem_usage=True,
+    use_flash_attention_2=USE_FLASH_ATTN_2,
+)
+# student = WhisperForCausalLM.from_pretrained("/home/patrick/distil_whisper_student", torch_dtype=DTYPE, variant="fp16", low_cpu_mem_usage=True, use_flash_attention_2=USE_FLASH_ATTN_2)
+
+student.generation_config = copy.deepcopy(teacher.generation_config)
+student.generation_config.num_assistant_tokens_schedule = "constant"
+
+# teacher = WhisperForConditionalGeneration.from_pretrained(
+#     "openai/whisper-large-v2", torch_dtype=DTYPE, variant="fp16", low_cpu_mem_usage=True
+# )
+# student = WhisperForConditionalGeneration.from_pretrained(
+#     "sanchit-gandhi/large-32-2-gpu-flat-lr", torch_dtype=DTYPE, variant="fp16", low_cpu_mem_usage=True
+# )
+#
+teacher.to(DEVICE)
+student.to(DEVICE)
+
+processor = AutoProcessor.from_pretrained("sanchit-gandhi/large-32-2-gpu-flat-lr")
+
+ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
+
+total_time_default = 0
+total_time_spec = 0
+total_time_spec_2 = 0
+
+input_values = ds[0]["audio"]["array"]
+inputs = processor(input_values, return_tensors="pt", sampling_rate=SAMPLING_RATE)
+input_features = inputs.input_features.to(device=DEVICE, dtype=DTYPE)
+
+_ = teacher.generate(input_features, max_length=100)
+
+end_idx = ds.shape[0]
+for audio_idx in range(0, end_idx, BATCH_SIZE):
+    input_values = ds[audio_idx : audio_idx + BATCH_SIZE]
+    input_values = [i["array"] for i in input_values["audio"]]
+
+    inputs = processor(input_values, return_tensors="pt", sampling_rate=SAMPLING_RATE)
+    input_features = inputs.input_features.to(device=DEVICE, dtype=DTYPE)
+
+    start_time = time.time()
+    out = teacher.generate(input_features, max_length=100)
+    run_time = time.time() - start_time
+    print(f"Normal Decoding: {run_time}")
+    total_time_default += run_time
+
+    default_out = processor.batch_decode(out, skip_special_tokens=True)
+    # print("Output", default_out)
+
+    # start_time = time.time()
+    # with torch.no_grad():
+    #     encoder_outputs = teacher.get_encoder()(input_features).last_hidden_state
+
+    # out, ratio = speculative_decoding(teacher, student, encoder_outputs, max_length=100, gamma=5)
+    # run_time = time.time() - start_time
+    # print(20 * "=")
+    # print(f"Speculative Decoding: {run_time}")
+    # total_time_spec += run_time
+
+    # spec_out = processor.batch_decode(out)
+
+    start_time = time.time()
+    with torch.no_grad():
+        encoder_outputs = teacher.get_encoder()(input_features)
+
+    out = teacher.generate(
+        assistant_model=student,
+        assistant_encoder_outputs=encoder_outputs,
+        encoder_outputs=encoder_outputs,
+        max_length=100,
+    )
+    run_time = time.time() - start_time
+
+    spec_out_2 = processor.batch_decode(out, skip_special_tokens=True)
+
+    print(f"Speculative Decoding 2: {run_time}")
+    total_time_spec_2 += run_time
+
+    if spec_out_2 != default_out:
+        COUNT += 1
+        print(f"Audio {audio_idx} does not match. Spec: {spec_out_2}, True: {default_out}")
+
+
+print(20 * "=")
+print("Total time", total_time_default)
+print(f"Overall speed-up spec 2 {total_time_default / total_time_spec_2}")
+# print(f"Overall speed-up {total_time_default / total_time_spec}")
diff --git a/flax/run_speed.sh b/flax/run_speed.sh
new file mode 100644
index 0000000000000000000000000000000000000000..9bb362510b90b29d88d23390d1c3d3bd0d0eb528
--- /dev/null
+++ b/flax/run_speed.sh
@@ -0,0 +1,22 @@
+#!/usr/bin/env bash
+# --wandb_project "distil-whisper-speed-bench-1024-no-timestamps" \
+batch_sizes=(1 16)
+names=("openai/whisper-large-v2" "openai/whisper-medium.en" "openai/whisper-small.en" "openai/whisper-base.en" "openai/whisper-tiny.en" "patrickvonplaten/whisper-large-v2-32-2" "patrickvonplaten/whisper-medium-24-2")
+
+# Double loop
+for name in "${names[@]}"; do
+  for batch_size in "${batch_sizes[@]}"; do
+      CUDA_VISIBLE_DEVICES="1" python ./run_speed_pt.py \
+        --dataset_name "google/fleurs+distil-whisper/chime4+distil-whisper/earnings22+kensho/spgispeech" \
+        --wandb_name "T4-bsz${batch_size}-${name}" \
+        --model_name_or_path ${name} \
+        --wandb_project "beam-search-distil-whisper-speed-bench-256-no-timestamps" \
+        --dataset_config_name "en_us+1-channel+chunked+test" \
+        --dataset_split_name "test+test+test+test" \
+        --text_column_name "transcription+text+transcription+transcript" \
+        --samples_per_dataset "256" \
+	--attn_type "flash2" \
+	--num_beams 5 \
+        --batch_size ${batch_size}
+    done
+done
diff --git a/flax/run_speed_pt.py b/flax/run_speed_pt.py
new file mode 100644
index 0000000000000000000000000000000000000000..30ffd5357cd3ba65a2b302c53a24c10f54e6d198
--- /dev/null
+++ b/flax/run_speed_pt.py
@@ -0,0 +1,775 @@
+# coding=utf-8
+# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Evaluating a Whisper model on one or more evaluation datasets.
+"""
+# You can also adapt this script for your own speech recognition validation. Pointers for this are left as comments.
+
+import json
+import logging
+import os
+import string
+import subprocess
+import sys
+import tempfile
+import time
+from dataclasses import dataclass, field
+from functools import partial
+from typing import Optional
+
+import datasets
+import evaluate
+import numpy as np
+import torch
+import transformers
+import whisper
+from datasets import DatasetDict, IterableDatasetDict, load_dataset
+from tqdm import tqdm
+from transformers import (
+    HfArgumentParser,
+    WhisperForConditionalGeneration,
+    WhisperProcessor,
+    is_wandb_available,
+    pipeline,
+)
+from transformers.models.whisper.english_normalizer import EnglishTextNormalizer
+from transformers.models.whisper.modeling_whisper import WhisperForCausalLM
+from transformers.utils import check_min_version
+from transformers.utils.versions import require_version
+
+
+# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
+check_min_version("4.27.0.dev0")
+
+require_version(
+    "datasets>=1.18.0",
+    "To fix: pip install -r examples/flax/speech-recogintion/requirements.txt",
+)
+
+logger = logging.getLogger(__name__)
+
+PIPELINE_BATCH_SIZE = 16
+
+
+@dataclass
+class DataTrainingArguments:
+    """
+    Arguments pertaining to what data we are going to input our model for training and eval.
+    """
+
+    dataset_name: str = field(
+        default=None,
+        metadata={
+            "help": "The name of the dataset to use (via the datasets library). Load and combine "
+            "multiple datasets by separating dataset hours by a '+' symbol."
+        },
+    )
+    model_name_or_path: str = field(
+        default=None,
+        metadata={"help": "The name of the model to use (via the transformers library). "},
+    )
+    assistant_model_name_or_path: str = field(
+        default=None,
+        metadata={
+            "help": "The name of the assistant model to use to do speculative decoding. If None, no speculative decoding will be done."
+        },
+    )
+    use_fp16: bool = field(
+        default=True,
+        metadata={"help": "Whether to evaluate in fp16"},
+    )
+    use_torch_compile: bool = field(
+        default=False,
+        metadata={"help": "Whether to compile the model"},
+    )
+    use_orig_whisper: bool = field(
+        default=False,
+        metadata={"help": "Whether to evaluate with orig whisper"},
+    )
+    use_bf16: bool = field(
+        default=False,
+        metadata={"help": "Whether to evaluate in bf16"},
+    )
+    use_pipeline: bool = field(
+        default=False,
+        metadata={"help": "Whether to evaluate with Transformers pipeline"},
+    )
+    chunk_length_s: float = field(
+        default=30.0, metadata={"help": "Chunk length to use when `use_pipeline` is enabled."}
+    )
+    return_timestamps: bool = field(
+        default=False,
+        metadata={
+            "help": "Whether to decode with timestamps. This can help for improved WER for long form evaluation."
+        },
+    )
+    attn_type: Optional[str] = field(
+        default=None,
+        metadata={"help": "Which attn type to use: None, 'flash', 'compile', 'flash+compile'"},
+    )
+    batch_size: int = field(
+        default=1,
+        metadata={"help": "The batch size used for evluation."},
+    )
+    num_beams: int = field(
+        default=1,
+        metadata={"help": "The beam size used for evluation."},
+    )
+    samples_per_dataset: Optional[int] = field(
+        default=None,
+        metadata={"help": "Number of samples per dataset used to measure speed."},
+    )
+    dataset_config_name: Optional[str] = field(
+        default=None,
+        metadata={"help": "The configuration name of the dataset to use (via the datasets library)."},
+    )
+    dataset_split_name: Optional[str] = field(
+        default=None,
+        metadata={"help": "The split name of the dataset to use (via the datasets library)."},
+    )
+    dataset_cache_dir: Optional[str] = field(
+        default=None,
+        metadata={"help": "Path to cache directory for saving and loading datasets"},
+    )
+    overwrite_cache: bool = field(
+        default=False,
+        metadata={"help": "Overwrite the cached training and evaluation sets"},
+    )
+    preprocessing_num_workers: Optional[int] = field(
+        default=None,
+        metadata={"help": "The number of processes to use for the preprocessing."},
+    )
+    audio_column_name: str = field(
+        default="audio",
+        metadata={"help": "The name of the dataset column containing the audio data. Defaults to 'audio'"},
+    )
+    text_column_name: str = field(
+        default=None,
+        metadata={"help": "The name of the dataset column containing the text data. Defaults to `text`."},
+    )
+    max_duration_in_seconds: float = field(
+        default=30.0,
+        metadata={"help": "Filter audio files that are longer than `max_duration_in_seconds` seconds"},
+    )
+    min_duration_in_seconds: float = field(
+        default=0.0,
+        metadata={"help": "Filter audio files that are shorter than `min_duration_in_seconds` seconds"},
+    )
+    max_label_length: int = field(
+        default=128,
+        metadata={"help": "Truncate transcriptions that are longer `max_label_length` tokens."},
+    )
+    max_gen_length: int = field(default=128, metadata={"help": "Generate up until max_gen_length tokens."})
+    pad_target_to_multiple_of: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": (
+                "If set will pad the target sequence to a multiple of the provided"
+                " value. This is important to avoid triggering recompilations on TPU."
+                " If unspecified, will default to padding the targets to max length."
+            )
+        },
+    )
+    preprocessing_only: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether to only do data preprocessing and skip training. This is"
+                " especially useful when data preprocessing errors out in distributed"
+                " training due to timeout. In this case, one should run the"
+                " preprocessing in a non-distributed setup with"
+                " `preprocessing_only=True` so that the cached datasets can"
+                " consequently be loaded in distributed training"
+            )
+        },
+    )
+    wandb_project: str = field(
+        default="distil-whisper-speed-benchmark",
+        metadata={"help": "The name of the wandb project."},
+    )
+    wandb_name: str = field(
+        default=None,
+        metadata={"help": "The name of the wandb run."},
+    )
+    wandb_job_type: str = field(
+        default="distil-whisper",
+        metadata={"help": "The name of the wandb job type."},
+    )
+    wandb_dir: str = field(
+        default=None,
+        metadata={"help": "The absolute path to save the wandb logs."},
+    )
+    save_code_to_wandb: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether to save main script to wandb. This is valuable for improving"
+                " experiment reproducibility and to diff code across experiments in"
+                " the UI."
+            )
+        },
+    )
+    streaming: bool = field(
+        default=True,
+        metadata={"help": "Whether to use Datasets' streaming mode to load and the data."},
+    )
+    max_eval_samples: Optional[int] = field(
+        default=None,
+        metadata={"help": "For debugging purposes, truncate the number of eval examples to this value if set."},
+    )
+    log_audio: Optional[bool] = field(
+        default=False,
+        metadata={"help": "For debugging purposes, record the audio samples as well as the ground truths / preds."},
+    )
+
+
+def write_metric(summary_writer, eval_metrics, step, prefix="eval"):
+    for metric_name, value in eval_metrics.items():
+        summary_writer.scalar(f"{prefix}/{metric_name}", value, step)
+
+
+def write_wandb_metric(wandb_logger, metrics, train_time, prefix):
+    log_metrics = {}
+    for k, v in metrics.items():
+        log_metrics[f"{prefix}/{k}"] = v
+    log_metrics[f"{prefix}/time"] = train_time
+    wandb_logger.log(log_metrics)  # TODO(SG): bug with wandb means we can't log the step count
+
+
+def convert_dataset_str_to_list(
+    dataset_names, dataset_config_names, splits=None, text_column_names=None, dataset_hours=None, default_split="train"
+):
+    if isinstance(dataset_names, str):
+        dataset_names = dataset_names.split("+")
+
+        # we assume that all the datasets we're using derive from the distil-whisper org on the Hub - prepend the org name if necessary
+        for i in range(len(dataset_names)):
+            ds_name = dataset_names[i]
+            dataset_names[i] = f"distil-whisper/{ds_name}" if "/" not in ds_name else ds_name
+
+        dataset_config_names = dataset_config_names.split("+")
+        splits = splits.split("+") if splits is not None else None
+        text_column_names = text_column_names.split("+") if text_column_names is not None else None
+        dataset_hours = dataset_hours.split("+") if dataset_hours is not None else None
+
+    # basic checks to ensure we've got the right number of datasets/configs/splits/columns/probs
+    if len(dataset_names) != len(dataset_config_names):
+        raise ValueError(
+            f"Ensure one config is passed for each dataset, got {len(dataset_names)} datasets and"
+            f" {len(dataset_config_names)} configs."
+        )
+
+    if splits is not None and len(splits) != len(dataset_names):
+        raise ValueError(
+            f"Ensure one split is passed for each dataset, got {len(dataset_names)} datasets and {len(splits)} splits."
+        )
+
+    if text_column_names is not None and len(text_column_names) != len(dataset_names):
+        raise ValueError(
+            f"Ensure one text column name is passed for each dataset, got {len(dataset_names)} datasets and"
+            f" {len(text_column_names)} text column names."
+        )
+
+    if dataset_hours is not None:
+        if len(dataset_hours) != len(dataset_names):
+            raise ValueError(
+                f"Ensure one probability is passed for each dataset, got {len(dataset_names)} datasets and "
+                f"{len(dataset_hours)} hours."
+            )
+        dataset_hours = [float(ds_hours) for ds_hours in dataset_hours]
+    else:
+        dataset_hours = [None] * len(dataset_names)
+
+    text_column_names = (
+        text_column_names if text_column_names is not None else ["text" for _ in range(len(dataset_names))]
+    )
+    splits = splits if splits is not None else [default_split for _ in range(len(dataset_names))]
+
+    dataset_names_dict = []
+    for i, ds_name in enumerate(dataset_names):
+        dataset_names_dict.append(
+            {
+                "name": ds_name,
+                "config": dataset_config_names[i],
+                "split": splits[i],
+                "text_column_name": text_column_names[i],
+                "hours": dataset_hours[i],
+            }
+        )
+    return dataset_names_dict
+
+
+def main():
+    # 1. Parse input arguments
+    # See all possible arguments in src/transformers/training_args.py
+    # or by passing the --help flag to this script.
+    # We now keep distinct sets of args, for a cleaner separation of concerns.
+    parser = HfArgumentParser([DataTrainingArguments])
+
+    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
+        # If we pass only one argument to the script and it's the path to a json file,
+        # let's parse it to get our arguments.
+        data_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))[0]
+    else:
+        data_args = parser.parse_args_into_dataclasses()[0]
+
+    # 2. Setup logging
+    # Make one log on every process with the configuration for debugging.
+    logging.basicConfig(
+        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+        datefmt="%m/%d/%Y %H:%M:%S",
+        handlers=[logging.StreamHandler(sys.stdout)],
+    )
+
+    if data_args.use_pipeline and data_args.batch_size > 1:
+        raise ValueError("Make sure that `batch_size` is set to 1 when `use_pipeline=True`.")
+
+    has_wandb = is_wandb_available()
+    if has_wandb:
+        import wandb
+        import wandb as wandb_logger
+
+        # Set up wandb run
+        wandb_logger.init(
+            project=data_args.wandb_project,
+            name=data_args.wandb_name,
+            job_type=data_args.wandb_job_type,
+            dir=data_args.wandb_dir,
+            save_code=data_args.save_code_to_wandb,
+        )
+        wandb_logger.log({"torch_version": str(torch.__version__)})
+        wandb_logger.log({"transformers_version": str(transformers.__version__)})
+        wandb_logger.log({"batch_size": data_args.batch_size})
+
+        if data_args.use_pipeline:
+            wandb_logger.log({"chunk_length_s": data_args.chunk_length_s})
+    else:
+        raise ValueError("Wandb logging requires wandb to be installed. Run `pip install wandb` to enable.")
+
+    # 3. Load dataset
+    raw_datasets = IterableDatasetDict() if data_args.streaming else DatasetDict()
+
+    # Convert lists of dataset names/configs/splits to a dict
+    # names: "librispeech_asr+gigaspeech", configs: "all+l", splits: "validation.clean+validation"
+    # -> [{"name: "librispeech_asr": "config": "all", "split": "validation.clean"}, {"name: "gigaspeech": "config": "l", "split": "validation"}
+    dataset_names_dict = convert_dataset_str_to_list(
+        data_args.dataset_name,
+        data_args.dataset_config_name,
+        splits=data_args.dataset_split_name,
+        text_column_names=data_args.text_column_name,
+    )
+
+    if len(dataset_names_dict) == 1:
+        # load a single eval set
+        dataset_dict = dataset_names_dict[0]
+        raw_datasets["eval"] = load_dataset(
+            dataset_dict["name"],
+            dataset_dict["config"],
+            split=dataset_dict["split"],
+            cache_dir=data_args.dataset_cache_dir,
+            use_auth_token=True,
+            streaming=data_args.streaming,
+        )
+        if dataset_dict["text_column_name"] not in list(raw_datasets["eval"].features.keys()):
+            raise ValueError(
+                f"--text column name {dataset_dict['text_column_name']} not found in the evaluation "
+                f"dataset {dataset_dict['name']}. Ensure `text_column_name` is set to the correct column "
+                f"for the target text. Should be one of {' '.join(list(raw_datasets['eval'].features.keys()))}"
+            )
+        if dataset_dict["text_column_name"] != "text":
+            raw_datasets["eval"] = raw_datasets["eval"].rename_column(dataset_dict["text_column_name"], "text")
+    else:
+        # load multiple eval sets
+        for dataset_dict in tqdm(dataset_names_dict, desc="Loading datasets..."):
+            # Clean-up the dataset name for pretty logging
+            # ("distil-whisper/librispeech_asr", "validation.clean") -> "librispeech_asr/validation-clean"
+            pretty_name = f"{dataset_dict['name'].split('/')[-1]}/{dataset_dict['split'].replace('.', '-')}"
+            raw_datasets[pretty_name] = load_dataset(
+                dataset_dict["name"],
+                dataset_dict["config"],
+                split=dataset_dict["split"],
+                cache_dir=data_args.dataset_cache_dir,
+                use_auth_token=True,
+                streaming=data_args.streaming,
+            )
+            if dataset_dict["text_column_name"] not in list(raw_datasets[pretty_name].features.keys()):
+                raise ValueError(
+                    f"`--text_column_name` {dataset_dict['text_column_name']} not found in the evaluation "
+                    f"dataset {dataset_dict['name']}. Ensure `text_column_name` is set to the correct column "
+                    f"for the target text. Should be one of {' '.join(list(raw_datasets[pretty_name].features.keys()))}"
+                )
+            if dataset_dict["text_column_name"] != "text":
+                raw_datasets[pretty_name] = raw_datasets[pretty_name].rename_column(
+                    dataset_dict["text_column_name"], "text"
+                )
+
+    # 5. Load pretrained model, tokenizer, and feature extractor
+    processor = WhisperProcessor.from_pretrained(data_args.model_name_or_path)
+
+    dtype = torch.float16 if data_args.use_fp16 else torch.float32
+    if data_args.use_bf16:
+        dtype = torch.bfloat16
+
+    use_flash_attention_2 = data_args.attn_type is not None and "flash2" in data_args.attn_type
+
+    # make sure we're not using a T4
+    result = subprocess.run(["nvidia-smi"], capture_output=True, text=True)
+    gpu_type = [x for x in result.stdout.split("=") if len(x) > 1][1].split("0")[1].split()
+
+    use_sdpa = False
+    if gpu_type[0] == "Tesla" and use_flash_attention_2:
+        use_flash_attention_2 = False
+        use_sdpa = True
+
+    use_orig_whisper = False
+    if data_args.use_orig_whisper:
+        use_orig_whisper = True
+
+        model_name = data_args.model_name_or_path.split("/")[-1].split("whisper-")[-1]
+        model = whisper.load_model(model_name)
+        model.cuda()
+    else:
+        model = WhisperForConditionalGeneration.from_pretrained(
+            data_args.model_name_or_path, torch_dtype=dtype, use_flash_attention_2=use_flash_attention_2
+        )
+        model.cuda()
+
+    if use_sdpa:
+        logger.info("Use SDPA via BetterTransformers...")
+        model.to_bettertransformer()
+
+    if data_args.use_torch_compile:
+        logger.info("Enabling torch compile for the encoder.")
+        # let's compile the encoder forward path
+        model.model.encoder.forward = torch.compile(
+            model.model.encoder.forward, mode="reduce-overhead", fullgraph=True
+        )
+
+        # init torch compile once to create binaries
+        input_values = np.random.randn(data_args.batch_size, 16_000)
+        input_features = processor(input_values, return_tensors="pt", sampling_rate=16_000).input_features
+        input_features = input_features.to(dtype=dtype, device=model.device)
+
+        # run generation three times to that model is compiled
+        for _ in range(3):
+            _ = model.generate(input_features)
+
+    model_pipeline = None
+    if data_args.use_pipeline:
+        model_pipeline = pipeline(
+            "automatic-speech-recognition",
+            model=model,
+            tokenizer=processor.tokenizer,
+            feature_extractor=processor.feature_extractor,
+            torch_dtype=dtype,
+            device=model.device,
+            chunk_length_s=data_args.chunk_length_s,
+        )
+        model_pipeline_forward = model_pipeline._forward
+
+    assistant_model = None
+    if data_args.assistant_model_name_or_path is not None:
+        logger.info("Loading assistant model...")
+
+        if data_args.assistant_model_name_or_path.startswith("openai"):
+            assistant_model = WhisperForConditionalGeneration.from_pretrained(
+                data_args.assistant_model_name_or_path, torch_dtype=dtype, use_flash_attention_2=use_flash_attention_2
+            )
+        else:
+            assistant_model = WhisperForCausalLM.from_pretrained(
+                data_args.assistant_model_name_or_path, torch_dtype=dtype, use_flash_attention_2=use_flash_attention_2
+            )
+
+        assistant_model.cuda()
+
+    # 6. Resample speech dataset: `datasets` takes care of automatically loading and resampling the audio,
+    # so we just need to set the correct target sampling rate.
+    raw_datasets = raw_datasets.cast_column(
+        data_args.audio_column_name,
+        datasets.features.Audio(sampling_rate=processor.feature_extractor.sampling_rate),
+    )
+
+    # 7. Preprocessing the datasets.
+    # We need to read the audio files as arrays and tokenize the targets.
+    max_label_length = (
+        data_args.max_label_length if data_args.max_label_length is not None else model.config.max_length
+    )
+    audio_column_name = data_args.audio_column_name
+    num_workers = data_args.preprocessing_num_workers
+    model_input_name = processor.feature_extractor.model_input_names[0]
+    normalizer = EnglishTextNormalizer(processor.tokenizer.english_spelling_normalizer)
+
+    if data_args.max_eval_samples is not None:
+        for split in raw_datasets:
+            raw_datasets[split] = (
+                raw_datasets[split].take(data_args.max_eval_samples)
+                if data_args.streaming
+                else raw_datasets[split].select(range(data_args.max_eval_samples))
+            )
+
+    def prepare_dataset(batch):
+        # process audio
+        sample = batch[audio_column_name]
+
+        if model_pipeline is None and not use_orig_whisper:
+            inputs = processor.feature_extractor(
+                sample["array"], sampling_rate=sample["sampling_rate"], return_tensors="pt"
+            )
+            batch[model_input_name] = inputs.get(model_input_name)
+        else:
+            batch[model_input_name] = sample["array"]
+
+        # process audio length
+        batch["length_in_s"] = len(sample["array"]) / sample["sampling_rate"]
+
+        # process targets
+        input_str = batch["text"]
+        batch["labels"] = processor.tokenizer(input_str, max_length=max_label_length, truncation=True).input_ids
+        return batch
+
+    vectorized_datasets = IterableDatasetDict() if data_args.streaming else DatasetDict()
+
+    for split in raw_datasets:
+        raw_datasets_features = list(raw_datasets[split].features.keys())
+
+        map_fn = partial(
+            raw_datasets[split].map,
+            function=prepare_dataset,
+            remove_columns=raw_datasets_features,
+        )
+
+        vectorized_datasets[split] = (
+            map_fn(num_proc=num_workers, desc="preprocess eval dataset")
+            if not data_args.streaming
+            else map_fn()  # In streaming, we can't run multiproc - errors out if we try to
+        )
+
+    # for large datasets it is advised to run the preprocessing on a
+    # single machine first with `args.preprocessing_only` since there will mostly likely
+    # be a timeout when running the script in distributed mode.
+    # In a second step `args.preprocessing_only` can then be set to `False` to load the
+    # cached dataset
+    if data_args.preprocessing_only:
+        cache = {k: v.cache_files for k, v in vectorized_datasets.items()}
+        logger.info(f"Data preprocessing finished. Files cached at {cache}.")
+        return
+
+    # 8. Load Metric
+    metric = evaluate.load("wer")
+    # convention is that we space all punctuation *except* apostrophes
+    list(string.punctuation.replace("'", ""))
+
+    def compute_metrics(pred_str, label_str):
+        # normalize everything and re-compute the WER
+        norm_pred_str = [normalizer(pred) for pred in pred_str]
+        norm_label_str = [normalizer(label) for label in label_str]
+        # for logging, we need the pred/labels to match the norm_pred/norm_labels, so discard any filtered samples here
+        pred_str = [pred_str[i] for i in range(len(norm_pred_str)) if len(norm_label_str[i]) > 0]
+        label_str = [label_str[i] for i in range(len(norm_label_str)) if len(norm_label_str[i]) > 0]
+        # filtering step to only evaluate the samples that correspond to non-zero normalized references:
+        norm_pred_str = [norm_pred_str[i] for i in range(len(norm_pred_str)) if len(norm_label_str[i]) > 0]
+        norm_label_str = [norm_label_str[i] for i in range(len(norm_label_str)) if len(norm_label_str[i]) > 0]
+
+        # if any of the two lengths is 0, return 0 WER
+        if len(norm_pred_str) == 0 or len(norm_label_str) == 0:
+            return 0.0
+
+        wer = 100 * metric.compute(predictions=norm_pred_str, references=norm_label_str)
+
+        return wer
+
+    result_datasets = DatasetDict()
+
+    def benchmark(batch):
+        if model_pipeline is None and not use_orig_whisper:
+            inputs = torch.cat(batch[model_input_name], dim=0).cuda()
+            if data_args.use_fp16:
+                inputs = inputs.to(torch.float16)
+            if data_args.use_bf16:
+                inputs = inputs.to(torch.bfloat16)
+
+            inner_batch_size = inputs.shape[0]
+        else:
+            inner_batch_size = 1
+
+            inputs = batch[model_input_name]
+
+        gen_kwargs = {
+            "return_timestamps": data_args.return_timestamps,
+            "max_length": data_args.max_gen_length,
+        }
+
+        # if not data_args.model_name_or_path.endswith(".en"):
+        if not data_args.model_name_or_path.endswith(".en") and not data_args.model_name_or_path.endswith("24-2"):
+            gen_kwargs["language"] = "<|en|>"
+            gen_kwargs["task"] = "transcribe"
+            gen_kwargs["num_beams"] = data_args.num_beams
+
+        # Time forward
+        if use_orig_whisper:
+            raw_audio = inputs[0].astype(np.float32)
+            out_dict = model.transcribe(raw_audio)
+
+            batch["transcription"] = [out_dict["text"]]
+            batch["time"] = [out_dict["all_time"]]
+        elif model_pipeline is not None:
+            # if model is pipeline let's make sure that only forward is timed and not pre- and post-process
+            time_result = []
+
+            def _forward_time(*args, **kwargs):
+                start_time = time.time()
+                result = model_pipeline_forward(*args, **kwargs)
+                end_time = time.time() - start_time
+
+                time_result.append(end_time)
+
+                return result
+
+            model_pipeline._forward = _forward_time
+
+            result = model_pipeline(inputs, batch_size=PIPELINE_BATCH_SIZE, generate_kwargs=gen_kwargs)[0]["text"]
+            batch["transcription"] = [result]
+            batch["time"] = [sum(time_result)]
+        elif assistant_model is not None:
+            gen_kwargs["assistant_model"] = assistant_model
+
+            start_time = time.time()
+            with torch.no_grad():
+                encoder_outputs = model.get_encoder()(inputs)
+
+            gen_kwargs["encoder_outputs"] = encoder_outputs
+
+            if data_args.assistant_model_name_or_path.startswith("openai"):
+                with torch.no_grad():
+                    assistant_encoder_outputs = assistant_model.get_encoder()(inputs)
+
+                gen_kwargs["assistant_encoder_outputs"] = assistant_encoder_outputs
+            else:
+                gen_kwargs["assistant_encoder_outputs"] = encoder_outputs
+
+            output_ids = model.generate(**gen_kwargs)
+            batch["time"] = inner_batch_size * [(time.time() - start_time) / inner_batch_size]
+
+            batch["transcription"] = processor.batch_decode(output_ids, skip_special_tokens=True)
+        else:
+            start_time = time.time()
+            output_ids = model.generate(inputs, **gen_kwargs)
+            batch["time"] = inner_batch_size * [(time.time() - start_time) / inner_batch_size]
+
+            batch["transcription"] = processor.batch_decode(output_ids, skip_special_tokens=True)
+
+        batch["length_in_s"] = batch["length_in_s"]
+        batch["reference"] = processor.batch_decode(batch["labels"], skip_special_tokens=True)
+        batch["num_words"] = [len(r.split()) for r in batch["reference"]]
+
+        return batch
+
+    for split in vectorized_datasets:
+        vectorized_datasets_features = [model_input_name]
+
+        map_fn = partial(
+            vectorized_datasets[split].map,
+            function=benchmark,
+            remove_columns=vectorized_datasets_features,
+            batch_size=data_args.batch_size,
+            batched=True,
+        )
+
+        result_datasets[split] = (
+            map_fn(num_proc=1, desc="benchmark eval dataset") if not data_args.streaming else map_fn()
+        )
+
+    stats_dataset = DatasetDict()
+
+    all_stats = {
+        "times_audio_total": 0,
+        "times_transcription_total": 0,
+        "num_words_total": 0,
+        "num_samples": 0,
+        "time_per_sample": 0,
+        "rtf": 0,
+        "words_per_s": 0,
+        "wer": 0,
+    }
+
+    count = 0
+    for split in result_datasets:
+        transcriptions = []
+        references = []
+        stats = {k: 0 for k in all_stats.keys()}
+
+        print(f"Start benchmarking {split}...")
+        if data_args.streaming:
+            result_iter = iter(result_datasets[split])
+
+        for result in result_iter:
+            stats["times_audio_total"] += result["length_in_s"]
+            stats["times_transcription_total"] += result["time"]
+            stats["num_words_total"] += result["num_words"]
+            stats["num_samples"] += 1
+            transcriptions.append(result["transcription"])
+            references.append(result["reference"])
+
+            count += 1
+            print(f"Processed {count} samples...")
+
+            if data_args.samples_per_dataset is not None and stats["num_samples"] == data_args.samples_per_dataset:
+                break
+
+        stats["time_per_sample"] = stats["times_transcription_total"] / stats["num_samples"]
+        stats["avg_length_sample"] = stats["times_audio_total"] / stats["num_samples"]
+        stats["wer"] = compute_metrics(transcriptions, references)
+        stats["rtf"] = stats["times_audio_total"] / stats["times_transcription_total"]
+        stats["words_per_s"] = stats["num_words_total"] / stats["times_transcription_total"]
+
+        stats_dataset[split] = stats
+
+        log_stats = {f"{split}_{k}": v for k, v in stats.items()}
+        wandb_logger.log(log_stats)
+
+        all_stats["times_audio_total"] += stats["times_audio_total"]
+        all_stats["times_transcription_total"] += stats["times_transcription_total"]
+        all_stats["wer"] += stats["wer"]
+        all_stats["num_samples"] += stats["num_samples"]
+        all_stats["num_words_total"] += stats["num_words_total"]
+
+    all_stats["time_per_sample"] = all_stats["times_transcription_total"] / all_stats["num_samples"]
+    all_stats["avg_length_sample"] = all_stats["times_audio_total"] / all_stats["num_samples"]
+    all_stats["wer"] = all_stats["wer"] / len(result_datasets)
+    all_stats["rtf"] = all_stats["times_audio_total"] / all_stats["times_transcription_total"]
+    all_stats["words_per_s"] = all_stats["num_words_total"] / all_stats["times_transcription_total"]
+
+    stats_dataset["all"] = all_stats
+
+    log_all_stats = {f"all_{k}": v for k, v in all_stats.items()}
+    wandb_logger.log(log_all_stats)
+
+    benchmark_artifact = wandb.Artifact("Benchmark", type="datasets")
+    with tempfile.TemporaryDirectory() as temp_dir:
+        for split in stats_dataset:
+            file_name = os.path.join(temp_dir, f"{'_'.join(split.split('/'))}.json")
+
+            with open(file_name, "w") as json_file:
+                json.dump(stats_dataset[split], json_file)
+
+            benchmark_artifact.add_file(file_name, split)
+
+        wandb_logger.log_artifact(benchmark_artifact)
+
+    print("Done!")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/flax/setup.py b/flax/setup.py
new file mode 100644
index 0000000000000000000000000000000000000000..9a0ba04e0d18efc84791e2489f86ec1b72dbeae4
--- /dev/null
+++ b/flax/setup.py
@@ -0,0 +1,65 @@
+# Copyright 2023 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+import os
+
+import setuptools
+
+
+_deps = [
+    "transformers>=4.34.0",
+    "datasets[audio]>=2.14.5",
+    "jax>=0.4.13",
+    "flax>=0.7.2",
+    "optax",
+    "evaluate",
+    "jiwer",
+    "torch",
+    "torchdata",
+    "tokenizers",
+]
+
+_extras_dev_deps = [
+    "black~=23.1",
+    "isort>=5.5.4",
+    "ruff>=0.0.241,<=0.0.259",
+]
+
+here = os.path.abspath(os.path.dirname(__file__))
+
+with open(os.path.join(here, "README.md"), encoding="utf-8") as f:
+    long_description = f.read()
+
+# read version
+with open(os.path.join(here, "distil_whisper", "__init__.py"), encoding="utf-8") as f:
+    for line in f:
+        if line.startswith("__version__"):
+            version = line.split("=")[1].strip().strip('"')
+            break
+    else:
+        raise RuntimeError("Unable to find version string.")
+
+setuptools.setup(
+    name="distil_whisper",
+    version=version,
+    description="Toolkit for distilling OpenAI's Whisper model.",
+    long_description=long_description,
+    long_description_content_type="text/markdown",
+    packages=setuptools.find_packages(),
+    install_requires=_deps,
+    extras_require={
+        "dev": [_extras_dev_deps],
+    },
+)
diff --git a/flax/tpu_connect.sh b/flax/tpu_connect.sh
new file mode 100644
index 0000000000000000000000000000000000000000..9cc33a6f47eac5a4ceefc7883ba96c3dbdf0084a
--- /dev/null
+++ b/flax/tpu_connect.sh
@@ -0,0 +1,82 @@
+#!/bin/bash
+
+# This script is adapted from https://github.com/peregilk/ttconnect#ttconnect
+
+zone="us-central2-b"  # TPU v4's always are in us-central2-b. Update if using TPU v2/v3's
+name=$1
+
+echo "Connecting to $name";
+
+## Some basic checks if the input is valid
+output=$(gcloud compute tpus describe $name --zone $zone 2>/dev/null)
+if [ $? != 0 ]; then
+	echo "Could not find a tpu-v4 with this name in the zone $zone. Exiting."
+	exit 1
+fi
+
+tputype=$(echo $output | awk '{print $2}')
+tpusize=$(echo $tputype| cut -c4-)
+size="$(($tpusize / 8))"
+
+if (( $size < 1 )); then
+	echo "This is reported as a $tputype with $size tpu(s). This is not a valid tpu-v4 resource. Exiting."
+	exit 1
+fi
+
+
+# Check if the session exists, if not create it
+# If there already is a session with this name, it will just attach
+
+tmux has-session -t $name 2>/dev/null
+
+
+if [ $? != 0 ]; then
+	tmux new-session -d -s $name
+	tmux select-layout main-vertical
+
+	for i in $(seq $(($size-1))); do
+		tmux split-window -v -d -t $name
+		# Making sure there is space to split
+		tmux select-layout main-horizontal
+	done
+
+	for i in $(seq $(($size))); do
+		worker=$(($i -1))
+                command="gcloud alpha compute tpus tpu-vm ssh $name --zone $zone --worker $worker"
+		tmux select-pane -t $name:0.$worker
+		tmux send-keys -t $name "$command" Enter
+
+        done
+
+	# Select the final layout
+	if ((size >= 16));then
+		tmux select-layout tiled
+	else
+		tmux select-layout tiled
+		tmux select-layout main-vertical
+	fi
+
+	# Enable mouse control - for changing pane size
+	# Disabled for now since it makes copying more difficult
+	# tmux set-mouse on
+
+	# Move cursor to worker 0
+	tmux select-pane -t $name:0.0
+
+	# Resize the left window
+        tmux resize-pane -L 50
+
+
+
+	# Set pane synchronization
+	tmux set-window-option -t $name:0 synchronize-panes on
+
+	# Set pane border format
+	tmux set-option -t $name pane-border-status top
+	tmux set-option -t $name pane-border-format "worker #{pane_index} "
+
+
+fi
+
+# Attach to the session
+tmux attach -t $name
diff --git a/generation_config.json b/generation_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..9f4dced2a5a6e8902cd9e1d0f695f6217e02debd
--- /dev/null
+++ b/generation_config.json
@@ -0,0 +1,255 @@
+{
+  "alignment_heads": [
+    [
+      7,
+      0
+    ],
+    [
+      10,
+      17
+    ],
+    [
+      12,
+      18
+    ],
+    [
+      13,
+      12
+    ],
+    [
+      16,
+      1
+    ],
+    [
+      17,
+      14
+    ],
+    [
+      19,
+      11
+    ],
+    [
+      21,
+      4
+    ],
+    [
+      24,
+      1
+    ],
+    [
+      25,
+      6
+    ]
+  ],
+  "begin_suppress_tokens": [
+    220,
+    50257
+  ],
+  "bos_token_id": 50257,
+  "decoder_start_token_id": 50258,
+  "eos_token_id": 50257,
+  "is_multilingual": true,
+  "lang_to_id": {
+    "<|af|>": 50327,
+    "<|am|>": 50334,
+    "<|ar|>": 50272,
+    "<|as|>": 50350,
+    "<|az|>": 50304,
+    "<|ba|>": 50355,
+    "<|be|>": 50330,
+    "<|bg|>": 50292,
+    "<|bn|>": 50302,
+    "<|bo|>": 50347,
+    "<|br|>": 50309,
+    "<|bs|>": 50315,
+    "<|ca|>": 50270,
+    "<|cs|>": 50283,
+    "<|cy|>": 50297,
+    "<|da|>": 50285,
+    "<|de|>": 50261,
+    "<|el|>": 50281,
+    "<|en|>": 50259,
+    "<|es|>": 50262,
+    "<|et|>": 50307,
+    "<|eu|>": 50310,
+    "<|fa|>": 50300,
+    "<|fi|>": 50277,
+    "<|fo|>": 50338,
+    "<|fr|>": 50265,
+    "<|gl|>": 50319,
+    "<|gu|>": 50333,
+    "<|haw|>": 50352,
+    "<|ha|>": 50354,
+    "<|he|>": 50279,
+    "<|hi|>": 50276,
+    "<|hr|>": 50291,
+    "<|ht|>": 50339,
+    "<|hu|>": 50286,
+    "<|hy|>": 50312,
+    "<|id|>": 50275,
+    "<|is|>": 50311,
+    "<|it|>": 50274,
+    "<|ja|>": 50266,
+    "<|jw|>": 50356,
+    "<|ka|>": 50329,
+    "<|kk|>": 50316,
+    "<|km|>": 50323,
+    "<|kn|>": 50306,
+    "<|ko|>": 50264,
+    "<|la|>": 50294,
+    "<|lb|>": 50345,
+    "<|ln|>": 50353,
+    "<|lo|>": 50336,
+    "<|lt|>": 50293,
+    "<|lv|>": 50301,
+    "<|mg|>": 50349,
+    "<|mi|>": 50295,
+    "<|mk|>": 50308,
+    "<|ml|>": 50296,
+    "<|mn|>": 50314,
+    "<|mr|>": 50320,
+    "<|ms|>": 50282,
+    "<|mt|>": 50343,
+    "<|my|>": 50346,
+    "<|ne|>": 50313,
+    "<|nl|>": 50271,
+    "<|nn|>": 50342,
+    "<|no|>": 50288,
+    "<|oc|>": 50328,
+    "<|pa|>": 50321,
+    "<|pl|>": 50269,
+    "<|ps|>": 50340,
+    "<|pt|>": 50267,
+    "<|ro|>": 50284,
+    "<|ru|>": 50263,
+    "<|sa|>": 50344,
+    "<|sd|>": 50332,
+    "<|si|>": 50322,
+    "<|sk|>": 50298,
+    "<|sl|>": 50305,
+    "<|sn|>": 50324,
+    "<|so|>": 50326,
+    "<|sq|>": 50317,
+    "<|sr|>": 50303,
+    "<|su|>": 50357,
+    "<|sv|>": 50273,
+    "<|sw|>": 50318,
+    "<|ta|>": 50287,
+    "<|te|>": 50299,
+    "<|tg|>": 50331,
+    "<|th|>": 50289,
+    "<|tk|>": 50341,
+    "<|tl|>": 50348,
+    "<|tr|>": 50268,
+    "<|tt|>": 50351,
+    "<|uk|>": 50280,
+    "<|ur|>": 50290,
+    "<|uz|>": 50337,
+    "<|vi|>": 50278,
+    "<|yi|>": 50335,
+    "<|yo|>": 50325,
+    "<|yue|>": 50358,
+    "<|zh|>": 50260
+  },
+  "max_initial_timestamp_index": 50,
+  "max_length": 448,
+  "no_timestamps_token_id": 50364,
+  "pad_token_id": 50257,
+  "prev_sot_token_id": 50362,
+  "return_timestamps": false,
+  "suppress_tokens": [
+    1,
+    2,
+    7,
+    8,
+    9,
+    10,
+    14,
+    25,
+    26,
+    27,
+    28,
+    29,
+    31,
+    58,
+    59,
+    60,
+    61,
+    62,
+    63,
+    90,
+    91,
+    92,
+    93,
+    359,
+    503,
+    522,
+    542,
+    873,
+    893,
+    902,
+    918,
+    922,
+    931,
+    1350,
+    1853,
+    1982,
+    2460,
+    2627,
+    3246,
+    3253,
+    3268,
+    3536,
+    3846,
+    3961,
+    4183,
+    4667,
+    6585,
+    6647,
+    7273,
+    9061,
+    9383,
+    10428,
+    10929,
+    11938,
+    12033,
+    12331,
+    12562,
+    13793,
+    14157,
+    14635,
+    15265,
+    15618,
+    16553,
+    16604,
+    18362,
+    18956,
+    20075,
+    21675,
+    22520,
+    26130,
+    26161,
+    26435,
+    28279,
+    29464,
+    31650,
+    32302,
+    32470,
+    36865,
+    42863,
+    47425,
+    49870,
+    50254,
+    50258,
+    50359,
+    50360,
+    50361,
+    50362,
+    50363
+  ],
+  "task_to_id": {
+    "transcribe": 50360,
+    "translate": 50359
+  },
+  "transformers_version": "4.41.2"
+}
diff --git a/merges.txt b/merges.txt
new file mode 100644
index 0000000000000000000000000000000000000000..6038932a2a1f09a66991b1c2adae0d14066fa29e
--- /dev/null
+++ b/merges.txt
@@ -0,0 +1,50001 @@
+#version: 0.2
+Ġ t
+Ġ a
+Ġt h
+i n
+e r
+Ġ w
+Ġ s
+o u
+Ġth e
+r e
+o n
+a t
+e n
+Ġ c
+i t
+i s
+Ġ b
+n d
+Ġ d
+Ġ m
+Ġ h
+Ġ o
+in g
+e s
+Ġ p
+Ġt o
+a n
+Ġ f
+o r
+l l
+Ġ I
+Ġ l
+Ġ y
+a r
+Ġ g
+Ġy ou
+e d
+Ġa nd
+Ġ in
+Ġo f
+a s
+Ġ n
+o m
+i c
+Ġth at
+u s
+e t
+v e
+a l
+o w
+l e
+Ġ is
+Ġ e
+Ġ it
+o t
+' s
+Ġb e
+i on
+Ġ T
+Ġw h
+Ġ A
+en t
+Ġ S
+Ġ re
+a y
+Ġw e
+Ġ on
+er e
+Ġh a
+u t
+a c
+i d
+i g
+o s
+k e
+v er
+i m
+Ġ Ð
+ĠT h
+a m
+a ll
+Ġf or
+e l
+c h
+r o
+Ġth is
+Ġs t
+Ġ W
+Ġ u
+a d
+ou t
+i r
+l d
+c t
+Ġ k
+i f
+Ġg o
+. .
+Ð ¾
+it h
+l y
+h t
+q u
+Ġ -
+Ġd o
+Ġ j
+Ġha ve
+Ġ B
+Ġa n
+Ġw ith
+Ġa re
+Ġ r
+Ġd e
+Ġs e
+Ġs o
+Ġ v
+s t
+i ll
+u r
+Ġl i
+Ġ M
+es t
+o d
+all y
+' t
+us t
+Ġa s
+Ġ C
+c e
+Ġm e
+Ð °
+Ð µ
+i l
+Ġ H
+Ġw as
+t er
+t h
+Ġc an
+an t
+Ġc om
+ou r
+ig ht
+Ġ Y
+at ion
+ĠA nd
+o l
+Ġs h
+Ñ Ĥ
+o p
+s e
+Ġn ot
+ĠS o
+Ġn e
+u n
+Ġa b
+Ġli ke
+Ġa t
+Ġ D
+i e
+Ġh e
+Ġc on
+Ġc h
+o re
+Ġa l
+Ġo r
+Ġ qu
+Ġ O
+om e
+r a
+u l
+Ġ N
+p p
+Ġyou r
+ou ld
+Ġ P
+Ġf r
+g e
+er s
+' re
+Ð ¸
+Ġthe y
+Ġwh at
+us e
+Ġa ll
+ĠTh e
+Ġ L
+es s
+e m
+Ġk n
+Ġj ust
+ar t
+Ġp ro
+ver y
+u m
+Ġl o
+Ġ ì
+Ġm y
+o k
+Ġe x
+a b
+Ġth ere
+Ġb ut
+Ġkn ow
+Ġs u
+Ġ G
+Ñ ģ
+Ġ E
+Ġm a
+о Ð
+Ġ en
+Ġab out
+ĠI t
+is t
+Ġw or
+r i
+in d
+Ġon e
+at e
+a nd
+in k
+Ġl e
+or t
+' m
+Ġ F
+ic h
+Ñ Ģ
+id e
+Ġg et
+Ġ out
+.. .
+Ġw ill
+ã ģ
+i ve
+Ð ½
+Ġfr om
+a in
+ĠW e
+Ġu p
+p e
+re s
+c a
+Ġ R
+Ġ if
+Ġp l
+Ġd on
+ac k
+Ġ 1
+Ġ "
+Ġt r
+Ġ us
+ĠW h
+it y
+Ġ J
+ĠY ou
+Ġh ere
+h er
+Ġs ome
+ou g
+a k
+ar d
+Ġgo ing
+Ġu n
+m ent
+Ġth ink
+Ġp e
+en d
+Ġ (
+ca use
+Ġt im
+as t
+Ã ©
+Ġ our
+Ġw ant
+am e
+i es
+Ġ ë
+u d
+in e
+Ġre ally
+Ġt e
+Ġse e
+c i
+Ġb y
+s o
+u re
+os e
+Ġ [
+a re
+Ġm ore
+a h
+on e
+c k
+op le
+а Ð
+Ġthe n
+Ġth ing
+Ġthe m
+v en
+ou nd
+os t
+on g
+e ct
+Ġr ight
+a g
+Ġin t
+Ġpe ople
+Ġwh en
+ou s
+p l
+Ġtim e
+Ġ im
+Ġwh o
+Ġ 2
+a p
+Ġbe cause
+h ing
+Ġn o
+ic e
+Ġlo ok
+Ġh as
+Ġw ould
+Ġh ow
+ac t
+Ġf e
+n t
+oug h
+Ġp r
+ĠB ut
+Ġs ay
+Ñ ĥ
+Ġn ow
+Ġm an
+Ġ very
+Ġwor k
+i z
+Ġ K
+i v
+it t
+Ġa r
+e p
+Ġc l
+Ġwh ich
+Ġc o
+an s
+' ve
+Ġs a
+f f
+' ll
+Ġan y
+Ġa ct
+Ġy e
+b er
+ac h
+a ge
+p er
+Ġal so
+f er
+Ġthe se
+Ġa d
+е Ð
+th er
+ac e
+ic k
+a ke
+re at
+i re
+u e
+Ġa g
+Ġ U
+u ch
+ion s
+r y
+0 0
+n a
+Ġd id
+Ġqu e
+Ġha d
+Ġe very
+ĠH e
+Ġl a
+Ġw ay
+Ġs p
+b le
+ĠTh is
+as s
+Ġthe ir
+it e
+Ġne ed
+Ġp art
+Ġw ere
+Ġb ack
+i p
+ow n
+om et
+b e
+as e
+Ġma ke
+ir st
+i a
+en ce
+an g
+an k
+Ġg ot
+Ġp re
+Ġcon t
+Ġo ther
+p t
+ĠTh at
+o g
+Ġgo od
+Ġint o
+al k
+Ġbe en
+Ġa m
+Ġo ver
+u ally
+Ġ â
+ì Ŀ
+Ġu nd
+h e
+w ay
+Ġg r
+Ñ Į
+Ġd if
+Ġp er
+Ñ ı
+ĠI n
+Ġt w
+on d
+ar s
+in t
+or m
+Ġl ot
+Ġwh ere
+Ġ Ã
+Ġ V
+Ġs omet
+Ð »
+en s
+Ġg u
+Ġa c
+u g
+Ñ ĭ
+Ä ±
+Ġf irst
+re e
+Ġh is
+itt le
+Ġim p
+Ġm o
+a v
+Ġl ittle
+ĠWh at
+Ġm uch
+Ġ z
+Ġ ê
+ab le
+ĠÐ ¿
+Ġp o
+Ġcom p
+n e
+Ġd is
+Ġl et
+an ce
+Ġh er
+Ġthing s
+Ġst art
+ul t
+Ġa pp
+Ġre s
+Ġf o
+Ġc ould
+Ġin ter
+Ġth ose
+Ġd es
+Ġwe ll
+Ġtw o
+Ġk ind
+x t
+res s
+el y
+Ã ¤
+Ġb r
+Ġth r
+ĠÐ ²
+Ġ i
+is h
+Ġdif fer
+Ġ ro
+ĠS t
+Ġsomet hing
+Ġt ake
+Ġb o
+y s
+Ġsh e
+Ġt alk
+l o
+Ñ ĩ
+Ġe ven
+Ð º
+ã Ģ
+ĠÐ ½
+Ġb u
+ĠI f
+Ġd own
+ĠC h
+ad e
+ation s
+Ġ use
+or d
+Ġof f
+Ġact ually
+Ġs pe
+d u
+at ed
+at er
+os s
+n ing
+Ã ¼
+Ġdo es
+Ġ Ñģ
+Ġne w
+Ġb et
+ve l
+c ess
+p le
+Ġha pp
+t ing
+on na
+Ġ es
+Ġd ay
+Ġon ly
+ig n
+k ay
+s el
+ent s
+ou nt
+i ld
+i le
+Ġs c
+Ġh im
+Ġag ain
+v ing
+Ġg onna
+Ġcom m
+Ġh el
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diff --git a/normalizer.json b/normalizer.json
new file mode 100644
index 0000000000000000000000000000000000000000..dd6ae819ad738ac1a546e9f9282ef325c33b9ea0
--- /dev/null
+++ b/normalizer.json
@@ -0,0 +1,1742 @@
+{
+  "accessorise": "accessorize",
+  "accessorised": "accessorized",
+  "accessorises": "accessorizes",
+  "accessorising": "accessorizing",
+  "acclimatisation": "acclimatization",
+  "acclimatise": "acclimatize",
+  "acclimatised": "acclimatized",
+  "acclimatises": "acclimatizes",
+  "acclimatising": "acclimatizing",
+  "accoutrements": "accouterments",
+  "aeon": "eon",
+  "aeons": "eons",
+  "aerogramme": "aerogram",
+  "aerogrammes": "aerograms",
+  "aeroplane": "airplane",
+  "aeroplanes": "airplanes",
+  "aesthete": "esthete",
+  "aesthetes": "esthetes",
+  "aesthetic": "esthetic",
+  "aesthetically": "esthetically",
+  "aesthetics": "esthetics",
+  "aetiology": "etiology",
+  "ageing": "aging",
+  "aggrandisement": "aggrandizement",
+  "agonise": "agonize",
+  "agonised": "agonized",
+  "agonises": "agonizes",
+  "agonising": "agonizing",
+  "agonisingly": "agonizingly",
+  "almanack": "almanac",
+  "almanacks": "almanacs",
+  "aluminium": "aluminum",
+  "amortisable": "amortizable",
+  "amortisation": "amortization",
+  "amortisations": "amortizations",
+  "amortise": "amortize",
+  "amortised": "amortized",
+  "amortises": "amortizes",
+  "amortising": "amortizing",
+  "amphitheatre": "amphitheater",
+  "amphitheatres": "amphitheaters",
+  "anaemia": "anemia",
+  "anaemic": "anemic",
+  "anaesthesia": "anesthesia",
+  "anaesthetic": "anesthetic",
+  "anaesthetics": "anesthetics",
+  "anaesthetise": "anesthetize",
+  "anaesthetised": "anesthetized",
+  "anaesthetises": "anesthetizes",
+  "anaesthetising": "anesthetizing",
+  "anaesthetist": "anesthetist",
+  "anaesthetists": "anesthetists",
+  "anaesthetize": "anesthetize",
+  "anaesthetized": "anesthetized",
+  "anaesthetizes": "anesthetizes",
+  "anaesthetizing": "anesthetizing",
+  "analogue": "analog",
+  "analogues": "analogs",
+  "analyse": "analyze",
+  "analysed": "analyzed",
+  "analyses": "analyzes",
+  "analysing": "analyzing",
+  "anglicise": "anglicize",
+  "anglicised": "anglicized",
+  "anglicises": "anglicizes",
+  "anglicising": "anglicizing",
+  "annualised": "annualized",
+  "antagonise": "antagonize",
+  "antagonised": "antagonized",
+  "antagonises": "antagonizes",
+  "antagonising": "antagonizing",
+  "apologise": "apologize",
+  "apologised": "apologized",
+  "apologises": "apologizes",
+  "apologising": "apologizing",
+  "appal": "appall",
+  "appals": "appalls",
+  "appetiser": "appetizer",
+  "appetisers": "appetizers",
+  "appetising": "appetizing",
+  "appetisingly": "appetizingly",
+  "arbour": "arbor",
+  "arbours": "arbors",
+  "archaeologically": "archeologically",
+  "archaeologist": "archeologist",
+  "archaeologists": "archeologists",
+  "archaeology": "archeology</span>",
+  "archeological": "archaeological",
+  "ardour": "ardor",
+  "armour": "armor",
+  "armoured": "armored",
+  "armourer": "armorer",
+  "armourers": "armorers",
+  "armouries": "armories",
+  "armoury": "armory",
+  "artefact": "artifact",
+  "artefacts": "artifacts",
+  "authorise": "authorize",
+  "authorised": "authorized",
+  "authorises": "authorizes",
+  "authorising": "authorizing",
+  "axe": "ax",
+  "backpedalled": "backpedaled",
+  "backpedalling": "backpedaling",
+  "bannister": "banister",
+  "bannisters": "banisters",
+  "baptise": "baptize",
+  "baptised": "baptized",
+  "baptises": "baptizes",
+  "baptising": "baptizing",
+  "bastardise": "bastardize",
+  "bastardised": "bastardized",
+  "bastardises": "bastardizes",
+  "bastardising": "bastardizing",
+  "battleax": "battleaxe",
+  "baulk": "balk",
+  "baulked": "balked",
+  "baulking": "balking",
+  "baulks": "balks",
+  "bedevilled": "bedeviled",
+  "bedevilling": "bedeviling",
+  "behaviour": "behavior",
+  "behavioural": "behavioral",
+  "behaviourism": "behaviorism",
+  "behaviourist": "behaviorist",
+  "behaviourists": "behaviorists",
+  "behaviours": "behaviors",
+  "behove": "behoove",
+  "behoved": "behooved",
+  "behoves": "behooves",
+  "bejewelled": "bejeweled",
+  "belabour": "belabor",
+  "belaboured": "belabored",
+  "belabouring": "belaboring",
+  "belabours": "belabors",
+  "bevelled": "beveled",
+  "bevvies": "bevies",
+  "bevvy": "bevy",
+  "biassed": "biased",
+  "biassing": "biasing",
+  "bingeing": "binging",
+  "bougainvillaea": "bougainvillea",
+  "bougainvillaeas": "bougainvilleas",
+  "bowdlerise": "bowdlerize",
+  "bowdlerised": "bowdlerized",
+  "bowdlerises": "bowdlerizes",
+  "bowdlerising": "bowdlerizing",
+  "breathalyse": "breathalyze",
+  "breathalysed": "breathalyzed",
+  "breathalyser": "breathalyzer",
+  "breathalysers": "breathalyzers",
+  "breathalyses": "breathalyzes",
+  "breathalysing": "breathalyzing",
+  "brutalise": "brutalize",
+  "brutalised": "brutalized",
+  "brutalises": "brutalizes",
+  "brutalising": "brutalizing",
+  "busses": "buses",
+  "bussing": "busing",
+  "caesarean": "cesarean",
+  "caesareans": "cesareans",
+  "calibre": "caliber",
+  "calibres": "calibers",
+  "calliper": "caliper",
+  "callipers": "calipers",
+  "callisthenics": "calisthenics",
+  "canalise": "canalize",
+  "canalised": "canalized",
+  "canalises": "canalizes",
+  "canalising": "canalizing",
+  "cancelation": "cancellation",
+  "cancelations": "cancellations",
+  "cancelled": "canceled",
+  "cancelling": "canceling",
+  "candour": "candor",
+  "cannibalise": "cannibalize",
+  "cannibalised": "cannibalized",
+  "cannibalises": "cannibalizes",
+  "cannibalising": "cannibalizing",
+  "canonise": "canonize",
+  "canonised": "canonized",
+  "canonises": "canonizes",
+  "canonising": "canonizing",
+  "capitalise": "capitalize",
+  "capitalised": "capitalized",
+  "capitalises": "capitalizes",
+  "capitalising": "capitalizing",
+  "caramelise": "caramelize",
+  "caramelised": "caramelized",
+  "caramelises": "caramelizes",
+  "caramelising": "caramelizing",
+  "carbonise": "carbonize",
+  "carbonised": "carbonized",
+  "carbonises": "carbonizes",
+  "carbonising": "carbonizing",
+  "carolled": "caroled",
+  "carolling": "caroling",
+  "catalogue": "catalog",
+  "catalogued": "cataloged",
+  "catalogues": "catalogs",
+  "cataloguing": "cataloging",
+  "catalyse": "catalyze",
+  "catalysed": "catalyzed",
+  "catalyses": "catalyzes",
+  "catalysing": "catalyzing",
+  "categorise": "categorize",
+  "categorised": "categorized",
+  "categorises": "categorizes",
+  "categorising": "categorizing",
+  "cauterise": "cauterize",
+  "cauterised": "cauterized",
+  "cauterises": "cauterizes",
+  "cauterising": "cauterizing",
+  "cavilled": "caviled",
+  "cavilling": "caviling",
+  "centigramme": "centigram",
+  "centigrammes": "centigrams",
+  "centilitre": "centiliter",
+  "centilitres": "centiliters",
+  "centimetre": "centimeter",
+  "centimetres": "centimeters",
+  "centralise": "centralize",
+  "centralised": "centralized",
+  "centralises": "centralizes",
+  "centralising": "centralizing",
+  "centre": "center",
+  "centred": "centered",
+  "centrefold": "centerfold",
+  "centrefolds": "centerfolds",
+  "centrepiece": "centerpiece",
+  "centrepieces": "centerpieces",
+  "centres": "centers",
+  "channelled": "channeled",
+  "channelling": "channeling",
+  "characterise": "characterize",
+  "characterised": "characterized",
+  "characterises": "characterizes",
+  "characterising": "characterizing",
+  "cheque": "check",
+  "chequebook": "checkbook",
+  "chequebooks": "checkbooks",
+  "chequered": "checkered",
+  "cheques": "checks",
+  "chilli": "chili",
+  "chimaera": "chimera",
+  "chimaeras": "chimeras",
+  "chiselled": "chiseled",
+  "chiselling": "chiseling",
+  "circularise": "circularize",
+  "circularised": "circularized",
+  "circularises": "circularizes",
+  "circularising": "circularizing",
+  "civilise": "civilize",
+  "civilised": "civilized",
+  "civilises": "civilizes",
+  "civilising": "civilizing",
+  "clamour": "clamor",
+  "clamoured": "clamored",
+  "clamouring": "clamoring",
+  "clamours": "clamors",
+  "clangour": "clangor",
+  "clarinettist": "clarinetist",
+  "clarinettists": "clarinetists",
+  "collectivise": "collectivize",
+  "collectivised": "collectivized",
+  "collectivises": "collectivizes",
+  "collectivising": "collectivizing",
+  "colonisation": "colonization",
+  "colonise": "colonize",
+  "colonised": "colonized",
+  "coloniser": "colonizer",
+  "colonisers": "colonizers",
+  "colonises": "colonizes",
+  "colonising": "colonizing",
+  "colour": "color",
+  "colourant": "colorant",
+  "colourants": "colorants",
+  "coloured": "colored",
+  "coloureds": "coloreds",
+  "colourful": "colorful",
+  "colourfully": "colorfully",
+  "colouring": "coloring",
+  "colourize": "colorize",
+  "colourized": "colorized",
+  "colourizes": "colorizes",
+  "colourizing": "colorizing",
+  "colourless": "colorless",
+  "colours": "colors",
+  "commercialise": "commercialize",
+  "commercialised": "commercialized",
+  "commercialises": "commercializes",
+  "commercialising": "commercializing",
+  "compartmentalise": "compartmentalize",
+  "compartmentalised": "compartmentalized",
+  "compartmentalises": "compartmentalizes",
+  "compartmentalising": "compartmentalizing",
+  "computerise": "computerize",
+  "computerised": "computerized",
+  "computerises": "computerizes",
+  "computerising": "computerizing",
+  "conceptualise": "conceptualize",
+  "conceptualised": "conceptualized",
+  "conceptualises": "conceptualizes",
+  "conceptualising": "conceptualizing",
+  "connexion": "connection",
+  "connexions": "connections",
+  "contextualise": "contextualize",
+  "contextualised": "contextualized",
+  "contextualises": "contextualizes",
+  "contextualising": "contextualizing",
+  "cosier": "cozier",
+  "cosies": "cozies",
+  "cosiest": "coziest",
+  "cosily": "cozily",
+  "cosiness": "coziness",
+  "cosy": "cozy",
+  "councillor": "councilor",
+  "councillors": "councilors",
+  "counselled": "counseled",
+  "counselling": "counseling",
+  "counsellor": "counselor",
+  "counsellors": "counselors",
+  "crenelated": "crenellated",
+  "criminalise": "criminalize",
+  "criminalised": "criminalized",
+  "criminalises": "criminalizes",
+  "criminalising": "criminalizing",
+  "criticise": "criticize",
+  "criticised": "criticized",
+  "criticises": "criticizes",
+  "criticising": "criticizing",
+  "crueller": "crueler",
+  "cruellest": "cruelest",
+  "crystallisation": "crystallization",
+  "crystallise": "crystallize",
+  "crystallised": "crystallized",
+  "crystallises": "crystallizes",
+  "crystallising": "crystallizing",
+  "cudgelled": "cudgeled",
+  "cudgelling": "cudgeling",
+  "customise": "customize",
+  "customised": "customized",
+  "customises": "customizes",
+  "customising": "customizing",
+  "cypher": "cipher",
+  "cyphers": "ciphers",
+  "decentralisation": "decentralization",
+  "decentralise": "decentralize",
+  "decentralised": "decentralized",
+  "decentralises": "decentralizes",
+  "decentralising": "decentralizing",
+  "decriminalisation": "decriminalization",
+  "decriminalise": "decriminalize",
+  "decriminalised": "decriminalized",
+  "decriminalises": "decriminalizes",
+  "decriminalising": "decriminalizing",
+  "defence": "defense",
+  "defenceless": "defenseless",
+  "defences": "defenses",
+  "dehumanisation": "dehumanization",
+  "dehumanise": "dehumanize",
+  "dehumanised": "dehumanized",
+  "dehumanises": "dehumanizes",
+  "dehumanising": "dehumanizing",
+  "demeanour": "demeanor",
+  "demilitarisation": "demilitarization",
+  "demilitarise": "demilitarize",
+  "demilitarised": "demilitarized",
+  "demilitarises": "demilitarizes",
+  "demilitarising": "demilitarizing",
+  "demobilisation": "demobilization",
+  "demobilise": "demobilize",
+  "demobilised": "demobilized",
+  "demobilises": "demobilizes",
+  "demobilising": "demobilizing",
+  "democratisation": "democratization",
+  "democratise": "democratize",
+  "democratised": "democratized",
+  "democratises": "democratizes",
+  "democratising": "democratizing",
+  "demonise": "demonize",
+  "demonised": "demonized",
+  "demonises": "demonizes",
+  "demonising": "demonizing",
+  "demoralisation": "demoralization",
+  "demoralise": "demoralize",
+  "demoralised": "demoralized",
+  "demoralises": "demoralizes",
+  "demoralising": "demoralizing",
+  "denationalisation": "denationalization",
+  "denationalise": "denationalize",
+  "denationalised": "denationalized",
+  "denationalises": "denationalizes",
+  "denationalising": "denationalizing",
+  "deodorise": "deodorize",
+  "deodorised": "deodorized",
+  "deodorises": "deodorizes",
+  "deodorising": "deodorizing",
+  "depersonalise": "depersonalize",
+  "depersonalised": "depersonalized",
+  "depersonalises": "depersonalizes",
+  "depersonalising": "depersonalizing",
+  "deputise": "deputize",
+  "deputised": "deputized",
+  "deputises": "deputizes",
+  "deputising": "deputizing",
+  "desensitisation": "desensitization",
+  "desensitise": "desensitize",
+  "desensitised": "desensitized",
+  "desensitises": "desensitizes",
+  "desensitising": "desensitizing",
+  "destabilisation": "destabilization",
+  "destabilise": "destabilize",
+  "destabilised": "destabilized",
+  "destabilises": "destabilizes",
+  "destabilising": "destabilizing",
+  "dialled": "dialed",
+  "dialling": "dialing",
+  "dialogue": "dialog",
+  "dialogues": "dialogs",
+  "diarrhoea": "diarrhea",
+  "digitise": "digitize",
+  "digitised": "digitized",
+  "digitises": "digitizes",
+  "digitising": "digitizing",
+  "disc": "disk",
+  "discolour": "discolor",
+  "discoloured": "discolored",
+  "discolouring": "discoloring",
+  "discolours": "discolors",
+  "discs": "disks",
+  "disembowelled": "disemboweled",
+  "disembowelling": "disemboweling",
+  "disfavour": "disfavor",
+  "dishevelled": "disheveled",
+  "dishonour": "dishonor",
+  "dishonourable": "dishonorable",
+  "dishonourably": "dishonorably",
+  "dishonoured": "dishonored",
+  "dishonouring": "dishonoring",
+  "dishonours": "dishonors",
+  "disorganisation": "disorganization",
+  "disorganised": "disorganized",
+  "distil": "distill",
+  "distils": "distills",
+  "dramatisation": "dramatization",
+  "dramatisations": "dramatizations",
+  "dramatise": "dramatize",
+  "dramatised": "dramatized",
+  "dramatises": "dramatizes",
+  "dramatising": "dramatizing",
+  "draught": "draft",
+  "draughtboard": "draftboard",
+  "draughtboards": "draftboards",
+  "draughtier": "draftier",
+  "draughtiest": "draftiest",
+  "draughts": "drafts",
+  "draughtsman": "draftsman",
+  "draughtsmanship": "draftsmanship",
+  "draughtsmen": "draftsmen",
+  "draughtswoman": "draftswoman",
+  "draughtswomen": "draftswomen",
+  "draughty": "drafty",
+  "drivelled": "driveled",
+  "drivelling": "driveling",
+  "duelled": "dueled",
+  "duelling": "dueling",
+  "economise": "economize",
+  "economised": "economized",
+  "economises": "economizes",
+  "economising": "economizing",
+  "editorialise": "editorialize",
+  "editorialised": "editorialized",
+  "editorialises": "editorializes",
+  "editorialising": "editorializing",
+  "edoema": "edema",
+  "empathise": "empathize",
+  "empathised": "empathized",
+  "empathises": "empathizes",
+  "empathising": "empathizing",
+  "emphasise": "emphasize",
+  "emphasised": "emphasized",
+  "emphasises": "emphasizes",
+  "emphasising": "emphasizing",
+  "enamelled": "enameled",
+  "enamelling": "enameling",
+  "enamoured": "enamored",
+  "encyclopaedia": "encyclopedia",
+  "encyclopaedias": "encyclopedias",
+  "encyclopaedic": "encyclopedic",
+  "endeavour": "endeavor",
+  "endeavoured": "endeavored",
+  "endeavouring": "endeavoring",
+  "endeavours": "endeavors",
+  "energise": "energize",
+  "energised": "energized",
+  "energises": "energizes",
+  "energising": "energizing",
+  "enrol": "enroll",
+  "enrols": "enrolls",
+  "enthral": "enthrall",
+  "enthrals": "enthralls",
+  "epaulette": "epaulet",
+  "epaulettes": "epaulets",
+  "epicentre": "epicenter",
+  "epicentres": "epicenters",
+  "epilogue": "epilog",
+  "epilogues": "epilogs",
+  "epitomise": "epitomize",
+  "epitomised": "epitomized",
+  "epitomises": "epitomizes",
+  "epitomising": "epitomizing",
+  "equalisation": "equalization",
+  "equalise": "equalize",
+  "equalised": "equalized",
+  "equaliser": "equalizer",
+  "equalisers": "equalizers",
+  "equalises": "equalizes",
+  "equalising": "equalizing",
+  "eulogise": "eulogize",
+  "eulogised": "eulogized",
+  "eulogises": "eulogizes",
+  "eulogising": "eulogizing",
+  "evangelise": "evangelize",
+  "evangelised": "evangelized",
+  "evangelises": "evangelizes",
+  "evangelising": "evangelizing",
+  "exorcise": "exorcize",
+  "exorcised": "exorcized",
+  "exorcises": "exorcizes",
+  "exorcising": "exorcizing",
+  "extemporisation": "extemporization",
+  "extemporise": "extemporize",
+  "extemporised": "extemporized",
+  "extemporises": "extemporizes",
+  "extemporising": "extemporizing",
+  "externalisation": "externalization",
+  "externalisations": "externalizations",
+  "externalise": "externalize",
+  "externalised": "externalized",
+  "externalises": "externalizes",
+  "externalising": "externalizing",
+  "factorise": "factorize",
+  "factorised": "factorized",
+  "factorises": "factorizes",
+  "factorising": "factorizing",
+  "faecal": "fecal",
+  "faeces": "feces",
+  "familiarisation": "familiarization",
+  "familiarise": "familiarize",
+  "familiarised": "familiarized",
+  "familiarises": "familiarizes",
+  "familiarising": "familiarizing",
+  "fantasise": "fantasize",
+  "fantasised": "fantasized",
+  "fantasises": "fantasizes",
+  "fantasising": "fantasizing",
+  "favour": "favor",
+  "favourable": "favorable",
+  "favourably": "favorably",
+  "favoured": "favored",
+  "favouring": "favoring",
+  "favourite": "favorite",
+  "favourites": "favorites",
+  "favouritism": "favoritism",
+  "favours": "favors",
+  "feminise": "feminize",
+  "feminised": "feminized",
+  "feminises": "feminizes",
+  "feminising": "feminizing",
+  "fertilisation": "fertilization",
+  "fertilise": "fertilize",
+  "fertilised": "fertilized",
+  "fertiliser": "fertilizer",
+  "fertilisers": "fertilizers",
+  "fertilises": "fertilizes",
+  "fertilising": "fertilizing",
+  "fervour": "fervor",
+  "fibre": "fiber",
+  "fibreglass": "fiberglass",
+  "fibres": "fibers",
+  "fictionalisation": "fictionalization",
+  "fictionalisations": "fictionalizations",
+  "fictionalise": "fictionalize",
+  "fictionalised": "fictionalized",
+  "fictionalises": "fictionalizes",
+  "fictionalising": "fictionalizing",
+  "fillet": "filet",
+  "filleted": "fileted",
+  "filleting": "fileting",
+  "fillets": "filets",
+  "finalisation": "finalization",
+  "finalise": "finalize",
+  "finalised": "finalized",
+  "finalises": "finalizes",
+  "finalising": "finalizing",
+  "flautist": "flutist",
+  "flautists": "flutists",
+  "flavour": "flavor",
+  "flavoured": "flavored",
+  "flavouring": "flavoring",
+  "flavourings": "flavorings",
+  "flavourless": "flavorless",
+  "flavours": "flavors",
+  "flavoursome": "flavorsome",
+  "flyer / flier": "flier / flyer",
+  "foetal": "fetal",
+  "foetid": "fetid",
+  "foetus": "fetus",
+  "foetuses": "fetuses",
+  "formalisation": "formalization",
+  "formalise": "formalize",
+  "formalised": "formalized",
+  "formalises": "formalizes",
+  "formalising": "formalizing",
+  "fossilisation": "fossilization",
+  "fossilise": "fossilize",
+  "fossilised": "fossilized",
+  "fossilises": "fossilizes",
+  "fossilising": "fossilizing",
+  "fraternisation": "fraternization",
+  "fraternise": "fraternize",
+  "fraternised": "fraternized",
+  "fraternises": "fraternizes",
+  "fraternising": "fraternizing",
+  "fulfil": "fulfill",
+  "fulfilment": "fulfillment",
+  "fulfils": "fulfills",
+  "funnelled": "funneled",
+  "funnelling": "funneling",
+  "gage": "gauge",
+  "gaged": "gauged",
+  "gages": "gauges",
+  "gaging": "gauging",
+  "galvanise": "galvanize",
+  "galvanised": "galvanized",
+  "galvanises": "galvanizes",
+  "galvanising": "galvanizing",
+  "gambolled": "gamboled",
+  "gambolling": "gamboling",
+  "gaol": "jail",
+  "gaolbird": "jailbird",
+  "gaolbirds": "jailbirds",
+  "gaolbreak": "jailbreak",
+  "gaolbreaks": "jailbreaks",
+  "gaoled": "jailed",
+  "gaoler": "jailer",
+  "gaolers": "jailers",
+  "gaoling": "jailing",
+  "gaols": "jails",
+  "gasses": "gases",
+  "generalisation": "generalization",
+  "generalisations": "generalizations",
+  "generalise": "generalize",
+  "generalised": "generalized",
+  "generalises": "generalizes",
+  "generalising": "generalizing",
+  "ghettoise": "ghettoize",
+  "ghettoised": "ghettoized",
+  "ghettoises": "ghettoizes",
+  "ghettoising": "ghettoizing",
+  "gipsies": "gypsies",
+  "glamor": "glamour",
+  "glamorise": "glamorize",
+  "glamorised": "glamorized",
+  "glamorises": "glamorizes",
+  "glamorising": "glamorizing",
+  "globalisation": "globalization",
+  "globalise": "globalize",
+  "globalised": "globalized",
+  "globalises": "globalizes",
+  "globalising": "globalizing",
+  "glueing": "gluing",
+  "goitre": "goiter",
+  "goitres": "goiters",
+  "gonorrhoea": "gonorrhea",
+  "gramme": "gram",
+  "grammes": "grams",
+  "gravelled": "graveled",
+  "grey": "gray",
+  "greyed": "grayed",
+  "greying": "graying",
+  "greyish": "grayish",
+  "greyness": "grayness",
+  "greys": "grays",
+  "grovelled": "groveled",
+  "grovelling": "groveling",
+  "groyne": "groin",
+  "groynes": "groins",
+  "gruelling": "grueling",
+  "gruellingly": "gruelingly",
+  "gryphon": "griffin",
+  "gryphons": "griffins",
+  "gynaecological": "gynecological",
+  "gynaecologist": "gynecologist",
+  "gynaecologists": "gynecologists",
+  "gynaecology": "gynecology",
+  "haematological": "hematological",
+  "haematologist": "hematologist",
+  "haematologists": "hematologists",
+  "haematology": "hematology",
+  "haemoglobin": "hemoglobin",
+  "haemophilia": "hemophilia",
+  "haemophiliac": "hemophiliac",
+  "haemophiliacs": "hemophiliacs",
+  "haemorrhage": "hemorrhage",
+  "haemorrhaged": "hemorrhaged",
+  "haemorrhages": "hemorrhages",
+  "haemorrhaging": "hemorrhaging",
+  "haemorrhoids": "hemorrhoids",
+  "harbour": "harbor",
+  "harboured": "harbored",
+  "harbouring": "harboring",
+  "harbours": "harbors",
+  "harmonisation": "harmonization",
+  "harmonise": "harmonize",
+  "harmonised": "harmonized",
+  "harmonises": "harmonizes",
+  "harmonising": "harmonizing",
+  "homoeopath": "homeopath",
+  "homoeopathic": "homeopathic",
+  "homoeopaths": "homeopaths",
+  "homoeopathy": "homeopathy",
+  "homogenise": "homogenize",
+  "homogenised": "homogenized",
+  "homogenises": "homogenizes",
+  "homogenising": "homogenizing",
+  "honour": "honor",
+  "honourable": "honorable",
+  "honourably": "honorably",
+  "honoured": "honored",
+  "honouring": "honoring",
+  "honours": "honors",
+  "hospitalisation": "hospitalization",
+  "hospitalise": "hospitalize",
+  "hospitalised": "hospitalized",
+  "hospitalises": "hospitalizes",
+  "hospitalising": "hospitalizing",
+  "humanise": "humanize",
+  "humanised": "humanized",
+  "humanises": "humanizes",
+  "humanising": "humanizing",
+  "humour": "humor",
+  "humoured": "humored",
+  "humouring": "humoring",
+  "humourless": "humorless",
+  "humours": "humors",
+  "hybridise": "hybridize",
+  "hybridised": "hybridized",
+  "hybridises": "hybridizes",
+  "hybridising": "hybridizing",
+  "hypnotise": "hypnotize",
+  "hypnotised": "hypnotized",
+  "hypnotises": "hypnotizes",
+  "hypnotising": "hypnotizing",
+  "hypothesise": "hypothesize",
+  "hypothesised": "hypothesized",
+  "hypothesises": "hypothesizes",
+  "hypothesising": "hypothesizing",
+  "idealisation": "idealization",
+  "idealise": "idealize",
+  "idealised": "idealized",
+  "idealises": "idealizes",
+  "idealising": "idealizing",
+  "idolise": "idolize",
+  "idolised": "idolized",
+  "idolises": "idolizes",
+  "idolising": "idolizing",
+  "immobilisation": "immobilization",
+  "immobilise": "immobilize",
+  "immobilised": "immobilized",
+  "immobiliser": "immobilizer",
+  "immobilisers": "immobilizers",
+  "immobilises": "immobilizes",
+  "immobilising": "immobilizing",
+  "immortalise": "immortalize",
+  "immortalised": "immortalized",
+  "immortalises": "immortalizes",
+  "immortalising": "immortalizing",
+  "immunisation": "immunization",
+  "immunise": "immunize",
+  "immunised": "immunized",
+  "immunises": "immunizes",
+  "immunising": "immunizing",
+  "impanelled": "impaneled",
+  "impanelling": "impaneling",
+  "imperilled": "imperiled",
+  "imperilling": "imperiling",
+  "individualise": "individualize",
+  "individualised": "individualized",
+  "individualises": "individualizes",
+  "individualising": "individualizing",
+  "industrialise": "industrialize",
+  "industrialised": "industrialized",
+  "industrialises": "industrializes",
+  "industrialising": "industrializing",
+  "inflexion": "inflection",
+  "inflexions": "inflections",
+  "initialise": "initialize",
+  "initialised": "initialized",
+  "initialises": "initializes",
+  "initialising": "initializing",
+  "initialled": "initialed",
+  "initialling": "initialing",
+  "instal": "install",
+  "instalment": "installment",
+  "instalments": "installments",
+  "instals": "installs",
+  "instil": "instill",
+  "instils": "instills",
+  "institutionalisation": "institutionalization",
+  "institutionalise": "institutionalize",
+  "institutionalised": "institutionalized",
+  "institutionalises": "institutionalizes",
+  "institutionalising": "institutionalizing",
+  "intellectualise": "intellectualize",
+  "intellectualised": "intellectualized",
+  "intellectualises": "intellectualizes",
+  "intellectualising": "intellectualizing",
+  "internalisation": "internalization",
+  "internalise": "internalize",
+  "internalised": "internalized",
+  "internalises": "internalizes",
+  "internalising": "internalizing",
+  "internationalisation": "internationalization",
+  "internationalise": "internationalize",
+  "internationalised": "internationalized",
+  "internationalises": "internationalizes",
+  "internationalising": "internationalizing",
+  "ionisation": "ionization",
+  "ionise": "ionize",
+  "ionised": "ionized",
+  "ioniser": "ionizer",
+  "ionisers": "ionizers",
+  "ionises": "ionizes",
+  "ionising": "ionizing",
+  "italicise": "italicize",
+  "italicised": "italicized",
+  "italicises": "italicizes",
+  "italicising": "italicizing",
+  "itemise": "itemize",
+  "itemised": "itemized",
+  "itemises": "itemizes",
+  "itemising": "itemizing",
+  "jeopardise": "jeopardize",
+  "jeopardised": "jeopardized",
+  "jeopardises": "jeopardizes",
+  "jeopardising": "jeopardizing",
+  "jewelled": "jeweled",
+  "jeweller": "jeweler",
+  "jewellers": "jewelers",
+  "jewellery": "jewelry",
+  "judgement": "judgment",
+  "kilogramme": "kilogram",
+  "kilogrammes": "kilograms",
+  "kilometre": "kilometer",
+  "kilometres": "kilometers",
+  "labelled": "labeled",
+  "labelling": "labeling",
+  "labour": "labor",
+  "laboured": "labored",
+  "labourer": "laborer",
+  "labourers": "laborers",
+  "labouring": "laboring",
+  "labours": "labors",
+  "lacklustre": "lackluster",
+  "legalisation": "legalization",
+  "legalise": "legalize",
+  "legalised": "legalized",
+  "legalises": "legalizes",
+  "legalising": "legalizing",
+  "legitimise": "legitimize",
+  "legitimised": "legitimized",
+  "legitimises": "legitimizes",
+  "legitimising": "legitimizing",
+  "leukaemia": "leukemia",
+  "levelled": "leveled",
+  "leveller": "leveler",
+  "levellers": "levelers",
+  "levelling": "leveling",
+  "libelled": "libeled",
+  "libelling": "libeling",
+  "libellous": "libelous",
+  "liberalisation": "liberalization",
+  "liberalise": "liberalize",
+  "liberalised": "liberalized",
+  "liberalises": "liberalizes",
+  "liberalising": "liberalizing",
+  "licence": "license",
+  "licenced": "licensed",
+  "licences": "licenses",
+  "licencing": "licensing",
+  "likeable": "likable",
+  "lionisation": "lionization",
+  "lionise": "lionize",
+  "lionised": "lionized",
+  "lionises": "lionizes",
+  "lionising": "lionizing",
+  "liquidise": "liquidize",
+  "liquidised": "liquidized",
+  "liquidiser": "liquidizer",
+  "liquidisers": "liquidizers",
+  "liquidises": "liquidizes",
+  "liquidising": "liquidizing",
+  "litre": "liter",
+  "litres": "liters",
+  "localise": "localize",
+  "localised": "localized",
+  "localises": "localizes",
+  "localising": "localizing",
+  "louvre": "louver",
+  "louvred": "louvered",
+  "louvres": "louvers",
+  "lustre": "luster",
+  "magnetise": "magnetize",
+  "magnetised": "magnetized",
+  "magnetises": "magnetizes",
+  "magnetising": "magnetizing",
+  "manoeuvrability": "maneuverability",
+  "manoeuvrable": "maneuverable",
+  "manoeuvre": "maneuver",
+  "manoeuvred": "maneuvered",
+  "manoeuvres": "maneuvers",
+  "manoeuvring": "maneuvering",
+  "manoeuvrings": "maneuverings",
+  "marginalisation": "marginalization",
+  "marginalise": "marginalize",
+  "marginalised": "marginalized",
+  "marginalises": "marginalizes",
+  "marginalising": "marginalizing",
+  "marshalled": "marshaled",
+  "marshalling": "marshaling",
+  "marvelled": "marveled",
+  "marvelling": "marveling",
+  "marvellous": "marvelous",
+  "marvellously": "marvelously",
+  "materialisation": "materialization",
+  "materialise": "materialize",
+  "materialised": "materialized",
+  "materialises": "materializes",
+  "materialising": "materializing",
+  "maximisation": "maximization",
+  "maximise": "maximize",
+  "maximised": "maximized",
+  "maximises": "maximizes",
+  "maximising": "maximizing",
+  "meagre": "meager",
+  "mechanisation": "mechanization",
+  "mechanise": "mechanize",
+  "mechanised": "mechanized",
+  "mechanises": "mechanizes",
+  "mechanising": "mechanizing",
+  "mediaeval": "medieval",
+  "memorialise": "memorialize",
+  "memorialised": "memorialized",
+  "memorialises": "memorializes",
+  "memorialising": "memorializing",
+  "memorise": "memorize",
+  "memorised": "memorized",
+  "memorises": "memorizes",
+  "memorising": "memorizing",
+  "mesmerise": "mesmerize",
+  "mesmerised": "mesmerized",
+  "mesmerises": "mesmerizes",
+  "mesmerising": "mesmerizing",
+  "metabolise": "metabolize",
+  "metabolised": "metabolized",
+  "metabolises": "metabolizes",
+  "metabolising": "metabolizing",
+  "metre": "meter",
+  "metres": "meters",
+  "mhm": "hmm",
+  "micrometre": "micrometer",
+  "micrometres": "micrometers",
+  "militarise": "militarize",
+  "militarised": "militarized",
+  "militarises": "militarizes",
+  "militarising": "militarizing",
+  "milligramme": "milligram",
+  "milligrammes": "milligrams",
+  "millilitre": "milliliter",
+  "millilitres": "milliliters",
+  "millimetre": "millimeter",
+  "millimetres": "millimeters",
+  "miniaturisation": "miniaturization",
+  "miniaturise": "miniaturize",
+  "miniaturised": "miniaturized",
+  "miniaturises": "miniaturizes",
+  "miniaturising": "miniaturizing",
+  "minibusses": "minibuses",
+  "minimise": "minimize",
+  "minimised": "minimized",
+  "minimises": "minimizes",
+  "minimising": "minimizing",
+  "misbehaviour": "misbehavior",
+  "misdemeanour": "misdemeanor",
+  "misdemeanours": "misdemeanors",
+  "misspelt": "misspelled",
+  "mitre": "miter",
+  "mitres": "miters",
+  "mm": "hmm",
+  "mmm": "hmm",
+  "mobilisation": "mobilization",
+  "mobilise": "mobilize",
+  "mobilised": "mobilized",
+  "mobilises": "mobilizes",
+  "mobilising": "mobilizing",
+  "modelled": "modeled",
+  "modeller": "modeler",
+  "modellers": "modelers",
+  "modelling": "modeling",
+  "modernise": "modernize",
+  "modernised": "modernized",
+  "modernises": "modernizes",
+  "modernising": "modernizing",
+  "moisturise": "moisturize",
+  "moisturised": "moisturized",
+  "moisturiser": "moisturizer",
+  "moisturisers": "moisturizers",
+  "moisturises": "moisturizes",
+  "moisturising": "moisturizing",
+  "monologue": "monolog",
+  "monologues": "monologs",
+  "monopolisation": "monopolization",
+  "monopolise": "monopolize",
+  "monopolised": "monopolized",
+  "monopolises": "monopolizes",
+  "monopolising": "monopolizing",
+  "moralise": "moralize",
+  "moralised": "moralized",
+  "moralises": "moralizes",
+  "moralising": "moralizing",
+  "motorised": "motorized",
+  "mould": "mold",
+  "moulded": "molded",
+  "moulder": "molder",
+  "mouldered": "moldered",
+  "mouldering": "moldering",
+  "moulders": "molders",
+  "mouldier": "moldier",
+  "mouldiest": "moldiest",
+  "moulding": "molding",
+  "mouldings": "moldings",
+  "moulds": "molds",
+  "mouldy": "moldy",
+  "moult": "molt",
+  "moulted": "molted",
+  "moulting": "molting",
+  "moults": "molts",
+  "moustache": "mustache",
+  "moustached": "mustached",
+  "moustaches": "mustaches",
+  "moustachioed": "mustachioed",
+  "multicoloured": "multicolored",
+  "nationalisation": "nationalization",
+  "nationalisations": "nationalizations",
+  "nationalise": "nationalize",
+  "nationalised": "nationalized",
+  "nationalises": "nationalizes",
+  "nationalising": "nationalizing",
+  "naturalisation": "naturalization",
+  "naturalise": "naturalize",
+  "naturalised": "naturalized",
+  "naturalises": "naturalizes",
+  "naturalising": "naturalizing",
+  "neighbour": "neighbor",
+  "neighbourhood": "neighborhood",
+  "neighbourhoods": "neighborhoods",
+  "neighbouring": "neighboring",
+  "neighbourliness": "neighborliness",
+  "neighbourly": "neighborly",
+  "neighbours": "neighbors",
+  "neutralisation": "neutralization",
+  "neutralise": "neutralize",
+  "neutralised": "neutralized",
+  "neutralises": "neutralizes",
+  "neutralising": "neutralizing",
+  "normalisation": "normalization",
+  "normalise": "normalize",
+  "normalised": "normalized",
+  "normalises": "normalizes",
+  "normalising": "normalizing",
+  "odour": "odor",
+  "odourless": "odorless",
+  "odours": "odors",
+  "oesophagus": "esophagus",
+  "oesophaguses": "esophaguses",
+  "oestrogen": "estrogen",
+  "offence": "offense",
+  "offences": "offenses",
+  "omelette": "omelet",
+  "omelettes": "omelets",
+  "optimise": "optimize",
+  "optimised": "optimized",
+  "optimises": "optimizes",
+  "optimising": "optimizing",
+  "organisation": "organization",
+  "organisational": "organizational",
+  "organisations": "organizations",
+  "organise": "organize",
+  "organised": "organized",
+  "organiser": "organizer",
+  "organisers": "organizers",
+  "organises": "organizes",
+  "organising": "organizing",
+  "orthopaedic": "orthopedic",
+  "orthopaedics": "orthopedics",
+  "ostracise": "ostracize",
+  "ostracised": "ostracized",
+  "ostracises": "ostracizes",
+  "ostracising": "ostracizing",
+  "outmanoeuvre": "outmaneuver",
+  "outmanoeuvred": "outmaneuvered",
+  "outmanoeuvres": "outmaneuvers",
+  "outmanoeuvring": "outmaneuvering",
+  "overemphasise": "overemphasize",
+  "overemphasised": "overemphasized",
+  "overemphasises": "overemphasizes",
+  "overemphasising": "overemphasizing",
+  "oxidisation": "oxidization",
+  "oxidise": "oxidize",
+  "oxidised": "oxidized",
+  "oxidises": "oxidizes",
+  "oxidising": "oxidizing",
+  "paederast": "pederast",
+  "paederasts": "pederasts",
+  "paediatric": "pediatric",
+  "paediatrician": "pediatrician",
+  "paediatricians": "pediatricians",
+  "paediatrics": "pediatrics",
+  "paedophile": "pedophile",
+  "paedophiles": "pedophiles",
+  "paedophilia": "pedophilia",
+  "palaeolithic": "paleolithic",
+  "palaeontologist": "paleontologist",
+  "palaeontologists": "paleontologists",
+  "palaeontology": "paleontology",
+  "panelled": "paneled",
+  "panelling": "paneling",
+  "panellist": "panelist",
+  "panellists": "panelists",
+  "paralyse": "paralyze",
+  "paralysed": "paralyzed",
+  "paralyses": "paralyzes",
+  "paralysing": "paralyzing",
+  "parcelled": "parceled",
+  "parcelling": "parceling",
+  "parlour": "parlor",
+  "parlours": "parlors",
+  "particularise": "particularize",
+  "particularised": "particularized",
+  "particularises": "particularizes",
+  "particularising": "particularizing",
+  "passivisation": "passivization",
+  "passivise": "passivize",
+  "passivised": "passivized",
+  "passivises": "passivizes",
+  "passivising": "passivizing",
+  "pasteurisation": "pasteurization",
+  "pasteurise": "pasteurize",
+  "pasteurised": "pasteurized",
+  "pasteurises": "pasteurizes",
+  "pasteurising": "pasteurizing",
+  "patronise": "patronize",
+  "patronised": "patronized",
+  "patronises": "patronizes",
+  "patronising": "patronizing",
+  "patronisingly": "patronizingly",
+  "pedalled": "pedaled",
+  "pedalling": "pedaling",
+  "pedestrianisation": "pedestrianization",
+  "pedestrianise": "pedestrianize",
+  "pedestrianised": "pedestrianized",
+  "pedestrianises": "pedestrianizes",
+  "pedestrianising": "pedestrianizing",
+  "penalise": "penalize",
+  "penalised": "penalized",
+  "penalises": "penalizes",
+  "penalising": "penalizing",
+  "pencilled": "penciled",
+  "pencilling": "penciling",
+  "personalise": "personalize",
+  "personalised": "personalized",
+  "personalises": "personalizes",
+  "personalising": "personalizing",
+  "pharmacopoeia": "pharmacopeia",
+  "pharmacopoeias": "pharmacopeias",
+  "philosophise": "philosophize",
+  "philosophised": "philosophized",
+  "philosophises": "philosophizes",
+  "philosophising": "philosophizing",
+  "philtre": "filter",
+  "philtres": "filters",
+  "phoney": "phony",
+  "plagiarise": "plagiarize",
+  "plagiarised": "plagiarized",
+  "plagiarises": "plagiarizes",
+  "plagiarising": "plagiarizing",
+  "plough": "plow",
+  "ploughed": "plowed",
+  "ploughing": "plowing",
+  "ploughman": "plowman",
+  "ploughmen": "plowmen",
+  "ploughs": "plows",
+  "ploughshare": "plowshare",
+  "ploughshares": "plowshares",
+  "polarisation": "polarization",
+  "polarise": "polarize",
+  "polarised": "polarized",
+  "polarises": "polarizes",
+  "polarising": "polarizing",
+  "politicisation": "politicization",
+  "politicise": "politicize",
+  "politicised": "politicized",
+  "politicises": "politicizes",
+  "politicising": "politicizing",
+  "popularisation": "popularization",
+  "popularise": "popularize",
+  "popularised": "popularized",
+  "popularises": "popularizes",
+  "popularising": "popularizing",
+  "pouffe": "pouf",
+  "pouffes": "poufs",
+  "practise": "practice",
+  "practised": "practiced",
+  "practises": "practices",
+  "practising": "practicing",
+  "praesidium": "presidium",
+  "praesidiums": "presidiums",
+  "pressurisation": "pressurization",
+  "pressurise": "pressurize",
+  "pressurised": "pressurized",
+  "pressurises": "pressurizes",
+  "pressurising": "pressurizing",
+  "pretence": "pretense",
+  "pretences": "pretenses",
+  "primaeval": "primeval",
+  "prioritisation": "prioritization",
+  "prioritise": "prioritize",
+  "prioritised": "prioritized",
+  "prioritises": "prioritizes",
+  "prioritising": "prioritizing",
+  "privatisation": "privatization",
+  "privatisations": "privatizations",
+  "privatise": "privatize",
+  "privatised": "privatized",
+  "privatises": "privatizes",
+  "privatising": "privatizing",
+  "professionalisation": "professionalization",
+  "professionalise": "professionalize",
+  "professionalised": "professionalized",
+  "professionalises": "professionalizes",
+  "professionalising": "professionalizing",
+  "programme": "program",
+  "programmes": "programs",
+  "prologue": "prolog",
+  "prologues": "prologs",
+  "propagandise": "propagandize",
+  "propagandised": "propagandized",
+  "propagandises": "propagandizes",
+  "propagandising": "propagandizing",
+  "proselytise": "proselytize",
+  "proselytised": "proselytized",
+  "proselytiser": "proselytizer",
+  "proselytisers": "proselytizers",
+  "proselytises": "proselytizes",
+  "proselytising": "proselytizing",
+  "psychoanalyse": "psychoanalyze",
+  "psychoanalysed": "psychoanalyzed",
+  "psychoanalyses": "psychoanalyzes",
+  "psychoanalysing": "psychoanalyzing",
+  "publicise": "publicize",
+  "publicised": "publicized",
+  "publicises": "publicizes",
+  "publicising": "publicizing",
+  "pulverisation": "pulverization",
+  "pulverise": "pulverize",
+  "pulverised": "pulverized",
+  "pulverises": "pulverizes",
+  "pulverising": "pulverizing",
+  "pummelled": "pummel",
+  "pummelling": "pummeled",
+  "pyjama": "pajama",
+  "pyjamas": "pajamas",
+  "pzazz": "pizzazz",
+  "quarrelled": "quarreled",
+  "quarrelling": "quarreling",
+  "radicalise": "radicalize",
+  "radicalised": "radicalized",
+  "radicalises": "radicalizes",
+  "radicalising": "radicalizing",
+  "rancour": "rancor",
+  "randomise": "randomize",
+  "randomised": "randomized",
+  "randomises": "randomizes",
+  "randomising": "randomizing",
+  "rationalisation": "rationalization",
+  "rationalisations": "rationalizations",
+  "rationalise": "rationalize",
+  "rationalised": "rationalized",
+  "rationalises": "rationalizes",
+  "rationalising": "rationalizing",
+  "ravelled": "raveled",
+  "ravelling": "raveling",
+  "realisable": "realizable",
+  "realisation": "realization",
+  "realisations": "realizations",
+  "realise": "realize",
+  "realised": "realized",
+  "realises": "realizes",
+  "realising": "realizing",
+  "recognisable": "recognizable",
+  "recognisably": "recognizably",
+  "recognisance": "recognizance",
+  "recognise": "recognize",
+  "recognised": "recognized",
+  "recognises": "recognizes",
+  "recognising": "recognizing",
+  "reconnoitre": "reconnoiter",
+  "reconnoitred": "reconnoitered",
+  "reconnoitres": "reconnoiters",
+  "reconnoitring": "reconnoitering",
+  "refuelled": "refueled",
+  "refuelling": "refueling",
+  "regularisation": "regularization",
+  "regularise": "regularize",
+  "regularised": "regularized",
+  "regularises": "regularizes",
+  "regularising": "regularizing",
+  "remodelled": "remodeled",
+  "remodelling": "remodeling",
+  "remould": "remold",
+  "remoulded": "remolded",
+  "remoulding": "remolding",
+  "remoulds": "remolds",
+  "reorganisation": "reorganization",
+  "reorganisations": "reorganizations",
+  "reorganise": "reorganize",
+  "reorganised": "reorganized",
+  "reorganises": "reorganizes",
+  "reorganising": "reorganizing",
+  "revelled": "reveled",
+  "reveller": "reveler",
+  "revellers": "revelers",
+  "revelling": "reveling",
+  "revitalise": "revitalize",
+  "revitalised": "revitalized",
+  "revitalises": "revitalizes",
+  "revitalising": "revitalizing",
+  "revolutionise": "revolutionize",
+  "revolutionised": "revolutionized",
+  "revolutionises": "revolutionizes",
+  "revolutionising": "revolutionizing",
+  "rhapsodise": "rhapsodize",
+  "rhapsodised": "rhapsodized",
+  "rhapsodises": "rhapsodizes",
+  "rhapsodising": "rhapsodizing",
+  "rigour": "rigor",
+  "rigours": "rigors",
+  "ritualised": "ritualized",
+  "rivalled": "rivaled",
+  "rivalling": "rivaling",
+  "romanticise": "romanticize",
+  "romanticised": "romanticized",
+  "romanticises": "romanticizes",
+  "romanticising": "romanticizing",
+  "rumour": "rumor",
+  "rumoured": "rumored",
+  "rumours": "rumors",
+  "sabre": "saber",
+  "sabres": "sabers",
+  "saltpetre": "saltpeter",
+  "sanitise": "sanitize",
+  "sanitised": "sanitized",
+  "sanitises": "sanitizes",
+  "sanitising": "sanitizing",
+  "satirise": "satirize",
+  "satirised": "satirized",
+  "satirises": "satirizes",
+  "satirising": "satirizing",
+  "saviour": "savior",
+  "saviours": "saviors",
+  "savour": "savor",
+  "savoured": "savored",
+  "savouries": "savories",
+  "savouring": "savoring",
+  "savours": "savors",
+  "savoury": "savory",
+  "scandalise": "scandalize",
+  "scandalised": "scandalized",
+  "scandalises": "scandalizes",
+  "scandalising": "scandalizing",
+  "sceptic": "skeptic",
+  "sceptical": "skeptical",
+  "sceptically": "skeptically",
+  "scepticism": "skepticism",
+  "sceptics": "skeptics",
+  "sceptre": "scepter",
+  "sceptres": "scepters",
+  "scrutinise": "scrutinize",
+  "scrutinised": "scrutinized",
+  "scrutinises": "scrutinizes",
+  "scrutinising": "scrutinizing",
+  "secularisation": "secularization",
+  "secularise": "secularize",
+  "secularised": "secularized",
+  "secularises": "secularizes",
+  "secularising": "secularizing",
+  "sensationalise": "sensationalize",
+  "sensationalised": "sensationalized",
+  "sensationalises": "sensationalizes",
+  "sensationalising": "sensationalizing",
+  "sensitise": "sensitize",
+  "sensitised": "sensitized",
+  "sensitises": "sensitizes",
+  "sensitising": "sensitizing",
+  "sentimentalise": "sentimentalize",
+  "sentimentalised": "sentimentalized",
+  "sentimentalises": "sentimentalizes",
+  "sentimentalising": "sentimentalizing",
+  "sepulchre": "sepulcher",
+  "sepulchres": "sepulchers",
+  "serialisation": "serialization",
+  "serialisations": "serializations",
+  "serialise": "serialize",
+  "serialised": "serialized",
+  "serialises": "serializes",
+  "serialising": "serializing",
+  "sermonise": "sermonize",
+  "sermonised": "sermonized",
+  "sermonises": "sermonizes",
+  "sermonising": "sermonizing",
+  "sheikh": "sheik",
+  "shovelled": "shoveled",
+  "shovelling": "shoveling",
+  "shrivelled": "shriveled",
+  "shrivelling": "shriveling",
+  "signalise": "signalize",
+  "signalised": "signalized",
+  "signalises": "signalizes",
+  "signalising": "signalizing",
+  "signalled": "signaled",
+  "signalling": "signaling",
+  "smoulder": "smolder",
+  "smouldered": "smoldered",
+  "smouldering": "smoldering",
+  "smoulders": "smolders",
+  "snivelled": "sniveled",
+  "snivelling": "sniveling",
+  "snorkelled": "snorkeled",
+  "snorkelling": "snorkeling",
+  "snowplough": "snowplow",
+  "snowploughs": "snowplow",
+  "socialisation": "socialization",
+  "socialise": "socialize",
+  "socialised": "socialized",
+  "socialises": "socializes",
+  "socialising": "socializing",
+  "sodomise": "sodomize",
+  "sodomised": "sodomized",
+  "sodomises": "sodomizes",
+  "sodomising": "sodomizing",
+  "solemnise": "solemnize",
+  "solemnised": "solemnized",
+  "solemnises": "solemnizes",
+  "solemnising": "solemnizing",
+  "sombre": "somber",
+  "specialisation": "specialization",
+  "specialisations": "specializations",
+  "specialise": "specialize",
+  "specialised": "specialized",
+  "specialises": "specializes",
+  "specialising": "specializing",
+  "spectre": "specter",
+  "spectres": "specters",
+  "spiralled": "spiraled",
+  "spiralling": "spiraling",
+  "splendour": "splendor",
+  "splendours": "splendors",
+  "squirrelled": "squirreled",
+  "squirrelling": "squirreling",
+  "stabilisation": "stabilization",
+  "stabilise": "stabilize",
+  "stabilised": "stabilized",
+  "stabiliser": "stabilizer",
+  "stabilisers": "stabilizers",
+  "stabilises": "stabilizes",
+  "stabilising": "stabilizing",
+  "standardisation": "standardization",
+  "standardise": "standardize",
+  "standardised": "standardized",
+  "standardises": "standardizes",
+  "standardising": "standardizing",
+  "stencilled": "stenciled",
+  "stencilling": "stenciling",
+  "sterilisation": "sterilization",
+  "sterilisations": "sterilizations",
+  "sterilise": "sterilize",
+  "sterilised": "sterilized",
+  "steriliser": "sterilizer",
+  "sterilisers": "sterilizers",
+  "sterilises": "sterilizes",
+  "sterilising": "sterilizing",
+  "stigmatisation": "stigmatization",
+  "stigmatise": "stigmatize",
+  "stigmatised": "stigmatized",
+  "stigmatises": "stigmatizes",
+  "stigmatising": "stigmatizing",
+  "storey": "story",
+  "storeys": "stories",
+  "subsidisation": "subsidization",
+  "subsidise": "subsidize",
+  "subsidised": "subsidized",
+  "subsidiser": "subsidizer",
+  "subsidisers": "subsidizers",
+  "subsidises": "subsidizes",
+  "subsidising": "subsidizing",
+  "succour": "succor",
+  "succoured": "succored",
+  "succouring": "succoring",
+  "succours": "succors",
+  "sulphate": "sulfate",
+  "sulphates": "sulfates",
+  "sulphide": "sulfide",
+  "sulphides": "sulfides",
+  "sulphur": "sulfur",
+  "sulphurous": "sulfurous",
+  "summarise": "summarize",
+  "summarised": "summarized",
+  "summarises": "summarizes",
+  "summarising": "summarizing",
+  "swivelled": "swiveled",
+  "swivelling": "swiveling",
+  "symbolise": "symbolize",
+  "symbolised": "symbolized",
+  "symbolises": "symbolizes",
+  "symbolising": "symbolizing",
+  "sympathise": "sympathize",
+  "sympathised": "sympathized",
+  "sympathiser": "sympathizer",
+  "sympathisers": "sympathizers",
+  "sympathises": "sympathizes",
+  "sympathising": "sympathizing",
+  "synchronisation": "synchronization",
+  "synchronise": "synchronize",
+  "synchronised": "synchronized",
+  "synchronises": "synchronizes",
+  "synchronising": "synchronizing",
+  "synthesise": "synthesize",
+  "synthesised": "synthesized",
+  "synthesiser": "synthesizer",
+  "synthesisers": "synthesizers",
+  "synthesises": "synthesizes",
+  "synthesising": "synthesizing",
+  "syphon": "siphon",
+  "syphoned": "siphoned",
+  "syphoning": "siphoning",
+  "syphons": "siphons",
+  "systematisation": "systematization",
+  "systematise": "systematize",
+  "systematised": "systematized",
+  "systematises": "systematizes",
+  "systematising": "systematizing",
+  "tantalise": "tantalize",
+  "tantalised": "tantalized",
+  "tantalises": "tantalizes",
+  "tantalising": "tantalizing",
+  "tantalisingly": "tantalizingly",
+  "tasselled": "tasseled",
+  "technicolour": "technicolor",
+  "temporise": "temporize",
+  "temporised": "temporized",
+  "temporises": "temporizes",
+  "temporising": "temporizing",
+  "tenderise": "tenderize",
+  "tenderised": "tenderized",
+  "tenderises": "tenderizes",
+  "tenderising": "tenderizing",
+  "terrorise": "terrorize",
+  "terrorised": "terrorized",
+  "terrorises": "terrorizes",
+  "terrorising": "terrorizing",
+  "theatre": "theater",
+  "theatregoer": "theatergoer",
+  "theatregoers": "theatergoers",
+  "theatres": "theaters",
+  "theorise": "theorize",
+  "theorised": "theorized",
+  "theorises": "theorizes",
+  "theorising": "theorizing",
+  "tonne": "ton",
+  "tonnes": "tons",
+  "towelled": "toweled",
+  "towelling": "toweling",
+  "toxaemia": "toxemia",
+  "tranquillise": "tranquilize",
+  "tranquillised": "tranquilized",
+  "tranquilliser": "tranquilizer",
+  "tranquillisers": "tranquilizers",
+  "tranquillises": "tranquilizes",
+  "tranquillising": "tranquilizing",
+  "tranquillity": "tranquility",
+  "tranquillize": "tranquilize",
+  "tranquillized": "tranquilized",
+  "tranquillizer": "tranquilizer",
+  "tranquillizers": "tranquilizers",
+  "tranquillizes": "tranquilizes",
+  "tranquillizing": "tranquilizing",
+  "tranquilly": "tranquility",
+  "transistorised": "transistorized",
+  "traumatise": "traumatize",
+  "traumatised": "traumatized",
+  "traumatises": "traumatizes",
+  "traumatising": "traumatizing",
+  "travelled": "traveled",
+  "traveller": "traveler",
+  "travellers": "travelers",
+  "travelling": "traveling",
+  "travelog": "travelogue",
+  "travelogs": "travelogues",
+  "trialled": "trialed",
+  "trialling": "trialing",
+  "tricolour": "tricolor",
+  "tricolours": "tricolors",
+  "trivialise": "trivialize",
+  "trivialised": "trivialized",
+  "trivialises": "trivializes",
+  "trivialising": "trivializing",
+  "tumour": "tumor",
+  "tumours": "tumors",
+  "tunnelled": "tunneled",
+  "tunnelling": "tunneling",
+  "tyrannise": "tyrannize",
+  "tyrannised": "tyrannized",
+  "tyrannises": "tyrannizes",
+  "tyrannising": "tyrannizing",
+  "tyre": "tire",
+  "tyres": "tires",
+  "unauthorised": "unauthorized",
+  "uncivilised": "uncivilized",
+  "underutilised": "underutilized",
+  "unequalled": "unequaled",
+  "unfavourable": "unfavorable",
+  "unfavourably": "unfavorably",
+  "unionisation": "unionization",
+  "unionise": "unionize",
+  "unionised": "unionized",
+  "unionises": "unionizes",
+  "unionising": "unionizing",
+  "unorganised": "unorganized",
+  "unravelled": "unraveled",
+  "unravelling": "unraveling",
+  "unrecognisable": "unrecognizable",
+  "unrecognised": "unrecognized",
+  "unrivalled": "unrivaled",
+  "unsavoury": "unsavory",
+  "untrammelled": "untrammeled",
+  "urbanisation": "urbanization",
+  "urbanise": "urbanize",
+  "urbanised": "urbanized",
+  "urbanises": "urbanizes",
+  "urbanising": "urbanizing",
+  "utilisable": "utilizable",
+  "utilisation": "utilization",
+  "utilise": "utilize",
+  "utilised": "utilized",
+  "utilises": "utilizes",
+  "utilising": "utilizing",
+  "valour": "valor",
+  "vandalise": "vandalize",
+  "vandalised": "vandalized",
+  "vandalises": "vandalizes",
+  "vandalising": "vandalizing",
+  "vaporisation": "vaporization",
+  "vaporise": "vaporize",
+  "vaporised": "vaporized",
+  "vaporises": "vaporizes",
+  "vaporising": "vaporizing",
+  "vapour": "vapor",
+  "vapours": "vapors",
+  "verbalise": "verbalize",
+  "verbalised": "verbalized",
+  "verbalises": "verbalizes",
+  "verbalising": "verbalizing",
+  "victimisation": "victimization",
+  "victimise": "victimize",
+  "victimised": "victimized",
+  "victimises": "victimizes",
+  "victimising": "victimizing",
+  "videodisc": "videodisk",
+  "videodiscs": "videodisks",
+  "vigour": "vigor",
+  "visualisation": "visualization",
+  "visualisations": "visualizations",
+  "visualise": "visualize",
+  "visualised": "visualized",
+  "visualises": "visualizes",
+  "visualising": "visualizing",
+  "vocalisation": "vocalization",
+  "vocalisations": "vocalizations",
+  "vocalise": "vocalize",
+  "vocalised": "vocalized",
+  "vocalises": "vocalizes",
+  "vocalising": "vocalizing",
+  "vulcanised": "vulcanized",
+  "vulgarisation": "vulgarization",
+  "vulgarise": "vulgarize",
+  "vulgarised": "vulgarized",
+  "vulgarises": "vulgarizes",
+  "vulgarising": "vulgarizing",
+  "waggon": "wagon",
+  "waggons": "wagons",
+  "watercolour": "watercolor",
+  "watercolours": "watercolors",
+  "weaselled": "weaseled",
+  "weaselling": "weaseling",
+  "westernisation": "westernization",
+  "westernise": "westernize",
+  "westernised": "westernized",
+  "westernises": "westernizes",
+  "westernising": "westernizing",
+  "womanise": "womanize",
+  "womanised": "womanized",
+  "womaniser": "womanizer",
+  "womanisers": "womanizers",
+  "womanises": "womanizes",
+  "womanising": "womanizing",
+  "woollen": "woolen",
+  "woollens": "woolens",
+  "woollies": "woolies",
+  "woolly": "wooly",
+  "worshipped": "worshiped",
+  "worshipper": "worshiper",
+  "worshipping": "worshiping",
+  "yodelled": "yodeled",
+  "yodelling": "yodeling",
+  "yoghourt": "yogurt",
+  "yoghourts": "yogurts",
+  "yoghurt": "yogurt",
+  "yoghurts": "yogurts"
+}
diff --git a/preprocessor_config.json b/preprocessor_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..931c77a740890c46365c7ae0c9d350ba3cca908f
--- /dev/null
+++ b/preprocessor_config.json
@@ -0,0 +1,14 @@
+{
+  "chunk_length": 30,
+  "feature_extractor_type": "WhisperFeatureExtractor",
+  "feature_size": 128,
+  "hop_length": 160,
+  "n_fft": 400,
+  "n_samples": 480000,
+  "nb_max_frames": 3000,
+  "padding_side": "right",
+  "padding_value": 0.0,
+  "processor_class": "WhisperProcessor",
+  "return_attention_mask": false,
+  "sampling_rate": 16000
+}
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000000000000000000000000000000000000..16e06c81028351f32d5445f7db8f7730accdfa7c
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,16 @@
+[tool.black]
+line-length = 119
+target-version = ['py37']
+
+[tool.ruff]
+# Never enforce `E501` (line length violations).
+ignore = ["C901", "E501", "E741", "W605"]
+select = ["C", "E", "F", "I", "W"]
+line-length = 119
+
+# Ignore import violations in all `__init__.py` files.
+[tool.ruff.per-file-ignores]
+"__init__.py" = ["E402", "F401", "F403", "F811"]
+
+[tool.ruff.isort]
+lines-after-imports = 2
diff --git a/run_distillation.py b/run_distillation.py
new file mode 100644
index 0000000000000000000000000000000000000000..3f4ff343f44872de6501c8788f29fed362d62294
--- /dev/null
+++ b/run_distillation.py
@@ -0,0 +1,1737 @@
+#!/usr/bin/env python
+# coding=utf-8
+# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Training the Whisper model for sequence to sequence speech recognition via teacher-student distillation.
+"""
+# You can also adapt this script for your own distillation tasks. Pointers for this are left as comments.
+
+import logging
+import os
+import re
+import shutil
+import sys
+import time
+from dataclasses import dataclass, field
+from functools import partial
+from pathlib import Path
+from typing import Any, Dict, List, Optional, Union
+
+import datasets
+import evaluate
+import numpy as np
+import torch
+import torch.nn as nn
+import transformers
+from accelerate import Accelerator
+from accelerate.logging import get_logger
+from accelerate.utils import set_seed
+from datasets import (
+    DatasetDict,
+    IterableDataset,
+    IterableDatasetDict,
+    concatenate_datasets,
+    interleave_datasets,
+    load_dataset,
+)
+from huggingface_hub import create_repo, get_full_repo_name, upload_folder
+from torch.utils.data import DataLoader
+from tqdm import tqdm
+from transformers import (
+    AddedToken,
+    HfArgumentParser,
+    Seq2SeqTrainingArguments,
+    WhisperConfig,
+    WhisperFeatureExtractor,
+    WhisperForConditionalGeneration,
+    WhisperProcessor,
+    WhisperTokenizerFast,
+    get_scheduler
+)
+from transformers.modeling_outputs import BaseModelOutput
+from transformers.models.whisper.english_normalizer import BasicTextNormalizer, EnglishTextNormalizer
+from transformers.utils import check_min_version
+from transformers.utils.versions import require_version
+
+
+# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
+check_min_version("4.34.0.dev0")
+
+require_version("datasets>=2.14.6", "To fix: `pip install --upgrade datasets`")
+
+logger = get_logger(__name__)
+
+
+@dataclass
+class ModelArguments:
+    """
+    Arguments pertaining to which model/config/tokenizer we are going to distill from.
+    """
+
+    model_name_or_path: str = field(
+        metadata={"help": "Path to pretrained Whisper model or model identifier from huggingface.co/models"}
+    )
+    teacher_model_name_or_path: str = field(
+        metadata={"help": "Path to pretrained teacher model or model identifier from huggingface.co/models"}
+    )
+    config_name: Optional[str] = field(
+        default=None,
+        metadata={"help": "Pretrained config name or path if not the same as model_name"},
+    )
+    tokenizer_name: Optional[str] = field(
+        default=None,
+        metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"},
+    )
+    feature_extractor_name: Optional[str] = field(
+        default=None,
+        metadata={"help": "feature extractor name or path if not the same as model_name"},
+    )
+    cache_dir: Optional[str] = field(
+        default=None,
+        metadata={"help": "Where to store the pretrained models downloaded from huggingface.co"},
+    )
+    use_fast_tokenizer: bool = field(
+        default=True,
+        metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
+    )
+    model_revision: str = field(
+        default="main",
+        metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
+    )
+    subfolder: str = field(
+        default="",
+        metadata={
+            "help": "In case the relevant files are located inside a subfolder of the model repo on huggingface.co, you can"
+            "specify the folder name here."
+        },
+    )
+    token: str = field(
+        default=None,
+        metadata={
+            "help": (
+                "The token to use as HTTP bearer authorization for remote files. If not specified, will use the token "
+                "generated when running `huggingface-cli login` (stored in `~/.huggingface`)."
+            )
+        },
+    )
+    attn_implementation: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": (
+                "Which attention implementation to use in the encoder and decoder attention layers. Can be one of:\n"
+                "1. `eager` or `None`: default Transformers attention implementation.\n"
+                "2. `sdpa`: Flash Attention through PyTorch SDPA. Requires `torch>=2.1`. Recommended for hardware where Flash Attention 2 is not supported, e.g. Turing GPUs, (T4, RTX 2080).\n"
+                "3. `flash_attn_2`: Flash Attention 2 through the Flash Attention package https://github.com/Dao-AILab/flash-attention. **Always** recommended on supported hardware (Ampere, Ada, or Hopper GPUs, e.g., A100, RTX 3090, RTX 4090, H100)."
+            )
+        },
+    )
+
+    def __post_init__(self):
+        if self.attn_implementation not in [None, "eager", "sdpa", "flash_attention_2"]:
+            raise ValueError(
+                f"Got `--attn_implementation={self.attn_implementation}`, which is an invalid attention type. Should be one of:\n"
+                "1. `eager` or `None`: default Transformers attention implementation.\n"
+                "2. `sdpa`: Flash Attention through PyTorch SDPA. Requires `torch>=2.1`. Recommended for hardware where Flash Attention 2 is not supported, e.g. Turing GPUs, (T4, RTX 2080).\n"
+                "3. `flash_attn_2`: Flash Attention 2 through the Flash Attention package https://github.com/Dao-AILab/flash-attention. **Always** recommended on supported hardware (Ampere, Ada, or Hopper GPUs, e.g., A100, RTX 3090, RTX 4090, H100)."
+            )
+
+
+@dataclass
+class DataTrainingArguments:
+    """
+    Arguments pertaining to what data we are going to input our model for training and eval.
+    """
+
+    train_dataset_name: str = field(
+        default=None,
+        metadata={
+            "help": "The name of the training dataset to use (via the datasets library). Load and combine "
+            "multiple datasets by separating dataset ids by a '+' symbol. For example, to load LibriSpeech "
+            "and Common Voice, set `train_dataset_name='librispeech_asr+common_voice'`."
+        },
+    )
+    train_dataset_config_name: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": "The configuration name of the training dataset to use (via the datasets library). Load and combine "
+            "multiple datasets by separating dataset configs by a '+' symbol. Note that the order of the configs should "
+            "match the order of the datasets."
+        },
+    )
+    train_dataset_samples: str = field(
+        default=None,
+        metadata={
+            "help": "Number of samples in each dataset when loading multiple datasets with streaming mode. "
+            "Not required when using one dataset or non-streaming mode. The sample values provide the sampling "
+            "probability for each dataset. Setting them equal to the number of sample values ensures that every "
+            "sample from every dataset is used once per epoch."
+        },
+    )
+    eval_dataset_name: str = field(
+        default=None,
+        metadata={
+            "help": "The name of the evaluation dataset to use (via the datasets library). Defaults to the training "
+            "dataset name if unspecified. Load multiple evaluation datasets by separating dataset "
+            "ids by a '+' symbol."
+        },
+    )
+    eval_dataset_config_name: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": "The configuration name of the evaluation dataset to use (via the datasets library). Defaults to the "
+            "training dataset config name if unspecified."
+        },
+    )
+    dataset_cache_dir: Optional[str] = field(
+        default=None,
+        metadata={"help": "Path to cache directory for saving and loading datasets"},
+    )
+    overwrite_cache: bool = field(
+        default=False,
+        metadata={"help": "Overwrite the cached training and evaluation sets"},
+    )
+    preprocessing_num_workers: Optional[int] = field(
+        default=None,
+        metadata={"help": "The number of processes to use for the preprocessing if using non-streaming mode."},
+    )
+    preprocessing_batch_size: Optional[int] = field(
+        default=256,
+        metadata={"help": "Number of examples per batch provided to the `prepare_dataset` function."},
+    )
+    max_train_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": (
+                "For debugging purposes or quicker training, truncate the number of training examples to this value if set."
+            )
+        },
+    )
+    max_eval_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": (
+                "For debugging purposes or quicker training, truncate the number of evaluation examples to this value if set."
+            )
+        },
+    )
+    audio_column_name: str = field(
+        default="audio",
+        metadata={"help": "The name of the dataset column containing the audio data. Defaults to 'audio'"},
+    )
+    text_column_name: str = field(
+        default=None,
+        metadata={"help": "The name of the dataset column containing the text data in the training set."},
+    )
+    eval_text_column_name: str = field(
+        default="text",
+        metadata={"help": ("The name of the dataset column containing the text data in the evaluation set.")},
+    )
+    max_duration_in_seconds: float = field(
+        default=30.0,
+        metadata={"help": "Filter audio files that are longer than `max_duration_in_seconds` seconds"},
+    )
+    min_duration_in_seconds: float = field(
+        default=0.0,
+        metadata={"help": "Filter audio files that are shorter than `min_duration_in_seconds` seconds"},
+    )
+    max_label_length: int = field(
+        default=448,
+        metadata={"help": "Truncate transcriptions that are longer `max_label_length` tokens."},
+    )
+    pad_target_to_multiple_of: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": (
+                "If set will pad the target sequence to a multiple of the provided"
+                " value. This is important to avoid triggering recompilations on TPU."
+                " If unspecified, will default to padding the targets to max length."
+            )
+        },
+    )
+    preprocessing_only: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether to only do data preprocessing and skip training. This is"
+                " especially useful when data preprocessing errors out in distributed"
+                " training due to timeout. In this case, one should run the"
+                " preprocessing in a non-distributed setup with"
+                " `preprocessing_only=True` so that the cached datasets can"
+                " consequently be loaded in distributed training"
+            )
+        },
+    )
+    train_split_name: str = field(
+        default="train",
+        metadata={
+            "help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
+        },
+    )
+    eval_split_name: str = field(
+        default="validation",
+        metadata={
+            "help": (
+                "The name of the evaluation data set split to use (via the datasets library). Defaults to 'validation'"
+            )
+        },
+    )
+    streaming: bool = field(
+        default=True,
+        metadata={"help": "Whether to use Datasets' streaming mode to load and pre-process the data."},
+    )
+    wer_threshold: float = field(
+        default=None,
+        metadata={
+            "help": "Filter training data with Whisper transcriptions that have greater than `wer_threshold` "
+            "WER with the normalised transcriptions. This only takes effect if training on pseudo-labels targets."
+            "If `--use_pseudo_labels=False`, then no WER filtering is performed, since we train directly on the text"
+            "transcriptions."
+        },
+    )
+    use_pseudo_labels: bool = field(
+        default=True,
+        metadata={
+            "help": "Whether or not to use pseudo-label transcriptions as the targets. If True, the pseudo-labels "
+            "must be in the dataset column `whisper_transcript` from the previous pseudo-labelling step. This is "
+            "not currently yet configurable."
+        },
+    )
+    timestamp_probability: float = field(
+        default=0.2, metadata={"help": "Probability for training on timestamped tokens if the data contains it."}
+    )
+    condition_on_prev_probability: float = field(
+        default=0.2, metadata={"help": "Probability for conditioning on the previous text example."}
+    )
+    return_timestamps: bool = field(
+        default=False, metadata={"help": "Whether or not to predict timestamps in the generation step."}
+    )
+    language: str = field(
+        default=None,
+        metadata={
+            "help": (
+                "Language for multilingual distillation. This argument should be set for multilingual distillation "
+                "only. For English speech recognition, it should be left as `None`."
+            )
+        },
+    )
+    task: str = field(
+        default="transcribe",
+        metadata={
+            "help": "Task, either `transcribe` for speech recognition or `translate` for speech translation."
+            "This argument should be set for multilingual distillation only. For English speech recognition, it should be left as `None`."
+        },
+    )
+    wandb_project: str = field(
+        default="distil-whisper",
+        metadata={"help": "The name of the wandb project."},
+    )
+    wandb_name: str = field(
+        default=None,
+        metadata={"help": "The name of the wandb run."},
+    )
+    wandb_dir: str = field(
+        default="./wandb",
+        metadata={"help": "The dir where wandb metadata will be stored."},
+    )
+
+
+@dataclass
+class DistillationTrainingArguments(Seq2SeqTrainingArguments):
+    freeze_encoder: Optional[bool] = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether to freeze the entire encoder model. Only recommended when the entire encoder has been "
+                "copied from the teacher model."
+            )
+        },
+    )
+    freeze_decoder: Optional[bool] = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether to freeze the entire decoder model. Note that the decoder input embeddings are **not** frozen, since they are tied to the LM head."
+            )
+        },
+    )
+    freeze_embed_positions: Optional[bool] = field(
+        default=False,
+        metadata={"help": "Whether to freeze the decoder embedding positions."},
+    )
+    temperature: Optional[float] = field(
+        default=2.0, metadata={"help": "Temperature to anneal the logits when computing the softmax."}
+    )
+    kl_weight: Optional[float] = field(
+        default=1.0,
+        metadata={
+            "help": (
+                "Weighting assigned to the MSE loss in the KD formulation. MSE loss is "
+                "computed between the teacher-student hidden states and attentions."
+            )
+        },
+    )
+    dtype: Optional[str] = field(
+        default="float32",
+        metadata={
+            "help": (
+                "The data type (dtype) in which to run training. One of `float32` (full-precision), "
+                "`float16` or `bfloat16` (both half-precision)."
+            )
+        },
+    )
+
+
+@dataclass
+class DataCollatorSpeechSeq2SeqWithPadding:
+    """
+    Data collator that will dynamically pad the inputs received.
+    Args:
+        processor ([`Wav2Vec2Processor`])
+            The processor used for proccessing the data.
+        decoder_start_token_id (:obj: `int`)
+            The start-of-sequence token id of the decoder.
+        decoder_prev_token_id (:obj: `int`)
+            The start-of-prompt token id of the decoder
+        input_padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`, `optional`, defaults to :obj:`True`):
+            Select a strategy to pad the returned input sequences (according to the model's padding side and padding index)
+            among:
+            * :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
+              sequence if provided).
+            * :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the
+              maximum acceptable input length for the model if that argument is not provided.
+            * :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of
+              different lengths).
+        target_padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`, `optional`, defaults to :obj:`True`):
+            Select a strategy to pad the returned target sequences (according to the model's padding side and padding index).
+            See above for details.
+        max_target_length (:obj:`int`, `optional`):
+            Maximum length of the ``labels`` of the returned list and optionally padding length (see above).
+    """
+
+    processor: Any
+    decoder_start_token_id: int
+    decoder_prev_token_id: int
+    input_padding: Union[bool, str] = "max_length"
+    target_padding: Union[bool, str] = "max_length"
+    max_target_length: Optional[int] = None
+
+    def __call__(self, features: List[Dict[str, Union[List[int], np.ndarray]]]) -> Dict[str, np.ndarray]:
+        # split inputs and labels since they have to be of different lengths and need
+        # different padding methods
+
+        # dataloader returns a list of features which we convert to a dict
+        input_features = {"input_features": [feature["input_features"] for feature in features]}
+        label_features = {"input_ids": [feature["labels"] for feature in features]}
+
+        # reformat list to dict and set to pytorch format
+        batch = self.processor.feature_extractor.pad(
+            input_features,
+            padding=self.input_padding,
+            return_tensors="pt",
+        )
+
+        labels_batch = self.processor.tokenizer.pad(
+            label_features,
+            max_length=self.max_target_length,
+            padding=self.target_padding,
+            return_tensors="pt",
+        )
+
+        # shift labels to the right to get decoder input ids
+        labels = labels_batch["input_ids"]
+        decoder_input_ids = labels[:, :-1]
+        labels = labels[:, 1:]
+        labels_mask = labels_batch.attention_mask[:, 1:]
+
+        # replace padding with -100 to ignore correctly when computing the loss
+        labels = labels.masked_fill(labels_mask.ne(1), -100)
+
+        # replace initial prompt tokens with -100 to ignore correctly when computing the loss
+        bos_index = torch.argmax((labels == self.decoder_start_token_id).long(), dim=1)
+        bos_index = torch.where(bos_index > 0, bos_index + 1, bos_index)
+        prompt_mask = torch.arange(labels.shape[1]) < bos_index[:, None]
+        labels = torch.where(prompt_mask, -100, labels)
+
+        batch["labels"] = labels
+        batch["decoder_input_ids"] = decoder_input_ids
+
+        return batch
+
+
+def log_metric(
+    accelerator,
+    metrics: Dict,
+    train_time: float,
+    step: int,
+    epoch: int,
+    learning_rate: float = None,
+    prefix: str = "train",
+):
+    """Helper function to log all training/evaluation metrics with the correct prefixes and styling."""
+    log_metrics = {}
+    for k, v in metrics.items():
+        log_metrics[f"{prefix}/{k}"] = v
+    log_metrics[f"{prefix}/time"] = train_time
+    log_metrics[f"{prefix}/epoch"] = epoch
+    if learning_rate is not None:
+        log_metrics[f"{prefix}/learning_rate"] = learning_rate
+    accelerator.log(log_metrics, step=step)
+
+
+def log_pred(
+    accelerator,
+    pred_str: List[str],
+    label_str: List[str],
+    norm_pred_str: List[str],
+    norm_label_str: List[str],
+    step: int,
+    prefix: str = "eval",
+    num_lines: int = 200000,
+):
+    """Helper function to log target/predicted transcriptions to weights and biases (wandb)."""
+    if accelerator.is_main_process:
+        wandb_tracker = accelerator.get_tracker("wandb")
+        # pretty name for current step: step 50000 -> step 50k
+        cur_step_pretty = f"{int(step // 1000)}k" if step > 1000 else step
+        prefix_pretty = prefix.replace("/", "-")
+
+        # convert str data to a wandb compatible format
+        str_data = [[label_str[i], pred_str[i], norm_label_str[i], norm_pred_str[i]] for i in range(len(pred_str))]
+        # log as a table with the appropriate headers
+        wandb_tracker.log_table(
+            table_name=f"predictions/{prefix_pretty}-step-{cur_step_pretty}",
+            columns=["Target", "Pred", "Norm Target", "Norm Pred"],
+            data=str_data[:num_lines],
+            step=step,
+        )
+
+        # log incorrect normalised predictions
+        str_data = np.asarray(str_data)
+        str_data_incorrect = str_data[str_data[:, -2] != str_data[:, -1]]
+        # log as a table with the appropriate headers
+        wandb_tracker.log_table(
+            table_name=f"incorrect_predictions/{prefix_pretty}-step-{cur_step_pretty}",
+            columns=["Target", "Pred", "Norm Target", "Norm Pred"],
+            data=str_data_incorrect[:num_lines],
+            step=step,
+        )
+
+
+def convert_dataset_str_to_list(
+    dataset_names,
+    dataset_config_names,
+    splits=None,
+    text_column_names=None,
+    dataset_samples=None,
+    default_split="train",
+) -> List[Dict]:
+    """
+    Given three lists of dataset names, configs and splits, this function groups the corresponding
+    names/configs/splits. Each dataset is assigned a unique dictionary with these metadata values, and the
+    function returns a list of dictionaries, one for each dataset.
+    """
+    if isinstance(dataset_names, str):
+        dataset_names = dataset_names.split("+")
+        dataset_config_names = dataset_config_names.split("+") if dataset_config_names is not None else None
+        splits = splits.split("+") if splits is not None else None
+        text_column_names = text_column_names.split("+") if text_column_names is not None else None
+        dataset_samples = dataset_samples.split("+") if dataset_samples is not None else None
+
+    # basic checks to ensure we've got the right number of datasets/configs/splits/columns/probs
+    if dataset_config_names is not None and len(dataset_names) != len(dataset_config_names):
+        raise ValueError(
+            f"Ensure one config is passed for each dataset, got {len(dataset_names)} datasets and"
+            f" {len(dataset_config_names)} configs."
+        )
+
+    if splits is not None and len(splits) != len(dataset_names):
+        raise ValueError(
+            f"Ensure one split is passed for each dataset, got {len(dataset_names)} datasets and {len(splits)} splits."
+        )
+
+    if text_column_names is not None and len(text_column_names) != len(dataset_names):
+        raise ValueError(
+            f"Ensure one text column name is passed for each dataset, got {len(dataset_names)} datasets and"
+            f" {len(text_column_names)} text column names."
+        )
+
+    if dataset_samples is not None:
+        if len(dataset_samples) != len(dataset_names):
+            raise ValueError(
+                f"Ensure one sample is passed for each dataset, got {len(dataset_names)} datasets and "
+                f"{len(dataset_samples)} samples."
+            )
+        dataset_samples = [float(ds_sample) for ds_sample in dataset_samples]
+    else:
+        dataset_samples = [None] * len(dataset_names)
+
+    dataset_config_names = (
+        dataset_config_names if dataset_config_names is not None else ["default" for _ in range(len(dataset_names))]
+    )
+    text_column_names = (
+        text_column_names if text_column_names is not None else ["text" for _ in range(len(dataset_names))]
+    )
+    splits = splits if splits is not None else [default_split for _ in range(len(dataset_names))]
+
+    dataset_names_dict = []
+    for i, ds_name in enumerate(dataset_names):
+        dataset_names_dict.append(
+            {
+                "name": ds_name,
+                "config": dataset_config_names[i],
+                "split": splits[i],
+                "text_column_name": text_column_names[i],
+                "samples": dataset_samples[i],
+            }
+        )
+    return dataset_names_dict
+
+
+def load_multiple_datasets(
+    dataset_names: Union[List, str],
+    dataset_config_names: Union[List, str],
+    splits: Optional[Union[List, str]] = None,
+    text_column_names: Optional[List] = None,
+    sampling_rate: Optional[int] = 16000,
+    stopping_strategy: Optional[str] = "first_exhausted",
+    dataset_samples: Optional[Union[List, np.array]] = None,
+    streaming: Optional[bool] = True,
+    seed: Optional[int] = None,
+    accelerator: Optional[Accelerator] = None,
+    use_pseudo_labels: float = None,
+    **kwargs,
+) -> IterableDataset:
+    dataset_names_dict = convert_dataset_str_to_list(
+        dataset_names, dataset_config_names, splits, text_column_names, dataset_samples
+    )
+
+    if dataset_samples is not None:
+        dataset_samples = [ds_dict["samples"] for ds_dict in dataset_names_dict]
+        probabilities = np.array(dataset_samples) / np.sum(dataset_samples)
+    else:
+        probabilities = None
+
+    all_datasets = []
+    # iterate over the datasets we want to interleave
+    for dataset_dict in tqdm(
+        dataset_names_dict,
+        desc="Combining datasets...",
+        disable=not accelerator.is_local_main_process if accelerator is not None else False,
+    ):
+        dataset = load_dataset(
+            dataset_dict["name"],
+            dataset_dict["config"],
+            split=dataset_dict["split"],
+            streaming=streaming,
+            **kwargs,
+        )
+        # resample to specified sampling rate
+        dataset = dataset.cast_column("audio", datasets.features.Audio(sampling_rate))
+        dataset_features = dataset.features.keys()
+        columns_to_keep = {"audio", "text"}
+
+        if dataset_dict["text_column_name"] not in dataset_features:
+            raise ValueError(
+                f"Text column name {dataset_dict['text_column_name']} not found in dataset"
+                f" '{dataset_dict['name']}'. Make sure to set `--text_column_name` to the"
+                f" correct text column - one of {', '.join(dataset_features)}."
+            )
+
+        # blanket renaming of all transcription columns to text
+        if dataset_dict["text_column_name"] != "text":
+            dataset = dataset.rename_column(dataset_dict["text_column_name"], "text")
+
+        if use_pseudo_labels:
+            if "whisper_transcript" not in dataset_features:
+                raise ValueError(
+                    f"Pseudo-label column `whisper_transcript` not found in dataset {dataset_dict['name']}. Ensure"
+                    "pseudo-labels are present in the dataset under this column name, or train directly on the text "
+                    "labels by setting `--use_pseudo_labels=False` and defining the appropriate `--text_column_name`."
+                )
+            columns_to_keep.add("whisper_transcript")
+
+        if "condition_on_prev" in dataset_features:
+            columns_to_keep.add("condition_on_prev")
+
+        dataset_features = dataset.features.keys()
+        dataset = dataset.remove_columns(set(dataset_features - columns_to_keep))
+        all_datasets.append(dataset)
+
+    if len(all_datasets) == 1:
+        # we have a single dataset so just return it as is
+        return all_datasets[0]
+
+    if streaming:
+        interleaved_dataset = interleave_datasets(
+            all_datasets,
+            stopping_strategy=stopping_strategy,
+            probabilities=probabilities,
+            seed=seed,
+        )
+    else:
+        interleaved_dataset = concatenate_datasets(all_datasets)
+
+    return interleaved_dataset
+
+
+def sorted_checkpoints(output_dir=None, checkpoint_prefix="checkpoint") -> List[str]:
+    """Helper function to sort saved checkpoints from oldest to newest."""
+    ordering_and_checkpoint_path = []
+
+    glob_checkpoints = [str(x) for x in Path(output_dir).glob(f"{checkpoint_prefix}-*") if os.path.isdir(x)]
+
+    for path in glob_checkpoints:
+        regex_match = re.match(f".*{checkpoint_prefix}-([0-9]+)", path)
+        if regex_match is not None and regex_match.groups() is not None:
+            ordering_and_checkpoint_path.append((int(regex_match.groups()[0]), path))
+
+    checkpoints_sorted = sorted(ordering_and_checkpoint_path)
+    checkpoints_sorted = [checkpoint[1] for checkpoint in checkpoints_sorted]
+    return checkpoints_sorted
+
+
+def rotate_checkpoints(save_total_limit=None, output_dir=None, checkpoint_prefix="checkpoint") -> None:
+    """Helper function to delete old checkpoints."""
+    if save_total_limit is None or save_total_limit <= 0:
+        return
+    # Check if we should delete older checkpoint(s)
+    checkpoints_sorted = sorted_checkpoints(output_dir=output_dir, checkpoint_prefix=checkpoint_prefix)
+    if len(checkpoints_sorted) <= save_total_limit:
+        return
+
+    number_of_checkpoints_to_delete = max(0, len(checkpoints_sorted) - save_total_limit)
+    checkpoints_to_be_deleted = checkpoints_sorted[:number_of_checkpoints_to_delete]
+    for checkpoint in checkpoints_to_be_deleted:
+        logger.info(f"Deleting older checkpoint [{checkpoint}] due to args.save_total_limit")
+        shutil.rmtree(checkpoint, ignore_errors=True)
+
+
+_RE_CHECKPOINT = re.compile(r"^checkpoint-(\d+)-epoch-(\d+)$")
+
+
+def get_last_checkpoint(folder):
+    content = os.listdir(folder)
+    checkpoints = [
+        path
+        for path in content
+        if _RE_CHECKPOINT.search(path) is not None and os.path.isdir(os.path.join(folder, path))
+    ]
+    if len(checkpoints) == 0:
+        return
+    return os.path.join(folder, max(checkpoints, key=lambda x: int(_RE_CHECKPOINT.search(x).groups()[0])))
+
+
+def get_parameter_names(model, forbidden_layer_types, forbidden_module=None):
+    """
+    Returns the names of the model parameters that are not inside a forbidden layer or forbidden module.
+    Can be used to get a subset of parameter names for decay masks, or to exclude parameters from an optimiser
+    (e.g. if the module is frozen).
+    """
+    result = []
+    for name, child in model.named_children():
+        result += [
+            f"{name}.{n}"
+            for n in get_parameter_names(child, forbidden_layer_types, forbidden_module)
+            if not (
+                isinstance(child, tuple(forbidden_layer_types))
+                or (child in tuple(forbidden_module) if forbidden_module is not None else False)
+            )
+        ]
+    # Add model specific parameters (defined with nn.Parameter) since they are not in any child.
+    result += list(model._parameters.keys())
+    return result
+
+
+def main():
+    # 1. Parse input arguments
+    # We keep distinct sets of args, for cleaner separation of model/data/training related args
+    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, DistillationTrainingArguments))
+
+    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
+        # If we pass only one argument to the script and it's the path to a json file,
+        # let's parse it to get our arguments.
+        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
+    else:
+        model_args, data_args, training_args = parser.parse_args_into_dataclasses()
+
+    # 2. Initialize the accelerator
+    # We will let the accelerator handle device placement for us in this example
+    # We simply have to specify the training precision and any trackers being used
+    # We'll use the same dtype arguments as our JAX/Flax training script and convert
+    # it to accelerate format
+    if training_args.dtype == "float16":
+        mixed_precision = "fp16"
+        teacher_dtype = torch.float16
+    elif training_args.dtype == "bfloat16":
+        mixed_precision = "bf16"
+        teacher_dtype = torch.bfloat16
+    else:
+        mixed_precision = "no"
+        teacher_dtype = torch.float32
+
+    accelerator = Accelerator(
+        gradient_accumulation_steps=training_args.gradient_accumulation_steps,
+        mixed_precision=mixed_precision,
+        log_with=training_args.report_to,
+        project_dir=training_args.output_dir,
+    )
+
+    accelerator.init_trackers(
+        project_name=data_args.wandb_project,
+        init_kwargs={
+            "wandb": {"name": data_args.wandb_name,
+                      "dir": data_args.wandb_dir}
+        }
+
+    )
+
+    # 3. Set-up basic logging
+    # Create one log on every process with the configuration for debugging
+    logging.basicConfig(
+        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+        datefmt="%m/%d/%Y %H:%M:%S",
+        level=logging.INFO,
+    )
+    # Log a small summary on each proces
+    logger.warning(
+        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, "
+        f"distributed training: {training_args.parallel_mode.value == 'distributed'}, 16-bits training: {training_args.fp16}"
+    )
+
+    # Set the verbosity to info of the Transformers logger (on main process only)
+    if accelerator.is_local_main_process:
+        datasets.utils.logging.set_verbosity_warning()
+        transformers.utils.logging.set_verbosity_info()
+    else:
+        datasets.utils.logging.set_verbosity_error()
+        transformers.utils.logging.set_verbosity_error()
+    logger.info("Training/evaluation parameters %s", training_args)
+
+    # 4. Detecting last checkpoint and eventually continue from last checkpoint
+    last_checkpoint = None
+    if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
+        last_checkpoint = get_last_checkpoint(training_args.output_dir)
+        if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
+            raise ValueError(
+                f"Output directory ({training_args.output_dir}) already exists and is not empty. "
+                "Use --overwrite_output_dir to overcome."
+            )
+        elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
+            logger.info(
+                f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
+                "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
+            )
+
+    # 5. Handle the repository creation
+    if accelerator.is_main_process:
+        if training_args.push_to_hub:
+            if training_args.hub_model_id is None:
+                repo_name = get_full_repo_name(
+                    Path(training_args.output_dir).absolute().name,
+                    token=training_args.hub_token,
+                )
+            else:
+                repo_name = training_args.hub_model_id
+            create_repo(repo_name, exist_ok=True, token=training_args.hub_token)
+
+            with open(os.path.join(training_args.output_dir, ".gitignore"), "w+") as gitignore:
+                if "wandb" not in gitignore:
+                    gitignore.write("wandb\n")
+        elif training_args.output_dir is not None:
+            os.makedirs(training_args.output_dir, exist_ok=True)
+    accelerator.wait_for_everyone()
+
+    # 6. Load dataset - either streaming or non-streaming (offline)
+    raw_datasets = IterableDatasetDict() if data_args.streaming else DatasetDict()
+
+    # set seed for determinism
+    set_seed(training_args.seed)
+
+    if training_args.do_train:
+        raw_datasets["train"] = load_multiple_datasets(
+            data_args.train_dataset_name,
+            data_args.train_dataset_config_name,
+            splits=data_args.train_split_name,
+            text_column_names=data_args.text_column_name,
+            use_pseudo_labels=data_args.use_pseudo_labels,
+            streaming=data_args.streaming,
+            dataset_samples=data_args.train_dataset_samples,
+            seed=training_args.seed,
+            accelerator=accelerator,
+            cache_dir=data_args.dataset_cache_dir,
+            token=model_args.token,
+        )
+        raw_datasets_train_features = list(raw_datasets["train"].features.keys())
+
+    if training_args.do_eval:
+        dataset_names_dict = convert_dataset_str_to_list(
+            data_args.eval_dataset_name if data_args.eval_dataset_name else data_args.train_dataset_name,
+            (
+                data_args.eval_dataset_config_name
+                if data_args.eval_dataset_config_name
+                else data_args.train_dataset_config_name
+            ),
+            splits=data_args.eval_split_name,
+            text_column_names=data_args.eval_text_column_name,
+        )
+        all_eval_splits = []
+        if len(dataset_names_dict) == 1:
+            # load a single eval set
+            dataset_dict = dataset_names_dict[0]
+            all_eval_splits.append("eval")
+            raw_datasets["eval"] = load_dataset(
+                dataset_dict["name"],
+                dataset_dict["config"],
+                split=dataset_dict["split"],
+                cache_dir=data_args.dataset_cache_dir,
+                token=model_args.token,
+                streaming=data_args.streaming,
+            )
+            if data_args.eval_text_column_name != "text":
+                raw_datasets["eval"] = raw_datasets["eval"].rename_column(data_args.eval_text_column_name, "text")
+        else:
+            # load multiple eval sets
+            for dataset_dict in dataset_names_dict:
+                if dataset_dict["name"] == "esb/diagnostic-dataset":
+                    # for the ESB diagnostic dataset, the dataset name is effectively the config
+                    pretty_name = f"{dataset_dict['config']}-diagnostic/{dataset_dict['split']}"
+                else:
+                    pretty_name = f"{dataset_dict['name'].split('/')[-1]}/{dataset_dict['split'].replace('.', '-')}"
+                all_eval_splits.append(pretty_name)
+                raw_datasets[pretty_name] = load_dataset(
+                    dataset_dict["name"],
+                    dataset_dict["config"],
+                    split=dataset_dict["split"],
+                    cache_dir=data_args.dataset_cache_dir,
+                    token=model_args.token,
+                    streaming=data_args.streaming,
+                )
+                # make column names consistent (text, audio)
+                if dataset_dict["text_column_name"] != "text":
+                    raw_datasets[pretty_name] = raw_datasets[pretty_name].rename_column(
+                        dataset_dict["text_column_name"], "text"
+                    )
+                raw_datasets[pretty_name] = raw_datasets[pretty_name].remove_columns(
+                    set(raw_datasets[pretty_name].features.keys()) - {"audio", "text"}
+                )
+
+    if not training_args.do_train and not training_args.do_eval:
+        raise ValueError(
+            "Cannot not train and not do evaluation. At least one of training or evaluation has to be performed."
+        )
+
+    # 7. Load pretrained model, tokenizer, and feature extractor
+    config = WhisperConfig.from_pretrained(
+        (model_args.config_name if model_args.config_name else model_args.model_name_or_path),
+        cache_dir=model_args.cache_dir,
+        revision=model_args.model_revision,
+        token=model_args.token,
+    )
+    feature_extractor = WhisperFeatureExtractor.from_pretrained(
+        (model_args.feature_extractor_name if model_args.feature_extractor_name else model_args.model_name_or_path),
+        cache_dir=model_args.cache_dir,
+        revision=model_args.model_revision,
+        token=model_args.token,
+    )
+    tokenizer = WhisperTokenizerFast.from_pretrained(
+        (model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path),
+        cache_dir=model_args.cache_dir,
+        use_fast=model_args.use_fast_tokenizer,
+        revision=model_args.model_revision,
+        token=model_args.token,
+    )
+
+    # override timestamp tokens until tokenizer issues are fixed in transformers
+    timestamps = [AddedToken("<|%.2f|>" % (i * 0.02), lstrip=False, rstrip=False) for i in range(1500 + 1)]
+    tokenizer.add_tokens(timestamps)
+
+    # The teacher model can safely be cast to the dtype of training since we don't
+    # update the params
+    teacher_model = WhisperForConditionalGeneration.from_pretrained(
+        model_args.teacher_model_name_or_path,
+        cache_dir=model_args.cache_dir,
+        token=model_args.token,
+        low_cpu_mem_usage=True,
+        torch_dtype=teacher_dtype,
+        attn_implementation=model_args.attn_implementation,
+    )
+
+    student_model = WhisperForConditionalGeneration.from_pretrained(
+        model_args.model_name_or_path,
+        config=config,
+        cache_dir=model_args.cache_dir,
+        revision=model_args.model_revision,
+        subfolder=model_args.subfolder,
+        token=model_args.token,
+        low_cpu_mem_usage=True,
+        attn_implementation=model_args.attn_implementation,
+    )
+
+    if student_model.config.decoder_start_token_id is None or teacher_model.config.decoder_start_token_id is None:
+        raise ValueError(
+            f"Make sure that `config.decoder_start_token_id` is correctly defined for both the "
+            f"student and teacher model. Got {student_model.config.decoder_start_token_id} for the "
+            f"student and {teacher_model.config.decoder_start_token_id} for the teacher."
+        )
+
+    # enable gradient checkpointing if necessary
+    if training_args.gradient_checkpointing:
+        student_model.gradient_checkpointing_enable()
+
+    def set_trainable_parameters(module, requires_grad=False):
+        for param in module.parameters():
+            param.requires_grad = requires_grad
+        module._requires_grad = requires_grad
+
+    # freeze student encoder if necessary
+    if training_args.freeze_encoder:
+        set_trainable_parameters(student_model.model.encoder, requires_grad=False)
+        student_model.model.encoder.gradient_checkpointing = False
+    
+    if training_args.freeze_decoder:
+        set_trainable_parameters(student_model.model.decoder, requires_grad=False)
+        student_model.model.decoder.gradient_checkpointing = False
+        # un-freeze LM head parameters (and consequently word embeddings), frozen when frozing decoder since tied word embedding and LM head
+        set_trainable_parameters(student_model.proj_out, requires_grad=True) 
+        
+
+    if training_args.freeze_embed_positions:
+        # set_trainable_parameters(student_model.model.decoder.embed_tokens, requires_grad=False)
+        set_trainable_parameters(student_model.model.decoder.embed_positions, requires_grad=False)
+        if student_model.model.decoder.gradient_checkpointing:
+            logger.info(
+                "Disabling gradient checkpointing in the decoder since it's incompatible with `freeze_embed_positions`."
+            )
+    
+    logger.info(
+        f"Number of trainable parameters: {sum(p.numel() for p in student_model.parameters() if p.requires_grad):.3e}"
+    )
+
+    share_hidden_states = training_args.freeze_encoder and student_model.config.d_model == teacher_model.config.d_model
+    if share_hidden_states:
+        # tie the weights for the teacher encoder if we're freezing the student and it's the same as the teacher
+        teacher_model.model.encoder = student_model.model.encoder
+
+    if hasattr(teacher_model.generation_config, "is_multilingual") and teacher_model.generation_config.is_multilingual:
+        # We need to set the language and task ids for previously multilingual checkpoints
+        is_multilingual = True
+        tokenizer.set_prefix_tokens(language=data_args.language, task=data_args.task, predict_timestamps=False)
+        student_model.generation_config.update(
+            **{
+                "language": data_args.language,
+                "task": data_args.task,
+            }
+        )
+    elif data_args.language is not None:
+        raise ValueError(
+            "Setting language token for an English-only checkpoint is not permitted. The language argument should "
+            "only be set for multilingual checkpoints."
+        )
+    else:
+        is_multilingual = False
+
+    # 8. Create a single speech processor - make sure all processes wait until data is saved
+    if accelerator.is_main_process:
+        feature_extractor.save_pretrained(training_args.output_dir)
+        tokenizer.save_pretrained(training_args.output_dir)
+        # save the config and generation config as well
+        config.save_pretrained(training_args.output_dir)
+        student_model.generation_config.save_pretrained(training_args.output_dir)
+
+    accelerator.wait_for_everyone()
+    processor = WhisperProcessor.from_pretrained(training_args.output_dir)
+
+    # 9. Resample speech dataset: `datasets` takes care of automatically loading and resampling the audio,
+    # so we just need to set the correct target sampling rate.
+    sampling_rate = feature_extractor.sampling_rate
+    raw_datasets = raw_datasets.cast_column(
+        data_args.audio_column_name,
+        datasets.features.Audio(sampling_rate=sampling_rate),
+    )
+
+    # 10. Preprocessing the datasets: we need to read the audio files as arrays and tokenize the targets.
+    # 10.1: Define the pre-processing constants
+    max_input_length = int(data_args.max_duration_in_seconds * sampling_rate)
+    min_input_length = int(data_args.min_duration_in_seconds * sampling_rate)
+    max_label_length = (
+        data_args.max_label_length if data_args.max_label_length is not None else student_model.config.max_length
+    )
+
+    timestamp_probability = data_args.timestamp_probability
+    condition_on_prev_probability = data_args.condition_on_prev_probability
+    return_timestamps = data_args.return_timestamps if timestamp_probability > 0 else False
+
+    timestamp_ids = tokenizer.timestamp_ids()
+    timestamp_begin = tokenizer.all_special_ids[-1]
+    timestamp_position = 3 if is_multilingual else 1
+
+    decoder_start_token_id = student_model.config.decoder_start_token_id  # <|startoftranscript|>
+    decoder_prev_token_id = tokenizer.all_special_ids[-3]  # <|startofprev|>
+    prompt_cutoff_length = max_label_length // 2
+
+    num_workers = data_args.preprocessing_num_workers
+    dataloader_num_workers = training_args.dataloader_num_workers
+    prefetch_factor = training_args.dataloader_prefetch_factor
+
+    metric = evaluate.load("wer")
+    normalizer = (
+        BasicTextNormalizer()
+        if data_args.language is not None
+        else EnglishTextNormalizer(tokenizer.english_spelling_normalizer)
+    )
+    wer_threshold = data_args.wer_threshold
+    use_pseudo_labels = data_args.use_pseudo_labels
+    train_text_column_name = "whisper_transcript" if use_pseudo_labels else "text"
+
+    # 10.2: filter based on maximum number of training/evaluation samples
+    if training_args.do_train and data_args.max_train_samples is not None:
+        raw_datasets["train"] = (
+            raw_datasets["train"].take(data_args.max_train_samples)
+            if data_args.streaming
+            else raw_datasets["train"].select(range(data_args.max_train_samples))
+        )
+
+    if training_args.do_eval and data_args.max_eval_samples is not None:
+        for eval_split in all_eval_splits:
+            raw_datasets[eval_split] = (
+                raw_datasets[eval_split].take(data_args.max_eval_samples)
+                if data_args.streaming
+                else raw_datasets[eval_split].select(range(data_args.max_eval_samples))
+            )
+
+    # 10.3: filter training data based on WER threshold -> this is KEY to good distillation performance
+    def is_wer_in_range(ground_truth, whisper_transcript):
+        norm_ground_truth = normalizer(ground_truth)
+        if whisper_transcript is not None and whisper_transcript.upper() == whisper_transcript:
+            # filter entirely upper-case transcriptions: these are erroneous generations from large-v3
+            return False
+        elif len(norm_ground_truth) > 0 and whisper_transcript is not None:
+            norm_whisper_transcript = normalizer(whisper_transcript)
+            wer = 100 * metric.compute(predictions=[norm_whisper_transcript], references=[norm_ground_truth])
+            return wer < wer_threshold
+        else:
+            # filter automatically since we can't know the WER
+            return False
+
+    filter_by_wer_threshold = partial(
+        raw_datasets["train"].filter,
+        function=is_wer_in_range,
+        input_columns=["text", "whisper_transcript"],
+    )
+
+    if wer_threshold is not None and use_pseudo_labels:
+        with accelerator.main_process_first():
+            raw_datasets["train"] = (
+                filter_by_wer_threshold(num_proc=num_workers, desc="filtering train dataset by wer")
+                if not data_args.streaming
+                else filter_by_wer_threshold()
+            )
+
+    # 10.4: pre-process training/evaluation datasets
+    def prepare_train_dataset(batch):
+        """
+        Pre-process the raw dataset in a three stage process:
+            1. Convert the audio arrays to log-mel spectrogram inputs
+            2. Possibly filter the timestamp tokens from the token ids (depending on the timestamp probability)
+            3. Possibly add prompt tokens if conditioning on previous text (depending on the conditioning probability)
+        """
+        # process audio input
+        audio = [sample["array"] for sample in batch["audio"]]
+        inputs = feature_extractor(audio, sampling_rate=sampling_rate)
+        batch["input_features"] = inputs.input_features
+        batch["input_length"] = [len(sample) for sample in audio]
+
+        # process text targets - for training these are the Whisper-generated pseudo-labels
+        input_str_batched = batch[train_text_column_name]
+        condition_on_prev_batched = batch.get("condition_on_prev", len(input_str_batched) * [None])
+
+        all_token_ids = []
+        all_token_ids_unprompted = []
+        for prev_ids, input_str in zip(condition_on_prev_batched, input_str_batched):
+            token_ids = tokenizer(input_str, add_special_tokens=not use_pseudo_labels).input_ids
+
+            # check whether we have timestamps in the PLs and filter if required
+            has_timestamps = len(set(token_ids) & set(timestamp_ids)) > 0
+            if has_timestamps:
+                # sample from binomial distribution to get probability of training on timestamps
+                predict_timestamps = bool(np.random.binomial(1, timestamp_probability))
+                if not predict_timestamps:
+                    # filter timestamps and insert the <|notimestamps|> task token
+                    token_ids = [token for token in token_ids if token < timestamp_begin]
+                    token_ids.insert(timestamp_position, timestamp_begin)
+
+            all_token_ids_unprompted.append(token_ids)
+            # check whether to condition on previous text - we do this with probability condition_on_prev_probability
+            condition_on_prev = bool(np.random.binomial(1, condition_on_prev_probability))
+            if not condition_on_prev:
+                prev_ids = None
+            elif "condition_on_prev" not in batch and len(all_token_ids_unprompted) > 1:
+                # prompt ids are the penultimate token ids in the batch
+                prev_ids = all_token_ids_unprompted[-2]
+
+            if prev_ids is not None:
+                if has_timestamps and not predict_timestamps:
+                    # filter timestamp ids from prompt when not predicting timestamps
+                    prev_ids = [token for token in prev_ids if token < timestamp_begin]
+
+                # check that the length of the prompt does not exceed more than half the max label length (224)
+                if len(prev_ids) > prompt_cutoff_length:
+                    prev_ids = prev_ids[-prompt_cutoff_length + 1 :]
+                    prev_ids = [decoder_prev_token_id] + prev_ids
+
+                # and that the total length of the labels does not exceed the max label length (448)
+                if len(prev_ids + token_ids) > max_label_length:
+                    trim_length = len(prev_ids + token_ids) - max_label_length + 1
+                    prev_ids = prev_ids[trim_length:]
+                    prev_ids = [decoder_prev_token_id] + prev_ids
+
+                token_ids = prev_ids + token_ids
+
+            all_token_ids.append(token_ids)
+
+        batch["labels"] = all_token_ids
+        return batch
+
+    def prepare_eval_dataset(batch):
+        # process audio input
+        sample = batch["audio"]
+        inputs = feature_extractor(sample["array"], sampling_rate=sample["sampling_rate"])
+        batch["input_features"] = inputs.input_features[0]
+        batch["input_length"] = len(sample["array"])
+
+        # process targets - for evaluation these are the ground-truth transcriptions
+        input_str = batch["text"]
+        batch["labels"] = tokenizer(input_str).input_ids
+        return batch
+
+    vectorized_datasets = IterableDatasetDict() if data_args.streaming else DatasetDict()
+    if training_args.do_train:
+        # with streaming mode we can only have 1 worker, whereas with non-streaming
+        # we can use `num_workers` (which is much faster)
+        # We gate the pre-processing function accordingly
+        map_fn_train = partial(
+            raw_datasets["train"].map,
+            function=prepare_train_dataset,
+            remove_columns=raw_datasets_train_features,
+            batched=True,
+            batch_size=data_args.preprocessing_batch_size,
+        )
+        with accelerator.main_process_first():
+            vectorized_datasets["train"] = (
+                map_fn_train(num_proc=num_workers, desc="preprocess train dataset")
+                if not data_args.streaming
+                else map_fn_train()
+            )
+    if training_args.do_eval:
+        for eval_split in all_eval_splits:
+            raw_datasets_eval_features = list(raw_datasets[eval_split].features.keys())
+            map_fn_eval = partial(
+                raw_datasets[eval_split].map, function=prepare_eval_dataset, remove_columns=raw_datasets_eval_features
+            )
+            with accelerator.main_process_first():
+                vectorized_datasets[eval_split] = (
+                    map_fn_eval(num_proc=num_workers, desc="preprocess eval dataset")
+                    if not data_args.streaming
+                    else map_fn_eval()
+                )
+
+    # 10.5: Filter training data with inputs longer than `max_input_length`
+    def is_audio_in_length_range(length):
+        return min_input_length < length < max_input_length
+
+    filter_by_audio_fn = partial(
+        vectorized_datasets.filter, function=is_audio_in_length_range, input_columns=["input_length"]
+    )
+    with accelerator.main_process_first():
+        vectorized_datasets = (
+            filter_by_audio_fn(num_proc=num_workers, desc="filtering train dataset by audio length")
+            if not data_args.streaming
+            else filter_by_audio_fn()
+        )
+
+    # 10.6: Filter training data with labels longer than `max_label_length`
+    def is_labels_in_length_range(labels):
+        return 0 < len(labels) <= max_label_length
+
+    filter_by_labels_fn = partial(
+        vectorized_datasets.filter, function=is_labels_in_length_range, input_columns=["labels"]
+    )
+    with accelerator.main_process_first():
+        vectorized_datasets = (
+            filter_by_labels_fn(num_proc=num_workers, desc="filtering train dataset")
+            if not data_args.streaming
+            else filter_by_labels_fn()
+        )
+
+    # Pre-processing complete!
+    # For large datasets it is advised to run the preprocessing on a
+    # single machine first with `--preprocessing_only` since there will mostly likely
+    # be a timeout when running the script in distributed mode.
+    # In a second step, `--preprocessing_only` can then be set to `False` to load the
+    # cached dataset
+    if data_args.preprocessing_only:
+        if data_args.streaming:
+            raise ValueError(
+                "When using streaming mode, dataset pre-processing is performed on the fly, hence there is no notion"
+                "of a cached pre-processed dataset. Remove the argument `--preprocessing_only` to run pre-processing "
+                "on the fly with streaming mode."
+            )
+        cache = {k: v.cache_files for k, v in vectorized_datasets.items()}
+        logger.info(f"Data preprocessing finished. Files cached at {cache}.")
+        return
+
+    # 11. Define Evaluation Metrics
+    def compute_metrics(preds, labels):
+        # replace padded labels by the padding token
+        for idx in range(len(labels)):
+            labels[idx][labels[idx] == -100] = tokenizer.pad_token_id
+
+        pred_str = tokenizer.batch_decode(preds, skip_special_tokens=True, decode_with_timestamps=return_timestamps)
+        # we do not want to group tokens when computing the metrics
+        label_str = tokenizer.batch_decode(labels, skip_special_tokens=True)
+        wer_ortho = 100 * metric.compute(predictions=pred_str, references=label_str)
+
+        # normalize everything and re-compute the WER
+        norm_pred_str = [normalizer(pred) for pred in pred_str]
+        norm_label_str = [normalizer(label) for label in label_str]
+        # for logging, we need the pred/labels to match the norm_pred/norm_labels, so discard any filtered samples here
+        pred_str = [pred_str[i] for i in range(len(norm_pred_str)) if len(norm_label_str[i]) > 0]
+        label_str = [label_str[i] for i in range(len(norm_label_str)) if len(norm_label_str[i]) > 0]
+        # filtering step to only evaluate the samples that correspond to non-zero normalized references:
+        norm_pred_str = [norm_pred_str[i] for i in range(len(norm_pred_str)) if len(norm_label_str[i]) > 0]
+        norm_label_str = [norm_label_str[i] for i in range(len(norm_label_str)) if len(norm_label_str[i]) > 0]
+
+        wer = 100 * metric.compute(predictions=norm_pred_str, references=norm_label_str)
+        return {"wer": wer, "wer_ortho": wer_ortho}, pred_str, label_str, norm_pred_str, norm_label_str
+
+    # 12. Define Training Schedule
+    # Store some constants
+    per_device_train_batch_size = int(training_args.per_device_train_batch_size)
+    train_batch_size = per_device_train_batch_size * accelerator.num_processes
+    gradient_accumulation_steps = int(training_args.gradient_accumulation_steps)
+    per_device_eval_batch_size = int(training_args.per_device_eval_batch_size)
+
+    if not data_args.streaming and training_args.max_steps < 0:
+        num_epochs = int(training_args.num_train_epochs)
+        steps_per_epoch = len(vectorized_datasets["train"]) // (train_batch_size * gradient_accumulation_steps)
+        total_train_steps = steps_per_epoch * num_epochs
+    elif training_args.max_steps > 0:
+        logger.info("max_steps is given, it will override any value given in num_train_epochs")
+        total_train_steps = int(training_args.max_steps)
+        if not data_args.streaming:
+            steps_per_epoch = len(vectorized_datasets["train"]) // (train_batch_size * gradient_accumulation_steps)
+            num_epochs = int(np.ceil(total_train_steps / steps_per_epoch))
+        else:
+            # Setting a very large number of epochs so we go as many times as necessary over the iterator.
+            num_epochs = sys.maxsize
+            steps_per_epoch = total_train_steps
+    else:
+        raise ValueError("max_steps must be specified when training with a streaming (iterable) dataset")
+
+    if training_args.eval_steps is None:
+        logger.info(
+            f"eval_steps is not set, evaluating at the end of {'each epoch' if not data_args.streaming else 'training'}"
+        )
+        eval_steps = steps_per_epoch
+    else:
+        eval_steps = training_args.eval_steps
+
+    # 13. Define optimizer, LR scheduler, collator
+    
+    forbidden_module = [
+        module
+        for module, flag in [
+            (student_model.model.encoder, training_args.freeze_encoder),
+            (student_model.model.decoder, training_args.freeze_decoder)
+        ]
+        if flag
+    ] or None
+
+    decay_parameters = get_parameter_names(
+        student_model,
+        [nn.LayerNorm],
+        forbidden_module=forbidden_module,
+    )
+    decay_parameters = [name for name in decay_parameters if "bias" not in name]
+    optimizer_grouped_parameters = [
+        {
+            "params": [param for name, param in student_model.named_parameters() if name in decay_parameters],
+            "weight_decay": training_args.weight_decay,
+        },
+        {
+            "params": [param for name, param in student_model.named_parameters() if name not in decay_parameters],
+            "weight_decay": 0.0,
+        },
+    ]
+    optimizer = torch.optim.AdamW(
+        params=optimizer_grouped_parameters,
+        lr=training_args.learning_rate,
+        betas=(training_args.adam_beta1, training_args.adam_beta2),
+        eps=training_args.adam_epsilon,
+    )
+
+    # LR scheduler gets stepped by `num_processes` each time -> account for this in warmup / total steps
+    lr_scheduler = get_scheduler(
+        name=training_args.lr_scheduler_type,
+        optimizer=optimizer,
+        num_warmup_steps=training_args.warmup_steps * accelerator.num_processes,
+        num_training_steps=total_train_steps * accelerator.num_processes,
+    )
+
+    data_collator = DataCollatorSpeechSeq2SeqWithPadding(
+        processor=processor,
+        decoder_start_token_id=decoder_start_token_id,
+        decoder_prev_token_id=decoder_prev_token_id,
+        input_padding="longest",
+        target_padding="max_length",
+        max_target_length=max_label_length,
+    )
+
+    # 14. Define generation arguments - we need to do this before we wrap the models in DDP
+    # so that we can still access the configs
+    num_beams = (
+        training_args.generation_num_beams
+        if training_args.generation_num_beams is not None
+        else getattr(student_model.generation_config, "num_beams", 1)
+    )
+
+    gen_kwargs = {
+        "max_length": max_label_length,
+        "num_beams": num_beams,
+        "return_timestamps": return_timestamps,
+    }
+    if is_multilingual:
+        # forcing the language and task tokens helps multilingual models in their generations
+        gen_kwargs.update(
+            {
+                "language": data_args.language,
+                "task": data_args.task,
+            }
+        )
+
+    # 15. Prepare everything with accelerate
+    student_model, teacher_model, optimizer, lr_scheduler = accelerator.prepare(
+        student_model, teacher_model, optimizer, lr_scheduler
+    )
+
+    def kl_divergence(target_distribution, log_predicted_distribution, labels):
+        kl_loss = nn.KLDivLoss(reduction="none")
+        divergence = kl_loss(log_predicted_distribution, target_distribution)
+        # ignore padded tokens from divergence, i.e. where labels are not set to -100
+        padding_mask = labels >= 0
+        padding_mask = padding_mask.unsqueeze(-1)
+        divergence = divergence * padding_mask
+        # take the average over the mini-batch
+        divergence = divergence.sum() / padding_mask.sum()
+        return divergence
+
+    # Define gradient update step fn
+    def train_step(
+        batch,
+        temperature=2.0,
+    ):
+        student_model.train()
+        teacher_model.eval()
+
+        student_outputs = student_model(**batch)
+        with torch.no_grad():
+            if share_hidden_states:
+                # if the student and teacher share the same frozen encoder then we don't have to recompute the
+                # encoder hidden-states for the teacher model, we can just re-use from the student
+                encoder_outputs = BaseModelOutput(student_outputs.encoder_last_hidden_state.to(dtype=teacher_dtype))
+                teacher_outputs = teacher_model(encoder_outputs=encoder_outputs, labels=batch["labels"])
+            else:
+                # do the full forward pass for the teacher model (encoder + decoder)
+                teacher_outputs = teacher_model(**batch)
+
+        # CE (data) loss
+        ce_loss = student_outputs.loss
+        # rescale distribution by temperature to ensure gradients scale correctly
+        teacher_distribution = nn.functional.softmax(teacher_outputs.logits / temperature, dim=-1)
+        # log softmax of student predictions for numerical stability
+        student_distribution = nn.functional.log_softmax(student_outputs.logits / temperature, dim=-1)
+        # KL-divergence loss (scaled by temperature)
+        kl_loss = kl_divergence(teacher_distribution, student_distribution, batch["labels"]) * temperature**2
+
+        # use Distil-Whisper formulation (fix weight of CE loss and tune KL weight)
+        loss = 0.8 * ce_loss + training_args.kl_weight * kl_loss
+        metrics = {"loss": loss, "ce_loss": ce_loss, "kl_loss": kl_loss}
+        return loss, metrics
+
+    # Define eval fn
+    def eval_step(batch):
+        student_model.eval()
+        teacher_model.eval()
+
+        with torch.no_grad():
+            student_outputs = student_model(**batch)
+            if share_hidden_states:
+                encoder_outputs = BaseModelOutput(student_outputs.encoder_last_hidden_state.to(dtype=teacher_dtype))
+                teacher_outputs = teacher_model(encoder_outputs=encoder_outputs, labels=batch["labels"])
+            else:
+                teacher_outputs = teacher_model(**batch)
+
+        # CE (data) loss
+        ce_loss = student_outputs.loss
+
+        # log softmax / softmax for numerical stability
+        student_distribution = nn.functional.log_softmax(student_outputs.logits, dim=-1)
+        teacher_distribution = nn.functional.softmax(teacher_outputs.logits, dim=-1)
+        # temperature is always 1 for eval
+        kl_loss = kl_divergence(teacher_distribution, student_distribution, batch["labels"])
+
+        # use Distil-Whisper formulation (fix weight of CE loss and tune KL weight)
+        loss = 0.8 * ce_loss + training_args.kl_weight * kl_loss
+        metrics = {"loss": loss, "ce_loss": ce_loss, "kl_loss": kl_loss}
+        return metrics
+
+    def generate_step(batch):
+        student_model.eval()
+        output_ids = accelerator.unwrap_model(student_model).generate(batch["input_features"], **gen_kwargs)
+        output_ids = accelerator.pad_across_processes(output_ids, dim=1, pad_index=tokenizer.pad_token_id)
+        return output_ids
+
+    logger.info("***** Running training *****")
+    logger.info(f"  Num examples = {total_train_steps * train_batch_size * gradient_accumulation_steps}")
+    if not data_args.streaming:
+        logger.info(f"  Num epochs = {num_epochs}")
+    logger.info("  Instantaneous batch size per device =" f" {training_args.per_device_train_batch_size}")
+    logger.info("  Gradient accumulation steps =" f" {gradient_accumulation_steps}")
+    logger.info(
+        f"  Total train batch size (w. parallel & distributed) = {train_batch_size * gradient_accumulation_steps}"
+    )
+    logger.info(f"  Total optimization steps = {total_train_steps}")
+
+    # ======================== Training ================================
+    train_time = 0
+    train_start = time.time()
+    steps_trained_progress_bar = tqdm(
+        range(total_train_steps), desc="Train steps ... ", position=0, disable=not accelerator.is_local_main_process
+    )
+    continue_training = True
+    epochs_trained = 0
+    cur_step = 0
+
+    checkpoint = None
+    if training_args.resume_from_checkpoint is not None:
+        checkpoint = training_args.resume_from_checkpoint
+    elif last_checkpoint is not None:
+        checkpoint = last_checkpoint
+
+    if checkpoint is not None:
+        accelerator.load_state(checkpoint)
+        # Find num steps and epoch from saved state string pattern
+        pattern = r"checkpoint-(\d+)-epoch-(\d+)"
+        match = re.search(pattern, checkpoint)
+        cur_step = int(match.group(1))
+        epochs_trained = int(match.group(2))
+
+        logger.info("  Continuing training from checkpoint, will skip to saved global_step")
+        logger.info(f"  Continuing training from epoch {epochs_trained}")
+        logger.info(f"  Continuing training from global step {cur_step}")
+
+        steps_trained_progress_bar.update(cur_step)
+
+        for epoch in range(0, epochs_trained):
+            vectorized_datasets["train"] = vectorized_datasets["train"].shuffle(training_args.seed)
+
+        if not data_args.streaming and training_args.max_steps < 0:
+            # we know exactly the number of steps per epoch, so can skip through the required number of batches
+            resume_step = (cur_step - epochs_trained * steps_per_epoch) * gradient_accumulation_steps
+        else:
+            # Currently we don't know how many steps we've taken in the current epoch
+            # So we just shuffle the dataset one extra time and start from a fresh epoch
+            # This is "good enough" for our purposes but not fully correct
+            resume_step = None
+            vectorized_datasets["train"] = vectorized_datasets["train"].shuffle(training_args.seed)
+    else:
+        resume_step = None
+
+    for epoch in range(epochs_trained, num_epochs):
+        vectorized_datasets["train"] = vectorized_datasets["train"].shuffle(training_args.seed)
+        train_dataloader = DataLoader(
+            vectorized_datasets["train"],
+            collate_fn=data_collator,
+            batch_size=per_device_train_batch_size,
+            num_workers=dataloader_num_workers,
+            prefetch_factor=prefetch_factor,
+            pin_memory=training_args.dataloader_pin_memory,
+        )
+        train_dataloader = accelerator.prepare(train_dataloader)
+        if hasattr(train_dataloader, "dataset") and isinstance(train_dataloader.dataset, IterableDataset):
+            train_dataloader.dataset.set_epoch(epoch)
+
+        if resume_step is not None:
+            # Skip the first N batches in the dataloader when resuming from a checkpoint
+            train_dataloader = accelerator.skip_first_batches(train_dataloader, resume_step)
+            resume_step = None
+
+        for batch in train_dataloader:
+            with accelerator.accumulate(student_model):
+                loss, train_metric = train_step(batch, temperature=training_args.temperature)
+                accelerator.backward(loss)
+                if accelerator.sync_gradients:
+                    accelerator.clip_grad_norm_(student_model.parameters(), training_args.max_grad_norm)
+                optimizer.step()
+                lr_scheduler.step()
+                optimizer.zero_grad()
+
+            # Check if the accelerator has performed an optimization step behind the scenes
+            if accelerator.sync_gradients:
+                steps_trained_progress_bar.update(1)
+                cur_step += 1
+
+                if cur_step % training_args.logging_steps == 0:
+                    steps_trained_progress_bar.write(
+                        f"Step... ({cur_step} / {total_train_steps} | Loss:"
+                        f" {train_metric['loss']}, Learning Rate:"
+                        f" {lr_scheduler.get_last_lr()[0]})"
+                    )
+                    log_metric(
+                        accelerator,
+                        metrics=train_metric,
+                        learning_rate=lr_scheduler.get_last_lr()[0],
+                        train_time=train_time + time.time() - train_start,
+                        step=cur_step,
+                        epoch=epoch,
+                        prefix="train",
+                    )
+
+                # save checkpoint and weights after each save_steps and at the end of training
+                if (cur_step % training_args.save_steps == 0) or cur_step == total_train_steps:
+                    intermediate_dir = os.path.join(training_args.output_dir, f"checkpoint-{cur_step}-epoch-{epoch}")
+                    accelerator.save_state(output_dir=intermediate_dir)
+                    accelerator.wait_for_everyone()
+                    if accelerator.is_main_process:
+                        rotate_checkpoints(training_args.save_total_limit, output_dir=training_args.output_dir)
+
+                        if training_args.push_to_hub:
+                            upload_folder(
+                                folder_path=training_args.output_dir,
+                                repo_id=repo_name,
+                                repo_type="model",
+                                commit_message=f"Saving train state of step {cur_step}",
+                            )
+
+                if training_args.do_eval and (cur_step % eval_steps == 0 or cur_step == total_train_steps):
+                    train_time += time.time() - train_start
+                    student_model.eval()
+                    # ======================== Evaluating ==============================
+                    for eval_split in all_eval_splits:
+                        eval_metrics = []
+                        eval_preds = []
+                        eval_labels = []
+                        eval_start = time.time()
+
+                        validation_dataloader = DataLoader(
+                            vectorized_datasets[eval_split],
+                            collate_fn=data_collator,
+                            batch_size=per_device_eval_batch_size,
+                            drop_last=False,
+                            num_workers=dataloader_num_workers,
+                            prefetch_factor=prefetch_factor,
+                            pin_memory=training_args.dataloader_pin_memory,
+                        )
+                        validation_dataloader = accelerator.prepare(validation_dataloader)
+
+                        for batch in tqdm(
+                            validation_dataloader,
+                            desc=f"Evaluating {eval_split}...",
+                            position=2,
+                            disable=not accelerator.is_local_main_process,
+                        ):
+                            # Model forward
+                            eval_metric = eval_step(batch)
+                            eval_metric = accelerator.gather_for_metrics(eval_metric)
+                            eval_metrics.append(eval_metric)
+
+                            # generation
+                            if training_args.predict_with_generate:
+                                generated_ids = generate_step(batch)
+                                # Gather all predictions and targets
+                                generated_ids, labels = accelerator.gather_for_metrics(
+                                    (generated_ids, batch["labels"])
+                                )
+                                eval_preds.extend(generated_ids)
+                                eval_labels.extend(labels)
+
+                        eval_time = time.time() - eval_start
+                        # normalize eval metrics
+                        eval_metrics = {
+                            key: torch.mean(torch.stack([d[key] for d in eval_metrics])) for key in eval_metrics[0]
+                        }
+
+                        # compute WER metric
+                        wer_desc = ""
+                        if training_args.predict_with_generate:
+                            wer_metric, pred_str, label_str, norm_pred_str, norm_label_str = compute_metrics(
+                                eval_preds, eval_labels
+                            )
+                            eval_metrics.update(wer_metric)
+                            wer_desc = " ".join([f"Eval {key}: {value} |" for key, value in wer_metric.items()])
+                            log_pred(
+                                accelerator,
+                                pred_str,
+                                label_str,
+                                norm_pred_str,
+                                norm_label_str,
+                                step=cur_step,
+                                prefix=eval_split,
+                            )
+
+                        # Print metrics and update progress bar
+                        steps_trained_progress_bar.write(
+                            f"Eval results for step ({cur_step} / {total_train_steps} | Eval Loss: {eval_metrics['loss']} |"
+                            f" {wer_desc})"
+                        )
+
+                        log_metric(
+                            accelerator,
+                            metrics=eval_metrics,
+                            train_time=eval_time,
+                            step=cur_step,
+                            epoch=epoch,
+                            prefix=eval_split,
+                        )
+
+                    # flush the train metrics
+                    train_start = time.time()
+
+                # break condition
+                if cur_step == total_train_steps:
+
+                    # un-wrap student model for save
+                    student_model = accelerator.unwrap_model(student_model)
+                    student_model.save_pretrained(training_args.output_dir)
+
+                    if training_args.push_to_hub:
+                        upload_folder(
+                            folder_path=training_args.output_dir,
+                            repo_id=repo_name,
+                            repo_type="model",
+                            commit_message=f"Saving final weights of step {cur_step}",
+                        )
+
+                    continue_training = False
+                    break
+
+        if not continue_training:
+            break
+
+    accelerator.end_training()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/run_eval.py b/run_eval.py
new file mode 100644
index 0000000000000000000000000000000000000000..768ac33624edc9e33ee4db68e1c2575fb95f005a
--- /dev/null
+++ b/run_eval.py
@@ -0,0 +1,822 @@
+# coding=utf-8
+# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Evaluating a Whisper model on one or more speech recognition datasets.
+"""
+# You can also adapt this script for your own speech recognition validation. Pointers for this are left as comments.
+
+import json
+import logging
+import os
+import sys
+import tempfile
+import time
+from dataclasses import dataclass, field
+from typing import Optional
+
+import datasets
+import evaluate
+import numpy as np
+import torch
+import transformers
+from datasets import DatasetDict, IterableDatasetDict, load_dataset
+from tqdm import tqdm
+from transformers import (
+    HfArgumentParser,
+    WhisperForConditionalGeneration,
+    WhisperProcessor,
+    is_wandb_available,
+    pipeline,
+    set_seed,
+)
+from transformers.models.whisper.english_normalizer import EnglishTextNormalizer, BasicTextNormalizer
+from transformers.models.whisper.modeling_whisper import WhisperForCausalLM
+from transformers.models.whisper.tokenization_whisper import TO_LANGUAGE_CODE
+from transformers.utils import check_min_version, is_accelerate_available
+from transformers.utils.versions import require_version
+
+
+# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
+check_min_version("4.34.0.dev0")
+
+require_version("datasets>=2.14.6", "To fix: `pip install --upgrade datasets`")
+
+logger = logging.getLogger(__name__)
+
+PIPELINE_BATCH_SIZE = 16
+
+
+@dataclass
+class DataTrainingArguments:
+    """
+    Arguments pertaining to what data we are going to input our model for training and eval.
+    """
+
+    dataset_name: str = field(
+        default=None,
+        metadata={
+            "help": "The name of the dataset to use (via the datasets library). Load and combine "
+            "multiple datasets by separating dataset hours by a '+' symbol."
+        },
+    )
+    model_name_or_path: str = field(
+        default=None,
+        metadata={"help": "The name of the model to use (via the transformers library). "},
+    )
+    subfolder: str = field(
+        default="",
+        metadata={"help": "If specified load weights from a subfolder in the model repository"},
+    )
+    model_variant: str = field(
+        default=None,
+        metadata={"help": "If specified load weights from `variant` filename, *e.g.* pytorch_model.<variant>.bin. "},
+    )
+    cache_dir: Optional[str] = field(
+        default=None,
+        metadata={"help": "Where to store the pretrained models downloaded from huggingface.co"},
+    )
+    assistant_model_name_or_path: str = field(
+        default=None,
+        metadata={
+            "help": "The name of the assistant model to use to do speculative decoding. If None, no speculative decoding will be done."
+        },
+    )
+    dtype: Optional[str] = field(
+        default="float16",
+        metadata={
+            "help": (
+                "Floating-point format in which the model weights should be initialized"
+                " and the computations run. Choose one of `[float32, float16, bfloat16]`."
+            )
+        },
+    )
+    use_pipeline: bool = field(
+        default=False,
+        metadata={"help": "Whether to evaluate with Transformers pipeline"},
+    )
+    chunk_length_s: float = field(
+        default=30.0, metadata={"help": "Chunk length to use when `use_pipeline` is enabled."}
+    )
+    return_timestamps: bool = field(
+        default=True,
+        metadata={
+            "help": "Whether to decode with timestamps. This can help for improved WER for long form evaluation."
+        },
+    )
+    language: str = field(
+        default=None,
+        metadata={
+            "help": (
+                "Language for multilingual evaluation. This argument should be set for multilingual evaluation "
+                "only. For English speech recognition, it should be left as `None`."
+            )
+        },
+    )
+    task: str = field(
+        default="transcribe",
+        metadata={
+            "help": "Task, either `transcribe` for speech recognition or `translate` for speech translation."
+            "This argument should be set for multilingual evaluation only. For English speech recognition, it should be left as `None`."
+        },
+    )
+    attn_implementation: Optional[str] = field(
+        default=None,
+        metadata={"help": "Which attn type to use: ['eager', 'sdpa', 'flash_attention_2']"},
+    )
+    batch_size: int = field(
+        default=1,
+        metadata={"help": "The batch size to be used for generation."},
+    )
+    num_beams: int = field(
+        default=1,
+        metadata={"help": "The beam size to be used for evaluation. Set to 1 for greedy, or >1 for beam search."},
+    )
+    temperature_fallback: bool = field(
+        default=True,
+        metadata={"help": "Whether to use temperature fallback for evaluation."},
+    )
+    logprob_threshold: float = field(
+        default=-1.0,
+        metadata={"help": "Whether to use temperature fallback for evaluation."},
+    )
+    no_speech_threshold: float = field(
+        default=0.6,
+        metadata={
+            "help": "Only relevant for long-form transcription. If defined, the 'no-speech' token combined with the `logprob_threshold`"
+            "is used to determine whether a segment contains only silence. In this case, the transcription for this segment"
+            "is skipped."
+        },
+    )
+    compression_ratio_threshold: float = field(
+        default=1.35,
+        metadata={
+            "help": "Only relevant for long-form transcription. If defined, the zlib compression rate of each segment will be computed. If the compression rate of"
+            "a segment is higher than `compression_ratio_threshold`, temperature fallback is activated: the generated segment is discarded and the generation is"
+            "repeated using a higher temperature. The intuition behind this feature is that segments with very high compression rates"
+            "suffer from a lot of repetition. The unwanted repetition can be reduced by injecting more randomness by increasing the temperature. "
+            "If `compression_ratio_threshold` is defined make sure that `temperature` is a list of values. The default value for `compression_ratio_threshold` is 1.35."
+        },
+    )
+    condition_on_prev_tokens: bool = field(
+        default=False,
+        metadata={"help": "Whether to condition on previous tokens or not"},
+    )
+    samples_per_dataset: Optional[int] = field(
+        default=None,
+        metadata={"help": "Number of samples per dataset used to measure speed."},
+    )
+    dataset_config_name: Optional[str] = field(
+        default=None,
+        metadata={"help": "The configuration name of the dataset to use (via the datasets library)."},
+    )
+    dataset_split_name: Optional[str] = field(
+        default=None,
+        metadata={"help": "The split name of the dataset to use (via the datasets library)."},
+    )
+    dataset_cache_dir: Optional[str] = field(
+        default=None,
+        metadata={"help": "Path to cache directory for saving and loading datasets"},
+    )
+    overwrite_cache: bool = field(
+        default=False,
+        metadata={"help": "Overwrite the cached training and evaluation sets"},
+    )
+    preprocessing_num_workers: Optional[int] = field(
+        default=None,
+        metadata={"help": "The number of processes to use for the preprocessing."},
+    )
+    audio_column_name: str = field(
+        default="audio",
+        metadata={"help": "The name of the dataset column containing the audio data. Defaults to 'audio'"},
+    )
+    text_column_name: str = field(
+        default=None,
+        metadata={"help": "The name of the dataset column containing the text data. Defaults to `text`."},
+    )
+    generation_max_length: int = field(
+        default=256, metadata={"help": "Generate up until `generation_max_length` tokens."}
+    )
+    log_predictions: Optional[bool] = field(
+        default=True,
+        metadata={"help": "Whether or not to log the ground truths / pred text to the wandb logger."},
+    )
+    preprocessing_only: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether to only do data preprocessing and skip training. This is"
+                " especially useful when data preprocessing errors out in distributed"
+                " training due to timeout. In this case, one should run the"
+                " preprocessing in a non-distributed setup with"
+                " `preprocessing_only=True` so that the cached datasets can"
+                " consequently be loaded in distributed training"
+            )
+        },
+    )
+    wandb_project: str = field(
+        default="distil-whisper-speed-benchmark",
+        metadata={"help": "The name of the wandb project."},
+    )
+    wandb_name: str = field(
+        default=None,
+        metadata={"help": "The name of the wandb run."},
+    )
+    wandb_job_type: str = field(
+        default="distil-whisper",
+        metadata={"help": "The name of the wandb job type."},
+    )
+    wandb_dir: str = field(
+        default=None,
+        metadata={"help": "The absolute path to save the wandb logs."},
+    )
+    save_code_to_wandb: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether to save main script to wandb. This is valuable for improving"
+                " experiment reproducibility and to diff code across experiments in"
+                " the UI."
+            )
+        },
+    )
+    streaming: bool = field(
+        default=True,
+        metadata={"help": "Whether to use Datasets' streaming mode to load and the data."},
+    )
+    max_eval_samples: Optional[int] = field(
+        default=None,
+        metadata={"help": "For debugging purposes, truncate the number of eval examples to this value if set."},
+    )
+    seed: int = field(default=42, metadata={"help": "RNG seed for reproducibility."})
+    use_fast_tokenizer: bool = field(
+        default=True,
+        metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
+    )
+    prompt_text: str = field(
+        default=None,
+        metadata={
+            "help": "Text prompt to condition the generation on. Useful for controlling the style of transcription and predicting named entities."
+        },
+    )
+
+
+def write_metric(summary_writer, eval_metrics, step, prefix="eval"):
+    for metric_name, value in eval_metrics.items():
+        summary_writer.scalar(f"{prefix}/{metric_name}", value, step)
+
+
+def write_wandb_metric(wandb_logger, metrics, prefix):
+    log_metrics = {}
+    for k, v in metrics.items():
+        log_metrics[f"{prefix}/{k}"] = v
+    wandb_logger.log(log_metrics)
+
+
+def write_wandb_pred(
+    wandb_logger,
+    pred_str,
+    label_str,
+    norm_pred_str,
+    norm_label_str,
+    wer_per_sample,
+    prefix="eval",
+):
+    columns = ["WER", "Target", "Pred", "Norm Target", "Norm Pred"]
+    # convert str data to a wandb compatible format
+    str_data = [
+        [wer_per_sample[i], label_str[i], pred_str[i], norm_label_str[i], norm_pred_str[i]]
+        for i in range(len(pred_str))
+    ]
+
+    # log as a table with the appropriate headers
+    wandb_logger.log(
+        {f"{prefix}/predictions": wandb_logger.Table(columns=columns, data=str_data)},
+    )
+
+
+def convert_dataset_str_to_list(
+    dataset_names, dataset_config_names, splits=None, text_column_names=None, dataset_hours=None, default_split="train"
+):
+    if isinstance(dataset_names, str):
+        dataset_names = dataset_names.split("+")
+
+        # we assume that all the datasets we're using derive from the distil-whisper org on the Hub - prepend the org name if necessary
+        for i in range(len(dataset_names)):
+            ds_name = dataset_names[i]
+            dataset_names[i] = f"distil-whisper/{ds_name}" if "/" not in ds_name else ds_name
+
+        dataset_config_names = dataset_config_names.split("+") if dataset_config_names is not None else None
+        splits = splits.split("+") if splits is not None else None
+        text_column_names = text_column_names.split("+") if text_column_names is not None else None
+        dataset_hours = dataset_hours.split("+") if dataset_hours is not None else None
+
+    # basic checks to ensure we've got the right number of datasets/configs/splits/columns/probs
+    if dataset_config_names is not None and len(dataset_names) != len(dataset_config_names):
+        raise ValueError(
+            f"Ensure one config is passed for each dataset, got {len(dataset_names)} datasets and"
+            f" {len(dataset_config_names)} configs."
+        )
+
+    if splits is not None and len(splits) != len(dataset_names):
+        raise ValueError(
+            f"Ensure one split is passed for each dataset, got {len(dataset_names)} datasets and {len(splits)} splits."
+        )
+
+    if text_column_names is not None and len(text_column_names) != len(dataset_names):
+        raise ValueError(
+            f"Ensure one text column name is passed for each dataset, got {len(dataset_names)} datasets and"
+            f" {len(text_column_names)} text column names."
+        )
+
+    if dataset_hours is not None:
+        if len(dataset_hours) != len(dataset_names):
+            raise ValueError(
+                f"Ensure one probability is passed for each dataset, got {len(dataset_names)} datasets and "
+                f"{len(dataset_hours)} hours."
+            )
+        dataset_hours = [float(ds_hours) for ds_hours in dataset_hours]
+    else:
+        dataset_hours = [None] * len(dataset_names)
+
+    dataset_config_names = (
+        dataset_config_names if dataset_config_names is not None else ["default" for _ in range(len(dataset_names))]
+    )
+    text_column_names = (
+        text_column_names if text_column_names is not None else ["text" for _ in range(len(dataset_names))]
+    )
+    splits = splits if splits is not None else [default_split for _ in range(len(dataset_names))]
+
+    dataset_names_dict = []
+    for i, ds_name in enumerate(dataset_names):
+        dataset_names_dict.append(
+            {
+                "name": ds_name,
+                "config": dataset_config_names[i],
+                "split": splits[i],
+                "text_column_name": text_column_names[i],
+                "hours": dataset_hours[i],
+            }
+        )
+    return dataset_names_dict
+
+
+def language_to_id(language: str, generation_config) -> str:
+    language = language.lower()
+    if language in generation_config.lang_to_id.keys():
+        language_token = language
+    elif language in TO_LANGUAGE_CODE.keys():
+        language_token = f"<|{TO_LANGUAGE_CODE[language]}|>"
+    elif language in TO_LANGUAGE_CODE.values():
+        language_token = f"<|{language}|>"
+    else:
+        is_language_code = len(language) == 2
+        raise ValueError(
+            f"Unsupported language: {language}. Language should be one of:"
+            f" {list(TO_LANGUAGE_CODE.values()) if is_language_code else list(TO_LANGUAGE_CODE.keys())}."
+        )
+    if language_token not in generation_config.lang_to_id:
+        raise ValueError(
+            f"{language_token} is not supported by this specific model as it is not in the `generation_config.lang_to_id`."
+            "(You should just add it to the generation config)"
+        )
+
+    return language_token
+
+
+def main():
+    # 1. Parse input arguments
+    # See all possible arguments in src/transformers/training_args.py
+    # or by passing the --help flag to this script.
+    # We now keep distinct sets of args, for a cleaner separation of concerns.
+    parser = HfArgumentParser([DataTrainingArguments])
+
+    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
+        # If we pass only one argument to the script and it's the path to a json file,
+        # let's parse it to get our arguments.
+        data_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))[0]
+    else:
+        data_args = parser.parse_args_into_dataclasses()[0]
+
+    # 2. Setup logging
+    # Make one log on every process with the configuration for debugging.
+    logger.setLevel(logging.INFO)
+    logging.basicConfig(
+        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+        datefmt="%m/%d/%Y %H:%M:%S",
+        handlers=[logging.StreamHandler(sys.stdout)],
+    )
+
+    # 3. Set seed for reproducibility
+    set_seed(data_args.seed)
+
+    if data_args.use_pipeline and data_args.batch_size > 1:
+        raise ValueError("Make sure that `batch_size` is set to 1 when `use_pipeline=True`.")
+
+    has_wandb = is_wandb_available()
+    if has_wandb:
+        import wandb
+        import wandb as wandb_logger
+
+        # store generation HPs for runs
+        generation_arguments = {
+            "torch_version": str(torch.__version__),
+            "transformers_version": str(transformers.__version__),
+            "attn_implementation": data_args.attn_implementation,
+            "model_name_or_path": data_args.model_name_or_path,
+            "subfolder": data_args.subfolder,
+            "assistant_model_name_or_path": data_args.assistant_model_name_or_path,
+            "seed": data_args.seed,
+            "batch_size": data_args.batch_size,
+            "num_beams": data_args.num_beams,
+            "return_timestamps": data_args.return_timestamps,
+            "condition_on_prev_tokens": data_args.condition_on_prev_tokens,
+            "temperature_fallback": data_args.temperature_fallback,
+            "logprob_threshold": data_args.logprob_threshold,
+            "no_speech_threshold": data_args.no_speech_threshold,
+            "use_pipeline": data_args.use_pipeline,
+            "chunk_length_s": data_args.chunk_length_s,
+        }
+
+        # Set up wandb run
+        wandb_logger.init(
+            project=data_args.wandb_project,
+            name=data_args.wandb_name,
+            job_type=data_args.wandb_job_type,
+            dir=data_args.wandb_dir,
+            save_code=data_args.save_code_to_wandb,
+            config=generation_arguments,
+        )
+
+    else:
+        raise ValueError("Wandb logging requires wandb to be installed. Run `pip install wandb` to enable.")
+
+    # 3. Load dataset
+    raw_datasets = IterableDatasetDict()
+
+    # Convert lists of dataset names/configs/splits to a dict
+    # names: "librispeech_asr+gigaspeech", configs: "all+l", splits: "validation.clean+validation"
+    # -> [{"name: "librispeech_asr": "config": "all", "split": "validation.clean"}, {"name: "gigaspeech": "config": "l", "split": "validation"}
+    dataset_names_dict = convert_dataset_str_to_list(
+        data_args.dataset_name,
+        data_args.dataset_config_name,
+        splits=data_args.dataset_split_name,
+        text_column_names=data_args.text_column_name,
+    )
+
+    # load multiple eval sets
+    for dataset_dict in tqdm(dataset_names_dict, desc="Loading datasets..."):
+        sub_dataset = load_dataset(
+            dataset_dict["name"],
+            dataset_dict["config"],
+            split=dataset_dict["split"],
+            cache_dir=data_args.dataset_cache_dir,
+            streaming=data_args.streaming,
+            num_proc=data_args.preprocessing_num_workers,
+        )
+        if dataset_dict["text_column_name"] not in list(sub_dataset.features.keys()):
+            raise ValueError(
+                f"`--text_column_name` {dataset_dict['text_column_name']} not found in the evaluation "
+                f"dataset {dataset_dict['name']}. Ensure `text_column_name` is set to the correct column "
+                f"for the target text. Should be one of {' '.join(list(sub_dataset.features.keys()))}"
+            )
+        if dataset_dict["text_column_name"] != "text":
+            sub_dataset = sub_dataset.rename_column(dataset_dict["text_column_name"], "text")
+        if not data_args.streaming:
+            sub_dataset = sub_dataset.to_iterable_dataset()
+
+        # Clean-up the dataset name for pretty logging
+        # ("distil-whisper/librispeech_asr", "validation.clean") -> "librispeech_asr/validation-clean"
+        pretty_name = f"{dataset_dict['name'].split('/')[-1]}/{dataset_dict['split'].replace('.', '-')}"
+        raw_datasets[pretty_name] = sub_dataset
+
+    # 5. Load pretrained model, tokenizer, and feature extractor
+    processor = WhisperProcessor.from_pretrained(
+        data_args.model_name_or_path,
+        subfolder=data_args.subfolder,
+        cache_dir=data_args.cache_dir,
+        use_fast=data_args.use_fast_tokenizer,
+    )
+    dtype = getattr(torch, data_args.dtype)
+    model = WhisperForConditionalGeneration.from_pretrained(
+        data_args.model_name_or_path,
+        subfolder=data_args.subfolder,
+        torch_dtype=dtype,
+        attn_implementation=data_args.attn_implementation,
+        low_cpu_mem_usage=is_accelerate_available(),
+        cache_dir=data_args.cache_dir,
+        variant=data_args.model_variant,
+    )
+    model.to("cuda:0", dtype=dtype)
+
+    model_pipeline = None
+    if data_args.use_pipeline:
+        model_pipeline = pipeline(
+            "automatic-speech-recognition",
+            model=model,
+            tokenizer=processor.tokenizer,
+            feature_extractor=processor.feature_extractor,
+            torch_dtype=dtype,
+            device=model.device,
+            chunk_length_s=data_args.chunk_length_s,
+        )
+        model_pipeline_forward = model_pipeline._forward
+
+    assistant_model = None
+    if data_args.assistant_model_name_or_path is not None:
+        logger.info("Loading assistant model...")
+
+        if data_args.assistant_model_name_or_path.startswith("openai"):
+            assistant_model = WhisperForConditionalGeneration.from_pretrained(
+                data_args.assistant_model_name_or_path,
+                torch_dtype=dtype,
+                attn_implementation=data_args.attn_implementation,
+                low_cpu_mem_usage=is_accelerate_available(),
+                cache_dir=data_args.cache_dir,
+            )
+        else:
+            assistant_model = WhisperForCausalLM.from_pretrained(
+                data_args.assistant_model_name_or_path,
+                torch_dtype=dtype,
+                attn_implementation=data_args.attn_implementation,
+                low_cpu_mem_usage=is_accelerate_available(),
+                cache_dir=data_args.cache_dir,
+            )
+
+        assistant_model.cuda()
+
+    # 6. Resample speech dataset: `datasets` takes care of automatically loading and resampling the audio,
+    # so we just need to set the correct target sampling rate.
+    raw_datasets = raw_datasets.cast_column(
+        data_args.audio_column_name,
+        datasets.features.Audio(sampling_rate=processor.feature_extractor.sampling_rate),
+    )
+
+    # 7. Preprocessing the datasets.
+    # We need to read the audio files as arrays and tokenize the targets.
+    audio_column_name = data_args.audio_column_name
+    language = language_to_id(data_args.language, model.generation_config) if data_args.language else None
+    if language is None or language == "<|en|>":
+        normalizer = EnglishTextNormalizer(processor.tokenizer.english_spelling_normalizer)
+    else:
+        normalizer = BasicTextNormalizer()
+
+    sampling_rate = processor.feature_extractor.sampling_rate
+
+    if data_args.samples_per_dataset is not None:
+        for split in raw_datasets:
+            raw_datasets[split] = raw_datasets[split].take(data_args.samples_per_dataset)
+
+    def prepare_dataset(batch):
+        # process audio
+        audio = [sample["array"].astype(np.float32) for sample in batch[audio_column_name]]
+
+        if model_pipeline is None:
+            inputs = processor.feature_extractor(
+                audio,
+                sampling_rate=sampling_rate,
+                return_tensors="pt",
+                truncation=False,
+                padding="longest",
+                return_attention_mask=True,
+            )
+            if inputs.input_features.shape[-1] < 3000:
+                inputs = processor.feature_extractor(
+                    audio,
+                    sampling_rate=sampling_rate,
+                    return_tensors="pt",
+                    return_attention_mask=True,
+                )
+            batch["input_features"] = inputs.input_features.to(dtype)
+            batch["attention_mask"] = inputs.attention_mask
+        else:
+            batch["input_features"] = audio
+
+        # process audio length
+        batch["length_in_s"] = [len(sample) / sampling_rate for sample in audio]
+        # process targets
+        batch["reference"] = batch["text"]
+        return batch
+
+    vectorized_datasets = IterableDatasetDict()
+
+    for split in raw_datasets:
+        raw_datasets_features = list(raw_datasets[split].features.keys())
+
+        vectorized_datasets[split] = raw_datasets[split].map(
+            function=prepare_dataset,
+            remove_columns=raw_datasets_features,
+            batch_size=data_args.batch_size,
+            batched=True,
+        )
+
+    # for large datasets it is advised to run the preprocessing on a
+    # single machine first with `args.preprocessing_only` since there will mostly likely
+    # be a timeout when running the script in distributed mode.
+    # In a second step `args.preprocessing_only` can then be set to `False` to load the
+    # cached dataset
+    if data_args.preprocessing_only:
+        cache = {k: v.cache_files for k, v in vectorized_datasets.items()}
+        logger.info(f"Data preprocessing finished. Files cached at {cache}.")
+        return
+
+    metric = evaluate.load("wer")
+
+    def compute_metrics(pred_str, label_str):
+        # normalize everything and re-compute the WER
+        norm_pred_str = [normalizer(pred) for pred in pred_str]
+        norm_label_str = [normalizer(label) for label in label_str]
+
+        # filtering step to only evaluate the samples that correspond to non-zero normalized references:
+        norm_pred_str = [norm_pred_str[i] for i in range(len(norm_pred_str)) if len(norm_label_str[i]) > 0]
+        norm_label_str = [norm_label_str[i] for i in range(len(norm_label_str)) if len(norm_label_str[i]) > 0]
+
+        wer = 100 * metric.compute(predictions=norm_pred_str, references=norm_label_str)
+        return wer
+
+    gen_kwargs = {
+        "max_length": data_args.generation_max_length,
+        "return_timestamps": data_args.return_timestamps,
+        "num_beams": data_args.num_beams,
+        "top_k": 0,
+    }
+
+    if hasattr(model.generation_config, "is_multilingual") and model.generation_config.is_multilingual:
+        gen_kwargs["language"] = data_args.language
+        gen_kwargs["task"] = data_args.task
+    elif data_args.language is not None:
+        raise ValueError(
+            "Setting language token for an English-only checkpoint is not permitted. The language argument should "
+            "only be set for multilingual checkpoints."
+        )
+
+    if assistant_model is not None:
+        gen_kwargs["assistant_model"] = assistant_model
+
+    if data_args.prompt_text is not None:
+        gen_kwargs["prompt_ids"] = processor.get_prompt_ids(data_args.prompt_text, return_tensors="pt").to("cuda:0")
+
+    long_form_gen_kwargs = {
+        "condition_on_prev_tokens": data_args.condition_on_prev_tokens,
+        "compression_ratio_threshold": data_args.compression_ratio_threshold,
+        "temperature": (0.0, 0.2, 0.4, 0.6, 0.8, 1.0) if data_args.temperature_fallback else 0,
+        "logprob_threshold": data_args.logprob_threshold,
+        "no_speech_threshold": data_args.no_speech_threshold,
+    }
+
+    def benchmark(batch):
+        if model_pipeline is None:
+            inputs = torch.stack(batch["input_features"], dim=0).cuda()
+            attention_mask = torch.stack(batch["attention_mask"], dim=0).cuda()
+            # automatically use long-form args if required
+            inner_batch_size, num_mels, seq_len = inputs.shape
+            if seq_len == 3000:
+                batch_gen_kwargs = gen_kwargs
+            else:
+                batch_gen_kwargs = {**gen_kwargs, **long_form_gen_kwargs}
+
+            set_seed(data_args.seed)
+            start_time = time.time()
+            output_ids = model.generate(inputs, attention_mask=attention_mask, **batch_gen_kwargs)
+            batch["time"] = inner_batch_size * [(time.time() - start_time) / inner_batch_size]
+
+            batch["transcription"] = processor.batch_decode(
+                output_ids, skip_special_tokens=True, decode_with_timestamps=data_args.return_timestamps
+            )
+
+        else:
+            inputs = batch["input_features"]
+            # Time forward: let's make sure that only forward is timed and not pre- and post-processing
+            time_result = []
+
+            def _forward_time(*args, **kwargs):
+                start_time = time.time()
+                result = model_pipeline_forward(*args, **kwargs)
+                end_time = time.time() - start_time
+                time_result.append(end_time)
+                return result
+
+            model_pipeline._forward = _forward_time
+
+            result = model_pipeline(inputs, batch_size=PIPELINE_BATCH_SIZE, generate_kwargs=gen_kwargs)[0]["text"]
+            batch["transcription"] = [result]
+            batch["time"] = [sum(time_result)]
+
+        batch["num_words"] = [len(r.split()) for r in batch["reference"]]
+        return batch
+
+    result_datasets = DatasetDict()
+
+    for split in vectorized_datasets:
+        result_datasets[split] = vectorized_datasets[split].map(
+            function=benchmark,
+            remove_columns=["input_features"],
+            batch_size=data_args.batch_size,
+            batched=True,
+        )
+
+    stats_dataset = DatasetDict()
+
+    all_stats = {"rtf": 0, "wer": 0}
+    rtf_stats = {
+        "times_audio_total": 0,
+        "times_transcription_total": 0,
+    }
+
+    logger.info("***** Running Evaluation *****")
+    for key in generation_arguments:
+        logger.info(f"  {key}: {generation_arguments[key]}")
+
+    datasets_evaluated_progress_bar = tqdm(result_datasets, desc="Datasets", position=0)
+    for split in datasets_evaluated_progress_bar:
+        transcriptions = []
+        references = []
+        stats = {}
+        times_audio_total = 0
+        times_transcription_total = 0
+
+        datasets_evaluated_progress_bar.write(f"Start benchmarking {split}...")
+        result_iter = iter(result_datasets[split])
+        for result in tqdm(result_iter, desc="Samples", position=1):
+            times_audio_total += result["length_in_s"]
+            times_transcription_total += result["time"]
+            # ensure prompt is removed from the transcription (awaiting fix in Transformers)
+            if data_args.prompt_text is not None:
+                result["transcription"] = result["transcription"].replace(data_args.prompt_text, "")
+            transcriptions.append(result["transcription"])
+            references.append(result["reference"])
+
+        norm_transcriptions = [normalizer(pred) for pred in transcriptions]
+        norm_references = [normalizer(label) for label in references]
+
+        transcriptions = [transcriptions[i] for i in range(len(transcriptions)) if len(norm_references[i]) > 0]
+        references = [references[i] for i in range(len(references)) if len(norm_references[i]) > 0]
+
+        norm_transcriptions = [
+            norm_transcriptions[i] for i in range(len(norm_transcriptions)) if len(norm_references[i]) > 0
+        ]
+        norm_references = [norm_references[i] for i in range(len(norm_references)) if len(norm_references[i]) > 0]
+
+        stats["wer"] = compute_metrics(norm_transcriptions, norm_references)
+
+        wer_per_sample = []
+        for pred, ref in zip(norm_transcriptions, norm_references):
+            wer_per_sample.append(compute_metrics([pred], [ref]))
+
+        stats["rtf"] = times_audio_total / times_transcription_total
+        stats_dataset[split] = stats
+
+        wer_desc = " ".join([f"Eval {key}: {value} |" for key, value in stats.items()])
+        datasets_evaluated_progress_bar.write(wer_desc)
+
+        write_wandb_metric(wandb_logger, stats, prefix=split)
+
+        if data_args.log_predictions:
+            write_wandb_pred(
+                wandb_logger,
+                transcriptions,
+                references,
+                norm_transcriptions,
+                norm_references,
+                wer_per_sample,
+                prefix=split,
+            )
+
+        rtf_stats["times_audio_total"] += times_audio_total
+        rtf_stats["times_transcription_total"] += times_transcription_total
+        all_stats["wer"] += stats["wer"]
+
+    all_stats["wer"] = all_stats["wer"] / len(result_datasets)
+    # technically this is the reciprocal of the RTF, but it makes the scale easier to read on wandb
+    all_stats["rtf"] = rtf_stats["times_audio_total"] / rtf_stats["times_transcription_total"]
+
+    stats_dataset["all"] = all_stats
+
+    write_wandb_metric(wandb_logger, all_stats, prefix="all")
+
+    benchmark_artifact = wandb.Artifact("Benchmark", type="datasets")
+    with tempfile.TemporaryDirectory() as temp_dir:
+        for split in stats_dataset:
+            file_name = os.path.join(temp_dir, f"{'_'.join(split.split('/'))}.json")
+
+            with open(file_name, "w") as json_file:
+                json.dump(stats_dataset[split], json_file)
+
+            benchmark_artifact.add_file(file_name, split)
+
+        wandb_logger.log_artifact(benchmark_artifact)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/run_pseudo_labelling.py b/run_pseudo_labelling.py
new file mode 100644
index 0000000000000000000000000000000000000000..d1a9234faeff0bc6c9e08ff95fbeb5cb3d41f4e8
--- /dev/null
+++ b/run_pseudo_labelling.py
@@ -0,0 +1,1021 @@
+#!/usr/bin/env python
+# coding=utf-8
+# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Pseudo-labelling audio data using the Whisper model in preparation for distillation.
+"""
+# You can also adapt this script for your own pseudo-labelling tasks. Pointers for this are left as comments.
+
+import csv
+import logging
+import os
+import sys
+import time
+import warnings
+from dataclasses import dataclass, field
+from datetime import timedelta
+from pathlib import Path
+from typing import Any, Dict, List, Optional, Union
+
+import datasets
+import evaluate
+import numpy as np
+import torch
+import transformers
+from accelerate import Accelerator, InitProcessGroupKwargs
+from accelerate.logging import get_logger
+from datasets import (
+    DatasetDict,
+    IterableDatasetDict,
+    load_dataset,
+)
+from huggingface_hub import HfFolder, create_repo, get_full_repo_name, snapshot_download, upload_folder
+from torch.utils.data import DataLoader
+from tqdm import tqdm
+from soundfile import LibsndfileError
+from datasets.arrow_dataset import table_iter
+from transformers import (
+    HfArgumentParser,
+    Seq2SeqTrainingArguments,
+    WhisperConfig,
+    WhisperFeatureExtractor,
+    WhisperForConditionalGeneration,
+    WhisperProcessor,
+    WhisperTokenizerFast,
+)
+from transformers.models.whisper.english_normalizer import BasicTextNormalizer, EnglishTextNormalizer
+from transformers.utils import check_min_version
+from transformers.utils.versions import require_version
+
+
+# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
+check_min_version("4.34.0.dev0")
+
+require_version("datasets>=2.14.6", "To fix: `pip install --upgrade datasets`")
+
+logger = get_logger(__name__)
+
+
+@dataclass
+class ModelArguments:
+    """
+    Arguments pertaining to which model/config/tokenizer we are going to distill from.
+    """
+
+    model_name_or_path: str = field(
+        metadata={"help": "Path to pretrained Whisper model or model identifier from huggingface.co/models"}
+    )
+    config_name: Optional[str] = field(
+        default=None,
+        metadata={"help": "Pretrained config name or path if not the same as model_name"},
+    )
+    tokenizer_name: Optional[str] = field(
+        default=None,
+        metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"},
+    )
+    feature_extractor_name: Optional[str] = field(
+        default=None,
+        metadata={"help": "feature extractor name or path if not the same as model_name"},
+    )
+    processor_name: Optional[str] = field(
+        default=None,
+        metadata={"help": "processor name or path if not the same as model_name"},
+    )
+    cache_dir: Optional[str] = field(
+        default=None,
+        metadata={"help": "Where to store the pretrained models downloaded from huggingface.co"},
+    )
+    use_fast_tokenizer: bool = field(
+        default=True,
+        metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
+    )
+    model_revision: str = field(
+        default="main",
+        metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
+    )
+    subfolder: str = field(
+        default="",
+        metadata={
+            "help": "In case the relevant files are located inside a subfolder of the model repo on huggingface.co, you can"
+            "specify the folder name here."
+        },
+    )
+    token: str = field(
+        default=None,
+        metadata={
+            "help": (
+                "The token to use as HTTP bearer authorization for remote files. If not specified, will use the token "
+                "generated when running `huggingface-cli login` (stored in `~/.huggingface`)."
+            )
+        },
+    )
+    dtype: Optional[str] = field(
+        default="float32",
+        metadata={
+            "help": (
+                "The data type (dtype) in which to load the model weights. One of `float32` (full-precision), "
+                "`float16` or `bfloat16` (both half-precision)."
+            )
+        },
+    )
+    attn_implementation: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": (
+                "Which attention implementation to use in the encoder and decoder attention layers. Can be one of:\n"
+                "1. `eager` or `None`: default Transformers attention implementation.\n"
+                "2. `sdpa`: Flash Attention through PyTorch SDPA. Requires `torch>=2.1`. Recommended for hardware where Flash Attention 2 is not supported, e.g. Turing GPUs, (T4, RTX 2080).\n"
+                "3. `flash_attn_2`: Flash Attention 2 through the Flash Attention package https://github.com/Dao-AILab/flash-attention. **Always** recommended on supported hardware (Ampere, Ada, or Hopper GPUs, e.g., A100, RTX 3090, RTX 4090, H100)."
+            )
+        },
+    )
+    attn_type: Optional[str] = field(
+        default=None,
+        metadata={"help": "Deprecated. Use `attn_implementation` instead."},
+    )
+
+    def __post_init__(self):
+        if self.attn_type is not None and self.attn_implementation is None:
+            # set attn_implementation in a backwards compatible way
+            if self.attn_type == "flash_attn":
+                self.attn_implementation = "sdpa"
+            elif self.attn_type == "flash_attn_2":
+                self.attn_implementation = "flash_attention_2"
+            elif self.attn_type in [None, "eager", "sdpa", "flash_attention_2"]:
+                self.attn_implementation = self.attn_type
+            else:
+                raise ValueError(
+                    f"Argument `--attn_type` is deprecated, and set to an invalid option `{self.attn_type}`. You should omit the argument `--attn_type`, and instead set `-attention_implementation` to one of the following:\n"
+                    "1. `eager` or `None`: default Transformers attention implementation.\n"
+                    "2. `sdpa`: Flash Attention through PyTorch SDPA. Requires `torch>=2.1`. Recommended for hardware where Flash Attention 2 is not supported, e.g. Turing GPUs, (T4, RTX 2080).\n"
+                    "3. `flash_attn_2`: Flash Attention 2 through the Flash Attention package https://github.com/Dao-AILab/flash-attention. **Always** recommended on supported hardware (Ampere, Ada, or Hopper GPUs, e.g., A100, RTX 3090, RTX 4090, H100)."
+                )
+            warnings.warn(
+                f"Argument `--attn_type` is deprecated. Use `--attn_implementation` instead. Inferring `--attn_implementation={self.attn_implementation} from argument `--attn_type={self.attn_type}`."
+            )
+        elif self.attn_type is not None and self.attn_implementation is not None:
+            raise ValueError(
+                "`--attn_type` and `--attn_implementation` are both specified. Only the argument `--attn_implementation`."
+            )
+
+
+@dataclass
+class DataTrainingArguments:
+    """
+    Arguments pertaining to what data we are going to input our model for training and eval.
+    """
+
+    dataset_name: str = field(
+        default=None,
+        metadata={"help": "The name of the dataset to use (via the datasets library)."},
+    )
+    dataset_config_name: Optional[str] = field(
+        default=None,
+        metadata={"help": "The configuration name of the dataset to use (via the datasets library)."},
+    )
+    dataset_cache_dir: Optional[str] = field(
+        default=None,
+        metadata={"help": "Path to cache directory for saving and loading datasets"},
+    )
+    overwrite_cache: bool = field(
+        default=False,
+        metadata={"help": "Overwrite the cached training and evaluation sets"},
+    )
+    preprocessing_num_workers: Optional[int] = field(
+        default=None,
+        metadata={"help": "The number of processes to use for the preprocessing."},
+    )
+    preprocessing_batch_size: Optional[int] = field(
+        default=500,
+        metadata={"help": "The batch size to use for the dataset pre-processing."},
+    )
+    audio_column_name: str = field(
+        default="audio",
+        metadata={"help": "The name of the dataset column containing the audio data. Defaults to 'audio'"},
+    )
+    text_column_name: str = field(
+        default="text",
+        metadata={"help": "The name of the dataset column containing the text data. Defaults to 'text'."},
+    )
+    id_column_name: str = field(
+        default="id",
+        metadata={"help": "The name of the dataset column containing the id data. Defaults to 'id'"},
+    )
+    speaker_id_column_name: str = field(
+        default=None,
+        metadata={"help": "The name of the dataset column containing the speaker id data. Defaults to None."},
+    )
+    max_duration_in_seconds: float = field(
+        default=30.0,
+        metadata={"help": "Filter audio files that are longer than `max_duration_in_seconds` seconds"},
+    )
+    max_label_length: int = field(
+        default=256,
+        metadata={"help": "Truncate transcriptions that are longer `max_label_length` tokens."},
+    )
+    concatenate_audio: bool = field(
+        default=True,
+        metadata={"help": "Whether or not to concatenate the audio samples to `max_duration_in_seconds`."},
+    )
+    preprocessing_only: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether to only do data preprocessing and skip training. This is"
+                " especially useful when data preprocessing errors out in distributed"
+                " training due to timeout. In this case, one should run the"
+                " preprocessing in a non-distributed setup with"
+                " `preprocessing_only=True` so that the cached datasets can"
+                " consequently be loaded in distributed training"
+            )
+        },
+    )
+    dataset_split_name: str = field(
+        default="train+validation+test",
+        metadata={
+            "help": (
+                "The name of the data set splits to use (via the datasets library)."
+                " Defaults to 'train+validation+test'. Multiple splits can be passed by splitting a"
+                " list through the '+' character, e.g. 'train+validation' will"
+                " pseudo-label both the 'train' and 'validation' splits sequentially."
+            )
+        },
+    )
+    wandb_project: str = field(
+        default="distil-whisper",
+        metadata={"help": "The name of the wandb project."},
+    )
+    streaming: bool = field(
+        default=False,
+        metadata={"help": "Whether to use dataset's streaming mode to load and pre-process the data."},
+    )
+    max_samples_per_split: Optional[int] = field(
+        default=None,
+        metadata={"help": "For debugging purposes, truncate the number of examples per split to this value if set."},
+    )
+    return_timestamps: bool = field(
+        default=False,
+        metadata={
+            "help": "Whether to return the timestamps with the text. This enables the `FlaxWhisperTimestampsLogitsProcessor`."
+        },
+    )
+    language: str = field(
+        default=None,
+        metadata={
+            "help": (
+                "Language for multilingual distillation. This argument should be set for multilingual distillation "
+                "only. For English speech recognition, it should be left as `None`."
+            )
+        },
+    )
+    task: str = field(
+        default="transcribe",
+        metadata={
+            "help": "Task, either `transcribe` for speech recognition or `translate` for speech translation."
+            "This argument should be set for multilingual distillation only. For English speech recognition, it should be left as `None`."
+        },
+    )
+    decode_token_ids: bool = field(
+        default=True,
+        metadata={"help": "Deprecated. The predicted token ids should always be decoded to text transcriptions."},
+    )
+    private_dataset: bool = field(
+        default=False,
+        metadata={"help": "Whether or not to create a private dataset for the pseudo-labelled data."},
+    )
+
+    def __post_init__(self):
+        if not self.decode_token_ids:
+            raise ValueError(
+                "The argument `--decode_token_ids` is deprecated. The token ids are now always decoded to "
+                "their corresponding text string. This is following a fix to the merges of the Whisper tokenizer"
+                "on the Hugging Face Hub: https://huggingface.co/openai/whisper-large-v2/discussions/100. "
+                "You should either omit the argument `--decode_token_ids`, or set it to True explicitly."
+            )
+
+
+def shift_tokens_right(label_ids: np.array, decoder_start_token_id: int) -> np.ndarray:
+    """
+    Shift label ids one token to the right.
+    """
+    shifted_label_ids = np.zeros_like(label_ids)
+    shifted_label_ids[:, 1:] = label_ids[:, :-1]
+    shifted_label_ids[:, 0] = decoder_start_token_id
+
+    return shifted_label_ids
+
+
+@dataclass
+class DataCollatorSpeechSeq2SeqWithPadding:
+    """
+    Data collator that will dynamically pad the inputs received.
+    Args:
+        processor ([`Wav2Vec2Processor`])
+            The processor used for proccessing the data.
+        decoder_start_token_id (:obj: `int`)
+            The start-of-sequence token id of the decoder.
+        input_padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`, `optional`, defaults to :obj:`True`):
+            Select a strategy to pad the returned input sequences (according to the model's padding side and padding index)
+            among:
+            * :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
+              sequence if provided).
+            * :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the
+              maximum acceptable input length for the model if that argument is not provided.
+            * :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of
+              different lengths).
+        target_padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`, `optional`, defaults to :obj:`True`):
+            Select a strategy to pad the returned target sequences (according to the model's padding side and padding index).
+            See above for details.
+        max_target_length (:obj:`int`, `optional`):
+            Maximum length of the ``labels`` of the returned list and optionally padding length (see above).
+    """
+
+    processor: Any
+    decoder_start_token_id: int
+    input_padding: Union[bool, str] = "max_length"
+    target_padding: Union[bool, str] = "max_length"
+    max_target_length: Optional[int] = None
+
+    def __call__(self, features: List[Dict[str, Union[List[int], np.ndarray]]]) -> Dict[str, np.ndarray]:
+        # split inputs and labels since they have to be of different lengths and need
+        # different padding methods
+        model_input_name = self.processor.model_input_names[0]
+
+        # dataloader returns a list of features which we convert to a dict
+        input_features = {model_input_name: [feature[model_input_name] for feature in features]}
+        label_features = {"input_ids": [feature["labels"] for feature in features]}
+
+        # reformat list to dict and set to pytorch format
+        batch = self.processor.feature_extractor.pad(
+            input_features,
+            padding=self.input_padding,
+            return_tensors="pt",
+        )
+
+        labels_batch = self.processor.tokenizer.pad(
+            label_features,
+            max_length=self.max_target_length,
+            padding=self.target_padding,
+            return_tensors="pt",
+        )
+
+        # replace padding with -100 to ignore correctly when computing the loss
+        labels = labels_batch["input_ids"].masked_fill(labels_batch.attention_mask.ne(1), -100)
+
+        # if bos token is appended in previous tokenization step,
+        # cut bos token here as it's append later anyways
+        if (labels[:, 0] == self.decoder_start_token_id).all().cpu().item():
+            labels = labels[:, 1:]
+
+        batch["labels"] = labels
+        return batch
+
+
+def log_metric(
+    accelerator,
+    metrics: Dict,
+    train_time: float,
+    prefix: str = "eval",
+):
+    """Helper function to log all evaluation metrics with the correct prefixes and styling."""
+    log_metrics = {}
+    for k, v in metrics.items():
+        log_metrics[f"{prefix}/{k}"] = v
+    log_metrics[f"{prefix}/time"] = train_time
+    accelerator.log(log_metrics)
+
+
+def log_pred(
+    accelerator,
+    pred_str: List[str],
+    label_str: List[str],
+    norm_pred_str: List[str],
+    norm_label_str: List[str],
+    prefix: str = "eval",
+    num_lines: int = 200000,
+):
+    """Helper function to log target/predicted transcriptions to weights and biases (wandb)."""
+    if accelerator.is_main_process:
+        wandb_tracker = accelerator.get_tracker("wandb")
+        # pretty name for split
+        prefix = prefix.replace("/", "-")
+
+        # convert str data to a wandb compatible format
+        str_data = [[label_str[i], pred_str[i], norm_label_str[i], norm_pred_str[i]] for i in range(len(pred_str))]
+        # log as a table with the appropriate headers
+        wandb_tracker.log_table(
+            table_name=f"{prefix}/all_predictions",
+            columns=["Target", "Pred", "Norm Target", "Norm Pred"],
+            data=str_data[:num_lines],
+        )
+
+        # log incorrect normalised predictions
+        str_data = np.asarray(str_data)
+        str_data_incorrect = str_data[str_data[:, -2] != str_data[:, -1]]
+        # log as a table with the appropriate headers
+        wandb_tracker.log_table(
+            table_name=f"{prefix}/incorrect_predictions",
+            columns=["Target", "Pred", "Norm Target", "Norm Pred"],
+            data=str_data_incorrect[:num_lines],
+        )
+
+
+def main():
+    # 1. Parse input arguments
+    # We keep distinct sets of args, for cleaner separation of model/data/training related args
+    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, Seq2SeqTrainingArguments))
+
+    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
+        # If we pass only one argument to the script and it's the path to a json file,
+        # let's parse it to get our arguments.
+        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
+    else:
+        model_args, data_args, training_args = parser.parse_args_into_dataclasses()
+
+    # 2. Initialize the accelerator
+    # We will let the accelerator handle device placement for us in this example
+    # We simply have to specify the training precision and any trackers being used
+    # We'll use the same dtype arguments as our JAX/Flax training script and convert
+    # it to accelerate format
+    if model_args.dtype == "float16":
+        mixed_precision = "fp16"
+        torch_dtype = torch.float16
+    elif model_args.dtype == "bfloat16":
+        mixed_precision = "bf16"
+        torch_dtype = torch.bfloat16
+    else:
+        mixed_precision = "no"
+        torch_dtype = torch.float32
+
+    kwargs = InitProcessGroupKwargs(timeout=timedelta(seconds=7200))
+
+    accelerator = Accelerator(
+        gradient_accumulation_steps=training_args.gradient_accumulation_steps,
+        mixed_precision=mixed_precision,
+        log_with=training_args.report_to,
+        project_dir=training_args.output_dir,
+        kwargs_handlers=[kwargs],
+    )
+
+    accelerator.init_trackers(project_name=data_args.wandb_project)
+
+    # 3. Set-up basic logging
+    # Create one log on every process with the configuration for debugging
+    logging.basicConfig(
+        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+        datefmt="%m/%d/%Y %H:%M:%S",
+        level=logging.INFO,
+    )
+    # Log a small summary on each proces
+    logger.warning(
+        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, "
+        f"distributed training: {training_args.parallel_mode.value == 'distributed'}, 16-bits training: {training_args.fp16}"
+    )
+
+    # Set the verbosity to info of the Transformers logger (on main process only)
+    if accelerator.is_local_main_process:
+        datasets.utils.logging.set_verbosity_warning()
+        transformers.utils.logging.set_verbosity_info()
+    else:
+        datasets.utils.logging.set_verbosity_error()
+        transformers.utils.logging.set_verbosity_error()
+    logger.info("Training/evaluation parameters %s", training_args)
+
+    # 3. Load dataset
+    raw_datasets = IterableDatasetDict() if data_args.streaming else DatasetDict()
+    token = model_args.token if model_args.token is not None else HfFolder().get_token()
+
+    data_splits = data_args.dataset_split_name.split("+")
+    for split in data_splits:
+        with accelerator.main_process_first():
+            raw_datasets[split] = load_dataset(
+                data_args.dataset_name,
+                data_args.dataset_config_name,
+                split=split,
+                cache_dir=data_args.dataset_cache_dir,
+                token=token,
+                streaming=data_args.streaming,
+                num_proc=data_args.preprocessing_num_workers if not data_args.streaming else None,
+            )
+
+    if data_args.audio_column_name not in next(iter(raw_datasets.values())).column_names:
+        raise ValueError(
+            f"--audio_column_name '{data_args.audio_column_name}' not found in dataset"
+            f" '{data_args.dataset_name}'. Make sure to set `--audio_column_name` to"
+            " the correct audio column - one of"
+            f" {', '.join(next(iter(raw_datasets.values())).column_names)}."
+        )
+
+    if data_args.text_column_name not in next(iter(raw_datasets.values())).column_names:
+        raise ValueError(
+            f"--text_column_name {data_args.text_column_name} not found in dataset"
+            f" '{data_args.dataset_name}'. Make sure to set `--text_column_name` to the"
+            " correct text column - one of"
+            f" {', '.join(next(iter(raw_datasets.values())).column_names)}."
+        )
+    
+    # 7. Load pretrained model, tokenizer, and feature extractor
+    config = WhisperConfig.from_pretrained(
+        (model_args.config_name if model_args.config_name else model_args.model_name_or_path),
+        cache_dir=model_args.cache_dir,
+        revision=model_args.model_revision,
+        token=token,
+    )
+    feature_extractor = WhisperFeatureExtractor.from_pretrained(
+        (model_args.feature_extractor_name if model_args.feature_extractor_name else model_args.model_name_or_path),
+        cache_dir=model_args.cache_dir,
+        revision=model_args.model_revision,
+        token=token,
+    )
+    tokenizer = WhisperTokenizerFast.from_pretrained(
+        (model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path),
+        cache_dir=model_args.cache_dir,
+        use_fast=model_args.use_fast_tokenizer,
+        revision=model_args.model_revision,
+        token=token,
+    )
+    processor = WhisperProcessor.from_pretrained(
+        (model_args.processor_name if model_args.processor_name else model_args.model_name_or_path),
+        cache_dir=model_args.cache_dir,
+        revision=model_args.model_revision,
+        token=token,
+    )
+
+    model = WhisperForConditionalGeneration.from_pretrained(
+        model_args.model_name_or_path,
+        config=config,
+        cache_dir=model_args.cache_dir,
+        revision=model_args.model_revision,
+        subfolder=model_args.subfolder,
+        token=token,
+        low_cpu_mem_usage=True,
+        torch_dtype=torch_dtype,
+        attn_implementation=model_args.attn_implementation,
+    )
+    model.eval()
+
+    if model.config.decoder_start_token_id is None:
+        raise ValueError("Make sure that `config.decoder_start_token_id` is correctly defined")
+
+    return_timestamps = data_args.return_timestamps
+    if hasattr(model.generation_config, "is_multilingual") and model.generation_config.is_multilingual:
+        is_multilingual = True
+        # We need to set the language and task ids for multilingual checkpoints
+        tokenizer.set_prefix_tokens(
+            language=data_args.language, task=data_args.task, predict_timestamps=return_timestamps
+        )
+    elif data_args.language is not None:
+        raise ValueError(
+            "Setting language token for an English-only checkpoint is not permitted. The language argument should "
+            "only be set for multilingual checkpoints."
+        )
+    else:
+        is_multilingual = False
+
+    # 6. Resample speech dataset: `datasets` takes care of automatically loading and resampling the audio,
+    # so we just need to set the correct target sampling rate.
+    raw_datasets = raw_datasets.cast_column(
+        data_args.audio_column_name,
+        datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate),
+    )
+
+    # 7. Preprocessing the datasets.
+    # We need to read the audio files as arrays and tokenize the targets.
+    max_input_length = int(data_args.max_duration_in_seconds * feature_extractor.sampling_rate)
+    max_label_length = (
+        data_args.max_label_length if data_args.max_label_length is not None else model.config.max_length
+    )
+    audio_column_name = data_args.audio_column_name
+    sampling_rate = feature_extractor.sampling_rate
+
+    preprocessing_batch_size = data_args.preprocessing_batch_size
+    num_workers = data_args.preprocessing_num_workers
+    dataloader_num_workers = training_args.dataloader_num_workers
+
+    text_column_name = data_args.text_column_name
+    model_input_name = feature_extractor.model_input_names[0]
+    id_column_name = data_args.id_column_name
+    speaker_id_column_name = data_args.speaker_id_column_name
+    normalizer = (
+        BasicTextNormalizer()
+        if data_args.language is not None
+        else EnglishTextNormalizer(tokenizer.english_spelling_normalizer)
+    )
+
+    timestamp_position = 3 if is_multilingual else 1
+    decoder_prev_token_id = tokenizer.convert_tokens_to_ids("<|startofprev|>")
+    decoder_eot_token_id = tokenizer.eos_token_id
+
+    if data_args.max_samples_per_split is not None:
+        for split in data_splits:
+            raw_datasets[split] = (
+                raw_datasets[split].take(data_args.max_samples_per_split)
+                if data_args.streaming
+                else raw_datasets[split].select(range(data_args.max_samples_per_split))
+            )
+
+    if speaker_id_column_name is not None:
+        raw_datasets = raw_datasets.sort(speaker_id_column_name)
+
+    def concatenate_dataset(batch):
+        audio_arrays, texts, speaker_ids = [], [], []
+
+        # skip corrupted samples
+        for row in table_iter(batch.pa_table, batch_size=1):
+            row = batch.formatter.format_row(row)
+            try:
+                sample_audio = row[audio_column_name]['array']
+                sample_text = row[text_column_name]
+                sample_speaker_id = row[speaker_id_column_name] if speaker_id_column_name else None
+            except LibsndfileError:
+                logger.warning(f"{row[id_column_name]} is corrupted! Skipping sample.")
+                continue
+            audio_arrays.append(sample_audio)
+            texts.append(sample_text)
+            speaker_ids.append(sample_speaker_id)
+
+        # initialize concatenations
+        concat_audio = [audio_arrays[0]]
+        concat_text = [texts[0]]
+        concat_speaker_id = [speaker_ids[0]]
+        condition_on_prev = [0]
+
+        for audio_array, text, speaker_id in zip(audio_arrays[1:], texts[1:], speaker_ids[1:]):
+            is_same_speaker = speaker_id == concat_speaker_id[-1]
+            is_concatenable = len(audio_array) + len(concat_audio[-1]) <= max_input_length 
+            if is_same_speaker and is_concatenable:
+                # inplace concatenation
+                concat_audio[-1] = np.append(concat_audio[-1], audio_array)
+                concat_text[-1] = concat_text[-1] + " " + text
+            else:
+                concat_audio.append(audio_array)
+                concat_text.append(text)
+                concat_speaker_id.append(speaker_id)
+                condition_on_prev.append(1 if is_same_speaker else 0)   
+
+        batch[audio_column_name] = [{"array": array, "sampling_rate": sampling_rate} for array in concat_audio]
+        batch[text_column_name] = concat_text
+        batch[id_column_name] = concat_speaker_id
+        batch["condition_on_prev"] = condition_on_prev
+
+        return batch
+
+    raw_datasets_features = list(next(iter(raw_datasets.values())).features.keys())
+    if data_args.concatenate_audio and not data_args.streaming:
+        with accelerator.main_process_first():
+            raw_datasets = raw_datasets.map(
+                concatenate_dataset,
+                batched=True,
+                batch_size=preprocessing_batch_size,
+                num_proc=num_workers,
+                remove_columns=set(raw_datasets_features)
+                - {audio_column_name, text_column_name, id_column_name, "condition_on_prev"},
+                desc="Concatenating dataset...",
+            )
+
+        raw_datasets = raw_datasets.cast_column(
+            audio_column_name, datasets.features.Audio(sampling_rate=sampling_rate)
+        )
+        pretty_name = data_args.dataset_name.split("/")[-1]
+
+        def postprocess_ids(speaker_ids, indices):
+            speaker_ids_formatted = []
+            for speaker, idx in zip(speaker_ids, indices):
+                formatted_idx = f"{pretty_name}-{speaker}-{idx}" if speaker is not None else f"{pretty_name}-{idx}"
+                speaker_ids_formatted.append(formatted_idx)
+            return {id_column_name: speaker_ids_formatted}
+        
+        with accelerator.main_process_first():
+            raw_datasets = raw_datasets.map(
+                postprocess_ids,
+                input_columns=[id_column_name],
+                with_indices=True,
+                desc="Setting sample idxs...",
+                batched=True,
+                batch_size=preprocessing_batch_size,
+                num_proc=num_workers,
+            )
+    elif data_args.concatenate_audio and data_args.streaming:
+        raise ValueError(
+            "Streaming mode is not yet compatible with concatenating audios to `max_duration_in_seconds`."
+            "Either set `--streaming=False` and download the audios locally, or open an issue on the Distil-Whisper repo to request this feature."
+        )
+
+    def prepare_dataset(batch):
+        # process audio
+        sample = batch[audio_column_name]
+        inputs = feature_extractor(sample["array"], sampling_rate=sample["sampling_rate"])
+        # process audio length
+        batch[model_input_name] = inputs.get(model_input_name)[0]
+
+        # process targets
+        input_str = batch[text_column_name]
+        batch["labels"] = tokenizer(input_str, max_length=max_label_length, truncation=True).input_ids
+        return batch
+
+    raw_datasets_features = list(next(iter(raw_datasets.values())).features.keys())
+    file_ids_dataset = IterableDatasetDict() if data_args.streaming else DatasetDict()
+    for split in raw_datasets:
+        file_ids_dataset[split] = raw_datasets[split][id_column_name]
+    if data_args.streaming:
+        with accelerator.main_process_first():
+            vectorized_datasets = raw_datasets.map(prepare_dataset, remove_columns=raw_datasets_features)
+    else:
+        with accelerator.main_process_first():
+            vectorized_datasets = raw_datasets.map(
+                prepare_dataset,
+                remove_columns=raw_datasets_features,
+                num_proc=num_workers,
+                desc="preprocess dataset",
+            )
+
+    # for large datasets it is advised to run the preprocessing on a
+    # single machine first with `args.preprocessing_only` since there will mostly likely
+    # be a timeout when running the script in distributed mode.
+    # In a second step `args.preprocessing_only` can then be set to `False` to load the
+    # cached dataset
+    if data_args.preprocessing_only:
+        cache = {k: v.cache_files for k, v in vectorized_datasets.items()}
+        logger.info(f"Data preprocessing finished. Files cached at {cache}.")
+        return
+
+    if data_args.streaming and dataloader_num_workers > 0:
+        logger.warning(
+            "Using multiple dataloader num workers with streaming mode will result in different shards of "
+            "data being transcribed in parallel. This is not advised if you want to preserve the order of the "
+            "audio-text data."
+        )
+
+    # Handle the repository creation
+    output_dir = training_args.output_dir
+    if accelerator.is_main_process:
+        if training_args.push_to_hub:
+            if training_args.hub_model_id is None:
+                repo_name = get_full_repo_name(
+                    Path(output_dir).absolute().name,
+                    token=training_args.hub_token,
+                )
+            else:
+                repo_name = training_args.hub_model_id
+            create_repo(repo_name, repo_type="dataset", exist_ok=True, token=training_args.hub_token)
+            snapshot_download(repo_id=repo_name, local_dir=output_dir)
+
+            # Ensure large txt files can be pushed to the Hub with git-lfs
+            with open(os.path.join(output_dir, ".gitattributes"), "r+") as f:
+                git_lfs_extensions = f.read()
+                if "*.csv" not in git_lfs_extensions:
+                    f.write("*.csv filter=lfs diff=lfs merge=lfs -text")
+
+        elif output_dir is not None:
+            # this is where we'll save our transcriptions
+            os.makedirs(output_dir, exist_ok=True)
+
+    accelerator.wait_for_everyone()
+
+    # 8. Load Metric
+    metric = evaluate.load("wer")
+
+    def compute_metrics(preds, labels, file_ids):
+        # replace padded labels by the padding token
+        for idx in range(len(labels)):
+            labels[idx][labels[idx] == -100] = tokenizer.pad_token_id
+
+        pred_str = tokenizer.batch_decode(preds, skip_special_tokens=False, decode_with_timestamps=return_timestamps)
+        # we do not want to group tokens when computing the metrics
+        label_str = tokenizer.batch_decode(labels, skip_special_tokens=True)
+
+        # normalize everything and re-compute the WER
+        norm_pred_str = [normalizer(pred) for pred in pred_str]
+        norm_label_str = [normalizer(label) for label in label_str]
+        # for logging, we need the pred/labels to match the norm_pred/norm_labels, so discard any filtered samples here
+        pred_str = [pred_str[i] for i in range(len(norm_pred_str)) if len(norm_label_str[i]) > 0]
+        label_str = [label_str[i] for i in range(len(norm_label_str)) if len(norm_label_str[i]) > 0]
+        file_ids = [file_ids[i] for i in range(len(file_ids)) if len(norm_label_str[i]) > 0]
+        # filtering step to only evaluate the samples that correspond to non-zero normalized references:
+        norm_pred_str = [norm_pred_str[i] for i in range(len(norm_pred_str)) if len(norm_label_str[i]) > 0]
+        norm_label_str = [norm_label_str[i] for i in range(len(norm_label_str)) if len(norm_label_str[i]) > 0]
+
+        wer = 100 * metric.compute(predictions=norm_pred_str, references=norm_label_str)
+
+        return {"wer": wer}, pred_str, label_str, norm_pred_str, norm_label_str, file_ids
+
+    def filter_eot_tokens(preds):
+        for idx in range(len(preds)):
+            # remove the EOT tokens to get the 'true' token length
+            token_ids = [token for token in preds[idx] if token != decoder_eot_token_id]
+            token_ids = token_ids + [decoder_eot_token_id]
+            preds[idx] = token_ids
+        return preds
+
+    # 12. Define Training Schedule
+    per_device_eval_batch_size = int(training_args.per_device_eval_batch_size)
+
+    data_collator = DataCollatorSpeechSeq2SeqWithPadding(
+        processor=processor,
+        decoder_start_token_id=model.config.decoder_start_token_id,  # <|startoftranscript|>
+        input_padding="longest",
+        target_padding="max_length",
+        max_target_length=max_label_length,
+    )
+
+    # 14. Define generation arguments - we need to do this before we wrap the models in DDP
+    # so that we can still access the configs
+    num_beams = (
+        training_args.generation_num_beams
+        if training_args.generation_num_beams is not None
+        else getattr(model.generation_config, "num_beams", 1)
+    )
+
+    gen_kwargs = {
+        "max_length": max_label_length,
+        "num_beams": num_beams,
+        "return_timestamps": return_timestamps,
+    }
+    if hasattr(model.generation_config, "is_multilingual") and model.generation_config.is_multilingual:
+        # forcing the language and task tokens helps multilingual models in their generations
+        gen_kwargs.update(
+            {
+                "language": data_args.language,
+                "task": data_args.task,
+            }
+        )
+    # remove any preset forced decoder ids since these are deprecated
+    model.generation_config.forced_decoder_ids = None
+    model.config.forced_decoder_ids = None
+
+    # 15. Prepare everything with accelerate
+    model = accelerator.prepare(model)
+
+    def eval_step_with_save(split="eval"):
+        # ======================== Evaluating ==============================
+        eval_preds = []
+        eval_labels = []
+        eval_ids = []
+        pred_str = []
+        eval_start = time.time()
+
+        eval_loader = DataLoader(
+            vectorized_datasets[split],
+            batch_size=per_device_eval_batch_size,
+            collate_fn=data_collator,
+            num_workers=dataloader_num_workers,
+            pin_memory=True,
+        )
+        file_loader = DataLoader(
+            file_ids_dataset[split],
+            batch_size=per_device_eval_batch_size * accelerator.num_processes,
+            num_workers=dataloader_num_workers,
+        )
+
+        eval_loader = accelerator.prepare(eval_loader)
+        batches = tqdm(eval_loader, desc=f"Evaluating {split}...", disable=not accelerator.is_local_main_process)
+
+        # make the split name pretty for librispeech etc
+        split = split.replace(".", "-").split("/")[-1]
+        output_csv = os.path.join(output_dir, f"{split}-transcription.csv")
+
+        for step, (batch, file_ids) in enumerate(zip(batches, file_loader)):
+            # Generate predictions and pad to max generated length
+            generate_fn = model.module.generate if accelerator.num_processes > 1 else model.generate
+            generated_ids = generate_fn(batch["input_features"].to(dtype=torch_dtype), **gen_kwargs)
+            generated_ids = accelerator.pad_across_processes(generated_ids, dim=1, pad_index=tokenizer.pad_token_id)
+            # Gather all predictions and targets
+            generated_ids, labels = accelerator.gather_for_metrics((generated_ids, batch["labels"]))
+            eval_preds.extend(generated_ids.cpu().numpy())
+            eval_labels.extend(labels.cpu().numpy())
+            eval_ids.extend(file_ids)
+
+            if step % training_args.logging_steps == 0 and step > 0:
+                batches.write(f"Saving transcriptions for split {split} step {step}")
+                accelerator.wait_for_everyone()
+                pred_ids = eval_preds[-(len(eval_preds) - len(pred_str)) :]
+                pred_ids = filter_eot_tokens(pred_ids)
+                pred_str.extend(
+                    tokenizer.batch_decode(
+                        pred_ids, skip_special_tokens=False, decode_with_timestamps=return_timestamps
+                    )
+                )
+                csv_data = [[eval_ids[i], pred_str[i]] for i in range(len(eval_preds))]
+
+                with open(output_csv, "w", encoding="UTF8", newline="") as f:
+                    writer = csv.writer(f)
+                    # write multiple rows
+                    writer.writerow(["file_id", "whisper_transcript"])
+                    writer.writerows(csv_data)
+
+                if training_args.push_to_hub and accelerator.is_main_process:
+                    upload_folder(
+                        folder_path=output_dir,
+                        repo_id=repo_name,
+                        repo_type="dataset",
+                        commit_message=f"Saving transcriptions for split {split} step {step}.",
+                    )
+
+        accelerator.wait_for_everyone()
+        eval_time = time.time() - eval_start
+
+        # compute WER metric for eval sets
+        wer_desc = ""
+        if "validation" in split or "test" in split:
+            eval_preds = filter_eot_tokens(eval_preds)
+            wer_metric, pred_str, label_str, norm_pred_str, norm_label_str, eval_ids = compute_metrics(
+                eval_preds, eval_labels, eval_ids
+            )
+            wer_desc = " ".join([f"Eval {key}: {value} |" for key, value in wer_metric.items()])
+            # Save metrics + predictions
+            log_metric(
+                accelerator,
+                metrics=wer_metric,
+                train_time=eval_time,
+                prefix=split,
+            )
+            log_pred(
+                accelerator,
+                pred_str,
+                label_str,
+                norm_pred_str,
+                norm_label_str,
+                prefix=split,
+            )
+        else:
+            pred_ids = eval_preds[-(len(eval_preds) - len(pred_str)) :]
+            pred_ids = filter_eot_tokens(pred_ids)
+            pred_str.extend(
+                tokenizer.batch_decode(pred_ids, skip_special_tokens=False, decode_with_timestamps=return_timestamps)
+            )
+
+        batches.write(f"Saving final transcriptions for split {split}.")
+        csv_data = [[eval_ids[i], eval_preds[i]] for i in range(len(eval_preds))]
+        with open(output_csv, "w", encoding="UTF8", newline="") as f:
+            writer = csv.writer(f)
+            # write multiple rows
+            writer.writerow(["file_id", "whisper_transcript"])
+            writer.writerows(csv_data)
+
+        # Print metrics
+        logger.info(wer_desc)
+
+        if not data_args.streaming:
+            raw_datasets[split] = raw_datasets[split].add_column("whisper_transcript", pred_str)
+            raw_datasets[split] = raw_datasets[split].add_column("eval_preds", eval_preds)
+
+            def add_concatenated_text(eval_preds, condition_on_prev):
+                concatenated_prev = [None]
+                for token_ids, condition in zip(eval_preds[:-1], condition_on_prev[1:]):
+                    if condition is False:
+                        concatenated_prev.append(None)
+                    else:
+                        prompt_ids = [token for token in token_ids if token != decoder_eot_token_id]
+                        prompt_ids = [decoder_prev_token_id] + prompt_ids[timestamp_position:]
+                        concatenated_prev.append(prompt_ids)
+                return {"condition_on_prev": concatenated_prev}
+
+            if data_args.concatenate_audio:
+                with accelerator.main_process_first():
+                    raw_datasets[split] = raw_datasets[split].map(
+                        add_concatenated_text,
+                        input_columns=["eval_preds", "condition_on_prev"],
+                        remove_columns=["eval_preds"],
+                        desc="Setting condition on prev...",
+                        batched=True,
+                        batch_size=preprocessing_batch_size,
+                        num_proc=num_workers,
+                    )
+
+    logger.info("***** Running Labelling *****")
+    logger.info("  Instantaneous batch size per device =" f" {training_args.per_device_eval_batch_size}")
+    logger.info(
+        f"  Total eval batch size (w. parallel & distributed) = {training_args.per_device_eval_batch_size * accelerator.num_processes}"
+    )
+    logger.info(f"  Predict labels with timestamps = {return_timestamps}")
+    for split in data_splits:
+        eval_step_with_save(split=split)
+        accelerator.wait_for_everyone()
+        if training_args.push_to_hub and accelerator.is_main_process:
+            upload_folder(
+                folder_path=output_dir,
+                repo_id=repo_name,
+                repo_type="dataset",
+                commit_message=f"Saving final transcriptions for split {split.replace('.', '-').split('/')[-1]}",
+            )
+    if not data_args.streaming and accelerator.is_main_process:
+        raw_datasets.save_to_disk(output_dir, num_proc=num_workers)
+        if training_args.push_to_hub:
+            raw_datasets.push_to_hub(repo_name, config_name=data_args.dataset_config_name)
+    accelerator.end_training()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/setup.py b/setup.py
new file mode 100644
index 0000000000000000000000000000000000000000..75077f52633ffd41652eae95e36da4ba6e6c5750
--- /dev/null
+++ b/setup.py
@@ -0,0 +1,52 @@
+# Copyright 2023 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+import os
+
+import setuptools
+
+_deps = [
+    "torch>=1.10",
+    "transformers>=4.35.1",
+    "datasets[audio]>=2.14.7",
+    "accelerate>=0.24.1",
+    "jiwer",
+    "evaluate>=0.4.1",
+    "wandb",
+    "tensorboard",
+    "nltk",
+]
+
+_extras_dev_deps = [
+    "ruff==0.1.5",
+]
+
+here = os.path.abspath(os.path.dirname(__file__))
+
+with open(os.path.join(here, "README.md"), encoding="utf-8") as f:
+    long_description = f.read()
+
+setuptools.setup(
+    name="distil_whisper",
+    description="Toolkit for distilling OpenAI's Whisper model.",
+    long_description=long_description,
+    long_description_content_type="text/markdown",
+    packages=setuptools.find_packages(),
+    install_requires=_deps,
+    extras_require={
+        "dev": [_extras_dev_deps],
+    },
+)
+
diff --git a/special_tokens_map.json b/special_tokens_map.json
new file mode 100644
index 0000000000000000000000000000000000000000..312bc106291bb51bf2cc1648df070bef963a0639
--- /dev/null
+++ b/special_tokens_map.json
@@ -0,0 +1,139 @@
+{
+  "additional_special_tokens": [
+    "<|startoftranscript|>",
+    "<|en|>",
+    "<|zh|>",
+    "<|de|>",
+    "<|es|>",
+    "<|ru|>",
+    "<|ko|>",
+    "<|fr|>",
+    "<|ja|>",
+    "<|pt|>",
+    "<|tr|>",
+    "<|pl|>",
+    "<|ca|>",
+    "<|nl|>",
+    "<|ar|>",
+    "<|sv|>",
+    "<|it|>",
+    "<|id|>",
+    "<|hi|>",
+    "<|fi|>",
+    "<|vi|>",
+    "<|he|>",
+    "<|uk|>",
+    "<|el|>",
+    "<|ms|>",
+    "<|cs|>",
+    "<|ro|>",
+    "<|da|>",
+    "<|hu|>",
+    "<|ta|>",
+    "<|no|>",
+    "<|th|>",
+    "<|ur|>",
+    "<|hr|>",
+    "<|bg|>",
+    "<|lt|>",
+    "<|la|>",
+    "<|mi|>",
+    "<|ml|>",
+    "<|cy|>",
+    "<|sk|>",
+    "<|te|>",
+    "<|fa|>",
+    "<|lv|>",
+    "<|bn|>",
+    "<|sr|>",
+    "<|az|>",
+    "<|sl|>",
+    "<|kn|>",
+    "<|et|>",
+    "<|mk|>",
+    "<|br|>",
+    "<|eu|>",
+    "<|is|>",
+    "<|hy|>",
+    "<|ne|>",
+    "<|mn|>",
+    "<|bs|>",
+    "<|kk|>",
+    "<|sq|>",
+    "<|sw|>",
+    "<|gl|>",
+    "<|mr|>",
+    "<|pa|>",
+    "<|si|>",
+    "<|km|>",
+    "<|sn|>",
+    "<|yo|>",
+    "<|so|>",
+    "<|af|>",
+    "<|oc|>",
+    "<|ka|>",
+    "<|be|>",
+    "<|tg|>",
+    "<|sd|>",
+    "<|gu|>",
+    "<|am|>",
+    "<|yi|>",
+    "<|lo|>",
+    "<|uz|>",
+    "<|fo|>",
+    "<|ht|>",
+    "<|ps|>",
+    "<|tk|>",
+    "<|nn|>",
+    "<|mt|>",
+    "<|sa|>",
+    "<|lb|>",
+    "<|my|>",
+    "<|bo|>",
+    "<|tl|>",
+    "<|mg|>",
+    "<|as|>",
+    "<|tt|>",
+    "<|haw|>",
+    "<|ln|>",
+    "<|ha|>",
+    "<|ba|>",
+    "<|jw|>",
+    "<|su|>",
+    "<|yue|>",
+    "<|translate|>",
+    "<|transcribe|>",
+    "<|startoflm|>",
+    "<|startofprev|>",
+    "<|nospeech|>",
+    "<|notimestamps|>"
+  ],
+  "bos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}
diff --git a/tokenizer.json b/tokenizer.json
new file mode 100644
index 0000000000000000000000000000000000000000..45c4df3f3a2e0e3dc4b867d4160168aa9c89e8bf
--- /dev/null
+++ b/tokenizer.json
@@ -0,0 +1,114883 @@
+{
+  "version": "1.0",
+  "truncation": null,
+  "padding": null,
+  "added_tokens": [
+    {
+      "id": 50257,
+      "content": "<|endoftext|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 50258,
+      "content": "<|startoftranscript|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 50259,
+      "content": "<|en|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 50260,
+      "content": "<|zh|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 50261,
+      "content": "<|de|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 50262,
+      "content": "<|es|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 50263,
+      "content": "<|ru|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 50264,
+      "content": "<|ko|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 50265,
+      "content": "<|fr|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 50266,
+      "content": "<|ja|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 50267,
+      "content": "<|pt|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 50268,
+      "content": "<|tr|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 50269,
+      "content": "<|pl|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 50270,
+      "content": "<|ca|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 50271,
+      "content": "<|nl|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 50272,
+      "content": "<|ar|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 50273,
+      "content": "<|sv|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 50274,
+      "content": "<|it|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 50275,
+      "content": "<|id|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 50276,
+      "content": "<|hi|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 50277,
+      "content": "<|fi|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 50278,
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+      "Ġa s",
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+      "а ÑĤ",
+      "Ġma in",
+      "Ġrec ord",
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+      "Ġdefin itely",
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+      "Ġn uc",
+      "ĠA pr",
+      "é Ľ",
+      "ĠKore a",
+      "eg o",
+      "ĠCan ada",
+      "Ġkön nen",
+      "Ġcomp ar",
+      "Ġg anz",
+      "ĠM ais",
+      "Ġthem e",
+      "Ġk i",
+      "Ġdraw ing",
+      "az on",
+      "ĠO ff",
+      "t t",
+      "ĠW ind",
+      "Ġtod os",
+      "Ġob vious",
+      "на Ñı",
+      "I M",
+      "ĠÐ ł",
+      "we ll",
+      "Ġbl ow",
+      "Ġho ok",
+      "Ġcir cle",
+      "Ġë³ ´",
+      "Ġarch itect",
+      "ĠK r",
+      "Ġc ó",
+      "Ġprotect ion",
+      "eg a",
+      "å ĩ",
+      "Ġwatch ed",
+      "Ġans wers",
+      "Ġdi et",
+      "iv o",
+      "Ġpow der",
+      "Ġyour s",
+      "Ġhigh est",
+      "çĤ º",
+      "F F",
+      "å º",
+      "Ġbo ys",
+      "ö yle",
+      "Ġl unch",
+      "è¬ Ŀ",
+      "ĠI I",
+      "Ġset s",
+      "Ġmo le",
+      "Û ģ",
+      "Ġwin ter",
+      "Ġluck y",
+      "Ġrespons ibility",
+      "Ġsign al",
+      "Ġwond ering",
+      "Ġa x",
+      "Ġcook ing",
+      "ов оÑĢ",
+      "le g",
+      "Ġп оÑĤ",
+      "Ġsurpr ise",
+      "Ġdem ocr",
+      "Ġlo op",
+      "Ġj ag",
+      "Ġcur ious",
+      "Ġmarket ing",
+      "Ð Ŀ",
+      "ar on",
+      "ĠApp le",
+      "Ġvirt ual",
+      "Ġ19 8",
+      "no on",
+      "ĠM et",
+      "оÑģ ÑĤо",
+      "об Ñĭ",
+      "it u",
+      "ĠA w",
+      "Ġbu ying",
+      "Ġrestaur ant",
+      "ĠB ud",
+      "Ġdou bt",
+      "Ġgr ant",
+      "Ġver d",
+      "Ġc ash",
+      "Ġfac ulty",
+      "Th at",
+      "ĠE in",
+      "å¤ ļ",
+      "Ġw ed",
+      "it ness",
+      "ĠM ag",
+      "n el",
+      "Ġn arr",
+      "Ġacc ident",
+      "Ġmed ium",
+      "em ents",
+      "Ġcr ow",
+      "n ight",
+      "ìĿ ¼",
+      "ä¹ Ł",
+      "Ġlibr ary",
+      "аÑİ ÑĤ",
+      "Ġtamb ién",
+      "Ġrefer ence",
+      "Ġfour th",
+      "h ouse",
+      "v ention",
+      "Ġfill ed",
+      "ĠC our",
+      "ib r",
+      "Ġn g",
+      "Ġdevelop ing",
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+      "Ġpo ll",
+      "Ġtra ffic",
+      "arent ly",
+      "à® Ł",
+      "Ġform s",
+      "Ġcl ient",
+      "Ġg entle",
+      "Ġmus s",
+      "ĠCong ress",
+      "ĠInd ian",
+      "ce an",
+      "Ġp il",
+      "Ġc zy",
+      "st ood",
+      "ut y",
+      "Ġn ä",
+      "Ġsp ending",
+      "Ġconst ruction",
+      "ina udible",
+      "Ġë§ Ī",
+      "Īë¬ ´",
+      "Ġìĥ Ŀ",
+      "om a",
+      "os en",
+      "ag o",
+      "Ġlar gest",
+      "ãħĭ ãħĭ",
+      "Ġun iverse",
+      "b es",
+      "os a",
+      "Ġе го",
+      "Ġd ude",
+      "ĠM AR",
+      "Ġind eed",
+      "ε ι",
+      "Ġman aged",
+      "ĠSh ould",
+      "S o",
+      "Ġappl ied",
+      "Ġfair ly",
+      "ĠD en",
+      "Ġanal y",
+      "Ġconst antly",
+      "Ñģ п",
+      "H ow",
+      "ĠS ay",
+      "en cies",
+      "ĠP C",
+      "Ġegg s",
+      "à® °",
+      "Ġet h",
+      "ĠEnt ão",
+      "in ar",
+      "i ot",
+      "Ġc z",
+      "ĠEurope an",
+      "ãģ Ī",
+      "ĠA M",
+      "Ġc á",
+      "Ġrad io",
+      "§ Į",
+      "Ġh ide",
+      "ä» Ĭ",
+      "ĠSt art",
+      "Ġcl ub",
+      "ĠH ope",
+      "Ġeff orts",
+      "lus ion",
+      "Ġc ities",
+      "h one",
+      "Ġreach ed",
+      "Ġgu id",
+      "ro id",
+      "Ġhar m",
+      "Ġcut ting",
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+      "1 8",
+      "i est",
+      "ĠMe x",
+      "Ġ iron",
+      "çŁ ¥",
+      "Ġafter noon",
+      "Ġha ll",
+      "Ġpr zy",
+      "Ġg osh",
+      "Ġinflu ence",
+      "Ġв ид",
+      "Ġincre ased",
+      "ĠMin ister",
+      "Ġdis ci",
+      "ĠP eter",
+      "Ġver t",
+      "Ġmen u",
+      "Ġse lling",
+      "ur ally",
+      "Ġqu ote",
+      "ĠÂ ¡",
+      "Ġcontin ues",
+      "mp re",
+      "ĠÅŁ ey",
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+      "Ġна Ñģ",
+      "c les",
+      "ĠGerm an",
+      "c zy",
+      "ĠÐ £",
+      "B e",
+      "Ġk itchen",
+      "ĠT ry",
+      "i pe",
+      "Ġic on",
+      "ar p",
+      "Ġprov iding",
+      "ĠTr ans",
+      "Ġtechn ique",
+      "Ġh är",
+      "Ġinf rast",
+      "Ġsus p",
+      "ü ck",
+      "ic ip",
+      "ĠÐ ķ",
+      "Ġc in",
+      "ìĸ ´ë",
+      "Ġpr z",
+      "Ġcompon ent",
+      "Ġby e",
+      "ĠB ible",
+      "iz er",
+      "C h",
+      "Ġsol utions",
+      "Ġaccom pl",
+      "Ġ201 6",
+      "I E",
+      "ĠT a",
+      "Ġass ume",
+      "Ġliqu id",
+      "Ġë¨ ¹",
+      "Ġquar ter",
+      "Ġfem ale",
+      "ĠTh ink",
+      "Ġstat us",
+      "it ute",
+      "Ġco ach",
+      "Ġre in",
+      "Ġcomb ination",
+      "è ·",
+      "ĠT er",
+      "Ġobject s",
+      "Ġdist rict",
+      "Ġmake up",
+      "Ġmur der",
+      "w as",
+      "f en",
+      "Ġbow l",
+      "Ġpub lished",
+      "Ġsp orts",
+      "ãģ ¡",
+      "Ġident ity",
+      "Ġseem ed",
+      "Ġact ing",
+      "л Ñİ",
+      "ri x",
+      "Ġup load",
+      "Ġh ast",
+      "Ġbo at",
+      "ĠM od",
+      "ri o",
+      "Ġ =",
+      "Ġcy cle",
+      "¯ ¸",
+      "Ġl oud",
+      "ust ed",
+      "com ing",
+      "Ġ201 7",
+      "Ġon t",
+      "Ġleg isl",
+      "Ġst ruct",
+      "ĠSomet hing",
+      "Ġconf lict",
+      "Ġu pper",
+      "Ġman ager",
+      "Ġm ort",
+      "Ġf ra",
+      "ĠÄ °",
+      "ĠM ike",
+      "ĠW ork",
+      "Ġn ó",
+      "ph ere",
+      "ĠìĤ ¬ë",
+      "ĠL and",
+      "Ġfil ter",
+      "Ġprom ot",
+      "æ °",
+      "æĻ Ĥ",
+      "ķ ¼",
+      "Ġrecord ing",
+      "× Ŀ",
+      "Ġassoci ated",
+      "Ġf uel",
+      "und er",
+      "Ġele ction",
+      "Ġemploy ees",
+      "ĠCom p",
+      "ÑĢÑĥ г",
+      "ĠW o",
+      "ro l",
+      "Ġsa ved",
+      "ĠH on",
+      "ĠV i",
+      "åĪ Ĩ",
+      "ac a",
+      "p ret",
+      "Ġw et",
+      "Ġst upid",
+      "Ġl ad",
+      "Ġf est",
+      "Ġw ake",
+      "Ġи н",
+      "Ġgreat est",
+      "ĠJ im",
+      "Ġserious ly",
+      "Ġì ¹",
+      "Ġfeel ings",
+      "Ġ3 00",
+      "i ation",
+      "Ġbeaut y",
+      "Ġìŀ ĺ",
+      "Ġs an",
+      "ĵ ł",
+      "Ġ- (",
+      "Ġcons cious",
+      "Ġд ел",
+      "b ye",
+      "ç Ļ",
+      "M an",
+      "Ġlet s",
+      "Ġsho es",
+      "y d",
+      "ä¹ Ī",
+      "Ġdisapp e",
+      "ĠCount y",
+      "ĠSc ott",
+      "Ġbut t",
+      "Ġaqu ÃŃ",
+      "Ġconf ig",
+      "resp ond",
+      "LAU GH",
+      "© ëĭĪëĭ¤",
+      "Ġdivid ed",
+      "Ġac qu",
+      "Ġz one",
+      "Ġk omm",
+      "a ção",
+      "ì§ ľ",
+      "c ut",
+      "Ġ2 3",
+      "Ġmaxim um",
+      "ro g",
+      "Ġrun s",
+      "Ġcompon ents",
+      "Ġarri ved",
+      "Ġconf ident",
+      "ÑĢ ов",
+      "Ġhe ight",
+      "Ġpro ced",
+      "E M",
+      "ĠÐŃ ÑĤо",
+      "ĠM en",
+      "Ġtalk s",
+      "Ġconf idence",
+      "ĠChr is",
+      "Ġlead s",
+      "Ġn ose",
+      "f all",
+      "b b",
+      "ĠNot hing",
+      "is er",
+      "Ġindepend ent",
+      "Ġmin or",
+      "Ġsy m",
+      "l en",
+      "ci ence",
+      "Ġf ashion",
+      "Ġsex ual",
+      "Ġb un",
+      "h ere",
+      "Ġso il",
+      "Ġdies e",
+      "Ġsh ap",
+      "Ġempt y",
+      "Ġjour nal",
+      "ag on",
+      "ĠThe ir",
+      "Ġweek end",
+      "ÃŃ t",
+      "Ġer ror",
+      "Ġn ar",
+      "Ã ¸",
+      "è ©",
+      "an cy",
+      "Ġìķ Ĭ",
+      "Ġfore st",
+      "Ġha cer",
+      "Ġmiss ed",
+      "ãģ ķ",
+      "åı¯ 以",
+      "Ġev il",
+      "Ġstor age",
+      "Ġsing ing",
+      "in ha",
+      "Ġkn ock",
+      "Ġimp ress",
+      "ĠоÑĩ енÑĮ",
+      "ĠGo ld",
+      "ĠS ur",
+      "ĠP ort",
+      "åİ »",
+      "ĠL ond",
+      "Ġfaz er",
+      "ot y",
+      "ot o",
+      "Ġan x",
+      "ĠWill iam",
+      "Ġexist ing",
+      "pl ace",
+      "ĠC D",
+      "Î ³",
+      "ĠColl ege",
+      "l or",
+      "ĠE ast",
+      "s en",
+      "f ach",
+      "o ft",
+      "Ġexperien ced",
+      "Ġlo ves",
+      "im m",
+      "Ġpo ly",
+      "Ġes se",
+      "ì ¤",
+      "ĠG rand",
+      "è §",
+      "ch er",
+      "Ġvict im",
+      "ĠG es",
+      "л ÑĮ",
+      "v ision",
+      "Ġt all",
+      "Ġl ens",
+      "Ġз на",
+      "ĠB oth",
+      "Ġì ²",
+      "Ġsust ain",
+      "Ġarg ument",
+      "Ġfact ors",
+      "Ġautom atically",
+      "Ġfr uit",
+      "Ġli ber",
+      "Ġa le",
+      "ĠP ress",
+      "ĠB a",
+      "ĠÐ ³Ð¾",
+      "Ġhundred s",
+      "th at",
+      "ĠR ich",
+      "Ġreci pe",
+      "ĠI T",
+      "è ĩ",
+      "Ạ¥",
+      "Ġdescri be",
+      "Ġdri ver",
+      "ĠO ct",
+      "ĠM at",
+      "д е",
+      "Ġme al",
+      "Ġlat est",
+      "Ġth erap",
+      "Ġcomp are",
+      "ĠAm azon",
+      "Ġì¢ Ģ",
+      "ĠRuss ia",
+      "Ġstr ing",
+      "Ġk a",
+      "ĠComm un",
+      "Ġd ia",
+      "I s",
+      "Ġmill ions",
+      "Ġcor por",
+      "Ġcor respond",
+      "Ġfix ed",
+      "ĠJo e",
+      "Ù İ",
+      "Ġview s",
+      "Ġr iver",
+      "Ġstud io",
+      "ig ger",
+      "Ġfl avor",
+      "Ġpres ence",
+      "Ġun its",
+      "Ġsa ving",
+      "av our",
+      "Ġp esso",
+      "or ith",
+      "Ġh ers",
+      "ĠN at",
+      "as ion",
+      "ĠFr ank",
+      "о ÑĪ",
+      "ÅĤ y",
+      "í Ħ",
+      "Ġein em",
+      "Ġfun ctions",
+      "um an",
+      "Ġn orth",
+      "Ġìł Ħ",
+      "Ġhor se",
+      "v id",
+      "Ġple asure",
+      "а ÑĪ",
+      "é es",
+      "ind a",
+      "Ġt ail",
+      "Ġexpl ore",
+      "S T",
+      "Ġcommer cial",
+      "ĠD uring",
+      "ar l",
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+      "f it",
+      "Ġr ates",
+      "æ ³",
+      "M USIC",
+      "Ġhous ing",
+      "Ġein er",
+      "Ġsitu ations",
+      "æ ĭ",
+      "Ġdec re",
+      "Ġappropri ate",
+      "ен но",
+      "% .",
+      "Ġb ac",
+      "Ġw at",
+      "ens ity",
+      "ä h",
+      "kn own",
+      "it z",
+      "Ġemot ional",
+      "erv ation",
+      "Ġbl ind",
+      "1 6",
+      "í ĥ",
+      "大 家",
+      "Ġjo ined",
+      "Ġloc ated",
+      "ĠÑģ м",
+      "ad as",
+      "ber g",
+      "Ġd ess",
+      "Ġde ar",
+      "ed en",
+      "c os",
+      "Ġad opt",
+      "1 00",
+      "ow e",
+      "ĠChe ck",
+      "ism o",
+      "Ġsim pl",
+      "Ġang ry",
+      "Ġмен Ñı",
+      "ĠC am",
+      "Ġp ad",
+      "Ġatt end",
+      "Ġsam ple",
+      "æĹ ¥",
+      "Ġì Ľ",
+      "ĠI N",
+      "ul ous",
+      "ĠS ar",
+      "ĠSh ow",
+      "Ġinfrast ructure",
+      "ĠAug ust",
+      "Ġless on",
+      "Ġn iet",
+      "æ İ",
+      "Ġfo i",
+      "Ġbro ke",
+      "t r",
+      "ç ķ",
+      "Ġ4 5",
+      "Ġg ew",
+      "Ñĥ п",
+      "at i",
+      "Ġmaint ain",
+      "Ġart ists",
+      "ing er",
+      "æĿ ¥",
+      "er ved",
+      "I A",
+      "Ġequ als",
+      "Ġoper ation",
+      "ill y",
+      "ĠëĤ ´",
+      "Ġcrow d",
+      "Ġintern al",
+      "Ġtest s",
+      "ĠR ock",
+      "ĠC ons",
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+      "w ar",
+      "Ġs ou",
+      "Ġch art",
+      "ĠJ une",
+      "ĠApr il",
+      "g ent",
+      "Ġv ent",
+      "Ġqu and",
+      "ĠKore an",
+      "im o",
+      "ç ī",
+      "id ers",
+      "Ġmount ain",
+      "ÑģÑĤ ав",
+      "æľ Ī",
+      "ij k",
+      "Ġdiscover ed",
+      "ĠS und",
+      "ĠS il",
+      "Ġso lo",
+      "Â ´",
+      "Ġsch ol",
+      "ĠE ach",
+      "ç µ",
+      "Ġb are",
+      "Ġí Į",
+      "ĠvÃŃ de",
+      "Ġingred ients",
+      "ĠIt s",
+      "Ŀ¼ ê³ł",
+      "Ġì Ĭ",
+      "Ï į",
+      "ĠLe e",
+      "Ġsc ary",
+      "Ġprinci p",
+      "Ġspirit ual",
+      "ì ħ",
+      "ĠH old",
+      "æ²Ĵ æľī",
+      "Ġdef ine",
+      "ĠL es",
+      "ĠN or",
+      "ĠE nd",
+      "Ġbl og",
+      "ĠG reen",
+      "аеÑĤ ÑģÑı",
+      "p art",
+      "el es",
+      "äº ĭ",
+      "ĠUnd er",
+      "Ġpart e",
+      "Ġ3 5",
+      "Ġse ctor",
+      "ĠS ept",
+      "Ġaut h",
+      "à® ®",
+      "om in",
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+      "Ġc i",
+      "ĠFr iday",
+      "er as",
+      "Ġtw e",
+      "ul ated",
+      "Ġcult ural",
+      "ĠÑģв о",
+      "Ġëį Ķ",
+      "ĠÃ º",
+      "Ġpar ce",
+      "à® ²",
+      "Ġtrad ition",
+      "Ġjud ge",
+      "ĠGen eral",
+      "Ġdeterm ine",
+      "ĠIs n",
+      "ĠP L",
+      "ne ath",
+      "Ġmatter s",
+      "íķ ´ì",
+      "! ]",
+      "а Ñħ",
+      "Ġpo ol",
+      "Ġvari able",
+      "Ġvacc ine",
+      "Ġcaus ed",
+      "Ġw est",
+      "ĠY ep",
+      "f ast",
+      "Ġph ilos",
+      "hor a",
+      "Ġcontinu ed",
+      "Ġunf ortunately",
+      "ãģ į",
+      "æ ķ",
+      "Ġfl ight",
+      "Ġw rap",
+      "Ġhu h",
+      "ĠAbs olutely",
+      "Ġp ink",
+      "Ġrem ains",
+      "Ġn é",
+      "Ġf le",
+      "ĠS ol",
+      "Ġlos ing",
+      "Ġalg orith",
+      "Ġrequ ires",
+      "Ġfound ation",
+      "ĠB ur",
+      "Ġprofess ion",
+      "ĠM id",
+      "Ġë ŃIJ",
+      "c an",
+      "ĠM il",
+      "Ġyoung er",
+      "Ġappe ars",
+      "ter m",
+      "íķĺ ê³ł",
+      "ac le",
+      "ĠLond on",
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+      "ภ¢",
+      "Ġadv ent",
+      "ìĦ¸ ìļĶ",
+      "Ġê¸ °",
+      "ĠM aj",
+      "ÑĢ ем",
+      "ing u",
+      "ĠU K",
+      "u ro",
+      "s pe",
+      "Ġt ent",
+      "Ġreport ed",
+      "ĠA L",
+      "H ey",
+      "Ġë§ IJ",
+      "Ġd ent",
+      "ĠAustral ia",
+      "ĠJan uary",
+      "³ ´",
+      "ag ues",
+      "ars h",
+      "r ig",
+      "Ġtien e",
+      "ภ£",
+      "Î ®",
+      "Ġmach en",
+      "un te",
+      "Ñĥ Ñģ",
+      "Ġelect r",
+      "Ġtut orial",
+      "Ġpl aced",
+      "ĠìĿ´ ê±°",
+      "ĠCoun cil",
+      "í ĸĪ",
+      "°ë ¦¬",
+      "ah ren",
+      "Ġê·¸ë ŀĺ",
+      "Ġpro ve",
+      "f ol",
+      "Ġqu er",
+      "Ġche ap",
+      "ĠF ather",
+      "ĠP ower",
+      "ĵ ľ",
+      "Ġpur s",
+      "Ġes p",
+      "ĠB re",
+      "ê¸ °ë",
+      "om as",
+      "æĥ ³",
+      "ил ÑĮ",
+      "Ġge ht",
+      "os ter",
+      "ê³ ¼",
+      "Ġfil es",
+      "ĠÐ §",
+      "be ll",
+      "Ġwh om",
+      "Ġë ĺ",
+      "Ġex cellent",
+      "Ġdat ab",
+      "Ġg ö",
+      "Ġì§Ħ ì§ľ",
+      "Ġbelie f",
+      "j et",
+      "Ġj ack",
+      "Ġsw im",
+      "ri al",
+      "um in",
+      "a uc",
+      "Ġso ll",
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+      "ĠR el",
+      "ur u",
+      "Ġr hy",
+      "Ġg ig",
+      "Ġengage ment",
+      "ü ÅŁ",
+      "ãĤ ĩ",
+      "Ġoffer ing",
+      "wh el",
+      "Ġact or",
+      "Ġ å°į",
+      "AP P",
+      "w est",
+      "ĠR oy",
+      "Ġreturn ed",
+      "Ġsil ver",
+      "r ating",
+      "Ġest ar",
+      "Ġs ke",
+      "Ġt i",
+      "ic ation",
+      "Ġann oy",
+      "Ġdeep ly",
+      "ìļ ©",
+      "Ġnat ürlich",
+      "EL L",
+      "ĠC ath",
+      "Ġr ail",
+      "н ов",
+      "Ġpray er",
+      "c ol",
+      "G B",
+      "ĠТ ак",
+      "Ġg la",
+      "ĠW ater",
+      "Ñı ÑĤÑĮ",
+      "ĠN on",
+      "ô t",
+      "ag ers",
+      "Ġh ug",
+      "Ġdo ctors",
+      "an cing",
+      "ĠT alk",
+      "z ing",
+      "Ġhad n",
+      "Ġl ui",
+      "Ġat é",
+      "Ġê·¸ë ¦¬ê³ł",
+      "ê¹Į ì§Ģ",
+      "ic i",
+      "Ġincor por",
+      "ĠD i",
+      "z il",
+      "any a",
+      "ª ħ",
+      "ĠÂ »",
+      "3 5",
+      "Ġbe er",
+      "Ġbe aucoup",
+      "ĠM C",
+      "Ġe ars",
+      "og en",
+      "ĠQu est",
+      "ed a",
+      "æľ ¬",
+      "ĠSat urday",
+      "Ġfall s",
+      "st on",
+      "b les",
+      "Ġth us",
+      "ĠëĦ ¤",
+      "๠Ħ",
+      "Ġth erm",
+      "Ġdivers ity",
+      "Ġso y",
+      "az u",
+      "im p",
+      "Ġtele vision",
+      "éģ İ",
+      "Ġש ׾",
+      "Ġw ur",
+      "Ġed ges",
+      "Ġless ons",
+      "ĠA ud",
+      "ãģĹ ãģ¦",
+      "vo ir",
+      "ament o",
+      "Ġexplain ed",
+      "Ġо на",
+      "Ġtem ps",
+      "Ï İ",
+      "The y",
+      "Ġsurpr ising",
+      "ани Ñı",
+      "ĠD rag",
+      "éĿ ¢",
+      "ĠC le",
+      "Ġn am",
+      "ĠлÑİ Ð´",
+      "Ġhard ware",
+      "Ġth umbs",
+      "Ġκ αι",
+      "ĠT op",
+      "ĠÃ ¥",
+      "é Ļ",
+      "×ķ× ¨",
+      "Ġê·¸ëŀĺ ìĦľ",
+      "ĠBud d",
+      "ther n",
+      "Ġinterest s",
+      "Ø °",
+      "Ġdevelop ers",
+      "Ġh itting",
+      "Ġopp osed",
+      "Ġheart s",
+      "ĠAnd roid",
+      "ĠH and",
+      "Ġrepres ents",
+      "g lich",
+      "íĬ ¸",
+      "Ġ3 2",
+      "Ġdom in",
+      "ĠAn n",
+      "ä¸Ģ ä¸ĭ",
+      "Ġét é",
+      "Ġzo om",
+      "Ġktó re",
+      "Ġadult s",
+      "Ġorder ed",
+      "Ġpick ing",
+      "ĠH ong",
+      "Ġfilm ing",
+      "æĢ Ŀ",
+      "Ġse ed",
+      "ĠA T",
+      "Ġcalcul ate",
+      "Ġк огда",
+      "ĠO s",
+      "ic it",
+      "Ġrem aining",
+      "Ġse gu",
+      "Ã »",
+      "Ġìĺ¤ ëĬĺ",
+      "Ġarri ve",
+      "Ġcon gr",
+      "Ġgrand e",
+      "Ġhealth care",
+      "Ġмож но",
+      "S A",
+      "est e",
+      "Ġaware ness",
+      "Ġsqu ared",
+      "xt ure",
+      "ĠBe ing",
+      "Ġsold iers",
+      "Ñĥ б",
+      "Ġrev olution",
+      "Ġtra ined",
+      "end en",
+      "è °",
+      "Ġdan cing",
+      "Ġinstall ed",
+      "pr ise",
+      "Ġv eter",
+      "Ġmen os",
+      "ne ll",
+      "ĠBr other",
+      "Ġn un",
+      "Ġimportant ly",
+      "all ed",
+      "ia ÅĤ",
+      "ab led",
+      "ĠSy stem",
+      "ĠV ol",
+      "Ġe ld",
+      "Ġemot ion",
+      "ic an",
+      "ĠB ank",
+      "ik es",
+      "Ġv log",
+      "Ġв оз",
+      "Ġpu ede",
+      "ìĺ ¤",
+      "Ġte en",
+      "Ġse vere",
+      "% ,",
+      "Ġclean ing",
+      "z Äħ",
+      "Ĺ IJ",
+      "ĠTh rough",
+      "ĠS et",
+      "E P",
+      "\" ?",
+      "ĠM other",
+      "Ġfigure d",
+      "Ġm ud",
+      "ĠÑ ĸ",
+      "ĠOff ice",
+      "Ġr aw",
+      "Ġdestroy ed",
+      "ent a",
+      "Ġag gress",
+      "Ġо Ñģ",
+      "Ġëª ¨ë",
+      "ä ä",
+      "ĠA R",
+      "Ġcorrect ly",
+      "åī į",
+      "Ġst ir",
+      "Ġext ract",
+      "Ġveh icles",
+      "éĸ ĭ",
+      "ĠR un",
+      "Ġв ÑĢем",
+      "Ġparall el",
+      "Ġl ag",
+      "j u",
+      "Ġd are",
+      "ĠM ot",
+      "on o",
+      "Ġbeing s",
+      "Ġst ro",
+      "Ġexc use",
+      "Ġal pha",
+      "Ġask s",
+      "Ġpo cket",
+      "... ?",
+      "Ġk ita",
+      "ü m",
+      "Ġappear ance",
+      "ord an",
+      "Ġins ert",
+      "Ġна Ñĩ",
+      "Ľ i",
+      "Ġtem po",
+      "Ġfac ility",
+      "Ġvis ible",
+      "å Ĵ",
+      "ĠS cience",
+      "ur os",
+      "ĠÙģ ÙĬ",
+      "ĠV an",
+      "Ġt ension",
+      "Ġíķ ł",
+      "Ġdel ivery",
+      "Ġst im",
+      "Ġsur vey",
+      "ĠG ra",
+      "Ġb ol",
+      "æ ł",
+      "Ġwe iter",
+      "ÃŁ en",
+      "ä¸Ģ åĢĭ",
+      "Ġpro ceed",
+      "Ġimpress ive",
+      "ĠV oc",
+      "ious ly",
+      "Ġд а",
+      "h ale",
+      "o ch",
+      "Ġgl ue",
+      "ph et",
+      "c ont",
+      "Ġf its",
+      "Ġbox es",
+      "Ġcontrol s",
+      "ĠCh ild",
+      "Ġscen ario",
+      "Ġt rop",
+      "Ġprocess ing",
+      "ĠÑĤ олÑĮко",
+      "Ġbird s",
+      "ĠCh ic",
+      "Ġн ап",
+      "Ġ201 3",
+      "Ġmü ssen",
+      "ĠJ ag",
+      "Ġs Äħ",
+      "Ġper ce",
+      "re h",
+      "ĠF ore",
+      "Ġconf used",
+      "a ire",
+      "Ġaccompl ish",
+      "Ġcas a",
+      "c lock",
+      "Ġinflu en",
+      "ĠR O",
+      "Ġb one",
+      "ic ian",
+      "ĠS C",
+      "Ġstrateg ies",
+      "g h",
+      "д Ñĥ",
+      "Ġit u",
+      "Ġperson ality",
+      "Ġbard zo",
+      "Ġaccept ed",
+      "Ġst om",
+      "ie v",
+      "ĠH ist",
+      "ĠA us",
+      "Ġë° Ķë",
+      "AT OR",
+      "æĦ ı",
+      "o ir",
+      "Ġmag az",
+      "Ġexpl an",
+      "Ġcor n",
+      "Ġil s",
+      "Ġcirc uit",
+      "Ġg ay",
+      "h op",
+      "ãĤ ĥ",
+      "Ġequ ival",
+      "Ġdies er",
+      "er ves",
+      "com es",
+      "k lich",
+      "Ġëķ Įë",
+      "ab et",
+      "Ġex ha",
+      "Ġman ner",
+      "ĠâĻª âĻª",
+      "é c",
+      "ä l",
+      "Ġconf irm",
+      "Ġenter ed",
+      "empl o",
+      "ĠF ar",
+      "Ġo ù",
+      "ess ions",
+      "Ġn urs",
+      "Ġent ão",
+      "Ġab andon",
+      "l ife",
+      "Ġw is",
+      "N arrator",
+      "Ġìĸ ´",
+      "Th ere",
+      "ĠR am",
+      "ast e",
+      "Ġatt rib",
+      "ĠA y",
+      "Ġmes mo",
+      "Ġν α",
+      "é «",
+      "ens es",
+      "Ġc rop",
+      "Ġзд еÑģÑĮ",
+      "ĠUnt il",
+      "ste in",
+      "Ġo ven",
+      "Ġsus pect",
+      "h et",
+      "Ġpu is",
+      "Ġcar ried",
+      "é g",
+      "ĠDe v",
+      "em s",
+      "re ens",
+      "ber ry",
+      "Ġtem pl",
+      "ĠB it",
+      "Ġvari ables",
+      "Ġover whel",
+      "μ ε",
+      "Ġinit ially",
+      "ìķ ĺ",
+      "ot hing",
+      "еÑĤ ÑĮ",
+      "ĠH ill",
+      "Ġdep art",
+      "Ġmy st",
+      "az z",
+      "Ġflu id",
+      "ĠD C",
+      "Ġclin ical",
+      "ĠR yan",
+      "ĠFlor ida",
+      "ĠT ak",
+      "Ġanx iety",
+      "b ro",
+      "Ġcircumst ances",
+      "ĠÙ ĥ",
+      "Ġexist ence",
+      "Ġt ong",
+      "Ġ201 2",
+      "ĠSecret ary",
+      "Ġsp icy",
+      "Ġ[ (",
+      "ĠWith out",
+      "Ġfact s",
+      "Ġt ons",
+      "A pp",
+      "ĠSt and",
+      "Ġli es",
+      "ĠA D",
+      "w in",
+      "ÏĦ ε",
+      "app lause",
+      "I P",
+      "st a",
+      "ĠS up",
+      "ph ones",
+      "ŀ ij",
+      "p ie",
+      "ĠP ot",
+      "ĠN O",
+      "èµ ·",
+      "Ġ× ŀ",
+      "ĠÐĶ а",
+      "ic as",
+      "ĠI r",
+      "Ġpush ed",
+      "Ġun cle",
+      "ĠÙħ ÙĨ",
+      "Ġl on",
+      "Ġprinci ples",
+      "ĠIntern ational",
+      "ĠÃ ĸ",
+      "Å ¾",
+      "Ġsay a",
+      "Ġê³ ł",
+      "Ġr ib",
+      "Ġpast e",
+      "Ġwar ning",
+      "Ġmus ical",
+      "Ġagre ed",
+      "оÑĢ м",
+      "Ġgar lic",
+      "Ġox ygen",
+      "ìĺ Ī",
+      "A l",
+      "Ġë§ ŀ",
+      "el ines",
+      "LAU SE",
+      "ç¾ İ",
+      "gy pt",
+      "G E",
+      "ck er",
+      "t u",
+      "Ġsh el",
+      "Ġstay ed",
+      "Ġг од",
+      "Ġl apt",
+      "ĠMart in",
+      "Ġinv ited",
+      "Ġconf ir",
+      "Ġemb arrass",
+      "ac iones",
+      "ĠC amp",
+      "Ġhold s",
+      "ax y",
+      "Ġd ive",
+      "uck les",
+      "Ġbo ost",
+      "Ġw ür",
+      "st al",
+      "ĠÑĢ абоÑĤ",
+      "Ġdé c",
+      "Ġoffic ers",
+      "ĠìķĦ ë",
+      "olog ist",
+      "× ŀ×",
+      "Ġse eds",
+      "Ġbu ff",
+      "Ġupd ates",
+      "ãĤ ı",
+      "d ed",
+      "Ġfriend ly",
+      "Ġcoun cil",
+      "ĠProb ably",
+      "Ġp iano",
+      "Ġredu ced",
+      "ÏĦ α",
+      "Ġauth ent",
+      "Ġexpl os",
+      "p ass",
+      "ĠH it",
+      "j ud",
+      "ĠN av",
+      "om i",
+      "Ġcomm ission",
+      "Ġg ym",
+      "Ð Ł",
+      "Ġp on",
+      "ÑĢ оÑģ",
+      "Ġinter face",
+      "Ġstruct ures",
+      "ĠJ en",
+      "Ġy ok",
+      "Ġme u",
+      "ì§Ģ ë§Į",
+      "n ed",
+      "ĠW ie",
+      "Ġident ified",
+      "Ġchann els",
+      "ı na",
+      "Ġphilos op",
+      "ke it",
+      "Ġbit s",
+      "ent es",
+      "Ġf rag",
+      "ĠK ind",
+      "Ġdo ch",
+      "Ġs ne",
+      "ind ing",
+      "ĠJew ish",
+      "оÑĢ оÑĪ",
+      "Ġf ue",
+      "æĸ ¹",
+      "Ġí ı",
+      "Ġm ı",
+      "Ġke ine",
+      "Ġloc ations",
+      "çĶ ¨",
+      "Ġmet er",
+      "Ġbe ef",
+      "ãģ ĺ",
+      "Ġman ip",
+      "Ġson o",
+      "zz le",
+      "ç ¶",
+      "Ġp es",
+      "Ġhor rible",
+      "ĠS n",
+      "Ġfact ory",
+      "Ġfif th",
+      "Ġcook ed",
+      "Ġmo od",
+      "Ġveloc ity",
+      "Ġob lig",
+      "Ġconnect ions",
+      "ÄŁ im",
+      "Ġê³ µ",
+      "Ġdom ain",
+      "Ġapply ing",
+      "Ġrid ic",
+      "Ġc el",
+      "Ġchild hood",
+      "ĠT est",
+      "rat ulations",
+      "ĠVir gin",
+      "ĠC EO",
+      "Ġп л",
+      "Ġalgorith m",
+      "Ġinter action",
+      "ag a",
+      "Ġkid ding",
+      "Ġtom ato",
+      "Ġcontinu ing",
+      "l ad",
+      "st ream",
+      "ож е",
+      "Ġìĺ ģ",
+      "ел ов",
+      "B A",
+      "Ġn ap",
+      "ĠNo body",
+      "Ġth umb",
+      "ĠO N",
+      "Ġr ush",
+      "D R",
+      "Ġstri ke",
+      "Ġev olution",
+      "ich e",
+      "Ġì »",
+      "Ġê·¸ëŁ °",
+      "ا ت",
+      "Ġa k",
+      "Ġwind ows",
+      "Ġex cess",
+      "ãģª ãģĦ",
+      "Ġconc lud",
+      "Ġepis odes",
+      "Ġstrugg ling",
+      "ĠD at",
+      "Ŀ ¼ë",
+      "Ġke ys",
+      "Ġk le",
+      "æŀ ľ",
+      "Ġveget ables",
+      "y stem",
+      "ên cia",
+      "r ick",
+      "Ġreven ue",
+      "ĠH aw",
+      "Ġl an",
+      "ant es",
+      "in iz",
+      "ãģĵ ãĤĮ",
+      "и ÑģÑĤ",
+      "Ġsu p",
+      "© ´ìĦľ",
+      "Ġmoment o",
+      "ist o",
+      "ãģ ¤",
+      "ĠE ric",
+      "i ors",
+      "b aj",
+      "Ġintrodu ction",
+      "ir ty",
+      "Ġde ck",
+      "re al",
+      "ĠMar io",
+      "Ġlo ving",
+      "ภĶ",
+      "Ġsupport s",
+      "иÑĩ еÑģ",
+      "Ġinc ident",
+      "ut ch",
+      "u v",
+      "Ġbo om",
+      "еÑĢ ÑĮ",
+      "Ġн Ñĥж",
+      "Ġcomb ined",
+      "ĠL in",
+      "2 3",
+      "or ation",
+      "nt e",
+      "Ġs or",
+      "Ġdir ty",
+      "if er",
+      "ĠAP I",
+      "Ġcollabor ation",
+      "i able",
+      "Ġprior ity",
+      "ĠA le",
+      "ĠPr in",
+      "ĠEx c",
+      "Ġv ais",
+      "Ġgr an",
+      "Ġst ood",
+      "Ġrec ru",
+      "ĠM ur",
+      "es is",
+      "as p",
+      "Ġlock ed",
+      "ĠP ero",
+      "ĠHar ry",
+      "Ġt udo",
+      "ĠT en",
+      "Ø µ",
+      "force ment",
+      ") )",
+      "ol i",
+      "ĠìĿ ¸",
+      "Ġsupp l",
+      "Ġcroch et",
+      "Ġphen omen",
+      "l os",
+      "ath an",
+      "ĠSu pp",
+      "Ġem br",
+      "Ġbe k",
+      "ĠZe it",
+      "g end",
+      "Ġroom s",
+      "ª ½",
+      "V ER",
+      "ny ch",
+      "Ġdon t",
+      "Ġcab in",
+      "Ġaccount s",
+      "ĠE aster",
+      "×ķ× ľ",
+      "ãĥ «",
+      "Ġfac ilities",
+      "be it",
+      "Ġlink ed",
+      "ĠG er",
+      "Ġprogram ming",
+      "ot ic",
+      "Ġdr ama",
+      "Ġ2 9",
+      "Ġí ģ",
+      "Ġinstruct ions",
+      "Ġimportant e",
+      "Ġw aves",
+      "Ġa id",
+      "C K",
+      "ê²ł ìĬµëĭĪëĭ¤",
+      "ĠM ir",
+      "Ġt id",
+      "ĠH ot",
+      "Ġarr ange",
+      "ĠB aby",
+      "Ġt ack",
+      "ĠÑ ī",
+      "í Ŀ",
+      "Ġvert ical",
+      "Ġhe el",
+      "ĠC ut",
+      "Ġnar row",
+      "ĠA ri",
+      "Ġkn ee",
+      "ĠBra zil",
+      "ĠF ive",
+      "Ġpost ed",
+      "U D",
+      "Ġro lling",
+      "Î ¸",
+      "Ġclaim s",
+      "ĠIn s",
+      "O K",
+      "ãģĦ ãģĨ",
+      "u in",
+      "ĠInst itute",
+      "Ġint ense",
+      "i ar",
+      "ĠN ick",
+      "Ġse lection",
+      "Ġleg end",
+      "Ġun iform",
+      "ú n",
+      "Ġstud ied",
+      "å¤ ª",
+      "ĠÐ ¥",
+      "Ġìķ Į",
+      "g ers",
+      "Ġd ow",
+      "ĠC S",
+      "Ġag ent",
+      "ĠA uf",
+      "è¦ º",
+      "Ġj og",
+      "Ġair craft",
+      "ëĭ ĺ",
+      "Ġv it",
+      "ul s",
+      "Ġseg ment",
+      "Ġord ers",
+      "ĠCl ass",
+      "Ġap olog",
+      "Ġplatform s",
+      "Ġmy th",
+      "аж е",
+      "ĠB ook",
+      "Ġsens itive",
+      "Ġпол ÑĥÑĩ",
+      "Ġdam it",
+      "ĠC apt",
+      "so le",
+      "Ġarchitect ure",
+      "ĠW il",
+      "Ġin her",
+      "ca p",
+      "ĠB oy",
+      "æ¬ ¡",
+      "Ġbur ning",
+      "ĠP ublic",
+      "Ġbeh alf",
+      "Ġìľ Ħ",
+      "Ġtherap y",
+      "ubs cribe",
+      "Ġinvol ve",
+      "Ġexp osed",
+      "i ÅŁ",
+      "ä» ¬",
+      "ê tre",
+      "Ġto il",
+      "Ġs ink",
+      "p ir",
+      "å ĥ",
+      "I I",
+      "Ġag encies",
+      "Ġ q",
+      "ĠD own",
+      "au f",
+      "Ġë§ Ľ",
+      "ãĥ» ãĥ»",
+      "Ġpro c",
+      "ok ed",
+      "Ġst ores",
+      "p ower",
+      "ĠTh ings",
+      "Ġaccess ible",
+      "Ġte ż",
+      "ĠE duc",
+      "Ġspeak ers",
+      "ĠSar ah",
+      "Ķ Ķ",
+      "Ġdi verse",
+      "ìŀ ĸ",
+      "ĠU lt",
+      "Ãł y",
+      "ĠChic ago",
+      "S he",
+      "ath y",
+      "Ġen able",
+      "Ġtrad ing",
+      "Ġmus cles",
+      "æ Ľ",
+      "ĠC are",
+      "ĠU r",
+      "ĠSc ot",
+      "Ġphr ase",
+      "EN T",
+      "Ġê² ½",
+      "ĠJ ac",
+      "p ack",
+      "Ġdeterm ined",
+      "ü nd",
+      "Ġneg oti",
+      "Ġvid é",
+      "Ġro z",
+      "ĠS us",
+      "Ġr iding",
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+      "È Ļ",
+      "Ġelectric ity",
+      "Ġto x",
+      "ÅĤ ad",
+      "Ġì ´",
+      "Ġdifficult y",
+      "ł ×Ļ",
+      "nes day",
+      "иÑģ ÑĮ",
+      "Ġall eg",
+      "ĠG O",
+      "Ġqu it",
+      "ĠHer r",
+      "Ġestá n",
+      "Ġgirl friend",
+      "Ġt eng",
+      "ific ial",
+      "ĠJ am",
+      "Ġcan cel",
+      "Ġfrequ ently",
+      "I V",
+      "å¯ ¦",
+      "Ġclos ing",
+      "Ġdec ade",
+      "Ġrepresent ed",
+      "ĠCan ad",
+      "ĠкоÑĤоÑĢ Ñĭе",
+      "Ġest amos",
+      "ĠTh ursday",
+      "ĠG a",
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+      "b ble",
+      "S ON",
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+      "ĥ ¥",
+      "Ġo c",
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+      "Ġ1 20",
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+      "ç ¨",
+      "Ġant icip",
+      "ł Ī",
+      "s l",
+      "Ġvot ing",
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+      "or ney",
+      "szy st",
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+      "ìĭ ľë",
+      "ĠW in",
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+      "ra f",
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+      "аз Ñĭв",
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+      "aw a",
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+      "Ġinj ury",
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+      "ĠCol or",
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+      "Ġd ip",
+      "ÑĢ Ñĭ",
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+      "g t",
+      "ĠP D",
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+      "os c",
+      "Ġ usted",
+      "ĠW oo",
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+      "è¦ ĭ",
+      "Ġden n",
+      "ĠP et",
+      "Ġover come",
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+      "ĠM ove",
+      "Ġlic ense",
+      "Ġrepe ated",
+      "௠ĩ",
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+      "Ġnood les",
+      "Ġflo od",
+      "ĠM ass",
+      "Ġn uts",
+      "ĠJ ess",
+      "ĠI h",
+      "Ġch ances",
+      "IJ ĺ",
+      "Ġdon de",
+      "I G",
+      "Ġand ere",
+      "Ġb ones",
+      "ìŀ ij",
+      "Ġeffic iency",
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+      "ill er",
+      "Ġom ega",
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+      "ĠGr oup",
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+      "Ġj ail",
+      "ĠJ ones",
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+      "Ġbrief ly",
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+      "Ġkn ees",
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+      "Ġmin ister",
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+      "Ġto wer",
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+      "ĠP en",
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+      "× ĺ",
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+      "Ġener g",
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+      "ил а",
+      "ĠWed nesday",
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+      "ĠBe aut",
+      "Ġreason able",
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+      "ier a",
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+      "ê± ´",
+      "Ġoppon ent",
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+      "Ġho ly",
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+      "Ġh ij",
+      "Ð ľ",
+      "Ġb iss",
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+      "Ġì ½",
+      "lle icht",
+      "ÙĬ Ø©",
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+      "ert e",
+      "ĠS ame",
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+      "Ġben ch",
+      "Ġpo co",
+      "Ġmark s",
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+      "åĸ Ķ",
+      "ĠÎ ³",
+      "Ġdist inct",
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+      "Ġmole c",
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+      "ur ning",
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+      "e ze",
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+      "Ġact s",
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+      "ĠN am",
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+      "Ġre ly",
+      "Ġt ens",
+      "Ġd ating",
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+      "Ġacknow ledge",
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+      "Ġvo ir",
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+      "Ġmo ż",
+      "Ġk m",
+      "ĠD O",
+      "Ġf ert",
+      "Ġë ıĦ",
+      "ĠP ac",
+      "Ġf ont",
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+      "ĠIt aly",
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+      "Ġnot ion",
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+      "ĠÃ ľ",
+      "ĠMo on",
+      "ĠBus iness",
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+      "ĠY out",
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+      "Ġfin ance",
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+      "Ø £",
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+      "Ġsuccess fully",
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+      "d em",
+      "Ġret ire",
+      "Ġlapt op",
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+      "ĠAssoci ation",
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+      "Ġíķ ľë",
+      "Ġemploy ee",
+      "Ġm olt",
+      "R L",
+      "Ð ¯",
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+      "Ġu g",
+      "ĠB all",
+      "ĠJ ava",
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+      "Ġproced ure",
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+      "ĠëĬ IJë",
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+      "Ġcomp reh",
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+      "Ġterm in",
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+      "ÑĤ а",
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+      "н Ñĸ",
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+      "ge b",
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+      "Ġris ks",
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+      "Ġche ering",
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+      "Ġir gend",
+      "Ġf el",
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+      "Ġfem in",
+      "it ure",
+      "ÑĢ аÑĤ",
+      "Ġh ire",
+      "Ġnow here",
+      "ä½ į",
+      "Ạ§",
+      "Ġw ing",
+      "Ġsa v",
+      "ĠSec urity",
+      "Ġr ural",
+      "ĠF un",
+      "ay er",
+      "Ġac cus",
+      "Ġm m",
+      "ĠJose ph",
+      "Ġsc reens",
+      "Ġb orrow",
+      "Ġsw ing",
+      "Ġ4 8",
+      "Ġtouch ing",
+      "th is",
+      "int endo",
+      "é ĥ",
+      "Ð ł",
+      "ĠScot land",
+      "ĠJ ason",
+      "ĠV en",
+      "Ġexcept ion",
+      "Ġnear by",
+      "Ġbrows er",
+      "ang ers",
+      "ĠS in",
+      "ÏĢ ο",
+      "ä½Ĩ æĺ¯",
+      "osp el",
+      "Ġwur de",
+      "Ġdr unk",
+      "í ļ",
+      "ìĨ į",
+      "ãĥ ī",
+      "ĠìĬ ¤í",
+      "ĠL ie",
+      "oc o",
+      "ĠLe ague",
+      "Ġign ore",
+      "Ġ: )",
+      "Ġland ing",
+      "Ġع ÙĦ",
+      "ĠT ag",
+      "2 8",
+      "Ġdra ft",
+      "Ġa er",
+      "Ġê·¸ë ĥ¥",
+      "Ġp ense",
+      "Ġд аже",
+      "Ġbed room",
+      "Ġna j",
+      "ì§Ģ ê³ł",
+      "igen ous",
+      "Ġde als",
+      "ell o",
+      "äº Į",
+      "Ġpos it",
+      "ê »",
+      "Ġvis ited",
+      "if ies",
+      "Ġpre mi",
+      "Ġcan t",
+      "ĠR ick",
+      "Ġrais ing",
+      "Ġperm ission",
+      "Ġpub l",
+      "un ci",
+      "Ġb end",
+      "Ġchamp ions",
+      "d ie",
+      "Ġaw ful",
+      "Ġjump ing",
+      "Ġll eg",
+      "Ġsustain able",
+      "ĠT ot",
+      "Ġcand y",
+      "åĢ Ļ",
+      "Ġsatisf ied",
+      "Ġpi pe",
+      "Ġco ck",
+      "Ø ¶",
+      "st one",
+      "Ġmoment um",
+      "ĠÐĿ а",
+      "Ġal ors",
+      "Ġreturn s",
+      "am men",
+      "ç ®",
+      "Ñĭ м",
+      "a wn",
+      "ott ed",
+      "Ġwoll en",
+      "ict ed",
+      "Ġcandid ates",
+      "ĠL ady",
+      "Ġy ield",
+      "Ġmainten ance",
+      "ff ect",
+      "Ġexpans ion",
+      "ĠL ED",
+      "Ġdark ness",
+      "Ġout fit",
+      "ìķ Ī",
+      "Ġи Ñģп",
+      "ru ption",
+      "ãģĦ ãģ¾ãģĻ",
+      "Ġeng aging",
+      "Ġins ight",
+      "ĠAl ways",
+      "Ġge f",
+      "ra k",
+      "Ġp ix",
+      "覺 å¾Ĺ",
+      "Ġquant ity",
+      "Ġin k",
+      "ĠKing dom",
+      "Ġc ort",
+      "å¸ ¸",
+      "Ġgovern ments",
+      "Ġprot est",
+      "po on",
+      "ĠÑĤ ого",
+      "å® ĥ",
+      "uch en",
+      "qu ality",
+      "ĠPor que",
+      "ĠCl ub",
+      "Ġr it",
+      "Ġart icles",
+      "B I",
+      "ig ible",
+      "Ġdis aster",
+      "и г",
+      "Ġн ик",
+      "Ùĩ ا",
+      "ë ¥¼",
+      "are t",
+      "Ġun able",
+      "ĠÃ ®",
+      "Ġer st",
+      "Ġ× ł",
+      "v ard",
+      "Ġannoy ing",
+      "ĠK ir",
+      "еÑĢ ж",
+      "enn is",
+      "Ġunc ertain",
+      "3 6",
+      "ö s",
+      "Ġecos ystem",
+      "z ed",
+      "j Ãł",
+      "s un",
+      "ìĸ´ì Ħľ",
+      "Ġże by",
+      "Ġma ps",
+      "ë Ĥĺ",
+      "åħ ¨",
+      "ĠJust in",
+      "Ġtr ash",
+      "Ġenorm ous",
+      "Ġst ated",
+      "Ġbrand s",
+      "Ġyou t",
+      "ĠÑĩелов ек",
+      "ĠMat th",
+      "Ġtransport ation",
+      "Ġlegisl ation",
+      "Ġprov iders",
+      "ĠØ Ń",
+      "Ġmagaz ine",
+      "Ġse hen",
+      "ĠDesp ite",
+      "Ġpass es",
+      "æĪ IJ",
+      "Ġal ter",
+      "ad an",
+      "Ġfarm ers",
+      "è° ¢",
+      "Ġconfir med",
+      "Ġes a",
+      "it os",
+      "Ġroad s",
+      "V IS",
+      "Ġwork er",
+      "Ġdesign s",
+      "ĠSo viet",
+      "br id",
+      "Ġpract icing",
+      "Ġë¶ Ģ",
+      "ĠSe a",
+      "ãĥ ©",
+      "ĠпÑĢ од",
+      "Ġch ill",
+      "Ġlem on",
+      "ìĹIJ ëĬĶ",
+      "Ġflex ible",
+      "ĠExc use",
+      "Ġterrit ory",
+      "åķ ı",
+      "ãģ ¿",
+      "Ġl ux",
+      "Ġlif etime",
+      "Ġdist ingu",
+      "ĠT imes",
+      "Ġgr oss",
+      "en z",
+      "Ġsc roll",
+      "m Ä±ÅŁ",
+      "ci p",
+      "£ ¼",
+      "D P",
+      "Ġpub lish",
+      "Ġe ben",
+      "Ġreg ist",
+      "Ġed ition",
+      "ĠL E",
+      "Ġchar ging",
+      "ut ation",
+      "yr ics",
+      "id as",
+      "ĠÎ ¿",
+      "Ġк оÑĢ",
+      "ĠT on",
+      "çĽ ®",
+      "Ġwho ever",
+      "ĠF ox",
+      "æī ĭ",
+      "ê±°ëĵł ìļĶ",
+      "Ġf ought",
+      "Ġdr ill",
+      "ĠAf ghan",
+      "~ !",
+      "ĠTo o",
+      "Ġsecond ary",
+      "r ä",
+      "ĠHe ad",
+      "in nen",
+      "Ġy arn",
+      "Ġн ам",
+      "Ġwid th",
+      "Ġengine er",
+      "i Äħ",
+      "Ġw ings",
+      "ĠëķĮë ¬¸",
+      "Ġtra uma",
+      "Ġrep rodu",
+      "Ġch ip",
+      "Ġpassion ate",
+      "Ġaw kward",
+      "Ġí Ĭ",
+      "аж д",
+      "ĠBit coin",
+      "Ġkh ông",
+      "Ġr ó",
+      "re ction",
+      "Ġг де",
+      "ĠUs ually",
+      "Ġimplement ation",
+      "Ġgame play",
+      "Ġmyst ery",
+      "Ġо к",
+      "Ġa ños",
+      "and y",
+      "им и",
+      "Ġpriv acy",
+      "ac o",
+      "ãĤ ģ",
+      "Ġd ump",
+      "ĠP ay",
+      "Ġdi pl",
+      "Ġf urn",
+      "Ġsh ips",
+      "L A",
+      "ĠÑħ оÑĢоÑĪ",
+      "Ġe c",
+      "Ġdrop s",
+      "ch l",
+      "3 2",
+      "Ġobs erve",
+      "ĠDe velop",
+      "M üzik",
+      "ann el",
+      "owa Äĩ",
+      "Ġfac ed",
+      "á l",
+      "Ġvictim s",
+      "Ġg ifts",
+      "Ġbo ot",
+      "ÃŁ e",
+      "ro d",
+      "Ġ200 9",
+      "ör t",
+      "Ġunivers al",
+      "Ġn ouve",
+      "Ġboy friend",
+      "Ġcet era",
+      "ÑģÑĤ а",
+      "' S",
+      "Ġn ive",
+      "Ġcru cial",
+      "Ġsur ve",
+      "Ġco in",
+      "Ġs ondern",
+      "Ġsh ade",
+      "Ġl ugar",
+      "Ġsure ly",
+      "Ġma x",
+      "Ġimpro ving",
+      "åĽł çĤº",
+      "Ġw en",
+      "Ġ× ij",
+      "Ġìĸ ´ì",
+      "Ġen forcement",
+      "ib l",
+      "Ġli v",
+      "ler i",
+      "Ġme jor",
+      "ĠCarol ina",
+      "Ġv as",
+      "Ġcomp rom",
+      "Ġd irt",
+      "Ġup grade",
+      "ĠBe ll",
+      "Ġrestaur ants",
+      "Ġtra p",
+      "Ġte as",
+      "b lic",
+      "ĠG reg",
+      "s an",
+      "Ġo w",
+      "u est",
+      "Ġpropos al",
+      "ĠR et",
+      "fr ont",
+      "Ġpass age",
+      "Ġsurround ing",
+      "Ġú lt",
+      "Ġup coming",
+      "Ġhor ror",
+      "Ġcl othing",
+      "Ġìķ ½",
+      "Ġd il",
+      "r ome",
+      "ĠI d",
+      "ĠRo ad",
+      "ĠÑįÑĤо ÑĤ",
+      "ch ain",
+      "ĠбÑĭ ÑĤÑĮ",
+      "ĠO ffic",
+      "ĠÐĿ е",
+      "Ġins an",
+      "Ġdecre ase",
+      "ĠÑħ оÑĤ",
+      "bu ild",
+      "ĠDrag on",
+      "å Ĥ",
+      "Ġinvest ors",
+      "ant i",
+      "Ġsacr ifice",
+      "Ġtro ops",
+      "ĠB ad",
+      "Ġpass word",
+      "Ġconst ra",
+      "ภķ",
+      "ĠÃĩ a",
+      "ad ow",
+      "th rough",
+      "ÑĨ а",
+      "C an",
+      "Ġcandid ate",
+      "Ġant ib",
+      "ìĹ ħ",
+      "Ġtast y",
+      "ÙĪ ÙĨ",
+      "ĠIn f",
+      "ĠB ang",
+      "iÃŁ t",
+      "in ity",
+      "f ather",
+      "Ġcontro vers",
+      "ĠP ak",
+      "il ty",
+      "êµ ¬ë",
+      "Ġlight er",
+      "Ġfall en",
+      "Ġz us",
+      "ĠGu ard",
+      "Ġc ott",
+      "ĠF ree",
+      "Ġiniti ative",
+      "al ous",
+      "Ġnot ification",
+      "ĠMed ic",
+      "ĠComm ittee",
+      "ìĹ °",
+      "ĠW ood",
+      "Ġmus h",
+      "ul um",
+      "è ²",
+      "ah ah",
+      "Ġsu fficient",
+      "Ġsing er",
+      "к ой",
+      "AL I",
+      "ät t",
+      "ĠP R",
+      "ĠL ar",
+      "cul es",
+      "iem po",
+      "Ġune x",
+      "Ġintegr al",
+      "Ġtransm ission",
+      "Ġic i",
+      "Ñĥ Ñħ",
+      "g ic",
+      "ĠN intendo",
+      "ĠC op",
+      "ĠTr ust",
+      "en as",
+      "Ġab ilities",
+      "Ġch ips",
+      "p at",
+      "Ġan che",
+      "л ен",
+      "Ġapproach es",
+      "Ġth or",
+      "Ġs isters",
+      "Ġdri vers",
+      "Ġall a",
+      "0 3",
+      "Ġrub ber",
+      "Ġn Ã¥",
+      "AC K",
+      "Ġdisappe ar",
+      "ê° ľ",
+      "Ġcomp ens",
+      "Ġv ibr",
+      "ç¬ ij",
+      "G O",
+      "Ġs izes",
+      "Ġtrack ing",
+      "íĻ Ķ",
+      "ĠìĦ ¸",
+      "Ġimpact s",
+      "ib il",
+      "f ish",
+      "B R",
+      "Ġar row",
+      "Ġlarg ely",
+      "ann y",
+      "Ġlaw yer",
+      "æĢİ éº¼",
+      "j ours",
+      "Ú º",
+      "v ia",
+      "Ġde lla",
+      "Ġmath emat",
+      "ĠM ine",
+      "ĠK oll",
+      "Ø ²",
+      "ĠC ross",
+      "Ġ6 5",
+      "Ġgr ac",
+      "Ġinvol ves",
+      "Ġdel ight",
+      "ĠHolly wood",
+      "Ġimmedi ate",
+      "on ic",
+      "Ġl ado",
+      "Ġbull et",
+      "ĠN ote",
+      "Ġun lock",
+      "Ġdisc ount",
+      "Ġris ing",
+      "p ress",
+      "Ġp ace",
+      "Ġsh orter",
+      "Ġten er",
+      "ge on",
+      "Ġman aging",
+      "Ġc ere",
+      "C hr",
+      "Wh en",
+      "ach en",
+      "Ġì ĵ",
+      "ĠH un",
+      "Ġof t",
+      "Ġ2 50",
+      "ier ung",
+      "Ġst abil",
+      "ĠCon nect",
+      "Ġy ani",
+      "Ġdow nt",
+      "μ α",
+      "Ġvoc al",
+      "ν α",
+      "Ġle an",
+      "Ġvidé o",
+      "ĠFam ily",
+      "res ent",
+      "Ġamount s",
+      "ì§ ģ",
+      "cl ass",
+      "Ġv ib",
+      "ĠA v",
+      "ar se",
+      "Ġgentle men",
+      "Ġsee king",
+      "Ġun ion",
+      "Ġregular ly",
+      "æ ı",
+      "ĠJ ahr",
+      "ĠF ood",
+      "ĠPro blem",
+      "Ġart ificial",
+      "ĠS ix",
+      "Ġimp ressed",
+      "Ġto oth",
+      "ĠK h",
+      "Ġy ard",
+      "Ġíķ ´",
+      "Ġown ed",
+      "Ġëı Ļ",
+      "ì² Ń",
+      "Ġto da",
+      "Ġport fol",
+      "ĠëĤ ¨",
+      "orge ous",
+      "Ġd ates",
+      "олÑĮ з",
+      "еÑĩ но",
+      "Ġconfig uration",
+      "Ġrequire ment",
+      "Ġbell y",
+      "Ġpain ful",
+      "Ġdemonst rate",
+      "Ġg leich",
+      "Ġvis iting",
+      "ĠCon f",
+      "Ġd al",
+      "Ù ij",
+      "Ġam end",
+      "ĠF ur",
+      "æ¯ Ķ",
+      "Ġv ital",
+      "á» ĭ",
+      "Ġm ate",
+      "ĠO u",
+      "Ġleg acy",
+      "ust ing",
+      "Ġaccom mod",
+      "Ġqu oi",
+      "au en",
+      "Ġlif estyle",
+      "C C",
+      "ä än",
+      "art en",
+      "Ġmin ha",
+      "r ó",
+      "Ġëª ¨",
+      "Ġform ation",
+      "Ġtra iler",
+      "per or",
+      "Ġг ÑĢ",
+      "Ġu d",
+      "z u",
+      "Ġk ommen",
+      "Ġc ave",
+      "ĠCouncill or",
+      "Ġthr own",
+      "Ġtrick s",
+      "LAUGH TER",
+      "ĠAcad emy",
+      "row d",
+      "¡ Ŀ",
+      "ìł Ģ",
+      "ĠIm agine",
+      "Ġinform ed",
+      "ro ph",
+      "Ġl ig",
+      "Ġsk ull",
+      "ab eth",
+      "Ġfunction al",
+      "ere k",
+      "O n",
+      "é ¦",
+      "Ġanc est",
+      "Ġsaf ely",
+      "ĠH T",
+      "ëĭ ¹",
+      "Ġd av",
+      "Ġdri ves",
+      "A meric",
+      "Ġt ire",
+      "Ġsa is",
+      "á ri",
+      "av ors",
+      "Ġcorrespond ing",
+      "Ġpr és",
+      "ch est",
+      "Ġbacter ia",
+      "Ġinfect ion",
+      "us al",
+      "Ġave z",
+      "Ġbasket ball",
+      "Ġsuppl ies",
+      "Ġexpert ise",
+      "ł ¥",
+      "f a",
+      "Ġt iempo",
+      "ĠW ant",
+      "Ġs illy",
+      "Ġu pp",
+      "Ġelect ed",
+      "Ġf ired",
+      "ĠØ ¯",
+      "Ġunivers ities",
+      "all e",
+      "Ġjack et",
+      "Â °",
+      "Ġtra v",
+      "l s",
+      "Ġdefe at",
+      "Ġco gn",
+      "Ġequ ations",
+      "uk i",
+      "ĠS her",
+      "Ġth irty",
+      "Ġstream ing",
+      "ot ros",
+      "ĠPro du",
+      "ne j",
+      "Ġdesign er",
+      "ĠëĬIJë Ĥ",
+      "Ġpaint ed",
+      "ra ine",
+      "m ail",
+      "Ġv ess",
+      "Ġrhy thm",
+      "les h",
+      "Ġ9 9",
+      "Ġa inda",
+      "ch ied",
+      "Ġét ait",
+      "Ġaffect s",
+      "é £",
+      "ĠF ind",
+      "Ġé l",
+      "Ġpotato es",
+      "Ġp ag",
+      "Ġп аÑĢ",
+      "art s",
+      "ĠN ach",
+      "Ġ3 3",
+      "ĠH ard",
+      "ĠIra q",
+      "Ġopin ions",
+      "w ith",
+      "erm an",
+      "Ã ½",
+      "è Ń",
+      "ĠS PEAK",
+      "¬ ¼",
+      "Ġst ability",
+      "ĠH E",
+      "Ġcapt ured",
+      "Ġbu cks",
+      "Ġmas ks",
+      "Ġcompet e",
+      "Ġforgot ten",
+      "лÑİ Ñĩ",
+      "se qu",
+      "ĠìĦ ł",
+      "ill ion",
+      "Ġgraph ics",
+      "Ġh ub",
+      "ĠìĹ °",
+      "em por",
+      "Ġcr own",
+      "Ġw ider",
+      "Ġocc urs",
+      "D S",
+      "æ ģ",
+      "ĠB attle",
+      "ãģª ãĤĵ",
+      "Ġd ual",
+      "Ġ6 00",
+      "ath ers",
+      "๠ģ",
+      "Ġsett le",
+      "Ġav ait",
+      "Ġdo is",
+      "ки Ñħ",
+      "ad ores",
+      "ĠÃ ³",
+      "ne go",
+      "ĠGeorg ia",
+      "ĠR og",
+      "Ġdiv or",
+      "ĠS ong",
+      "ĠSpe cial",
+      "Ġm un",
+      "Ġpret end",
+      "M AN",
+      "Ġviol ent",
+      "Ġbes ides",
+      "v y",
+      "ĠN az",
+      "2 9",
+      "Ġswe at",
+      "Ġz w",
+      "ĠH u",
+      "ĠBu ild",
+      "Ġh orm",
+      "ĠC ard",
+      "Ġìľ ł",
+      "Ġrecommend ation",
+      "c alled",
+      "st ick",
+      "ĠPol ice",
+      "Ġconsum ers",
+      "Ġgro cer",
+      "Ġst un",
+      "ĠÐĴ Ñĭ",
+      "Ð £",
+      "ĠD ata",
+      "Ġsubst ant",
+      "ire ct",
+      "à ²",
+      "Ġв з",
+      "ĠAr m",
+      "Ġsem ester",
+      "ĠBr ad",
+      "Ġour s",
+      "ĠкоÑĤоÑĢ Ñĭй",
+      "§ a",
+      "Ġgr ams",
+      "Ġexerc ises",
+      "7 5",
+      "Ġswe ar",
+      "Ġinc ent",
+      "Ïģ ο",
+      "Ġilleg al",
+      "ä½ ķ",
+      "ĠDam n",
+      "Ġn ú",
+      "Ġne ces",
+      "Ġcu z",
+      "ic on",
+      "Ġh ors",
+      "ĠCom o",
+      "ä½ ľ",
+      "Ġëij IJ",
+      "Ġover se",
+      "Ġhar vest",
+      "Ġth rew",
+      "ĠпоÑĤ омÑĥ",
+      "×Ļ× Ķ",
+      "Ġot ro",
+      "ĠпеÑĢ в",
+      "Ġsc ope",
+      "Ġgl ory",
+      "ĠMich igan",
+      "Ġassum ing",
+      "ĠÑĥ д",
+      "Ġbo ld",
+      "g ue",
+      "m other",
+      "Ġw aren",
+      "è¬ Ľ",
+      "ĠØ ¥",
+      "ĠK am",
+      "isp iel",
+      "Ġtou jours",
+      "Ġconst itution",
+      "Ġ ~",
+      "Ġfrank ly",
+      "ol en",
+      "ons cious",
+      "Ġwür de",
+      "th on",
+      "ĠO F",
+      "ìŀ IJë",
+      "und a",
+      "Ġ æĺ¯",
+      "Ġп оÑĢ",
+      "Ġemploy ment",
+      "Ñij ÑĤ",
+      "ãģ ģ",
+      "Ġste am",
+      "ĠD I",
+      "Ġprofession als",
+      "Ġengine ers",
+      "ĠX ia",
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+      "ĠD un",
+      "Ġbit ch",
+      "ĠF ord",
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+      "× ¦",
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+      "Ġbu eno",
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+      "l ers",
+      "ĠY ang",
+      "ĠH A",
+      "b at",
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+      "ĠEl iz",
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+      "Ġб ол",
+      "Ġf are",
+      "ĠG ab",
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+      "Ġs atell",
+      "ĠAny thing",
+      "Ġass et",
+      "Ġsched ul",
+      "Ġrad ical",
+      "Ġzwe i",
+      "ĠM E",
+      "rel ated",
+      "Ġsepar ated",
+      "ĠL ibr",
+      "Ġg rip",
+      "Ġà® ª",
+      "è¨ Ģ",
+      "Ġbe ans",
+      "ĠO p",
+      "ìĨ Į",
+      "Ġp ound",
+      "Ġentr ance",
+      "Ï Ĩ",
+      "ĠN ie",
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+      "Ġperson as",
+      "ĠAl i",
+      "ä hr",
+      "å¤ ĸ",
+      "Ġdram atic",
+      "ĠF ine",
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+      "è Ļ",
+      "Ġdé jÃł",
+      "Ġafford able",
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+      "c u",
+      "Ġ \\",
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+      "Ġrec y",
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+      "Ġ lle",
+      "ĠвÑĢем Ñı",
+      "ĠW W",
+      "Ġv s",
+      "ĠD ude",
+      "ĠR ome",
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+      "ĠB ak",
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+      "ĠUn ter",
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+      "h n",
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+      "ä»Ĭ 天",
+      "Ġh int",
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+      "ivers ary",
+      "Ġпо Ñĩ",
+      "r ac",
+      "ĠSPEAK ER",
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+      "Ġfr ames",
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+      "h u",
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+      "å® ļ",
+      "ĠM ach",
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+      "Ġd iz",
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+      "Ġse vent",
+      "ĠQu ick",
+      "Ġb aking",
+      "Ġassemb ly",
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+      "ĠL ad",
+      "éĿ ŀ",
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+      "ag s",
+      "Ġgrav ity",
+      "Ġì§ ij",
+      "em ploy",
+      "Ġdies es",
+      "Ġdisco very",
+      "ÑģÑĤв а",
+      "Ġheb ben",
+      "Ġger ade",
+      "ĠD R",
+      "Ġ' '",
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+      "ĠÐŁ о",
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+      "ass er",
+      "¶Ģ íĦ°",
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+      "ash a",
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+      "ah n",
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+      "ãĤ ¿",
+      "Ġ6 4",
+      "ĠM mm",
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+      "Ġfr uits",
+      "ĠH D",
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+      "ĠF air",
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+      "ĠA u",
+      "Ñĥ н",
+      "ul ating",
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+      "Ġdrag on",
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+      "Ġwork out",
+      "им еÑĢ",
+      "m ond",
+      "ĠN ope",
+      "Ġ× ĸ×Ķ",
+      "ĠÙ Ĥ",
+      "Ġadopt ed",
+      "b ul",
+      "Ġs ans",
+      "Ġposs ibilities",
+      "Ġp end",
+      "Ġz aman",
+      "h ou",
+      "Ġsha res",
+      "Ġcal endar",
+      "Ġperson a",
+      "Ġse al",
+      "Ġgen e",
+      "Ġst ored",
+      "Ġп оз",
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+      "Ġinstr uments",
+      "ĠM A",
+      "Ģ ìĿ´",
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+      "h ot",
+      "Ạ¯",
+      "Ġê°Ļ ìķĦìļĶ",
+      "ĠEmp ire",
+      "Ġb io",
+      "w ind",
+      "ie go",
+      "ĠEu rop",
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+      "ed ge",
+      "Ġback wards",
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+      "Ġque en",
+      "Ġsh ine",
+      "Ġç ık",
+      "Ġc ad",
+      "ĠO d",
+      "ĠìĤ¬ëŀ Į",
+      "Ġbu bble",
+      "ô i",
+      "z es",
+      "Ġreact ions",
+      "Ġjud gment",
+      "ĠDemocr ats",
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+      "ash ed",
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+      "ÅĽ nie",
+      "ê ´",
+      "Ġexem ple",
+      "M P",
+      "á» ¯",
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+      "Ġres il",
+      "Ġm á",
+      "ÅĦ st",
+      "bel iev",
+      "ĠV or",
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+      "ond a",
+      "Ġpod emos",
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+      "ĠS S",
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+      "Ġem bed",
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+      "gg ed",
+      "Ġredu cing",
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+      "Ġc ord",
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+      "olog ists",
+      "az a",
+      "Ġt ends",
+      "ĠC ook",
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+      "erem ony",
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+      "ĠMAR ISHA",
+      "Ġen roll",
+      "ĠC ry",
+      "ES S",
+      "ĠS ad",
+      "Ġimplement ed",
+      "Ġd ÃŃa",
+      "Ã ľ",
+      "Ġp ist",
+      "D r",
+      "Ġs abe",
+      "ĠSo ci",
+      "ä re",
+      "Ġк ÑĤо",
+      "ĠFranc isco",
+      "Ġìŀ ¥",
+      "Ġcreat ures",
+      "aw s",
+      "Ġearn ed",
+      "Ġche aper",
+      "Ġd la",
+      "Ġw arn",
+      "s che",
+      "Ġbl ah",
+      "Ġnut r",
+      "è ¼",
+      "Ġg orgeous",
+      "ĠAng eles",
+      "Ġgem acht",
+      "Ġhom eless",
+      "ograph ic",
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+      "ĠS om",
+      "ĠH ad",
+      "act ions",
+      "Ġpost s",
+      "Ġout ra",
+      "ĠMe an",
+      "k ar",
+      "Ġc ous",
+      "Ġbr ack",
+      "иÑĤÑĮ ÑģÑı",
+      "Ġbelie ves",
+      "Ġsu icide",
+      "Ġequ ally",
+      "Ġca res",
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+      "Ġst em",
+      "ĠM uch",
+      "Ġprodu cer",
+      "×ķ× IJ",
+      "Ġprotect ing",
+      "ĠTRA VIS",
+      "Ġinterview s",
+      "Ġal ien",
+      "ĠAs k",
+      "Ġso le",
+      "C O",
+      "ĠS ud",
+      "Ġsurv iv",
+      "Ġsk etch",
+      "Ġw ÅĤa",
+      "Ġcol oc",
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+      "we ight",
+      "Ġ5 5",
+      "Ġ >",
+      "Ġhero es",
+      "ĠB oston",
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+      "Ġgu ilty",
+      "ĠJ enn",
+      "ing en",
+      "Ġgrant ed",
+      "ĠK elly",
+      "ĠS av",
+      "ĠUn cle",
+      "ĠHon estly",
+      "EL I",
+      "Ġnavig ate",
+      "Ġbless ed",
+      "c ore",
+      "Ġear ning",
+      "Ġsign als",
+      "Ġdis k",
+      "ial s",
+      "Ġag es",
+      "æ ħ",
+      "Ġpartic le",
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+      "Ġcan n",
+      "Ġt ier",
+      "Ġstat ements",
+      "ê³ł ìļĶ",
+      "ĠëķĮ문 ìĹIJ",
+      "ĠCh o",
+      "Ġpol ar",
+      "an ç",
+      "ĠK enn",
+      "ĠN i",
+      "ĠF ight",
+      "or gan",
+      "é ķ",
+      "ĠCh a",
+      "ĠS ÃŃ",
+      "ãĥ ª",
+      "Ġs lic",
+      "Ġcert ific",
+      "Ġtempl ate",
+      "ĠFed eral",
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+      "Ġexpl o",
+      "ĠM ain",
+      "ĠN E",
+      "Ġalong side",
+      "Ġd ressed",
+      "ĠP oint",
+      "Ġenviron ments",
+      "Ġpró xim",
+      "Ġda ar",
+      "Ġprom pt",
+      "Ġpurs ue",
+      "Ġentertain ment",
+      "Ġth roat",
+      "Ġproblem a",
+      "Ġm art",
+      "ì ¼",
+      "Ġprov ider",
+      "Ø Į",
+      "Ġ× Ĺ",
+      "int e",
+      "m aking",
+      "Ġstro ke",
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+      "U n",
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+      "ĠAr ts",
+      "ink ing",
+      "ĠÐŀ н",
+      "Ġи Ñģ",
+      "n ah",
+      "ĠÐķ Ñģли",
+      "Ġcor ners",
+      "Ġtrick y",
+      "in ch",
+      "l ijk",
+      "Ġpress ing",
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+      "ìĦ ł",
+      "Ġconf ront",
+      "Ġv et",
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+      "Ġcra p",
+      "ĠDe c",
+      "Ġr amp",
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+      "u és",
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+      "int on",
+      "ĠJ ah",
+      "èĢ ħ",
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+      "Ġk ay",
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+      "ight y",
+      "Ġsp oon",
+      "Ġh unt",
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+      "Ġdis m",
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+      "Ġíĺ ķ",
+      "Ġweek ly",
+      "Ġm ismo",
+      "Ġgen etic",
+      "ul pt",
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+      "Ġarrest ed",
+      "Ġguid elines",
+      "Ġ×ľ× IJ",
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+      "Ġrequ ests",
+      "ĥ IJ",
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+      "Ġinter ess",
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+      "Ġd uck",
+      "Ġwith d",
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+      "ĠÏ Į",
+      "ĠHist ory",
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+      "ÄŁ i",
+      "ĠP M",
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+      "é ¢",
+      "ÑĨи и",
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+      "íģ ¬",
+      "Ġpr ose",
+      "oo oo",
+      "r ates",
+      "W S",
+      "Ġautom atic",
+      "Ġcollect ing",
+      "Å ij",
+      "Ġneighb ors",
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+      "ĠEx pl",
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+      "co ver",
+      "we g",
+      "Ġstick s",
+      "Ġe ller",
+      "Ġw ww",
+      "Ġd orm",
+      "ĠEx per",
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+      "Ġemail s",
+      "Ġgra ve",
+      "im iz",
+      "H S",
+      "Ġu it",
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+      "ĠÑĤ ем",
+      "Ñĭ ÑĪ",
+      "Ñī Ñij",
+      "Ġgen au",
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+      "Ġmed itation",
+      "ĠOr gan",
+      "Ġest imate",
+      "Ġë¬ ´ì",
+      "l ets",
+      "Ġn Ãły",
+      "Ġmind set",
+      "Ġres on",
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+      "Ġnumer ous",
+      "Ġvie lleicht",
+      "ĠTh ird",
+      "u ous",
+      "ĠDe ad",
+      "ан д",
+      "H N",
+      "Ġrac ing",
+      "Ġag ents",
+      "ĠU t",
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+      "ĠH P",
+      "Ġchem istry",
+      "Ġsurv ival",
+      "æĸ °",
+      "Ġconvin ced",
+      "Ġ ;",
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+      "ĠE S",
+      "åĴ Į",
+      "3 00",
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+      "Ġì µ",
+      "Ġd ict",
+      "G A",
+      "Ġah ÃŃ",
+      "åĭ ķ",
+      "Ġte j",
+      "Ġо ÑģÑĤ",
+      "ĠE lect",
+      "Ġintellect ual",
+      "Ġbi as",
+      "Ġbur den",
+      "çĤ ¹",
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+      "Ġche er",
+      "Ġso ph",
+      "Ġportfol io",
+      "ub a",
+      "Ġest os",
+      "T V",
+      "F or",
+      "Ġas h",
+      "Ġkom mer",
+      "Ġcollect ive",
+      "Ġw rest",
+      "ĠJ etzt",
+      "ĠW at",
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+      "Ġprim er",
+      "act ive",
+      "Ġm ie",
+      "ick ed",
+      "Ġhun ting",
+      "Ġtest im",
+      "Ġcompass ion",
+      "ĠØ ±",
+      "Ġbr ut",
+      "Ġsal ad",
+      "об Ñīе",
+      "Ġsol ving",
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+      "Ġattract ive",
+      "ÙĪ ÙĦ",
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+      "Ġsc ulpt",
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+      "ĠWe ek",
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+      "Ġn ad",
+      "Ġmer ch",
+      "ĠíĻ ķ",
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+      "ĠÎ ¸",
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+      "3 8",
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+      "ĠNet work",
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+      "Ġpre v",
+      "Ġs ö",
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+      "ĠP oke",
+      "ĠP od",
+      "ĠTur key",
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+      "Ġsn ake",
+      "ĠAm y",
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+      "Ġrac ism",
+      "× ij×",
+      "ĠS ams",
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+      "Ġà® ¨",
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+      "M e",
+      "ix ò",
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+      "an ne",
+      "Ġexp ressed",
+      "м еÑĢ",
+      "Ġmo et",
+      "Ġret urning",
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+      "Ġexp on",
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+      "M L",
+      "Ġl amp",
+      "Ġsh y",
+      "Ġcomp osition",
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+      "Ġmagn etic",
+      "Ġprem ier",
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+      "Ġs its",
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+      "Ġma i",
+      "ĠDeuts ch",
+      "as ı",
+      "Ġobt ain",
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+      "Ġso it",
+      "Ġë° Ķ",
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+      "Ġconse gu",
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+      "Ġc um",
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+      "ĠRoy al",
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+      "il ib",
+      "Ġan ch",
+      "íĸ ī",
+      "Æ°á» £",
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+      "ĠAn th",
+      "ĠS Ã¥",
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+      "Ġst ere",
+      "ĠO S",
+      "ony m",
+      "ter y",
+      "ĠMar ia",
+      "ê² ĥ",
+      "Ġexpl oring",
+      "Ġtransp arent",
+      "Ġf ate",
+      "ĠJ ung",
+      "Ġgr up",
+      "Ġdark er",
+      "ĠD oug",
+      "Ġman e",
+      "æĶ ¾",
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+      "d ri",
+      "lo ok",
+      "ĠDes ign",
+      "Ġtut aj",
+      "Ġhorizont al",
+      "re on",
+      "ort e",
+      "ĠCor rect",
+      "ĠSte ven",
+      "Ġv ine",
+      "0 2",
+      "i Äĩ",
+      "Ġsie mpre",
+      "ĠK ey",
+      "åĥ ı",
+      "ĠG ames",
+      "Ġna ar",
+      "Ġshock ed",
+      "el ve",
+      "ĠR ose",
+      "ìĭ ¬",
+      "Ġstop ping",
+      "oh l",
+      "ĠM ix",
+      "Ġsuff ered",
+      "Ġsig ma",
+      "Ġweak ness",
+      "ĠO w",
+      "ี à¹Ī",
+      "I F",
+      "Ġà® ħ",
+      "ad ed",
+      "ĠNet flix",
+      "an es",
+      "Ġrem ained",
+      "ir y",
+      "Ġr ip",
+      "ell t",
+      "Ġsil ent",
+      "Ġpro ven",
+      "Ġtox ic",
+      "Ġal umin",
+      "Ġmulti pl",
+      "al and",
+      "Ġ3 4",
+      "0 6",
+      "ĠB ru",
+      "Ġìłķ ë§IJ",
+      "J ust",
+      "b oy",
+      "Ġsho e",
+      "Ġcreat ure",
+      "Ġhead ed",
+      "ĠоÑĤ к",
+      "æ ±",
+      "Ġess ence",
+      "Ġremark able",
+      "Ġnú mer",
+      "Ġd rew",
+      "Ġpu zzle",
+      "ĠLibr ary",
+      "ĠF u",
+      "ash es",
+      "k k",
+      "ĠI st",
+      "¦ °",
+      "ĠB ry",
+      "Ġc eremony",
+      "Ġà® İ",
+      "Ġc ri",
+      "e qu",
+      "ãĤ ¢",
+      "Ġpri ze",
+      "Ġdim ensions",
+      "og ram",
+      "Ġle ather",
+      "Ġpop ulations",
+      "u um",
+      "Ġve gan",
+      "Ñı д",
+      "Ġcó mo",
+      "å Ħ",
+      "Ġstri p",
+      "å £",
+      "Ġvac ation",
+      "ħ ķ",
+      "Ġme als",
+      "ili pp",
+      "Ġ ents",
+      "ar am",
+      "ric ht",
+      "Ġgra in",
+      "ĠSp ain",
+      "Ġche ek",
+      "ĠA ff",
+      "I ON",
+      "ĠBr ing",
+      "Ġ3 8",
+      "iel en",
+      "ul u",
+      "ĠболÑĮ ÑĪе",
+      "Ġannounce ment",
+      "ĠÑĤ ÑĥÑĤ",
+      "ĠPro phet",
+      "ard o",
+      "3 7",
+      "Ġw oke",
+      "Ġtransl ation",
+      "ĠN OT",
+      "ĠC L",
+      "Ġd Ã¼ÅŁ",
+      "ÑĨ Ñĸ",
+      "ac er",
+      "ĠL oc",
+      "Ġper ception",
+      "N O",
+      "Ġdies en",
+      "L ook",
+      "he art",
+      "av ed",
+      "Ġbound ary",
+      "Ġfl ows",
+      "Ñij м",
+      "Ġarg uments",
+      "Ġelect ions",
+      "ı s",
+      "Ġhe ck",
+      "Ġsuit able",
+      "Ġf iber",
+      "ĠSt ra",
+      "x y",
+      "ĠH um",
+      "Ġmonth ly",
+      "u per",
+      "Ġgol f",
+      "Ġl ately",
+      "ĠG ard",
+      "ĠR en",
+      "ĠA st",
+      "ĠF ant",
+      "аÑģ Ñģ",
+      "Ġobs er",
+      "ë ¡ľ",
+      "Ġeas iest",
+      "į Ķë",
+      "Ġwebs ites",
+      "p ol",
+      "Ġco con",
+      "Ġà® ĩ",
+      "ĠV eg",
+      "Ġwalk s",
+      "Ġint ro",
+      "Ġdirect ed",
+      "ĠAn na",
+      "Ġëĵ¤ ìĸ´",
+      "ĠEaster n",
+      "ĠS aint",
+      "ĠB ow",
+      "Ġro ast",
+      "ĠU RL",
+      "Ġjed en",
+      "ur as",
+      "aj a",
+      "Ġse mi",
+      "Ġrapid ly",
+      "Ġtarget s",
+      "ĠCont rol",
+      "Ġb ah",
+      "Ġref lection",
+      "Ġcreat ivity",
+      "hold ers",
+      "Ġìĺ ¬ë",
+      "Ġamong st",
+      "Ġfeed ing",
+      "ÑįÑĤ омÑĥ",
+      "Ġвид е",
+      "Ġë§Įë ĵ¤",
+      "ĠSm art",
+      "Ġrel iable",
+      "Ġvez es",
+      "Ġ× ¨",
+      "ch uckles",
+      "az ione",
+      "ĠWilliam s",
+      "Ġa ç",
+      "Ġsle e",
+      "е Ñī",
+      "Ġtim eline",
+      "Ġthor ough",
+      "á» į",
+      "ĠO t",
+      "ạ n",
+      "Ġimag ination",
+      "Ġmechan ics",
+      "r ist",
+      "Ġclaim ed",
+      "ÏĦ η",
+      "ê te",
+      "ĠHur ry",
+      "ĠiP ad",
+      "Ġconst ru",
+      "ĠC la",
+      "ĠAl s",
+      "ä¼ ļ",
+      "ut z",
+      "Ġcult ures",
+      "Ġìĸ´ëĸ» ê²Į",
+      "Ġbelong s",
+      "Ġy er",
+      "ĠDoes n",
+      "Ġge omet",
+      "Ġb id",
+      "Ġfo am",
+      "Ġh ob",
+      "ĠBrit ain",
+      "Ġsubst ance",
+      "Ġann iversary",
+      "ĠëĦ Ī",
+      "Ġnot ed",
+      "Ġgovern or",
+      "Ġstock s",
+      "3 1",
+      "Ġdi ye",
+      "ìĬ ¤ë",
+      "Ġre b",
+      "z el",
+      "Ġmultip ly",
+      "Ġoper ator",
+      "Ħ¤ ìļĶ",
+      "Ġwat ers",
+      "Ġd är",
+      "Ġuns er",
+      "ĠEliz abeth",
+      "é« ĺ",
+      "Ġincreasing ly",
+      "ĠG ro",
+      "Ġen gines",
+      "ir s",
+      "Ø «",
+      "Ġtre asure",
+      "P C",
+      "in ction",
+      "ir i",
+      "Ġacc um",
+      "Ġvari ation",
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+      "Ġtit les",
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+      "Ġeld er",
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+      "r un",
+      "Ñı в",
+      "Ġinnov ative",
+      "Ġnom bre",
+      "Ġco inc",
+      "Ġfr anch",
+      "Ġent onces",
+      "Ġnicht s",
+      "Ġexc lusive",
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+      "ĠB i",
+      "u je",
+      "æŃ ¡",
+      "Ġp ok",
+      "ĠP rem",
+      "Ġrock et",
+      "ELI PE",
+      "Ġhosp itals",
+      "ri um",
+      "Ġjust e",
+      "Ġham mer",
+      "Ġquant um",
+      "Ġrespons es",
+      "ll y",
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+      "Ġfr idge",
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+      "Ġgather ed",
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+      "Ġd azu",
+      "Ġreci pes",
+      "Ġbu zz",
+      "c en",
+      "Ġany time",
+      "ons ense",
+      "Ġcirc les",
+      "Ġsol ved",
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+      "ĠLu ke",
+      "Ġbu bb",
+      "Ġcont empor",
+      "r zy",
+      "ĠJ ane",
+      "Ġд ом",
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+      "ĠÄ IJ",
+      "ĠCol umb",
+      "ĠÑħ оÑĩ",
+      "Ġbu cket",
+      "Ġevalu ate",
+      "Ġid ol",
+      "Ġrep utation",
+      "ĠìĨ Įë",
+      "ÙĪ ر",
+      "Ġhe cho",
+      "Ġpo em",
+      "Ġsubject s",
+      "pl ant",
+      "ĠBe h",
+      "ĠSpe aking",
+      "Ġbatter ies",
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+      "Ġg ently",
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+      "Ġparam eter",
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+      "ĠT our",
+      "ĠD J",
+      "ot te",
+      "ĠJ ahren",
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+      "Ġд Ñĥм",
+      "Ġ8 00",
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+      "ik ing",
+      "Ġë¬ ¸",
+      "Ġн Ñĥ",
+      "Ġл еÑĤ",
+      "åIJ Į",
+      "ĠI de",
+      "Ġì¡° ê¸Ī",
+      "Ġla ughter",
+      "Ġmole cules",
+      "ĠR est",
+      "Ġobs erved",
+      "d zie",
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+      "ert o",
+      "Ġmo ins",
+      "ĠM IT",
+      "Ġexc it",
+      "Ġt um",
+      "Ġty l",
+      "Ġinvest ed",
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+      "Ġph i",
+      "oty pe",
+      "we ise",
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+      "jour d",
+      "Ġhors es",
+      "n Äħ",
+      "= \"",
+      "ĠS M",
+      "Ġf ib",
+      "Ġcl ips",
+      "çķ ¶",
+      "å¦Ĥ æŀľ",
+      "Ġreg ime",
+      "Ġrot ate",
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+      "Ġarm or",
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+      "åº ¦",
+      "ĠO ch",
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+      "ane ously",
+      "m ek",
+      "éĮ ¯",
+      "ĠX iao",
+      "Ġexist ed",
+      "w orth",
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+      "Ġna ught",
+      "Ġhe iÃŁt",
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+      "ĠR us",
+      "Ġgr ows",
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+      "×ij ר",
+      "ĠЧ ÑĤо",
+      "Ġout ro",
+      "Ġinnoc ent",
+      "Ġm og",
+      "ĠSams ung",
+      "Ġmer cy",
+      "Ġhand ling",
+      "Ġinter vention",
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+      "Ġstitch es",
+      "ÃŃ vel",
+      "Ġtun nel",
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+      "Ġg ost",
+      "im y",
+      "Ġcz as",
+      "Ġm é",
+      "Ġcat al",
+      "ĠSim on",
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+      "is as",
+      "ĠC PU",
+      "ĠD ou",
+      "Ġnä ch",
+      "Ġinfin ity",
+      "Ġr if",
+      "ĠPe ace",
+      "ĠC u",
+      "Ġminim al",
+      "Ġlisten ed",
+      "Ġpo le",
+      "hal b",
+      "Ġload ed",
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+      "Ġl ap",
+      "Ġco op",
+      "Ġfriends hip",
+      "w orld",
+      "Ġge h",
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+      "ĠLa ura",
+      "Ġsurround ed",
+      "ĠE vent",
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+      "ĠW onder",
+      "bre ak",
+      "Ġdro ve",
+      "Ġbroad er",
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+      "F i",
+      "Ġge hen",
+      "Ġwest ern",
+      "Ġintellig ent",
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+      "cks å",
+      "Ġhand y",
+      "Ġsy mp",
+      "Ġr ows",
+      "Ġnut ri",
+      "b ur",
+      "ĠLe on",
+      "Ġsist ema",
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+      "Ġnight s",
+      "Ġcá c",
+      "Ġcount ing",
+      "ĠM ust",
+      "all ow",
+      "еÑģ Ñģ",
+      "M om",
+      "Ġнад о",
+      "Ġbar rel",
+      "ãĥ ŀ",
+      "AR D",
+      "Ġinstall ation",
+      "Ġin sect",
+      "Ġëħ ¸ë",
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+      "ok u",
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+      "Ġassign ed",
+      "Ġinteract ions",
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+      "F ELIPE",
+      "Ġresc ue",
+      "Ġindust ries",
+      "ĠAnd y",
+      "Ġpra ise",
+      "Ġfl ame",
+      "Ġsn ack",
+      "í Ĥ",
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+      "Ġsw o",
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+      "Ġbo ards",
+      "ĠÑĤ ом",
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+      "Ġhat te",
+      "Ġcoll eg",
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+      "ãģĵ ãģ®",
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+      "Ġb um",
+      "Ġwho a",
+      "ìŀ Ħ",
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+      "ĠL ang",
+      "ĠSy ria",
+      "Ġses i",
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+      "Ġappro val",
+      "4 8",
+      "Ġод ин",
+      "Ġë ĸ",
+      "ĠH arr",
+      "ĠAd minist",
+      "Ġ× ¤",
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+      "Ġcitiz en",
+      "Ġsh ark",
+      "0 5",
+      "Ġbo il",
+      "Ġindic ate",
+      "å ¡",
+      "A re",
+      "Ġlay out",
+      "Ġref r",
+      "ĠPac ific",
+      "AA AA",
+      "ĠAustral ian",
+      "g ression",
+      "V oice",
+      "ал ÑģÑı",
+      "Ġshel ter",
+      "T o",
+      "au pt",
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+      "ig os",
+      "ãģ °",
+      "Ġo ct",
+      "Ġro yal",
+      "è ³",
+      "as il",
+      "ĠChild ren",
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+      "Ġbar rier",
+      "Ġej emplo",
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+      "Ġfew er",
+      "Ġdis abilities",
+      "Ġ ....",
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+      "Ġfe es",
+      "Ġwid ely",
+      "em i",
+      "Ġoverwhel ming",
+      "Ġz omb",
+      "Ġb ere",
+      "Ġho od",
+      "ĠA ye",
+      "ĠHar vard",
+      "e v",
+      "ĠÏĦ οÏħ",
+      "Ġcup s",
+      "ĠA uch",
+      "z ona",
+      "Ġ199 0",
+      "Ġwe iÃŁ",
+      "Ġcr unch",
+      "æ ¥",
+      "Ġз ав",
+      "Ġmeas uring",
+      "Ġst ations",
+      "ĠStep hen",
+      "Ġshort ly",
+      "Ġsig ning",
+      "Ġcom edy",
+      "om o",
+      "Ġsuggest ions",
+      "Ġsign ature",
+      "ĠпÑĢ ив",
+      "Ġdis order",
+      "as ka",
+      "Ġworld s",
+      "Ġprecis ely",
+      "n orm",
+      "ra v",
+      "ĠC ivil",
+      "In ter",
+      "ĠC ertain",
+      "Ġinj ured",
+      "Ġsuggest s",
+      "ĠGold en",
+      "Ġcy ber",
+      "ĠØ ´",
+      "Ġtempor ary",
+      "Ġco oper",
+      "Ġvot ed",
+      "Ġ ought",
+      "ấ y",
+      "x ual",
+      "Ġpan els",
+      "Ġ9 5",
+      "Ġhands ome",
+      "ĠпÑĢ ов",
+      "Ġper mit",
+      "Ġke in",
+      "Ġbad ly",
+      "Ġnot ifications",
+      "iz a",
+      "ĠNot ice",
+      "Ġinc lusive",
+      "Ġanswer ing",
+      "Ġí Ĺ",
+      "u ld",
+      "íħ Į",
+      "Ġnow adays",
+      "Ġ3 7",
+      "Ġb olt",
+      "Ġstat ic",
+      "ĠH op",
+      "Ġav ant",
+      "aj o",
+      "Ġ맼 ìŀĪ",
+      "Ġfif ty",
+      "ĠF inal",
+      "Ġsc ores",
+      "ĠT ap",
+      "Ġcy l",
+      "Ġconv ince",
+      "Ġany ways",
+      "od a",
+      "Ġìķ ¼",
+      "Ġser ves",
+      "ĠÑĤак ой",
+      "ĠZo om",
+      "Ġsaving s",
+      "ul o",
+      "Ġs outhern",
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+      "Ġho je",
+      "Ġse ja",
+      "Ġrepresent ing",
+      "Īë įĺ",
+      "l ik",
+      "ĠSome body",
+      "Ġbe ast",
+      "Ġstick ing",
+      "Ġins ist",
+      "Ġtal ented",
+      "Ġexplain ing",
+      "Ġatt orney",
+      "éĥ ¨",
+      "Ġst airs",
+      "ĠD og",
+      "í ĭ",
+      "Ġc ig",
+      "Ġshap ed",
+      "Ġs ons",
+      "Ïģ ι",
+      "ut t",
+      "Ġì Ķ",
+      "Ġpar ad",
+      "ìĿ¸ë į°",
+      "Ġh orn",
+      "ĠJ our",
+      "ann o",
+      "Ġworld wide",
+      "åĬ Ľ",
+      "Ġparticip ation",
+      "¦ Ħ",
+      "Ġm ów",
+      "Ġburn ed",
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+      "all ah",
+      "ĠF und",
+      "Ġcle ver",
+      "ĠLe ute",
+      "b in",
+      "Ġbe ating",
+      "f oot",
+      "ĠìĽ IJ",
+      "ĠStud io",
+      "Ġv ag",
+      "be y",
+      "r ze",
+      "Ġoppos ition",
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+      "w ho",
+      "Ġê± ´",
+      "Ġtr ace",
+      "Ġд енÑĮ",
+      "Ġep id",
+      "Ġges ch",
+      "ĠN ar",
+      "ĠB E",
+      "Ñĥ й",
+      "ĠS ign",
+      "ed ly",
+      "Ġcl ay",
+      "Ġinst antly",
+      "Ġgather ing",
+      "ĠGal axy",
+      "Ġb ored",
+      "ĠBudd h",
+      "c é",
+      "Ġm am",
+      "Ġsl ope",
+      "Ġëĭ¤ ìĿĮ",
+      "Ġsch ön",
+      "Ġp ir",
+      "ge f",
+      "am er",
+      "Ġh ö",
+      "Ġcolle ague",
+      "Ġpres ents",
+      "ad ium",
+      "Ġà® µ",
+      "Ġfal ar",
+      "be ep",
+      "Ġdri ed",
+      "ism s",
+      "Ġro pe",
+      "Ġworks hop",
+      "Ġest ud",
+      "Ġb ands",
+      "Ġthem es",
+      "åħ ¬",
+      "ÙĬ ر",
+      "åIJ İ",
+      "Ġremind er",
+      "ÑĤ Ñĥ",
+      "ĠB h",
+      "Ġcocon ut",
+      "ĠÑģ ÑĤо",
+      "ĠCh annel",
+      "Ġimmig ration",
+      "ä s",
+      ".. ...",
+      "ä¸ »",
+      "çĻ ½",
+      "st op",
+      "Ġк аÑĢ",
+      "Ġco ins",
+      "ĠÑĩ аÑģ",
+      "Ġdest ruction",
+      "l ined",
+      "Ġbar riers",
+      "ant ine",
+      "Ġprint ed",
+      "Ġcongrat ulations",
+      "ĠHe art",
+      "Ġin qu",
+      "th a",
+      "Ġhard ly",
+      "ĠA ven",
+      "Ġt inha",
+      "ĠS ony",
+      "ĠN F",
+      "Ġgradu ates",
+      "Ġsque eze",
+      "ere my",
+      "ÏĦ ι",
+      "Ġep ic",
+      "ĠJ u",
+      "Ġol m",
+      "ĠLa ughter",
+      "Ġbelief s",
+      "ĠC ru",
+      "ĠTr ue",
+      "ĠS oul",
+      "owe en",
+      "Ġrom antic",
+      "Ġз в",
+      "Ġan os",
+      "ĠY up",
+      "éĺ ¿",
+      "d im",
+      "Ġin fer",
+      "Ġз ам",
+      "Ġso c",
+      "uk a",
+      "Ġprec ise",
+      "Ġdro pping",
+      "Ġcl ue",
+      "Ġer rors",
+      "char ge",
+      "ĠP u",
+      "omet er",
+      "Ġlamb da",
+      "ac ional",
+      "ĠD ong",
+      "Ġcham ber",
+      "Ġthank ful",
+      "ĠN u",
+      "ĠHaw ai",
+      "Ġinf o",
+      "Ġactiv ate",
+      "ĠQ ual",
+      "Ġqu ed",
+      "Ñĥ лÑĮ",
+      "Ġcl oth",
+      "åĸ ľ",
+      "Ġw ichtig",
+      "5 5",
+      "Ġot ra",
+      "ograp her",
+      "Ġcur ios",
+      "Ġ19 80",
+      "Ġemp res",
+      "d ess",
+      "e ur",
+      "Ġcl uster",
+      "ar ter",
+      "ob ile",
+      "ĠY an",
+      "ĠAd v",
+      "Ġdiscipl ine",
+      "Ġìłķ ëıĦ",
+      "ĠPl ace",
+      "ĠSe lect",
+      "T E",
+      "ĠбÑĭ ла",
+      "Ġwh is",
+      "Ġb ay",
+      "ĠD or",
+      "en cing",
+      "Ġrep et",
+      "Ġf icar",
+      "p ad",
+      "Ġf og",
+      "u yor",
+      "Ġsn ap",
+      "ib t",
+      "Ġso bie",
+      "Ġappoint ment",
+      "ĠR y",
+      "Ġce iling",
+      "our se",
+      "Ġwr ites",
+      "ĠAfghan istan",
+      "Ġm os",
+      "az e",
+      "Ġpen al",
+      "Ġcry stal",
+      "IC E",
+      "ê° IJ",
+      "é Ł",
+      "ĠTes la",
+      "Ġthe ories",
+      "Ġappe al",
+      "Ġnewsp aper",
+      "Ġcook ies",
+      "æ ©",
+      "ĠاÙĦ ÙĦ",
+      "Ġma j",
+      "ĠGet ting",
+      "k ommen",
+      "ĠHe aven",
+      "ell s",
+      "Ġdiv ine",
+      "Ä «",
+      "Ġa kt",
+      "Ġhop es",
+      "ĠCh en",
+      "we gen",
+      "** *",
+      "ĠFra ge",
+      "Ġн и",
+      "ภ¹",
+      "min ister",
+      "nes ota",
+      "wh ich",
+      "Ġexpl icit",
+      "Ġverd ad",
+      "Ġgradu ated",
+      "ĠPh ilipp",
+      "Q L",
+      "ĠM I",
+      "Ġdev ot",
+      "Ġc ure",
+      "Ġclos est",
+      "ĠÃ Ħ",
+      "Ġsex y",
+      "ãģ Ľ",
+      "ĠDe ath",
+      "ok o",
+      "ug u",
+      "ĠAn ne",
+      "itar ian",
+      "es a",
+      "ег од",
+      "ĠD ur",
+      "Ġ 000",
+      "ze it",
+      "Ġtour nament",
+      "Ġmel hor",
+      "ภª",
+      "Ġin du",
+      "Ġf law",
+      "Ġw ars",
+      "ĠM ind",
+      "ĠI ron",
+      "ÑĤ ак",
+      "ĠV R",
+      "Ġs iz",
+      "ĠS outhern",
+      "Ġê·¸ëŁ ¬ë",
+      "Ġaw ak",
+      "Ġìķ ŀ",
+      "Ġc ube",
+      "believ able",
+      "if all",
+      "d is",
+      "Ġabandon ed",
+      "m ind",
+      "Ġpar l",
+      "Ġclass ical",
+      "è ĭ",
+      "á»Ļ t",
+      "ĠAut o",
+      "ĠB or",
+      "ç ©",
+      "4 00",
+      "ĠSoci ety",
+      "Ġsubt le",
+      "Ġmiss ions",
+      "Ġremember ed",
+      "ĠE ither",
+      "Ġda für",
+      "OR D",
+      "Ġint ensity",
+      "ES IN",
+      "ĠC up",
+      "Ġrare ly",
+      "Ġto ys",
+      "ĠChar lie",
+      "á» Ł",
+      "Ġgla ube",
+      "Ġround s",
+      "T IN",
+      "Ġcap ability",
+      "Ġderiv ative",
+      "Ġrefer ring",
+      "Ġd Ã¥",
+      "ĠT ALI",
+      "Ġcott on",
+      "Ġcon fer",
+      "Ġcolum ns",
+      "Ġliber al",
+      "Ġnun ca",
+      "Ġμ ε",
+      "Ġind o",
+      "ib en",
+      "ĠBe ispiel",
+      "Ġê·¸ë łĩ",
+      "ĠÑĥ Ñĩ",
+      "Ġh oy",
+      "Ġfr y",
+      "ĠScott ish",
+      "è Ĭ",
+      "Ġc iv",
+      "Ġconserv ative",
+      "Ġair pl",
+      "Ġs ar",
+      "r us",
+      "Ġinvest ments",
+      "Ġinfin ite",
+      "Ġà® ķ",
+      "ĠTALI ESIN",
+      "ĠG ary",
+      "ue ll",
+      "Ġа к",
+      "ĠC ir",
+      "Ġrit ual",
+      "Ġ>> >",
+      "Ġtem pt",
+      "ĠTe ch",
+      "ĠPoke mon",
+      "Ġimprove ments",
+      "Ġsp are",
+      "Ġtransl ate",
+      "Ġson ra",
+      "ĠFil m",
+      "w ort",
+      "Ġм и",
+      "Ġperiod s",
+      "Ġje alous",
+      "ãģĦ ãģĦ",
+      "Ġt ir",
+      "M I",
+      "Ġconduct ed",
+      "ĠìķĪë ħķ",
+      "0 9",
+      "ĠPol it",
+      "ĠWhere as",
+      "Ġmoist ure",
+      "Ġs ins",
+      "Ġk ap",
+      "ĠÑį к",
+      "Ġben im",
+      "Ġelimin ate",
+      "Ġathlet es",
+      "ĠMan ager",
+      "Ġfeature d",
+      "ap ore",
+      "äº Ľ",
+      "Ġë° ľ",
+      "Ġper f",
+      "ĠTh us",
+      "Ġdeb ut",
+      "об ÑĢ",
+      "Ġse ñ",
+      "Ġmyster ious",
+      "w ords",
+      "Ķ ê°Ģ",
+      "Ġcheck s",
+      "Ġvolunte er",
+      "Ġwas hing",
+      "ĠMar vel",
+      "ĠA B",
+      "iss ors",
+      "! '",
+      "ĠF ull",
+      "ye on",
+      "Ġwe igh",
+      "ĠJO HN",
+      "Ġv os",
+      "Ġproced ures",
+      "Ġaddress ed",
+      "ĠBer lin",
+      "put er",
+      "ĠB an",
+      "Ġmedic ation",
+      "Ġdr one",
+      "ĠÑĥ б",
+      "ĠJe an",
+      "Ġcap s",
+      "Ġdisappoint ed",
+      "Ġw ore",
+      "Ġêµ Ń",
+      "Ġorgan ize",
+      "ĠHall oween",
+      "Ġfant asy",
+      "y ard",
+      "Ġnos otros",
+      "Ġjump ed",
+      "Ġphot ography",
+      "ĠN ame",
+      "re c",
+      "A B",
+      "Ġbless ing",
+      "ĠSh ut",
+      "Ġbit ter",
+      "p op",
+      "ãģĿ ãĤĮ",
+      "Ġde i",
+      "Ġfulf ill",
+      "çIJ Ĩ",
+      "Ġden gan",
+      "Ġbe lo",
+      "ĠMean while",
+      "Ġdep ois",
+      "Ġdi abetes",
+      "Ġbu nd",
+      "ĠZe aland",
+      "Ġdig est",
+      "Ġt ires",
+      "Ġdo d",
+      "ag ne",
+      "ế t",
+      "Ġpe el",
+      "Ġз аб",
+      "Ġn odes",
+      "Ġtrend s",
+      "ĠSw itch",
+      "ĠA ward",
+      "ĠOr ig",
+      "ĠH al",
+      "Ġest as",
+      "Ġ3 60",
+      "Ġsim ult",
+      "Ġcom ic",
+      "Ġm Ãł",
+      "Ġbal anced",
+      "ĠPrin cess",
+      "Ġkilomet ers",
+      "á» ©",
+      "Ġpart ir",
+      "ì¤ ij",
+      "so ft",
+      "ĠV iew",
+      "Ġbi ological",
+      "in st",
+      "4 4",
+      "Ġman era",
+      "Ġcompreh ensive",
+      "ĠS ab",
+      "Ġcr imes",
+      "y ers",
+      "ĠComp any",
+      "ĠPh ot",
+      "Ġpou co",
+      "i ac",
+      "Ġbe im",
+      "in ate",
+      "Ġsub sequ",
+      "ĠMay or",
+      "Ġcent uries",
+      "è res",
+      "ìŀĸ ìķĦìļĶ",
+      "Ġê·¸ëŁ ¼",
+      "ĠFra u",
+      "ĠO H",
+      "Ġëģ Ŀ",
+      "ĠN ah",
+      "ĠSer ies",
+      "Ġover night",
+      "íĴ Ī",
+      "ĠâĢ ¢",
+      "Ġtra ve",
+      "atter ed",
+      "Ġwar ri",
+      "ĠGru nd",
+      "ĠInd ones",
+      "Ġsc ra",
+      "ob y",
+      "ĠBro ok",
+      "Ġcur s",
+      "Ġë ¸",
+      "Ġexpl ains",
+      "ram atic",
+      "Ġparticip ating",
+      "Ġmin ut",
+      "Ġcontract s",
+      "Ġg egen",
+      "Ġdisappe ared",
+      "ĠS N",
+      "Ġrob ust",
+      "ap h",
+      "Ġsh rim",
+      "Ġdev ast",
+      "c ope",
+      "Ġme ets",
+      "Ġpeace ful",
+      "m ate",
+      "Ġwe ld",
+      "Ġ× ª",
+      "d on",
+      "Ñĥ ÑĤÑĮ",
+      "Ġregister ed",
+      "ĠN ik",
+      "j in",
+      "Ġc av",
+      "Ġe cht",
+      "io x",
+      "Ġflow ing",
+      "но ÑģÑĤи",
+      "Ġto e",
+      "Ġent ity",
+      "ов а",
+      "f its",
+      "ĠPat rick",
+      "ÑĤ ÑĢ",
+      "Ġle verage",
+      "Ġcor rel",
+      "i ah",
+      "Ġstr ings",
+      "ist inct",
+      "Ġg ue",
+      "arch y",
+      "Ġteng o",
+      "ım ız",
+      "Ġor bit",
+      "ä¸ º",
+      "Ġе ÑīÑij",
+      "ca ke",
+      "Ġ׾ ×Ķ",
+      "ĠMin nesota",
+      "Ġbra ke",
+      "ow ie",
+      "Ġcra w",
+      "ê¸°ë ¥¼",
+      "Ġprogram me",
+      "ĠÑģл ÑĥÑĩ",
+      "åı ª",
+      "ien ces",
+      "ĠO ui",
+      "ĠP ers",
+      "im iento",
+      "ĠIn vest",
+      "Ġsl ower",
+      "æĻĤ åĢĻ",
+      "ĠB eth",
+      "Ġnur se",
+      "ĠSpr ing",
+      "S p",
+      "Ġun employ",
+      "д и",
+      "Ġgen ius",
+      "ĠA aron",
+      "Ġê·¸ëŁ ¬",
+      "Ġe i",
+      "ãģĹ ãĤĩ",
+      "Ġtank s",
+      "Ġau jourd",
+      "Ġcomplex ity",
+      "ĠÑĢ еÑĪ",
+      "Ġold est",
+      "Ġlet z",
+      "åħ ¥",
+      "Ġphenomen on",
+      "pr int",
+      "ĠBund es",
+      "it at",
+      "ê» ĺ",
+      "Ġ4 2",
+      "ĠW i",
+      "Ġinc om",
+      "Ġg ek",
+      "Ġembr ace",
+      "Ġt ies",
+      "out e",
+      "Ġd ose",
+      "ĠF riends",
+      "Ñĭ ÑĤ",
+      "егод нÑı",
+      "Ġor g",
+      "Ħë ¡ľ",
+      "ó g",
+      "Ġex ceed",
+      "Ġgod s",
+      "Ġê±° ìĺĪìļĶ",
+      "Ġsoci et",
+      "ĠUn ivers",
+      "it ät",
+      "Ġword en",
+      "Ġsm oking",
+      "Ġint ens",
+      "ab ul",
+      "em ia",
+      "è ij",
+      "4 7",
+      "f ly",
+      "Ġ200 6",
+      "ĠSer iously",
+      "Ġprze z",
+      "æ ¼",
+      "c re",
+      "Ġn an",
+      "Ġmod es",
+      "ов аÑĤÑĮ",
+      "ĠH ang",
+      "em en",
+      "Ġbenefic ial",
+      "Ġvot ers",
+      "ĠBro ad",
+      "Ġb ent",
+      "W ow",
+      "Ġm ul",
+      "åĵ ¥",
+      "ĠU C",
+      "Ġdam aged",
+      "ĠUk raine",
+      "Ġw ipe",
+      "Ġst ones",
+      "Ġman agers",
+      "Ġr ab",
+      "ÑģÑĤÑĢ о",
+      "l at",
+      "Ġde ce",
+      "Ġgraph ic",
+      "Ġf oss",
+      "Ġdisag ree",
+      "ĠAm en",
+      "Ġsec rets",
+      "ho le",
+      "ink le",
+      "Ġfortun ate",
+      "Ġì ±",
+      "ìľ Ħ",
+      "èIJ ¬",
+      "Ġhab its",
+      "Ġbur ied",
+      "Ġh in",
+      "Ġvirt ually",
+      "ol as",
+      "ĠR P",
+      "ĠT ab",
+      "l ow",
+      "Ġsacr ific",
+      "Ġestim ated",
+      "ol n",
+      "Ù ĭ",
+      "c ur",
+      "ĠFe el",
+      "Ġcast le",
+      "Ġus eless",
+      "Ġdis g",
+      "ĠJac ob",
+      "Ġga an",
+      "Ġup side",
+      "Ġpare ce",
+      "ãĥ³ ãĥ",
+      "Ġsh ipping",
+      "ĠC R",
+      "Ġdis rupt",
+      "ac ter",
+      "UN D",
+      "f u",
+      "å® Į",
+      "ĠP ick",
+      "ĠChar l",
+      "ĠB ull",
+      "Ġenter prise",
+      "Ġpunish ment",
+      "ack ing",
+      "Ġfr action",
+      "Ġtab let",
+      "Ġch ord",
+      "Ġsimilar ly",
+      "åħ¶ 實",
+      "ĠTor onto",
+      "Ġcour ts",
+      "ÄŁ l",
+      "esz cze",
+      "Ġpron oun",
+      "ĠS ister",
+      "ĠM P",
+      "Ġgreat ly",
+      "ĠD ank",
+      "ic op",
+      "Ġgar bage",
+      "Ġresol ve",
+      "ĠS af",
+      "ĠG un",
+      "Ġcomp ound",
+      "Ġë° °",
+      "ĠMus ik",
+      "âĻ «",
+      "Ġcha os",
+      "ĠWhen ever",
+      "Ġe uros",
+      "Ġor chest",
+      "Ġrefr iger",
+      "al an",
+      "ภ·",
+      "ĠAm azing",
+      "Ġp ud",
+      "ag an",
+      "Ġj eszcze",
+      "is y",
+      "Ġaccur acy",
+      "ĠA ma",
+      "is ode",
+      "ë ĮĢ",
+      "Ġinterpret ation",
+      "ĠL iber",
+      "æ ·",
+      "c am",
+      "Ġevol ved",
+      "ĠK ay",
+      "ÑĨ Ñĭ",
+      "Ġcreat or",
+      "it as",
+      "Ġal arm",
+      "Ġcelebr ation",
+      "z ent",
+      "Ġfun cion",
+      "Ġo v",
+      "umb ling",
+      "Ġ %",
+      "ภĪ",
+      "Ġrestrict ions",
+      "Ġн ав",
+      "ĠK inder",
+      "Ġban ana",
+      "ÑĮ Ñı",
+      "Ġdiam eter",
+      "Ġnor thern",
+      "ur ers",
+      "ĠP as",
+      "æĪij çļĦ",
+      "Ġwork force",
+      "Ġj ung",
+      "Ġguar ante",
+      "Ġequ ilib",
+      "Ġsu ite",
+      "Ġeu ro",
+      "Ġdel iber",
+      "S te",
+      "Ġdownt own",
+      "Ġch in",
+      "Ġc odes",
+      "ed ia",
+      "Ġshe ep",
+      "res hold",
+      "wn ie",
+      "ó b",
+      "Ġunder lying",
+      "l ia",
+      "j er",
+      "ÏĢ ÏĮ",
+      "ç Ŀ",
+      "th rop",
+      "Ġz ap",
+      "Ġvac uum",
+      "ĠH ab",
+      "Ġwra pped",
+      "ì ¢",
+      "Ġinvent ory",
+      "м а",
+      "Ġco ord",
+      "Ġpl ates",
+      "Ġsy mm",
+      "T e",
+      "ĠwÅĤa ÅĽnie",
+      "Ġreach es",
+      "Ġlon ely",
+      "S cript",
+      "le e",
+      "ess er",
+      "Ġê± ¸",
+      "ĠGes ch",
+      "ĠMo ving",
+      "Ġré p",
+      "ĠV ill",
+      "åIJ Ī",
+      "ĠR achel",
+      "Ġtem os",
+      "ON E",
+      "Ġstra in",
+      "Ġang el",
+      "Ġf Ã¥",
+      "T r",
+      "Ġach o",
+      "Ġhighlight s",
+      "ĠW er",
+      "ĠCar l",
+      "Ġbl ur",
+      "Ġreg ards",
+      "Â ·",
+      "ил ÑģÑı",
+      "Ġrec re",
+      "ĠY ani",
+      "U CK",
+      "ł ¸",
+      "Ġelectr ons",
+      "ĠSp iel",
+      "Ġv ed",
+      "Ú ¾",
+      "Ġbe am",
+      "Ġid iot",
+      "ë ĵ¤",
+      "на Ñĩ",
+      "id d",
+      "Ġsk i",
+      "it ative",
+      "Ġhyp othes",
+      "ãģ§ãģĻ ãģŃ",
+      "ent er",
+      "ĠìķĦëĭĪ ë",
+      "Ġih re",
+      "Ġpre view",
+      "ang el",
+      "Ġdem on",
+      "Ġd us",
+      "Ġd ic",
+      "ĠK om",
+      "LE Y",
+      "... !",
+      "Ġsie ht",
+      "ĠSon ic",
+      "Ġten ho",
+      "an as",
+      "Ġdig it",
+      "ĠMa ar",
+      "Ġunder grad",
+      "oun cer",
+      "uff y",
+      "Ġconvers ion",
+      "Ġdis connect",
+      "Ġe cho",
+      "om er",
+      "Ġcurric ulum",
+      "Ġper ché",
+      "Ġw and",
+      ".. ?",
+      "Ġroll ed",
+      "Ġentreprene ur",
+      "Ġtheore t",
+      "ĠÑī о",
+      "Ġins ights",
+      "Ġzus ammen",
+      "o in",
+      "ret t",
+      "p rodu",
+      "Ġvisit ors",
+      "e ous",
+      "Ġgrand mother",
+      "Ġhum or",
+      "Ġн иÑħ",
+      "zen ia",
+      "ins on",
+      "Ġres et",
+      "Ġbase ball",
+      "Ġmatch ing",
+      "ëĭ¤ ê°Ģ",
+      "Ġpun to",
+      "ì ¡",
+      "Ġre de",
+      "Ġaddress ing",
+      "Ġfore cast",
+      "ĠB ol",
+      "Ġcol ored",
+      "Ġdocument ation",
+      "Ġexpect ation",
+      "ĠNor thern",
+      "Ġcre o",
+      "Ġà® ļ",
+      "f on",
+      "Ġuns ere",
+      "U M",
+      "Ġcop ies",
+      "Ġexpand ed",
+      "Ġveter ans",
+      "ĠAl m",
+      "Ġво обÑīе",
+      "Ġpsych ological",
+      "Ġnos so",
+      "Ġpay ments",
+      "im eters",
+      "Ġ-- >",
+      "ĠJenn ifer",
+      "Ġvolunte ers",
+      "os se",
+      "or ious",
+      "ĠбÑĭ ли",
+      "è Ĥ",
+      "ĠEs s",
+      "w s",
+      "ĠB C",
+      "ĠI C",
+      "W oman",
+      "Ġv ont",
+      "Ġeth nic",
+      "EN N",
+      "им о",
+      "Ġlo b",
+      "Ġou i",
+      "c s",
+      "Ġre he",
+      "Ġìł ģ",
+      "Ġch ick",
+      "ús ica",
+      "Ġk ont",
+      "ĠDist rict",
+      "Ġp ile",
+      "Ġа в",
+      "ей ÑģÑĤв",
+      "ĠÂ £",
+      "Ġiss ued",
+      "Ġком п",
+      "Ġpros per",
+      "Ġprof ound",
+      "ĠDe ar",
+      "Ġãģ ĵ",
+      "Ġfund ed",
+      "Ġb isa",
+      "ŀ ĺë",
+      "× Ł",
+      "ĠìĿ ĺ",
+      "Ġtw elve",
+      "ĠChamp ions",
+      "éĿŀ 常",
+      "Ñģ л",
+      "Ġ200 5",
+      "p m",
+      "Ġon de",
+      "Ġdiff é",
+      "ĠCh all",
+      "Ġdifficult ies",
+      "Ġgar age",
+      "Ġd á",
+      "ün k",
+      "Ġë¬ ¼",
+      "Ġtr an",
+      "Ġsubm itted",
+      "z w",
+      "ÙĪ ا",
+      "Ġar k",
+      "ĠìĦ ±",
+      "Ġgrocer y",
+      "он а",
+      "i ere",
+      "Ġa est",
+      "Ġexhib ition",
+      "Ġr és",
+      "Ġconsist ency",
+      "Ġcook ie",
+      "н ей",
+      "Ġrepl acement",
+      "æ² ¹",
+      "ĠS em",
+      "ĠìĤ¬ ìļ©",
+      "8 00",
+      "Ġgen es",
+      "Ġtrans action",
+      "ĠE L",
+      "Ġdur ante",
+      "ib les",
+      "ĠE at",
+      "t ail",
+      "iss ance",
+      "Ġto ss",
+      "Ġsurv ived",
+      "Ġoff ices",
+      "Ġsupport ive",
+      "Wh ere",
+      "Ġtout es",
+      "Ġë§ ī",
+      "Ġj okes",
+      "ier on",
+      "ap ers",
+      "Ġm ature",
+      "ĠM arsh",
+      "Ġs ido",
+      "k ind",
+      "Ġreal mente",
+      "ĠChe f",
+      "Ġquel que",
+      "Ġjud ges",
+      "e ft",
+      "ER S",
+      "Ġj et",
+      "Ġpers ons",
+      "è »",
+      "iz ations",
+      "ri k",
+      "Ġsh ops",
+      "ĠW y",
+      "Ġele g",
+      "qu è",
+      "qu oi",
+      "Ġjug a",
+      "Ġíķľë ²Ī",
+      "ĠQuest ion",
+      "ĠGlo bal",
+      "Ġìķ½ ê°Ħ",
+      "ĠSt ation",
+      "æİ ¥",
+      "ĠOh io",
+      "Ġstick y",
+      "Ġst ressed",
+      "Ġg ün",
+      "Ġí Ŀ",
+      "ÑģÑĤ Ñĥп",
+      "é ¡Į",
+      "ĠPh D",
+      "im mer",
+      "Ġment or",
+      "Ġinv ented",
+      "Ġre un",
+      "Ġine vit",
+      "Ġpol ÃŃt",
+      "Ġexec ute",
+      "ĠSt ory",
+      "Ġout standing",
+      "Ġgu er",
+      "ĠR ain",
+      "Ġch oses",
+      "ĠT it",
+      "ĠÑģ еÑĢ",
+      "ĠSing apore",
+      "ĠN one",
+      "Ġch ronic",
+      "°ë į°",
+      "Ġe go",
+      "æł ·",
+      "ES T",
+      "ãģĤ ãĤĬ",
+      "ĠW ang",
+      "ĠN AT",
+      "Ġa ug",
+      "Ġdes ktop",
+      "Ġetern al",
+      "ĠìĤ¬ ìĭ¤",
+      "ĠConst itution",
+      "ìĤ ¬ë",
+      "×Ļ× ľ",
+      "p res",
+      "ĠТ Ñĭ",
+      "Ġinter f",
+      "Ġlist s",
+      "Ġfight s",
+      "ft en",
+      "ĠI owa",
+      "Ġmotiv ated",
+      "ĠH osp",
+      "Ġelse where",
+      "Ġpath s",
+      "Ġinst ances",
+      "B l",
+      "r ange",
+      "á» ±",
+      "ĠS it",
+      "man a",
+      "Ġìĭľ ìŀij",
+      "Ġm ình",
+      "ans as",
+      "Ġs na",
+      "Ġphilos oph",
+      "Ġpas se",
+      "Æ°á» Ŀi",
+      "ak h",
+      "ent al",
+      "Ġih n",
+      "ru ctor",
+      "Ġв аÑĪ",
+      "Ġgener ous",
+      "Ġp ivot",
+      "п ол",
+      "Ġjam ais",
+      "Ġcom ent",
+      "ĠL ew",
+      "od zi",
+      "ĠX box",
+      "Ġв од",
+      "Ġcons ent",
+      "ī ìŀ¥",
+      "Ġdis par",
+      "l ass",
+      "ĠGovern or",
+      "Be ifall",
+      "Ġê° ľ",
+      "Ġbelo ved",
+      "׳ ×ķ",
+      "se ll",
+      "Ġhon ored",
+      "le h",
+      "Ġw äre",
+      "un ting",
+      "Ġfra ud",
+      "ĠR AM",
+      "ê± ¸",
+      "Ġkill s",
+      "Ġeconom ics",
+      "0 4",
+      "п еÑĢ",
+      "Ġco isas",
+      "Ġи гÑĢ",
+      "ÃŃ m",
+      "Ġmö chte",
+      "Ġìµ ľ",
+      "Ġstim ul",
+      "Ġfast est",
+      "l v",
+      "Ġg én",
+      "ĠS ounds",
+      "Ġ19 70",
+      "Ġhome work",
+      "spe aking",
+      "Ġencour aging",
+      "Ġqu ery",
+      "Ġre vers",
+      "pro fit",
+      "Ġd y",
+      "Ġìŀ ij",
+      "ëĬĶëį° ìļĶ",
+      "Ġso ap",
+      "ĠG all",
+      "ĠC N",
+      "ĠAn s",
+      "Ġf ic",
+      "ank s",
+      "Ġdess ert",
+      "ĠìłĢ íĿ¬",
+      "ĠM aking",
+      "Ġcome ç",
+      "ê³ Ħ",
+      "Ġassoci ation",
+      "D ad",
+      "he e",
+      "Ġh ogy",
+      "Ġap ro",
+      "Ġinvis ible",
+      "Americ an",
+      "í İ",
+      "Ġvi be",
+      "Ġem issions",
+      "Ġadvoc ate",
+      "Ġkick ed",
+      "Ġ vel",
+      "Ġsum mar",
+      "Ġfre aking",
+      "ch ron",
+      "Ġpin ch",
+      "Ġwszyst k",
+      "isc al",
+      "Ġpro ved",
+      "Ġmind ful",
+      "Ġt ä",
+      "Ġno ises",
+      "Ġisol ated",
+      "Ġcross ed",
+      "Ġê° ķ",
+      "Ġvo ilÃł",
+      "Ġch ore",
+      "ĠR A",
+      "C om",
+      "Ġrelax ed",
+      "at ro",
+      "Ġpre vention",
+      "Voice over",
+      "O D",
+      "ĠCo vid",
+      "Ġsepar ation",
+      "Ġ- [",
+      "иÑĩ его",
+      "çĻ ¼",
+      "ĠS D",
+      "ble ep",
+      "Ġindepend ence",
+      "Ġpart ial",
+      "Ġalgorith ms",
+      "ĠAny one",
+      "Ġassoci ate",
+      "h um",
+      "ic ular",
+      "Ġb ạn",
+      "Ġbatt les",
+      "G ood",
+      "App lause",
+      "Ġbast ante",
+      "Ġadv ant",
+      "ĠS weet",
+      "Ġref used",
+      "ãĤ ¸",
+      "ĠÑĤеб е",
+      "pl et",
+      "Ġencour aged",
+      "åĵ ¦",
+      "Ġmir acle",
+      "ĠB un",
+      "ĠV ar",
+      "rim ination",
+      "e lect",
+      "ĠM ult",
+      "Ġdeliver ing",
+      "e ing",
+      "Ġc m",
+      "ne hmen",
+      "ĠL ine",
+      "Ġë§ Į",
+      "en ced",
+      "ĠS ound",
+      "ĠCont in",
+      "ij d",
+      "UN G",
+      "k le",
+      "Ġth reshold",
+      "Ġcomp act",
+      "ad t",
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+      "ĠP ur",
+      "own ed",
+      "ment ed",
+      "Ġdes igning",
+      "Ġvacc inated",
+      "Ġexha ust",
+      "Ġbas ics",
+      "Ġcons ists",
+      "ĠGu y",
+      "ac zy",
+      "Ġm ÃŃ",
+      "w on",
+      "å® ³",
+      "Ġ8 5",
+      "æ Ĥ",
+      "Ġm um",
+      "Ġign or",
+      "Ġprint ing",
+      "ac ular",
+      "p ow",
+      "Ġexpand ing",
+      "Ġg ir",
+      "ĠC ab",
+      "íĺ ¸",
+      "ÑĤÑĮ ÑģÑı",
+      "ĠìĹ¬ëŁ¬ë ¶Ħ",
+      "Ġang les",
+      "Ġterm inal",
+      "ĠW on",
+      "ĠInter esting",
+      "Ġcross ing",
+      "Ġbond s",
+      "Ġpu eden",
+      "Ġor b",
+      "lar ın",
+      "Ġcreep y",
+      "Ġnutr ition",
+      "Ġall ies",
+      "Ġwire less",
+      "Ġdes ired",
+      "Ġcomp ute",
+      "ĠAri zona",
+      "ĠBeaut iful",
+      "Ġprodu ces",
+      "Ġnuest ro",
+      "t ed",
+      "Ġel igible",
+      "ĠÑģ оз",
+      "ic ial",
+      "ĠH ero",
+      "Ġcons ume",
+      "Ġrob ots",
+      "Ġpurch ased",
+      "c ción",
+      "Ġ iz",
+      "ượ c",
+      "ίν αι",
+      "ĠØ£ ÙĨ",
+      "Ġshad ows",
+      "ĠMed ia",
+      "Ġprin cess",
+      "Ġk lar",
+      "Ġwood en",
+      "Ġus ar",
+      "Ġg üzel",
+      "Ġsl ot",
+      "r ade",
+      "Ġë Ĵ",
+      "Ġhar mon",
+      "Ġingred ient",
+      "ors hip",
+      "ek i",
+      "Ġgrand father",
+      "Ġexcit ement",
+      "Ġpolit icians",
+      ".. !",
+      "Ġout s",
+      "Ġsepar ately",
+      "ĠÑı к",
+      "ĠW elt",
+      "ĠP ow",
+      "j an",
+      "Ġorient ation",
+      "åı ĭ",
+      "L C",
+      "age m",
+      "ÛĮ Úº",
+      "åIJ Ĺ",
+      "Ġbran ches",
+      "ad en",
+      "rent e",
+      "ĠI hr",
+      "as m",
+      "Ġest ão",
+      "ĠN ic",
+      "Ġsla ve",
+      "Ġcomp ress",
+      "c rowd",
+      "Ġclim bing",
+      "ĠMan agement",
+      "ĠB ah",
+      "Ġpan ic",
+      "Ġk or",
+      "Ġcool ing",
+      "Ġb ind",
+      "Ġз ад",
+      "Ġr ack",
+      "Ġent it",
+      "Ġs ends",
+      "Ġyour selves",
+      "d es",
+      "ĠMuslim s",
+      "Ġí ļ",
+      "ism a",
+      "cy cle",
+      "un kt",
+      "ĠC ore",
+      "Ġinj uries",
+      "Ġident ical",
+      "ка Ñı",
+      "ĠDeutsch land",
+      "Ġе е",
+      "is an",
+      "Ġtr uc",
+      "let on",
+      "Ġback up",
+      "Ġult ra",
+      "Ġab und",
+      "ille urs",
+      "Ġby ÅĤo",
+      "åħ ĥ",
+      "ort ed",
+      "Ġearth qu",
+      "Ġк л",
+      "Ġobs ervation",
+      "Ġmainten ant",
+      "el en",
+      "Ġsett led",
+      "Ġp ela",
+      "ĠE conom",
+      "Ġ Õ",
+      "Ġste ering",
+      "ĠAL L",
+      "ĠC her",
+      "Ġpat ience",
+      "ĠS now",
+      "Ġb or",
+      "Ġworth y",
+      "Ġcá i",
+      "Ġ× §",
+      "Ġκ α",
+      "d og",
+      "ĠK aren",
+      "ill es",
+      "Î ²",
+      "Ġagric ulture",
+      "×ķ× Ł",
+      "ĠSe an",
+      "Ġsens ors",
+      "íķ ´ë",
+      "ag h",
+      "Ġpublic ly",
+      "Ġpe ux",
+      "ĠAlex ander",
+      "Ġprior it",
+      "Ġla zy",
+      "ard on",
+      "atter ing",
+      "Ġcost ume",
+      "س ت",
+      "è¿ ĺ",
+      "Ġun w",
+      "Ð Ľ",
+      "Ġthick ness",
+      "qu ito",
+      "g unt",
+      "ist as",
+      "ne ys",
+      "ĠëIJĺ ê²Į",
+      "ĠBr asil",
+      "Ġto ken",
+      "Ġaff ili",
+      "l on",
+      "Ġf Ã¥r",
+      "ĠBe ach",
+      "Ġw itch",
+      "ĠSe ven",
+      "Ġp ant",
+      "λ λ",
+      "Ġcapt ain",
+      "å Ŀ",
+      "Ġve ut",
+      "Ġpou voir",
+      "ac z",
+      "ĠBar b",
+      "Ġut ility",
+      "Ġcontempor ary",
+      "Ġobt ained",
+      "Ġpainting s",
+      "e ar",
+      "Ġpe an",
+      "ĠO g",
+      "Ġc ust",
+      "л ем",
+      "Ĥ ĺë",
+      "ĠIs so",
+      "Ġac onte",
+      "ĠTe le",
+      "ĠAss istant",
+      "Ã ī",
+      "íĸĪ ìĬµëĭĪëĭ¤",
+      "Ġcount s",
+      "Ġbu ck",
+      "ĠDe ep",
+      "Ġtack le",
+      "Ġh arsh",
+      "Ġdec ides",
+      "éĹ ľ",
+      ". âĢĭ",
+      "éĤ Ĭ",
+      "ĠAng el",
+      "Ġlay ing",
+      "Ġcal ories",
+      "Ġcontro lling",
+      "Ġadvant ages",
+      "ĠÑįÑĤ ой",
+      "Ġappro aching",
+      "Ġthreat s",
+      "ak an",
+      "em atic",
+      "m ann",
+      "ê³ µ",
+      "m umbles",
+      "ac ió",
+      "Ġmaint aining",
+      "Ġfound er",
+      "l ah",
+      "f ight",
+      "Ġadm itted",
+      "âĢ¦ .",
+      "ķ Į",
+      "ab ol",
+      "Ġus age",
+      "Ġn onsense",
+      "ĠPal est",
+      "Ġcont re",
+      "ĠDemocr atic",
+      "ĠE R",
+      "j ekt",
+      "Ġar bit",
+      "Ġг ол",
+      "ĠMich elle",
+      "ich er",
+      "es h",
+      "ĠP ho",
+      "к ом",
+      "4 9",
+      "ĠEner gy",
+      "ο Ïį",
+      "Ġc ents",
+      "Ġref ers",
+      "Ġg ospel",
+      "ĠSh a",
+      "ĠSh are",
+      "×Ļ× ł",
+      "Ġclin ic",
+      "ĠëĦ £",
+      "Ġequ ality",
+      "ug s",
+      "Ġsh ed",
+      "Ġplan es",
+      "Ġtout e",
+      "re ck",
+      "Ġstra nd",
+      "Ġbi ology",
+      "Ġle ague",
+      "ĠP ok",
+      "Ġnúmer o",
+      "ĠCo ast",
+      "Ġconsist ently",
+      "Ġnuc le",
+      "OO OO",
+      "Ġob jet",
+      "Ġch or",
+      "Ġg inger",
+      "Ġd abei",
+      "Ġcoop eration",
+      "à¯į .",
+      "nt en",
+      "ç ¤",
+      "l Ãł",
+      "ìĸ ij",
+      "r ado",
+      "Ġpass ive",
+      "Ġglo ves",
+      "Ġunder ground",
+      "Ġlog ical",
+      "Ġk et",
+      "Ġfunction ality",
+      "¸ë ¦¬",
+      "Ġport al",
+      "ell er",
+      "×Ļ× ¨",
+      "ĠT ed",
+      "ĠG re",
+      "IJ ľ",
+      "Ġperson nel",
+      "Ġemer ging",
+      "ĠF ür",
+      "Ġmeant ime",
+      "usal em",
+      "ĠC lear",
+      "Ġtra pped",
+      "Ġìļ °",
+      "Ġdis pl",
+      "Ġmet tre",
+      "Ġmun icip",
+      "Ġwithd raw",
+      "Ġsp at",
+      "un es",
+      "Ġaccess ibility",
+      "æĪij 们",
+      "Ġap are",
+      "Ġpros pect",
+      "Ġн аз",
+      "Ġcop per",
+      "ĠP RO",
+      "Ïħ ÏĦ",
+      "Ġattack ing",
+      "ĠV in",
+      "ĠSt one",
+      "Ġinvestig ate",
+      "st yle",
+      "ĠÎ »",
+      "ë ¡Ŀ",
+      "ë§ Ī",
+      "Ġins pect",
+      "Ġli ver",
+      "ал иÑģÑĮ",
+      "Ġser a",
+      "hal ten",
+      "em an",
+      "Ġmin istry",
+      "' '",
+      "Ġd ots",
+      "ãħĭãħĭ ãħĭãħĭ",
+      "Ñĥ ÑģÑĤ",
+      "ĠJ ak",
+      "AK E",
+      "Ġg aps",
+      "uck er",
+      "ĠинÑĤеÑĢ еÑģ",
+      "ĠEm ily",
+      "Ġinter val",
+      "Ġt ender",
+      "ĠTechn ology",
+      "g ame",
+      "Ġtri b",
+      "ÙĦ ا",
+      "ĠDevelop ment",
+      "Ùħ ا",
+      "Ġwr ist",
+      "Ġf ires",
+      "Ġtarget ed",
+      "ìł IJ",
+      "Ġso d",
+      "íļ Į",
+      "Ġoldu ÄŁ",
+      "Ġse asons",
+      "vent ions",
+      "Ġн его",
+      "Ġsomet ime",
+      "ли в",
+      "n é",
+      "Ġt ú",
+      "ĠDe us",
+      "Ġexec ution",
+      "á p",
+      "ĠCh ange",
+      "ĠInd eed",
+      "Ġreg ulation",
+      "ĠH ung",
+      "é is",
+      "Ġwish es",
+      "Ġj azz",
+      "Ġstruct ural",
+      "Ġblow ing",
+      "Ġby Äĩ",
+      "Ġtherm al",
+      "ph ant",
+      "ÑĢÑĥ з",
+      "ан ÑĤ",
+      "ĠP ull",
+      "Ġconf usion",
+      "нÑĭ ми",
+      "Ġscen arios",
+      "ìłģ ìľ¼ë¡ľ",
+      "Ġд еÑĤ",
+      "Ġtatto o",
+      "Ġaut re",
+      "Ġhe ating",
+      "Ġtreat ing",
+      "Ġпон им",
+      "Ġexc lus",
+      "ĠL OL",
+      "we ar",
+      "ag le",
+      "Ġzur ück",
+      "Ġr ational",
+      "s u",
+      "Ġdet er",
+      "ĠN ative",
+      "à®ķ ள",
+      "ach ed",
+      "Ġ ãĥ",
+      "ĠEnt onces",
+      "Ġhor a",
+      "ìĿ´ìĹIJ ìļĶ",
+      "Ġl ite",
+      "Ã «",
+      "Ġsix th",
+      "Ġбол ее",
+      "act or",
+      "Ġpsych ology",
+      "çĽ ¸",
+      "Ġdem ands",
+      "Ġpe er",
+      "Ġnew ly",
+      "ĠWW E",
+      "Don ald",
+      "ĠBo x",
+      "Ġp ine",
+      "Ġload ing",
+      "ĠN ico",
+      "Ġs ÅĤ",
+      "omm e",
+      "AR T",
+      "Ġrecru it",
+      "Ġbug s",
+      "arent s",
+      "ĠпÑĢ об",
+      "ĠIn side",
+      "ipp er",
+      "d ramatic",
+      "Ġplan ets",
+      "ord e",
+      "Ġy oga",
+      "ch ild",
+      "ĠMar ie",
+      "Ġãģ Ĥ",
+      "ĠB L",
+      "Ġfil med",
+      "Ġref resh",
+      "Ġtomato es",
+      "Ġf et",
+      "Qu é",
+      "Ġ !!",
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+      "Ġt in",
+      "ino is",
+      "ë ¶Ħ",
+      "×Ļ× Ł",
+      "ĠC AR",
+      "åIJ į",
+      "irst y",
+      "Ġout door",
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+      "ãħ İ",
+      "ff en",
+      "Ġl ud",
+      "H ello",
+      "Ġroll er",
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+      "ĠPol and",
+      "Ġap a",
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+      "Ġcertific ate",
+      "ĠT own",
+      "аÑİÑĤ ÑģÑı",
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+      "Ġdeterm in",
+      "P R",
+      "Ġfree ze",
+      "Ġmain stream",
+      "Ġobject ives",
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+      "Ġtak ie",
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+      "Ġë°Ķë ¡ľ",
+      "el et",
+      "ĠI V",
+      "ĠF ast",
+      "Ġd ere",
+      "em p",
+      "ĠD ra",
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+      "Ġdisc rimination",
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+      "æ ®",
+      "ıģ ı",
+      "Ġpost ing",
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+      "Ġl ub",
+      "Ġol ive",
+      "Ġr im",
+      "Ġmodel ing",
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+      "Ġover l",
+      "Ġinf lam",
+      "N E",
+      "ìĹIJ ê²Į",
+      "Ġatt ended",
+      "Ġdeal t",
+      "ĠAl t",
+      "ĠL incoln",
+      "Ġaw ake",
+      "Ġfil ters",
+      "ĠWith in",
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+      "Ġs û",
+      "ĠJohn ny",
+      "Ġintegr ity",
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+      "ĠAl ice",
+      "Ġsm iling",
+      "en ix",
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+      "Î ¶",
+      "Ġbeg un",
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+      "Ġtra va",
+      "Ġih m",
+      "Ġcru el",
+      "ĠAg ora",
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+      "Ġz ones",
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+      "Ġtr ay",
+      "×ķ× ł",
+      "Ġir rit",
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+      "ĠBe at",
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+      "ie lle",
+      "Ġtrust ed",
+      "Ġb ikes",
+      "ĠÑĥ п",
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+      "Ġher itage",
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+      "C ome",
+      "Ġf iring",
+      "ĠBu eno",
+      "ĠТ о",
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+      "ett es",
+      "Ġk es",
+      "Ġtri ps",
+      "Ġdivor ce",
+      "ĠK l",
+      "Ġcons ol",
+      "ke ep",
+      "기 ê°Ģ",
+      "ĠRep ort",
+      "Ġhost ing",
+      "Ġdiam ond",
+      "Ġcompl ic",
+      "Ġhel icop",
+      "Ġdep uis",
+      "d s",
+      "ĠCh an",
+      "Ñı л",
+      "Ġsc issors",
+      "il ation",
+      "Ġprop ortion",
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+      "ĠÙĪ اÙĦ",
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+      "u ation",
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+      "æĬ Ĭ",
+      "Ñı Ñī",
+      "Ġni in",
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+      "ET H",
+      "æ º",
+      "Ġconst itu",
+      "åĽ ½",
+      "Ġperform ances",
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+      "go v",
+      "ĠL iter",
+      "Ġincorpor ate",
+      "Ġeduc ate",
+      "ĠN in",
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+      "Ùĩ Ùħ",
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+      "am el",
+      "Ġê·¸ë łĩê²Į",
+      "ĠE ste",
+      "Ñħод иÑĤ",
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+      "Ġg ent",
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+      "ĠD ance",
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+      "Ġpregn ancy",
+      "Ġlo ops",
+      "ал оÑģÑĮ",
+      "ĠM oh",
+      "Ġcatch ing",
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+      "Ġ[ ?",
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+      "lo oking",
+      "Ġsurf aces",
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+      "0 8",
+      "Î ¾",
+      "K A",
+      "Ġ ży",
+      "Ġpick s",
+      "ann on",
+      "Ġbul k",
+      "ĠR oss",
+      "Ġdescri bing",
+      "ĠG el",
+      "Ġloc ally",
+      "Ġend less",
+      "Ġmass age",
+      "Ġclean ed",
+      "Ġtravel ed",
+      "ен Ñĭ",
+      "Ġsent iment",
+      "ig ma",
+      "ĠN as",
+      "Ġchemical s",
+      "Ġright eous",
+      "ĠMag ic",
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+      "Ġtruck s",
+      "Ġ19 60",
+      "åĪ ¥",
+      "Ġapp et",
+      "Ġsn acks",
+      "ĠSum mer",
+      "Ġy üz",
+      "Ġpr is",
+      "ĠMex ican",
+      "Ġtransp aren",
+      "Ġminor ity",
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+      "Ġl assen",
+      "4 6",
+      "л ек",
+      "é p",
+      "ĠÑĦ илÑĮ",
+      "Ġi yi",
+      "Ġsp an",
+      "íķĺ ì§Ģ",
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+      "qu ar",
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+      "ĠLGB T",
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+      "Ġmin ist",
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+      "ĠR ap",
+      "Ġcons ervation",
+      "çĽ ´",
+      "ĠH oney",
+      "ĠBe i",
+      "id el",
+      "Ġrespons ibilities",
+      "Ġmess y",
+      "ĠEx cept",
+      "OR E",
+      "Ġiniti atives",
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+      "Ġdesign ers",
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+      "Ġindic ates",
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+      "Ġemploy er",
+      "å® ī",
+      "Ġcous in",
+      "Ġbo iling",
+      "Ġch rom",
+      "Ġç al",
+      "Ġper pet",
+      "Ġcont ained",
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+      "Ð «",
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+      "ĠStart ing",
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+      "è¥ ¿",
+      "Ġz d",
+      "Ġfranch ise",
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+      "Ġint uit",
+      "ĠG L",
+      "re ach",
+      "ĠE lle",
+      "Ġnh Æ°",
+      "ĠN ord",
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+      "0 7",
+      "Ġple asant",
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+      "v iron",
+      "Ġgrad ient",
+      "z us",
+      "ĠE M",
+      "Ġess ay",
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+      "ế n",
+      "n u",
+      "á» «",
+      "ĠÃī s",
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+      "ĠGirl s",
+      "Ġperson nes",
+      "ĠاÙĦØ £",
+      "b ild",
+      "ĠSt at",
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+      "ĠK ate",
+      "Ġoptim al",
+      "Ġh id",
+      "د ÙĬ",
+      "Ġquick er",
+      "w all",
+      "E n",
+      "IN E",
+      "?? ?",
+      "ì² ´",
+      "ĠA ction",
+      "å Ł",
+      "Ġpenal ty",
+      "ĠK az",
+      "' ?",
+      "Ġc ried",
+      "Ġcan vas",
+      "ft e",
+      "Ġexc lud",
+      "¸ë ¡ľ",
+      "Ġemphas is",
+      "Ġen zy",
+      "ĠH ou",
+      "Ġoverse as",
+      "ÃŃ amos",
+      "å¸ «",
+      "ö glich",
+      "Ġhead phones",
+      "c n",
+      "ĠA ge",
+      "Ġa kan",
+      "Ġcharacter istic",
+      "íķĺë ©´",
+      "get s",
+      "Ġë¶ Ī",
+      "Ġr ival",
+      "Ġb orders",
+      "em ente",
+      "em ás",
+      "Ġy ol",
+      "Ġcom pe",
+      "end ers",
+      "ınd an",
+      "Ġmö glich",
+      "Ġbubb les",
+      "nat ural",
+      "Ġar med",
+      "Ġel abor",
+      "ĠìĿ´ë ²Ī",
+      "Ġwash ed",
+      "οÏħ με",
+      "è« ĭ",
+      "Ġfl avors",
+      "Ġexist e",
+      "Ġpre st",
+      "ĠThe ma",
+      "оп ÑĢоÑģ",
+      "er on",
+      "U E",
+      "er i",
+      "Ġconc er",
+      "Ġa ixò",
+      "åħ ©",
+      "Ġprotect ive",
+      "Ġзна Ñİ",
+      "ĠëĤ ł",
+      "ĠII I",
+      "Ġme er",
+      "ĠSh op",
+      "ll i",
+      "ĠOr der",
+      "ĠM Y",
+      "ĠG host",
+      "ãĤĤ ãģĨ",
+      "ad el",
+      "Ġst ole",
+      "Ġrele asing",
+      "ĠCom ment",
+      "Ġtra ins",
+      "ë ªħ",
+      "Ġw issen",
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+      "Ġdesc end",
+      "Ġf ier",
+      "Ġrad i",
+      "Ġpers u",
+      "ç ¢",
+      "Ġм н",
+      "ĠD est",
+      "Ġwor ries",
+      "it et",
+      "b as",
+      "Ġst ab",
+      "n ame",
+      "or ic",
+      "ĠCl ose",
+      "Ġalum ni",
+      "ĠS elf",
+      "ff e",
+      "it ating",
+      "ather ine",
+      "ĠRight s",
+      "Ġell os",
+      "Ġwar rant",
+      "Ġn erve",
+      "Ġveget able",
+      "ĠTe il",
+      "Ġê°Ļ ìĿ´",
+      "R Y",
+      "Ġsustain ability",
+      "Ġste ht",
+      "Ġbr id",
+      "ada ÅŁ",
+      "Ġt v",
+      "Ġdur ation",
+      "Ġpesso a",
+      "Ġmet rics",
+      "Ġad am",
+      "c as",
+      "аÑĢ и",
+      "Ġev ident",
+      "Ġdisplay ed",
+      "Ø§Ø ¦",
+      "Ġre ck",
+      "ĠBudd ha",
+      "Ġde le",
+      "ĠDie go",
+      "os ph",
+      "Ġb la",
+      "ĠM ik",
+      "ul ator",
+      "Ġ200 1",
+      "Ġpromot ing",
+      "y ch",
+      "ĠE X",
+      "Ġlast ly",
+      "Ġout line",
+      "Ġspir its",
+      "Ġve ux",
+      "Ġsubt ract",
+      "ĠÅŁ imdi",
+      "Ġp ins",
+      "Ġbur ger",
+      "Ġmol to",
+      "Ġhab ÃŃa",
+      "Ġë° ĺ",
+      "ig u",
+      "er st",
+      "Ġn en",
+      "Ġbac on",
+      "it ious",
+      "Ġcar ries",
+      "Ġprom ises",
+      "nd e",
+      "ĠLe ft",
+      "ĠL im",
+      "æ £",
+      "Ġ4 4",
+      "Ġcare ers",
+      "Ġì£ ¼ë",
+      "Ġspeed s",
+      "qu é",
+      "m ad",
+      "mark et",
+      "is me",
+      "Ġ200 3",
+      "Ġre cess",
+      "ĠJ UD",
+      "Ġrac ist",
+      "ĠSch l",
+      "Ġpar ler",
+      "Ġot ros",
+      "ish es",
+      "Ġconvert ed",
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+      "ĠAr k",
+      "ĠCh ance",
+      "Ġelement ary",
+      "ε ν",
+      "ink s",
+      "Inter viewer",
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+      "al ah",
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+      "Ġrequest ed",
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+      "no ÅĽci",
+      "ou red",
+      "ĠSt aff",
+      "Ġst ain",
+      "ĠAl an",
+      "Ġv ere",
+      "ĠW inter",
+      "Ġdef ect",
+      "ied y",
+      "Ġbe ats",
+      "Ġh á",
+      "um n",
+      "o ons",
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+      "Ġse it",
+      "o ly",
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+      "vis or",
+      "Ġhand ful",
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+      "uc cess",
+      "Ġup stairs",
+      "ĠSqu are",
+      "Ġhe in",
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+      "ol is",
+      "Ġpr ince",
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+      "Ñĸ й",
+      "Ġا ÙĨ",
+      "um ble",
+      "ê¹Į ìļĶ",
+      "Ġass ass",
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+      "Ġqual ities",
+      "Ġh mm",
+      "Ġbl own",
+      "ĠL iz",
+      "ĠK ur",
+      "ĠS A",
+      "Ġfind ings",
+      "Ġcol ours",
+      "Ġde lle",
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+      "ĠA th",
+      "ĠD ub",
+      "ĠO x",
+      "ĠØ ®",
+      "Ġpo ckets",
+      "Ġgr ill",
+      "Ġswitch ing",
+      "Ġprefer red",
+      "ĠW ales",
+      "Ġex emplo",
+      "Ġchop ped",
+      "Ġvacc ination",
+      "Ġne uro",
+      "Ġspec ify",
+      "iv os",
+      "Ġser á",
+      "Ġz ie",
+      "Ġà® ®",
+      "Ġresult ing",
+      "ĠU gh",
+      "Ġmess ed",
+      "C D",
+      "Ġpa ar",
+      "Ġcom er",
+      "Ġcou ch",
+      "ĠFest ival",
+      "Ġ4 9",
+      "v ous",
+      "z ens",
+      "ç¨ ®",
+      "ĠKenn edy",
+      "ĠT s",
+      "Ġë³´ì Ĺ",
+      "Ġdemonst ration",
+      "Ġun to",
+      "Ġfrust rating",
+      "Ġlabor atory",
+      "Ġe gy",
+      "Ġbeaut ifully",
+      "Ġìŀ ¬ë",
+      "Ġal gu",
+      "Ġö yle",
+      "ä½ł çľĭ",
+      "ĠP H",
+      "Ġfort une",
+      "Ġclean er",
+      "ĠRob in",
+      "Ġsa us",
+      "ĠG eld",
+      "Ġk at",
+      "o bs",
+      "Ġol ur",
+      "Ġm att",
+      "Ġquest a",
+      "Ġsuggest ion",
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+      "о ÑģÑĤ",
+      "Ġrad ar",
+      "Ġìŀ ¡",
+      "ish a",
+      "à® ¨",
+      "ãĤĵ ãģª",
+      "j es",
+      "Ġve el",
+      "ìĤ °",
+      "Ġauth ors",
+      "ãĢ İ",
+      "pl an",
+      "Ġcollabor ative",
+      "Ġinst inct",
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+      "au ge",
+      "E du",
+      "Ġmembers hip",
+      "Ġsimult aneously",
+      "Ġb ake",
+      "Ġk ä",
+      "Ġlect ures",
+      "Ñĩ еÑģ",
+      "Ġprend re",
+      "Ġcoll aps",
+      "ĠS aya",
+      "ĠF ut",
+      "Ġy og",
+      "ĠR ather",
+      "ر ÙĬ",
+      "Ġcamp s",
+      "ол од",
+      "Ġsim ulation",
+      "ĠM ak",
+      "La ughs",
+      "Ġgre y",
+      "Ġsent ences",
+      "y en",
+      "ĠUn less",
+      "J e",
+      "ĠSat an",
+      "ĠÑĤак же",
+      "ĠN A",
+      "Ġbr on",
+      "Ġ? ]",
+      "Ġsoul s",
+      "Ġlight ning",
+      "Ġimag ined",
+      "Ġczy li",
+      "ps ilon",
+      "et ta",
+      "Ġbelie ving",
+      "Ġstrong est",
+      "ĠC ON",
+      "Ġquel ques",
+      "Ġimmig rants",
+      "Ġwall et",
+      "éĢĻ æĺ¯",
+      "ĠJer sey",
+      "Ġimplic ations",
+      "Ġfor b",
+      "ãĢ ı",
+      "Ġun believable",
+      "Ø§Ø ¡",
+      "Ġoper ational",
+      "ü s",
+      "ĠG M",
+      "Ġê·¸ëŁ °ëį°",
+      "Ġgrac ias",
+      "Ġent end",
+      "ĠReg ard",
+      "ro b",
+      "ĠÑĤ еÑħ",
+      "è ı",
+      "ĠRev olution",
+      "Ġwa ar",
+      "ĠB iz",
+      "th eless",
+      "Ġspons ored",
+      "qu ier",
+      "ĠìĿ ¼ë",
+      "Ġte k",
+      "ĠëIJ ł",
+      "ig keit",
+      "ĠL uck",
+      "ĠCertain ly",
+      "Ġto ll",
+      "Ġн иÑĩего",
+      "ĠM oney",
+      "ĠÑģ ÑĤоÑĢ",
+      "ĠDou ble",
+      "ĠW olf",
+      "Ġch unk",
+      "ά ν",
+      "it és",
+      "on ing",
+      "M ar",
+      "Ġgrand es",
+      "Ġcollect ions",
+      "ĠEurop a",
+      "Ġа ÑĢ",
+      "ĠâĢĭâĢĭ âĢĭ",
+      "Ġê·¸ëŁ¬ë ©´",
+      "Ġоб ÑĬ",
+      "Ġãģ ª",
+      "Ġìĭľ ê°Ħ",
+      "ĠC ustom",
+      "Ġì² ĺ",
+      "Ñĸ лÑĮ",
+      "Ġindivid ually",
+      "í Ĺ",
+      "Ġdo zen",
+      "Ġo we",
+      "ĠVict oria",
+      "åı¯ èĥ½",
+      "Ġbe et",
+      "ur b",
+      "Ġanal og",
+      "i ção",
+      "Ĥ ľ",
+      "so ever",
+      "Ġmod o",
+      "Ġsubscri bed",
+      "ìŀ ¬",
+      "Ġent ities",
+      "çī ĩ",
+      "Ġclos et",
+      "Ġrespond ing",
+      "Ġprin ter",
+      "ĠStep han",
+      "Ġby ÅĤ",
+      "ĠD om",
+      "ĠF ern",
+      "ĠP ier",
+      "ĠwiÄĻ c",
+      "Ġh ence",
+      "Ġmod ules",
+      "ãĥ ¬",
+      "ĠëĶ ±",
+      "ĠDann y",
+      "ĠÑģеб е",
+      "Ġv ad",
+      "ĠìĹ Ħ",
+      "Ġs ous",
+      "Ġsp here",
+      "B Y",
+      "ĠP ed",
+      "ign ed",
+      "Ġwhe at",
+      "Ġund ers",
+      "Ġevol ve",
+      "Ġdec lar",
+      "Ġlight ly",
+      "Ġident ifying",
+      "æĦı æĢĿ",
+      "Ġlegend ary",
+      "Ġgen uine",
+      "Ġgr ind",
+      "ĠU ne",
+      "ge ben",
+      "Ġb icy",
+      "Ġjump s",
+      "Ġprov ince",
+      "zi ÄĻ",
+      "Ġ×IJ× ł×Ļ",
+      "Ġh oc",
+      "Ġб л",
+      "ĠGr ad",
+      "Ġreven ge",
+      "ĠاÙĦ ت",
+      "o oh",
+      "æĭ ľ",
+      "аÑĨи и",
+      "å¹ ³",
+      "Ġelect ro",
+      "ĠëIJ IJ",
+      "ãģ§ ãģ¯",
+      "Ġf als",
+      "ri el",
+      "ok er",
+      "ĠEx cellent",
+      "ĠMor gan",
+      "Ġbr ick",
+      "Ġsubstant ial",
+      "Ġpoll ution",
+      "ĠT ür",
+      "ĠEv et",
+      "Ġl ung",
+      "ãģ ĸ",
+      "×Ļ× ©",
+      "omm es",
+      "Ġreal izing",
+      "Ġhum ble",
+      "ĠL ock",
+      "Ġb od",
+      "Ġìĸ ¸",
+      "Ġpe ers",
+      "uz z",
+      "Ġembed ded",
+      "Ġclar o",
+      "Ġag greg",
+      "Ġemploy ers",
+      "ĠR aj",
+      "Ġãģ ¨",
+      "ĠY i",
+      "Ġje u",
+      "at ers",
+      "Ġstri kes",
+      "n os",
+      "aut res",
+      "d r",
+      "op her",
+      "ĠApp arently",
+      "íĺ Ħ",
+      "Ġinf ant",
+      "ا ب",
+      "ÑĤ Ñĭ",
+      "í Ľ",
+      "Ú ¯",
+      "Ġred es",
+      "acaģ ım",
+      "ĠDA VID",
+      "ĠCh icken",
+      "Ġperspect ives",
+      "Ġview er",
+      "Ġsh ar",
+      "ĠпÑĢо из",
+      "lig t",
+      "er os",
+      "it able",
+      "ил оÑģÑĮ",
+      "Ġdif ÃŃ",
+      "´ë į°",
+      "Ġret ired",
+      "Ġthat s",
+      "zen ie",
+      "be iten",
+      "Ġmy cket",
+      "ĠR ab",
+      "Ġinflam m",
+      "ì° ®",
+      "Ġd um",
+      "Ġdad dy",
+      "æľ Ł",
+      "Ġimm ers",
+      "Ġplay list",
+      "௠Ĩ",
+      "Ġtra um",
+      "Ġref use",
+      "st ep",
+      "à® ļ",
+      "c up",
+      "Ġpop s",
+      "r imin",
+      "ay ım",
+      "Ġa ld",
+      "Ġun necess",
+      "Ġd ah",
+      "ĠIr ish",
+      "Ġcomp r",
+      "la ÅŁ",
+      "T P",
+      "Ġtransl ated",
+      "S c",
+      "ce ÄŁim",
+      "´ IJ",
+      "Ġd rei",
+      "ĠлÑİд ей",
+      "Ġqu iero",
+      "Ġhe le",
+      "z lich",
+      "Ġapp les",
+      "Ġdistrict s",
+      "Ġcred its",
+      "Ġas p",
+      "Ġëĭ ¨",
+      "or al",
+      "å½ ±",
+      "Ġste pping",
+      "ĠV a",
+      "Ġg ains",
+      "6 5",
+      "Ġnuest ra",
+      "ed ay",
+      "ass ador",
+      "ĠL ind",
+      "Ġcrop s",
+      "ci endo",
+      "ig ue",
+      "Ġb ana",
+      "A m",
+      "Ġp ent",
+      "Ġadd iction",
+      "Ġpack aging",
+      "ä d",
+      "ª ¨",
+      "Ġper què",
+      "Ġcampaign s",
+      "Ġste ep",
+      "Ġne ue",
+      "Ġembarrass ed",
+      "Ġdist inction",
+      "it zer",
+      "åij Ĭ",
+      "Ġregist ration",
+      "Ġll am",
+      "ĠAlm ighty",
+      "li est",
+      "Ġu z",
+      "n ak",
+      "ç º",
+      "Ġter az",
+      "iam ente",
+      "Ġtrans actions",
+      "Ġc ôt",
+      "Ġswitch ed",
+      "Ġcom bo",
+      "Ġpray ers",
+      "Ġintern ship",
+      "Ġaddress es",
+      "Ġchar ity",
+      "ĠW OO",
+      "Ġb ait",
+      "è¿ ĩ",
+      "Ġ �",
+      "Ġf ica",
+      "ĠTy ler",
+      "ar u",
+      "Ġat oms",
+      "ĠLe vel",
+      "ĠпоÑĤ ом",
+      "Ġf ame",
+      "ul k",
+      "Ġteach es",
+      "Ġre build",
+      "ед ÑĮ",
+      "ĠIndones ia",
+      "ush i",
+      "ĠSh ort",
+      "Ġens uring",
+      "f s",
+      "e le",
+      "Ġmargin al",
+      "Ġconclud e",
+      "am t",
+      "Ġver ify",
+      "ĠMc Donald",
+      "Ġsk al",
+      "Ġrec onst",
+      "ĠM ann",
+      "Ġbas ement",
+      "Ġtransform ed",
+      "Ġoccasion ally",
+      "z one",
+      "ĠD ans",
+      "Ġкак ой",
+      "Ġdiagn osed",
+      "ĠÏĦ α",
+      "Ġcomm ands",
+      "Ġpresident ial",
+      "Ġab b",
+      "Ġbrack et",
+      "ĠL em",
+      "Ã¥ ng",
+      "Ġfavor ites",
+      "Ġrev ol",
+      "ĠíĬ ¹",
+      "Ġhar ass",
+      "é ħ",
+      "Ġcle ans",
+      "st änd",
+      "Ġknock ed",
+      "Ġpe oples",
+      "Ġmusic ians",
+      "Ġmut ual",
+      "ĠC old",
+      "8 8",
+      "ze j",
+      "at ie",
+      "ĠHon or",
+      "Ġobs essed",
+      "ĠM USIC",
+      "ĠBre ak",
+      "ú ng",
+      "Ġmod ify",
+      "Ġs öyle",
+      "Ġ×ŀ ×Ķ",
+      "ĠOn line",
+      "f o",
+      "ĠMill er",
+      "Ġlik ing",
+      "Ġin hab",
+      "Ġgrat itude",
+      "ĠJour nal",
+      "arn ess",
+      "J ohn",
+      "ĠG it",
+      "åī Ľ",
+      "Ġsin cere",
+      "ĠS ci",
+      "ĠE li",
+      "Ġsymbol s",
+      "Ġman ually",
+      "ε ÏĤ",
+      "Ġв Ñĸд",
+      "ĠF at",
+      "Ġlab els",
+      "Ġsophistic ated",
+      "ump s",
+      "Ġrele ases",
+      "Ġ4 7",
+      "ĠO M",
+      "ê°Ģ ë",
+      "ĠB ien",
+      "ĠRe f",
+      "è¨ ĺ",
+      "ĠSt a",
+      "ĠE gg",
+      "Ġindic ator",
+      "ps on",
+      "Ġnas ıl",
+      "R ight",
+      "Ġcon vey",
+      "Ġkn ot",
+      "Ġconnect s",
+      "ul as",
+      "Ġpre ced",
+      "Ġine quality",
+      "am iento",
+      "Ġrep ly",
+      "O Y",
+      "Ġdism iss",
+      "ĠëIJ ľ",
+      "çĦ ¡",
+      "ĠÑħоÑĢоÑĪ о",
+      "Ġm éd",
+      "Ġrandom ly",
+      "ĠO nt",
+      "u ard",
+      "Ġpull s",
+      "ĠÑĤ епеÑĢÑĮ",
+      "ĠNe ed",
+      "ĠSo ft",
+      "Ġstrength s",
+      "Ġgo ed",
+      "um en",
+      "æŃ »",
+      "Ġíİ ¸",
+      "Ġд об",
+      "Ġclar ity",
+      "ĠA i",
+      "Ġball oon",
+      "ĠP and",
+      "ĠìķĦ ëĭ",
+      "Ġsh iny",
+      "Ġsmall est",
+      "on ia",
+      "h ill",
+      "ot ing",
+      "Ġe ing",
+      "Ġmere ly",
+      "Ġse us",
+      "Ġн еп",
+      "Ġí Ĩµ",
+      "Ġgu ides",
+      "Ġspecial ist",
+      "Ġste ak",
+      "ãĤĪ ãģĨ",
+      "Ġmig ration",
+      "que le",
+      "Ġru ined",
+      "Ġpu pp",
+      "å¥ ³",
+      "Ġk end",
+      "ang an",
+      "Ġpal m",
+      "Ġunf air",
+      "Ġz m",
+      "ĠD V",
+      "ch ester",
+      "и Ñİ",
+      "Ġo oh",
+      "er g",
+      "AT H",
+      "° ©",
+      "åĵ ª",
+      "r ison",
+      "Ġinvol ving",
+      "Ġpart ly",
+      "anç ais",
+      "Ġv ow",
+      "Ġprom inent",
+      "Ġcry st",
+      "ib a",
+      "Ġdes erves",
+      "Ġover t",
+      "Ġsens it",
+      "ĠWh e",
+      "Ġtight en",
+      "Ġintim id",
+      "Ġal iment",
+      "w ill",
+      "Ġstrength en",
+      "ĠT an",
+      "åı Ī",
+      "ãģĹ ãģ¾ãģĻ",
+      "on i",
+      "ĠM un",
+      "Ġpro ph",
+      "Ġrehe ars",
+      "ĠK le",
+      "Ġve ces",
+      "Ġwonder ed",
+      "ok i",
+      "Ġsens es",
+      "´ì ĭ",
+      "Æ°á» Ľ",
+      "ĠÈĻ i",
+      "Ġmuch os",
+      "Ġwatch es",
+      "ortun ate",
+      "ĠJ uan",
+      "ìŀĸ ìķĦ",
+      "ÑĢ е",
+      "e i",
+      "ion en",
+      "Ġexperiment al",
+      "Ġda ughters",
+      "ภĽ",
+      "Ġment ally",
+      "bec ca",
+      "aw are",
+      "ìĦ Ŀ",
+      "Ġwhat soever",
+      "Ġen ables",
+      "ĠL ow",
+      "o id",
+      "ภĬ",
+      "ó d",
+      "Ø º",
+      "Ġconstruct ed",
+      "ĠLad ies",
+      "Ġaccus ed",
+      "Ġа н",
+      "D an",
+      "Ġsp awn",
+      "Ġcontain ers",
+      "Ġart istic",
+      "ı p",
+      "Ġdisc l",
+      "Ġaut res",
+      "in as",
+      "ĠN ation",
+      "Ġn ag",
+      "be an",
+      "w he",
+      "ľë ıĦ",
+      "ĠSe oul",
+      "Ġíı ¬",
+      "ĠN ich",
+      "Ġcomp lement",
+      "Ġinter ven",
+      "ĠMod el",
+      "ĠOr ange",
+      "nam on",
+      "Ġcalcul ation",
+      "se e",
+      "Ġusted es",
+      "Ġle b",
+      "Ġdo ct",
+      "Ñĸ н",
+      "Ġf oster",
+      "Ġel astic",
+      "ĠAh h",
+      "Ġa ce",
+      "ĠP ink",
+      "ĠJ eg",
+      "Ġde er",
+      "ãģĹ ãģĦ",
+      "s is",
+      "Ġjak o",
+      "ĠEm ma",
+      "ÑģÑĤв енно",
+      "Ġport rait",
+      "Ġmak er",
+      "Ġa ument",
+      "ÑĢ об",
+      "Ġairpl ane",
+      "Ġtransparen cy",
+      "Ġadjust ment",
+      "ĠCD C",
+      "ç on",
+      "Ġupload ed",
+      "Ġд ейÑģÑĤв",
+      "Ġго ÑĤов",
+      "Ġit er",
+      "Ġcur se",
+      "ô n",
+      "mer ce",
+      "ar an",
+      "Ġle ak",
+      "çµ IJ",
+      "Ġabs ence",
+      "Ñģ кий",
+      "Ġread ers",
+      "al er",
+      "Ġbene ath",
+      "ang o",
+      "h etic",
+      "Ġfin ns",
+      "Ġpo op",
+      "Ġdu plic",
+      "H i",
+      "ig s",
+      "olog ically",
+      "op p",
+      "Ġd izer",
+      "ĠAll en",
+      "Ġgl i",
+      "Ġacc eleration",
+      "Ġvit amin",
+      "ãĥ Ń",
+      "v ä",
+      "ĠAc cess",
+      "à® Ļ",
+      "r ás",
+      "Ġappreci ated",
+      "Ġn ah",
+      "Ġpos ter",
+      "Ġt ale",
+      "Ġhighlight ed",
+      "æĸ ĩ",
+      "ż eli",
+      "Ġblock chain",
+      "Ġmic row",
+      "Ġcin ema",
+      "ĠCh ang",
+      "ĠSe arch",
+      "ust ers",
+      "ĠZ ero",
+      "ĠDiv ision",
+      "ÑĢ аÑģ",
+      "Ġsca re",
+      "Ġj elly",
+      "ĠAdminist ration",
+      "S O",
+      "Ġl ined",
+      "Ġê° Ħ",
+      "Ġge ben",
+      "Ġso da",
+      "Ġwin ners",
+      "³ ¼",
+      "Ù Ĵ",
+      "ĠAm b",
+      "åķı é¡Į",
+      "å Ķ",
+      "Ġpe g",
+      "å· ±",
+      "4 3",
+      "Ġra us",
+      "Ġre wards",
+      "Ġinc lus",
+      "Ġhigh way",
+      "Ġha h",
+      "Ġmultipl ied",
+      "Ġs ẽ",
+      "Ġdisci ples",
+      "Ġn ing",
+      "Ġdress ing",
+      "Ġattrib utes",
+      "ĠM osc",
+      "ĠGree ce",
+      "Ġse k",
+      "ĠLe arn",
+      "Ġj us",
+      "rend re",
+      "Ġperson ne",
+      "pl ete",
+      "Ġpl acing",
+      "Ġl uego",
+      "ill ance",
+      "Ġоб Ñī",
+      "Ġprov ision",
+      "Ġl ion",
+      "t ra",
+      "bo ards",
+      "Ġbehavi our",
+      "he y",
+      "Ġsubscri ption",
+      "Ġprot agon",
+      "ãĥ £",
+      "Ġvar a",
+      "ĠÅŁ u",
+      "Ġha ha",
+      "Ġteas poon",
+      "æ Ł",
+      "av oir",
+      "Ġcrypt o",
+      "ĠÑģÑĤ аÑĢ",
+      "ĠSt ore",
+      "ab s",
+      "ĠStud ents",
+      "Ġla und",
+      "int o",
+      "Ġapproach ed",
+      "° ľ",
+      "ÑĥÑİ Ñī",
+      "ĠL abor",
+      "ot es",
+      "iat ric",
+      "Ġgro ÃŁ",
+      "ut ive",
+      "Ġи д",
+      "ĠG ib",
+      "Ġpl acement",
+      "ĠdifÃŃ cil",
+      "Ġf rog",
+      "ĠвÑģе Ñħ",
+      "ĠJ r",
+      "az ed",
+      "Ñĥ Ñī",
+      "Ġê ¼",
+      "fr ame",
+      "а еÑĪÑĮ",
+      "Ġlock down",
+      "åij ³",
+      "Ġmed i",
+      "Ġ×Ķ× ŀ×",
+      "ени й",
+      "em ale",
+      "ì¢ ħ",
+      "ater al",
+      "Ġdist ant",
+      "Ġbe ars",
+      "Ġjournal ist",
+      "è§ £",
+      "ĠMarsh all",
+      "ĠIh nen",
+      "uet ooth",
+      "b ag",
+      "ĠÄij ã",
+      "ĠHigh ness",
+      "Ġì° į",
+      "и ка",
+      "ĠW u",
+      "ĠFr an",
+      "Ġp eng",
+      "Ġf on",
+      "Ġhypothes is",
+      "ĠÑĢ Ñĥ",
+      "Ġl y",
+      "× ļ",
+      "ìĽ Ķ",
+      "ĠRad io",
+      "ภŀ",
+      "D av",
+      "Ġembarrass ing",
+      "ĠìŀĪ ìĸ´",
+      "Ġcast ing",
+      "Ġc age",
+      "ĠP sych",
+      "ĠìĿ¼ ëĭ¨",
+      "ĠÅ ¾",
+      "im b",
+      "Ġdirect ors",
+      "S H",
+      "ĠÏĦη ν",
+      "á»ģ u",
+      "Ġkon uÅŁ",
+      "Ġoption al",
+      "quar ters",
+      "ik er",
+      "ĠS ant",
+      "Ġvers es",
+      "ë ¶Ģ",
+      "Ġo lar",
+      "ĠÏ ĩ",
+      "ãĥ ķ",
+      "Ġγ ια",
+      "ĠI mm",
+      "Ġcontrovers ial",
+      "Ġer sten",
+      "Ġreci p",
+      "ĠChristian ity",
+      "Ġê´ ľ",
+      "ord on",
+      "×ķ× ©",
+      "Ġsl ash",
+      "ĠP f",
+      "Ñĥд ÑĮ",
+      "×ķ× Ŀ",
+      "ĠPer ry",
+      "Ġm amy",
+      "Ġbackground s",
+      "Ġà®İ ன",
+      "Ġpend ant",
+      "ĠColumb ia",
+      "Ġin verse",
+      "ĠÑĩеÑĢ ез",
+      "Ġs v",
+      "Ġdig ging",
+      "4 1",
+      "ch em",
+      "Ġnavig ation",
+      "ĠSh in",
+      "ĠFr ont",
+      "P D",
+      "Ġbe aring",
+      "ĠW asser",
+      "Ġw ax",
+      "ĠCH RIS",
+      "ch ing",
+      "Ġpress ed",
+      "E l",
+      "ĠD al",
+      "ons in",
+      "Ġb inding",
+      "Ñģк ой",
+      "po ons",
+      "Ġmo ck",
+      "are st",
+      "к ÑĢа",
+      "M M",
+      "Ġcor rupt",
+      "st orm",
+      "Ġref res",
+      "ĠCo ach",
+      "ll ä",
+      "ĠTH IS",
+      "Ġpar ag",
+      "Ġìĵ °",
+      "p ool",
+      "Ġbill ions",
+      "Ġê¹ Ģ",
+      "gr oup",
+      "Ġwel coming",
+      "cell ence",
+      "ĠDu ke",
+      "ê¸ ´",
+      "Ġprim era",
+      "ìł ¸",
+      "Ġp ond",
+      "Ġstat ue",
+      "Ġêµ ¬ë",
+      "Ġh atch",
+      "Ġinstrument al",
+      "Ġresident ial",
+      "ì» ¤",
+      "Ġaccept ing",
+      "osh i",
+      "d ate",
+      "ĠìĶ ¨",
+      "Ġplant ed",
+      "Ġj oking",
+      "Ġì Ħľ",
+      "Ġh ated",
+      "ĠÑĢаÑģ Ñģк",
+      "Ġsle pt",
+      "Ġpack ages",
+      "Ġisland s",
+      "es en",
+      "ģ ı",
+      "Ġdi agon",
+      "ĠO sc",
+      "Ġmes h",
+      "Ġsc ales",
+      "ar ity",
+      "ĠDef ense",
+      "ãģ¡ ãĤĩ",
+      "ĠLew is",
+      "ĠÑģ егоднÑı",
+      "Ġfl ies",
+      "uin ely",
+      "ĠCons ider",
+      "Ġst ark",
+      "he w",
+      "ĠAs ÃŃ",
+      "³ ´ë",
+      "Ġprop ose",
+      "Ġíķĺë ©´",
+      "od o",
+      "ĠNorm ally",
+      "Ġhe eft",
+      "ĠHarr is",
+      "g ro",
+      "ĠBlo od",
+      "b ase",
+      "Ġi OS",
+      "Ġtouch es",
+      "Ġinsp ir",
+      "Ġ× ĵ",
+      "Ġb inary",
+      "Ġì¶ Ķ",
+      "Ġser ial",
+      "Ġ ion",
+      "Ġunemploy ment",
+      "Ġodd s",
+      "ĠF ab",
+      "ĠF BI",
+      "BR UN",
+      "Ġweight s",
+      "ν ο",
+      "at ile",
+      "Ġnurs es",
+      "Ġinvolve ment",
+      "ĠíĶ ¼",
+      "Ġgovern ance",
+      "Ġâ Ĥ¬",
+      "ÑĢÑĥ п",
+      "ier ra",
+      "íĺ ķ",
+      "ĠJ erry",
+      "Ġbe ard",
+      "Ġsal vation",
+      "ĠAl ong",
+      "g entle",
+      "ĠK i",
+      "b ol",
+      "ĠPl at",
+      "Ġhas ht",
+      "è¿ ij",
+      "Ġw are",
+      "Ġpart ie",
+      "y cz",
+      "Ġint r",
+      "F ih",
+      "n ent",
+      "Ġche at",
+      "il en",
+      "Ġë ¯",
+      "or ie",
+      "Ġfá cil",
+      "et ric",
+      "Ġaffect ing",
+      "unci ation",
+      "Ġaff airs",
+      "Ġbe e",
+      "Ġview ing",
+      "Ġor ang",
+      "ĠL an",
+      "ĠС ÑĤ",
+      "ä¸ ĸ",
+      "ĠM es",
+      "ĥ ģ",
+      "er ie",
+      "Ġes pa",
+      "Ġinter pre",
+      "Ġposs ess",
+      "Ġpure ly",
+      "rit o",
+      "f ound",
+      "as ma",
+      "ìłģ ìĿ¸",
+      "Ġexam ine",
+      "ĠÑĥ м",
+      "Ġbes ch",
+      "ĠTom orrow",
+      "ĠB lock",
+      "Ġvari ant",
+      "Ġprefer ence",
+      "Ġcoach es",
+      "Ġmedic ations",
+      "Ġíĺ Ħ",
+      "Ġemp ire",
+      "ë Ħ¤",
+      "ĠIll inois",
+      "Ġcris py",
+      "Ġth ì",
+      "Ġbe es",
+      "7 7",
+      "Ġgl ow",
+      "è º",
+      "ĠStud ies",
+      "åIJ Ħ",
+      "ĠChall enge",
+      "Ġunlike ly",
+      "Ð §",
+      "ıy orsun",
+      "DI E",
+      "Ġminim ize",
+      "iz ard",
+      "Ġú n",
+      "Ġencont rar",
+      "ĠK ill",
+      "å »",
+      "Ġvan illa",
+      "ĠGr ant",
+      "ĠG T",
+      "se a",
+      "Ġs ought",
+      "в од",
+      "Ġnä m",
+      "ĠA unt",
+      "OW N",
+      "Ġpump kin",
+      "st ellen",
+      "Ġr ag",
+      "ег да",
+      "Ġstory t",
+      "Ġfor um",
+      "æ© Ł",
+      "Ġestab a",
+      "uch e",
+      "Ġcon gress",
+      "ĠRe y",
+      "Ġdram atically",
+      "ĠSp ort",
+      "ĠYe llow",
+      "Ġê³Ħ ìĨį",
+      "Ġdisg usting",
+      "ĠRe cent",
+      "Ġacqu ired",
+      "Ġc ables",
+      "çĶ ļ",
+      "d in",
+      "Ġv isto",
+      "Ġcommunic ating",
+      "ÑģÑĤав лÑı",
+      "еÑģ ÑĤо",
+      "ãĥ»ãĥ» ãĥ»",
+      "Ġré g",
+      "Ġso cks",
+      "Ġpro ces",
+      "be cause",
+      "Ġut ter",
+      "Ġcoloc ar",
+      "Ġnew est",
+      "Ġgr amm",
+      "è¡ ¨",
+      "ä¸į çŁ¥éģĵ",
+      "Ġsh ifting",
+      "Ġcar rier",
+      "ĠÑģк оÑĢ",
+      "ĠSch w",
+      "Ġexec uted",
+      "Ġmaint ained",
+      "ĠÏ Ĩ",
+      "ĠM oses",
+      "Ġdis se",
+      "Ġhor r",
+      "ãĢ ľ",
+      "Ġr ally",
+      "Ġall em",
+      "ĠEvent ually",
+      "Ġdi yor",
+      "lv ania",
+      "Ġsch nell",
+      "Ġê³ ¼",
+      "Ġë§ ¤",
+      "Ġstrugg les",
+      "l ate",
+      "Ġclar ify",
+      "é ment",
+      "Ġmulti plic",
+      "иб о",
+      "Ġjour n",
+      "Ġfra gr",
+      "Ġsurprising ly",
+      "Ġdesper ate",
+      "5 2",
+      "Ġs ul",
+      "ĠRe ad",
+      "ĠF ried",
+      "Ġm ond",
+      "w oo",
+      "Ġorgan izing",
+      "ãģĹãĤĩ ãģĨ",
+      "ĠSo on",
+      "Ġв опÑĢоÑģ",
+      "ĠN ur",
+      "ĠÐĹ Ð´",
+      "Ġsp ider",
+      "е ÑģÑı",
+      "Ġtutorial s",
+      "Ġnutri ents",
+      "or er",
+      "Ġcoe fficient",
+      "Ġarrange ment",
+      "Ġpr icing",
+      "n an",
+      "y u",
+      "B L",
+      "Ġtri be",
+      "ĠHow ard",
+      "un ks",
+      "Ġnew er",
+      "Ġprov in",
+      "Ġpred iction",
+      "h os",
+      "Ġol sun",
+      "ĠAr ound",
+      "Ġv ier",
+      "ĠÑģÑĤоÑĢ он",
+      "Ġv alley",
+      "ĠE la",
+      "if i",
+      "Ġgal axy",
+      "Ġtran qu",
+      "Ġad vers",
+      "ĠTem ple",
+      "iff s",
+      "ig ence",
+      "èĩª å·±",
+      "Ġkön nte",
+      "ĠÄij ó",
+      "D id",
+      "Ġphotograph s",
+      "ĠA WS",
+      "ÑĨи Ñı",
+      "Ġgu ards",
+      "Ġappoint ed",
+      "ĠG il",
+      "Ġм ом",
+      "Ġc od",
+      "ĠUn like",
+      "Ġeven ly",
+      "isc onsin",
+      "Ġest ou",
+      "Ġm nie",
+      "ĠEx ec",
+      "ĠM V",
+      "ĠE ine",
+      "ä¿ ¡",
+      "ĠRog er",
+      "ĠF ac",
+      "ĠL ist",
+      "Ġf uer",
+      "аеÑĤ е",
+      "om ed",
+      "Ġattract ion",
+      "èī ²",
+      "Ġter rain",
+      "ĠD rop",
+      "Ġcorpor ations",
+      "Ġsci ences",
+      "Ġthr one",
+      "ãģĦ ãģŁ",
+      "Ġa j",
+      "ĠR ot",
+      "çī ¹",
+      "Ġsupp orters",
+      "ĠB ere",
+      "H ere",
+      "Ġdifer entes",
+      "Ġsignific ance",
+      "Ïĥ η",
+      "æĪij 覺å¾Ĺ",
+      "Ġcl amp",
+      "Ġë ĮĢë",
+      "Ġfab ulous",
+      "re z",
+      "æĮ ģ",
+      "Ġassum ptions",
+      "ut her",
+      "w id",
+      "p ot",
+      "è¿ İ",
+      "Ġy an",
+      "ul in",
+      "ÑĢ Ñĭв",
+      "ĠSl ow",
+      "ĠPenn sy",
+      "Ġíķ ´ìĦľ",
+      "Ġme io",
+      "Ġwealth y",
+      "ĠE ight",
+      "Ġpul se",
+      "Ġfr iction",
+      "id ity",
+      "ĠH oll",
+      "i yorum",
+      "Ġsound ed",
+      "ĠC arr",
+      "Ġfor k",
+      "â ĺ",
+      "ĠP A",
+      "Ġcons pir",
+      "Ġc oding",
+      "r t",
+      "ĠTy p",
+      "Ġìĸ ij",
+      "Ġп ог",
+      "Ġmis er",
+      "ĠÑģм оÑĤÑĢ",
+      "ĠSw eden",
+      "Ġolar ak",
+      "ĠZh ang",
+      "ĠCh i",
+      "ĠT itan",
+      "Ġscreen ing",
+      "ĠSp ider",
+      "ĠÅŀ imdi",
+      "Ġobst acles",
+      "lar a",
+      "Ġchalleng ed",
+      "p se",
+      "T ON",
+      "á» ¥",
+      "ĠP i",
+      "Ġlag i",
+      "ie urs",
+      "Ġhur ting",
+      "Ġneg lect",
+      "Ġgener ating",
+      "Ġyoung est",
+      "Ġaud it",
+      "ĠÑĢ ез",
+      "Ïģ ά",
+      "Ġdon ate",
+      "ĠPD F",
+      "Ġvis its",
+      "Ġcru ise",
+      "P P",
+      "as er",
+      "Ġw sp",
+      "back s",
+      "iv als",
+      "ãģĨ ãĤĵ",
+      "Ġde ve",
+      "Ġprop ort",
+      "Ġc ath",
+      "ĠE ffect",
+      "Ġwind s",
+      "ĠìĻ Ķ",
+      "Ġchart s",
+      "Ġs ama",
+      "Ġautom ation",
+      "Ġпок а",
+      "Ġol an",
+      "Ġbo ats",
+      "Ġca fe",
+      "Ġden ied",
+      "ĠM ama",
+      "Ġblock ing",
+      "ĠTh or",
+      "Ġphenomen al",
+      "Ġstake holders",
+      "Ġun os",
+      "Ñĥ еÑĤ",
+      "ĠAb raham",
+      "ãģ§ ãĤĤ",
+      "Ġdetect ion",
+      "Ġjur is",
+      "Ġpower ed",
+      "z ial",
+      "Ġwel fare",
+      "Ġup grad",
+      "Ġmoż na",
+      "ĠC ase",
+      "c ular",
+      "Ķ ìĿ´",
+      "ãĥ ģ",
+      "ĠGu ess",
+      "Ġcy cles",
+      "ä¾ ĭ",
+      "çµ ¦",
+      "ro ck",
+      "um i",
+      "Ġel ite",
+      "Ġqu è",
+      "åł ±",
+      "ÑĤ ом",
+      "Ġsh ore",
+      "gun ta",
+      "Ġk u",
+      "Ġfaith ful",
+      "ĠJ eremy",
+      "a id",
+      "à ·",
+      "ug al",
+      "å°į åķĬ",
+      "ĠV el",
+      "Ġvra i",
+      "st ell",
+      "¨ ¸",
+      "Ġk ol",
+      "è ½",
+      "Ġquant o",
+      "Ġз аÑĢ",
+      "Ġ200 2",
+      "es y",
+      "Ġres erve",
+      "Ġмом енÑĤ",
+      "Ġdeploy ed",
+      "Ġdefin ing",
+      "Ġsa u",
+      "Ġga at",
+      "\" )",
+      "Ġtrans mit",
+      "Ġpubl ishing",
+      "Ġrank ing",
+      "Ġoff ense",
+      "Ġ4 6",
+      "p in",
+      "ĠT aking",
+      "Ġentit led",
+      "Ġgen uinely",
+      "Ġvari ations",
+      "Ġfind e",
+      "Ġt au",
+      "Ġunf ortunate",
+      "ĠR ah",
+      "port s",
+      "Ġc Å",
+      "Ġmon key",
+      "Ġbr ac",
+      "we i",
+      "l ung",
+      "Ġart if",
+      "Ġsy rup",
+      "ĠÐĶ ав",
+      "Ġlift ed",
+      "Ġche z",
+      "ĠAd vent",
+      "ĠSt ock",
+      "Ġdo l",
+      "м ен",
+      "иÑĪ ÑĮ",
+      "Ġy n",
+      "g io",
+      "d et",
+      "Ġdes se",
+      "Ġg ri",
+      "ĠChair man",
+      "ç ħ",
+      "Ġcu enta",
+      "an im",
+      "Ġcra b",
+      "Ġesc al",
+      "Ġpremi ère",
+      "ĠGe f",
+      "Ġd ining",
+      "Ġsevent h",
+      "Ġch asing",
+      "ĠT ower",
+      "Ġbrut al",
+      "Ġfundament ally",
+      "ãģ¨ ãģĨ",
+      "л ениÑı",
+      "st age",
+      "Ġacqu is",
+      "Ġcyl inder",
+      "Ġcomm ander",
+      "m em",
+      "ĠU V",
+      "ha ppy",
+      "Ġe psilon",
+      "Ġinv itation",
+      "Ġfar mer",
+      "ch air",
+      "Ġdest iny",
+      "Ġso vere",
+      "ĠHeb rew",
+      "Ġserv ant",
+      "Ġbe w",
+      "Ġg ast",
+      "ut ies",
+      "Ġadministr ative",
+      "ĠComm and",
+      "é ta",
+      "Ġnit rogen",
+      "ê· ¼",
+      "Ġab i",
+      "Ġvill ain",
+      "Ġblank et",
+      "ĠS end",
+      "Ġbeat en",
+      "² Ħ",
+      "Ġvol unt",
+      "Ġschol ar",
+      "ĠEm peror",
+      "Ġ4 3",
+      "v able",
+      "ĠD us",
+      "ĠG U",
+      "Ġtarget ing",
+      "ww w",
+      "Ġamend ment",
+      "ìĨ Įë",
+      "Ġt ing",
+      "Ġn asty",
+      "Ġg auge",
+      "ĠÑĢ од",
+      "ĠH ans",
+      "Y our",
+      "α ν",
+      "Ġpro jet",
+      "ĠHawai i",
+      "Ġsusp icious",
+      "Ġsch w",
+      "Ġremo val",
+      "Ġint rig",
+      "ĠM U",
+      "Ġp onto",
+      "ठ¾",
+      "Ġоб ÑĢаз",
+      "Ġguess ing",
+      "p ace",
+      "Ġm others",
+      "Ġmill imeter",
+      "л ение",
+      "没 æľī",
+      "Ġavail ability",
+      "ic z",
+      "æŃ ¤",
+      "Ġfr act",
+      "Ġbas es",
+      "k m",
+      "ĠB TS",
+      "ĠF ield",
+      "Ġd zie",
+      "Ġseg undo",
+      "ĠëĤĺ ëĬĶ",
+      "Ġlegit imate",
+      "im as",
+      "Ġв н",
+      "Ġcor ruption",
+      "Ġsm ash",
+      "ĠVal ent",
+      "Ġalign ed",
+      "ĠPennsy lvania",
+      "Ġg ab",
+      "ĠE un",
+      "ent h",
+      "ĠMor ning",
+      "Ġcand le",
+      "Ġback pack",
+      "ĠIslam ic",
+      "a ções",
+      "Ġenc ry",
+      "Ġmushroom s",
+      "íĮ Į",
+      "d it",
+      "Ġtrans it",
+      "ĠW isconsin",
+      "Ġparticip ated",
+      "ĠIl s",
+      "Ġunf old",
+      "¶ Ģë",
+      "Ġprof its",
+      "Ġwar ming",
+      "ĠG ang",
+      "Ġnetwork ing",
+      "Ġme ga",
+      "Ġthorough ly",
+      "le ments",
+      "ĠH m",
+      "Ġdec iding",
+      "Ġemotion ally",
+      "Ġexha usted",
+      "ĠÐŁ оÑĤ",
+      "c ido",
+      "ĠHT ML",
+      "Ġcopy right",
+      "Ġmel ody",
+      "y im",
+      "Ġand ers",
+      "osh op",
+      "Ġë³ ¼",
+      "Ġathlet e",
+      "ĠG E",
+      "Ġfrequ ent",
+      "Ġdes ires",
+      "Ġneed ing",
+      "ĠY un",
+      "Ġrif le",
+      "Ġlo ver",
+      "' T",
+      "Ġd ense",
+      "Ġt ão",
+      "Ġnot ified",
+      "Ġid i",
+      "ìĹ Ń",
+      "í Ĩ",
+      "Ġinteract ing",
+      "Ġrapp ort",
+      "еÑĢ и",
+      "s ki",
+      "Ġb esser",
+      "Ġmanufact urer",
+      "ĠK yle",
+      "Ġaccount able",
+      "ĠS ak",
+      "ĠP il",
+      "ĠD omin",
+      "Ġpres um",
+      "ĠÐĴÑģ е",
+      "Ġvine gar",
+      "Ġguarante ed",
+      "çľĭ åĪ°",
+      "Ġhand led",
+      "éŁ ³",
+      "c at",
+      "Ġcivil ization",
+      "Ġaccom p",
+      "ĠV M",
+      "é mon",
+      "Ġde ze",
+      "Ġgrad es",
+      "Ġsoll te",
+      "Ġst aring",
+      "×IJ× ª",
+      "ar nt",
+      "Ġhoriz on",
+      "Ġtrav ail",
+      "h our",
+      "第 ä¸Ģ",
+      "ĠE D",
+      "ĠD ak",
+      "Ġn y",
+      "Ġcon ve",
+      "ĠCh am",
+      "Ġfir ms",
+      "ĠL iu",
+      "ĠÑģÑĤ ÑĢан",
+      "Ġli bert",
+      "Ġlens es",
+      "Ġint ake",
+      "ĠвÑĭ б",
+      "Ġmens en",
+      "h el",
+      "Ġpract ition",
+      "Ġ3 50",
+      "ãĤ ³",
+      "F O",
+      "Ġbed s",
+      "Ġancest ors",
+      "ĠìĹĦ ì²Ń",
+      "Ġdistur b",
+      "ĠLast ly",
+      "ĠSupp ort",
+      "ี à¹ī",
+      "ĠCor ona",
+      "Ġenthus i",
+      "Ġвоз м",
+      "ĠìĤ¬ëŀ Įë",
+      "Ġ5 2",
+      "b ird",
+      "Ġredu ces",
+      "ĠìŀĪ ìĿĦ",
+      "ĠG ene",
+      "êµ IJ",
+      "ÄĻ p",
+      "ĠÃľ ber",
+      "Ġconcer ning",
+      "us er",
+      "Ġconcent rate",
+      "ĠWH AT",
+      "ish op",
+      "onym ous",
+      "no ld",
+      "Ġsuggest ing",
+      "© °",
+      "ĠF ish",
+      ".... ....",
+      "Ġvess el",
+      "Ġtrabaj o",
+      "ãģ µ",
+      "ĠO cean",
+      "å§ IJ",
+      "y g",
+      "Ġtown s",
+      "d el",
+      "Ġterr ifying",
+      "Ġçal Ä±ÅŁ",
+      "Ġs ino",
+      "Ġe ats",
+      "Ġge z",
+      "Ġg eme",
+      "ĠìĻ Ħ",
+      "Ġcomp art",
+      "Ġimplement ing",
+      "ĠPot ter",
+      "ĠGerm ans",
+      "Ġg ÅĤ",
+      "Ġt ennis",
+      "Ġcar pet",
+      "au er",
+      "ĠSaud i",
+      "ye ong",
+      "Ġcur ry",
+      "ĠFore st",
+      "Ñĭ л",
+      "Ġfif teen",
+      "Ġbol ts",
+      "Ġ{ \\",
+      "¬ ´",
+      "Ġsett lement",
+      "Ġl ange",
+      "Ġb am",
+      "G et",
+      "íķ Ļ",
+      "Ġsw ap",
+      "ĠK han",
+      "Ġcomm ence",
+      "Ġquar antine",
+      "Ġsc ored",
+      "ç ĸ",
+      "Ġ19 50",
+      "Ġthick er",
+      "Ġsû r",
+      "åı £",
+      "ĠLar ry",
+      "Ġall ez",
+      "ìĭľ ëĬĶ",
+      "Ġg ü",
+      "Ġspect acular",
+      "/ /",
+      "b oth",
+      "Ġst ats",
+      "å¦ ³",
+      "ĠN ancy",
+      "Ġbun u",
+      "Ġcr ust",
+      "Ġactiv ated",
+      "Ġê·¸ë ŀ",
+      "out he",
+      "Ġport s",
+      "Ġne ural",
+      "Ġj aw",
+      "Ġobserv ations",
+      "Ġvo it",
+      "ab an",
+      "ả i",
+      "¦¬ë ¥¼",
+      "om es",
+      "௠ĭ",
+      "qu i",
+      "Ġkind ness",
+      "Ð ij",
+      "Ġ4 1",
+      "Ġmoder ate",
+      "Ġang els",
+      "ĠT amb",
+      "è t",
+      "Ġch lor",
+      "ĠBill y",
+      "ì² ĺë",
+      "ac on",
+      "Ġselect ing",
+      "ĠDel ta",
+      "Ġn ull",
+      "den ly",
+      "Ġci ud",
+      "Ġtend ency",
+      "Ġbreak down",
+      "Ġm int",
+      "ÑĦ оÑĢм",
+      "or ph",
+      "Ġda wn",
+      "s pr",
+      "ĠW ILL",
+      "äch lich",
+      "Ġpu ppy",
+      "7 00",
+      "Ġà® ¤",
+      "Ġfail s",
+      "ĠCon c",
+      "Ġrel atives",
+      "Ġinv iting",
+      "Ġaut onom",
+      "Ġcomp osed",
+      "Ġun ity",
+      "Ġdec is",
+      "Ġaccess ories",
+      "ĠC ass",
+      "Ġb ist",
+      "ĠT ip",
+      "ì§ ¸",
+      "Ġp unt",
+      "Ġr áp",
+      "éĢ ²",
+      "AN K",
+      "ãģ ļ",
+      "ex ist",
+      "Ġcompat ible",
+      "Ġn er",
+      "Ġе мÑĥ",
+      "Ġa plic",
+      "Ġb apt",
+      "Ġfail ing",
+      "ĠTam am",
+      "Ġos cill",
+      "Ġletz ten",
+      "Ġrepeated ly",
+      "Ġjung le",
+      "ĠP ush",
+      "h ai",
+      "ĠÎ ·",
+      "Ġdead ly",
+      "Ñı ж",
+      "wi Äħ",
+      "ĠComm on",
+      "ĠÎ ķ",
+      "Ġsk ate",
+      "T C",
+      "ĠMin i",
+      "Ġhob by",
+      "ầ n",
+      "Ġrout es",
+      "Ġam igos",
+      "Ġcon jun",
+      "Ġpartners hips",
+      "Ġno vo",
+      "Ġa ver",
+      "Ġpou vez",
+      "br idge",
+      "Ġpre oc",
+      "h im",
+      "Ġtur b",
+      "Ġso b",
+      "ĠSn ap",
+      "Ġì° ¸",
+      "min ute",
+      "Ġtra ject",
+      "uj ÄĻ",
+      "Ġe ager",
+      "Ġregul atory",
+      "Ġbank ing",
+      "b ling",
+      "ÑĪ ÑĮ",
+      "a ż",
+      "Ġbiz arre",
+      "it ated",
+      "d ire",
+      "Ġthreat ened",
+      "Ġsh ining",
+      "Ġn esse",
+      "Ġcor ps",
+      "ĠÑģ Ñĥ",
+      "Ġt eles",
+      "Ġtem p",
+      "t em",
+      "Ġк ан",
+      "Ġfe ver",
+      "N ew",
+      "Ġheav ier",
+      "ĠS ah",
+      "b ud",
+      "Ġout ros",
+      "Ġì° ¾",
+      "Ġëª ħ",
+      "arr ing",
+      "Ġê´ľ ì°®",
+      "ĠN ap",
+      "Ġse min",
+      "ĠTh an",
+      "if s",
+      "Ġdes en",
+      "ĠÑĤак ое",
+      "Ġlos es",
+      "ĠB alt",
+      "k on",
+      "Ġнап ÑĢ",
+      "Ġvo is",
+      "ĠMosc ow",
+      "Ġch airs",
+      "h is",
+      "Ġrefuge es",
+      "k g",
+      "Ġk ole",
+      "į ¨",
+      "аÑģ ибо",
+      "¦ ½",
+      "ĠUn iverse",
+      "ĠDire ct",
+      "Ġche ating",
+      "ĠC in",
+      "Ġpat ri",
+      "Ġadv ise",
+      "ĠN ether",
+      "Ġprime iro",
+      "Ġmention ing",
+      "n ut",
+      "5 6",
+      "ar ı",
+      "Ġpet ite",
+      "b led",
+      "Ġpens ar",
+      "ic io",
+      "IN D",
+      "Ġveter an",
+      "Ġlad der",
+      "Ġconsequ ence",
+      "ож ал",
+      "ĠB urn",
+      "Ġr ug",
+      "ĠM ade",
+      "Ġg it",
+      "\" ...",
+      "Ġcompet itors",
+      "Ġprz ed",
+      "Ġapp arent",
+      "ĠArgent ina",
+      "ĠWork ing",
+      "Ġcollabor ate",
+      "w oman",
+      "Ġret ain",
+      "Ġle urs",
+      "Ġdash board",
+      "×Ļ× ĵ",
+      "ĠEar ly",
+      "B M",
+      "Ġе Ñij",
+      "ол ог",
+      "Ġsatisf ying",
+      "Ġoft entimes",
+      "Ġma pping",
+      "ünk ü",
+      "ar th",
+      "f old",
+      "Ġlaunch ing",
+      "Ġa ura",
+      "Ġprec ision",
+      "work s",
+      "G od",
+      "Ġstra p",
+      "ĠIm per",
+      "Ġr ivers",
+      "Ġ |",
+      "Ġcu er",
+      "reg on",
+      "Ġarri val",
+      "ка Ñħ",
+      "ĠM iami",
+      "ан Ñĭ",
+      "Ġsurviv ors",
+      "ĠSen ior",
+      "Dav id",
+      "Ġest ado",
+      "Ġse ctors",
+      "Ġpop ping",
+      "Ġch im",
+      "ay ı",
+      "Ġkun nen",
+      "Ġgall ery",
+      "Ġsun light",
+      "ese hen",
+      "Ġye lling",
+      "ĠMe in",
+      "ĠPho enix",
+      "Ġman o",
+      "Ġhistor ia",
+      "Ġoccur ring",
+      "æ¬ ¸",
+      "ì ¸",
+      "ад и",
+      "å¾ ħ",
+      "Ġinstitution al",
+      "ĠT ut",
+      "ç ²",
+      "Ġsl aves",
+      "ãģ© ãģĨ",
+      "Ġforg iveness",
+      "Ġtw in",
+      "ĠHy un",
+      "н ÑĮ",
+      "ĠK omm",
+      "and ra",
+      "sh ot",
+      "ss ä",
+      "ĠÑĨ е",
+      "at ta",
+      "Ġexp ense",
+      "ĠG PU",
+      "ĠP ast",
+      "rib ly",
+      "ĠëŃIJ ìķ¼",
+      "Ġгод а",
+      "Ġresp ir",
+      "æĿ ±",
+      "ĠQue ens",
+      "h ops",
+      "Ġs érie",
+      "Ġpre f",
+      "Ġcom ed",
+      "Ġpl ut",
+      "ĠOver all",
+      "Ġãģ Ŀ",
+      "Ġc ush",
+      "Ġring ing",
+      "Ġincor rect",
+      "ĠÑģÑĤ ÑĢ",
+      "Ġgeomet ry",
+      "Ġadvert is",
+      "ĠÐ ¨",
+      "Ġreview ed",
+      "ãģĤ ãģĤ",
+      "Ġdo zens",
+      "Ġdeterm ination",
+      "ĠPh ill",
+      "Ġcontrib uted",
+      "ĠC it",
+      "Ġpass engers",
+      "Ġcôt é",
+      "Ġre ver",
+      "Ġtechn ological",
+      "Ġall en",
+      "Ġr aining",
+      "av i",
+      "Ġsal ty",
+      "Ġtyp ing",
+      "ĠÑĤ е",
+      "Ġt ilt",
+      "Ġì¹ ĺ",
+      "Ġо ÑĢ",
+      "ĠпÑĢ Ñıм",
+      "Ġr ou",
+      "Ġare na",
+      "ar at",
+      "åĪ «",
+      "HH HH",
+      "Ġmanufact urers",
+      "ĠEd ward",
+      "Ġt uck",
+      "Ġbl ows",
+      "ing o",
+      "ĠMar c",
+      "ìķĦ ìĦľ",
+      "M ich",
+      "ĠCle an",
+      "è ´",
+      "est o",
+      "ĠP ack",
+      "Ġsha ft",
+      "BRUN O",
+      "Ġa ven",
+      "u ur",
+      "Ñģк олÑĮко",
+      "ê´ Ģ",
+      "Ġautom ated",
+      "Ġvent ure",
+      "Ġsurve illance",
+      "ĠG row",
+      "ĠE mer",
+      "Ġд оÑĢ",
+      "Ġinvest or",
+      "ĠY ok",
+      "Ġl atter",
+      "ĠN I",
+      "Ġfunction ing",
+      "ĠHam ilton",
+      "Ġ5 1",
+      "Ġmurder ed",
+      "Ġanch or",
+      "Ġc uc",
+      "ĠSC P",
+      "ĠMad am",
+      "Ġconstra ints",
+      "Ġb arn",
+      "ank en",
+      "Ġë§İ ìĿĢ",
+      "ĠMot or",
+      "ĠDo ing",
+      "Ġam en",
+      "et ts",
+      "Ġinst ructor",
+      "eg t",
+      "ak o",
+      "Ġpost ure",
+      "iv ia",
+      "ĠPol ish",
+      "Ġдв а",
+      "Ġcolor ful",
+      "Ġel bow",
+      "Ġpar le",
+      "Ġpass er",
+      "Ġcond em",
+      "ort al",
+      "Ġfert il",
+      "ا د",
+      "ĠCol omb",
+      "Ġalign ment",
+      "Ġastron aut",
+      "ĠM ut",
+      "Ġsal mon",
+      "Ġstructure d",
+      "ŀ ר",
+      "Ġclick s",
+      "Ġm iej",
+      "æĶ ¿",
+      "ãģĦ ãĤĦ",
+      "ĠR ound",
+      "Ġrain bow",
+      "ĠV A",
+      "ãģĶ ãģĸ",
+      "ì§ Ī",
+      "ot z",
+      ", </",
+      "ĠNico le",
+      "l ishing",
+      "Ġwh ilst",
+      "Ġrep ublic",
+      "Ġtam am",
+      "vert ed",
+      "Ġrecogn izing",
+      "Ġгл ав",
+      "Ġd ub",
+      "ĠJ os",
+      "fall s",
+      "ich i",
+      "Ġcz ÄĻ",
+      "ĠÐ ¦",
+      "ĠM itch",
+      "C R",
+      "cl ick",
+      "ãģĦ ãģ¦",
+      "Ġstun ning",
+      "ĠJul ia",
+      "m ers",
+      "ĠPo ly",
+      "Ġdess a",
+      "Ġint é",
+      "Ġê³ łë",
+      "Ġdo ÄŁ",
+      "Ġdi ver",
+      "Ġstri king",
+      "ap hor",
+      "Ġap enas",
+      "ous es",
+      "Ġtraged y",
+      "ĠF an",
+      "ĠTurk ish",
+      "Ġpro phet",
+      "Ġdist ancing",
+      "ĠH em",
+      "Ġcart oon",
+      "K e",
+      "ant ing",
+      "ĠCl ark",
+      "ç ¿",
+      "Ġdav on",
+      "Ġí ħ",
+      "Ġy ummy",
+      "Ġcomprom ise",
+      "Ġstart up",
+      "r itt",
+      "Ġcert ified",
+      "Ġpill ow",
+      "b ere",
+      "ì¤ Ģ",
+      "Ġsegu ir",
+      "Ġst adium",
+      "at ivo",
+      "Ġsimpl er",
+      "³ ¸",
+      "Ġvis a",
+      "Ġpath way",
+      "Ġnue vo",
+      "Ġr ay",
+      "ãĥ IJ",
+      "é ľ",
+      "ö ÃŁ",
+      "Ġз ан",
+      "Ġcelebr ity",
+      "з а",
+      "Ġein es",
+      "ĠG iven",
+      "ĠA ra",
+      "ĠJ ob",
+      "Ġy ak",
+      "ĠAr beit",
+      "ress ing",
+      "á nd",
+      "Ġgrab bed",
+      "p end",
+      "Ġs ine",
+      "ir k",
+      "ĠÐŀ ÑĤ",
+      "ĠF le",
+      "ich en",
+      "ç ¦",
+      "ĠNe il",
+      "èĻ Ł",
+      "Ġrepe ating",
+      "Ġdraw ings",
+      "r ise",
+      "Ġgl itter",
+      "f ive",
+      "Ġsur t",
+      "Ġsich er",
+      "Ġadjust ments",
+      "Ġ éĤ£",
+      "ipp i",
+      "c ke",
+      "Ġrepresent atives",
+      "Ġmid st",
+      "Ġspo il",
+      "me ye",
+      "Ġtag s",
+      "Ġy ep",
+      "ĠStephan ie",
+      "Ġg ere",
+      "ĠR ud",
+      "ç ĭ",
+      "Ġgr os",
+      "Ġque ue",
+      "Ġacc ord",
+      "Ġorgan isation",
+      "end y",
+      "ĠTe xt",
+      "ü yor",
+      "ĠÃ Ń",
+      "Ġconc lus",
+      "Ġì¤ Ģë",
+      "Ġam p",
+      "ĠL ess",
+      "ĠëIJĺ ëĬĶ",
+      "c ano",
+      "ĠP ix",
+      "ap ed",
+      "Ġdar auf",
+      "u o",
+      "yn th",
+      "ab el",
+      "ĠD one",
+      "Ġd ick",
+      "ath on",
+      "Ġh ilar",
+      "ac co",
+      "ĠìĨ į",
+      "ĠO regon",
+      "ĠWe il",
+      "Ġmathemat ics",
+      "Ġal m",
+      "Ġpix els",
+      "ĠfrÃ¥ n",
+      "б о",
+      "F C",
+      "н Ñİ",
+      "he im",
+      "g os",
+      "ĠFor get",
+      "f end",
+      "ĠVo ilÃł",
+      "ĠG reet",
+      "Ġα ÏħÏĦ",
+      "Ġrec ur",
+      "æĶ ¶",
+      "5 1",
+      "ĠìŀĪ ê³ł",
+      "A t",
+      "Ġy ards",
+      "иÑĤ и",
+      "Ġoff set",
+      "ro lling",
+      "ĠÐŁ оÑģ",
+      "Ġen light",
+      "ĠP ad",
+      "lim ited",
+      "илÑĮ но",
+      "ĠS ara",
+      "ĠÑģдел аÑĤÑĮ",
+      "m art",
+      "ĠJ ump",
+      "Ġad orable",
+      "or se",
+      "che ering",
+      "Ġemp athy",
+      "ĠTon ight",
+      "or p",
+      "ĠHun ter",
+      "P oint",
+      "г а",
+      "Ġpass enger",
+      "ĠK night",
+      "Ġseem ingly",
+      "h uh",
+      "Ġthe atre",
+      "Ġto mb",
+      "Ġdep ressed",
+      "Ġsum mon",
+      "Ġsatisf action",
+      "do ors",
+      "ĠHou ston",
+      "аÑİ Ñī",
+      "ĠR io",
+      "г лÑı",
+      "Ġarr anged",
+      "Ġhand les",
+      "Ġtr illion",
+      "Ġnight mare",
+      "ĠQu ando",
+      "Ġo le",
+      "ĠGu ide",
+      "oo o",
+      "Ġb ile",
+      "Ġem pez",
+      "Ġ7 2",
+      "cri bed",
+      "Ġpro gression",
+      "ĠLin ux",
+      "ë ¦¬",
+      "Ġì²ĺ ìĿĮ",
+      "Ġfoss il",
+      "Ġqu ero",
+      "ìĨ ¡",
+      "at iva",
+      "Ġpu zz",
+      "ĠZ us",
+      "ãĤ ª",
+      "Ġthr illed",
+      "ĠC B",
+      "Ġmin er",
+      "ÑĢа Ñī",
+      "ĠS AR",
+      "ĠN os",
+      "Ġго ÑĢод",
+      "Ġcam b",
+      "ĠÑĤ а",
+      "Ġresult ed",
+      "ĠD ick",
+      "ou ng",
+      "Ġcom ics",
+      "Ġabs olut",
+      "st an",
+      "dim ensional",
+      "Ġt ense",
+      "m us",
+      "ĠInt ell",
+      "ĠÑįÑĤ Ñĥ",
+      "Ġph ases",
+      "Ġvol ta",
+      "Ġv ão",
+      "b ound",
+      "ĠAnd erson",
+      "Ġcurios ity",
+      "Ġp ont",
+      "éĢĻ 裡",
+      "Ġdemonst rated",
+      "ol ine",
+      "ĠSpe ed",
+      "Ġm ama",
+      "Ġshock ing",
+      "Ġk iedy",
+      "Ġearthqu ake",
+      "Ġimpl ies",
+      "Ġent ers",
+      "ŀ Ģ",
+      "Ġelev ator",
+      "Ġdelight ed",
+      "ĠM itt",
+      "ĠB ased",
+      "ĠD ol",
+      "Ġk en",
+      "Ġworry ing",
+      "Ġfil ed",
+      "ail and",
+      "Ġм еÑĤ",
+      "Ġmas c",
+      "ĠÎ ij",
+      "ĠJul ie",
+      "Ġdim ensional",
+      "h uman",
+      "T ok",
+      "Ã ¿",
+      "Ġun st",
+      "Ġse ule",
+      "Ġemb ar",
+      "Ġíķ ©ëĭĪëĭ¤",
+      "ac ion",
+      "Ġì ī",
+      "Ġë¶Ģë ¶Ħ",
+      "Ġhe ated",
+      "âĢ¦ âĢ¦",
+      "\" !",
+      "Ġreal ise",
+      "еÑĤ Ñĭ",
+      "ien ia",
+      "ie z",
+      "Ġf üh",
+      "ĠEs se",
+      "Ġp s",
+      "Ġd ó",
+      "as ters",
+      "Ġon s",
+      "P M",
+      "Ġret ro",
+      "m aker",
+      "wh en",
+      "Ġe lla",
+      "ĠL iving",
+      "ĠL am",
+      "Ġtr ong",
+      "Ġappro ve",
+      "Ġθ α",
+      "Ġs ung",
+      "ени Ñİ",
+      "ĠRem ove",
+      "è ne",
+      "ire n",
+      "Ġstr anger",
+      "и нÑĭ",
+      "Ġv æ",
+      "a fter",
+      "ot to",
+      "Ķë ¡ľ",
+      "ĠA hora",
+      "m ill",
+      "IS H",
+      "Ġgradu ating",
+      "k te",
+      "Ġre nov",
+      "Ġprocess ed",
+      "ke ys",
+      "ек о",
+      "Ġen rich",
+      "ĠÅŁ ek",
+      "Ġin sec",
+      "ĠN an",
+      "ca kes",
+      "Ġill usion",
+      "ĺë ¥¼",
+      "Ġa irl",
+      "im s",
+      "Ġan ten",
+      "ữ ng",
+      "s n",
+      "Ġprec isa",
+      "기 ìŀIJ",
+      "ĠاÙĦ ع",
+      "Ġfore most",
+      "Ġparag raph",
+      "av ais",
+      "Ġв оÑģ",
+      "Ġman s",
+      "ÃŃ fic",
+      "b ot",
+      "Ġع ÙĨ",
+      "Ġbr oth",
+      "Ġaltern ate",
+      "ĠCh apter",
+      "Ġve ctors",
+      "es ar",
+      "Ġindic ation",
+      "ĠNe in",
+      "¶ ģ",
+      "Ġje ans",
+      "Y E",
+      "c ond",
+      "Ġun ited",
+      "ab i",
+      "ĠSer ge",
+      "Ġpart ially",
+      "Ġmac ro",
+      "æī į",
+      "å¼ µ",
+      "Ġeth ical",
+      "ru it",
+      "Ġshift ed",
+      "Ġc abe",
+      "Ġmathemat ical",
+      "Ġr ude",
+      "×Ļ ×ķת",
+      "ĠM erc",
+      "Ġgan ze",
+      "ic ion",
+      "Ġunc onscious",
+      "Ġbur nt",
+      "ĠÑĢ еб",
+      "íĬ ¸ë",
+      "Ġchar m",
+      "and al",
+      "ì² ľ",
+      "oth y",
+      "ĠH adi",
+      "Ġappreci ation",
+      "EN D",
+      "Ġré al",
+      "¶ Ħëĵ¤",
+      "ĠN ag",
+      "ł¤ ê³ł",
+      "ĠLa uren",
+      "Ġv Ỽi",
+      "ĠBr idge",
+      "ĠU mm",
+      "ĠWe g",
+      "Ġcha que",
+      "ĠS oph",
+      "Ġg dzie",
+      "í ijľ",
+      "Ġst er",
+      "ĠB la",
+      "Ġreflect s",
+      "Ġbench mark",
+      "в аÑĤ",
+      "am ine",
+      "ãģ¡ ãĤĥ",
+      "Ġan h",
+      "Ġcontin ent",
+      "ĠF DA",
+      "ì ¡°",
+      "Ġê tes",
+      "×Ļ× IJ",
+      "å¼ Ģ",
+      "Ġblo ody",
+      "ĠN ine",
+      "iel t",
+      "em and",
+      "Ġë³´ ê³ł",
+      "Ġtid ak",
+      "ĠS cient",
+      "ple x",
+      "ost en",
+      "Ġanim ated",
+      "ass a",
+      "Ġder ived",
+      "ĠиÑģ ÑĤоÑĢ",
+      "ĠM ig",
+      "ìħ ĺ",
+      "Ġr os",
+      "pl us",
+      "os aur",
+      "Ġ ^",
+      "Ġint ensive",
+      "Ġglob ally",
+      "Ġdif eren",
+      "ìĿ´ ê³ł",
+      "ä½ł çļĦ",
+      "Äħ d",
+      "Ġd és",
+      "Ġpresent ations",
+      "ĠC ro",
+      "Ġess es",
+      "ĠBet ween",
+      "P a",
+      "Ġn aw",
+      "à¸Ń à¸ĩ",
+      "Ġbre ed",
+      "icht e",
+      "ĠÐŀ ни",
+      "ĠBuild ing",
+      "Ġcon form",
+      "M O",
+      "ĠÐ ĸ",
+      "ĠK id",
+      "n as",
+      "ĠD ue",
+      "r és",
+      "Ġd iox",
+      "ĠB in",
+      "Ġtax i",
+      "Ġs ap",
+      "ĠH ub",
+      "çĤº ä»Ģ麼",
+      "Ġcenter ed",
+      "Ġsur ge",
+      "Ġav ons",
+      "Ġle arnt",
+      "ĠY am",
+      "ĠDies e",
+      "ни ки",
+      "ĠBe ij",
+      "W ill",
+      "Ġattempt ed",
+      "Ġgr ief",
+      "ó j",
+      "Ġkid ney",
+      "Ġoppon ents",
+      "æĽ ´",
+      "Ġn ome",
+      "5 7",
+      "ÑıÑĤ но",
+      "Ġmid night",
+      "Ann ouncer",
+      "ac ity",
+      "on ed",
+      "Ġpued es",
+      "Ġproblem atic",
+      "Ġcop s",
+      "ĠP ete",
+      "r int",
+      "unt ed",
+      "Ġb ip",
+      "æ ¢",
+      "ĠÃ Ģ",
+      "Ġc ens",
+      "ative ly",
+      "Ġ ä¸į",
+      "Ġur gent",
+      "Ġstrugg led",
+      "ach us",
+      "Ġmicrow ave",
+      "ĠS ide",
+      "ĠD enn",
+      "ĠÑı в",
+      "Ġur ge",
+      "Ġfor cing",
+      "w ang",
+      "ĠкоÑĤоÑĢ аÑı",
+      "Ġm amm",
+      "ĠðŁ İ",
+      "Ġtri bes",
+      "ĠSh adow",
+      "ĠS ang",
+      "ĠHit ler",
+      "Ġl un",
+      "Ġsc ent",
+      "ì§ ij",
+      "Ġoverwhel med",
+      "Ġbom bs",
+      "Ġcr imin",
+      "Ġconsol id",
+      "Ġmolec ular",
+      "×ķ× §",
+      "n or",
+      "Ġperce ived",
+      "Ġv é",
+      "Ġalt ogether",
+      "Ġor th",
+      "Ġve m",
+      "Ġz war",
+      "iz o",
+      "Å «",
+      "Ġmelt ed",
+      "ord en",
+      "ĠCharl otte",
+      "ĠEx cel",
+      "art a",
+      "ìľ ł",
+      "ĠG ew",
+      "Ġrom ance",
+      "ere mos",
+      "Ġcolon ial",
+      "Ġtradition ally",
+      "Ġqu an",
+      "ho o",
+      "Ġchampions hip",
+      "Ġarbit r",
+      "ìħ Ķ",
+      "Ġм ин",
+      "Ġself ish",
+      "Ġble w",
+      "ry ing",
+      "Ġoper ators",
+      "Ġjuris d",
+      "ı ħ",
+      "u ition",
+      "ĠE C",
+      "ĠAny body",
+      "v ate",
+      "iet ies",
+      "Ġanaly st",
+      "´ì ĹIJ",
+      "ĠвÑģ егда",
+      "ç ek",
+      "ĠK un",
+      "Ġag ing",
+      "Õ ¡",
+      "ÑĢа ÑĦ",
+      "ĠMom ent",
+      "ĠH ua",
+      "è ĥ",
+      "th en",
+      "ел а",
+      "est one",
+      "Ġend e",
+      "Ġaward ed",
+      "Ġnäch sten",
+      "ĠSp ot",
+      "ĠN eg",
+      "Ġfair y",
+      "ä» £",
+      "ĠCo ver",
+      "Ġdep osit",
+      "Ġstress ful",
+      "Ġj unk",
+      "Ġmet abol",
+      "J a",
+      "Ġê· Ģ",
+      "Ġundergrad uate",
+      "Ġcan cell",
+      "Ġcons ensus",
+      "Ġo so",
+      "éĩ ij",
+      "Ạ·",
+      "ÄŁ er",
+      "r ada",
+      "ĠPal ace",
+      "Ġped al",
+      "Ġex agger",
+      "Ġbehavior al",
+      "play er",
+      "ll es",
+      "Ġconnect or",
+      "Ġske pt",
+      "įĶë Ŀ¼ê³ł",
+      "Ġm itt",
+      "ĠH aha",
+      "Ġpe que",
+      "ĠG ott",
+      "f ang",
+      "à °",
+      "j os",
+      "Ġkick ing",
+      "Ġmount ed",
+      "Ġrepl acing",
+      "v os",
+      "Ġquiet ly",
+      "Ġmil it",
+      "Ġown s",
+      "Ġnive au",
+      "Ġa ur",
+      "ĠBu y",
+      "Ġpredict ed",
+      "Ġc ows",
+      "Ġpon er",
+      "ĠD ri",
+      "Ġremark s",
+      "Ġrep orter",
+      "Ġark adaÅŁ",
+      "е ÑģÑĤи",
+      "Ġsa ves",
+      "Ġç oc",
+      "Ġmet aphor",
+      "ĠK el",
+      "st ation",
+      "semb ly",
+      "Ġadvis or",
+      "Ġworks hops",
+      "Ġaccount ing",
+      "Ġto k",
+      "n ier",
+      "in ner",
+      "Ġbur ada",
+      "ĠB B",
+      "ĠOlymp ic",
+      "ĠP ract",
+      "Chr ist",
+      "ĠÑģ Ñİ",
+      "Ġk as",
+      "Ġview ed",
+      "Ġmark ers",
+      "Ġf oto",
+      "get ic",
+      "ĠLuc as",
+      "Ġp ads",
+      "ĠJ oh",
+      "ĠCD U",
+      "aff en",
+      "are m",
+      "ĠBe ck",
+      "ĠG osh",
+      "sh it",
+      "ãģĮ ãģ¨ãģĨ",
+      "ĠM ater",
+      "abul ary",
+      "ĠRo om",
+      "ll en",
+      "ĠFollow ing",
+      "Ġdo it",
+      "ball s",
+      "ix a",
+      "Ġground s",
+      "ĠìŀĪ ëĬĶëį°",
+      "L S",
+      "Ġwild life",
+      "ĠS QL",
+      "Ġsh ifts",
+      "ä¸Ģ é»ŀ",
+      "B ook",
+      "Ġhost ed",
+      "ll or",
+      "Ġsn aps",
+      "Ġbes oin",
+      "Ġש ×Ķ",
+      "Ġpean ut",
+      "ä ft",
+      "¹ ł",
+      "ÅĽ l",
+      "Aud ience",
+      "ĠBarb ara",
+      "Ġadopt ion",
+      "Ġw olf",
+      "ĠоÑģ нов",
+      "ard a",
+      "Ġexp ose",
+      "Ġì ¦",
+      "j as",
+      "Ä ĵ",
+      "Ġcount less",
+      "Ġì§ ģ",
+      "he alth",
+      "u ent",
+      "is o",
+      "ot ion",
+      "Ġhung er",
+      "Ġmo is",
+      "off s",
+      "Ġclaim ing",
+      "ĠÎ ļ",
+      "ĠBel g",
+      "Ġн ай",
+      "기ë ıĦ",
+      "Ġun pre",
+      "Ġg ed",
+      "ĠI o",
+      "ĠпоÑģ моÑĤÑĢ",
+      "Ġco ÅĽ",
+      "ĠN arrator",
+      "ĠÃĩ ok",
+      "íĻ ©",
+      "à¸Ń ย",
+      "ci pl",
+      "Ġtim er",
+      "Ġdef ic",
+      "av in",
+      "Ġcateg or",
+      "Ġthr ows",
+      "ĠëĤ ľ",
+      "ĠпоÑģ лед",
+      "ĠTh ai",
+      "Ġmas cul",
+      "Ġbek ommen",
+      "Ġintern ation",
+      "ul se",
+      "Ġa ye",
+      "Ġpo i",
+      "Ġpix el",
+      "Chr is",
+      "Ġst ove",
+      "ο ι",
+      "Ġgener ator",
+      "Ġì» ¬ë",
+      "Ġacad em",
+      "Ġpract iced",
+      "Ġaqu est",
+      "Ġcontrib uting",
+      "ĠI g",
+      "Ġ ợ",
+      "Ġcont aining",
+      "Ġwrest ling",
+      "ĠÑĩ его",
+      "h aupt",
+      "Ġess as",
+      "vel ope",
+      "Ġexcept ional",
+      "Y U",
+      "ĠApp lause",
+      "ric ane",
+      "Ġconven ience",
+      "Ġдел аÑĤÑĮ",
+      "или ÑģÑĮ",
+      "ĠEn viron",
+      "8 5",
+      "Ġc â",
+      "ĠìķĪëħķ íķĺìĦ¸ìļĶ",
+      "ĠM O",
+      "ĠP ope",
+      "Ġs ah",
+      "ob i",
+      "Ġmas ters",
+      "ain es",
+      "Ġbless ings",
+      "Ġo bey",
+      "Ġfl ux",
+      "Ġbr ow",
+      "Ġìĭ ¤",
+      "Ġpopular ity",
+      "ĠL amb",
+      "ze ug",
+      "ìĻ Ķ",
+      "ıĦ ë¡Ŀ",
+      "itu ation",
+      "Ġaccom pan",
+      "Ġdial og",
+      "ĠJam ie",
+      "åĬł æ²¹",
+      "Ġse wing",
+      "Ġble eding",
+      "Ġb ail",
+      "Ġthread s",
+      "od ge",
+      "ĠSh ang",
+      "Ġdeploy ment",
+      "ch ed",
+      "Ġsatisf y",
+      "Ġla z",
+      "Ġmiss ile",
+      "ĠLink ed",
+      "Ġmak ers",
+      "ci um",
+      "f re",
+      "Ġë¨ ¼",
+      "Ġë¬ ´ë",
+      "ĠEd ge",
+      "Ġsociet ies",
+      "Ġag ua",
+      "Ġsyn chron",
+      "¡ ł",
+      "un ft",
+      "Ġun m",
+      "Ġtri ang",
+      "Ġin just",
+      "t op",
+      "Ġor al",
+      "k or",
+      "Ġíķ ¨",
+      "ld igt",
+      "ce ÄŁ",
+      "qu et",
+      "ĠLe o",
+      "Ġsa voir",
+      "Ġeas tern",
+      "ie u",
+      "Ġexp ed",
+      "ĠС п",
+      "Ġunnecess ary",
+      "ĠPer form",
+      "ĠM ing",
+      "ĠÑĢ ав",
+      "Ġintent ions",
+      "Ġcomp ression",
+      "ĠS ac",
+      "ο λ",
+      "ars on",
+      "Ġtrou ve",
+      "ĠMuh ammad",
+      "ĠвÑĭ Ñģ",
+      "Ġfin ite",
+      "Ġна Ñħод",
+      "ug a",
+      "ÑĢаз Ñĥ",
+      "Ġcelebr ated",
+      "Ġconf ess",
+      "Ġsqu ares",
+      "ĠG ordon",
+      "ĠëĤĺ ìĺ",
+      "Ġsynd rome",
+      "Ġcomplet ion",
+      "Ġback ing",
+      "Ġdar f",
+      "ĠQ uran",
+      "Ġintermedi ate",
+      "Ġk er",
+      "Ġd ü",
+      "hes ive",
+      "Ġaccount ability",
+      "ĠRe becca",
+      "èij Ĺ",
+      "ĠS leep",
+      "Ġdiffé rent",
+      "ol s",
+      "ĠR ice",
+      "Ġë³ ¸",
+      "Ġantib iot",
+      "ÏĦ ά",
+      "r z",
+      "amb ling",
+      "Ġsensit ivity",
+      "Ġch ron",
+      "all as",
+      "6 4",
+      "Ġfle et",
+      "Ġoptim istic",
+      "Ñģк ого",
+      "Ġj adi",
+      "a illeurs",
+      "ĠEn ough",
+      "Ġsen in",
+      "Ġpack s",
+      "b n",
+      "ĠAre a",
+      "ĠT ro",
+      "¨ë ¦¬",
+      "а ÑĶ",
+      "ĠTh om",
+      "Ġharm ony",
+      "ни ка",
+      "Ġsom eday",
+      "IS E",
+      "ĠBroad way",
+      "la res",
+      "ern ess",
+      "à¹Ħ ม",
+      "ĠT enn",
+      "ĠNAT O",
+      "ãĤĬ ãģ¾ãģĻ",
+      "Ġminut os",
+      "ĠK ansas",
+      "ĠM ong",
+      "Ġcompt e",
+      "åĽ Ľ",
+      "Ĭ ¤",
+      "ĠìĹ Ń",
+      "Ġsuper hero",
+      "ĠGard en",
+      "ĠM os",
+      "Ġattach ment",
+      "Ġb ust",
+      "௠Ĭ",
+      "ĠTh ailand",
+      "st at",
+      "Ġsp ice",
+      "ĠLe b",
+      "Ġle ap",
+      "ze ch",
+      "G L",
+      "Ġver l",
+      "Ġfix ing",
+      "Ġë³´ë ©´",
+      "Ġpor n",
+      "Ġb üy",
+      "ĠÙħ ا",
+      "ĠV irt",
+      "ĠT ommy",
+      "Ġcar go",
+      "ĠOl ha",
+      "Ġro ku",
+      "Ùĥ ÙĨ",
+      "Ġbak ed",
+      "Ġtact ics",
+      "Ġmarket place",
+      "Ġktó ra",
+      "ar lo",
+      "Ġswitch es",
+      "Ġc ache",
+      "ĠH R",
+      "ĠG an",
+      "ĠG PS",
+      "Ġdu as",
+      "her es",
+      "еÑĢ ÑĪ",
+      "tr ack",
+      "Ġl ungs",
+      "St ation",
+      "igg les",
+      "Ġcamp ing",
+      "åĵ ĩ",
+      "Ġcomplet ing",
+      "am as",
+      "Ġcy cl",
+      "Ġprot otype",
+      "ĠJud ge",
+      "oty pes",
+      "Ġinfect ions",
+      "ł ¤ë",
+      "еÑĢ г",
+      "ob a",
+      "ĠB od",
+      "ĠSecond ly",
+      "Ġap ost",
+      "Ġso gar",
+      "Ġre ass",
+      "ie k",
+      "æĸ ¼",
+      "Ġash amed",
+      "Ġcur ves",
+      "Ġв аж",
+      "Ġense mble",
+      "at ur",
+      "Ġphot ographer",
+      "Ġeight h",
+      "Ġwas ted",
+      "ç® Ĺ",
+      "Ġd amp",
+      "Ġм ал",
+      "are na",
+      "Ġintern ally",
+      "Ġhe els",
+      "ĠS alt",
+      "Ġbl ir",
+      "Īë Ĥĺ",
+      "Ġcontr ary",
+      "Ġprim a",
+      "Ġos s",
+      "Ġrab bit",
+      "Ġaut or",
+      "Ġbroad ly",
+      "ÃŃ st",
+      "Ġback s",
+      "íĶ Ħ",
+      "et o",
+      "Ġj ury",
+      "è ±",
+      "Ġprost u",
+      "Ġbar a",
+      "Ġpar liament",
+      "or ient",
+      "ил аÑģÑĮ",
+      "Ġind irect",
+      "á m",
+      "ĠÃ¥ r",
+      "Ġtra its",
+      "Ġd ÃŃas",
+      "ÙĦ Ùħ",
+      "ĠC T",
+      "aly st",
+      "Ġli vest",
+      "Ġk os",
+      "M ay",
+      "ĠJ ing",
+      "Ġjournal ists",
+      "Ñĩ ик",
+      "ar ms",
+      "Ġê°IJ ìĤ¬",
+      "Ġим е",
+      "Ġé gal",
+      "ĠNew ton",
+      "Ġrecover ed",
+      "Ġbra uchen",
+      "ĠBr on",
+      "а но",
+      "Ġp ale",
+      "pr ises",
+      "Ġhor as",
+      "ch ts",
+      "éĢ ļ",
+      "ÿ ÿ",
+      "ak ers",
+      "ĠAl aska",
+      "zie j",
+      "Ġsc oop",
+      "ìĿ´ ê°Ģ",
+      "ãģķ ãģĦ",
+      "c or",
+      "él é",
+      "Ġsur g",
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+      "ĠKr ist",
+      "5 4",
+      "Ġb anned",
+      "Ġsmooth ly",
+      "Ġtreat s",
+      "Ġpron ounce",
+      "Ġfl ush",
+      "Ġcam bi",
+      "Ġmusic ian",
+      "ĠAsh ley",
+      "ĠSP D",
+      "ĠBob by",
+      "Ġgl oss",
+      "res pect",
+      "Ġreview ing",
+      "Ġgener ic",
+      "Æ°á»Ľ c",
+      "ats ächlich",
+      "Ġhealth ier",
+      "ub ers",
+      "Ġд ан",
+      "ĠMedic are",
+      "5 3",
+      "Ġcomplain ts",
+      "j ac",
+      "Ġagric ultural",
+      "S pe",
+      "ĠJ ong",
+      "Ġdiox ide",
+      "ê² ¨",
+      "el ijk",
+      "ĠSh it",
+      "ain ts",
+      "ĠI an",
+      "ĠSim ply",
+      "ĠSt re",
+      "æľ ĭ",
+      "ĠG DP",
+      "5 9",
+      "as z",
+      "ĠKat ie",
+      "Ġб ÑĢ",
+      "Ġpe ek",
+      "owy ch",
+      "Ġres ort",
+      "Ġres idence",
+      "Ġsp ices",
+      "ci ó",
+      "Ġjed er",
+      "Ġem o",
+      "ar ium",
+      "Ġp uff",
+      "ë§ ī",
+      "ÑĥлÑĮ ÑĤ",
+      "Ġmet a",
+      "Ġìł Ħë",
+      "Ġoptim ization",
+      "g ang",
+      "Ġíķ Ħ",
+      "Ġefficient ly",
+      "Ġvis ually",
+      "Ġfr ost",
+      "ĠAr thur",
+      "Ġ ż",
+      "Ġachie ving",
+      "Ġrot ating",
+      "Ġl ining",
+      "Ġoccup ied",
+      "å¼ Ł",
+      "ment ation",
+      "Ġstretch ing",
+      "Ġst all",
+      "ost ic",
+      "ĠS ever",
+      "Ġgl uc",
+      "Ġró ż",
+      "Ġout reach",
+      "st ra",
+      "ik en",
+      "Ġìĸĺ 기",
+      "ĠJo in",
+      "Ġim pe",
+      "Ġcompens ation",
+      "ĠT at",
+      "ĠCar los",
+      "ühr t",
+      "ĠFranc is",
+      "c ji",
+      "ye ah",
+      "Ġmembr ane",
+      "Ġex hale",
+      "Ġrel i",
+      "ĠO R",
+      "Ġrefriger ator",
+      "ĠV enez",
+      "L ike",
+      "Ġrais es",
+      "ott le",
+      "at ura",
+      "Ġrul er",
+      "Ġwe er",
+      "Ġgu ided",
+      "ĠM agn",
+      "ĠCor por",
+      "į Ķ",
+      "Ġattrib ute",
+      "ĠWo ah",
+      "Ġarr ows",
+      "Ġaw ait",
+      "ĠPr im",
+      "Ġdign ity",
+      "ĠOnt ario",
+      "isch er",
+      "Ġìĭ Ŀ",
+      "im en",
+      "ou ver",
+      "AS S",
+      "á»ĩ n",
+      "op y",
+      "achus etts",
+      "Ġelder ly",
+      "åİ Ł",
+      "F A",
+      "ĠDa ily",
+      "sh ine",
+      "Ġ5 6",
+      "è ¢",
+      "ier no",
+      "Ġskill ed",
+      "Ġgro ÃŁe",
+      "ĠO ak",
+      "第 äºĮ",
+      "igg le",
+      "ел ей",
+      "Ġbir az",
+      "Ġargu ing",
+      "Ġпо ÑįÑĤомÑĥ",
+      "Ġdr ift",
+      "Ġh arness",
+      "Ġdeix ar",
+      "aut re",
+      "ĠSee ing",
+      "Ġcapital ism",
+      "ĠE ld",
+      "z ione",
+      "ĠBe yond",
+      "Ġperfect ion",
+      "Ġho e",
+      "Ġdecl are",
+      "ал аÑģÑĮ",
+      "Ġpo ke",
+      "Ġ× ¡",
+      "Ġfight ers",
+      "ê²ł ëĭ¤",
+      "оÑĢ ов",
+      "Ġaccording ly",
+      "ĠIs a",
+      "Ġoptim ize",
+      "ĠMin istry",
+      "Ġsa ge",
+      "ìĭľë ©´",
+      "Ġben i",
+      "Ġdon ation",
+      "Ġcle ared",
+      "ĠLuck ily",
+      "Ġharm ful",
+      "µ 커",
+      "Ġc ement",
+      "п иÑģ",
+      "Ġded i",
+      "ĠCra ig",
+      "Ġdem ons",
+      "Ġcustom ize",
+      "Ġign ored",
+      "ĠT ian",
+      "Ġhop ed",
+      "ĠB ureau",
+      "Ġr i",
+      "ĠY ah",
+      "Ġso cket",
+      "Ġfeat uring",
+      "Ġpar f",
+      "ĠT E",
+      "ĠTe acher",
+      "Ġcatal og",
+      "ê°Ģ ì§Ģê³ł",
+      "ĠSe ite",
+      "Ġcon e",
+      "ĠPalest in",
+      "Ġgew oon",
+      "Ġg aining",
+      "ĠØ ¢",
+      "Ġcat ast",
+      "Ġneighb our",
+      "IS T",
+      "Ġste aling",
+      "Ġtro is",
+      "Ġint end",
+      "ĠSh oot",
+      "Ġp ione",
+      "ĠInt el",
+      "ĠL IN",
+      "Ġbright er",
+      "ĠY esterday",
+      "Ġs ow",
+      "s in",
+      "od s",
+      "Ġeth ics",
+      "Ġinterview ed",
+      "re ll",
+      "Ġrefres hing",
+      "s å",
+      "Ġabs urd",
+      "Ġph osph",
+      "f il",
+      "Ġste hen",
+      "v als",
+      "Ġcare d",
+      "æĪ ĸ",
+      "Ġde ll",
+      "b one",
+      "Ġho ch",
+      "Ġp up",
+      "Ġi o",
+      "Ġaconte ce",
+      "ell es",
+      "ĠS pl",
+      "ig i",
+      "Ġt än",
+      "Ġele phant",
+      "Ġg ates",
+      "Ġslic es",
+      "Ġpr ank",
+      "ok rat",
+      "Ġhilar ious",
+      "ĠS id",
+      "ĠëĴ ¤",
+      "Ġess ere",
+      "Ġtele phone",
+      "in ally",
+      "r ator",
+      "Ġhelicop ter",
+      "ĠiÅŁ te",
+      "Ġg id",
+      "Ġtour ist",
+      "Ġconflict s",
+      "аÑĤ а",
+      "Ġt é",
+      "Ġass ert",
+      "Ġlaund ry",
+      "ĠB om",
+      "Ġspecial ized",
+      "ĠMod ern",
+      "og raf",
+      "Ġan o",
+      "Ġret rie",
+      "ĠPut in",
+      "ĠH AR",
+      "Ġм аÑĪ",
+      "Ġα ÏĢÏĮ",
+      "Ġtut ti",
+      "Ġв ÑĤоÑĢ",
+      "ìĸ µ",
+      "ĠB ul",
+      "ëĭ¤ë ©´",
+      "ÅĤ e",
+      "æľĭ åıĭ",
+      "ar in",
+      "Ġtherap ist",
+      "Ġg Ã¥r",
+      "ĠC zy",
+      "pp e",
+      "m ir",
+      "ĠT erm",
+      "ĠBe ar",
+      "l ace",
+      "ĠMore over",
+      "ĠDis c",
+      "Ġíĥ Ģ",
+      "Ġtit led",
+      "Ġstri ps",
+      "ĠF ahr",
+      "ĠR ing",
+      "rand o",
+      "af a",
+      "èº «",
+      "Ġshort s",
+      "Ġtr unk",
+      "Ġsent ido",
+      "Ïī ν",
+      "Ġac res",
+      "Ġover d",
+      "ĠOlymp ics",
+      "åı «",
+      "ĠMer ci",
+      "ĠëĤĺ ìĺ¤",
+      "Ġg erm",
+      "am med",
+      "Ġpre gunt",
+      "ĠN ut",
+      "Ġ< /",
+      "Ġtravel s",
+      "Ġvoc abulary",
+      "et en",
+      "od er",
+      "Ġconsum ing",
+      "wr iting",
+      "è¶ ħ",
+      "Ġappe aring",
+      "Ġadjust ed",
+      "se m",
+      "Ġf rente",
+      "Ġmaxim ize",
+      "Ġzw ischen",
+      "Ġz am",
+      "c onscious",
+      "z ek",
+      "è°¢ è°¢",
+      "ha o",
+      "ì²ĺë Ł¼",
+      "ĠEp isode",
+      "Ġvis ibility",
+      "Ġm ijn",
+      "Ġviel en",
+      "ĠBr others",
+      "×Ļ× ij",
+      "Ġvä ldigt",
+      "Ġcrush ed",
+      "uf en",
+      "ä½ł åĢij",
+      "act ic",
+      "ĠB ed",
+      "ĠF A",
+      "iss ippi",
+      "Ġrem ot",
+      "Ġp ets",
+      "Ġth under",
+      "ĠM am",
+      "ìķ µì»¤",
+      "p arents",
+      "Ġb ı",
+      "Ġsurt out",
+      "Ġseg ments",
+      "Ġne hmen",
+      "Ġutil iz",
+      "ĠRub y",
+      "Ġr á»ĵi",
+      "Ġhapp ily",
+      "Ġbus h",
+      "ult an",
+      "çİ ©",
+      "Ø ¸",
+      "ĠH il",
+      "Ġla wn",
+      "Ġeyebr ows",
+      "me z",
+      "ĠSy d",
+      "re p",
+      "in f",
+      "éł Ń",
+      "Ġover head",
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+      "Ġox id",
+      "ĠW ol",
+      "Ġdestro ying",
+      "ĠAdd itionally",
+      "umb led",
+      "d ep",
+      "Ġdep os",
+      "Ġcomm od",
+      "Ġc akes",
+      "Ġtal ents",
+      "Ġpour quoi",
+      "Ġcont empl",
+      "n els",
+      "о Ñī",
+      "ĠArab ic",
+      "ĠMary land",
+      "çİ ĭ",
+      "ow o",
+      "ĠP la",
+      "ÄŁl um",
+      "Ġprop he",
+      "ĠRep resent",
+      "op ol",
+      "acc ord",
+      "ĠMe aning",
+      "Ġjoint s",
+      "Ġbra kes",
+      "ck t",
+      "Ġ199 9",
+      "Ġpublic ation",
+      "ĠRe view",
+      "ой д",
+      "Ġn iche",
+      "Ġsignific a",
+      "Ġde br",
+      "Ġoverl ap",
+      "Ġdemand ing",
+      "ĠS ó",
+      "Ġsubsequ ent",
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+      "ĠCurrent ly",
+      "Ġprevent ing",
+      "Ġ1 30",
+      "ĠC el",
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+      "wnie ż",
+      "ìķ ½",
+      "Ġкак ие",
+      "AC H",
+      "Ġg um",
+      "ĠIsrael i",
+      "ìľ¼ ëĭĪê¹Į",
+      "å ¨",
+      "ru kt",
+      "Ġcl apping",
+      "ĠMass achusetts",
+      "Ġresil ience",
+      "Ġsubscri bing",
+      "Ġjewel ry",
+      "ge bra",
+      "Ġcor rection",
+      "bo o",
+      "Ø ¦",
+      "l io",
+      "s am",
+      "Ġen velope",
+      "k al",
+      "ĠF arm",
+      "Ġc attle",
+      "Ġbr as",
+      "Ġrep ent",
+      "Ġt ones",
+      "os ion",
+      "pe ction",
+      "Ġden en",
+      "È Ľi",
+      "ĠMar g",
+      "Ġacqu ire",
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+      "Ġas pir",
+      "Ġs ized",
+      "Ġal c",
+      "Ġvib ration",
+      "t il",
+      "em in",
+      "Ġcorrel ation",
+      "Ġsing ular",
+      "Ġпо Ñıв",
+      "re k",
+      "Ġchap ters",
+      "mb re",
+      "Ġaud ition",
+      "ç as",
+      "Ġv amp",
+      "Ġt es",
+      "ĠÑĢаз в",
+      "Ġrespect ed",
+      "c in",
+      "Ġfuck in",
+      "Ġüber haupt",
+      "Ġп об",
+      "Ġal ike",
+      "¶ Ī",
+      "ro bi",
+      "î t",
+      "ĠT ouch",
+      "an za",
+      "Ġfirm ly",
+      "ĠGreet ings",
+      "sc ale",
+      "d ad",
+      "ак ÑĤи",
+      "Ġback yard",
+      "ож д",
+      "G r",
+      "ĠST E",
+      "оÑĢ ÑĤ",
+      "Ġhät te",
+      "ĠFirst ly",
+      "ĠO ften",
+      "as ures",
+      "Ġdraw s",
+      "red it",
+      "AT E",
+      "P e",
+      "C P",
+      "Ġcompe lling",
+      "Ġsubs id",
+      "Ġneighborhood s",
+      "Ġdipl om",
+      "Ġent ender",
+      "per ing",
+      "a ug",
+      "ch at",
+      "ÐĿ Ñĥ",
+      "ĠDo ll",
+      "Ġìł IJ",
+      "Ġh ose",
+      "n ar",
+      "Ġreward ing",
+      "ĠSo ld",
+      "Ġtak i",
+      "Ġbl ades",
+      "ĠK ath",
+      "Ġjog o",
+      "Ġsens ation",
+      "u ana",
+      "p el",
+      "ĠRecent ly",
+      "Ġpoly mer",
+      "ĠU P",
+      "-- -",
+      "Ġho ver",
+      "Ġrul ed",
+      "æµ ·",
+      "Ġ×Ķ× IJ×",
+      "Ġaffect ion",
+      "ĠÄij á»ĥ",
+      "Ġb ree",
+      "ç§ ģ",
+      "ĠL ay",
+      "ĠY ong",
+      "Ġrece iver",
+      "ľë ¥¼",
+      "Ġdis so",
+      "ĠQ ing",
+      "Ġé v",
+      "Ġm úsica",
+      "Ġaest hetic",
+      "ĠB reat",
+      "ĠT A",
+      "Ġaccur ately",
+      "? âĢĭ",
+      "Ġw ages",
+      "rawd ÄĻ",
+      "Ġsw allow",
+      "Ġcompl aint",
+      "Ġli ed",
+      "bec ue",
+      "Ġrelax ing",
+      "ĠPok émon",
+      "Ġte cn",
+      "b ang",
+      "³ ´ì",
+      "Ġqu ien",
+      "н омÑĥ",
+      "Ġhabit at",
+      ".... ..",
+      "ab ling",
+      "ĠÑĤак ие",
+      "Ġbes ond",
+      "Ġemploy ed",
+      "Ġarri ves",
+      "Ġvess els",
+      "ĠA x",
+      "Ġdisplay s",
+      "1 50",
+      "olog ie",
+      "ĠìĹ IJ",
+      "Ġcl o",
+      "Ġд ов",
+      "ĠÐŀ д",
+      "Ġv uel",
+      "èĬ ±",
+      "w end",
+      "Ġsl ipp",
+      "ur p",
+      "ĠL ot",
+      "Ġbull ets",
+      "Ġr age",
+      "Ġsk irt",
+      "ient es",
+      "Ġnh ững",
+      "ĠNat ural",
+      "Ġh ind",
+      "Ġwork load",
+      "m u",
+      "íĥ ľ",
+      "Ġsun set",
+      "в ол",
+      "p it",
+      "åį ģ",
+      "ĠAS H",
+      "Ġë¶ Ħëĵ¤",
+      "Ġdown stairs",
+      "é Ń",
+      "Ġcount ed",
+      "Ġn az",
+      "×ķ× ¤",
+      "ĠPhilipp ines",
+      "Ġ11 0",
+      "ĠPark er",
+      "Ġg itu",
+      "Ġinter es",
+      "Ġum bre",
+      "ĠN ature",
+      "Ġj er",
+      "en os",
+      "Ġpanel ists",
+      "Ġco ating",
+      "Ġch erry",
+      "ĠP ent",
+      "ĠM ist",
+      "reg ation",
+      "Ġv ind",
+      "ĠCor ps",
+      "ĠM ission",
+      "Ġno ble",
+      "Ġfon ction",
+      "Ġwarri or",
+      "Ġprot ests",
+      "our i",
+      "Ġconstitution al",
+      "ÅĤ am",
+      "Ġemer ged",
+      "Ġd ye",
+      "ĠTry ing",
+      "ig m",
+      "ä¸Ģ 个",
+      "é qu",
+      "L O",
+      "ĠV erm",
+      "er ving",
+      "ĠT IM",
+      "ĠC i",
+      "Ġfree zer",
+      "Ġgrup o",
+      "ĠSp orts",
+      "ĠпÑĢ ог",
+      "ĠÙĦ ا",
+      "other ap",
+      "iff any",
+      "b ian",
+      "Ġrank ed",
+      "Ġpropos als",
+      "ĠÄij ây",
+      "Ġfree zing",
+      "Ġinsect s",
+      "v il",
+      "Ġcomp ost",
+      "çİ °",
+      "Ġse mana",
+      "Ġdistingu ish",
+      "Ġfacil itate",
+      "Ġplus ieurs",
+      "Ġver g",
+      "Ġalgun s",
+      "ĠTik Tok",
+      "ĠEx press",
+      "м енÑĤ",
+      "S U",
+      "Ġint imate",
+      "ĠAut hor",
+      "Ġwitness es",
+      "Ġkal au",
+      "Ġargu ed",
+      "Ġavo iding",
+      "ct ive",
+      "Ġpurs uing",
+      "Ġsy ll",
+      "á vel",
+      "ĠAtl anta",
+      "ĠUt ah",
+      "ĠT ill",
+      "Ġer f",
+      "Ġ20 22",
+      "ä ter",
+      "Ġfun eral",
+      "ĠFl ash",
+      "ĠAtl antic",
+      "Ġge le",
+      "ì¦ Ī",
+      "Ġmort gage",
+      "ĠëĦ ĺ",
+      "lic ht",
+      "Ġamb itious",
+      "ĠBeij ing",
+      "Ġd iving",
+      "Ġun box",
+      "ill as",
+      "Ġot ras",
+      "Ġev ac",
+      "Ġmar ine",
+      "ĠÑģоз д",
+      "ĠCre ate",
+      "Ġg j",
+      "Ġfrequ encies",
+      "ing ton",
+      "ĠRom ans",
+      "Ġaim ing",
+      "ĠB uff",
+      "Ġem peror",
+      "ĠMo i",
+      "Ġprom ising",
+      "ãģ ľ",
+      "Ġalg uma",
+      "Ġpas a",
+      "Ġdis orders",
+      "S I",
+      "Ġsucceed ed",
+      "Ġcuer po",
+      "Ġsod ium",
+      "Ġst ub",
+      "he iro",
+      "Ġdelay ed",
+      "et era",
+      "t w",
+      "Ġsyn c",
+      "h d",
+      "Ġtour ists",
+      "Ġsy st",
+      "Ġm ét",
+      "Ġqual ify",
+      "ĠO thers",
+      "ll ers",
+      "аÑĤелÑĮ но",
+      "ĠÐŀ на",
+      "Ġperce ive",
+      "Ġê² Ģ",
+      "Ġê°Ģ ìŀ¥",
+      "Ġи Ñģк",
+      "ĠM atter",
+      "ĠBl uetooth",
+      "Ġpe arl",
+      "Ġar ise",
+      "Ġmon ument",
+      "Ġим енно",
+      "ag i",
+      "ÙĦ ÙĬ",
+      "Ġr ho",
+      "Ġsm arter",
+      "Ġcon j",
+      "ок а",
+      "Ġke en",
+      "ĠT reat",
+      "к лÑİÑĩ",
+      "Ġpack et",
+      "els ius",
+      "ĠAl ab",
+      "и ни",
+      "Ġp si",
+      "Ġenjoy able",
+      "ĠEll en",
+      "Ġв м",
+      "Ġelimin ated",
+      "ĠR ow",
+      "Ġzomb ie",
+      "ĠK u",
+      "Ġphr ases",
+      "Ġg ren",
+      "ut er",
+      "Ġdire kt",
+      "× ĸ",
+      "en en",
+      "us a",
+      "ĠÑģл ов",
+      "Ä °",
+      "ĠG h",
+      "Ġcor rid",
+      "Ġque er",
+      "ĠL inda",
+      "Ġon a",
+      "Ġoblig ation",
+      "d ar",
+      "ĠØ µ",
+      "em ment",
+      "ac ies",
+      "Ġscrew ed",
+      "Ġn ak",
+      "Ġay ud",
+      "ä¸ Ķ",
+      "á r",
+      "le z",
+      "Ġdr own",
+      "ĠMedic ine",
+      "Ġlab s",
+      "Ġjus qu",
+      "ĠG onna",
+      "Ġterror ist",
+      "qu est",
+      "Ġfar ther",
+      "Ġrepl ied",
+      "ĠS W",
+      "ĠMiss issippi",
+      "ish na",
+      "Ġhold er",
+      "Ġre ign",
+      "Ġaccept ance",
+      "Ġu l",
+      "¶ Į",
+      "ĠHot el",
+      "ĠCo oper",
+      "t an",
+      "ĠG rab",
+      "Ġv apor",
+      "Ġact ed",
+      "ĠK ang",
+      "f an",
+      "ĠìĿ´ì ĥģ",
+      "çĶļ 麼",
+      "ut et",
+      "Ġword t",
+      "Ġfar ms",
+      "d at",
+      "Ġcou ples",
+      "Ġbe ads",
+      "ient os",
+      "Th en",
+      "ä¿ Ĥ",
+      "os ity",
+      "ĠStan ford",
+      ". -",
+      "W ait",
+      "Ġdat as",
+      "o ire",
+      "Ġhasht ag",
+      "im me",
+      "Ġencounter ed",
+      "Ġshout ing",
+      "Ġresist ant",
+      "ĠSe ung",
+      "Ġtra gic",
+      "ĠD raw",
+      ", ,",
+      "Ġshow case",
+      "ĠA F",
+      "ĠSt ri",
+      "Ġback ed",
+      "ĠÑĥ г",
+      "ĠбÑĥд ÑĥÑĤ",
+      "ĠCo le",
+      "e urs",
+      "( ?)",
+      "Ġesca ped",
+      "AS T",
+      "ĠAs sembly",
+      "Ġstick er",
+      "Ġmie ux",
+      "Ġentertain ing",
+      "ĠD ON",
+      "ĠAm end",
+      "ĠK arl",
+      "Ġin hib",
+      "s st",
+      "ie g",
+      "~~ ~",
+      "Ġhook ed",
+      "Ġliter al",
+      "Ġsun ny",
+      "st eps",
+      "Ġë° ľë",
+      "ĠMar ine",
+      "Ġsu e",
+      "Ġprison ers",
+      "ĠE b",
+      "5 8",
+      "Ġdr ums",
+      "Ġgu ilt",
+      "al g",
+      "Ġhapp ier",
+      "ĠC M",
+      "ĠìķĦëĭĪ ìķ¼",
+      "ĠÐŁ еÑĢ",
+      "Ñĥ лÑı",
+      "Ġkey word",
+      "ĠPar ce",
+      "ĠFore ign",
+      "ĠAm anda",
+      "ç¥ ŀ",
+      "Ġëª ©",
+      "pl ess",
+      "Ī ¬",
+      "ó mo",
+      "Ġqual quer",
+      "ìĿ´ë Ŀ¼ê³ł",
+      "Ġconspir acy",
+      "Ġstraw berry",
+      "Ġhat ten",
+      "E s",
+      "Ġsp os",
+      "Ġvill ages",
+      "Ġle v",
+      "ĠÑģ ÑĢед",
+      "Ġw aking",
+      "Ġcalcul ations",
+      "ĠÙħ ع",
+      "Ġpour ing",
+      "Ġleb ih",
+      "Ġpol ish",
+      "ĠT out",
+      "Ġfun ktion",
+      "м о",
+      "ĠT i",
+      "Ġwas ting",
+      "ist ically",
+      "Ġmanip ulate",
+      "Ġsimpl ify",
+      "Ġteam mates",
+      "Ġб о",
+      "Ġcont am",
+      "ĠQu ite",
+      "Ġkur z",
+      "ĠC and",
+      "ty pe",
+      "outhe ast",
+      "Ġfinan cially",
+      "ол н",
+      "els on",
+      "Ġfore head",
+      "u age",
+      "na udible",
+      "ĠBeh ind",
+      "Ġnegoti ations",
+      "Ġë§Ī ìĿĮ",
+      "Ġaltern atives",
+      "r ank",
+      "hold er",
+      "æĩ ī",
+      "Ġhe aled",
+      "ÑĤо Ñĩ",
+      "ĠSpe c",
+      "ä» ¶",
+      "ä»ĸ åĢij",
+      "Ġexhib it",
+      "Ġshall ow",
+      "Ġgo b",
+      "Ġë ľ",
+      "Ġfrust ration",
+      "ÃŃ o",
+      "Ġmel ting",
+      "ĠSt orm",
+      "Ġpat ent",
+      "ĠBar cel",
+      "Ġped est",
+      "ÙĪ Ùħ",
+      "Ġt ai",
+      "ĠM ode",
+      "Ġw il",
+      "Ġëª¨ë ¥´",
+      "Ġégal ement",
+      "éĤ£ 麼",
+      "Ġ×IJ× Ĺ",
+      "ay an",
+      "Ġam azed",
+      "ì§Ģ ëĬĶ",
+      "Ġha ciendo",
+      "ĠìĿ´ì ķ¼",
+      "λ α",
+      "ภĤ",
+      "еÑĤ а",
+      "Ġexam s",
+      "Ġtrave lling",
+      "P ress",
+      "и ÑĢÑĥ",
+      "Ġbas eline",
+      "Ġbus es",
+      "Ġrein for",
+      "ven ant",
+      "ĠTr uth",
+      "Ŀ ½",
+      "o be",
+      "Ġye ll",
+      "Ġsaus age",
+      "T F",
+      "ĠEv il",
+      "Ġme iner",
+      "×Ļ× §",
+      "Ġhope ful",
+      "Ġró wnież",
+      "ĠPer ò",
+      "t wo",
+      "nd er",
+      "Ġм иÑĢ",
+      "Ġcons cience",
+      "ĠWar ren",
+      "ick y",
+      "Ġaim ed",
+      "Ġgö ra",
+      "X T",
+      "Ġpy ram",
+      "R ed",
+      "éĽ »",
+      "at u",
+      "ĠEst a",
+      "Ġearning s",
+      "Ġh ats",
+      "ĠSt adt",
+      "ick et",
+      "point s",
+      "in ander",
+      "Ġmotor cycle",
+      "Ġëı Į",
+      "Ġíķ´ì ķ¼",
+      "k om",
+      "ĠD ing",
+      "æ Ĵ",
+      "Ġrec urs",
+      "Ġestim ates",
+      "Ġder ni",
+      "Ġvers ch",
+      "ãģĿ ãģ®",
+      "ĠM IC",
+      "ив аÑĤÑĮ",
+      "ĠпÑĢо ÑĪ",
+      "Ġd ost",
+      "Ġв ÑģÑĤÑĢ",
+      "Ġw iel",
+      "Ġs iblings",
+      "Ġд ев",
+      "Ġear liest",
+      "Ġfat igue",
+      "Ġn hi",
+      "Ġgust a",
+      "Ġbon ne",
+      "æľĢ å¾Į",
+      "f rom",
+      "ĠJen ny",
+      "Ġsupposed ly",
+      "int age",
+      "Ġcount ies",
+      "Ġun re",
+      "Ġplant ing",
+      "ĠGr ac",
+      "ĠGen esis",
+      "ĠAl pha",
+      "ys z",
+      "Ġt ile",
+      "Ġê²½ ìļ°",
+      "Ġ×Ļ ש",
+      "qu el",
+      "Ġdistrib ute",
+      "de f",
+      "é ral",
+      "Ġcl utch",
+      "adel ph",
+      "ĠPlay Station",
+      "Ħ ¸",
+      "Ġs j",
+      "bre aking",
+      "ĠëIJ ĺë",
+      "ĠC uba",
+      "ĠRuss ians",
+      "ĠMAR K",
+      "Ġper se",
+      "Ġrestrict ed",
+      "ig es",
+      "ĠTra vel",
+      "Ġelectron ics",
+      "Ġquar ters",
+      "ĠKe ith",
+      "s ized",
+      "Ġdead line",
+      "aren th",
+      "ĠvÃŃde os",
+      "Ġprotocol s",
+      "am ment",
+      "ĠTra ining",
+      "ĠÃ ¢",
+      "Ġsequ el",
+      "н ак",
+      "Ġke inen",
+      "Ġmatt ress",
+      "lud ing",
+      "Ġclass ified",
+      "Ġreact or",
+      "ĠK ont",
+      "Ġpass ar",
+      "Ġhon our",
+      "or ig",
+      "IN A",
+      "ĠN athan",
+      "в а",
+      "ĠÑģказ аÑĤÑĮ",
+      "t ır",
+      "Ġexclus ively",
+      "Ġsh ades",
+      "ĠпÑĢо ÑĨ",
+      "Ġoccas ions",
+      "ij a",
+      "çļĦ æĻĤåĢĻ",
+      "åİ ²",
+      "æħ ¢",
+      "f ig",
+      "Ġt us",
+      "Ġrem em",
+      "ĠChrist opher",
+      "Ġsl ime",
+      "Ġalg una",
+      "ĠF ortunately",
+      "Ġl ors",
+      "v oll",
+      "a ver",
+      "Ġout let",
+      "ĠLinked In",
+      "ĠExec utive",
+      "Ġorg ans",
+      "ĠBe gin",
+      "ĠíĻ Ķ",
+      "Ġtrans plant",
+      "ra gen",
+      "V O",
+      "ĠF ör",
+      "Ġب اÙĦ",
+      "ĠAnd re",
+      "is ine",
+      "Ġlast s",
+      "Ġhist ória",
+      "Ġl uz",
+      "Ġcoll ar",
+      "Ġkid na",
+      "Ġopt ical",
+      "io v",
+      "Ġto b",
+      "Ġex terior",
+      "Ġmet ric",
+      "ie ur",
+      "Ġt roll",
+      "ĠÑĢ оз",
+      "æĺ Ł",
+      "Ġt ô",
+      "ĠìĺĪ ìģ",
+      "ĠGes etz",
+      "Ġе д",
+      "Ġdenomin ator",
+      "ì ³",
+      "Ġlet t",
+      "åħ ī",
+      "Ġgr Ã¶ÃŁ",
+      "é¡ ĺ",
+      "ĠL uther",
+      "Ġrest e",
+      "Ġrese mb",
+      "Ġperm et",
+      "ks i",
+      "Ġf isher",
+      "ãģŁ ãģĦ",
+      "ĠV on",
+      "íĶ ¼",
+      "ĠÏĥ ÏĦο",
+      "Ġlo cks",
+      "Ġsho ots",
+      "Ġkam u",
+      "ĠK er",
+      "ĠO bs",
+      "çĿ Ģ",
+      "Ġb ili",
+      "Ġë° ±",
+      "Ġtort ure",
+      "ass y",
+      "Ġи г",
+      "Ġlast ing",
+      "好 çļĦ",
+      "Ġtien es",
+      "Ġrece ives",
+      "ĠOsc ar",
+      "Ġremember ing",
+      "Ġproblem as",
+      "Ġ ia",
+      "åĺ Ľ",
+      "Ġmemor able",
+      "Ġjour s",
+      "Ġfa çon",
+      "am ic",
+      "Ġë´ ¤",
+      "at ique",
+      "ĠëŃ Ķê°Ģ",
+      "Ġz ip",
+      "h alt",
+      "ĠðŁ ĺ",
+      "Ġfr ies",
+      "Ġfind en",
+      "g ra",
+      "ÑĢÑĥ д",
+      "im port",
+      "Ġëĭ ¬ë",
+      "Ġi ki",
+      "Ġcompl aining",
+      "Ġfaz endo",
+      "Ġgo ogle",
+      "Ġtab s",
+      "Ġëĵ¤ ìĸ´ì",
+      "ãĤ ¦",
+      "ug o",
+      "ier to",
+      "au fen",
+      "Ġ먼 ìłĢ",
+      "Ġskull e",
+      "Ġsu iv",
+      "Ġsp y",
+      "ĠK ai",
+      "éĤ£ åĢĭ",
+      "Ġmart ial",
+      "Ġon der",
+      "èª °",
+      "at ility",
+      "Ġirgend wie",
+      "Ġcl ap",
+      "int ell",
+      "Ġinstall ing",
+      "Ġun iqu",
+      "ĠCent re",
+      "ast s",
+      "u ar",
+      "Ġrev is",
+      "Ġthreat ening",
+      "ra is",
+      "Ġcu id",
+      "s ka",
+      "Ġresol ved",
+      "Ġr ides",
+      "Ġfail ures",
+      "Ġse mb",
+      "Ġmal es",
+      "U FF",
+      "å¾Ī å¤ļ",
+      "Ġtr ês",
+      "app ed",
+      "Ġnewsp apers",
+      "ri et",
+      "Ġapplaud s",
+      "Ð ĵ",
+      "Ġãģ ¯",
+      "ĠN C",
+      "åį ĥ",
+      "æĻĤ éĸĵ",
+      "Ġh eter",
+      "Ġhaz ard",
+      "Ġr y",
+      "Ġstrict ly",
+      "Ġ5 4",
+      "Ġëĵ¤ìĸ´ ê°Ģ",
+      "Ġsp ont",
+      "Ġt atsächlich",
+      "Ġë§IJ ìĶ",
+      "la ub",
+      "Ġabsor bed",
+      "acaģ ız",
+      "Ġon u",
+      "ĠÐIJ н",
+      "Ġexplicit ly",
+      "Ġìŀ ¬",
+      "ĠFut ure",
+      "acht en",
+      "Ãł o",
+      "y on",
+      "Ġser ia",
+      "ĠHer ren",
+      "ce j",
+      "ĠAl bert",
+      "ìĿ´ ëĬĶ",
+      "ect or",
+      "Ġpack ing",
+      "Ġvirt ue",
+      "Ġven ir",
+      "D D",
+      "Ġy az",
+      "Ġlog s",
+      "ĠPhot oshop",
+      "Ġs id",
+      "l ings",
+      "Ġremot ely",
+      "ĠD ifferent",
+      "Ġoper ated",
+      "light s",
+      "Ġdisc rimin",
+      "ist ance",
+      "ĠG RE",
+      "Ġpl ac",
+      "Ġsh irts",
+      "Ġjust ify",
+      "Ġtrabal ho",
+      "ut il",
+      "v oc",
+      "Ġqu art",
+      "ĠÎ ¤",
+      "S C",
+      "ĠS R",
+      "Ġ- \"",
+      "Ġhes itate",
+      "Ġp ak",
+      "èĩ ³",
+      "gu a",
+      "J o",
+      "Ġsou vent",
+      "ĠAng ela",
+      "esse e",
+      "adelph ia",
+      "ark s",
+      "Ġwe ed",
+      "Ġkann st",
+      "åĤ Ļ",
+      "Ġê·¸ëŁ¬ ëĭĪê¹Į",
+      "Ġplut ôt",
+      "ĠComm ander",
+      "Ġsummar ize",
+      "௠Ģ",
+      "Ġ9 8",
+      "ãģ ĩ",
+      "Ġdevelop ments",
+      "ĠC ost",
+      "Ġtheoret ical",
+      "Ġo re",
+      "Ġmet all",
+      "οÏħ ν",
+      "f ahr",
+      "Ðļ ÐIJ",
+      "Ġch uck",
+      "Ġadapt ed",
+      "ĠOk lah",
+      "ĠNether lands",
+      "Ġpo et",
+      "st o",
+      "k at",
+      "Ġwe ars",
+      "ç ¯",
+      "Ġìĸ´ë ĶĶ",
+      "ĠEst o",
+      "Ġlaugh ed",
+      "Ġdon ner",
+      "Ġë į°",
+      "ĠìĽ IJë",
+      "oc ur",
+      "ĠK ick",
+      "ĠDet roit",
+      "Ġbicy cle",
+      "Ġlack ing",
+      "ph abet",
+      "ĠK end",
+      "A ss",
+      "Ġreve als",
+      "ĠÎ ł",
+      "ĠNo ah",
+      "¦¬ ëĬĶ",
+      "Ġsell s",
+      "ĠAlab ama",
+      "Ġterr ific",
+      "ĠE lement",
+      "Ġí Ĩ",
+      "Ġtur bo",
+      "ĠH om",
+      "Ġtheore m",
+      "Ġadvent ures",
+      "Ġpurch asing",
+      "ĠT á",
+      "Ġм аÑĤ",
+      "Ġve mos",
+      "Ġd uties",
+      "Ġwen ig",
+      "Ġbo oth",
+      "Ġent rar",
+      "V A",
+      "Ġge ars",
+      "ĠJa e",
+      "è n",
+      "Ġcal cium",
+      "ĠRober ts",
+      "ĠпÑĢоб лем",
+      "Ġrib bon",
+      "Ġн азÑĭв",
+      "Ġla v",
+      "Ġinter ventions",
+      "ĠUlt ra",
+      "Ġnam ely",
+      "Ġadequ ate",
+      "Ġre cap",
+      "Ġdo ck",
+      "f ting",
+      "Ġvo i",
+      "Ġconsult ation",
+      "ĠÑģ ем",
+      "Ġpod em",
+      "Ġposs ession",
+      "Ġcl ues",
+      "ĠRus sell",
+      "Ġrenew able",
+      "åݲ 害",
+      "ĠÑĥ з",
+      "in formation",
+      "igg ers",
+      "W ith",
+      "wn o",
+      "Ġelabor ate",
+      "ctor al",
+      "ĠD ow",
+      "Ġram en",
+      "æı IJ",
+      "á» ķ",
+      "Ġer ste",
+      "ĠZ el",
+      "ãĥ Ĺ",
+      "Ġqu asi",
+      "Ġн ак",
+      "ç§ Ĵ",
+      "ĠSt ars",
+      "Ġtri bal",
+      "Ġse ated",
+      "Ġw ol",
+      "Ġch ol",
+      "äm ä",
+      "Ġout break",
+      "Ġc res",
+      "Ġunser er",
+      "Ġí ijľ",
+      "Ġunder water",
+      "Ġass ure",
+      "OO D",
+      "Ġnap rawdÄĻ",
+      "Ġestablish ment",
+      "Ġinc on",
+      "Ġdifer ente",
+      "Ġex cus",
+      "ĠD im",
+      "о Ñħ",
+      "ĠL ing",
+      "ro log",
+      "Ġãģ ¾",
+      "Ġout doors",
+      "na j",
+      "Ġepid emic",
+      "Ġun ters",
+      "Ġ3 000",
+      "ĠGab riel",
+      "ĠìĹĨ ëĬĶ",
+      "Ġenc l",
+      "ĠO der",
+      "ĠF oot",
+      "p as",
+      "ĠZ uk",
+      "åĵ ¡",
+      "Ġwork flow",
+      "Ġun p",
+      "Ġall iance",
+      "ens chaft",
+      "Ġyog urt",
+      "ин е",
+      "Ġer u",
+      "Ġf iz",
+      "äº Ķ",
+      "Ġa ÅŁ",
+      "Ġap rend",
+      "Ġcual quier",
+      "Ġcarr ots",
+      "ın ın",
+      "af ood",
+      "Ġflo ors",
+      "Ġkey words",
+      "Ġsp otted",
+      "Ġdr ank",
+      "Ġpar as",
+      "Ġúlt imo",
+      "Ġhab lar",
+      "Ġprose cut",
+      "ìĹIJ ëıĦ",
+      "éĸĭ å§ĭ",
+      "Ġé p",
+      "Ġstick ers",
+      "Ġpush es",
+      "k h",
+      "Ġrest art",
+      "ĠTh under",
+      "á» Ŀi",
+      "Ġmuit a",
+      "Ġfo x",
+      "arde ÅŁ",
+      "ĠZ ach",
+      "ĠMine craft",
+      "ç ¸",
+      "Ġ== ==",
+      "Ġgö re",
+      "Ġst ance",
+      "ig ung",
+      "Ùİ Ùij",
+      "k ä",
+      "Ġteaching s",
+      "é Ĩ",
+      "Ġdec ay",
+      "Ġr ic",
+      "om ena",
+      "ĠвÑģ ем",
+      "ch ten",
+      "ĠV ert",
+      "Ġíķľ êµŃ",
+      "¬ ´ë",
+      "Ġco c",
+      ": )",
+      "ke iten",
+      "ĠB A",
+      "eth eless",
+      "Ġhead quarters",
+      "Ġsp ike",
+      "ĠB ase",
+      "Ġ10 1",
+      "Ġcoordin ates",
+      "Ġt ard",
+      "Ġbo iled",
+      "ĠMon ster",
+      "Ġnote book",
+      "Ġê´ Ģ",
+      "ĠW ake",
+      "ĠSet ting",
+      "ìĿ´ì Ĺ",
+      "ĠSyd ney",
+      "ĠFin n",
+      "Ġlob by",
+      "å¾ ŀ",
+      "Ġsen iors",
+      "ни Ñħ",
+      "av an",
+      "ĠJ E",
+      "Ġtra ff",
+      "th ink",
+      "Ġsl ap",
+      "ĠCast le",
+      "© ng",
+      "Ġalgun os",
+      "ĠPerson ally",
+      "ĠM ale",
+      "íĭ °",
+      "ĠGener ally",
+      "ĠP el",
+      "Ġdi as",
+      "Ġevol ving",
+      "it ol",
+      "в оÑĢ",
+      "Ġple in",
+      "Ġfl ights",
+      "Ġele ven",
+      "owe j",
+      "á»ij ng",
+      "Ġa ku",
+      "Ġgl ance",
+      "Ġconnect ivity",
+      "Ġbal d",
+      "Ñĭ Ñĩ",
+      "Ġint est",
+      "á g",
+      "ĠGR Ãľ",
+      "ibl ical",
+      "ĠP apa",
+      "Ġp ity",
+      "Ġf aint",
+      "Ġwur den",
+      "Ġleg ally",
+      "Ġpre y",
+      "ĠSci ences",
+      "ĠпÑĢ оÑģ",
+      "Ġtrain er",
+      "Ġprobl ème",
+      "Ġkil o",
+      "к ого",
+      "Ġbrid ges",
+      "8 9",
+      "Ġlast ed",
+      "Ġeleg ant",
+      "b ows",
+      "Ġpal ab",
+      "Ġdirect ory",
+      "ä¸į æľĥ",
+      "Ġbul b",
+      "pe ople",
+      "I X",
+      "Ġge b",
+      "Ġ6 6",
+      "ĠTenn essee",
+      "ah len",
+      "ie val",
+      "Ġca ut",
+      "ĠDam en",
+      "pl o",
+      "ian e",
+      "ал е",
+      "att an",
+      "ĠاÙĦ س",
+      "Ġrisk y",
+      "Ġslee ve",
+      "Ġinc idents",
+      "Ġë° ķ",
+      "C o",
+      "Ġapplic able",
+      "Ġimper ial",
+      "ĠPhil ip",
+      "ĠYe a",
+      "еÑĢ о",
+      "Ġпок аз",
+      "ü ne",
+      "ìĺ Ģ",
+      "H ub",
+      "t or",
+      "Ġsig u",
+      "c end",
+      "Ġpolit ically",
+      "ĠìĤ ´",
+      "Ġp ars",
+      "Ġou v",
+      "Ġprime ira",
+      "ĠSh ah",
+      "Ġsat ur",
+      "Ġcomb ust",
+      "Ġpromot ed",
+      "ì£ ¼ë",
+      "æĢ ķ",
+      "Ġtempl ates",
+      "Ġëĭ ¬",
+      "Ġha ul",
+      "ĠÑĤ еÑĢ",
+      "Ġsl iding",
+      "ced ented",
+      "Ġãģ ®",
+      "child ren",
+      "M R",
+      "ĠWe i",
+      "Ġb ör",
+      "æĹ ©",
+      "Ġpróxim o",
+      "ar ÃŃa",
+      "Ġsam pling",
+      "ел ен",
+      "es i",
+      "ĠDan ielle",
+      "ĠOklah oma",
+      "è ħ",
+      "çķ Į",
+      "еÑģ п",
+      "ĠDV D",
+      "ĠвÑĭ п",
+      "r ous",
+      "c ons",
+      "Ġenhan ced",
+      "éĽ £",
+      "Ġpast or",
+      "ĠSud denly",
+      "è® ĵ",
+      "f ar",
+      "P ER",
+      "ĠN g",
+      "1 000",
+      "Ġche w",
+      "Ġrum ors",
+      "ĠA na",
+      "Ġann ées",
+      "ĠÑĥ ÑģÑĤ",
+      "ĠPhil adelphia",
+      "åĹ ¯",
+      "еж дÑĥ",
+      "Ġeffect iveness",
+      "è¿Ļ æł·",
+      "ét é",
+      "Ġd ing",
+      "Ġrelig ions",
+      "Ġag ed",
+      "zie Äĩ",
+      "ĠR ic",
+      "ĠK ap",
+      "ĠP age",
+      "Ġs ü",
+      "Ġnäm lich",
+      "Ġman kind",
+      "Ġrest ing",
+      "Ġinflu ences",
+      "ĠSch ul",
+      "Ġн ев",
+      "Ġman a",
+      "Ġconsum ed",
+      "ĠP om",
+      "ç¾İ åľĭ",
+      "Ġconsegu ir",
+      "ĠThanks giving",
+      "ĠHind u",
+      "la is",
+      "Ġth rive",
+      "Ġcont our",
+      "аÑĨи Ñı",
+      "Ġfal ando",
+      "ĠJ á",
+      "z an",
+      "иÑĤ Ñĥ",
+      "ip her",
+      "j amin",
+      "ĠHall o",
+      "Ġ16 0",
+      "ĠоÑģ об",
+      "Ġmet e",
+      "Ġìķ Įë",
+      "ĠBarcel ona",
+      "let ter",
+      "ĠÐĿ еÑĤ",
+      "å Ļ",
+      "Ġad emás",
+      "Ġcoord ination",
+      "un ts",
+      "Ġsl op",
+      "ĠпÑĢ ид",
+      "ì§Ģ ë§ī",
+      "Ġquestion ing",
+      "Ġdies el",
+      "Ġde j",
+      "Ġaff irm",
+      "įĶëĿ¼ê³ł ìļĶ",
+      "ien ne",
+      "Ġcr ank",
+      "Ġpredict ions",
+      "Ġphys i",
+      "ch sel",
+      "Ġcomb inations",
+      "Ġex cellence",
+      "éĢĻ 麼",
+      "á» Ŀ",
+      "wid th",
+      "we ed",
+      "Ħë ¥¼",
+      "Ħë §Ī",
+      "Ġal to",
+      "Ġda iry",
+      "ĠNorm al",
+      "pp en",
+      "Ġob en",
+      "Ġdevast ating",
+      "Ġpo z",
+      "ĠH us",
+      "m az",
+      "Ġwarn ed",
+      "Ġden k",
+      "ĠA uss",
+      "Ġtrad es",
+      "he ll",
+      "Ġprim ero",
+      "Ġm ia",
+      "в аÑĢ",
+      "ب ÙĬ",
+      "Ġkick s",
+      "Ġa ÄŁ",
+      "ĠM ü",
+      "Ġl uc",
+      "ени ем",
+      "ĠStand ard",
+      "r ice",
+      "ĠC ub",
+      "Ġg ou",
+      "ĠJo ão",
+      "Ñĥ Ñģк",
+      "Ġen qu",
+      "£ Į",
+      "ge w",
+      "Ġíģ °",
+      "ow ania",
+      "ian i",
+      "Ġf akt",
+      "Ñı ни",
+      "Ġbe f",
+      "Ġthumb na",
+      "Ġce ux",
+      "æŃ¡ è¿İ",
+      "app le",
+      "N EN",
+      "Ġg ad",
+      "ap on",
+      "ĠFant astic",
+      "Ġconcent rated",
+      "g irl",
+      "l ene",
+      "ĠÐĶ лÑı",
+      "Ġé ta",
+      "a an",
+      "Ġout ta",
+      "Ġnar c",
+      "ĠB ody",
+      "br ush",
+      "Ġlegisl ative",
+      "ĠMeg an",
+      "Ġmist aken",
+      "ĠMiss ouri",
+      "Ġlabel ed",
+      "лÑı еÑĤÑģÑı",
+      "Ġreal ised",
+      "y orsun",
+      "ãģĤãĤĬ ãģĮãģ¨ãģĨ",
+      "ĠSaf ety",
+      "Ġacceler ate",
+      "Ġsan ctions",
+      "Ġpe e",
+      "Ġj uego",
+      "Ġpe ppers",
+      "Ġw al",
+      "ê¸ ī",
+      "ell ow",
+      "Ġж ен",
+      "Ġcin co",
+      "ĠÑģ иÑģÑĤ",
+      "co very",
+      "Ġgr am",
+      "Ġé po",
+      "ĠBM W",
+      "iv ol",
+      "ĠCh em",
+      "çļĦ 話",
+      "use ment",
+      "ĠSupp ose",
+      "Ġê°Ģ ì§Ģê³ł",
+      "Ġmill enn",
+      "ĠT un",
+      "Ġmed al",
+      "Ġha cia",
+      "Ġstimul us",
+      "Ġbright ness",
+      "a ient",
+      "ĠH ands",
+      "in et",
+      "Ġcoal ition",
+      "åŃ ¸",
+      "Ġris es",
+      "r ina",
+      "Ġsc oot",
+      "Ġ ãģ§",
+      "Ġdef ending",
+      "Ġin vers",
+      "Ġh ills",
+      "Ġfulf illed",
+      "åĪ° äºĨ",
+      "ll ie",
+      "Ġad oles",
+      "ĠCh ase",
+      "åĸľ æŃ¡",
+      "ĠJ J",
+      "Ġne uen",
+      "ĠT ru",
+      "Ġinher it",
+      "Ġsixt y",
+      "ĠEx p",
+      "ĠCl ay",
+      "оÑģ об",
+      "ar na",
+      "ĠImper ial",
+      "ĠÑįÑĤ а",
+      "Ġso cially",
+      "at y",
+      "ody nam",
+      "Ġrib s",
+      "om ic",
+      "ĠT ol",
+      "ол ж",
+      "Ġ199 8",
+      "Ġfr am",
+      "Ġrank s",
+      "ĠбÑĥд Ñĥ",
+      "ĠCol on",
+      "H z",
+      "Ġaccommod ate",
+      "Ġexpl ode",
+      "íĦ °ë",
+      "HA EL",
+      "ĠH art",
+      "Ġжиз ни",
+      "æ ¡",
+      "Ġdel icate",
+      "ł ×Ĺ",
+      "Ġto fu",
+      "Ġachieve ments",
+      "ĠS or",
+      "Ġagre ements",
+      "Ġ5 7",
+      "Ġt amp",
+      "Ġfr ançais",
+      "Ġher bs",
+      "c orn",
+      "Ġkon k",
+      "AN A",
+      "ĠQ i",
+      "Ġpró p",
+      "Ġt iger",
+      "Ġëij ĺ",
+      "Äĥ m",
+      "Ġapp rent",
+      "ah an",
+      "Ġrul ing",
+      "Ġt sp",
+      "Ġtw itter",
+      "Ġteen ager",
+      "b us",
+      "Ġí Ĵ",
+      "ĠAmend ment",
+      "Ġt apping",
+      "ĠAP Is",
+      "åł ´",
+      "Ġmatch ed",
+      "ë ©´",
+      "W A",
+      "ĠBeaut y",
+      "Ġinevit able",
+      "Ġg ases",
+      "ĠÙ ¾",
+      "h igh",
+      "ĠO pt",
+      "Ġpred omin",
+      "Ïģ ÏĮ",
+      "Ġtub es",
+      "Ġìķ ł",
+      "ĠA a",
+      "Ġ æľī",
+      "omet own",
+      "ĠI M",
+      "Ġdes ar",
+      "ä ren",
+      "Ġм аÑģ",
+      "ĠM öglich",
+      "Ġrent al",
+      "Ġíķ¨ ê»ĺ",
+      "ĠD iana",
+      "Ġaut ism",
+      "ĠPu erto",
+      "ı ld",
+      "Ġfal an",
+      "Ġdream ing",
+      "Ġg ute",
+      "Ġк ам",
+      "Ġw reck",
+      "Ġstoryt elling",
+      "ĠLeg end",
+      "ĠUk rain",
+      "ĠпÑĢо иÑģ",
+      "ĠS K",
+      "Ġíĸ ī",
+      "ĠÅĽ wi",
+      "ĠBel ieve",
+      "Ġmost rar",
+      "ĠTod d",
+      "ĠN iger",
+      "ic ting",
+      "h ard",
+      ": //",
+      "ir able",
+      "ig ation",
+      "ĠMem bers",
+      "Ġìłľ íĴĪ",
+      "Ġdisc our",
+      "Ł ½",
+      "ri ka",
+      "ĠD N",
+      "ĠF if",
+      "ĠCap ital",
+      "ÑĢ ом",
+      "ĠS ans",
+      "y un",
+      "Ġpil ots",
+      "Ġtr at",
+      "Ġn yt",
+      "Ġë ¯¼",
+      "Ġexpon ential",
+      "Ġemer ge",
+      "Ġtraject ory",
+      "ĠпоÑĩ емÑĥ",
+      "Ġse aled",
+      "att i",
+      "Ġw ides",
+      "Ġо гÑĢ",
+      "ian ces",
+      "Ġwitness ed",
+      "O r",
+      "os i",
+      "ĠJo el",
+      "on al",
+      "èģ ½",
+      "ĠInt e",
+      "ced es",
+      "ĠGot ta",
+      "an ium",
+      "Ġfem ales",
+      "ĠLeb ens",
+      "Ġmoist ur",
+      "ĠSim ple",
+      "ĠDo ch",
+      "ar á",
+      "Ġg esehen",
+      "US T",
+      "Æ¡ i",
+      "Ġclass ification",
+      "Ġdiagon al",
+      "Ġperm ett",
+      "com p",
+      "ĠاÙĦ ØŃ",
+      "ĠMal ays",
+      "Ġgeh ört",
+      "Ġpop ped",
+      "Ġcontact ed",
+      "Ġ׼ ׾",
+      "Ġ14 0",
+      "Ġadapt ation",
+      "Ġman us",
+      "Ġtur key",
+      "Ġpre ach",
+      "br ight",
+      "Ġdown s",
+      "Ġunpre cedented",
+      "Ġmight y",
+      "Ġc ater",
+      "itt i",
+      "g s",
+      "ĠDep uty",
+      "wr ite",
+      "ĠB less",
+      "á c",
+      "Ġsum mit",
+      "Ġëı¼ ìļĶ",
+      "Ġthought ful",
+      "Ġsh red",
+      "s inging",
+      "ĠлÑĥÑĩ ÑĪе",
+      "Ġy en",
+      "Ġvibr ant",
+      "ĠWal ter",
+      "Ġhost s",
+      "Ġamb ul",
+      "Ġinv asion",
+      "og an",
+      "Ġreason ing",
+      "Ġsu cc",
+      "л екÑĤ",
+      "Ġfal a",
+      "Ġk ings",
+      "Ġgo in",
+      "Ġcal ib",
+      "ĠGRÃľ NEN",
+      "ot er",
+      "Ġein z",
+      "Ġins ulin",
+      "Ĭ ¨",
+      "Ġsc aling",
+      "ĠC orn",
+      "hy d",
+      "Ġmat te",
+      "P L",
+      "Ġali ens",
+      "ĠSe g",
+      "è¯ Ŀ",
+      "est i",
+      "ast ics",
+      "Ġwar mer",
+      "Ġing en",
+      "ĠM L",
+      "Ġro de",
+      "ĠE ye",
+      "be its",
+      "ĠB arn",
+      "» ,",
+      "ĠCh uck",
+      "Ġprofit able",
+      "ugu ese",
+      "ĠArab ia",
+      "Ġco co",
+      "Ġpued o",
+      "Ġinflamm ation",
+      "cl ip",
+      "Ġtables poons",
+      "Ġìł ij",
+      "ĠSw ed",
+      "Ġan at",
+      "ìĪ ł",
+      "Ġar rib",
+      "Ġdan cer",
+      "ĠCar ter",
+      "Ġmagn ific",
+      "st ore",
+      "éģ ¸",
+      "Ġf ade",
+      "Ġaccomp any",
+      "Ġw ahr",
+      "Ġye ast",
+      "Ġmin eral",
+      "Ġlegisl ature",
+      "ä½ ı",
+      "ir os",
+      "Ġcrowd ed",
+      "ÑĢа ÑĪ",
+      "oc ado",
+      "ìĸ´ì ķ¼",
+      "ĠíĽ Ħ",
+      "ĠBar ry",
+      "m aster",
+      "Ġnick name",
+      "Ġ\" ...",
+      "ĠR s",
+      "ĠMo ore",
+      "Ġven ue",
+      "Ġб Ñĥ",
+      "ãĥ ¡",
+      "li hood",
+      "ĠA gency",
+      "л ов",
+      "Ġk ah",
+      "ĠìĨĮë ¦¬",
+      "Ġm arsh",
+      "Ġincorpor ated",
+      "ant wort",
+      "Ġkim chi",
+      "Ġw oo",
+      "Ġdistract ed",
+      "er ies",
+      "Ġinform ación",
+      "ĠCho ose",
+      "ĠJ adi",
+      "Ġanal ogy",
+      "s ay",
+      "uff le",
+      "b ok",
+      "Ġac ids",
+      "Ġacquis ition",
+      "Ġvari ants",
+      "èµ· ä¾Ĩ",
+      "Ġpass iert",
+      "ìĿ´ë Ĥĺ",
+      "ruct ive",
+      "br ig",
+      "Ġ ãĢĮ",
+      "ep her",
+      "Ġp H",
+      "ut lich",
+      "å· ®",
+      "Ġrel ie",
+      "u ite",
+      "Ġre ception",
+      "Ġco h",
+      "ĠPre p",
+      "Ġanticip ate",
+      "æĢ §",
+      "ke e",
+      "Ġdesign ated",
+      "Ñı ÑĤи",
+      "ĠK or",
+      "ĠAn im",
+      "üh l",
+      "ĠWh it",
+      "Ġun cover",
+      "ĠMay a",
+      "ĠÑĤ огда",
+      "° ķ",
+      "uten ant",
+      "Ġìĸ ¼ë",
+      "Ġforest s",
+      "Ġmem e",
+      "Ġdistingu ished",
+      "ĠMar x",
+      "ĠL ion",
+      "Ġserv ants",
+      "ĠD iam",
+      "çķ¶ çĦ¶",
+      "ĠPol icy",
+      "į ¼",
+      "Ġtrigger ed",
+      "abil ir",
+      "ĠìĿ ij",
+      "Ġnegoti ate",
+      "Ġfe z",
+      "Ġer w",
+      "Ġvar ies",
+      "Ġj emand",
+      "Ġdis charge",
+      "ÑģÑı Ñĩ",
+      "ĠP AR",
+      "ĠAff airs",
+      "Ġvot er",
+      "Ġat en",
+      "Ġcro is",
+      "ob il",
+      "ĠO ops",
+      "ĠAr c",
+      "ĠHe ather",
+      "ank a",
+      "Ġsim ples",
+      "ο ν",
+      "\" >",
+      "Ġch ords",
+      "ĠSand ers",
+      "Ġë¶ Ħë",
+      "B en",
+      "Ġdar über",
+      "ili ans",
+      "Ġorder ing",
+      "ĠMan h",
+      "Ġkil ogram",
+      "Ġkar ÅŁ",
+      "Ġgr asp",
+      "Ġghost s",
+      "al en",
+      "ĠJ edi",
+      "Ġб ли",
+      "Ġdownload ed",
+      "Ġconduct ing",
+      "ĠH ak",
+      "Ġresearch er",
+      "il an",
+      "go od",
+      "ĠH annah",
+      "ĠdÃ¼ÅŁ ün",
+      "ĠMess iah",
+      "u ity",
+      "ion a",
+      "Ġprob able",
+      "ĠY E",
+      "Ġindepend ently",
+      "Ġbuff er",
+      "b urn",
+      "our d",
+      "ĠMc K",
+      "Ġl ingu",
+      "uj emy",
+      "еÑĢ ÑĤ",
+      "Ġintuit ive",
+      "Ġcrack s",
+      "app ropri",
+      "nt y",
+      "Ġge en",
+      "Ġl end",
+      "Ġcert ification",
+      "ID S",
+      "un ter",
+      "pe es",
+      "Ġtr ump",
+      "Ġbank rupt",
+      "Ġfe as",
+      "è Ĺ",
+      "Ġdu ż",
+      "æ¸ ħ",
+      "Ġvirus es",
+      "Ġ5 8",
+      "g od",
+      "Ġж ел",
+      "Ġst alk",
+      "I nd",
+      "ach i",
+      "ĠC F",
+      "ĠC ond",
+      "Ġsan ct",
+      "Ġcont en",
+      "Ġfre ed",
+      "ĠR T",
+      "Ġment ors",
+      "ì¡ ±",
+      "Ġport able",
+      "ĠPaul o",
+      "r ane",
+      "HA HA",
+      "ĠS ection",
+      "ç Ĩ",
+      "hy un",
+      "ĠÎŃ Ïĩ",
+      "ĠP ub",
+      "ĠInd epend",
+      "Ġcomp ounds",
+      "ĠÑģ Ñĭ",
+      "Ġmess aging",
+      "Ġded ication",
+      "Ġnot icing",
+      "Ġdevot ed",
+      "ÑİÑĤ ÑģÑı",
+      "Ġsn akes",
+      "Ġbattle field",
+      "p ers",
+      "Ġdel a",
+      "9 2",
+      "Ġha i",
+      "ill ä",
+      "ér er",
+      "e very",
+      "Ġrespons ive",
+      "×Ļ ×ķ",
+      "op f",
+      "é ī",
+      "Ĭ ¸",
+      "Be cause",
+      "Ġtour ism",
+      "Ġê·¸ ê²Į",
+      "×ķ× ¦",
+      "Ġcan s",
+      "st üt",
+      "Ġdon ne",
+      "ĠD ios",
+      "ĠU ber",
+      "act ory",
+      "Ġorient ed",
+      "ĠH erm",
+      "Ġpat ron",
+      "ur f",
+      "be i",
+      "Ġprogram a",
+      "ĠOh h",
+      "gen er",
+      "Ġf ist",
+      "ĠW endy",
+      "Ġand a",
+      "Ġguess ed",
+      "Ġfre ak",
+      "ä¸Ń åľĭ",
+      "ĠK ings",
+      "ch ool",
+      "Ġoff line",
+      "ĠIndian a",
+      "ĠAll iance",
+      "Ġ5 3",
+      "Ġpartic ul",
+      "ĠF ocus",
+      "Ġinhab it",
+      "Ġê°ĻìĿĢ ëį°",
+      "ĠMc G",
+      "ows ki",
+      "ĠìĿ´ ê±´",
+      "Ġpa ÅĦst",
+      "он и",
+      "itt a",
+      "Ġconfirm ation",
+      "ĠBrook lyn",
+      "Ġnood le",
+      "f und",
+      "it ud",
+      "Ġgrand parents",
+      "Ġbar becue",
+      "ει ÏĤ",
+      "Ġ á",
+      "Ġball ot",
+      "ĠV eter",
+      "Ġpip es",
+      "ig ious",
+      "ĠG raph",
+      "est ed",
+      "Ġë¸ Įë",
+      "ĠK E",
+      "ãģ¡ãĤĩ ãģ£ãģ¨",
+      "Ġe ins",
+      "Ġhat red",
+      "ãģij ãģ©",
+      "Ġd ang",
+      "ee ee",
+      "Ġarch ae",
+      "ĠJes se",
+      "Ġdetect ed",
+      "Ġsen i",
+      "burg h",
+      "Ġdispl acement",
+      "Ġdo p",
+      "Ġcondition ing",
+      "Ġне ÑģколÑĮко",
+      "Ġdistur bing",
+      "P H",
+      "Ġthin ner",
+      "Ġwound ed",
+      "ĠCu ando",
+      "Ġcush ion",
+      "Ġwh ites",
+      "Ġprefer ences",
+      "Ġì¤Ģë ¹Ħ",
+      "Ġka ż",
+      "ĠG ate",
+      "ĠP ath",
+      "d les",
+      "à¸Ħ ร",
+      "im ore",
+      "Ġë³´ìĹ ¬",
+      "Ġdiscipl ines",
+      "á» ı",
+      "Ġmes ma",
+      "Ġìĥ Īë",
+      "Ġìĭ ¬",
+      "Ġg ing",
+      "Ġumbre lla",
+      "IGH T",
+      "Ġp ension",
+      "Ġcomb ining",
+      "S S",
+      "Ġrect angle",
+      "á»ĩ t",
+      "Ġpro xim",
+      "ĠC ow",
+      "¸ Į",
+      "Ġintention al",
+      "æķ Ļ",
+      "Ġdec id",
+      "ĠÑģк аж",
+      "ĠU ma",
+      "ias m",
+      "b uz",
+      "Ġdebr is",
+      "Ġc ass",
+      "ĠP rop",
+      "is ka",
+      "ë ł¥",
+      "ester ol",
+      "uss ian",
+      "ìĿ´ë ŀij",
+      "Ġun limited",
+      "Ġadm ire",
+      "Ġtight ly",
+      "Ġgen ome",
+      "ĠJun ior",
+      "ven ir",
+      "g us",
+      "Ġc Äĥ",
+      "ĠV lad",
+      "Ġí Ĥ",
+      "Ġrel ativ",
+      "in ci",
+      "Ġaun que",
+      "ĠBo ys",
+      "ÑĨи он",
+      "ĠSw iss",
+      "Ġphys icians",
+      "Ġíı ī",
+      "ĠP ET",
+      "Ġw ounds",
+      "ab out",
+      "Ãł i",
+      "on z",
+      "ur ities",
+      "ĠÑĥв ид",
+      "å· ¦",
+      "Ġment ality",
+      "Ġvari ance",
+      "Ġseg unda",
+      "Ġvol cano",
+      "al ie",
+      "ॠĩ",
+      "Ġt iles",
+      "ĠT erry",
+      "ĠاÙĦÙĦ Ùĩ",
+      "Ġcan on",
+      "Ġsc attered",
+      "pt on",
+      "Ġdefin itions",
+      "Ġal gebra",
+      "ot en",
+      "ab lo",
+      "ij uana",
+      "Ġwra pping",
+      "Ġses ame",
+      "ĠнаÑĩ ина",
+      "ĠAl f",
+      "ĠÐł оÑģÑģ",
+      "or no",
+      "Ġan kle",
+      "Ġspecial ty",
+      "Ġattempt ing",
+      "ili ation",
+      "Ġ19 20",
+      "Ġphen omena",
+      "ĠPro duct",
+      "ĠB uck",
+      "ĠA ww",
+      "se en",
+      "Ġvo id",
+      "ĠFrank lin",
+      "Ġadvoc acy",
+      "ĠS ep",
+      "Ġcool est",
+      "ĠÑģ ÑĢазÑĥ",
+      "ĠQu and",
+      "Ġ9 00",
+      "ĠTr ad",
+      "d ies",
+      "Ġhas h",
+      "æĪij å°±",
+      "ä¹Ł æĺ¯",
+      "Ġpot s",
+      "Ġsad ly",
+      "Ġvi able",
+      "ĠT iger",
+      "ĠON E",
+      "Ġneur ons",
+      "ow anie",
+      "Ä Ĺ",
+      "ĠSh ar",
+      "ĠLand es",
+      "Ġconfer ences",
+      "è© ²",
+      "Ġcred ential",
+      "Ġl ime",
+      "ine e",
+      "x it",
+      "p ay",
+      "Ġinc ons",
+      "Ġ>> :",
+      "èª į",
+      "Ġí ŀĺë",
+      "Ġless er",
+      "Ġsp ill",
+      "Ġprem ise",
+      "Ġ36 5",
+      "ĠH ost",
+      "Ġtom ar",
+      "×IJ× ľ",
+      "ë ²Ī",
+      "ĠWhat s",
+      "Ġlight weight",
+      "ĠM ap",
+      "f ia",
+      "ells chaft",
+      "Ġvend ors",
+      "uest o",
+      "ĠM ister",
+      "ĠÐŁ ÑĢи",
+      "åı ³",
+      "h ma",
+      "Ġintention ally",
+      "ĠT ang",
+      "éĹ ®",
+      "Ġident ification",
+      "Ġetc etera",
+      "ĠN ee",
+      "ĠÑĤ ÑĢи",
+      "ê· ¸",
+      "Ġcrypt ocur",
+      "Ġin hale",
+      "Ġadd ict",
+      "åIJĦ ä½į",
+      "Ġma u",
+      "ĠÑĤак аÑı",
+      "Ġë² Ħ",
+      "Ġcomp rar",
+      "ied zieÄĩ",
+      "ĠоÑĤ но",
+      "Ġbegin ner",
+      "Ġм Ñĥж",
+      "Ġobs c",
+      "Ġlim iting",
+      "asc ular",
+      "Ġins pection",
+      "ac i",
+      "Ġre jo",
+      "M us",
+      "Ġz aten",
+      "Ġsz cz",
+      "ĠMad rid",
+      "Ġvar ieties",
+      "Ġest Ãł",
+      "ĠSh akes",
+      "Ġk its",
+      "Ġad minister",
+      "Ġla va",
+      "Ġg Ã¥",
+      "è© ¦",
+      "ת ×Ļ",
+      "ĠWay ne",
+      "Ġinst agram",
+      "Ġr ated",
+      "p aper",
+      "Ġb ild",
+      "Ġpret ending",
+      "Ġobser ving",
+      "ĠÑģам ом",
+      "Ġtr or",
+      "Ġorgan isms",
+      "Ġfal ta",
+      "Ġh ometown",
+      "ç ±",
+      "Ġí ĭ",
+      "Ġche g",
+      "Ġì ¡",
+      "Ġcomm a",
+      "is é",
+      "Ġlike lihood",
+      "av ored",
+      "Ġgel di",
+      "ни ков",
+      "Ġmed io",
+      "Ġjak ie",
+      "ĠJ up",
+      "Ġgreen house",
+      "Ġsp it",
+      "ко е",
+      "Ġк аж",
+      "ĠG ram",
+      "ĠCon ference",
+      "Ġdef icit",
+      "s ın",
+      "in se",
+      "u ÄŁ",
+      "Ġr icht",
+      "Ġcoinc idence",
+      "åı į",
+      "Ġeu rop",
+      "Ġbutter fly",
+      "p read",
+      "Ġìĸ ¼",
+      "èĢ ¶",
+      "Ġwa vel",
+      "ĠIn fin",
+      "ĠPlan et",
+      "Ġself ie",
+      "ient ras",
+      "Ġar rog",
+      "os er",
+      "id al",
+      "ł×Š׳×ķ",
+      "üt ün",
+      "Ġfresh man",
+      "ĠMach ine",
+      "Ïĥ ÏĦ",
+      "ĠD ia",
+      "ìĿ´ ëĭ¤",
+      "ãģĵ ãģĨ",
+      "ne a",
+      "Ġlist ing",
+      "Ġconfig ure",
+      "ut or",
+      "U p",
+      "ts chaft",
+      "ri ère",
+      "Ġup wards",
+      "ĠÑħоÑĩ Ñĥ",
+      "Ġswe ep",
+      "B r",
+      "Ġexpress ing",
+      "Ġun happy",
+      "Ġmand atory",
+      "g ender",
+      "ĠA ÃŃ",
+      "Ġindic ators",
+      "Ġoil s",
+      "n ote",
+      "Ġseg ur",
+      "ож еÑĤ",
+      "yn asty",
+      "Ġdist ances",
+      "Ġmer ge",
+      "BER T",
+      "Ġsur render",
+      "Ġbu at",
+      "ĠA wards",
+      "Ġseñ or",
+      "od ox",
+      "Ġfl avour",
+      "Ġab dom",
+      "Ġconfig ur",
+      "8 6",
+      "ĠDI Y",
+      "Ġrig id",
+      "° ĺ",
+      "Ġcorpor ation",
+      "Ġg room",
+      "j aw",
+      "ĠNe ar",
+      "ил о",
+      "Ġoper a",
+      "ĠIn nov",
+      "и ÑĢа",
+      "ĵ ±",
+      "Ġspec ified",
+      "Ġcos m",
+      "ĠFre edom",
+      "Ġcl own",
+      "ĠN em",
+      "Ġв ол",
+      "Ñij н",
+      "Ġchar ger",
+      "à¹ģ ล",
+      "Ġinflu ential",
+      "äs ident",
+      "é ¤",
+      "ĠìĦ łë",
+      "Ġvol umes",
+      "æ IJ",
+      "Ġout ras",
+      "ĠTw itch",
+      "Ġfound ing",
+      "Ġa while",
+      "Ġco il",
+      "ê° Ļ",
+      "Ġc ả",
+      "ĠTh row",
+      "ĠH ence",
+      "omm t",
+      "ĠBen jamin",
+      "глÑı д",
+      "T ime",
+      "ob ic",
+      "Ġm our",
+      "Ġd read",
+      "ĠL Ãł",
+      "ĠCh ile",
+      "Ġpre val",
+      "Ġv ain",
+      "Ġart ık",
+      "Ġpres erved",
+      "ĠоÑĤ д",
+      "Ġware house",
+      "Ġbest e",
+      "ĠSever al",
+      "ĠS ituation",
+      "Ġcard board",
+      "T od",
+      "er na",
+      "Ġgar ant",
+      "Ġgest ure",
+      "Ġh en",
+      "Ġspe lling",
+      "ose xual",
+      "Ġan ne",
+      "Ġm ice",
+      "ĠMe ine",
+      "c ard",
+      "Ġre bell",
+      "Ġcert o",
+      "Ġìľ łë",
+      "Ġvers chied",
+      "ĠB os",
+      "Ġinv ention",
+      "Ġtr ze",
+      "Ġman ière",
+      "ĠCh ad",
+      "Ġsp re",
+      "Ġorganis ations",
+      "Ġpoor ly",
+      "Ġan terior",
+      "Ġst air",
+      "к ÑĢ",
+      "Ġatom ic",
+      "Ġsymp ath",
+      "Ġcontin ually",
+      "Ġkle ine",
+      "è te",
+      "и Ñī",
+      "ο ÏĤ",
+      "pe ut",
+      "Ġrep osit",
+      "Ġent ra",
+      "E m",
+      "Ġfinan cing",
+      "Ġмн ог",
+      "Ġthe sis",
+      "ĠCom puter",
+      "e au",
+      "ĠT ree",
+      "Ġbr ide",
+      "ons ieur",
+      "sh ire",
+      "w ic",
+      "D E",
+      "ĠìĪ ĺë",
+      "Ġac om",
+      "ĠP O",
+      "ers ch",
+      "Ġпом оÑī",
+      "ĠAr men",
+      "Ġì£ ½",
+      "Ġz or",
+      "Ġprint s",
+      "ĠD ass",
+      "æ¸ ¯",
+      "Ġdur able",
+      "ĠTrans port",
+      "ìŀIJ ê°Ģ",
+      "Ġл ег",
+      "Ġdé t",
+      "ô le",
+      "am ous",
+      "Y N",
+      "Ġcl iff",
+      "Ġgramm ar",
+      "ĠÐŁÐ¾ ÑįÑĤомÑĥ",
+      "ĠlÃł m",
+      "es ch",
+      "Ġmiser able",
+      "Ġvol ts",
+      "ĠC ad",
+      "uk an",
+      "ÑĤ ив",
+      "r ust",
+      "Ġìĺ¬ë Ŀ¼",
+      "Ġver k",
+      "Ġchick ens",
+      "ĠY oo",
+      "Ġout fits",
+      "c ode",
+      "Ġhier archy",
+      "net es",
+      "Ġcounter part",
+      "Ġt ôi",
+      "Ġt ed",
+      "ĠB art",
+      "Ġë Ŀ¼",
+      "ĠGen au",
+      "Ġinc oming",
+      "ĠA BC",
+      "ri que",
+      "ĠоÑĤ п",
+      "qu al",
+      "Ġincent ive",
+      "Ġih ren",
+      "׳ ×Ļ",
+      "lo e",
+      "Ġ19 30",
+      "Ġbar g",
+      "Ġd iction",
+      "Ġön ce",
+      "IN S",
+      "Ġre h",
+      "isia j",
+      "m outh",
+      "Ġsc oring",
+      "l ık",
+      "ĠìķĦ 주",
+      "OR IA",
+      "ĠEst ados",
+      "Ġcompan ion",
+      "Ġasse mble",
+      "Ġpun ished",
+      "Ġit al",
+      "Ġprev ents",
+      "ist es",
+      "ĠKent ucky",
+      "Ġloc ate",
+      "Ġfast ing",
+      "ãģ¨ æĢĿ",
+      "ĥ Ģ",
+      "ĠSe b",
+      "ĠCr own",
+      "op ia",
+      "Ġwh ip",
+      "us z",
+      "к ами",
+      "Ġdatab ases",
+      "åŃ Ĺ",
+      "Ġprose c",
+      "Ġ199 7",
+      "ĠìĤ´ì §Ŀ",
+      "ĠSol ar",
+      "ĠP ues",
+      "ĠZ en",
+      "oll o",
+      "ĠG uru",
+      "Ġsque ez",
+      "ĠÐĹ Ð°",
+      "ĠÄ į",
+      "cept ions",
+      "c ca",
+      "iz able",
+      "m and",
+      "Ġbreak through",
+      "Ġtables poon",
+      "ĠS EC",
+      "ik h",
+      "ĠS ão",
+      "Ġп ло",
+      "am en",
+      "Ġpr ac",
+      "Ġdar ling",
+      "Ġtall er",
+      "Ġrend ering",
+      "Ġìļ°ë¦¬ ê°Ģ",
+      "ĠÏĦη ÏĤ",
+      "Ġm ã",
+      "Ġes os",
+      "uer do",
+      "ĠÑģ ÑĩиÑĤ",
+      "all er",
+      "ìĹĪ ìĸ´ìļĶ",
+      "Ġmill ones",
+      "ler in",
+      "Ġpe gar",
+      "on ne",
+      "Ġenroll ment",
+      "Ġli egt",
+      "Ġbo a",
+      "w iÄĻ",
+      "bs p",
+      "Ġcy cling",
+      "ĠBern ie",
+      "Ġ198 9",
+      "Ġд алÑĮ",
+      "ĠDak ota",
+      "ĠÑģв Ñıз",
+      "ĠC P",
+      "Ġst are",
+      "íĤ ¤",
+      "Ġprosper ity",
+      "Ġarrange ments",
+      "Ġarri ving",
+      "m ä",
+      "Ġkay ak",
+      "ip t",
+      "Ġp ardon",
+      "Ġrel at",
+      "Ġver ste",
+      "ĠF ig",
+      "Ġfo il",
+      "ĠTalk ing",
+      "pe are",
+      "Ġno i",
+      "ĠпÑĢи ÑĪ",
+      "Ġhoc key",
+      "Ġad o",
+      "ĠO UT",
+      "6 7",
+      "Ġhorm ones",
+      "ĠAven ue",
+      "ĠSuper man",
+      "Ġpres cription",
+      "uber netes",
+      "C L",
+      "ot ive",
+      "N IS",
+      "ien en",
+      "Ġsad ness",
+      "ĠV it",
+      "T y",
+      "Ġstar ter",
+      "Ġbed e",
+      "Ġfound ations",
+      "Ġso re",
+      "åº Ĺ",
+      "Ñīе ÑģÑĤв",
+      "ìļ °ë",
+      "ĠÑĩ Ñĥв",
+      "l ink",
+      "Ġmane u",
+      "work ing",
+      "Ãł n",
+      "ĠAtt ack",
+      "ĠC art",
+      "ve is",
+      "ĠRes p",
+      "ens ing",
+      "Ġì¢ĭ ìķĦìļĶ",
+      "Ġesc uch",
+      "ĠR NA",
+      "Ĥ ´",
+      "Ġad op",
+      "Ġb ending",
+      "ع د",
+      "Ġman ages",
+      "us p",
+      "Ġt art",
+      "Ġrout er",
+      "B o",
+      "Ġestab lishing",
+      "Ġbal ancing",
+      "Ġathlet ic",
+      "ĠS lo",
+      "Ġf ills",
+      "Ġн аб",
+      "Ġд ал",
+      "Ġpos so",
+      "ĠV ielen",
+      "Ġcrit ics",
+      "Ġlaws uit",
+      "ĠIsa ac",
+      "ĠÑĦилÑĮ м",
+      "Ġtr as",
+      "Ġpra w",
+      "ĠCra zy",
+      "Ġne u",
+      "Ġk ull",
+      "Ġtum or",
+      "ĠAP P",
+      "g ate",
+      "ĠA RE",
+      "9 8",
+      "ĠSte am",
+      "Ġfuck ed",
+      "l age",
+      "ĠâĻ ¬",
+      "ĠM D",
+      "f y",
+      "Ġshell s",
+      "ĠSe ems",
+      "iz ers",
+      "Ġr anges",
+      "ĠAnton io",
+      "AT ION",
+      "ĠB aba",
+      "Ġìĥ ī",
+      "k un",
+      "Ġpray ed",
+      "ÑĢ Ñı",
+      "ĠпÑĢоÑĤ ив",
+      "Ġse as",
+      "b ury",
+      "Ġ×Ķ× ©",
+      "Ġtra it",
+      "ĠDep ending",
+      "Ġd re",
+      "Ġkön nt",
+      "ÑĨ Ñĥ",
+      "Ġlip stick",
+      "ee z",
+      "ĠпÑĢ имеÑĢ",
+      "Ġassign ments",
+      "B ob",
+      "Ġmet als",
+      "Ġspe cially",
+      "å°į ä¸įå°į",
+      "Ġìĺ Īë",
+      "ĠÅ ¡",
+      "Ġv ista",
+      "ĠÎ ¬",
+      "Ġtw ins",
+      "Ġnot able",
+      "ĠS au",
+      "Ġdé velop",
+      "Ġç ek",
+      "Ġpoly nom",
+      "av am",
+      "Ġtamb é",
+      "он ом",
+      "Ġpl asma",
+      "Ġe fect",
+      "Ġlä ng",
+      "Ġcas i",
+      "Ñģ а",
+      "ım ı",
+      "ãģĻ ãĤĭ",
+      "ĵ¤ ìĿĢ",
+      "Ġlab our",
+      "oss en",
+      "ĠP un",
+      "r if",
+      "Ġd oses",
+      "Ġoper ates",
+      "ил ли",
+      "Ġja ar",
+      "st aw",
+      "ĠìĤ¬ëŀ ij",
+      "Ġat m",
+      "Ġprotect s",
+      "Ġimp ed",
+      "H O",
+      "Ġc ima",
+      "Ġto ch",
+      "ab is",
+      "Ġsend o",
+      "la us",
+      "Ġcur l",
+      "ĠN um",
+      "Ġspons ors",
+      "Ġdé but",
+      "ĠAlex a",
+      "ĠB ür",
+      "ĠA mer",
+      "Ġc ope",
+      "Ġиз в",
+      "j al",
+      "Ġ199 5",
+      "ap at",
+      "res se",
+      "ĠPri ze",
+      "ĠCla ire",
+      "ĠBrand on",
+      "Ġwszyst ko",
+      "Ġval ued",
+      "à¸Ļ ะ",
+      "Ġse ct",
+      "Ġsecret ly",
+      "Ġdiam onds",
+      "ĠEv an",
+      "ĠRP G",
+      "ãģ« ãģª",
+      "Īë ıĦ",
+      "ĠUnivers al",
+      "Ġdoub ts",
+      "ĠP in",
+      "wiÄħ z",
+      "ļ ©",
+      "Ġal bo",
+      "Ġbra ucht",
+      "AU L",
+      "ĠM obile",
+      "gr ades",
+      "Ġsch em",
+      "wh y",
+      "ĠN icht",
+      "p i",
+      "g le",
+      "Ġchor us",
+      "Ġg ly",
+      "Ġrein force",
+      "Ġm uff",
+      "ĠSh en",
+      "ĠH ola",
+      "Ñĥ г",
+      "vid emment",
+      "v ial",
+      "ac ious",
+      "laim ed",
+      "ĠR ico",
+      "Ġve gg",
+      "Ġillust ration",
+      "ĠBut ter",
+      "ow ad",
+      "Ġeu x",
+      "Ġenf ants",
+      "ĠLe ader",
+      "ĠVill age",
+      "et ically",
+      "ÙĨ ÙĬ",
+      "Ġst ew",
+      "Ġsurpr ises",
+      "Ġc ue",
+      "ĠGrand ma",
+      "ĠC elsius",
+      "ĠR icht",
+      "en c",
+      "Ġpet ition",
+      "Ġher b",
+      "Ġw icked",
+      "Ġsch le",
+      "oc aly",
+      "Ġtrans f",
+      "Ġtok ens",
+      "ĠGr ay",
+      "ĠB BC",
+      "I K",
+      "Ġ15 00",
+      "z n",
+      "ĠNe v",
+      "Ġk oy",
+      "Ġz ar",
+      "Ġbull shit",
+      "ĠColomb ia",
+      "ul ative",
+      "Ġwides pread",
+      "y ect",
+      "k it",
+      "Ġempres a",
+      "Ġn our",
+      "Ġburn s",
+      "at in",
+      "a ired",
+      "Ġrevolution ary",
+      "Ġгод Ñĥ",
+      "ĠLog an",
+      "Ġ199 6",
+      "ĠGra ham",
+      "re b",
+      "ĠN HS",
+      "æľ Ľ",
+      "Ġcost umes",
+      "Ġnaw et",
+      "Ġlo vers",
+      "ĠLuc y",
+      "ĠInd igenous",
+      "íķĺ 기",
+      "Ġimmun ity",
+      "¥ ´ë",
+      "uit o",
+      "Ġexcess ive",
+      "Ġdon ations",
+      "Ġ×Ķ ר",
+      "Ġì² «",
+      "éī Ħ",
+      "Ġdry ing",
+      "mel on",
+      "Ġsurve ys",
+      "Ġ무ì Ĭ¨",
+      "é¢ ¨",
+      "aa a",
+      "Ġpro be",
+      "an cial",
+      "Ġlou der",
+      "Ġhot els",
+      "ü ÄŁ",
+      "ag ner",
+      "Ġorig ins",
+      "Ġë§Ī ì§Ģë§ī",
+      "Ġ* *",
+      "Ġstr angers",
+      "ĠHa us",
+      "com ed",
+      "Ġan throp",
+      "Ġus o",
+      "ĠìķĦ ì§ģ",
+      "ĠY uan",
+      "ĠíķĦ ìļĶ",
+      "pl er",
+      "ress ive",
+      "Ġsp raw",
+      "ĠSt ew",
+      "Ġ199 4",
+      "Ġeld ers",
+      "Ġme inen",
+      "Ġj unt",
+      "Ġac oust",
+      "ĠW ohn",
+      "Ġban anas",
+      "Ġproject ion",
+      "ĠSt ick",
+      "leg t",
+      "spe ed",
+      "ĠcÅ ©ng",
+      "ĠW ort",
+      "ĠBalt imore",
+      "ĠÑĨ ел",
+      "Ġdun no",
+      "å¼ ·",
+      "? ,",
+      "ãĥī ãĥ³",
+      "ĠLoc al",
+      "ost o",
+      "Ð Ń",
+      "од а",
+      "ĠPort uguese",
+      "Ġtheir s",
+      "Ġdé m",
+      "åı ¦",
+      "Ġdra uf",
+      "ĠBuddh ist",
+      "ert a",
+      "G e",
+      "Ġcar rot",
+      "ĠWonder ful",
+      "Ġso ak",
+      "Ġchair man",
+      "gg i",
+      "IC A",
+      "f ried",
+      "Ġfl ick",
+      "ĠThrough out",
+      "Ġìļ °ë",
+      "Ġc ough",
+      "Ġfl uffy",
+      "sch ool",
+      "Ġr ipped",
+      "---- ----",
+      "ĠZuk unft",
+      "Ġн еб",
+      "Ġst o",
+      "ĠB O",
+      "p ent",
+      "ĠLaw rence",
+      "Ïī ÏĤ",
+      "st icks",
+      "ĠE ins",
+      "ĠÑĢ Ñĭ",
+      "ĠStr ong",
+      "Ġcar amel",
+      "Ġsp ite",
+      "az ar",
+      "éĥ½ æĺ¯",
+      "Ġcrit ically",
+      "Ġob ra",
+      "ow itz",
+      "ĠZ one",
+      "ĠÑĢ ек",
+      "Ġsu g",
+      "ard ed",
+      "Ġg ì",
+      "ff entlich",
+      "an che",
+      "Ø Ł",
+      "ast ically",
+      "ìĿ ¼ë",
+      "л ав",
+      "Ġsimpl est",
+      "ĠF riend",
+      "Ġque llo",
+      "Ġamb ition",
+      "Ġabb iamo",
+      "åº ķ",
+      "ĠÑĦ оÑĢм",
+      "ĠEs sa",
+      "Ġeduc ators",
+      "Ġstatist ical",
+      "éĢĻ éĤĬ",
+      "Ġchang er",
+      "Ġat au",
+      "éta is",
+      "ĠShakes peare",
+      "ë IJĺ",
+      "Ġtr iggers",
+      "Ġreal iz",
+      "Ġcel ui",
+      "whe el",
+      "Ġloyal ty",
+      "Ġscream s",
+      "ke hr",
+      "ĠM ega",
+      "e ast",
+      "Ġtop s",
+      "ĠTot ally",
+      "ount ain",
+      "l ord",
+      "Ġviol ation",
+      "ĠG A",
+      "Ġnic er",
+      "ĠF resh",
+      "ĠMel issa",
+      "fun ction",
+      "Ġra pe",
+      "Ġexcept ions",
+      "Ġsil icon",
+      "Ġliber ty",
+      "Ġhousehold s",
+      "ãģį ãģ¾ãģĻ",
+      "ĠC A",
+      "ĠÐŀ б",
+      "Ġli b",
+      "ŀ Į",
+      "c ific",
+      "Ġtrop ical",
+      "Ġinvestig ating",
+      "H D",
+      "Ġad apter",
+      "ĠP itt",
+      "an cia",
+      "ĠShe ll",
+      "friend ly",
+      "Ġconclus ions",
+      "Ġtur tle",
+      "Ġdec omp",
+      "Ġanim ations",
+      "ĠÑģ ек",
+      "ins i",
+      "Ġret ention",
+      "k ie",
+      "Ġinject ion",
+      "ĠMad ison",
+      "ì° °",
+      "Ġv ient",
+      "Ġvar ied",
+      "Ġviol in",
+      "ĠB il",
+      "Ġluck ily",
+      "Ġh tt",
+      "l ä",
+      "Ġr anch",
+      "çľĭ çľĭ",
+      "Ġsó lo",
+      "ìķ ħ",
+      "ĠD erek",
+      "ĠScript ure",
+      "оÑĢ а",
+      "Ġclassroom s",
+      "av il",
+      "form ed",
+      "Ġbefore hand",
+      "ĠG em",
+      "pre ch",
+      "Ġl in",
+      "Ġgre ens",
+      "ÑĨ ев",
+      "ĠMer cedes",
+      "Ġdr ought",
+      "gas ps",
+      "Ġab ortion",
+      "Ġter ribly",
+      "Ġspos ób",
+      "Ġsec ured",
+      "Ġat rás",
+      "Ġwavel ength",
+      "Ġgra ins",
+      "ect ive",
+      "Ġspace craft",
+      "Ġtour s",
+      "Ġprof es",
+      "Ġsur geon",
+      "ĠP ie",
+      "Ġide ally",
+      "arn er",
+      "U P",
+      "op ard",
+      "s ce",
+      "Ġimm ense",
+      "ĠOr t",
+      "roll er",
+      "ĠD allas",
+      "ĠNich olas",
+      "Ġs ulf",
+      "ĠToy ota",
+      "Ġquant ities",
+      "ce ans",
+      "Ġcu i",
+      "an ça",
+      "ĠC AN",
+      "itzer land",
+      "åĦ ¿",
+      "Ġz ou",
+      "ĠCy ber",
+      "le gen",
+      "ĠIn it",
+      "ed u",
+      "Ġa pert",
+      "Ġad jac",
+      "ou v",
+      "èĢĮ ä¸Ķ",
+      "r s",
+      "Ġcab bage",
+      "Ġwheel chair",
+      "iny l",
+      "ĠD ynam",
+      "ĠìķĦëĭĪë Ŀ¼",
+      "Ġl ing",
+      "h l",
+      "Ġмог Ñĥ",
+      "Ġcris p",
+      "Ġm ij",
+      "Ġd ug",
+      "n in",
+      "Ġbl oss",
+      "Ġbelong ing",
+      "Ġloud ly",
+      "Ġminer als",
+      "Ġconclud ed",
+      "Ġsearch ed",
+      "9 6",
+      "ĠMe et",
+      "ĠS EO",
+      "ĠС к",
+      "ĠH ob",
+      "ot ta",
+      "Ġpropag anda",
+      "Ġcin namon",
+      "Ġhun ter",
+      "Ġgeme ins",
+      "Ġsculpt ure",
+      "uls ion",
+      "Ġv äl",
+      "Ġmagaz ines",
+      "Ġcontrovers y",
+      "ä¸Ģ 樣",
+      "Ġsequ ences",
+      "ãģĦ ãĤĭ",
+      "Ġíļ Į",
+      "Ġdel eted",
+      "ä½ ¿",
+      "IJë ıĦ",
+      "Ġvary ing",
+      "ãĥ Ĩ",
+      "Ġmount ing",
+      "Ġaff air",
+      "Ġpath ways",
+      "æ ¦",
+      "Ġdig o",
+      "äº ®",
+      "Ġд ок",
+      "A lex",
+      "Ġtob acco",
+      "ĠC V",
+      "Ġbother ed",
+      "Ġamb ient",
+      "ink y",
+      "ĠS L",
+      "Ġh ates",
+      "Ġje żeli",
+      "Ġcon greg",
+      "Ġel as",
+      "Ġde uts",
+      "ĠStud ios",
+      "ch ÄĻ",
+      "Ġdocument ed",
+      "ĠCru z",
+      "ĠL en",
+      "ĠDoug las",
+      "ĠPort ugal",
+      "ent i",
+      "Ġsp ouse",
+      "Ġanal ys",
+      "av ia",
+      "Ġed ited",
+      "Ġl ại",
+      "bu ilt",
+      "Ġv ille",
+      "ad ora",
+      "Ġbrac elet",
+      "Ġs ushi",
+      "Ġp m",
+      "Ġtra ils",
+      "Ġl ug",
+      "Ġö ver",
+      "Ġs orrow",
+      "Ġcol ony",
+      "ado x",
+      "Ġser ie",
+      "any ak",
+      "ĠØ ·",
+      "ĠG ulf",
+      "æĺ¯ ä¸įæĺ¯",
+      "ĠP V",
+      "ĠSam uel",
+      "ĠK it",
+      "ĠR al",
+      "ont in",
+      "ex pl",
+      "Ġent ries",
+      "Ġactiv ists",
+      "P s",
+      "Ġs ant",
+      "ĠÑĤо Ñĩ",
+      "ĠBr uno",
+      "ke ley",
+      "Ġtut to",
+      "é Ķ",
+      "Ġv intage",
+      "Ġterr ified",
+      "Ġпо Ñħ",
+      "us ive",
+      "ow ers",
+      "ай ÑĤ",
+      "ë ıĻ",
+      "Ġtwist ed",
+      "ĠTh ought",
+      "Ġt ah",
+      "Ġshr ink",
+      "Ġshe er",
+      "l it",
+      "Ġdal am",
+      "Ġd ib",
+      "Ġv ard",
+      "ow ane",
+      "Ġdo br",
+      "ĠR ena",
+      "ĠÑģво Ñİ",
+      "ĠpaÃŃs es",
+      "ĠE ra",
+      "ãģ® ãģ§",
+      "ĠB UT",
+      "s ighs",
+      "Ġê·¸ ê±°",
+      "Ġgro ÃŁen",
+      "Ġë¹ ¨ë¦¬",
+      "Ġn erves",
+      "Ġconst it",
+      "Ġpreoc up",
+      "ĠG ay",
+      "ĠX u",
+      "keep er",
+      "he ure",
+      ".. )",
+      "ĠCal m",
+      "ĠUn idos",
+      "ĠìĿ´ ê²ĥ",
+      "ĠAqu i",
+      "Ġìłľ ìĿ¼",
+      "d ır",
+      "ì¦ ĺ",
+      "y our",
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+      "20 20",
+      "Ġr und",
+      "ĠH O",
+      "ĠC atherine",
+      "iel i",
+      "Ġf usion",
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+      "Ġfor am",
+      "sh aped",
+      "ĠíĽ Ħë",
+      "Ġw t",
+      "Ġret r",
+      "Ġpr éc",
+      "Ġê° ij",
+      "Ġopen ly",
+      "v ity",
+      "구 ìļĶ",
+      "Ġobst acle",
+      "Ġbo o",
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+      "ic orn",
+      "Ġeigen lijk",
+      "Ġhead er",
+      "are mos",
+      "Ġso fter",
+      "ĠÐŁ од",
+      "Ġpre jud",
+      "Ġdefin es",
+      "ier te",
+      "Ġbl ending",
+      "Ġbelie vers",
+      "ĠWo chen",
+      "Ġник ак",
+      "ĠÐļ огда",
+      "ĠTyp ically",
+      "Ġíģ ¬",
+      "ç® ¡",
+      "ci os",
+      "Ġmiss iles",
+      "Ġsp onge",
+      "ĠK itchen",
+      "Ġt ren",
+      "ning en",
+      "Ġsc rap",
+      "Ġser ait",
+      "´ì ł",
+      "ç ¹",
+      "Ġë° ĺë",
+      "Ġrest ored",
+      "Ġprzy kÅĤad",
+      "ĠK ubernetes",
+      "Ġsa it",
+      "Ġu w",
+      "Ġen abling",
+      "Ġtra vers",
+      "amp s",
+      "åı Ĺ",
+      "ĠOM G",
+      "ens or",
+      "Ġz osta",
+      "Ġpronoun ced",
+      "A ng",
+      "norm al",
+      "Ġeconom ies",
+      "t in",
+      "ĠChamp ion",
+      "iz en",
+      "Ġar beiten",
+      "ĠG ospel",
+      "ĠZ u",
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+      "Ġliter acy",
+      "ĠM ans",
+      "Ġcircul ation",
+      "Ġad ap",
+      "ĠTot al",
+      "Ġmere ka",
+      "Ġol acak",
+      "ÑģÑĤ аÑĤи",
+      "J ack",
+      "Ġm und",
+      "Ġth ief",
+      "b ies",
+      "Ġê² ģ",
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+      "ĠÚ© ÛĮ",
+      "ĠSc ar",
+      "å ²",
+      "Ġab ol",
+      "Ġdev ote",
+      "Ġ0 1",
+      "Ġs itten",
+      "ĠVis ual",
+      "we ek",
+      "s ome",
+      "ing t",
+      "Ġjournal ism",
+      "ĠH ir",
+      "ĠB achelor",
+      "in ery",
+      "Ãľ ND",
+      "ãĥ Ł",
+      "ç» Ļ",
+      "Ġcolor ing",
+      "ĠCr ist",
+      "Ġcelebr ities",
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+      "ĠC rit",
+      "Ġdifferent iate",
+      "ĠÐľ не",
+      "el im",
+      "Ġse afood",
+      "Ġalgum as",
+      "otherap y",
+      "æĪ °",
+      "Ġgla ub",
+      "Ġarbitr ary",
+      "g ens",
+      "ĠбÑĥд ем",
+      "Ġt av",
+      "Ġcream y",
+      "ĠCount ry",
+      "a ñ",
+      "м еÑĤ",
+      "Ġh inter",
+      "Ġm ism",
+      "Ġillust rate",
+      "ÃľND NIS",
+      "Ġdecre asing",
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+      "AK I",
+      "ix on",
+      "Ġн ей",
+      "Ġfat to",
+      "Ġn erd",
+      "ç ł",
+      "Ġb itte",
+      "P er",
+      "Ġt ane",
+      "Ġgö z",
+      "Ġfor te",
+      "ĠE y",
+      "Ġнав еÑĢ",
+      "è¢ «",
+      "ĠWord Press",
+      "ĠM is",
+      "Å ¯",
+      "z äh",
+      "Ġinté ress",
+      "osa urs",
+      "ĠFall s",
+      "Ġn essa",
+      "9 7",
+      "Ġmuseum s",
+      "Ġcorrespond s",
+      "Ġs ings",
+      "f our",
+      "Ġed er",
+      "ĠCommun ist",
+      "o a",
+      "ne k",
+      "ĠWH O",
+      "Ġcor po",
+      "Ġmess ing",
+      "ÏĦ αι",
+      "Ġbrush es",
+      "Ġb isc",
+      "ĠAr beits",
+      "ĠT ax",
+      "Ġse le",
+      "Ġflag s",
+      "ou pe",
+      "Ġanticip ated",
+      "ãĥ ij",
+      "ĠN ad",
+      "Ġpou red",
+      "Ġm l",
+      "Ġll ama",
+      "Ġvisual ize",
+      "Ġlisten ers",
+      "ÙĦ Ùĥ",
+      "al ten",
+      "Mich ael",
+      "Ġcos ì",
+      "Õ¡ Õ",
+      "op us",
+      "Ġíķ´ì £¼",
+      "Ġh ike",
+      "ĠAtt orney",
+      "ĠHill ary",
+      "ud ed",
+      "Ġíķĺ ì§Ģë§Į",
+      "Ġdo ve",
+      "Ġstorm s",
+      "ак Ñģ",
+      "Ġdoct rine",
+      "Ġhe x",
+      "ik s",
+      "no ÅĽÄĩ",
+      "Ġscript s",
+      "Ġδ εν",
+      "ĠÑįÑĤи Ñħ",
+      "ĠÐ Ĩ",
+      "ab er",
+      "ĠV as",
+      "Ġcent imeters",
+      "×ŀ ×Ķ",
+      "ни б",
+      "Ġrid ers",
+      "ĠT rib",
+      "åĮ ħ",
+      "Ġtak że",
+      "Ġn oun",
+      "Ġic ons",
+      "Ġsole ly",
+      "mind ed",
+      "Ġdisp on",
+      "ĠSw itzerland",
+      "Ġcl usters",
+      "Ġqu eda",
+      "ail ing",
+      "Ġman ga",
+      "Ġ6 8",
+      "Ħ Ī",
+      "Ġt et",
+      "g ins",
+      "ha us",
+      "ç© º",
+      "å· ¥",
+      "ĠO P",
+      "ot ed",
+      "Ġnouve au",
+      "AL LY",
+      "ÙĪ د",
+      "ò n",
+      "Ġmort ality",
+      "ĠGit Hub",
+      "d rop",
+      "Ġdis gu",
+      "Ġrec om",
+      "Ġloc als",
+      "Ġhome made",
+      "amb a",
+      "Ġpron unciation",
+      "Ġal phabet",
+      "ан ÑĮ",
+      "ow any",
+      "ir as",
+      "id ency",
+      "OM E",
+      "ĠÑĢаÑģ Ñģ",
+      "ar ak",
+      "v iamente",
+      "Ġnon profit",
+      "ĠYouT uber",
+      "Ġp arenth",
+      "ĠB oo",
+      "v at",
+      "ĠSt ir",
+      "Ġpre cip",
+      "Ġan ts",
+      "Ġall y",
+      "ĠMa ori",
+      "ĠëĮĢ íķľ",
+      "åı¯ æĺ¯",
+      "og ene",
+      "ĠLab our",
+      "aret te",
+      "Ġrecy cling",
+      "ens a",
+      "Ġpurs uit",
+      "Ġs ak",
+      "ĠÐĹд еÑģÑĮ",
+      "Ġtoler ance",
+      "Ġsa at",
+      "Ġclick ed",
+      "âĻ ¥",
+      "Ġface book",
+      "ĠInt o",
+      "Ġincent ives",
+      "기 ëĬĶ",
+      "ĠD ennis",
+      "ĠW ik",
+      "ges ch",
+      "à¹ĢภĽ",
+      "ĠÏĢ α",
+      "ĠWh oo",
+      "Ġround ed",
+      "Ġdo pe",
+      "Ġcapt uring",
+      "ĠWar ri",
+      "Ġcivil ian",
+      "Ġchar ming",
+      "Ġes as",
+      "Ġsust ained",
+      "Ġle aning",
+      "Ġabund ance",
+      "ÃŃ lia",
+      "алÑĮ нÑĭй",
+      "Ġph ải",
+      "ac ja",
+      "Ġê°Ļ ìķĦ",
+      "act iv",
+      "า ย",
+      "Ġ9 7",
+      "Ġм ой",
+      "c ro",
+      "ĠJack ie",
+      "itt ees",
+      "br acht",
+      "ul ent",
+      "Ġìł ľë",
+      "Ġplug in",
+      "v antage",
+      "part y",
+      "Ġsu as",
+      "Ġan te",
+      "Ñĥ л",
+      "ÐĿ ÐIJ",
+      "æĤ ¨",
+      "ĠÏĥ Ïħ",
+      "Ġmet h",
+      "Ġenthus iasm",
+      "ÑıÑĤ ÑģÑı",
+      "íĻ Ķë",
+      "Ġsynth etic",
+      "Ġseason ing",
+      "ĠL ost",
+      "on omy",
+      "ĠSp ark",
+      "Ġb ure",
+      "Ġass ured",
+      "Ġimag in",
+      "Ġcar ro",
+      "S ha",
+      "Äħ t",
+      "нÑĥ ÑĤÑĮ",
+      "át ica",
+      "T Y",
+      "Ġk ern",
+      "ĠBrazil ian",
+      "Ã °",
+      "Ġsusp ended",
+      "ĠCar ib",
+      "Ġbiz im",
+      "ĠOl iver",
+      "ãģ ¶",
+      "T om",
+      "Ġпл ан",
+      "Ġn ope",
+      "omet hing",
+      "Ġbe iden",
+      "ÑĨ ен",
+      "Ġflu ct",
+      "Ġμ οÏħ",
+      "Ġf athers",
+      "ĠBl ake",
+      "Ġup ward",
+      "ĠD ash",
+      "ĠL il",
+      "ĠìĪ ĺëıĦ",
+      "Ġrevel ation",
+      "Ġelev ated",
+      "ĠJi ang",
+      "LE D",
+      "ĠThom pson",
+      "Ġмог ÑĥÑĤ",
+      "ÑģÑĤ ÑĢÑĥ",
+      "if iers",
+      "Ġcome back",
+      "Ġbuy ers",
+      "ê² °",
+      "ĠS ales",
+      "иÑĩ е",
+      "c iones",
+      "Ġwh istle",
+      "Ġd ull",
+      "LE X",
+      "Ġíķĺ ê²łìĬµëĭĪëĭ¤",
+      "Ġcrimin als",
+      "Ġdes cent",
+      "ipp le",
+      "mas ı",
+      "Ġfool ish",
+      "ĠдÑĥм аÑİ",
+      "t ar",
+      "Ġman go",
+      "Ġchore ography",
+      "M att",
+      "Ġterr itor",
+      "Ġac aba",
+      "ĠEin stein",
+      "ĠI BM",
+      "ĠMet al",
+      "ĠCry stal",
+      "Ġr ah",
+      "Ġf oul",
+      "ĠIsland s",
+      "Ġint act",
+      "ĠR ail",
+      ". :",
+      "Ġac á",
+      "ĠпÑĢ оп",
+      "еÑĢ е",
+      "ĠWr ite",
+      "he he",
+      "ĠF O",
+      "ĠÏĥ ÏĦη",
+      "Ġdo in",
+      "h eld",
+      "Ġappropri ately",
+      "Ġdeliber ately",
+      "Ġarch ive",
+      "Ġgive away",
+      "ãģĵ ãģĵ",
+      "Ġfin ale",
+      "л аÑģ",
+      "ен о",
+      "Æ¡ n",
+      "æ£ Ĵ",
+      "og o",
+      "çī ©",
+      "ĠAud ience",
+      "ãħ ł",
+      "Ġsub ur",
+      "Ġhead ache",
+      "ан нÑı",
+      "ĠW itch",
+      "ĠSwed ish",
+      "ĠB I",
+      "Ġer ase",
+      "Ġk hi",
+      "Ġcomment ary",
+      "ĠS ultan",
+      "íĥ Ŀ",
+      "ĠLe ban",
+      "Ġë³´ì ĭ",
+      "ĠP am",
+      "pe kt",
+      "mon th",
+      "Ġground ed",
+      "ê ¾",
+      "ĠÅŁek ilde",
+      "2 50",
+      "ĠS CH",
+      "ios o",
+      "Ġin aug",
+      "he imer",
+      "Ġreflect ing",
+      "ĠR uth",
+      "ĠO il",
+      "Ġtrou ver",
+      "u ep",
+      ".. ]",
+      "Ġìŀ Īë",
+      "Ġol ha",
+      "Ġreason ably",
+      "Ġgl itch",
+      "U B",
+      "ĠGr an",
+      "Ġad alah",
+      "Ġl ent",
+      "ر ا",
+      "Ġtr action",
+      "Ġadjust ing",
+      "´ ¤",
+      "ниб ÑĥдÑĮ",
+      "Ġд оп",
+      "Ġstretch ed",
+      "Ġor t",
+      "Ġcos ine",
+      "vi ol",
+      "Ġì ħ",
+      "c ir",
+      "Ġbast ard",
+      "ä¸ ĩ",
+      "ĠÑħ од",
+      "Ġqu ier",
+      "Ġpress ures",
+      "ĠAn h",
+      "å¹ ¾",
+      "Ġell es",
+      "Ġд ÑĢÑĥз",
+      "ĠможеÑĤ е",
+      "Ġch á»",
+      "ĠM é",
+      "ö k",
+      "ầ u",
+      "ìł Ī",
+      "z in",
+      "Ġca ution",
+      "ib an",
+      "Ġjud ging",
+      "ÑĥÑİ ÑĤ",
+      "Ġb aj",
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+      "ĠPo or",
+      "ĠNaz i",
+      "Ġup beat",
+      "y ang",
+      "Ġweek ends",
+      "ĠEss entially",
+      "Ġol uyor",
+      "Ġspat ial",
+      "ack er",
+      "Ġsell er",
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+      "ij ׾",
+      "Ġv ivid",
+      "ĠB ond",
+      "ê ¶Į",
+      "is kt",
+      "ãĤ µ",
+      "Ġgo at",
+      "dri ver",
+      "Ġm ug",
+      "ict ional",
+      "Ġall t",
+      "ĠIn iti",
+      "ĠR and",
+      "Ġfinish es",
+      "Ġê° Ī",
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+      "Ġteen agers",
+      "ĠMor ris",
+      "ì¤ Ħ",
+      "ĠO ri",
+      "i ya",
+      "Ġmy ös",
+      "St ep",
+      "ĠK re",
+      "è¾ ¦",
+      "Ġdin osaur",
+      "Ġëª ĩ",
+      "aff e",
+      "ĠëIJ ©ëĭĪëĭ¤",
+      "Ġz eg",
+      "åĪ ĩ",
+      "ĠManh attan",
+      "Ġsu jet",
+      "ue lle",
+      "st off",
+      "Ġd ür",
+      "Ġsub mar",
+      "es es",
+      "Ġa quele",
+      "Ġn ou",
+      "ĠFa ith",
+      "t z",
+      "ĠÑĤ омÑĥ",
+      "ace ut",
+      "li ers",
+      "Ġband width",
+      "Æ°á» Ŀ",
+      "Ġrespect ive",
+      "ĠA ve",
+      "Ġspread she",
+      "ĠS ent",
+      "ic amente",
+      "Ġinf ra",
+      "Ġlearn ers",
+      "Ġà® ī",
+      "ai ah",
+      "ren al",
+      "Ġmust ard",
+      "Ġhab t",
+      "ç ĥ",
+      "ĠQu é",
+      "Ġanaly zing",
+      "æ¯ ı",
+      "Ġso lic",
+      "Ġ×Ķ ×ķ×IJ",
+      "Ġcaus a",
+      "Ġwel comed",
+      "ĠS uccess",
+      "Ġfac ile",
+      "ĠÐŁÐ¾ÑĤ омÑĥ",
+      "sche in",
+      "Ġf etch",
+      "Ġstr at",
+      "ĠÑģÑĤо иÑĤ",
+      "ìĹIJìĦľ ëĬĶ",
+      "ĠÑģп оÑģоб",
+      "m am",
+      "Ġser ÃŃa",
+      "nam ents",
+      "wr iter",
+      "Ġconsult ing",
+      "íĺ Ģ",
+      "ĠBer keley",
+      "e u",
+      "as ive",
+      "U U",
+      "ĠAnal yt",
+      "Ġsubm ission",
+      "Ġmagnific ent",
+      "en za",
+      "Ġe con",
+      "Ġprof iles",
+      "Ġinc ar",
+      "A b",
+      "ĠN un",
+      "Ġh ic",
+      "scream ing",
+      "Ġresil ient",
+      "åĪ ©",
+      "gr und",
+      "Ġconc ur",
+      "Ġbere its",
+      "L D",
+      "Ġnur t",
+      "ì ī",
+      "Ġfe ast",
+      "Ġenc uent",
+      "ĠMich el",
+      "Ġsup rem",
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+      "Ġfeed s",
+      "ĠKoll egen",
+      "iss er",
+      "ĠF eng",
+      "ĠW en",
+      "m un",
+      "Ġten ÃŃa",
+      "ĠW rest",
+      "Ġìĺ¤ëĬĺ ìĿĢ",
+      "Ġst ead",
+      "Ġrest oration",
+      "Ġdon ated",
+      "Ġdel s",
+      "Ġc ensus",
+      "Ġdesper ately",
+      "worth y",
+      "H E",
+      "ĠSp a",
+      "ĠBry an",
+      "Ġh j",
+      "ĠR aw",
+      "ìķĦ ë",
+      "ĠCam era",
+      "Ġz ien",
+      "Ġst yl",
+      "ĠT W",
+      "ĠChe ese",
+      "bor ne",
+      "Ġob l",
+      "ĠAl ready",
+      "Ġunst able",
+      "Ġfl ames",
+      "p ost",
+      "H a",
+      "rom agn",
+      "ĠìĹ Ħë§Ī",
+      "d est",
+      "Ġkole j",
+      "Ġtempor arily",
+      "Ġdeterm ining",
+      "ĠGl ass",
+      "ÑĢ он",
+      "ol an",
+      "Ġdom inated",
+      "åĮ ĸ",
+      "__ __",
+      "ĠÙĩ ذا",
+      "ĠD ana",
+      "Ġdin heiro",
+      "a qu",
+      "ë ¯¼",
+      "ĠÃł s",
+      "ĠJo ey",
+      "ĠGr iff",
+      "Ġatt ain",
+      "Ġtrans itions",
+      "ĠLiter ally",
+      "ен д",
+      "ĠHa ven",
+      "Ġgrab bing",
+      "Ġcryst als",
+      "ĠFour th",
+      "Ġcand les",
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+      "ric o",
+      "Ġ5 000",
+      "et to",
+      "Ġund o",
+      "Ġk to",
+      "Ġdi vert",
+      "Ġch ir",
+      "Ġper sec",
+      "Ġh iking",
+      "Ġannounce ments",
+      "çĶ ±",
+      "з Ñĭ",
+      "Ġa uc",
+      "Ġsystem ic",
+      "ĠR M",
+      "Ïĥ α",
+      "ĠÐĶ ж",
+      "Ġy ar",
+      "ĠW ard",
+      "Ġpiss ed",
+      "Ġcar n",
+      "Ġautonom ous",
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+      "so ver",
+      "æ²Ĵ éĮ¯",
+      "å¾Ī 好",
+      "Ġref lex",
+      "Ġgard ens",
+      "Ġd ated",
+      "ì ±",
+      "ami ÄĻ",
+      "Ġcontinu ity",
+      "Ġcitizens hip",
+      "Ġsch wer",
+      "Ġz ak",
+      "t able",
+      "ĠÑģ Ñĩ",
+      "è§ ģ",
+      "ĠÏĥ ε",
+      "Ġgener ates",
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+      "ö h",
+      "ó m",
+      "al am",
+      "ĠJUD Y",
+      "ĠB ug",
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+      "Ġdr ones",
+      "Ġá gua",
+      "ac aks",
+      "æ ļ",
+      "ĠÐļ он",
+      "× ĸ×Ķ",
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+      "Ġne arest",
+      "Ġpro yect",
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+      "ĠASH LEY",
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+      "Ġre play",
+      "Ġt ara",
+      "ĠInd ians",
+      "ãĤ °",
+      "ica id",
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+      "Ġappe aling",
+      "ĠW es",
+      "Ġment ions",
+      "Ġдел е",
+      "Ġk w",
+      "Ġfrag ile",
+      "is z",
+      "k ów",
+      "h ang",
+      "col or",
+      "Ġpresident e",
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+      "е ÑĦ",
+      "çĪ ¸",
+      "Ġдоб ав",
+      "ĠN elson",
+      "á fic",
+      "ĠMIC HAEL",
+      "Ġmechan ic",
+      "Ġmet res",
+      "Ġo czywiÅĽcie",
+      "ĠC ind",
+      "Ġog sÃ¥",
+      "Ġlands ca",
+      "AC E",
+      "Ġhead lines",
+      "Ġcat alyst",
+      "ĠC atch",
+      "ink les",
+      "Ġp ills",
+      "ord o",
+      "Ġimmig rant",
+      "Ġexam ination",
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+      "zÄħ d",
+      "Ġqui ere",
+      "Ġne lla",
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+      "Ġpass a",
+      "Ġsuper fic",
+      "ist or",
+      "Ġno v",
+      "ëĭ µ",
+      "Ġmand ate",
+      "is ons",
+      "ĠVirt ual",
+      "Ġsel ber",
+      "Ġcounsel ing",
+      "ĠN BA",
+      "Ġse pt",
+      "Ġbelie ver",
+      "Ġmar vel",
+      "ĠInte gr",
+      "Ġм Ñĸ",
+      "Ġor ph",
+      "Ġback ward",
+      "ĠGen eration",
+      "ĠP ict",
+      "ĠÑĤо ÑĤ",
+      "Ġtap i",
+      "pro chen",
+      "Ġhall way",
+      "ht e",
+      "ĠÛģ ÛĴ",
+      "ĠZ um",
+      "èĢģ 師",
+      "ach ment",
+      "iqu er",
+      "fol g",
+      "ĠEd die",
+      "ĠK il",
+      "Ġwell ness",
+      "st ock",
+      "è¼ ĥ",
+      "Ġka ç",
+      "Ġterror ism",
+      "Ġpo inter",
+      "O f",
+      "her ic",
+      "ĠUlt imately",
+      "Ġmes es",
+      "ĠTr ade",
+      "Ġp int",
+      "Ġtu ition",
+      "Ġdisag re",
+      "Ġê²Į ìŀĦ",
+      "Ġmanus cript",
+      "Ġro omm",
+      "Ġoutput s",
+      "е ÑĨи",
+      "Ġr ies",
+      "Ġsal ud",
+      "otz dem",
+      "Ġmass es",
+      "Ġby ÅĤa",
+      "Ġclear ing",
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+      "ats on",
+      "Ġfold ed",
+      "ĠJ ar",
+      "ÙĦ Ùī",
+      "9 00",
+      "ĠÑĥ Ñģп",
+      "Ġprophe cy",
+      "Ġinterf ere",
+      "иÑħ од",
+      "๠Į",
+      "Ġth ri",
+      "Ġ×ŀ× ©",
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+      "Ġ199 2",
+      "Ġfut uro",
+      "Ġlock ing",
+      "Ġembar go",
+      "ĠNe ither",
+      "iv amente",
+      "ĠmÃ¥ ste",
+      "Ġm ik",
+      "Ġcollect or",
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+      "ĠG and",
+      "Ġsent ir",
+      "ĠM ight",
+      "å¡ Ķ",
+      "Ġgan zen",
+      "U C",
+      "Ġrel ating",
+      "S D",
+      "Ġmos quito",
+      "G R",
+      "Ġho llow",
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+      "ĠWalk er",
+      "Ġaffili ate",
+      "Ġduplic ate",
+      "н ем",
+      "Ġgra pe",
+      "ĠOrgan ization",
+      "Ġsy nt",
+      "J oe",
+      "Ġg eg",
+      "Ġreve aling",
+      "ĠEth an",
+      "out er",
+      "Ġy ay",
+      "é« Ķ",
+      "л аÑĢ",
+      "Ġreported ly",
+      "Ġihr er",
+      "Ġrecogn ise",
+      "Ġbum per",
+      "ĠR andy",
+      "ĠVen us",
+      "t les",
+      "Ġappet ite",
+      "Ġgluc ose",
+      "Ġch odzi",
+      "ĠFurther more",
+      "t ir",
+      "Ġcont a",
+      "Ġint uition",
+      "Ġalt itude",
+      "Ġch unks",
+      "ĠJosh ua",
+      "ıģ ım",
+      "ry lic",
+      "le ans",
+      "ĠíĶ ¼ë",
+      "L L",
+      "Q ue",
+      "Ġg or",
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+      "Ġpo ems",
+      "Ġexc el",
+      "Ġexpl ored",
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+      "Ġinclus o",
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+      "ĠG avin",
+      "all ing",
+      "ĠÏĦο ν",
+      "é ©",
+      "ar beit",
+      "ĠG as",
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+      "rie ben",
+      "Ġsp am",
+      "Ġindo or",
+      "Ġthr ust",
+      "ĠA ld",
+      "ĠPri or",
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+      "o ca",
+      "AS H",
+      "£ ł",
+      "ĠChrist ine",
+      "Ġdra wer",
+      "Ġno on",
+      "Ġìŀ ĺë",
+      "Ġperman ently",
+      "æ· ±",
+      "ĠнапÑĢ имеÑĢ",
+      "Ġpodcast s",
+      "era peut",
+      "pr it",
+      "Ġstain less",
+      "ĠÚ© ÛĴ",
+      "Ġfamil ia",
+      "ĠÑĢаз ÑĢ",
+      "un to",
+      "ĠÑģÑĤ ол",
+      "Ġh ä",
+      "ĠH ai",
+      "ĠP B",
+      "iz on",
+      "Ġkon nte",
+      "Ġbüy ük",
+      "Ġutil izar",
+      "Ú Ĩ",
+      "Ġaqu esta",
+      "Ġmix er",
+      "ud ent",
+      "лек Ñģ",
+      "ÅĤ u",
+      "ĠÑģиÑģÑĤ ем",
+      "Ġн оÑĢм",
+      "Ġfat al",
+      "Ġconsider ations",
+      "Ġvalid ation",
+      "Ġo li",
+      "Ġk ardeÅŁ",
+      "ĠGL ORIA",
+      "Ġp all",
+      "еÑģÑĤ е",
+      "Ġrect ang",
+      "Ġmed ieval",
+      "allah i",
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+      "ĠSy rian",
+      "Ġshe ar",
+      "Ġdeb ug",
+      "ĠM ai",
+      "Ġknock ing",
+      "ĠLe x",
+      "ard an",
+      "ro v",
+      "Ġmem orial",
+      "æ° £",
+      "ook y",
+      "Ġstuff ed",
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+      "Ġw ig",
+      "Ĥ ł",
+      "Ġpróxim a",
+      "Ġ199 1",
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+      "ĠBe ast",
+      "Ġsm o",
+      "atch ed",
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+      "Ġм од",
+      "Ġge e",
+      "Ġconcept ual",
+      "ĠÃ ´",
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+      "Ġquer ies",
+      "олÑĮ ÑĪ",
+      "ĠA part",
+      "Ġex empl",
+      "å± ±",
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+      "ĠO FF",
+      "gg ak",
+      "Ġbe ad",
+      "h ir",
+      "l ies",
+      "ĠClear ly",
+      "ı lar",
+      "Ġch ess",
+      "Ġwhich ever",
+      "Ġ9 6",
+      "Ạ±",
+      "Ġrespect s",
+      "Ġм оÑĢ",
+      "Ġorgan ism",
+      "Ġgrand pa",
+      "ĠV ie",
+      "è·Ł ä½ł",
+      "Ġflo oding",
+      "Ġupgrad ed",
+      "Ñij ÑĢ",
+      "Ġcheek s",
+      "Ġcon quer",
+      "Ġstub born",
+      "Ġpuzz les",
+      "Ġau ction",
+      "Ġre lying",
+      "ĠPRO F",
+      "ĠEs per",
+      "ĠÐľ У",
+      "Ġhy pe",
+      "Ġposs ibil",
+      "Ġimp rison",
+      "ĠEr n",
+      "ìĹĪ ìĬµëĭĪëĭ¤",
+      "Ġenv ie",
+      "Ġresur rection",
+      "ä¸į è¡Į",
+      "Ġs per",
+      "ĠVenez uela",
+      "s om",
+      "Ġìŀł ê¹",
+      "Ġnouve lle",
+      "Ġclos es",
+      "Ġ19 40",
+      "Ġqu a",
+      "ĠJ ared",
+      "ĠP ir",
+      "Ġind e",
+      "Ġscr ub",
+      "uk u",
+      "Ġrequ iring",
+      "Ġв ами",
+      "Ġconsider able",
+      "åIJ Ľ",
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+      "Ġin ne",
+      "Ġmein em",
+      "Ġhard ship",
+      "Ġtra ps",
+      "ro c",
+      "ĠìĦ ¤ë",
+      "Ġresearch ing",
+      "ĠMarg aret",
+      "Ġpen ny",
+      "Ġbı rak",
+      "Ñij л",
+      "Ġw ool",
+      "Ġr het",
+      "Ġflat ten",
+      "ç ĩ",
+      "à¹Ģภ£",
+      "Ġp ied",
+      "ĠCh ap",
+      "Ġunder m",
+      "Ġf ret",
+      "Ġcrash ed",
+      "ĠFra uen",
+      "Ø° Ùĩ",
+      "iv an",
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+      "late go",
+      "Ġsp äter",
+      "Ġsimilar ities",
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+      "ĠCor on",
+      "ĠC reek",
+      "Ġboss es",
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+      "Ġdeb ates",
+      "Ġassemb led",
+      "ĠÃ ģ",
+      "ĠV ai",
+      "Ġtr act",
+      "Ġsimple ment",
+      "ĠAr in",
+      "Ġvulner ability",
+      "Ġhorm one",
+      "I EL",
+      "OO K",
+      "Ġrel ay",
+      "ĠAnd rea",
+      "r il",
+      "Ġnecess ity",
+      "aceut ical",
+      "Ñİ Ñī",
+      "ous ing",
+      "nah men",
+      "Ġfoot print",
+      "m ap",
+      "ĠT ier",
+      "ann ya",
+      "int end",
+      "åĸ ®",
+      "å ¢",
+      "Ġdecor ate",
+      "Ġzomb ies",
+      "ĠHy d",
+      "ĠSu z",
+      "Ġcampus es",
+      "ĠE mb",
+      "Ġthr ottle",
+      "Ġad min",
+      "Ġop ortun",
+      "Ġmir rors",
+      "Ġident ities",
+      "ĠCl in",
+      "Ġë¹ Ħë",
+      "á¹ £",
+      "ĠO tt",
+      "Ġbl ues",
+      "Ġimpress ions",
+      "- ,",
+      "Ġv ague",
+      "a fe",
+      "Ġinfer ior",
+      "eral d",
+      "Ġmedic ines",
+      "Ġpre gunta",
+      "os ely",
+      "Ġt élé",
+      "ĠMon th",
+      "ĠLe aders",
+      "ĠEgypt ian",
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+      "k ers",
+      "he its",
+      "Ġre cht",
+      "P lay",
+      "Ġe g",
+      "Ġpoll s",
+      "ĠWOO DR",
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+      "j am",
+      "B oth",
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+      "Ġsing ers",
+      "Ġlo gin",
+      "Ġt êm",
+      "l ation",
+      "ĠM um",
+      "Æ°á»Ŀ ng",
+      "ĠEd itor",
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+      "h ave",
+      "ĠS ek",
+      "Ġwe aker",
+      "ĠG ob",
+      "A fter",
+      "´ì §Ģ",
+      "Ġ문 ìłľ",
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+      "Ġdisad vantage",
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+      "Ġg aze",
+      "ĠM ack",
+      "Ïģ ί",
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+      "b ab",
+      "ä¿ Ŀ",
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+      "д еÑĢж",
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+      "ĠComm e",
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+      "ĠChampions hip",
+      "Ġwel che",
+      "ĠY outh",
+      "z em",
+      "Ġod pow",
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+      "ìĶ ©",
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+      "la ir",
+      "ik u",
+      "Ġvend or",
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+      "Ġfinan ci",
+      "Ġover ly",
+      "â u",
+      "Ġgl uten",
+      "Ġ18 00",
+      "Ġdiv isions",
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+      "Ġob ed",
+      "Ġwar um",
+      "Ġe her",
+      "Ġel im",
+      "ĠÐĴ о",
+      "Ġpeu vent",
+      "ĠW anna",
+      "Ġattend ance",
+      "Ġassess ments",
+      "ĠB og",
+      "Ġimag ery",
+      "Ġcollect ively",
+      "Ġinform al",
+      "ĠSch we",
+      "Ġde utlich",
+      "ĠCh el",
+      "ĠP E",
+      "ow ed",
+      "Ġb anner",
+      "Ġshel ves",
+      "ĠRet urn",
+      "æĭ ¿",
+      "LAUGH S",
+      "Ġcongrat ulate",
+      "ĠNor way",
+      "Ġd well",
+      "ĠCarib bean",
+      "Ġnorm s",
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+      "ĠValent ine",
+      "Ġext ending",
+      "ĠV ou",
+      "or r",
+      "ĠCh eng",
+      "Â ¡",
+      "ĠдоÑĢ ог",
+      "Ġve g",
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+      "em et",
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+      "ric es",
+      "I Z",
+      "ĠUS D",
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+      "ĠB ag",
+      "Ġê ½",
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+      "Ġweakness es",
+      "P h",
+      "ĠVi ol",
+      "ä¸į çĶ¨",
+      "Ġdra gging",
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+      "ĠCS S",
+      "Ġmill imeters",
+      "Ġest ás",
+      "Ġac ute",
+      "Ġde jar",
+      "i ÄŁ",
+      "ob ra",
+      "L ove",
+      "Ġsil k",
+      "** **",
+      "Ġjo ins",
+      "Ġpro l",
+      "Ġê°IJìĤ¬ íķ©ëĭĪëĭ¤",
+      "æĶ ¯",
+      "ØŃ Ø¯",
+      "agh etti",
+      "än ner",
+      "Ġstr ang",
+      "Ġdoub led",
+      "Ġdescri ptions",
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+      "Ġpart i",
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+      "² Ħë",
+      "Ġö ÄŁ",
+      "ig hing",
+      "Ġang ular",
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+      "Æ° Æ¡",
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+      "á»ij c",
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+      "Ġcarb oh",
+      "ĠTra vis",
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+      "ĠPh ase",
+      "Ġactiv ation",
+      "Ġop io",
+      "· ¨",
+      "Ġdecre ased",
+      "C ar",
+      "Ġbund le",
+      "Ġexp end",
+      "orm al",
+      "Ġadjac ent",
+      "Ġme e",
+      "ĠоÑĢ г",
+      "Ġtrans cript",
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+      "G S",
+      "è§ ī",
+      "Ġse ul",
+      "Ãł nh",
+      "Ġn ya",
+      "ning s",
+      "Ġìĭ ľë",
+      "ĠëĶ°ë Ŀ¼",
+      "ĠA gr",
+      "ÃŃ d",
+      "çķ Ļ",
+      "Ġab y",
+      "ĠNe o",
+      "ıyor uz",
+      "ĠThink ing",
+      "a ime",
+      "Ġv ite",
+      "Ġtrav és",
+      "Ġ×ij× ¢",
+      "Ġм ед",
+      "O ur",
+      "ho ot",
+      "Ġl iner",
+      "ĠP izza",
+      "Ġhy g",
+      "fl ies",
+      "ĠContin ue",
+      "Ġdent al",
+      "ĠT ib",
+      "Ġreg ulate",
+      "lie ÃŁ",
+      "AL K",
+      "ĠTa e",
+      "ê¸ ¸",
+      "ĠBre xit",
+      "ĠG ut",
+      "Ġoccup ation",
+      "Ġz robi",
+      "â m",
+      "Ġwh isk",
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+      "Ġkans ke",
+      "om on",
+      "ro be",
+      "Ġwar fare",
+      "Ġth á»ĥ",
+      "Ġjak i",
+      "Ġstro kes",
+      "Ġpe as",
+      "ĠDam it",
+      "H AN",
+      "Ġinter ference",
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+      "N ER",
+      "out ing",
+      "Ġtext ures",
+      "Ł ī",
+      "ow i",
+      "Ġíķ Ļ",
+      "Ġd ens",
+      "Ġprotagon ist",
+      "än n",
+      "Ġgod dess",
+      "Ġwoll te",
+      "ij o",
+      "ĠWo che",
+      "ĠV PN",
+      "st ory",
+      "Ġkind erg",
+      "Ġfun nel",
+      "Ġdist ress",
+      "ноÑģÑĤÑĮ Ñİ",
+      "Ġno isy",
+      "ĠпÑĢод олж",
+      "Ġdar an",
+      "Ġenzy me",
+      "л ож",
+      "Ġm ute",
+      "Ġd war",
+      "Ġا س",
+      "Ġkom pl",
+      "Ġmer it",
+      "Ġf osse",
+      "ĠDr ink",
+      "Ġfor a",
+      "Ġw ohl",
+      "Ġbree ze",
+      "Ġsan it",
+      "Ġdr in",
+      "ĠìĿ´ê±° ëĬĶ",
+      "Ġ6 2",
+      "Ġì° ¨ë",
+      "aby tes",
+      "Ġde eds",
+      "ĠÐ ¹",
+      "i ème",
+      "igg ling",
+      "Ġ\" '",
+      "ĠÑĩа ÑģÑĤÑĮ",
+      "ĠAns wer",
+      "Ġev angel",
+      "Ġ10 80",
+      "ĠVis it",
+      "ic ient",
+      "Ġreli ability",
+      "Ñİ ÑģÑĮ",
+      "ĠEar lier",
+      "Ġf id",
+      "çŃī ä¸Ģä¸ĭ",
+      "Ġslee ves",
+      "iy orsun",
+      "Ġb ib",
+      "ĠAcc ount",
+      "Ñı ли",
+      "cipl inary",
+      "z as",
+      "Ġб еÑĢ",
+      "Ġneck lace",
+      "Ġbl ender",
+      "ĠPhill ips",
+      "et i",
+      "ĠJup iter",
+      "Ġprov oc",
+      "ĠYe ars",
+      "ent re",
+      "ac io",
+      "Ġk ü",
+      "Ġanten na",
+      "Ġnovel s",
+      "Ġf art",
+      "ĠS ugar",
+      "ĠJud y",
+      "Ġcollaps ed",
+      "ç °",
+      "rit is",
+      "Ġìĥģ íĻ©",
+      "ÐĹ Ð«",
+      "ĠVer f",
+      "rane an",
+      "ere um",
+      "ĠTar get",
+      "Ġ8 8",
+      "ĠÐĺ з",
+      "ide o",
+      "Ġreg ression",
+      "ì¶ ľ",
+      "Ġmów i",
+      "Ġstud ios",
+      "i ens",
+      "ip h",
+      "Ġfr ying",
+      "Ġfasc inated",
+      "ĠW ah",
+      "b ucks",
+      "m aya",
+      "ĠSat urn",
+      "ĠM ommy",
+      "Ġrating s",
+      "Ġaut umn",
+      "Æ°Æ¡ ng",
+      "Ġlos er",
+      "Ġcent ro",
+      "érie ur",
+      "ĠF old",
+      "Ġsuper visor",
+      "ĠNo bel",
+      "Ġunder est",
+      "ob ia",
+      "Ġв ÑģÑı",
+      "Ġver w",
+      "Ġfu els",
+      "Ġartif acts",
+      "Ġë¶ Ļ",
+      "ĠAut om",
+      "çļĦ æĺ¯",
+      "Û Ķ",
+      "×ķ× ¡",
+      "Ġih nen",
+      "Ġ5 9",
+      "ound ing",
+      "еÑĢ Ñĭ",
+      "in ars",
+      "ch ant",
+      "Ġadd icted",
+      "Ġexplos ive",
+      "Ġdisp ers",
+      "â ĸĪ",
+      "ax is",
+      "AR Y",
+      "Ġl um",
+      "ĠÑĥ Ñģл",
+      "ĠØ Į",
+      "Ġru pees",
+      "ĠPe arl",
+      "c amp",
+      "t v",
+      "oy a",
+      "Ġconclud es",
+      "Ġcoll ision",
+      "Ġbuy er",
+      "Ġplay ground",
+      "Ġspr ings",
+      "Ġfemin ine",
+      "ĠR as",
+      "Ġincar cer",
+      "íĹ ĺ",
+      "Ġdial ect",
+      "Ġclos ure",
+      "Ġchat ting",
+      "Ġb abe",
+      "Ġspot light",
+      "Ġnot ation",
+      "è· ¯",
+      "St ar",
+      "i ão",
+      "Ġt ête",
+      "Ġt ide",
+      "Ġjun to",
+      "Ġsen ator",
+      "Ð ¥",
+      "Ġexcus es",
+      "Ġbl ink",
+      "Ġadm ission",
+      "ĠL ily",
+      "Ñĭ ми",
+      "Ġam igo",
+      "Ġl ust",
+      "ëĭ ¬",
+      "Ġam ino",
+      "äºĭ æĥħ",
+      "Ġconsult ant",
+      "ĠElect ric",
+      "Ġëħ¸ë ŀĺ",
+      "uj ah",
+      "Ġshoot er",
+      "icht en",
+      "ĠUkrain ian",
+      "Ġaim s",
+      "ĠEnter tain",
+      "Ġmir acles",
+      "èŃ °",
+      "Ġze igen",
+      "Ġl am",
+      "Ġres s",
+      "ĠJ ill",
+      "yl an",
+      "Ġro ok",
+      "Ġh aya",
+      "Ġpass port",
+      "ad ata",
+      "Ġju icy",
+      "con f",
+      "л ей",
+      "ĠS z",
+      "Ġinter cept",
+      "ãģĤãĤĬãģĮãģ¨ãģĨ ãģĶãģĸ",
+      "ĠTe ams",
+      "Ġmak en",
+      "ir rel",
+      "ĠLI KE",
+      "áºŃ y",
+      "êµ °",
+      "Ġshort age",
+      "Ġparad igm",
+      "Ġpap el",
+      "Ġast ero",
+      "ãģ¾ ãģŁ",
+      "Ġsoll en",
+      "ĠMic key",
+      "ĠOr leans",
+      "Ġchol esterol",
+      "Ġgo ose",
+      "ÑĨи Ñİ",
+      "ãģĤ ãĤĭ",
+      "ĠF L",
+      "Ġгол ов",
+      "Ġtrib ute",
+      "ĠG am",
+      "Ġé videmment",
+      "Ñı Ñħ",
+      "å® ŀ",
+      "çĶ °",
+      "Ġin appropri",
+      "uh an",
+      "Ġorganiz ational",
+      "ail ed",
+      "Ġend ure",
+      "Ġ7 6",
+      "Ġshot gun",
+      "Ġliv re",
+      "Ġsu ited",
+      "Ġwarm th",
+      "ĠS IM",
+      "Ġenv ision",
+      "Ġde grad",
+      "î ne",
+      "La ughing",
+      "ĠWho ever",
+      "ĠBuddh ism",
+      "Ġspr inkle",
+      "ceÄŁ iz",
+      "Ġru ins",
+      "Ġst arch",
+      "ĠHer z",
+      "Ġinjust ice",
+      "Ġhum idity",
+      "ожал Ñĥй",
+      "ĠOb ject",
+      "ĠI gn",
+      "ĠEx am",
+      "ig ers",
+      "Ġth ou",
+      "ĠSo y",
+      "iv as",
+      "Ġpol es",
+      "m ath",
+      "Ġв ним",
+      "ING ING",
+      "ed ral",
+      "Ġexpl or",
+      "Ġroast ed",
+      "Ġcraw l",
+      "Ġco ff",
+      "Ġan om",
+      "Ġw ij",
+      "Ġimpro ves",
+      "Ġtreat y",
+      "Ġdiscover ing",
+      "Ġstat ute",
+      "Ġmerc ado",
+      "ĠÑģ ил",
+      "Ġint el",
+      "ĠChance llor",
+      "ĠMed icaid",
+      "ug i",
+      "Ġver bal",
+      "Ġd ön",
+      "Ġscript ure",
+      "Ġit eration",
+      "ek s",
+      "ĠOx ford",
+      "Ġw äh",
+      "ĠV ad",
+      "ĠA K",
+      "ĠìķĦ ìĿ´ë",
+      "Ġi ets",
+      "Ġneed les",
+      "Ùĥ Ùħ",
+      "Ġpas ado",
+      "Ġalbum s",
+      "Ġye a",
+      "et zen",
+      "Ħë ıĦ",
+      "Ġdeterm ines",
+      "Ġthe e",
+      "ĠPlay ing",
+      "är t",
+      "Ġ× ¦",
+      "c led",
+      "Ġdown ward",
+      "al one",
+      "Ġsol u",
+      "Ġpart ition",
+      "Ġw z",
+      "d d",
+      "Ġpesso al",
+      "å ª½",
+      "Ġfact ories",
+      "Ġble ibt",
+      "ม า",
+      "als a",
+      "ĠNF L",
+      "Ġfu era",
+      "Ġres erved",
+      "ĠE arn",
+      "Ġhel t",
+      "Ġshort cut",
+      "Ġconvin cing",
+      "sp ace",
+      "Ġen force",
+      "Ġc ores",
+      "Ġe fter",
+      "Ġrecess ion",
+      "x ico",
+      "Ġprop osition",
+      "ar ians",
+      "rop ol",
+      "Ġëª °ë",
+      "ĠÎ ľ",
+      "ĠìļĶ ì¦ĺ",
+      "Ġactiv ist",
+      "Ġconv iction",
+      "Ġz ab",
+      "Ġcancel ed",
+      "ÑĤо Ñĩно",
+      "ĠÎ ®",
+      "éĢĻ樣 åŃIJ",
+      "n ite",
+      "Ġfund ra",
+      "buz zer",
+      "ел о",
+      "ic ations",
+      "Ġz ona",
+      "Ġte ens",
+      "Ġmethod ology",
+      "Ġì¤ij ìļĶ",
+      "th an",
+      "ĠU l",
+      "ĠG rey",
+      "Ġh og",
+      "IN K",
+      "ĠS ung",
+      "ĠC laud",
+      "ĠCN N",
+      "Ġdel ivers",
+      "al in",
+      "ĠAd obe",
+      "ot he",
+      "ĠDes wegen",
+      "ภ³",
+      "Ġwer de",
+      "Ġgre ase",
+      "Ġup grades",
+      "ĠFin land",
+      "ac cept",
+      "Ġinter rog",
+      "be e",
+      "Ġãģ «",
+      "Ġpre de",
+      "ĠN ep",
+      "ĠCam bridge",
+      "Ġgraph s",
+      "Ġha unted",
+      "Ñģ ем",
+      "æ §",
+      "åħ ĭ",
+      "S ome",
+      "ĠM all",
+      "Ġrehears al",
+      "ĠUr ban",
+      "ĠL ag",
+      "Ġn im",
+      "ê° ķ",
+      "Ġposition ed",
+      "Ġavo ided",
+      "EM A",
+      "Ġlleg ar",
+      "Ġráp ido",
+      "Ġgou vern",
+      "Ġh ing",
+      "Ġdeal er",
+      "Ġreform s",
+      "Ġfat ty",
+      "к ол",
+      "ĠA ce",
+      "Ġne p",
+      "Ġì² Ń",
+      "Ġcomput ation",
+      "ĠSt ream",
+      "bour ne",
+      "t ur",
+      "P or",
+      "Ġsleep y",
+      "Ġbang et",
+      "ãģĤ ãģ®",
+      "Ġwe ighs",
+      "Ġble iben",
+      "ĠG ren",
+      "Ġun ions",
+      "Ġêµ IJ",
+      "Ġap render",
+      "uit ar",
+      "ĠJ est",
+      "um ing",
+      "ĠPlay er",
+      "ĠExt rem",
+      "Ġinteg er",
+      "аÑĩ е",
+      "Ġconcert s",
+      "×ķ× Ľ",
+      "Ġtro chÄĻ",
+      "ĠRe pe",
+      "éĩį è¦ģ",
+      "๠Ĥ",
+      "ż en",
+      "Ġsound ing",
+      "Ġan onymous",
+      "Ġex ca",
+      "ĠIran ian",
+      "Ġener getic",
+      "Ġw ives",
+      "ĠÑĨ веÑĤ",
+      "Ġa is",
+      "ãģĭ ãģª",
+      "Ġsud ah",
+      "Ġunder wear",
+      "Ġcrunch y",
+      "ĠP ain",
+      "Ġger çek",
+      "red ict",
+      "Ġm isma",
+      "Ñĸ ÑĤ",
+      "Ġsurv iving",
+      "ÎŃ ÏĤ",
+      "Ġparticip ant",
+      "ĠH essen",
+      "ári as",
+      "Ġsub way",
+      "ist ä",
+      "Ġcor al",
+      "Ġmar ijuana",
+      "ĠMem orial",
+      "ÑĪ ий",
+      "ri z",
+      "Ġsatell ites",
+      "Ġle ase",
+      "ĠCam eron",
+      "um ph",
+      "Ġclass mates",
+      "äh än",
+      "ÑģÑĤв е",
+      "Ġh ue",
+      "ĵ¤ ìĿĦ",
+      "Ġproport ional",
+      "Ġn oss",
+      "Ġl aps",
+      "r å",
+      "Ġbit coin",
+      "ÐĹЫ ÐļÐIJ",
+      "Ġì¶ ©",
+      "ĠÙĦ ÙĦ",
+      "ĠM ort",
+      "ĠEs p",
+      "arn os",
+      "ĠÑģказ ал",
+      "Ġä nd",
+      "åħ Ħ",
+      "×Ļ ×Ļ×Ŀ",
+      "ĠGe b",
+      "ge hen",
+      "I naudible",
+      "bor ough",
+      "ÑĦ ÑĦ",
+      "Ġfellow ship",
+      "ĠP aper",
+      "Ġcur ved",
+      "ĠGE OR",
+      "Ġcalcul ator",
+      "ĠCat al",
+      "ĠvÃł o",
+      "Ġby pass",
+      "л еÑĤ",
+      "à ³",
+      "tr ans",
+      "ren cies",
+      "ì ¡Į",
+      "ig ent",
+      "Ġtast ed",
+      "Ġo ceans",
+      "u ft",
+      "erv ice",
+      "ĠÐľÐ£ ÐĹЫÐļÐIJ",
+      "ĠClass ic",
+      "Ġrespect ively",
+      "~ )",
+      "î tre",
+      "ĠN ash",
+      "Ġz it",
+      "ĠìĽ ĥ",
+      "ĠëĨ Ĵ",
+      "qu ote",
+      "ĠUn s",
+      "Ġt ac",
+      "Ġpro ves",
+      "ĠPort land",
+      "b ly",
+      "Ġ ere",
+      "ì¶ Ķ",
+      "Ġépo ca",
+      "ĠÑĤÑĭ ÑģÑıÑĩ",
+      "7 6",
+      "Ġhad e",
+      "ĠF ro",
+      "ĠpolÃŃt ica",
+      "t ag",
+      "Ġíķ Ń",
+      "Ġsch ö",
+      "are tt",
+      "Ġprov isions",
+      "Ġmot ors",
+      "Ġimag ing",
+      "Ġdo k",
+      "ul ously",
+      "Ġme ille",
+      "çİ° åľ¨",
+      "ë IJ",
+      "ĠIS O",
+      "ĠST EM",
+      "ĠBow l",
+      "Ġto wers",
+      "ĠE e",
+      "ĠPerform ance",
+      "Ġlo in",
+      "cuss ion",
+      "Ġcoast al",
+      "ial e",
+      "com pass",
+      "Ġspell s",
+      "Ġdisappoint ing",
+      "Ġë²Ī 째",
+      "E ER",
+      "Ġvers atile",
+      "as ury",
+      "Ġen fin",
+      "Ġdown side",
+      "Ġgu iding",
+      "ĠاÙĦ ÙĤ",
+      "Ġnin ety",
+      "char ged",
+      "ĠF ans",
+      "Ġphilosoph ical",
+      "Ġg arn",
+      "ĠmÃ¥ nga",
+      "Ġwilling ness",
+      "Ġport ions",
+      "ab en",
+      "Ġ ï",
+      "Â ¿",
+      "ra ul",
+      "Ġspr int",
+      "if en",
+      "ıy la",
+      "Ġк Ñĥп",
+      "ãģı ãģłãģķãģĦ",
+      "Ġens uite",
+      "ĠCap itol",
+      "Ġ6 3",
+      "ĠговоÑĢ иÑĤ",
+      "Ġappoint ments",
+      "æī ¾",
+      "omi ast",
+      "Ġcare g",
+      "Ġpubl isher",
+      "Ġher aus",
+      "Ġε ί",
+      "ĠV S",
+      "ãģĿ ãģĹãģ¦",
+      "ä¸Ń åħ±",
+      "Ġsacrific es",
+      "th ird",
+      "Ġhuman itarian",
+      "ĠëĤ ´ì",
+      "im on",
+      "Ġine qu",
+      "Ġz ob",
+      "Ġcomfort ably",
+      "ĠD inge",
+      "Ġcancell ed",
+      "ĠPS AKI",
+      "ĠRob inson",
+      "Ġfin s",
+      ") ?",
+      "ĠHist or",
+      "ĠÑĩеловек а",
+      "Ġt bsp",
+      "te xt",
+      "k im",
+      "Ġupd ating",
+      "Ġgel d",
+      "f eld",
+      "ı ¼",
+      "Ġm ä",
+      "Ġcaf é",
+      "Ö Ģ",
+      "ĠS ri",
+      "ĠReg ion",
+      "ĠH ahaha",
+      "Ġfin ances",
+      "ĠاÙĦØ ´",
+      "Ġb unk",
+      "ru k",
+      "ha ft",
+      "Ġlater al",
+      "Ġext ensions",
+      "ĠìķĦ ìĿ´",
+      "Ġdefin ite",
+      "ĠZ hao",
+      "ĠLu is",
+      "st y",
+      "Ġcas os",
+      "ĠK lim",
+      "Ġ199 3",
+      "Ġreal ization",
+      "Ġhistor ian",
+      "Ġcrack ed",
+      "ëĤ ´",
+      "Ġsyst ème",
+      "ĠC IA",
+      "ĠÑĤ во",
+      "osp heric",
+      "Ġfle e",
+      "Ġr ất",
+      "ĠRegard less",
+      "Ġrel uct",
+      "Ġtim ely",
+      "ĠJul ian",
+      "G M",
+      "é Ĵ",
+      "ad ura",
+      "é£ Ł",
+      "Ġdress es",
+      "çģ £",
+      "ĠëĶ Ķ",
+      "Ġnom inated",
+      "Ġadvoc ates",
+      "ym ph",
+      "Ġrecord ings",
+      "Ġdev iation",
+      "Ġpriorit ize",
+      "Ġspir al",
+      "ĠYOU R",
+      "Ġtransp ose",
+      "amp oo",
+      "ĠìĽIJë ŀĺ",
+      "ĠV ision",
+      "Ġpol ite",
+      "Ġha mb",
+      "ĠPat ient",
+      "æ¯Ķ è¼ĥ",
+      "íģ ¬ë",
+      "Ġs ia",
+      "Ġê³ ³",
+      "Ġž e",
+      "è§ Ģ",
+      "Ġsuper market",
+      "ë ¹",
+      "ĠS ierra",
+      "Ġgr illed",
+      "ĠUp on",
+      "Ġabs ent",
+      "Ġme c",
+      "ĠAp ollo",
+      "Ġp unk",
+      "ĠPa ÅĦst",
+      "ĠÑģв ой",
+      "Ġê±° 기",
+      "G irl",
+      "Ġskin ny",
+      "ĠPrem ier",
+      "Ġterrit ories",
+      "Ġli ability",
+      "Ġj erk",
+      "r atic",
+      "Ġdan cers",
+      "ĠÑĥ ÑĢов",
+      "Ġê´ Ģë",
+      "on ly",
+      "ĠSt u",
+      "Ġske leton",
+      "ĠëŃ IJë",
+      "Ġзак он",
+      "ı kt",
+      "ĠMI KE",
+      "Ġl ö",
+      "m ie",
+      "Ġre iter",
+      "ãģĵãĤĮ ãģ¯",
+      "ĠKoll eg",
+      "ĠAd ams",
+      "lich er",
+      "Ġçoc uk",
+      "Ñı г",
+      "Ġbl ush",
+      "Ġsun shine",
+      "Ġe z",
+      "ĠDev il",
+      "Ġê¸ ¸",
+      "Ġãģ Ĭ",
+      "ad d",
+      "Ġlic ensed",
+      "Ġv inyl",
+      "ĠC zech",
+      "im ag",
+      "Ġcrack ing",
+      "Ġì º",
+      "Ġud ah",
+      "Ġs ommes",
+      "Ġìĸ¼ êµ",
+      "wa Äĩ",
+      "Ġf res",
+      "åij ½",
+      "ĠWal mart",
+      "ĠТ епеÑĢÑĮ",
+      "at isf",
+      "C I",
+      "l ang",
+      "Ġdiff usion",
+      "çĶ ·",
+      "Ġsom os",
+      "ĠM akes",
+      "æĪij æĥ³",
+      "ĠRick y",
+      "Ġmuch a",
+      "íķ ¨",
+      "Ġhorse power",
+      "as ia",
+      "Ġfib ers",
+      "Ġ erm",
+      "Ñģ кие",
+      "Ġjest e",
+      "Ġfire fight",
+      "Ġcu isine",
+      "Ġbesond ers",
+      "d ig",
+      "Ġì¢ ħ",
+      "ĠÑĥ ж",
+      "Ġtr acing",
+      "Ġcertain s",
+      "ĠApp ly",
+      "Ñĭв аÑĤÑĮ",
+      "ç Į",
+      "Ġbr u",
+      "ĠY ES",
+      "ĠB ai",
+      "ĠD it",
+      "ĠB is",
+      "Ġun le",
+      "ÑģÑĤа ÑĤоÑĩно",
+      "ĠAw ak",
+      ".. \"",
+      "Ġ12 5",
+      "Ġroot ed",
+      "Ġcaut ious",
+      "con st",
+      "Ġorchest ra",
+      "çľ ¼",
+      "Ġвн ÑĥÑĤ",
+      "Ġquel qu",
+      "ĠоÑĤ веÑĤ",
+      "ĠMet hod",
+      "ì¹ ľ",
+      "Ġμ αÏĤ",
+      "l ü",
+      "ĠìķĦ ê¹Į",
+      "Ġn aming",
+      "C har",
+      "ĠS icher",
+      "Ġprivile ged",
+      "ĠF ly",
+      "Ġãģ ĭ",
+      "áºŃ t",
+      "Ġadv ances",
+      "ĠZel da",
+      "Ġand ra",
+      "Ġgr inding",
+      "ĠEd ition",
+      "p f",
+      "Ġwarri ors",
+      "Ġh edge",
+      "Ġuns eren",
+      "ĠÑģÑİ Ð´Ð°",
+      "el iness",
+      "Ġpersonal ities",
+      "Ġf ö",
+      "' M",
+      "ĠÑĤо Ñĩно",
+      "Ġsh ipped",
+      "Ġmete or",
+      "Ġsurround ings",
+      "ĠF ill",
+      "u esta",
+      "ĠPerson al",
+      "ĠAll e",
+      "OR T",
+      "ä¹ ħ",
+      "ĠS che",
+      "V I",
+      "Ġcompar able",
+      "dam n",
+      "Ġd itch",
+      "Y AN",
+      "ism us",
+      "Ġpick up",
+      "Ġd ak",
+      "ĠE P",
+      "b est",
+      "ĠS ue",
+      "äll t",
+      "Ġpop corn",
+      "Ġfold ing",
+      "h ome",
+      "ив аеÑĤ",
+      "å·² ç¶ĵ",
+      "Ġan not",
+      "ch uck",
+      "Ġfier ce",
+      "Ġdam aging",
+      "Ġfl op",
+      "Ġpas ar",
+      "Ġre ef",
+      "ĠÑģво ей",
+      "Ġz oo",
+      "o vers",
+      "j ets",
+      "Ġpr ès",
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+      "Ġside walk",
+      "ìĪ ĺë",
+      "Ġжиз нÑĮ",
+      "ĠMon ica",
+      "ä¾Ĩ äºĨ",
+      "Ġcop ied",
+      "ĠTer ra",
+      "ist ent",
+      "ç³ »",
+      "Ġо но",
+      "Ġwh ale",
+      "ĠW ITH",
+      "л ÑĥÑĪ",
+      "å½± çīĩ",
+      "ĠE en",
+      "ĠÑģво и",
+      "Ġord in",
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+      "Ġsp okes",
+      "Ġdisp ute",
+      "Ġsens ible",
+      "Ġpre aching",
+      "Ġktó rzy",
+      "pt ed",
+      "av ier",
+      "Ġpist ol",
+      "ĠTap i",
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+      "ff ff",
+      "Ġac rylic",
+      "Ġignor ance",
+      "ĠZ iel",
+      "r ans",
+      "Ġweld ing",
+      "m id",
+      "æĪij ä¸į",
+      "Ġзан им",
+      "Ġlan es",
+      "Ġmin es",
+      "Ġmom s",
+      "×ķ× Ĺ",
+      "ĠCham ber",
+      "t ier",
+      "Ġmod est",
+      "ĠìĹ¬ê¸° ìĦľ",
+      "Ġun as",
+      "Ġw rench",
+      "hand ed",
+      "Ġsatur ated",
+      "ĠF ang",
+      "ĠCommission er",
+      "ठ°",
+      "Ġ× ĸ",
+      "ĠLouis iana",
+      "ĠM ask",
+      "Ġcub es",
+      "ìĶ ¨",
+      "Ġvidé os",
+      "ĠnÃ¥ gon",
+      "Ġr ider",
+      "Ġì¶ ľ",
+      "Ġs ón",
+      "ĠLat ino",
+      "b ank",
+      "íķ´ì £¼",
+      "ĠB rend",
+      "Ġsexual ity",
+      "... ,",
+      "Ġforget ting",
+      "Ġ ÛĮ",
+      "ĠAven gers",
+      "ĠBon jour",
+      "cess or",
+      "кÑĢа ÑĹ",
+      "c ence",
+      "Ġge ograph",
+      "cul o",
+      "о ÑģÑĤÑĮ",
+      "Ġswe ating",
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+      "Ġsymm etry",
+      "ts å",
+      "Ġj an",
+      "ĠFer r",
+      "é¦ ĸ",
+      "Ġamb assador",
+      "ziÄĻ k",
+      "Ġmus un",
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+      "ĠL G",
+      "iss ent",
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+      "Ġbron ze",
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+      "åĦ Ħ",
+      "ĠPROF ESS",
+      "h alf",
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+      "ĠB omb",
+      "л аг",
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+      "Ġteam mate",
+      "èģ ŀ",
+      "F T",
+      "Ġk ı",
+      "heart ed",
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+      "Ġbell s",
+      "ĠOu ais",
+      "Ġspecial ists",
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+      "dep th",
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+      "g ies",
+      "ĠCo ffee",
+      "Ġmark ing",
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+      "ĠB ot",
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+      "e ye",
+      "ĠB ub",
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+      "Ġdon or",
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+      "Ġen for",
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+      "Ġbr icks",
+      "Ġkn itting",
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+      "H Y",
+      "r ide",
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+      "Ġìĺ ¨",
+      "Ġв д",
+      "Ġrest ra",
+      "Ġevalu ated",
+      "ÑĢ ев",
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+      "Ġche gar",
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+      "Ġdom ains",
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+      "Ġshut ter",
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+      "M ike",
+      "Ġinc lu",
+      "Ġdon ors",
+      "Ġa pl",
+      "ĠL ower",
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+      "Ġacad emy",
+      "Ġfin als",
+      "Ġdisappe ars",
+      "ÙĬ ا",
+      "Ġadministr ator",
+      "j s",
+      "Ġcut ter",
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+      "ĠO oo",
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+      "emet ery",
+      "Ġsort e",
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+      "ĠV oy",
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+      "ĠNin ja",
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+      "Ġë³ Ģ",
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+      "Ġpro pe",
+      "ĠW right",
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+      "ĠSh i",
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+      "Ġìĺ ¤í",
+      "ĠìĻĦ ìłĦ",
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+      "Ġl akes",
+      "ünd e",
+      "I nt",
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+      "ĠZh ou",
+      "Ġcorrid or",
+      "Ġesc ol",
+      "Ġindic ating",
+      "ia ÅĤa",
+      "Ġm ommy",
+      "Ġarch ives",
+      "Ġfound ers",
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+      "ĠDie u",
+      "Ġsick ness",
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+      "Ġar b",
+      "Ġn ed",
+      "ĠCh op",
+      "Ġco vid",
+      "Ġsl am",
+      "Ġpublic ations",
+      "D C",
+      "Ġsp ends",
+      "æ ¾",
+      "Ġrefuge e",
+      "Ġd ile",
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+      "ific ar",
+      "ĠS ach",
+      "G u",
+      "Ġre load",
+      "?? ??",
+      "Ġje ÅĽli",
+      "ĠÑģ оÑģÑĤо",
+      "Ġsim plicity",
+      "Ġbull ying",
+      "Ġм ол",
+      "Ġreal idad",
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+      "le vant",
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+      "Ġdev am",
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+      "ĠCop y",
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+      "ĠP le",
+      "H ave",
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+      "Ġde u",
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+      "Ġsigu iente",
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+      "ĠвÑģÑĤÑĢ еÑĩ",
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+      "Ġtwe ets",
+      "Ġsch auen",
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+      "ĠðŁİ µ",
+      "Ġst att",
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+      "ân cia",
+      "Ġsuper natural",
+      "Ġplug ged",
+      "F l",
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+      "ĠTamb ién",
+      "Ġencourage ment",
+      "ĠSer ver",
+      "ëĤ ľ",
+      "up a",
+      "Ġast on",
+      "Ġhe ars",
+      "ÑĢа Ñħ",
+      "Ġsch e",
+      "Ġr ats",
+      "Ġrec uper",
+      "Ġun ten",
+      "ĠFight ing",
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+      "ç¤ º",
+      "ĠS ü",
+      "Ñģ киÑħ",
+      "Ġpa ired",
+      "Ģ ìĿĦ",
+      "Ġá rea",
+      "Ġsweet ness",
+      "åı Ĭ",
+      "Ġde fer",
+      "Ġmuit as",
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+      "Ġdra gged",
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+      "ìĿ´ì §Ģ",
+      "Ġcell e",
+      "ck ing",
+      "ĠاÙĦØ ¬",
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+      "Ġgl imp",
+      "Ġspread s",
+      "ong o",
+      "ĠM ason",
+      "ĠIn g",
+      "Ġê°Ģ ëĬ¥",
+      "ÏĦ ικ",
+      "Ġsec ular",
+      "Ġb ater",
+      "Ġinqu iry",
+      "Ġenerg ies",
+      "Ġmanufact ured",
+      "Ġveget arian",
+      "Ġpine apple",
+      "ÑıÑĤ а",
+      "Ġpractition ers",
+      "2 000",
+      "Ġíķ´ì ļĶ",
+      "ĠìĹ¬ëŁ¬ë ¶Ħëĵ¤",
+      "Ġë¶ Īë",
+      "ĠJeff erson",
+      "ĠJo an",
+      "Ġtr am",
+      "å® ¹",
+      "ch mal",
+      "ĠH ait",
+      "á¹ ĩ",
+      "Ġun real",
+      "Ġsymbol ic",
+      "Ġste alth",
+      "Ġspl ash",
+      "ĠEntertain ment",
+      "Ġmetall ic",
+      "?\" .",
+      "è¶ Ĭ",
+      "ar ound",
+      "Ġdesp air",
+      "ĠNev ada",
+      "ĠFin ance",
+      "Ġk rie",
+      "ĠL ux",
+      "ĠSm ash",
+      "ke eping",
+      "Ġз аг",
+      "Ġnarc iss",
+      "Ġdz isiaj",
+      "Ġtoler ate",
+      "o ard",
+      "Ġlink ing",
+      "ĠEconom ic",
+      "Ġì ¼",
+      "Ġmor ph",
+      "ĠN ak",
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+      "at on",
+      "r ings",
+      "ĠP eng",
+      "ĠAir port",
+      "ãģĭ ãģ£ãģŁ",
+      "íķĺ ëĭ¤",
+      "§ ģ",
+      "pr ints",
+      "Ġhad i",
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+      "ann ers",
+      "Ġн им",
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+      "Ġinter fer",
+      "Ġfulf illing",
+      "Ġvisual ization",
+      "éĹľ ä¿Ĥ",
+      "ĠPr ice",
+      "� �",
+      "Ġscen ery",
+      "Ġpr one",
+      "Ġw izard",
+      "Ġb anyak",
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+      "Ġwish ed",
+      "Ġrail way",
+      "Ġü zer",
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+      "ĠA W",
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+      "Ġreact ing",
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+      "ภ¶",
+      "Ġan th",
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+      "Ġh ust",
+      "ĠSc reen",
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+      "Ġë ¿",
+      "Ġé qu",
+      "Ġwra ps",
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+      "Ġrecre ate",
+      "л аÑĤ",
+      "Ġbo leh",
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+      "Ġglimp se",
+      "reg ierung",
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+      "Ġreposit ory",
+      "Ġeng ra",
+      "Ġtraff icking",
+      "ass is",
+      "ĠTre k",
+      "Ġë² Į",
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+      "ĠK ab",
+      "ani u",
+      "g ive",
+      "Ġdin osaurs",
+      "Ġfe ather",
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+      "Ġpl um",
+      "ĠR S",
+      "ĠAn fang",
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+      "ĠìĬ ¤",
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+      "Ġsk ies",
+      "ĠA j",
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+      "ĠSh ane",
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+      "æ° Ĺ",
+      "is u",
+      "Ġb ord",
+      "Ġinteresting ly",
+      "çĻ »",
+      "oug hing",
+      "Ġr ushing",
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+      "Ġp yt",
+      "Ġform ats",
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+      "Ġê¼ Ń",
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+      "ĠA H",
+      "Ġpe w",
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+      "ĠH ugh",
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+      "ĠP ic",
+      "Ġenroll ed",
+      "Ġjed nak",
+      "Ġoffer ings",
+      "Ġcor az",
+      "L ife",
+      "Ġ !!!",
+      "Ġcl er",
+      "ĠVide os",
+      "ĠRod rig",
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+      "ĠP os",
+      "ĠSt age",
+      "ĠR ace",
+      "Ġen act",
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+      "ĠG y",
+      "ĠHis pan",
+      "Ġdef ence",
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+      "Ġrele v",
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+      "Ħ¸ ìļĶ",
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+      "Ġannoy ed",
+      "æĮ ĩ",
+      "la x",
+      "Ġexplo it",
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+      "ate urs",
+      "Ġhe ights",
+      "E ven",
+      "Ñģ он",
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+      "ĠVietnam ese",
+      "ãģ¯ ãģ¯",
+      "T R",
+      "Ġe er",
+      "Ġcann on",
+      "ĠCom b",
+      "IJë §Į",
+      "è» Ĭ",
+      "Ġê²ĥ ëıĦ",
+      "Ġaccomplish ments",
+      "ĠAnalyt ics",
+      "Ġshap ing",
+      "re iben",
+      "Ġb achelor",
+      "Ġfing ert",
+      "ack ed",
+      "Ġpyram id",
+      "ĠStew art",
+      "á st",
+      "Ġsurviv or",
+      "Ġdu ct",
+      "Ġdeal ers",
+      "æ´ »",
+      "ع Ùħ",
+      "ли н",
+      "Ġed e",
+      "×ķ× ¢",
+      "ĠÙĥ اÙĨ",
+      "ĠÏĦ ι",
+      "Ġcho oses",
+      "ĠO wn",
+      "го ÑĤов",
+      "h ire",
+      "алÑĮ нÑĭе",
+      "ĠÐĽ Ñİ",
+      "Ġо ÑģÑĤав",
+      "te ch",
+      "Ġdro it",
+      "Ġsubject ive",
+      "en es",
+      "Ġdiv is",
+      "ave z",
+      "Ġmaneu ver",
+      "à¹Ħ à¸Ķ",
+      "ade ce",
+      "ĠEn s",
+      "ac ial",
+      "ĠProt ection",
+      "ĸ ´",
+      "Ġform ally",
+      "Ġwy d",
+      "ingu ém",
+      "Ġz iem",
+      "Ġrecru iting",
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+      "n em",
+      "Ġforb idden",
+      "ĠB apt",
+      "×IJ× ł×Ļ",
+      "Ġsubs et",
+      "ĠMag az",
+      "n ement",
+      "Ġaqu ela",
+      "rag on",
+      "Ġcomm ittees",
+      "Ġéta ient",
+      "ud i",
+      "ĠDa wn",
+      "Ġb ore",
+      "Ġcompos er",
+      "ĠwiÄĻ cej",
+      "ang a",
+      "Ġdis like",
+      "ĠD ays",
+      "åŁ º",
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+      "Ġheaven s",
+      "ãģ Ĵ",
+      "he id",
+      "Ġtrad ers",
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+      "Ġwhis per",
+      "ĠMus k",
+      "éĽ Ĩ",
+      "ĠFamil ie",
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+      "ĠOl ivia",
+      "ĠPr os",
+      "Ġol ika",
+      "il im",
+      "Ġrép ond",
+      "ĠP eters",
+      "Ġ å¾Ī",
+      "Ġbit es",
+      "Ġv ic",
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+      "Ġ4 50",
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+      "w ait",
+      "Ġnotice able",
+      "uch a",
+      "Ġreh abil",
+      "Ġreject ion",
+      "ĠÑģлед ÑĥÑİÑī",
+      "Ġsl ider",
+      "Ġregard ed",
+      "Ġgrav it",
+      "ĠRes erve",
+      "c ount",
+      "Ġbre eding",
+      "Ġlon ge",
+      "ale b",
+      "Ġkn ight",
+      "Ġв ой",
+      "Ġprés ent",
+      "Ĥĺ ìļĶ",
+      "ĠSpec ifically",
+      "Ġpos es",
+      "Ġve ure",
+      "ok ay",
+      "em as",
+      "Ġ ãģ§ãģĻ",
+      "Ġma jÄħ",
+      "Ġweb inars",
+      "Ġcann abis",
+      "Ġdam als",
+      "ĠNorth west",
+      "Ġp ada",
+      "Ġcrowd s",
+      "Ġfut ures",
+      "Ġä n",
+      "Ġciv ilians",
+      "ĠS achen",
+      "æ į",
+      "Ġtr aces",
+      "Ġ먹 ê³ł",
+      "Q U",
+      "é¡ĺ ãģĦ",
+      "ĠI F",
+      "an ın",
+      "ìĤ ´",
+      "Ġb iblical",
+      "ĠV ed",
+      "Ġst oring",
+      "ÑĢав лÑı",
+      "æĩī 該",
+      "Ġn ast",
+      "Ġd ö",
+      "ÑĢ оп",
+      "el ia",
+      "Ġside ways",
+      "ĠUnder stand",
+      "ĠQ ur",
+      "Ġper pend",
+      "ĠMill ionen",
+      "Ġwater melon",
+      "ĠDiv ine",
+      "ult ur",
+      "ab ord",
+      "Ġsuccess es",
+      "Ġhom bre",
+      "Ġcar p",
+      "Ġsus cept",
+      "ung kin",
+      "Ġk ij",
+      "ul us",
+      "Ø§Ø ¬",
+      "Ġnot ch",
+      "Ġpolynom ial",
+      "å¹ ²",
+      "å ©",
+      "Ġún ico",
+      "Ġteles cope",
+      "Ġpolit ique",
+      "k iem",
+      "ĠÎŃ Î½Î±",
+      "Ġaggreg ate",
+      "ĠGe off",
+      "Ġtr il",
+      "ĠG RA",
+      "Ġsubscri ber",
+      "im et",
+      "Ġдол лаÑĢ",
+      "op ing",
+      "Ġth erapeut",
+      "ĠCan cer",
+      "Ġpar ade",
+      "Ġir rig",
+      "âĻª âĻª",
+      "Ġclear er",
+      "Ġb og",
+      "ĠM aur",
+      "า à¸ĩ",
+      "ĠShang hai",
+      "acht e",
+      "ĠK ol",
+      "el ujah",
+      "Ġha v",
+      "ĠCr ime",
+      "se k",
+      "Ġë ¡ľ",
+      "ien na",
+      "ĠG or",
+      "è Ľ",
+      "ĠпоÑĤ ÑĢ",
+      "Ġкаж еÑĤÑģÑı",
+      "ĠL ift",
+      "ĠS ort",
+      "ĠP sal",
+      "Ġp ing",
+      "ĵ Ŀ",
+      "ph is",
+      "ĠF UCK",
+      "ĠS yn",
+      "Ġbam boo",
+      "¬ ìĺģ",
+      "c uts",
+      "Ġm mm",
+      "Ġfunktion iert",
+      "Ġ _",
+      "ÃŃ cio",
+      "St op",
+      "Ġimag inary",
+      "Ġnot amment",
+      "ĠIniti ative",
+      "ãĥ ¥",
+      "ĠK urt",
+      "Ġlo osen",
+      "Ġbus car",
+      "çģ «",
+      "Ġz elf",
+      "Ġpro ps",
+      "åĽ ī",
+      "Ġmoet en",
+      "Ġmill i",
+      "Ġhall s",
+      "ĠM atch",
+      "Ġbrack ets",
+      "ĠC ou",
+      "æ¦ Ĥ",
+      "ĠÐľ аÑĢ",
+      "IS A",
+      "Ġcig arette",
+      "Ġcompet itions",
+      "ĠM IN",
+      "Ġbeh ö",
+      "vo or",
+      "Ġ ust",
+      "ĠZ i",
+      "ĠO cc",
+      "ul ates",
+      "Ġball oons",
+      "Ġpr onto",
+      "ĠM iy",
+      "ĠF ile",
+      "Ġкл аÑģÑģ",
+      "нÑĥ л",
+      "Ġcere al",
+      "Ġincre ment",
+      "Ġref ined",
+      "åı¦ å¤ĸ",
+      "pr ising",
+      "ĠR F",
+      "Ġrespect ful",
+      "Ġlo ot",
+      "ask et",
+      "Ġdeix a",
+      "ing le",
+      "Ġfuncion a",
+      "ĠRe vel",
+      "Ġso ber",
+      "Ġperform s",
+      "ĠG entle",
+      "ãĤ ¨",
+      "Ġrecip ient",
+      "ĠHa use",
+      "Ġë ĥ",
+      "F rom",
+      "Ġmin isters",
+      "Ġpar adox",
+      "å°±æĺ¯ èªª",
+      "Ġtast ing",
+      "Ġ×Ķ× Ĺ",
+      "Ġre use",
+      "ĠL ane",
+      "ĠÑģов еÑĢÑĪ",
+      "Ġremem bers",
+      "Ġfemin ist",
+      "Ġcommit ments",
+      "Ġproject ed",
+      "Ġg az",
+      "iyor uz",
+      "Ġoblig ations",
+      "R o",
+      "z ar",
+      "Ġch w",
+      "ĠJ AM",
+      "ĠbÄĻd Äħ",
+      "asp berry",
+      "Ġм еÑģÑĤо",
+      "ë² ķ",
+      "Ġreg ulated",
+      "Ġw icht",
+      "ĠTre vor",
+      "Ġsecond ly",
+      "ĠIh re",
+      "els h",
+      "Ġrep orters",
+      "ÑĤоÑĢ а",
+      "oy o",
+      "G I",
+      "Ġinter connect",
+      "é IJĺ",
+      "OS H",
+      "æŃ ²",
+      "Ġbr ass",
+      "Ġign oring",
+      "ä»Ĭ æĹ¥",
+      "in fect",
+      "Ġpro jekt",
+      "ore t",
+      "ÏĦα ν",
+      "ĠÑĤ ип",
+      "Ġmut ta",
+      "Ġunbox ing",
+      "Ħ °",
+      "å¡ Ĭ",
+      "Ġadv ised",
+      "ĠDen ver",
+      "Ġsevere ly",
+      "ĠM hm",
+      "Ġfl ipped",
+      "Ġp ien",
+      "Ġkomm un",
+      "ĠF RE",
+      "Ġà®ĩ à®°",
+      "aint ed",
+      "Ġkn ives",
+      "Ġhab l",
+      "Ġgew orden",
+      "arett es",
+      "C S",
+      "Ġмал енÑĮ",
+      "Ġgal ax",
+      "Ġnin ete",
+      "ê±°ë Ĥĺ",
+      "Ġs is",
+      "Ġadvis ory",
+      "Ġdr illing",
+      "ĠWould n",
+      "ün f",
+      "gest ellt",
+      "ĠHel en",
+      "Ġ×ŀ× IJ",
+      "ap olis",
+      "Ġrze czy",
+      "Ġter ra",
+      "Ġhe p",
+      "Ġalg ún",
+      "ik k",
+      "Ġastron om",
+      "ĠStar bucks",
+      "k Äħ",
+      "Ġpat rol",
+      "Ġì½ Ķ",
+      "Ġg on",
+      "Ġ ãĢIJ",
+      "Ġson st",
+      "Ġencoun ters",
+      "Ġret rou",
+      "Ġshark s",
+      "Ġd or",
+      "ĠR ever",
+      "Ġev apor",
+      "Ġreserv oir",
+      "Ġalleg ed",
+      "ul er",
+      "Ġver m",
+      "Ġcommer ce",
+      "Ġf itted",
+      "ge m",
+      "Ġtact ical",
+      "Ġl ith",
+      "éīĦ å¡Ķ",
+      "h ad",
+      "è® Ĭ",
+      "Ġcarboh yd",
+      "Ġlength s",
+      "ι ο",
+      "Ġdem ographic",
+      "R ob",
+      "ĠS kin",
+      "cc oli",
+      "Ġsimpl ified",
+      "Ġread ily",
+      "ĠC um",
+      "ades h",
+      "ĠD Ã¥",
+      "us st",
+      "ig ne",
+      "et on",
+      "Ġmen or",
+      "q i",
+      "OO M",
+      "à¸Ń à¸Ļ",
+      "Ġpsych iat",
+      "Ġeight y",
+      "Ġм илли",
+      "ĠT ob",
+      "ed o",
+      "ç¶ ²",
+      "ĠÄij ến",
+      "Ġcirc uits",
+      "ĠLAU GH",
+      "ic ism",
+      "em or",
+      "Ġreg ener",
+      "eg ree",
+      "Ġbure auc",
+      "ĠAl ber",
+      "ä¹ĭ å¾Į",
+      "ĠW or",
+      "å¤ «",
+      "Ġres in",
+      "Ġby ÅĤy",
+      "ĠI G",
+      "à¯į ,",
+      "Ġ7 8",
+      "Ġwe eds",
+      "ĠMy th",
+      "9 3",
+      "æ ¿",
+      "ĠëĤĺ ìĻĶ",
+      "é v",
+      "á ½",
+      "ö ren",
+      "ç ar",
+      "ĠP AUL",
+      "Ġdisad vant",
+      "Ġposition ing",
+      "Ġcock tail",
+      "Ġagre es",
+      "n n",
+      "ĠS ally",
+      "M s",
+      "Ġinher ent",
+      "Ġmonet ary",
+      "Ġnat ur",
+      "ĠN h",
+      "ĠImp ort",
+      "Ġle ben",
+      "Ġw i",
+      "uss y",
+      "Ġob es",
+      "Ġwand ering",
+      "Ġìĭ łë",
+      "Äħ da",
+      "etch up",
+      "Ġdispos al",
+      "ĠJ A",
+      "ĠC er",
+      "z illa",
+      "Ġvir gin",
+      "ĠSl ide",
+      "and el",
+      "Ġrighteous ness",
+      "ĠÎ £",
+      "Ġide ia",
+      "ä½ł 好",
+      "иÑĢов аÑĤÑĮ",
+      "ר ×IJ",
+      "Com ment",
+      "Ġpre lim",
+      "ĠV ale",
+      "Ġì§Ģë Ĥľ",
+      "ĠV anc",
+      "OM AN",
+      "Ġп Ñĸд",
+      "Ġy um",
+      "st re",
+      "ce m",
+      "Ġpo cz",
+      "Ġfrag ment",
+      "ĠÑģлÑĥÑĩа е",
+      "Ġunder go",
+      "ĠH ank",
+      "ce ks",
+      "ĠF PS",
+      "Ġoc ur",
+      "Ġdeter ior",
+      "æ³ ¨",
+      "Ġempres as",
+      "Pa ul",
+      "Ġ) ))",
+      "ĠвÑĢем ени",
+      "Ġsc old",
+      "×Ļ× ¢",
+      "Ġsuspect ed",
+      "Ġaccess ing",
+      "Ġsubst it",
+      "Ġhistor ians",
+      "ä» »",
+      "Ġдел о",
+      "Ġsoci ed",
+      "r one",
+      "Ġre den",
+      "Ġext ends",
+      "epher d",
+      "Ġbal con",
+      "ä¸į èµ·",
+      "ĠSol o",
+      "Ġpolit ician",
+      "олÑĮ но",
+      "Ġirgend w",
+      "Ġtraum atic",
+      "Ġrapp er",
+      "ĠRO BERT",
+      "Re ally",
+      "æģ ¯",
+      "Ġline up",
+      "AS E",
+      "Ġcontract or",
+      "ĠCorpor ation",
+      "g or",
+      "ĠTod o",
+      "ÑģÑĤÑĢ ой",
+      "F BE",
+      "Ġnews letter",
+      "Ġko ÅĦ",
+      "alt ies",
+      "ĠпÑĢ иÑĩ",
+      "ĠHe avy",
+      "Ġsw ords",
+      "Ġmanip ulation",
+      "Ġfun k",
+      "Ġv Ã¥r",
+      "ĠTal iban",
+      "Ġë° ¥",
+      "Ġac ne",
+      "ür ü",
+      "Ġdes wegen",
+      "ĠD ust",
+      "Ġsil ic",
+      "Ġhook s",
+      "Ġbl ij",
+      "Ġpet its",
+      "Ġfil me",
+      "ĠBere ich",
+      "ĠSa id",
+      "Ġimp osed",
+      "Ġdi ary",
+      "Ġго ÑĢ",
+      "ĠG ates",
+      "Ġal ta",
+      "å¸ Į",
+      "Ġch cia",
+      "ple asant",
+      "Ġë° Ŀ",
+      "Ġmoż emy",
+      "ĠAust ria",
+      "Ġbro ker",
+      "Ġsuck ed",
+      "èĢ ĥ",
+      "Ġcomp artment",
+      "Ġcl one",
+      "Ġ×Ķ× ¢",
+      "ĠDan ke",
+      "Ġnoch mal",
+      "ез д",
+      "Ġad renal",
+      "Ġkle inen",
+      "ãģ¾ ãģĹãĤĩãģĨ",
+      "Ġsubsequ ently",
+      "Ġdecent ral",
+      "Ġgen etics",
+      "Ġê´ ij",
+      "Ġmon itors",
+      "ĠApp lic",
+      "ĠRep orter",
+      "w ert",
+      "Ġwie m",
+      "ĠMove ment",
+      "Ġinterview ing",
+      "Ġhair s",
+      "Ġpu ò",
+      "ĠChel sea",
+      "Ġco her",
+      "Ġc ot",
+      "Ġz as",
+      "Ġpatch es",
+      "Ġl ah",
+      "Ñĥн к",
+      "ĠRe agan",
+      "ĠMar co",
+      "c ity",
+      "Ġdef ender",
+      "Ġdecor ation",
+      "ij i",
+      "Ġl itter",
+      "Ð ¨",
+      "Ġj ego",
+      "RE W",
+      "ĠP ik",
+      "ĠHe e",
+      "ĠI v",
+      "Ġи де",
+      "ĠThe ater",
+      "ĠÑĩаÑģ ÑĤо",
+      "Ġswe ater",
+      "Ġhighlight ing",
+      "Ġa insi",
+      "Ġdipl omatic",
+      "ĠNever theless",
+      "å ³",
+      "AS ON",
+      "Ġpúblic o",
+      "Ġf erm",
+      "reat ed",
+      "c od",
+      "Ġë¬ ¼ë",
+      "Ġm ister",
+      "ĠVanc ouver",
+      "Ġrecogn izes",
+      "ec d",
+      "Ġcomplic ations",
+      "en cial",
+      "ãģĹ ãģı",
+      "Ġê°Ģ ì§Ģ",
+      "ĠUlt imate",
+      "Ġva ig",
+      "ĠM erry",
+      "×ķ× Ĵ",
+      "ĠMar cus",
+      "ç¸ ½",
+      "ow ego",
+      "Ġm ente",
+      "S m",
+      "Ġa ja",
+      "ĠTa o",
+      "Ġjud icial",
+      "Ġentrepreneurs hip",
+      "Ġнем ного",
+      "Ġp is",
+      "Ġer g",
+      "Ġch rist",
+      "ĠC urt",
+      "ĠÑĢаÑģ п",
+      "λ ε",
+      "ens ch",
+      "ÃŃ re",
+      "Ġfo cal",
+      "ĠDiam ond",
+      "av ÃŃa",
+      "Ġh anno",
+      "ĠSqu ad",
+      "Ġassoci ations",
+      "ĠCreat ive",
+      "Ġmess enger",
+      "Ġbe gging",
+      "Ġdec imal",
+      "Ġd Ä±ÅŁ",
+      "Ġmet adata",
+      "sel s",
+      "ĠÄ° ÅŁ",
+      "ữ a",
+      "Ġdiffic ile",
+      "d ı",
+      "Ġs laughter",
+      "ĠVer g",
+      "Ġ×Ĵ ×Ŀ",
+      "ç° ¡",
+      "æĮ ī",
+      "ĠTe a",
+      "ass es",
+      "O k",
+      "Ġsynth es",
+      "ot iation",
+      "Ġpain ter",
+      "Ġel bows",
+      "Ġarchitect ural",
+      "ĠÑĢ ад",
+      "Ġgl or",
+      "im age",
+      "amp a",
+      "cul iar",
+      "ł ¨",
+      "Ġte ve",
+      "ĠSt elle",
+      "ĠB am",
+      "Ġì´ Ī",
+      "as is",
+      "ip edia",
+      "ĠG I",
+      "ĠAct ive",
+      "çĦ¶ åIJİ",
+      "az i",
+      "ãĤĮ ãģ¦",
+      "ĠL ucky",
+      "íķ ©",
+      "ĠпÑĢ иÑħод",
+      "Ġrun way",
+      "Ġauthent ication",
+      "Ġpos ible",
+      "Ġsupp lements",
+      "Ġsurg ical",
+      "G en",
+      "Ġfeas ible",
+      "D O",
+      "Ġout look",
+      "Ġinter vals",
+      "Ġan ecd",
+      "Ãł ng",
+      "Ġstra ps",
+      "ĠSh u",
+      "ud d",
+      "iss enschaft",
+      "Ġport e",
+      "Ġcomm itting",
+      "Ġall ey",
+      "Ġco venant",
+      "ĠPed ro",
+      "less ness",
+      "ĠSol id",
+      "ĠM olly",
+      "Ġн екоÑĤоÑĢ",
+      "Ġcooper ate",
+      "åĮ Ĺ",
+      "oll en",
+      "Ġtun a",
+      "Ġkinderg arten",
+      "ĠS iz",
+      "Ġduż o",
+      "ĠM BA",
+      "ĠGEOR GE",
+      "ĠF isher",
+      "å¿ ĺ",
+      "ĠCa esar",
+      "ĠкÑĢаÑģ ив",
+      "ĠDel hi",
+      "zy m",
+      "Ġexpl icar",
+      "ê°Ģ ì§Ģ",
+      "un s",
+      "gr ow",
+      "ĠпÑĢ иÑģ",
+      "Ġ8 6",
+      "Ġst ating",
+      "Ġmass a",
+      "ch ter",
+      "Ġì»¬ë Ł¬",
+      "Ġdep uty",
+      "S M",
+      "n oc",
+      "Ġge ography",
+      "ĠEnter prise",
+      "ĠC ant",
+      "ö z",
+      "Ġun pack",
+      "ĠíĻ Ķë",
+      "Ġsearch es",
+      "Ġpres idency",
+      "Ġtri vial",
+      "Ġp ige",
+      "ou bt",
+      "ãĤ ļ",
+      "ì¼ ĢìĿ´",
+      "Ġbudget s",
+      "Ġu b",
+      "Ġp ne",
+      "ĠY ale",
+      "ĠÅŁ öyle",
+      "reg ular",
+      "Ġimper fect",
+      "AR A",
+      "Ġfam ÃŃlia",
+      "ur m",
+      "ĠAdvent ure",
+      "ãĥ Ĭ",
+      "c is",
+      "em ark",
+      "Ġne go",
+      "Ġinappropri ate",
+      "ĠпÑĢи з",
+      "ĠÑĢ ол",
+      "Ġdream ed",
+      "B ry",
+      "Ġshut tle",
+      "Ġpill ars",
+      "Ġb ik",
+      "in um",
+      "ĠÑĥ Ñģ",
+      "ĠNe br",
+      "Ġperpend icular",
+      "Ġbook ed",
+      "ber y",
+      "Ġv ikt",
+      "be ar",
+      "es us",
+      "Ġвозм ожно",
+      "¨ ¹",
+      "Ġpresum ably",
+      "ĠMem phis",
+      "Ġambul ance",
+      "×ķ× ŀר",
+      "Ġthumbna il",
+      "Ġmod ification",
+      "éĩ ı",
+      "Ġinterpret ed",
+      "Ġprom o",
+      "Ġκ ά",
+      "Ġε ÏĢ",
+      "Ġacoust ic",
+      "ĠD B",
+      "åĵ İ",
+      "Ġnon etheless",
+      "ou le",
+      "Ġpe qu",
+      "Ġkn ob",
+      "ãĤ £",
+      "ĠëıĮ ìķĦ",
+      "Ġpurch ases",
+      "ĠÃĩ ünkü",
+      "Ġdivid ing",
+      "per form",
+      "ract ion",
+      "health y",
+      "ĠTit le",
+      "Ġu k",
+      "Ġcer ca",
+      "Ġargu ably",
+      "Ġf ale",
+      "ë³ µ",
+      "Ġgam ers",
+      "Ġutil izing",
+      "Ġoff ended",
+      "Ġt ava",
+      "al ı",
+      "Ġmed ian",
+      "Ġinfect ious",
+      "ĠAn nie",
+      "Ġsmart phones",
+      "Ġpar ole",
+      "åĸ Ŀ",
+      "ĠEp ic",
+      "z za",
+      "Ġun ified",
+      "Ġê·¸ë ķĮ",
+      "Ġcur tain",
+      "ĠÄ ĥ",
+      "Ġsex ually",
+      "Ġuns erem",
+      "ĠCon vention",
+      "Ġalleg edly",
+      "Y a",
+      "ĠH oo",
+      "en ment",
+      "æĢ ª",
+      "íĽ Ħ",
+      "Ġgig antic",
+      "Ġnot ing",
+      "Ġre bo",
+      "ĠJ ama",
+      "ĠAl z",
+      "Ġborrow ed",
+      "ì¹ ¨",
+      "Ġper ipher",
+      "оÑĤ а",
+      "ĠG B",
+      "ĠGe ar",
+      "Ġeconom ically",
+      "Ġtele fon",
+      "Ġqu eremos",
+      "ĠдалÑĮ ÑĪе",
+      "Ġr as",
+      "ĠTe ach",
+      "ic ios",
+      "at os",
+      "Ġpl edge",
+      "b au",
+      "ĠHim self",
+      "L ink",
+      "Ġesper o",
+      "Ġchrom os",
+      "ĠP ER",
+      "Ġer le",
+      "Ġpod ium",
+      "ç os",
+      "Ġnie u",
+      "Ġf en",
+      "ĠGO D",
+      "ĠCh ocolate",
+      "wer k",
+      "Ġt ừ",
+      "Ġsupp ress",
+      "λ η",
+      "Ġ24 0",
+      "Ġsit ä",
+      "Ġhonest y",
+      "ĠB io",
+      "ĠB ard",
+      "ĠобÑī ем",
+      "Ġм Ñĥз",
+      "Ġmar ble",
+      "ĠÑĨ енÑĤ",
+      "Ġproc ure",
+      "Ġrot or",
+      "ber n",
+      "Ġtu h",
+      "Ġhead set",
+      "at em",
+      "Ġwarrant y",
+      "à® ´",
+      "Ġfil ing",
+      "ι ά",
+      "Ġcomp rendre",
+      "Ġimp ulse",
+      "Ġsal v",
+      "wr itten",
+      "Ġinstit ute",
+      "K im",
+      "ĠLGBT Q",
+      "fic iente",
+      "H is",
+      "ĠαÏħÏĦ ÏĮ",
+      "Ġteen age",
+      "or us",
+      "ĠÑĢаз б",
+      "S ee",
+      "ĠCons erv",
+      "á»ģ n",
+      "ful ness",
+      "Ġstraw berries",
+      "ĠAb u",
+      "и он",
+      "Ġo lla",
+      "NO ISE",
+      "ĠEm ploy",
+      "Ġwip ed",
+      "ur ger",
+      "Ġmod ifications",
+      "Ġíķĺ ì§Ģ",
+      "Ġfoot steps",
+      "Ġhon ors",
+      "Ġad ul",
+      "Ġfl ipping",
+      "ĠH U",
+      "Z Y",
+      "Ġintegr ating",
+      "ب ر",
+      "ull a",
+      "Ġnatuur lijk",
+      "ĠíĹ Ī",
+      "ĠEth ereum",
+      "ÙĬ ÙĦ",
+      "w ed",
+      "Ġpe aks",
+      "ĠK es",
+      "Ġblo om",
+      "Ġcr ashing",
+      "Ġ9 11",
+      "ĠоÑĤ лиÑĩ",
+      "Ġcontro llers",
+      "ĠD od",
+      "Ġвм еÑģÑĤе",
+      "Ġsort ir",
+      "å¥ ĩ",
+      "ĠStra ight",
+      "ĠGrac ias",
+      "Ġgro ove",
+      "Ġto gg",
+      "Ġìĭ¶ ìĿĢ",
+      "é ro",
+      "Ġout ward",
+      "ĠW A",
+      "ĠRock y",
+      "Ġsc am",
+      "Ġhay at",
+      "ig nty",
+      "â Ħ",
+      "pl ings",
+      "Ġantibiot ics",
+      "Ġ ä¸Ģ",
+      "Ġnever theless",
+      "j ang",
+      "com merce",
+      "Ġspo iler",
+      "Ġglo ve",
+      "Ġch atter",
+      "ĠB Y",
+      "~ ?",
+      "Ġíĺ ¸",
+      "Ġdem ol",
+      "we chsel",
+      "im ir",
+      "Ġra id",
+      "еÑĢ Ñħ",
+      "ìŀIJ 기",
+      "en f",
+      "Ġcomment ed",
+      "Ġoptim ized",
+      "Ġconv icted",
+      "Ġb ats",
+      "ĠS B",
+      "ĠA ur",
+      "ĠT ong",
+      "Ġimplic it",
+      "ĠJan et",
+      "Ġre ag",
+      "ãģ ²",
+      "ĠAdv anced",
+      "Ġimp ose",
+      "ש ×Ķ",
+      "Ġschem es",
+      "oug her",
+      "ab olic",
+      "Ġê±° ì£ł",
+      "Ġslow ing",
+      "Ġwt edy",
+      "Ġdest ructive",
+      "Ġоп ÑĢед",
+      "Ġland mark",
+      "Ġëı Ī",
+      "ĠWalk ing",
+      "Ạ¹",
+      "Ġt ijd",
+      "ĠK N",
+      "ĠQu ant",
+      "ìĺ ¤ë",
+      "Ġк ÑĢÑĥ",
+      "Ġper der",
+      "Ġno ve",
+      "änd e",
+      "Ġãģ Ĺ",
+      "b ia",
+      "Ġcust ody",
+      "Ġb iod",
+      "æĿ± 西",
+      "Ġdirect ing",
+      "... âĢĭ",
+      "Ġre loc",
+      "Ġdemand e",
+      "ãĤĵ ãģł",
+      "Ġo ÄŁlum",
+      "Ġод на",
+      "ĠMil k",
+      "åı ·",
+      "ĠK ra",
+      "ĠH onda",
+      "Ġp ue",
+      "Ġele kt",
+      "Ġbegin ners",
+      "Ġspe ar",
+      "ÃŃ nh",
+      "ĠLu ft",
+      "Ġn ig",
+      "ĠSchool s",
+      "Ġfor ums",
+      "ĠQ in",
+      "pp o",
+      "Ġz ag",
+      "ĠÐ ®",
+      "Ġtooth p",
+      "ĠSt yle",
+      "ì´ Ī",
+      "Ġpun ct",
+      "Ġrep s",
+      "ĠA ly",
+      "Ġamend ments",
+      "Ġö z",
+      "Ġdig its",
+      "ur ai",
+      "Ġcha otic",
+      "ĠMas ters",
+      "e on",
+      "ĠC ash",
+      "ĠC uz",
+      "Ġbede utet",
+      "Ġscan ning",
+      "Ġж д",
+      "н еÑĤ",
+      "Ġcertain ty",
+      "j ek",
+      "Ġdi jo",
+      "ĠCl imate",
+      "Ġr inse",
+      "Ġk rij",
+      "vel and",
+      "Ġsound track",
+      "ĠSa fe",
+      "ĠNo va",
+      "9 4",
+      "Ġa the",
+      "ĠVer b",
+      "ol er",
+      "ìĿ´ì £ł",
+      "Ġv in",
+      "Ġrespir atory",
+      "ĠStud y",
+      "ĠC AM",
+      "Ġav ocado",
+      "ĠZ hen",
+      "Ġlat ency",
+      "Ġfe athers",
+      "Ġcont ar",
+      "Ġв еÑī",
+      "Ġf ark",
+      "Ġbl ended",
+      "Ġexpl oded",
+      "ĠX X",
+      "ĠBen im",
+      "Ġalgu ém",
+      "isto ire",
+      "Ġconfident ial",
+      "Ġm ast",
+      "Ġì ¿",
+      "ge h",
+      "Ġdis respect",
+      "ĠSystem s",
+      "Æ° a",
+      "E d",
+      "Ġw ys",
+      "Ġex otic",
+      "Ġgl owing",
+      "ù ng",
+      "oun ge",
+      "è Ħ",
+      "ани з",
+      "Ġpal av",
+      "ĠSw ord",
+      "Ġg im",
+      "ĠC row",
+      "Ġpot ent",
+      "b ish",
+      "Ġab used",
+      "ĠJ ed",
+      "Ġg ambling",
+      "ĠS pect",
+      "Ġinvestig ators",
+      "æĻ ļ",
+      "Ġr att",
+      "Ġdo b",
+      "ĠD ES",
+      "h og",
+      "ĠоÑĤк ÑĢÑĭ",
+      "íĮ ħ",
+      "ĠденÑĮ ги",
+      "Ġíĺ ¹",
+      "Ġë¨ ¸ë¦¬",
+      "Ġsat uration",
+      "Ġinher ited",
+      "ĠInnov ation",
+      "ìĹ Īëįĺ",
+      "Ġtang ible",
+      "Ġdep ri",
+      "h ed",
+      "Ġпом ог",
+      "Ġslic ed",
+      "ॠį",
+      "Ġth ế",
+      "Å ¥",
+      "6 8",
+      "Ġcor ona",
+      "Ġgift ed",
+      "Ġso ir",
+      "Ġhum ility",
+      "ĠìĿ´ 걸",
+      "Ġflaw s",
+      "ĠпÑĢ акÑĤи",
+      "Ġk ald",
+      "wa ż",
+      "y w",
+      "ãĤĵ ãģ§ãģĻ",
+      "ir teen",
+      "Ġcroch ets",
+      "¦¬ ê°Ģ",
+      "ĠìłĦ ìĹIJ",
+      "Ġdes e",
+      "æ¥ Ń",
+      "Ġм аг",
+      "Ġdz iaÅĤ",
+      "Ġl ég",
+      "ch anging",
+      "Ġlle v",
+      "ÅĦ sk",
+      "çĶ »",
+      "Ġ198 4",
+      "orn s",
+      "ĠW elsh",
+      "Ġpharm aceutical",
+      "Ġpump ing",
+      "ĠSh aw",
+      "p unk",
+      "Ġva ult",
+      "Ġkin etic",
+      "Ġhur ricane",
+      "ĠInc luding",
+      "ứ c",
+      "ĠGrand pa",
+      "ans hip",
+      "é¦Ļ 港",
+      "ĠвÑĭ Ñħод",
+      "н ож",
+      "ľ ł",
+      "ut ta",
+      "Ġê²ģ ëĭĪëĭ¤",
+      "Ġb az",
+      "Ġпо ÑĪ",
+      "Ġpe culiar",
+      "zy Äĩ",
+      "ĠEll ie",
+      "Ġlearn s",
+      "ĠKr ishna",
+      "Ġconse cut",
+      "Ġemp ath",
+      "ĠD in",
+      "Ġtrad ed",
+      "ĠBor is",
+      "ugg age",
+      "oll a",
+      "Ġназ в",
+      "Ġetern ity",
+      "Ġв п",
+      "è mes",
+      "Ġgra pp",
+      "b é",
+      "ĠпÑĢед ÑģÑĤав",
+      "ĠF C",
+      "į ëĭĪëĭ¤",
+      "e ven",
+      "ĠNebr aska",
+      "ortun e",
+      "Ġk arena",
+      "ĠAg ent",
+      "Ġst ing",
+      "ĠP I",
+      "Ġmunicip al",
+      "power ed",
+      "Ġconse gue",
+      "ĠMan chester",
+      "Ġrain y",
+      "Ġbl i",
+      "Ġk ost",
+      "Ġhal ten",
+      "ĠAh hh",
+      "ins ula",
+      "er ting",
+      "ĠاÙĦ Ùģ",
+      "Ġrel acion",
+      "Ġk omen",
+      "Ġd ome",
+      "Ġpri ests",
+      "ĠInt rodu",
+      "rop he",
+      "sh ore",
+      "vel t",
+      "clip se",
+      "ĠÑĢ ÑĥÑģ",
+      "×Ļ× ¡",
+      "Ġsab emos",
+      "ĠHoll and",
+      "og i",
+      "ank i",
+      "ĠM ats",
+      "Ġsm oked",
+      "ull ie",
+      "Ġeuro pe",
+      "ĠдейÑģÑĤв иÑĤелÑĮно",
+      "Ġbard ziej",
+      "Ġtransform ing",
+      "ĠE z",
+      "op ath",
+      "Ġìĸ¸ ëĭĪ",
+      "ĠÑģÑĤ ан",
+      "ằ ng",
+      "ั à¹ī",
+      "ĠO uch",
+      "Ġclear ance",
+      "ust ain",
+      "Ġsolid arity",
+      "Ġpro ving",
+      "ĠÐĺ н",
+      "ĠÑģ ÑĬ",
+      "Ġpro long",
+      "ад но",
+      "Ġs os",
+      "ĠDe al",
+      "Ġ17 0",
+      "m ons",
+      "Ġз ем",
+      "Ġlo gged",
+      "Ġlif elong",
+      "Ġsens ory",
+      "Ġbe hold",
+      "ĠF AR",
+      "èt ement",
+      "ĠFed eration",
+      "Ġdod ge",
+      "ĠSh ir",
+      "Ġdrag ons",
+      "ĠAr ctic",
+      "Äħ ż",
+      "Å į",
+      "Â º",
+      "Ġden ke",
+      "Ġpodr ÃŃa",
+      "co le",
+      "ÑĥлÑĮÑĤ аÑĤ",
+      "Ġsystem atic",
+      "ам а",
+      "ch os",
+      "Ġclin ics",
+      "ĠB S",
+      "Ġtal es",
+      "us ions",
+      "Ġí Ī¬",
+      "Ġpres ervation",
+      "Ġl ore",
+      "ĠProt est",
+      "á» Ľ",
+      "å¸ Ĥ",
+      "Ġacknowled ged",
+      "ĠIs aiah",
+      "ĠëķĮ ëĬĶ",
+      "Ġ× ĺ",
+      "Ġcompet itor",
+      "Ġadv ancing",
+      "z ip",
+      "Ġtent h",
+      "ĠLa ure",
+      "Ġh ints",
+      "Ġexerc ising",
+      "ŀ ľë",
+      "ĠIntell igence",
+      "u ated",
+      "OU T",
+      "op ed",
+      "Ġaut onomy",
+      "Ġbrand ing",
+      "ĠMediter ranean",
+      "Ñĸ к",
+      "Ġscrew driver",
+      "Ġsu pre",
+      "Ġst ap",
+      "Ġjurisd iction",
+      "ĠSetting s",
+      "Ġfore front",
+      "ĠF emale",
+      "com fort",
+      "Ġmultiplic ation",
+      "ĠMur ray",
+      "Ġbo b",
+      "ĠT as",
+      "Ġt ahu",
+      "Ġon un",
+      "et ter",
+      "Ġproph ets",
+      "l ag",
+      "Ġreven ues",
+      "Ġpr á",
+      "Ġupload ing",
+      "Ġmach inery",
+      "asc al",
+      "ĠEst á",
+      "ĠG oth",
+      "ĠB ald",
+      "ĠS aw",
+      "Ġstri pes",
+      "ìł ij",
+      "Ġpow in",
+      "æĹ¥ æľ¬",
+      "Ġhost ile",
+      "Ġdar um",
+      "Ġprevent ed",
+      "ожалÑĥй ÑģÑĤа",
+      "Ġalgun as",
+      "Ġhop eless",
+      "Ġz naj",
+      "Ġread ings",
+      "Ġcra ving",
+      "t at",
+      "ĠP ig",
+      "Ġli ar",
+      "çĪ ±",
+      "Ġmulti player",
+      "Ġd ale",
+      "ĠCour se",
+      "íģ ¼",
+      "ĠK ita",
+      "Ġcustom s",
+      "Ġrespond s",
+      "end ra",
+      "è¦ ĸ",
+      "Ġmet ro",
+      "Ñģ ол",
+      "Ġmitig ate",
+      "Ġopp ression",
+      "Ġ æĪijåĢij",
+      "qu inho",
+      "Ġam mo",
+      "Ġen fer",
+      "Ġp ony",
+      "Ġ ounces",
+      "° Ķ",
+      "ĠìĪĺ ê°Ģ",
+      "Ġdich o",
+      "ĠDe b",
+      "Ġwond ers",
+      "ĠRo ose",
+      "Ġpri zes",
+      "ĠA LEX",
+      "Ġthank fully",
+      "Ġtiss ues",
+      "ĠÑĢав но",
+      "ĠL una",
+      "intell igible",
+      "ĠìĻ ¸",
+      "ê° ij",
+      "ĠHe at",
+      "ĠÑģ ид",
+      "ĠQu i",
+      "Ġ ions",
+      "Ġaccommod ation",
+      "ä¾ ¿",
+      "ĠK art",
+      "ien st",
+      "Ġt arde",
+      "Ġso aked",
+      "ĠCase y",
+      "Ġì´ Ŀ",
+      "ĠÑĢ Ñĥб",
+      "Ġdifferent i",
+      "Ġleft over",
+      "Ġexch anges",
+      "sec ond",
+      "Ġfirst ly",
+      "Ġbuild er",
+      "ri en",
+      "Ġd w",
+      "Ġboun cing",
+      "? </",
+      "ĠëĮĢ íķ´ìĦľ",
+      "ĠÑģ е",
+      "ĠM iles",
+      "ien ie",
+      "Ġпод пиÑģ",
+      "Ġë¬ ´",
+      "Ġar ises",
+      "Ġsub conscious",
+      "ĠSand y",
+      "Ġlot tery",
+      "âĢ ij",
+      "am iliar",
+      "Ġcoordin ator",
+      "è Į",
+      "Ġextra ordin",
+      "ĠRon ald",
+      "ĠM ON",
+      "g reen",
+      "Ġmanufact ure",
+      "ĠRec ord",
+      "ĠMark eting",
+      "и ÑĨ",
+      "Ġcredential s",
+      "Ġup right",
+      "ĠHer itage",
+      "Ġgör d",
+      "æľ į",
+      "exp ensive",
+      "áºŃ n",
+      "Ġì± Ħ",
+      "Ġout lined",
+      "ĠO ooh",
+      "orient ed",
+      "Ġw ired",
+      "Ġout lets",
+      "Ġhug ely",
+      "ĠíĸĪ ëĬĶëį°",
+      "аÑĢ ÑĤ",
+      "Ġlog istics",
+      "Ġseason al",
+      "Ġde be",
+      "Ġthe or",
+      "Ġpir ate",
+      "app y",
+      "Ġkn ots",
+      "Ġfem me",
+      "ĠSoft ware",
+      "g ende",
+      "ÑĤак и",
+      "Ġtem ples",
+      "Ġlim itation",
+      "Ġampl itude",
+      "Ġha cen",
+      "Ġaud i",
+      "Ġëĸ ¨",
+      "ĠW ahl",
+      "Ġni h",
+      "Ġampl ifier",
+      "ari us",
+      "iz ado",
+      "ach a",
+      "Ġkull an",
+      "ĠTw in",
+      "ĠFor ces",
+      "Ġab rir",
+      "ĠE PA",
+      "ĠA ha",
+      "Ġê·¸ëŀ ĺëıĦ",
+      "Ġbi om",
+      "ĠТ ам",
+      "Ġsa iling",
+      "ĠJ oker",
+      "F irst",
+      "è¿Ļ æĺ¯",
+      "~ ]",
+      "ors ch",
+      "Ġvæ re",
+      "Ġbeet je",
+      "ĠSpa ÃŁ",
+      "pol it",
+      "Ġtur bul",
+      "ĠìłĢíĿ¬ ê°Ģ",
+      "Ġc ic",
+      "ĠDra ke",
+      "ĠB RI",
+      "iz ação",
+      "ĠìŀĪ ëĭ¤",
+      "ĠLyn n",
+      "Ġtrans gender",
+      "Ġres ign",
+      "Ġchar ter",
+      "ĠJ H",
+      "ĠHol mes",
+      "ĠL ip",
+      "d as",
+      "Ġped iatric",
+      "Ġmemor ize",
+      "Ġevalu ating",
+      "ĠðŁ IJ",
+      "ca k",
+      "Ġconjun ction",
+      "Ġres erves",
+      "Ġsh ampoo",
+      "Ġjud ged",
+      "Ġwid z",
+      "V IN",
+      "Ġab oard",
+      "ar is",
+      "ĠR oh",
+      "Ġcool ed",
+      "ÑģÑĤ е",
+      "ce p",
+      "r ost",
+      "h ots",
+      "ĠMel bourne",
+      "оÑĩ ÑĮ",
+      "Ġvent il",
+      "ин ов",
+      "Ġmot ions",
+      "ìĹĪ ëĬĶëį°",
+      "меÑĢ ик",
+      "ĠCh at",
+      "Ġgouvern ement",
+      "ä¸Ģ 次",
+      "ĠK ivol",
+      "ĠKivol owitz",
+      "Ġnó i",
+      "Ġк Ñĥда",
+      "Ġhyd raul",
+      "ĠBer g",
+      "yl um",
+      "ĠPr äsident",
+      "rop y",
+      "Ġsem ic",
+      "Ñı еÑĤ",
+      "ĠCa pe",
+      "Ġcan e",
+      "Ġbring en",
+      "Ġwir ing",
+      "un ya",
+      "Ġrep ay",
+      "ª ©",
+      "Ġw ont",
+      "á nt",
+      "Ġgo ver",
+      "ĠLiber ty",
+      "Ġelect romagn",
+      "ĠSing h",
+      "Ġг ÑĢÑĥп",
+      "г ов",
+      "Īë¬ ´ë",
+      "ĠR ule",
+      "Ġunder way",
+      "ĠFred er",
+      "Ġturb ine",
+      "ish i",
+      "Ġf ÃŃs",
+      "ĠC ulture",
+      "ac re",
+      "Ġw ander",
+      "Ġguer ra",
+      "Ġsö y",
+      "ĠJ ur",
+      "a ways",
+      "Ġschw ier",
+      "gu ard",
+      "ĠAb d",
+      "u ction",
+      "ĠarkadaÅŁ lar",
+      "ĠH amb",
+      "? .",
+      "s ize",
+      "ĠOr th",
+      "Ġs way",
+      "ĠÎ Ķ",
+      "Ġabsor ption",
+      "ine es",
+      "Ġpat rons",
+      "Ġbeach es",
+      "G G",
+      "Ġcont amin",
+      "intend ent",
+      "Ġн ÑĢав",
+      "Ġд еÑĢж",
+      "Ġqu ilt",
+      "Ġevolution ary",
+      "ìĿ´ë Ŀ¼",
+      "az ioni",
+      "Ġer kl",
+      "ĠBut ler",
+      "Ġdo o",
+      "Ġneg otiation",
+      "end um",
+      "Ġtermin ology",
+      "Ġk ul",
+      "ĠUnter nehmen",
+      "é ric",
+      "x i",
+      "b ad",
+      "Ġдолж нÑĭ",
+      "ĠMitch ell",
+      "th ree",
+      "å¼ ı",
+      "Ġsubst rate",
+      "ĠIn hale",
+      "ĠAgr ic",
+      "un ge",
+      "Ġз ÑĢ",
+      "Ġad verse",
+      "ĠìłĢë ıĦ",
+      "Ġpill ar",
+      "ĠMin uten",
+      "ĠM ate",
+      "ĠPl atz",
+      "Ġhel pless",
+      "Ġal ar",
+      "Ġf rench",
+      "Ġalloc ation",
+      "Ġst ems",
+      "Ġmar athon",
+      "ĠHAR F",
+      "iz ación",
+      "J ess",
+      "Ġзна Ñĩ",
+      "Ġdeclar ation",
+      "EER ING",
+      "ster dam",
+      "ass ium",
+      "Ġse iz",
+      "Ġpres idents",
+      "t ake",
+      "Ġwild erness",
+      "Ġcos mic",
+      "Ġ모ë ijIJ",
+      "st ro",
+      "Ġpow iedz",
+      "ĠMagaz ine",
+      "ĠV I",
+      "Ġд еÑĢ",
+      "Ġwür den",
+      "Ġtab lets",
+      "Ġpier ws",
+      "Ġmort al",
+      "Ġsuppl ied",
+      "ĠN ós",
+      "ĠPro per",
+      "Ġкажд Ñĭй",
+      "ol óg",
+      "ë° ©",
+      "Ġmis con",
+      "Ġproxim ity",
+      "ĠAll es",
+      "Ġгл аз",
+      "Ġl ame",
+      "Ġvib es",
+      "Ġde emed",
+      "Ġur ine",
+      "Ġremind ing",
+      "Ġcircumst ance",
+      "ë ĵ¤ìĿ´",
+      "Ġlapt ops",
+      "Â ²",
+      "íķ´ì ķ¼",
+      "ĠOm ega",
+      "ãģªãĤĵ ãģĭ",
+      "N Y",
+      "Ġpump s",
+      "Ġr ails",
+      "Ġsur pass",
+      "ĠBr os",
+      "Ġnation ally",
+      "Ġgew esen",
+      "äº «",
+      "³´ ëĭ¤",
+      "os hing",
+      "ê° Ī",
+      "ç¤ ¾",
+      "Ġc rian",
+      "ĠìĤ¬ëŀĮ ìĿ´",
+      "ca ust",
+      "æķ ´",
+      "ÑĨи п",
+      "ĠO ber",
+      "ĠD AY",
+      "ĠCan on",
+      "z ung",
+      "Ġê° ĸ",
+      "Ġав ÑĤом",
+      "Ġdivor ced",
+      "×Ļ× ¤",
+      "Ïģ ε",
+      "cel and",
+      "ci er",
+      "ÑĢ ез",
+      "Tod ay",
+      "Ġorb ital",
+      "Ġst ret",
+      "Ġsat u",
+      "Ġíģ ¬ë",
+      "z os",
+      "ĠS co",
+      "μ ÎŃ",
+      "ĠGuard ian",
+      "inter est",
+      "ĠV ER",
+      "ünd en",
+      "ĠÑħоÑĤ ел",
+      "t it",
+      "B y",
+      "Ġan lat",
+      "S how",
+      "Ġo ily",
+      "ç¯ Ģ",
+      "Ġleg ends",
+      "Ġspec ulation",
+      "ĠW ish",
+      "Ġmon k",
+      "G AN",
+      "Ġh á»į",
+      "Ġd angers",
+      "ĠB ene",
+      "iqu ement",
+      "ĠëĤĺ ìĻĢ",
+      "Ġа д",
+      "Ġdisc rete",
+      "Ã ĩ",
+      "Ġcond itional",
+      "ĠG ill",
+      "u ates",
+      "ĠÑģов Ñģем",
+      "Ġscreens hot",
+      "c ado",
+      "Ġ모ë ĵł",
+      "Ġfingert ips",
+      "ĠM AC",
+      "Ġd udes",
+      "c ost",
+      "Ġbump s",
+      "ond o",
+      "Ġdat os",
+      "Ġbe eps",
+      "ĠP ron",
+      "ĠK hal",
+      "z ego",
+      "ĠAb by",
+      "U h",
+      "Y o",
+      "ĠT el",
+      "Ġμ ÎŃ",
+      "K I",
+      "Ġstress es",
+      "Ġspreadshe et",
+      "ĠN OW",
+      "D B",
+      "Ġliber ation",
+      "Ġpredict able",
+      "ĠQuest ions",
+      "Ġsp acing",
+      "Ġinhabit ants",
+      "Ġz wiÄħz",
+      "ç± ³",
+      "ĠS AP",
+      "Ġl uggage",
+      "Ġh ipp",
+      "è ĸ",
+      "Ġtang ent",
+      "Ġv Ã¥",
+      "алÑĮ ной",
+      "se hen",
+      "Ġprocess ors",
+      "Ġfind et",
+      "Ġcart ridge",
+      "Ġadministr ators",
+      "Ġìĸ´ì ļ",
+      "Ġsupre me",
+      "ĠAnt i",
+      "ĠíĶ Ħë¡ľ",
+      "Ġinform ative",
+      "Ġkom t",
+      "æĪij ä¹Ł",
+      "×Ļ× ĺ",
+      "Ass istant",
+      "Ġlist a",
+      "ö ll",
+      "Ġdistinct ive",
+      "ĠH ud",
+      "Ġsal on",
+      "ä¸ĭ ä¾Ĩ",
+      "m ême",
+      "ĠMot ion",
+      "Ġseule ment",
+      "ĠMens ch",
+      "Ġpump ed",
+      "ü her",
+      "ib o",
+      "Ġwa ż",
+      "Ġquant itative",
+      "Ù ¾",
+      "Ġ모 ìĬµ",
+      "Ġp ouch",
+      "ĠThe atre",
+      "ah i",
+      "Ġspin ach",
+      "Ġreal ities",
+      "Ġle y",
+      "ĠMar tha",
+      "Ġre cher",
+      "e ches",
+      "Ġperiod ic",
+      "oc ide",
+      "ĠInc red",
+      "Ġth ấy",
+      "ot on",
+      "ĠE so",
+      "Ġgén éral",
+      "il ight",
+      "Ġimag ining",
+      "he a",
+      "et ical",
+      "á» Ń",
+      "ĠDem okrat",
+      "Ġen jo",
+      "Ġadjust able",
+      "Ġr ains",
+      "iew aż",
+      "Ġjust ement",
+      "Ġjust ified",
+      "ĠSh ake",
+      "v iv",
+      "ìĤ¬ë ¥¼",
+      "Ġmet t",
+      "ĠEnviron mental",
+      "Ġsol amente",
+      "Ġinter sect",
+      "Ġ198 8",
+      "Ġsim ulate",
+      "J A",
+      "Ġз аÑģ",
+      "Ġcont ing",
+      "ĠT ek",
+      "Ġtor ch",
+      "ĠдÑĢÑĥг ой",
+      "Ġins cre",
+      "Ġmodel o",
+      "ĠG eg",
+      "ĠDemocr at",
+      "к в",
+      "ĠBud dy",
+      "Ġredu nd",
+      "Ġcraft s",
+      "ĠH ij",
+      "Ġj ue",
+      "ĠKir k",
+      "Ġk ab",
+      "á» £",
+      "Ġaest het",
+      "ĠJ ON",
+      "Ġsuper com",
+      "ĠÑģ иÑĤÑĥ",
+      "ĠÏĮ ÏĦι",
+      "Ùħ ÙĨ",
+      "ĠE VER",
+      "ìķĺ ìĸ´",
+      "o it",
+      "ĠCle veland",
+      "Ġsixt een",
+      "Ġwater fall",
+      "ï ¸",
+      "in fl",
+      "Ġcounsel or",
+      "ĠP unk",
+      "Ġspre chen",
+      "æµ ģ",
+      "ex c",
+      "ĠSk ills",
+      "ro z",
+      "ad amente",
+      "Ġpan cakes",
+      "ê¸°ë ¡ľ",
+      "Ġpl ank",
+      "Ġsovere ignty",
+      "Ġf ui",
+      "Ġне об",
+      "ĠW ii",
+      "ĠSch ol",
+      "âĢ İ",
+      "ĠSpe ak",
+      "èĭ ±",
+      "c iliation",
+      "Ġth igh",
+      "Ġê±° ìĿĺ",
+      "Ġj ot",
+      "Ġì´ ¬ìĺģ",
+      "ĠÙħ ÛĮÚº",
+      "ĠCC P",
+      "Ġпо ÑģÑĤ",
+      "Ġobser ver",
+      "á b",
+      "Ġst igma",
+      "Ġprop riet",
+      "Ġc idade",
+      "ĠbaÅŁ ka",
+      "ع ة",
+      "k re",
+      "Ġpow iedzieÄĩ",
+      "Ġce ase",
+      "Ġsk ins",
+      "Ġvegg ies",
+      "Ġoppos ing",
+      "op oly",
+      "ĠJ ug",
+      "ĠY oon",
+      "ĠUn it",
+      "Ġ198 6",
+      "Ġk ons",
+      "Ġdiagn ostic",
+      "Ġempower ed",
+      "Ġth o",
+      "Ġc en",
+      "é ration",
+      "ĠÑ Ĺ",
+      "Ġphys ic",
+      "ĠPract ice",
+      "å· Ŀ",
+      "ĠS outheast",
+      "ĠEs pa",
+      "è¯ ·",
+      "ĠGe or",
+      "rop ortion",
+      "Ġspec s",
+      "Ġadapt ive",
+      "ĠUn ity",
+      "ĠWork s",
+      "ug en",
+      "ĠMont ana",
+      "Thank s",
+      "Ġwh ipped",
+      "Ġdun geon",
+      "Ġvitam ins",
+      "S P",
+      "Ġsc andal",
+      "Ġdin ero",
+      "o va",
+      "Ġemb ro",
+      "ĠE agle",
+      "Ġthe ology",
+      "ĠVan essa",
+      "ĠA IDS",
+      "ë IJľ",
+      "Ġfre el",
+      "ĠAlz heimer",
+      "ĠÅ ļ",
+      "H er",
+      "Ġtorn ado",
+      "ag ens",
+      "ĠìŀĪ ìĸ´ìĦľ",
+      "ĠTrans form",
+      "Ġprocess o",
+      "Ġmill ise",
+      "Ġprofession ally",
+      "Ġmem b",
+      "oc ation",
+      "Ġsty ling",
+      "Ġоб Ñıз",
+      "ĠOper ation",
+      "Ġwy gl",
+      "ĠR an",
+      "Ġ çļĦ",
+      "ĠK in",
+      "á»± c",
+      "ĠB AR",
+      "Ġpaper work",
+      "Ġt ule",
+      "Ġquer ia",
+      "Ġcomp ly",
+      "ĠH air",
+      "×Ļ× Ľ",
+      "ĠпÑĢо ÑģÑĤ",
+      "Ġmut ation",
+      "Ġrep rés",
+      "Ġoct opus",
+      "Ġimportant es",
+      "Ġdes erved",
+      "et r",
+      "Ġdis asters",
+      "l ında",
+      "iqu é",
+      "ĠDes halb",
+      "so o",
+      "oss ip",
+      "Ġrelie ved",
+      "ĠColl ins",
+      "Ġwater proof",
+      "ĠY uk",
+      "Ġcop ying",
+      "Ġb ütün",
+      "ĠHe ute",
+      "ĠEnt re",
+      "Ġresid ual",
+      "Ġcolon ies",
+      "Ġé norm",
+      "ĠEr in",
+      "Ġst an",
+      "Ġtremend ously",
+      "Ġcapt ures",
+      "ĠS ai",
+      "â ce",
+      "Ġm iaÅĤ",
+      "Ġ8 7",
+      "Ġlo gging",
+      "Ġinsert ed",
+      "Ġinher ently",
+      "ìĿ ij",
+      "la ve",
+      "ни Ñĩ",
+      "Ġfem mes",
+      "Ġdé p",
+      "u ks",
+      "ac ia",
+      "ĠW ade",
+      "Ġj ij",
+      "ĠVin cent",
+      "ĠI celand",
+      "h em",
+      "Ġap ology",
+      "ĠP eg",
+      "Ġgl ued",
+      "Ġcompan ions",
+      "ĠL iver",
+      "Ġcritic ized",
+      "le ading",
+      "Ġsä ga",
+      "æ¼ Ĥ",
+      "Ġsqu id",
+      "Ġnarr atives",
+      "Ġtak a",
+      "ne z",
+      "we it",
+      "Ġtrip od",
+      "Ġexpl ic",
+      "Ġsp inal",
+      "Ġapproxim ation",
+      "Ġpag ar",
+      "ĠCal vin",
+      "Ġв едÑĮ",
+      "Ġl ac",
+      "Ġpro active",
+      "ĠTra in",
+      "or f",
+      "Ġst en",
+      "Ġgra pes",
+      "Ġme us",
+      "Ġautom at",
+      "Ġbi ased",
+      "Ġcha îne",
+      "co al",
+      "Ġren cont",
+      "ĠK um",
+      "Ġfest ivals",
+      "Ġstart ups",
+      "Ġa ka",
+      "ãģ ¹",
+      "Ġcyl ind",
+      "s na",
+      "C RI",
+      "Ġresult ado",
+      "Ġmil estone",
+      "ĠÏ ħ",
+      "Ġtele port",
+      "zy ch",
+      "6 2",
+      "åħ ³",
+      "ĠFe ar",
+      "Ġnucle us",
+      "Ġsh ines",
+      "ho v",
+      "ĠPart ners",
+      "ĠK as",
+      "Ġnad ie",
+      "Ġalert s",
+      "ĠB ILL",
+      "str ong",
+      "ĠN ate",
+      "ĠDen mark",
+      "ĠC av",
+      "OS T",
+      "h ält",
+      "ĠìķĦëĭ Į",
+      "any on",
+      "Ġencour ages",
+      "Ġпо ÑģÑĤав",
+      "ĠHu ang",
+      "ãģĬ é¡ĺãģĦ",
+      "ST A",
+      "Ġpain ts",
+      "ãģĻ ãģĶ",
+      "Ġsched ules",
+      "Ġche ated",
+      "Ġappro x",
+      "Ġï ·",
+      "Ġ» .",
+      "Ġsm iles",
+      "is ure",
+      "Ġn ered",
+      "ard en",
+      "Ġcur t",
+      "Ġë Į",
+      "ĠR oth",
+      "Ġpuis que",
+      "ĠG ET",
+      "ĠVe get",
+      "Ġprodu z",
+      "ĠBelg ium",
+      "ĠCamp us",
+      "ר ×Ļ×Ŀ",
+      "ic ut",
+      "ĠÑģ ним",
+      "Ġré uss",
+      "Ġslipp ery",
+      "ĠE w",
+      "Å ³",
+      "ĠLeg ends",
+      "ĠT iffany",
+      "али з",
+      "ĠпеÑĢ ев",
+      "ĠогÑĢ ом",
+      "Ġcr os",
+      "ĠC E",
+      "B u",
+      "Ġens ures",
+      "Ġgrand children",
+      "Ġac uerdo",
+      "Ġprison er",
+      "Ġth irsty",
+      "b ane",
+      "Ġë¹ ł",
+      "Ġúlt ima",
+      "ĠLa unch",
+      "n ity",
+      "Ġcombust ion",
+      "Ġun icorn",
+      "Ġfam ille",
+      "Ġlower ing",
+      "ĠY ing",
+      "build ing",
+      "Ġdu o",
+      "ĠMé xico",
+      "ast ian",
+      "Ġ먹 ìĿĦ",
+      "ĠRal ph",
+      "Ġre write",
+      "Ġgl am",
+      "if ique",
+      "E r",
+      "ĠRun ning",
+      "он ов",
+      "Ġmean ings",
+      "Ġche wy",
+      "ĠLes lie",
+      "Ġfin est",
+      "Ġhah aha",
+      "ĠST EP",
+      "Ġlon eliness",
+      "ri ans",
+      "Ġquestion ed",
+      "Ġes que",
+      "Ġsink ing",
+      "Ġpes o",
+      "ĠWr ong",
+      "asm ine",
+      "Ġdefin itive",
+      "Ġbu ys",
+      "Ġcru c",
+      "c ool",
+      "Ġë łĪ",
+      "Ġp ó",
+      "Ġutil ized",
+      "Ġworth while",
+      "ĠD ylan",
+      "ES E",
+      "Ġverte x",
+      "t ı",
+      "ĠF ir",
+      "Ġz aw",
+      "ĠG ed",
+      "ĠÐĿ ап",
+      "d z",
+      "Ġcurs or",
+      "Ġsw ipe",
+      "Ġinevit ably",
+      "Ġpos ters",
+      "Ġinc lined",
+      "Ġgreet ing",
+      "Ġdisappoint ment",
+      "ãģ¾ ãģ§",
+      "Ġrela ção",
+      "T T",
+      "Ġr abb",
+      "ĠM aine",
+      "Ġanaly zed",
+      "F E",
+      "ĠÐŁ ол",
+      "ĠSand ra",
+      "Ġpl ague",
+      "AR E",
+      "Ġv är",
+      "ĠV iv",
+      "um ed",
+      "h ando",
+      "hou ette",
+      "ĠBa iley",
+      "ä¸į éģİ",
+      "ys on",
+      "Ġsem ua",
+      "Ġhard core",
+      "â Ĥ¬",
+      "Ñĸ м",
+      "é ra",
+      "OT H",
+      "Ġforeign ers",
+      "ĠPalestin ian",
+      "Ġprop rio",
+      "ани й",
+      "Ġmyth s",
+      "W H",
+      "Ġnin th",
+      "ĠCreat or",
+      "л ом",
+      "ĠFl ip",
+      "Ġem an",
+      "Ġki ÅŁ",
+      "zie h",
+      "ĠEarn est",
+      "sy stem",
+      "ĸ ìĹIJ",
+      "Ġarm ies",
+      "ĠOut side",
+      "Ġhar us",
+      "æº ĸ",
+      "од аÑĢ",
+      "Ġvis itor",
+      "çŃ Ķ",
+      "Ġstrength ening",
+      "Ġ9 2",
+      "v io",
+      "Ġë ¦¬",
+      "Ġgre edy",
+      "Ġpo quito",
+      "ud er",
+      "ĠK opf",
+      "Ġëĭ¤ìĿĮ ìĹIJ",
+      "Ġse is",
+      "át ico",
+      "Ġtrust ing",
+      "ÃŃ p",
+      "ĠE mm",
+      "le en",
+      "ĠاÙĦ ÙĨ",
+      "Ġrecruit ment",
+      "ĠFil ip",
+      "ĠÙĥ ÙĦ",
+      "Cl int",
+      "Ġв еÑģ",
+      "au ft",
+      "Ġdomin ate",
+      "Ġrest o",
+      "Ġk ra",
+      "á i",
+      "ĠC ait",
+      "r ows",
+      "Ġcountry side",
+      "Ġ19 45",
+      "аÑĨи Ñİ",
+      "Ġд и",
+      "Ġkern el",
+      "lo v",
+      "Ġcalcul ating",
+      "د ا",
+      "ĠW alt",
+      "Ġempower ing",
+      "Ġch assis",
+      "line ar",
+      "г Ñĥ",
+      "Ġno va",
+      "Ġu y",
+      "Ġ6 9",
+      "Ġen compass",
+      "tr l",
+      "Ġcomput ational",
+      "Ġwor ms",
+      "Ġnhi á»ģu",
+      "Ġastronaut s",
+      "Ġv es",
+      "Ġsy tu",
+      "Ġdemand ed",
+      "Ġc s",
+      "ĠM ol",
+      "Ġ `",
+      "Ġch ant",
+      "Ġthere by",
+      "Ġpen is",
+      "Ġem oc",
+      "w yn",
+      "Ñĥ же",
+      "Ġtre ad",
+      "ó le",
+      "Ġdeep est",
+      "Ġmach e",
+      "ĠV ent",
+      "ĠAm sterdam",
+      "ãĥ Ľ",
+      "Ġre bel",
+      "Ġ6 1",
+      "Ġв кÑĥÑģ",
+      "uff s",
+      "ĠdoÄŁ ru",
+      "ĠNap ole",
+      "ή Ïĥ",
+      "Ġwork outs",
+      "ĠGl ad",
+      "н еÑģ",
+      "Ġt ensions",
+      "ĠSh ift",
+      "ĠGu er",
+      "íĮ IJ",
+      "Ġì¹ľ 구",
+      "Ð ĸ",
+      "Ġimpl ant",
+      "ê u",
+      "ê¸ Ģ",
+      "Ġauthor ized",
+      "C ER",
+      "ĠR V",
+      "Ġh il",
+      "le v",
+      "c imento",
+      "ĠU FO",
+      "ìĥ Ī",
+      "è¨ Ĥ",
+      "w or",
+      "Ġd ances",
+      "ĠPix el",
+      "çľĭ ä¸Ģä¸ĭ",
+      "Ġtr otzdem",
+      "Ġob ten",
+      "ĠAlf red",
+      "Ġcost ly",
+      "ĠStan ley",
+      "Ġterror ists",
+      "ĠW id",
+      "ħ ëĭĪëĭ¤",
+      "Ġle icht",
+      "ìĿ´ì Ĭ¤",
+      "Ġdobr ze",
+      "Ġhes it",
+      "Ġer zäh",
+      "Ġein ige",
+      "Ġhe bt",
+      "Ñģ е",
+      "Ġunp redict",
+      "C ómo",
+      "rem os",
+      "ĠThank fully",
+      "Ġpur se",
+      "ch s",
+      "an cer",
+      "ul os",
+      "st ud",
+      "æľī æ²Ĵæľī",
+      "Ġneuro log",
+      "ĠAn cient",
+      "O ut",
+      "aws ze",
+      "Ġopp ose",
+      "Ġantib odies",
+      "ĠSome how",
+      "ropol itan",
+      "kt or",
+      "ĠÑģÑĤоÑĢон Ñĭ",
+      "Ġrock ets",
+      "Ġdis able",
+      "Ġcatast roph",
+      "´ì ŀ",
+      "Ġc yn",
+      "ĠдÑĢÑĥз ÑĮÑı",
+      "Ġinstruct ors",
+      "ema al",
+      "Ġet wa",
+      "Ġy uan",
+      "ĠGr ound",
+      "Ġpremi ere",
+      "Ñĩ ив",
+      "Ġs aint",
+      "y ba",
+      "Ġk ok",
+      "Ġcontract ors",
+      "Ġê° ģ",
+      "Ġ×IJ× ľ",
+      "Ġhead line",
+      "Ġcomplet amente",
+      "Ġin expensive",
+      "Ġvi u",
+      "ĠGrand e",
+      "Ġble ed",
+      "ë ¬¼",
+      "Ġ7 3",
+      "Ġtod avÃŃa",
+      "ĠR ush",
+      "ĠEld er",
+      "ê°Ģ ëĬĶ",
+      "ĠR ou",
+      "Ġжен Ñī",
+      "ĠM ira",
+      "Ġde ine",
+      "Ġkar ma",
+      "Ġum m",
+      "Ġents che",
+      "ĠHolo caust",
+      "Ġdiscover ies",
+      "am ents",
+      "Ġrais on",
+      "Ġbur gers",
+      "B ack",
+      "Ġg dy",
+      "ĠA G",
+      "ĠD aw",
+      "ìķ ł",
+      "head ed",
+      "ĠCl ar",
+      "In st",
+      "ĠLie utenant",
+      "ĠAf D",
+      "ĠC es",
+      "Ġpersonal ized",
+      "Ġinterf aces",
+      "à¸Ī ะ",
+      "ĠÑĢ еж",
+      "Ġsu ic",
+      "Ġstar ving",
+      "Ġox ide",
+      "Ġdecor ated",
+      "ĠD U",
+      "ĠìĺĪìģ ĺ",
+      "Ġqu o",
+      "Ġdist ortion",
+      "æ® µ",
+      "Ġ먹 ìĸ´ë",
+      "Ġst akes",
+      "æĺİ çĻ½",
+      "Ġsynt ax",
+      "Ġbi ết",
+      "th y",
+      "ic ie",
+      "Ġbras ile",
+      "is is",
+      "R C",
+      "Ġsh ook",
+      "Ġdepth s",
+      "ĠC osta",
+      "Ġvoc als",
+      "Ġco aster",
+      "Ġfal ou",
+      "ett le",
+      "Ġk ennen",
+      "Ġder ive",
+      "Ġa ids",
+      "ĠÐĿ ик",
+      "Ġent wic",
+      "Ġvert ically",
+      "Ġ Í",
+      "ĠSU V",
+      "Ġfire works",
+      "Ġspecific s",
+      "äº ¤",
+      "Ġinsist ed",
+      "Ġdes halb",
+      "ĠG onz",
+      "lo ve",
+      "ĠMil itary",
+      "ĠPier re",
+      "Ġâ Ī",
+      "ĠWh ose",
+      "Ġperf ume",
+      "ĠÏĢ ε",
+      "Ġlower ed",
+      "Ġcross es",
+      "Ġtransl ates",
+      "Ġarrib a",
+      "ÃŃ do",
+      "ĠLe v",
+      "åħ §",
+      "ĠC iao",
+      "Ġscholars hips",
+      "Ġgest ures",
+      "ĠÑĢез ÑĥлÑĮÑĤаÑĤ",
+      "Ġquest ão",
+      "ĠColon el",
+      "ĠB ott",
+      "ر Ùģ",
+      "N ING",
+      "ĠWatch ing",
+      "ĠPur ple",
+      "ÑģÑĤÑĢ ан",
+      "Ġexecut ives",
+      "ĠK ris",
+      "or neys",
+      "ен нÑĭй",
+      "Ġco ated",
+      "Ä ©",
+      "Ġpark ed",
+      "ĠÑģв еÑĤ",
+      "!! !!!",
+      "ĠFlo yd",
+      "ıs ı",
+      "zi Äĩ",
+      "Ġmotiv ate",
+      "ĠEl on",
+      "le an",
+      "Ĩ ĵ",
+      "Ġ ip",
+      "Ġni ż",
+      "ĠExper ience",
+      "ĠT ina",
+      "ĠKoll ege",
+      "ĠAmb assador",
+      "in ya",
+      "Ġthe ft",
+      "Ġhe ures",
+      "ĠMy st",
+      "Ġmais on",
+      "le b",
+      "Ġbowl s",
+      "ĠBür ger",
+      "ĠRoose velt",
+      "R P",
+      "ê°Ģ ìļĶ",
+      "ĠDel icious",
+      "erd ings",
+      "ĠAssoci ate",
+      "ous se",
+      "ĠC ort",
+      "ĠRepe at",
+      "ĠGl ory",
+      "Ġcont ag",
+      "à¹Ģภ¥",
+      "ĠPar ad",
+      "ĠK erry",
+      "Ġê ¿",
+      "ĠW ave",
+      "å¿ ħ",
+      "Ġgate way",
+      "çIJ ĥ",
+      "! ãĢį",
+      "Ġtrans cend",
+      "Ġdam ages",
+      "Ġt ails",
+      "Ġgravit ational",
+      "ĠSh ield",
+      "Ġprim itive",
+      "Ġcar riers",
+      "ĠHua wei",
+      "ÙĤ د",
+      "Ġfel iz",
+      "ĠM ia",
+      "åĥ ķ",
+      "ĠпÑĢÑıм о",
+      "ĠпÑĢоиÑģ ÑħодиÑĤ",
+      "ĠMur phy",
+      "ĠAct iv",
+      "ãĥĥ ãĤ¯",
+      "Ġdis comfort",
+      "×ij ×Ķ",
+      "ĠK ell",
+      "ĠCent ury",
+      "Ġsp aghetti",
+      "ĠD urch",
+      "Ġc ierto",
+      "ĠEmp ress",
+      "Ġg uts",
+      "ne g",
+      "Ġдо ÑģÑĤаÑĤоÑĩно",
+      "Ġvolunt ary",
+      "å¤ ±",
+      "Ġsqu irrel",
+      "æ¬ ¢",
+      "ãģ¡ ãĤī",
+      "ĠM az",
+      "´ìĭ ¬",
+      "Ġв и",
+      "ãĤ §",
+      "ĠÑĤак иÑħ",
+      "ĠSh aron",
+      "Ġenthusi astic",
+      "ire ment",
+      "Ġíŀĺë ĵ¤",
+      "Ġpot rze",
+      "Ġiniti ated",
+      "ãĥ §",
+      "ĠÅĽ rod",
+      "ĠìĿ´ë ¦Ħ",
+      "Ġrem ake",
+      "Ġcul min",
+      "Ġconf use",
+      "mi yor",
+      "ur ar",
+      "CT OR",
+      "Ġbun ny",
+      "Ġ 大",
+      "ä¸į èĥ½",
+      "el p",
+      "Ġvamp ire",
+      "Ġill umin",
+      "ĠH end",
+      "Ġк аÑĩе",
+      "ĠSal v",
+      "Ġкан ал",
+      "Ġport a",
+      "Ġass hole",
+      "Ġsupp orter",
+      "Ġskept ical",
+      "Ġkn ead",
+      "Ġìĺ ¬",
+      "e za",
+      "Ġqu ê",
+      "ĠD H",
+      "Ġrod z",
+      "own ers",
+      "Ġpl ots",
+      "Ġdel ays",
+      "Ġbelong ed",
+      "Ġa hh",
+      "Ġcar ved",
+      "Ġris en",
+      "Ġor den",
+      "ph ony",
+      "iss y",
+      "!!!! !!!!",
+      "ĠolduÄŁ unu",
+      "Ġr oses",
+      "Ġintr ins",
+      "ĠAng st",
+      "Ġfinal ement",
+      "ì§ Ŀ",
+      "SO UND",
+      "Ġind ul",
+      "° Į",
+      "Ġ×ķ ×Ķ",
+      "ch y",
+      "акÑģ им",
+      "Ġn ggak",
+      "Ġli z",
+      "Ġele ctoral",
+      "ĠSh awn",
+      "ric ia",
+      "Ġar sen",
+      "ĠP ep",
+      "Ġ20 30",
+      "Ġtro phy",
+      "Ġsmo other",
+      "Ġer re",
+      "Ġcrash es",
+      "Ġsch ne",
+      "Ġas i",
+      "ĠMa ÃŁ",
+      "Ñĥ ли",
+      "ÑĩеÑģ ки",
+      "ie ves",
+      "RE AM",
+      "Ġstir ring",
+      "ãĥ Ģ",
+      "ust a",
+      "Ġin ver",
+      "s ight",
+      "ord u",
+      "o or",
+      "ĠÄĥ n",
+      "Ġperm itted",
+      "ÑĢ ÑĮ",
+      "Ġch alk",
+      "ãĤĪ ãģĹ",
+      "Ġtatto os",
+      "ĠRel ations",
+      "ĠH oy",
+      "ks am",
+      "Ġdent ist",
+      "Ġ미 êµŃ",
+      "Ġso fa",
+      "ĠÑ Ķ",
+      "Ġform e",
+      "ÙĤ Ø©",
+      "Ġë² ł",
+      "Ġembr aced",
+      "m il",
+      "Ġsung lasses",
+      "Ġê° Ķ",
+      "Ġseam less",
+      "Ġbe ep",
+      "äch st",
+      "Ġswe ets",
+      "Ġsem aine",
+      "Ġirre levant",
+      "Ġdesen vol",
+      "Ïģ Ïī",
+      "ĠпÑĢоиз вод",
+      "ang s",
+      "Ġar oma",
+      "Ġpool s",
+      "Ġgi á»Ŀ",
+      "ĠU g",
+      "Ġclim bed",
+      "Ġtrend ing",
+      "Ġseper ti",
+      "ĠB arr",
+      "Ġp ÅĤ",
+      "ĠOrig inally",
+      "Ġ Ú¯",
+      "ut to",
+      "Ĭ ¸ë",
+      "ĠкоÑĤоÑĢ ÑĭÑħ",
+      "Ġза Ñħ",
+      "Ġeigen en",
+      "Ġmurder er",
+      "ern ame",
+      "Å ŀ",
+      "Ġannoun cing",
+      "ĠPlat form",
+      "Ġexplan ations",
+      "Ġpres ente",
+      "ĠNas ıl",
+      "Ġorph an",
+      "ĠFort nite",
+      "ros pect",
+      "ered ith",
+      "ĠìĹĨ ìĸ´",
+      "ĠNI H",
+      "w agen",
+      "Ġrem ed",
+      "§ Ģë",
+      "m ont",
+      "ĠJeff rey",
+      "pr om",
+      "Ġf ünf",
+      "Ġназ ад",
+      "Ġcuc umber",
+      "ĠSum mit",
+      "åĪ Ŀ",
+      "§ ¤",
+      "ÐĿÐIJ Я",
+      "ĠJ et",
+      "Ġcamb io",
+      "Ñĥй ÑĤе",
+      "Ġcub ic",
+      "Ġdisp roportion",
+      "ere z",
+      "Ġmad ness",
+      "çĹ Ľ",
+      "Ġt int",
+      "Ġfuer on",
+      "Ġk y",
+      "Ġbip art",
+      "ãģ¾ ãģĽ",
+      "S am",
+      "Ġë ½",
+      "Ġr iv",
+      "ĠT ank",
+      "ĠëĨ ĵ",
+      "Ġrend ered",
+      "ÅĽl ÄĻ",
+      "c onds",
+      "Ġdis ruption",
+      "Ġincon ven",
+      "Ġqu iser",
+      "Ġden ial",
+      "Ġgalax ies",
+      "Ġsovere ign",
+      "Ġpol sk",
+      "Ïģ Ïİ",
+      "Ġme x",
+      "Ġcar acter",
+      "ĠL ego",
+      "and en",
+      ".' \"",
+      "ĠíĶ Įë",
+      "Ġcompress or",
+      "ĠMo vie",
+      "Ġapplic ants",
+      "zie hen",
+      "Ġveget ation",
+      "Ġbe lle",
+      "ĠG OOD",
+      "ĠB au",
+      "Ġres ent",
+      "se x",
+      "ament os",
+      "Ġ×Ķ× ĸ×Ķ",
+      "Ġover load",
+      "Ġsilic one",
+      "еÑģÑĤ но",
+      "Ġden ken",
+      "Ġdefin it",
+      "ĠWas n",
+      "Ġalter ed",
+      "ĠSo o",
+      "ĠW ing",
+      "ind re",
+      "ĠN PC",
+      "Ïģ ÎŃ",
+      "ĠTw enty",
+      "ĠLie be",
+      "Ġhomeless ness",
+      "ould er",
+      "ĠÐĺ ÑĤак",
+      "Ñģ каÑı",
+      "Ġcu atro",
+      "ĠHar vey",
+      "Ġph ilan",
+      "ĠBe et",
+      "Ġpol icing",
+      "ĠAlex and",
+      "Ġм олод",
+      "Ġmü s",
+      "Ġh izo",
+      "ë³´ ëĭ¤",
+      "Ġпоз вол",
+      "Ġп ÑĭÑĤ",
+      "оÑĩ емÑĥ",
+      "Ġíĥ ľ",
+      "Ġcryptocur rency",
+      "Ġl oro",
+      "Ġsumm ation",
+      "Ġbak alım",
+      "Ġne uros",
+      "Ø ¥",
+      "Ġмож ем",
+      "Ġü st",
+      "Ġprelim inary",
+      "Ġhorn s",
+      "ĠT I",
+      "Ùĥ ÙĦ",
+      "Y O",
+      "Ġh inge",
+      "Ġrep airs",
+      "Ġbond ing",
+      "Ġb ize",
+      "ĠÑĪ ÑĤ",
+      "Ġmot ive",
+      "ĠNiger ia",
+      "1 20",
+      "b lock",
+      "Ġav iation",
+      "ĠKomm un",
+      "Ġок аз",
+      "Ġten ha",
+      "Ġeduc ating",
+      "Ġsta at",
+      "æ ¶Ī",
+      "ĠÑģк олÑĮко",
+      "Ġfright ened",
+      "Ġsee ks",
+      "ÑĢÑĥ ÑĪ",
+      "qu ent",
+      "ĠN ou",
+      "Ġpr at",
+      "ĠSh ot",
+      "W ork",
+      "kar ang",
+      "ĠLight ning",
+      "nold s",
+      "roll ed",
+      "gl ass",
+      "Ġcred ibility",
+      "IT Y",
+      "Ġatm ospheric",
+      "Ġha via",
+      "änd ern",
+      "che ers",
+      "Th ese",
+      "ĠC ell",
+      "Ġmag nes",
+      "ĠBra vo",
+      "se ason",
+      "ĠÅŁey ler",
+      "ðŁ İ",
+      "wh ite",
+      "ĠM B",
+      "Ġstack ed",
+      "Ġ7 4",
+      "Ġдав ай",
+      "Ġp ave",
+      "Ġо Ñħ",
+      "Ġdatas et",
+      "Ġret our",
+      "Ġmat urity",
+      "Ġqu ase",
+      "Ġ9 3",
+      "ĠSy m",
+      "Ġbrief ing",
+      "Ġcult urally",
+      "Ġì ·¨",
+      "inh as",
+      "Ġmad am",
+      "Ġajud ar",
+      "ĠTib et",
+      "Ġle aks",
+      "ci le",
+      "Ġthe aters",
+      "ìĺ ¨",
+      "ãĥ ĸ",
+      "7 2",
+      "ĠW ash",
+      "ĠQual ity",
+      "ĠI van",
+      "ĠB ent",
+      "ig ator",
+      "ĠGesch ichte",
+      "Ġreact ive",
+      "Ġ19 00",
+      "æ¡ Ī",
+      "Ġcontrad ict",
+      "Ġziem lich",
+      "Ġcoh ort",
+      "á» §",
+      "Ġp estic",
+      "Ġor az",
+      "Ġtell ement",
+      "é ¾",
+      "ĠNow adays",
+      "c rew",
+      "Ste ve",
+      "Ġf ictional",
+      "Ġil k",
+      "ãģĤ ãģ£",
+      "Ġgas oline",
+      "z am",
+      "Ġpan cake",
+      "èn cia",
+      "Ġmuit os",
+      "Ġb ury",
+      "Ġk op",
+      "ĠI Q",
+      "Ġres ervation",
+      "ĠUp date",
+      "Ġje j",
+      "ĠE yes",
+      "åı ij",
+      "Ġv ive",
+      "Ġch ce",
+      "ĠIn i",
+      "resp ons",
+      "Ġreflect ive",
+      "ĠW an",
+      "Ñĸ з",
+      "Ġen ca",
+      "Ġemb od",
+      "ĠBur ger",
+      "Ġacad emia",
+      "ĠCir c",
+      "ĠпÑĢ ек",
+      "Ġan lam",
+      "Ġphilan throp",
+      "ĠBa ÅŁ",
+      "ĠAud i",
+      "Ġv ost",
+      "ä½ł çŁ¥éģĵ",
+      "Ġre per",
+      "P eter",
+      "Ġcons oles",
+      "Ġscr ut",
+      "ĠTurn er",
+      "ĠбÑĭ в",
+      "II I",
+      "è¨ ´",
+      "ĠF light",
+      "ภĸ",
+      "ĠR aven",
+      "Ġcor ros",
+      "fer n",
+      "Ġprov a",
+      "ĠSe v",
+      "Ġreci pro",
+      "Ġ198 5",
+      "Ġnue va",
+      "Ġd ab",
+      "ãĢģ ãĢĮ",
+      "Ġme z",
+      "ĠSt ark",
+      "pp ings",
+      "о ÑģÑĤи",
+      "ì¦ Ŀ",
+      "Ġfr aming",
+      "ĠÐł аз",
+      "Ġpost p",
+      "ĠSh annon",
+      "Ġк ÑĥÑĢ",
+      "Ġjak by",
+      "ien nent",
+      "ĠM aps",
+      "ĠRevel ation",
+      "ĠÑģÑĤ ал",
+      "ìļ ´ëį°",
+      "Ġdev ant",
+      "ĠG iving",
+      "ĠW AS",
+      "Ġк ого",
+      "Ġrem a",
+      "ĠR C",
+      "n ÃŃ",
+      "Ġsl ipped",
+      "ĠR ams",
+      "Ġwe et",
+      "Ġmascul ine",
+      "ĠE c",
+      "Ġre op",
+      "ĠPl ant",
+      "ĠM AY",
+      "Ġsp ikes",
+      "Ġno zzle",
+      "ĠWik ipedia",
+      "ĠC oh",
+      "IS SA",
+      "chl ossen",
+      "ì§Ģ 를",
+      "Ġë¯ ¸ë",
+      "ĠN eder",
+      "J osh",
+      "ĠÐłÐ¾ÑģÑģ ии",
+      "Ġ198 7",
+      "ĠThe ory",
+      "ek k",
+      "Ġut an",
+      "Ġдом а",
+      "ch u",
+      "ĠÑģ б",
+      "Ġapro ve",
+      "V EN",
+      "uep rint",
+      "Ġ8 4",
+      "æ¼Ĥ 亮",
+      "C or",
+      "Ġrich er",
+      "Ġsandwich es",
+      "ats u",
+      "ÑĪ иÑħ",
+      "Ġl att",
+      "~~ ~~",
+      "f riends",
+      "Ġderni ère",
+      "Ġstere o",
+      "ĠÑįк Ñģп",
+      "Ġprotect ions",
+      "Ġha ut",
+      "Every one",
+      "Ġenter prises",
+      "ĠMost ly",
+      "ĠSpot ify",
+      "ĠSe x",
+      "Ġun g",
+      "Įë ¥¼",
+      "Ġactiv ism",
+      "ct ica",
+      "orig inal",
+      "ĠпÑĢог ÑĢам",
+      "Ġbro ccoli",
+      "à ¦",
+      "ог ÑĢаÑĦ",
+      "Ġse karang",
+      "Ġcra fting",
+      "Ġб ан",
+      "ãģ» ãģ©",
+      "ĠR az",
+      "Ġna ive",
+      "Ġsc rolling",
+      "Ġnumer ical",
+      "Ġschedul ing",
+      "Ġapart ments",
+      "ç į",
+      "Ġstret ches",
+      "ace y",
+      "ĠH ER",
+      "ãĤ º",
+      "Ġz inc",
+      "Ġd arn",
+      "Ġc él",
+      "Ġward robe",
+      "Ġred irect",
+      "Ġj um",
+      "ĠStr ange",
+      "Ġn Ãło",
+      "Ġexperiment ing",
+      "ér é",
+      "Ġvou lez",
+      "Ġge be",
+      "ĠK ann",
+      "ĠÄij á»Ļ",
+      "ĠMax im",
+      "ĠK ön",
+      "ĠGl as",
+      "Ġpol ished",
+      "Ġnum a",
+      "I ch",
+      "Ġritual s",
+      "ĠS I",
+      "иÑĤ ели",
+      "Ġinf ilt",
+      "Ġscar f",
+      "op hy",
+      "Ġy ine",
+      "Ġciv ic",
+      "ĠM eng",
+      "än ge",
+      "Õ ¥",
+      "h istoire",
+      "ĠO ke",
+      "Ġìĺ Ĩ",
+      "Ġsoll ten",
+      "Ġ8 2",
+      "é ¦¬",
+      "Ġpres cribed",
+      "ĠDub ai",
+      "ĠEl tern",
+      "Ġnation wide",
+      "Ġsk ating",
+      "i ary",
+      "Ġreward ed",
+      "Ġmor ality",
+      "ĠMag gie",
+      "ĠOh hh",
+      "ĠF ahren",
+      "ol ved",
+      "æŶ åĢĻ",
+      "Ġdeux ième",
+      "te chn",
+      "ro le",
+      "Ġle ider",
+      "ĠJ AY",
+      "Ġин ÑĦоÑĢм",
+      "Ġca ffe",
+      "reich en",
+      "Ġk art",
+      "ĠC ute",
+      "ffect ive",
+      "Ġbull y",
+      "ag ar",
+      "Ġcommod ity",
+      "Ġob rig",
+      "OU R",
+      "Ġun pleasant",
+      "no x",
+      "J ul",
+      "ol ith",
+      "ÑĤо ÑıÑī",
+      "ĠBe lla",
+      "Ġdoll s",
+      "ĠHo ff",
+      "Ġadvis ors",
+      "Ġtransf ers",
+      "ĠG oku",
+      "Ġ12 00",
+      "inh os",
+      "P al",
+      "Ġëĺ ij",
+      "Ġre pt",
+      "Ġaccomplish ment",
+      "Ġwe ave",
+      "Ġovers ight",
+      "Ġun healthy",
+      "Ġfil t",
+      "Ġpud ding",
+      "ĠMig uel",
+      "Ġch uckles",
+      "åı° çģ£",
+      "vers ion",
+      "Ġconf ession",
+      "val ue",
+      "Ġtri umph",
+      "Ġsa ir",
+      "Ġëħ ¸",
+      "Ġar te",
+      "ĠMater ial",
+      "ut i",
+      "Ġliqu or",
+      "ĠBay ern",
+      "ĠM ail",
+      "Ġíĸ ¥",
+      "Ñģк ом",
+      "Ġcheap est",
+      "ĠÑĩа ÑģÑĤи",
+      "ĠJo bs",
+      "ĠC anyon",
+      "har ma",
+      "ale y",
+      "and ro",
+      "Ġappear ances",
+      "pro f",
+      "Ġо з",
+      "l agen",
+      "Ġ/ /",
+      "Ġли ÑĪÑĮ",
+      "Ġrecover ing",
+      "д ж",
+      "ps y",
+      "ãĥ ¢",
+      "Ġsw ift",
+      "ĠSp in",
+      "å¸ Ī",
+      "Ġsein em",
+      "Ġdol ph",
+      "f ühr",
+      "â t",
+      "Ġalt ijd",
+      "ĠMart y",
+      "ĠHo ch",
+      "Ġpred ators",
+      "Ġvor her",
+      "ĠÐĶав ай",
+      "Ġfrag ments",
+      "Ġpast ry",
+      "Ġcomm en",
+      "ĠS ana",
+      "Ġê± ´ëį°",
+      "uss en",
+      "Ġt ela",
+      "ĠN ina",
+      "le k",
+      "Ġc ries",
+      "Ġth ighs",
+      "ĠF lex",
+      "ĠB uzz",
+      "ã Ħ",
+      "U s",
+      "Ġpas o",
+      "Ġdecl ined",
+      "ĠN y",
+      "bal ance",
+      "Ġmas a",
+      "Ġj os",
+      "ãģª ãĤĭ",
+      "ĠСп аÑģибо",
+      "ach u",
+      "l oud",
+      "Ġpen a",
+      "ĠW ald",
+      "Ġelim ination",
+      "Ġв еÑģÑĮ",
+      "or age",
+      "Ġmisunder standing",
+      "Ġend orse",
+      "Ġog óle",
+      "Ġgre ed",
+      "Ġkle in",
+      "׾ ×Ķ",
+      "RE Y",
+      "ĠE ating",
+      "Ġsemin ar",
+      "ĠBirth day",
+      "Ġque lle",
+      "ĠMult i",
+      "Ġtir ar",
+      "Ġper ch",
+      "Ġla vor",
+      "ĠJ ia",
+      "Ġmut ations",
+      "Ġcig arettes",
+      "ÙĪ ج",
+      "Ġcous ins",
+      "Ġcaps ule",
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+      "Ġst ur",
+      "Ġze igt",
+      "n uts",
+      "Ġmean while",
+      "ĠCol in",
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+      "Ġg w",
+      "Ġuh h",
+      "ĠJ ER",
+      "spe cific",
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+      "Ġë© ĭ",
+      "ĠE lla",
+      "ook ed",
+      "ĠF it",
+      "aff le",
+      "ĠApr ès",
+      "ĠD uck",
+      "Ġcell ular",
+      "c ów",
+      "ĠÑĩÑĥв ÑģÑĤв",
+      "gen ommen",
+      "ìĬ¤í Ĭ¸",
+      "Ġl ain",
+      "is ol",
+      "Ġhold ers",
+      "Ġbo oster",
+      "ĠS asha",
+      "Ñĭв аеÑĤ",
+      "ģ ¼",
+      "Ġsepar ating",
+      "Ġrein forcement",
+      "Ġод ной",
+      "ìĹ Ĩ",
+      "ID E",
+      "ĠO ption",
+      "ph on",
+      "Ġpl ais",
+      "ĠC amb",
+      "ĠíĻ ĺ",
+      "Ġuncom mon",
+      "\" :",
+      "mi yorum",
+      "mo i",
+      "ac je",
+      "аж Ñĥ",
+      "Õ ¶",
+      "Ġgem s",
+      "ü ler",
+      "ool s",
+      "Ġenzy mes",
+      "Ġkidna pped",
+      "Ġk etchup",
+      "t alk",
+      "Ġz ach",
+      "Ġwas her",
+      "ãĢĤ ãĢĤ",
+      "ĠArch itect",
+      "ven ue",
+      "ĠPlan ning",
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+      "ĠSav ior",
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+      "íĬ ¼",
+      "ary a",
+      "Ġproces o",
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+      "i ander",
+      "F S",
+      "aj i",
+      "Ġun ite",
+      "ĠìĿ ĺë",
+      "Ġposs ÃŃvel",
+      "ra its",
+      "ĠAg re",
+      "ÛĮ Ú©",
+      "ìĦ ľëıĦ",
+      "æİ ī",
+      "Ġв ел",
+      "Ġм еÑģÑı",
+      "an or",
+      "P at",
+      "Ġder nier",
+      "Ïĥ ÏĦε",
+      "Ġкак аÑı",
+      "Ġlä sst",
+      "æİ °",
+      "ĠMe h",
+      "Ġng h",
+      "Ġam ateur",
+      "è« ĸ",
+      "F e",
+      "Ġê ¶ģ",
+      "Ġsitu ación",
+      "Ġsed an",
+      "Ġcleans ing",
+      "last ing",
+      "Ġcommun ist",
+      "AN E",
+      "Ġir regular",
+      "Ġs out",
+      "ĠCar ney",
+      "Ġall emaal",
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+      "Ġli bro",
+      "ÐŃ ÑĤо",
+      "Ġа п",
+      "Ġcontinu ation",
+      "ĠL or",
+      "?\" ,",
+      "qu in",
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+      "Ġs ights",
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+      "ĠU hh",
+      "è· ³",
+      "bir th",
+      "d ong",
+      "Ġhab lando",
+      "Ġsympt om",
+      "çµ Ĥ",
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+      "Ġtransport ed",
+      "Ġignor ant",
+      "Ġник огда",
+      "Ġdri p",
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+      "Ġref und",
+      "ĠZ w",
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+      "ĠG iant",
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+      "οÏħ ÏĤ",
+      "M o",
+      "ĠEnt wick",
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+      "ÙĨ ت",
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+      "Ġte aring",
+      "Ġtrou bled",
+      "p rem",
+      "Ġrepet itive",
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+      "Ġheaven ly",
+      "ĠAm ber",
+      "Ġпол ож",
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+      "Ġvow el",
+      "ank ing",
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+      "Ġir r",
+      "Ġco zy",
+      "Ġunf amiliar",
+      "ĠP ors",
+      "Ġë§ŀ ìķĦ",
+      "ĠTim othy",
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+      "pe x",
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+      "ĠExt ra",
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+      "d ings",
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+      "ì° ¬",
+      "ĠG C",
+      "åı ĸ",
+      "be h",
+      "J eremy",
+      "ĠB att",
+      "ãģ ¸",
+      "ath a",
+      "ĠZus ammen",
+      "scream s",
+      "Ġgr as",
+      "aff t",
+      "ĠInit ially",
+      "ĠB rett",
+      "Ġspecific ations",
+      "Ġsea weed",
+      "Ġo ath",
+      "Ġf ountain",
+      "ĠкоÑĤоÑĢ ой",
+      "ĠSte in",
+      "èģ ²",
+      "ĠCorin th",
+      "Ġconj ug",
+      "å·¦ åı³",
+      "Ġcompens ate",
+      "ĠëĬIJëĤĮ ìĿ´",
+      "Ġon ze",
+      "Ġskin care",
+      "B rian",
+      "Ġserv ir",
+      "} }",
+      "ĠV ik",
+      "Ġun int",
+      "Ġsupp liers",
+      "Ġbalcon y",
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+      "omet ric",
+      "з Ñı",
+      "Ġs igh",
+      "ĠT OM",
+      "ĠP ure",
+      "yt t",
+      "Ñĭ Ñģ",
+      "ĠRain bow",
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+      "×Ļ× ŀ",
+      "Ġstat ues",
+      "head s",
+      "Ġcou pled",
+      "éĮ ¢",
+      "Ġher d",
+      "ä½ ĵ",
+      "Ġexclud ed",
+      "Ġg ilt",
+      "ĠÑ İ",
+      "Ġswo je",
+      "ĠS ver",
+      "6 3",
+      "iss ant",
+      "Ġdür fen",
+      "ł Īë",
+      "Ġkiss ing",
+      "oo f",
+      "以 ä¸Ĭ",
+      "Ġcurs ed",
+      "Ġshow ers",
+      "Ġsw inging",
+      "Ġreprodu ce",
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+      "el come",
+      "Ġfundament als",
+      "Ġal mond",
+      "Ġp é",
+      "Ġwell being",
+      "Ġhun ters",
+      "å¾ Ģ",
+      "S ec",
+      "ĵľë ¦´",
+      "Ġem ission",
+      "Ġpsych ologist",
+      "Ġbetray ed",
+      "ĠRey nolds",
+      "L ES",
+      "Ġpo lling",
+      "Ġnegative ly",
+      "Ġcomb ines",
+      "׾ ×IJ",
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+      "λλ ά",
+      "ĠTurn s",
+      "OT T",
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+      "Ġrestrict ion",
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+      "ĠLeban on",
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+      "Ñĸ ÑĹ",
+      "Ġmother f",
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+      "Ġfe u",
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+      "Ġs aints",
+      "Ġpot assium",
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+      "Clint us",
+      "Ġmanifest ation",
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+      "ĠIns pect",
+      "Ġvent ilation",
+      "Ġhel m",
+      "Ġk ara",
+      "า à¸Ļ",
+      "Ġfavor able",
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+      "ĠHispan ic",
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+      "Ġstat istic",
+      "ĠìĤ °",
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+      "ull ah",
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+      "æĩ Ĥ",
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+      "ĠPar l",
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+      "ìĹ Ķ",
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+      "av ana",
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+      "ist les",
+      "ac jÄĻ",
+      "9 1",
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+      "ê²ł ì£ł",
+      "Ġл иÑĩ",
+      "AUDI O",
+      "Ġпло Ñħ",
+      "Ġprop osing",
+      "éł »",
+      "Ġtempt ed",
+      "Ġconver ting",
+      "ĠLe hr",
+      "Ġpers one",
+      "ĠFe eling",
+      "ìĸ´ì £¼",
+      "omb res",
+      "Ġ׾ ×Ļ",
+      "Ġg uru",
+      "Ġde ment",
+      "ни з",
+      "иÑĤ елей",
+      "Ġcomp añ",
+      "æľ ª",
+      "å¸Į æľĽ",
+      "Ġred o",
+      "Ġcondu ctor",
+      "m ia",
+      "Ġidol s",
+      "ĠM ul",
+      "Ġin ex",
+      "Ġt ämä",
+      "Ġimpact ing",
+      "Ġday light",
+      "g il",
+      "Ġhel fen",
+      "Ġents prech",
+      "ĠwiÄĻ ks",
+      "Ġscript ures",
+      "Ġdismiss ed",
+      "ãĥ³ ãĥĪ",
+      "ĠPod cast",
+      "Ùħ ر",
+      "Ġann ually",
+      "Ġus able",
+      "Ġli bre",
+      "оз м",
+      "Ġrub bish",
+      "çļĦ 人",
+      "Ġcontinu ar",
+      "Ġhum ili",
+      "Ġspe eches",
+      "ÑĢа Ñĩ",
+      "b ard",
+      "7 1",
+      "> <",
+      "olog ÃŃa",
+      "we alth",
+      "Ġmed itate",
+      "ĵ¤ ìĿĺ",
+      "ĠC raft",
+      "è§ī å¾Ĺ",
+      "æĻ ®",
+      "ri v",
+      "ĠAgain st",
+      "Ġcer amic",
+      "esp ère",
+      "Ġcompet ent",
+      "ĠHop kins",
+      "Ġkil os",
+      "Ġgra vel",
+      "Ġpist on",
+      "Ġfriends hips",
+      "Ġesc re",
+      "Ġvo z",
+      "ĠGes ellschaft",
+      "Ġunter stüt",
+      "Ġmu j",
+      "Ġwarning s",
+      "p os",
+      "ĠProfess ional",
+      "w szy",
+      "od le",
+      "b ands",
+      "Ġteam work",
+      "stell ung",
+      "Ġd x",
+      "åį Ĭ",
+      "Ġatt orneys",
+      "Ġweit ere",
+      "ãħĭãħĭ ãħĭ",
+      "ĠOrig inal",
+      "×Ļ× Ĺ",
+      "Ġbroadcast ing",
+      "ĠпеÑĢв Ñĭй",
+      "uch i",
+      "Ġhe ure",
+      "Ġgra bs",
+      "ĠW OR",
+      "ĠPla id",
+      "M in",
+      "Ġp az",
+      "ĠP uis",
+      "um u",
+      "it ates",
+      "Ġco ats",
+      "Ġbu en",
+      "Ġhe ir",
+      "Ġpne um",
+      "ש ר",
+      "ens er",
+      "ĠJUD GE",
+      "Ġbl onde",
+      "á¹ Ľ",
+      "Ġg ak",
+      "Ġs ık",
+      "Ġquot ed",
+      "Ġequip o",
+      "Ġw ishing",
+      "ÃŃ cia",
+      "Ġver bs",
+      "çµ Ħ",
+      "ĠCanad ians",
+      "Ġgover ning",
+      "ĠEv ans",
+      "E uro",
+      "Ġgen res",
+      "Ġunters chied",
+      "ĠBeck y",
+      "³¼ ê²ĮìļĶ",
+      "Ġe inge",
+      "ĠRa ise",
+      "ol and",
+      "ĠStr ateg",
+      "Ġer es",
+      "ĠVeter ans",
+      "Ġbreak out",
+      "Ġsant é",
+      "Ġad el",
+      "Ġinvestig ated",
+      "Ġpe ur",
+      "Ġag ile",
+      "Ġrail road",
+      "ans ka",
+      "Ġе й",
+      "Ġexp os",
+      "ator ies",
+      "ĠCont ent",
+      "Ġtruth s",
+      "ĠTra il",
+      "Ġgu a",
+      "Ġp ores",
+      "Ġwrit ings",
+      "ĠU hr",
+      "ĠThat s",
+      "Ġic ing",
+      "O C",
+      "ĠProdu ction",
+      "Ġcar ne",
+      "IS S",
+      "Ġn inguém",
+      "n on",
+      "Ġv icious",
+      "×ķ× Ķ",
+      "Ġrecon nect",
+      "Ġcent res",
+      "ĠK em",
+      "Ġcre ase",
+      "ĠìĿ´ë ¯¸",
+      "айÑĤ еÑģÑĮ",
+      "Ġб оÑĢ",
+      "ĠHay ır",
+      "ĠÑģ Ñĥд",
+      "Ġún ica",
+      "owa ÅĤ",
+      "Ġad her",
+      "h ua",
+      "Z Z",
+      "Ġprecis o",
+      "Ġcurrent s",
+      "Ġseason ed",
+      "ĠIo T",
+      "ĠB ishop",
+      "è¨ Ī",
+      "st ed",
+      "ĠBern ard",
+      "ì¤ ĺ",
+      "æ² »",
+      "ĠGl enn",
+      "Ġktóry m",
+      "ื à¹Ī",
+      "Ġast rolog",
+      "ĠK ot",
+      "å¤ ľ",
+      "Ġparf ois",
+      "Ġfor wards",
+      "ĠW iÄĻ",
+      "ĠÎ ĺ",
+      "Ġn ano",
+      "è» į",
+      "s ub",
+      "ĠBr ill",
+      "Ġgr it",
+      "Ġc ited",
+      "g ado",
+      "Ġmel ts",
+      "Ġfor cé",
+      "âĸĪ âĸĪ",
+      "Ġb ajo",
+      "Ġdiscret ion",
+      "° °",
+      "at ivity",
+      "Ġsitu ated",
+      "ãĥ« ãĤ¯",
+      "Ñīе е",
+      "åľ° æĸ¹",
+      "ĠпÑĢин ÑĨип",
+      "am az",
+      "Ġaqu arium",
+      "Ġdissol ve",
+      "ĠGod s",
+      "S uper",
+      "Ġam id",
+      "z k",
+      "Ġ ãģĦ",
+      "éł IJ",
+      "amp f",
+      "Ġhel a",
+      "' !",
+      "Ġdevelopment al",
+      "ĠD ise",
+      "ĠÑĢабоÑĤ аеÑĤ",
+      "Ġsnaps hot",
+      "好 好",
+      "Õ ¸",
+      "ĠY ue",
+      "ĠH ulk",
+      "ĠDo om",
+      "ĠFel ix",
+      "Ġré f",
+      "M ale",
+      "ç· Ĭ",
+      "ph ants",
+      "EN S",
+      "ĠMe chan",
+      "ĠG olf",
+      "åĨį è¦ĭ",
+      "Ġgener osity",
+      "ät ze",
+      "Ġunlock ed",
+      "Ġ ãĤĴ",
+      "íĥ ģ",
+      "ocaly pse",
+      "Al right",
+      "Ġê° ľë",
+      "Ġ×IJ× ij׾",
+      "ĠKeep ing",
+      "Ġcollabor ating",
+      "ch ief",
+      "ĠFern ando",
+      "Ġchef s",
+      "ĠíĶ¼ë ¶Ģ",
+      "Ġsk ipped",
+      "Ġperson n",
+      "Ġax e",
+      "che z",
+      "Ġextract ion",
+      "ĠA V",
+      "ĠGib bs",
+      "Ġí ľ",
+      "Ġs ı",
+      "I AM",
+      "V iew",
+      "ĠGR ANT",
+      "Ġëª ¸",
+      "Ġver ification",
+      "Ġdep icted",
+      "ĠMo z",
+      "ou x",
+      "Ġt ul",
+      "Ġsc anner",
+      "Ġcomed ian",
+      "ĠVol ks",
+      "ĠJE FF",
+      "è¨Ĥ éĸ±",
+      "§ Ħ",
+      "Ġdistract ion",
+      "r á",
+      "ĠIN TER",
+      "Ġsin cer",
+      "Ġ×ŀ× ª",
+      "Ġש ׳",
+      "Ġconstruct ive",
+      "ar f",
+      "ĠëĪ Ħë",
+      "Ġe co",
+      "r amos",
+      "Ġrenew ed",
+      "in ement",
+      "ĠU b",
+      "ĠPe pper",
+      "ì§Ģ ê°Ģ",
+      "ĠDar win",
+      "Ġmerch and",
+      "Ġv árias",
+      "è ce",
+      "N G",
+      "ĠìľĦ íķ´ìĦľ",
+      "Ġак ÑĤив",
+      "ĠUn ters",
+      "ع ÙĦ",
+      "Ġint ric",
+      "omm a",
+      "ie ving",
+      "ĠCarol ine",
+      "åĵ ģ",
+      "ĠPR ES",
+      "Ġperform er",
+      "Ġaut our",
+      "ãģ¾ãģĽ ãĤĵ",
+      "Ġutter ly",
+      "Ġsynth esis",
+      "Ġles bian",
+      "Ġretrie ve",
+      "Ġmane ira",
+      "Ġimp air",
+      "Ġment oring",
+      "ĠSoul s",
+      "ĠGo Pro",
+      "ÑĢ аÑĤÑĮ",
+      "Ġc ose",
+      "ĠSS D",
+      "I RE",
+      "Ġup front",
+      "ĠA un",
+      "Ġgam er",
+      "Ġl itt",
+      "Ġag gression",
+      "ĠLike wise",
+      "ĠBet ty",
+      "ĠD art",
+      "ĠD LC",
+      "ish ment",
+      "ìŀ¥ ìĿĦ",
+      "Ġ 对",
+      "ç» ı",
+      "c ream",
+      "ĠBaby lon",
+      "Ġn ug",
+      "br ar",
+      "Ġa ynı",
+      "am ily",
+      "b ike",
+      "ahah aha",
+      "lo yd",
+      "Ġmir a",
+      "Ġper me",
+      "ĠG aming",
+      "Ġfirm ware",
+      "M a",
+      "Ġassist ed",
+      "at ics",
+      "Ġìķŀ ìľ¼ë¡ľ",
+      "ĠM ental",
+      "niej s",
+      "ĠI z",
+      "ow Äħ",
+      "Ġt ougher",
+      "Ġde ed",
+      "èĭ ¦",
+      "Ġsty lish",
+      "ĠTool s",
+      "ĠH amp",
+      "Ġsun screen",
+      "Ġartic ulate",
+      "i ye",
+      "и ÑĦ",
+      "ĠSp read",
+      "ĠHA VE",
+      "Ġsw irl",
+      "Ġspons oring",
+      "ä» ĭ",
+      "iov ascular",
+      "mes i",
+      "Ġrelax ation",
+      "ĠÑģво иÑħ",
+      "Ġmar gins",
+      "Ġsa ÄŁ",
+      "ĠPr ide",
+      "ĠÏĦοÏħ ÏĤ",
+      "и ÑĨи",
+      "en ci",
+      "Do es",
+      "Ġcor pse",
+      "Ġend urance",
+      "Ġí ŀĺ",
+      "ì¹ ´",
+      "Ġhair cut",
+      "Ġinterrupt ed",
+      "Ġwind y",
+      "ĠC aleb",
+      "Ïģ Ïĩ",
+      "ĠPour quoi",
+      "Ġhol istic",
+      "uc lear",
+      "ĠWho le",
+      "å£ «",
+      "A ct",
+      "Ġgall on",
+      "c ade",
+      "ĠReg ional",
+      "ro ads",
+      "ĠSch ne",
+      "á ng",
+      "Ġиз мен",
+      "ãĤĪ ãģŃ",
+      "Ġmen us",
+      "Ġspl itting",
+      "Ġpr iced",
+      "ĠÎ ĵ",
+      "Ġus ername",
+      "ĠÐŀ Ñĩ",
+      "Ġcomp ressed",
+      "y in",
+      "Ġguard ian",
+      "Ġgo of",
+      "Ġcheck list",
+      "Ġinter change",
+      "Ġexped ition",
+      "Ġex tern",
+      "Ġinfra red",
+      "eng o",
+      "Ġden ying",
+      "Ġpack ets",
+      "on ent",
+      "B B",
+      "ĠInc re",
+      "Ġsin i",
+      "ÃŁ er",
+      "è g",
+      "ma al",
+      "gen eration",
+      "Ġminor ities",
+      "Ġlle var",
+      "Ġnom ination",
+      "Ġcons id",
+      "Ġ×ľ× ¢",
+      "m uÅŁ",
+      "ĠEs c",
+      "Ġnumer ator",
+      "Ġka ik",
+      "Ġktóry ch",
+      "ies en",
+      "Ġv ê",
+      "ĠUS S",
+      "ĠPri vate",
+      "Ġод но",
+      "Ġal ém",
+      "ÃŃt ulo",
+      "Ġlim b",
+      "Ġforg iven",
+      "Ġdiscl osure",
+      "ÏĦ ί",
+      "Ġning ún",
+      "Ġtherapeut ic",
+      "Ġnegoti ating",
+      "ĠN ike",
+      "ense ful",
+      "Ġin cap",
+      "Ġflag ship",
+      "t own",
+      "â Ī",
+      "ĠÏĢ ολ",
+      "Ġwol ves",
+      "Ġviol ations",
+      "ĠAr nold",
+      "Ġinterven e",
+      "Ġhe ater",
+      "Ġrecurs os",
+      "Ġma id",
+      "ê² ¼",
+      "Ġдав айÑĤе",
+      "ĠCe lebr",
+      "Ġca pe",
+      "ĠSt y",
+      "ain en",
+      "s ite",
+      "b ij",
+      "Ġп олÑĮз",
+      "Ġfr amed",
+      "Ġpublish ers",
+      "ĠÑĩ ÑĥÑĤÑĮ",
+      "Ġtempt ation",
+      "Ġcert eza",
+      "Ġex empt",
+      "ìĬ ¹",
+      "se lling",
+      "ĠT ask",
+      "ho on",
+      "ĠC oc",
+      "ĠPark s",
+      "Ġrepet ition",
+      "ĠÑĤ Ñĥда",
+      "Ġens l",
+      "ĠdeÄŁ iÅŁ",
+      "ĠOr lando",
+      "ĠMain ten",
+      "æŃ ¢",
+      "oc ument",
+      "ĠH C",
+      "Ġscoot er",
+      "Ġнап иÑģ",
+      "Ġtight er",
+      "Ġte ase",
+      "Ġremo ves",
+      "Ġkij ken",
+      "ĠÑģÑĥ ÑīеÑģÑĤв",
+      "Ġth é",
+      "ĠвÑĭ глÑıд",
+      "Ġrel ieve",
+      "Ġmit ä",
+      "Ġstation ary",
+      "ö ff",
+      "p able",
+      "Ġar ter",
+      "Ġdé f",
+      "r ative",
+      "Ġcon ect",
+      "Ġsad dle",
+      "ĠD iane",
+      "Ġcomm emor",
+      "fend im",
+      "S ÃŃ",
+      "Ġíģ ´ë",
+      "Ġman ge",
+      "at te",
+      "Ġarrog ant",
+      "Ġrobot ic",
+      "Ġgi Ãł",
+      "æĺ¯ çļĦ",
+      "Ġneighbour hood",
+      "iss on",
+      "Ġдв иж",
+      "ĠR I",
+      "ĠNorm an",
+      "b rand",
+      "am ation",
+      "Ġraz or",
+      "Ġmur ders",
+      "ĠÑĤ Ñĥ",
+      "Ġwszystk im",
+      "Ġut ilities",
+      "Ġmicros cop",
+      "ê ¿",
+      "Ġda qui",
+      "oll ar",
+      "ĠÐĶав айÑĤе",
+      "Ġann ée",
+      "Ġkilomet res",
+      "Ġhom osexual",
+      "Ġarchitect s",
+      "ãģ¡ ãģ¯",
+      "Ġni ye",
+      "L ER",
+      "Ġmicro phones",
+      "ĠSt unden",
+      "Ġconsecut ive",
+      "iend a",
+      "v änd",
+      "D ER",
+      "Ġlif ts",
+      "ĠMe at",
+      "Ġsave z",
+      "íĸ Īëįĺ",
+      "M en",
+      "Ġdism ant",
+      "ê±°ë ¥¼",
+      "Ġins ulation",
+      "Ġsc all",
+      "Ġsp ooky",
+      "Ġpar c",
+      "Ġball et",
+      "ĠWhats App",
+      "Ġfr anc",
+      "Ġdeliber ate",
+      "Ġíħ Į",
+      "Ġm ars",
+      "ĠZ ur",
+      "P r",
+      "dis ciplinary",
+      "Ġobs ession",
+      "м е",
+      "Ġmarch ing",
+      "ĠEmer gency",
+      "ig uous",
+      "Ġs zy",
+      "ĠL ands",
+      "Ġboard ing",
+      "ĠпоÑĩ ÑĤи",
+      "Ġenv y",
+      "Ġcompassion ate",
+      "Ġmer ci",
+      "Ġdes irable",
+      "d ale",
+      "Ġcan ım",
+      "ĠAnt ar",
+      "tem ps",
+      "Ġconfig ured",
+      "ĠComp ared",
+      "ne h",
+      "ic ating",
+      "Ġnic kel",
+      "ÙĪ ÙĤ",
+      "Ùĥ ÙĪÙĨ",
+      "op es",
+      "Ġform ulas",
+      "ĠÐķ ÑģÑĤÑĮ",
+      "Ġpo bl",
+      "ĠP J",
+      "ĠL ud",
+      "ä»Ĭ åĽŀ",
+      "ĠBr id",
+      "ĠH og",
+      "ĠBr is",
+      "J en",
+      "Ġshad ing",
+      "ĠY as",
+      "Ġdistur bed",
+      "Ġrecomm ending",
+      "Ġc é",
+      "ĠH OW",
+      "ìĹĪ ìĸ´",
+      "Ġrevers ed",
+      "ĠInteresting ly",
+      "iox id",
+      "åħ Ń",
+      "Ġìĺ¤ ì¼ĢìĿ´",
+      "ế u",
+      "x x",
+      "Ġou ais",
+      "ĠYouT ubers",
+      "ĠR osa",
+      "ĠH aupt",
+      "j adi",
+      "Ġvlog s",
+      "Ġcult ura",
+      "ĠLeaders hip",
+      "ĠH ep",
+      "Ġill um",
+      "´ë ıĻ",
+      "Ġcustom ized",
+      "Ġmar ca",
+      "Ġqu atro",
+      "Ġн аг",
+      "ĠSpace X",
+      "ĠE igen",
+      "ast ing",
+      "ĠolduÄŁ u",
+      "Ġfor ts",
+      "ãģ ī",
+      "r iment",
+      "ien cia",
+      "Ġten ir",
+      "ro ffen",
+      "Ġ197 9",
+      "Ġc ie",
+      "ĠëIJĺ ê³ł",
+      "Ġes cri",
+      "ÏĮ ÏĤ",
+      "íı ¬",
+      "uz zy",
+      "C ong",
+      "ìĿ¸ ìĿ´",
+      "G reat",
+      "s il",
+      "é ch",
+      "ãģ¨ ãģĭ",
+      "Ġmult ic",
+      "ĠDis k",
+      "² ķ",
+      "Ġfaz la",
+      "Ġle vant",
+      "Ġab ajo",
+      "ur ry",
+      "st ru",
+      "Ġ먹 ëĬĶ",
+      "Ġaccess ory",
+      "Ġдв иг",
+      "ĠR id",
+      "20 19",
+      "Ġdown stream",
+      "æķ ¸",
+      "Ġk az",
+      "ut an",
+      "Ġchar coal",
+      "Ġa fect",
+      "w u",
+      "Ġcontext s",
+      "Ġfe ared",
+      "ĠìĦ ¤",
+      "Ġhist ories",
+      "Ġf as",
+      "ens ible",
+      "Ġcoco a",
+      "ill ar",
+      "ge ons",
+      "Ġspiritual ity",
+      "ĠP ew",
+      "Ġpharm acy",
+      "Ġpass ions",
+      "Ġb os",
+      "Ġall á",
+      "Ġthri ving",
+      "ĠRe act",
+      "Ġoccup y",
+      "Ġwithdraw al",
+      "Ġallow ance",
+      "ĠFra ktion",
+      "Ġbud dies",
+      "Ġid le",
+      "Ġdissol ved",
+      "Ġpreval ent",
+      "Ġmil itar",
+      "Ġsens ing",
+      "Ġpo jaw",
+      "Ġanc ora",
+      "Ġabund ant",
+      "Ġha irst",
+      "ãģĤ ãĤĮ",
+      "Ġtw ee",
+      "Ġnäch ste",
+      "ĠMöglich keit",
+      "Ġho o",
+      "uff icient",
+      "Ġfant ast",
+      "Ġed ible",
+      "Ġëĸ¨ ìĸ´ì",
+      "ìĽ ĥ",
+      "Ġve in",
+      "uc ci",
+      "Ġdevot ion",
+      "Ġconce aler",
+      "in come",
+      "Ġrecy cled",
+      "ĠìĬ¤í ĥĢ",
+      "Ġpont os",
+      "Ġdess us",
+      "Ġvé rit",
+      "Ġreflect ions",
+      "ĠA A",
+      "Ġtake away",
+      "b are",
+      "ĠCont act",
+      "e il",
+      "ĠHe ar",
+      "Ġmir ac",
+      "ĠGer ilim",
+      "ĠÑģам Ñĭй",
+      "Ġv ivo",
+      "Ġkilogram s",
+      "ĠCr im",
+      "û t",
+      "7 8",
+      "Ġsincere ly",
+      "ra z",
+      "Ġë³ µ",
+      "Ġarri v",
+      "Ġconcept ion",
+      "ĠPers ian",
+      "Ġsj äl",
+      "Ġst arring",
+      "ĠìķĦë ¬´",
+      "ĠFore ver",
+      "е ÑģÑĤÑĮ",
+      "Ġve il",
+      "Ġsubt it",
+      "od ka",
+      "ĠоÑĤно ÑĪ",
+      "Ġcook s",
+      "ен Ñı",
+      "K ay",
+      "Ġni ños",
+      "ĠPh one",
+      "Ġstitch ing",
+      "Ġfinger print",
+      "é¢ ĺ",
+      "λ ά",
+      "Ġded icate",
+      "ĠL ob",
+      "Ġblack s",
+      "ĠB le",
+      "b out",
+      "ĠÄij ang",
+      "Ġe ks",
+      "Ġsqu ash",
+      "ĠK ü",
+      "od i",
+      "Ġn Æ°á»Ľc",
+      "Ġvoy age",
+      "Ġplay ful",
+      "ĠØ¥ ÙĦÙī",
+      "an ic",
+      "Ġcondem n",
+      "ĠB öyle",
+      "ĠPol ize",
+      "ãĤ¿ ãĥ¼",
+      "Ġay uda",
+      "Ġp am",
+      "à¹Ħ à¸Ľ",
+      "ĠK athy",
+      "ед ин",
+      "нов а",
+      "Ġbr ig",
+      "eg er",
+      "Ġe agle",
+      "Ġvis ions",
+      "ĠíķŃ ìĥģ",
+      "Ġsh itty",
+      "Ġh ott",
+      "ĠBr itt",
+      "ut ors",
+      "ENT E",
+      "æĽ ²",
+      "Ġph on",
+      "ĠB ing",
+      "Ġпод деÑĢж",
+      "spr ing",
+      "æĸ ¯",
+      "et ten",
+      "Ġpil gr",
+      "Ġed iyor",
+      "енÑĤ Ñĭ",
+      "ag gio",
+      "Ġj ul",
+      "Ġcomp rend",
+      "te il",
+      "ĠØ ²",
+      "Ġperform ers",
+      "Ġinf amous",
+      "ĠM K",
+      "ç ª",
+      "æ³ ģ",
+      "ot le",
+      "e ff",
+      "ĠH ash",
+      "Ġcow ard",
+      "ĠB RA",
+      "ĠD D",
+      "Ġcom ida",
+      "Ġpl ata",
+      "Ġfl ap",
+      "ĠMe hr",
+      "rib ution",
+      "ĠY emen",
+      "Ġmyster ies",
+      "ĠÄ° yi",
+      "Ġst ell",
+      "Ġeyel iner",
+      "Ġdel es",
+      "Ġnail ed",
+      "Ġillness es",
+      "Ġst acks",
+      "Ġtrabaj ar",
+      "fl ower",
+      "ci u",
+      "Ġcr ude",
+      "Ġsubstant ially",
+      "Ġhome m",
+      "Ġnep hew",
+      "Ġstamp s",
+      "Ġcar bs",
+      "ÑĮ ÑĤе",
+      "mo oth",
+      "Ġtun nels",
+      "ac ie",
+      "æ³ ¢",
+      "ĠSe ñ",
+      "ĠH era",
+      "ĠìķĦëĭĪ ìĹIJìļĶ",
+      "ĠWy oming",
+      "ĠHD MI",
+      "ĠL is",
+      "u ción",
+      "Ġste er",
+      "о Ñİ",
+      "иÑĤ а",
+      "N T",
+      "Ġìĸ¼êµ ´",
+      "Ġpal ms",
+      "Ġne on",
+      "ов аниÑı",
+      "Ġfilter ing",
+      "Ġjou er",
+      "ĠH ö",
+      "Ġне Ñģ",
+      "ê²ł ìĸ´ìļĶ",
+      "Ġ8 1",
+      "Ġstory line",
+      "Ġprz ep",
+      "Ġthank ing",
+      "ĠBo eing",
+      "Ġsoft ly",
+      "j em",
+      "алÑĮ нÑĭÑħ",
+      "Ġflash light",
+      "Ġп Ñĥ",
+      "ĠW OMAN",
+      "ắ c",
+      "ÃŃ ch",
+      "Ġlux urious",
+      "Ġw ün",
+      "Ġimpact ful",
+      "Ġcons on",
+      "re u",
+      "ir ring",
+      "if ter",
+      "Ġconstitu ents",
+      "èIJ ½",
+      "Ġ9 4",
+      "ĠT ou",
+      "g om",
+      "ĠìĥĿê°ģ ìĿĦ",
+      "Ġstere otypes",
+      "Ġmoż li",
+      "åĪĨ 享",
+      "Ĥ ¨",
+      "Ġpencil s",
+      "ĠÑģл ож",
+      "Ġih rem",
+      "ĠBes ch",
+      "ĠK oh",
+      "ĠEnt scheid",
+      "Ġle k",
+      "Ġför s",
+      "Ġtotal mente",
+      "Ġlive ly",
+      "Ġent ropy",
+      "Ġdisc ern",
+      "ĠÐĹ Ð½Ð°",
+      "Ġdo v",
+      "Ġmyth ology",
+      "è¨ĺ å¾Ĺ",
+      "apan ese",
+      "Ġapprox imate",
+      "аÑĤ ив",
+      "if iable",
+      "ĠSe o",
+      "åĢ Ĵ",
+      "´ìĭ¬ íŀĪ",
+      "Ġìĺ ·",
+      "Ġtempor al",
+      "Ġi T",
+      "Ġest at",
+      "к им",
+      "Ġspr ink",
+      "Ġgr und",
+      "Ġinfant ry",
+      "Ġsch affen",
+      "ç´ Ħ",
+      "Ġan k",
+      "ri ages",
+      "ĠYe on",
+      "ĠMor oc",
+      "Ġinv asive",
+      "ģ Ķ",
+      "Ġparent ing",
+      "ĠR is",
+      "ib ile",
+      "Ġmod s",
+      "å½ ¢",
+      "ĠпÑĢов еÑĢ",
+      "ĠTh ing",
+      "ĠWhere ver",
+      "Ġacknowled ging",
+      "Ġpa wn",
+      "um mer",
+      "or b",
+      "6 9",
+      "Ġretr ouve",
+      "Ġrel ies",
+      "ĠHigh way",
+      "Ġa we",
+      "ãģ§ãģĻ ãģĭ",
+      "ita ire",
+      "Ġapplic ant",
+      "Ġais le",
+      "w orm",
+      "Ġpay load",
+      "Ġcar re",
+      "ĠB ach",
+      "æł ¼",
+      "Ġì¹ľ 구ë",
+      "ни е",
+      "Ġit ÃŃs",
+      "onna ise",
+      "s ol",
+      "èı ¯",
+      "alg ia",
+      "Ġrock ing",
+      "Ġbest en",
+      "rit es",
+      "^ ^",
+      "ин ой",
+      "Ġba ixo",
+      "Ġ기 ìĸµ",
+      "оÑĤ ÑĢи",
+      "s im",
+      "Ġinc arn",
+      "ëĭ¤ ìĿĮ",
+      "Ġl ick",
+      "s ided",
+      "Ġ7 1",
+      "f order",
+      "Ġreson ance",
+      "Ġte gen",
+      "Ġmet aph",
+      "ows er",
+      "Ġ×IJ× ł×Ĺ׳×ķ",
+      "? ãĢį",
+      "Ġsp ielen",
+      "Ġvoll ey",
+      "ĶìĿ´íģ¬ ìĹħ",
+      "lo oked",
+      "Ġsent enced",
+      "Ġmultip lying",
+      "Ġide als",
+      "Ġwahr scheinlich",
+      "Ġdepos its",
+      "bil ir",
+      "Ġeff et",
+      "ill on",
+      "Īë §Į",
+      "Ġtestim on",
+      "Ġz awsze",
+      "ĠпÑĢоÑĨ еÑģÑģ",
+      "ĠL av",
+      "ä¸į éĮ¯",
+      "Ġtrava iller",
+      "Ġla isse",
+      "ĠMount ains",
+      "ĠÑĢ об",
+      "Ġexam ined",
+      "it us",
+      "W as",
+      "л Ñĭ",
+      "Ġattrib uted",
+      "ĠìĬ ¹",
+      "ĠBar on",
+      "Ġg ep",
+      "Ġatt ent",
+      "ĠColl ection",
+      "Ġthe at",
+      "ĠC ai",
+      "Ġwell s",
+      "Ġhuman o",
+      "çĹ ħ",
+      "ĠH ast",
+      "ĠÑħоÑĤ Ñı",
+      "cz as",
+      "Ġperm its",
+      "Ġle gg",
+      "Ġe po",
+      "ĠF en",
+      "Ġth i",
+      "ĠF oi",
+      "Ġé lect",
+      "Ġ8 3",
+      "Ġover th",
+      "Ġ è¬Ŀè¬Ŀ",
+      "Ġten ant",
+      "è² ·",
+      "N ext",
+      "Ġpra ised",
+      "sec urity",
+      "ĠImp act",
+      "为 ä»Ģä¹Ī",
+      "Ġv ouch",
+      "Ġneg ó",
+      "Ġun ve",
+      "Ġcritic ize",
+      "ĠKen ya",
+      "Ġtact ic",
+      "Ġlo gr",
+      "Ġpo is",
+      "Ġpap a",
+      "spe aks",
+      "ðŁ ij",
+      "isp ers",
+      "Ġsur plus",
+      "Ġcold er",
+      "åį Ĺ",
+      "åIJ ¬",
+      "pl ets",
+      "ĠV ienna",
+      "ĠLe ad",
+      "Ġaer ial",
+      "ĠT ah",
+      "енÑĤ ов",
+      "ĠGree ks",
+      "C am",
+      "Ġmá xim",
+      "Ġk uin",
+      "ch io",
+      "Ġdemonst rates",
+      "an os",
+      "ĠC ert",
+      "ĠÑį н",
+      "Ġblog s",
+      "ĠìĦľ ìļ¸",
+      "Ġbe ams",
+      "ик ов",
+      "Ġprompt ed",
+      "Ġfright ening",
+      "ĠPors che",
+      "ãģĪ ãģ¦",
+      "lar ını",
+      "Ġch illing",
+      "is phere",
+      "Ġfl ashing",
+      "ĠK ard",
+      "b read",
+      "Ġex h",
+      "Ġty cker",
+      "Ġec ological",
+      "ĠMa e",
+      "Ġ×ŀ×IJ ×ķ×ĵ",
+      "ĠëĤ ĺëıĦ",
+      "л он",
+      "ys s",
+      "Ġper gunt",
+      "Ġpri x",
+      "izz ard",
+      "Ġcan cers",
+      "Ġ9 1",
+      "s usp",
+      "ĠIt em",
+      "ÅŁ a",
+      "Ġp est",
+      "Ġtak Äħ",
+      "Ġl ymph",
+      "ĠPat ri",
+      "f ill",
+      "Ġrec onna",
+      "Ġoptim ism",
+      "Ġmim ic",
+      "Ġì² ľ",
+      "ĠMad ame",
+      "oc y",
+      "l ining",
+      "åijĬ 訴",
+      "erm e",
+      "Ġfold ers",
+      "Ġcz ÅĤ",
+      "uch ar",
+      "Ġcur so",
+      "Ġbre ach",
+      "ни ÑĤÑĮ",
+      "Ġp amiÄĻ",
+      "Ġel ig",
+      "Ġaut op",
+      "F low",
+      "Ġprogram med",
+      "ĠPro cess",
+      "Ġfig ur",
+      "ĠS F",
+      "ĠE les",
+      "Ġprogram mes",
+      "Ġdiz zy",
+      "ìĭľ ê°Ħ",
+      "Ġли бо",
+      "Ġsn iff",
+      "ĠSeb astian",
+      "ĠH ye",
+      "Ġ4 000",
+      "Ġperm ite",
+      "æ¢ Ŀ",
+      "Ġза Ñī",
+      "Ġgu it",
+      "ĠD ais",
+      "Ġaccord ance",
+      "Ġmod ular",
+      "ogene ous",
+      "æĭ į",
+      "Ġpou quinho",
+      "Ġart illery",
+      "Ġlub ric",
+      "Ġvol can",
+      "ĠN H",
+      "ðŁ ¤",
+      "Ġde an",
+      "R h",
+      "Ġminist re",
+      "åĿ IJ",
+      "ĠIn v",
+      "ĠBul gar",
+      "ĠD aten",
+      "è İ",
+      "I m",
+      "Ġorigin ated",
+      "ĠN ixon",
+      "inte gr",
+      "Ġlack s",
+      "ĠN acht",
+      "ìĸ´ë Ĥĺ",
+      "cam era",
+      "Ġrad ish",
+      "ki ye",
+      "Ġang es",
+      "Ġpré f",
+      "j uk",
+      "ĠBe e",
+      "ĠB U",
+      "ĠвоÑģ п",
+      "ĠB T",
+      "ê mes",
+      "ĠSt ück",
+      "ĠIn k",
+      "æĪĸ èĢħ",
+      "ĠSerge ant",
+      "ĠMult ip",
+      "Ġhiç bir",
+      "ĠС ам",
+      "ĠD é",
+      "ol ph",
+      "ìĸ ¸",
+      "Ġimp at",
+      "ĠìķĬ ê³ł",
+      "ĠÑĤак ого",
+      "ĠнавеÑĢ ное",
+      "Ġunpredict able",
+      "Ġm end",
+      "ĠìĹĨ ìĸ´ìļĶ",
+      "Ġjakie ÅĽ",
+      "Ġann i",
+      "Ġdon né",
+      "ĠK irsty",
+      "Ġrectang ular",
+      "Ġempez ar",
+      "ĠEx change",
+      "ê° Ķ",
+      "Ġé conom",
+      "ãģĵ ãĤĵ",
+      "el in",
+      "re ibt",
+      "Ġ×Ķ× ¤",
+      "Ġc emetery",
+      "Ġespañ ol",
+      "ol in",
+      "лÑİ Ð´",
+      "Ġgr âce",
+      "all en",
+      "ĠPh ilos",
+      "ĠEr st",
+      "Ġìĥ Ī",
+      "ĠV id",
+      "G ive",
+      "O H",
+      "μ ο",
+      "ĠP are",
+      "Ġmetabol ism",
+      "Ġma ple",
+      "Ġax le",
+      "ĠD y",
+      "Ġkomm e",
+      "Ïİ Î½",
+      "Ġgreat ness",
+      "Ġver ified",
+      "Ġsp é",
+      "ĠFahren heit",
+      "ĠB ren",
+      "ĠConf eder",
+      "Ġhist oire",
+      "Ġelimin ating",
+      "ĠAd ding",
+      "ĠAb i",
+      "æĿ İ",
+      "Ġhospital ity",
+      "t im",
+      "Ġbon ito",
+      "Ġpart es",
+      "ĠдÑĢÑĥг иÑħ",
+      "ĠSh ay",
+      "ĠS ed",
+      "Ġreg rets",
+      "Ñı ми",
+      "Ġten ants",
+      "éĢ Ł",
+      "ĠP TS",
+      "Ġdev i",
+      "ĠL ate",
+      "ue z",
+      "Ġsö yl",
+      "ãĤ »",
+      "Ġìŀ¬ë °Į",
+      "Ġtogg le",
+      "Ġmas king",
+      "алÑĮ ного",
+      "Ġpers ön",
+      "Ġamer ican",
+      "f ik",
+      "ĠR GB",
+      "ens on",
+      "ĠK A",
+      "ww ww",
+      "ĠÑĢ ег",
+      "met ics",
+      "Ġeduc ator",
+      "ãĤ· ãĥ«ãĤ¯",
+      "p ark",
+      "елÑĮ зÑı",
+      "ar us",
+      "ÑĢ еÑĤ",
+      "Ġfe ito",
+      "Ġcho ir",
+      "Ġlar go",
+      "Ġe ens",
+      "Ġwat ts",
+      "ĠSing le",
+      "Ġsuscept ible",
+      "ic er",
+      "Ġв клÑİÑĩ",
+      "Ġp us",
+      "íĻ ĺ",
+      "E ng",
+      "Ġfant as",
+      "Ġspecific ation",
+      "Ġconfront ed",
+      "ĠColumb us",
+      "ив еÑĤ",
+      "ar ım",
+      "Ġcaffe ine",
+      "mun ition",
+      "Ġmig rants",
+      "l ide",
+      "it ations",
+      "ĠG eme",
+      "Ạ«",
+      "Ġpl anner",
+      "Ġstim ulate",
+      "Ġapro xim",
+      "ce u",
+      "ĠN om",
+      "Ġv og",
+      "ĠÑĢ аÑģÑĤ",
+      "Ġense ñ",
+      "Ġsell ers",
+      "Ġgut en",
+      "z d",
+      "C al",
+      "Ġdescri pt",
+      "Ġrecon ciliation",
+      "z inho",
+      "á¹ĩ a",
+      "ãģĺãĤĥ ãģĤ",
+      "acy j",
+      "ĠCO L",
+      "s aw",
+      "ĠíĻķ ìĿ¸",
+      "Ġvar it",
+      "Ġpartner ing",
+      "Ġdet ention",
+      "Ġbomb ing",
+      "c lapping",
+      "ien cies",
+      "ond u",
+      "AM E",
+      "Ġê°Ļ ìĬµëĭĪëĭ¤",
+      "c ÃŃa",
+      "ĠпоÑģ ÑĤо",
+      "ĠAS MR",
+      "Ġhome page",
+      "Ġsi è",
+      "an tha",
+      "ĠP oll",
+      "Ġ igen",
+      "cy ch",
+      "Ġê°ij ìŀIJ기",
+      "Ġconsider ably",
+      "ä»ĸ çļĦ",
+      "ĠAr ist",
+      "Ġwith stand",
+      "Ġqual itative",
+      "ĠK raft",
+      "ĠÑį лекÑĤ",
+      "ĠBe ad",
+      "екÑĤ ив",
+      "Ġcr ushing",
+      "ì³ IJ",
+      "Ġnav y",
+      "ÙĪ Úº",
+      "s ho",
+      "Ġo ak",
+      "ipp ers",
+      "Ġso ils",
+      "Ġpig ment",
+      "Ġev itar",
+      "ãĥ ĩ",
+      "Ġf use",
+      "ĠD ale",
+      ": \"",
+      "Ġcompl ètement",
+      "Ġke l",
+      "๠Ĩ",
+      "Ġqu atre",
+      "ĠU M",
+      "Ġë§ IJë",
+      "æł ¹",
+      "ÃŃ r",
+      "Ġle isure",
+      "ĠH ousing",
+      "Ġfold s",
+      "est ion",
+      "AR S",
+      "Ġm ash",
+      "urp ose",
+      "Ġaccum ulated",
+      "ĠSt uff",
+      "èª ŀ",
+      "Ġtap es",
+      "ĠÑģ илÑĮно",
+      "ĠLO VE",
+      "Ġ198 2",
+      "Ġsc ars",
+      "Ġcapital ist",
+      "ĠN ed",
+      "Ġsoft en",
+      "Ġnot ably",
+      "Ġforcé ment",
+      "ĠRa um",
+      "Ġнеоб Ñħод",
+      "Ġtrad emark",
+      "Ġfert ig",
+      "Ġ? !",
+      "æĹ ł",
+      "Ġreinfor ced",
+      "Ġre charge",
+      "ĠPut ting",
+      "Ġvill ains",
+      "Ġhand ic",
+      "Ġadvertis ement",
+      "ت ÙĬ",
+      "ĠÑģ Ñĥм",
+      "ĠR iley",
+      "×ķ× ij×",
+      "äº ¬",
+      "O s",
+      "Ø§Ø ²",
+      "B oy",
+      "Ġsqu ish",
+      "ock et",
+      "Ġtest ify",
+      "æ¼ Ķ",
+      "Ġ×ľ× ŀ×",
+      "Ġм аÑģÑģ",
+      "man uel",
+      "ĠArk ansas",
+      "if fe",
+      "Ġanalyst s",
+      "ĠDe af",
+      "Ġj ó",
+      "Ġgrocer ies",
+      "ĠWhe el",
+      "ĠÑĢ иÑģ",
+      "Ġc òn",
+      "ĠC ob",
+      "Ġpris ons",
+      "è ve",
+      "ĠCab inet",
+      "Ġpos ed",
+      "Ġguer re",
+      "ĠL loyd",
+      "Ġcl erk",
+      "Ġcr ises",
+      "ĠSh o",
+      "ĠO re",
+      "ĠFoot ball",
+      "ĠAd vis",
+      "ĠZh eng",
+      "è į",
+      "ĠAM Y",
+      "Ġun for",
+      "Ġmon aster",
+      "Ġcomp ile",
+      "Ġimm ortal",
+      "at able",
+      "Ġpar ano",
+      "Ġt iver",
+      "ĠStep h",
+      "ĠFu ÃŁ",
+      "Ġdisc ontin",
+      "Ġr ipe",
+      "Ġhack ing",
+      "Ġs iendo",
+      "Ġsegu ro",
+      "alt res",
+      "Ġand eres",
+      "Ġë ¦¬ë",
+      "Ġexp orts",
+      "æŃ ¥",
+      "Ġtab ii",
+      "Ġ기 ëĭ¤ë",
+      "Ġbother ing",
+      "Ġpick le",
+      "ĠBRI AN",
+      "Ġalt ar",
+      "ĠпÑĢи б",
+      "Ġtransfer ring",
+      "ĠV ors",
+      "ĠÙĩ ÙĪ",
+      "ĠZ a",
+      "ĠFr ances",
+      "Ġbrow se",
+      "em it",
+      "Ġche wing",
+      "ĠFred dy",
+      "Ġedit ors",
+      "ä lle",
+      "Ġí ĮĢ",
+      "ĠS que",
+      "ĠC ultural",
+      "aw k",
+      "ĠS ache",
+      "ĠCar bon",
+      "ắ t",
+      "F L",
+      "ĠN GO",
+      "pe ÅĤ",
+      "ĠS ou",
+      "Ġh vor",
+      "un intelligible",
+      "Ġë² ķ",
+      "ĠÂ °",
+      "i in",
+      "Ġ×¢ ×Ŀ",
+      "Ġder rière",
+      "Ġczy m",
+      "ĠAp ost",
+      "Ġregard er",
+      "Ġag rade",
+      "ĠC andy",
+      "Ġma re",
+      "Ġintrodu ces",
+      "bird s",
+      "Ġuniqu ely",
+      "Ġm uk",
+      "Ġcook er",
+      "Ġcrew s",
+      "Ġje ito",
+      "ER T",
+      "¶ Ħë",
+      "n isse",
+      "Ġe f",
+      "Ġcart e",
+      "ĠY ak",
+      "ĠP AT",
+      "и но",
+      "bok ki",
+      "Ġm ates",
+      "Ġdist int",
+      "Ġì½Ķë¡ľ ëĤĺ",
+      "Ġy ıl",
+      "Ġκ άν",
+      "Ġconfigur ations",
+      "eng a",
+      "re cht",
+      "H appy",
+      "ãĤĦ ãģ£ãģ¦",
+      "in vest",
+      "Ġreconst ruct",
+      "ĠÑįÑĤ омÑĥ",
+      "Ġmos que",
+      "ra um",
+      "Ġvoy ez",
+      "ĠN BC",
+      "ĠìŀIJ ìĭł",
+      "Ġstur dy",
+      "Ġк ап",
+      "Ġans ch",
+      "al id",
+      "Ġmas ih",
+      "ĠR EP",
+      "Ġì½ Ķë",
+      "Ġded uct",
+      "Ġsal ir",
+      "w urf",
+      "il ot",
+      "ĠM utter",
+      "old s",
+      "ĠF EMA",
+      "ĠB ib",
+      "Ġneighb oring",
+      "Ġbl iss",
+      "Ġíĺ ¼",
+      "ли ÑģÑĮ",
+      "ĠÑĤÑĢ еб",
+      "Ġ å°±æĺ¯",
+      "Ġgren ade",
+      "Ġe gal",
+      "Ġfin ely",
+      "Ġpet als",
+      "Ġke er",
+      "Ġch yba",
+      "Ġsk ipping",
+      "Ġth irteen",
+      "Ġgrav y",
+      "ĠS AT",
+      "6 1",
+      "Ġн ог",
+      "Ġmin s",
+      "IT E",
+      "Ġso zial",
+      "íķĺë ©´ìĦľ",
+      "rukt ur",
+      "Ġвозм ож",
+      "Ġоп ÑıÑĤÑĮ",
+      "Ġar th",
+      "ĠCub an",
+      "Ġtre asures",
+      "Ġfertil izer",
+      "Ġawak ening",
+      "Ġë°± ìĭł",
+      "Ġr all",
+      "Ġdep ict",
+      "ĠP ablo",
+      "Ġninete en",
+      "Ġw att",
+      "Ġentire ty",
+      "K S",
+      "ĠWood s",
+      "S ch",
+      "ĠÚ© ÙĪ",
+      "ĠD ry",
+      "ãģ ŀ",
+      "u ve",
+      "Ġreconst ruction",
+      "Ġanat omy",
+      "Īë ¥¼",
+      "Ġb aba",
+      "Ġlisten er",
+      "Ġshar pen",
+      "ĠPer u",
+      "ĠвÑĭ з",
+      "Ġrecre ation",
+      "Ġiniti ate",
+      "Ġcal or",
+      "ĠN aj",
+      "ge e",
+      "ĠFe els",
+      "ĠSnap chat",
+      "ĠT et",
+      "ĠN est",
+      "ĠD af",
+      "ĠFin ish",
+      "ĠÑĤак им",
+      "ú c",
+      "iz ens",
+      "Ġsp ins",
+      "Ġemb ry",
+      "Ġpass ages",
+      "Ġc ient",
+      "Ġjust ification",
+      "ä»ĸ 說",
+      "Ġolm az",
+      "Ġflood ed",
+      "Ġemo ji",
+      "Ġembr acing",
+      "Ġdisc ard",
+      "ĠBas ic",
+      "ag og",
+      "ĠìľĦ íķ´",
+      "Ġas ylum",
+      "er in",
+      "Ġf im",
+      "Ġnin ja",
+      "Ġautom ate",
+      "Ġaller gic",
+      "ÿÿ ÿÿ",
+      "am am",
+      "Ġм аÑĢ",
+      "ĠO i",
+      "ä us",
+      "Ġin duct",
+      "ĠB EN",
+      "Ġz ÅĤ",
+      "Ġkaż dy",
+      "ĠAM P",
+      "n ÄĽ",
+      "S ure",
+      "Ġqu il",
+      "Ġespe c",
+      "ro k",
+      "BS CRI",
+      "Ġlie be",
+      "p us",
+      "ach sen",
+      "Ġcr icket",
+      "ëĬ IJ",
+      "ĠFr ame",
+      "ekk ür",
+      "ar b",
+      "Ġp ÅĻ",
+      "иÑģ Ñģ",
+      "Ġzeg gen",
+      "Ġdou bles",
+      "ĠD re",
+      "t est",
+      "ins p",
+      "bo ys",
+      "Ġm ão",
+      "ĠVer se",
+      "Ġmus cular",
+      "ĠMA LE",
+      "Ġd ulu",
+      "Ġoccas ional",
+      "L o",
+      "conom ic",
+      "Ġv ak",
+      "Ġrem edy",
+      "å¤ ł",
+      "ĠâĻªâĻª âĻª",
+      "ve m",
+      "Ġön em",
+      "ĠkarÅŁ ı",
+      "ĠSh arp",
+      "h ur",
+      "Ġë°© ë²ķ",
+      "Ġgrand son",
+      "Ġakt iv",
+      "ĠTh rones",
+      "ĠìķĪ ìĹIJ",
+      "Ġto ts",
+      "Ġsub d",
+      "ĠPa ula",
+      "Ġgra ves",
+      "ĠB rent",
+      "Ġник ÑĤо",
+      "Ġsö z",
+      "Ġcre c",
+      "ĠVlad imir",
+      "çĸ «",
+      "Ġп ой",
+      "Ġ\" -",
+      "Ġp sy",
+      "at ri",
+      "id an",
+      "Ġa ún",
+      "Ġstandard ized",
+      "ì¹ ĺë",
+      "Ġк ÑĢов",
+      "ĠZh u",
+      "s omething",
+      "Ġ7 50",
+      "Ġmuj eres",
+      "Ġa it",
+      "éĹ ´",
+      "ag u",
+      "Ġcorrect ed",
+      "ik ka",
+      "el ed",
+      "ĠCare er",
+      "ow ym",
+      "Ġroomm ate",
+      "Ġdescend ants",
+      "ĠNapole on",
+      "ĠÐĶ о",
+      "íĸĪ ìĸ´ìļĶ",
+      "Ġbun un",
+      "ĠMich a",
+      "ç· ļ",
+      "Ġdesc ob",
+      "P I",
+      "Ġpalab ra",
+      "Ġtrack ed",
+      "Ġdepend ence",
+      "ĠBar ack",
+      "åģ ĩ",
+      "Ġfert ility",
+      "ĠSouth west",
+      "Ġincom plete",
+      "Ġcomun ic",
+      "Ġcomp ris",
+      "ĠRest aur",
+      "Ġac ron",
+      "κ α",
+      "Ġapprent ices",
+      "Ġmus st",
+      "ĠA br",
+      "Ġpent ru",
+      "ĠCons ort",
+      "ĠAve c",
+      "Ġdum plings",
+      "L R",
+      "Ġwszystk ie",
+      "Ġsw amp",
+      "н ев",
+      "ugg le",
+      "Ġwater color",
+      "Ġprot on",
+      "ĠEspa ña",
+      "ock ing",
+      "ов ал",
+      "Ġtak im",
+      "V ery",
+      "Ġdement ia",
+      "ĠÅŁey i",
+      "J ac",
+      "ĠMac Book",
+      "ĠL iv",
+      "ffic ients",
+      "ĠH unt",
+      "Ġover lay",
+      "æĦŁ 覺",
+      "ĠSky pe",
+      "p unkt",
+      "Ġconf ined",
+      "ĠAd rian",
+      "ر Ùĥ",
+      "ĠJe ep",
+      "Ġenqu anto",
+      "Ġan est",
+      "оÑĤ веÑĤ",
+      "Ġм енÑĮ",
+      "Ġirrig ation",
+      "á»ij n",
+      "Ġeight een",
+      "ĠP on",
+      "Ġresc ued",
+      "Ġ198 3",
+      "r ü",
+      "ja e",
+      "ĠJe ong",
+      "Ġamazing ly",
+      "ĠF DP",
+      "Ġback stage",
+      "c ue",
+      "ĠÏĥÏĦη ν",
+      "ĠاÙĦØ µ",
+      "Ġlivest ock",
+      "ĠW arner",
+      "Ġmaj ors",
+      "ãĥģ ãĥ£",
+      "Ġcooper ative",
+      "ĠBr ady",
+      "ra ined",
+      "rie b",
+      "Ġ×ij× ŀ×",
+      "Ġдов олÑĮно",
+      "ĠF E",
+      "Ġle aked",
+      "ĠMerc ury",
+      "Ġpersu ade",
+      "Ġtransform er",
+      "ĠNor weg",
+      "ĠìĹ¬ë Ł¬",
+      "Ġzrobi Äĩ",
+      "Ġcard iovascular",
+      "ĠCr ash",
+      "Ġg ossip",
+      "а ÑģÑĤÑĮ",
+      "Ġì ª½",
+      "Ġsw ept",
+      "ĠH orn",
+      "ĠAt é",
+      "Ġbu kan",
+      "ĠK aw",
+      "K Y",
+      "ĠSt ories",
+      "G ary",
+      "Ġgard ening",
+      "ĠQuick ly",
+      "ĠFal con",
+      "Ġov at",
+      "c ı",
+      "ĠCom plet",
+      "ĠD ate",
+      "ĠпÑĢ им",
+      "Ġlä uft",
+      "ĠAud rey",
+      "ĠW ent",
+      "Ġpel ÃŃcul",
+      "Ġcar riage",
+      "Ġun acceptable",
+      "ny mi",
+      "ĠÑģл ÑĭÑĪ",
+      "Ġter re",
+      "uell ement",
+      "EE EE",
+      "Ġpharm ac",
+      "h ões",
+      "Ġz ich",
+      "Ġmig rate",
+      "ĠF ry",
+      "ñ ana",
+      "ĠM uito",
+      "EO VER",
+      "Ġfort ress",
+      "ĠCom pan",
+      "ĠJ SON",
+      "ord nung",
+      "Ġw arto",
+      "Ġun gef",
+      "ìħĶ ìĦľ",
+      "ĠÑĢ ок",
+      "Ġpad dle",
+      "J ared",
+      "Ġsubm itting",
+      "Ġl atch",
+      "Ġf ug",
+      "Ġк оÑģ",
+      "ĠE f",
+      "Ġlaunch es",
+      "Ġf t",
+      "ote chn",
+      "Ġtrave lled",
+      "ا Ùģ",
+      "éģ ķ",
+      "Ġpro ch",
+      "Ġded im",
+      "8 3",
+      "Ġreb ound",
+      "ĠL U",
+      "p ath",
+      "ĠÑģп ÑĢав",
+      "Ġö l",
+      "ĠíĤ ¤",
+      "Ġpriv at",
+      "Ġtr actor",
+      "ĠAtt ention",
+      "S er",
+      "Ġcos es",
+      "á ria",
+      "p al",
+      "ĠìĿ Ģ",
+      "Ġsuccess or",
+      "Ġconnect ors",
+      "ĠÑĥÑģÑĤ анов",
+      "Ġgen ocide",
+      "Ġsufficient ly",
+      "ĠA ixò",
+      "Ġstabil ize",
+      "Ġcon gest",
+      "Ġcar ving",
+      "Ġz ost",
+      "ĠбÑĭ ÑģÑĤÑĢо",
+      "Ġshort est",
+      "Ġli vel",
+      "Ġ8 9",
+      "éģ Ĭ",
+      "Ġer k",
+      "Ġport raits",
+      "ॠĢ",
+      "è ĺ",
+      "bo at",
+      "ll ah",
+      "AN C",
+      "Ġempir ical",
+      "ĠE cho",
+      "ĠNeder land",
+      "è¿Ļ ä¹Ī",
+      "N et",
+      "Ġcuid ado",
+      "ĠR oma",
+      "Ġc alf",
+      "Ġgi ants",
+      "ĠExpl orer",
+      "ĠColl ect",
+      "al ition",
+      "ĠDest iny",
+      "Ġaus ge",
+      "ĠE du",
+      "ĠC lo",
+      "Ġear rings",
+      "ĠTr ack",
+      "ĠR OS",
+      "ĠBe lle",
+      "çĻ ¾",
+      "Ġpu eda",
+      "Ġday time",
+      "Ġsupp lier",
+      "ĠS V",
+      "ĠEx hale",
+      "Ġgal era",
+      "c ourse",
+      "Ġcent imeter",
+      "ĠB ast",
+      "m ud",
+      "Ġsang at",
+      "ĠPhys ical",
+      "Ġpriv ately",
+      "Ġtr ata",
+      "lyn n",
+      "ill i",
+      "Ġë© ĶìĿ´íģ¬ìĹħ",
+      "Ġcryst all",
+      "Ġpod s",
+      "ả n",
+      "in ator",
+      "ĠRec ords",
+      "å® ĺ",
+      "ÄŁim iz",
+      "isse ment",
+      "h are",
+      "h adow",
+      "ĠD K",
+      "ĠìķĮ ê³ł",
+      "Ġw yn",
+      "Ġrequest ing",
+      "ĠD onna",
+      "ĠìĹ ´ìĭ¬íŀĪ",
+      "ine a",
+      "Ġex ert",
+      "ĠDun can",
+      "Ġв еÑĩ",
+      "ĠH ah",
+      "ठĤ",
+      "ĠL if",
+      "ĠF inding",
+      "ĠNo v",
+      "Ġзн ак",
+      "Ġо ÑĦ",
+      "ĠQu è",
+      "Ġquarter back",
+      "ĠÑĦ ак",
+      "Ġbipart isan",
+      "ÄŁ in",
+      "Ġné cess",
+      "Ġrefer endum",
+      "Ġcomp iler",
+      "Ġprob abil",
+      "ед и",
+      "Ġtrad er",
+      "æĺ ĵ",
+      "ĠR um",
+      "ge me",
+      "Ġd io",
+      "ĠbÄĻdzie my",
+      "ĠÏĢ ά",
+      "ê¾ ¸",
+      "×ķ× ĺ",
+      "Ġठķ",
+      "Ġбл аг",
+      "Ġscal p",
+      "ĠPa use",
+      "Ġcapt ion",
+      "Ġend anger",
+      "Ġen lar",
+      "Ġrot ten",
+      "ãĥĥ ãĥĪ",
+      "Ġw ah",
+      "èĤ ī",
+      "Ġd zi",
+      "ĠInst all",
+      "A y",
+      "Ġcre ar",
+      "енÑĤ а",
+      "Ġwe ighing",
+      "Ġbutter flies",
+      "ĠG ast",
+      "äº ķ",
+      "h orn",
+      "war z",
+      "IC EOVER",
+      "Ġнай ÑĤи",
+      "Ġcoe fficients",
+      "ç°¡ åĸ®",
+      "ĠSp encer",
+      "ĠH igher",
+      "Ġcow ork",
+      "å¨ ĺ",
+      "ĠкоÑĤоÑĢ ое",
+      "Ġmon it",
+      "Ġdys function",
+      "ĠÑģÑĤ анов",
+      "Ġtour naments",
+      "Ġoy ster",
+      "B N",
+      "Ġtr ud",
+      "sl ow",
+      "ĠPen ny",
+      "ĠOd ys",
+      "æ r",
+      "Ġf ou",
+      "Ġenjoy ment",
+      "аÑĤ Ñĭ",
+      "Ġwygl Äħda",
+      "алÑĮ наÑı",
+      "ĠProt ect",
+      "Ġmo y",
+      "Ġcl aw",
+      "Ġsusp icion",
+      "Ġsacrific ed",
+      "Ġgost o",
+      "B ig",
+      "Ġaggress ively",
+      "Ġvor ne",
+      "ãĥ ł",
+      "Ġbl amed",
+      "ĠSe hr",
+      "פ ר",
+      "c ito",
+      "Ġse als",
+      "Ġmu jer",
+      "ĠWe ird",
+      "Ġfore ns",
+      "Ġcontrib utes",
+      "est ra",
+      "Ġp og",
+      "L OL",
+      "Ġhacer lo",
+      "о ÑĤÑĮ",
+      "f iction",
+      "7 9",
+      "λ ο",
+      "大 æ¦Ĥ",
+      "å£ °",
+      "ĠÑĤ об",
+      "ĠG S",
+      "ĠCl ara",
+      "ite z",
+      "Ġadvoc ating",
+      "ĠíĶ Ħë",
+      "s ung",
+      "Ġvert ices",
+      "Ġnavig ating",
+      "Ġeurop é",
+      "çļ Ĩ",
+      "Ġslow ed",
+      "Ġfore ground",
+      "ĠIndust rial",
+      "Ġad ore",
+      "ìĭ Ń",
+      "Ġcré er",
+      "æŀ Ĺ",
+      "chn itt",
+      "Ġun aware",
+      "Ġcur ly",
+      "ent ar",
+      "Ġl er",
+      "Ġprohib ited",
+      "ĠHero es",
+      "ĠRe ed",
+      "u ca",
+      "Ġsm ok",
+      "Ġkun na",
+      "zeit ig",
+      "im men",
+      "ĠL un",
+      "Ġаб ÑģолÑİÑĤ",
+      "Ġdeg li",
+      "Ġvill agers",
+      "Ġpres et",
+      "z ept",
+      "ud s",
+      "Ġem it",
+      "ä½ł è¦ģ",
+      "Ġë ī",
+      "ëĬĶ ì§Ģ",
+      "нак о",
+      "Ġos ób",
+      "Ġ196 9",
+      "ĠÐIJ ÑĢ",
+      "Ġman chmal",
+      "ĠBro ck",
+      "Ġmant ra",
+      "ĠW IL",
+      "b ach",
+      "in ä",
+      "el as",
+      "kel n",
+      "Ġdisci ple",
+      "Ġqual c",
+      "Ġde hyd",
+      "ìĿ´ë Ŀ¼ëĬĶ",
+      "A f",
+      "ìĦ± ìĿ´",
+      "R yan",
+      "Ġpupp et",
+      "ĠдÑĢÑĥг ие",
+      "Ġr ud",
+      "Ġp ending",
+      "P lus",
+      "ĠìķĬ ìĿĦ",
+      "Ġb á»ĭ",
+      "ĠSe ga",
+      "ç e",
+      "Ġprogram mer",
+      "b li",
+      "Ġun l",
+      "Ġensl aved",
+      "Ġsoci été",
+      "Äģ h",
+      "Ġinherit ance",
+      "ĠBang l",
+      "erm aid",
+      "Ġpractition er",
+      "ĠSt alin",
+      "ĠUs er",
+      "ci ble",
+      "Ġcard iac",
+      "ĠKore ans",
+      "Ġdump ed",
+      "Ġ×Ķ ×Ļ×Ķ",
+      "á is",
+      "Ġhydraul ic",
+      "oubt edly",
+      "ĠP it",
+      "Ġpic nic",
+      "Ġbehö ver",
+      "ĠÑģм ог",
+      "Ġbra king",
+      "é» ij",
+      "ut ar",
+      "ĠìĦ ¸ë",
+      "ub l",
+      "Ġü z",
+      "Ġmaj esty",
+      "Ġb ers",
+      "ut able",
+      "Ġhot ter",
+      "çħ §",
+      "ÛĮ ÙĨ",
+      "Ġbi ases",
+      "Ġsubject ed",
+      "Ġnaught y",
+      "Ġcir cus",
+      "ãģĹ ãģĭ",
+      "ĠIm medi",
+      "ĠSte fan",
+      "ĠTri ple",
+      "en k",
+      "Ġw it",
+      "Ġrecy cle",
+      "em ie",
+      "d ated",
+      "Ġun load",
+      "Ġpop ula",
+      "ch in",
+      "Ġyield s",
+      "Ġeng lish",
+      "ĠBon nie",
+      "Ġsp iders",
+      "Ã ģ",
+      "Ġer osion",
+      "éĥ¨ åĪĨ",
+      "ĠN ICK",
+      "иÑı Ñħ",
+      "Ġimp art",
+      "Ġк ни",
+      "Ġres olutions",
+      "Ġlith ium",
+      "Ġconver gence",
+      "ĠT ara",
+      "Ġдв е",
+      "th s",
+      "ĠCind y",
+      "æĪij è¦ģ",
+      "å¹ «",
+      "ĠD IE",
+      "Ġass urance",
+      "Ġоп иÑģ",
+      "Ġbu ckets",
+      "Ġc ues",
+      "ĠQu iet",
+      "Ġsimilar ity",
+      "Ġfound ational",
+      "ĠMin ist",
+      "æ» ¿",
+      "Ġp ian",
+      "Ġcent r",
+      "Ġnum b",
+      "Ġmon ks",
+      "uj ourd",
+      "en zie",
+      "Ġskate board",
+      "Ġd latego",
+      "ĠÑģ оÑĤ",
+      "ĠA E",
+      "Ġmaster piece",
+      "ĠSol omon",
+      "ĠRed dit",
+      "Ġr iot",
+      "ab l",
+      "ĠJ azz",
+      "Ġelectromagn etic",
+      "Ġinsec ure",
+      "ĠComp et",
+      "ger ies",
+      "об од",
+      "ł ×ķ",
+      "ðŁ Ĵ",
+      "Ġsen ators",
+      "ĠBris bane",
+      "ĠAl b",
+      "utter ing",
+      "ĠAll ow",
+      "z ero",
+      "Ġp ai",
+      "ĠÐIJ лекÑģ",
+      "ĠDis play",
+      "ĠBl ade",
+      "ĠApp s",
+      "Ġp ä",
+      "Ġд еÑģÑı",
+      "Ġque lla",
+      "ĠGa o",
+      "ен нÑĭÑħ",
+      "Ġspoil ers",
+      "Ġgall ons",
+      "ĠÙĦ ÙĬ",
+      "ĠZ ion",
+      "æľī ä¸Ģ",
+      "on ie",
+      "rag t",
+      "ĠCh and",
+      "Ġë³ ij",
+      "Ġbl unt",
+      "Ġus u",
+      "ĠK ad",
+      "ra kt",
+      "Ġcin ematic",
+      "Ġam munition",
+      "re ne",
+      "Ġfour teen",
+      "ĠC arn",
+      "c rit",
+      "Ġten ure",
+      "v u",
+      "Ġprincipal mente",
+      "Ġalle en",
+      "éĢĻ ä¸Ģ",
+      "Ġkompl ett",
+      "Ġdü ny",
+      "J ames",
+      "Ġrecept or",
+      "Ġones elf",
+      "g uru",
+      "Ġmerch ant",
+      "l iness",
+      "Ġover looked",
+      "Ġharmon ic",
+      "éķ ¿",
+      "ies o",
+      "×ķ× ŀ",
+      "col m",
+      "ĠпÑĢо екÑĤ",
+      "ĠAd a",
+      "ا س",
+      "T im",
+      "Ġrecur ring",
+      "Ġproceed s",
+      "ĠPart icularly",
+      "ĠDown load",
+      "et rical",
+      "Ġmat rices",
+      "Ġproyect o",
+      "anc ies",
+      "ĠUh m",
+      "Ġc aves",
+      "Ġìĸ´ë ł¤",
+      "ĠLe af",
+      "Ġоб ÑĭÑĩ",
+      "ĠìĿ´ì ľł",
+      "Euro pe",
+      "Ġt Äħ",
+      "Ġpul s",
+      "Ġtak iego",
+      "ÐĿ е",
+      "G U",
+      "Ġfor s",
+      "Ïģ γ",
+      "Ġfot os",
+      "Ġ) )",
+      "Ġë© ¤ë",
+      "Ġaqu ilo",
+      "ĠK urd",
+      "ï¸ ı",
+      "pt ic",
+      "ĠD ort",
+      "Ġmis ery",
+      "aus o",
+      "åĬ Ł",
+      "chuck ling",
+      "ĠR idge",
+      "ĠíĸĪ ìĬµëĭĪëĭ¤",
+      "Ġ* **",
+      "å® ¢",
+      "ĠHmm m",
+      "Ġge ographic",
+      "Ġany s",
+      "Ġtal vez",
+      "Ġske let",
+      "Ġsign atures",
+      "Ġlit ers",
+      "IJë ©´",
+      "ĠÑģво его",
+      "Ġski ing",
+      "ĠÐľ оÑģ",
+      "Ġadop ting",
+      "Ġha ft",
+      "Ġsymm etric",
+      "ĠL iqu",
+      "Ġthy roid",
+      "Ġmis in",
+      "lud e",
+      "Ġh ull",
+      "ĠX D",
+      "ĠG ust",
+      "ze ich",
+      "Ġvibr ations",
+      "Ġes emp",
+      "ĠвÑģ Ñİ",
+      "ĠQu em",
+      "Ġü brig",
+      "ĠS ke",
+      "ĠLyn ch",
+      "room s",
+      "art et",
+      "f est",
+      "Ġfr üher",
+      "Ġl ure",
+      "ä¸į好 æĦıæĢĿ",
+      "ĠìķĮ ìķĦ",
+      "ĠW IN",
+      "ĠR YAN",
+      "ĠкоÑĤоÑĢ ÑĥÑİ",
+      "ĠK ash",
+      "Ġ×Ķ× ŀ",
+      "Ġsaf eg",
+      "ĠHall elujah",
+      "Ġдв ÑĥÑħ",
+      "Ġstap le",
+      "Ġsed iment",
+      "ĠAct s",
+      "Ġbl aming",
+      "Ġmain land",
+      "Ġsport ing",
+      "Ġdecor ations",
+      "Ġexecut ing",
+      "Ġpar an",
+      "ĠDoll ar",
+      "Ġproject ions",
+      "Ġcommission ed",
+      "Ġb our",
+      "ö m",
+      "Ġste amed",
+      "ĠëŃ ĺ",
+      "Ġpet rol",
+      "Ġcel ular",
+      "å¸ ¶",
+      "ĠHung ary",
+      "Ġrent ed",
+      "Ġв аÑĢи",
+      "bb ie",
+      "Ġsé cur",
+      "ü ll",
+      "Ġsw ings",
+      "bet ween",
+      "Ġи ÑĤ",
+      "est ro",
+      "Ġnie mand",
+      "ĠìĤ ¼",
+      "ĠP ardon",
+      "ess es",
+      "ĠM ID",
+      "Ġcentral ized",
+      "ĠAl ien",
+      "cul os",
+      "Ġcr ise",
+      "裡 éĿ¢",
+      "Ġcl asse",
+      "beit et",
+      "i ÄŁi",
+      "Ġwh ales",
+      "Ġper imeter",
+      "Ġty ing",
+      "Ġstr ony",
+      "Ġlike wise",
+      "ĠP unch",
+      "D a",
+      "ĠBapt ist",
+      "Ġsort ing",
+      "Ġ iv",
+      "Ġíķ ©",
+      "Ġre hab",
+      "Ġet a",
+      "ri ver",
+      "Ġsa i",
+      "ãģĦãģŁ ãģł",
+      "od us",
+      "ãģĬé¡ĺãģĦ ãģĹãģ¾ãģĻ",
+      "Ġess ayer",
+      "Ġtur tles",
+      "ĠHaz rat",
+      "Ġfab rics",
+      "Ġcav ity",
+      "Ġpon ieważ",
+      "Ġschle cht",
+      "Ġs alsa",
+      "ÅŁ ekkür",
+      "Ġse ating",
+      "Ġeconom ists",
+      "Ġman g",
+      "Ġsegu inte",
+      "Ġr ang",
+      "Ġrat ios",
+      "Ġconst ell",
+      "Ġlong temps",
+      "u ating",
+      "Ġspo iled",
+      "Ġrecip ients",
+      "Ġsn iper",
+      "ä¹ĭ åīį",
+      "ìĬµ ëĭĪê¹Į",
+      "Ġw p",
+      "ĠLIN KE",
+      "Ġfl are",
+      "ĠAd ri",
+      "ñ as",
+      "Ġback l",
+      "mä ÃŁ",
+      "ĠB end",
+      "Ġworkload s",
+      "ĠÑģ Ñĥп",
+      "Ġ197 5",
+      "им ÑģÑı",
+      "ан е",
+      "Ġм он",
+      "Ġaspir ations",
+      "ĠA er",
+      "ĠговоÑĢ иÑĤÑĮ",
+      "ĠQ ian",
+      "å¦ Ī",
+      "Ġcomprom ised",
+      "Ġyol k",
+      "ла ÑģÑĤ",
+      "Ġhe men",
+      "ro ve",
+      "d ens",
+      "Ġком менÑĤ",
+      "Ġ- --",
+      "Ġflu ores",
+      "но Ñģ",
+      "ĠLiver pool",
+      "ĠÑģоб ой",
+      "ĠZ we",
+      "Ġl umin",
+      "ĠO G",
+      "á ¸",
+      "hol m",
+      "pro fits",
+      "S N",
+      "Ġproport ions",
+      "Ġm ica",
+      "ĠB oh",
+      "ĠAt las",
+      "Ġuns ure",
+      "Ġtour ing",
+      "Ġn ied",
+      "Ġt ÄĻ",
+      "Ġimper ative",
+      "Ġdem ek",
+      "ĠSher iff",
+      "r ance",
+      "Ġhom eland",
+      "ĠH ail",
+      "ĠG anz",
+      "y mm",
+      "M on",
+      "åĨ ·",
+      "v ida",
+      "Ġdesar roll",
+      "æĬ Ģ",
+      "Ġintrig uing",
+      "ĠH ugo",
+      "Ġ ãĤĤ",
+      "é ¬",
+      "а ÑĨ",
+      "ĠWiÄĻ c",
+      "att ed",
+      "ĠìķĦëĭĪ ê³ł",
+      "ĠV ari",
+      "á d",
+      "Ġsur real",
+      "Ġdispar ities",
+      "Ġm ó",
+      "ull en",
+      "ĠìŀĪ ëĭ¤ê³ł",
+      "Ġп ожалÑĥйÑģÑĤа",
+      "Ġma ins",
+      "Ġe ject",
+      "Ġmeth ane",
+      "Ġmarginal ized",
+      "Ġchill i",
+      "r ès",
+      "Ġy em",
+      "ä½ł æĺ¯",
+      "ĠCh un",
+      "Ġdeb ts",
+      "Ġdownload ing",
+      "ĠAth ens",
+      "is ierung",
+      "ry n",
+      "Ġte kn",
+      "ĠQu indi",
+      "éľ Ģ",
+      "Ġtara f",
+      "Ġh é",
+      "Ġconscious ly",
+      "Ġfix es",
+      "uck le",
+      "may ın",
+      "Ġfre i",
+      "Ġsp a",
+      "Ġì§Ħ íĸī",
+      "ĠاÙĦØ °",
+      "ĠÑĥ к",
+      "let t",
+      "Ġolm uÅŁ",
+      "Ġche esy",
+      "า à¸ģ",
+      "na ire",
+      "Ġw iden",
+      "Ġli en",
+      "Ġesca ping",
+      "igg s",
+      "ĠBl ick",
+      "c Äħ",
+      "ĠìĦ ľë",
+      "Ġ×Ķ× ¡",
+      "Ġв пеÑĢ",
+      "oph one",
+      "ie ll",
+      "ĠSU BSCRI",
+      "Ġl ions",
+      "Ġê·¸ ê²ĥ",
+      "Ġinsp ires",
+      "Ġguarante es",
+      "Ġcome ça",
+      "ĠGrow ing",
+      "Ġneg lig",
+      "ĠFrank f",
+      "Ġge geben",
+      "ĠÄij ầu",
+      "Ġend lich",
+      "Ġì į¨",
+      "ĠT T",
+      "ĠL ith",
+      "ÏĢ α",
+      "aster n",
+      "ĠA zer",
+      "Ġlun ar",
+      "h ic",
+      "Ġна ÑĢод",
+      "Ġnen hum",
+      "è· ij",
+      "ĠSalv ador",
+      "ĠPro gress",
+      "Ġprivile ges",
+      "ĠëıĻ ìķĪ",
+      "Ġant agon",
+      "ĠImp f",
+      "Ġdesc ub",
+      "ĠLe i",
+      "ĠìĥĪë ¡ľ",
+      "Ñĩ е",
+      "Ġdó lares",
+      "ĠMeg han",
+      "ĠW ire",
+      "to o",
+      "ay ing",
+      "us c",
+      "Ġt ud",
+      "Ġappe als",
+      "ed uc",
+      "Ġp ane",
+      "Ġj i",
+      "Ġde cks",
+      "ĠAl ter",
+      "Ġ å°±",
+      "ìĦ ¤",
+      "åĪĨ éIJĺ",
+      "Ġproduct ions",
+      "ĠWILL IAM",
+      "Ġimpl ied",
+      "Ġfulfill ment",
+      "ĠA ah",
+      "Ġsa ja",
+      "x us",
+      "ĠÎļ αι",
+      "Ãł s",
+      "uc ch",
+      "ок о",
+      "ĠDisc ord",
+      "ĠS Y",
+      "j sk",
+      "ĠWall ace",
+      "un ction",
+      "Dan iel",
+      "Ġk öt",
+      "ij ah",
+      "Ġmarch e",
+      "Ġdis gr",
+      "Ġm ungkin",
+      "Ġal ma",
+      "³ µ",
+      "Ġextensive ly",
+      "ĠFl oren",
+      "ĠAll ison",
+      "ãĤ ±",
+      "ÙĬ Ùħ",
+      "Ġju ven",
+      "ĠRena issance",
+      "Ġfundra ising",
+      "ĠCha os",
+      "Ġpar aly",
+      "Ġnarr ator",
+      "Ġecosystem s",
+      "A sh",
+      "Ġmitig ation",
+      "ĠA ujourd",
+      "ĠIde e",
+      "! ,",
+      "ĠÂ ½",
+      "Ġland lord",
+      "Ġdefect s",
+      "Ġac re",
+      "uls ive",
+      "Ġalg ae",
+      "pe k",
+      "Ġem ba",
+      "ĠR oc",
+      "éĽ ¢",
+      "ks om",
+      "ä che",
+      "Ġle uk",
+      "Ġlever aging",
+      "Ġê·¸ëłĩ ì§Ģ",
+      "ĠPal m",
+      "Ġä ven",
+      "Ġl is",
+      "ĠIn sp",
+      "ĠR ita",
+      "ĠAb b",
+      "ith m",
+      "Ġsuper vision",
+      "Ġrevis it",
+      "Ġpi ÄĻ",
+      "Ġeu h",
+      "Ġf ades",
+      "Ġmot to",
+      "åį ¡",
+      "ез ж",
+      "ĠSh im",
+      "Ġrelev ance",
+      "Ġo o",
+      "Ġo stat",
+      "n ica",
+      "Ġcho ix",
+      "ĠFac ulty",
+      "Ġì¤ij ìĹIJ",
+      "ĠAb ove",
+      "Ġнеб олÑĮÑĪ",
+      "Ġsequ encing",
+      "Ġnutri ent",
+      "Ġconqu ered",
+      "Ġdigest ive",
+      "Ġback drop",
+      "ĠL ori",
+      "ail able",
+      "G ame",
+      "Ġneglect ed",
+      "om orph",
+      "ill ah",
+      "Ġkn e",
+      "Ġsi itä",
+      "Ġworks pace",
+      "ĠVen ice",
+      "ĠK ne",
+      "Ñī о",
+      "ħ Ģ",
+      "ĠH ass",
+      "Ġv ita",
+      "Ŀ¼ë ©´",
+      "Ġlay s",
+      "ên cias",
+      "é rica",
+      "ĠL l",
+      "æ± Ĥ",
+      "ĠCo ca",
+      "ĠWH Y",
+      "èĪ ŀ",
+      "Ġrout ing",
+      "Ġperm issions",
+      "Ġd ings",
+      "pre nd",
+      "pro gram",
+      "Ġcro cod",
+      "br al",
+      "AAAA AAAA",
+      "ag it",
+      "ĠN ä",
+      "Ġgek ommen",
+      "at ten",
+      "Ġrefer enced",
+      "Ġpair ing",
+      "ĠPart ner",
+      "ĠCoron avirus",
+      "Ñĸ Ñģ",
+      "è½ ī",
+      "Ġ×Ķ× ĵ",
+      "Ġespec ÃŃfic",
+      "ars i",
+      "qu elle",
+      "Ġspont aneous",
+      "çĨ ±",
+      "Ġê²ĥ ìĿĦ",
+      "ĠÐŁÐ¾Ñģ ле",
+      "ĠاÙĦ د",
+      "ĠSh out",
+      "Ġн ал",
+      "Ġdisgu ise",
+      "ĠJ ord",
+      "Ġwe e",
+      "Ġmiej sc",
+      "Ġser um",
+      "Ġplais ir",
+      "Ġcred ible",
+      "Ġb Ã¥",
+      "ĠA J",
+      "ma res",
+      "Ġrod s",
+      "Ġer an",
+      "ãģ¾ ãģĤ",
+      "Ġp ää",
+      "ĠU A",
+      "ĠUn known",
+      "ĠÙĦ Ùħ",
+      "ĠRab bi",
+      "Ġla at",
+      "Ġhairst yle",
+      "ĠØ º",
+      "éģ ĭ",
+      "Ġc ach",
+      "ĠWr iting",
+      "оÑĩ ки",
+      "ab ad",
+      "Ġstraight en",
+      "-- \"",
+      "w ife",
+      "Ġhott est",
+      "Ġpun ya",
+      "ĠF ashion",
+      "gr iff",
+      "ĠQ R",
+      "ot ch",
+      "ĠÐľ ожеÑĤ",
+      "Cl oud",
+      "ĠStri ke",
+      "ĠHe in",
+      "Ġ 羣çļĦ",
+      "Ġle i",
+      "ĠFl ow",
+      "weg s",
+      "Ġha br",
+      "åīĽ åīĽ",
+      "nah me",
+      "Ì ģ",
+      "Ġple asing",
+      "op ping",
+      "Ġ구ë ıħ",
+      "Ġdr an",
+      "Ġbang s",
+      "Ġ7 9",
+      "Ġsk et",
+      "Ġcav al",
+      "ĠMac ron",
+      "Ġweight ed",
+      "Ġm uted",
+      "Ġnuest ras",
+      "EE P",
+      "Ġmath ematic",
+      "ĠM RI",
+      "ag us",
+      "Ġtherap ies",
+      "θ ε",
+      "Ġun pl",
+      "Ġcomm encer",
+      "f ull",
+      "Ġtow els",
+      "Ġpr ue",
+      "Ġlic enses",
+      "׼ ×ķ׾",
+      "ĠÐŁ оÑĩемÑĥ",
+      "Ġpoint less",
+      "B ye",
+      "Ġelig ibility",
+      "Ġscra pe",
+      "Ġab usive",
+      "ĠM ant",
+      "Ġje unes",
+      "t al",
+      "ĠPrin cip",
+      "ĠOrth odox",
+      "Ġmel od",
+      "ĠмаÑĤ еÑĢи",
+      "Ġprosecut or",
+      "Ġopio id",
+      "ĠÑĥ веÑĢ",
+      "ĠBe en",
+      "Ġìłij ì¢ħ",
+      "Ġd ynasty",
+      "Ġajud a",
+      "Ġent reg",
+      "Ġweigh ed",
+      "Ġe ure",
+      "ĠB em",
+      "Ġab normal",
+      "8 2",
+      "ĠJ R",
+      "ĠA kt",
+      "ĠB ri",
+      "ú t",
+      "Ġst agn",
+      "! *",
+      "Ġwe gen",
+      "Ġle aking",
+      "ĠW ords",
+      "ĠM au",
+      "Ġv ue",
+      "ĠL iam",
+      "ани ем",
+      "Ġclin icians",
+      "ĠP ump",
+      "Ġför st",
+      "? ...",
+      "Ġautom otive",
+      "ĠOw en",
+      "zus agen",
+      "ĠH undred",
+      "Ġdecentral ized",
+      "Ġbul bs",
+      "Ġ×ľ× Ľ",
+      "Ġprovin ces",
+      "ĠMil an",
+      "8 1",
+      "k as",
+      "Ġëĵ £",
+      "Ġfor ça",
+      "Ġright ly",
+      "å³ ¶",
+      "r Äħ",
+      "Ġven ues",
+      "Ġw ai",
+      "Ġpred icting",
+      "ĠWi Fi",
+      "Ġê¶ģ ê¸Ī",
+      "ر ÙĪ",
+      "Ġ×Ķ× ĸ",
+      "cent ury",
+      "Ġgrad ual",
+      "ĠProblem e",
+      "ĠìĹ ħ",
+      "Ġcop ing",
+      "ĠBr us",
+      "Ġpean uts",
+      "irts chaft",
+      "Ġз ал",
+      "ĠT roy",
+      "Ġsper m",
+      "ĠM itar",
+      "ĠTür kiye",
+      "g rand",
+      "¦ Ń",
+      "Ġ×ŀ× ¡",
+      "Ġp ans",
+      "ĠKnow ledge",
+      "ber ly",
+      "ĠÐķ го",
+      "Ġdan ced",
+      "ĠFr ost",
+      "ĠB urg",
+      "Ġbit ing",
+      "ìłķ ìĿĦ",
+      "me al",
+      "Ġhero ic",
+      "Ġmother board",
+      "ĠL icht",
+      "ãģ£ ãģ",
+      "ll an",
+      "ай н",
+      "ĠÑĢ Ñıд",
+      "Ġ à¹Ģà¸",
+      "on en",
+      "ir ie",
+      "Ar t",
+      "r ang",
+      "ν η",
+      "Ġnew born",
+      "Ġam is",
+      "Ġا ÙĪر",
+      "Ġsoph om",
+      "ĠCare ful",
+      "Ġprospect s",
+      "ens en",
+      "Ġthr ill",
+      "ĠVi á»ĩt",
+      "A dam",
+      "r ition",
+      "ent ric",
+      "ud en",
+      "Ġcertific ates",
+      "Ġas hes",
+      "èª ¿",
+      "play ing",
+      "Ġs adece",
+      "Ġo st",
+      "Ġairpl anes",
+      "ÑĢ ок",
+      "on er",
+      "Ġmagnes ium",
+      "Ġgod damn",
+      "Ġ197 2",
+      "ĠSch ule",
+      "Ġtem at",
+      "Ġpart out",
+      "௠Ĥ",
+      "Ġin ve",
+      "ĠScient ists",
+      "ĠHud son",
+      "win ning",
+      "ceks in",
+      "Ġcongress ional",
+      "or u",
+      "Ġro pes",
+      "в ед",
+      "Ġmad re",
+      "Ġf erry",
+      "ĠCoh en",
+      "ĠP red",
+      "Ġvag y",
+      "Ġб еÑģп",
+      "Ġmult im",
+      "Ġdrain age",
+      "Ġsim ulator",
+      "g iggles",
+      "ĠSt adium",
+      "об Ñī",
+      "Ġnot ices",
+      "Ġcraw ling",
+      "Ġgr oupe",
+      "åı ¸",
+      "Ġkto ÅĽ",
+      "ĠY oga",
+      "Ġmed ida",
+      "ĠÑħ ваÑĤ",
+      "ĠL ite",
+      "Ġr av",
+      "or ama",
+      "Ġdisc ord",
+      "ĠDI RE",
+      "Ġte h",
+      "ĠN urs",
+      "ç² ī",
+      "Ġpitch ed",
+      "Ġbark ing",
+      "ĠC oke",
+      "wi ad",
+      "Ġpop ulated",
+      "éĻ ¤",
+      "pe lled",
+      "Ġб ог",
+      "Ġpe wno",
+      "ĠC ube",
+      "Ġrecru ited",
+      "éĢĻ 種",
+      "ĠC ara",
+      "ıģ ını",
+      "im ated",
+      "ĠÑĪ кол",
+      "ic ional",
+      "ĠпÑĢо ÑĦ",
+      "Ġcontam ination",
+      "Ġúlt imos",
+      "Ġfear ful",
+      "Ġele phants",
+      "us i",
+      "ĠiT unes",
+      "ĠSw ami",
+      "ê ¼",
+      "ĠìĦ¤ë ªħ",
+      "ĠRich ards",
+      "Ġmagn ets",
+      "ĠRicht ung",
+      "ĠLeg ion",
+      "èı ľ",
+      "Ġk itty",
+      "Ġkiss ed",
+      "Ġwater ing",
+      "Ġcon o",
+      "ĠPalest ine",
+      "id ir",
+      "Ġma ze",
+      "Ġflu ids",
+      "ĠProdu cer",
+      "ĠKr sna",
+      "好 åķ¦",
+      "la f",
+      "Ġ×IJ ×ķ",
+      "Ġm iesz",
+      "ĠX ing",
+      "oint ed",
+      "se in",
+      "ĠF uk",
+      "ĠDep ression",
+      "ĠD uty",
+      "ĠPan ther",
+      "Ġsu nd",
+      "Ġref ere",
+      "Ġexc lusion",
+      "Ġnav al",
+      "ĠWin ston",
+      "Ġsl ogan",
+      "Ġhypoth etical",
+      "Ġelev ate",
+      "ë ł¹",
+      "Ġcabe ça",
+      "ĠGes und",
+      "m eter",
+      "ĠìķĦëĭĪë ©´",
+      "Ġcloud y",
+      "âĢ¦ ?",
+      "ĠSch ritt",
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+      "ì į",
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+      "ĠB atter",
+      "· °",
+      "Ġtail or",
+      "ĠPTS D",
+      "ĠG ent",
+      "Ġba ÄŁ",
+      "Ġspat ula",
+      "Ġcr ay",
+      "ĠLeg isl",
+      "Ġs ú",
+      "Ġle ve",
+      "า ม",
+      "Ġer ad",
+      "Ġdon g",
+      "Ġd erm",
+      "ĠBank s",
+      "ich o",
+      "åħĪ çĶŁ",
+      "ĠFr anz",
+      "ra vel",
+      "éģ Ķ",
+      "ол о",
+      "Ġfl ute",
+      "ĠE k",
+      "Ġjoy ful",
+      "Ġch ased",
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+      "O ver",
+      "Ġentrepreneur ial",
+      "Ġcons iders",
+      "Ñĥ ем",
+      "op a",
+      "Ġdorm ir",
+      "ĠElement ary",
+      "Ġprzy pad",
+      "ÑĥÑģ ка",
+      "ĠоÑĩ еÑĢ",
+      "ug ene",
+      "Ġten ido",
+      "Ġlug ares",
+      "ë ¥",
+      "ĠÑĩ аÑģÑĤ",
+      "Ġsa o",
+      "Ġbra id",
+      "ĠV ere",
+      "ĠRe ich",
+      "ĠP oss",
+      "Ġin an",
+      "w and",
+      "re f",
+      "Ġmont rer",
+      "Ġ198 1",
+      "çķ ª",
+      "as ında",
+      "Ġch rome",
+      "ĠTr inity",
+      "Ġexplo itation",
+      "ĠS ense",
+      "ĠC MS",
+      "ĠNo ble",
+      "ĠìĦł íĥĿ",
+      "Ġswe lling",
+      "elect ronic",
+      "] ?",
+      "Ġbr ushing",
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+      "ĠH ook",
+      "ĠCon nor",
+      "ĠAl um",
+      "Ġgu cken",
+      "su ite",
+      "Ġwie le",
+      "Ġbarrel s",
+      "ĠReg el",
+      "ĠM ent",
+      "ĠT rip",
+      "ĠBr ush",
+      "ĠE rik",
+      "ur ate",
+      "ÉĻ r",
+      "ĠC yr",
+      "ou ble",
+      "ĠBe cca",
+      "Ġpass words",
+      "Å ±",
+      "bor g",
+      "Ġv endo",
+      "ĠCla us",
+      "ĠF az",
+      "ind est",
+      "Ġdece ased",
+      "Ġcompar isons",
+      "ĠL CD",
+      "ĠP ork",
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+      "Ġpat reon",
+      "Ġin ability",
+      "Ġext inction",
+      "Ġì¢ĭìķĦ íķĺëĬĶ",
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+      "aj u",
+      "Ġ×ij× IJ×",
+      "Ġso fort",
+      "Ġdest ined",
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+      "Ġlim p",
+      "é» ¨",
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+      "ин г",
+      "Ġexp osing",
+      "ĠÎ ¾",
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+      "la ugh",
+      "Ġhis s",
+      "ãģł ãģĭãĤī",
+      "Ġind ie",
+      "Ġdet al",
+      "ÑĢав ÑģÑĤв",
+      "Ġtr ên",
+      "æķ °",
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+      "00 00",
+      "OL D",
+      "Ġpes os",
+      "Ġbon uses",
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+      "Ġrev ival",
+      "ĠHor se",
+      "Ġs ack",
+      "T alk",
+      "Ġmul her",
+      "ĠпоÑģÑĤо Ñıн",
+      "ĠH ood",
+      "H uh",
+      "Ġë¶ ģ",
+      "Ġhy ung",
+      "ĠMe eting",
+      "Ġimport a",
+      "Ġì°¾ ìķĦ",
+      "ĠV ern",
+      "Ġstri pped",
+      "Ġref uses",
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+      "Ģë ıĦ",
+      "ix ÃŃ",
+      "Ġdi ab",
+      "it ime",
+      "fl ows",
+      "Ġin ac",
+      "ĠG ong",
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+      "h ist",
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+      "Ġviol ated",
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+      "ĠEd it",
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+      "Ġgeomet ric",
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+      "g re",
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+      "ist ed",
+      "ĠاÙĦ Ùĥ",
+      "ĠCh ain",
+      "ĠPres ent",
+      "ĠY in",
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+      "ĠV log",
+      "Ġìĸ´ë ¨¸",
+      "Ġdon n",
+      "Ġh itch",
+      "uck ing",
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+      "w ald",
+      "ris k",
+      "Ġhar i",
+      "ĠK ens",
+      "ĠId ol",
+      "Ġвним ание",
+      "Ġtod d",
+      "Ġsm ashed",
+      "Ġinv ari",
+      "Ġкон ÑĤÑĢ",
+      "Ġaut istic",
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+      "R es",
+      "д Ñĭ",
+      "ch au",
+      "Ġsel v",
+      "Ġhät ten",
+      "ठ¿",
+      "Ġexpect s",
+      "Ïģ η",
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+      "ĠHT TP",
+      "le ÅŁ",
+      "Ġswe eping",
+      "ĠBet a",
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+      "ĠSim s",
+      "C s",
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+      "s qu",
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+      "Ġrun ter",
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+      "ĠTe en",
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+      "Ġrock y",
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+      "un al",
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+      "Ġо г",
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+      "w ig",
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+      "ì¤ij ìĹIJ",
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+      "Ġcalcul us",
+      "Ġab rupt",
+      "ĠInspect or",
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+      "int ense",
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+      "Ġast hma",
+      "ĠHi ç",
+      "ª »",
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+      "Ġk ır",
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+      "Ġtestoster one",
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+      "Ġi ba",
+      "T s",
+      "Ġsort a",
+      "Ġ( ?)",
+      "Ġت ÙĪ",
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+      "Ġbek ommt",
+      "Ġspirit ually",
+      "Ġdist orted",
+      "M ad",
+      "Ġre im",
+      "á nh",
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+      "ĠRel ig",
+      "ĠEl s",
+      "Ġret ained",
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+      "ĠS AS",
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+      "Ġbankrupt cy",
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+      "ĠD j",
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+      "Ġl ace",
+      "Ġfur ious",
+      "ĠY U",
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+      "law s",
+      "Ġrein vent",
+      "ĠK atherine",
+      "ĠCh oi",
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+      "Ġr ans",
+      "od en",
+      "ĠMid west",
+      "n ın",
+      "Ġdep ort",
+      "ĠD ip",
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+      "ĠÚ© Ûģ",
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+      "ĠBrook s",
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+      "Ġmal icious",
+      "Ġk ir",
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+      "est ab",
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+      "Ġe llo",
+      "ĠT M",
+      "ĠP aw",
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+      "f ruit",
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+      "ãĤ ¬",
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+      "ĠT ages",
+      "Ġs á»±",
+      "Ġbull ied",
+      "Ø ¤",
+      "Ġbenef ited",
+      "ĠPre viously",
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+      "Ù į",
+      "Ġsen ate",
+      "ĠM orm",
+      "ij ke",
+      "ĠF lu",
+      "Ġincorpor ating",
+      "j ack",
+      "Ġп иÑĤ",
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+      "Ġha cks",
+      "ĠR ICH",
+      "Ġк ваÑĢ",
+      "ĠпÑĢек ÑĢаÑģ",
+      "Ġdepend ency",
+      "Ġìļ ©",
+      "Ġì± ħ",
+      "Ġwäh rend",
+      "Ġsu lla",
+      "ĠPitts burgh",
+      "Ġesemp io",
+      "¼ë ¡ľ",
+      "pr ot",
+      "ĠR osen",
+      "ĠIndepend ence",
+      "Ġpars ley",
+      "ie gen",
+      "Ġha w",
+      "Ġaqu ell",
+      "ĠC AP",
+      "ĠÑĢабоÑĤ аÑĤÑĮ",
+      "ĠCl iff",
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+      "ν ε",
+      "Ġutil is",
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+      "ĠP ing",
+      "Ġtre k",
+      "Ġf ak",
+      "Ġenorm e",
+      "Ġìĭ «",
+      "è® ©",
+      "Ġdoub ling",
+      "ĠнÑĢав иÑĤÑģÑı",
+      "Ġh ed",
+      "ho ven",
+      "ĠStand ing",
+      "Ġm ÃŃn",
+      "ĠJ imin",
+      "Ġmon arch",
+      "Ġco ke",
+      "Ġm r",
+      "Ġcl ic",
+      "Ã į",
+      "Ġimpe achment",
+      "Ġdur ability",
+      "Ġvar ios",
+      "Ġcommercial s",
+      "Ġgreet ings",
+      "ĠR i",
+      "ĠApp reci",
+      "ìŀĪ ëĬĶ",
+      "Ġrés ult",
+      "ér t",
+      "Ġsal ute",
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+      "Ġsun rise",
+      "ve ck",
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+      "Ġcommission er",
+      "å¿ µ",
+      "â te",
+      "ĠKen ny",
+      "ĠSir i",
+      "ãĥĥ ãĥĹ",
+      "ĠëĬ ĺ",
+      "ĠE E",
+      "Ġun ch",
+      "к он",
+      "ĠاÙĦØ ¥",
+      "Ġbel ts",
+      "Ġhas s",
+      "Ġмо Ñı",
+      "Ġdispl aced",
+      "Ġab ra",
+      "ÎŃ Î»",
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+      "Ġauthor ization",
+      "ĠL LC",
+      "Ġprodu k",
+      "Ġrehabil itation",
+      "å ŀ",
+      "Ñĸ Ñĩ",
+      "ud ing",
+      "ol it",
+      "Ġ10 5",
+      "Ġexp ands",
+      "Ġalt ri",
+      "ĠKom ment",
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+      "P l",
+      "ĠM ana",
+      "f ed",
+      "Ġb ri",
+      "Ġor a",
+      "G s",
+      "ĠG ur",
+      "uck land",
+      "Ġjun ction",
+      "Ġiron ic",
+      "ĠFe ed",
+      "Ġpra kt",
+      "ĠHam mer",
+      "Įë ıĦ",
+      "ĠTr acy",
+      "çµ ±",
+      "ĠAs ide",
+      "н его",
+      "ĠиÑģполÑĮз оваÑĤÑĮ",
+      "Ġz aj",
+      "Ġequ itable",
+      "Ġcur b",
+      "Ġãģĵ ãĤĮ",
+      "Ġderiv atives",
+      "Ġpupp ies",
+      "ĠKenn eth",
+      "ĠCom pl",
+      "ig ram",
+      "ĠGar cia",
+      ") \"",
+      "ĠHar bor",
+      "est ial",
+      "Ġ ä¾Ĩ",
+      "Ġ ers",
+      "æ ¹",
+      "Ġunw anted",
+      "Ġbel ang",
+      "аР³Ð¾",
+      "em b",
+      "d os",
+      "ĠìĻ ľë",
+      "ĠBud get",
+      "Ġbatt ling",
+      "ØŃ Øª",
+      "k ok",
+      "наÑĩ ала",
+      "Ġpl ag",
+      "Ġcant idad",
+      "Ġgrup os",
+      "Ġplug ins",
+      "ler ini",
+      "Ġиме еÑĤ",
+      "Ġso zusagen",
+      "ol ics",
+      "Ġpue blo",
+      "Ġrem inis",
+      "r än",
+      "ĠMor rison",
+      "Ġl inha",
+      "Ġbreath s",
+      "ĠT aste",
+      "Ġenf rent",
+      "ĠDo cker",
+      "Ġд ен",
+      "Ġethnic ity",
+      "Ġw ob",
+      "Ġsuff ers",
+      "Ġtransition ing",
+      "ĠR ange",
+      "ÄĻd zy",
+      "Ġк аÑĤ",
+      "Ġsy ner",
+      "Ġdon ut",
+      "Ġprob abilities",
+      "ĠO mar",
+      "Wh ich",
+      "u ish",
+      "is in",
+      "Ġdem os",
+      "ĠìłĢ 기",
+      "Ġëĺij ê°Ļ",
+      "Ġед ин",
+      "Ġc erve",
+      "Ġj oka",
+      "I AN",
+      "Ġkilomet er",
+      "Ġhorizont ally",
+      "ĠBh ag",
+      "Ġ- >",
+      "ĠMon itor",
+      "Ġknowledge able",
+      "Ġf av",
+      "Ġpin ned",
+      "Ġe Bay",
+      "ick er",
+      "Ġìŀłê¹ IJë§Į",
+      "ĠXia omi",
+      "Ġcap it",
+      "Ġn p",
+      "Ġ196 5",
+      "ho e",
+      "Ġn ok",
+      "ĠS age",
+      "Ġн елÑĮзÑı",
+      "ĠT ow",
+      "g am",
+      "Ġdic en",
+      "ĠSUBSCRI BE",
+      "Ġrebo ot",
+      "Ġp aj",
+      "Ġë³´ìĹ ¬ë",
+      "Ġth icken",
+      "ĠRe ality",
+      "id än",
+      "N a",
+      "Ġê²ĥ ìĿĢ",
+      "!! )",
+      "Ġrout ines",
+      "Ġод ного",
+      "Ġex ting",
+      "Ġì¦ Ŀ",
+      "Ġsulf ur",
+      "Ġcar ve",
+      "Ġastero id",
+      "ĠWarri or",
+      "Ġphotograph ers",
+      "Ġpe ll",
+      "Ġcros sover",
+      "æĪij çŁ¥éģĵ",
+      "Ġhace mos",
+      "ĠNe j",
+      "Ġsett ling",
+      "Ġir m",
+      "ĠBook s",
+      "ient ôt",
+      "Ġesp acio",
+      "ĠSchol ars",
+      "Ġdo omed",
+      "ĠIR S",
+      "w ohl",
+      "Ġseg ue",
+      "ĠëĪĦ ê°Ģ",
+      "Ġpr atic",
+      "B T",
+      "ĠConsider ing",
+      "ĠBuff alo",
+      "Ġtrain ings",
+      "Ġge bru",
+      "ĠG leich",
+      "Ġpir ates",
+      "Ġen velop",
+      "Ġre open",
+      "im at",
+      "Ġte e",
+      "Ġsu ed",
+      "fe h",
+      "Ġ×Ķ× §",
+      "Ġdi ets",
+      "Ġjunt os",
+      "ast o",
+      "Ġmisunder stood",
+      "Ġru im",
+      "Ġclass ify",
+      "ĠпÑĢод Ñĥк",
+      "Ġin se",
+      "Ġillust rated",
+      "Ġcorros ion",
+      "Ġacc red",
+      "ĠAunt ie",
+      "ĠпÑĢив еÑĤ",
+      "ĠLI VE",
+      "Ġre k",
+      "Ġrece ipt",
+      "åĪ° åºķ",
+      "ĠBar bie",
+      "ĠSn ake",
+      "t urn",
+      "Je ff",
+      "ãģĬ ãģĬ",
+      "ķ Ħ",
+      "VO ICEOVER",
+      "co ll",
+      "Ġrun ners",
+      "ìł ľë",
+      "os os",
+      "mo on",
+      "Ġkey note",
+      "ĠInst it",
+      "S PEAK",
+      "Ġplug s",
+      "Ġcur v",
+      "ĠY uri",
+      "ĠTh eres",
+      "ĠP s",
+      "Ġμ ÏĢο",
+      "Ġconver ter",
+      "Ġref ine",
+      "Ġbad ass",
+      "Ġο ι",
+      "Ġreg en",
+      "az zi",
+      "ÙĬ Ùģ",
+      "Ġse ized",
+      "Ġiç er",
+      "ile e",
+      "Ġup stream",
+      "Ġbud s",
+      "Ġp im",
+      "Ġíķĺë £¨",
+      "Ġall uded",
+      "Ġthem ed",
+      "Ġconsist ing",
+      "Ġb ons",
+      "un uz",
+      "ĠпÑĢов од",
+      "ĠLove ly",
+      "ॠĭ",
+      "Ġpar ach",
+      "ĠSta ats",
+      "éļ Ĭ",
+      "Ġselect ive",
+      "Ġf ase",
+      "ĠGeor get",
+      "Ġcoc aine",
+      "Ġreprodu ction",
+      "ĠL ara",
+      "ĠL D",
+      "Ġg h",
+      "J on",
+      "Ġl Ã¥",
+      "Ġëij IJë",
+      "Ġtyp ed",
+      "ĠB ana",
+      "ë ĵľë",
+      "Ġsav ory",
+      "ĠZ omb",
+      "stand en",
+      "Ġpedest rian",
+      "Ġdifférent s",
+      "Ġìĭ ¸",
+      "èī ¯",
+      "Ġcompl ained",
+      "ç¦ ı",
+      "ĠÐļ ÑĤо",
+      "Ġ×ľ× ¤",
+      "ali ÅĽmy",
+      "Ġmort ar",
+      "Ġverd ict",
+      "Ġsu ficiente",
+      "ĠMill ion",
+      "mitt el",
+      "in als",
+      "ĠاÙĦØ ®",
+      "аÑİ ÑģÑĮ",
+      "Ġmi ÄĻdzy",
+      "ĠO le",
+      "Ġin vert",
+      "czy Äĩ",
+      "озм ожно",
+      "star ter",
+      "Ġaud itor",
+      "ĠSc out",
+      "ch ien",
+      "ĠSver ige",
+      "uff led",
+      "Ġze hn",
+      "ĠA uckland",
+      "Ġarg ent",
+      "Ġ197 6",
+      "ĠHo e",
+      "Ġboth ers",
+      "Ġsocial ist",
+      "Ġpl iers",
+      "Ġemer gen",
+      "ĠX P",
+      "еÑĢ ов",
+      "M ore",
+      "ĠLe vi",
+      "ĠAnd ers",
+      "ibil idad",
+      "ĠP arents",
+      "Ġindu ced",
+      "ìĸ´ì ¤",
+      "Ġbal ances",
+      "ĠвÑĭ ÑĪ",
+      "Ġsubmar ine",
+      "St art",
+      "Ġdri es",
+      "Ġvol ver",
+      "Ġtick ing",
+      "c ott",
+      "Ġf aj",
+      "pr és",
+      "ĠS abb",
+      "Ġза Ñĩ",
+      "Ġпок Ñĥп",
+      "Ġbapt ized",
+      "ĠBrill iant",
+      "ĠÐij ог",
+      "Ġm ots",
+      "b its",
+      "Ġlatt ice",
+      "æĪij è·Łä½ł",
+      "Ġcor iander",
+      "Ġresid ency",
+      "yn c",
+      "Ġpier wszy",
+      "ĠKn ock",
+      "ĠZ ap",
+      "ĠÐķ в",
+      "ê² ¬",
+      "å°ı å¿ĥ",
+      "Ġune ven",
+      "ĠJ as",
+      "od or",
+      "ç¿ Ĵ",
+      "7 4",
+      "ĠS ite",
+      "Ġacontece u",
+      "ym pt",
+      "Ġtril ogy",
+      "Ġlan tern",
+      "ĠZ ucker",
+      "v ari",
+      "we lling",
+      "ĠPot ato",
+      "gom ery",
+      "Ġreact ed",
+      "ĠChr on",
+      "Ġj ede",
+      "be eld",
+      "Ġtw ent",
+      "Ġl act",
+      "æ¨ Ĥ",
+      "Ġré se",
+      "Ġrel ent",
+      "Ġfurn ace",
+      "Ġwid get",
+      "Ġearthqu akes",
+      "ĠAd just",
+      "il it",
+      "ĠØ£ ÙĪ",
+      "Ġhear ings",
+      "Ġdefend ant",
+      "irs iniz",
+      "Ġbas k",
+      "c ja",
+      "ľ ¨",
+      "Ġrif les",
+      "Ġinst al",
+      "ĠFor give",
+      "p ical",
+      "ĠÐŀÑĩ енÑĮ",
+      "Ġpet ites",
+      "Ġh p",
+      "Ġren owned",
+      "ĠIn n",
+      "Ġ주 ìĦ¸ìļĶ",
+      "Ġemphas ized",
+      "éĹ® é¢ĺ",
+      "ĠìŀĪ ì£ł",
+      "Ġê²ĥ ìľ¼ë¡ľ",
+      "ãĤ Ĩ",
+      "Å ĵ",
+      "g ili",
+      "D ave",
+      "Ġexha usting",
+      "ÅĤ ug",
+      "Ġsch ema",
+      "μ ά",
+      "cy cl",
+      "Ġaut ant",
+      "Ġpar cel",
+      "Ġmater ia",
+      "ĠB erry",
+      "ĠÑģ ами",
+      "Ġextract ed",
+      "ĠSay ing",
+      "ism atic",
+      "Ġпоп ÑĢоб",
+      "Ġneur on",
+      "g raph",
+      "ľë ©´",
+      "Ġencl osure",
+      "ĠJoh ann",
+      "Ġafter math",
+      "ÑĤ об",
+      "Ġu ży",
+      "Ġs amp",
+      "3 60",
+      "ĠMe i",
+      "Ġt aco",
+      "Ġrecept ors",
+      "Ġpunch es",
+      "ĠHo je",
+      "ĠÙĩ ÙĨا",
+      "=\" #",
+      "ĠAng ular",
+      "Ġmus ique",
+      "Ġro l",
+      "ĠÃ ±",
+      "ster reich",
+      "Ġcl am",
+      "ĠTre asury",
+      "chem ical",
+      "Ġap ar",
+      "Ġapp end",
+      "Ġforb id",
+      "ĠHamb urg",
+      "ак ов",
+      "Ġê¸ Ī",
+      "ild a",
+      "Ġprepar ations",
+      "Ġmog Äħ",
+      "Ġcam ino",
+      "E ric",
+      "ĠBl ind",
+      "èĪ ĩ",
+      "å¹´ çļĦ",
+      "ĠDis covery",
+      "ì¸ ł",
+      "çĪ ¶",
+      "Ġinterpre ter",
+      "Ġb red",
+      "ĠPsal m",
+      "Ġdef ended",
+      "ìī ¬",
+      "ĠEr fahr",
+      "ĠPe ach",
+      "Ġmo ons",
+      "ĠO st",
+      "Ġspé cial",
+      "Ġarri ver",
+      "ĠW is",
+      "u ci",
+      "Ġrobot ics",
+      "I VE",
+      "Ġsie ge",
+      "ar la",
+      "Ġsepar ates",
+      "ĠT C",
+      "íı °",
+      "quis ite",
+      "Ġparenth eses",
+      "ик е",
+      "ç« Ļ",
+      "Ġtr ous",
+      "å» º",
+      "ĠÑģ илÑĮ",
+      "Ġbe ers",
+      "Ġпл аÑĤ",
+      "ãģĻãģĶ ãģĦ",
+      "Ġso la",
+      "Ġd ès",
+      "ming ham",
+      "ik te",
+      "Ġo ops",
+      "Ġtw itch",
+      "å° ĩ",
+      "Ï Ī",
+      "ĠShould n",
+      "uv re",
+      "Ġle er",
+      "cript ions",
+      "Ġeyes hadow",
+      "ĠGu o",
+      "ĠPow ell",
+      "Ġsup uesto",
+      "Ġan a",
+      "r als",
+      "ĠMont real",
+      "Ġsurf ing",
+      "ĠÐŁÐµÑĢ в",
+      "×ŀ ×ķ",
+      "Ġmillise conds",
+      "Ġsubur bs",
+      "Ġplanet a",
+      "ÑĥÑĪ ка",
+      "hr lich",
+      "ĠH Y",
+      "Ġس ÛĴ",
+      "ĠM M",
+      "ĠE ff",
+      "åı¯ æĦĽ",
+      "ĠH S",
+      "ans on",
+      "Ġì§ģ ìłij",
+      "Ġsu o",
+      "Ġdeploy ing",
+      "Ġk unt",
+      "ter ing",
+      "Ġere ct",
+      "ìŀ¥ ìĿ´",
+      "ĠìĿĮ ìĭĿ",
+      "Ġspec imen",
+      "! ...",
+      "æĪij 說",
+      "Ġlig ne",
+      "Ġk onst",
+      "ade qu",
+      "Ġìĥģ íĥľ",
+      "Ġaccess ed",
+      "ĠP ole",
+      "k ill",
+      "Ġë² Ħë",
+      "Ġauthentic ity",
+      "Ġapp elle",
+      "ull e",
+      "Ġrev ision",
+      "Ġgo ats",
+      "г ли",
+      "Ġp au",
+      "ĠR anger",
+      "ĠIm ag",
+      "aut hor",
+      "Ġe ve",
+      "ĠMess enger",
+      "Ġn ay",
+      "Ġwh oles",
+      "ät te",
+      "Ġon wards",
+      "ĠDep ois",
+      "Ġíijľ íĺĦ",
+      "ĠSAR S",
+      "Ġwszystk ich",
+      "Ġdest ru",
+      "umb ing",
+      "Ġcompat ibility",
+      "Ġmis information",
+      "od ore",
+      "ĠF avor",
+      "ek o",
+      "ı Į",
+      "w aukee",
+      "ĠTe aching",
+      "ĠK O",
+      "Ġbet ting",
+      "Ġquest s",
+      "Ġviv re",
+      "ĠмÑĥз Ñĭ",
+      "Ġs aga",
+      "Ġswe ll",
+      "Ġge he",
+      "æĢİ麼 樣",
+      "ĠоÑĢг аниз",
+      "Ġg ide",
+      "ĠG ross",
+      "Ġdale j",
+      "Ġcl aws",
+      "á»Ļ c",
+      "Ġprejud ice",
+      "Ġins ign",
+      "i hood",
+      "Ġpl ed",
+      "Ġdó nde",
+      "ĠPolit ical",
+      "Ġprem ises",
+      "und ert",
+      "ع ت",
+      "on nen",
+      "Ġespa ço",
+      "Ġf é",
+      "ĠHarr ison",
+      "ĠC ensus",
+      "Ġcard io",
+      "Ġdi y",
+      "Ġmil ieu",
+      "Ġjourn ée",
+      "ĠRe lease",
+      "N IE",
+      "ĠM uk",
+      "id ée",
+      "á»į i",
+      "Ġiç inde",
+      "ŀ Ļ",
+      "Ġreson ate",
+      "Ġm oles",
+      "ĠF lying",
+      "ĠGl oria",
+      "ĠPast or",
+      "ĠAre na",
+      "好 ä¸į好",
+      "N ON",
+      "ол ов",
+      "Ġall ÃŃ",
+      "om at",
+      "ìĸ´ë ıĦ",
+      "Ġcaracter ÃŃst",
+      "Ġdecl ining",
+      "Ñĸ Ñı",
+      "an co",
+      "ĠIn form",
+      "Ġbarg ain",
+      "Ġbus hes",
+      "ĠNat urally",
+      "Ġre chts",
+      "ĠT ensor",
+      "ĠPat ricia",
+      "Ġprincip io",
+      "ĠM umbai",
+      "Ġwom b",
+      "Ġnost ra",
+      "Ġdile mma",
+      "Ġirgendw ann",
+      "Ġ196 4",
+      "Ġenerg ÃŃa",
+      "Ġна ÑĢ",
+      "Ġseg regation",
+      "ĠA thlet",
+      "Ġ» ,",
+      "Ġy eni",
+      "ĠSe it",
+      "Ġven om",
+      "Ġdak ika",
+      "Ġëı Įë",
+      "ĠÃī l",
+      "Ġf us",
+      "ĠM og",
+      "¦½ ëĭĪëĭ¤",
+      "Ġrem ar",
+      "ĠTed dy",
+      "Ġbreast s",
+      "ic ans",
+      "æĶ¶ çľĭ",
+      "k ap",
+      "Ġh Æ¡n",
+      "ĠJ P",
+      "ãĥ³ ãĤ¿",
+      "Ġresur rect",
+      "ĠìĿ ¸ë",
+      "her ical",
+      "Ġfot ograf",
+      "ĠJos é",
+      "Ġlivel ihood",
+      "Ġbib li",
+      "ter i",
+      "Ġvor stellen",
+      "ĠA AA",
+      "Ġassess ing",
+      "Y A",
+      "Ġspl end",
+      "Ġexca v",
+      "Ġbapt ism",
+      "y ll",
+      "w ow",
+      "M ac",
+      "Ġpl astics",
+      "teok bokki",
+      "Ġintéress ant",
+      "Ġcommand ed",
+      "Ġfamous ly",
+      "ĠÐĺ ли",
+      "ĠMan uel",
+      "Ġsouth west",
+      "Ġde formation",
+      "ÃŃcul o",
+      "ĠнаÑħод иÑĤÑģÑı",
+      "ĠP atter",
+      "d egree",
+      "ĠczÄĻ sto",
+      "\" -",
+      "Ġìħ ĭ",
+      "Ġman ger",
+      "ĠTrust ee",
+      "Ģë ¦¬",
+      "Ġpunt os",
+      "iv able",
+      "Ġvol atile",
+      "ĠëĬ IJ",
+      "Ġinst ability",
+      "Ġc iel",
+      "ci Äħ",
+      "Ġpur ity",
+      "но ÑģÑĤ",
+      "S il",
+      "ed ar",
+      "åĻ ¨",
+      "NOUN CER",
+      "Ġspe lled",
+      "G ER",
+      "Ġsanct uary",
+      "Ġacceler ating",
+      "Ġsc out",
+      "ĠпÑĢ ев",
+      "f ahren",
+      "ãģĵ ãģ¡ãĤī",
+      "ĠëĤĺìĺ ¨",
+      "Ġpocz Äħt",
+      "ĠMe u",
+      "ka ar",
+      "³´ ê³ł",
+      "ak ra",
+      "D own",
+      "ĠÃĦ r",
+      "ĠEl ite",
+      "Ġall ons",
+      "Ġmay onnaise",
+      "ĠS ustain",
+      "prising ly",
+      "Ġsuper vis",
+      "Ġê·¸ëłĩ ì£ł",
+      "Ġunemploy ed",
+      "Ġfresh ly",
+      "Ġ×ŀ× ¢",
+      "ĠD h",
+      "Ġtack ling",
+      "Ġo gr",
+      "Ġì´ Īë",
+      "ãĤĪ ãĤį",
+      "Ġlo ft",
+      "ar ah",
+      "ĠA irl",
+      "ĠD ir",
+      "ĠÐľ ожно",
+      "Ġbook ing",
+      "ĠC RA",
+      "Ġhtt ps",
+      "Ġcho ke",
+      "Ġg own",
+      "Ġno ite",
+      "Ġz ac",
+      "ist ol",
+      "Ġsec re",
+      "Ġresemb les",
+      "Ġcu ad",
+      "ìĤ¬ ê°Ģ",
+      "sh ow",
+      "Ġbl anc",
+      "Ġag u",
+      "ĠPr int",
+      "ast ed",
+      "ĠWe ather",
+      "i pl",
+      "Ġobsc ure",
+      "Ġcont e",
+      "ough s",
+      ") ;",
+      "ĠD ame",
+      "ä¸Ģ 缴",
+      "Ġclar ification",
+      "Ġintim acy",
+      "Ġup hold",
+      "ĠMir ror",
+      "Ġw agon",
+      "x ide",
+      "Ġcl og",
+      "app er",
+      "ĠImmedi ately",
+      "ú de",
+      "Ġtouch down",
+      "Ġro oft",
+      "аÑĪ а",
+      "Ġç ıkt",
+      "Ġla isser",
+      "ĠUn real",
+      "ens itive",
+      "Ġ12 3",
+      "Ġpl aster",
+      "Ġduck s",
+      "Ġet me",
+      "Ġb ishop",
+      "bre vi",
+      "Ġb ic",
+      "ä¸ĭ åİ»",
+      "Ġrun time",
+      "Ġamb itions",
+      "м аÑĤ",
+      "ĠWe in",
+      "ĠMar i",
+      "ĠíĬ ¸ë",
+      "Ġresol ver",
+      "Ġng Ãły",
+      "ĠR ise",
+      "ãĤĪãģĨ ãģ«",
+      "ĠCr us",
+      "Ġmerchand ise",
+      "Ġel i",
+      "Ġstate wide",
+      "Ġow l",
+      "éģ ł",
+      "æĶ ¹",
+      "Ġtwist ing",
+      "Ġcontam inated",
+      "ĠCom merce",
+      "hy thm",
+      "ĠÃ Ī",
+      "Ġìĭ ¤ë",
+      "Ġmus ste",
+      "u ir",
+      "Ġsum s",
+      "ĠSome where",
+      "ãĥ İ",
+      "Ġk ami",
+      "Ġa ired",
+      "ĠAND REW",
+      "Ġê º",
+      "Ġv iendo",
+      "Ġantib ody",
+      "Ġabsol ument",
+      "Ġprotest ers",
+      "ĠQué bec",
+      "st adt",
+      "Sha un",
+      "Ġcham bers",
+      "ĠWe ar",
+      "ĠEffect s",
+      "Ġhaz ards",
+      "Ġne i",
+      "Ġcoraz ón",
+      "Ġá ¼",
+      "ĠS G",
+      "Ķ ©",
+      "ĠìĹŃ ìĭľ",
+      "Ġcom fy",
+      "ĠC ody",
+      "Ġpens ando",
+      "Ġg anska",
+      "ĠAc ross",
+      "öll ig",
+      "aby te",
+      "Ġwed ge",
+      "Ġkal ian",
+      "Ġsig ue",
+      "end es",
+      "ĠGro ÃŁ",
+      "Ġutil iser",
+      "Ġfl own",
+      "ани Ñİ",
+      "Ġle var",
+      "rest rial",
+      "Ġillust rations",
+      "Ġas lında",
+      "BLE EP",
+      "Ġдо ÑģÑĤ",
+      "Ġtur ret",
+      "Ġsuit case",
+      "ziÄĻ ki",
+      "Ġsket ches",
+      "Ġac red",
+      "ĠRe i",
+      "Ġt sun",
+      "ĠS ag",
+      "Ġthird s",
+      "ĠKIR BY",
+      "ra i",
+      "Ġhuman os",
+      "Ġrecomm ends",
+      "Ġextraordin arily",
+      "Ġcommence ment",
+      "K N",
+      "ope z",
+      "Ġ×ij× ©",
+      "Ġlet hal",
+      "ĠEst amos",
+      "Ġinspect or",
+      "ĠSe ok",
+      "e un",
+      "Ġoff shore",
+      "Ġget tin",
+      "ye ars",
+      "ĠSil ence",
+      "ĠNat ur",
+      "up un",
+      "Ġtr zy",
+      "Ġno get",
+      "Ġhamb urger",
+      "ĠPra ise",
+      "é nd",
+      "Ġ197 1",
+      "yl ie",
+      "k rit",
+      "ĠìĥĿê°ģ ìĿ´",
+      "çļ ®",
+      "Ġmoment os",
+      "Ġest é",
+      "Ġdisse min",
+      "Ġgig s",
+      "Ġdes af",
+      "Ġav is",
+      "ĠZ oo",
+      "ĠìķĬ ìĿĢ",
+      "h äng",
+      "åı ¥",
+      "h ake",
+      "ĠB ism",
+      "Ġre think",
+      "ĠMal colm",
+      "Ġident ifies",
+      "l ower",
+      "ix el",
+      "Ġtv Ã¥",
+      "k ed",
+      "ier z",
+      "Ġö ffentlich",
+      "Ġproc laim",
+      "so on",
+      "l ol",
+      "Ġlo i",
+      "Ġb itten",
+      "ro llo",
+      "Ġser mon",
+      "Ġes qu",
+      "Ġjack ets",
+      "Ġgr áfic",
+      "Ġпок азÑĭв",
+      "Ġcabe za",
+      "ch odzi",
+      "Ġpel vis",
+      "Ġnost algia",
+      "Ġbre w",
+      "Ġshort cuts",
+      "ĠAd emás",
+      "Ġsuperfic ial",
+      "åħ© åĢĭ",
+      "Ġbo ca",
+      "ĠæĪij æĺ¯",
+      "iment os",
+      "åĽł 为",
+      "Ġspr outs",
+      "é£ Ľ",
+      "ĠJon as",
+      "ĠFloren ce",
+      "st atic",
+      "da ughter",
+      "* )",
+      "ÅĤ by",
+      "f ashion",
+      "ĠG inger",
+      "Ġë§ ¤ë",
+      "Ġhust le",
+      "ut os",
+      "ĠÑĤ Ñıж",
+      "ĠL ös",
+      "ש ×Ļ×Ŀ",
+      "any ch",
+      "tu ber",
+      "Ġtid y",
+      "Ġfront al",
+      "Ġwhis key",
+      "Ġhum id",
+      "ĠÎ Ł",
+      "Ġr idge",
+      "Ġmar in",
+      "Ġb ientôt",
+      "ĠCarr ie",
+      "ch w",
+      "Ġtah un",
+      "ĠEr geb",
+      "F R",
+      "Ġìłķ ë¶Ģ",
+      "ĠSold ier",
+      "Ġenlight enment",
+      "Ġexam ining",
+      "ĠNot re",
+      "Ġer am",
+      "ĠSun ny",
+      "Ġlay ered",
+      "ĠD azu",
+      "r ades",
+      "好 åIJĥ",
+      "ĠнаÑĪ ей",
+      "Ġtim ber",
+      "Ġman ners",
+      "ĠBir mingham",
+      "Ġmini ature",
+      "omet ers",
+      "Ġfill er",
+      "ĠR ip",
+      "ĠK omb",
+      "own er",
+      "ì ¿",
+      "id ian",
+      "Ġdem ás",
+      "ĠÙĪ ت",
+      "Ġpreca utions",
+      "Ġgovern o",
+      "z elf",
+      "ĠCom plete",
+      "å¸ ĥ",
+      "ĠPh antom",
+      "ãģ¾ ãģļ",
+      "Ġн ез",
+      "ĠкаÑĢ ÑĤ",
+      "ĠAnt wort",
+      "ĠPf izer",
+      "ĠFran co",
+      "Ġw ÅĤ",
+      "Ġfr ig",
+      "es per",
+      "Ġk ale",
+      "Ġfilm maker",
+      "Ġk urt",
+      "Ġinv alid",
+      "å± Ģ",
+      "are lla",
+      "Äĥ ng",
+      "ram ento",
+      "Ġnutr itional",
+      "Ġdict ators",
+      "Ġaf in",
+      "Ġf uzzy",
+      "ĠG ina",
+      "ó t",
+      "ĠExtrem adura",
+      "Ġdemonst rations",
+      "ĠMont gomery",
+      "íķ´ì Ħ¤",
+      "ĠGand hi",
+      "ãĥ Ŀ",
+      "ç½ ®",
+      "Ġreun ion",
+      "Ġjaki ÅĽ",
+      "ĠZ ug",
+      "OU GH",
+      "l ifting",
+      "Ġ à²",
+      "á¹Ľ á¹£",
+      "e b",
+      "ĠW OW",
+      "ĠSh iva",
+      "omet ry",
+      "Ġwild ly",
+      "Ġt ended",
+      "Ġmeg ap",
+      "ì² ĺ",
+      "Ġna use",
+      "Ġg erek",
+      "ãĥ ĭ",
+      "ĠMar cel",
+      "Ġn este",
+      "خ ر",
+      "Ġfe h",
+      "åĨ ħ",
+      "susp enseful",
+      "ĠWrest le",
+      "ĠPalestin ians",
+      "ĠG ORD",
+      "iy et",
+      "ĠÑĢ ади",
+      "Ġvers uchen",
+      "Ġtrans istor",
+      "ĠÐŁÑĢ оÑģÑĤо",
+      "Ġпон ÑĢав",
+      "Ġrhy me",
+      "ĠVerm ont",
+      "pl atz",
+      "è® °",
+      "ĠÄ°ÅŁ te",
+      "ĠH ag",
+      "ĠÐĺ м",
+      "ĠÑĢаÑģÑģк аз",
+      "Ġmet ros",
+      "ĠInfin ity",
+      "w olf",
+      "ib al",
+      "ft ig",
+      "Ġ ÚĨ",
+      "Ġíĺ¹ ìĭľ",
+      "Ġo ggi",
+      "Ġdisp osit",
+      "ĠпÑĢ ил",
+      "ĠвÑĭ пол",
+      "Ġth ôi",
+      "ĠK ENN",
+      "Ġhand ing",
+      "act us",
+      "Ġtac os",
+      "Ġformer ly",
+      "ĠCorinth ians",
+      "ãģ« ãģ¯",
+      "ÑĨÑĸ ÑĹ",
+      "Ġpad re",
+      "Ġcongreg ation",
+      "æ ij",
+      "fer t",
+      "Ġsub ir",
+      "ais er",
+      "qu a",
+      "ara oh",
+      "ĠCur ry",
+      "ĠìķĬ ëĬĶ",
+      "ел Ñİ",
+      "Ġf uss",
+      "Ġbo oty",
+      "Ġl ows",
+      "Ġh ommes",
+      "ĠM H",
+      "ĠDisney land",
+      "w ent",
+      "Ġresid ue",
+      "Ġbe eping",
+      "è¼ ķ",
+      "ät ta",
+      "Ġm ould",
+      "ĠPro jekt",
+      "st alk",
+      "Ġartif act",
+      "ĠAnt rag",
+      "ĠAM D",
+      "ĠCry pt",
+      "Ġë© Ķ",
+      "ĠFel ipe",
+      "ĠCO B",
+      "el u",
+      "Ġself ies",
+      "ĠS anti",
+      "ch utz",
+      "ĠУ кÑĢаÑĹ",
+      "ges amt",
+      "Ġflo ck",
+      "j az",
+      "pl ain",
+      "Ġwr inkles",
+      "Ġre ais",
+      "Ġpal jon",
+      "Ġempower ment",
+      "Ġattend ees",
+      "pp a",
+      "Ġn eden",
+      "он Ñĭ",
+      "Ġtime frame",
+      "ĠCher ry",
+      "Ġid ée",
+      "Ġg ag",
+      "Ġdon key",
+      "Ġô ng",
+      "ĠH are",
+      "éļ Ľ",
+      "ĠK ara",
+      "Ġacom pan",
+      "pl aces",
+      "im ientos",
+      "ĠH amm",
+      "б и",
+      "ub en",
+      "ili yor",
+      "Ġth irst",
+      "Ġk ry",
+      "ĠGeorget own",
+      "׳ ×Ķ",
+      "Ġor ch",
+      "Ġheart beat",
+      "Ġtransform ations",
+      "est ones",
+      "ĠK H",
+      "Ġcart oons",
+      "Ġan ci",
+      "Ġworth less",
+      "Ġtail ored",
+      "p u",
+      "Americ ans",
+      "Ġp iles",
+      "ĠMon key",
+      "Ġbas in",
+      "ĠTem per",
+      "ĠP aint",
+      "Ġpunch ing",
+      "Ġba ik",
+      "ĠOak land",
+      "v re",
+      "ÅŁ allah",
+      "yd d",
+      "Ġcas ually",
+      "od u",
+      "Ġc oded",
+      "ĠNorweg ian",
+      "ĠV ince",
+      "Ġprem ature",
+      "ĠProm ise",
+      "ек ÑģÑĤ",
+      "Ġdevast ated",
+      "ĠPrem ium",
+      "ĠPar am",
+      "ĠÃĸ yle",
+      "um uz",
+      "P O",
+      "r ators",
+      "Ġlamp s",
+      "Ġterritor ial",
+      "Ġback bone",
+      "list ed",
+      "D Y",
+      "ĠاÙĦ ر",
+      "Ġpurs ued",
+      "ĠComm ons",
+      "Ġê³ ¡",
+      "lo cks",
+      "ed or",
+      "Ġconce ived",
+      "g ere",
+      "Ġdisappe aring",
+      "ĠS ull",
+      "ĠìĹ °ë",
+      "Ġho ffe",
+      "Ġdet ox",
+      "íĶ Į",
+      "Ġret ir",
+      "ĠëģĿ ëĤ",
+      "Ġper gunta",
+      "ĠB OY",
+      "ç² ¾",
+      "Ġp enn",
+      "æĿ¥ äºĨ",
+      "h és",
+      "h on",
+      "Ġcatastroph ic",
+      "Ġa ust",
+      "Ġtor so",
+      "Ġìĸ´ ëĬIJ",
+      "ĠìĤ¬ëŀĮë ĵ¤ìĿ´",
+      "Ġmarvel ous",
+      "ĠHar ley",
+      "ach ine",
+      "Ġti ế",
+      "itt o",
+      "ĠI ÃŃm",
+      "yl on",
+      "Ġshut down",
+      ".' '",
+      "Ġap ologies",
+      "ĠCommun ication",
+      "ĠговоÑĢ Ñİ",
+      "ãģĤ ãĥ¼",
+      "âĦ ¢",
+      "ÃŃ veis",
+      "ac un",
+      "Ġret aining",
+      "Ġcontrad iction",
+      "ĠAD AM",
+      "C OM",
+      "Bry an",
+      "ĠM onsieur",
+      "Ġadap ting",
+      "Ш ÐIJ",
+      "ĠSc r",
+      "änd ert",
+      "Ġpl aus",
+      "ä»Ĭ天 çļĦ",
+      "Ġon set",
+      "Ġassist ants",
+      "Ġval ves",
+      "Ġsc atter",
+      "ĠR ust",
+      "aw ia",
+      "Ġread iness",
+      "Ġp ais",
+      "Ġb ible",
+      "Ġamb iente",
+      "Ġа меÑĢик",
+      "Ġunc ond",
+      "Ġk alk",
+      "åĬ ¨",
+      "Ġmo c",
+      "un n",
+      "Ġact u",
+      "Ġhum ming",
+      "iss imo",
+      "ĠPat rol",
+      "g ow",
+      "ãĥ ¤",
+      "ĠTHE Y",
+      "ĠBod en",
+      "ĠB ie",
+      "Ġre el",
+      "ĠÑĥÑģл ов",
+      "Ġende avor",
+      "ĠPer iod",
+      "ustom ed",
+      "m als",
+      "al on",
+      "B ox",
+      "ĠÏĥ αÏĤ",
+      "Ġom dat",
+      "Ġal tre",
+      "ĠHe h",
+      "k ad",
+      "Ġprotect or",
+      "Ġdomin ance",
+      "odynam ic",
+      "Ġcommunic ated",
+      "k ö",
+      "Ġprede cessor",
+      "ĠL uk",
+      "ĠFl ower",
+      "Ġãģ ©",
+      "po que",
+      "ÑĤи ÑĢов",
+      "Ġret rospect",
+      "Ġdecis ive",
+      "Ġexem pel",
+      "{ \\",
+      "ĠR ück",
+      "r ite",
+      "ĠZe us",
+      "Ġcal orie",
+      "Ġattract ions",
+      "ĠH inter",
+      "Ġuh m",
+      "ĠíĮ IJ",
+      "Ġrul ers",
+      "Ġdiscour aged",
+      "Ġaconte cer",
+      "Ġacc ents",
+      "ĠOpt im",
+      "ĠAl g",
+      "k ids",
+      "20 21",
+      "ĠLind say",
+      "Ġfilm makers",
+      "pr owad",
+      "Ġter ug",
+      "ëĭ ´",
+      "ĠSom mer",
+      "20 18",
+      "Ġborrow ing",
+      "ĠTrans fer",
+      "н оп",
+      "ari as",
+      "Ġhead phone",
+      "ì¼ ľ",
+      "Ġtransl ating",
+      "Ġauf ge",
+      "ப à®Ł",
+      "we is",
+      "av ant",
+      "pa id",
+      "b aby",
+      "Ġtough est",
+      "Ġrepe ats",
+      "ĠTer esa",
+      "L ord",
+      "Ġacab ar",
+      "ĠR ide",
+      "d ir",
+      "Ġl eng",
+      "Ġd wa",
+      "Ġhead aches",
+      "Ġn ữa",
+      "ĠнаÑģ ÑĤоÑıÑī",
+      "Ġbo ils",
+      "Ġlong ing",
+      "ri as",
+      "ó rio",
+      "ĠParad ise",
+      "ĠSeñ or",
+      "erd em",
+      "Ġrein st",
+      "Ġsal aries",
+      "Ġinsec urity",
+      "ÅĤo ÅĽci",
+      "ĠабÑģолÑİÑĤ но",
+      "ink en",
+      "ĠEd dy",
+      "ud os",
+      "Ġd ummy",
+      "Ðļ ак",
+      "s ix",
+      "Ġin box",
+      "Ạ©",
+      "Pe ople",
+      "á»ĵ ng",
+      "Ġorganiz ers",
+      "f ind",
+      "Ġü l",
+      "ĠCO M",
+      "ż a",
+      "we ile",
+      "Comment ary",
+      "íĬ¸ë ¥¼",
+      "ĠMitt el",
+      "k us",
+      "èĽ ĭ",
+      "ठ¨",
+      "ir al",
+      "Ġgar ment",
+      "ικ ά",
+      "Ġst ool",
+      "pay ers",
+      "Ġsh immer",
+      "ĠO llie",
+      "ĠJe żeli",
+      "è¿ĺ æľī",
+      "Ġ197 7",
+      "Ġje ux",
+      "Ġext inct",
+      "ĠTransport ation",
+      "ĠM aker",
+      "Ġj ohn",
+      "Ġrich est",
+      "Ġtraum at",
+      "Ġli egen",
+      "´ë ¥¼",
+      "è¿Ļ éĩĮ",
+      "Ġun rest",
+      "ĠSt raw",
+      "æĭľ æĭľ",
+      "Ġcom a",
+      "ĠKr isten",
+      "ĠÐļон еÑĩно",
+      "ĠBry ce",
+      "ĠÑıк Ñĸ",
+      "Ġpearl s",
+      "Ġпоним аÑİ",
+      "Ġadd itions",
+      "Ġas ympt",
+      "ĠменÑĮ ÑĪе",
+      "Ġsc ans",
+      "Ch ild",
+      "ĠH ide",
+      "к ÑĥÑİ",
+      "et as",
+      "Ġd ank",
+      "Ġple as",
+      "Ġess ays",
+      "Ġj ets",
+      "åħ Ĵ",
+      "Ġв ед",
+      "Ġposit ives",
+      "ho f",
+      "- )",
+      "zz o",
+      "Ġstar ters",
+      "Ġsm iled",
+      "Ġ194 4",
+      "qu iera",
+      "Ġro k",
+      "Ġpu esto",
+      "N ico",
+      "Ġsim ulations",
+      "Ġ à¶",
+      "Ġintrig ued",
+      "ĠOver watch",
+      "åĸ Ĥ",
+      "s igh",
+      "b ai",
+      "Ġë§IJ ê³ł",
+      "id é",
+      "Ġcra bs",
+      "áºŃ p",
+      "ĠIraq i",
+      "ìĿ´ë ¥¼",
+      "ÑĤ Ñı",
+      "ĠSoph ia",
+      "ĠDN S",
+      "Ġönem li",
+      "ĠLu o",
+      "Ŀ ¤",
+      "ĠCoun sel",
+      "l igen",
+      "анÑĮ ÑĪе",
+      "Ġtrump et",
+      "Ġd apat",
+      "ĠJ M",
+      "ĠEVER Y",
+      "Ġå°į ä¸įå°į",
+      "å¤ ¢",
+      "ĠL ayer",
+      "Ġc ô",
+      "н ал",
+      "ĠJ oo",
+      "ĠH ack",
+      "Ġs unt",
+      "ĠLeon ard",
+      "ĠFire base",
+      "äng er",
+      "Ġexpl oding",
+      "v oy",
+      "Ġì¦ IJ",
+      "ĠÑģ еÑĢÑĮ",
+      "Ġsever ity",
+      "Ġbest imm",
+      "çµIJ æŀľ",
+      "Ġt iring",
+      "Ġprocure ment",
+      "Ġdiplom acy",
+      "Ġdecor ative",
+      "ĠÙĬ ا",
+      "Ġpenet ration",
+      "Õ «",
+      "Ġout right",
+      "EN E",
+      "ĠUn i",
+      "od les",
+      "Ġz eros",
+      "Ġdelight ful",
+      "j m",
+      "Ġdo po",
+      "没 äºĭ",
+      "Ġposit ivity",
+      "ĠVIS TA",
+      "ĠRes ource",
+      "íĥ Ģë",
+      "ÑĪ ие",
+      "C arl",
+      "Ġpip ing",
+      "Ġchop ping",
+      "ĠGan ze",
+      "ü ss",
+      "ĠA o",
+      "Ġsh attered",
+      "ĠDet ective",
+      "Ġund oubtedly",
+      "Ġhall uc",
+      "Ġen ch",
+      "Ñĭ Ñĩно",
+      "ÑĥлÑı ÑĢ",
+      "is esti",
+      "Ġped als",
+      "Ġdur um",
+      "¤í Ķ",
+      "la imer",
+      "Ġprop re",
+      "C u",
+      "Ġtransl ator",
+      "Ġca ÅĤ",
+      "Ġê·¸ 걸",
+      "Ġca ÅĤy",
+      "U A",
+      "Ġrev ised",
+      "Ġпод об",
+      "ĠArt icle",
+      "ĠHait i",
+      "ĠÃ ĵ",
+      "ĠC trl",
+      "Ġroz m",
+      "la it",
+      "Ġletz te",
+      "is pering",
+      "dis play",
+      "Ġalumin ium",
+      "Ġpalab ras",
+      "Ġconoc er",
+      "Ġz itten",
+      "Ġdir ig",
+      "åıª æľī",
+      "Ġbrain storm",
+      "Ġw ifi",
+      "ĠPart icip",
+      "Ġview point",
+      "ĠQu an",
+      "Ġhier arch",
+      "W elcome",
+      "å¯ ¾",
+      "Ġoff en",
+      "ĠRe covery",
+      "gan o",
+      "W ould",
+      "Ġrep ro",
+      "Ġper ceptions",
+      "Ġdem asi",
+      "ĠBangl adesh",
+      "ĠIncred ible",
+      "Ġlet zt",
+      "Ġbehav ing",
+      "Ġaston ishing",
+      "Ġâ Ĩ",
+      "ĠëĤ¨ ìŀIJ",
+      "èµ° äºĨ",
+      "ãĥ Ķ",
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+      "3 50",
+      "Ġíĺ¼ ìŀIJ",
+      "Ġë¶Ħë ĵ¤ìĿ´",
+      "Ġwar p",
+      "Ġteng a",
+      "Ġsympath etic",
+      "Ġbiz i",
+      "ĠZ ack",
+      "ied o",
+      "Ġëī ´ì",
+      "p iel",
+      "ĠÑĤ ол",
+      "Ġsc aled",
+      "ĠPET ER",
+      "ĠCO MM",
+      "ĠC ame",
+      "Ġcatast rophe",
+      "Ġsweat y",
+      "ig ration",
+      "Ġstuff ing",
+      "ĠÏĢολ Ïį",
+      "ĠDri ver",
+      "zy st",
+      "T ech",
+      "Ġassess ed",
+      "ĠSur face",
+      "ır ım",
+      "s ur",
+      "ler weile",
+      "Ġд ог",
+      "Ġshut ting",
+      "Ġfr actions",
+      "ĠÑģ ол",
+      "every one",
+      "Ġer n",
+      "ĠÐĿ ов",
+      "Ġdefend ers",
+      "Ġvers ucht",
+      "ãĥ³ãĥ Ģ",
+      "Ġpol ity",
+      "ĠÐŁ он",
+      "ver ständ",
+      "Ġbrows ers",
+      "Ġtransform ative",
+      "Ġdict ate",
+      "ĠLE GO",
+      "Ġning una",
+      "ê´ ij",
+      "Ġp izz",
+      "ĠHar old",
+      "ĠL opez",
+      "Ú¾ ÛĮ",
+      "an ız",
+      "atch et",
+      "ÙĬ ت",
+      "Ġl ernen",
+      "Ġê·Ģ ìŬ",
+      "Ġhous ed",
+      "Ġclean se",
+      "ĠW AT",
+      "lar ation",
+      "Ġby tes",
+      "Ġtuck ed",
+      "Ġfault s",
+      "д о",
+      "F X",
+      "Ġìĸ¼ë§ ĪëĤĺ",
+      "Ġde form",
+      "Ġcontract ing",
+      "ĠTIM E",
+      "ir se",
+      "Ġne ben",
+      "Ġc erc",
+      "ĠArm strong",
+      "Ġtest er",
+      "Ġparf ait",
+      "Ġjealous y",
+      "Ġtox ins",
+      "Ġdis bel",
+      "ÑĥÑĢ Ñĭ",
+      "imp ression",
+      "Ġprost ate",
+      "Ġfire wall",
+      "Ġclass ics",
+      "еÑĩ ÑĮ",
+      "Ġsocial ism",
+      "Ġgrac ious",
+      "ĠÑģ нова",
+      "Ġд нÑı",
+      "Ġburn er",
+      "ĠMin or",
+      "Ġìļ°ë ¦¬ë",
+      "Ġjed es",
+      "Ġcontinu um",
+      "Ġh ots",
+      "Ġoccur rence",
+      "Ġadminister ed",
+      "Ġзам еÑĤ",
+      "Ġhes itation",
+      "Ġdr ills",
+      "er ca",
+      "ĠвÑĤоÑĢ ой",
+      "Ġstead ily",
+      "Ġinsan lar",
+      "Ġi han",
+      "í ij",
+      "Ġhel per",
+      "ĠSen in",
+      "åģ ľ",
+      "ов ание",
+      "ĠER IC",
+      "b la",
+      "ĠAcad emic",
+      "Ġhuman ities",
+      "bl ack",
+      "ump y",
+      "ort ex",
+      "Ġìł Īë",
+      "ĠØ¥ ÙĨ",
+      "Ġdiscl ose",
+      "ĠEl ijah",
+      "Ġλ ÎŃ",
+      "ĠQu er",
+      "ب ÙĦ",
+      "ãĤ ¡",
+      "T ell",
+      "ar le",
+      "Ñĸ ÑĢ",
+      "Ġaug mented",
+      "Ġë¹Ħ ìĬ·",
+      "Ġand roid",
+      "ठ¤",
+      "ar ma",
+      "Ġs zer",
+      "ge ord",
+      "Ġge ek",
+      "Ġye ux",
+      "Ġp ong",
+      "ĠãģĿ ãģĨ",
+      "Ġtort ured",
+      "ĠB ath",
+      "z ig",
+      "ason able",
+      "Ġn ets",
+      "Ġbar u",
+      "ĠFl at",
+      "ĠV ater",
+      "ĠTer ror",
+      "ĠA vo",
+      "Ġceremon ies",
+      "ro e",
+      "Ùģ س",
+      "O ps",
+      "Ġhy vin",
+      "Ġap resent",
+      "ol or",
+      "ĠигÑĢ Ñĭ",
+      "ort on",
+      "Ġê·¸ëŀ ¬",
+      "Ġlook in",
+      "ĠT Y",
+      "ĠM int",
+      "Ad d",
+      "Ġm ite",
+      "ĠSm oke",
+      "Ġnot a",
+      "Ġm oss",
+      "ĠAb end",
+      "Ġì» ¨",
+      "Ġexagger ated",
+      "f ires",
+      "Ġred ist",
+      "ff iti",
+      "Ġopen ness",
+      "ê°IJ ìĿ´",
+      "ende u",
+      "ен ной",
+      "W atch",
+      "Ġav atar",
+      "ĠP ey",
+      "ur un",
+      "Ġsen za",
+      "Ġì§Ģ ìĹŃ",
+      "ĠNat omiast",
+      "Ġemer gence",
+      "ray s",
+      "Ġcraft ed",
+      "g ary",
+      "ãģł ãģij",
+      "ü ng",
+      "- \"",
+      "Ġhack ed",
+      "Ġstr ay",
+      "en cie",
+      "em o",
+      "Ġcom en",
+      "ĠK ız",
+      "ĠJ asmine",
+      "ĠH indi",
+      "man as",
+      "Ġinfin itely",
+      "em on",
+      "ìĿ¸ëį° ìļĶ",
+      "j ak",
+      "Ġro aring",
+      "éri que",
+      "s weise",
+      "ĠRo lex",
+      "åł± å°İ",
+      "ĠStu art",
+      "bn b",
+      "Ġdiagn ose",
+      "Ġcoher ent",
+      "ĠM J",
+      "æºĸ åĤĻ",
+      "Ġp ike",
+      "l av",
+      "Ġorchest ral",
+      "а ÑģÑĤи",
+      "Ġterm inar",
+      "Ġgather ings",
+      "Ġcompl iant",
+      "Ġupgrad ing",
+      "Ġregul ator",
+      "Ġlan ç",
+      "éĢ £",
+      "Ġmerch ants",
+      "ta wa",
+      "Ġmonit ored",
+      "Ġrend re",
+      "ä¸ ¤",
+      "Ġunter wegs",
+      "ang uard",
+      "g ard",
+      "ĠBel ow",
+      "du ino",
+      "ĠЦ е",
+      "Ġimped ance",
+      "ìľ ¡",
+      "ä» ½",
+      "Ġakt uell",
+      "ĠV atic",
+      "åŃ ©",
+      "Ġste wards",
+      "Ġbright est",
+      "Ġk enn",
+      "Ġk au",
+      "ĠMat rix",
+      "ĠB ark",
+      "ĠðŁ ij",
+      "Ġt aper",
+      "Ġcas ino",
+      "ר ×Ķ",
+      "ys ical",
+      "Ġbuild ers",
+      "ĠczÅĤ owie",
+      "ĠNep al",
+      "Ġ! \"",
+      "Ġterm e",
+      "Ġin nych",
+      "Ġmath s",
+      "Ġdraft ed",
+      "ĠB alk",
+      "Ġhesit ant",
+      "Ġvolt ar",
+      "Ġrev ive",
+      "ĠÑĦилÑĮ ма",
+      "Ġassass in",
+      "ĠS olutions",
+      "Ġdu el",
+      "Ġbear ings",
+      "à¸Ħ ะ",
+      "Ġrook ie",
+      "ik at",
+      "Ġbisc uits",
+      "Ġc ords",
+      "Ñĥв аÑĤи",
+      "AR IN",
+      "Ġprogress ing",
+      "ĠG ir",
+      "Ġpenet rate",
+      "ĠSt orage",
+      "e ight",
+      "ĠÑĤ ÑĢÑĥ",
+      "Ġdon ÃŃt",
+      "Ġsiz in",
+      "Ġout dated",
+      "ĠнаÑĪ и",
+      "Ġaff ir",
+      "Ġspo ons",
+      "Ġon i",
+      "Ġfl ank",
+      "ĠG ol",
+      "h ã",
+      "Ġp éri",
+      "Ġhonor able",
+      "ĠBreat he",
+      "sc enes",
+      "Ġob viamente",
+      "ик Ñģ",
+      "Ġש ×ŀ×",
+      "Ġsmooth ie",
+      "ŀ Īë",
+      "Ġd ime",
+      "ĠíĸĪ ìĸ´ìļĶ",
+      "Ġapp el",
+      "ĠCath olics",
+      "Ġsing les",
+      "Ġlat en",
+      "Ġç ünkü",
+      "ĠV ader",
+      "æı Ľ",
+      "Ġvard ı",
+      "ĠIst anbul",
+      "gr é",
+      "ĠEl sa",
+      "ë l",
+      "Ġinve ce",
+      "Ġcr ane",
+      "Ġo be",
+      "ĠSh ark",
+      "Ġsm ack",
+      "Ġrest oring",
+      ". \\",
+      "Ġë¹ łë",
+      "Ġf aded",
+      "um bers",
+      "S inging",
+      "Ġdep ressing",
+      "th est",
+      "ĠW ahr",
+      "Ġmult itude",
+      "ÑĢавÑģÑĤв ÑĥйÑĤе",
+      "rij k",
+      "ek a",
+      "Ġcomplet es",
+      "ĠWell s",
+      "Ġro y",
+      "ĠPr ay",
+      "ĠKal au",
+      "iz in",
+      "iaÅĤ em",
+      "Ġlo com",
+      "ĠNash ville",
+      "ĠPent agon",
+      "ë ¯¸",
+      "ĠNE W",
+      "Äħ Äĩ",
+      "ÃŃ ss",
+      "Ġmarry ing",
+      "Ġfe ud",
+      "íĻ ķ",
+      "æĢ ¥",
+      ") !",
+      "ĠOper ations",
+      "Ñĥ ÑĶ",
+      "Ġmo je",
+      "Ġinstruct ed",
+      "ĠëĪĦ 구",
+      "Ġ×Ķ× Ĵ",
+      "ĠпомоÑī ÑĮÑİ",
+      "Ġsab ia",
+      "ìķĺ ìĸ´ìļĶ",
+      "pl ane",
+      "p ri",
+      "Ġпол ноÑģÑĤÑĮÑİ",
+      "ĠK itty",
+      "Ġpróp rio",
+      "ed ere",
+      "Ġinteres ante",
+      "Ġд е",
+      "Ġcond ensed",
+      "Ġav ent",
+      "T OR",
+      "Ġgre asy",
+      "AR K",
+      "ort a",
+      "A J",
+      "Ġdis reg",
+      "Ġcorrect ions",
+      "Ġst ero",
+      "Ġinfluen za",
+      "Ġdess es",
+      "Ġball ots",
+      "Ġme get",
+      "Ġma fia",
+      "Ġb öl",
+      "n ost",
+      "ĠÑģÑĤ аÑĤÑĮ",
+      "Ġrespond er",
+      "Ġhint en",
+      "g rav",
+      "à¸Ń ะ",
+      "yn chron",
+      "Ġvi ens",
+      "Ġsam o",
+      "Ġd t",
+      "pan nt",
+      "ĠÅĽwi at",
+      "Ġзап иÑģ",
+      "Ġmer ged",
+      "Ġke p",
+      "Ġmis leading",
+      "Ġdig amos",
+      "Ġam mon",
+      "è¾ Ľ",
+      "ch et",
+      "Ġê°Ģ ìł¸",
+      "Ġun i",
+      "ĠëIJĺ ëĬĶëį°",
+      "Ġнап ÑĢав",
+      "ĠкоÑĤоÑĢ ого",
+      "Ġanim ate",
+      "×ķ× IJ×",
+      "еÑĢ в",
+      "Ġmin ced",
+      "Ġka um",
+      "ãģĤ ãģģ",
+      "ÏĢ ε",
+      "л ег",
+      "exist ing",
+      "Ġplata form",
+      "ĠK RIS",
+      "ìĽ ł",
+      "ĠFamil ien",
+      "ĠLib ya",
+      "Ġbiod iversity",
+      "Ġidi ots",
+      "ird i",
+      "Ġszy b",
+      "ĠRoll ing",
+      "ü cht",
+      "ĠÑĥд ив",
+      "Ñģ Ñĥд",
+      "Ġreal izar",
+      "Ġcan ned",
+      "ĠÑĢ ан",
+      "Ġmet abolic",
+      "ĠBe ef",
+      "Ġkil ka",
+      "лÑİ Ñģ",
+      "Ġreg istry",
+      "моÑĤÑĢ иÑĤе",
+      "Ġviel ä",
+      "Ġod c",
+      "Ġcondem ned",
+      "æ© ĭ",
+      "f al",
+      "ĠD il",
+      "wo ÅĽci",
+      "A w",
+      "Ġstatist ically",
+      "Ġso gen",
+      "ĠB ETH",
+      "Ġsh aving",
+      "å¹ ¸",
+      "oc al",
+      "ĠFun ny",
+      "Ġpeace fully",
+      "Ġaddict ive",
+      "ĠIns ert",
+      "la uf",
+      "Ġexperien cia",
+      "é¦ĸ åħĪ",
+      "иÑĤ елÑı",
+      "ÃŃ gen",
+      "ág ina",
+      "Ġabdom en",
+      "íķľ ëĭ¤",
+      "ic us",
+      "im ana",
+      "ì į¨",
+      "arch ing",
+      "Ġkonk ret",
+      "ìķ ĺë",
+      "ек а",
+      "ou fl",
+      "ive l",
+      "Ġn ude",
+      "èt res",
+      "Ġm onsieur",
+      "Ġcl ash",
+      "Ġtherap ists",
+      "Ġcub ed",
+      "Ġretrou ver",
+      "Ġwave form",
+      "Ġpot em",
+      "ĠForm er",
+      "is ión",
+      "åº ľ",
+      "Ġ×IJ× Ŀ",
+      "und os",
+      "ĠMein ung",
+      "ص ÙĦ",
+      "ĠJ ude",
+      "Ġn Ã¥r",
+      "ĠLeon ardo",
+      "ĠCr isto",
+      "ĠG OT",
+      "ÑģÑĤÑĢÑĥ к",
+      "L AN",
+      "Ġg Ã¥ng",
+      "Ġdé b",
+      "ĠFrankf urt",
+      "Ġcra ppy",
+      "Ġli l",
+      "ann ée",
+      "ĠмеÑģÑĤ е",
+      "RE T",
+      "ĠN er",
+      "ĠCO STA",
+      "Ġjed em",
+      "Ġcurt ains",
+      "Ġiter ations",
+      "Ġun av",
+      "Ġpla que",
+      "or um",
+      "ĠÎ ¶",
+      "Ġnúmer os",
+      "Ġdes ap",
+      "² ½",
+      "Ġcomp iled",
+      "Ġref le",
+      "Ġrank ings",
+      "Ġrep aired",
+      "ĠÐĿап ÑĢ",
+      "Ġdownload s",
+      "Ġarm our",
+      "Ġ×Ļ ×ķתר",
+      "Ġlonge vity",
+      "ĠTON ER",
+      "ĠкомменÑĤ аÑĢ",
+      "Ġcz ego",
+      "Ġnot ify",
+      "Ġairport s",
+      "Ġend uring",
+      "let te",
+      "Ġapp arat",
+      "Ġhab il",
+      "á»ĩ c",
+      "n ad",
+      "IC O",
+      "ĠBra h",
+      "Ġseg ún",
+      "Ġgovern ors",
+      "k aha",
+      "ĠSchl uss",
+      "Ġodpow ied",
+      "ir ting",
+      "Ġrem pl",
+      "ĠAb original",
+      "ident ally",
+      "Ġenhan cing",
+      "lic ting",
+      "ĠHawai ian",
+      "Ġstri ving",
+      "ĠN iet",
+      "Ġzn aczy",
+      "Ġobed ience",
+      "ĠnÃ¥ got",
+      "Ġexp ired",
+      "Ġ19 18",
+      "pres ented",
+      "Ġpr owad",
+      "ĠTer r",
+      "ĠPrinc eton",
+      "Ġmor gen",
+      "Ġattract ing",
+      "ĠS igma",
+      "ign er",
+      "ĠRe chts",
+      "ĠP eki",
+      "Ġmet hy",
+      "Ġha mm",
+      "Ġdire ito",
+      "Ġdeleg ation",
+      "ив аÑİÑĤ",
+      "Ġg in",
+      "You ng",
+      "Ġdepend encies",
+      "ĠBrad ley",
+      "bud s",
+      "Ġf is",
+      "Ġpyt anie",
+      "Ġinterconnect ed",
+      "Ġemba ixo",
+      "ĠS as",
+      "Ġr uh",
+      "ĠS icht",
+      "S ur",
+      "Ġsuper b",
+      "ĠSabb ath",
+      "ĠD anger",
+      "k ol",
+      "Ġh ou",
+      "s upp",
+      "ĠN acional",
+      "Ġsuccess ion",
+      "Ġv á",
+      "ĠMaÃŁ nahmen",
+      "ĠJess ie",
+      "ĠId aho",
+      "fore st",
+      "ħ ĺ",
+      "Ġ×ŀ× ĵ",
+      "ĠØ£ ÙĬ",
+      "Ġsweet heart",
+      "Ġneat ly",
+      "ĠEv angel",
+      "ê³ ¡",
+      "ĠSu ite",
+      "úblic a",
+      "ĠÑĥ ли",
+      "ĠAnn ouncer",
+      "l igh",
+      "Ġsens ations",
+      "Ġshel ters",
+      "Ġh art",
+      "Ġsqueez ing",
+      "ĠR ivers",
+      "ĠCook ing",
+      "ì± ħ",
+      "person al",
+      "Ġman os",
+      "ÑijÑĤ ÑģÑı",
+      "w ij",
+      "Ġgo gg",
+      "ĠMill i",
+      "ĠF P",
+      "ün st",
+      "ĠL S",
+      "Ġspray ing",
+      "Ġf aux",
+      "Ġaut ograph",
+      "olog ic",
+      "Ġtor ment",
+      "Ġencry pted",
+      "á» ħ",
+      "Ġest re",
+      "ç¹ ¼",
+      "à ±",
+      "Ġst umbled",
+      "Ġa ider",
+      "Ġsab en",
+      "x ter",
+      "ĠC ities",
+      "ĠTür k",
+      "ëĭ ¥",
+      "ch ine",
+      "Ġto pping",
+      "Ġpoison ed",
+      "ĠRoman ia",
+      "×ĵ ×Ļ",
+      "Ģë ¡ľ",
+      "ĠпоÑĢ Ñıд",
+      "Ġchir ping",
+      "ĠìĻ Ħë",
+      "×ij× ¢",
+      "Ġcu anto",
+      "Ġdon ating",
+      "ĠReg ent",
+      "ĠBer uf",
+      "Ġdistract ing",
+      "Ġstam ina",
+      "ĠDar ren",
+      "Ġì¶ ķ",
+      "l ists",
+      "d al",
+      "ch uss",
+      "Ġeconom ist",
+      "ãģĪ ãĥ¼",
+      "org t",
+      "Ġist iyorum",
+      "è¿ Ľ",
+      "ĠSur prise",
+      "ĠHa o",
+      "Ġìµľ ê³ł",
+      "ĠG W",
+      "ĠIn ner",
+      "Ġqu ieren",
+      "Ġmind ed",
+      "Ġsupercom puter",
+      "Ġdiagram s",
+      "íĬ ľë",
+      "ê²ł ìĸ´",
+      "ĠобÑĬ ÑıÑģ",
+      "Ġestab an",
+      "Ġdestro ys",
+      "ĠBre aking",
+      "Ġkar Ä±ÅŁ",
+      "Ġrebuild ing",
+      "ľë ĮĢ",
+      "ли во",
+      "ĠSau ce",
+      "ĠF usion",
+      "×ķ× ŀ×",
+      "ĠQu inn",
+      "Ġga uche",
+      "ĠÙĪ Ø£",
+      "Ġ È",
+      "ç ĵľ",
+      "Ġtechn o",
+      "Ġdisp atch",
+      "ĠaÅŁ k",
+      "Ġein zel",
+      "ĠG mail",
+      "ç ŀ",
+      "Ġê°ľ ìĿ¸",
+      "ĠÑģем ÑĮ",
+      "Ġjour neys",
+      "Ġi ht",
+      "Ġfib re",
+      "Ġdram as",
+      "ouch ed",
+      "Ġren ame",
+      "Ġоп еÑĢ",
+      "Ġpo o",
+      "ĠD ru",
+      "ĠиÑĤ ог",
+      "Ġz ast",
+      "Ġco z",
+      "Ġz ucch",
+      "Ġobt aining",
+      "Ġcomm ute",
+      "Ġsub mer",
+      "ĠV ish",
+      "ĠR abb",
+      "og g",
+      "Ġh ut",
+      "íĸĪ ìĸ´",
+      "æ¯Ķ å¦Ĥ",
+      "ere mi",
+      "Ġμ α",
+      "Ġdisk ut",
+      "Ġб Ñĥк",
+      "Ġimp aired",
+      "d epend",
+      "ĠÙĪ ا",
+      "ĠÑĢ Ñĥк",
+      "Ġб аÑĢ",
+      "Ġoxid ation",
+      "Ġsitu ação",
+      "ÉĻ n",
+      "u ção",
+      "Ġsag te",
+      "ĠS ER",
+      "ĠC ake",
+      "Ġtur meric",
+      "ĠK ak",
+      "b ung",
+      "ĠK á¹Ľá¹£á¹ĩa",
+      "Ġpoison ing",
+      "Ġsl ipping",
+      "ĠS ays",
+      "å°± åı¯ä»¥",
+      "ò ng",
+      "çŁ ³",
+      "Â «",
+      "ĠClaud ia",
+      "ĠChar acter",
+      "ни ÑĨ",
+      "co at",
+      "Ġprogress ed",
+      "ĠFer gus",
+      "Ġìĺ¤ ëĬ",
+      "Ġo at",
+      "ord able",
+      "ĠLe y",
+      "ĠHera us",
+      "Ġresult ados",
+      "ĠKay la",
+      "Ġr iff",
+      "Ġcheg ou",
+      "Ġx i",
+      "Ġsp acious",
+      "Ġrecogn ised",
+      "Ġe ch",
+      "ĠT ie",
+      "Ġlaunch er",
+      "J im",
+      "Ġsupp ression",
+      "ĠImp ossible",
+      "Ġguit ars",
+      "ĠFour ier",
+      "иÑĩеÑģ кий",
+      "ĠTh erap",
+      "ĠK af",
+      "cent ered",
+      "ĠÑģо оÑĤвеÑĤ",
+      "Ġk lim",
+      "Ġcarbohyd rates",
+      "ign ant",
+      "ĠAst ron",
+      "Ġem ple",
+      "Ġdr astic",
+      "ĠмиÑĢ е",
+      "в ин",
+      "u w",
+      "Ġpret tier",
+      "Ġdon uts",
+      "ĠAth ena",
+      "Ġdiss ert",
+      "Ġpl ante",
+      "Ġur anium",
+      "ìĿ Įë",
+      "ar é",
+      "Ġrze cz",
+      "Ġdisplay ing",
+      "æĪ ²",
+      "Ġsar c",
+      "r ão",
+      "Ġtamp oco",
+      "Ġphilosoph ers",
+      "ĠRe cht",
+      "æĵ ļ",
+      "Ġcoment arios",
+      "y se",
+      "Ġìľ ¤",
+      "Ġm ise",
+      "ĠG in",
+      "Ġн ом",
+      "ĠFR OM",
+      "l iner",
+      "at if",
+      "Ġspo ÅĤec",
+      "x a",
+      "ĠÑĤ ÑĢÑĥд",
+      "Ġw ag",
+      "기 ìĹIJ",
+      "ĠM G",
+      "Ġoff spring",
+      "ĠUnder standing",
+      "åıª æĺ¯",
+      "OR A",
+      "Ġwh irring",
+      "Ġsur rend",
+      "Ġpok er",
+      "Ġmon uments",
+      "ĠâĻ ©",
+      "Ġorgan ised",
+      "ĠSo zial",
+      "ĠF actory",
+      "Ñħ а",
+      "Ġrese mble",
+      "з д",
+      "Ġexplos ions",
+      "Ġpay roll",
+      "Ġom n",
+      "ĠJ orge",
+      "ι Ïĥ",
+      "Ġfract ure",
+      "Ġpersec ution",
+      "Ġdem ais",
+      "E CH",
+      ", )",
+      "Ġcri ar",
+      "ĠJ OSH",
+      "Ġdem ographics",
+      "Ġ16 00",
+      "Ġcur rencies",
+      "ĠT ips",
+      "Ġ éĢĻåĢĭ",
+      "ĠRe fer",
+      "ĠDan cing",
+      "Ġincons istent",
+      "Ġde h",
+      "Ġimm ens",
+      "Ġme ist",
+      "Ġimpat ient",
+      "Ġbehav es",
+      "æĿ ¾",
+      "ĠëĤ´ì ļ©",
+      "Ġback story",
+      "Ġagree ing",
+      "ĠÅ ģ",
+      "ih in",
+      "Ġtemper atura",
+      "ĠBack ground",
+      "Ġnut zen",
+      "Ġëħ ¹",
+      "ĠM änner",
+      "Ġcollabor ations",
+      "ĠK os",
+      "éģİ åİ»",
+      "Ġnight mares",
+      "ë ĵ±",
+      "ĠQueens land",
+      "Ġassoci ates",
+      "ĠK ok",
+      "Ġfact orial",
+      "ĠHy ung",
+      "Ġê·¸ ëĭ¤ìĿĮ",
+      "Ġfil ho",
+      "Ġel ét",
+      "Ġíĸī ë³µ",
+      "° ±",
+      "Ġgef unden",
+      "Ġsemic ondu",
+      "Ġcounsel ors",
+      "ĠU pper",
+      "ĠA ub",
+      "ick ers",
+      "V er",
+      "Ġnorth west",
+      "ĠMainten ant",
+      "ĠL akes",
+      "аÑı в",
+      "int é",
+      "ì° ½",
+      "Ġг аз",
+      "Ġgi orn",
+      "Ġdigit ally",
+      "ĠCirc uit",
+      "ì¼ Ģ",
+      "ãĤĬ ãģ¾ãģĹãģŁ",
+      "Ġcheer ful",
+      "ĠPet erson",
+      "ĠDan ish",
+      "ativ os",
+      "Ġli ken",
+      "Ġhar bor",
+      "али ÑģÑĤ",
+      "x e",
+      "Ġcur ls",
+      "ĠR hod",
+      "E nd",
+      "ĠE T",
+      "Ġacqu aint",
+      "ĠKel vin",
+      "Ġtr if",
+      "ĠA way",
+      "ìŀIJ ëĬĶ",
+      "v s",
+      "Ġp ágina",
+      "Ġin let",
+      "ĠSant os",
+      "Ġìļ° ìĻĢ",
+      "Ġyap ıyorsun",
+      "th eme",
+      "Ġsou ff",
+      "Ġinject ed",
+      "Ġpó źniej",
+      "iver so",
+      "amp ed",
+      "Ġda her",
+      "Ġd agger",
+      "ĠлÑİб им",
+      "Ġt ummy",
+      "Ġenlight ened",
+      "c ents",
+      "ĠD ah",
+      "Ġcu est",
+      "ä¾Ĩ 說",
+      "IL Y",
+      "Ġ×ij ר",
+      "Ġbang ing",
+      "ĠEm il",
+      "ĠC ler",
+      "ĠB order",
+      "иж Ñĥ",
+      "Ġpresent ers",
+      "ĠST UD",
+      "co ins",
+      "ĠíĻ į",
+      "Ġper ks",
+      "Ġpar ap",
+      "Ġcertain es",
+      "ĠL ore",
+      "ö st",
+      "ĠMAR TIN",
+      "Ġb ios",
+      "Ġwhere by",
+      "ver ts",
+      "ĠMir anda",
+      "Ġst ip",
+      "æ¾ ¤",
+      "and ez",
+      "׼ ׾",
+      "uj in",
+      "Ġê ¾",
+      "Ġaller gies",
+      "pl ate",
+      "Ġyap ıl",
+      "Ġundert ake",
+      "ĠëĤĺ ê°Ģ",
+      "P art",
+      "Ġkız ım",
+      "h guru",
+      "ãģĤ ãģ¨",
+      "ĠJohn s",
+      "Ġeyel ashes",
+      "Ġdra ined",
+      "Ġst Ã¥r",
+      "ãģĤãĤĬ ãģ¾ãģĻ",
+      "ĠJ ade",
+      "Ġcal end",
+      "fil m",
+      "Ġmes a",
+      "Ġlud zie",
+      "Ġattract s",
+      "Ġju ices",
+      "Ġк ил",
+      "Ġnieu we",
+      "Ġmen cion",
+      "Ġign ition",
+      "Ġbl adder",
+      "anda ag",
+      "ĠExt ension",
+      "íĤ ¨",
+      "fe ed",
+      "ĠÙĪ Ùĩ",
+      "Ġsp un",
+      "Ġt ät",
+      "оÑĢ оÑĤ",
+      "ty ard",
+      "ron ics",
+      "ĠH uge",
+      "Ñĥж д",
+      "st ring",
+      "Ġun just",
+      "Ġpra wn",
+      "Ġfrost ing",
+      "Ġdisappear ance",
+      "ios a",
+      "Ġcard i",
+      "ĠPri est",
+      "Ġcient ÃŃfic",
+      "åĵª 裡",
+      "ĠÐĴ аÑģ",
+      "Ġë¶Ģ íĥģ",
+      "Ġth ieves",
+      "Ġphys ique",
+      "ĠE ugene",
+      "Ġбли з",
+      "Ġmon opoly",
+      "Ġbi ography",
+      "Ġho ÅŁ",
+      "Ġt ö",
+      "m ac",
+      "Ġshock s",
+      "ìĦ ¸ë",
+      "h it",
+      "Ġsn ug",
+      "Ġinc l",
+      "Ġded ic",
+      "Ġult ras",
+      "Ġизв еÑģÑĤ",
+      "Ġutil ization",
+      "ĠÑģовеÑĢÑĪ енно",
+      "Ġserv i",
+      "st ag",
+      "1 80",
+      "Ġse wer",
+      "ĠCh oice",
+      "Ġdis charged",
+      "ĠJ D",
+      "ол еÑĤ",
+      "ĠкваÑĢ ÑĤи",
+      "Ġteles cop",
+      "ĠJe ÅĽli",
+      "ĠN ana",
+      "c ale",
+      "ĠÑĤ он",
+      "mm m",
+      "äºĨ åIJ§",
+      "Ġge habt",
+      "ëĤ ł",
+      "æĬ ķ",
+      "à¸Ļ à¸Ļ",
+      "Ġet her",
+      "Ġz en",
+      "Ġresearch ed",
+      "ĠCzy li",
+      "å®Į åħ¨",
+      "work ers",
+      "Ġê²½ ì°°",
+      "Ġsher iff",
+      "all o",
+      "Ġtip os",
+      "Ġprosec ution",
+      "Ġfrog s",
+      "Ġf alt",
+      "j d",
+      "ĠíĮ Ķ",
+      "Ġfilter ed",
+      "ĠO ft",
+      "Ġì į",
+      "Ġdis fr",
+      "ĠMust ang",
+      "Ġwo ah",
+      "ĠRE ALLY",
+      "Ġмог ли",
+      "Ġentr ada",
+      "Ġиг ÑĢа",
+      "Ġmix es",
+      "ĠавÑĤом об",
+      "Ð Ļ",
+      "Ġsh in",
+      "Ġparan ormal",
+      "Ġsome place",
+      "Ġdish on",
+      "eta an",
+      "Ġfu erte",
+      "Ù ¹",
+      "Ġdo om",
+      "ìĪ ľ",
+      "Ġexist ential",
+      "Ġbu ld",
+      "ĠSD K",
+      "ĠпÑĢав да",
+      "Ġturn over",
+      "ĠìĹ¬ê¸° ìĹIJ",
+      "Ġठ¹",
+      "Ġmodel ed",
+      "Ġbug ün",
+      "Ġexperiment ation",
+      "Ġmorning s",
+      "Ġmed o",
+      "Ste vie",
+      "Ġplay able",
+      "Ġairl ines",
+      "g ments",
+      "Ġê¸°ë ¶Ħ",
+      "ĠT omb",
+      "ĠMV P",
+      "AUDI ENCE",
+      "Ġcheck out",
+      "Ġpas st",
+      "Ġbe ispiel",
+      "ĠLink s",
+      "he avy",
+      "Ġquestion able",
+      "Ġìĵ °ë",
+      "Ġs ill",
+      "Ġmanip ulated",
+      "ĠL oren",
+      "Ġìľ ¼",
+      "Ġver ge",
+      "á k",
+      "I ES",
+      "Ġsab ot",
+      "ĠCustom er",
+      "ale ży",
+      "Ġnom inee",
+      "ĠG ad",
+      "Ġnouve lles",
+      "ĠS PE",
+      "ist ling",
+      "Ġo val",
+      "обÑĢ аж",
+      "if ty",
+      "éĩ İ",
+      "Ġbez el",
+      "y et",
+      "Ġfre ight",
+      "ĠHan ım",
+      "r ÃŃa",
+      "Ġz oning",
+      "Ġind em",
+      "ĠB ü",
+      "Ġfemin ism",
+      "Ġvo ix",
+      "Ġof icial",
+      "Ġdi yorum",
+      "» IJ",
+      "Ġar ose",
+      "Ġpar ar",
+      "ìĿ¸ ì§Ģ",
+      "ĠMart ine",
+      "ĠL ect",
+      "Ġrest er",
+      "Ġdrown ing",
+      "u ya",
+      "c ida",
+      "ĠAri el",
+      "Ġ0 2",
+      "Ġ×Ķ ×Ķ",
+      "ç´ ł",
+      "ĠW ert",
+      "Т Ñĭ",
+      "Ġwid ow",
+      "Ġparch ment",
+      "Ġcott age",
+      "ĠX L",
+      "ĠSl ack",
+      "ĠN ES",
+      "Ġro be",
+      "Ġg imm",
+      "Ġcam inho",
+      "ĠHar per",
+      "Ġcit rus",
+      "Ġfirefight ers",
+      "Ġdop amine",
+      "el ets",
+      "Ġdemocr at",
+      "ìł ľë¡ľ",
+      "Ġplay back",
+      "o j",
+      "ĠпÑĢ ок",
+      "ĠSull ivan",
+      "se mble",
+      "ĠW orth",
+      "ĠMust afa",
+      "า ร",
+      "Ġmet s",
+      "éĸ Ģ",
+      "л оÑģÑĮ",
+      "Ġinert ia",
+      "Ġuniform s",
+      "è¶ ³",
+      "é rio",
+      "×ķר ×Ķ",
+      "é nt",
+      "Ġà® Ĵ",
+      "ĠÑģам ÑĭÑħ",
+      "Ġvou lais",
+      "ĠZ immer",
+      "ê² łë",
+      "Ġн оÑģ",
+      "en cias",
+      "Ġrel ación",
+      "Ġê± ¸ë",
+      "Ġfact ion",
+      "Ġg osp",
+      "пол ож",
+      "n ap",
+      "h ak",
+      "Ġproceed ings",
+      "ĠìĨ Ķ",
+      "ìķĦ ëĭĪ",
+      "ĠìŀIJ 기",
+      "Ġwer d",
+      "Ġso f",
+      "Ġsch lim",
+      "Ġfl avored",
+      "Ġquad ratic",
+      "ĠBo ot",
+      "Ġpublic ity",
+      "ĠCar o",
+      "Ġ ?\"",
+      "ни ÑĨа",
+      "man ia",
+      "ĠS UR",
+      "ĠB UR",
+      "l ance",
+      "ét ica",
+      "Ġzob aczy",
+      "Ġtri o",
+      "s ama",
+      "Ġta ÅŁ",
+      "Ġas ymm",
+      "ress er",
+      "Ġت ع",
+      "Ġп еÑģ",
+      "Ġbeginning s",
+      "lad ım",
+      "ĠбÑĭ ÑģÑĤÑĢ",
+      "Ġmo o",
+      "ĠGene va",
+      "Ġ åľ¨",
+      "er us",
+      "bor ah",
+      "Ġref using",
+      "b ull",
+      "ĠWait ing",
+      "ĠInd ividual",
+      "Ġan onym",
+      "im ens",
+      "Ġmed idas",
+      "Ġfragr ant",
+      "Ġdirect ement",
+      "ĠìķĦ ë§Ī",
+      "ur ia",
+      "Ġsp herical",
+      "Ġab ge",
+      "ĠVictor ian",
+      "Ġspect acle",
+      "ĠRodrig uez",
+      "Ġoc up",
+      "ĠN är",
+      "mark s",
+      "ng ulo",
+      "ĠLu ci",
+      "Ġshout ed",
+      "Ġregul ators",
+      "ÄŁ ini",
+      "Ġdis ent",
+      "ĠÑĢÑĭ н",
+      "ëĤ ¨",
+      "ĠìĤ ´ë",
+      "Ġprobl èmes",
+      "ĠF inger",
+      "asse mble",
+      "Ġpe ar",
+      "Ġdro ite",
+      "ĠEvery where",
+      "t am",
+      "оÑĤ ив",
+      "в ой",
+      "ordin ate",
+      "ĠL ak",
+      "Ġm Ỽi",
+      "ĠTele vision",
+      "Ġexpon entially",
+      "av as",
+      "Ġble v",
+      "ĠM T",
+      "ä¿ º",
+      "Con nell",
+      "ĠêµŃ 민",
+      "ĠÑģво им",
+      "Ġach a",
+      "ĠD ynasty",
+      "J in",
+      "Ġto re",
+      "Ġfl or",
+      "Ġмног ие",
+      "æ²Ĵ äºĭ",
+      "ow an",
+      "b ah",
+      "Ġì£ Ħ",
+      "ĠC ela",
+      "Ġìµľ ê·¼",
+      "Ġpermett re",
+      "Ġab ras",
+      "Ġverste hen",
+      "Ġesc ort",
+      "ĠThe m",
+      "är ke",
+      "por ter",
+      "Ġkah kaha",
+      "Ġhe ct",
+      "Ġda u",
+      "w ah",
+      "ol ve",
+      "ĠAg es",
+      "s chaft",
+      "ĠSt ell",
+      "ne lle",
+      "ĠEn suite",
+      "ĠÐĴÑģ ем",
+      "Ġcr éd",
+      "ĠP P",
+      "l ords",
+      "gr unting",
+      "Ġcontract ion",
+      "G ot",
+      "Ġacqu iring",
+      "Ġso pr",
+      "Ġpoison ous",
+      "R NA",
+      "Ġan ar",
+      "ĠH of",
+      "' )",
+      "Ġremark ably",
+      "Ġintern acional",
+      "ü cke",
+      "in qu",
+      "Ġdu y",
+      "Ġbeast s",
+      "ĠL AN",
+      "Ġpreced ent",
+      "ĠRP M",
+      "åij ¨",
+      "Ġsel on",
+      "Ġmort e",
+      "Ġcomeç ou",
+      "Ñı ла",
+      "Ġinterpre ting",
+      "ĠBur ke",
+      "ÑĤ ÑĢа",
+      "ĠìĿ´ë Ł¬",
+      "Ġpess im",
+      "ĠN ok",
+      "íĮ Ŀ",
+      "F emale",
+      "Ġìĭ ¤í",
+      "Ļ Ģ",
+      "Ġstim ulation",
+      "Ġsl ick",
+      "Ġê°Ģ ëĬĶ",
+      "Ġк аз",
+      "ĠH BO",
+      "Ġpap ier",
+      "Ġkön nten",
+      "Ñĥб ли",
+      "ĠConst ant",
+      "SPEAK ING",
+      "Ġktó rÄħ",
+      "Ġcos metics",
+      "ĠT rend",
+      "Ġrob bery",
+      "Ġt itt",
+      "Ġgj ort",
+      "Ġdiet ary",
+      "ł Į",
+      "ĠKir by",
+      "ĠпÑĢимеÑĢ но",
+      "Ġqual ification",
+      "Ġìķ ī",
+      "Ġcabin ets",
+      "Ġhtt p",
+      "ĠEric a",
+      "ç¾ ©",
+      "Ġdisadvant ages",
+      "Ġch attering",
+      "y z",
+      "fe it",
+      "Ġgu ild",
+      "ĠE TF",
+      "ĠDrag ons",
+      "ĠH ERE",
+      "vent h",
+      "ÙĦ اÙħ",
+      "Ġmarch é",
+      "D am",
+      "Ġphot on",
+      "Ġest able",
+      "M ag",
+      "Ġol har",
+      "Ġcou pling",
+      "ĠHil fe",
+      "ĠW izard",
+      "Ġм ало",
+      "hel p",
+      "ĠlÃŃ nea",
+      "Ġì «",
+      "Ġstand alone",
+      "Ġmor ale",
+      "Ġzwe ite",
+      "ãĤĪãĤį ãģĹãģı",
+      "ähr t",
+      "Ġd otted",
+      "Ġdri pping",
+      "ĠFl ag",
+      "éĿ Ĵ",
+      "ro cket",
+      "rate gy",
+      "ir im",
+      "Ġíķĺë ©´ìĦľ",
+      "Ġsogen an",
+      "ĠUn o",
+      "ĠSch utz",
+      "Ġest ilo",
+      "ĠS ubs",
+      "ĠDais y",
+      "ÐĿ еÑĤ",
+      "' ...",
+      "Ġplat inum",
+      "Ġb irl",
+      "ĠSo vi",
+      "Ġviol ate",
+      "Ñĥ еÑĤÑģÑı",
+      "r ill",
+      "Ġtra z",
+      "Ġsn ip",
+      "Ġcum pl",
+      "à¸Ń à¸ģ",
+      "Ġc uk",
+      "éħ Ĵ",
+      "ĠParl ament",
+      "Ġhyper t",
+      "Ġpul p",
+      "Ġtong ues",
+      "at to",
+      "Ġbus ca",
+      "ih n",
+      "ER O",
+      "ĠÙĬ ع",
+      "Ġvari as",
+      "ĠMar ian",
+      "Ġbound ed",
+      "Ġpitch ing",
+      "Ġdefic iency",
+      "ĠBless ed",
+      "ĠEx erc",
+      "uch s",
+      "ĠnhÆ° ng",
+      "æľ¬ å½ĵ",
+      "Ġrap ed",
+      "h ales",
+      "Ġmal a",
+      "p ic",
+      "Ġ40 1",
+      "ÅĽ niej",
+      "ar ina",
+      "ëĵ¤ ìĿĦ",
+      "ott i",
+      "Ġдол го",
+      "Ġtrack er",
+      "ĠShel by",
+      "Ġvan ished",
+      "Ġbak ery",
+      "Kap ı",
+      "J esus",
+      "ĠK R",
+      "J O",
+      "ħ ¸",
+      "Ġdisc s",
+      "ìĦ ¯",
+      "ì§Ģ ë",
+      "×Ļ× ¦",
+      "em ary",
+      "K endra",
+      "Ġy ük",
+      "ück t",
+      "Ġv az",
+      "Ġk up",
+      "akt u",
+      "ĠÑģп аÑģибо",
+      "Ġa ik",
+      "Ġnurs ery",
+      "Ġendanger ed",
+      "êm ement",
+      "emat ics",
+      "Ġrespond ers",
+      "ĠRepresent atives",
+      "Ġsculpt ures",
+      "ig keiten",
+      "Ġde pl",
+      "Ġinterpret ations",
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+      "Ġ194 2",
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+      "Ġsug ars",
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+      "l ively",
+      "Ġrecre ational",
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+      "and in",
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+      "Ġdi arr",
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+      "zi est",
+      "Ġtooth brush",
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+      "Ġbackl ash",
+      "h ind",
+      "Ġris que",
+      "z ess",
+      "ĠìĿ´ìķ¼ 기",
+      "Ġesper ar",
+      "Ġtransl ations",
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+      "Ġhapp iest",
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+      "Ġmus i",
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+      "Ġìŀħ ëĭĪëĭ¤",
+      "ĠÑĢ ай",
+      "Ġbe vor",
+      "BL ANK",
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+      "W ER",
+      "Ġdec ÃŃa",
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+      "Ġcontract ed",
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+      "Ġal k",
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+      "Ġ? ?",
+      "ĠëĮĢ íijľ",
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+      "Ġacab ou",
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+      "ê· ¹",
+      "ĠM eter",
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+      "O l",
+      "Ġch ats",
+      "ĠBu enos",
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+      "ĠCongress man",
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+      "id able",
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+      "ĠAd miral",
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+      "Ġter cer",
+      "Ġp act",
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+      "ĠNav al",
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+      "IN TER",
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+      "Ġmiej sce",
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+      "Ġorth og",
+      "om n",
+      "int rodu",
+      "ĠDen ise",
+      "ìŀIJë ¥¼",
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+      "AM A",
+      "Ġinf lict",
+      "Ġf and",
+      "ĠG ü",
+      "ĠÑĩ еÑĤ",
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+      "Ġpat ents",
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+      "ä¸Ń åĽ½",
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+      "d id",
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+      "ĠAnal ysis",
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+      "Ġas eg",
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+      "ĠCa o",
+      "Ġtu v",
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+      "주 ê³ł",
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+      "ocr acy",
+      "иÑĩеÑģ ки",
+      "ĠM ush",
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+      "Ġwar med",
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+      "log o",
+      "ĠF ach",
+      "¤ë ¡ľ",
+      "Ġübrig ens",
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+      "iling ual",
+      "°ë ¥¼",
+      "eg al",
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+      "ov y",
+      "а ÑĤо",
+      "Ġr uth",
+      "ĠL ights",
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+      "ĠRe e",
+      "ĠAir bnb",
+      "Ġdr owned",
+      "ĠZ oe",
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+      "Ġbar r",
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+      "Ġpag an",
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+      "æĪ ¿",
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+      "m ember",
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+      "س Ùħ",
+      "Ġlie ber",
+      "Ġin ference",
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+      "ĠCamp aign",
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+      "Ġvict ories",
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+      "bl ick",
+      "Ġмен ее",
+      "oth s",
+      "ĠCou ple",
+      "J ason",
+      "ĠNic olas",
+      "ек Ñģ",
+      "l ib",
+      "Ġher ramient",
+      "Ġ×IJ ×ķ×ŀר",
+      "Ġвид им",
+      "mill imeter",
+      "Ġsil houette",
+      "Ġdrive way",
+      "Ġcher ish",
+      "ãħł ãħł",
+      "Ġrans om",
+      "Ġinter disciplinary",
+      "ĠPort al",
+      "Ġtra g",
+      "th ood",
+      "Ġted ious",
+      "Ġgloss y",
+      "Ġpré par",
+      "ĠC ay",
+      "ĠT ook",
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+      "Ġz ig",
+      "å «",
+      "åį ±",
+      "re presented",
+      "à¹Ģล ย",
+      "Ġdesar rollo",
+      "ìĦ ľë",
+      "Ġvis cos",
+      "Ġmill igram",
+      "ĠG und",
+      "Ġfer ment",
+      "d rum",
+      "Ġdraw ers",
+      "La ugh",
+      "Ġpel os",
+      "Ġpave ment",
+      "Ġmem oir",
+      "av ait",
+      "Ġ20 50",
+      "¤ë ¥¼",
+      "Ġraz ón",
+      "Ġflour ish",
+      "Ġst ern",
+      "ä¸ Ī",
+      "ĠCh ung",
+      "Ġser pent",
+      "ĠGentle men",
+      "羣çļĦ å¾Ī",
+      "k ook",
+      "Ġl ut",
+      "import e",
+      "p arent",
+      "Ġw sz",
+      "Ġsc ree",
+      "ĠMitar beiter",
+      "å· ´",
+      "m ut",
+      "Ġìĸĺ 기를",
+      "Ġsem ble",
+      "ĠO W",
+      "Ġinvestig ator",
+      "ĠCher yl",
+      "ĠG erald",
+      "Ġpr ere",
+      "Ġcomp ares",
+      "ny t",
+      "Ġdiferen ça",
+      "? -",
+      "Ġqu á",
+      "ר ×Ļ",
+      "S en",
+      "Ġhe ps",
+      "Ġgrat uit",
+      "Ġcons ort",
+      "ĠST OP",
+      "ĠProtest ant",
+      "Ġelectro de",
+      "â Ĺ",
+      "Ġsecure ly",
+      "иÑĩеÑģ кой",
+      "Ġt ää",
+      "Ġreg isters",
+      "ĠHeaven ly",
+      "og ly",
+      "iss ä",
+      "ĠPhys ics",
+      "ĠMer kel",
+      "Ġré v",
+      "éĻ ¢",
+      "Ġer ased",
+      "ĠSac ramento",
+      "Ġcoff in",
+      "Ġex acer",
+      "Ġl anz",
+      "Ġpo ets",
+      "ul if",
+      "Ġì¹ ĺë",
+      "ĠN erd",
+      "ĠN CT",
+      "ĠH our",
+      "neh mer",
+      "ŀ ĺëıĦ",
+      "ĠPrin ci",
+      "S w",
+      "m ies",
+      "ar med",
+      "ĠBeat les",
+      "Ġpropag ation",
+      "Ġexch anged",
+      "Ġcum ulative",
+      "Ġì§ij ìĹIJ",
+      "Ġdefe ating",
+      "æĬ ±",
+      "b els",
+      "Ġw es",
+      "ĠOdys sey",
+      "ä½ł æĥ³",
+      "av ior",
+      "ĠìľĦ ìĹIJ",
+      "Ġbr it",
+      "Ġhij o",
+      "D AY",
+      "ĠاÙĦت ÙĬ",
+      "ĠС еÑĢг",
+      "Ñĥ ка",
+      "eds iÄĻ",
+      "Ġimp os",
+      "Ġell as",
+      "Ġfire arms",
+      "ĠN R",
+      "Ġ×ij× IJ",
+      "ĠÐŁ ока",
+      "aw i",
+      "ĠìĦ± ê³µ",
+      "Ġpup ils",
+      "ĠT ack",
+      "Ġfr ase",
+      "ĠSh ip",
+      "Ġst ad",
+      "ä¸ ľ",
+      "ĠGreat er",
+      "un un",
+      "imm ung",
+      "gr own",
+      "ĠN XT",
+      "ĠAmeric as",
+      "f ox",
+      "Ġmant en",
+      "éłIJ åĤĻ",
+      "ĠÑģ ок",
+      "Ġr ikt",
+      "lect ric",
+      "de ep",
+      "Ġзна еÑĪÑĮ",
+      "Ġben ut",
+      "ĠInf rast",
+      "ĠEm ir",
+      "ĠоÑĤп ÑĢав",
+      "ĠKim chi",
+      "ĠFinn ish",
+      "´ìł ģ",
+      "ina ire",
+      "Ġo ike",
+      "æ¸ħ æ¥ļ",
+      "Ġhost age",
+      "ĠBut ton",
+      "ÙĤ ÙĬ",
+      "ek ing",
+      "ĠKaz akh",
+      "Ġcomfort ing",
+      "Ġso g",
+      "Ġgreet ed",
+      "g uitar",
+      "p ayer",
+      "Ġrel ational",
+      "Ġconstru ir",
+      "çī¹ åĪ¥",
+      "op ian",
+      "ĠVol ume",
+      "iet h",
+      "ÑģÑĤв ом",
+      "ur rection",
+      "li ÅĽmy",
+      "Ġhem isphere",
+      "ĠBe an",
+      "IG N",
+      "Ġköt ü",
+      "ĠFall out",
+      "Ġbr ace",
+      "ç¹¼ çºĮ",
+      "ÏĢ ά",
+      "ĠH AS",
+      "Ġg é",
+      "Ġcharacter ize",
+      "ặ c",
+      "ĠMil ky",
+      "Ġtum ors",
+      "Ġn uit",
+      "ĠG az",
+      "ĠìŀĪ ëĭ¤ëĬĶ",
+      "Ġг аÑĢ",
+      "ess ment",
+      "ĠA be",
+      "Ġë½ ij",
+      "ĠEins atz",
+      "J IN",
+      "j ä",
+      "C ry",
+      "ĠProm ised",
+      "ĠÑģеÑĢ д",
+      "ok us",
+      "Ġscal able",
+      "ĠпоÑģмоÑĤÑĢ еÑĤÑĮ",
+      "ück lich",
+      "Ġreal ism",
+      "Ġmay o",
+      "Ġjuven ile",
+      "Ġhead lights",
+      "Ġgör Ã¼ÅŁ",
+      "ĠRe form",
+      "Ġhal ves",
+      "cz ne",
+      "Ġbreak up",
+      "że j",
+      "Ġr ätt",
+      "D ay",
+      "ĠìĿ¼ë ³¸",
+      "Ġmu erte",
+      "Ġtun es",
+      "ĠSm ile",
+      "rec ord",
+      "Ġrecher che",
+      "atisf ied",
+      "Ġpo zi",
+      "Ġcelebr ations",
+      "ise xual",
+      "ĠRO B",
+      "third s",
+      "ĠF ortune",
+      "ĠÑĤ ой",
+      "Ġbrand ed",
+      "lo o",
+      "Ġd ud",
+      "Ġrandom ized",
+      "Ġcomb in",
+      "ä¸Ģ äºĽ",
+      "ier an",
+      "c zenia",
+      "į ãĥ«",
+      "Ġcur ator",
+      "Ġar tery",
+      "ĠÑĥ ÑĪ",
+      "ĠÑĩ иÑĤ",
+      "Ġsubsid ies",
+      "Ġbloss om",
+      "ĠTw ilight",
+      "Ġhy vä",
+      "ĠPom pe",
+      "ĠC isco",
+      "ĠÐŁÑĢ о",
+      "Ġbir i",
+      "Ġg ern",
+      "Ġre built",
+      "Ġw cze",
+      "Ġbenefic i",
+      "Ġdrum mer",
+      "Ġsol ids",
+      "Ġdi yorsun",
+      "ãģĤãĤĬãģĮãģ¨ãģĨãģĶãģĸ ãģĦãģ¾ãģĹãģŁ",
+      "l ated",
+      "Ġmud dy",
+      "Ġh olog",
+      "Ġcl aps",
+      "ĠR ings",
+      "ĠO key",
+      "ĠBra ve",
+      "Ġvalu ation",
+      "Ġmig rant",
+      "Ġinter mitt",
+      "Ġeig ene",
+      "ili ary",
+      "ãĥ¼ ãĥĪ",
+      "mark t",
+      "k r",
+      "ĠR ib",
+      "á»Ļ i",
+      "Ġaccus ations",
+      "Ġa rab",
+      "w ash",
+      "ĠBard zo",
+      "Ġu gh",
+      "est ers",
+      "oph ren",
+      "Ġaliment os",
+      "ĠU z",
+      "Ö Ĥ",
+      "Ġ6 50",
+      "ĠпÑĢи еÑħ",
+      "F I",
+      "Ġsamp ai",
+      "Ġparl é",
+      "hes ion",
+      "Ġs ır",
+      "Ġapparat us",
+      "Ġcor related",
+      "ĠPrincip al",
+      "Ġcor r",
+      "ĠOffic ial",
+      "иÑĩеÑģ кие",
+      "Ġtermin als",
+      "Sh ould",
+      "Ġvac un",
+      "Ġst ellt",
+      "Ġmo oi",
+      "etz ung",
+      "Ġк ÑĢа",
+      "Ġda i",
+      "Ġп ож",
+      "Te am",
+      "ĠP PE",
+      "ĠÐŀ Ñģ",
+      "ĠLe ah",
+      "ĠI vy",
+      "y st",
+      "Ġuh hh",
+      "Ġnight time",
+      "Ġtrend y",
+      "Ġsec urities",
+      "Ġcontin ents",
+      "Ġfirst hand",
+      "ĠVer on",
+      "ĠëĤ ®",
+      "Ġbrows ing",
+      "ĠC ada",
+      "t ro",
+      "Ġtr amp",
+      "re ib",
+      "Ġerst mal",
+      "irl er",
+      "Ġps ic",
+      "Ġget ir",
+      "ĠN P",
+      "Ġdzie ci",
+      "об ÑĢаз",
+      "Ġmagic ian",
+      "Ġscrut iny",
+      "Ġsl ab",
+      "ĠO T",
+      "ist y",
+      "ir ies",
+      "ore st",
+      "Ġtask ed",
+      "Ġmor ally",
+      "ìķ¼ ì§Ģ",
+      "ust ered",
+      "Ġfool s",
+      "Ġir respons",
+      "Ġein f",
+      "Ġvi á»ĩc",
+      "Ġsc or",
+      "Ġpill ows",
+      "ĠG egen",
+      "Ġtut te",
+      "Ġquarter ly",
+      "Ġdid nt",
+      "ĠG ym",
+      "ĠE ther",
+      "ĠØ «",
+      "лиÑĪ ком",
+      "Ġsign aling",
+      "ĠN ode",
+      "ĠDonc s",
+      "Ġy ah",
+      "ĠKan al",
+      "Ġf ading",
+      "et in",
+      "Ġinfluen cers",
+      "Ġmed als",
+      "Ġengine ered",
+      "Ġfer mented",
+      "ê²ł ì§Ģë§Į",
+      "ĠBeet hoven",
+      "×ŀ× ©",
+      "inent al",
+      "ĠìķĮë ł¤",
+      "üt fen",
+      "al nya",
+      "Ġo vere",
+      "Ġden kt",
+      "ак ÑĤеÑĢ",
+      "Ġâ ĺ",
+      "Ġneces it",
+      "Ġgener ators",
+      "gr ass",
+      "Ġпод Ñĥм",
+      "lie ÃŁen",
+      "B ar",
+      "ľë ıĻ",
+      "ĠдеÑĤ ей",
+      "Ġsuck ing",
+      "Ġsten cil",
+      "Ġprim o",
+      "ĠBreat h",
+      "st rom",
+      "Ġimmens ely",
+      "Ġapp reh",
+      "ìłķ ìĿ´",
+      "P op",
+      "Ġj ong",
+      "ĠGi ul",
+      "ĠAD HD",
+      "Ġhö ren",
+      "Ġe lo",
+      "iv ent",
+      "Ġr us",
+      "Ġoutrage ous",
+      "Ġmaster ed",
+      "Ġì» ¤",
+      "ÙĪ Ùģ",
+      "ip es",
+      "ĠRud y",
+      "Jac ob",
+      "Ġbull ish",
+      "Ġt apped",
+      "Ġfa ud",
+      "iz ophren",
+      "ĠÑģо Ñħ",
+      "ĠDar ling",
+      "Ġ196 3",
+      "ĠPre vention",
+      "² Ķ",
+      "Ġabdom inal",
+      "st ones",
+      "Ġav aient",
+      "á»ķ i",
+      "m ake",
+      "Ġs are",
+      "ĠInst ant",
+      "к ам",
+      "Ġkeep er",
+      "Ġblank ets",
+      "ãģ§ ãģĹãĤĩãģĨ",
+      "Ġswe ats",
+      "ĠMinne apolis",
+      "åħ¨ éĥ¨",
+      "Ġgen ommen",
+      "Ġfast en",
+      "ĠBrus sels",
+      "åij ¼",
+      "Ġcaf eter",
+      "Ġabsor bing",
+      "Ġha go",
+      "ĠEl mo",
+      "Ġgust o",
+      "ĠY ap",
+      "M úsica",
+      "Ġt ert",
+      "Ġband a",
+      "Ġm ily",
+      "Ġthere after",
+      "ĠStock holm",
+      "ĠC arson",
+      "Ġcalib ration",
+      "ava ÅŁ",
+      "ans a",
+      "ik ke",
+      "Ġfore see",
+      "Ġqual che",
+      "Ġdest e",
+      "æ ¤",
+      "ün üz",
+      "Ġfor ge",
+      "D is",
+      "est en",
+      "Ġδ ια",
+      "Ġenca ps",
+      "ĠGes pr",
+      "Ġcher cher",
+      "ick ets",
+      "ÑĤоÑĢ Ñĭ",
+      "C r",
+      "ĠТак же",
+      "Ġrabb its",
+      "ĠD ot",
+      "he iten",
+      "Ġcaus al",
+      "ĠF oster",
+      "ajÄħ c",
+      "Ġbere it",
+      "Ġayud ar",
+      "é« Ļ",
+      "ãģ ³",
+      "s ong",
+      "com b",
+      "Ġfr inge",
+      "Ġcyber security",
+      "Ġëľ ¨",
+      "Ġk ier",
+      "Ġbesch äft",
+      "Ġкон ÑĨе",
+      "Ġfacil it",
+      "ĠNam en",
+      "Ġbil ateral",
+      "t x",
+      "ĠW issenschaft",
+      "Ġnu ances",
+      "Ġr ipping",
+      "Ġf y",
+      "ĠSicher heit",
+      "ĠGh ana",
+      "ol on",
+      "Ġto pped",
+      "ĠMoroc co",
+      "Ġrad ial",
+      "ĠL EE",
+      "ĠAndre as",
+      "ed d",
+      "ĠìĹ ´ë",
+      "ĠAirl ines",
+      "ãģĵ ãĤį",
+      "Ġval ores",
+      "ê· ľ",
+      "H y",
+      "Ġзад аÑĩ",
+      "ĠKend all",
+      "ĠÑħ аÑĢ",
+      "ĠV amp",
+      "Ġpy thon",
+      "Ġmanage able",
+      "ĠG ente",
+      "o ise",
+      "ici ary",
+      "Ġimp oss",
+      "ĠBun ny",
+      "iest a",
+      "And rew",
+      "Ġser t",
+      "ĠC ec",
+      "zz arella",
+      "Ġautom obile",
+      "ĠT iere",
+      "all ows",
+      "åĨ Ĩ",
+      "Ġë° Ģ",
+      "ĠSc orp",
+      "ĠJ elly",
+      "ag ara",
+      "ĠSt retch",
+      "Ġrede f",
+      "Ġexacer b",
+      "ĠS HA",
+      "é f",
+      "ors a",
+      "Ġflaw ed",
+      "ĠNo el",
+      "?! ?",
+      "Ġpro cent",
+      "Ġmen stru",
+      "ĠпÑĢо Ñĩ",
+      "Ġinf ants",
+      "ðŁİ µ",
+      "pa use",
+      "ĠR acing",
+      "Ġ194 8",
+      "Ġsuper intendent",
+      "id ores",
+      "id y",
+      "bra him",
+      "Ġunl ucky",
+      "Ġper k",
+      "an ci",
+      "Ġë§Įë Ĥĺ",
+      "ĠÐľÐ¾Ñģ кв",
+      "Ġfin ans",
+      "Ġdiferen cia",
+      "łĪ ìĿ´",
+      "éħ į",
+      "OR Y",
+      "ĠT ac",
+      "ÛĮ ا",
+      "Ġdes em",
+      "Ġваж но",
+      "ĠJ U",
+      "ĠìŀĪ ìŀĸìķĦìļĶ",
+      "ĠÎ Ŀ",
+      "Ġinform ations",
+      "ĠH EL",
+      "h st",
+      "Ġпог овоÑĢ",
+      "Ġvo iture",
+      "Ġre us",
+      "änd ig",
+      "ĠпоÑħ ож",
+      "j ing",
+      "Ġd ru",
+      "alt ra",
+      "Ġprodu its",
+      "Ġk ite",
+      "Ġeye ball",
+      "ĠB elt",
+      "ĠRestaur ant",
+      "Ġg amb",
+      "Ġpor ridge",
+      "it ters",
+      "Ġconver ts",
+      "Ġyard ım",
+      "Ġmáxim o",
+      "w irtschaft",
+      "Ġíķĺë Ĥĺë",
+      "Ġì¤ Ģ",
+      "Ġice berg",
+      "Ġvor bei",
+      "Ġ25 6",
+      "ocr atic",
+      "Ġreck less",
+      "on ner",
+      "Ġm ús",
+      "Ġlog ically",
+      "ĠPr ison",
+      "ĠNet z",
+      "Ġvac ant",
+      "Ġn immt",
+      "ĠH ARR",
+      "Ġз ов",
+      "ĠDe e",
+      "ring e",
+      "ni est",
+      "ĠR ules",
+      "ìĬ¤ë Ł½",
+      "cuss ions",
+      "Ġfl oral",
+      "Ġconstra ined",
+      "Ġdifferent iation",
+      "ĠQue bec",
+      "ĠÛģ ÛĮÚº",
+      "Ġpúblic a",
+      "it el",
+      "Ġaccommod ations",
+      "ĠGr ü",
+      "í ľ",
+      "Ġpick les",
+      "иÑĩеÑģ киÑħ",
+      "Ġcomm issions",
+      "ĠBa ek",
+      "Ġçoc uÄŁ",
+      "ĠMed ium",
+      "Ġperiod ically",
+      "Ġwonder fully",
+      "Ġstaff ing",
+      "ìĽ IJë",
+      "ri re",
+      "f le",
+      "ĠMc L",
+      "ĠÑĤ еп",
+      "ĠпеÑĢ ек",
+      "н олог",
+      "Ġíģ¬ ê²Į",
+      "çĻ¼ çı¾",
+      "Ġprosper ous",
+      "ĠSpirit ual",
+      "ĠCh ick",
+      "DI A",
+      "ĠÐŁÑĢ ивеÑĤ",
+      "Ġper ÃŃ",
+      "ÑĮ ÑİÑĤ",
+      "Ġconsult ants",
+      "ĠEar l",
+      "ä»Ĭ å¹´",
+      "Ġru ining",
+      "оÑĢ е",
+      "Ġpens er",
+      "Ġtak iej",
+      "Ġstrength ened",
+      "ĠLiqu id",
+      "он еÑĨ",
+      "ав аÑĤÑĮ",
+      "Ġcam er",
+      "Ġdisagre ement",
+      "Ġbat hing",
+      "ĠY osh",
+      "a al",
+      "pre chen",
+      "RIS ADAS",
+      "Ġsuper star",
+      "æģ Ń",
+      "лÑı ÑĤÑĮ",
+      "Ġn ib",
+      "ĠTh erm",
+      "ĠDAN IEL",
+      "Ġp aw",
+      "Ġliqu ids",
+      "Ġcapac it",
+      "ark en",
+      "Ġvag ina",
+      "Ġm ashed",
+      "Ġemer ges",
+      "ys cy",
+      "Ġun related",
+      "ĠGu ild",
+      "Ġin verted",
+      "it ives",
+      "T ra",
+      "Ġbe gr",
+      "Ġal te",
+      "ì§ ķ",
+      "ãĤģ ãģ¦",
+      "ĠÑĢазÑĢ абоÑĤ",
+      "f inder",
+      "Ġдал ее",
+      "Ġблаг одаÑĢ",
+      "walk er",
+      "Ġcr ater",
+      "ass adors",
+      "ren ces",
+      "ins ki",
+      "ĠK IM",
+      "ĠEll iot",
+      "20 17",
+      "ĠS r",
+      "ink a",
+      "ano v",
+      "Ġìŀĺë ª»",
+      "Ġpropriet ary",
+      "display style",
+      "ĠÑģ им",
+      "Ġиз б",
+      "ĠPan el",
+      "Ġinstinct s",
+      "ĠCommun ications",
+      "éº »",
+      "mid t",
+      "Ġë§Įëĵ¤ ìĸ´",
+      "ĠÑģл ова",
+      "ĠGil bert",
+      "缮 åīį",
+      "Т ак",
+      "voor beeld",
+      "е ÑİÑģÑĮ",
+      "ary n",
+      "que z",
+      "Ġd art",
+      "Ñĸ ÑĪ",
+      "ĠH ut",
+      "S al",
+      "Ġs outheast",
+      "Ġpestic ides",
+      "Ġhelicop ters",
+      "Ġend ured",
+      "i ada",
+      "Ġbre wing",
+      "ìĹ ¬ë",
+      "ĠÑģв обод",
+      "ĠS aints",
+      "ĠFr ançais",
+      "ĠEconom ics",
+      "Ġdis loc",
+      "oph obia",
+      "C amer",
+      "Ġnegoti ated",
+      "ĠÑģÑĤ али",
+      "ìĬ¤í ģ",
+      "og ie",
+      "Ġtsun ami",
+      "Ġpeel ed",
+      "Ġmotiv ations",
+      "è¨ Ń",
+      "ost at",
+      "fl an",
+      "ĠD AC",
+      "Ġk av",
+      "' RE",
+      "ĠPe arson",
+      "b be",
+      "c zenie",
+      "Ġaten ção",
+      "íĨµ ëł¹",
+      "ãģ£ ãģ¡",
+      "ĠÑĥд аÑĢ",
+      "Ġintrodu ctory",
+      "ĠI ci",
+      "ë ĮĢë",
+      "ak at",
+      "Ġt rench",
+      "Ġproceed ed",
+      "ĠCo in",
+      "Ġdere cho",
+      "ĠRed e",
+      "æ¯ Ľ",
+      "ан нÑĭй",
+      "Ġincarcer ated",
+      "ĠRich mond",
+      "R ock",
+      "ĠP av",
+      "ĠKar ma",
+      "ug es",
+      "Ġconte ú",
+      "ë ¹Ħ",
+      "Ġê·¸ë §Į",
+      "ĠG one",
+      "Ġwsp óÅĤ",
+      "ĠRah men",
+      "un ken",
+      "Ġì¤ijìļĶ íķľ",
+      "Ġi b",
+      "Ġatt aching",
+      "H ay",
+      "Ġsu ka",
+      "ìį ¹",
+      "Ġpivot al",
+      "ĠRes pect",
+      "ÃŃ da",
+      "I B",
+      "ĠVer antwort",
+      "w iet",
+      "Ġforens ic",
+      "ÑĢи ÑģÑĤ",
+      "ĠпÑĢинÑĨип е",
+      "Ġmark ings",
+      "Ġk ettle",
+      "ĠOper a",
+      "ĠDo ctors",
+      "Ġshred ded",
+      "Ġrec uer",
+      "Ġvig il",
+      "ĠF ail",
+      "Ġentre v",
+      "Ġд ÑĥÑĪ",
+      "Ġout breaks",
+      "èµ° åIJ§",
+      "ĠÏĢ ο",
+      "Ġro gue",
+      "ang led",
+      "Ġyear ly",
+      "ĠCre ed",
+      "Ġw am",
+      "Ġlot us",
+      "ê³ ¼ë",
+      "ãĢģ ãĢģ",
+      "ĠSp it",
+      "ĠIt u",
+      "Ġstra ins",
+      "Ġstamp ed",
+      "Ġpl aint",
+      "Ġpot ion",
+      "Ġconsolid ation",
+      "è© ķ",
+      "оÑĩ кÑĥ",
+      "Ġvlog ging",
+      "Ġsl ate",
+      "ĠAu ft",
+      "ĠInc or",
+      "ừ ng",
+      "§ IJ",
+      "en h",
+      "Ġhe iÃŁ",
+      "Ġdom est",
+      "ĠSt rom",
+      "åį ³",
+      "ak is",
+      "Ġfra gen",
+      "Ġfin er",
+      "ĠS ug",
+      "Ġup hill",
+      "Ġé én",
+      "âĢ¦ )",
+      "ĠÑģ оп",
+      "ĠCore y",
+      "Ġsie bie",
+      "Ġm use",
+      "Ġclo ves",
+      "Ġp ous",
+      "ĠFin anz",
+      "ĠR oute",
+      "am at",
+      "Ġmut ually",
+      "ĠвнÑĥÑĤ ÑĢи",
+      "ĠSel ena",
+      "ë Ķ",
+      "ĠGa ussian",
+      "ë ¶ĢíĦ°",
+      "Ġ×ij× Ľ",
+      "Ġej erc",
+      "å¾ ®",
+      "ke a",
+      "ĠG erry",
+      "ĠS ic",
+      "大 çļĦ",
+      "Ġ196 6",
+      "ies e",
+      "Ġfoss ils",
+      "Ġest ad",
+      "ĠK ane",
+      "ci Äĩ",
+      "Ġìľł íĬľë",
+      "Ġп ам",
+      "ĠCru ise",
+      "int érieur",
+      "Ġbe kannt",
+      "ĠP ode",
+      "Ġdem ander",
+      "R em",
+      "Ġinv ade",
+      "Ġdecor ating",
+      "rop ic",
+      "Ġcow boy",
+      "ĠPh oto",
+      "opol it",
+      "Ġì»¬ë Ł¬ë",
+      "Ġre ap",
+      "Ġhand writing",
+      "à¹Ħ ร",
+      "Ġë ļ",
+      "Ġب عد",
+      "ĠM t",
+      "Ù Ģ",
+      "Ġspaces hip",
+      "Ġnational ism",
+      "Ġcouncil s",
+      "ĠGriff in",
+      "ĠAh med",
+      "Ġcl ich",
+      "ĠO L",
+      "w l",
+      "ĠPil ot",
+      "å® ®",
+      "Ġacron ym",
+      "Ġg els",
+      "Ġelectro ly",
+      "è ĵ",
+      "Ġм ной",
+      "Ġepis od",
+      "ĠDies es",
+      "ĠAT P",
+      "Ġed iyorum",
+      "Ġexpress es",
+      "Ġexhib its",
+      "C omm",
+      "Ġк ÑĢÑĥп",
+      "Ġmat ar",
+      "Ġ20 25",
+      "ĠArt em",
+      "vas ive",
+      "r Ãł",
+      "Ġbe ÅŁ",
+      "é» ĥ",
+      "Ġliz ard",
+      "Ġfill e",
+      "Ġì§ Ī문",
+      "Ġмо Ñī",
+      "Ġt ür",
+      "Ġcul prit",
+      "Ġwo ven",
+      "ĠAN Y",
+      "n im",
+      "Ġt ay",
+      "Ġprom in",
+      "Ġacom pa",
+      "Ġid é",
+      "Ġbo iler",
+      "ĠThe men",
+      "Ġaven ue",
+      "ĠM ud",
+      "Ġнов Ñĭе",
+      "Ġwitness ing",
+      "Ġl ance",
+      "ĠCH AN",
+      "ĠBe ver",
+      "ت Ùħ",
+      "Ġchem otherapy",
+      "K ing",
+      "ĠbÄĻd ÄĻ",
+      "Ġat ual",
+      "Ġt ive",
+      "Ġtalk in",
+      "Ġqued ar",
+      "ie ÃŁ",
+      "ed el",
+      "Ġìĸ´ì łľ",
+      "Ġjog ar",
+      "Ġö r",
+      "Ġundert aking",
+      "ĠStre ngth",
+      "Ġmil hões",
+      "ĠW ine",
+      "ĠM olt",
+      "è® ²",
+      "ãģij ãĤĮ",
+      "Ġunderm ine",
+      "ĠArch ives",
+      "v ana",
+      "mer cial",
+      "M C",
+      "Ġcast e",
+      "п ÑĢ",
+      "Ġlegisl ators",
+      "ul ators",
+      "ên io",
+      "Ġëį °ë",
+      "ĠÑħоÑĤ иÑĤе",
+      "Ġн ек",
+      "Ġs urn",
+      "Ġcons ci",
+      "ĠP OW",
+      "Ġcul inary",
+      "ĠK AT",
+      "ĠFol ks",
+      "Ñĭв аем",
+      "Ġв ок",
+      "ãģij ãĤĭ",
+      "s ervice",
+      "pt s",
+      "Ġпоб ед",
+      "æĺ¯ åķĬ",
+      "Ġt ents",
+      "Ġn ord",
+      "ST E",
+      "Ġrepublic an",
+      "Ġwy k",
+      "Ġmin ions",
+      "èĻ ķ",
+      "Ġmem ang",
+      "j est",
+      "Ġcompar ative",
+      "Ġty le",
+      "car bon",
+      "bed ingt",
+      "ks en",
+      "Ġneg ativity",
+      "Ġsjäl v",
+      "Ġd ú",
+      "æīĢ æľī",
+      "Ġrec alled",
+      "c ra",
+      "ĠT ada",
+      "ĠÑĢÑĥ ки",
+      "ĠопÑĢед ел",
+      "Ġproc rast",
+      "Ġjog os",
+      "ĠO o",
+      "ĠHe arts",
+      "Ġé ch",
+      "Ġksi Äħż",
+      "Ġco arse",
+      "ĠT ube",
+      "ĠG reens",
+      "Ġé n",
+      "Ġdumb bell",
+      "ĠÑĤ и",
+      "Ġquer er",
+      "ا ØŃ",
+      "Ïĥ ει",
+      "ĠпÑĢав илÑĮно",
+      "Ġп ап",
+      "Ġcomp ra",
+      "Ġt ér",
+      "ĠAnt es",
+      "Ġoptim um",
+      "Ġbisc uit",
+      "κ ι",
+      "acz ego",
+      "Ġìĭľê°Ħ ìĿ´",
+      "ĠMar ines",
+      "ver o",
+      "Ġvacc inations",
+      "Ġpet ty",
+      "rit ers",
+      "Ġа л",
+      "count ry",
+      "Ġcoun ters",
+      "Ġattend ant",
+      "ĠH ui",
+      "ãģ¨ãģĦãģĨãģĵãģ¨ ãģ§",
+      "ck a",
+      "ÑģÑĤвен нÑĭй",
+      "gu y",
+      "Ġtrick ed",
+      "ĠR ED",
+      "Ġthr illing",
+      "ÏĢο ι",
+      "Ġpig gy",
+      "Ġan unci",
+      "OR TER",
+      "ĠVal ue",
+      "Ġr ond",
+      "ĠA DA",
+      "Ġpos er",
+      "h ores",
+      "ĠR oland",
+      "ĵ ¯",
+      "Ġno ir",
+      "Ġש ×IJ×",
+      "ë° ľ",
+      "iem and",
+      "ĠпоÑĤ еÑĢ",
+      "ê³ ³",
+      "Ġê± ±",
+      "Ġformat ting",
+      "ĠL ed",
+      "è§Ģ çľ¾",
+      "Ġkill ers",
+      "ĠÄij ấy",
+      "Ġha ar",
+      "ag ain",
+      "! </",
+      "Ġsomet hin",
+      "Ġc oughing",
+      "Ġn ave",
+      "Ġprospect ive",
+      "ĠH K",
+      "ĠRes cue",
+      "may be",
+      "g ger",
+      "ĠÑĢабоÑĤ Ñĥ",
+      "×ķ׾ ×Ŀ",
+      "t ails",
+      "íķĺ íķĺ",
+      "Ġeyel id",
+      "Ġcustom ization",
+      "avil ion",
+      "Ġpro chain",
+      "Ġgla ze",
+      "æĥħ æ³ģ",
+      "S im",
+      "Ġоп аÑģ",
+      "Ġmosquito es",
+      "Ġf ent",
+      "Ġcapac ities",
+      "Ġapost les",
+      "Ġalt ura",
+      "Ġë¬ »",
+      "Ġser ont",
+      "ĠAny time",
+      "¥´ ëĬĶ",
+      "Ġcos play",
+      "Ġsp ac",
+      "Ġsam en",
+      "ãĥ Ħ",
+      "uc c",
+      "i ères",
+      "Ġsib ling",
+      "ĠC ock",
+      "Ġëı ħ",
+      "ĠпÑĢед ÑģÑĤавлÑı",
+      "Ġinstall ment",
+      "Ġdi je",
+      "ĠMC U",
+      "ĠE H",
+      "ĠN ing",
+      "Ġprep ares",
+      "Ġhyp ocr",
+      "pt y",
+      "Ġkad ın",
+      "ĠFro zen",
+      "ha ul",
+      "ĠK ylie",
+      "éĢĻ樣 çļĦ",
+      "Ġsh uffle",
+      "Ġelement al",
+      "Ġau ÃŁer",
+      "ĠKN OW",
+      "ĠAL ISSA",
+      "Z A",
+      "ì² ł",
+      "ç¾İ åħĥ",
+      "Ġrec ite",
+      "Ġsc rib",
+      "Ġ11 5",
+      "ä¼ ij",
+      "Ġstar red",
+      "Ġle quel",
+      "Ġbre wer",
+      "ĠOpp ortun",
+      "Ġr ä",
+      "Ġchop sticks",
+      "ĠK ah",
+      "ĠEthi opia",
+      "Ġhand made",
+      "Ġer folg",
+      "ĠD z",
+      "itt ens",
+      "èªį çĤº",
+      "в ал",
+      "η ν",
+      "åĬ ŀ",
+      "ãĥ ĵ",
+      "br ingen",
+      "Ġunpl ug",
+      "Ġoff s",
+      "Ġher man",
+      "l ied",
+      "ason ic",
+      "ĠSer bia",
+      "ĠGu atem",
+      "Ġ... \"",
+      "Ġer reichen",
+      "Ġamb iguous",
+      "ĠWhit ney",
+      "z uf",
+      "M AND",
+      "ł µ",
+      "Ġsqueez ed",
+      "ãģĿãģĨ ãģł",
+      "y as",
+      "é¾ į",
+      "ĠSh ock",
+      "Ġutil ise",
+      "uk o",
+      "b olt",
+      "Ġmot if",
+      "Ġin mates",
+      "Ġcorrupt ed",
+      "Ġconc ret",
+      "ĠCrit ical",
+      "ĠSing ing",
+      "ĠÑĦ Ñĥнк",
+      "éŃ Ķ",
+      "no va",
+      "reb be",
+      "d t",
+      "U nis",
+      "Ġweb cam",
+      "Ġcam oufl",
+      "K en",
+      "Ġlaws uits",
+      "ĠCons umer",
+      "Ġrec oll",
+      "Ġkle iner",
+      "ĠF IFA",
+      "Ġ196 2",
+      "èŃ ¦",
+      "Ġmal ad",
+      "Ġì° ½",
+      "ĠÃ¥ t",
+      "Ġinfluen cer",
+      "ĠArt ist",
+      "st i",
+      "ãģªãĤĭ ãģ»ãģ©",
+      "ว ย",
+      "ys ÅĤ",
+      "ĠB ian",
+      "Īë Ħ¤",
+      "Ġfire place",
+      "ĠApplic ation",
+      "Ġm niej",
+      "Ġacid ic",
+      "ĠMorm on",
+      "ss a",
+      "åĭ Ļ",
+      "Ġsneak y",
+      "Ġo jos",
+      "Ġv oud",
+      "ĠD ai",
+      "Ġgrass roots",
+      "ĠUn believable",
+      "ĠG abe",
+      "ĠExt reme",
+      "Ġhass le",
+      "Ġco b",
+      "m umbling",
+      "P ass",
+      "Įë Ł¬",
+      "Ġsystem atically",
+      "Ġsev enteen",
+      "ÏĢ ει",
+      "âĻ ¡",
+      "Ġк оÑĤ",
+      "Ġsend iri",
+      "Ġbathroom s",
+      "ĠS tern",
+      "ĠAr duino",
+      "è ¹",
+      "cri bing",
+      "Ġreop ening",
+      "Ġc erv",
+      "pe e",
+      "AR I",
+      "Ġcad re",
+      "ĠAn ch",
+      "L ee",
+      "ĠMA X",
+      "Ġm änn",
+      "Ġch ores",
+      "Ġad esso",
+      "æĿ ij",
+      "ĠN ig",
+      "Ġdissert ation",
+      "ĠV ay",
+      "ST ALK",
+      "ак а",
+      "av at",
+      "çł ´",
+      "Ġpun kt",
+      "Ġpad ding",
+      "ĠT empl",
+      "Ġe je",
+      "Ġí Ħ°",
+      "Ġa zt",
+      "ĠëĮĢ íĨµëł¹",
+      "Ġrearr ange",
+      "á ch",
+      "ĠìĤ¬ëŀĮë ĵ¤",
+      "Ġfre akin",
+      "cri re",
+      "Ġì» ¤ë",
+      "ĠExpl ain",
+      "ĠÏĦ Ïīν",
+      "Ġbod ily",
+      "ĠLe ist",
+      "Ġsig ui",
+      "Ġbunk er",
+      "Ġaz ul",
+      "ĠHa ush",
+      "S ub",
+      "ĠÐIJн д",
+      "ĠкÑĢ ай",
+      "Ġilleg ally",
+      "ĠM uy",
+      "ĠFe i",
+      "ĠBan ana",
+      "Ġscholar ly",
+      "ĠPr zy",
+      "ĠM oss",
+      "ĠFil ter",
+      "Ġìĸ´ëĸ ¡",
+      "ĠMax well",
+      "ten se",
+      "Ġlong itud",
+      "Ġlang sam",
+      "Ġ×ŀ× §",
+      "sm ith",
+      "iz ada",
+      "ĠноÑĢм алÑĮно",
+      "ĠV oll",
+      "ĠEl ena",
+      "æĸ¹ éĿ¢",
+      "ĠÑħ оÑĤÑĮ",
+      "ĠD abei",
+      "Ġconserv atives",
+      "Ġpróp ria",
+      "ĠDies er",
+      "ĠBrend a",
+      "ook ie",
+      "Ġb anc",
+      "ãĥ ¯",
+      "ìĿ´ì ¦",
+      "ìĽĥ ìĿĮ",
+      "Ġke h",
+      "Ġwed dings",
+      "Ġthunder storm",
+      "æĶ¾ å¿ĥ",
+      "ĠCo ordin",
+      "ìĪĺ ê°Ģ",
+      "Ġprze ci",
+      "éĴ ±",
+      "OS STALK",
+      "ma an",
+      "Ġê± ´ë",
+      "Ġب Ùĩ",
+      "Ġż ad",
+      "Ġy acht",
+      "Ġgö t",
+      "Ġble ach",
+      "Ġshort en",
+      "ĠÑģÑĤ ало",
+      "us an",
+      "ĠìŀIJ ìĹ°",
+      "Ġd ers",
+      "x is",
+      "įĶ ëĭĪ",
+      "Ġquant idade",
+      "Ġopp ressed",
+      "Ġзакон Ñĩ",
+      "ä¸Ī 夫",
+      "ãģĪ ãģĪ",
+      "ĠÑĩ еÑĤÑĭ",
+      "ĠÐĿапÑĢ имеÑĢ",
+      "ul p",
+      "æĢ ĸ",
+      "ÙĤ ÙĪÙĦ",
+      "оÑĩ е",
+      "ά λ",
+      "zen iu",
+      "Ġform ations",
+      "Ġspark ed",
+      "ĠEntwick lung",
+      "all s",
+      "Ġviv ir",
+      "Ġexp iration",
+      "ot ine",
+      "ĠЧ еÑĢ",
+      "ĠTur ning",
+      "Ġtar iffs",
+      "Ġnast ÄĻp",
+      "Ġab ide",
+      "ik si",
+      "Ġflash es",
+      "Ġdisp utes",
+      "Ġì² ´",
+      "Ġmer ak",
+      "Ġenorm ously",
+      "z ahl",
+      "Ġf ührt",
+      "в он",
+      "Ġзав иÑģ",
+      "Ġpersever ance",
+      "Ġdivid ends",
+      "Ġcontest ants",
+      "Ġpros zÄĻ",
+      "ĠFrank en",
+      "ãĤį ãģĨ",
+      "Ġexpl orer",
+      "Ġbuff alo",
+      "âĢ ķ",
+      "Ġec ology",
+      "Ġsc alar",
+      "Ġcr an",
+      "ε ÏĦαι",
+      "ży Äĩ",
+      "ĠìļĶ ë",
+      "Ġg ia",
+      "ĠG og",
+      "ĠPri v",
+      "Ġë§IJ ìĿĦ",
+      "ĠRe ason",
+      "ra ktion",
+      "ĠDe borah",
+      "Ġk itten",
+      "ĠEd in",
+      "ä¹ ¾",
+      "p iej",
+      "Ġëĭ ´",
+      "Ġmá qu",
+      "Ġbid ding",
+      "Ġaff inity",
+      "Ġa ika",
+      "fol k",
+      "ĠCon se",
+      "Ġdeuts chen",
+      "è Ĩ",
+      "Ġdeb it",
+      "ıģ ın",
+      "is el",
+      "Ġì¤ij êµŃ",
+      "ĠëŃIJ ê°Ģ",
+      "Ġtrust worthy",
+      "ĠStart ed",
+      "æķ ij",
+      "ür d",
+      "Ġпон ÑıÑĤно",
+      "Ġscient ifically",
+      "P ods",
+      "CR OSSTALK",
+      "Ġpregunt as",
+      "Ġcal ming",
+      "ĠPrem iere",
+      "׼ ש",
+      "ĠÑħ олод",
+      "Ġcap ita",
+      "Ġto ma",
+      "Ġmur m",
+      "Ġfuer za",
+      "ĠH ani",
+      "æĪij æľī",
+      "ü f",
+      "arl os",
+      "Ġhä uf",
+      "ãģij ãģ¦",
+      "Ġoso by",
+      "j ego",
+      "Ġп иÑģ",
+      "Ġcalm ly",
+      "id et",
+      "b uch",
+      "g one",
+      "Ġviscos ity",
+      "Ġmod al",
+      "Ġges am",
+      "ĠH z",
+      "Ġmunicip alities",
+      "Ġcircul ating",
+      "ol ina",
+      "S ho",
+      "é¢ ij",
+      "ĠBen ed",
+      "ol u",
+      "Ġrest s",
+      "Ġl Ã¥ng",
+      "ĠÐŀд нако",
+      "Ġprz ew",
+      "Ġpe pp",
+      "Ġmar riages",
+      "ĠB IG",
+      "and an",
+      "Ġmag ically",
+      "Ġbab ys",
+      "ĠëĮ ĵ",
+      "Ġhack ers",
+      "B aby",
+      "ĠM onst",
+      "Ġc ier",
+      "ĠAra bs",
+      "Ġмаг аз",
+      "ĠIndones ian",
+      "ãģĦãģĨ ãģĵãģ¨",
+      "ĠMark t",
+      "Ġd achte",
+      "ĠSch üler",
+      "ĠV ND",
+      "Ġsp ielt",
+      "Ġper lu",
+      "ãĤ ´",
+      "åŃ ĺ",
+      "ĠпÑĢо Ñħод",
+      "Ġsalt ed",
+      "Ġimpro vis",
+      "ĠInst r",
+      "vel mente",
+      "Ġn ess",
+      "Ġfun gus",
+      "Ġcollabor ators",
+      "ĠVir us",
+      "est ar",
+      "Ġproject or",
+      "ĠÐŁ ÑĢав",
+      "Ġag ility",
+      "×Ļ׳ ×ķ",
+      "ere l",
+      "Ġвоз в",
+      "Ġб аз",
+      "ĠCath y",
+      "ÄŁ u",
+      "ĠговоÑĢ ил",
+      "b ility",
+      "ĠL anc",
+      "ĠKim berly",
+      "ĠBr ief",
+      "åħ ·",
+      "Ġut veck",
+      "Ġgogg les",
+      "Ġpres chool",
+      "ç§ į",
+      "ATH ER",
+      "Ġmot ives",
+      "ĠB ong",
+      "E X",
+      "Ġch illy",
+      "ĠAdvis ory",
+      "âĢĭ âĢĭ",
+      "ĠкоÑĤоÑĢ ом",
+      "Ġtra itor",
+      "Ġdemasi ado",
+      "ĠÑĨ ен",
+      "Ġмо и",
+      "åŀ ĭ",
+      "Ġmult if",
+      "ìĶ ¬",
+      "ĠAlex is",
+      "Ġz iet",
+      "ĠR ama",
+      "br ance",
+      "Ġsan ction",
+      "it ous",
+      "×ķ× ļ",
+      "Ġë³´ë Ĥ",
+      "ÑģÑĤ анов",
+      "è¶ £",
+      "ĠÑĢ еÑģ",
+      "ĠChurch ill",
+      "ĠпÑĢ ез",
+      "ĠI O",
+      "ĠG ee",
+      "ĠG ather",
+      "ator i",
+      "Ty ler",
+      "Ġнем нож",
+      "ĠbÃ¥ de",
+      "ĠK iller",
+      "Ġtu ber",
+      "ĠRam adan",
+      "á ¿",
+      "ie ht",
+      "Ġstrang ely",
+      "л Ñĥ",
+      "Ġredes ign",
+      "Ġinc umb",
+      "Ġber aber",
+      "ĠVolks wagen",
+      "met al",
+      "d zy",
+      "p ción",
+      "ĠìķĬ ìķĦ",
+      "åĶ ±",
+      "å¤ ´",
+      "ĠGood ness",
+      "ив аеÑĤÑģÑı",
+      "b ahn",
+      "ĠAntar ctica",
+      "ек ÑĤоÑĢ",
+      "Ġhome owners",
+      "ze igt",
+      "ĠíĺĦ ìŀ¬",
+      "ì§Ģ ëıĦ",
+      "Ġgeograph ical",
+      "th inking",
+      "Ġg osta",
+      "ĠIm am",
+      "ulif lower",
+      "d ag",
+      "an nt",
+      "ak ov",
+      "Ġdown wards",
+      "ì²´ ê°Ģ",
+      "CU BE",
+      "ĠÐļ ÑģÑĤаÑĤи",
+      "Ġпол ов",
+      "Ġplate au",
+      "ãģĦ ãģį",
+      "Ḡ¥",
+      "Ġchlor ine",
+      "Ġacceler ator",
+      "Ġsol ves",
+      "ĠGr ass",
+      "p iano",
+      "ĠÚ© ا",
+      "Ġب ت",
+      "ĠRo chester",
+      "ĠÙĩ ÙĬ",
+      "Ġcollect s",
+      "įĶë Ŀ¼",
+      "ĠChe er",
+      "ling en",
+      "ĠÑĢаз г",
+      "Ġam éric",
+      "ht a",
+      "EC T",
+      "Ġart ific",
+      "ĠPay Pal",
+      "h ana",
+      "Step hen",
+      "ĠG est",
+      "ph alt",
+      "Ġrepl ication",
+      "ĠWill ie",
+      "Ġneut r",
+      "Ġirr ational",
+      "Ġdad os",
+      "ĠA id",
+      "k am",
+      "an ter",
+      "Ġд Ñĥже",
+      "Ġdet on",
+      "Ġha re",
+      "Ġbet s",
+      "bag ai",
+      "Ġst ained",
+      "Ġplaus ible",
+      "Ġpe eling",
+      "Ġcr ÃŃt",
+      "Ġgr ote",
+      "ì¶ °",
+      "¥´ ê²Į",
+      "alt et",
+      "P hone",
+      "F il",
+      "S QL",
+      "Ġge fallen",
+      "åı Ķ",
+      "Ġsa úde",
+      "ĠTam il",
+      "c ous",
+      "Ġглав ное",
+      "Ġatrav és",
+      "uss ia",
+      "Ġzwe iten",
+      "ĠEl vis",
+      "Ġmo ver",
+      "Ġlim ite",
+      "è¿ ½",
+      "are z",
+      "¥´ ê³ł",
+      "ĠKr anken",
+      "ü re",
+      "ĠìķĬ ìķĦìļĶ",
+      "Ġth Ãłnh",
+      "Ġprofound ly",
+      "Ġbedroom s",
+      "Ġtoothp aste",
+      "ĠAc cept",
+      "ét ico",
+      "Ġkü ç",
+      "ĠA ry",
+      "ad in",
+      "Ġgran ular",
+      "ect ed",
+      "Ġmen jadi",
+      "Ġcompet ence",
+      "d oc",
+      "Ġspark ling",
+      "Ġì¢ĭ ìĿĦ",
+      "Ġconstruct ing",
+      "Ġam usement",
+      "ĠIns urance",
+      "ĠFe uer",
+      "Ġrenov ation",
+      "s uch",
+      "pl at",
+      "Ġpros th",
+      "Ġbe y",
+      "ĠComplet ely",
+      "Ġz od",
+      "al n",
+      "V ict",
+      "Ġconfir ms",
+      "ät z",
+      "â ĸ",
+      "ham mer",
+      "Ġзна еÑĤ",
+      "Ġadm ired",
+      "łë ¥¼",
+      "ĠF ruit",
+      "ert en",
+      "Ġnie ce",
+      "ĠT iny",
+      "Ġpl umbing",
+      "erm a",
+      "Ġлег ко",
+      "Ġwind shield",
+      "ĠÑģм еÑĢ",
+      "Ġb zw",
+      "Ġabol ition",
+      "ĠSad hguru",
+      "Ġpre ached",
+      "ĠCreat ing",
+      "çī Ľ",
+      "per ed",
+      "Ġvol ont",
+      "Ġqu int",
+      "Ġprin ters",
+      "Ġneg ro",
+      "Ġgr osse",
+      "ĠTh y",
+      "ĠFell ows",
+      "æİ¥ ä¸ĭä¾Ĩ",
+      "Ġst anie",
+      "Ġnew com",
+      "ĠH ue",
+      "ĠFre unde",
+      "ĠConst ruction",
+      "Ġadvers ity",
+      "Ġneg atives",
+      "Ġhazard ous",
+      "Ġcompe lled",
+      "Ġw ok",
+      "ĠO y",
+      "п а",
+      "ª ¨ë",
+      "Ġrend ez",
+      "Ġover c",
+      "Ġwe aving",
+      "Ġид еÑĤ",
+      "Ġprosecut ors",
+      "Ġaudi obook",
+      "Ġancest or",
+      "Ġunder going",
+      "Ġpound ing",
+      "ãģĤãĤĬãģĮãģ¨ãģĨãģĶãģĸ ãģĦãģ¾ãģĻ",
+      "ĠíĴ Ģ",
+      "Ġì¶ ¤",
+      "Ġtule e",
+      "ĠìĹ ´ì",
+      "Ġzo als",
+      "Ġne in",
+      "éŃ ļ",
+      "Ġo ke",
+      "ĠJoy ce",
+      "Ġn ud",
+      "Ġdil igence",
+      "ĠLab s",
+      "Ġv ents",
+      "Ġancest ral",
+      "ห ม",
+      "ĠмÑĥж Ñĩ",
+      "Ġnom és",
+      "表 示",
+      "w ali",
+      "q ing",
+      "ĠMultip le",
+      "ĠCons ult",
+      "Ġist edi",
+      "ĠD oy",
+      "ak ah",
+      "Ġdiscipl ined",
+      "Ġaltern ating",
+      "ç Ĵ",
+      "Ġver me",
+      "Ġо Ñī",
+      "Ġto ta",
+      "ĠP rag",
+      "Ġsw orn",
+      "Ġbe ber",
+      "ĠAufg abe",
+      "ìļ ´ë",
+      "辦 æ³ķ",
+      "Ġy up",
+      "Ġrec laim",
+      "on ut",
+      "Ġauc une",
+      "Ġam ph",
+      "ĠÅĽ wie",
+      "Ġa a",
+      "isco ver",
+      "ĠAr g",
+      "cie ż",
+      "Ġdess as",
+      "ĠW äh",
+      "á» ¹",
+      "Ġдав но",
+      "Ġsil ently",
+      "ar c",
+      "ĠíĽĦë ³´",
+      "Ġtwe eting",
+      "ĠO nd",
+      "é¡ ŀ",
+      "¦¬ë ©´",
+      "Ġbow el",
+      "ìħ¨ ìĸ´ìļĶ",
+      "èģ Ĭ",
+      "OS E",
+      "Ġprop io",
+      "ĠKun st",
+      "k ung",
+      "Ġdonn ées",
+      "ĠHor izon",
+      "ĠF rog",
+      "åĢĭ 人",
+      "Ġar ist",
+      "â l",
+      "Ġк ож",
+      "Ġseg undos",
+      "ĠShort ly",
+      "ĠC rowd",
+      "ir an",
+      "ĠwÅĤa ÅĽci",
+      "ĠL ac",
+      "ident e",
+      "Ġê°Ģ ìŀIJ",
+      "Ġl en",
+      "ĠS US",
+      "ĠMot ors",
+      "ĠT rent",
+      "om ie",
+      "Ġtransmit ter",
+      "ĠAss ad",
+      "Ġpsych iatric",
+      "Ġж иÑĤÑĮ",
+      "Ġout lines",
+      "Ġeffective ment",
+      "ĠRelig ion",
+      "pre h",
+      "Ġдолж на",
+      "ĠÍ¡ °",
+      "ĠCons ervation",
+      "Ġ á»",
+      "Ġз ай",
+      "Ġres ide",
+      "Ġcomplet o",
+      "K EN",
+      "ĠëĤĺìĺ¤ ëĬĶ",
+      "Ġsubur ban",
+      "Ġrépond re",
+      "ĠÑĢаз лиÑĩ",
+      "Ġgall eries",
+      "Ġr apt",
+      "æĦŁ è¬Ŀ",
+      ") ...",
+      "Ġcruel ty",
+      "ĠVM ware",
+      "í Ī¬",
+      "Ġhay ır",
+      "Ġgroup ing",
+      "ĠR ider",
+      "Ġsyll able",
+      "Ġbeispiel sweise",
+      "Ġsafeg uard",
+      "ĠpelÃŃcul a",
+      "art i",
+      "ĠС о",
+      "Ġche ga",
+      "Ġк омÑĥ",
+      "Ġse ism",
+      "Ġharm less",
+      "ĠWarri ors",
+      "ãģĦ ãģ¤",
+      "Ġп Ñģ",
+      "Ġsham eless",
+      "ĠBa um",
+      "inst all",
+      "Ġtool kit",
+      "Ġpip elines",
+      "Ġp ussy",
+      "Ġconce al",
+      "Ġprot esting",
+      "och ond",
+      "Ġdu a",
+      "ĠP ose",
+      "Ġhel ium",
+      "ĠU X",
+      "ik le",
+      "ĠS uff",
+      "ĠìĦ¸ ê³Ħ",
+      "ing ers",
+      "ĠÑģлÑĥÑĩ ай",
+      "Ġdesc ending",
+      "Ġ æ²Ĵæľī",
+      "Ġmont age",
+      "H igh",
+      "ĠìĿ´ì ĸ",
+      "ĠI di",
+      "Ġ×ij× ¡",
+      "Ġexpress ive",
+      "ç§ ĭ",
+      "Ġпол ез",
+      "Ġp one",
+      "Ġadoles cent",
+      "ан нÑĭе",
+      "Ġassass ination",
+      "we isen",
+      "em atically",
+      "aut h",
+      "Ġur g",
+      "Ġgan har",
+      "Ġfund o",
+      "ĠRh ode",
+      "ĠиÑģÑĤоÑĢ ии",
+      "Ġcompart il",
+      "æķ ¢",
+      "Ġdimin ished",
+      "Ġapprent ice",
+      "ĠÐij Ñĥд",
+      "Ġphot ons",
+      "Ġcó d",
+      "å¹ ķ",
+      "æ¬ Ĭ",
+      "on ak",
+      "Ġadel ante",
+      "Ġch u",
+      "op ic",
+      "Ġa ixÃŃ",
+      "ed dar",
+      "ĠCong rats",
+      "m or",
+      "好 åIJ§",
+      "Ġreserv ations",
+      "ĠT oby",
+      "ĠK ern",
+      "Ġraz em",
+      "Ġfor ged",
+      "Ġhorr ifying",
+      "ÙĬ ع",
+      "ĠJo ining",
+      "ãĥ© ãĤ¤",
+      "ĠA uth",
+      "d ah",
+      "Ġcons ig",
+      "Ġintimid ated",
+      "Ġperipher al",
+      "Ġmen o",
+      "Ġdetect ing",
+      "Ġte or",
+      "Ġtag ged",
+      "Ġnost algic",
+      "Ġ미 ìķĪ",
+      "åĢ ¼",
+      "Ġver di",
+      "Ġlabel ing",
+      "п од",
+      "ast es",
+      "Ġv ist",
+      "Ġcy t",
+      "Ġfl ips",
+      "ÑĢи з",
+      "bal anced",
+      "ãģª ãģı",
+      "Ġо ÑĪиб",
+      "Ġdest in",
+      "las se",
+      "ere i",
+      "Ġkal o",
+      "Ġar qu",
+      "Ġplan o",
+      "Ġordin ance",
+      "Ġcomp ilation",
+      "ĠVocê s",
+      "ĠE co",
+      "Ġì¶Ķ ì²ľ",
+      "Ġenc ima",
+      "ĠGar rett",
+      "ĠC ord",
+      "öl ker",
+      "ĠAr row",
+      "Ġprot ons",
+      ", âĢĭ",
+      "Ġì² ĺë",
+      "Ġsc and",
+      "Ġbe ige",
+      "c ong",
+      "Ġb iking",
+      "ĠT L",
+      "Ñĥн д",
+      "ĠìĨĶ ì§ģ",
+      "ĠV illa",
+      "ĠJ ACK",
+      "以 åıĬ",
+      "ĠÃ¶ÄŁ ren",
+      "Ġtem as",
+      "ĠKy ung",
+      "J enn",
+      "Ġc ud",
+      "Ġimp osing",
+      "Ġcommand ments",
+      "ĠMe ans",
+      "ĠD är",
+      "Ġrecom end",
+      "Ġdisp osition",
+      "ا Ùĩ",
+      "Ġth u",
+      "Ġredu ctions",
+      "Ġdi u",
+      "Ġ×ķ ×IJ×",
+      "ĠиÑģ Ñģлед",
+      "th ren",
+      "Ġl ados",
+      "ĠR B",
+      "ix ed",
+      "Ġì ı",
+      "F r",
+      "st ill",
+      "Ġol mas",
+      "CH UCK",
+      "ĠíĨ ł",
+      "ĠIndepend ent",
+      "ÐĴ Ðŀ",
+      "Ġp its",
+      "Ġundert aken",
+      "Ġf ør",
+      "ĠN aw",
+      "Ġìŀij ìĹħ",
+      "Ġsh epherd",
+      "Ġlang ue",
+      "ĠJ ab",
+      "ĠDr um",
+      "ĠEle kt",
+      "æĭ ¬",
+      "ãģĺãĤĥ ãģªãģĦ",
+      "á»ij t",
+      "ĠìĿ´ì ª½",
+      "Ġbegin nen",
+      "ĠF ury",
+      "á»ĥ u",
+      "se ctions",
+      "Ġspray ed",
+      "Ġmá r",
+      "ĠV olt",
+      "ĠSe ong",
+      "иÑĤ ел",
+      "du ction",
+      "as an",
+      "Ġjud gments",
+      "ima an",
+      "ŀ× ª",
+      "Ġs iento",
+      "ĠAC T",
+      "ĠB H",
+      "de v",
+      "Ġì¢ĭìķĦ íķĺ",
+      "Ġj orn",
+      "IS TIN",
+      "Ġro ar",
+      "Ġimmers ion",
+      "aff les",
+      "Ġtra inee",
+      "ĠBill board",
+      "ress es",
+      "ĠWar m",
+      "ĠRober to",
+      "Ġutil izz",
+      "ĠIg or",
+      "Ġr ash",
+      "Ġanalyt ic",
+      "ir am",
+      "Ġsymm etrical",
+      "Ġlifes pan",
+      "Ġe ater",
+      "ĠBloom berg",
+      "ater ial",
+      "Ġë¯ ¿",
+      "Ġis ter",
+      "Ġinv aluable",
+      "Ġassist ing",
+      "Ġsh ack",
+      "μα ÏĦα",
+      "j is",
+      "en iz",
+      "ĠпÑĢед лож",
+      "Ġdecl aring",
+      "ĠV iking",
+      "ĠAss im",
+      "Ġexpend iture",
+      "Ġpos ing",
+      "ĠOn un",
+      "Ġin ic",
+      "аÑİ ÑĤÑĮ",
+      "re v",
+      "Ġm iedo",
+      "Ġfil thy",
+      "ĠI B",
+      "ĠDis cover",
+      "icht et",
+      "m illion",
+      "¶Ħë ĵ¤ìĿ´",
+      "Ġamb igu",
+      "ĠF lynn",
+      "bard ziej",
+      "Ġinc omp",
+      "ав но",
+      "z ia",
+      "Ġinfluen cing",
+      "Ġworld ly",
+      "ĠSales force",
+      "z et",
+      "Ġparticul ier",
+      "ĠK och",
+      "Ġ194 3",
+      "Ġton er",
+      "ĠÑįкÑģп еÑĢ",
+      "Ġsus cri",
+      "Ġtrigger ing",
+      "IC ES",
+      "ìĬ¤ ê°Ģ",
+      "δ α",
+      "ÑĢ абоÑĤ",
+      "Ġafter ward",
+      "p ine",
+      "ĠI L",
+      "are th",
+      "Ġп ал",
+      "Ġs aker",
+      "Ġ194 7",
+      "A F",
+      "uy orsun",
+      "ĠìĬ ¤ë",
+      "Ġquant ify",
+      "Ġment orship",
+      "Ġll ega",
+      "ĠTam ara",
+      "Ġoptim izing",
+      "Ġfront s",
+      "os ters",
+      "Ġes quer",
+      "Ġsubm issions",
+      "Ġann ih",
+      "Ġsu ction",
+      "lu ence",
+      "chied en",
+      "ING S",
+      "Ġ×ij ×Ķ",
+      "ĠÑģ ÑĨен",
+      "Ġwiel u",
+      "Ġobjet o",
+      "Ġbo obs",
+      "ĠGesch äft",
+      "Ġear buds",
+      "ĠÑĢ анÑĮÑĪе",
+      "Ġrout inely",
+      "Ġcoll agen",
+      "од Ñĭ",
+      "ĠCin namon",
+      "Ġba ix",
+      "د Ùħ",
+      "f rage",
+      "Ġк ноп",
+      "Ġde ception",
+      "Ġunexpected ly",
+      "Ġsmell ed",
+      "Ġlo os",
+      "Ġhighlight er",
+      "Ġê¸°ë ³¸",
+      "ĠGlas gow",
+      "ow ana",
+      "m n",
+      "ĠJ eremiah",
+      "ĠDat ab",
+      "iet e",
+      "Ġb aw",
+      "Ġprop ia",
+      "Ġprop ri",
+      "OOOO OOOO",
+      "ink er",
+      "Ġpert urb",
+      "ĠF ake",
+      "ìĿ´ì ĸ",
+      "im ming",
+      "Ġund ocumented",
+      "Ġtrabaj ando",
+      "Ġro am",
+      "Ġдолж но",
+      "Ġar be",
+      "Ġan i",
+      "at al",
+      "Ġar ada",
+      "ĠAnd a",
+      "ĠìĽ Ģ",
+      "ĠBr anch",
+      "o ires",
+      "Ġouts ider",
+      "d ollar",
+      "å½ĵ çĦ¶",
+      "iss es",
+      "be ans",
+      "ĠG ig",
+      "çĿ ¡",
+      "r ados",
+      "ĠS ut",
+      "ĠL ance",
+      "edsiÄĻ bior",
+      "Ġcol a",
+      "on ents",
+      "Ġrec onsider",
+      "ãĤ¹ ãĥĪ",
+      "Ġmond o",
+      "ãĥ³ãĥ įãĥ«",
+      "Ġuns uccess",
+      "ĠK ä",
+      "è¾ ¹",
+      "Ġreg el",
+      "Ġbis og",
+      "et us",
+      "Ġun ravel",
+      "Ġsweet ie",
+      "Ġreprés ent",
+      "our ing",
+      "Ġground water",
+      "ĠBe w",
+      "Ġscratch ed",
+      "Ġcass ette",
+      "Ġc ider",
+      "p is",
+      "ĠÑģам а",
+      "Ġglobal ization",
+      "Ġdegrad ation",
+      "Ġde gener",
+      "ĠRos ie",
+      "ick t",
+      "Ġover weight",
+      "ĠM EM",
+      "Ġguard ians",
+      "Ġconse c",
+      "H mm",
+      "æĪij åľ¨",
+      "ĠпоÑĤÑĢ еб",
+      "Ġme va",
+      "Ġgra ffiti",
+      "Ġfl irt",
+      "ĠB P",
+      "Ġjust o",
+      "ĠThous ands",
+      "çĶ ľ",
+      "Ł¬ ìļ´",
+      ". *",
+      "ĠRA W",
+      "Ġflu or",
+      "iy i",
+      "ant al",
+      "j ed",
+      "ĠSh eng",
+      "ĠEl ise",
+      "ĠChar ge",
+      "ìĿ´ íĬ¸",
+      "Ġcon es",
+      "n ies",
+      "g ia",
+      "ĠнаÑĩ ала",
+      "ĠD harma",
+      "Ġëĭ¤ ìĸij",
+      "Ġf avors",
+      "ĠTr ung",
+      "het to",
+      "Ġpo zw",
+      "Ġlong o",
+      "Ġke lu",
+      "Ġdigest ion",
+      "ĠE ig",
+      "ĠTH ERE",
+      "Ġt iers",
+      "Ġs unk",
+      "Ġmyst ical",
+      "z ub",
+      "ĠÃī t",
+      "Ġanticip ating",
+      "ĠV ine",
+      "Y Y",
+      "Ġconcent rating",
+      "ĠAgre ement",
+      "Ġок оло",
+      "Ġlid t",
+      "ĠYa o",
+      "ĠÑģ лиÑĪком",
+      "r ÃŃ",
+      "ISTIN CT",
+      "ĠOFF IC",
+      "Ġso aking",
+      "Ġsi ihen",
+      "Ġrefer encing",
+      "ĠT ampa",
+      "ane y",
+      "Ġresp uesta",
+      "ĠCo alition",
+      "ĠÑģог лаÑģ",
+      "ank ind",
+      "Ġë Ľ",
+      "ĠY ummy",
+      "ë° °",
+      "Ġon c",
+      "ui ção",
+      "Ġthe o",
+      "Ġm ural",
+      "ĠTeach ers",
+      "Ġwait s",
+      "Ġrent ing",
+      "ĠHar mon",
+      "Ġe ÅŁ",
+      "ĠMun ich",
+      "íĻ ľ",
+      "ìĸ ¼",
+      "c ards",
+      "Ġrou ge",
+      "Ġn ên",
+      "cl ub",
+      "Ġun seen",
+      "Ġdep reci",
+      "Ġcomput ed",
+      "Ġwip ing",
+      "ĠEll i",
+      "ident ified",
+      "Ġcl utter",
+      "role um",
+      "Ġtele f",
+      "Ġlevel ing",
+      "ĠWo ody",
+      "ĠG us",
+      "ĠBenn ett",
+      "Ġsit io",
+      "i ÅĤ",
+      "Ġposs essions",
+      "ĠNat asha",
+      "old own",
+      "ĠÑģо обÑī",
+      "ĠL ic",
+      "Ġë§Įë ĵł",
+      "Ġlors que",
+      "we h",
+      "Ġм ам",
+      "l iter",
+      "ad omo",
+      "Ġfin i",
+      "Ïİ ÏĤ",
+      "ĠÑĥб ий",
+      "Ġind isp",
+      "Ġtele vis",
+      "Ġp á",
+      "ĠCre o",
+      "ÃŃ ll",
+      "Ġg ur",
+      "ĠM AL",
+      "ĠÑĢаз нÑĭÑħ",
+      "Ġzie hen",
+      "Ġfashion ed",
+      "Ġdeb ating",
+      "ĠS oup",
+      "ĠProv ince",
+      "ê·¸ë łĩ",
+      "Ġimpro per",
+      "Ġimag en",
+      "ĠÑģдел ал",
+      "Ġlog os",
+      "Ġevent o",
+      "è§ Ĩ",
+      "ả o",
+      "l arda",
+      "ĠназÑĭв аеÑĤÑģÑı",
+      "Ġver f",
+      "Ġscreens hots",
+      "×ķ×ĵ ×¢",
+      "ĠAur ora",
+      "ĠB ali",
+      "ter ed",
+      "Ġcontag ious",
+      "Ġcompart ir",
+      "ven idos",
+      "ri ke",
+      "ĠвÑĭглÑıд иÑĤ",
+      "Ġfreed oms",
+      "nic as",
+      "ł¤ ìĦľ",
+      "Ġredu z",
+      "ĠE cu",
+      "Ġab onn",
+      "ĠSE Ãij",
+      "ĠB itch",
+      "Ġprojet o",
+      "иÑĩ но",
+      "ett re",
+      "AN NA",
+      "th ank",
+      "ĠA O",
+      "æīĢ以 åij¢",
+      "arn ish",
+      "ie ÃŁen",
+      "Ġr ipple",
+      "Ġpant ry",
+      "ĠG H",
+      "γ α",
+      "ĠìĿ´ë²Ī ìĹIJ",
+      "Ġvalid ated",
+      "Ġbrush ed",
+      "ĠE min",
+      "ĠDar th",
+      "es in",
+      ", .",
+      "Ġv alle",
+      "Ġjer sey",
+      "ul an",
+      "R ead",
+      "ĠR angers",
+      "Ġso othing",
+      "Ġcomplement ary",
+      "ĠVer kehr",
+      "ac akt",
+      "Ġbat ht",
+      "ĠN D",
+      "S on",
+      "ĠíĻĶ ìŀ¥",
+      "ĠA vi",
+      "ĠS AL",
+      "ais se",
+      "Ġsem aines",
+      "ĠSur v",
+      "w ier",
+      "Ġвид ел",
+      "Ġsi ete",
+      "Ķë ıĦ",
+      "ĠRams ay",
+      "ĠQueens borough",
+      "ĠM enge",
+      "ĠFood s",
+      "Ġthe ological",
+      "Ġ[ #",
+      "Ġв они",
+      "Ġim min",
+      "ios ity",
+      "ĠAb geord",
+      "ĠA cho",
+      "ĠÃ Ķ",
+      "Ġst ains",
+      "Ġreal istically",
+      "Ġfashion able",
+      "ĠCEO s",
+      "ĠSk ill",
+      "Ġв же",
+      "Ġde ver",
+      "ĠPl ug",
+      "æ ª",
+      "P od",
+      "Ġlo af",
+      "Ġge bracht",
+      "Ġabsor bs",
+      "ĠGr anny",
+      "Ġmal ware",
+      "ag ÄĻ",
+      "Ġcivil izations",
+      "ĠÏ ģ",
+      "Ġh ält",
+      "С Т",
+      "g reat",
+      "Ġlay ering",
+      "s ings",
+      "Ġв Ñĸн",
+      "Ġrecogn izable",
+      "Ġwo j",
+      "Ġwet en",
+      "第 ä¸ĢåĢĭ",
+      "γ ο",
+      "St udent",
+      "Ġdé fin",
+      "ple ase",
+      "en ch",
+      "Ġatt ic",
+      "ĠOt tawa",
+      "Ġopt ed",
+      "Ġcapt iv",
+      "Ġm ÅĤ",
+      "ĠY A",
+      "ĠW and",
+      "Ġb ounty",
+      "Ġ2 70",
+      "Ġspec ulate",
+      "Ġenhance ment",
+      "Ġcommod ities",
+      "ĠMil ton",
+      "e j",
+      "al om",
+      "D as",
+      "Ġco oldown",
+      "ר ×IJ׾",
+      "Ġ×IJ× ¤",
+      "Ġwcze ÅĽniej",
+      "Ġel ong",
+      "Ġdi ode",
+      "ina ção",
+      "ĠI ris",
+      "ĠI b",
+      "Ġsummon ed",
+      "Ġres pe",
+      "ĠR ach",
+      "注 æĦı",
+      "Ġ» :",
+      "éĨ Ĵ",
+      "Ġv ur",
+      "Ġmov imento",
+      "Ġflu ent",
+      "ĠEv olution",
+      "ĠBut t",
+      "ific ación",
+      "ĶĶ ìĸ´",
+      "ĠÑįн еÑĢг",
+      "Ġmanip ulating",
+      "Ġposit iv",
+      "м оÑģ",
+      "Ġw iz",
+      "Ġinto x",
+      "ÎŃ Ïģ",
+      "ем ÑģÑı",
+      "ives se",
+      "imiz i",
+      "Ġìļ ¸",
+      "Ġknock s",
+      "Ġcongest ion",
+      "ĠIde ally",
+      "ĠHold ing",
+      "Ġpo bre",
+      "ĠJ UL",
+      "Ġë¶Ħëĵ¤ ìĿĢ",
+      "Ġα κ",
+      "ĠFergus on",
+      "ĠLabor atory",
+      "richt en",
+      "roph y",
+      "produ ction",
+      "ass ung",
+      "IT A",
+      "Ġsiè cle",
+      "ר ת",
+      "c ision",
+      "Ġפ ×Ķ",
+      "ĠIre ne",
+      "an ca",
+      "ĠìĤ¬ ê³ł",
+      "Ġpin point",
+      "Ġdesign ation",
+      "ÅŁ am",
+      "l Ä±ÅŁ",
+      "a at",
+      "ĠnÃ¥ gra",
+      "Ġmyth ical",
+      "ĠDec laration",
+      "Ġìŀ¡ ìķĦ",
+      "Ġby te",
+      ". âĻª",
+      "D el",
+      "Ġí į¼",
+      "Ġnutrit ious",
+      "ĠÑĢÑĥб лей",
+      "åĤ ³",
+      "S AY",
+      "M aster",
+      "ĠÑĦоÑĤ огÑĢаÑĦ",
+      "ĠëĴ¤ ìĹIJ",
+      "Ġne h",
+      "Ġdok ument",
+      "çª ģ",
+      "Ġczas u",
+      "Ġcontinu a",
+      "ĠSil ent",
+      "Ġtens or",
+      "Ġt anta",
+      "Ġirgend wo",
+      "ĠL ET",
+      "ĠSh akt",
+      "l ama",
+      "chl ag",
+      "Ġd ingen",
+      "ÑģÑĤ ÑĢа",
+      "Ġe hrlich",
+      "ĠM acht",
+      "rel s",
+      "Ãł cies",
+      "v ideo",
+      "Ġnatur ale",
+      "ĠSTE VE",
+      "um m",
+      "B ACK",
+      "Ġ7 20",
+      "ãģ§ ãģĹãģŁ",
+      "Ġmom encie",
+      "ĠSw an",
+      "Ġtechn icians",
+      "Ġgee hr",
+      "ĠM end",
+      "R eg",
+      "Ġsca ff",
+      "Ġa ide",
+      "Ġë³´ ëĬĶ",
+      "Ġpress es",
+      "ler de",
+      "\\ '",
+      "Ġultras ound",
+      "Ġdisc laimer",
+      "ĠM its",
+      "ĠHol iday",
+      "Ġextern ally",
+      "ĠF ate",
+      "IN O",
+      "ĠC ats",
+      "ë° ķ",
+      "um o",
+      "cont rol",
+      "Ġthe CUBE",
+      "t ic",
+      "ier ungs",
+      "Ġзнак ом",
+      "Ġfre estyle",
+      "MAND ARIN",
+      "Ġis e",
+      "aur us",
+      "è¨ ±",
+      "ĠSt rategy",
+      "ĠBe am",
+      "rä ge",
+      "Ġexplo ited",
+      "ãģĪ ãģ£",
+      "id is",
+      "Ġch ime",
+      "ĠPen insula",
+      "Ġmer its",
+      "Ġalt ro",
+      "ĠTO P",
+      "ĠS ens",
+      "ĠK ant",
+      "or as",
+      "Ġroyal ty",
+      "ĠID E",
+      "å¤ ī",
+      "r acy",
+      "ĠTH OM",
+      "om os",
+      "Ġläng er",
+      "Ġnumber ed",
+      "U m",
+      "ĠNi ye",
+      "θ η",
+      "zy ka",
+      "l ime",
+      "ĠPerson en",
+      "Ġvalid ity",
+      "Ġcont rat",
+      "ĠCom ic",
+      "ç ons",
+      "ĠHe idi",
+      "Ġz g",
+      "Ġren amed",
+      "Ġc umin",
+      "ĠJ F",
+      "in el",
+      "Ġenfor ced",
+      "Ġch ama",
+      "ли Ñĩно",
+      "Ạ»",
+      "Ġден ег",
+      "Ġprof und",
+      "Ġpel vic",
+      "Ġpalav ra",
+      "Ġext ras",
+      "Ġank les",
+      "ìĹIJ ìĦľëıĦ",
+      "ĠT F",
+      "Ġinsan ely",
+      "Ġм ÑıÑģ",
+      "Ġrép onse",
+      "Ġgö ster",
+      "ĠBB Q",
+      "ĠÑĥÑĩ аÑģÑĤ",
+      "Ġsh aken",
+      "ãĤ« ãĥ³ãĤ¿",
+      "Ġalm onds",
+      "d ish",
+      "ĠP G",
+      "ĠBl izzard",
+      "ÑĮ ого",
+      "Ġ ãħ",
+      "Ġkn app",
+      "T oo",
+      "Ġund e",
+      "Ġmount s",
+      "ом ина",
+      "Ġnorth east",
+      "Ġcens orship",
+      "ÑıÑĤÑĮ ÑģÑı",
+      "l r",
+      "Ġlaw makers",
+      "ĠsÃ¥ dan",
+      "Ġins ider",
+      "Ġclean up",
+      "ĠN ada",
+      "ó c",
+      "Ġharvest ed",
+      "ĠDesp ués",
+      "íļ į",
+      "Ġredund ant",
+      "EN A",
+      "Ġdeleg ate",
+      "Ġbur g",
+      "ĠAl ison",
+      "æĸ° èģŀ",
+      "Ġcel estial",
+      "Ġsin ners",
+      "Ġmart yr",
+      "ĠP erm",
+      "Ġspec imens",
+      "Ġmit ochond",
+      "Ġmar avil",
+      "Ġcaval ry",
+      "Ġarray s",
+      "Ġanne x",
+      "Ġlabor atories",
+      "ĠBy z",
+      "Ġat ac",
+      "ĠÑģл ожно",
+      "Ġto pl",
+      "Ġger i",
+      "ĠCom bat",
+      "ÑģÑı ÑĤ",
+      "ek en",
+      "ĠÐĴ лад",
+      "Ġa just",
+      "Ġmar que",
+      "Ġlook out",
+      "ĠL ol",
+      "Ġrooft op",
+      "ĠOr ion",
+      "Ġб ой",
+      "Ġheart breaking",
+      "Ġdet to",
+      "z h",
+      "ät ter",
+      "c era",
+      "Ġhe ats",
+      "Ġant iqu",
+      "Ġunf inished",
+      "ĠK azu",
+      "ıl ı",
+      "Ġslight est",
+      "le o",
+      "ĠvÃ¥ ra",
+      "Ġverschied enen",
+      "Ġlot ion",
+      "ä½ł å°±",
+      "æĮ º",
+      "ÑĪ его",
+      "ction al",
+      "ĠìĿ´ì ł",
+      "d ragon",
+      "Ġreson ates",
+      "Ġin m",
+      "av ic",
+      "Ġfulf il",
+      "Ġ기ë ĮĢ",
+      "Ġjust amente",
+      "Ġдо ÑģÑĤÑĥп",
+      "Ġê·¸ ê±´",
+      "Ġrecon cile",
+      "ĠSch ön",
+      "ĠAvo id",
+      "ê¹ Ģ",
+      "' D",
+      "Ġconf inement",
+      "Ġí ij",
+      "Ġmotiv ating",
+      "ĠBritt any",
+      "Ġãģ Ļ",
+      "Ġscream ed",
+      "ob ject",
+      "Ġdec ree",
+      "Ġtrava ille",
+      "iss ible",
+      "Ġb usted",
+      "pro cess",
+      "Ġmass acre",
+      "Ġngh Ä©",
+      "ily n",
+      "Ġв ÑĢоде",
+      "Ġpo etic",
+      "Ġnh ất",
+      "Ġiron ically",
+      "us u",
+      "n io",
+      "Ġst aging",
+      "omed ical",
+      "le ased",
+      "ĠìĥĪë¡ľ ìļ´",
+      "ĠN Z",
+      "act ing",
+      "ĠBattle field",
+      "play ful",
+      "V i",
+      "Ġseñ ora",
+      "Ġprompt s",
+      "lich keit",
+      "Ġçık ar",
+      "ji ang",
+      "Ġpick y",
+      "ĠC ave",
+      "Ġmirac ulous",
+      "ĠHugh es",
+      "20 16",
+      "Ġx u",
+      "ĠDor othy",
+      "Ġvirt ues",
+      "Ġret ract",
+      "Ġty r",
+      "Ġchar ismatic",
+      "Ġb ola",
+      "é ¼",
+      "Ġë§IJìĶ Ģë",
+      "Ġparent al",
+      "Ġmillion aire",
+      "ari at",
+      "æĶ¿ åºľ",
+      "Ġinv oke",
+      "żen ie",
+      "Ġextrem es",
+      "ĠA ku",
+      "ivid ade",
+      "Ġï· º",
+      "Ġìĭľ ì²Ń",
+      "ĠGar lic",
+      "RI A",
+      "Ġд оÑģ",
+      "ĠP ont",
+      "Ġmil j",
+      "ell i",
+      "Ġrack et",
+      "Ġcompet it",
+      "ĠWh is",
+      "Ġreal t",
+      "ign ment",
+      "est re",
+      "Ġper nah",
+      "ĠOp ening",
+      "ĠF S",
+      "ĠDemokrat en",
+      "ac ements",
+      "Ġworld view",
+      "Ġplay offs",
+      "ĠC AD",
+      "Ġét ant",
+      "Ġyem ek",
+      "Ġsent iments",
+      "od el",
+      "b uster",
+      "a ÅŁ",
+      "ĠK Y",
+      "cz ÄĻ",
+      "Ġschö ne",
+      "a pe",
+      "ĠR aspberry",
+      "Ġcred ited",
+      "ĠH idden",
+      "Ġsaus ages",
+      "ru ce",
+      "ĠBe v",
+      "ilant ro",
+      "Ġpoke mon",
+      "Ġê°Ģ 격",
+      "Ġproceed ing",
+      "Ġve io",
+      "Ġ17 5",
+      "è ¸",
+      "ma x",
+      "Ġfr ater",
+      "ìłĦ ìĹIJ",
+      "Ġe gent",
+      "Ġ25 00",
+      "us ch",
+      "T ube",
+      "Ġampl ify",
+      "Ġpraw d",
+      "Ġod or",
+      "ĠSc an",
+      "Ġplot ting",
+      "ithm etic",
+      "Ġres igned",
+      "ĠSC OTT",
+      "Ġstere oty",
+      "Ġdo able",
+      "ĠCom plex",
+      "Ùģ ÙĬ",
+      "t ım",
+      "ÑĢи г",
+      "l ardan",
+      "es o",
+      "D EN",
+      "Ġhood ie",
+      "ĠC AT",
+      "Ø§Ø ·",
+      "Ġbond ed",
+      "ĠBurn s",
+      "оп аÑģ",
+      "Ġr ÄĻ",
+      "ει α",
+      "ĠоÑĤд елÑĮ",
+      "Ġtim eless",
+      "ĠV ij",
+      "ĠPan ama",
+      "Ġre organ",
+      "ĠT ä",
+      "ĠPl uto",
+      "O range",
+      "Ġп ойд",
+      "ĠBr istol",
+      "u ced",
+      "ĠëIJĺ ìĸ´",
+      "Ġun bedingt",
+      "ad le",
+      "Ġvolunte ered",
+      "Ġm ieli",
+      "ĠEdin burgh",
+      "ik al",
+      "Ġal ten",
+      "ĠAr sen",
+      "Ġmouve ment",
+      "Ġant ique",
+      "Ġb h",
+      "ĠH ers",
+      "Ġsa ute",
+      "Ġasp ire",
+      "Ġsp heres",
+      "ĠW am",
+      "ắ m",
+      "Ġwip es",
+      "Ġ2 80",
+      "ĠVe h",
+      "Ġcol oca",
+      "а ÑĦ",
+      "Ġвозмож ноÑģÑĤÑĮ",
+      "Ġphysi ological",
+      "h wa",
+      "et u",
+      "Ġprolong ed",
+      "Ġexperi ência",
+      "Ġвид но",
+      "Ġquar ant",
+      "Ġpued an",
+      "è Ķ",
+      "v ine",
+      "ĠUS DA",
+      "ph em",
+      "Ġform idable",
+      "Ġfl atter",
+      "ìĸ´ì §Ģ",
+      "Ġb én",
+      "à¹ģ à¸ķ",
+      "Ġë¬¼ë ¡ł",
+      "Ġfact ions",
+      "ĠLe aving",
+      "Ġ×IJת ×Ķ",
+      "ĠExper t",
+      "d io",
+      "ĠVer d",
+      "ãģ¿ ãģŁãģĦ",
+      "Ġs int",
+      "ÙĨ د",
+      "n umber",
+      "Ġow ed",
+      "Ġindu ce",
+      "ĠFred die",
+      "ab o",
+      "ĠFilip ino",
+      "¯ ¼ë",
+      "believ ably",
+      "ath lon",
+      "ama an",
+      "Ġde venir",
+      "ĠG os",
+      "ĠJen kins",
+      "b ait",
+      "Ġb ins",
+      "ĠM ICH",
+      "u yorum",
+      "ig rade",
+      "is so",
+      "ĠìĹ ´",
+      "ĠìķĦë ¹ł",
+      "Ġdiarr hea",
+      "Ġtorn ar",
+      "ad din",
+      "Ġungef ähr",
+      "Ġrest room",
+      "Ġpsychiat rist",
+      "ĠKick starter",
+      "Ġg era",
+      "Ġal red",
+      "ĠW rap",
+      "ÏĮ Ïĥ",
+      "Ġsin ner",
+      "CH EERING",
+      "Ġkil ow",
+      "Ġdetermin ant",
+      "Ġdem onic",
+      "id ences",
+      "ch as",
+      "ĠD ed",
+      "å¼ ķ",
+      "Ġst umble",
+      "ĠUr s",
+      "Ġdece ived",
+      "ĠT ER",
+      "ĠC ó",
+      "ell ed",
+      "Ġnot wend",
+      "Ġì§Ģê¸Ī ê¹Įì§Ģ",
+      "Ġpart ido",
+      "Ġdesc ended",
+      "Ġvard ır",
+      "Ġenact ed",
+      "ĠczÄĻ ÅĽci",
+      "å·¥ ä½ľ",
+      "Ġtra inees",
+      "Ġaud ible",
+      "Ġm alf",
+      "Ġve o",
+      "ì n",
+      "ĠG PA",
+      "ĠApp e",
+      "åĤ ·",
+      "Ġr ut",
+      "ĠCar la",
+      "k ach",
+      "Ġsav ior",
+      "itch ed",
+      "Ġclim ax",
+      "аÑĤ елÑı",
+      "ĠMc Connell",
+      "ол Ñı",
+      "ere ye",
+      "ĠÑģоз н",
+      "Ġcab o",
+      "ĠS ne",
+      "ĠAff ordable",
+      "Ġsar Ãł",
+      "Ġlegitim acy",
+      "Ġscar ce",
+      "... </",
+      "Ġ10 8",
+      "Ġac um",
+      "ĠFrank ly",
+      "Ġradi ator",
+      "Ġgener als",
+      "Ġdivid es",
+      "Ġcheese cake",
+      "Ġsor cer",
+      "Ġmiscon ception",
+      "Ġhardship s",
+      "ĠOne Plus",
+      "üy orsun",
+      "ĠSovi ets",
+      "ĠItal ia",
+      "ick i",
+      "ĠAfter wards",
+      "Ġridic ulously",
+      "Ġgdzie ÅĽ",
+      "ĠNot es",
+      "Ùĥ اÙĨ",
+      "Ġr oman",
+      "Ġorganiz er",
+      "Ġcour tyard",
+      "ĠÑĩелов еÑĩ",
+      "ĠW itness",
+      "Ġп ÑıÑĤ",
+      "ĠCh ill",
+      "ĠVal ve",
+      "Ġά λλ",
+      "ĠK P",
+      "chl uss",
+      "Ġdef lect",
+      "ĠTon i",
+      "Ġcl air",
+      "Ġstack ing",
+      "ä½ İ",
+      "ras zam",
+      "ĠSon ra",
+      "ãģ£ ãģ¡ãĤĥ",
+      "ĠAt ari",
+      "Ġpas ó",
+      "Ġchar ms",
+      "an st",
+      "Ġter ce",
+      "ĠL illy",
+      "Ġpsych ologically",
+      "ĠcÅ ĵ",
+      "ust e",
+      "¥ ´ì",
+      "CT V",
+      "Ġm iel",
+      "çļ ĩ",
+      "C are",
+      "ĠâĢ ij",
+      "Ġsna pped",
+      "ãģ© ãĤĤ",
+      "Ġê° IJë",
+      "оÑĤ Ñĭ",
+      "Ġm ês",
+      ". ?",
+      "Ġton nes",
+      "×ķ×ĵ ×Ķ",
+      "à¸Ħ à¸Ļ",
+      "T u",
+      "Ġdistrib uting",
+      "Ġcrack ers",
+      "Ġcor ação",
+      "äm än",
+      "ä½ł åľ¨",
+      "cl amation",
+      "оÑĢ д",
+      "ĵľë¦´ ê²ĮìļĶ",
+      "ĠUnters chied",
+      "F ine",
+      "ck o",
+      "ĠÑĢеб ен",
+      "Ġsp ic",
+      "Ġdoctor al",
+      "ĠÑģкоÑĢ ее",
+      "un ivers",
+      "ac ula",
+      "ĠÃĸ sterreich",
+      "Ġgr inder",
+      "Ġamb os",
+      "Ġvast ly",
+      "éĢĻåĢĭ æĺ¯",
+      "Ġconf essed",
+      "ĠSh h",
+      "and ers",
+      "ĠGu an",
+      "ĠнеобÑħод имо",
+      "Ġchampions hips",
+      "ĠV ul",
+      "ĠPh i",
+      "ĠMe asure",
+      "æľ ¨",
+      "Ġins gesamt",
+      "æħ¢ æħ¢",
+      "v ette",
+      "Ġgen om",
+      "ind ung",
+      "g li",
+      "D et",
+      "Ġunm ute",
+      "ãģ¾ ãĤĬ",
+      "Ġsau ces",
+      "ĠD w",
+      "×ij× ª",
+      "ĠB RE",
+      "Ġnurt ure",
+      "Ġdet ained",
+      "ĠBe er",
+      "Ġми ÑĢа",
+      "в е",
+      "ĠBird s",
+      "Ġmeille ur",
+      "Ġre wind",
+      "Ġp ore",
+      "×Ļ× ĸ",
+      "é ger",
+      "qu ela",
+      "Ġtrous ers",
+      "Ġsi inä",
+      "ĠG aga",
+      "ĠBR AND",
+      "le ben",
+      "Ġr aspberry",
+      "ä» ĺ",
+      "il ik",
+      "Ġvers ão",
+      "l ak",
+      "Ġlo gar",
+      "ĠMID I",
+      "ĠìľĦ íķľ",
+      "ĠпÑĢоиз оÑĪ",
+      "Ġster il",
+      "Ġhar med",
+      "ав лив",
+      "ĠÑģ ÑģÑĭл",
+      "Ġlack ed",
+      "Ġcontact ing",
+      "Ġ기 ìŀIJ",
+      "Ġgef ähr",
+      "Ġco y",
+      "ike l",
+      "Ġb inge",
+      "Ġorthog onal",
+      "Ġentend u",
+      "ĠTh irty",
+      "Ġsmart est",
+      "å¤ļ å°ij",
+      "Ġr asa",
+      "ĠQu á»ijc",
+      "Ñĭв аÑİÑĤ",
+      "Ġsl ut",
+      "л ÑĥÑĩ",
+      "ig ten",
+      "ĠÑĢ аб",
+      "Ġt aman",
+      "Ġqual idade",
+      "Ġdom ination",
+      "Ġsin us",
+      "Ġprogram mers",
+      "Ġaller gy",
+      "ĠTor res",
+      "ĠAust rian",
+      "n ants",
+      "å®Į æĪIJ",
+      "M el",
+      "ĠÑĥв елиÑĩ",
+      "ĠA gg",
+      "Ġso k",
+      "Ġpl uck",
+      "Ġbind s",
+      "Ġprop or",
+      "ĠM af",
+      "Ġoso b",
+      "ĠV IC",
+      "é ¥",
+      "ĠзаÑĩ ем",
+      "Ġexhib itions",
+      "Ġett i",
+      "c za",
+      "ĠнаÑĪ иÑħ",
+      "ĠM itte",
+      "обÑĭ ÑĤи",
+      "Ġclock s",
+      "Ġr ico",
+      "æĶ »",
+      "ĠиÑģÑĤоÑĢ иÑı",
+      "Ġsch izophren",
+      "Ġfl uff",
+      "ĠÑģоб иÑĢ",
+      "Ġap oy",
+      "Ġprin ces",
+      "Ġbr aces",
+      "ĠF IR",
+      "ĠS na",
+      "Ġ; )",
+      "ven es",
+      "Ġvuel ta",
+      "Ġm ies",
+      "Ġbro om",
+      "Ġmer ry",
+      "Ġespecial mente",
+      "ĠAl ban",
+      "ĠпоÑģÑĤоÑıн но",
+      "ĠL ena",
+      "ĠC ult",
+      "al so",
+      "Ġquot ing",
+      "Ġgen ere",
+      "ĠY ar",
+      "ĠL age",
+      "Ġdem ost",
+      "Ġd age",
+      "ĠEcu ador",
+      "Ġan vänd",
+      "u ÃŁen",
+      "Ġë°Ľ ìķĦ",
+      "Ġpsych ologists",
+      "ĠL ars",
+      "Ġposs a",
+      "Ġout going",
+      "Ġmet ic",
+      "Ġbag gage",
+      "er ia",
+      "Ġricht ige",
+      "ìĭľ ìĹIJ",
+      "ĠÑģоÑħ ÑĢан",
+      "Ġroot ing",
+      "Ġdro plets",
+      "çļĨ ãģķãĤĵ",
+      "Ġnas al",
+      "ĠCo x",
+      "X i",
+      "Ġdispos able",
+      "Ġbut cher",
+      "ĠZ ar",
+      "ĠArmen ian",
+      "Ġë¿ Įë",
+      "ĠF ool",
+      "ĠCB D",
+      "Ġs ost",
+      "Ġper ish",
+      "ĠR ép",
+      "ç´ °",
+      "ãģĿãĤĮ ãģ§ãģ¯",
+      "ĠFre ud",
+      "Ġf andom",
+      "Ġblo que",
+      "Ġinvent or",
+      "Ġab re",
+      "Ġénorm ément",
+      "Ġimport s",
+      "é Ī",
+      "Ġot ur",
+      "ĠRy u",
+      "ĠâĨ Ĵ",
+      "Ġsecond o",
+      "Ġincom pet",
+      "Ġincarcer ation",
+      "Ġasc end",
+      "b ene",
+      "åĸľ 欢",
+      "Ġol urs",
+      "no ch",
+      "Ġbre eds",
+      "ли з",
+      "ĠVerf üg",
+      "Ġma iling",
+      "re ally",
+      "Ġes f",
+      "Ġpe le",
+      "Ġle ash",
+      "Ġdis ks",
+      "Ġзам еÑĩ",
+      "ìķĦ ìķĦ",
+      "ab outs",
+      "ĠM ull",
+      "ĠD ent",
+      "edere en",
+      "D rive",
+      "Ġt ipping",
+      "Ġnig ga",
+      "ord um",
+      "Ġpor ter",
+      "Ġkara oke",
+      "Ġdocument aries",
+      "ĠR IGHT",
+      "ĠP urd",
+      "ĠоÑģÑĤ ан",
+      "к лад",
+      "é rence",
+      "Ġê± ¸ë¡ľ",
+      "ĠÑĤ оп",
+      "ĠW ong",
+      "ä¸į 对",
+      "ĠпÑĢ иÑĢ",
+      "Ġnom inal",
+      "Ġa ula",
+      "ĠÑįк ÑĢан",
+      "Ġcher che",
+      "ĠTh r",
+      "åħ¶ å®ŀ",
+      "Ġla ufen",
+      "ĠKath leen",
+      "Ġreact ors",
+      "ih at",
+      "Ġs ided",
+      "ĠSim one",
+      "Ġguid eline",
+      "import ant",
+      "b umps",
+      "t one",
+      "Ġentre prises",
+      "Ġconst itute",
+      "osc ope",
+      "ĠMyst ery",
+      "cy cles",
+      "ĠWars aw",
+      "Ġburst s",
+      "ĠZh ong",
+      "å®Į äºĨ",
+      "ĠSAR AH",
+      "ĠëĬIJ ê»",
+      "é į",
+      "Ġbe acon",
+      "åį ĩ",
+      "AD E",
+      "Ġì§Ģë Ĥĺ",
+      "Ġ ersch",
+      "Ġinteg ers",
+      "ĠCross ing",
+      "s ource",
+      "Ġschool ing",
+      "ĠR OM",
+      "ator ium",
+      "ĠìŀĪ ê²Į",
+      "Ġr ôle",
+      "Ðķ ÐĿ",
+      "Ch at",
+      "Ġshr inking",
+      "Ġreim burse",
+      "Ġl umber",
+      "ü cks",
+      "Ġsal ah",
+      "M other",
+      "Ġk ali",
+      "ĠQ atar",
+      "ot ional",
+      "Ġop acity",
+      "Ġne e",
+      "ĠC ory",
+      "Ġì¸ ¡",
+      "Ġturbul ent",
+      "z ers",
+      "ĠÑĤ еÑģÑĤ",
+      "Ġéc rit",
+      "Ġë³´ íĨµ",
+      "Ġdisgr ace",
+      "Ġì¹ ´",
+      "Ġcourt esy",
+      "ing a",
+      "Ġhug ging",
+      "ĠA BS",
+      "m ith",
+      "Ġins ufficient",
+      "Ġcro oked",
+      "Ġê·¸ë ĮĢë¡ľ",
+      "ìĭ ¤í",
+      "Ġsim ulated",
+      "ĠëĦ¤ ê°Ģ",
+      "Ġb ö",
+      "ĠOt to",
+      "L ING",
+      "Ġillust rates",
+      "ĠDest roy",
+      "Ġ196 1",
+      "ĠT agen",
+      "Ġmel on",
+      "ĠP ascal",
+      "Q UE",
+      "ĠполÑĥÑĩ иÑĤÑĮ",
+      "Ġinc idence",
+      "ĠSteven s",
+      "ĠG ins",
+      "r ue",
+      "Ġunre asonable",
+      "ĠJ ie",
+      "ys ics",
+      "Ġ몰ë Ŀ¼",
+      "Ġfish es",
+      "© ´ì",
+      "Ġprec urs",
+      "Ġmog ÄĻ",
+      "t ight",
+      "et é",
+      "Ġmund ial",
+      "ìĹĪ ëĭ¤",
+      "âĢ¦ !",
+      "B U",
+      "Ġsoci ology",
+      "Ġbrut ality",
+      "Ġperson aje",
+      "Ġn ÃŃvel",
+      "Ġfaz em",
+      "Ġess en",
+      "Ġd welling",
+      "Ġcommer cially",
+      "Ġed its",
+      "Ġd ues",
+      "ĠG SA",
+      "ìĿ¸ ê°Ģ",
+      "ĠíĹĪ íĮĿ",
+      "ĠYah oo",
+      "ен еÑĢ",
+      "ìľ ¨",
+      "ÑĥÑĪ ки",
+      "le ft",
+      "Ġcapt ive",
+      "cip her",
+      "Ġ×ŀ× ŀ×",
+      "ĠгÑĢ ом",
+      "Ġinn ate",
+      "Ġimp ul",
+      "ĠìŬ ìŀIJ",
+      "Ġswallow ed",
+      "ĠTab ii",
+      "ìĿ´ì ĭ",
+      "ĠÑģо ÑģÑĤав",
+      "Ġoy un",
+      "Ġobrig ado",
+      "ĠA ph",
+      "K atie",
+      "Ġc ena",
+      "ĠAll Äģh",
+      "ÙĪ س",
+      "Ġprzy p",
+      "Ġpe pt",
+      "Ġvolunt arily",
+      "ĠO ÄŁlum",
+      "ĠE lo",
+      "ou e",
+      "B ir",
+      "bur ger",
+      "ĠS BS",
+      "Ġ6 000",
+      "Ġpromot ional",
+      "ĠHerr n",
+      "Ġstamp ing",
+      "Ġqual ifying",
+      "Ġcos mos",
+      "Ġaf ar",
+      "æ± Ł",
+      "ab us",
+      "Ġdad s",
+      "ãģŃ ãģĩ",
+      "ĠÑįк оном",
+      "inc arn",
+      "Ġìĸ´ë Ķ",
+      "Ġл еж",
+      "ĠB ET",
+      "Ġнай д",
+      "on ter",
+      "Ġreus able",
+      "Ġkomm a",
+      "ĠB ij",
+      "ĠTer az",
+      "ĠOl á",
+      "ĠìķĦ 침",
+      "ĠÑĢаз меÑĢ",
+      "aw an",
+      "Ġcart a",
+      "æIJ ŀ",
+      "ic eless",
+      "Ġsm e",
+      "ĠTut aj",
+      "ĠÈĺ i",
+      "Ġprob ation",
+      "Ġadequ ately",
+      "ĠPresident ial",
+      "ind ruck",
+      "bl ade",
+      "Ġve ulent",
+      "Ġc ioè",
+      "åĮħ æĭ¬",
+      "Ġrever b",
+      "Ġgegen über",
+      "ĠEsper o",
+      "Ġbe ge",
+      "ĠSTUD ENT",
+      "s ound",
+      "ĠD ü",
+      "Ġoff end",
+      "Ġ\" ..",
+      "ken nt",
+      "ĠÑģл ÑĥÑĪ",
+      "Ġpurp osely",
+      "ĠL it",
+      "ĠíĽ ¨",
+      "uch er",
+      "Ġh ina",
+      "ý ch",
+      "ign on",
+      "TH E",
+      "Ġgl ide",
+      "our cing",
+      "ĠØ£ ÙĨا",
+      "Ġoll ut",
+      "Ġarch ety",
+      "Ġsh ady",
+      "Ġs omm",
+      "Ġep ile",
+      "Ke ep",
+      "Ġnaj bardziej",
+      "ठķ",
+      "itution al",
+      "Ġм ай",
+      "Ġsin ful",
+      "ĠBron x",
+      "Ġгл Ñĥб",
+      "Ġv am",
+      "Ġpres ets",
+      "ĠD ag",
+      "ĠìĻĦ ìĦ±",
+      "Ġc reek",
+      "it ures",
+      "ĠLord s",
+      "ö tt",
+      "UN T",
+      "R a",
+      "Ġinequ alities",
+      "Ġcoll ateral",
+      "Ġwr ists",
+      "Ġgroup ed",
+      "Ġоб ÑĭÑĩно",
+      "Ġarm ored",
+      "Ġt ung",
+      "Ġconver ge",
+      "Ġb ok",
+      "ĠD odge",
+      "нÑı Ñı",
+      "Ġfle eing",
+      "ĠMartine z",
+      "ĠDream s",
+      "ke k",
+      "Ġsocial e",
+      "ĠPla za",
+      "د ة",
+      "Ġke ll",
+      "ĠSt ellen",
+      "f elt",
+      "ĠÑģп аÑģ",
+      "ĠP v",
+      "Ġcan ción",
+      "ĠH ert",
+      "ĠBal ance",
+      "Ġsel ves",
+      "Ġv andaag",
+      "Ġpr y",
+      "Ġnaj le",
+      "Ġвид иÑĤе",
+      "Ġvel vet",
+      "Ġgro ot",
+      "Ġf out",
+      "æ¨ ¡",
+      "ĠSchul en",
+      "ĠMoh ammed",
+      "ĠCent ers",
+      "Ġha ver",
+      "Ġfre uen",
+      "¤í Ĭ¸",
+      "л ан",
+      "P OS",
+      "ink i",
+      "Ġëĭ µ",
+      "Ġparaly zed",
+      "GL ISH",
+      "Ġcast s",
+      "ĠV C",
+      "ìĿ´ì ħĺ",
+      "Ġت Ú¾",
+      "ç¥ ¨",
+      "Ġì¤ ĺ",
+      "Ġר ×ķצ",
+      "Ġsu ced",
+      "Ġprogress es",
+      "ĠE ÄŁer",
+      "°ë ıĦ",
+      "Ġinstall ations",
+      "ped o",
+      "еÑĢ б",
+      "inter pret",
+      "Ġê³łë ¯¼",
+      "ĠAzer bai",
+      "ivid ades",
+      "Ġì£Ħ ìĨ¡",
+      "Ġent fer",
+      "Ġchw il",
+      "ĠHer bert",
+      "ĠAlexand ria",
+      "y ty",
+      "Ġse chs",
+      "Ġcal iber",
+      "ĠWe ise",
+      "ĠHe ck",
+      "ĠY ug",
+      "ĠاÙĦØ ·",
+      "Ġpes ar",
+      "Ġcig ar",
+      "Ġm él",
+      "Ġha ird",
+      "Ġprzypad ku",
+      "Ġconfident ly",
+      "Ġan arch",
+      "ĠG ian",
+      "Ġdo bre",
+      "c jÄĻ",
+      "aw y",
+      "ĠRe ce",
+      "ĠGob ierno",
+      "Ġcar ga",
+      "um sy",
+      "Ġn orte",
+      "Ġhand ler",
+      "Ġrespect ing",
+      "Ġall ied",
+      "ĠP iet",
+      "icht lich",
+      "Ġold s",
+      "Ġdust y",
+      "Ġg ry",
+      "Ġ- ...",
+      "GH T",
+      "Ġne o",
+      "Ñĩ ики",
+      "еж д",
+      "a ide",
+      "ĠбÑĥ ло",
+      "í į¼",
+      "Ġtempor ada",
+      "Ġd oute",
+      "âĺ Ĩ",
+      "ĠìĪ ł",
+      "ĠJ USTIN",
+      "aut o",
+      "Ġration ale",
+      "pro b",
+      "Ġfish y",
+      "Ġdoor way",
+      "Ġempt iness",
+      "ен наÑı",
+      "Ġbra g",
+      "ĠÐĵ де",
+      "çĪ ¾",
+      "Ġtrans ient",
+      "Ġmitt lerweile",
+      "ĠB ret",
+      "Ġf ij",
+      "Ġdepos ited",
+      "N S",
+      "Ġìķŀ ìĹIJ",
+      "Ġkim se",
+      "Ġchar ities",
+      "ĠMill enn",
+      "dog s",
+      "Ġmo yen",
+      "Ġnue vos",
+      "ĠCook ie",
+      "par able",
+      "do ing",
+      "ĠS ail",
+      "Ġ icy",
+      "h aba",
+      "Ġque ens",
+      "Ġchocol ates",
+      "ĠN ay",
+      "ĠÑĦ ин",
+      "Ġve c",
+      "Ġhelm ets",
+      "T M",
+      "ĠAr med",
+      "Ġimpair ment",
+      "ĠT us",
+      "ĠM ême",
+      "ome z",
+      "ĠRe qu",
+      "ĠInvest ig",
+      "íİ ĺ",
+      "Ġgol pe",
+      "ĠR ac",
+      "ig raph",
+      "Ġk west",
+      "Ġsail ors",
+      "Ġstatut ory",
+      "Ġmil estones",
+      "ĠM ash",
+      "ĠGesetzent wurf",
+      "é Ĭ",
+      "Ġcol oured",
+      "h uma",
+      "Ġy ere",
+      "Ġsubtit les",
+      "Ġembod ied",
+      "Ġmiss chien",
+      "ĠiP h",
+      "üt zen",
+      "Ġdet ached",
+      "Ġdescri ção",
+      "ci amo",
+      "Ġreco il",
+      "ĠÐŃÑĤо ÑĤ",
+      "Ġexport ed",
+      "ĠAl one",
+      "ant ry",
+      "Ġest an",
+      "ĠS od",
+      "Ġlavor o",
+      "æĬĬ å®ĥ",
+      "ר ×ij",
+      "ĠÄij á»ĭ",
+      "Ġsw ag",
+      "ĠPC B",
+      "ĠK aiser",
+      "ĠMod er",
+      "j ug",
+      "Ġtext ile",
+      "T w",
+      "Ġn ac",
+      "f rei",
+      "Ġret ard",
+      "isc ern",
+      "Ġtall est",
+      "ĠLu ca",
+      "R ah",
+      "Ġpre acher",
+      "Ġj ut",
+      "ĠR ica",
+      "ic iency",
+      "ĠÄiji á»ģu",
+      "Ġk aufen",
+      "Ġnet t",
+      "Ġdisc ut",
+      "Ġdepri ved",
+      "¡ Ń",
+      "Ġsp richt",
+      "Ġencl osed",
+      "ĠSub st",
+      "ç§ ij",
+      "ĠRab bit",
+      "pr ised",
+      "Ġbit ches",
+      "ì Łģ",
+      "çī Ī",
+      "Ġtap a",
+      "ĠEs sen",
+      "ĠBa o",
+      "Ġdev ient",
+      "ĠW uhan",
+      "ĠT ipp",
+      "Ġdis ast",
+      "ÑģÑĤв Ñĥ",
+      "ubl ique",
+      "Ġqual ité",
+      "Ġinadequ ate",
+      "Ġbarg aining",
+      "ĠGot cha",
+      "ев иÑĩ",
+      "iev ous",
+      "ert on",
+      "bl ue",
+      "ĠìĽĢ ì§ģ",
+      "Ġsand box",
+      "ĠRe in",
+      "è¦ ª",
+      "ĠìĿ´ê²ĥ ëıĦ",
+      "Ġsa x",
+      "z ogen",
+      "un ächst",
+      "Ġher kes",
+      "Ġ- ,",
+      "zen i",
+      "r ising",
+      "Ġresp osta",
+      "Ġpromot ions",
+      "ĠUnter stüt",
+      "ĠM AS",
+      "N othing",
+      "ot ics",
+      "ĠвÑĭ й",
+      "Ġrot ates",
+      "k ien",
+      "Ġhab la",
+      "ĠDan i",
+      "un ion",
+      "Ġw ack",
+      "Ġarchae ological",
+      "ĠCurt is",
+      "ĠHor iz",
+      "Ġê³ ¨ë",
+      "Ġwai ver",
+      "åĺ ¿",
+      "B on",
+      "Ġrot ated",
+      "Ġpitch er",
+      "Ġin ad",
+      "Ġhug s",
+      "ĠNorth east",
+      "×Ļת ×Ļ",
+      "Ġple a",
+      "Ġcup cake",
+      "ĠL Y",
+      "Ġfam ili",
+      "Ġgro o",
+      "ĠBla ir",
+      "Ġli j",
+      "Ġhabit ats",
+      "Ġcommun ism",
+      "os ium",
+      "b ars",
+      "ĠFre eman",
+      "ne o",
+      "Ġdiff use",
+      "Ġcylind ers",
+      "ĠDe bat",
+      "íĸĪ ëĬĶëį°",
+      "еÑĪ е",
+      "Ġfinger prints",
+      "Ġam ar",
+      "в ид",
+      "ĠìłķëıĦ ë¡ľ",
+      "Ġaffili ated",
+      "ĠÑħоÑĩ еÑĤ",
+      "ãģ° ãģĦ",
+      "Ġet iqu",
+      "Ġch ÃŃnh",
+      "æģŃ åĸľ",
+      "Ġcru ising",
+      "ĠWe ihn",
+      "çĶ µ",
+      "ĠTitan ic",
+      "ç´ Ģ",
+      "ĠN ast",
+      "Ġëĵ¤ ë",
+      "Ġв ал",
+      "Ġdem i",
+      "ĠKrist in",
+      "M IN",
+      "Ġrig or",
+      "Ġmot o",
+      "ĠL AKE",
+      "ĠíĻ ľ",
+      "Ġë§Į ìķ½",
+      "ĠSt ro",
+      "Ġprot otypes",
+      "ĠL C",
+      "ìĿ¸ ìĿĦ",
+      "ÑĢ им",
+      "Ġviol ating",
+      "Ġgi orno",
+      "Ġchild ish",
+      "æ° Ķ",
+      "Ġ×IJ×Ĺ ×ĵ",
+      "Ġoverd ose",
+      "ag ogue",
+      "ад ÑĨ",
+      "he us",
+      "ĠговоÑĢ Ñı",
+      "Ġinc r",
+      "Ġdeb ated",
+      "Ùħ ÙĦ",
+      "Ġch icks",
+      "Ġqu in",
+      "LAUGH ING",
+      "Ġtight ening",
+      "Ġsupervis ors",
+      "ĠHaw k",
+      "ĠB az",
+      "Ġпов ÑĤоÑĢ",
+      "Ġбл ок",
+      "Äģ n",
+      "Ġdump ing",
+      "Ġfact o",
+      "ber ger",
+      "Ġarsen al",
+      "ĠAfric ans",
+      "¡ Ģ",
+      "Ġcafeter ia",
+      "fe eding",
+      "qu ila",
+      "ĠpaÅĦst wo",
+      "ı nt",
+      "Ħ ±",
+      "Ġenvironment ally",
+      "Ġdes prés",
+      "ĠWill y",
+      "ĠPaÅĦst wo",
+      "ĠG G",
+      "Ġch acun",
+      "Ġdirection al",
+      "Ġh ört",
+      "Ġ ðĿ",
+      "en ary",
+      "Ġvo iced",
+      "a ģı",
+      "Ġp ope",
+      "Ġcom rades",
+      "ĠGib son",
+      "ĠAC C",
+      "v ik",
+      "Ġmod elling",
+      "Ġag gi",
+      "ãģªãĤĵ ãģ§ãģĻ",
+      "Ġconvers ions",
+      "Ġaver ages",
+      "E llie",
+      "Ġgest ellt",
+      "ĠU E",
+      "osa ic",
+      "ÐĴ оÑĤ",
+      "S ay",
+      "ĠÑģам ого",
+      "Ġmes ures",
+      "is iert",
+      "g asp",
+      "vo ice",
+      "Ġcheck point",
+      "Ġpercent ages",
+      "Ġdisrupt ed",
+      "ĠT uc",
+      "ĠH omer",
+      "ĠW AY",
+      "ĠTur ks",
+      "he en",
+      "im oto",
+      "ĠO C",
+      "ÃŃ na",
+      "z iel",
+      "Ġmud ar",
+      "ãĥIJ ãĤ¤",
+      "ges etzt",
+      "Ġmej ores",
+      "ĠC J",
+      "на ÑĢÑĥж",
+      "Ġmod ulus",
+      "Ġmod ulation",
+      "Ġrepl ies",
+      "Ġlar va",
+      "Ġg ider",
+      "ĠMand arin",
+      "ĠпоÑģмоÑĤÑĢ им",
+      "Ġsacrific ing",
+      "Ġpre ço",
+      "Ġoy sters",
+      "ĠMy an",
+      "olog ue",
+      "ĠW it",
+      "Ġd û",
+      "ĠLe uten",
+      "Ġp ater",
+      "ĠKENN ETH",
+      "аб аÑĤ",
+      "arth y",
+      "Ġsocied ad",
+      "Ġni ño",
+      "ев ой",
+      "Ġj ÄĻ",
+      "Ġadvert ised",
+      "ĠPep si",
+      "ute ur",
+      "Ġmas se",
+      "Ġsc attering",
+      "Ġy ön",
+      "Ġdesap are",
+      "ĠHub ble",
+      "ĠH é",
+      "k rä",
+      "ĠD are",
+      "Ġover ride",
+      "ĠEl aine",
+      "ĠDub lin",
+      "du llah",
+      "M at",
+      "ĠG arr",
+      "... '",
+      "Ġadul thood",
+      "E Z",
+      "Ġbelang rijk",
+      "ien za",
+      "Ġun iverso",
+      "Ġstell ar",
+      "íĶ Ħë",
+      "Ġê²° êµŃ",
+      "Ġconstell ation",
+      "ĠShell ey",
+      "Ġmult it",
+      "Ġmasc ot",
+      "Ġhospital ized",
+      "Ġ ðĿĺ",
+      "оÑĢ Ñĭ",
+      "ad ia",
+      "ĠMike y",
+      "ĠAmer ika",
+      "Ġhair y",
+      "H old",
+      "ắ n",
+      "k iego",
+      "è§ Ĥ",
+      "à¹ĢภĶ",
+      "Ġrival ry",
+      "ĠJon ah",
+      "Ġsurge ons",
+      "Ġrelat able",
+      "è Ĵ",
+      "Ġswim s",
+      "Ġbillion aire",
+      "mod ern",
+      "Ġdocument ing",
+      "ĠDa e",
+      "Ġsw atch",
+      "Ġpu isse",
+      "Ġmas uk",
+      "Ġmar c",
+      "Ġk ró",
+      "ĠPeters burg",
+      "ĠArist otle",
+      "ix e",
+      "P rodu",
+      "Ġн ими",
+      "Ġk ana",
+      "ĠÐ ©",
+      "Ġvom it",
+      "ĠWork ers",
+      "pop ular",
+      "ĠBie ber",
+      "еÑĤ и",
+      "ét ique",
+      "Ġenc ant",
+      "gr an",
+      "f ir",
+      "Ġanth em",
+      "ÑģÑĥд аÑĢ",
+      "L ast",
+      "Ġha g",
+      "Ġvic inity",
+      "rench ed",
+      "and ing",
+      "Ġгол оÑģ",
+      "ĠCor ner",
+      "ÐĴ Ñĭ",
+      "os as",
+      "ie vers",
+      "c ional",
+      "Ġvig or",
+      "Ġrejo ice",
+      "Ġci Äħ",
+      "Ġк оп",
+      "Ġqualc osa",
+      "dess us",
+      "Ġе в",
+      "ĠSc andin",
+      "ĠS mooth",
+      "ä½ł 说",
+      "ha pe",
+      "Ġëĭ¬ë Ŀ¼",
+      "ĠT U",
+      "Ġly ric",
+      "Ġb ess",
+      "é IJ",
+      "ÑģÑĤÑĢÑĥ менÑĤ",
+      "ĠAct ing",
+      "ĠOr chest",
+      "é cole",
+      "Ġdo lor",
+      "Ġíĭ °",
+      "Ġverg essen",
+      "Ġeyel ids",
+      "ĠT anz",
+      "веÑĢ ж",
+      "Ġìķ łë",
+      "u é",
+      "Ġsc ène",
+      "Ġìļ°ë¦¬ ëĬĶ",
+      "Ġcr ate",
+      "k ick",
+      "ĠThe me",
+      "Ġ3 20",
+      "Ġgarn ish",
+      "Ġmet re",
+      "Ġconve x",
+      "pl ants",
+      "es ian",
+      "Ġê±° ì§Ģ",
+      "Ġmé di",
+      "ĠMed al",
+      "1 30",
+      "ĠAl ma",
+      "æľī é»ŀ",
+      "C ola",
+      "ĠваÑĢи анÑĤ",
+      "Ġg ord",
+      "Ġav anz",
+      "Ġwhis pering",
+      "Ġintest ine",
+      "Ðł Ðķ",
+      "ĠL ISA",
+      "am ız",
+      "S PD",
+      "Ġpe c",
+      "Ġpast ors",
+      "Ġmu á»ijn",
+      "oc re",
+      "S un",
+      "ĠÑĤак ÑĥÑİ",
+      "Ġrev ital",
+      "Ġincom es",
+      "Ġdetail ing",
+      "ĠB acon",
+      "Ġëħ¸ë ŀĺë",
+      "Ġpar rot",
+      "Ġcollabor ated",
+      "hes ia",
+      "Ġse va",
+      "Ġphysic ist",
+      "ĠB ACK",
+      "׾ ×Ļ",
+      "Ġbip olar",
+      "Ïģ εί",
+      "c ros",
+      "Ġk ed",
+      "Ġeconom ical",
+      "Ġend ings",
+      "Ġtick s",
+      "Ġê· ¼",
+      "ĠOl iv",
+      "ong s",
+      "Ġcontin ental",
+      "Ġweiter hin",
+      "Ġactiv ating",
+      "Ġpoll en",
+      "ĠAn k",
+      "b ay",
+      "Ġ×ľ× Ĺ",
+      "ĠEgg s",
+      "ĠRAM SAY",
+      "ĠB ER",
+      "ĠíĽ¨ ìĶ¬",
+      "Ġpass ado",
+      "Ġground breaking",
+      "pres a",
+      "Ġhil ft",
+      "ĠTechn ically",
+      "ÑĨи й",
+      "N I",
+      "Ġturn out",
+      "ĠL ap",
+      "ĠG wen",
+      "ĠV ikt",
+      "Ġesc ola",
+      "ĠCin ema",
+      "æ° ¸",
+      "Ġãģ Ĩ",
+      "Ġconsum o",
+      "ĠPurd ue",
+      "Ġse manas",
+      "ĠPRES ID",
+      "Æ° ng",
+      "Ġs ach",
+      "æĢİ麼 辦",
+      "Ġsav age",
+      "ĠR W",
+      "Ġ5 50",
+      "bo ld",
+      "ĠSim mons",
+      "Ġsl ang",
+      "ĠNar u",
+      "ĠThe o",
+      "íĸĪ ëĭ¤",
+      ". �",
+      "Ġseiz ure",
+      "Ġh ive",
+      "Ġcell phone",
+      "å¥ ¶",
+      "ii ii",
+      "ĠMus ical",
+      "ĠN uclear",
+      "è¡ Ĺ",
+      "á veis",
+      "Ġprest ige",
+      "Ġbal m",
+      "Ġref ill",
+      "y ah",
+      "h art",
+      "Ġt aps",
+      "Ġdisp ose",
+      "ĠM ick",
+      "Ġtherm ometer",
+      "ãģª ãĤī",
+      "Ġobed ient",
+      "Ġinform ações",
+      "ĠW ide",
+      "m om",
+      "S ud",
+      "Ġsusp end",
+      "ĠObs erv",
+      "Ġл еÑģ",
+      "Ġtr atar",
+      "ĠKat rina",
+      "Ġth eres",
+      "äº ŀ",
+      "Ġtext ed",
+      "Ġst ör",
+      "Ġsna il",
+      "ĠF iona",
+      "Ġvict orious",
+      "Ġlibr arian",
+      "pr act",
+      "Ġfin o",
+      "ĠAr ms",
+      "pp t",
+      "l uk",
+      "Ġty res",
+      "Ġto c",
+      "ĠKommun en",
+      "ç¯Ģ 缮",
+      "Ġrev olt",
+      "Ġmotiv ates",
+      "Ġb isexual",
+      "Ġw us",
+      "Ġhand lar",
+      "ĠMU ELLER",
+      "Ġexpect ancy",
+      "Ġem body",
+      "ĠPrim ary",
+      "åİŁ åĽł",
+      "ÑĢ ей",
+      "Ġuns crew",
+      "i antly",
+      ", âĢ¦",
+      "Ġsn el",
+      "Ġpreval ence",
+      "Ġeru ption",
+      "Ġdescript ive",
+      "v ag",
+      "ĠбÑĥк в",
+      "Ġm êmes",
+      "Ġeth n",
+      "Ġhij os",
+      "ĠAbd ul",
+      "ĠZ ahl",
+      "b elt",
+      "Ġgö st",
+      "ĠTheres a",
+      "ĠS UN",
+      "ĠB ake",
+      "Ġ å¿«",
+      "Ġopt ics",
+      "Ġap ocalypse",
+      "p urpose",
+      "Ġróż nych",
+      "Ġcr us",
+      "ĠÐĹ ÐµÐ¼",
+      "Ġhard ened",
+      "ĠT D",
+      "Ġgra veyard",
+      "ĠSi ber",
+      "ĠPor ter",
+      "Ġexpl odes",
+      "ĠSo fia",
+      "ĠÐĴ едÑĮ",
+      "Ġweak ened",
+      "æĺ¯ æĪij",
+      "UL L",
+      "Ġpink y",
+      "Ġchap el",
+      "ĠF res",
+      "ĠпÑĢи г",
+      "M ER",
+      "ĠSch midt",
+      "ĠD ud",
+      "æŁ ¥",
+      "est ens",
+      "Ġnu ance",
+      "Ġmod ifying",
+      "ĠMöglich keiten",
+      "ĠAn at",
+      "Ġecc entric",
+      "ĠSc rew",
+      "ĠLe h",
+      "Ġhom ogeneous",
+      "ĠT all",
+      "ĠRic ardo",
+      "Ã ļ",
+      "ign s",
+      "Ġли ÑĪ",
+      "Ġgef ragt",
+      "R un",
+      "c aster",
+      "no ise",
+      "Ġas ynchron",
+      "ÄĻd zie",
+      "Ġ×ŀ× Ĺ",
+      "Ġsupp ressed",
+      "Ar thur",
+      "ή ÏĤ",
+      "â r",
+      "d ist",
+      "Ġк ад",
+      "Ġh ör",
+      "Ġ13 5",
+      "ĠMoz art",
+      "ĠÑģ обÑĭÑĤи",
+      "ĠNurs ing",
+      "ĠH ahah",
+      "ĠD op",
+      "Ġpolic eman",
+      "´ìĹIJ ìĦľ",
+      "Ġê´Ģë ł¨",
+      "hy uk",
+      "Ġrug ged",
+      "Ġnug gets",
+      "ĠComm s",
+      "St ud",
+      "ĠÑģв ое",
+      "Ġczas ie",
+      "ãĤ ½",
+      "Ġrég ion",
+      "Ġfisher men",
+      "ĠL T",
+      "Ã ĵ",
+      "cia ż",
+      "he i",
+      "Ġcr umbs",
+      "ĠIm mer",
+      "ĠF eld",
+      "th ese",
+      "Ġadvertis ers",
+      "Ġro aming",
+      "Ġfun niest",
+      "ĠN YU",
+      "Ġhe he",
+      "Ġp oking",
+      "ĠìķĪë ı¼",
+      "ist ical",
+      "Ġop aque",
+      "u ç",
+      "w ire",
+      "ĠWe ber",
+      "ĠJac ques",
+      "Ġ2 10",
+      "ü p",
+      "uy u",
+      "Ġenfer med",
+      "Ġbump ed",
+      "ĠS ew",
+      "ĠChan el",
+      "Ġpersön lich",
+      "Ġbetray al",
+      "Ġallevi ate",
+      "Ġv ähän",
+      "Ġguess es",
+      "ĠC eline",
+      "ass ing",
+      "stro ke",
+      "Ġì¡ °ë",
+      "å¤ ı",
+      "ĠÑĤеÑħ нолог",
+      "Ġо ÑģÑĤÑĢ",
+      "Ġso ient",
+      "De ar",
+      "Ġj s",
+      "Ġges prochen",
+      "ath i",
+      "ç¿ »",
+      "Å¡ e",
+      "S et",
+      "og er",
+      "ĠR ig",
+      "Ġм еÑĩ",
+      "Ġserv icios",
+      "ĠR ut",
+      "ĠÐŀ й",
+      "ĠMyan mar",
+      "if ie",
+      "Ġsna pping",
+      "ĠKam era",
+      "Ġfest ive",
+      "ĠF Y",
+      "ĠCaro lyn",
+      "Ñĸ б",
+      "Ġlegg ings",
+      "Ġy at",
+      "Ġer gon",
+      "Ġepis ód",
+      "Ġanom aly",
+      "uest os",
+      "I d",
+      "Ġevac uation",
+      "Ġgig abytes",
+      "Ġand are",
+      "ĠR ent",
+      "m t",
+      "ist ine",
+      "Ġest rat",
+      "ett u",
+      "Ġrece ber",
+      "Ġdram at",
+      "ric ular",
+      "aln ız",
+      "ĠSen i",
+      "Ġo yn",
+      "ĠChem ical",
+      "ĠÑģ Ñħ",
+      "Ġtur f",
+      "Ġ19 17",
+      "iscern ible",
+      "Ġmant ener",
+      "Ġexc er",
+      "Ġspect ral",
+      "Ġneuros cience",
+      "Ġmicro f",
+      "Ġforeign er",
+      "ĠL anka",
+      "ä½ł åı¯ä»¥",
+      "ĠÑĤ воÑĢ",
+      "Ġtoss ed",
+      "Ġpobl ación",
+      "Ġmate ix",
+      "Ġsie llä",
+      "Ġot t",
+      "Ġcomp uls",
+      "ak ukan",
+      "Ġmanifest ed",
+      "Ġìĵ ¸",
+      "Ġut most",
+      "Ġrevers al",
+      "Ġplace bo",
+      "Ġbl at",
+      "ĠSt unde",
+      "m anship",
+      "Ġatt e",
+      "ĠìĨĮ ê°ľ",
+      "Ġist em",
+      "Ġann at",
+      "ĠPlay station",
+      "Ġz ad",
+      "Ġqu itting",
+      "Ġfam ine",
+      "ĠR ough",
+      "ĠFl ame",
+      "Ġhe ut",
+      "Ġoportun idad",
+      "Ġfais ait",
+      "ĠD P",
+      "Ġdic iendo",
+      "ĠMel anie",
+      "ĠCar ne",
+      "m eg",
+      "pet to",
+      "J UN",
+      "ĠлÑİб ой",
+      "Ġo ste",
+      "ĠJJ onak",
+      "Ġtheat rical",
+      "Ġinv inci",
+      "Ġcommun ion",
+      "voc al",
+      "E h",
+      "ĠDet ails",
+      "Ġst roll",
+      "ĠRay mond",
+      "ĠAm elia",
+      "ij ¥",
+      "Ġprodu kt",
+      "Ġnue vas",
+      "Ġmust n",
+      "may ı",
+      "col ored",
+      "de c",
+      "Ġhj äl",
+      "Ġsentiment al",
+      "Ġreal ms",
+      "Ġk rit",
+      "Ġse xt",
+      "ĠPsych ology",
+      "èĪ ī",
+      "h il",
+      "ĠкоÑĢ аб",
+      "ĠëĤ´ ìĿ¼",
+      "ĠUnder stood",
+      "ĠG uten",
+      "Ġgang s",
+      "Ġeven ings",
+      "æĢİ æ¨£",
+      "E nt",
+      "ĠLeg acy",
+      "ĠCong o",
+      "Ġdurch aus",
+      "Ġbu oy",
+      "ere lla",
+      "W AN",
+      "P re",
+      "ĠÑĢ ед",
+      "ĠCr isis",
+      "ãģª ãģŁ",
+      "ĠìĿ¼ ìĿ´",
+      "Ġmanuscript s",
+      "еÑĤ ÑĢ",
+      "Ġnon profits",
+      "Ġdict ator",
+      "Ġbask ets",
+      "ĠIs h",
+      "Ġper to",
+      "Ġdatas ets",
+      "Ġam ple",
+      "geb aut",
+      "Ġcontrib utor",
+      "Ġc iao",
+      "Ġconfir ming",
+      "ĠUC LA",
+      "âĻ ¬",
+      "ĠÑģ н",
+      "Ġovert urn",
+      "åIJ ī",
+      "Ġunreal istic",
+      "ĠPie ce",
+      "oc ate",
+      "Ġf ällt",
+      "po x",
+      "Ġë³´ìĭ ľë©´",
+      "Ġë© Ķë",
+      "ĠCre ation",
+      "Ñİ Ð´Ð°",
+      "Ġ×Ķ× IJ",
+      "Ġwh ack",
+      "olith ic",
+      "c ely",
+      "ĠÑģов ÑĢем",
+      "Ġsequ ential",
+      "Ġprofes ional",
+      "Ġcool s",
+      "Ġrep ente",
+      "Ġa ire",
+      "enn es",
+      "rit os",
+      "ĠÐĴ ид",
+      "Ġk ör",
+      "ĠB itte",
+      "ul ars",
+      "Ġincorrect ly",
+      "Ġshar ply",
+      "Ġbomb ard",
+      "ëĭĺ ìĿ´",
+      "Ġchromos ome",
+      "Ġadvertis ements",
+      "h un",
+      "ĠÑī об",
+      "ĠÐĶ аже",
+      "Ġbatht ub",
+      "ĠS no",
+      "ÙIJ Ùij",
+      "Ġbuff et",
+      "ĠGr id",
+      "ĠB rew",
+      "is et",
+      "ĠImport ant",
+      "üm üz",
+      "Ġvet o",
+      "ĠW erk",
+      "ĠSh am",
+      "k ra",
+      "ile en",
+      "he ard",
+      "Ġdra ining",
+      "Ġkl ass",
+      "Ġbak ayım",
+      "ct ure",
+      "ä½ł 說",
+      "am our",
+      "Ġspons orship",
+      "Ġdist ill",
+      "Ġpat io",
+      "Ġk omb",
+      "Ġoverwhelming ly",
+      "ĠJama ica",
+      "uit en",
+      "L ittle",
+      "ĠL OT",
+      "ta Äĩ",
+      "Ġcommand ers",
+      "ĠWat ts",
+      "ĠO ptions",
+      "ìĿ´ë ©´",
+      "AC T",
+      "Ġindisp ens",
+      "ĠF orsch",
+      "ot om",
+      "ĠÎŃÏĩ ει",
+      "Ġpra ising",
+      "Ġìĺģìĥģ ìĿĦ",
+      "Ġam an",
+      "Ġhyp not",
+      "th ms",
+      "Ġnas zej",
+      "Ġmour ning",
+      "ĠS AY",
+      "cy j",
+      "Ġго ÑģÑĥдаÑĢ",
+      "Ġca u",
+      "me e",
+      "Ġt adi",
+      "M ed",
+      "Ġcal idad",
+      "ãĥŁ ãĥ¼",
+      "Ġstri pe",
+      "Ġε ν",
+      "ĠKat y",
+      "ĠEs cape",
+      "Ġ ãĤĵ",
+      "Ġmüs ste",
+      "ĠاÙĦ ا",
+      "к ÑĤ",
+      "Ġjob bar",
+      "ĠJe ju",
+      "or ar",
+      "ĠSer á",
+      "ĠMess i",
+      "á z",
+      "ĠTr an",
+      "Ġpier cing",
+      "Ġar ithmetic",
+      "Ġstagger ing",
+      "Ġplug ging",
+      "ĠK AR",
+      "v l",
+      "´ì ĺ",
+      "ĠReg ierung",
+      "ĠO czywiÅĽcie",
+      "ĠEd gar",
+      "Ġconduct ivity",
+      "y elling",
+      "v ais",
+      "ad ian",
+      "Ġbul ky",
+      "ĠÑģ ÑĢав",
+      "ĠпÑĢ ом",
+      "Ġp aved",
+      "Ġb ends",
+      "ĠSkills hare",
+      "ĠMmm m",
+      "ĠHor ror",
+      "Ġt umb",
+      "Ġgoof y",
+      "ĠMe ow",
+      "×Ļ׾ ×ķ",
+      "ĠW ass",
+      "ĠSc ale",
+      "ĠR ak",
+      "Ġproject ing",
+      "Ġlingu istic",
+      "ĠWorld s",
+      "ense mble",
+      "Ġpe ga",
+      "stop pable",
+      "Ġim balance",
+      "ĠÃ ¸",
+      "Ġthr iller",
+      "колÑĮ кÑĥ",
+      "Ġleft overs",
+      "Ġcave at",
+      "ĠST R",
+      "und ai",
+      "Ġwater y",
+      "ĠMar in",
+      "ãĥ³ ãĤ°",
+      "Ġegg plant",
+      "ĠJ B",
+      "Ùħ ÙĥÙĨ",
+      "vid ia",
+      "ĠF IN",
+      "ic able",
+      "Ġpod ob",
+      "Ġco hesive",
+      "ĠVerfüg ung",
+      "ĠPl ato",
+      "аÑĢи Ñī",
+      "Ġk ot",
+      "ĠÐŁ ом",
+      "Ġдок Ñĥм",
+      "Ġimpl ants",
+      "isse z",
+      "B re",
+      "Ġgas ps",
+      "ĠT ED",
+      "r ato",
+      "J I",
+      "Ġaven ues",
+      "ĠCh ong",
+      "lad ı",
+      "ر ض",
+      "Ġin ici",
+      "ĠSub aru",
+      "æķ ħ",
+      "éģĬ æĪ²",
+      "ภĭ",
+      "Ġach t",
+      "ĠArchitect ure",
+      "ĠвеÑī и",
+      "ĠDev Ops",
+      "Ġto ppings",
+      "Ġobs ol",
+      "ain a",
+      "ĠBang kok",
+      "est ruct",
+      "Ġk ob",
+      "Ġëĵ ¯",
+      "ĠÑĢаз нÑĭе",
+      "Ġre e",
+      "Ġbij voorbeeld",
+      "ĠDemocr acy",
+      "à¹Ģร า",
+      "Ġкон ÑĤ",
+      "Ġse ç",
+      "Ġrah at",
+      "Ġparliament ary",
+      "ĠB ash",
+      "æĬ ĵ",
+      "z iaÅĤ",
+      "IT CH",
+      "ĠBub ble",
+      "kt ó",
+      "Who a",
+      "Ġfl ats",
+      "æķ Ī",
+      "z ne",
+      "Ġserv icio",
+      "ĠD ew",
+      "Õ¸ ÖĤ",
+      "Ġunterstüt zen",
+      "ĠWind s",
+      "éĤ£ 个",
+      "Ġìĸĺ ëĬĶ",
+      "Ġevalu ations",
+      "Ġre ca",
+      "Ġel ves",
+      "che er",
+      "Ġj al",
+      "Ġrest ed",
+      "Ġquien es",
+      "ĠBro oke",
+      "Ġë§ĪìĿĮ ìĹIJ",
+      "Ġint en",
+      "Ġo ats",
+      "Ġrefere e",
+      "Ġpneum onia",
+      "Ġdel ve",
+      "pe ace",
+      "en y",
+      "Ġmost ra",
+      "ĠC annon",
+      "Ïģο Ïį",
+      "ĠÐIJ л",
+      "Ġmonument al",
+      "οÏį με",
+      "imm ers",
+      "av ian",
+      "Ġдел аеÑĤ",
+      "Ġpitch es",
+      "ĠGro ve",
+      "Ġsemin ars",
+      "Ġré cup",
+      "ĠVo or",
+      "Ġde ven",
+      "Ġd B",
+      "Ġboost ing",
+      "eg an",
+      "Ġwel t",
+      "ĠGuatem ala",
+      "Ġmile age",
+      "Ġbeh and",
+      "ĠWa ar",
+      "ĠSur f",
+      "Ġca uliflower",
+      "ĠTy r",
+      "Ġmite inander",
+      "Ġd aring",
+      "ĠS itting",
+      "d led",
+      "Ġresent ment",
+      "mÃ¤ÃŁ ig",
+      "Ġfilm making",
+      "w arts",
+      "th ought",
+      "olog ique",
+      "ĠC OR",
+      "Ġaccount ed",
+      "Ġa per",
+      "ĠIN T",
+      "ol are",
+      "Ġacompa ñ",
+      "èŃ ĺ",
+      "Ġ Æ¡i",
+      "ä¹ Ŀ",
+      "Ġm ermaid",
+      "ĠBent ley",
+      "at ore",
+      "Ġpre n",
+      "Ġet hanol",
+      "Ġastronom ers",
+      "se at",
+      "keep ers",
+      "Ġexem ption",
+      "Ġam o",
+      "ĠëĤĺ ìĦľ",
+      "Ġin hal",
+      "Ġb ows",
+      "Ñģк ÑĥÑİ",
+      "3 000",
+      "Ġfer mentation",
+      "Ġsink s",
+      "Ġcomer cial",
+      "Ġst ump",
+      "Ġce le",
+      "ĠS isters",
+      "ĠReg ister",
+      "Ġso ort",
+      "Ġnat omiast",
+      "Ġê·¸ë ¦¼",
+      "ĠÅŀ ey",
+      "Ġhy ped",
+      "ĠRaf ael",
+      "ĠE is",
+      "ĠBas il",
+      "ĠAssass in",
+      "ĠA de",
+      "rå n",
+      "Ġon lar",
+      "Ġmov imiento",
+      "Ġaddition ally",
+      "Ġsl it",
+      "ĠCh ry",
+      "ĠInter viewer",
+      "׾ ק",
+      "Ġdis l",
+      "Ġl igger",
+      "Ñĥ ки",
+      "ber ish",
+      "ĠÑĢÑıд ом",
+      "AR ON",
+      "], ,",
+      "Ġlum ière",
+      "Ġol vid",
+      "Ġfre ue",
+      "ĠT ing",
+      "ĠK ö",
+      "Ġge o",
+      "Ġdy ed",
+      "ãģ§ ãģį",
+      "ÑĪ ей",
+      "Ġży cie",
+      "Ġ ie",
+      "Ġtax payer",
+      "Ġpe ÅĤ",
+      "Ġdéc idé",
+      "ĠcÅĵ ur",
+      "Ġentwic kelt",
+      "ĠH Q",
+      "K K",
+      "od ar",
+      "Ġh one",
+      "Ġconf iance",
+      "Ġiss uing",
+      "Ġdiagn ost",
+      "Ġìŀ Ħ",
+      "ĠкÑĢÑĥ ÑĤ",
+      "Ġк аÑģ",
+      "ĠÃ ¾",
+      "Ġrestrict ive",
+      "ĠCast ro",
+      "Ġu ÄŁ",
+      "Ġem pre",
+      "ĠM oo",
+      "ĠFig ure",
+      "phon etic",
+      "Pro f",
+      "ĠпÑĢ е",
+      "Ġtilt ed",
+      "ĠNeg ative",
+      "ĠLim ited",
+      "men o",
+      "lam ation",
+      "Ġtrust ees",
+      "Ġintens ely",
+      "Ġaç ıl",
+      "ĠUs ed",
+      "Ġz ul",
+      "Ġappreci ative",
+      "Ġt inc",
+      "Ġconqu est",
+      "ĠعÙĨ د",
+      "Ġsuic idal",
+      "Ġmul heres",
+      "Ġdet ach",
+      "Ġkam era",
+      "ĠAir Pods",
+      "IND ISTINCT",
+      "гли й",
+      "Ġëĥ Ħ",
+      "Ġwrest le",
+      "æ´ Ĺ",
+      "Ġfire arm",
+      "Ġli re",
+      "p ra",
+      "Ġjew els",
+      "ĠCor nell",
+      "Ġíķł ê²ĮìļĶ",
+      "Ġsu cker",
+      "Ġnombre ux",
+      "ĠF erm",
+      "ìĽIJ ìĿ´",
+      "ĠP is",
+      "Ġиз ÑĥÑĩ",
+      "Ġmit en",
+      "Ġce v",
+      "ĠURL s",
+      "ĠC AS",
+      "Ġ åı¯ä»¥",
+      "f inden",
+      "Ġbra very",
+      "ĠÑģлов о",
+      "Ġnen huma",
+      "Ġencuent ra",
+      "ĠShir ley",
+      "Ġper cept",
+      "fr ames",
+      "ĠRo ver",
+      "ĠAlber ta",
+      "oc c",
+      "Ġë Ŀ¼ê³ł",
+      "Ġsú per",
+      "Ġpres ume",
+      "Ġgl and",
+      "Ġp acing",
+      "Ġneur ot",
+      "Ġs no",
+      "Ġpl otted",
+      "ĠpaÅĦst wa",
+      "ĠOwn er",
+      "ĠDef ence",
+      "rid ges",
+      "Ġwall paper",
+      "on ian",
+      "B ro",
+      "ĠAri ana",
+      "缴 æİ¥",
+      "k ry",
+      "Ġnarr ation",
+      "Ġcrian ça",
+      "ĠAlright y",
+      "ĠìĿ ½",
+      "Ġìĵ° ê³ł",
+      "Ġliber ated",
+      "Ġexceed s",
+      "Ġdom inating",
+      "Ġbak ın",
+      "l k",
+      "Ġsla pped",
+      "ÐĹ Ð´",
+      "ument al",
+      "get table",
+      "ĠRo z",
+      "ĠG ul",
+      "ou vert",
+      "Ġsm ashing",
+      "azu je",
+      "S ir",
+      "Ġgr ated",
+      "ä½ł æľī",
+      "AT T",
+      "Ġartic ulated",
+      "Ġst ora",
+      "Ġextr ater",
+      "á» ī",
+      "Ïĥ Ïī",
+      "w ir",
+      "ĠM ete",
+      "I mp",
+      "Ġho or",
+      "ph ase",
+      "ĠÑĩ Ñĥд",
+      "Ġб ÑĢаÑĤ",
+      "Ġid ag",
+      "Ġcin q",
+      "Ġapare cer",
+      "ĠI CE",
+      "åĪ Ĺ",
+      "Ġquiet er",
+      "Ġfals ch",
+      "ad ic",
+      "Ġп лÑİÑģ",
+      "ĠMen u",
+      "ux e",
+      "ĠT ôi",
+      "ĠM IL",
+      "ĠH aj",
+      "ver bs",
+      "Ġtub ing",
+      "Ġmach st",
+      "Ġd all",
+      "T er",
+      "Ġgel en",
+      "Ġcuc umbers",
+      "Ġwid gets",
+      "Ġdev rait",
+      "Ġm ike",
+      "Ġint ra",
+      "íķ Ń",
+      "ĠÃ ħ",
+      "ĠH und",
+      "æ§ ĭ",
+      "qu arter",
+      "Ġe w",
+      "Ġkelu ar",
+      "Ġm ats",
+      "ĠTr ick",
+      "ĠInfin ite",
+      "ŀ ¨",
+      "Ġpe ac",
+      "ĠPr ote",
+      "ॠĪ",
+      "Ġ17 00",
+      "ĠR ais",
+      "๠Ĭ",
+      "äh lt",
+      "ific a",
+      "a imer",
+      "a Äĩ",
+      "Ġa kl",
+      "ĠVol vo",
+      "ĠT yson",
+      "ĠR ong",
+      "irs in",
+      "ĠâĻ ¥",
+      "Ġpar ody",
+      "n ational",
+      "p od",
+      "ay d",
+      "amb led",
+      "Ġgovernment al",
+      "Ġconf ort",
+      "ic ides",
+      "Ġnas ze",
+      "ĠSh epherd",
+      "ĠKont akt",
+      "Ġdisproportion ately",
+      "Ġк лÑİÑĩ",
+      "Ġt ÃŃtulo",
+      "Ġs ina",
+      "Ġcompos itions",
+      "ĠP F",
+      "Ġver kl",
+      "Ġsuiv re",
+      "Ġast a",
+      "Ġstake holder",
+      "Ġsam ma",
+      "ĠBL ACK",
+      "Ġnod ig",
+      "Ġle va",
+      "Ġjue gos",
+      "Ġern st",
+      "Ġbottom s",
+      "ĠSign al",
+      "Ġpoll ut",
+      "Ġd ura",
+      "Mus ik",
+      "Ġком на",
+      "ĠвÑģ ей",
+      "al ter",
+      "ĠSte f",
+      "ĠBig Query",
+      "ĠVerantwort ung",
+      "Ġëĭ¹ ìĹ°",
+      "Ġqu izz",
+      "ĠLet ter",
+      "ĠInvest ment",
+      "ÑĪ ÑĤ",
+      "IJë į°",
+      "Ġenc oding",
+      "Ġtän ker",
+      "ĠK w",
+      "ann ie",
+      "åĭ Ŀ",
+      "1 10",
+      "Ġz wy",
+      "Ġì§ §",
+      "Ġda w",
+      "est ä",
+      "Ġdece ive",
+      "ĠL änder",
+      "is ko",
+      "Ġpod staw",
+      "ĠPh araoh",
+      "ì³ ¤",
+      "éĻ IJ",
+      "ú lt",
+      "Ġty ö",
+      "Ġmus imy",
+      "è³ ª",
+      "Ġp c",
+      "ĠN T",
+      "ĠCost co",
+      "Ġ å°ı",
+      "ĠÏĥ οÏħ",
+      "Ġun in",
+      "r ounds",
+      "Ġremind ers",
+      "Ġpuis qu",
+      "Ġkrij gen",
+      "Ġwork flows",
+      "net en",
+      "ĠëIJĺ ì§Ģ",
+      "Ġsle ek",
+      "Ġcowork ers",
+      "am ientos",
+      "Ġwitch es",
+      "ba ar",
+      "et ies",
+      "Ġun natural",
+      "ĠS ick",
+      "ĠEf endi",
+      "ãĥ³ãĥĢ ãĥĽ",
+      "j cie",
+      "Ġcham ado",
+      "ìĺĢ ìĬµëĭĪëĭ¤",
+      "Ġprz edsiÄĻbior",
+      "Ġbook store",
+      "Ġìŀłê¹ IJ",
+      "ĠSep ar",
+      "ang i",
+      "E vet",
+      "Ġemergen cies",
+      "ĠX ML",
+      "н д",
+      "¥´ë ©´",
+      "Ġê¿ Ī",
+      "Ġëĵ¤ ê³ł",
+      "Ġs ut",
+      "ĠW iz",
+      "å± ķ",
+      "Ġdynam ically",
+      "op eration",
+      "d ot",
+      "Ġine fficient",
+      "cle ars",
+      "Ġmund ane",
+      "ĠVeron ica",
+      "èĮ ¶",
+      "ر ت",
+      "p ose",
+      "p ai",
+      "Ġn ylon",
+      "Ġaument ar",
+      "Ġall tsÃ¥",
+      "v ak",
+      "Ġcapac idad",
+      "ĠWrest ling",
+      "Ġfert ile",
+      "Ġm ég",
+      "ĠN ano",
+      "аÑĤ ели",
+      "Ġìĸ´ì ©",
+      "Ġto ca",
+      "ĠE g",
+      "â ģ",
+      "Ġì ³",
+      "lu ent",
+      "Ġso lem",
+      "Ġcin emat",
+      "ĠQu el",
+      "Ġorb its",
+      "ĠHar m",
+      "ric anes",
+      "Ġblur red",
+      "å¦Ĥ ä½ķ",
+      "ĠاÙĦØ° ÙĬ",
+      "Ġj in",
+      "Ġgren ades",
+      "Ġat roc",
+      "Ġwhere in",
+      "Ġrepl en",
+      "ĠCom ics",
+      "eda an",
+      "Ġden im",
+      "Ġembarrass ment",
+      "ĠG omez",
+      "ĠBus an",
+      "iv ities",
+      "Ġsal iva",
+      "Ġmer k",
+      "Ġil gili",
+      "Ġк ÑĢÑĥг",
+      "Ġoccup ational",
+      "ĠSah ib",
+      "S ta",
+      "Ġadv iser",
+      "ĠTru ly",
+      "ĠYE AH",
+      "ĠìŀĪ ëĬĶëį°ìļĶ",
+      "z ew",
+      "b aren",
+      "Ġst ol",
+      "Ġbelong ings",
+      "ĠResearch ers",
+      "Ġe fendim",
+      "Ïħ Ïĩ",
+      "ÅĤÄħ cz",
+      "ĠU ng",
+      "ĠJ ub",
+      "Ġcere bral",
+      "á»ĩ u",
+      "Ġצ ר",
+      "Ġпод аÑĢ",
+      "Ġmarch ed",
+      "Ġaw aken",
+      "Ġa ko",
+      "Ġa cept",
+      "Ġiniti ation",
+      "è¯ ī",
+      "l ot",
+      "ĠwÅĤ as",
+      "ĠMong ol",
+      "ut ral",
+      "Ġtent ang",
+      "Ġinvers ion",
+      "ĠìĿ´ íĽĦ",
+      "Ġlo k",
+      "ÅĤby m",
+      "R S",
+      "Ġst os",
+      "Ġinteract s",
+      "ĠCal endar",
+      "Ġvan ish",
+      "Ġphysi ology",
+      "Ġlinear ly",
+      "ĠJ Y",
+      "ÄŁ an",
+      "fund ed",
+      "iz iert",
+      "Ġzm ian",
+      "ĠGr ill",
+      "Ġun believably",
+      "otechn ology",
+      "ĠC ars",
+      "ĠÙĨ Ûģ",
+      "ĠFol ge",
+      "ĠBever ly",
+      "ä ischen",
+      "Ġaument o",
+      "ìĽĮ ìĦľ",
+      "Ġmail box",
+      "Ġste eds",
+      "ĠPe ak",
+      "å· §",
+      "Ġwy kor",
+      "Ġpraw da",
+      "иÑĤ Ñĭ",
+      "Ġdisc ours",
+      "Ġacc use",
+      "cess o",
+      "u ire",
+      "Ġпоп ад",
+      "Ġth a",
+      "Ġmeas urable",
+      "be eping",
+      "ĠIn nen",
+      "Ġп ÑıÑĤÑĮ",
+      "Ġcompet ed",
+      "ĠItal ians",
+      "Ġencont ra",
+      "Ġn iew",
+      "Ġfilt ration",
+      "ĠпÑĢоÑĦ еÑģÑģ",
+      "Ġpaj amas",
+      "Ġc ilantro",
+      "ĠSo c",
+      "L uc",
+      "Ġê¹ Ģë",
+      "ĠOd d",
+      "Ġhyd ration",
+      "м ов",
+      "Ġply wood",
+      "ĠCompet ition",
+      "из неÑģ",
+      "f light",
+      "ĠBe it",
+      "bour g",
+      "Ġco ils",
+      "Ġcâ mera",
+      "Ġam ended",
+      "Äģ m",
+      "Ang el",
+      "ĠSt acy",
+      "f lo",
+      "Ġnorm ale",
+      "Ġconson ant",
+      "Ġaccompany ing",
+      "к Ñĸ",
+      "Ġirrit ated",
+      "ĠfÃ¥ tt",
+      "Ġcrocod ile",
+      "IJĺ ëĬĶ",
+      "Ġal beit",
+      "ĠPhilos ophy",
+      "ç´ ¯",
+      "Å Ĩ",
+      "yt ic",
+      "Ġr èg",
+      "Ġfr ança",
+      "Ġattent ive",
+      "H am",
+      "Ġalred edor",
+      "æĿ ¿",
+      "se i",
+      "ĠÑģв ид",
+      "Ġgim bal",
+      "Ġch ina",
+      "ĠðŁİ ¶",
+      "ĠÐĴ ам",
+      "Ġstim ulating",
+      "ĠO ra",
+      "yt es",
+      "Ġhe ft",
+      "Ġhat ers",
+      "Ġcomplex es",
+      "Ġ0 3",
+      "ró d",
+      "cle ar",
+      "Ġbeste ht",
+      "çķĻ è¨Ģ",
+      "wn y",
+      "mo il",
+      "Ġslop py",
+      "Ġinsign ificant",
+      "Ġdub bed",
+      "Ġëĸ ł",
+      "Ġcons igo",
+      "лÑĥÑĪ ай",
+      "S n",
+      "Ġ×Ķ× ¦",
+      "ĠÎ Į",
+      "Ġnad zie",
+      "Ġfresh men",
+      "ta a",
+      "Ġuw agÄĻ",
+      "ĠFavor ite",
+      "ĠCrim inal",
+      "Ġev iden",
+      "Ġsy mb",
+      "L es",
+      "ĠBe au",
+      "un ed",
+      "ple ment",
+      "A c",
+      "Ġderm at",
+      "ĠN olan",
+      "Ñĭ п",
+      "Ġs itt",
+      "Ġever lasting",
+      "Ġest avam",
+      "Ġм ик",
+      "Ġkh ác",
+      "Ġinv it",
+      "Ġtre ble",
+      "Ġj ig",
+      "man i",
+      "Ġtu vo",
+      "ĠR US",
+      "ĠEr de",
+      "ĠD ziÄĻkujÄĻ",
+      "Ġblue berries",
+      "ke ll",
+      "ac ions",
+      "çĪ ·",
+      "в и",
+      "LE T",
+      "Ġspr out",
+      "Ġsp or",
+      "Ġb ên",
+      "ĠM ona",
+      "ĠCont ain",
+      "ĠKe ys",
+      "оз Ñı",
+      "Ġfun ción",
+      "Ġrapp elle",
+      "Ġevol ves",
+      "Ġscra ping",
+      "Ġcoment ários",
+      "Ġpr atique",
+      "Ġaux iliary",
+      "ĠSp onge",
+      "Ñģк им",
+      "u vo",
+      "ĠÑģам о",
+      "Ġs ank",
+      "Ġhigh ways",
+      "Ġinvent ions",
+      "Ġин огда",
+      "Ġcreat ively",
+      "Ġbenchmark s",
+      "on cé",
+      "al al",
+      "Ġs otto",
+      "Ġcal ves",
+      "ĠMo v",
+      "Ġlav ender",
+      "Ġeye balls",
+      "Ġawait ing",
+      "ĠPat y",
+      "ÙĦ Ùĩ",
+      "Ġembroider y",
+      "Ġdu h",
+      "Ġcam ar",
+      "ĠBO B",
+      "Ġsp aced",
+      "ĠgÅĤ os",
+      "аем ÑģÑı",
+      "Ġesca pes",
+      "ĠR ogue",
+      "z cz",
+      "è ŀ",
+      "¬ë ¥¼",
+      "ĠMo że",
+      "Ġе ÑģÑĤе",
+      "ĠBur ada",
+      "éĮ ²",
+      "w d",
+      "uu uu",
+      "Ġs ash",
+      "ĠL ub",
+      "Ġnote books",
+      "Ġma e",
+      "Ġconf licting",
+      "Ġsumm ertime",
+      "ac as",
+      "Ġb auen",
+      "bl owing",
+      "ạ o",
+      "Ġìĸ¸ ìłľ",
+      "ä»ĬæĹ¥ ãģ¯",
+      "ĠSen hor",
+      "ĠiPh ones",
+      "ĠQu arter",
+      "Ġìłľë ĮĢë¡ľ",
+      "u ÃŁ",
+      "Ġë§ Ī무ë",
+      "Ġsett lers",
+      "Ġcre st",
+      "Ġtrans c",
+      "æĽ ¾",
+      "Ġri ots",
+      "Ġcl ones",
+      "ĠOp rah",
+      "ί ζ",
+      "Ġp als",
+      ".... ...",
+      "ãģĶãģĸ ãģĦãģ¾ãģĻ",
+      "ĠÑĢ оÑģÑģ",
+      "ĠLas er",
+      "Ġz aczy",
+      "Ġse vi",
+      "Ġregen eration",
+      "ìĹ ¼",
+      "w ould",
+      "Ġüzer ine",
+      "ĠStra ÃŁe",
+      "Ġvenge ance",
+      "Ġr er",
+      "ĠSaf ari",
+      "ĠHE Y",
+      "çķ «",
+      "Ġsac ar",
+      "Ġimage m",
+      "ĠBund est",
+      "mes an",
+      "ĠP aste",
+      "Ġs izz",
+      "Ġпо ÑģÑĤÑĥп",
+      "×Ķ ×ķ",
+      "t rad",
+      "Ġfrança ise",
+      "ĠB ou",
+      "Ġbar re",
+      "ĠZ hi",
+      "ĠG eez",
+      "ih ad",
+      "Ġrecon oc",
+      "Ġpel ig",
+      "Ġind ices",
+      "Ġë°Ķë Ģ",
+      "Ġcondu ction",
+      "Ġìķ ħ",
+      "Ġz eker",
+      "Ġf um",
+      "ĠW ür",
+      "bre aker",
+      "Ġspr ite",
+      "C rowd",
+      "Ġopen er",
+      "Ġol v",
+      "Ġbu enas",
+      "ĠSil k",
+      "ĠH IM",
+      "k op",
+      "com pl",
+      "Ġposs ono",
+      "³ Ģ",
+      "Ġoscill ator",
+      "ĠS ith",
+      "èĥ ¡",
+      "аж и",
+      "Ġra ft",
+      "h all",
+      "Ġschne ller",
+      "Ġimport ing",
+      "Ġassemb ling",
+      "Ġub iqu",
+      "Ġactiv ates",
+      "ac ci",
+      "ĵľë ¥¼",
+      "Ġcompos ers",
+      "ĠAC L",
+      "Con f",
+      "Ġì½ ĺ",
+      "ĠнекоÑĤоÑĢ Ñĭе",
+      "Ġcand ies",
+      "åĬł åħ¥",
+      "ĠM uss",
+      "à¹ĥ à¸Ĭ",
+      "Ġd uda",
+      "ник ом",
+      "med en",
+      "Ġìĸ´ë ķĮ",
+      "ĠYes hua",
+      "z ag",
+      "hod ou",
+      "Ġal oud",
+      "ĠPal mer",
+      "im ize",
+      "ãĤ· ãĥ§",
+      "Ġmar itime",
+      "Ġcommun al",
+      "Ġbad ges",
+      "Ġrug by",
+      "Ġmarshm allow",
+      "Ġfier y",
+      "Ġaccount ant",
+      "Ġab la",
+      "ĠMon roe",
+      "ĠF ont",
+      "ĠBo ost",
+      "ĠBarn es",
+      "ans wer",
+      "ĠBur ning",
+      "Ġ ä¸įæĺ¯",
+      "Ġan gef",
+      "ĠWes ley",
+      "ll s",
+      "ì µ",
+      "ש ׾",
+      "ili ÅĽmy",
+      "×IJ× Ł",
+      "am ura",
+      "ĠF uj",
+      "Ġpan i",
+      "ĠT rop",
+      "ar beiten",
+      "Ġr ue",
+      "ĠR are",
+      "äng en",
+      "ĠÑģмоÑĤÑĢ еÑĤÑĮ",
+      "ĠÐļ аÑĢ",
+      "ĠM TV",
+      "board ing",
+      "] [",
+      "Ġë łĪë",
+      "stan bul",
+      "p ielt",
+      "ĠHard y",
+      "ĠEng agement",
+      "ĠD ienst",
+      "Ġw ären",
+      "Ġfue go",
+      "Ġest ruct",
+      "Ġcal am",
+      "ĠResp onse",
+      "Ġ ãĤĦ",
+      "ĠMoh ammad",
+      "Ġresist ing",
+      "Ġdur ant",
+      "èģ ¯",
+      "åĨ µ",
+      "ĠO LED",
+      "Ġver z",
+      "m än",
+      "ĠÙĨ ÛĴ",
+      "Ġparano id",
+      "ĠA ware",
+      "ĠEngine ers",
+      "Ġproced ural",
+      "Ġpersonn age",
+      "Ġfark lı",
+      "é¡ Ĩ",
+      "fl owing",
+      "ĠмеÑģÑĤ а",
+      "ĠB are",
+      "ist em",
+      "ĠpoczÄħt ku",
+      "Ġperson ajes",
+      "Ġìĸ´ë łµ",
+      "Ń ī",
+      "ĠХоÑĤ Ñı",
+      "Ġuns ett",
+      "ĠAbs ol",
+      "Ġ ấy",
+      "ĠMAY OR",
+      "пол не",
+      "Ġinform ing",
+      "Ġam ps",
+      "ÐŁ ÑĢ",
+      "ĠëŃ Ķ",
+      "a eda",
+      "Ġ×Ķ× ij×",
+      "ấ n",
+      "kel ijk",
+      "Ġathe ist",
+      "Ġtr out",
+      "Ġne ues",
+      "ĠNok ia",
+      "m achen",
+      "Ġwholes ale",
+      "ır d",
+      "I ns",
+      "ĠÑį п",
+      "Ġpr ick",
+      "ĠKind ern",
+      "à¸Ĺ ำ",
+      "Ġclass y",
+      "Ġî nt",
+      "ĠShop ify",
+      "ĠÑģ оÑĢ",
+      "Ġзак ÑĢÑĭ",
+      "z uk",
+      "Ġunivers ally",
+      "Ġteas poons",
+      "Ġrec ount",
+      "ĠnÃ¥gon ting",
+      "ĠX ue",
+      "isi ème",
+      "Ġweak est",
+      "Ġte ÅŁekkür",
+      "Ġmath ematically",
+      "ĠH os",
+      "Ġíķľ ëĭ¤",
+      "Ġpart ager",
+      "ĠD arr",
+      "ê º",
+      "Ġε κ",
+      "Ġgerm s",
+      "Ġgel ir",
+      "Ġd ul",
+      ", -",
+      "Ġìĸ ¸ë",
+      "Ġ×ŀ× ¦",
+      "ĠÑı ÑĢ",
+      "Ġquot id",
+      "Ġprz ysz",
+      "Ġhard ness",
+      "Ġaqu atic",
+      "ĠJung le",
+      "ĠPC R",
+      "ĠEli ot",
+      "Ġo str",
+      "Ġma pa",
+      "ess ä",
+      "ĠG IR",
+      "ĠDri ving",
+      "ĠS ami",
+      "ĠMed ien",
+      "ĠCompan ies",
+      "ĠPh arm",
+      "se its",
+      "ĠR im",
+      "Ġο ÏĢο",
+      "Ġweiter en",
+      "Ġpizz as",
+      "ĠL ydia",
+      "ĠHe ights",
+      "Ġsincer ity",
+      "Ġnoss as",
+      "Ġd ÅĤ",
+      "Ġalar ming",
+      "ĠC auc",
+      "ĠÑģм ÑĭÑģ",
+      "f acing",
+      "b ags",
+      "W W",
+      "ĠØ´ ÙĬ",
+      "Ġcourt room",
+      "ĠPhill ip",
+      "Ġê²ĥ ì²ĺëŁ¼",
+      "ĠSpiel er",
+      "ãĤı ãģĭ",
+      "Ġk ant",
+      "Ġadm itting",
+      "ãĥģãĥ£ ãĥ³ãĥįãĥ«",
+      "Ġcontain ment",
+      "å¼ ł",
+      "Ġremo vable",
+      "Ġjum per",
+      "f ocused",
+      "ĠиÑĤог е",
+      "ĠТ ем",
+      "Ġv ase",
+      "ĠUS C",
+      "ĠMon ate",
+      "ĠJac obs",
+      "ĠH OL",
+      "ik ed",
+      "er weise",
+      "Ġgood ies",
+      "Ġhom age",
+      "׼ש ×Ļ×ķ",
+      "Ġqu ais",
+      "Ġin icial",
+      "Ġguard ing",
+      "Ġd azz",
+      "Ġcomb os",
+      "ĠÑĥп ÑĢав",
+      "ĠTal ent",
+      "å¥ĩ æĢª",
+      "Ġó r",
+      "Ġintermitt ent",
+      "ĠMcC arthy",
+      "Ġsp ans",
+      "Ġty re",
+      "Ġqu y",
+      "èĪ Ī",
+      "j ut",
+      "ĠZ ent",
+      "Ġg at",
+      "大 åĵ¥",
+      "Ġscaff old",
+      "Ġneces ario",
+      "ĠZ ahlen",
+      "ĠS AND",
+      "ĠP U",
+      "Every thing",
+      "-------- --------",
+      "Ġвз ÑıÑĤÑĮ",
+      "Ġspark s",
+      "Ġpend ulum",
+      "×ŀ× Ł",
+      "Ġìĥī ê¹",
+      "Ġmultipl ier",
+      "Ġл адно",
+      "ur at",
+      "Ġupset ting",
+      "è¡ Ģ",
+      "b ak",
+      "Ġìµ ľëĮĢ",
+      "Ġan ál",
+      "ĠJO E",
+      "Ġk osten",
+      "ĠPat ty",
+      "ĠGu in",
+      "ck ed",
+      "ĠEgypt ians",
+      "ĠCit izens",
+      "ר ׼",
+      "ĠÐķ Ñīе",
+      "Ġй ого",
+      "Ġsnow fl",
+      "Ġlek ker",
+      "Ġac ost",
+      "ĠB abe",
+      "Ġgam ble",
+      "Ġadject ive",
+      "к ими",
+      "o ys",
+      "Ġmont re",
+      "ĠHy undai",
+      "Ġmoistur izing",
+      "Ġmo zzarella",
+      "OO O",
+      "Ġfac ult",
+      "Ġdo et",
+      "Ġfear less",
+      "Ġesp resso",
+      "Ġall ora",
+      "ĠC inc",
+      "ãĥ¼ ãĤ¸",
+      "Ġconteú do",
+      "ĠPel osi",
+      "Ġmind er",
+      "ro ot",
+      "Ġíķ łë",
+      "Ġп ад",
+      "ĠCall ing",
+      "ĠConf ig",
+      "ĠCon sole",
+      "ins ky",
+      "éner gie",
+      "Ġsol itary",
+      "од е",
+      "Ġguard ed",
+      "1 60",
+      "ĠпÑģ иÑħ",
+      "ĠSh ap",
+      "Ġtit re",
+      "olog ne",
+      "ĠпаÑĢ Ñĥ",
+      "ĠP RE",
+      "ãĥ¼ ãĥī",
+      "Ġl n",
+      "ĠMit gl",
+      "ĠCar ry",
+      "Ġsp ind",
+      "ĠCant on",
+      "Ġkingdom s",
+      "rem o",
+      "Ġr aging",
+      "Ġincap able",
+      "ĠW R",
+      "åĨį è§ģ",
+      "ĠÑģоб ÑģÑĤвен",
+      "Ġкак иÑħ",
+      "ĠSH E",
+      "ëĭ¹ íŀĪ",
+      "Ġscar city",
+      "Ġper de",
+      "Ġex its",
+      "ĠS inger",
+      "Ġsupp er",
+      "Ġmunicip ality",
+      "ĠD iversity",
+      "Ġt iro",
+      "iel s",
+      "ĠlÃŃ der",
+      "Ġbl uff",
+      "Ġat ra",
+      "ly s",
+      "Ġma hd",
+      "Ġcód igo",
+      "ĠHar lem",
+      "ru le",
+      "ic ity",
+      "Ġsimpl istic",
+      "ĠK onst",
+      "åģ ¥",
+      "ELL I",
+      "Ġför sta",
+      "Ġconstit utes",
+      "ĠÑģÑĤоÑĢон Ñĥ",
+      "Ġur ged",
+      "ĠP anda",
+      "ì° ¨ë",
+      "re ce",
+      "Ġpatri ot",
+      "ĠCr ush",
+      "Ġw ink",
+      "ой ÑĤи",
+      "uran ça",
+      "Ġseiz ures",
+      "Ġelect rod",
+      "ĠDon key",
+      "ĠI U",
+      "ĠM OS",
+      "Ġal kal",
+      "ì´ ī",
+      "bes ondere",
+      "Ġparall els",
+      "Ġbitter ness",
+      "ät tre",
+      "ess ional",
+      "Ġsoy bean",
+      "Ġcoll ab",
+      "ĠReport ing",
+      "å§ Ķ",
+      "Ġкомп ании",
+      "Ġwsz yscy",
+      "ĠCr unch",
+      "ise en",
+      "Ġamb assadors",
+      "ĠChe v",
+      "åį Ī",
+      "ов Ñĭе",
+      "s ca",
+      "ĠÑĢеÑĪ ил",
+      "о ÑĤо",
+      "Ġgleich zeitig",
+      "mer n",
+      "ü st",
+      "ĠHa e",
+      "³´ ê²łìĬµëĭĪëĭ¤",
+      "Ġsh ores",
+      "Ġdep ress",
+      "Ġa hor",
+      "ĠSte uer",
+      "ah h",
+      "Ġrev ise",
+      "ĠÑģам Ñĭе",
+      "j at",
+      "Ġher bal",
+      "Ġcu ánt",
+      "Ġb una",
+      "niejs ze",
+      "F inally",
+      "×ķ× ĸ",
+      "c je",
+      "ĠìŀĪ ê±°ëĵłìļĶ",
+      "ĠëĤĺë Ī",
+      "Ġprz est",
+      "ãĥ¼ãĥ ł",
+      "lic a",
+      "ĠD uch",
+      "å°į å°į",
+      "Ñĸй ÑģÑĮ",
+      "p assen",
+      "Ġsatisf ies",
+      "ĠAdd itional",
+      "Ġcá mara",
+      "еÑĩ ение",
+      "Ġp omp",
+      "Ġë§IJ ìĿ´",
+      "ĠM ills",
+      "ев ид",
+      "Ġrespect able",
+      "Ġfil ament",
+      "Ġv ender",
+      "Ġmatter ed",
+      "ou re",
+      "ì¸ µ",
+      "K orean",
+      "Ġestud io",
+      "Ġc actus",
+      "ĠV ive",
+      "ĠR ag",
+      "Ġcompl iqué",
+      "ĠÙĪ Ûģ",
+      "Ġta o",
+      "¦ ¿",
+      "S ince",
+      "Ġje opard",
+      "ĠS ell",
+      "åº Ķ",
+      "Ġìĺ Ľ",
+      "Ġket o",
+      "Ġintel ig",
+      "ĠAn geb",
+      "Ġt iden",
+      "Ġsoci o",
+      "Ġreminis cent",
+      "Ġcareg iver",
+      "Sp ace",
+      "ĠExerc ise",
+      "ĠBe come",
+      "ê ts",
+      "ak k",
+      "! ..",
+      "ĠÑģп ÑĢоÑģ",
+      "Ġα ÏĢο",
+      "Ġshoot ings",
+      "Ġa pe",
+      "ĠSam my",
+      "ĠK ung",
+      "Ġcu ál",
+      "ĠL up",
+      "æĿ Ł",
+      "ä¾Ĩ åĪ°",
+      "ĠÑģÑĤ Ñĥд",
+      "Ġswe eter",
+      "Ġcom um",
+      "ĠAd s",
+      "hy ung",
+      "ĠбÑĥд ÑĥÑī",
+      "Ġw affle",
+      "ĠOr b",
+      "Ġla ut",
+      "Ġforecast ing",
+      "å ª",
+      "Ġrapp ing",
+      "Ġpref ers",
+      "Ġben z",
+      "Ġn ik",
+      "ĠB ahn",
+      "Ġsand ing",
+      "Ġimmin ent",
+      "ĠпÑĢоблем Ñĭ",
+      "Ġdo ivent",
+      "ол а",
+      "Ġży cia",
+      "ih u",
+      "Ġexist em",
+      "ĠInter ior",
+      "ĠT akes",
+      "Ġtodd ler",
+      "Ġdictators hip",
+      "ĠSmith son",
+      "ĠAllah u",
+      "Ïİ Ïģα",
+      "ìķĺ ìĬµëĭĪëĭ¤",
+      "ĠV ote",
+      "ĠSm ells",
+      "од но",
+      "Ġhind sight",
+      "V R",
+      "ĠP atch",
+      "ĠJah res",
+      "Ġsou venir",
+      "Ġneut ron",
+      "Ġlong time",
+      "Ġsay in",
+      "ä¹ IJ",
+      "as aki",
+      "ĠоÑģÑĤ анов",
+      "Ġex pelled",
+      "Ġcryptocur rencies",
+      "ĠMur der",
+      "ĠCit izen",
+      "W AY",
+      "Ġpl u",
+      "Ġlemon ade",
+      "Ġconvenient ly",
+      "ĠH I",
+      "Ġ20 23",
+      "ש ×ķת",
+      "аÑĨи он",
+      "ĠëĽ °",
+      "ĠÙĦ ÙĥÙĨ",
+      "Ġнемнож ко",
+      "Ġun used",
+      "Ġmaior ia",
+      "Ġastrolog y",
+      "ĠDow nt",
+      "N ick",
+      "Ġpreoc cup",
+      "Ġdem ain",
+      "×ŀ× ¢",
+      "Ġвод Ñĭ",
+      "ĠSans krit",
+      "Ġpr êt",
+      "Ġstrand ed",
+      "Ġref in",
+      "ĠпÑĢи ним",
+      "Ġпов еÑĢÑħ",
+      "à¯į ?",
+      "Ġz rob",
+      "Ġinter tw",
+      "ĠDavid son",
+      "лен а",
+      "Ġпон ÑıÑĤÑĮ",
+      "ĠR eno",
+      "ĠполÑĥÑĩ илоÑģÑĮ",
+      "Ġcorrespond ent",
+      "ĠU ran",
+      "el se",
+      "· ·",
+      "Ġtut oring",
+      "Ġgrand daughter",
+      "lud ed",
+      "Ġst esso",
+      "Ġh ết",
+      "Ġgeg angen",
+      "ĠÐĿ ÐIJ",
+      "Ġant ig",
+      "back ground",
+      "Ġg edaan",
+      "Ġf avored",
+      "ĠEm manuel",
+      "Ġi od",
+      "Ġclamp s",
+      "Ġcomp le",
+      "ĠAdv ance",
+      "ĠìŀĪ ê³łìļĶ",
+      "ĠRo x",
+      "ĠìĹ IJë",
+      "Ġintest ines",
+      "Ġper cussion",
+      "Ġlegit imately",
+      "ĠE ternal",
+      "f amily",
+      "al og",
+      "B rad",
+      "ени ÑĤÑĮ",
+      "ĠÑģ наÑĩала",
+      "Ġcert a",
+      "Ġak kor",
+      "Ġε δÏİ",
+      "Ġoct ave",
+      "ĠV ac",
+      "м оÑĤÑĢи",
+      "ĠÃīt ats",
+      "Ġlong ue",
+      "Ġdis soci",
+      "ÑĢ Ñıд",
+      "he in",
+      "Ġpant alla",
+      "Ġindic ations",
+      "ĠL t",
+      "ĠGr ade",
+      "è£ Ŀ",
+      "o ine",
+      "b ug",
+      "ĠVer izon",
+      "ĠAl ém",
+      "Ġv iennent",
+      "ĠÑĩ иÑģÑĤ",
+      "ĠBen i",
+      "ĠT sch",
+      "ĠT P",
+      "Ġinsult ing",
+      "ĠWe ight",
+      "Ġadapt ations",
+      "Ġhab ÃŃan",
+      "Ġcl ique",
+      "o ÅĽci",
+      "j una",
+      "Ġsuch en",
+      "ĠGo es",
+      "ĠEx odus",
+      "C ho",
+      "Ġant is",
+      "ĠíĮ Įë",
+      "se ven",
+      "ĠÑĩаÑģ ов",
+      "Ġball istic",
+      "z ony",
+      "IC IA",
+      "ĠпÑĢ еÑģÑĤ",
+      "Ġsimples mente",
+      "ĠColl abor",
+      "F red",
+      "ĠÑĤелеÑĦ он",
+      "ĠR avi",
+      "íķ´ì ¤",
+      "пеÑĢ в",
+      "ĠìŀĪ ìľ¼ëĭĪê¹Į",
+      "Ġó t",
+      "Ġa leg",
+      "ú p",
+      "Ġdisreg ard",
+      "Ġind ent",
+      "cl oud",
+      "chl agen",
+      "Ġiter ate",
+      "Ġgeneral ized",
+      "ãģĹ ãģ¾ãģĹãģŁ",
+      "ठ¹",
+      "eler i",
+      "Ġdisast rous",
+      "ĠÑģÑĤ ала",
+      "³ ij",
+      "KN OWN",
+      "Ġrich ness",
+      "Ġcons cient",
+      "icht s",
+      "ĠÑį лем",
+      "ب د",
+      "ire ns",
+      "Ġha unting",
+      "ruct ures",
+      "att ack",
+      "Ġcup cakes",
+      "s que",
+      "Ġnas zego",
+      "Ġanthrop ology",
+      "ãģŁ ãģł",
+      "ãģµ ãģµ",
+      "cha e",
+      "Ġdisco vers",
+      "ĠPerson ality",
+      "ĠΤ ο",
+      "Ġdi ÄŁer",
+      "åį Ģ",
+      "Ġне Ñij",
+      "ĠAn ita",
+      "Ġ[ âĻª",
+      "ĠCar m",
+      "ĠBen ny",
+      "ìĬ ¬",
+      "Ġpup il",
+      "Ġoc as",
+      "äll et",
+      "j ÅĽÄĩ",
+      "大 ä¸Ī夫",
+      "ament al",
+      "ĠоÑĤно Ñģ",
+      "Ġp id",
+      "Ġar mp",
+      "RE E",
+      "ĠоÑĤк ÑĢÑĭв",
+      "Ġu da",
+      "ĠSynd rome",
+      "ĠStand ards",
+      "ãģĪ ãĤĭ",
+      "Ġpo inters",
+      "Ġen am",
+      "ĠT ig",
+      "ÃŃ z",
+      "Ġн ами",
+      "Ġunch anged",
+      "Ġtur moil",
+      "ứ ng",
+      "!! \"",
+      "5 000",
+      "Ġ물 ìĸ´ë",
+      "Ġmer ging",
+      "Ġentsche iden",
+      "åĩº æĿ¥",
+      "form e",
+      "Ġtrim med",
+      "Ġd ared",
+      "Ġasp iration",
+      "ĠMyth ical",
+      "ĠHe j",
+      "ĠAle j",
+      "ÑĨ о",
+      "оÑĤ Ñĥ",
+      "Z e",
+      "Ġин ÑģÑĤÑĢÑĥменÑĤ",
+      "ĠRT X",
+      "Ġlocal ized",
+      "çļĦ è¯Ŀ",
+      "Ġsur rounds",
+      "Ġemp ieza",
+      "Ġcl ase",
+      "Ġ à¸ģ",
+      "ĠRap id",
+      "omin ous",
+      "ig ail",
+      "ĠÑĪ иÑĢ",
+      "Ġl æ",
+      "Ġzas ad",
+      "Ġunfold ing",
+      "?! ?!",
+      "ĠìĪľ ê°Ħ",
+      "ĠPol ski",
+      "ĠK auf",
+      "ĠC elt",
+      "it ic",
+      "Ġtool box",
+      "ĠP ocket",
+      "ĠìĦ ľë¡ľ",
+      "Ġbel ki",
+      "Ġadm iration",
+      "ph r",
+      "ĠProdu kt",
+      "ĠT ruck",
+      "ãģ İ",
+      "Ġdra uÃŁen",
+      "wa ÅĤ",
+      "ĠHebrew s",
+      "Ġíķĺ ê²Į",
+      "ĠA CE",
+      "urg ence",
+      "aur ais",
+      "Ġchar itable",
+      "ı t",
+      "Ġarm as",
+      "ĠGed anken",
+      "reat ing",
+      "port e",
+      "Ġimp rint",
+      "f äh",
+      "Ġпод Ñħод",
+      "Ġout set",
+      "ว à¸ģ",
+      "ен ного",
+      "Cl ass",
+      "Ġvan ity",
+      "ĠVO ICES",
+      "Ġ2 60",
+      "res ident",
+      "US E",
+      "Ġê°Ģ ìļ´ëį°",
+      "é ½",
+      "Ġthrough put",
+      "Ġc uma",
+      "ìļ ±",
+      "ãĥ¼ ãĥ³",
+      "Ġпло Ñī",
+      "Ġpart is",
+      "ĠAnim ation",
+      "§ Īë",
+      "C re",
+      "öt zlich",
+      "Ġma gg",
+      "Ġcl umsy",
+      "Ġbott lene",
+      "Ġbirl ikte",
+      "ĠG amb",
+      "Ġ׼ ף",
+      "Ġmet ropolitan",
+      "è¯ ¥",
+      "æİ Ĵ",
+      "O oh",
+      "Ġobject ions",
+      "ĠÙħ ت",
+      "Ġм ел",
+      "Ġrem nants",
+      "ĠX avier",
+      "R ich",
+      "Ġol sa",
+      "ĠP ill",
+      "Ġgro ans",
+      "ĠNaru hodou",
+      "ĠCont ract",
+      "ад а",
+      "na i",
+      "ĠÑĦ из",
+      "Ġop s",
+      "ạ t",
+      "Ġparach ute",
+      "Ġne ll",
+      "ĠEntscheid ung",
+      "׾ ×Ļ×Ŀ",
+      "Ġtruth ful",
+      "Ġshar per",
+      "Ġbureauc racy",
+      "c art",
+      "Ġин ÑĤ",
+      "wie k",
+      "Ġwilling ly",
+      "ĠH erman",
+      "Ġmehr ere",
+      "Ġel ites",
+      "ĠArm or",
+      "ãĥĪ ãĥŁãĥ¼",
+      "Ġemb ora",
+      "ĠRec ogn",
+      "ĠлÑİб лÑİ",
+      "ĠEx cellence",
+      "ib el",
+      "Ġexport ing",
+      "ì² ´ìłģ",
+      "K elly",
+      "Camer aman",
+      "Ġsl ips",
+      "Ġfig ura",
+      "Ġãģ ¡",
+      "Ġk oll",
+      "ĠPand emie",
+      "çı Ń",
+      "Ġtim ed",
+      "lieÃŁ lich",
+      "Ġ×ŀ× Ľ",
+      "ĠperÃŃ odo",
+      "å¿ Ĺ",
+      "iv at",
+      "Ġquestion naire",
+      "Ġpéri ode",
+      "ç© ¶",
+      "Ġs ighs",
+      "Ġalleg iance",
+      "ĠX V",
+      "ĠKens uke",
+      "ĠGesund heits",
+      "Ġposit ivo",
+      "ĠJane iro",
+      "ĠS EE",
+      "Ġا ست",
+      "ĠKel sey",
+      "to ber",
+      "Ġα λλά",
+      "ĠP arent",
+      "ĠDay ton",
+      "ĠBild er",
+      "our age",
+      "Ġser es",
+      "ĠmuchÃŃs imo",
+      "ĠReal m",
+      "ĠOFFIC ER",
+      "erson ic",
+      "ãĤĤ ãģ®",
+      "ony a",
+      "Ġê¸ ī",
+      "Ġancest ry",
+      "ĠJur assic",
+      "Ġcent igrade",
+      "ấ u",
+      "ujÄħ c",
+      "m ans",
+      "Ġt io",
+      "ĠMo ż",
+      "Ġtra gen",
+      "Ġst ared",
+      "Ġsch ematic",
+      "Ġpass ou",
+      "Ġmeat balls",
+      "ÅĤo ÅĽÄĩ",
+      "Ġsynchron ous",
+      "Ġperm is",
+      "ar ial",
+      "Ġz er",
+      "Ġpar ity",
+      "ĠAv atar",
+      "inde er",
+      "est on",
+      "Ġme idän",
+      "ĠC ly",
+      "´ ī",
+      "Ġest rogen",
+      "Ġcent imet",
+      "çĻ º",
+      "Ġconv ictions",
+      "Ġposs iamo",
+      "Ġper du",
+      "Ġpath ogens",
+      "ĠQu in",
+      "ĠProgram s",
+      "ĠPoint s",
+      "ram ent",
+      "ra il",
+      "Ġv y",
+      "Ġgra ft",
+      "Ġb art",
+      "ĠLot us",
+      "à ¨",
+      "Ġë³´ìĭ ľ",
+      "ram er",
+      "F ather",
+      "Ġëľ »",
+      "Ġ×Ķ× Ŀ",
+      "Ġtra zer",
+      "Ġt ark",
+      "è ces",
+      "f orth",
+      "ĠÑģдел али",
+      "Ġzucch ini",
+      "Ġw aktu",
+      "Ġentertain ed",
+      "ĠMilli arden",
+      "Ġsh aky",
+      "Ġprz ede",
+      "¸ Įë",
+      "Ġrevers ible",
+      "ĠN AU",
+      "u ins",
+      "ér êt",
+      "ann en",
+      "ĠHun ting",
+      "ĠF ellow",
+      "él ior",
+      "Ġrot ations",
+      "Ġgr anny",
+      "xt on",
+      "ĠÑģÑĤанов иÑĤÑģÑı",
+      "ĠнаÑĩ ал",
+      "Ġarter ies",
+      "ri ó",
+      "ĠполÑĮз ов",
+      "ĠÐij Ñĭ",
+      "Ġnovel ty",
+      "p ound",
+      "Ġweird est",
+      "Ġbo is",
+      "ém ie",
+      "u pl",
+      "AT A",
+      "Ġte hd",
+      "ĠN ir",
+      "s ınız",
+      "! \",",
+      "åijĬ è¯ī",
+      "Ġimm ort",
+      "Ġel k",
+      "ани Ñĩ",
+      "Ġfabric ation",
+      "ĠNo ise",
+      "ĠA vant",
+      "ر ÛĮ",
+      "w at",
+      "Ġwho oshing",
+      "Ġ׼ ×Ļ",
+      "ĠÐĹна ÑĩиÑĤ",
+      "Ġcent rif",
+      "ans ing",
+      "S ound",
+      "Ġë Ŀ¼ë",
+      "Ġcapt ions",
+      "à³ į",
+      "Ġor gas",
+      "Ġdolph ins",
+      "ĠBl end",
+      "ĠT aj",
+      "ĠC CTV",
+      "Ġin om",
+      "Ġed itions",
+      "Ġburn out",
+      "Ġb ättre",
+      "ĠC asa",
+      "ov ich",
+      "Ġmol ten",
+      "Ġblind fold",
+      "ĠG ue",
+      "æŶ éĹ´",
+      "Ġspin ner",
+      "Ġmöglich st",
+      "ĠV Ãł",
+      "ene ca",
+      "Ġméd ico",
+      "å¹¹ åĺĽ",
+      "ást ico",
+      "Ġar d",
+      "ĠSund ays",
+      "ĠRem ote",
+      "Ġìĸ¼ë §Ī",
+      "Ġtr Æ°á»Ľc",
+      "ìħ ¨ë",
+      "Ġdo pp",
+      "Ġbe ÄŁ",
+      "ic ana",
+      "ĠëĤĺ ì¤ijìĹIJ",
+      "çİ ĩ",
+      "Ġhol iness",
+      "d irect",
+      "Ġìĺģ íĻĶ",
+      "Ġcul pa",
+      "ĠSt itch",
+      "light ly",
+      "ам ен",
+      "Ġм еÑĪ",
+      "Ġп еÑĩ",
+      "Ġy hte",
+      "os phere",
+      "Ġìĵ° ëĬĶ",
+      "é k",
+      "Ġserious ness",
+      "Ġgar ments",
+      "Ġconc ise",
+      "ĠS J",
+      "Ġverl oren",
+      "Ġpare cer",
+      "ĠUN C",
+      "ìĬ¤í ĥĢ",
+      "Ġenf ant",
+      "Ġbom ber",
+      "ĠG ift",
+      "Ġì¢ĭ ëĭ¤",
+      "Ġrhy thms",
+      "ĠK lar",
+      "人 æ°ij",
+      "own ik",
+      "ĠRever end",
+      "Ġem itted",
+      "l assen",
+      "Ġreven ir",
+      "Ġar ising",
+      "Ġprecis amente",
+      "Ġinter pol",
+      "ĠTen emos",
+      "ob ed",
+      "Ġtecn ologia",
+      "Ġnered e",
+      "ĠV isa",
+      "Ġsa va",
+      "Ġescre ver",
+      "Ġassault ed",
+      "ĠFle isch",
+      "ĠCouncill ors",
+      "Ġê°Ģ ê¹Į",
+      "Ġbe gg",
+      "ĠDevelop er",
+      "ĠBron ze",
+      "ĠBon us",
+      "Ġר ק",
+      "f act",
+      "Ġend lessly",
+      "Ġmac am",
+      "Ġrze czywiÅĽcie",
+      "Ġhover ing",
+      "è ge",
+      "Ġpo orest",
+      "ĠSch ed",
+      "m ile",
+      "isse ments",
+      "ac Äĥ",
+      "Ġë ¦½",
+      "Ġvacc in",
+      "Ġfutur istic",
+      "ĠWind ow",
+      "п аÑĢ",
+      "ĠÑĢ оÑģ",
+      "Ġlow ers",
+      "ac s",
+      "ĠÐIJлекÑģ анд",
+      "ĠAl ert",
+      "ie me",
+      "ĠCauc as",
+      "Ġj aws",
+      "Ġhunt ed",
+      "ìĹ ½",
+      "Ġب ÙĨ",
+      "Ġ×ľ× ł×ķ",
+      "Ġturb ines",
+      "Ġl umps",
+      "ĠAll ies",
+      "ah lt",
+      "Ġsubscri ptions",
+      "Ġnouve aux",
+      "ug er",
+      "b ones",
+      "Ġb erry",
+      "ĠìĦłë ¬¼",
+      "ĠMan ufact",
+      "ĠL unch",
+      "ê·¸ë ŀĺ",
+      "Ġhyd rated",
+      "Ġache i",
+      "ĠY az",
+      "ĠTibet an",
+      "ĠQuant um",
+      "ĠJer ome",
+      "ĠоÑī ÑĥÑī",
+      "ов ан",
+      "m otion",
+      "ĠCont roller",
+      "ener getic",
+      "ĠÑģкоÑĢ о",
+      "Ġvow els",
+      "ĠÑĥж аÑģ",
+      "Ġho of",
+      "ĠBull et",
+      "imag in",
+      "׳ ×Ļ×Ŀ",
+      "Ġengage ments",
+      "ĠBl ues",
+      "Ġañ ad",
+      "Ġf ps",
+      "Ġcater p",
+      "Ġs á»ij",
+      "ĠTri be",
+      "ç¶ ļ",
+      "п он",
+      "if eration",
+      "Ġrum ah",
+      "ĠPun j",
+      "l ab",
+      "Ġcompreh ension",
+      "br inging",
+      "W o",
+      "Ġt ik",
+      "Ġany how",
+      "以 åīį",
+      "át icas",
+      "Ġsit zen",
+      "Ġkol ay",
+      "ĠConfeder ate",
+      "ĠCall ed",
+      "Ġnas zych",
+      "Ġd ziÄĻki",
+      "Ġclo ak",
+      "ĠGo og",
+      "ĠAs he",
+      "è± ¡",
+      "en an",
+      "ĠмÑĭ ÑĪ",
+      "Ġв еÑĤ",
+      "ĠSp o",
+      "ĠS ket",
+      "ĠHend erson",
+      "il ah",
+      "Ġбез опаÑģ",
+      "Ġsek ali",
+      "ìĸ´ ê°Ģ",
+      "Ġsn are",
+      "Ġr ằng",
+      "Ġförs ö",
+      "szy ch",
+      "Ġü bers",
+      "Ġstrat ég",
+      "Ġìº IJë",
+      "Ġrapp ers",
+      "Ġc ep",
+      "ĠH asta",
+      "Ġhor ribly",
+      "Ġfr üh",
+      "Ġب ع",
+      "Ġmant le",
+      "ãĢ ħ",
+      "fund ing",
+      "Ġz ust",
+      "ĠP ens",
+      "s ed",
+      "ĠíĹ ¤",
+      "Ġgere ki",
+      "Ġal arms",
+      "ĠWh a",
+      "ĠMark us",
+      "aks i",
+      "ĠÐIJ ле",
+      "kl ore",
+      "Ġé ner",
+      "Ġt ilde",
+      "box ing",
+      "ĠìĦ ŀ",
+      "Ġencont ramos",
+      "ĠPh ar",
+      "нак ом",
+      "ó st",
+      "ĠÄ° s",
+      "Ġëĭ ĺ",
+      "Ġsqu ats",
+      "Ġpret ended",
+      "Ġde z",
+      "Ġê´ľì°® ìķĦ",
+      "j ach",
+      "ë Ŀ¼ê³ł",
+      "ĠíĻķ ì§Ħ",
+      "ĠAn sch",
+      "imer k",
+      "Ġconjug ate",
+      "Ġpen insula",
+      "Ġgor illa",
+      "Ġphotograph ed",
+      "ĠAun que",
+      "Ġent ren",
+      "ĠDeuts chen",
+      "ĠAl addin",
+      "Ġë¬ ´ìĦľ",
+      "ĠSt ella",
+      "ĠE lection",
+      "out ine",
+      "G rand",
+      "ĠW ak",
+      "ĠSer gio",
+      "hor se",
+      "ah on",
+      "ĠFamil ies",
+      "Ġh ating",
+      "ĠB ett",
+      "à¸Ļะ à¸Ħะ",
+      "Ġcur ling",
+      "ĠIsrael is",
+      "Ġ×ľ× IJ×",
+      "ĠMy ers",
+      "Ġsc anned",
+      "ĠB EC",
+      "iler i",
+      "Ġcall e",
+      "ĠMin h",
+      "Ġmic ron",
+      "Ġcon duc",
+      "ÃŃ v",
+      "Ġвоз ÑĮ",
+      "Ġaction able",
+      "ĠTrust ees",
+      "Ġt ief",
+      "Ġhead ers",
+      "Ġanim ales",
+      "ìĽ Ģ",
+      "ло Ñħ",
+      "un ity",
+      "ly a",
+      "Ġj angan",
+      "Ġh ani",
+      "Ġcas ing",
+      "Ġjó venes",
+      "ĠSpl it",
+      "ĠCar lo",
+      "ĠBe im",
+      "å°į ä¸įèµ·",
+      "Ġnu anced",
+      "Ġted dy",
+      "ĠCl an",
+      "ä chen",
+      "p ier",
+      "Ġдоп олн",
+      "Ġdi aper",
+      "e ffective",
+      "ĠNi agara",
+      "Ġw art",
+      "Ġcor ro",
+      "ĠK ampf",
+      "z te",
+      "Ġdévelopp ement",
+      "Ġattack ers",
+      "ĠSher man",
+      "Ġ19 14",
+      "Ġme ow",
+      "ĠP Ã¥",
+      "ì º",
+      "c it",
+      "Ġcou pe",
+      "Ġê·¸ëĭ¤ìĿĮ ìĹIJ",
+      "Ġhum our",
+      "Ġco le",
+      "ĠW arning",
+      "ĠT il",
+      "cal m",
+      "bu at",
+      "Ġc ine",
+      "k iej",
+      "Ke vin",
+      "Ġmilligram s",
+      "×ĵ ר",
+      "ar iamente",
+      "Ġo ro",
+      "ĠH od",
+      "ert os",
+      "Ġli hat",
+      "Ġfull est",
+      "Ġgrand i",
+      "Ġб ок",
+      "Ġwho lly",
+      "Ġmahd oll",
+      "Ġcontro ll",
+      "ĠBun un",
+      "èĬ Ĥ",
+      "Ġdi pped",
+      "Ġreg ión",
+      "ĠÙĦ ÙĪ",
+      "Ġб аг",
+      "Ġpremi ers",
+      "Ġch á»ĭ",
+      "Ġ æīĢ以",
+      "è± Ĩ",
+      "ide z",
+      "Ġqu ota",
+      "Ġg hee",
+      "ark an",
+      "Ġgel atin",
+      "ĠCler k",
+      "bb les",
+      "ĠPa ige",
+      "Ġst aged",
+      "Ġsoci ais",
+      "ĠBiz im",
+      "Ġveloc idade",
+      "Ġmal aria",
+      "Ġshort ened",
+      "Ġsal ut",
+      "ĠHe he",
+      "Ġv á»ĭ",
+      "ĠTaiwan ese",
+      "ĠAr ri",
+      "g res",
+      "åİ» äºĨ",
+      "( )",
+      "ri ad",
+      "ij IJë",
+      "Ġãģ¾ ãģĻ",
+      "Ġmascul inity",
+      "L P",
+      "Ġëĸ ¡",
+      "Ġtér min",
+      "ĠV ä",
+      "ĠSe iten",
+      "Ġrespect fully",
+      "á o",
+      "Ġtotal ement",
+      "Ġscra ps",
+      "Ġinf ring",
+      "ĠB ose",
+      "am ar",
+      "ĠLu iza",
+      "ĠAR M",
+      "ĠплоÑħ о",
+      "Ġme illä",
+      "ĠD ion",
+      "å¼Ģ å§ĭ",
+      "Ġsou ha",
+      "Ġgesch afft",
+      "Ġconv olution",
+      "ĠâĢij âĢij",
+      "Ġ14 4",
+      "ling t",
+      "Ġmänn isk",
+      "Ġgust ado",
+      "Ġco ined",
+      "ĠL ulu",
+      "å®ĥ çļĦ",
+      "op ot",
+      "ĠPr ayer",
+      "Ġroast ing",
+      "Ġchromos omes",
+      "é£ ¯",
+      "ел е",
+      "Bl ue",
+      "ĠEr folg",
+      "èĩª çĶ±",
+      "ĠпÑĢид Ñĥм",
+      "Ġrisk ing",
+      "ĠGuard ians",
+      "Ġ20 24",
+      "è se",
+      "ĠбÑĥд ÑĤо",
+      "Ġcons erve",
+      "ĠBr inging",
+      "ĠAst ra",
+      "à¹ĢภĤ",
+      "Ġкак ÑĥÑİ",
+      "resp ace",
+      "ĠÐŀ п",
+      "Ġвок ÑĢÑĥг",
+      "æħ ĭ",
+      "Ġmask ed",
+      "ĠSh y",
+      "ĠN im",
+      "end as",
+      "Ġíı¬ ìĿ¸",
+      "Ġ모 ìĸij",
+      "Ġvale ur",
+      "ĠNeg ro",
+      "ĠCD s",
+      "ink ling",
+      "Ġmont ón",
+      "ĠH ond",
+      "Re al",
+      "Ġfull ness",
+      "ĠWho ops",
+      "ĠSh ank",
+      "ĠBr an",
+      "Ġtransl uc",
+      "Ġer r",
+      "ĠGard ens",
+      "oy u",
+      "Ġaffirm ative",
+      "ä¸ĭ ä¸Ģ",
+      "Ġpot tery",
+      "l ive",
+      "ia u",
+      "m ount",
+      "Ġfluct uations",
+      "åŁ İ",
+      "ÃŃ em",
+      "Ġpul ses",
+      "Ġcrian ças",
+      "ία ÏĤ",
+      "Ġbast a",
+      "ENN IS",
+      "ĠкоÑĢ п",
+      "ĠF unk",
+      "Ġ éĢĻ",
+      "Ã¥r t",
+      "ĠзаÑĤ ем",
+      "Ġparas ites",
+      "ãĥ Ļ",
+      "Ġair flow",
+      "ĠX uan",
+      "G ülme",
+      "Ġblo oming",
+      "Ġm ummy",
+      "Ġba o",
+      "ĠCl ap",
+      "ant ics",
+      "sk in",
+      "cent ric",
+      "be fore",
+      "ĠRICH ARD",
+      "ĠH ahn",
+      "T AKE",
+      "ĠÑĤÑĢ еÑĤÑĮ",
+      "Ġpressure d",
+      "ĠKur z",
+      "ist i",
+      "ĠнаÑĪ его",
+      "Ġsemicondu ctor",
+      "ĠCl int",
+      "Ġpl up",
+      "ĠOrig in",
+      "ĠEv ents",
+      "Ġê±± ìłķ",
+      "mp fen",
+      "NE Y",
+      "ĠD W",
+      "Ġë¶ģ íķľ",
+      "Ġinform s",
+      "Ġfor sk",
+      "Ġam iga",
+      "ĠCinc inn",
+      "St r",
+      "Ġpar ish",
+      "Ġì» ¤íĶ",
+      "Ġsiz i",
+      "Ġplant ation",
+      "Ġbl iver",
+      "Ġпол иÑĤ",
+      "Ġsubd iv",
+      "Ġr ant",
+      "Ġprincip als",
+      "åIJ ¦",
+      "Ġkun ne",
+      "ü gen",
+      "a respace",
+      "Ġv allahi",
+      "Ġcollaps ing",
+      "اÙĦ Ùħ",
+      "Ġl ider",
+      "Ġt ama",
+      "Ġg agner",
+      "ro lle",
+      "Ġë§IJìĶĢë ĵľë",
+      "Ġcath edral",
+      "ĠWe bs",
+      "ĠPolit ics",
+      "ãģĹ ãģ¾",
+      "ãģ£ãģ¦ ãĤĭ",
+      "ĠDen is",
+      "Ġtu o",
+      "Ġref ract",
+      "Ġdis integr",
+      "st es",
+      "ĠлÑİб ов",
+      "Ġw ilt",
+      "Ġtrust s",
+      "Ġkom un",
+      "ĠB asket",
+      "~ !!",
+      "na e",
+      "ĠÐļ ол",
+      "Ġsyll ables",
+      "ĠHen ri",
+      "ĠN ab",
+      "ÙĪ ع",
+      "Ġw n",
+      "Ġk amp",
+      "ĠPrag ue",
+      "ĠBreak fast",
+      "Ġê·¸ëŁ ´",
+      "Ġch ut",
+      "Ġ3 30",
+      "ĠIndust ries",
+      "ä¸į 管",
+      "ĠiÅŁ i",
+      "ĠGold man",
+      "ĠÄ° ns",
+      "uss a",
+      "ith e",
+      "Ħ IJ",
+      "ĠSO UND",
+      "алÑĮ нÑĭм",
+      ". (",
+      "Ġго ÑĢаз",
+      "Ġdage gen",
+      "Ġë ®",
+      "Ġwait er",
+      "l ength",
+      "ĠÏĥ ÏĦα",
+      "Ġchunk y",
+      "S a",
+      "Ġrust y",
+      "ĠJud ith",
+      "7 50",
+      "Ġepo xy",
+      "ì¹ ł",
+      "åı ²",
+      "met ro",
+      "Ġreject ing",
+      "Ġsquish y",
+      "Ġplup art",
+      "Ġmé th",
+      "Ġasp iring",
+      "ĠD rama",
+      "Ġup lift",
+      "§Ī ëĭ¤",
+      "........ ........",
+      "ł¤ ìļĶ",
+      "Ġtéc nica",
+      "Ġpas ando",
+      "Th ose",
+      "ĠÑĢаз дел",
+      "Ġmedi ocre",
+      "ĠNic kel",
+      "Ġsuperhero es",
+      "Ġmission ary",
+      "ĠPare ce",
+      "Ġrot ational",
+      "Ġpret t",
+      "ãģĿãģĨ ãģĿãģĨ",
+      "Ġl ama",
+      "Ġcan yon",
+      "Ġbet er",
+      "ĠProv ost",
+      "Ġh vis",
+      "Ġde activ",
+      "ĠH els",
+      "pf licht",
+      "S omething",
+      "ĠPier ce",
+      "Ġê²Ģ ì°°",
+      "l ungen",
+      "Ġs izing",
+      "Ġlat itude",
+      "ĠNon etheless",
+      "om nia",
+      "ĠSab rina",
+      "ĠDynam ic",
+      "åĥ ¹",
+      "ont a",
+      "ìĨ IJ",
+      "Ġdirect ive",
+      "ĠDep ot",
+      "Ġfuel ed",
+      "Ġexp ire",
+      "Ġcom ún",
+      "ĠSe xual",
+      "ĠG ore",
+      "Ġrest less",
+      "ĠJ AKE",
+      "ÑĤеÑĢ еÑģ",
+      "ĠÑĤÑĢ ан",
+      "ĠHol z",
+      "å° Ĩ",
+      "ĠA ctor",
+      "æĿ ¯",
+      "c all",
+      "Ġemail ed",
+      "ĠPe ar",
+      "Ñĥд и",
+      "ÑĢ ал",
+      "Ġm Ãły",
+      "ĠCH EERING",
+      "å®ī åħ¨",
+      "Ġretail er",
+      "Ġprot r",
+      "Ġdisc arded",
+      "ĠH IS",
+      "Ġevangel ical",
+      "ĠEl se",
+      "Ġexpl ores",
+      "Ġcritic izing",
+      "if ik",
+      "Ġwh ipping",
+      "Ġop is",
+      "ous ed",
+      "F ree",
+      "ĠíĮ ¬",
+      "Ġm ics",
+      "run ning",
+      "O b",
+      "iti é",
+      "Ġneces ita",
+      "ĠDomin ican",
+      "ĠB agh",
+      "Ġtend encies",
+      "ĠMet ropolitan",
+      "Åij l",
+      "Ġзна ем",
+      "ĠZ am",
+      "ĠDead pool",
+      "ale ż",
+      "Ġinvestig ative",
+      "ĠPron unciation",
+      "Ġem ulate",
+      "Ġban co",
+      "Ġ- âĻª",
+      "åĪ »",
+      "Ġover arching",
+      "lich es",
+      "Ġвозв ÑĢаÑī",
+      "ĠSc ary",
+      "ĠK ia",
+      "åľ Ł",
+      "ron ting",
+      "inn ed",
+      "ĠÛģ ÙĪ",
+      "ìĪ ĺ를",
+      "ç¾İ åij³",
+      "w el",
+      "Ġë³ Ħë¡ľ",
+      "Ġunint ention",
+      "aa S",
+      "Ġnic est",
+      "ĠT esting",
+      "ĠIS IL",
+      "ogen ous",
+      "ĠØ Ł",
+      "Ġlie utenant",
+      "Ġbra uch",
+      "ĠT ir",
+      "d rive",
+      "Ġtoler ant",
+      "Ġshoot ers",
+      "ĠìĺĪë »IJ",
+      "æ® º",
+      "ont on",
+      "Ġter ia",
+      "iet et",
+      "R on",
+      "le igh",
+      "ga e",
+      "Ġol mak",
+      "ĠCl one",
+      "so ld",
+      "Ġskelet ons",
+      "Ġincumb ent",
+      "ом е",
+      "C ON",
+      "Ġle ven",
+      "Ġmillenn ials",
+      "Ġequ ator",
+      "ĠFed er",
+      "ĠAlex andra",
+      "Ġv rij",
+      "ĠHealth care",
+      "Ġíķ ij",
+      "Ġemphas izing",
+      "Ġdialog ues",
+      "Ġch illed",
+      "Ġpr ow",
+      "ĠPass ion",
+      "ĠLad en",
+      "ari est",
+      "aph rag",
+      "Ġadd itive",
+      "ĠSta at",
+      "ĠN ept",
+      "ĠH AM",
+      "à¹Ģ à¸Ń",
+      "day s",
+      "Ġíĸ Īëįĺ",
+      "Ġvo ila",
+      "ĠÑħ л",
+      "ĠDeuts che",
+      "qu ir",
+      "O pen",
+      "Ġr anged",
+      "Ġle vers",
+      "ĠMans ion",
+      "p ared",
+      "ĠTit ans",
+      "ato ire",
+      "Ġeng ages",
+      "ye z",
+      "n aden",
+      "Ġobst ruct",
+      "ĠEmm y",
+      "åķ Ĩ",
+      "° ¥",
+      "Ġtro ph",
+      "Ġtake aways",
+      "+ .",
+      "ty cznie",
+      "hés itez",
+      "Ġpod ÃŃa",
+      "Ġ주 ëĬĶ",
+      "Ġc itation",
+      "ĠAqu a",
+      "Ġdebug ging",
+      "в ан",
+      "Ġëĭ¹ ìĭł",
+      "ĠاÙĦ ÙĬ",
+      "Ġinstant aneous",
+      "ĠAut umn",
+      "Ġkep ada",
+      "Ġget an",
+      "h ini",
+      "ynth esis",
+      "ĠпеÑĢ и",
+      "ĠM aced",
+      "P ac",
+      "unt u",
+      "B ra",
+      "ĠгоÑĢаз до",
+      "Ġ195 9",
+      "ĠÑĤем пеÑĢ",
+      "Ġs ane",
+      "ĠO UR",
+      "as u",
+      "Ġ무ì Ĺ",
+      "Ġvalle ys",
+      "Ġlist ings",
+      "Ġprzed staw",
+      "Ġg ummy",
+      "Ġcort isol",
+      "ĠO brig",
+      "ĠAll ied",
+      "ож Ñĥ",
+      "Ġgén ér",
+      "Ġdoc s",
+      "ĠCh ili",
+      "ĠAb dullah",
+      "K it",
+      "Ġcontrib utors",
+      "го ÑĢ",
+      "л еÑĢ",
+      "Ġb inder",
+      "Ġmod èle",
+      "íħ IJ",
+      "Ġinte iro",
+      "m is",
+      "fer a",
+      "Ø§Ø °",
+      "M ania",
+      "ĠíĻ ľëıĻ",
+      "Ġë´IJ ìļĶ",
+      "ĠJ az",
+      "ç» ĵ",
+      "ÑĸлÑĮ ки",
+      "r ishna",
+      "Ġêµ °",
+      "Ġtaman ho",
+      "Ġappli ance",
+      "ĠRes istance",
+      "ĠL OOK",
+      "ĠHy p",
+      "ĠHe il",
+      "F ire",
+      "uj u",
+      "Ġhe als",
+      "Ġmal t",
+      "ĠVER Y",
+      "ĠÑħоÑĩ еÑĪÑĮ",
+      "Ġl inger",
+      "ĠN arr",
+      "ĠReg ular",
+      "ĠLo op",
+      "ĠL eno",
+      "Ġsort ie",
+      "ĠS erve",
+      "ĠìĿ µ",
+      "ĠL uego",
+      "itt ä",
+      "Ġund es",
+      "è³ ½",
+      "å¦Ĥæŀľ ä½ł",
+      "Ġslipp ers",
+      "Ġon da",
+      "ĠÄIJ ây",
+      "Ġtap ed",
+      "Ġtra verse",
+      "Ġrelat ivity",
+      "ĠY oshi",
+      "c jon",
+      "il ated",
+      "act ively",
+      "ĠС ов",
+      "æĪij è§īå¾Ĺ",
+      "ĠP OL",
+      "Ðł Ðĺ",
+      "infl amm",
+      "cheer ful",
+      "Ġ×ŀ× IJ×",
+      "Ġ>> [",
+      "min ster",
+      "Ġв ли",
+      "Ġident ifier",
+      "ĠLamb da",
+      "Ġtr os",
+      "Ġflaw less",
+      "Ġdetriment al",
+      "Ġbun ları",
+      "W ar",
+      "Ġreg ião",
+      "羣çļĦ æĺ¯",
+      "ĠB ike",
+      "cess ors",
+      "Ġc ùng",
+      "ĠR N",
+      "Ġê½ ĥ",
+      "Ġküç ük",
+      "ĠBegin ning",
+      "íĺ ¸ë",
+      "Ġge we",
+      "Ġden ote",
+      "ĠAlber to",
+      "Ġprob iot",
+      "Ġo de",
+      "Ġmol ar",
+      "Ġburst ing",
+      "ass umed",
+      "Ġfoot prints",
+      "ved a",
+      "Ġstero ids",
+      "Ġfl aming",
+      "ĠE ller",
+      "Ġerk ennen",
+      "ät zen",
+      "Ġlife cycle",
+      "ĠD OU",
+      "ĠK arena",
+      "ĠGuer ra",
+      "è¿ĺ æĺ¯",
+      "Ġsin ister",
+      "Ġpod éis",
+      "Ġpar ab",
+      "Ġok o",
+      "Ġmat éri",
+      "Ġcar ic",
+      "son aro",
+      "Ġpratic amente",
+      "ÑĥÑģ а",
+      "Ġcomun que",
+      "Ġvig ilant",
+      "Ġreg imes",
+      "ĠShoot ing",
+      "Ġra ids",
+      "ĠN ora",
+      "ĠW ieder",
+      "m ens",
+      "ĠÑģ од",
+      "Ġê²½ìļ° ìĹIJëĬĶ",
+      "Ġв Ñħод",
+      "Ġaut obi",
+      "ĠS chn",
+      "ĠRob bie",
+      "ĠF itness",
+      "Ġкон ÑĦ",
+      "Ġpeng uin",
+      "моÑĤÑĢ Ñı",
+      "Ġми ним",
+      "play s",
+      "Ġdeleg ates",
+      "M er",
+      "Ġsist em",
+      "ĠMicha els",
+      "m ale",
+      "ا ع",
+      "Ġcá ch",
+      "ĠH ä",
+      "Ġ×Ļ ×ķ×ĵ×¢",
+      "Ġsuper power",
+      "Ġstr on",
+      "Ġro ver",
+      "Ġdé pend",
+      "éĻ ³",
+      "Ġret iring",
+      "Ġvamp ires",
+      "Ġmer de",
+      "ĠCh anging",
+      "Ġt ame",
+      "Ġspokes person",
+      "Ġc ay",
+      "Ġfl irting",
+      "ĠGr ö",
+      "Ġw är",
+      "Ġwy b",
+      "Ġcoe ur",
+      "ạ nh",
+      "ĠìĻĢ ìĦľ",
+      "Ġconna is",
+      "ĠHundred s",
+      "ĠBe a",
+      "Ġα ÏĢ",
+      "pr uch",
+      "Ġsocied ade",
+      "ĠWh ilst",
+      "ĠK ait",
+      "esp ace",
+      "Ġch ia",
+      "ĠEr m",
+      "Ġë°Ķ ê¿",
+      "Ġf ences",
+      "ĠM ortal",
+      "ê² ģ",
+      "Ġг ÑĢаÑĦ",
+      "ĠHom eland",
+      "ĠJ UN",
+      "is st",
+      "Ġpar lar",
+      "Ġsport y",
+      "é o",
+      "Ġdeep en",
+      "ĠBeh avior",
+      "éĢ ı",
+      "åĵĪåĵĪ åĵĪ",
+      "Ġer rand",
+      "Ġrot ary",
+      "ĠWell ington",
+      "W ind",
+      "Ġmes ela",
+      "ả ng",
+      "iend e",
+      "Ġex cell",
+      "ĠGen ius",
+      "ĠEdu ardo",
+      "æľī 人",
+      "ĠÅŁ unu",
+      "ĠÄ° stanbul",
+      "Ġprod uto",
+      "Ġ ãħİãħİ",
+      "O FF",
+      "Ġwoll t",
+      "çĪ Ĩ",
+      "Ġëī´ì Ĭ¤",
+      "Ġl ass",
+      "Ġher tz",
+      "Ġar omatic",
+      "Ġзв он",
+      "Ġaut oc",
+      "ĠL ust",
+      "Ġ11 2",
+      "ĠÎ Ĺ",
+      "Ġreview ers",
+      "Ġrecept ive",
+      "å°į äºĨ",
+      "â nd",
+      "og lo",
+      "ĠìķĦëĭ Ļ",
+      "Ġn go",
+      "Ñĸ ÑĤи",
+      "Ã¥ t",
+      "con o",
+      "Ġtek rar",
+      "Ġ주 ê³ł",
+      "Ġgel miÅŁ",
+      "Ġbed time",
+      "ĠAr gh",
+      "AD A",
+      "ĠгоÑĢод а",
+      "ĠÄ ĩ",
+      "Ġall iances",
+      "g iggling",
+      "Ġyer de",
+      "Ġsp ies",
+      "Ġg utes",
+      "ç i",
+      "Ġallt id",
+      "ĠL ah",
+      "ŀ IJë",
+      "Ġdo kÅĤad",
+      "ÙĪ ÙĬ",
+      "Ġtoxic ity",
+      "Ġcancell ation",
+      "Ġ195 8",
+      "d ro",
+      "Ġìŀij ìĿĢ",
+      "ĠMotor ola",
+      "Ġmult in",
+      "Ġenthusi asts",
+      "ĠM ighty",
+      "ĠCoc onut",
+      ": ãĢĮ",
+      "ĠPict ures",
+      "Ġsang re",
+      "Ġbl inking",
+      "ol esome",
+      "ĠìĬ¤íĥĢ ìĿ¼",
+      "F P",
+      "Ġboom ing",
+      "ĠдеÑģÑı ÑĤ",
+      "Ġr atchet",
+      "Ġtim elines",
+      "len ess",
+      "Ġc ages",
+      "ĠGood night",
+      "omet imes",
+      "Ġc unning",
+      "ĠR isk",
+      "ul ed",
+      "d ade",
+      "Ġpr ata",
+      "Ġgust arÃŃa",
+      "am us",
+      "ĠJin ping",
+      "Ġest rut",
+      "Ġdescob rir",
+      "ĠM Äģ",
+      "ĠAll an",
+      "Ġ åĪĨ",
+      "Ġ×ľ× §",
+      "Ġpres erv",
+      "ĠStraw berry",
+      "Ä ı",
+      "L u",
+      "Ġk ro",
+      "ĠRep orts",
+      "ìħĶ ìķ¼",
+      "Ġval t",
+      "Ġpouv ait",
+      "Ġapp ar",
+      "ĠB one",
+      "Ġprefer ably",
+      "ĠRep ública",
+      "å°± åĪ°",
+      "Ġher zlich",
+      "Ġchim ney",
+      "Ġç ev",
+      "Ġvis as",
+      "Ġver r",
+      "Ġcultiv ation",
+      "ĠArmen ia",
+      "Ġвд ÑĢÑĥг",
+      "Ġcock ro",
+      "retch ed",
+      "art z",
+      "ĠлÑİд Ñıм",
+      "ĠpolÃŃt icas",
+      "ĠP anz",
+      "ĠA KA",
+      "ĠëĪ Į룬",
+      "Ġer ro",
+      "Ġcam per",
+      "Ġ10 2",
+      "ठ¸",
+      "d one",
+      "Ġho ard",
+      "ĠÐŁÐ¾ÑĤ ом",
+      "je ong",
+      "Ġdest a",
+      "p ak",
+      "Ġin im",
+      "Ġgrow ers",
+      "ĠMess age",
+      "Ġele ctor",
+      "eng age",
+      "ĠFor bes",
+      "ĠCincinn ati",
+      "Ġdiffé rence",
+      "d f",
+      "Ġsp ar",
+      "Ġawait s",
+      "ĠUSS R",
+      "ĠR ising",
+      "ĠHo ÅŁ",
+      "Ġfoot ing",
+      "Ġcond iciones",
+      "ÑĤоÑĢ ов",
+      "Ġclin ician",
+      "ĠDisk uss",
+      "å£ ĵ",
+      "ר ×Ĵ",
+      "× ¥",
+      "ite it",
+      "g ren",
+      "Ġchar isma",
+      "Ġle uke",
+      "Ġirrit ating",
+      "Ġcir ca",
+      "ĠRhod es",
+      "Ġp ior",
+      "Ġhandic ap",
+      "roy able",
+      "Ġv ull",
+      "O G",
+      "Ġin ÃŃcio",
+      "ier i",
+      "Ġspl ashing",
+      "Ġdem ise",
+      "Ġassist ir",
+      "Ñĩ ÑĤо",
+      "Ġcover t",
+      "ĠG ud",
+      "ภī",
+      "kl är",
+      "ĠìŀIJ 꾸",
+      "Ġver ändert",
+      "ĠR EM",
+      "ĠCon ven",
+      "at ge",
+      "Ġpierws ze",
+      "Ġcler gy",
+      "ling ton",
+      "l iv",
+      "V PN",
+      "ĠÑģ ожал",
+      "ĠH ate",
+      "ãģ¨ ãģĵãĤį",
+      "ÏĨ ο",
+      "ĠResp ons",
+      "оз д",
+      "Ġet mek",
+      "Ġchem in",
+      "Ùħ Ø©",
+      "Ġê°Ģ 족",
+      "T re",
+      "Ġum as",
+      "ĠBur ton",
+      "Ġpatri arch",
+      "ĠSmithson ian",
+      "¥ ĺ",
+      "M oon",
+      "A ir",
+      "Ġmed ios",
+      "Ġer aser",
+      "Ġwoll ten",
+      "Ġpare il",
+      "ĠBill ie",
+      "æĬ ½",
+      "еÑĢÑĤ в",
+      "Ġparl ament",
+      "Ġag ony",
+      "ĠQU E",
+      "sequ ently",
+      "An other",
+      "ĠWh ew",
+      "ĠAnn ual",
+      "Ġse ben",
+      "ìĥģ ìĿĦ",
+      "val ues",
+      "ŀľë §Į",
+      "Ġsin on",
+      "ere al",
+      "ĠEn light",
+      "ĠChem istry",
+      "ĠCatal unya",
+      "Ġdoct r",
+      "ant on",
+      "Ġst uk",
+      "ĠPl ate",
+      "ĠKardash ian",
+      "Ġfil os",
+      "ĠW et",
+      "Ġпоп ÑĭÑĤ",
+      "Ġunknown s",
+      "ĠSch on",
+      "ĠBald win",
+      "Ġtelescop es",
+      "ĠG ucci",
+      "ox ide",
+      "ĠConserv ative",
+      "ìĦ± ìĿĦ",
+      "Ġhina us",
+      "P ower",
+      "Ġê±´ ê°ķ",
+      "Ġprev ail",
+      "orm an",
+      "m achine",
+      "Ġ194 6",
+      "Ġun bel",
+      "Ġsch aut",
+      "Ġp iel",
+      "e enth",
+      "Ġobject ively",
+      "Ġch akra",
+      "aud io",
+      "Ġch icos",
+      "ĠV ault",
+      "å° Ī",
+      "Ġmedic inal",
+      "ĠT ail",
+      "Wh ile",
+      "Ġas phalt",
+      "Ġfro ze",
+      "ĠE K",
+      "unch ing",
+      "n osis",
+      "20 15",
+      "ĠG ri",
+      "Ġodd ly",
+      "ĠM är",
+      "ĠA eg",
+      "c olo",
+      "P ar",
+      "Ġëĵ¤ ìĸ´ë",
+      "Ġv inden",
+      "ĠO VER",
+      "Ġ iced",
+      "Ġsc orp",
+      "Ġha c",
+      "qual ified",
+      "ĠÑĥвид еÑĤÑĮ",
+      "erm o",
+      "H EN",
+      "Ġso i",
+      "Ġmulti ples",
+      "Ġlay outs",
+      "Ġblind ness",
+      "ĠB owser",
+      "Ġпод ÑĤ",
+      "ĠÃ İ",
+      "vention al",
+      "Ġm ata",
+      "mad ı",
+      "Ġge ez",
+      "Ġcad ence",
+      "Ġważ ne",
+      "ĠChrist ie",
+      "ven ge",
+      "C all",
+      "Ġturn around",
+      "Ġblo b",
+      "ĠЯ к",
+      "ĠVoice over",
+      "Ġper il",
+      "ĠJa ime",
+      "ĠH OY",
+      "l ane",
+      "Ġse bel",
+      "ĠDu o",
+      "ĠHistor ical",
+      "Ġd ni",
+      "Ġg ema",
+      "y k",
+      "Ġsab em",
+      "ắ ng",
+      "Ġv ars",
+      "ĠRon nie",
+      "ĠRon aldo",
+      "ĠPer què",
+      "ns inn",
+      "h air",
+      "Ġrelent less",
+      "Ġl yn",
+      "Ġtravel er",
+      "æĢİ麼 äºĨ",
+      "n ine",
+      "Ġant im",
+      "Ġì¼ Ģ",
+      "Ġsnow ball",
+      "ĠÑħаÑĢ акÑĤеÑĢ",
+      "Ġintern s",
+      "Ġconstitu ency",
+      "ĠÐĿ ам",
+      "׾ ׾",
+      "V EL",
+      "Ġvikt igt",
+      "Ġap oyo",
+      "ÙĦ ب",
+      "Ġj ard",
+      "Ġheight ened",
+      "ÑĢо ÑģÑĤ",
+      "ĠSM ITH",
+      "Ġдел а",
+      "Ġrepair ing",
+      "Ġr igt",
+      "ĠShe ikh",
+      "ĠBrit ney",
+      "Ġevery time",
+      "Ġadvent urous",
+      "oc key",
+      "er nt",
+      "Ġat aque",
+      "ĠAltern atively",
+      "e ffect",
+      "Ġpalav ras",
+      "ĠElli ott",
+      "Ġréuss i",
+      "Ġhypert ension",
+      "ĠMan ual",
+      "Ġproph etic",
+      "Ġhand c",
+      "ÑĮ е",
+      "Ġref rain",
+      "ĠSqu id",
+      "ìŀ ¡",
+      "Ġком ан",
+      "äll en",
+      "Ġlleg ó",
+      "Ġbas h",
+      "ion y",
+      "ĠÑģк лад",
+      "Ġк аб",
+      "Ġcare less",
+      "ĠP ool",
+      "Ġtr ás",
+      "Ġfil s",
+      "ĠSch r",
+      "Ġsp rawd",
+      "ĠMon aten",
+      "Ġunfor gettable",
+      "ĠCott on",
+      "Ġinconven ient",
+      "ĠR X",
+      "or is",
+      "Ġhum bled",
+      "ת ×Ĺ",
+      "ĠØ¢ Ù¾",
+      "Ġincre ÃŃ",
+      "ĠKomment are",
+      "èĪ Ĵ",
+      "r ación",
+      "Ġv antage",
+      "ĠSe al",
+      "ĠìĿ´ 거를",
+      "Ġjou e",
+      "ãģĿãģĨ ãģ§ãģĻãģŃ",
+      "Ġìĺ¤ë ŀĺ",
+      "ĠиÑģп ÑĭÑĤ",
+      "ob en",
+      "Ġgr ate",
+      "Ġcontro le",
+      "ĠPer cy",
+      "ÅĤ ada",
+      "Ġsimult aneous",
+      "Ġprot oty",
+      "ĠgroÃŁ er",
+      "Ġbew usst",
+      "iniz i",
+      "Ġpass ieren",
+      "ĠHapp iness",
+      "åī ĩ",
+      "sh i",
+      "ge ht",
+      "Ġstation ed",
+      "ĠErgeb nis",
+      "Ġdirect amente",
+      "Ġsurv ives",
+      "Ġperson es",
+      "BER G",
+      "Ġvom iting",
+      "Ġconhe cer",
+      "Ġad jour",
+      "ĠCiv ic",
+      "pe i",
+      "bur st",
+      "Ġëĭ¤ ëĭĪ",
+      "é ı",
+      "Ġsl ed",
+      "Ġplataform a",
+      "ĠS ect",
+      "ĠDe fin",
+      "çĻ» éĮ²",
+      "én om",
+      "chn et",
+      "Ġprofit ability",
+      "Ġerre icht",
+      "á»ı i",
+      "c ation",
+      "Ġì§Ģ ê¸",
+      "Ġperd re",
+      "Ġfel ony",
+      "Ġ195 7",
+      "æĪij å¾Ī",
+      "Ġunsuccess ful",
+      "Ġnag yon",
+      "Ġelastic ity",
+      "Ġfac ade",
+      "Ġearth ly",
+      "ĠамеÑĢик ан",
+      "Ġcon n",
+      "c la",
+      "D u",
+      "Ġpolit iques",
+      "Ġhal o",
+      "iant es",
+      "Ġмо ей",
+      "ãĥ³ ãĥī",
+      "ton es",
+      "el ier",
+      "è® ļ",
+      "ht aking",
+      "Ġwicht ige",
+      "Ġan no",
+      "ĠL ok",
+      "ill ions",
+      "Ġv iver",
+      "Ġsol chen",
+      "Ġsu f",
+      "ĠSal z",
+      "ĠN vidia",
+      "z uge",
+      "ĠSp ike",
+      "V ideo",
+      "Ġtw or",
+      "ĠA la",
+      "èij ī",
+      "Ġh anya",
+      "ĠAd m",
+      "ìĿ µ",
+      "ĠPatient en",
+      "ĠOn ion",
+      "ĠKo be",
+      "ĠSc ene",
+      "ĠR ash",
+      "æ¨ Ļ",
+      "ÑĢа ÑģÑĤ",
+      "ist ani",
+      "Gen eral",
+      "le ye",
+      "imb ap",
+      "Ġconce aled",
+      "ĠFr idays",
+      "ĠW ool",
+      "Ġнов ÑĭÑħ",
+      "ش ر",
+      "Ġê²° ê³¼",
+      "Ġjed och",
+      "´ìĭ ľ",
+      "ĵ¤ ëıĦ",
+      "Ġìŀ¥ ëĤľ",
+      "uk t",
+      "L ou",
+      "Ġ먹 ìĸ´",
+      "ĠEx pect",
+      "Ġдом ой",
+      "Ġirrespons ible",
+      "Ġac erca",
+      "ĠZ ust",
+      "ר ×ĺ",
+      "U I",
+      "Ġyout ubers",
+      "ĠPos itive",
+      "Ġsoci oe",
+      "Ġsn atch",
+      "èĥ Į",
+      "Ġrefresh ed",
+      "Ġnom inations",
+      "ĠP att",
+      "Ġobsol ete",
+      "Ġdem iÅŁ",
+      "åı ¤",
+      "orm uÅŁ",
+      "ĠìĨĶì§ģ íŀĪ",
+      "Ġf la",
+      "Ġcra ziest",
+      "ĠZ ie",
+      "ĠT ú",
+      "z ep",
+      "ic em",
+      "Ġë©ĭ ìŀĪ",
+      "Ġcyn ical",
+      "ãģĿ ãĤĵãģª",
+      "Ġt resp",
+      "Ġcra z",
+      "Õ¥ Õ",
+      "Ġne lle",
+      "Ġm ph",
+      "ĠN ered",
+      "ĠK ob",
+      "ĠE ck",
+      "¨¸ ëĭĪ",
+      "J an",
+      "ĠТ огда",
+      "Ġde ci",
+      "ĠV og",
+      "Ġbubb ling",
+      "éĢ Ģ",
+      "ú a",
+      "Ġproduct os",
+      "iber al",
+      "Ġrepl icated",
+      "ĠImp rove",
+      "ill ary",
+      "C ha",
+      "Ġré du",
+      "ĥIJ íķĺë©´",
+      "Ġcon not",
+      "ĠK rit",
+      "ĠдÑĥÑħ ов",
+      "Ġtread mill",
+      "ĠP W",
+      "Ġзов ÑĥÑĤ",
+      "Ġcl ams",
+      "Ġdra fting",
+      "Ġ195 6",
+      "un ta",
+      "Ġexpend itures",
+      "ĠHoo ver",
+      "W OO",
+      "ÑĪе е",
+      "Ġded uction",
+      "mon ary",
+      "Ġreci b",
+      "Ġpo vo",
+      "Ġëį Ķë",
+      "ĠP AL",
+      "ĠBl ow",
+      "Ġwy p",
+      "Ġdest ac",
+      "de al",
+      "Gra eme",
+      "Ġnécess aire",
+      "Ġdamn ed",
+      "Ġ19 38",
+      "Ġìĭ¤ ìłľë¡ľ",
+      "Ġtro op",
+      "Ġinsight ful",
+      "ĠT J",
+      "ĠоÑģ в",
+      "Ġf idelity",
+      "ĠSk ip",
+      "ĠMay o",
+      "ë§ Ŀ",
+      "app e",
+      "Ġbl as",
+      "ĠW Y",
+      "ĠG N",
+      "ct ar",
+      "S u",
+      "Ġcu ent",
+      "he ws",
+      "Ġcorps es",
+      "A bs",
+      "Ġwaste water",
+      "Ġc iek",
+      "ĠOn u",
+      "Ġexplos ives",
+      "Ġar ma",
+      "ĠSTEP HAN",
+      "polit ik",
+      "ĠOs aka",
+      "ta ÅĤ",
+      "Ġyap ıyor",
+      "Ġiz quier",
+      "Ġbele za",
+      "ĠWy att",
+      "åIJ ¸",
+      "Ġsu k",
+      "Ġspec jal",
+      "Ġdan ke",
+      "wh istle",
+      "ĠfÃŃs ica",
+      "ĠHar riet",
+      "ĠìķĦ íĮĮ",
+      "Ġwill kommen",
+      "ip ing",
+      "ĠÑģмоÑĤÑĢ иÑĤе",
+      "Ġмож еÑĪÑĮ",
+      "Ġinacc urate",
+      "Ġarrog ance",
+      "ĠRem o",
+      "γ ά",
+      "ass ed",
+      "Ġdeliver ies",
+      "Ġst inky",
+      "ĠпеÑĢ еж",
+      "j ay",
+      "Ġtrans itional",
+      "Ġr ere",
+      "ĠNGO s",
+      "ĠAT M",
+      "خ ت",
+      "i ology",
+      "Ġв лад",
+      "Ġsch me",
+      "ĠSh ine",
+      "ìķ ¡",
+      "p ants",
+      "Ġser ge",
+      "Ġsen hor",
+      "Ġab duct",
+      "ĠBry ant",
+      "V ES",
+      "Ġawak ened",
+      "ĠL az",
+      "rop olis",
+      "ĠLa o",
+      "è¾Ľ èĭ¦",
+      "Ġvill a",
+      "Ġsumm ers",
+      "Ġent hal",
+      "Ġ194 9",
+      "V ia",
+      "Ġìĸ´ì ¨",
+      "Ġtend on",
+      "Ġviol et",
+      "Ġintellect ually",
+      "Ġboun ced",
+      "ara us",
+      "Ġ19 19",
+      "Ġvra ag",
+      "Ġsp el",
+      "ĠSch war",
+      "Sc ott",
+      "ĠInd o",
+      "Ġë§ Ŀ",
+      "Ġcanon ical",
+      "ĠI KE",
+      "Ġthat ÃŃs",
+      "Ġme llan",
+      "æ¯ Ĵ",
+      "ig mat",
+      "C ould",
+      "... ?)",
+      "Ġfo arte",
+      "ĠKum ar",
+      "rend o",
+      "Ġél é",
+      "à ´",
+      "val uation",
+      "c ases",
+      "Ġintuit ively",
+      "h ong",
+      "ett ed",
+      "Ġsou ven",
+      "Ġmor b",
+      "Ġc ors",
+      "ĠN V",
+      "ĠHas an",
+      "æĥħ åĨµ",
+      "ie ved",
+      "Ġì§Ģê¸Ī ìĿĢ",
+      "Ġdum pling",
+      "Ġcontr ôle",
+      "Ġambigu ity",
+      "æ©Ł æľĥ",
+      "Ġco g",
+      "ĠScript ures",
+      "Ġc ai",
+      "Ġbe ver",
+      "大家 éĥ½",
+      "Ġhu is",
+      "Ġa ime",
+      "Ġerkl ären",
+      "ĠL M",
+      "ĠF ey",
+      "éļ ¾",
+      "ற த",
+      "Ġsuper vised",
+      "Ġje we",
+      "s pl",
+      "ĠÑĨенÑĤ ÑĢ",
+      "Ġcoll isions",
+      "ÙĦ Ùģ",
+      "ĠHog warts",
+      "ĠDur ham",
+      "×ķ× £",
+      "Ġphosph ate",
+      "Ġoverse e",
+      "Ġinspect ions",
+      "Ġbr inc",
+      "ĠZ ak",
+      "Ġpay off",
+      "Ġch aud",
+      "ĠHung er",
+      "ã os",
+      "v ir",
+      "Ġf iance",
+      "Ġb oug",
+      "l ived",
+      "c ry",
+      "åĽŀ ä¾Ĩ",
+      "Ġjoint ly",
+      "Ġgirl friends",
+      "ĠNe xus",
+      "¦¬ ê²łìĬµëĭĪëĭ¤",
+      "ĠK wang",
+      "åĵĪ åĽī",
+      "å§ ij",
+      "ÅĤ ÄĻ",
+      "ĠN eden",
+      "ie ce",
+      "Ġins erting",
+      "æŁ ĵ",
+      "ĠM ummy",
+      "ĠGlo be",
+      "Ġle e",
+      "Ġg erman",
+      "Ġcre ams",
+      "ach o",
+      "Ġch Æ°a",
+      "ĠGal ile",
+      "Ġfür s",
+      "Ġest iver",
+      "c idos",
+      "Christ ian",
+      "Ġlors qu",
+      "Ġcut est",
+      "v ale",
+      "ĠкÑĢ еп",
+      "Ġw ary",
+      "Ġslic ing",
+      "Ġesper ando",
+      "ĠV ander",
+      "ĠDe ixa",
+      "Ġ195 4",
+      "Ġmów iÄħ",
+      "Ñĸ ÑĶ",
+      "Ġtool ing",
+      "Ġrest or",
+      "Ġpos ición",
+      "Ġintent ar",
+      "ĠAp ache",
+      "OU L",
+      "ĠÙĪ ب",
+      "Ġmat ière",
+      "ãĥ¼ ãĤĵ",
+      "Ġl inen",
+      "Ġestrat ég",
+      "ĠMut ta",
+      "é¡ ¯",
+      "è¡Į äºĨ",
+      "Ġpart ing",
+      "Ġminim izing",
+      "Ġapp rendre",
+      "æľ Ŀ",
+      "Ġан глий",
+      "ĠDo o",
+      "ĠFire fox",
+      "c ómo",
+      "Ġge opolit",
+      "Ġmak an",
+      "Ġmog elijk",
+      "ĠÏĢε Ïģι",
+      "Ġcá» ©",
+      "Ġinstall er",
+      "Ġdib uj",
+      "ĠHe ath",
+      "lo op",
+      "ĠBro ken",
+      "HY UN",
+      "sh elf",
+      "Ġf izer",
+      "Ġenh ances",
+      "ä¾ĭ ãģĪãģ°",
+      "Ġдо ÑģÑĤи",
+      "ĠP UB",
+      "ĠKolleg in",
+      "Ġatt ained",
+      "Ä ¾",
+      "Ġmist ress",
+      "ĠOft entimes",
+      "×ŀ ×Ļ×Ŀ",
+      "Ġbe we",
+      "ĠS ora",
+      "ra uen",
+      "ba um",
+      "Ġroll ers",
+      "Ġm ering",
+      "ĠP AC",
+      "Ġн Ñĸ",
+      "ĠRép ublique",
+      "ĠÑĤ ÑĢав",
+      "ĠV anguard",
+      "uc iones",
+      "Ġ무ë ĮĢ",
+      "Ġg our",
+      "¯ ¤",
+      "ĠÏ ī",
+      "Ġsa una",
+      "Ġpe ine",
+      "ĠVal erie",
+      "ĠS ikh",
+      "fend imiz",
+      "ber o",
+      "ĠÑĩ и",
+      "Ġdo ÅĽwiad",
+      "ĠE uros",
+      "Ġcomment aires",
+      "Ġtwe aks",
+      "ĠF aster",
+      "ĠÑĢаÑģ к",
+      "Ġprogress ively",
+      "ĠE uch",
+      "bor o",
+      "ĠIng red",
+      "C ap",
+      "Ġun check",
+      "Ġìĺ¤ë ¥¸",
+      "Ġw re",
+      "ĠF T",
+      "ör ung",
+      "Ġmemor ized",
+      "ĠD inner",
+      "ĠP hew",
+      "ou bl",
+      "Ġput a",
+      "Ġadm its",
+      "ез де",
+      "op od",
+      "Ġpand a",
+      "Ġhing es",
+      "ci pe",
+      "Ġtrans act",
+      "Ġpod ia",
+      "Ġp ics",
+      "Ġcriter ion",
+      "ĠOrchest ra",
+      "ĠBl og",
+      "Ġsolem n",
+      "ĠPix ar",
+      "Th ree",
+      "Ġв низ",
+      "ĠVol unte",
+      "ĠSav age",
+      "ĠPV C",
+      "ĠC af",
+      "Ġwy kon",
+      "Ġgrad ers",
+      "Ġcr ouch",
+      "Ġcl iche",
+      "Ġsoy beans",
+      "ĠM UR",
+      "ĠGonz alez",
+      "ĠM imi",
+      "ĠBol sonaro",
+      "Ġdi aphrag",
+      "Ġbil ang",
+      "ëIJĺ ëĬĶ",
+      "éĤ£ æĪijåĢij",
+      "Ġregul ating",
+      "M c",
+      "J udge",
+      "Ġн ож",
+      "Ġjak Äħ",
+      "ites se",
+      "ĠW ij",
+      "Ġl ata",
+      "gro aning",
+      "POS ING",
+      "Ġ×IJ×ķת ×ķ",
+      "Ġha ga",
+      "Ġground ing",
+      "Ġviol ently",
+      "Ġt ills",
+      "Ġeng ag",
+      "ĠHo llow",
+      "Ġпоп ÑĥлÑıÑĢ",
+      "Ġw prowad",
+      "Ġrepl aces",
+      "Ġfluores cent",
+      "urg ical",
+      "igg ly",
+      "ĠTrad itional",
+      "t te",
+      "ĠÙĦ Ùĩ",
+      "Ġphosph orus",
+      "Ġapr on",
+      "ĠWat ers",
+      "ĠK ultur",
+      "ав ай",
+      "Ġol ives",
+      "Ġ×Ķ×IJ× ľ",
+      "Ġteil weise",
+      "Ġsen cill",
+      "Ġprend s",
+      "Ġnarr ower",
+      "Ġj ätte",
+      "ĠInformation en",
+      "ìĥģ ìĿ´",
+      "Ġstar ve",
+      "Ġfr ick",
+      "ĠBe weg",
+      "ठ²",
+      "Ġdolph in",
+      "ĠLAUGH TER",
+      "ĠINTER VIE",
+      "åĶ ī",
+      "Ġyan lÄ±ÅŁ",
+      "Ġtor pedo",
+      "Ġshort ages",
+      "ìĿ´ë ĵľ",
+      "ıld ı",
+      "Ġp aws",
+      "Ġo zone",
+      "Ġcultiv ated",
+      "ĠF ot",
+      "Ġnot or",
+      "н оз",
+      "Ġко ÑĪ",
+      "Ġtouch screen",
+      "ĠAll y",
+      "æľĢ è¿ij",
+      "Ġ맼ìŀĪ ìĸ´ìļĶ",
+      "ĠС еÑĢ",
+      "Ġв полне",
+      "Ġpap rika",
+      "ĠDust in",
+      "Ġefect o",
+      "Ġop ini",
+      "Ġmu ut",
+      "Ġhá»į c",
+      "Ġinter ject",
+      "ÄĻ t",
+      "Ġbut ts",
+      "ure z",
+      "ĠP ike",
+      "ĠH ok",
+      "ĠGu inea",
+      "ĠCath edral",
+      "Ġ14 00",
+      "C ra",
+      "+ ,",
+      "ë§ Ľ",
+      "³´ë ıĦë¡Ŀ",
+      "aby rin",
+      "Ġvide og",
+      "Ġо ÑĢÑĥж",
+      "Ġu ž",
+      "Ġbus cando",
+      "ĠAss istance",
+      "éĻ ½",
+      "Ġmel hores",
+      "ì¡ ´",
+      "Ġëģ ¼",
+      "ĠR J",
+      "Ġت Ùħ",
+      "Ġo min",
+      "Ġmotor cycles",
+      "ĠS app",
+      "Ġsupply ing",
+      "ĠAl gun",
+      "Ġaer ospace",
+      "×¢ ׾",
+      "oc cup",
+      "le ist",
+      "Ġê±° ëĬĶ",
+      "Ġcomplet a",
+      "b res",
+      "! (",
+      "ĠÐŁÑĢ ед",
+      "Ġdisadvant aged",
+      "ĠAtt end",
+      "ĠJud ah",
+      "á»ĭ ch",
+      "yl ene",
+      "act ly",
+      "Ġset ups",
+      "Ġammon ia",
+      "ĠSchwe iz",
+      "ĠSh ame",
+      "Ġband e",
+      "ĠF uel",
+      "Ġtroubles ome",
+      "Ġnum ero",
+      "ĠM OM",
+      "ĠпÑĢед лаг",
+      "ment ioned",
+      "ĠболÑĮÑĪ ое",
+      "ĠVikt or",
+      "ĠSty les",
+      "Ġcruc ified",
+      "ructure d",
+      "en viron",
+      "Ġmor als",
+      "Ġmed itating",
+      "Ġax ial",
+      "is ance",
+      "ĠAb st",
+      "G reen",
+      "Ġê± ´ì",
+      "Ġquad rant",
+      "Ġper gi",
+      "Ġcamer aman",
+      "ĠSe qu",
+      "Ġpa used",
+      "ĠLa ughing",
+      "ê· Ģ",
+      "? ..",
+      "ĠÅ» e",
+      "Ġpermit ir",
+      "Ġdetect ors",
+      "ĠH UD",
+      "av al",
+      "ĠìĹ¬ê¸° ê¹Įì§Ģ",
+      "Ġh ubs",
+      "Ġbest immt",
+      "ĠбÑĥдеÑĤ е",
+      "INTER POSING",
+      "Ġten gan",
+      "Ġcra ve",
+      "ĠBundes regierung",
+      "ĠBlo ody",
+      "Ġus ability",
+      "ĠE as",
+      "ĠÄijá»Ļ ng",
+      "Ġ195 5",
+      "Ġkrie gen",
+      "Ġhabit ual",
+      "Ġessential s",
+      "rim inal",
+      "Ġroomm ates",
+      "éĤ£ å°±",
+      "ĠпеÑĢе Ñħод",
+      "Ġng hi",
+      "Ġmen ing",
+      "ĠSym phony",
+      "ĠH ug",
+      "ag gi",
+      "Ġw ied",
+      "Ġmit ad",
+      "ãģ£ãģ¦ ãģĦãģĨ",
+      "te enth",
+      "ida Äĩ",
+      "S ave",
+      "Ġrob iÄĩ",
+      "Ġboun ces",
+      "° ĸìĹIJ",
+      "st ars",
+      "Ġprag matic",
+      "Ġcogn ition",
+      "Ġwra pper",
+      "Ġw arten",
+      "ad h",
+      "Ġpens a",
+      "ĠHert z",
+      "Ġn ÄĽ",
+      "ĠRe id",
+      "ĠPC s",
+      "ĠMo le",
+      "Ġ.. ...",
+      "Ġpre cio",
+      "ĠChampions hips",
+      "ê°Ģë Ŀ½",
+      "Ġv ér",
+      "Ġcorrid ors",
+      "ĠElect ronic",
+      "S l",
+      "Ġа ле",
+      "Ġoverth row",
+      "Ġk abul",
+      "ĠR ES",
+      "ĠCyber punk",
+      "ог од",
+      "ĠÐĿ ав",
+      "Ġw an",
+      "Ġmanifest ations",
+      "Ġcual es",
+      "ĠW ise",
+      "ĠLös ung",
+      "Ġex fol",
+      "Ġearn s",
+      "ÑĥÑģÑĤ иÑĤÑĮ",
+      "Ġsa pp",
+      "ĠBra un",
+      "ĠBRAND ON",
+      "ì¹ Ļ",
+      "Ġs ano",
+      "ĠF EL",
+      "Ñĭв айÑĤеÑģÑĮ",
+      "ожд ениÑı",
+      "Ġse wn",
+      "F un",
+      "Ġrecipro cal",
+      "Ġexpans ive",
+      "ĠTra ffic",
+      "Ġktóre go",
+      "ĠÙĪ س",
+      "æĺ ¥",
+      "Ġë¹ ¨",
+      "pro ve",
+      "ig are",
+      "Ġlo h",
+      "Ø§Ø ¶",
+      "H ope",
+      "Ġdevote es",
+      "ĠG om",
+      "Ġste als",
+      "ĠU ms",
+      "ĠTw ice",
+      "ãĤ ²",
+      "iy im",
+      "Ġrhythm ic",
+      "ĠV orte",
+      "Ġpref ix",
+      "om ination",
+      "Ġdat o",
+      "Ġcust ard",
+      "ĠVO ICE",
+      "å· ŀ",
+      "Ġmen y",
+      "ist ors",
+      "Ġíĺ ij",
+      "ĠìĤ´ì ķĦ",
+      "Ġíĥ Ħ",
+      "Ġk ort",
+      "Ġab a",
+      "ĠV era",
+      "ep y",
+      "Ġì¹´ë©Ķë Ŀ¼",
+      "Ġsubmer ged",
+      "ĠC lock",
+      "Ġthumbna ils",
+      "Ġbo ast",
+      "ĠF are",
+      "!! ]",
+      "ĠÅĽ m",
+      "Ġkaik ki",
+      "ĠTechn ologies",
+      "ìĻ ¸",
+      "ãĥ Ĵ",
+      "иÑĤ ай",
+      "å°ı æĻĤ",
+      "Ġа ÑĤ",
+      "Ġkn obs",
+      "Ġre icht",
+      "ượ ng",
+      "gl io",
+      "Ġ맼 ìĿ´",
+      "ê°IJ ìĿĦ",
+      "Ġjot ka",
+      "ĠHand y",
+      "ĠHab en",
+      "n ous",
+      "Ġin land",
+      "Ġam azon",
+      "ho oting",
+      "S L",
+      "Ġle isten",
+      "~ \"",
+      "Ġprov oke",
+      "ĠTw ist",
+      "Ġ×ij× Ĺ",
+      "Ġdepart ed",
+      "ê° ľë¥¼",
+      "Ġk onse",
+      "ĠCar wyn",
+      "íķĺ ìĭł",
+      "ident al",
+      "ES CO",
+      "Ġt teokbokki",
+      "Ġdiz endo",
+      "ç· ´",
+      "ınd aki",
+      "imas u",
+      "af ar",
+      "Ġland fill",
+      "Ġcorrect ing",
+      "Ġcle ars",
+      "ĠNum mer",
+      "H AM",
+      "Ġcart ridges",
+      "ĠDies el",
+      "p aced",
+      "Ġobl iv",
+      "Ġmoy ens",
+      "ĠSin ne",
+      "ĠPre is",
+      "il iz",
+      "ĠÑģм ож",
+      "Ġbroad en",
+      "ä»ĸ æĺ¯",
+      "x es",
+      "Ġcarbohyd rate",
+      "íĺ ¹",
+      "se ok",
+      "Ġecho es",
+      "Ġc ess",
+      "ë° Ķ",
+      "Ġб изнеÑģ",
+      "Ġllam ado",
+      "Ġess ent",
+      "ĠìĿ¼ë °ĺ",
+      "ĠA ires",
+      "ph en",
+      "Ġze bra",
+      "Ġsymbol ism",
+      "On ce",
+      "Ġr acks",
+      "ĠKaf ka",
+      "ĠÑģеÑĢÑĮ ез",
+      "Ġsin n",
+      "p icious",
+      "ka a",
+      "Ġmotherf ucker",
+      "Ġapprentices hip",
+      "Ġr pm",
+      "Ġtax ation",
+      "Ġfur ry",
+      "ĠSac red",
+      "ĠÑĢаз м",
+      "por a",
+      "eng es",
+      "ĠíĹ Īë",
+      "ĠÑģ ин",
+      "Ġsanit izer",
+      "Ġcr inge",
+      "ĠS ca",
+      "оÑĩ но",
+      "Ġof ere",
+      "Ġmel odies",
+      "ĠVel vet",
+      "ĠIhr er",
+      "ĠHy brid",
+      "ĠG iov",
+      "Ġirgend was",
+      "Ġdep ende",
+      "ĠUs ers",
+      "Ġh ump",
+      "dri ving",
+      "Ġs f",
+      "Ġruth less",
+      "à¹ĢภĦ",
+      "Ġlem ons",
+      "Ġfö ret",
+      "ĠO j",
+      "Ġм ама",
+      "Ġinter personal",
+      "Ġge v",
+      "Ġab norm",
+      "иÑģ л",
+      "Ġин д",
+      "Ġkont roll",
+      "Ġreg res",
+      "Ġled ge",
+      "Ġerzäh lt",
+      "ĠT act",
+      "Ġarri vé",
+      "Ġsubstant ive",
+      "Ġspoon ful",
+      "zw ischen",
+      "oooo o",
+      "Ġconten ido",
+      "Ġbes l",
+      "á»ĥ m",
+      "k ten",
+      "Jam ie",
+      "Ġsand y",
+      "ä¸į åIJĮ",
+      "â ĭ",
+      "Ġp ase",
+      "Ġdet te",
+      "ĠBelg ian",
+      "ê° ľë",
+      "ula res",
+      "r ud",
+      "ig or",
+      "ĠíĮ ¬ë",
+      "Ġremed ies",
+      "Ġblast ing",
+      "ĠS ich",
+      "Ġож ид",
+      "Ġmon str",
+      "Ġmanif old",
+      "Ġglaub en",
+      "ĠE ST",
+      "Ġstream line",
+      "Ġlobb ying",
+      "ĠGoth ic",
+      "to ire",
+      ".. '",
+      "Ġdém ocr",
+      "Ġнаб лÑİд",
+      "Ġwsp ól",
+      "ĠczÄĻ ÅĽÄĩ",
+      "ä¸ĭ éĿ¢",
+      "is és",
+      "g angen",
+      "Ġbez pie",
+      "rem lin",
+      "ê° Ŀ",
+      "St ill",
+      "Ġres ides",
+      "Ġgele cek",
+      "Ġtélé phone",
+      "Ġpe wn",
+      "Ġle opard",
+      "Ġcompliment ary",
+      "Ġc rib",
+      "ĠAnim als",
+      "Ġge il",
+      "ess el",
+      "Ġgard er",
+      "Ġcatch y",
+      "æ¨ ¹",
+      "ĠE ts",
+      "ĠCom mercial",
+      "ĠD ENNIS",
+      "ĠCoordin ator",
+      "ĠAb igail",
+      "ffff ff",
+      "ấ p",
+      "Ġpeque ña",
+      "Ġinject ions",
+      "ce kt",
+      "Ġphilanthrop y",
+      "Ġp uck",
+      "Ġcelebr ates",
+      "ĠD unk",
+      "ĠD latego",
+      "ãģ¾ ãģł",
+      "δ ή",
+      "grad uate",
+      "ĠM obil",
+      "t ill",
+      "ac am",
+      "Ġyol ks",
+      "Ġtang led",
+      "Ġman iac",
+      "Ġoblig ed",
+      "ĠLa ink",
+      "Ġver der",
+      "ĠDam on",
+      "Ġmut ant",
+      "Ġhop ping",
+      "Ġre ins",
+      "Ġinver ter",
+      "Ġcont empt",
+      "׳ ס",
+      "le arning",
+      "M iss",
+      "ĠÐĵ оÑģ",
+      "ĠMe yer",
+      "ê»ĺ ìĦľ",
+      "é£ İ",
+      "×ķ׳ ×Ļ×Ŀ",
+      "ask ing",
+      "Ġtrim ming",
+      "Ġtre asury",
+      "Ġs ente",
+      "A ust",
+      "ĠUnterstüt zung",
+      "ĠCom edy",
+      "ĠAn akin",
+      "é ¹",
+      "ÑĢÑĥ ÑĤ",
+      "ĠH ari",
+      "ograph ers",
+      "Ġoat meal",
+      "ĠB ots",
+      "ä¸į äºĨ",
+      "Ġп алÑĮ",
+      "Ġacknowledge ment",
+      "x ic",
+      "Ġê´Ģ ìĭ¬",
+      "gas ping",
+      "Ġãģ ķ",
+      "Ġterr ace",
+      "Ġor naments",
+      "ĠM ER",
+      "comm ittee",
+      "ĠìĹĨ ìĬµëĭĪëĭ¤",
+      "Ġr ij",
+      "é ³",
+      "צ ×Ŀ",
+      "le me",
+      "Ġlibert ies",
+      "Ġfell as",
+      "ĠCop per",
+      "ben ch",
+      "ĠIde a",
+      "á»į n",
+      "ÑĪ а",
+      "Ġvers ión",
+      "ÏĦο Ïį",
+      "ĠÐľ и",
+      "ĠпÑĢил ож",
+      "Ġbox er",
+      "ĠT anner",
+      "ĠM oy",
+      "ì¹ĺ ëĬĶ",
+      "T hr",
+      "Ġtin ham",
+      "Ġpol ishing",
+      "Ġconsequ ently",
+      "Ġamen ities",
+      "ĠK I",
+      "ĠGRE EN",
+      "ĠFrank ie",
+      "н иÑĤ",
+      "itt el",
+      "Ñģ кое",
+      "urs ed",
+      "Ġup bringing",
+      "Ġth ứ",
+      "ĠìĭĿ ìľ¼ë¡ľ",
+      "Ġwh im",
+      "Ġchin ese",
+      "conf idence",
+      "ĠJ eder",
+      "ãģª ãģ®ãģ§",
+      "aj cie",
+      "ĠT ous",
+      "ĠPow ers",
+      "ừ a",
+      "other mal",
+      "ĠвÑĭ ÑĪе",
+      "r ale",
+      "Ø§Ø ®",
+      "Ġì§Ģ ìĽIJ",
+      "Ġép isode",
+      "Ġsul ph",
+      "Ġenc ara",
+      "k raft",
+      "alar ı",
+      "ĠCom es",
+      "Ġdiv ul",
+      "ĠRud olph",
+      "ĠM use",
+      "Ġut ens",
+      "ĠìŀIJ 주",
+      "Ġp ana",
+      "ĠVeget a",
+      "ĠPH P",
+      "ĠN SA",
+      "ent in",
+      "ĠCarne gie",
+      "ا ÙĬ",
+      "iÄĻ cy",
+      "H arry",
+      "Ġf ır",
+      "С п",
+      "Ġglad ly",
+      "Ġaver aging",
+      "íķĺ ê²łìĬµëĭĪëĭ¤",
+      "лÑı ÑİÑĤÑģÑı",
+      "ĠÐľ енÑı",
+      "Ġquot ation",
+      "ri res",
+      "itch ens",
+      "ay ed",
+      "Ġun att",
+      "ĠP erez",
+      "ĠоÑĤ меÑĤ",
+      "Ġtact ile",
+      "ĠEu h",
+      "is ini",
+      "b uh",
+      "Ġhat ır",
+      "ĠìŀĪ ìľ¼",
+      "Ġpolicy makers",
+      "³´ì Ħ¸ìļĶ",
+      "ac ı",
+      "Ġκ ι",
+      "Ġregister ing",
+      "re to",
+      "ĠSpr inkle",
+      "ĠGram my",
+      "ax ter",
+      "Ġб и",
+      "Ġsit ter",
+      "Ġpred ic",
+      "Ġthin ly",
+      "Ġstr um",
+      "Ġag grav",
+      "Ġa ha",
+      "ر ج",
+      "m ellow",
+      "Ġconst ante",
+      "ĠL aut",
+      "ist on",
+      "Ġtransition ed",
+      "ĠCamb odia",
+      "ãģĦ ãģįãģ¾ãģĻ",
+      "è·Ł 大家",
+      "art ed",
+      "Ġmis f",
+      "ĠPunk te",
+      "Įë ĵł",
+      "Ġtremb ling",
+      "Ġges pannt",
+      "ĠعÙĦÙĬ Ùĩ",
+      "Ġникак иÑħ",
+      "Ġë¶Ģë ĵľë",
+      "ĠÑĢазв иÑĤ",
+      "Ġit chy",
+      "Ġc iento",
+      "Ġpl ains",
+      "Ġk ittens",
+      "Ġback log",
+      "ĠPres iding",
+      "pt a",
+      "Ġha voc",
+      "ĠDarr in",
+      "ĠÐĽÑİ Ð±",
+      "Ġsegreg ated",
+      "Ġg hetto",
+      "Ġerle bt",
+      "Ġdrug iej",
+      "ĠSi xt",
+      "åı ĥ",
+      "ร ะ",
+      "uen cia",
+      "Ġíķĺ 기",
+      "ĠëĨ į",
+      "Ġrob i",
+      "Ġpione ers",
+      "Ġmilli ards",
+      "ĠWitch er",
+      "Ġ무ìĹ ĩ",
+      "or ro",
+      "m ass",
+      "Ġdiver gence",
+      "ĠRiver a",
+      "ĠNo odles",
+      "Ġend roit",
+      "ĠK osten",
+      "ĠдÑĢÑĥг а",
+      "ĠmÃŃn imo",
+      "ĠKazakh stan",
+      "ت Ùĩ",
+      "Ġвоз дÑĥ",
+      "Ġgesch rieben",
+      "ĠN il",
+      "Ñģ ки",
+      "ĠFr üh",
+      "Ġbever ages",
+      "æº IJ",
+      "ĠG on",
+      "æĺ ¨",
+      "Ar in",
+      "ĠInt ro",
+      "ocaly ptic",
+      "Ġexhaust ion",
+      "ĠStat us",
+      "ĠBatter y",
+      "és z",
+      "£ ¼ë",
+      "air y",
+      "Ġë³´ìŬë ĵľë",
+      "Ġdispar ity",
+      "Ù Į",
+      "ĠTuc son",
+      "Ġbright ly",
+      "pro blem",
+      "Ġbiom ass",
+      "éĻ į",
+      "§ ī",
+      "Ġhur dle",
+      "Ġwavelength s",
+      "Ġ< <",
+      "Ġteam ed",
+      "FF FF",
+      "ĠS lim",
+      "om ial",
+      "Ġunve iled",
+      "ĠVere in",
+      "ÙĤ Ø·",
+      "est ry",
+      "Ġcl ás",
+      "Ġch eddar",
+      "Ġaccus ing",
+      "ĠScient ific",
+      "ĠбÑĥд е",
+      "ĠCyr us",
+      "ε ÏĦε",
+      "Ĩĵ ê³ł",
+      "Ġë³ Ħ",
+      "Ġcur d",
+      "Ġrefer rals",
+      "sh ift",
+      "åį ķ",
+      "nik ów",
+      "Ġm ier",
+      "Ġconf ronting",
+      "ê²ĥ ëıĦ",
+      "aw l",
+      "Ġtry in",
+      "Ġê·¸ëŀĺ ìļĶ",
+      "Ġch iar",
+      "Ġìĺ¤ëĬ ĺëıĦ",
+      "æĶ¿ æ²»",
+      "es que",
+      "Ġmism os",
+      "ĠSh ak",
+      "Ġsoci aux",
+      "Ġpi ÅŁ",
+      "ĠkiÅŁ i",
+      "Ġcy an",
+      "h ay",
+      "be w",
+      "b od",
+      "ĠÎ ¹",
+      "ĠMain ly",
+      "Ñİ ÑĤÑĮ",
+      "hab itude",
+      "ĠÑģп окой",
+      "è·Ł æĪij",
+      "Ġpre con",
+      "ĠM andy",
+      "ðŁ¤ £",
+      "ill os",
+      "Ġgr upp",
+      "Ġcr umble",
+      "Ġconstru ctor",
+      "erv ices",
+      "Ġlight house",
+      "ĠCon cept",
+      "ан ÑĤи",
+      "alt ro",
+      "h ope",
+      "ĠAll eg",
+      "ìĸ´ë ¥¼",
+      "pie ces",
+      "oun ter",
+      "Ġíķĺ ëĭĪê¹Į",
+      "ĠìĿ¸ íĦ°ë",
+      "Ġvérit able",
+      "Ġthread ed",
+      "bl ind",
+      "Ĥĺë Ŀ¼",
+      "Ġtr ays",
+      "ĠEd ison",
+      "ĠÃĸ z",
+      "ĠSte vie",
+      "Ġl ender",
+      "Ġbrig ade",
+      "Ġdeuts che",
+      "m uffled",
+      "b art",
+      "Ġinsan ity",
+      "Ġsav vy",
+      "Ġsens ational",
+      "Ġdere chos",
+      "ĠM X",
+      "ĠпÑĢ еп",
+      "Ġthreat ens",
+      "Ġrealt Ãł",
+      "Ġindic ative",
+      "Ġch ops",
+      "Ġbenef iting",
+      "ĠVern on",
+      "ĠSt rand",
+      "n un",
+      "qu ently",
+      "10 1",
+      "Ġe el",
+      "ìĪ Ļ",
+      "r ints",
+      "ĠÙħ س",
+      "Ġب د",
+      "Ġпо ÑģÑĤÑĢо",
+      "Ġyap mÄ±ÅŁ",
+      "Ġol ması",
+      "Ġi edereen",
+      "ol é",
+      "ke f",
+      "Ġë°ľ ìĥĿ",
+      "Ġr ained",
+      "Ġalm ighty",
+      "ĠвÑĭ д",
+      "ĠC PR",
+      "F re",
+      "Ġinhab ited",
+      "Ġarb ets",
+      "Ġa kin",
+      "а ÑģÑĤв",
+      "v ania",
+      "Ġhäuf ig",
+      "ĠMat te",
+      "s orry",
+      "Jen ny",
+      "ĠгÑĢ ад",
+      "Ġwh it",
+      "Ġbro kers",
+      "å¯ Ł",
+      "Ġh ine",
+      "ast en",
+      "Ġг ÑĢÑĥ",
+      "M B",
+      "ĠP RI",
+      "S ab",
+      "Ġwrest ler",
+      "Ġfacil itating",
+      "Ġeh kä",
+      "ĠC red",
+      "Ġ12 7",
+      "Ġnot hin",
+      "Ġmand ated",
+      "å¯ Į",
+      "ÑĥÑĤ ÑģÑĤв",
+      "F rank",
+      "Ġwor s",
+      "Ġdzie ÅĦ",
+      "ĠUnder ground",
+      "Ġznaj du",
+      "ĠB ä",
+      "ĠPrin zip",
+      "аÑĤ елей",
+      "Ġveter inar",
+      "Ġsplend id",
+      "Ġroz p",
+      "Ġpsych opath",
+      "ig on",
+      "Ġh ops",
+      "Ġc ần",
+      "ĠX ian",
+      "Ġtro isième",
+      "Ġproduct o",
+      "ĠdeÄŁ er",
+      "ĠContin uing",
+      "ив ал",
+      "c ık",
+      "Ġmoistur izer",
+      "Wh ite",
+      "Ġsi is",
+      "ĠEver est",
+      "ien ced",
+      "Ġcả m",
+      "ĠJ apon",
+      "´ìł Ħ",
+      "Ġten ÃŃan",
+      "Ġenc anta",
+      "M m",
+      "Ġdrop down",
+      "ĠI ya",
+      "³´ë ©´",
+      "Ġword ing",
+      "ĠSque eze",
+      "ĠMap le",
+      "Ġclar ified",
+      "ĠMun icip",
+      "ĠRou ge",
+      "ĠNick i",
+      "ĠGo o",
+      "v olt",
+      "t ek",
+      "fect ure",
+      "f red",
+      "ar rive",
+      "ãĥ¼ ãģĦ",
+      "te z",
+      "E p",
+      "Ġob ras",
+      "ĠV ID",
+      "ĠR iv",
+      "ĠMod i",
+      "i be",
+      "Ġacontec endo",
+      "Ġim itation",
+      "Ġcamoufl age",
+      "Ġspan ning",
+      "ĠSEC RET",
+      "ĠOre o",
+      "ìĨĮë ¦¬",
+      "Ġh unch",
+      "Ġca ÅĤe",
+      "Ġspont aneously",
+      "ĠPer d",
+      "Ġet ap",
+      "ĠHo le",
+      "ĠDis ability",
+      "Ġafter life",
+      "æģ ©",
+      "Ġtest ified",
+      "Ġpres up",
+      "Ġpet roleum",
+      "Ġcontr ario",
+      "ĠAss essment",
+      "ÄŁ lu",
+      "Ġp ests",
+      "Ġdil ig",
+      "ĠвÑģÑĤÑĢ еÑĤ",
+      "Ġcons équ",
+      "Ġcann ons",
+      "Ġcan oe",
+      "ĠM ile",
+      "Ġcit oy",
+      "Ġbe gged",
+      "ĠMin nie",
+      "ÅĤy ch",
+      "Ġprinci pe",
+      "ÏĢÏĮ ν",
+      "m niej",
+      "Ġw ert",
+      "Ġëĭ¤ë ĵ¤",
+      "an se",
+      "Ġunc les",
+      "Ġprovoc ative",
+      "Ġinter sections",
+      "Ġdemocr ats",
+      "ĠJul ius",
+      "ин ки",
+      "yg usal",
+      "Ġ׾ ×ķ",
+      "Ġgj orde",
+      "Ġg asket",
+      "ĠB ock",
+      "ĠÄ° n",
+      "b reat",
+      "ĠEqu ity",
+      "ard ı",
+      "Ġкан але",
+      "Ġд ней",
+      "Ġt Ỽi",
+      "Ġfi xture",
+      "Ġab uses",
+      "Ġv aya",
+      "Ġou vert",
+      "Ġmultic ultural",
+      "Ġcontext o",
+      "ĠSes ame",
+      "Ġdé pl",
+      "Ġcons omm",
+      "ĠPart e",
+      "Ġp em",
+      "ĠCon an",
+      "Ġб ÑĸлÑĮ",
+      "Ġpersu aded",
+      "Ġdra ins",
+      "M oo",
+      "F ORE",
+      "Ġб аÑĤ",
+      "Ġf od",
+      "ĠProduct s",
+      "ì§Ħ ì§ľ",
+      "Ġ\" [",
+      "ĠW ick",
+      "ĠNar uto",
+      "н али",
+      "ry w",
+      "Ġl odge",
+      "Ġin h",
+      "Ġvont ade",
+      "Ġdi j",
+      "ĠJes ús",
+      "Look ing",
+      "Ġfore arm",
+      "ĠIntegr ation",
+      "ĠHARR IS",
+      "Ġtool bar",
+      "le ader",
+      "Ġsel dom",
+      "Ġб ÑĢоÑģ",
+      "ĠK ook",
+      "он д",
+      "Ġmon opol",
+      "Ġmill et",
+      "Ġl ira",
+      "ĠAs ians",
+      "Ġ18 90",
+      "ci ÄŁim",
+      "Ġed en",
+      "ĠIKE A",
+      "ĠNeigh bor",
+      "ĠKazu ya",
+      "ü d",
+      "Ġpsych edel",
+      "Ġenvision ed",
+      "åĿ Ĺ",
+      "Ġï· »",
+      "Ġw under",
+      "ĠBulgar ia",
+      "B rid",
+      "Ġmar row",
+      "Ġdep iction",
+      "ĠT in",
+      "ĠPhar ise",
+      "Ġeinz ige",
+      "Ġblind ly",
+      "ãģĽ ãģ¦",
+      "Ġdef ens",
+      "D ire",
+      "Ġvibr ating",
+      "Ġtroll s",
+      "Ġdisrespect ful",
+      "Ġw od",
+      "Ġstimul i",
+      "Ġcreep ing",
+      "Ġcla irement",
+      "Ġsc ariest",
+      "Ġdécouv rir",
+      "Ġ10 4",
+      "ĠвеÑĢ Ñħ",
+      "ĠÅĤ at",
+      "Ġróż ne",
+      "Ġbar ley",
+      "ĠRe pl",
+      "ĠT we",
+      "k ke",
+      "ĠãģĿ ãĤĮ",
+      "ĠRed mi",
+      "ĠMet roid",
+      "Ġή ÏĦαν",
+      "Che ck",
+      "ĠS EN",
+      "Ġ ido",
+      "ÑĤоÑĢ ии",
+      "ó p",
+      "UN KNOWN",
+      "Ġänd ern",
+      "ĠJu ice",
+      "ĠGes icht",
+      "å°± æľĥ",
+      "ĠнаÑģÑĤ олÑĮко",
+      "íĥ ķ",
+      "Â Ń",
+      "ex hales",
+      "Ġì´ ī",
+      "Ġj sem",
+      "ÏĢ ÏīÏĤ",
+      "Ġit t",
+      "ëªħ ìĿ´",
+      "Ġrem ix",
+      "Ġbloss oms",
+      "ĠR enee",
+      "is ations",
+      "ìĬ¤í Ħ°",
+      "Ġë³´ ìĿ´ëĬĶ",
+      "uest as",
+      "op edia",
+      "ĠA im",
+      "ìĿ´ì¦ Ī",
+      "sc ene",
+      "Ġleak age",
+      "uck t",
+      "S ad",
+      "A sk",
+      "Ġsusp ense",
+      "Ġimp ost",
+      "ĠStrateg ic",
+      "ĠIt ÃŃs",
+      "âĢ Į",
+      "Ġkey boards",
+      "Ġam using",
+      "og r",
+      "id erman",
+      "ŀ ĸ",
+      "Ġв ижÑĥ",
+      "Ġd ips",
+      "Ġapolog ized",
+      "ĠST AR",
+      "Ġesc uela",
+      "ĠC hing",
+      "н ениÑı",
+      "Ġë¶Ģë¶Ħ ìĿ´",
+      "ĠFle et",
+      "Ġs amb",
+      "Ġentsprech end",
+      "Ġelectrod es",
+      "ĠFrei heit",
+      "æĪij ä¸įçŁ¥éģĵ",
+      "ĠSh rim",
+      "iÃŁ e",
+      "Ġselect ions",
+      "Ġfor di",
+      "Ġd oss",
+      "Ñı Ñĩ",
+      "Ġdiscrimin ate",
+      "ĠAu ÃŁerdem",
+      "Ġdesenvol v",
+      "ĠIntern al",
+      "ĠBened ict",
+      "å¯ Ĩ",
+      "ĠSh iv",
+      "M issy",
+      "Ġоб наÑĢÑĥж",
+      "Ġна ÑģÑĤÑĢо",
+      "Ġcontrol ar",
+      "ĠL ia",
+      "Ġopio ids",
+      "ant u",
+      "Ġcup board",
+      "æģ IJ",
+      "г е",
+      "acht s",
+      "Ġcur ated",
+      "Ġx em",
+      "Ġwe ary",
+      "Ġbre thren",
+      "Ġbudget ing",
+      "Ġpour tant",
+      "éļ »",
+      "ais ia",
+      "ĠоÑĤв еÑĩ",
+      "ĠG IS",
+      "μ αι",
+      "Ġש×Ķ ×ķ×IJ",
+      "Ġsa ud",
+      "Ġl Ỽ",
+      "Ðķ Т",
+      "ub ine",
+      "ĠнÑĥж ен",
+      "Ġkidna pping",
+      "Ġbr at",
+      "ĠTer re",
+      "ĠMon et",
+      "Ġë§Ī ìĬ¤íģ",
+      "Ġflash y",
+      "ĠIS BN",
+      "Ġfreel ance",
+      "i age",
+      "Ġjun ge",
+      "ì¶ ©",
+      "cer al",
+      "ĠÑĤоÑĩ ки",
+      "Ġform ulate",
+      "ĠF ER",
+      "ĠDart mouth",
+      "ìľ¼ë ©´ìĦľ",
+      "å¢ ĥ",
+      "ow iÄħ",
+      "ĠëĶĶ ìŀIJ",
+      "Ġreg iment",
+      "Ġmetabol ismo",
+      "ĠP arr",
+      "Ġ충 ë¶Ħ",
+      "Ġsan ity",
+      "ĠL al",
+      "ĠG ö",
+      "ĠG la",
+      "Ġprot o",
+      "Ġmicroscop ic",
+      "Ġk ang",
+      "ĠSc alia",
+      "Ġp ug",
+      "ĠSc ore",
+      "ĠSav annah",
+      "Ġgard e",
+      "ĠN OR",
+      "å°į åIJ§",
+      "Ġsche int",
+      "Ġp óÅĤ",
+      "Ġcor ri",
+      "Ġbr ute",
+      "Ġ ÅĤad",
+      "ä»ĸ 们",
+      "Ġsucceed ing",
+      "Ġbicy cles",
+      "N on",
+      "Ġseek ers",
+      "Ġuncond itional",
+      "Ġrhy mes",
+      "ĠGar age",
+      "Ġinv oice",
+      "Ġcan vi",
+      "ne ck",
+      "Ġcustom izable",
+      "irit ual",
+      "Que en",
+      "íķĺ ìĭľëĬĶ",
+      "Ġpower less",
+      "Ġcs ak",
+      "ä¸į ä¼ļ",
+      "is oft",
+      "Ġìłķ íĻķ",
+      "Ġnh ân",
+      "ĠM AND",
+      "ĠH af",
+      "Ġrevol ves",
+      "ä¹Ł åı¯ä»¥",
+      "ov an",
+      "ar oo",
+      "ĠGr ind",
+      "éĽ ª",
+      "Ġindispens able",
+      "Ġconsult ed",
+      "ĠClin ical",
+      "A cc",
+      "Ġol hos",
+      "Ġmon ter",
+      "ĠH ana",
+      "et ah",
+      "Ġva an",
+      "Ġt igers",
+      "Ġcau cus",
+      "ðŁĺ Ĥ",
+      "³´ì ŀIJ",
+      "pow ers",
+      "ium s",
+      "ĠíĨ łë",
+      "Ġtrad icional",
+      "Ġreson ated",
+      "Ġìĭł 기",
+      "th em",
+      "Ro bert",
+      "Ġelement o",
+      "Ġant id",
+      "Ġоб Ñģ",
+      "Ġnat ives",
+      "Ġlo ca",
+      "ow ment",
+      "ĠT ight",
+      "Ġ æĢĿ",
+      "Ġmel an",
+      "ĠN ue",
+      "am is",
+      "Ġsor gen",
+      "as ına",
+      "H ome",
+      "ĠPUB G",
+      "Ġaw fully",
+      "ĠSh ore",
+      "ĠPer ché",
+      "ĠL au",
+      "ĠCind erella",
+      "ĠCh est",
+      "Ġsem antic",
+      "Ġdesert ed",
+      "ĠMom o",
+      "ĠHern andez",
+      "gen es",
+      "ĠAd ult",
+      "иÑĩеÑģ кого",
+      "osh ima",
+      "ĠcaracterÃŃst icas",
+      "ĠK L",
+      "´ìŀ ¥",
+      "oc ar",
+      "Ġfeh lt",
+      "Ġd ruk",
+      "ĠPop py",
+      "EN GLISH",
+      "ĠVerg leich",
+      "B rien",
+      "Ġrec omp",
+      "ĠÑģ д",
+      "Ġmer ger",
+      "Ġmarket ers",
+      "Ġhoney moon",
+      "Ġpen so",
+      "Ġbell i",
+      "еÑĤ Ñĥ",
+      "Ġbank er",
+      "Cam era",
+      "ĠSt all",
+      "ĠSt amp",
+      "ĠB ite",
+      "еж де",
+      "Ġs ür",
+      "Ġgü ç",
+      "ĠPas sover",
+      "ĠBug ün",
+      "ĠÑģожал ениÑİ",
+      "Ġн из",
+      "Ġman ure",
+      "Ġglac ier",
+      "è« ĩ",
+      "RA Y",
+      "ter ror",
+      "Ġsal ads",
+      "Ġhur ricanes",
+      "ĠDesign er",
+      "ator io",
+      "Ġfact ual",
+      "ĠTam my",
+      "Ġзв ÑĥÑĩ",
+      "Ġintrodu ctions",
+      "Ġhouse keeping",
+      "Ġh anger",
+      "ëĭ ĺë",
+      "ak te",
+      "ĠCol a",
+      "' ]",
+      "ĠG ender",
+      "оÑĢ он",
+      "ip se",
+      "ic ias",
+      "Ġsuccess ive",
+      "Ġpolit ic",
+      "Ġhö her",
+      "ĠQ iao",
+      "ĠG imme",
+      "Ġл ож",
+      "Ġse b",
+      "ĠWe iter",
+      "ĠSak ura",
+      "ĠB oulder",
+      "ĠAm érica",
+      "peÅĤ nie",
+      "Ġtecn ologÃŃa",
+      "ish ops",
+      "f ur",
+      "Ġmoon light",
+      "Ġdispers ed",
+      "Ġre z",
+      "ен ное",
+      "алÑĮ нÑĥÑİ",
+      "ĠTw elve",
+      "ĠH OR",
+      "ìĭ¤í ŀĪ",
+      "il age",
+      "Ġshad ed",
+      "Ġres umes",
+      "ĠPe anut",
+      "ĠM ILL",
+      "ap ons",
+      "ĠU FC",
+      "ĠSo le",
+      "Ġjoy stick",
+      "ĠOliv ier",
+      "war ming",
+      "Ġsyll abus",
+      "Ġоб Ñīе",
+      "Ġhi á»ĩn",
+      "Ġfest a",
+      "Ġcr adle",
+      "ĠZ ac",
+      "Ġremem brance",
+      "Ġê°Ļ ìķĦìĦľ",
+      "ĠpiÄĻ k",
+      "Ġco exist",
+      "ĠV II",
+      "Ġá reas",
+      "Ġu waż",
+      "Ġobser vers",
+      "Ġmännisk or",
+      "co on",
+      "ĠD AM",
+      "Ġnas zym",
+      "Ġall igator",
+      "ĠFree ze",
+      "ĠEst ate",
+      "ĠÑĤÑĢ ади",
+      "Ġunder cover",
+      "Ġn ies",
+      "ĠFeh ler",
+      "pl in",
+      "ĠK abul",
+      "il ate",
+      "Ġê³ł ìĸij",
+      "Ġm op",
+      "ìĦ ¼",
+      "Ġand erer",
+      "ĠK ELL",
+      "ок и",
+      "Ġж еÑģÑĤ",
+      "Ġgra zing",
+      "Ġda ÃŃ",
+      "Ġcapital ize",
+      "Ġa pex",
+      "Ġnurt uring",
+      "Ġcort ar",
+      "Ġcontr ac",
+      "ımız ı",
+      "Ġtand em",
+      "éĥ½ æľī",
+      "ge ment",
+      "ĠÑģиÑģÑĤем а",
+      "Ġman que",
+      "ia jÄħ",
+      "W OR",
+      "Ġا ب",
+      "Ġcart s",
+      "AN O",
+      "Ġë°Ľ ê³ł",
+      "ĠC ena",
+      "ĠBi ology",
+      "id ar",
+      "Ġa ż",
+      "er ne",
+      "an u",
+      "Ġthank ed",
+      "Ġsubmar ines",
+      "Ġman ic",
+      "Ġм оз",
+      "ä¼ Ĭ",
+      "inst ant",
+      "ess ential",
+      "Ġsam urai",
+      "Ġpast i",
+      "Ġal an",
+      "Ġbro ch",
+      "Ġb aker",
+      "ĠGu ill",
+      "¨ ¼",
+      "Ġwithd rawn",
+      "ëĭ Ŀ",
+      "Per fect",
+      "qu ency",
+      "Ġstream lined",
+      "Ġ13 00",
+      "´ë ıĦ",
+      "Ġëĸ łë",
+      "Ġãģ¯ ãģĦ",
+      "Ġh vad",
+      "ä¸Ģå®ļ è¦ģ",
+      "Ġverb ally",
+      "ĠK ons",
+      "Ġì¡° ìĭ¬",
+      "Ġdie z",
+      "æİ° æİ°",
+      "Ġchuck ling",
+      "ĠM ih",
+      "Ġrall ies",
+      "Ġman ter",
+      "Ġearn est",
+      "s uper",
+      "Ġge ce",
+      "ĠR end",
+      "ĠGer ade",
+      "jen igen",
+      "ĠV all",
+      "Ġìŀ ĪëĤĺ",
+      "ĠÑģказ ала",
+      "Ġtrabal h",
+      "ĠнаÑĪ ем",
+      "Ġм еÑħ",
+      "ik it",
+      "Ġnoun s",
+      "Ġneurolog ical",
+      "Ġmotiv ational",
+      "ĠMcM ahon",
+      "ĠFin ished",
+      "Ġë³´ ìĿ´",
+      "ĠField s",
+      "Ġadoles cents",
+      "ĠT isch",
+      "ĠNe ben",
+      "ĠFl owers",
+      "ĠEner g",
+      "Ġdire t",
+      "ĠTh i",
+      "ĠP icas",
+      "æĥ ľ",
+      "æĢİä¹Ī æł·",
+      "Ġav ete",
+      "ĠF ors",
+      "ĠChap el",
+      "N ão",
+      "E t",
+      "ĠÑģод еÑĢж",
+      "ren o",
+      "Ġs ven",
+      "Ġdost ÄĻp",
+      "ne e",
+      "ĠSnap dragon",
+      "ĠID s",
+      "ìķĺ ëĬĶëį°",
+      "ר ×ļ",
+      "Ġsun flower",
+      "Ġperpet ual",
+      "ç³ ĸ",
+      "Ġkn ights",
+      "Ġg ird",
+      "ĠTo ld",
+      "Ġvolcano es",
+      "Ġadvers ary",
+      "ĠEconom y",
+      "Ġextra pol",
+      "Ġbl uetooth",
+      "Ġzoom ing",
+      "Ġsk ys",
+      "Ġgen ial",
+      "ÃŃcul os",
+      "amb re",
+      "Ġм еÑĢ",
+      "Ġteen y",
+      "Ġstress ing",
+      "ìķ Į",
+      "ON Y",
+      "Ġtransluc ent",
+      "Ġround ing",
+      "Ġgr ues",
+      "×Ļ׳ ×Ķ",
+      "ap rès",
+      "Ġprue ba",
+      "Ġpoly gon",
+      "Ġblue berry",
+      "ĠProgram m",
+      "Ġtren ches",
+      "Ġse bagai",
+      "Ġpal ate",
+      "Ġla ude",
+      "Ġbehav ed",
+      "Ġlongitud inal",
+      "ĠMod ule",
+      "Ġadm ir",
+      "λ ι",
+      "G reg",
+      "Ġwy st",
+      "Ġpropag ate",
+      "Ġmold s",
+      "ĠT ub",
+      "ĠL oud",
+      "ust o",
+      "Ġun stoppable",
+      "Ġreinfor cing",
+      "éĿŀ常 çļĦ",
+      "ĠпÑĢоблем а",
+      "Ġpot encial",
+      "Ġhe mp",
+      "ìŀ Ķ",
+      "ठ¯",
+      "Ġopt ic",
+      "Ġerfolg reich",
+      "Ñģ Ñĭ",
+      "олÑĮ ÑĪе",
+      "ur st",
+      "ĠPo is",
+      "Ġrespond ents",
+      "Ġneh me",
+      "ĠEx ternal",
+      "ol ate",
+      "H yun",
+      "Ġquart z",
+      "Ġmathematic ian",
+      "Ġbás icamente",
+      "Ġa il",
+      "ìł ľë¥¼",
+      "att utto",
+      "Ġno oit",
+      "Ġaff lict",
+      "ĠOl ga",
+      "èŃ ·",
+      "Ġна ÑĤ",
+      "Ġd ites",
+      "Ġreal idade",
+      "Ġk än",
+      "Ġuniqu eness",
+      "Ġpad res",
+      "Ġsubs idi",
+      "Ġpige ons",
+      "β α",
+      "st ad",
+      "Ġder en",
+      "ĠС лед",
+      "d oo",
+      "ĠопиÑģ ании",
+      "Ġam ber",
+      "Ġgoose bumps",
+      "ĠfrÃ¥ gor",
+      "ĠV ital",
+      "ĠIsrael ites",
+      "w asser",
+      "Is n",
+      "Ġcomm its",
+      "ĠSTE VEN",
+      "ĠBev ölker",
+      "uit ive",
+      "Ġleg en",
+      "Ġbr uk",
+      "иÑĢов ан",
+      "yn en",
+      "hel m",
+      "Ġgener ational",
+      "ĠL ändern",
+      "οι ÏĢÏĮν",
+      "uz u",
+      "Ġcall er",
+      "он ÑĮ",
+      "üm ü",
+      "Ġbes ar",
+      "Ġpl ats",
+      "Ġmig rated",
+      "Ġj ap",
+      "ĠW AR",
+      "Ġdis sect",
+      "ĠZus ch",
+      "ĠZe iten",
+      "ĠL ions",
+      "ĠD F",
+      "â Ķ",
+      "ки в",
+      "Ġpedest rians",
+      "ĠMar ilyn",
+      "d ock",
+      "Ġy ht",
+      "Ġre incarn",
+      "ĠSon o",
+      "ĠGrow th",
+      "ÑĥÑģ ов",
+      "Ġdun geons",
+      "Ġbag us",
+      "k ich",
+      "ĠÑĥ кÑĢаÑĹ",
+      "éĨ «",
+      "ĠK eller",
+      "chem istry",
+      "J apanese",
+      "Ġwill st",
+      "Ġdecomp osition",
+      "ĠÑģÑĤ ен",
+      "Ġrev ived",
+      "íķĻ êµIJ",
+      "ĠÅ ĵ",
+      "ä½ IJ",
+      "ìĭ ¸",
+      "ipp y",
+      "Ġhour ly",
+      "j än",
+      "ĠWork shop",
+      "Ŀ¼ ìĦľ",
+      "Ġcu arto",
+      "Ġpat rim",
+      "ĠB urch",
+      "ĠìŀĪ 기",
+      "Ġhe pat",
+      "Ġh Ãłng",
+      "ĠëĮĢ íķ´",
+      "ĠваÑĪ и",
+      "Ġre work",
+      "Ġpar se",
+      "Ġçıkt ı",
+      "ĠS ax",
+      "ĠMong o",
+      "ĠAa ah",
+      "ram ble",
+      "D J",
+      "Ġstabil ized",
+      "ĠSpe ech",
+      "Book s",
+      "Ġhur dles",
+      "ĠW O",
+      "ĠLamb org",
+      "Ġ19 33",
+      "Ġvor bere",
+      "Ġclin ically",
+      "Ġbreat htaking",
+      "ĠGate way",
+      "пеÑĢв ÑĭÑħ",
+      "ut ers",
+      "Ġë¹ µ",
+      "Ġyet er",
+      "Ġpull ey",
+      "Ġmuff in",
+      "ĠPre fer",
+      "ĠP ence",
+      "Ġinform ação",
+      "ìĬ¤í Ĭ¸ë",
+      "ãĤ¸ ãĥ£",
+      "ĠTur tle",
+      "ĠReg ina",
+      "ĠLo ad",
+      "do es",
+      "pan ze",
+      "¸ Ķ",
+      "Ġmin a",
+      "ĠLatin os",
+      "amm ers",
+      "ĠT ort",
+      "ĠBey once",
+      "имо ÑģÑĤи",
+      "ĠвопÑĢоÑģ Ñĭ",
+      "Ġbul un",
+      "èĢĮ å·²",
+      "ine k",
+      "bere ich",
+      "Ġpast ure",
+      "ĠO A",
+      "ĠM elt",
+      "ĠEt t",
+      "ĠD Y",
+      "Ġob wohl",
+      "Ġle agues",
+      "ÑĤ еÑģÑĮ",
+      "Ġк ÑĥÑģ",
+      "Ġv ors",
+      "Ġto pp",
+      "ograph ical",
+      "as st",
+      "Ġl indo",
+      "Ġë°Ŀ íĺĶ",
+      "Ġré fl",
+      "Ġclim bs",
+      "Ġv arsa",
+      "Ġmethy l",
+      "ĠKar ere",
+      "Æ°á» Ł",
+      "R ad",
+      "Ġprepared ness",
+      "он Ñĩ",
+      "ĠO D",
+      "ĠC GI",
+      "Ġठ®",
+      "Ġspeech less",
+      "Ġlas ci",
+      "Ġbol ag",
+      "ĠÑħоÑĩ еÑĤÑģÑı",
+      "Ġgr ieving",
+      "ĠJohann es",
+      "ĠCar roll",
+      "ad aki",
+      "Ī ¬ë",
+      "ĠsÅĤ u",
+      "Ġinner halb",
+      "Ġgymn astics",
+      "п ÑĢи",
+      "if iques",
+      "Ġkar ate",
+      "Ġdom u",
+      "ãģĿãĤĮ ãģ§",
+      "OTH ER",
+      "Ġdemand é",
+      "Ġbook let",
+      "ĠKy oto",
+      "Ġw oh",
+      "ĠMar ÃŃa",
+      "viol ent",
+      "J E",
+      "Ġl óg",
+      "Ġbrut ally",
+      "c ot",
+      "ĠÙħ ÛĮ",
+      "ĠWars z",
+      "å® Ī",
+      "w ol",
+      "Ġmik ä",
+      "ĠPron ounce",
+      "ĠBrend an",
+      "Ġr oup",
+      "Ġital iano",
+      "å¦Ĥ æѤ",
+      "Ġкомп ÑĮÑİÑĤ",
+      "Ġur ging",
+      "ed es",
+      "Ġcarbon o",
+      "ĠRichards on",
+      "ĠÐĿ аÑĩ",
+      "ĠTra iner",
+      "ĠCrime a",
+      "Ġdi apers",
+      "Ġco vet",
+      "ĠMah ar",
+      "ĠH utch",
+      "ĠAus w",
+      "ber ty",
+      "Ġind ifferent",
+      "кÑĢ еÑĤ",
+      "uld ade",
+      "Ġhar ms",
+      "¢ ÙĨ",
+      "les ia",
+      "Ġg io",
+      "ĠMist ress",
+      "ĠK nox",
+      "ĠFRE E",
+      "Ġë £¨ë",
+      "ĠнаÑĪ а",
+      "Ġinvinci ble",
+      "Ġma iden",
+      "ĠJ eez",
+      "Ġbre ve",
+      "po le",
+      "Ġcritic isms",
+      "ĠRus ia",
+      "ठ®",
+      "ph in",
+      "ĠComp are",
+      "ĠB ON",
+      "Ġsne aking",
+      "ĠR ails",
+      "ĠG eral",
+      "Ġ195 3",
+      "H ola",
+      "Ġоп ÑĭÑĤ",
+      "Ġrain forest",
+      "Ġbel um",
+      "ĠOb i",
+      "ĠIS S",
+      "ãĤĮ ãģªãģĦ",
+      "ĠС в",
+      "Ġbl ond",
+      "Ġwz gl",
+      "Ġpowiedz iaÅĤ",
+      "Ġch oking",
+      "ĠSong s",
+      "ĠBir az",
+      "Ġyell s",
+      "Ġstyl ist",
+      "ÏĮ ÏĦε",
+      "Ġsch reiben",
+      "ĠJ aw",
+      "ĠEle ven",
+      "ĠR if",
+      "/ .",
+      "Ġìĺ¤ë ŀľë§Į",
+      "Ġtreat ies",
+      "uff ed",
+      "ĠâĪ Ĵ",
+      "Ġroof s",
+      "à¹Ģภª",
+      "Ġë »",
+      "Ġspark le",
+      "ĠK iev",
+      "ĠAr gu",
+      "ere cht",
+      "ĠÐĿад о",
+      "ĠF IL",
+      "Ġmol ta",
+      "ĠDe vi",
+      "Ġcam pe",
+      "Ġbene vol",
+      "ĠT ough",
+      "Ġmo im",
+      "Ġevac uate",
+      "Ġer rado",
+      "å© Ĩ",
+      "ÑĢÑĥ го",
+      "Ġíİ ĺ",
+      "ĠÎĵ ια",
+      "Ġweak en",
+      "Ġillum inated",
+      "Ġsig lo",
+      "ĠV acc",
+      "и ей",
+      "al is",
+      "ĠÑĥ ÑģÑĤÑĢой",
+      "Ġdon a",
+      "ÅĤ os",
+      "ü man",
+      "Ġprodu cción",
+      "Ġcl ot",
+      "ĠM ango",
+      "Ġune asy",
+      "Ġsh uts",
+      "ĠExam ples",
+      "ve ll",
+      "e be",
+      "Ġprompt ly",
+      "ĠT eles",
+      "ĠпÑĢоÑĪ л",
+      "Ġpu erta",
+      "Ġüber zeug",
+      "Ġco ch",
+      "so cial",
+      "ĠB enson",
+      "ĠM eth",
+      "ĠEx ped",
+      "Ġsupplement al",
+      "Ġconce ive",
+      "Ġ×ĺ ×ķ×ij",
+      "Ġcapt ivity",
+      "ıĻ ìķĪ",
+      "ĠÑħ Ñĥд",
+      "form ing",
+      "Ġupload s",
+      "Ġturbul ence",
+      "j oint",
+      "Ġsatisf actory",
+      "ĠAn ime",
+      "Ġwash es",
+      "Ġliber als",
+      "ĠSun shine",
+      "ĠRE AL",
+      "ub lik",
+      "b inary",
+      "T ony",
+      "Ġpolar ized",
+      "Ġenrich ed",
+      "t aking",
+      "ĠëģĿ ëĤĺ",
+      "Ġple asures",
+      "Ġex termin",
+      "in ese",
+      "at l",
+      "v är",
+      "аÑĢ Ñĭ",
+      "Ġmy ÅĽ",
+      "n arrator",
+      "Ġод ном",
+      "Ġnaj wiÄĻ",
+      "Ġmobil ize",
+      "Ġmill or",
+      "Ġat a",
+      "æ· ·",
+      "ĠpolÃŃt ico",
+      "Ġple ad",
+      "Ġpain ters",
+      "ĠS ow",
+      "о ÑĦ",
+      "ĠìĺĽ ëĤł",
+      "ĠÑĩ ÑĤоб",
+      "Ġs abor",
+      "ĠUnd ert",
+      "ĠJER RY",
+      "Å¡ ÃŃ",
+      "Ġë° ĸìĹIJ",
+      "Ġpréc éd",
+      "Ġannot ation",
+      "ĠI naudible",
+      "Ġtext ured",
+      "Ġfisher man",
+      "v ordan",
+      "icher ung",
+      "Ġìłģ ìĿ´",
+      "Ġge zeigt",
+      "Ġmand ates",
+      "Ġbe ak",
+      "ĠTW O",
+      "ĠAk bar",
+      "il ian",
+      "Ġtiế p",
+      "Ġsuperior ity",
+      "ink u",
+      "Ġl ys",
+      "ĠF CC",
+      "ĠC PA",
+      "ust ering",
+      "nic os",
+      "an ja",
+      "Ġch ills",
+      "ĠC age",
+      "Ġse aling",
+      "Ġsa ç",
+      "Ġded ans",
+      "ĠAl ger",
+      "Ġspe zie",
+      "Ġcol oss",
+      "ıy ı",
+      "clock wise",
+      "Ġexact amente",
+      "Ġ iemand",
+      "am ı",
+      "Ġmand ar",
+      "ra j",
+      "f aced",
+      "ag ua",
+      "Ġê¹ Ķë",
+      "Ġins besondere",
+      "Ġdri zzle",
+      "Ġdimin ish",
+      "ĠY oda",
+      "A I",
+      "Ġbil miyorum",
+      "ĠM MA",
+      "ateg ory",
+      "ĠпеÑĢ еп",
+      "Ġparticip ar",
+      "Ġnormal ized",
+      "Ġcomplex ities",
+      "æ´ ²",
+      "æİ §",
+      "аÑĢ ов",
+      "m ist",
+      "ich a",
+      "Gr oup",
+      "Ġresil iency",
+      "Ġnog le",
+      "ĠCN C",
+      "pr ü",
+      "Ġphysic ists",
+      "н ок",
+      "L I",
+      "Ġstuff s",
+      "Ġsist emas",
+      "Ġinterfer ing",
+      "ĠMar vin",
+      "ér cito",
+      "ĠìĹĨ ê³ł",
+      "Ġson ic",
+      "Ġequ iv",
+      "Ġab ord",
+      "ĠRam en",
+      "Ġ0 9",
+      "med im",
+      "at iques",
+      "Ġдел аÑİÑĤ",
+      "Ġunanim ously",
+      "Ġsk irts",
+      "ĠíĬ¹ ë³Ħ",
+      "ĠP rix",
+      "k ami",
+      "Ġfr uition",
+      "Ġbirthday s",
+      "ик ом",
+      "Ġinaug ural",
+      "Ġcorrel ate",
+      "ĠT ory",
+      "ĠëĤĺ ìģ",
+      "Ġde w",
+      "ĠPre cis",
+      "ih i",
+      "Ġë¬¸ìłľ ê°Ģ",
+      "Ġc iting",
+      "ĠL ana",
+      "ĠK ag",
+      "Ġplay through",
+      "ĠProt ocol",
+      "fr ist",
+      "hov ah",
+      "Ġmerc iful",
+      "Ġb ilingual",
+      "ĠG uitar",
+      "r h",
+      "Ġglam orous",
+      "ĠVik ings",
+      "ĠOoo oh",
+      "íķĺ ëĬĶëį°",
+      "ĠUg anda",
+      "Ġcollaps es",
+      "ent ry",
+      "Ġantioxid ants",
+      "ëĤ ĺë",
+      "ÑĪ аÑı",
+      "Ġtri via",
+      "Ġgä ller",
+      "Ġfun gi",
+      "Ġmil ks",
+      "Ġd icht",
+      "μ η",
+      "po ke",
+      "ĠвÑĭп ÑĥÑģк",
+      "Ġfeed er",
+      "ĠAl cohol",
+      "h ower",
+      "Ġdes erving",
+      "ĠRe bel",
+      "ios is",
+      "Ġ10 3",
+      "Ġhand out",
+      "Ġen m",
+      "Ġland lords",
+      "Ġge ology",
+      "r ils",
+      "Ġco bra",
+      "ĠV old",
+      "ĠP anch",
+      "ĠGRE G",
+      "Ġpr oss",
+      "Ġbrac elets",
+      "ĠV ega",
+      "Ġroz um",
+      "æ¬ ¾",
+      "аз д",
+      "ĠLy nd",
+      "ĠHon ors",
+      "Ġsurrend ered",
+      "Ġlibr arians",
+      "12 5",
+      "ĠÑģ иг",
+      "Ġuniform ly",
+      "ĠE agles",
+      "ìķ Ļ",
+      "иÑĤ ан",
+      "and id",
+      "ĠìłĪë ĮĢ",
+      "ĠØ ¶",
+      "Ġarrest s",
+      "ĠCS V",
+      "ĠAzerbai jan",
+      "ort ic",
+      "ĠD X",
+      "ĠAdvent ures",
+      "Ġab us",
+      "ĠF au",
+      "Ġschlim m",
+      "Ġratt ling",
+      "Ġconsum es",
+      "ĠTol kien",
+      "Ġresurrect ed",
+      "ĠX Y",
+      "íĬ¸ ê°Ģ",
+      "ĠвÑĭ ÑģÑĤÑĥп",
+      "ĠAng ie",
+      "żen ia",
+      "M ic",
+      "ĠShe ila",
+      "acht et",
+      "Ġover st",
+      "Ġl â",
+      "Ġine ffective",
+      "æĿ ¡",
+      "æĢİä¹Ī äºĨ",
+      "å¿ Ļ",
+      "Ġwicht iger",
+      "Ġv ino",
+      "Ġp um",
+      "Ġang led",
+      "ĠP ione",
+      "ĠM ỹ",
+      "ãģĿãĤĮ ãģ¯",
+      "wo ÅĽÄĩ",
+      "d raw",
+      "ั à¹Ī",
+      "mark ets",
+      "Ġcaf es",
+      "ĠC em",
+      "â Ŀ¤",
+      "ĠS uit",
+      "M K",
+      "Ġemphas izes",
+      "Ġtort illa",
+      "Ġmejor ar",
+      "ĠSur viv",
+      "cast ing",
+      "Ġeduc ación",
+      "ĠG um",
+      "u ely",
+      "ĠìĹ¬ê¸° ëĬĶ",
+      "Ġstretch y",
+      "en ça",
+      "Ġwith hold",
+      "Ġex iting",
+      "Ġenthal py",
+      "ĠTrans it",
+      "ıl mÄ±ÅŁ",
+      "al ies",
+      "Ġsal var",
+      "Ġlean ed",
+      "ĠgroÃŁ es",
+      "Ġf itt",
+      "ак и",
+      "S arah",
+      "Ġhost el",
+      "Ġfinger na",
+      "Ġnadzie jÄĻ",
+      "w ives",
+      "R ec",
+      "Ġsp ool",
+      "аÑĤ ов",
+      "ĠEn emy",
+      "Ġf ury",
+      "Ġdet ta",
+      "ĠF ay",
+      "éļ ¨",
+      "Ñı ÑİÑĤ",
+      "Ġaproxim adamente",
+      "Ġsil os",
+      "Ġmag ist",
+      "Ġc ree",
+      "ĠKr ank",
+      "ĠD OWN",
+      "Ġstart led",
+      "Ġre born",
+      "ĠUm welt",
+      "ĠSuz anne",
+      "ни ÑĨÑĭ",
+      "out ez",
+      "ĠJ AC",
+      "y ards",
+      "rad as",
+      "ra u",
+      "ip ts",
+      "h ail",
+      "Ġparagraph s",
+      "Ġme glio",
+      "Ġisol ating",
+      "Ġace ite",
+      "ĠH arsh",
+      "Ġcy st",
+      "ĠBlock chain",
+      "ĠÑħоÑĢоÑĪ ий",
+      "Ġvirt uous",
+      "Ġinvestig ación",
+      "Ġdev oir",
+      "Ġmast urb",
+      "ĠS ale",
+      "ÙĬر Ø©",
+      "ĠÎ §",
+      "ĠStra ÃŁen",
+      "Ġdi kk",
+      "Ġa fore",
+      "ĠJung kook",
+      "Ġcho ciaż",
+      "ĠDebat te",
+      "Ġweird ly",
+      "Ġvia je",
+      "reg ist",
+      "H elp",
+      "Ġkind eren",
+      "Ġform ulated",
+      "Ġenf im",
+      "ĠTow ards",
+      "ко ÑĹ",
+      "iver ing",
+      "ĠдеÑĤ и",
+      "char ger",
+      "Ġpur l",
+      "Ġacadem ically",
+      "ĠNur se",
+      "Ġdel eting",
+      "ay o",
+      "Ġref usal",
+      "Ġdepict s",
+      "ĠDr acula",
+      "Ġtoast ed",
+      "ĠZomb ie",
+      "ĠSuper ior",
+      "ĠB old",
+      "Ġquizz es",
+      "Ġg le",
+      "4 50",
+      "Ġcome ço",
+      "yn n",
+      "Ġver st",
+      "ĠO laf",
+      "Ġpom oc",
+      "ĠS ask",
+      "ë ĺ",
+      "ĠT CP",
+      "ĠProper ty",
+      "íķĺ ì£ł",
+      "à¸ľ ม",
+      "bo om",
+      "ar os",
+      "ĠÑĢоÑģÑģ ий",
+      "ĠбÑĭв аеÑĤ",
+      "åĩº åİ»",
+      "ĠìĿ´ìķ¼ 기를",
+      "Ġcomb ien",
+      "v acc",
+      "Ġeben falls",
+      "par a",
+      "Ġз м",
+      "Ġdesper ation",
+      "ord re",
+      "Ġש׾ ×Ļ",
+      "Ġgener ously",
+      "ĠÐŀ к",
+      "Ġorb iting",
+      "> </",
+      "Ġesp ÃŃ",
+      "ĠCO P",
+      "åŃ© åŃIJ",
+      "vis ible",
+      "ĠпÑĢеÑģÑĤ Ñĥп",
+      "Ġstitch ed",
+      "à¯Ī .",
+      "Ġlat ent",
+      "ĠP rab",
+      "ĠMc N",
+      "ĠHe aling",
+      "ĠCur iosity",
+      "c ert",
+      "Ġ민 주",
+      "Ġpatient ly",
+      "ĠY T",
+      "fore ign",
+      "Ġv ẫn",
+      "Ġindust ri",
+      "Ġcock tails",
+      "Ġbright en",
+      "Ġconsolid ated",
+      "аÑĢ д",
+      "lt ry",
+      "Ġgr ille",
+      "Ġb ona",
+      "Ġdilig ently",
+      "ĠWrestle Mania",
+      "er kt",
+      "ener gy",
+      "99 9",
+      "à®ķ வ",
+      "Ġto te",
+      "ion o",
+      "DI O",
+      "Ġschizophren ia",
+      "Ġpostp oned",
+      "ĠQ iu",
+      "ĠÏĥÏħ ν",
+      "Ġzd jÄĻ",
+      "Ġspann end",
+      "ĠD IS",
+      "R el",
+      "Ġr hin",
+      "imm une",
+      "O ld",
+      "Ġpl ötzlich",
+      "Ġm ound",
+      "Ġastronom ical",
+      "ĠGu id",
+      "ĠC ul",
+      "H I",
+      "ĠÅ ł",
+      "Ġrep o",
+      "ĠMaur ice",
+      "ä¸Ģ çĤ¹",
+      "Ġband its",
+      "ĠDes ktop",
+      "ä ss",
+      "ft a",
+      "Ġlic ence",
+      "Ġimag inar",
+      "ĠEntre prene",
+      "x o",
+      "Ġ맼ìŀĪ ëĬĶ",
+      "Ġ×Ķ× ij",
+      "Ġpump kins",
+      "Ġkans sa",
+      "ĠjÄĻ zy",
+      "Ġcommunaut é",
+      "b ür",
+      "Ġer hö",
+      "ĠWol ver",
+      "ĠSh aring",
+      "ä» ¤",
+      "Ġpak ai",
+      "Ġinsult ed",
+      "Ðľ Ñĭ",
+      "о ÑĹ",
+      "Ġconsist e",
+      "æĮ ij",
+      "Ġyoung sters",
+      "Ġgleich en",
+      "w eder",
+      "Ġm ote",
+      "Ġcla uses",
+      "ét at",
+      "pr us",
+      "Ġwas t",
+      "ç»Ļ æĪij",
+      "ĠCr isp",
+      "Ġ çĦ¶å¾Į",
+      "Ġoff enders",
+      "Ġconve ction",
+      "Ġconf ian",
+      "o llow",
+      "am et",
+      "ĠÑĹ Ñħ",
+      "第äºĮ åĢĭ",
+      "ffic iency",
+      "Ġung laub",
+      "ig ans",
+      "Ġmarket ed",
+      "ĠV AN",
+      "Ġproc laimed",
+      "Ġcél ulas",
+      "Ġcoll ide",
+      "ĠO culus",
+      "ad ore",
+      "J i",
+      "Ġsust aining",
+      "ĠF asc",
+      "Ġset zt",
+      "Ġnos altres",
+      "M ost",
+      "Ġв Ñĩ",
+      "Ġna uc",
+      "ĠB har",
+      "çĪ¸ çĪ¸",
+      "æĪijè·Łä½ł è¬Ľ",
+      "Ġy êu",
+      "Ġtim est",
+      "Ġpert ama",
+      "ir mi",
+      "Ġz wr",
+      "Ġverb ess",
+      "Ġv ortex",
+      "ĠST ACK",
+      "ث ر",
+      "¹ Ħë",
+      "ĶĶ ìĺ¤",
+      "Ġlink age",
+      "ĠFr aser",
+      "en ario",
+      "Ġë Ŀ¼ëĬĶ",
+      "ĠìĦłë °°",
+      "ht hal",
+      "Ġê¹ Į",
+      "ĠKh ông",
+      "Ã ĥ",
+      "Ġscr ambled",
+      "ĠE ink",
+      "Ġmicro organ",
+      "Ġnarciss ist",
+      "ĠKomb at",
+      "Ġë§ ¡",
+      "ĠA GA",
+      "Ġperf ekt",
+      "ĠSer ie",
+      "det erm",
+      "- '",
+      "Ġpony tail",
+      "Ġkos ka",
+      "ì ĵ",
+      "Ġo bec",
+      "Ġch ests",
+      "ve er",
+      "Ġup rising",
+      "Ġst oked",
+      "as soci",
+      "Ġprodu ção",
+      "ĠSha pe",
+      "ìłľ ê°Ģ",
+      "ĠëĶ °",
+      "Ġj on",
+      "Ġinad vert",
+      "ant as",
+      "Ġнак онеÑĨ",
+      "Ġå°į åķĬ",
+      "ĠArsen al",
+      "Ġprot eg",
+      "Ġlibert é",
+      "Ġgl are",
+      "åĪ ļ",
+      "å·² ç»ı",
+      "Ġvere in",
+      "Ġinsert s",
+      "ĠJ ana",
+      "Ġwyd aje",
+      "ÅĤ um",
+      "Ġ %.",
+      "orig ine",
+      "Ġsyn agogue",
+      "Ġfall ait",
+      "Ġdis obed",
+      "Ġant ic",
+      "ĠCy cl",
+      "Ġasynchron ous",
+      "Ġë²Į ìį¨",
+      "Ġges und",
+      "Ġg agn",
+      "Ġpe a",
+      "Ġgr in",
+      "é st",
+      "Ġsa uc",
+      "ĠM äd",
+      "íķ´ë ıĦ",
+      "pp s",
+      "ĠεÏĢ ι",
+      "Ġpeu ple",
+      "Ġde ben",
+      "ĠB ree",
+      "ĠÑĢ олÑĮ",
+      "Ġкак им",
+      "Ġú til",
+      "Ġdistrib utor",
+      "ал Ñĭ",
+      "Ġswo jÄħ",
+      "Ġfol klore",
+      "Ġrece ivers",
+      "ĠM OO",
+      "b ins",
+      "ast re",
+      "ìķ Īë",
+      "ĠëĦ£ ê³ł",
+      "Ġmultim edia",
+      "Ġgeb aut",
+      "ов ÑĭÑħ",
+      "ã y",
+      "Ġd ane",
+      "ok ol",
+      "emit ism",
+      "ONE Y",
+      "Ġya ÄŁ",
+      "Ġcha uff",
+      "容 æĺĵ",
+      "Ġesf uer",
+      "Äĥ n",
+      "ert as",
+      "Ġfonction ne",
+      "om ina",
+      "Ġiv ory",
+      "ĠYout uber",
+      "ĠSky walker",
+      "иÑĩеÑģ каÑı",
+      "to i",
+      "Ġve ya",
+      "Ġgel ernt",
+      "Ġchance llor",
+      "ĠStat istics",
+      "Ġweld ed",
+      "Ġon dan",
+      "ĠSe i",
+      "Ġmed ically",
+      "Ġenerg ized",
+      "ĠV ia",
+      "Ġв ик",
+      "Ġun inter",
+      "Ġhigh ness",
+      "ĠíĮ Ķë",
+      "Ġampl ified",
+      "ĠSerge y",
+      "ĠM ins",
+      "w arm",
+      "pe ll",
+      "oph ile",
+      "Ġh è",
+      "ĠBel o",
+      "ĠSket ch",
+      "Ġcharacter ization",
+      "ans en",
+      "ĠÑĤ ÑĥÑĢ",
+      "Ġ ãħĭãħĭãħĭ",
+      "N ote",
+      "Ġko ÅŁ",
+      "Ġc iert",
+      "f lu",
+      "Ġba ht",
+      "ĠDownt own",
+      "ĠC RIS",
+      "od ie",
+      "1 40",
+      "Ġlit res",
+      "Ġgr iev",
+      "æ§ ĺ",
+      "ĠìĶ¨ ê°Ģ",
+      "Ġsucceed s",
+      "Ġ __",
+      "ent ing",
+      "Ġv imos",
+      "Ġs ì",
+      "def ense",
+      "ĠMc D",
+      "ĠMar ion",
+      "ĠD ont",
+      "ĠD DR",
+      "ĠL azar",
+      "ĠD AR",
+      "Ġk uv",
+      "K n",
+      "Ġsemb la",
+      "Ġair borne",
+      "ĠViol ence",
+      "ëIJ IJ",
+      "Ġrestra int",
+      "Ġwh istles",
+      "Ġscold ed",
+      "Ġacces o",
+      "Ġabsolut amente",
+      "ĠT yl",
+      "ĠS ap",
+      "¶Ģë ¶Ħ",
+      "itä ten",
+      "ad em",
+      "ĠÃ ½",
+      "Ġpres cribe",
+      "ĠM age",
+      "ĠHel ena",
+      "å¾Ī æľī",
+      "äº ²",
+      "v t",
+      "Ġv ienen",
+      "Ġsne ez",
+      "Ġmol é",
+      "Æ°á»Ł ng",
+      "Ġtransport ing",
+      "ĠLe an",
+      "Ġk ung",
+      "Ñĥ ÑĢа",
+      "ÏĦ ÎŃ",
+      "ut ches",
+      "ond ers",
+      "li yor",
+      "N at",
+      "Ġz ij",
+      "Ġmamm al",
+      "Ġkä yt",
+      "ĠJo anna",
+      "s ent",
+      "Ġठ¸",
+      "Ġvest ed",
+      "ĠErfahr ung",
+      "oke e",
+      "Ġcl ipping",
+      "ĠList ening",
+      "Ġ( #",
+      "f ö",
+      "Ġvid are",
+      "Ġbr ittle",
+      "ĠST ART",
+      "ĠDam as",
+      "ĠY og",
+      "ãĤĵ ãģ¨",
+      "g art",
+      "Ġver lier",
+      "Ġheart felt",
+      "Ġdo ÅĽÄĩ",
+      "ì¹ĺ ê°Ģ",
+      ". »",
+      "Ġmaxim al",
+      "Ġdistint os",
+      "ĠìĻľë ĥIJíķĺë©´",
+      "Ġsa iled",
+      "Ġconvey ed",
+      "ĠT inder",
+      "ĠSU PER",
+      "ни ÑĨÑĥ",
+      "cont rolled",
+      "Ġfun z",
+      "Ġbast ards",
+      "ĠGins burg",
+      "Ġnuo vo",
+      "ĠP ere",
+      "ĠJ ES",
+      "ĠD ingen",
+      "ĠB ets",
+      "umb a",
+      "ac ción",
+      "ĠìŀĪ ì§Ģë§Į",
+      "Ġret ra",
+      "ĠLaure nt",
+      "Ġpo zy",
+      "Ġgroo ves",
+      "Ġmáqu ina",
+      "Ġmin ion",
+      "Ġde inen",
+      "ĠSha un",
+      "×Ļ ×Ļ",
+      "Ġhonor ary",
+      "osaur us",
+      "Ġze it",
+      "Ġespe cie",
+      "ĠB CE",
+      "аÑĤ е",
+      "Just in",
+      "ĠWhe els",
+      "ĠìĿ´ íķ´",
+      "Ġب ÙĬÙĨ",
+      "Ġprop ulsion",
+      "Ġperce ber",
+      "ĠNew man",
+      "å ´",
+      "cul osis",
+      "M i",
+      "Ġак кÑĥ",
+      "Ġmaster ing",
+      "Ġl äh",
+      "Ġf ists",
+      "ä» Ķ",
+      "Ġmarin ade",
+      "L illy",
+      "Ġëħ¸ë ł¥",
+      "ĠY H",
+      "Ġurg ently",
+      "Ġinform ational",
+      "Ġac ordo",
+      "iz zy",
+      "ãģĦ ãģı",
+      "ìĿ´ì ĸ´",
+      "im ar",
+      "ĠëĤĺìĺ ¤ë",
+      "Ġtwent ies",
+      "Ġr asp",
+      "Ġbump y",
+      "ب ة",
+      "work er",
+      "Ġquick est",
+      "Ġattach es",
+      "в иг",
+      "ĠëĤĺ íĥĢë",
+      "Ġpure e",
+      "Ġovers ized",
+      "Ġstir red",
+      "Ġjak im",
+      "Ġhom icide",
+      "ãĤĤ ãģĹ",
+      "isc illa",
+      "Ġì± Ļ",
+      "Ġspec ulative",
+      "Ġass ists",
+      "m ain",
+      "j ähr",
+      "ind et",
+      "ĠÅŁ ur",
+      "Ġforecast s",
+      "Ġdivers ion",
+      "Ġt are",
+      "Ġog l",
+      "ĠOrgan isation",
+      "ĠChe vy",
+      "Ġb aja",
+      "and ır",
+      "ĠÙĪ ÙĦا",
+      "Ġrad iant",
+      "Ġli aison",
+      "Ġdem okrat",
+      "ĠMAR C",
+      "ÏĢ οÏħ",
+      "Ġrun t",
+      "Ġpréc is",
+      "Ġge ven",
+      "Ġvé hic",
+      "ĠJ ESS",
+      "ST R",
+      "Ġìĸ ĺë",
+      "Ġvision ary",
+      "Ġbur adan",
+      "ĠãģĤ ãĤĬ",
+      "Ġre birth",
+      "Ġexhib ited",
+      "ĠMet all",
+      "ol ie",
+      "ely n",
+      "Ġflav ours",
+      "Ġesc rito",
+      "ĠDel ete",
+      "ĠìķĮ ìķĺìĸ´",
+      "ĠÑĥкÑĢаÑĹ Ð½",
+      "Ġinterrupt ing",
+      "Ġident ific",
+      "ĠSuz uki",
+      "ĠLand ing",
+      "件 äºĭæĥħ",
+      "and i",
+      "Ġest ran",
+      "Ġcoule ur",
+      "Ġag rad",
+      "ĠS ny",
+      "Ġà®ĩ ல",
+      "Ġand er",
+      "Ġr ua",
+      "Ġpr ise",
+      "Ġla ure",
+      "ĠíĬ Ģ",
+      "Ġmod eration",
+      "Ġerf ahren",
+      "Ġdec onst",
+      "ĠRe ese",
+      "ĠP K",
+      "et os",
+      "ãģĵãĤĮ ãģ§",
+      "ĠGra vity",
+      "ĠE ren",
+      "Ġover board",
+      "Ġmü sst",
+      "ĠEm ail",
+      "еÑĢ м",
+      "y di",
+      "iÄĻ dzy",
+      "ĠL OU",
+      "ĠFuÃŁ ball",
+      "ĠR D",
+      "al ts",
+      "ĠìĬ¤í Ĭ¸ë",
+      "ĠÐļ ÑĢаÑģ",
+      "Ġtele v",
+      "ĠÑĢ о",
+      "Ġresign ation",
+      "Ġj ingle",
+      "ĠStud ien",
+      "ĠI X",
+      "ĠSent inel",
+      "ĠP ang",
+      "é Ħ",
+      "J ake",
+      "Ġperson agem",
+      "Ġméd ia",
+      "ĠC hern",
+      "ant ically",
+      "Ġth á»Ŀi",
+      "Ġparal ysis",
+      "Ġj apanese",
+      "Ġcon ex",
+      "Ġe fic",
+      "Ġunders ide",
+      "Ġne ol",
+      "Ġf ian",
+      "имо ÑģÑĤÑĮ",
+      "Ġquir ky",
+      "Ġp ista",
+      "ĠC lement",
+      "not hing",
+      "Ġпо еÑħ",
+      "Ġhor rend",
+      "Ġconsolid ate",
+      "plo ys",
+      "em aker",
+      "Jenn ifer",
+      "Ġnum éro",
+      "Ġfam oso",
+      "ĠNept une",
+      "ĠíĸĪ ìĸ´",
+      "ĠпÑĢез ид",
+      "Ġsit com",
+      "Ġser io",
+      "Ġm ue",
+      "Ġgl ands",
+      "Ġbör jar",
+      "ĠY J",
+      "ĠR iot",
+      "par agus",
+      "Ġseg urança",
+      "Ġimm ature",
+      "ĠMad onna",
+      "ภį",
+      "Ġling ering",
+      "Ġac esso",
+      "ĠOri ent",
+      "ĠRe comm",
+      "Ġcompl ac",
+      "found ed",
+      "att end",
+      "Ġciel o",
+      "ĠZ han",
+      "na ires",
+      "c co",
+      "Ġ×IJ× ł",
+      "Ġstat a",
+      "Ġcontradict ory",
+      "ĠS é",
+      "ĠS AN",
+      "ĠCon nie",
+      "Ġëĭ¹ ìĭľ",
+      "ĠÑģам ой",
+      "Ġmaj estic",
+      "ĠPeng uin",
+      "ĠCO ME",
+      "ÃŃ cios",
+      "per o",
+      "Ġm g",
+      "Ġfa uc",
+      "Ġcor rer",
+      "ĠGott es",
+      "ĠAng lo",
+      "H ar",
+      "á»Ĺ i",
+      "Ġv itesse",
+      "Ġannoun cer",
+      "ĠOm aha",
+      "k um",
+      "Ġsp ared",
+      "ĠÑĢаз а",
+      "ĠполÑĥÑĩ иÑĤÑģÑı",
+      "Ġt ähän",
+      "Ġпон ад",
+      "Ġpert aining",
+      "ĠR ate",
+      "ier n",
+      "G old",
+      "Ġtest e",
+      "ĠdeÄŁ ild",
+      "Ġdamp ing",
+      "ĠPartners hip",
+      "zy sta",
+      "g eld",
+      "Ġsm okes",
+      "ĠMar riage",
+      "쪽 ìĹIJ",
+      "èħ ³",
+      "is ce",
+      "Ġtry na",
+      "ĠDirect ory",
+      "ĠëĤĺìĺ ¬",
+      "Ġshame ful",
+      "Ġment re",
+      "Ġass igning",
+      "æĺ¯ éĢĻ樣",
+      "Ġreper toire",
+      "Ġobjet os",
+      "ç¨ ±",
+      "Ġunder world",
+      "Ġende avors",
+      "Ġign ite",
+      "ĠÙĪ ج",
+      "Ġexper ient",
+      "ĠÐĹ Ð°Ð¿",
+      "Ġзак лÑİÑĩ",
+      "Ġvolt ages",
+      "Ġnie go",
+      "Ġdefic its",
+      "Ġbu enos",
+      "ĠSleep ing",
+      "ĠSal em",
+      "Ġunlock ing",
+      "Ġinteract ed",
+      "Ġent endeu",
+      "ĠSuper intendent",
+      "Ġszcz egól",
+      "Ġqu as",
+      "Ġpal ing",
+      "Ġk ho",
+      "ب ØŃ",
+      "Ġcol abor",
+      "ĠпÑĢи гоÑĤов",
+      "Ġma uv",
+      "ĠJud as",
+      "ĠAss ist",
+      "ĠÑĤеÑĢ ÑĢи",
+      "Ġна ÑģколÑĮко",
+      "Ġsubsid y",
+      "ĠEmb assy",
+      "Ġd agen",
+      "ĠS anto",
+      "èĪ ¬",
+      "ש ×ķ×ij",
+      "Ġabrupt ly",
+      "ĠAd apt",
+      "Ġva ak",
+      "Ġpost al",
+      "Ġinvest ir",
+      "Ġfique i",
+      "Ġdownt ime",
+      "ĠWe bb",
+      "ĠNC AA",
+      "ĠEst oy",
+      "ол оÑĤ",
+      "ĠìĤ¬ ê±´",
+      "Ġnational ist",
+      "ĠKath ryn",
+      "ĠK op",
+      "é ª",
+      "Se an",
+      "ON A",
+      "ĠB j",
+      "×¢ ×Ŀ",
+      "ÃŃ b",
+      "id amente",
+      "Ġглаз а",
+      "Ġun nie",
+      "Ġgema akt",
+      "ĠINTERVIE WER",
+      "ĠH aut",
+      "ί ο",
+      "ge ois",
+      "w ydd",
+      "Ġкол и",
+      "Ġtight ened",
+      "Ġpl anners",
+      "Ġher um",
+      "Ġgör ün",
+      "Ġelectron ically",
+      "Ġcer am",
+      "Ġëĭ¤ìĸij íķľ",
+      "Ġepile psy",
+      "Ġe ÄŁ",
+      "l ins",
+      "ĠSh iny",
+      "æł ¡",
+      "ĠÑģ олн",
+      "Ġmac aron",
+      "Ġimpact o",
+      "ĠVeg an",
+      "ze ÅĦ",
+      "ĠRap ha",
+      "ĠP ars",
+      "ĠL EO",
+      "ãģĬ ãģ£",
+      "c ü",
+      "Ġ׾×Ķ ×Ļ×ķת",
+      "Ġä hnlich",
+      "Ġfl oss",
+      "ĠA Z",
+      "Ġmö chten",
+      "Ġgroom ing",
+      "Ġgrass es",
+      "ran ch",
+      "Ġreci bir",
+      "Ġboun cy",
+      "ĠHob by",
+      "Ġvikt ig",
+      "Ġbeg itu",
+      "ĠPicas so",
+      "ĠK ush",
+      "ë ª¨",
+      "Ġobst ruction",
+      "Ġë¶Ħ ìľĦ",
+      "Ġmicro b",
+      "ĠWest minster",
+      "rop s",
+      "d ul",
+      "Ġdev o",
+      "ĠLehr er",
+      "ĠAd visor",
+      "uck en",
+      "Ġб Ñĥм",
+      "Ġfl attering",
+      "ĠTr uman",
+      "ĠSem pre",
+      "ĠMcC ain",
+      "ĠHind us",
+      "Jul ia",
+      "Ġwaters hed",
+      "Ġl ush",
+      "ìł Ħë",
+      "Be fore",
+      "ĠÐĴ ÑĤоÑĢ",
+      "ĠS aaS",
+      "Ġsit zt",
+      "Ġbeet le",
+      "ĠEss ential",
+      "en ko",
+      "ĠëķĮë ıĦ",
+      "Ġrev ving",
+      "Ġpoor er",
+      "Ġco erc",
+      "Ġide e",
+      "Ġco û",
+      "al et",
+      "Ġzd row",
+      "Ġf ender",
+      "grow th",
+      "D ING",
+      "Ġz de",
+      "ä¸Ĭ éĿ¢",
+      "EN TS",
+      "Ġfac ets",
+      "éļ ª",
+      "ush ima",
+      "ĠÅŁ eh",
+      "Ġparas ite",
+      "Ġl apse",
+      "ĠMe er",
+      "ĠK und",
+      "Ġsl og",
+      "Ġbr unch",
+      "ĠCh art",
+      "ar z",
+      "ĠM US",
+      "Ġoff enses",
+      "Ġingl és",
+      "Ġfol iage",
+      "op lan",
+      "A ut",
+      "ĠJac qu",
+      "t ak",
+      "ie mbre",
+      "Ġx en",
+      "Ġnom inees",
+      "Ġbi omedical",
+      "és us",
+      "Ġest uv",
+      "ÏĦ ÏĮ",
+      "ATH AN",
+      "Ġíķľë į°",
+      "Ġhe ed",
+      "cros stalk",
+      "B ill",
+      "Ġsp ouses",
+      "ĠÑģÑİ Ð¶",
+      "Ġver so",
+      "ĠS ven",
+      "ĠC au",
+      "c uz",
+      "Ġë³´ì Ħ¸ìļĶ",
+      "ĠÑħ озÑı",
+      "Ġmock ing",
+      "ĠO na",
+      "ĠD á",
+      "Ġfruit ful",
+      "Ġban quet",
+      "ud ding",
+      "in ctions",
+      "d ert",
+      "s ud",
+      "Ġdes con",
+      "ĠJ C",
+      "ĠÂ §",
+      "Ġpub li",
+      "ëĪ Ī",
+      "éģķ ãģĨ",
+      "Ġents chieden",
+      "ĠRO I",
+      "ãģį ãģŁ",
+      "ĠìĥĿ ê²¼",
+      "Ġkä ytt",
+      "y ani",
+      "sh aw",
+      "Ġunle ash",
+      "Ġman ne",
+      "Ġhist ogram",
+      "æĬ ¥",
+      "à¸Ńะ à¹Ħร",
+      "Ġg n",
+      "Ġfe lla",
+      "Ġeing es",
+      "ĠBu ilt",
+      "Ġrepresent a",
+      "Ġpun ishing",
+      "Ġouts iders",
+      "нÑĥ ÑĤÑĮÑģÑı",
+      "cur rent",
+      "Ġfamiliar ity",
+      "Ġд ив",
+      "Ġpro jets",
+      "Ġaqu eles",
+      "ĠGl ue",
+      "th ose",
+      "Ġin ception",
+      "Ġaquell os",
+      "Ġill usions",
+      "Ġatt ends",
+      "re se",
+      "Ġsw arm",
+      "Ġsw ab",
+      "Ġregard ez",
+      "Ġpos ição",
+      "Ġak hir",
+      "Ġextract ing",
+      "Ġanecd ote",
+      "ĠT ale",
+      "Ġв ин",
+      "Ġab ges",
+      "Ġol uÅŁ",
+      "Ġcomplic ado",
+      "Ġco vari",
+      "Ñĸ ÑĤÑĮ",
+      "D er",
+      "Ġ×Ļ ×Ķ",
+      "F orm",
+      "Ġìĸ´ì¨ Įëĵł",
+      "Ġread able",
+      "Ġinhib it",
+      "Ġde cipher",
+      "ĠAng ry",
+      "p g",
+      "வ த",
+      "ĠÑģоб ÑģÑĤвенно",
+      "Ġsam h",
+      "Ġesc r",
+      "Ġencompass es",
+      "Ġa uster",
+      "Ġconf isc",
+      "ĠMand al",
+      "Ġ }",
+      "atch er",
+      "= #",
+      "çļĦ æŶåĢĻ",
+      "Ġк ино",
+      "Ġst al",
+      "l ungs",
+      "Ġvo le",
+      "Ġrequ is",
+      "Ġ ãĤĪ",
+      "Ġp én",
+      "Ġlect urer",
+      "Ġins cription",
+      "Ġcerv ical",
+      "ĠTre asure",
+      "ĠJ W",
+      "com ings",
+      "Ġeyes ight",
+      "ĠT ails",
+      "ÃŃs imo",
+      "Ġworks heet",
+      "Ġswift ly",
+      "Ġcon os",
+      "Ġelimin ates",
+      "ĠBla ze",
+      "ал ог",
+      "Ġpicture d",
+      "Ġgir affe",
+      "ĠLog ic",
+      "åĺ ī",
+      "Ġenrich ment",
+      "F it",
+      "Ġunint ended",
+      "Ġpersec uted",
+      "ak ap",
+      "ë° ĺ",
+      "Ġbar ber",
+      "Ġar beitet",
+      "ĠSur prisingly",
+      "ĠAut ob",
+      "unk u",
+      "pro v",
+      "ĠLo ch",
+      "ob yl",
+      "Ġпод гоÑĤов",
+      "Ġéconom ique",
+      "Ġp att",
+      "Ġce ased",
+      "ĠÑģп иÑģ",
+      "Ġnucle i",
+      "Ġis te",
+      "ĠW ag",
+      "Ġzu peÅĤnie",
+      "Ġpro verb",
+      "ĠAh ÃŃ",
+      "åĽŀ åİ»",
+      "li amo",
+      "Ġreli ably",
+      "Ġp ik",
+      "ĠTr ading",
+      "ĠCole man",
+      "Ġα να",
+      "Ġmag ari",
+      "ĠPH IL",
+      "Ġshed ding",
+      "oh ner",
+      "Ġporn ography",
+      "Ġbenefici aries",
+      "âĢ ¢",
+      "en in",
+      "Ġresol ving",
+      "ĠÑģп оÑĢÑĤ",
+      "Ġб ег",
+      "Ġne ctar",
+      "ult ura",
+      "ims ical",
+      "ĮĢë ¥¼",
+      "å¹´ åīį",
+      "ãģĹ ãĤĥ",
+      "Ġvis ão",
+      "éģİ ä¾Ĩ",
+      "ÿÿÿÿ ÿÿÿÿ",
+      "att form",
+      "Ġë§ŀ ëĬĶ",
+      "Ġpilgr image",
+      "Ġm ating",
+      "ĠRe aper",
+      "ĠBre f",
+      "çĶŁ æ´»",
+      "Ġ×ij× ĵ",
+      "Ġnov amente",
+      "Ġgr illing",
+      "ĠWire less",
+      "ĠRoman ian",
+      "Ò Ľ",
+      "ìľ łë",
+      "h ait",
+      "ĠB ora",
+      "ARR Y",
+      "Ġhypothes es",
+      "é© ¬",
+      "ik ut",
+      "ĠìķĦë ²Ħ",
+      "ĠÑĸ з",
+      "Ġnation ale",
+      "ت Ùī",
+      "üll t",
+      "Ġélé ments",
+      "ĠW are",
+      "Ġ( -",
+      "алÑĮ ном",
+      "Ġind ict",
+      "ĠSt ones",
+      "ãģŁ ãĤģ",
+      "expl osion",
+      "ĠëĥĦ ìĥĪ",
+      "Ġfel ic",
+      "Ġjud iciary",
+      "Ġincarn ation",
+      "Ġin ning",
+      "Ġform ul",
+      "Ġship ment",
+      "Ġre indeer",
+      "æĴ Ń",
+      "Ġоз наÑĩ",
+      "Ġenv ol",
+      "und y",
+      "Ġзна ÑĤÑĮ",
+      "Ġвид ели",
+      "Ġexclud ing",
+      "de ath",
+      "Ġb erm",
+      "Ġsopr attutto",
+      "Ġdeb ido",
+      "ĠZ ig",
+      "ĠO v",
+      "ĠKE VIN",
+      "ĠP ale",
+      "ĠM ire",
+      "Ġand ar",
+      "inc luding",
+      "Ġsw apped",
+      "Ġmiscon ceptions",
+      "Ġsp ong",
+      "ré al",
+      "Ġorbit als",
+      "Ġhasht ags",
+      "or it",
+      "Ġmauv ais",
+      "иÑģ а",
+      "Ġliv res",
+      "ĠI PS",
+      "Ġ0 4",
+      "ö g",
+      "in str",
+      "Ġвн еÑĪ",
+      "Ġh ice",
+      "is ée",
+      "Ġow es",
+      "Ġes imerk",
+      "ĠU H",
+      "Ġirrit ation",
+      "Ġg iggles",
+      "Ġcolonial ism",
+      "ĠBl iss",
+      "str ings",
+      "Ġreun ited",
+      "ĠPs aki",
+      "w ach",
+      "Ġcl iffs",
+      "ĠFal se",
+      "ä g",
+      "p ipe",
+      "Ġwh opping",
+      "Ġmering ue",
+      "Ġb ung",
+      "indust rie",
+      "Ġle che",
+      "ĠL oy",
+      "Ġdr ie",
+      "Ġpass at",
+      "Ġo leh",
+      "Ġcé u",
+      "ĠGab rie",
+      "Ġreef s",
+      "Ġbom bers",
+      "Ġepisód io",
+      "ĠR ug",
+      "ĠPr ose",
+      "on os",
+      "Ġob ese",
+      "Ġgo og",
+      "Ġpi ace",
+      "flan zen",
+      "éĴ Ł",
+      "Ġfl aps",
+      "ĠAl to",
+      "é£Ł ãģ¹",
+      "F in",
+      "Ġres ize",
+      "ê·¸ë ŀ¨",
+      "è² »",
+      "N athan",
+      "ŀĪë ł¤",
+      "ĠÑĤ ай",
+      "ĠNF T",
+      "Ġsne eze",
+      "Ġshr oud",
+      "i é",
+      "Ġver amente",
+      "Ġcas cade",
+      "ĠO ok",
+      "ìĹĨ ìĿ´",
+      "Ġinf used",
+      "f ps",
+      "cent er",
+      "Ġgrapp ling",
+      "ĠWohn ung",
+      "ĠT umb",
+      "ĠIm ma",
+      "ĠDu ygusal",
+      "ен ÑĤи",
+      "Ġstewards hip",
+      "Ġhar p",
+      "Ġendors ed",
+      "ıl an",
+      "Ġод ним",
+      "Ġcompet ency",
+      "Ġb ert",
+      "ĠT ales",
+      "Ġr he",
+      "Ġoh h",
+      "Ġê°Ħ ëĭ¨",
+      "Ġm RNA",
+      "Ġgang ster",
+      "ĠRun ner",
+      "ен нÑĭм",
+      "ph oria",
+      "ĠwÅĤaÅĽci wie",
+      "Ġqu arto",
+      "Ġorgan ise",
+      "ĠV et",
+      "P ad",
+      "ĠÙħ Ø«",
+      "Ġst inks",
+      "ĠD ul",
+      "u em",
+      "is iej",
+      "T op",
+      "Ġt ussen",
+      "ĠE fendimiz",
+      "ĠB oule",
+      "ĠSlo ven",
+      "ĠL ö",
+      "Ñij з",
+      "ÑĢи п",
+      "ca ve",
+      "Ġbo î",
+      "Ġapolog ise",
+      "ĠMar ly",
+      "ĠEx port",
+      "ĠCait lin",
+      "Ġtav alla",
+      "Ġent ails",
+      "Ġbr om",
+      "ĠCop enh",
+      "Ġwal nut",
+      "Ġins ists",
+      "Ġcu á»Ļc",
+      "ĠQ uit",
+      "ĠDev ice",
+      "×Ĵ ×Ŀ",
+      "ĠD OT",
+      "Ġveloc idad",
+      "L IE",
+      "C ool",
+      "Ġsan itation",
+      "Ġol ho",
+      "ĠE B",
+      "ĠíĻķ ìĭ¤íŀĪ",
+      "ĠÐľ иÑħ",
+      "Ġz uk",
+      "Ġsurn ame",
+      "ĠSch uld",
+      "ru ff",
+      "c ultural",
+      "ĠÑģÑĤ олÑĮко",
+      "æĻļ ä¸Ĭ",
+      "Įë į°",
+      "Ġtor to",
+      "Ġback ups",
+      "ÑĢи й",
+      "re lax",
+      "Ġsyner gy",
+      "Ġbuff s",
+      "Ġap o",
+      "ĠWell ness",
+      "round ed",
+      "Ġunivers es",
+      "Ġf era",
+      "Ġstand by",
+      "ĠSil va",
+      "ĠJ I",
+      "ens ored",
+      "ĠìĹĨ ëĭ¤",
+      "ĠÐIJ в",
+      "ĠоÑĤ дел",
+      "Ġf ø",
+      "ĠRoc kef",
+      "ĠComp ass",
+      "ĠBe ars",
+      "Ġ ä¸įè¦ģ",
+      "T urn",
+      "Ġth á»±c",
+      "Ġposs ibile",
+      "Ġest em",
+      "ĠCroat ia",
+      "Ġtät ä",
+      "ĠC AL",
+      "à¹Ģภŀ",
+      "ĠÑģÑĤ ÑĢаÑħ",
+      "Ġsal ts",
+      "Ġminimal ist",
+      "Ġincorpor ates",
+      "ĠÙĨÛģ ÛĮÚº",
+      "aca o",
+      "Ġsl ammed",
+      "Ġcam a",
+      "T ext",
+      "!!!! !!",
+      "Ġalc anz",
+      "é ma",
+      "Ġinc ense",
+      "Ġhard en",
+      "Ġgrant ing",
+      "ĠN ai",
+      "ĠFir ma",
+      "Ġhyp oc",
+      "j ob",
+      "ĠR H",
+      "z ur",
+      "ил Ñı",
+      "ĠÅ º",
+      "Ġda res",
+      "an h",
+      "Ġë§Į íģ¼",
+      "Ġcuest ión",
+      "ĠL ima",
+      "æĻ ¯",
+      "Ġass unto",
+      "ĠIP O",
+      "ĠBeng al",
+      "ĠB ier",
+      "Ġpsy che",
+      "Ġacqu ainted",
+      "ĠG ün",
+      "оз и",
+      "ÅĽci Äħ",
+      "A G",
+      "Ġmalf unction",
+      "Ġastero ids",
+      "ire z",
+      "am orph",
+      "ĠÑģоÑĤ ÑĢÑĥд",
+      "Ġfresh water",
+      "Ġar ran",
+      "ĠпÑĢ Ñĭ",
+      "н ог",
+      "Ġdiab etic",
+      "ĠÙĤ اÙĦ",
+      "Ġopp ress",
+      "Ġcapacit ance",
+      "perform ance",
+      "cr ates",
+      "Ġapost le",
+      "ĠJ EN",
+      "OU LD",
+      "Int ro",
+      "Ġstall s",
+      "ĠAB OUT",
+      "ctic amente",
+      "Ġdil igent",
+      "Ġmanif ests",
+      "ĠPak istani",
+      "Ġ( '",
+      "åľ º"
+    ]
+  }
+}
\ No newline at end of file
diff --git a/tokenizer_config.json b/tokenizer_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..fdf84ba1b1ae9268ae28b3fe7abb2e5b22ee789d
--- /dev/null
+++ b/tokenizer_config.json
@@ -0,0 +1,12997 @@
+{
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "50257": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50258": {
+      "content": "<|startoftranscript|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50259": {
+      "content": "<|en|>",
+      "lstrip": false,
+      "normalized": false,
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+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "51853": {
+      "content": "<|29.76|>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "51854": {
+      "content": "<|29.78|>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "51855": {
+      "content": "<|29.80|>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "51856": {
+      "content": "<|29.82|>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "51857": {
+      "content": "<|29.84|>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "51858": {
+      "content": "<|29.86|>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "51859": {
+      "content": "<|29.88|>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "51860": {
+      "content": "<|29.90|>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "51861": {
+      "content": "<|29.92|>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "51862": {
+      "content": "<|29.94|>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "51863": {
+      "content": "<|29.96|>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "51864": {
+      "content": "<|29.98|>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "51865": {
+      "content": "<|30.00|>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    }
+  },
+  "additional_special_tokens": [
+    "<|startoftranscript|>",
+    "<|en|>",
+    "<|zh|>",
+    "<|de|>",
+    "<|es|>",
+    "<|ru|>",
+    "<|ko|>",
+    "<|fr|>",
+    "<|ja|>",
+    "<|pt|>",
+    "<|tr|>",
+    "<|pl|>",
+    "<|ca|>",
+    "<|nl|>",
+    "<|ar|>",
+    "<|sv|>",
+    "<|it|>",
+    "<|id|>",
+    "<|hi|>",
+    "<|fi|>",
+    "<|vi|>",
+    "<|he|>",
+    "<|uk|>",
+    "<|el|>",
+    "<|ms|>",
+    "<|cs|>",
+    "<|ro|>",
+    "<|da|>",
+    "<|hu|>",
+    "<|ta|>",
+    "<|no|>",
+    "<|th|>",
+    "<|ur|>",
+    "<|hr|>",
+    "<|bg|>",
+    "<|lt|>",
+    "<|la|>",
+    "<|mi|>",
+    "<|ml|>",
+    "<|cy|>",
+    "<|sk|>",
+    "<|te|>",
+    "<|fa|>",
+    "<|lv|>",
+    "<|bn|>",
+    "<|sr|>",
+    "<|az|>",
+    "<|sl|>",
+    "<|kn|>",
+    "<|et|>",
+    "<|mk|>",
+    "<|br|>",
+    "<|eu|>",
+    "<|is|>",
+    "<|hy|>",
+    "<|ne|>",
+    "<|mn|>",
+    "<|bs|>",
+    "<|kk|>",
+    "<|sq|>",
+    "<|sw|>",
+    "<|gl|>",
+    "<|mr|>",
+    "<|pa|>",
+    "<|si|>",
+    "<|km|>",
+    "<|sn|>",
+    "<|yo|>",
+    "<|so|>",
+    "<|af|>",
+    "<|oc|>",
+    "<|ka|>",
+    "<|be|>",
+    "<|tg|>",
+    "<|sd|>",
+    "<|gu|>",
+    "<|am|>",
+    "<|yi|>",
+    "<|lo|>",
+    "<|uz|>",
+    "<|fo|>",
+    "<|ht|>",
+    "<|ps|>",
+    "<|tk|>",
+    "<|nn|>",
+    "<|mt|>",
+    "<|sa|>",
+    "<|lb|>",
+    "<|my|>",
+    "<|bo|>",
+    "<|tl|>",
+    "<|mg|>",
+    "<|as|>",
+    "<|tt|>",
+    "<|haw|>",
+    "<|ln|>",
+    "<|ha|>",
+    "<|ba|>",
+    "<|jw|>",
+    "<|su|>",
+    "<|yue|>",
+    "<|translate|>",
+    "<|transcribe|>",
+    "<|startoflm|>",
+    "<|startofprev|>",
+    "<|nospeech|>",
+    "<|notimestamps|>"
+  ],
+  "bos_token": "<|endoftext|>",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|endoftext|>",
+  "errors": "replace",
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "<|endoftext|>",
+  "processor_class": "WhisperProcessor",
+  "tokenizer_class": "WhisperTokenizer",
+  "trust_remote_code": false,
+  "unk_token": "<|endoftext|>",
+  "use_fast": true
+}
diff --git a/train.log b/train.log
new file mode 100644
index 0000000000000000000000000000000000000000..a103073fd532a4939b9f64bf04a52265f4d0d253
--- /dev/null
+++ b/train.log
@@ -0,0 +1,2940 @@
+[W socket.cpp:464] [c10d] The server socket cannot be initialized on [::]:29500 (errno: 97 - Address family not supported by protocol).
+[W socket.cpp:697] [c10d] The client socket cannot be initialized to connect to [::ffff:127.0.0.1]:29500 (errno: 97 - Address family not supported by protocol).
+[W socket.cpp:697] [c10d] The client socket cannot be initialized to connect to [::ffff:127.0.0.1]:29500 (errno: 97 - Address family not supported by protocol).
+[W socket.cpp:697] [c10d] The client socket cannot be initialized to connect to [::ffff:127.0.0.1]:29500 (errno: 97 - Address family not supported by protocol).
+wandb: Currently logged in as: salomon-kisters. Use `wandb login --relogin` to force relogin
+wandb: WARNING Path ./wandb/wandb/ wasn't writable, using system temp directory.
+wandb: WARNING Path ./wandb/wandb/ wasn't writable, using system temp directory
+wandb: Tracking run with wandb version 0.17.1
+wandb: Run data is saved locally in /tmp/wandb/run-20240617_155355-q96nvxpq
+wandb: Run `wandb offline` to turn off syncing.
+wandb: Syncing run kind-shape-106
+wandb: ⭐️ View project at https://wandb.ai/salomon-kisters/distil-whisper
+wandb: 🚀 View run at https://wandb.ai/salomon-kisters/distil-whisper/runs/q96nvxpq
+06/17/2024 15:53:59 - WARNING - __main__ - Process rank: 0, device: cuda:0, n_gpu: 1, distributed training: True, 16-bits training: False
+06/17/2024 15:53:59 - INFO - __main__ - Training/evaluation parameters DistillationTrainingArguments(
+_n_gpu=1,
+accelerator_config={'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None},
+adafactor=False,
+adam_beta1=0.9,
+adam_beta2=0.999,
+adam_epsilon=1e-08,
+auto_find_batch_size=False,
+batch_eval_metrics=False,
+bf16=False,
+bf16_full_eval=False,
+data_seed=None,
+dataloader_drop_last=False,
+dataloader_num_workers=10,
+dataloader_persistent_workers=False,
+dataloader_pin_memory=True,
+dataloader_prefetch_factor=None,
+ddp_backend=None,
+ddp_broadcast_buffers=None,
+ddp_bucket_cap_mb=None,
+ddp_find_unused_parameters=None,
+ddp_timeout=7200,
+debug=[],
+deepspeed=None,
+disable_tqdm=False,
+dispatch_batches=None,
+do_eval=True,
+do_predict=False,
+do_train=True,
+dtype=bfloat16,
+eval_accumulation_steps=None,
+eval_delay=0,
+eval_do_concat_batches=True,
+eval_steps=5000.0,
+eval_strategy=no,
+evaluation_strategy=None,
+fp16=False,
+fp16_backend=auto,
+fp16_full_eval=False,
+fp16_opt_level=O1,
+freeze_decoder=False,
+freeze_embed_positions=False,
+freeze_encoder=True,
+fsdp=[],
+fsdp_config={'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False},
+fsdp_min_num_params=0,
+fsdp_transformer_layer_cls_to_wrap=None,
+full_determinism=False,
+generation_config=None,
+generation_max_length=None,
+generation_num_beams=None,
+gradient_accumulation_steps=1,
+gradient_checkpointing=True,
+gradient_checkpointing_kwargs=None,
+greater_is_better=None,
+group_by_length=False,
+half_precision_backend=auto,
+hub_always_push=False,
+hub_model_id=None,
+hub_private_repo=False,
+hub_strategy=every_save,
+hub_token=<HUB_TOKEN>,
+ignore_data_skip=False,
+include_inputs_for_metrics=False,
+include_num_input_tokens_seen=False,
+include_tokens_per_second=False,
+jit_mode_eval=False,
+kl_weight=1.0,
+label_names=None,
+label_smoothing_factor=0.0,
+learning_rate=0.0001,
+length_column_name=length,
+load_best_model_at_end=False,
+local_rank=0,
+log_level=passive,
+log_level_replica=warning,
+log_on_each_node=True,
+logging_dir=./runs/Jun17_15-53-54_6296a7809286,
+logging_first_step=False,
+logging_nan_inf_filter=True,
+logging_steps=25,
+logging_strategy=steps,
+lr_scheduler_kwargs={},
+lr_scheduler_type=linear,
+max_grad_norm=1.0,
+max_steps=100000,
+metric_for_best_model=None,
+mp_parameters=,
+neftune_noise_alpha=None,
+no_cuda=False,
+num_train_epochs=3.0,
+optim=adamw_torch,
+optim_args=None,
+optim_target_modules=None,
+output_dir=./,
+overwrite_output_dir=True,
+past_index=-1,
+per_device_eval_batch_size=8,
+per_device_train_batch_size=8,
+predict_with_generate=True,
+prediction_loss_only=False,
+push_to_hub=True,
+push_to_hub_model_id=None,
+push_to_hub_organization=None,
+push_to_hub_token=<PUSH_TO_HUB_TOKEN>,
+ray_scope=last,
+remove_unused_columns=True,
+report_to=['wandb'],
+restore_callback_states_from_checkpoint=False,
+resume_from_checkpoint=None,
+run_name=./,
+save_on_each_node=False,
+save_only_model=False,
+save_safetensors=True,
+save_steps=5000,
+save_strategy=steps,
+save_total_limit=5,
+seed=42,
+skip_memory_metrics=True,
+sortish_sampler=False,
+split_batches=None,
+temperature=2.0,
+tf32=None,
+torch_compile=False,
+torch_compile_backend=None,
+torch_compile_mode=None,
+torchdynamo=None,
+tpu_metrics_debug=False,
+tpu_num_cores=None,
+use_cpu=False,
+use_ipex=False,
+use_legacy_prediction_loop=False,
+use_mps_device=False,
+warmup_ratio=0.0,
+warmup_steps=500,
+weight_decay=0.0,
+)
+
Combining datasets...:   0%|          | 0/1 [00:00<?, ?it/s]
Combining datasets...: 100%|██████████| 1/1 [00:07<00:00,  7.80s/it]
Combining datasets...: 100%|██████████| 1/1 [00:07<00:00,  7.80s/it]
+loading configuration file ./distil-large-v3-init/config.json
+Model config WhisperConfig {
+  "_name_or_path": "GalaktischeGurke/swhisper_large_8552",
+  "activation_dropout": 0.0,
+  "activation_function": "gelu",
+  "apply_spec_augment": false,
+  "architectures": [
+    "WhisperForConditionalGeneration"
+  ],
+  "attention_dropout": 0.0,
+  "begin_suppress_tokens": [
+    220,
+    50257
+  ],
+  "bos_token_id": 50257,
+  "classifier_proj_size": 256,
+  "d_model": 1280,
+  "decoder_attention_heads": 20,
+  "decoder_ffn_dim": 5120,
+  "decoder_layerdrop": 0.0,
+  "decoder_layers": 2,
+  "decoder_start_token_id": 50258,
+  "dropout": 0.0,
+  "encoder_attention_heads": 20,
+  "encoder_ffn_dim": 5120,
+  "encoder_layerdrop": 0.0,
+  "encoder_layers": 32,
+  "eos_token_id": 50257,
+  "init_std": 0.02,
+  "is_encoder_decoder": true,
+  "mask_feature_length": 10,
+  "mask_feature_min_masks": 0,
+  "mask_feature_prob": 0.0,
+  "mask_time_length": 10,
+  "mask_time_min_masks": 2,
+  "mask_time_prob": 0.05,
+  "max_length": 448,
+  "max_source_positions": 1500,
+  "max_target_positions": 448,
+  "median_filter_width": 7,
+  "model_type": "whisper",
+  "num_hidden_layers": 32,
+  "num_mel_bins": 128,
+  "pad_token_id": 50256,
+  "scale_embedding": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.41.2",
+  "use_cache": true,
+  "use_weighted_layer_sum": false,
+  "vocab_size": 51866
+}
+
+loading configuration file ./distil-large-v3-init/preprocessor_config.json
+Feature extractor WhisperFeatureExtractor {
+  "chunk_length": 30,
+  "feature_extractor_type": "WhisperFeatureExtractor",
+  "feature_size": 128,
+  "hop_length": 160,
+  "n_fft": 400,
+  "n_samples": 480000,
+  "nb_max_frames": 3000,
+  "padding_side": "right",
+  "padding_value": 0.0,
+  "processor_class": "WhisperProcessor",
+  "return_attention_mask": false,
+  "sampling_rate": 16000
+}
+
+loading file vocab.json
+loading file tokenizer.json
+loading file merges.txt
+loading file normalizer.json
+loading file added_tokens.json
+loading file special_tokens_map.json
+loading file tokenizer_config.json
+Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
+Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
+loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--GalaktischeGurke--swhisper_large_8552/snapshots/15df14934a8130180e919f218e6e63ba431cc780/config.json
+Model config WhisperConfig {
+  "_name_or_path": "openai/whisper-large-v3",
+  "activation_dropout": 0.0,
+  "activation_function": "gelu",
+  "apply_spec_augment": false,
+  "architectures": [
+    "WhisperForConditionalGeneration"
+  ],
+  "attention_dropout": 0.0,
+  "begin_suppress_tokens": [
+    220,
+    50257
+  ],
+  "bos_token_id": 50257,
+  "classifier_proj_size": 256,
+  "d_model": 1280,
+  "decoder_attention_heads": 20,
+  "decoder_ffn_dim": 5120,
+  "decoder_layerdrop": 0.0,
+  "decoder_layers": 32,
+  "decoder_start_token_id": 50258,
+  "dropout": 0.0,
+  "encoder_attention_heads": 20,
+  "encoder_ffn_dim": 5120,
+  "encoder_layerdrop": 0.0,
+  "encoder_layers": 32,
+  "eos_token_id": 50257,
+  "init_std": 0.02,
+  "is_encoder_decoder": true,
+  "mask_feature_length": 10,
+  "mask_feature_min_masks": 0,
+  "mask_feature_prob": 0.0,
+  "mask_time_length": 10,
+  "mask_time_min_masks": 2,
+  "mask_time_prob": 0.05,
+  "max_length": 448,
+  "max_source_positions": 1500,
+  "max_target_positions": 448,
+  "median_filter_width": 7,
+  "model_type": "whisper",
+  "num_hidden_layers": 32,
+  "num_mel_bins": 128,
+  "pad_token_id": 50256,
+  "scale_embedding": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.41.2",
+  "use_cache": true,
+  "use_weighted_layer_sum": false,
+  "vocab_size": 51866
+}
+
+loading weights file model.safetensors from cache at /root/.cache/huggingface/hub/models--GalaktischeGurke--swhisper_large_8552/snapshots/15df14934a8130180e919f218e6e63ba431cc780/model.safetensors.index.json
+Instantiating WhisperForConditionalGeneration model under default dtype torch.bfloat16.
+Generate config GenerationConfig {
+  "begin_suppress_tokens": [
+    220,
+    50257
+  ],
+  "bos_token_id": 50257,
+  "decoder_start_token_id": 50258,
+  "eos_token_id": 50257,
+  "max_length": 448,
+  "pad_token_id": 50256
+}
+
+
Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]
Loading checkpoint shards:  50%|█████     | 1/2 [00:08<00:08,  8.26s/it]
Loading checkpoint shards:  50%|█████     | 1/2 [00:08<00:08,  8.27s/it]
Loading checkpoint shards: 100%|██████████| 2/2 [00:10<00:00,  4.57s/it]
Loading checkpoint shards: 100%|██████████| 2/2 [00:10<00:00,  5.12s/it]
+All model checkpoint weights were used when initializing WhisperForConditionalGeneration.
+
+All the weights of WhisperForConditionalGeneration were initialized from the model checkpoint at GalaktischeGurke/swhisper_large_8552.
+If your task is similar to the task the model of the checkpoint was trained on, you can already use WhisperForConditionalGeneration for predictions without further training.
+
Loading checkpoint shards: 100%|██████████| 2/2 [00:10<00:00,  4.58s/it]
Loading checkpoint shards: 100%|██████████| 2/2 [00:10<00:00,  5.13s/it]
+loading configuration file generation_config.json from cache at /root/.cache/huggingface/hub/models--GalaktischeGurke--swhisper_large_8552/snapshots/15df14934a8130180e919f218e6e63ba431cc780/generation_config.json
+Generate config GenerationConfig {
+  "alignment_heads": [
+    [
+      7,
+      0
+    ],
+    [
+      10,
+      17
+    ],
+    [
+      12,
+      18
+    ],
+    [
+      13,
+      12
+    ],
+    [
+      16,
+      1
+    ],
+    [
+      17,
+      14
+    ],
+    [
+      19,
+      11
+    ],
+    [
+      21,
+      4
+    ],
+    [
+      24,
+      1
+    ],
+    [
+      25,
+      6
+    ]
+  ],
+  "attn_implementation": null,
+  "begin_suppress_tokens": [
+    220,
+    50257
+  ],
+  "bos_token_id": 50257,
+  "decoder_start_token_id": 50258,
+  "eos_token_id": 50257,
+  "forced_decoder_ids": [
+    [
+      1,
+      null
+    ],
+    [
+      2,
+      50360
+    ]
+  ],
+  "is_multilingual": true,
+  "lang_to_id": {
+    "<|af|>": 50327,
+    "<|am|>": 50334,
+    "<|ar|>": 50272,
+    "<|as|>": 50350,
+    "<|az|>": 50304,
+    "<|ba|>": 50355,
+    "<|be|>": 50330,
+    "<|bg|>": 50292,
+    "<|bn|>": 50302,
+    "<|bo|>": 50347,
+    "<|br|>": 50309,
+    "<|bs|>": 50315,
+    "<|ca|>": 50270,
+    "<|cs|>": 50283,
+    "<|cy|>": 50297,
+    "<|da|>": 50285,
+    "<|de|>": 50261,
+    "<|el|>": 50281,
+    "<|en|>": 50259,
+    "<|es|>": 50262,
+    "<|et|>": 50307,
+    "<|eu|>": 50310,
+    "<|fa|>": 50300,
+    "<|fi|>": 50277,
+    "<|fo|>": 50338,
+    "<|fr|>": 50265,
+    "<|gl|>": 50319,
+    "<|gu|>": 50333,
+    "<|haw|>": 50352,
+    "<|ha|>": 50354,
+    "<|he|>": 50279,
+    "<|hi|>": 50276,
+    "<|hr|>": 50291,
+    "<|ht|>": 50339,
+    "<|hu|>": 50286,
+    "<|hy|>": 50312,
+    "<|id|>": 50275,
+    "<|is|>": 50311,
+    "<|it|>": 50274,
+    "<|ja|>": 50266,
+    "<|jw|>": 50356,
+    "<|ka|>": 50329,
+    "<|kk|>": 50316,
+    "<|km|>": 50323,
+    "<|kn|>": 50306,
+    "<|ko|>": 50264,
+    "<|la|>": 50294,
+    "<|lb|>": 50345,
+    "<|ln|>": 50353,
+    "<|lo|>": 50336,
+    "<|lt|>": 50293,
+    "<|lv|>": 50301,
+    "<|mg|>": 50349,
+    "<|mi|>": 50295,
+    "<|mk|>": 50308,
+    "<|ml|>": 50296,
+    "<|mn|>": 50314,
+    "<|mr|>": 50320,
+    "<|ms|>": 50282,
+    "<|mt|>": 50343,
+    "<|my|>": 50346,
+    "<|ne|>": 50313,
+    "<|nl|>": 50271,
+    "<|nn|>": 50342,
+    "<|no|>": 50288,
+    "<|oc|>": 50328,
+    "<|pa|>": 50321,
+    "<|pl|>": 50269,
+    "<|ps|>": 50340,
+    "<|pt|>": 50267,
+    "<|ro|>": 50284,
+    "<|ru|>": 50263,
+    "<|sa|>": 50344,
+    "<|sd|>": 50332,
+    "<|si|>": 50322,
+    "<|sk|>": 50298,
+    "<|sl|>": 50305,
+    "<|sn|>": 50324,
+    "<|so|>": 50326,
+    "<|sq|>": 50317,
+    "<|sr|>": 50303,
+    "<|su|>": 50357,
+    "<|sv|>": 50273,
+    "<|sw|>": 50318,
+    "<|ta|>": 50287,
+    "<|te|>": 50299,
+    "<|tg|>": 50331,
+    "<|th|>": 50289,
+    "<|tk|>": 50341,
+    "<|tl|>": 50348,
+    "<|tr|>": 50268,
+    "<|tt|>": 50351,
+    "<|uk|>": 50280,
+    "<|ur|>": 50290,
+    "<|uz|>": 50337,
+    "<|vi|>": 50278,
+    "<|yi|>": 50335,
+    "<|yo|>": 50325,
+    "<|yue|>": 50358,
+    "<|zh|>": 50260
+  },
+  "max_initial_timestamp_index": 50,
+  "max_length": 448,
+  "no_timestamps_token_id": 50364,
+  "pad_token_id": 50257,
+  "prev_sot_token_id": 50362,
+  "return_timestamps": false,
+  "suppress_tokens": [
+    1,
+    2,
+    7,
+    8,
+    9,
+    10,
+    14,
+    25,
+    26,
+    27,
+    28,
+    29,
+    31,
+    58,
+    59,
+    60,
+    61,
+    62,
+    63,
+    90,
+    91,
+    92,
+    93,
+    359,
+    503,
+    522,
+    542,
+    873,
+    893,
+    902,
+    918,
+    922,
+    931,
+    1350,
+    1853,
+    1982,
+    2460,
+    2627,
+    3246,
+    3253,
+    3268,
+    3536,
+    3846,
+    3961,
+    4183,
+    4667,
+    6585,
+    6647,
+    7273,
+    9061,
+    9383,
+    10428,
+    10929,
+    11938,
+    12033,
+    12331,
+    12562,
+    13793,
+    14157,
+    14635,
+    15265,
+    15618,
+    16553,
+    16604,
+    18362,
+    18956,
+    20075,
+    21675,
+    22520,
+    26130,
+    26161,
+    26435,
+    28279,
+    29464,
+    31650,
+    32302,
+    32470,
+    36865,
+    42863,
+    47425,
+    49870,
+    50254,
+    50258,
+    50359,
+    50360,
+    50361,
+    50362,
+    50363
+  ],
+  "task_to_id": {
+    "transcribe": 50360,
+    "translate": 50359
+  }
+}
+
+loading weights file ./distil-large-v3-init/model.safetensors
+Generate config GenerationConfig {
+  "begin_suppress_tokens": [
+    220,
+    50257
+  ],
+  "bos_token_id": 50257,
+  "decoder_start_token_id": 50258,
+  "eos_token_id": 50257,
+  "max_length": 448,
+  "pad_token_id": 50256
+}
+
+All model checkpoint weights were used when initializing WhisperForConditionalGeneration.
+
+All the weights of WhisperForConditionalGeneration were initialized from the model checkpoint at ./distil-large-v3-init.
+If your task is similar to the task the model of the checkpoint was trained on, you can already use WhisperForConditionalGeneration for predictions without further training.
+loading configuration file ./distil-large-v3-init/generation_config.json
+Generate config GenerationConfig {
+  "alignment_heads": [
+    [
+      7,
+      0
+    ],
+    [
+      10,
+      17
+    ],
+    [
+      12,
+      18
+    ],
+    [
+      13,
+      12
+    ],
+    [
+      16,
+      1
+    ],
+    [
+      17,
+      14
+    ],
+    [
+      19,
+      11
+    ],
+    [
+      21,
+      4
+    ],
+    [
+      24,
+      1
+    ],
+    [
+      25,
+      6
+    ]
+  ],
+  "begin_suppress_tokens": [
+    220,
+    50257
+  ],
+  "bos_token_id": 50257,
+  "decoder_start_token_id": 50258,
+  "eos_token_id": 50257,
+  "is_multilingual": true,
+  "lang_to_id": {
+    "<|af|>": 50327,
+    "<|am|>": 50334,
+    "<|ar|>": 50272,
+    "<|as|>": 50350,
+    "<|az|>": 50304,
+    "<|ba|>": 50355,
+    "<|be|>": 50330,
+    "<|bg|>": 50292,
+    "<|bn|>": 50302,
+    "<|bo|>": 50347,
+    "<|br|>": 50309,
+    "<|bs|>": 50315,
+    "<|ca|>": 50270,
+    "<|cs|>": 50283,
+    "<|cy|>": 50297,
+    "<|da|>": 50285,
+    "<|de|>": 50261,
+    "<|el|>": 50281,
+    "<|en|>": 50259,
+    "<|es|>": 50262,
+    "<|et|>": 50307,
+    "<|eu|>": 50310,
+    "<|fa|>": 50300,
+    "<|fi|>": 50277,
+    "<|fo|>": 50338,
+    "<|fr|>": 50265,
+    "<|gl|>": 50319,
+    "<|gu|>": 50333,
+    "<|haw|>": 50352,
+    "<|ha|>": 50354,
+    "<|he|>": 50279,
+    "<|hi|>": 50276,
+    "<|hr|>": 50291,
+    "<|ht|>": 50339,
+    "<|hu|>": 50286,
+    "<|hy|>": 50312,
+    "<|id|>": 50275,
+    "<|is|>": 50311,
+    "<|it|>": 50274,
+    "<|ja|>": 50266,
+    "<|jw|>": 50356,
+    "<|ka|>": 50329,
+    "<|kk|>": 50316,
+    "<|km|>": 50323,
+    "<|kn|>": 50306,
+    "<|ko|>": 50264,
+    "<|la|>": 50294,
+    "<|lb|>": 50345,
+    "<|ln|>": 50353,
+    "<|lo|>": 50336,
+    "<|lt|>": 50293,
+    "<|lv|>": 50301,
+    "<|mg|>": 50349,
+    "<|mi|>": 50295,
+    "<|mk|>": 50308,
+    "<|ml|>": 50296,
+    "<|mn|>": 50314,
+    "<|mr|>": 50320,
+    "<|ms|>": 50282,
+    "<|mt|>": 50343,
+    "<|my|>": 50346,
+    "<|ne|>": 50313,
+    "<|nl|>": 50271,
+    "<|nn|>": 50342,
+    "<|no|>": 50288,
+    "<|oc|>": 50328,
+    "<|pa|>": 50321,
+    "<|pl|>": 50269,
+    "<|ps|>": 50340,
+    "<|pt|>": 50267,
+    "<|ro|>": 50284,
+    "<|ru|>": 50263,
+    "<|sa|>": 50344,
+    "<|sd|>": 50332,
+    "<|si|>": 50322,
+    "<|sk|>": 50298,
+    "<|sl|>": 50305,
+    "<|sn|>": 50324,
+    "<|so|>": 50326,
+    "<|sq|>": 50317,
+    "<|sr|>": 50303,
+    "<|su|>": 50357,
+    "<|sv|>": 50273,
+    "<|sw|>": 50318,
+    "<|ta|>": 50287,
+    "<|te|>": 50299,
+    "<|tg|>": 50331,
+    "<|th|>": 50289,
+    "<|tk|>": 50341,
+    "<|tl|>": 50348,
+    "<|tr|>": 50268,
+    "<|tt|>": 50351,
+    "<|uk|>": 50280,
+    "<|ur|>": 50290,
+    "<|uz|>": 50337,
+    "<|vi|>": 50278,
+    "<|yi|>": 50335,
+    "<|yo|>": 50325,
+    "<|yue|>": 50358,
+    "<|zh|>": 50260
+  },
+  "max_initial_timestamp_index": 50,
+  "max_length": 448,
+  "no_timestamps_token_id": 50364,
+  "pad_token_id": 50257,
+  "prev_sot_token_id": 50362,
+  "return_timestamps": false,
+  "suppress_tokens": [
+    1,
+    2,
+    7,
+    8,
+    9,
+    10,
+    14,
+    25,
+    26,
+    27,
+    28,
+    29,
+    31,
+    58,
+    59,
+    60,
+    61,
+    62,
+    63,
+    90,
+    91,
+    92,
+    93,
+    359,
+    503,
+    522,
+    542,
+    873,
+    893,
+    902,
+    918,
+    922,
+    931,
+    1350,
+    1853,
+    1982,
+    2460,
+    2627,
+    3246,
+    3253,
+    3268,
+    3536,
+    3846,
+    3961,
+    4183,
+    4667,
+    6585,
+    6647,
+    7273,
+    9061,
+    9383,
+    10428,
+    10929,
+    11938,
+    12033,
+    12331,
+    12562,
+    13793,
+    14157,
+    14635,
+    15265,
+    15618,
+    16553,
+    16604,
+    18362,
+    18956,
+    20075,
+    21675,
+    22520,
+    26130,
+    26161,
+    26435,
+    28279,
+    29464,
+    31650,
+    32302,
+    32470,
+    36865,
+    42863,
+    47425,
+    49870,
+    50254,
+    50258,
+    50359,
+    50360,
+    50361,
+    50362,
+    50363
+  ],
+  "task_to_id": {
+    "transcribe": 50360,
+    "translate": 50359
+  }
+}
+
+06/17/2024 15:54:22 - INFO - __main__ - Number of trainable parameters: 1.194e+08
+Feature extractor saved in ./preprocessor_config.json
+tokenizer config file saved in ./tokenizer_config.json
+Special tokens file saved in ./special_tokens_map.json
+Some non-default generation parameters are set in the model config. These should go into a GenerationConfig file (https://huggingface.co/docs/transformers/generation_strategies#save-a-custom-decoding-strategy-with-your-model) instead. This warning will be raised to an exception in v4.41.
+Non-default generation parameters: {'max_length': 448, 'begin_suppress_tokens': [220, 50257]}
+Configuration saved in ./config.json
+Configuration saved in ./generation_config.json
+loading configuration file ./preprocessor_config.json
+Feature extractor WhisperFeatureExtractor {
+  "chunk_length": 30,
+  "feature_extractor_type": "WhisperFeatureExtractor",
+  "feature_size": 128,
+  "hop_length": 160,
+  "n_fft": 400,
+  "n_samples": 480000,
+  "nb_max_frames": 3000,
+  "padding_side": "right",
+  "padding_value": 0.0,
+  "processor_class": "WhisperProcessor",
+  "return_attention_mask": false,
+  "sampling_rate": 16000
+}
+
+loading file vocab.json
+loading file tokenizer.json
+loading file merges.txt
+loading file normalizer.json
+loading file added_tokens.json
+loading file special_tokens_map.json
+loading file tokenizer_config.json
+Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
+Processor WhisperProcessor:
+- feature_extractor: WhisperFeatureExtractor {
+  "chunk_length": 30,
+  "feature_extractor_type": "WhisperFeatureExtractor",
+  "feature_size": 128,
+  "hop_length": 160,
+  "n_fft": 400,
+  "n_samples": 480000,
+  "nb_max_frames": 3000,
+  "padding_side": "right",
+  "padding_value": 0.0,
+  "processor_class": "WhisperProcessor",
+  "return_attention_mask": false,
+  "sampling_rate": 16000
+}
+
+- tokenizer: WhisperTokenizer(name_or_path='./', vocab_size=50257, model_max_length=1000000000000000019884624838656, is_fast=False, padding_side='right', truncation_side='right', special_tokens={'bos_token': '<|endoftext|>', 'eos_token': '<|endoftext|>', 'unk_token': '<|endoftext|>', 'pad_token': '<|endoftext|>', 'additional_special_tokens': ['<|startoftranscript|>', '<|en|>', '<|zh|>', '<|de|>', '<|es|>', '<|ru|>', '<|ko|>', '<|fr|>', '<|ja|>', '<|pt|>', '<|tr|>', '<|pl|>', '<|ca|>', '<|nl|>', '<|ar|>', '<|sv|>', '<|it|>', '<|id|>', '<|hi|>', '<|fi|>', '<|vi|>', '<|he|>', '<|uk|>', '<|el|>', '<|ms|>', '<|cs|>', '<|ro|>', '<|da|>', '<|hu|>', '<|ta|>', '<|no|>', '<|th|>', '<|ur|>', '<|hr|>', '<|bg|>', '<|lt|>', '<|la|>', '<|mi|>', '<|ml|>', '<|cy|>', '<|sk|>', '<|te|>', '<|fa|>', '<|lv|>', '<|bn|>', '<|sr|>', '<|az|>', '<|sl|>', '<|kn|>', '<|et|>', '<|mk|>', '<|br|>', '<|eu|>', '<|is|>', '<|hy|>', '<|ne|>', '<|mn|>', '<|bs|>', '<|kk|>', '<|sq|>', '<|sw|>', '<|gl|>', '<|mr|>', '<|pa|>', '<|si|>', '<|km|>', '<|sn|>', '<|yo|>', '<|so|>', '<|af|>', '<|oc|>', '<|ka|>', '<|be|>', '<|tg|>', '<|sd|>', '<|gu|>', '<|am|>', '<|yi|>', '<|lo|>', '<|uz|>', '<|fo|>', '<|ht|>', '<|ps|>', '<|tk|>', '<|nn|>', '<|mt|>', '<|sa|>', '<|lb|>', '<|my|>', '<|bo|>', '<|tl|>', '<|mg|>', '<|as|>', '<|tt|>', '<|haw|>', '<|ln|>', '<|ha|>', '<|ba|>', '<|jw|>', '<|su|>', '<|yue|>', '<|translate|>', '<|transcribe|>', '<|startoflm|>', '<|startofprev|>', '<|nospeech|>', '<|notimestamps|>']}, clean_up_tokenization_spaces=True),  added_tokens_decoder={
+	50257: AddedToken("<|endoftext|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50258: AddedToken("<|startoftranscript|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50259: AddedToken("<|en|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50260: AddedToken("<|zh|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50261: AddedToken("<|de|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50262: AddedToken("<|es|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50263: AddedToken("<|ru|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50264: AddedToken("<|ko|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50265: AddedToken("<|fr|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50266: AddedToken("<|ja|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50267: AddedToken("<|pt|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50268: AddedToken("<|tr|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50269: AddedToken("<|pl|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50270: AddedToken("<|ca|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50271: AddedToken("<|nl|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50272: AddedToken("<|ar|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50273: AddedToken("<|sv|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50274: AddedToken("<|it|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50275: AddedToken("<|id|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50276: AddedToken("<|hi|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50277: AddedToken("<|fi|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50278: AddedToken("<|vi|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50279: AddedToken("<|he|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50280: AddedToken("<|uk|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50281: AddedToken("<|el|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50282: AddedToken("<|ms|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50283: AddedToken("<|cs|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50284: AddedToken("<|ro|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50285: AddedToken("<|da|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50286: AddedToken("<|hu|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50287: AddedToken("<|ta|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50288: AddedToken("<|no|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50289: AddedToken("<|th|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50290: AddedToken("<|ur|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50291: AddedToken("<|hr|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50292: AddedToken("<|bg|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50293: AddedToken("<|lt|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50294: AddedToken("<|la|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50295: AddedToken("<|mi|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50296: AddedToken("<|ml|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50297: AddedToken("<|cy|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50298: AddedToken("<|sk|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50299: AddedToken("<|te|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50300: AddedToken("<|fa|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50301: AddedToken("<|lv|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50302: AddedToken("<|bn|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50303: AddedToken("<|sr|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50304: AddedToken("<|az|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50305: AddedToken("<|sl|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50306: AddedToken("<|kn|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50307: AddedToken("<|et|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50308: AddedToken("<|mk|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50309: AddedToken("<|br|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50310: AddedToken("<|eu|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50311: AddedToken("<|is|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50312: AddedToken("<|hy|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50313: AddedToken("<|ne|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50314: AddedToken("<|mn|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50315: AddedToken("<|bs|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50316: AddedToken("<|kk|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50317: AddedToken("<|sq|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50318: AddedToken("<|sw|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50319: AddedToken("<|gl|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50320: AddedToken("<|mr|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50321: AddedToken("<|pa|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50322: AddedToken("<|si|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50323: AddedToken("<|km|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50324: AddedToken("<|sn|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50325: AddedToken("<|yo|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50326: AddedToken("<|so|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50327: AddedToken("<|af|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50328: AddedToken("<|oc|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50329: AddedToken("<|ka|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50330: AddedToken("<|be|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50331: AddedToken("<|tg|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50332: AddedToken("<|sd|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50333: AddedToken("<|gu|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50334: AddedToken("<|am|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50335: AddedToken("<|yi|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50336: AddedToken("<|lo|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50337: AddedToken("<|uz|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50338: AddedToken("<|fo|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50339: AddedToken("<|ht|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50340: AddedToken("<|ps|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50341: AddedToken("<|tk|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50342: AddedToken("<|nn|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50343: AddedToken("<|mt|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50344: AddedToken("<|sa|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50345: AddedToken("<|lb|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50346: AddedToken("<|my|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50347: AddedToken("<|bo|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50348: AddedToken("<|tl|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50349: AddedToken("<|mg|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50350: AddedToken("<|as|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50351: AddedToken("<|tt|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50352: AddedToken("<|haw|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50353: AddedToken("<|ln|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50354: AddedToken("<|ha|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50355: AddedToken("<|ba|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50356: AddedToken("<|jw|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50357: AddedToken("<|su|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50358: AddedToken("<|yue|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50359: AddedToken("<|translate|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50360: AddedToken("<|transcribe|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50361: AddedToken("<|startoflm|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50362: AddedToken("<|startofprev|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50363: AddedToken("<|nospeech|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50364: AddedToken("<|notimestamps|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
+	50365: AddedToken("<|0.00|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50366: AddedToken("<|0.02|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50367: AddedToken("<|0.04|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50368: AddedToken("<|0.06|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50369: AddedToken("<|0.08|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50370: AddedToken("<|0.10|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50371: AddedToken("<|0.12|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50372: AddedToken("<|0.14|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50373: AddedToken("<|0.16|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50374: AddedToken("<|0.18|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50375: AddedToken("<|0.20|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50376: AddedToken("<|0.22|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50377: AddedToken("<|0.24|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50378: AddedToken("<|0.26|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50379: AddedToken("<|0.28|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50380: AddedToken("<|0.30|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50381: AddedToken("<|0.32|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50382: AddedToken("<|0.34|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50383: AddedToken("<|0.36|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50384: AddedToken("<|0.38|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50385: AddedToken("<|0.40|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50386: AddedToken("<|0.42|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50387: AddedToken("<|0.44|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50388: AddedToken("<|0.46|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50389: AddedToken("<|0.48|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50390: AddedToken("<|0.50|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50391: AddedToken("<|0.52|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50392: AddedToken("<|0.54|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50393: AddedToken("<|0.56|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50394: AddedToken("<|0.58|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50395: AddedToken("<|0.60|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50396: AddedToken("<|0.62|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50397: AddedToken("<|0.64|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50398: AddedToken("<|0.66|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50399: AddedToken("<|0.68|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50400: AddedToken("<|0.70|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50401: AddedToken("<|0.72|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50402: AddedToken("<|0.74|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50403: AddedToken("<|0.76|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50404: AddedToken("<|0.78|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50405: AddedToken("<|0.80|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50406: AddedToken("<|0.82|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50407: AddedToken("<|0.84|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50408: AddedToken("<|0.86|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50409: AddedToken("<|0.88|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50410: AddedToken("<|0.90|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50411: AddedToken("<|0.92|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50412: AddedToken("<|0.94|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50413: AddedToken("<|0.96|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50414: AddedToken("<|0.98|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50415: AddedToken("<|1.00|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50416: AddedToken("<|1.02|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50417: AddedToken("<|1.04|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50418: AddedToken("<|1.06|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50419: AddedToken("<|1.08|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50420: AddedToken("<|1.10|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50421: AddedToken("<|1.12|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50422: AddedToken("<|1.14|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50423: AddedToken("<|1.16|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50424: AddedToken("<|1.18|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50425: AddedToken("<|1.20|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50426: AddedToken("<|1.22|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50427: AddedToken("<|1.24|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50428: AddedToken("<|1.26|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50429: AddedToken("<|1.28|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50430: AddedToken("<|1.30|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50431: AddedToken("<|1.32|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50432: AddedToken("<|1.34|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50433: AddedToken("<|1.36|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50434: AddedToken("<|1.38|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50435: AddedToken("<|1.40|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50436: AddedToken("<|1.42|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50437: AddedToken("<|1.44|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50438: AddedToken("<|1.46|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50439: AddedToken("<|1.48|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50440: AddedToken("<|1.50|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50441: AddedToken("<|1.52|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50442: AddedToken("<|1.54|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50443: AddedToken("<|1.56|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50444: AddedToken("<|1.58|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50445: AddedToken("<|1.60|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50446: AddedToken("<|1.62|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50447: AddedToken("<|1.64|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50448: AddedToken("<|1.66|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50449: AddedToken("<|1.68|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50450: AddedToken("<|1.70|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50451: AddedToken("<|1.72|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50452: AddedToken("<|1.74|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50453: AddedToken("<|1.76|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50454: AddedToken("<|1.78|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50455: AddedToken("<|1.80|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50456: AddedToken("<|1.82|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50457: AddedToken("<|1.84|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50458: AddedToken("<|1.86|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50459: AddedToken("<|1.88|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50460: AddedToken("<|1.90|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50461: AddedToken("<|1.92|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50462: AddedToken("<|1.94|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50463: AddedToken("<|1.96|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50464: AddedToken("<|1.98|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50465: AddedToken("<|2.00|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50466: AddedToken("<|2.02|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50467: AddedToken("<|2.04|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50468: AddedToken("<|2.06|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50469: AddedToken("<|2.08|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50470: AddedToken("<|2.10|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50471: AddedToken("<|2.12|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50472: AddedToken("<|2.14|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50473: AddedToken("<|2.16|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50474: AddedToken("<|2.18|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50475: AddedToken("<|2.20|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50476: AddedToken("<|2.22|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50477: AddedToken("<|2.24|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50478: AddedToken("<|2.26|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50479: AddedToken("<|2.28|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50480: AddedToken("<|2.30|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50481: AddedToken("<|2.32|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50482: AddedToken("<|2.34|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50483: AddedToken("<|2.36|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50484: AddedToken("<|2.38|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50485: AddedToken("<|2.40|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50486: AddedToken("<|2.42|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50487: AddedToken("<|2.44|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50488: AddedToken("<|2.46|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50489: AddedToken("<|2.48|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50490: AddedToken("<|2.50|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50491: AddedToken("<|2.52|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50492: AddedToken("<|2.54|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50493: AddedToken("<|2.56|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50494: AddedToken("<|2.58|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50495: AddedToken("<|2.60|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50496: AddedToken("<|2.62|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50497: AddedToken("<|2.64|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50498: AddedToken("<|2.66|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50499: AddedToken("<|2.68|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50500: AddedToken("<|2.70|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50501: AddedToken("<|2.72|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50502: AddedToken("<|2.74|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50503: AddedToken("<|2.76|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50504: AddedToken("<|2.78|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50505: AddedToken("<|2.80|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50506: AddedToken("<|2.82|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50507: AddedToken("<|2.84|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50508: AddedToken("<|2.86|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50509: AddedToken("<|2.88|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50510: AddedToken("<|2.90|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50511: AddedToken("<|2.92|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50512: AddedToken("<|2.94|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50513: AddedToken("<|2.96|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50514: AddedToken("<|2.98|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50515: AddedToken("<|3.00|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50516: AddedToken("<|3.02|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50517: AddedToken("<|3.04|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50518: AddedToken("<|3.06|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50519: AddedToken("<|3.08|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50520: AddedToken("<|3.10|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50521: AddedToken("<|3.12|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50522: AddedToken("<|3.14|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50523: AddedToken("<|3.16|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50524: AddedToken("<|3.18|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50525: AddedToken("<|3.20|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50526: AddedToken("<|3.22|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50527: AddedToken("<|3.24|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50528: AddedToken("<|3.26|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50529: AddedToken("<|3.28|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50530: AddedToken("<|3.30|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50531: AddedToken("<|3.32|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50532: AddedToken("<|3.34|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50533: AddedToken("<|3.36|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50534: AddedToken("<|3.38|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50535: AddedToken("<|3.40|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50536: AddedToken("<|3.42|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50537: AddedToken("<|3.44|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50538: AddedToken("<|3.46|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50539: AddedToken("<|3.48|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50540: AddedToken("<|3.50|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50541: AddedToken("<|3.52|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50542: AddedToken("<|3.54|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50543: AddedToken("<|3.56|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50544: AddedToken("<|3.58|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50545: AddedToken("<|3.60|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50546: AddedToken("<|3.62|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50547: AddedToken("<|3.64|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50548: AddedToken("<|3.66|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50549: AddedToken("<|3.68|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50550: AddedToken("<|3.70|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50551: AddedToken("<|3.72|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50552: AddedToken("<|3.74|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50553: AddedToken("<|3.76|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50554: AddedToken("<|3.78|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50555: AddedToken("<|3.80|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50556: AddedToken("<|3.82|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50557: AddedToken("<|3.84|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50558: AddedToken("<|3.86|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50559: AddedToken("<|3.88|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50560: AddedToken("<|3.90|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50561: AddedToken("<|3.92|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50562: AddedToken("<|3.94|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50563: AddedToken("<|3.96|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50564: AddedToken("<|3.98|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50565: AddedToken("<|4.00|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50566: AddedToken("<|4.02|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50567: AddedToken("<|4.04|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50568: AddedToken("<|4.06|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50569: AddedToken("<|4.08|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50570: AddedToken("<|4.10|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50571: AddedToken("<|4.12|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50572: AddedToken("<|4.14|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50573: AddedToken("<|4.16|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50574: AddedToken("<|4.18|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50575: AddedToken("<|4.20|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50576: AddedToken("<|4.22|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50577: AddedToken("<|4.24|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50578: AddedToken("<|4.26|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50579: AddedToken("<|4.28|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50580: AddedToken("<|4.30|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50581: AddedToken("<|4.32|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50582: AddedToken("<|4.34|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50583: AddedToken("<|4.36|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50584: AddedToken("<|4.38|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50585: AddedToken("<|4.40|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50586: AddedToken("<|4.42|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50587: AddedToken("<|4.44|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50588: AddedToken("<|4.46|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50589: AddedToken("<|4.48|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50590: AddedToken("<|4.50|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50591: AddedToken("<|4.52|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50592: AddedToken("<|4.54|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50593: AddedToken("<|4.56|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50594: AddedToken("<|4.58|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50595: AddedToken("<|4.60|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50596: AddedToken("<|4.62|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50597: AddedToken("<|4.64|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50598: AddedToken("<|4.66|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50599: AddedToken("<|4.68|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50600: AddedToken("<|4.70|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50601: AddedToken("<|4.72|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50602: AddedToken("<|4.74|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50603: AddedToken("<|4.76|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50604: AddedToken("<|4.78|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50605: AddedToken("<|4.80|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50606: AddedToken("<|4.82|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50607: AddedToken("<|4.84|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50608: AddedToken("<|4.86|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50609: AddedToken("<|4.88|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50610: AddedToken("<|4.90|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50611: AddedToken("<|4.92|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50612: AddedToken("<|4.94|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50613: AddedToken("<|4.96|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50614: AddedToken("<|4.98|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50615: AddedToken("<|5.00|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50616: AddedToken("<|5.02|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50617: AddedToken("<|5.04|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50618: AddedToken("<|5.06|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50619: AddedToken("<|5.08|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50620: AddedToken("<|5.10|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50621: AddedToken("<|5.12|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50622: AddedToken("<|5.14|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50623: AddedToken("<|5.16|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50624: AddedToken("<|5.18|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50625: AddedToken("<|5.20|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50626: AddedToken("<|5.22|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50627: AddedToken("<|5.24|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50628: AddedToken("<|5.26|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50629: AddedToken("<|5.28|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50630: AddedToken("<|5.30|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50631: AddedToken("<|5.32|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50632: AddedToken("<|5.34|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50633: AddedToken("<|5.36|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50634: AddedToken("<|5.38|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50635: AddedToken("<|5.40|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50636: AddedToken("<|5.42|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50637: AddedToken("<|5.44|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50638: AddedToken("<|5.46|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50639: AddedToken("<|5.48|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50640: AddedToken("<|5.50|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50641: AddedToken("<|5.52|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50642: AddedToken("<|5.54|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50643: AddedToken("<|5.56|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50644: AddedToken("<|5.58|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50645: AddedToken("<|5.60|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50646: AddedToken("<|5.62|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50647: AddedToken("<|5.64|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50648: AddedToken("<|5.66|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50649: AddedToken("<|5.68|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50650: AddedToken("<|5.70|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50651: AddedToken("<|5.72|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50652: AddedToken("<|5.74|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50653: AddedToken("<|5.76|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50654: AddedToken("<|5.78|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50655: AddedToken("<|5.80|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50656: AddedToken("<|5.82|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50657: AddedToken("<|5.84|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50658: AddedToken("<|5.86|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50659: AddedToken("<|5.88|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50660: AddedToken("<|5.90|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50661: AddedToken("<|5.92|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50662: AddedToken("<|5.94|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50663: AddedToken("<|5.96|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50664: AddedToken("<|5.98|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50665: AddedToken("<|6.00|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50666: AddedToken("<|6.02|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50667: AddedToken("<|6.04|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50668: AddedToken("<|6.06|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50669: AddedToken("<|6.08|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50670: AddedToken("<|6.10|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50671: AddedToken("<|6.12|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50672: AddedToken("<|6.14|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50673: AddedToken("<|6.16|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50674: AddedToken("<|6.18|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50675: AddedToken("<|6.20|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50676: AddedToken("<|6.22|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50677: AddedToken("<|6.24|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50678: AddedToken("<|6.26|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50679: AddedToken("<|6.28|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50680: AddedToken("<|6.30|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50681: AddedToken("<|6.32|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50682: AddedToken("<|6.34|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50683: AddedToken("<|6.36|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50684: AddedToken("<|6.38|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50685: AddedToken("<|6.40|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50686: AddedToken("<|6.42|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50687: AddedToken("<|6.44|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50688: AddedToken("<|6.46|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50689: AddedToken("<|6.48|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50690: AddedToken("<|6.50|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50691: AddedToken("<|6.52|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50692: AddedToken("<|6.54|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50693: AddedToken("<|6.56|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50694: AddedToken("<|6.58|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50695: AddedToken("<|6.60|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50696: AddedToken("<|6.62|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50697: AddedToken("<|6.64|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50698: AddedToken("<|6.66|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50699: AddedToken("<|6.68|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50700: AddedToken("<|6.70|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50701: AddedToken("<|6.72|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50702: AddedToken("<|6.74|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50703: AddedToken("<|6.76|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50704: AddedToken("<|6.78|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50705: AddedToken("<|6.80|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50706: AddedToken("<|6.82|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50707: AddedToken("<|6.84|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50708: AddedToken("<|6.86|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50709: AddedToken("<|6.88|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50710: AddedToken("<|6.90|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50711: AddedToken("<|6.92|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50712: AddedToken("<|6.94|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50713: AddedToken("<|6.96|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50714: AddedToken("<|6.98|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50715: AddedToken("<|7.00|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50716: AddedToken("<|7.02|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50717: AddedToken("<|7.04|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50718: AddedToken("<|7.06|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50719: AddedToken("<|7.08|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50720: AddedToken("<|7.10|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50721: AddedToken("<|7.12|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50722: AddedToken("<|7.14|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50723: AddedToken("<|7.16|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50724: AddedToken("<|7.18|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50725: AddedToken("<|7.20|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50726: AddedToken("<|7.22|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50727: AddedToken("<|7.24|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50728: AddedToken("<|7.26|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50729: AddedToken("<|7.28|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50730: AddedToken("<|7.30|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50731: AddedToken("<|7.32|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50732: AddedToken("<|7.34|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50733: AddedToken("<|7.36|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50734: AddedToken("<|7.38|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50735: AddedToken("<|7.40|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50736: AddedToken("<|7.42|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50737: AddedToken("<|7.44|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50738: AddedToken("<|7.46|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50739: AddedToken("<|7.48|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50740: AddedToken("<|7.50|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50741: AddedToken("<|7.52|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50742: AddedToken("<|7.54|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50743: AddedToken("<|7.56|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50744: AddedToken("<|7.58|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50745: AddedToken("<|7.60|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50746: AddedToken("<|7.62|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50747: AddedToken("<|7.64|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50748: AddedToken("<|7.66|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50749: AddedToken("<|7.68|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50750: AddedToken("<|7.70|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50751: AddedToken("<|7.72|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50752: AddedToken("<|7.74|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50753: AddedToken("<|7.76|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50754: AddedToken("<|7.78|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50755: AddedToken("<|7.80|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50756: AddedToken("<|7.82|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50757: AddedToken("<|7.84|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50758: AddedToken("<|7.86|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50759: AddedToken("<|7.88|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50760: AddedToken("<|7.90|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50761: AddedToken("<|7.92|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50762: AddedToken("<|7.94|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50763: AddedToken("<|7.96|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50764: AddedToken("<|7.98|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50765: AddedToken("<|8.00|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50766: AddedToken("<|8.02|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50767: AddedToken("<|8.04|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50768: AddedToken("<|8.06|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50769: AddedToken("<|8.08|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50770: AddedToken("<|8.10|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50771: AddedToken("<|8.12|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50772: AddedToken("<|8.14|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50773: AddedToken("<|8.16|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50774: AddedToken("<|8.18|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50775: AddedToken("<|8.20|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50776: AddedToken("<|8.22|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50777: AddedToken("<|8.24|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50778: AddedToken("<|8.26|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50779: AddedToken("<|8.28|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50780: AddedToken("<|8.30|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50781: AddedToken("<|8.32|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50782: AddedToken("<|8.34|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50783: AddedToken("<|8.36|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50784: AddedToken("<|8.38|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50785: AddedToken("<|8.40|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50786: AddedToken("<|8.42|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50787: AddedToken("<|8.44|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50788: AddedToken("<|8.46|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50789: AddedToken("<|8.48|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50790: AddedToken("<|8.50|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50791: AddedToken("<|8.52|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50792: AddedToken("<|8.54|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50793: AddedToken("<|8.56|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50794: AddedToken("<|8.58|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50795: AddedToken("<|8.60|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50796: AddedToken("<|8.62|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50797: AddedToken("<|8.64|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50798: AddedToken("<|8.66|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50799: AddedToken("<|8.68|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50800: AddedToken("<|8.70|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50801: AddedToken("<|8.72|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50802: AddedToken("<|8.74|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50803: AddedToken("<|8.76|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50804: AddedToken("<|8.78|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50805: AddedToken("<|8.80|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50806: AddedToken("<|8.82|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50807: AddedToken("<|8.84|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50808: AddedToken("<|8.86|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50809: AddedToken("<|8.88|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50810: AddedToken("<|8.90|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50811: AddedToken("<|8.92|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50812: AddedToken("<|8.94|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50813: AddedToken("<|8.96|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50814: AddedToken("<|8.98|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50815: AddedToken("<|9.00|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50816: AddedToken("<|9.02|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50817: AddedToken("<|9.04|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50818: AddedToken("<|9.06|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50819: AddedToken("<|9.08|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50820: AddedToken("<|9.10|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50821: AddedToken("<|9.12|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50822: AddedToken("<|9.14|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50823: AddedToken("<|9.16|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50824: AddedToken("<|9.18|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50825: AddedToken("<|9.20|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50826: AddedToken("<|9.22|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50827: AddedToken("<|9.24|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50828: AddedToken("<|9.26|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50829: AddedToken("<|9.28|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50830: AddedToken("<|9.30|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50831: AddedToken("<|9.32|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50832: AddedToken("<|9.34|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50833: AddedToken("<|9.36|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50834: AddedToken("<|9.38|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50835: AddedToken("<|9.40|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50836: AddedToken("<|9.42|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50837: AddedToken("<|9.44|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50838: AddedToken("<|9.46|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50839: AddedToken("<|9.48|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50840: AddedToken("<|9.50|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50841: AddedToken("<|9.52|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50842: AddedToken("<|9.54|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50843: AddedToken("<|9.56|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50844: AddedToken("<|9.58|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50845: AddedToken("<|9.60|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50846: AddedToken("<|9.62|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50847: AddedToken("<|9.64|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50848: AddedToken("<|9.66|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50849: AddedToken("<|9.68|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50850: AddedToken("<|9.70|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50851: AddedToken("<|9.72|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50852: AddedToken("<|9.74|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50853: AddedToken("<|9.76|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50854: AddedToken("<|9.78|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50855: AddedToken("<|9.80|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50856: AddedToken("<|9.82|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50857: AddedToken("<|9.84|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50858: AddedToken("<|9.86|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50859: AddedToken("<|9.88|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50860: AddedToken("<|9.90|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50861: AddedToken("<|9.92|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50862: AddedToken("<|9.94|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50863: AddedToken("<|9.96|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50864: AddedToken("<|9.98|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50865: AddedToken("<|10.00|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50866: AddedToken("<|10.02|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50867: AddedToken("<|10.04|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50868: AddedToken("<|10.06|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50869: AddedToken("<|10.08|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50870: AddedToken("<|10.10|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50871: AddedToken("<|10.12|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50872: AddedToken("<|10.14|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50873: AddedToken("<|10.16|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50874: AddedToken("<|10.18|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50875: AddedToken("<|10.20|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50876: AddedToken("<|10.22|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50877: AddedToken("<|10.24|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50878: AddedToken("<|10.26|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50879: AddedToken("<|10.28|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50880: AddedToken("<|10.30|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50881: AddedToken("<|10.32|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50882: AddedToken("<|10.34|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50883: AddedToken("<|10.36|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50884: AddedToken("<|10.38|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50885: AddedToken("<|10.40|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50886: AddedToken("<|10.42|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50887: AddedToken("<|10.44|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50888: AddedToken("<|10.46|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50889: AddedToken("<|10.48|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50890: AddedToken("<|10.50|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50891: AddedToken("<|10.52|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50892: AddedToken("<|10.54|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50893: AddedToken("<|10.56|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50894: AddedToken("<|10.58|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50895: AddedToken("<|10.60|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50896: AddedToken("<|10.62|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50897: AddedToken("<|10.64|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50898: AddedToken("<|10.66|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50899: AddedToken("<|10.68|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50900: AddedToken("<|10.70|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50901: AddedToken("<|10.72|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50902: AddedToken("<|10.74|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50903: AddedToken("<|10.76|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50904: AddedToken("<|10.78|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50905: AddedToken("<|10.80|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50906: AddedToken("<|10.82|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50907: AddedToken("<|10.84|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50908: AddedToken("<|10.86|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50909: AddedToken("<|10.88|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50910: AddedToken("<|10.90|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50911: AddedToken("<|10.92|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50912: AddedToken("<|10.94|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50913: AddedToken("<|10.96|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50914: AddedToken("<|10.98|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50915: AddedToken("<|11.00|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50916: AddedToken("<|11.02|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50917: AddedToken("<|11.04|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50918: AddedToken("<|11.06|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50919: AddedToken("<|11.08|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50920: AddedToken("<|11.10|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50921: AddedToken("<|11.12|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50922: AddedToken("<|11.14|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50923: AddedToken("<|11.16|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50924: AddedToken("<|11.18|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50925: AddedToken("<|11.20|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50926: AddedToken("<|11.22|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50927: AddedToken("<|11.24|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50928: AddedToken("<|11.26|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50929: AddedToken("<|11.28|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50930: AddedToken("<|11.30|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50931: AddedToken("<|11.32|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50932: AddedToken("<|11.34|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50933: AddedToken("<|11.36|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50934: AddedToken("<|11.38|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50935: AddedToken("<|11.40|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50936: AddedToken("<|11.42|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50937: AddedToken("<|11.44|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50938: AddedToken("<|11.46|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50939: AddedToken("<|11.48|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50940: AddedToken("<|11.50|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50941: AddedToken("<|11.52|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50942: AddedToken("<|11.54|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50943: AddedToken("<|11.56|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50944: AddedToken("<|11.58|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50945: AddedToken("<|11.60|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50946: AddedToken("<|11.62|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50947: AddedToken("<|11.64|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50948: AddedToken("<|11.66|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50949: AddedToken("<|11.68|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50950: AddedToken("<|11.70|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50951: AddedToken("<|11.72|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50952: AddedToken("<|11.74|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50953: AddedToken("<|11.76|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50954: AddedToken("<|11.78|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50955: AddedToken("<|11.80|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50956: AddedToken("<|11.82|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50957: AddedToken("<|11.84|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50958: AddedToken("<|11.86|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50959: AddedToken("<|11.88|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50960: AddedToken("<|11.90|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50961: AddedToken("<|11.92|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50962: AddedToken("<|11.94|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50963: AddedToken("<|11.96|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50964: AddedToken("<|11.98|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50965: AddedToken("<|12.00|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50966: AddedToken("<|12.02|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50967: AddedToken("<|12.04|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50968: AddedToken("<|12.06|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50969: AddedToken("<|12.08|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50970: AddedToken("<|12.10|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50971: AddedToken("<|12.12|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50972: AddedToken("<|12.14|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50973: AddedToken("<|12.16|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50974: AddedToken("<|12.18|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50975: AddedToken("<|12.20|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50976: AddedToken("<|12.22|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50977: AddedToken("<|12.24|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50978: AddedToken("<|12.26|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50979: AddedToken("<|12.28|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50980: AddedToken("<|12.30|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50981: AddedToken("<|12.32|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50982: AddedToken("<|12.34|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50983: AddedToken("<|12.36|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50984: AddedToken("<|12.38|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50985: AddedToken("<|12.40|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50986: AddedToken("<|12.42|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50987: AddedToken("<|12.44|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50988: AddedToken("<|12.46|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50989: AddedToken("<|12.48|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50990: AddedToken("<|12.50|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50991: AddedToken("<|12.52|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50992: AddedToken("<|12.54|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50993: AddedToken("<|12.56|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50994: AddedToken("<|12.58|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50995: AddedToken("<|12.60|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50996: AddedToken("<|12.62|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50997: AddedToken("<|12.64|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50998: AddedToken("<|12.66|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	50999: AddedToken("<|12.68|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51000: AddedToken("<|12.70|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51001: AddedToken("<|12.72|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51002: AddedToken("<|12.74|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51003: AddedToken("<|12.76|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51004: AddedToken("<|12.78|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51005: AddedToken("<|12.80|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51006: AddedToken("<|12.82|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51007: AddedToken("<|12.84|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51008: AddedToken("<|12.86|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51009: AddedToken("<|12.88|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51010: AddedToken("<|12.90|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51011: AddedToken("<|12.92|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51012: AddedToken("<|12.94|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51013: AddedToken("<|12.96|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51014: AddedToken("<|12.98|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51015: AddedToken("<|13.00|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51016: AddedToken("<|13.02|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51017: AddedToken("<|13.04|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51018: AddedToken("<|13.06|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51019: AddedToken("<|13.08|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51020: AddedToken("<|13.10|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51021: AddedToken("<|13.12|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51022: AddedToken("<|13.14|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51023: AddedToken("<|13.16|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51024: AddedToken("<|13.18|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51025: AddedToken("<|13.20|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51026: AddedToken("<|13.22|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51027: AddedToken("<|13.24|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51028: AddedToken("<|13.26|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51029: AddedToken("<|13.28|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51030: AddedToken("<|13.30|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51031: AddedToken("<|13.32|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51032: AddedToken("<|13.34|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51033: AddedToken("<|13.36|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51034: AddedToken("<|13.38|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51035: AddedToken("<|13.40|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51036: AddedToken("<|13.42|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51037: AddedToken("<|13.44|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51038: AddedToken("<|13.46|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51039: AddedToken("<|13.48|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51040: AddedToken("<|13.50|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51041: AddedToken("<|13.52|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51042: AddedToken("<|13.54|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51043: AddedToken("<|13.56|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51044: AddedToken("<|13.58|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51045: AddedToken("<|13.60|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51046: AddedToken("<|13.62|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51047: AddedToken("<|13.64|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51048: AddedToken("<|13.66|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51049: AddedToken("<|13.68|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51050: AddedToken("<|13.70|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51051: AddedToken("<|13.72|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51052: AddedToken("<|13.74|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51053: AddedToken("<|13.76|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51054: AddedToken("<|13.78|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51055: AddedToken("<|13.80|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51056: AddedToken("<|13.82|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51057: AddedToken("<|13.84|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51058: AddedToken("<|13.86|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51059: AddedToken("<|13.88|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51060: AddedToken("<|13.90|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51061: AddedToken("<|13.92|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51062: AddedToken("<|13.94|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51063: AddedToken("<|13.96|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51064: AddedToken("<|13.98|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51065: AddedToken("<|14.00|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51066: AddedToken("<|14.02|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51067: AddedToken("<|14.04|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51068: AddedToken("<|14.06|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51069: AddedToken("<|14.08|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51070: AddedToken("<|14.10|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51071: AddedToken("<|14.12|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51072: AddedToken("<|14.14|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51073: AddedToken("<|14.16|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51074: AddedToken("<|14.18|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51075: AddedToken("<|14.20|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51076: AddedToken("<|14.22|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51077: AddedToken("<|14.24|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51078: AddedToken("<|14.26|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51079: AddedToken("<|14.28|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51080: AddedToken("<|14.30|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51081: AddedToken("<|14.32|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51082: AddedToken("<|14.34|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51083: AddedToken("<|14.36|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51084: AddedToken("<|14.38|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51085: AddedToken("<|14.40|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51086: AddedToken("<|14.42|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51087: AddedToken("<|14.44|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51088: AddedToken("<|14.46|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51089: AddedToken("<|14.48|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51090: AddedToken("<|14.50|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51091: AddedToken("<|14.52|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51092: AddedToken("<|14.54|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51093: AddedToken("<|14.56|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51094: AddedToken("<|14.58|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51095: AddedToken("<|14.60|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51096: AddedToken("<|14.62|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51097: AddedToken("<|14.64|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51098: AddedToken("<|14.66|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51099: AddedToken("<|14.68|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51100: AddedToken("<|14.70|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51101: AddedToken("<|14.72|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51102: AddedToken("<|14.74|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51103: AddedToken("<|14.76|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51104: AddedToken("<|14.78|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51105: AddedToken("<|14.80|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51106: AddedToken("<|14.82|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51107: AddedToken("<|14.84|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51108: AddedToken("<|14.86|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51109: AddedToken("<|14.88|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51110: AddedToken("<|14.90|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51111: AddedToken("<|14.92|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51112: AddedToken("<|14.94|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51113: AddedToken("<|14.96|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51114: AddedToken("<|14.98|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51115: AddedToken("<|15.00|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51116: AddedToken("<|15.02|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51117: AddedToken("<|15.04|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51118: AddedToken("<|15.06|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51119: AddedToken("<|15.08|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51120: AddedToken("<|15.10|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51121: AddedToken("<|15.12|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51122: AddedToken("<|15.14|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51123: AddedToken("<|15.16|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51124: AddedToken("<|15.18|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51125: AddedToken("<|15.20|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51126: AddedToken("<|15.22|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51127: AddedToken("<|15.24|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51128: AddedToken("<|15.26|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51129: AddedToken("<|15.28|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51130: AddedToken("<|15.30|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51131: AddedToken("<|15.32|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51132: AddedToken("<|15.34|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51133: AddedToken("<|15.36|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51134: AddedToken("<|15.38|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51135: AddedToken("<|15.40|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51136: AddedToken("<|15.42|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51137: AddedToken("<|15.44|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51138: AddedToken("<|15.46|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51139: AddedToken("<|15.48|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51140: AddedToken("<|15.50|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51141: AddedToken("<|15.52|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51142: AddedToken("<|15.54|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51143: AddedToken("<|15.56|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51144: AddedToken("<|15.58|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51145: AddedToken("<|15.60|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51146: AddedToken("<|15.62|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51147: AddedToken("<|15.64|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51148: AddedToken("<|15.66|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51149: AddedToken("<|15.68|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51150: AddedToken("<|15.70|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51151: AddedToken("<|15.72|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51152: AddedToken("<|15.74|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51153: AddedToken("<|15.76|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51154: AddedToken("<|15.78|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51155: AddedToken("<|15.80|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51156: AddedToken("<|15.82|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51157: AddedToken("<|15.84|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51158: AddedToken("<|15.86|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51159: AddedToken("<|15.88|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51160: AddedToken("<|15.90|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51161: AddedToken("<|15.92|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51162: AddedToken("<|15.94|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51163: AddedToken("<|15.96|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51164: AddedToken("<|15.98|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51165: AddedToken("<|16.00|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51166: AddedToken("<|16.02|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51167: AddedToken("<|16.04|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51168: AddedToken("<|16.06|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51169: AddedToken("<|16.08|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51170: AddedToken("<|16.10|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51171: AddedToken("<|16.12|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51172: AddedToken("<|16.14|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51173: AddedToken("<|16.16|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51174: AddedToken("<|16.18|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51175: AddedToken("<|16.20|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51176: AddedToken("<|16.22|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51177: AddedToken("<|16.24|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51178: AddedToken("<|16.26|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51179: AddedToken("<|16.28|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51180: AddedToken("<|16.30|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51181: AddedToken("<|16.32|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51182: AddedToken("<|16.34|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51183: AddedToken("<|16.36|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51184: AddedToken("<|16.38|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51185: AddedToken("<|16.40|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51186: AddedToken("<|16.42|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51187: AddedToken("<|16.44|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51188: AddedToken("<|16.46|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51189: AddedToken("<|16.48|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51190: AddedToken("<|16.50|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51191: AddedToken("<|16.52|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51192: AddedToken("<|16.54|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51193: AddedToken("<|16.56|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51194: AddedToken("<|16.58|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51195: AddedToken("<|16.60|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51196: AddedToken("<|16.62|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51197: AddedToken("<|16.64|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51198: AddedToken("<|16.66|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51199: AddedToken("<|16.68|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51200: AddedToken("<|16.70|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51201: AddedToken("<|16.72|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51202: AddedToken("<|16.74|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51203: AddedToken("<|16.76|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51204: AddedToken("<|16.78|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51205: AddedToken("<|16.80|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51206: AddedToken("<|16.82|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51207: AddedToken("<|16.84|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51208: AddedToken("<|16.86|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51209: AddedToken("<|16.88|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51210: AddedToken("<|16.90|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51211: AddedToken("<|16.92|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51212: AddedToken("<|16.94|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51213: AddedToken("<|16.96|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51214: AddedToken("<|16.98|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51215: AddedToken("<|17.00|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51216: AddedToken("<|17.02|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51217: AddedToken("<|17.04|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51218: AddedToken("<|17.06|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51219: AddedToken("<|17.08|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51220: AddedToken("<|17.10|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51221: AddedToken("<|17.12|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51222: AddedToken("<|17.14|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51223: AddedToken("<|17.16|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51224: AddedToken("<|17.18|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51225: AddedToken("<|17.20|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51226: AddedToken("<|17.22|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51227: AddedToken("<|17.24|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51228: AddedToken("<|17.26|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51229: AddedToken("<|17.28|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51230: AddedToken("<|17.30|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51231: AddedToken("<|17.32|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51232: AddedToken("<|17.34|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51233: AddedToken("<|17.36|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51234: AddedToken("<|17.38|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51235: AddedToken("<|17.40|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51236: AddedToken("<|17.42|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51237: AddedToken("<|17.44|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51238: AddedToken("<|17.46|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51239: AddedToken("<|17.48|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51240: AddedToken("<|17.50|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51241: AddedToken("<|17.52|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51242: AddedToken("<|17.54|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51243: AddedToken("<|17.56|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51244: AddedToken("<|17.58|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51245: AddedToken("<|17.60|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51246: AddedToken("<|17.62|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51247: AddedToken("<|17.64|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51248: AddedToken("<|17.66|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51249: AddedToken("<|17.68|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51250: AddedToken("<|17.70|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51251: AddedToken("<|17.72|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51252: AddedToken("<|17.74|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51253: AddedToken("<|17.76|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51254: AddedToken("<|17.78|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51255: AddedToken("<|17.80|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51256: AddedToken("<|17.82|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51257: AddedToken("<|17.84|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51258: AddedToken("<|17.86|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51259: AddedToken("<|17.88|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51260: AddedToken("<|17.90|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51261: AddedToken("<|17.92|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51262: AddedToken("<|17.94|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51263: AddedToken("<|17.96|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51264: AddedToken("<|17.98|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51265: AddedToken("<|18.00|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51266: AddedToken("<|18.02|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51267: AddedToken("<|18.04|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51268: AddedToken("<|18.06|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51269: AddedToken("<|18.08|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51270: AddedToken("<|18.10|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51271: AddedToken("<|18.12|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51272: AddedToken("<|18.14|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51273: AddedToken("<|18.16|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51274: AddedToken("<|18.18|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51275: AddedToken("<|18.20|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51276: AddedToken("<|18.22|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51277: AddedToken("<|18.24|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51278: AddedToken("<|18.26|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51279: AddedToken("<|18.28|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51280: AddedToken("<|18.30|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51281: AddedToken("<|18.32|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51282: AddedToken("<|18.34|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51283: AddedToken("<|18.36|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51284: AddedToken("<|18.38|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51285: AddedToken("<|18.40|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51286: AddedToken("<|18.42|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51287: AddedToken("<|18.44|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51288: AddedToken("<|18.46|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51289: AddedToken("<|18.48|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51290: AddedToken("<|18.50|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51291: AddedToken("<|18.52|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51292: AddedToken("<|18.54|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51293: AddedToken("<|18.56|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51294: AddedToken("<|18.58|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51295: AddedToken("<|18.60|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51296: AddedToken("<|18.62|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51297: AddedToken("<|18.64|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51298: AddedToken("<|18.66|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51299: AddedToken("<|18.68|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51300: AddedToken("<|18.70|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51301: AddedToken("<|18.72|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51302: AddedToken("<|18.74|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51303: AddedToken("<|18.76|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51304: AddedToken("<|18.78|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51305: AddedToken("<|18.80|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51306: AddedToken("<|18.82|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51307: AddedToken("<|18.84|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51308: AddedToken("<|18.86|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51309: AddedToken("<|18.88|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51310: AddedToken("<|18.90|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51311: AddedToken("<|18.92|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51312: AddedToken("<|18.94|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51313: AddedToken("<|18.96|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51314: AddedToken("<|18.98|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51315: AddedToken("<|19.00|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51316: AddedToken("<|19.02|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51317: AddedToken("<|19.04|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51318: AddedToken("<|19.06|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51319: AddedToken("<|19.08|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51320: AddedToken("<|19.10|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51321: AddedToken("<|19.12|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51322: AddedToken("<|19.14|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51323: AddedToken("<|19.16|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51324: AddedToken("<|19.18|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51325: AddedToken("<|19.20|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51326: AddedToken("<|19.22|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51327: AddedToken("<|19.24|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51328: AddedToken("<|19.26|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51329: AddedToken("<|19.28|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51330: AddedToken("<|19.30|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51331: AddedToken("<|19.32|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51332: AddedToken("<|19.34|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51333: AddedToken("<|19.36|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51334: AddedToken("<|19.38|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51335: AddedToken("<|19.40|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51336: AddedToken("<|19.42|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51337: AddedToken("<|19.44|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51338: AddedToken("<|19.46|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51339: AddedToken("<|19.48|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51340: AddedToken("<|19.50|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51341: AddedToken("<|19.52|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51342: AddedToken("<|19.54|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51343: AddedToken("<|19.56|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51344: AddedToken("<|19.58|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51345: AddedToken("<|19.60|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51346: AddedToken("<|19.62|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51347: AddedToken("<|19.64|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51348: AddedToken("<|19.66|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51349: AddedToken("<|19.68|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51350: AddedToken("<|19.70|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51351: AddedToken("<|19.72|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51352: AddedToken("<|19.74|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51353: AddedToken("<|19.76|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51354: AddedToken("<|19.78|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51355: AddedToken("<|19.80|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51356: AddedToken("<|19.82|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51357: AddedToken("<|19.84|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51358: AddedToken("<|19.86|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51359: AddedToken("<|19.88|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51360: AddedToken("<|19.90|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51361: AddedToken("<|19.92|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51362: AddedToken("<|19.94|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51363: AddedToken("<|19.96|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51364: AddedToken("<|19.98|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51365: AddedToken("<|20.00|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51366: AddedToken("<|20.02|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51367: AddedToken("<|20.04|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51368: AddedToken("<|20.06|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51369: AddedToken("<|20.08|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51370: AddedToken("<|20.10|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51371: AddedToken("<|20.12|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51372: AddedToken("<|20.14|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51373: AddedToken("<|20.16|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51374: AddedToken("<|20.18|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51375: AddedToken("<|20.20|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51376: AddedToken("<|20.22|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51377: AddedToken("<|20.24|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51378: AddedToken("<|20.26|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51379: AddedToken("<|20.28|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51380: AddedToken("<|20.30|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51381: AddedToken("<|20.32|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51382: AddedToken("<|20.34|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51383: AddedToken("<|20.36|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51384: AddedToken("<|20.38|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51385: AddedToken("<|20.40|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51386: AddedToken("<|20.42|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51387: AddedToken("<|20.44|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51388: AddedToken("<|20.46|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51389: AddedToken("<|20.48|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51390: AddedToken("<|20.50|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51391: AddedToken("<|20.52|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51392: AddedToken("<|20.54|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51393: AddedToken("<|20.56|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51394: AddedToken("<|20.58|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51395: AddedToken("<|20.60|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51396: AddedToken("<|20.62|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51397: AddedToken("<|20.64|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51398: AddedToken("<|20.66|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51399: AddedToken("<|20.68|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51400: AddedToken("<|20.70|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51401: AddedToken("<|20.72|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51402: AddedToken("<|20.74|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51403: AddedToken("<|20.76|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51404: AddedToken("<|20.78|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51405: AddedToken("<|20.80|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51406: AddedToken("<|20.82|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51407: AddedToken("<|20.84|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51408: AddedToken("<|20.86|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51409: AddedToken("<|20.88|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51410: AddedToken("<|20.90|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51411: AddedToken("<|20.92|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51412: AddedToken("<|20.94|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51413: AddedToken("<|20.96|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51414: AddedToken("<|20.98|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51415: AddedToken("<|21.00|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51416: AddedToken("<|21.02|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51417: AddedToken("<|21.04|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51418: AddedToken("<|21.06|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51419: AddedToken("<|21.08|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51420: AddedToken("<|21.10|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51421: AddedToken("<|21.12|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51422: AddedToken("<|21.14|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51423: AddedToken("<|21.16|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51424: AddedToken("<|21.18|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51425: AddedToken("<|21.20|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51426: AddedToken("<|21.22|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51427: AddedToken("<|21.24|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51428: AddedToken("<|21.26|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51429: AddedToken("<|21.28|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51430: AddedToken("<|21.30|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51431: AddedToken("<|21.32|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51432: AddedToken("<|21.34|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51433: AddedToken("<|21.36|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51434: AddedToken("<|21.38|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51435: AddedToken("<|21.40|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51436: AddedToken("<|21.42|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51437: AddedToken("<|21.44|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51438: AddedToken("<|21.46|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51439: AddedToken("<|21.48|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51440: AddedToken("<|21.50|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51441: AddedToken("<|21.52|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51442: AddedToken("<|21.54|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51443: AddedToken("<|21.56|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51444: AddedToken("<|21.58|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51445: AddedToken("<|21.60|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51446: AddedToken("<|21.62|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51447: AddedToken("<|21.64|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51448: AddedToken("<|21.66|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51449: AddedToken("<|21.68|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51450: AddedToken("<|21.70|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51451: AddedToken("<|21.72|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51452: AddedToken("<|21.74|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51453: AddedToken("<|21.76|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51454: AddedToken("<|21.78|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51455: AddedToken("<|21.80|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51456: AddedToken("<|21.82|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51457: AddedToken("<|21.84|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51458: AddedToken("<|21.86|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51459: AddedToken("<|21.88|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51460: AddedToken("<|21.90|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51461: AddedToken("<|21.92|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51462: AddedToken("<|21.94|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51463: AddedToken("<|21.96|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51464: AddedToken("<|21.98|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51465: AddedToken("<|22.00|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51466: AddedToken("<|22.02|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51467: AddedToken("<|22.04|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51468: AddedToken("<|22.06|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51469: AddedToken("<|22.08|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51470: AddedToken("<|22.10|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51471: AddedToken("<|22.12|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51472: AddedToken("<|22.14|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51473: AddedToken("<|22.16|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51474: AddedToken("<|22.18|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51475: AddedToken("<|22.20|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51476: AddedToken("<|22.22|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51477: AddedToken("<|22.24|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51478: AddedToken("<|22.26|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51479: AddedToken("<|22.28|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51480: AddedToken("<|22.30|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51481: AddedToken("<|22.32|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51482: AddedToken("<|22.34|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51483: AddedToken("<|22.36|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51484: AddedToken("<|22.38|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51485: AddedToken("<|22.40|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51486: AddedToken("<|22.42|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51487: AddedToken("<|22.44|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51488: AddedToken("<|22.46|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51489: AddedToken("<|22.48|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51490: AddedToken("<|22.50|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51491: AddedToken("<|22.52|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51492: AddedToken("<|22.54|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51493: AddedToken("<|22.56|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51494: AddedToken("<|22.58|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51495: AddedToken("<|22.60|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51496: AddedToken("<|22.62|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51497: AddedToken("<|22.64|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51498: AddedToken("<|22.66|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51499: AddedToken("<|22.68|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51500: AddedToken("<|22.70|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51501: AddedToken("<|22.72|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51502: AddedToken("<|22.74|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51503: AddedToken("<|22.76|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51504: AddedToken("<|22.78|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51505: AddedToken("<|22.80|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51506: AddedToken("<|22.82|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51507: AddedToken("<|22.84|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51508: AddedToken("<|22.86|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51509: AddedToken("<|22.88|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51510: AddedToken("<|22.90|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51511: AddedToken("<|22.92|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51512: AddedToken("<|22.94|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51513: AddedToken("<|22.96|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51514: AddedToken("<|22.98|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51515: AddedToken("<|23.00|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51516: AddedToken("<|23.02|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51517: AddedToken("<|23.04|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51518: AddedToken("<|23.06|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51519: AddedToken("<|23.08|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51520: AddedToken("<|23.10|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51521: AddedToken("<|23.12|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51522: AddedToken("<|23.14|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51523: AddedToken("<|23.16|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51524: AddedToken("<|23.18|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51525: AddedToken("<|23.20|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51526: AddedToken("<|23.22|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51527: AddedToken("<|23.24|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51528: AddedToken("<|23.26|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51529: AddedToken("<|23.28|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51530: AddedToken("<|23.30|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51531: AddedToken("<|23.32|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51532: AddedToken("<|23.34|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51533: AddedToken("<|23.36|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51534: AddedToken("<|23.38|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51535: AddedToken("<|23.40|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51536: AddedToken("<|23.42|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51537: AddedToken("<|23.44|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51538: AddedToken("<|23.46|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51539: AddedToken("<|23.48|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51540: AddedToken("<|23.50|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51541: AddedToken("<|23.52|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51542: AddedToken("<|23.54|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51543: AddedToken("<|23.56|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51544: AddedToken("<|23.58|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51545: AddedToken("<|23.60|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51546: AddedToken("<|23.62|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51547: AddedToken("<|23.64|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51548: AddedToken("<|23.66|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51549: AddedToken("<|23.68|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51550: AddedToken("<|23.70|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51551: AddedToken("<|23.72|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51552: AddedToken("<|23.74|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51553: AddedToken("<|23.76|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51554: AddedToken("<|23.78|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51555: AddedToken("<|23.80|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51556: AddedToken("<|23.82|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51557: AddedToken("<|23.84|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51558: AddedToken("<|23.86|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51559: AddedToken("<|23.88|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51560: AddedToken("<|23.90|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51561: AddedToken("<|23.92|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51562: AddedToken("<|23.94|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51563: AddedToken("<|23.96|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51564: AddedToken("<|23.98|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51565: AddedToken("<|24.00|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51566: AddedToken("<|24.02|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51567: AddedToken("<|24.04|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51568: AddedToken("<|24.06|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51569: AddedToken("<|24.08|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51570: AddedToken("<|24.10|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51571: AddedToken("<|24.12|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51572: AddedToken("<|24.14|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51573: AddedToken("<|24.16|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51574: AddedToken("<|24.18|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51575: AddedToken("<|24.20|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51576: AddedToken("<|24.22|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51577: AddedToken("<|24.24|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51578: AddedToken("<|24.26|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51579: AddedToken("<|24.28|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51580: AddedToken("<|24.30|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51581: AddedToken("<|24.32|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51582: AddedToken("<|24.34|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51583: AddedToken("<|24.36|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51584: AddedToken("<|24.38|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51585: AddedToken("<|24.40|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51586: AddedToken("<|24.42|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51587: AddedToken("<|24.44|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51588: AddedToken("<|24.46|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51589: AddedToken("<|24.48|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51590: AddedToken("<|24.50|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51591: AddedToken("<|24.52|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51592: AddedToken("<|24.54|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51593: AddedToken("<|24.56|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51594: AddedToken("<|24.58|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51595: AddedToken("<|24.60|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51596: AddedToken("<|24.62|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51597: AddedToken("<|24.64|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51598: AddedToken("<|24.66|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51599: AddedToken("<|24.68|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51600: AddedToken("<|24.70|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51601: AddedToken("<|24.72|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51602: AddedToken("<|24.74|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51603: AddedToken("<|24.76|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51604: AddedToken("<|24.78|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51605: AddedToken("<|24.80|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51606: AddedToken("<|24.82|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51607: AddedToken("<|24.84|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51608: AddedToken("<|24.86|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51609: AddedToken("<|24.88|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51610: AddedToken("<|24.90|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51611: AddedToken("<|24.92|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51612: AddedToken("<|24.94|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51613: AddedToken("<|24.96|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51614: AddedToken("<|24.98|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51615: AddedToken("<|25.00|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51616: AddedToken("<|25.02|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51617: AddedToken("<|25.04|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51618: AddedToken("<|25.06|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51619: AddedToken("<|25.08|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51620: AddedToken("<|25.10|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51621: AddedToken("<|25.12|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51622: AddedToken("<|25.14|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51623: AddedToken("<|25.16|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51624: AddedToken("<|25.18|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51625: AddedToken("<|25.20|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51626: AddedToken("<|25.22|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51627: AddedToken("<|25.24|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51628: AddedToken("<|25.26|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51629: AddedToken("<|25.28|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51630: AddedToken("<|25.30|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51631: AddedToken("<|25.32|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51632: AddedToken("<|25.34|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51633: AddedToken("<|25.36|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51634: AddedToken("<|25.38|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51635: AddedToken("<|25.40|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51636: AddedToken("<|25.42|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51637: AddedToken("<|25.44|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51638: AddedToken("<|25.46|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51639: AddedToken("<|25.48|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51640: AddedToken("<|25.50|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51641: AddedToken("<|25.52|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51642: AddedToken("<|25.54|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51643: AddedToken("<|25.56|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51644: AddedToken("<|25.58|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51645: AddedToken("<|25.60|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51646: AddedToken("<|25.62|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51647: AddedToken("<|25.64|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51648: AddedToken("<|25.66|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51649: AddedToken("<|25.68|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51650: AddedToken("<|25.70|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51651: AddedToken("<|25.72|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51652: AddedToken("<|25.74|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51653: AddedToken("<|25.76|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51654: AddedToken("<|25.78|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51655: AddedToken("<|25.80|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51656: AddedToken("<|25.82|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51657: AddedToken("<|25.84|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51658: AddedToken("<|25.86|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51659: AddedToken("<|25.88|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51660: AddedToken("<|25.90|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51661: AddedToken("<|25.92|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51662: AddedToken("<|25.94|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51663: AddedToken("<|25.96|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51664: AddedToken("<|25.98|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51665: AddedToken("<|26.00|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51666: AddedToken("<|26.02|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51667: AddedToken("<|26.04|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51668: AddedToken("<|26.06|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51669: AddedToken("<|26.08|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51670: AddedToken("<|26.10|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51671: AddedToken("<|26.12|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51672: AddedToken("<|26.14|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51673: AddedToken("<|26.16|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51674: AddedToken("<|26.18|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51675: AddedToken("<|26.20|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51676: AddedToken("<|26.22|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51677: AddedToken("<|26.24|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51678: AddedToken("<|26.26|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51679: AddedToken("<|26.28|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51680: AddedToken("<|26.30|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51681: AddedToken("<|26.32|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51682: AddedToken("<|26.34|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51683: AddedToken("<|26.36|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51684: AddedToken("<|26.38|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51685: AddedToken("<|26.40|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51686: AddedToken("<|26.42|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51687: AddedToken("<|26.44|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51688: AddedToken("<|26.46|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51689: AddedToken("<|26.48|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51690: AddedToken("<|26.50|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51691: AddedToken("<|26.52|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51692: AddedToken("<|26.54|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51693: AddedToken("<|26.56|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51694: AddedToken("<|26.58|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51695: AddedToken("<|26.60|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51696: AddedToken("<|26.62|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51697: AddedToken("<|26.64|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51698: AddedToken("<|26.66|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51699: AddedToken("<|26.68|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51700: AddedToken("<|26.70|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51701: AddedToken("<|26.72|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51702: AddedToken("<|26.74|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51703: AddedToken("<|26.76|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51704: AddedToken("<|26.78|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51705: AddedToken("<|26.80|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51706: AddedToken("<|26.82|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51707: AddedToken("<|26.84|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51708: AddedToken("<|26.86|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51709: AddedToken("<|26.88|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51710: AddedToken("<|26.90|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51711: AddedToken("<|26.92|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51712: AddedToken("<|26.94|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51713: AddedToken("<|26.96|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51714: AddedToken("<|26.98|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51715: AddedToken("<|27.00|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51716: AddedToken("<|27.02|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51717: AddedToken("<|27.04|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51718: AddedToken("<|27.06|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51719: AddedToken("<|27.08|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51720: AddedToken("<|27.10|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51721: AddedToken("<|27.12|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51722: AddedToken("<|27.14|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51723: AddedToken("<|27.16|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51724: AddedToken("<|27.18|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51725: AddedToken("<|27.20|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51726: AddedToken("<|27.22|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51727: AddedToken("<|27.24|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51728: AddedToken("<|27.26|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51729: AddedToken("<|27.28|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51730: AddedToken("<|27.30|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51731: AddedToken("<|27.32|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51732: AddedToken("<|27.34|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51733: AddedToken("<|27.36|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51734: AddedToken("<|27.38|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51735: AddedToken("<|27.40|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51736: AddedToken("<|27.42|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51737: AddedToken("<|27.44|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51738: AddedToken("<|27.46|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51739: AddedToken("<|27.48|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51740: AddedToken("<|27.50|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51741: AddedToken("<|27.52|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51742: AddedToken("<|27.54|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51743: AddedToken("<|27.56|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51744: AddedToken("<|27.58|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51745: AddedToken("<|27.60|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51746: AddedToken("<|27.62|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51747: AddedToken("<|27.64|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51748: AddedToken("<|27.66|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51749: AddedToken("<|27.68|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51750: AddedToken("<|27.70|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51751: AddedToken("<|27.72|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51752: AddedToken("<|27.74|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51753: AddedToken("<|27.76|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51754: AddedToken("<|27.78|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51755: AddedToken("<|27.80|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51756: AddedToken("<|27.82|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51757: AddedToken("<|27.84|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51758: AddedToken("<|27.86|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51759: AddedToken("<|27.88|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51760: AddedToken("<|27.90|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51761: AddedToken("<|27.92|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51762: AddedToken("<|27.94|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51763: AddedToken("<|27.96|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51764: AddedToken("<|27.98|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51765: AddedToken("<|28.00|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51766: AddedToken("<|28.02|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51767: AddedToken("<|28.04|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51768: AddedToken("<|28.06|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51769: AddedToken("<|28.08|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51770: AddedToken("<|28.10|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51771: AddedToken("<|28.12|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51772: AddedToken("<|28.14|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51773: AddedToken("<|28.16|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51774: AddedToken("<|28.18|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51775: AddedToken("<|28.20|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51776: AddedToken("<|28.22|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51777: AddedToken("<|28.24|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51778: AddedToken("<|28.26|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51779: AddedToken("<|28.28|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51780: AddedToken("<|28.30|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51781: AddedToken("<|28.32|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51782: AddedToken("<|28.34|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51783: AddedToken("<|28.36|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51784: AddedToken("<|28.38|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51785: AddedToken("<|28.40|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51786: AddedToken("<|28.42|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51787: AddedToken("<|28.44|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51788: AddedToken("<|28.46|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51789: AddedToken("<|28.48|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51790: AddedToken("<|28.50|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51791: AddedToken("<|28.52|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51792: AddedToken("<|28.54|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51793: AddedToken("<|28.56|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51794: AddedToken("<|28.58|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51795: AddedToken("<|28.60|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51796: AddedToken("<|28.62|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51797: AddedToken("<|28.64|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51798: AddedToken("<|28.66|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51799: AddedToken("<|28.68|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51800: AddedToken("<|28.70|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51801: AddedToken("<|28.72|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51802: AddedToken("<|28.74|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51803: AddedToken("<|28.76|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51804: AddedToken("<|28.78|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51805: AddedToken("<|28.80|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51806: AddedToken("<|28.82|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51807: AddedToken("<|28.84|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51808: AddedToken("<|28.86|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51809: AddedToken("<|28.88|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51810: AddedToken("<|28.90|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51811: AddedToken("<|28.92|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51812: AddedToken("<|28.94|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51813: AddedToken("<|28.96|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51814: AddedToken("<|28.98|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51815: AddedToken("<|29.00|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51816: AddedToken("<|29.02|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51817: AddedToken("<|29.04|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51818: AddedToken("<|29.06|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51819: AddedToken("<|29.08|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51820: AddedToken("<|29.10|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51821: AddedToken("<|29.12|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51822: AddedToken("<|29.14|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51823: AddedToken("<|29.16|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51824: AddedToken("<|29.18|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51825: AddedToken("<|29.20|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51826: AddedToken("<|29.22|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51827: AddedToken("<|29.24|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51828: AddedToken("<|29.26|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51829: AddedToken("<|29.28|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51830: AddedToken("<|29.30|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51831: AddedToken("<|29.32|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51832: AddedToken("<|29.34|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51833: AddedToken("<|29.36|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51834: AddedToken("<|29.38|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51835: AddedToken("<|29.40|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51836: AddedToken("<|29.42|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51837: AddedToken("<|29.44|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51838: AddedToken("<|29.46|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51839: AddedToken("<|29.48|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51840: AddedToken("<|29.50|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51841: AddedToken("<|29.52|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51842: AddedToken("<|29.54|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51843: AddedToken("<|29.56|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51844: AddedToken("<|29.58|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51845: AddedToken("<|29.60|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51846: AddedToken("<|29.62|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51847: AddedToken("<|29.64|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51848: AddedToken("<|29.66|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51849: AddedToken("<|29.68|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51850: AddedToken("<|29.70|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51851: AddedToken("<|29.72|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51852: AddedToken("<|29.74|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51853: AddedToken("<|29.76|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51854: AddedToken("<|29.78|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51855: AddedToken("<|29.80|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51856: AddedToken("<|29.82|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51857: AddedToken("<|29.84|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51858: AddedToken("<|29.86|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51859: AddedToken("<|29.88|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51860: AddedToken("<|29.90|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51861: AddedToken("<|29.92|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51862: AddedToken("<|29.94|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51863: AddedToken("<|29.96|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51864: AddedToken("<|29.98|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+	51865: AddedToken("<|30.00|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),
+}
+
+{
+  "processor_class": "WhisperProcessor"
+}
+
+06/17/2024 15:54:23 - INFO - __main__ - max_steps is given, it will override any value given in num_train_epochs
+06/17/2024 15:54:26 - INFO - __main__ - ***** Running training *****
+06/17/2024 15:54:26 - INFO - __main__ -   Num examples = 1600000
+06/17/2024 15:54:26 - INFO - __main__ -   Num epochs = 6
+06/17/2024 15:54:26 - INFO - __main__ -   Instantaneous batch size per device = 8
+06/17/2024 15:54:26 - INFO - __main__ -   Gradient accumulation steps = 1
+06/17/2024 15:54:26 - INFO - __main__ -   Total train batch size (w. parallel & distributed) = 16
+06/17/2024 15:54:26 - INFO - __main__ -   Total optimization steps = 100000
+
Train steps ... :   0%|          | 0/100000 [00:00<?, ?it/s]`use_cache = True` is incompatible with gradient checkpointing. Setting `use_cache = False`...
+
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Train steps ... :   0%|          | 25/100000 [00:17<16:12:45,  1.71it/s]Step... (25 / 100000 | Loss: 10.779987335205078, Learning Rate: 5e-06)
+
                                                                        
Step... (25 / 100000 | Loss: 11.640220642089844, Learning Rate: 5e-06)
+
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Train steps ... :   0%|          | 50/100000 [00:32<16:11:58,  1.71it/s]Step... (50 / 100000 | Loss: 8.868854522705078, Learning Rate: 1e-05)
+
                                                                        
Step... (50 / 100000 | Loss: 8.45461368560791, Learning Rate: 1e-05)
+
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Train steps ... :   0%|          | 75/100000 [00:47<16:12:21,  1.71it/s]Step... (75 / 100000 | Loss: 7.8157219886779785, Learning Rate: 1.5e-05)
+
                                                                        
Step... (75 / 100000 | Loss: 7.479994773864746, Learning Rate: 1.5e-05)
+
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Step... (200 / 100000 | Loss: 6.09074592590332, Learning Rate: 4e-05)
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Step... (250 / 100000 | Loss: 5.898040771484375, Learning Rate: 5e-05)
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Step... (275 / 100000 | Loss: 5.8472089767456055, Learning Rate: 5.500000000000001e-05)
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Step... (650 / 100000 | Loss: 5.2585015296936035, Learning Rate: 9.984924623115578e-05)
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Step... (675 / 100000 | Loss: 4.475214004516602, Learning Rate: 9.982412060301508e-05)
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Step... (700 / 100000 | Loss: 5.005642414093018, Learning Rate: 9.979899497487438e-05)
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Step... (725 / 100000 | Loss: 4.459407806396484, Learning Rate: 9.977386934673367e-05)
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Step... (750 / 100000 | Loss: 3.93214750289917, Learning Rate: 9.974874371859297e-05)
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Step... (775 / 100000 | Loss: 4.25425910949707, Learning Rate: 9.972361809045227e-05)
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Step... (800 / 100000 | Loss: 3.9947874546051025, Learning Rate: 9.969849246231156e-05)
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Step... (825 / 100000 | Loss: 3.964719772338867, Learning Rate: 9.967336683417086e-05)
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Step... (850 / 100000 | Loss: 3.5669803619384766, Learning Rate: 9.964824120603016e-05)
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Step... (875 / 100000 | Loss: 2.844369411468506, Learning Rate: 9.962311557788945e-05)
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Step... (900 / 100000 | Loss: 3.759490966796875, Learning Rate: 9.959798994974875e-05)
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Step... (925 / 100000 | Loss: 3.6875388622283936, Learning Rate: 9.957286432160805e-05)
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Step... (950 / 100000 | Loss: 3.748227596282959, Learning Rate: 9.954773869346734e-05)
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Step... (975 / 100000 | Loss: 3.9362993240356445, Learning Rate: 9.952261306532664e-05)
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Step... (1000 / 100000 | Loss: 3.3357691764831543, Learning Rate: 9.949748743718594e-05)
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Step... (1025 / 100000 | Loss: 2.775944709777832, Learning Rate: 9.947236180904523e-05)
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Step... (1050 / 100000 | Loss: 2.628873348236084, Learning Rate: 9.944723618090453e-05)
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Step... (1075 / 100000 | Loss: 3.657226085662842, Learning Rate: 9.942211055276383e-05)
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Step... (1100 / 100000 | Loss: 2.8121204376220703, Learning Rate: 9.939698492462311e-05)
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Train steps ... :   1%|          | 1125/100000 [11:01<16:03:41,  1.71it/s]Step... (1125 / 100000 | Loss: 3.045886754989624, Learning Rate: 9.937185929648242e-05)
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Step... (1125 / 100000 | Loss: 3.2682456970214844, Learning Rate: 9.937185929648242e-05)
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Step... (1150 / 100000 | Loss: 2.920464277267456, Learning Rate: 9.934673366834172e-05)
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Train steps ... :   1%|          | 1175/100000 [11:30<16:03:28,  1.71it/s]Step... (1175 / 100000 | Loss: 2.675760269165039, Learning Rate: 9.932160804020101e-05)
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Step... (1175 / 100000 | Loss: 3.1925272941589355, Learning Rate: 9.932160804020101e-05)
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Step... (1200 / 100000 | Loss: 2.4822206497192383, Learning Rate: 9.929648241206031e-05)
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Step... (1225 / 100000 | Loss: 2.32077956199646, Learning Rate: 9.927135678391961e-05)
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Step... (1250 / 100000 | Loss: 2.6389994621276855, Learning Rate: 9.92462311557789e-05)
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Step... (1275 / 100000 | Loss: 2.7524683475494385, Learning Rate: 9.922110552763819e-05)
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Train steps ... :   1%|▏         | 1300/100000 [12:43<16:00:01,  1.71it/s]Step... (1300 / 100000 | Loss: 2.905158519744873, Learning Rate: 9.91959798994975e-05)
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Step... (1300 / 100000 | Loss: 2.408172607421875, Learning Rate: 9.91959798994975e-05)
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Train steps ... :   1%|▏         | 1325/100000 [12:57<15:59:49,  1.71it/s]Step... (1325 / 100000 | Loss: 2.6993136405944824, Learning Rate: 9.917085427135678e-05)
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Step... (1325 / 100000 | Loss: 2.3762319087982178, Learning Rate: 9.917085427135678e-05)
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Step... (1350 / 100000 | Loss: 2.500340461730957, Learning Rate: 9.914572864321609e-05)
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Step... (1375 / 100000 | Loss: 3.0102505683898926, Learning Rate: 9.912060301507539e-05)
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Step... (1400 / 100000 | Loss: 2.5264840126037598, Learning Rate: 9.909547738693468e-05)
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Step... (1425 / 100000 | Loss: 1.9740264415740967, Learning Rate: 9.907035175879398e-05)
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Step... (1450 / 100000 | Loss: 2.2392287254333496, Learning Rate: 9.904522613065326e-05)
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Step... (1475 / 100000 | Loss: 2.758131504058838, Learning Rate: 9.902010050251257e-05)
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Train steps ... :   2%|▏         | 1500/100000 [14:40<15:58:52,  1.71it/s]Step... (1500 / 100000 | Loss: 2.0899949073791504, Learning Rate: 9.899497487437186e-05)
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Step... (1500 / 100000 | Loss: 3.1465883255004883, Learning Rate: 9.899497487437186e-05)
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Step... (1525 / 100000 | Loss: 2.317763566970825, Learning Rate: 9.896984924623117e-05)
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Step... (1550 / 100000 | Loss: 2.720150947570801, Learning Rate: 9.894472361809046e-05)
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Step... (1575 / 100000 | Loss: 2.9906163215637207, Learning Rate: 9.891959798994975e-05)
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Step... (1600 / 100000 | Loss: 2.5789403915405273, Learning Rate: 9.889447236180906e-05)
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Step... (1625 / 100000 | Loss: 2.519101142883301, Learning Rate: 9.886934673366834e-05)
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Step... (1650 / 100000 | Loss: 2.567646026611328, Learning Rate: 9.884422110552765e-05)
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Step... (1675 / 100000 | Loss: 2.1535837650299072, Learning Rate: 9.881909547738693e-05)
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Step... (1700 / 100000 | Loss: 2.390073537826538, Learning Rate: 9.879396984924624e-05)
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Step... (1725 / 100000 | Loss: 3.432939052581787, Learning Rate: 9.876884422110553e-05)
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Step... (1750 / 100000 | Loss: 2.282086133956909, Learning Rate: 9.874371859296482e-05)
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Step... (1775 / 100000 | Loss: 1.9946322441101074, Learning Rate: 9.871859296482413e-05)
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Step... (1800 / 100000 | Loss: 2.925203800201416, Learning Rate: 9.869346733668342e-05)
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Step... (1825 / 100000 | Loss: 1.9889013767242432, Learning Rate: 9.866834170854273e-05)
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Step... (1850 / 100000 | Loss: 2.0378527641296387, Learning Rate: 9.864321608040201e-05)
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Step... (1875 / 100000 | Loss: 2.063296318054199, Learning Rate: 9.861809045226132e-05)
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Step... (1900 / 100000 | Loss: 1.9550762176513672, Learning Rate: 9.85929648241206e-05)
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Train steps ... :   2%|▏         | 1925/100000 [18:48<15:54:15,  1.71it/s]Step... (1925 / 100000 | Loss: 2.459440231323242, Learning Rate: 9.85678391959799e-05)
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Step... (1925 / 100000 | Loss: 2.0916812419891357, Learning Rate: 9.85678391959799e-05)
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Train steps ... :   2%|▏         | 1950/100000 [19:03<15:53:12,  1.71it/s]Step... (1950 / 100000 | Loss: 2.285675525665283, Learning Rate: 9.85427135678392e-05)
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Step... (1950 / 100000 | Loss: 2.617213726043701, Learning Rate: 9.85427135678392e-05)
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Train steps ... :   2%|▏         | 1975/100000 [19:17<15:52:51,  1.71it/s]Step... (1975 / 100000 | Loss: 1.8821467161178589, Learning Rate: 9.851758793969849e-05)
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Step... (1975 / 100000 | Loss: 1.9096872806549072, Learning Rate: 9.851758793969849e-05)
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Train steps ... :   2%|▏         | 2000/100000 [19:32<15:55:41,  1.71it/s]Step... (2000 / 100000 | Loss: 1.6118667125701904, Learning Rate: 9.84924623115578e-05)
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Step... (2000 / 100000 | Loss: 2.533165216445923, Learning Rate: 9.84924623115578e-05)
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Train steps ... :   2%|▏         | 2025/100000 [19:47<15:54:34,  1.71it/s]Step... (2025 / 100000 | Loss: 2.5816469192504883, Learning Rate: 9.846733668341709e-05)
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Step... (2025 / 100000 | Loss: 2.1250510215759277, Learning Rate: 9.846733668341709e-05)
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Train steps ... :   2%|▏         | 2050/100000 [20:01<15:54:23,  1.71it/s]Step... (2050 / 100000 | Loss: 2.293224334716797, Learning Rate: 9.84422110552764e-05)
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Step... (2050 / 100000 | Loss: 1.9091993570327759, Learning Rate: 9.84422110552764e-05)
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Train steps ... :   2%|▏         | 2075/100000 [20:16<15:52:17,  1.71it/s]Step... (2075 / 100000 | Loss: 2.1247358322143555, Learning Rate: 9.841708542713568e-05)
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Step... (2075 / 100000 | Loss: 2.001577854156494, Learning Rate: 9.841708542713568e-05)
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Train steps ... :   2%|▏         | 2100/100000 [20:30<15:52:33,  1.71it/s]Step... (2100 / 100000 | Loss: 2.43992018699646, Learning Rate: 9.839195979899497e-05)
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Step... (2100 / 100000 | Loss: 1.9887549877166748, Learning Rate: 9.839195979899497e-05)
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Step... (2125 / 100000 | Loss: 2.2253899574279785, Learning Rate: 9.836683417085427e-05)
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Step... (2150 / 100000 | Loss: 2.544222354888916, Learning Rate: 9.834170854271357e-05)
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Step... (2175 / 100000 | Loss: 1.7483419179916382, Learning Rate: 9.831658291457288e-05)
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Step... (2200 / 100000 | Loss: 2.5814757347106934, Learning Rate: 9.829145728643216e-05)
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Step... (2225 / 100000 | Loss: 1.893161654472351, Learning Rate: 9.826633165829147e-05)
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Step... (2250 / 100000 | Loss: 2.1789896488189697, Learning Rate: 9.824120603015075e-05)
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Step... (2275 / 100000 | Loss: 1.6578755378723145, Learning Rate: 9.821608040201005e-05)
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Step... (2300 / 100000 | Loss: 2.0857045650482178, Learning Rate: 9.819095477386935e-05)
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Step... (2325 / 100000 | Loss: 2.153782606124878, Learning Rate: 9.816582914572864e-05)
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Step... (2350 / 100000 | Loss: 1.9852826595306396, Learning Rate: 9.814070351758794e-05)
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Step... (2375 / 100000 | Loss: 2.0186283588409424, Learning Rate: 9.811557788944724e-05)
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Step... (2400 / 100000 | Loss: 2.3566975593566895, Learning Rate: 9.809045226130655e-05)
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Step... (2425 / 100000 | Loss: 2.206974506378174, Learning Rate: 9.806532663316583e-05)
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Step... (2450 / 100000 | Loss: 2.4200856685638428, Learning Rate: 9.804020100502513e-05)
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Step... (2475 / 100000 | Loss: 1.9558579921722412, Learning Rate: 9.801507537688442e-05)
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Step... (2500 / 100000 | Loss: 2.018594264984131, Learning Rate: 9.798994974874372e-05)
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Step... (2525 / 100000 | Loss: 2.2286834716796875, Learning Rate: 9.796482412060302e-05)
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Train steps ... :   3%|▎         | 2550/100000 [24:53<15:50:02,  1.71it/s]Step... (2550 / 100000 | Loss: 2.2637081146240234, Learning Rate: 9.793969849246231e-05)
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Step... (2550 / 100000 | Loss: 2.4222264289855957, Learning Rate: 9.793969849246231e-05)
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Step... (2575 / 100000 | Loss: 1.795831322669983, Learning Rate: 9.791457286432161e-05)
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Step... (2600 / 100000 | Loss: 1.7674096822738647, Learning Rate: 9.788944723618091e-05)
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Step... (2625 / 100000 | Loss: 1.5143308639526367, Learning Rate: 9.78643216080402e-05)
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Step... (2650 / 100000 | Loss: 1.9200074672698975, Learning Rate: 9.78391959798995e-05)
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Train steps ... :   3%|▎         | 2675/100000 [26:06<15:49:19,  1.71it/s]Step... (2675 / 100000 | Loss: 1.9957597255706787, Learning Rate: 9.78140703517588e-05)
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Step... (2675 / 100000 | Loss: 2.334860324859619, Learning Rate: 9.78140703517588e-05)
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Step... (2700 / 100000 | Loss: 2.691915988922119, Learning Rate: 9.77889447236181e-05)
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Step... (2725 / 100000 | Loss: 1.5675148963928223, Learning Rate: 9.776381909547739e-05)
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Train steps ... :   3%|▎         | 2750/100000 [26:50<15:47:01,  1.71it/s]Step... (2750 / 100000 | Loss: 1.716235637664795, Learning Rate: 9.773869346733669e-05)
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Step... (2750 / 100000 | Loss: 1.8575785160064697, Learning Rate: 9.773869346733669e-05)
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Step... (2775 / 100000 | Loss: 1.2865090370178223, Learning Rate: 9.771356783919598e-05)
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Step... (2800 / 100000 | Loss: 1.8294017314910889, Learning Rate: 9.768844221105528e-05)
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Train steps ... :   3%|▎         | 2825/100000 [27:34<15:47:47,  1.71it/s]Step... (2825 / 100000 | Loss: 1.4799518585205078, Learning Rate: 9.766331658291458e-05)
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Step... (2825 / 100000 | Loss: 1.7465558052062988, Learning Rate: 9.766331658291458e-05)
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Train steps ... :   3%|▎         | 2850/100000 [27:49<15:48:04,  1.71it/s]Step... (2850 / 100000 | Loss: 1.5640523433685303, Learning Rate: 9.763819095477387e-05)
+
                                                                          
Step... (2850 / 100000 | Loss: 2.1535427570343018, Learning Rate: 9.763819095477387e-05)
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Train steps ... :   3%|▎         | 2875/100000 [28:03<15:46:55,  1.71it/s]Step... (2875 / 100000 | Loss: 2.1185688972473145, Learning Rate: 9.761306532663317e-05)
+
                                                                          
Step... (2875 / 100000 | Loss: 2.0174813270568848, Learning Rate: 9.761306532663317e-05)
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Step... (2900 / 100000 | Loss: 2.3137707710266113, Learning Rate: 9.758793969849247e-05)
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Step... (2925 / 100000 | Loss: 1.5508296489715576, Learning Rate: 9.756281407035176e-05)
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Step... (2950 / 100000 | Loss: 1.8892719745635986, Learning Rate: 9.753768844221106e-05)
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Step... (2975 / 100000 | Loss: 1.9072906970977783, Learning Rate: 9.751256281407036e-05)
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Step... (3000 / 100000 | Loss: 1.9377498626708984, Learning Rate: 9.748743718592965e-05)
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Step... (3025 / 100000 | Loss: 1.1778452396392822, Learning Rate: 9.746231155778895e-05)
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Train steps ... :   3%|▎         | 3050/100000 [29:46<15:46:36,  1.71it/s]Step... (3050 / 100000 | Loss: 2.066643714904785, Learning Rate: 9.743718592964825e-05)
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Step... (3050 / 100000 | Loss: 2.391375780105591, Learning Rate: 9.743718592964825e-05)
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Step... (3075 / 100000 | Loss: 1.588186264038086, Learning Rate: 9.741206030150754e-05)
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Step... (3100 / 100000 | Loss: 1.8512842655181885, Learning Rate: 9.738693467336684e-05)
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Train steps ... :   3%|▎         | 3125/100000 [30:30<15:43:46,  1.71it/s]Step... (3125 / 100000 | Loss: 2.061491012573242, Learning Rate: 9.736180904522614e-05)
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Step... (3125 / 100000 | Loss: 1.906196117401123, Learning Rate: 9.736180904522614e-05)
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Step... (3150 / 100000 | Loss: 2.0033793449401855, Learning Rate: 9.733668341708543e-05)
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Step... (3175 / 100000 | Loss: 1.9570081233978271, Learning Rate: 9.731155778894473e-05)
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Step... (3200 / 100000 | Loss: 1.6003143787384033, Learning Rate: 9.728643216080403e-05)
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Step... (3225 / 100000 | Loss: 2.0375277996063232, Learning Rate: 9.726130653266332e-05)
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Train steps ... :   3%|▎         | 3250/100000 [31:43<15:43:59,  1.71it/s]Step... (3250 / 100000 | Loss: 1.4527112245559692, Learning Rate: 9.723618090452262e-05)
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Step... (3250 / 100000 | Loss: 1.8371045589447021, Learning Rate: 9.723618090452262e-05)
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Step... (3275 / 100000 | Loss: 1.9244837760925293, Learning Rate: 9.721105527638192e-05)
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Step... (3300 / 100000 | Loss: 1.532179594039917, Learning Rate: 9.718592964824121e-05)
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Step... (3325 / 100000 | Loss: 1.8486913442611694, Learning Rate: 9.716080402010051e-05)
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Step... (3350 / 100000 | Loss: 2.0695669651031494, Learning Rate: 9.71356783919598e-05)
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Step... (3375 / 100000 | Loss: 2.056501865386963, Learning Rate: 9.71105527638191e-05)
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Step... (3400 / 100000 | Loss: 2.549149990081787, Learning Rate: 9.70854271356784e-05)
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Step... (3425 / 100000 | Loss: 1.7898151874542236, Learning Rate: 9.706030150753768e-05)
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Step... (3450 / 100000 | Loss: 2.027005672454834, Learning Rate: 9.703517587939699e-05)
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Step... (3475 / 100000 | Loss: 2.385775566101074, Learning Rate: 9.701005025125629e-05)
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Step... (3500 / 100000 | Loss: 1.9265058040618896, Learning Rate: 9.698492462311559e-05)
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Step... (3525 / 100000 | Loss: 1.6537153720855713, Learning Rate: 9.695979899497488e-05)
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Step... (3550 / 100000 | Loss: 2.1290764808654785, Learning Rate: 9.693467336683418e-05)
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Step... (3575 / 100000 | Loss: 1.7071335315704346, Learning Rate: 9.690954773869348e-05)
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Step... (3600 / 100000 | Loss: 1.4668434858322144, Learning Rate: 9.688442211055276e-05)
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Step... (3625 / 100000 | Loss: 1.7880744934082031, Learning Rate: 9.685929648241207e-05)
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Train steps ... :   4%|▎         | 3650/100000 [35:37<15:40:07,  1.71it/s]Step... (3650 / 100000 | Loss: 1.9757217168807983, Learning Rate: 9.683417085427137e-05)
+
                                                                          
Step... (3650 / 100000 | Loss: 1.611654281616211, Learning Rate: 9.683417085427137e-05)
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Step... (3675 / 100000 | Loss: 1.9251561164855957, Learning Rate: 9.680904522613066e-05)
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Step... (3700 / 100000 | Loss: 2.0399255752563477, Learning Rate: 9.678391959798996e-05)
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Step... (3725 / 100000 | Loss: 1.9736084938049316, Learning Rate: 9.675879396984924e-05)
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Step... (3750 / 100000 | Loss: 1.828228235244751, Learning Rate: 9.673366834170855e-05)
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Step... (3775 / 100000 | Loss: 1.2374651432037354, Learning Rate: 9.670854271356784e-05)
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Step... (3800 / 100000 | Loss: 1.5388574600219727, Learning Rate: 9.668341708542715e-05)
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Step... (3825 / 100000 | Loss: 2.311981201171875, Learning Rate: 9.665829145728643e-05)
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Step... (3850 / 100000 | Loss: 1.4168236255645752, Learning Rate: 9.663316582914574e-05)
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Step... (3875 / 100000 | Loss: 2.8408865928649902, Learning Rate: 9.660804020100504e-05)
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Step... (3900 / 100000 | Loss: 1.586662769317627, Learning Rate: 9.658291457286432e-05)
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Step... (3925 / 100000 | Loss: 1.1214300394058228, Learning Rate: 9.655778894472363e-05)
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Step... (3950 / 100000 | Loss: 1.427931785583496, Learning Rate: 9.653266331658291e-05)
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Step... (3975 / 100000 | Loss: 2.093895196914673, Learning Rate: 9.650753768844222e-05)
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Step... (4000 / 100000 | Loss: 1.8173255920410156, Learning Rate: 9.64824120603015e-05)
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Step... (4025 / 100000 | Loss: 1.4212720394134521, Learning Rate: 9.645728643216081e-05)
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Step... (4050 / 100000 | Loss: 1.6796870231628418, Learning Rate: 9.64321608040201e-05)
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Step... (4075 / 100000 | Loss: 1.8444504737854004, Learning Rate: 9.64070351758794e-05)
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Step... (4100 / 100000 | Loss: 1.5247472524642944, Learning Rate: 9.63819095477387e-05)
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Step... (4125 / 100000 | Loss: 2.078380584716797, Learning Rate: 9.635678391959799e-05)
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Step... (4150 / 100000 | Loss: 1.6633261442184448, Learning Rate: 9.63316582914573e-05)
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Step... (4175 / 100000 | Loss: 1.8845267295837402, Learning Rate: 9.630653266331658e-05)
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Step... (4200 / 100000 | Loss: 1.623282790184021, Learning Rate: 9.628140703517589e-05)
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Step... (4225 / 100000 | Loss: 2.303323745727539, Learning Rate: 9.625628140703517e-05)
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Step... (4250 / 100000 | Loss: 1.5161821842193604, Learning Rate: 9.623115577889447e-05)
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Step... (4275 / 100000 | Loss: 1.334981918334961, Learning Rate: 9.620603015075378e-05)
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Step... (4300 / 100000 | Loss: 1.6932096481323242, Learning Rate: 9.618090452261306e-05)
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Step... (4325 / 100000 | Loss: 1.7333745956420898, Learning Rate: 9.615577889447237e-05)
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Step... (4350 / 100000 | Loss: 1.7114551067352295, Learning Rate: 9.613065326633166e-05)
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Step... (4375 / 100000 | Loss: 1.5088326930999756, Learning Rate: 9.610552763819097e-05)
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Step... (4400 / 100000 | Loss: 1.5172038078308105, Learning Rate: 9.608040201005025e-05)
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Step... (4425 / 100000 | Loss: 1.662022352218628, Learning Rate: 9.605527638190955e-05)
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Step... (4450 / 100000 | Loss: 1.947008490562439, Learning Rate: 9.603015075376884e-05)
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Step... (4475 / 100000 | Loss: 1.6117420196533203, Learning Rate: 9.600502512562814e-05)
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Step... (4500 / 100000 | Loss: 1.579805850982666, Learning Rate: 9.597989949748745e-05)
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Step... (4525 / 100000 | Loss: 1.3546942472457886, Learning Rate: 9.595477386934673e-05)
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Step... (4550 / 100000 | Loss: 1.4567748308181763, Learning Rate: 9.592964824120604e-05)
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Step... (4575 / 100000 | Loss: 1.6305551528930664, Learning Rate: 9.590452261306533e-05)
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Step... (4600 / 100000 | Loss: 1.1392271518707275, Learning Rate: 9.587939698492462e-05)
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Step... (4750 / 100000 | Loss: 1.7168644666671753, Learning Rate: 9.57286432160804e-05)
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Step... (5000 / 100000 | Loss: 1.512629508972168, Learning Rate: 9.547738693467337e-05)
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Train steps ... :   5%|▌         | 5000/100000 [48:47<15:26:15,  1.71it/s]06/17/2024 16:43:14 - INFO - accelerate.accelerator - Saving current state to ./checkpoint-5000-epoch-0
+06/17/2024 16:43:14 - WARNING - accelerate.utils.other - Removed shared tensor {'proj_out.weight'} while saving. This should be OK, but check by verifying that you don't receive any warning while reloading
+06/17/2024 16:43:17 - INFO - accelerate.checkpointing - Model weights saved in checkpoint-5000-epoch-0/model.safetensors
+06/17/2024 16:43:17 - WARNING - accelerate.utils.other - Removed shared tensor {'proj_out.weight'} while saving. This should be OK, but check by verifying that you don't receive any warning while reloading
+06/17/2024 16:43:21 - INFO - accelerate.checkpointing - Model weights saved in checkpoint-5000-epoch-0/model_1.safetensors
+06/17/2024 16:43:21 - INFO - accelerate.checkpointing - Optimizer state saved in checkpoint-5000-epoch-0/optimizer.bin
+06/17/2024 16:43:21 - INFO - accelerate.checkpointing - Scheduler state saved in checkpoint-5000-epoch-0/scheduler.bin
+06/17/2024 16:43:21 - INFO - accelerate.checkpointing - Sampler state for dataloader 0 saved in checkpoint-5000-epoch-0/sampler.bin
+06/17/2024 16:43:21 - INFO - accelerate.checkpointing - Random states saved in checkpoint-5000-epoch-0/random_states_0.pkl
+[2024-06-17 16:43:26,361] [INFO] [real_accelerator.py:203:get_accelerator] Setting ds_accelerator to cuda (auto detect)
+ [WARNING]  Please specify the CUTLASS repo directory as environment variable $CUTLASS_PATH
+ [WARNING]  sparse_attn requires a torch version >= 1.5 and < 2.0 but detected 2.3
+ [WARNING]  using untested triton version (2.3.1), only 1.0.0 is known to be compatible
+/opt/conda/lib/python3.10/site-packages/huggingface_hub/hf_api.py:3664: UserWarning: Warnings while validating metadata in README.md:
+- empty or missing yaml metadata in repo card
+  warnings.warn(f"Warnings while validating metadata in README.md:\n{message}")
+
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diff --git a/train.sh b/train.sh
new file mode 100644
index 0000000000000000000000000000000000000000..3ea3c55f40c8d204f6762da4000e3ebac9aa97cf
--- /dev/null
+++ b/train.sh
@@ -0,0 +1,75 @@
+accelerate launch --mixed_precision=bf16 run_distillation.py \
+  --model_name_or_path "./distil-large-v3-init" \
+  --teacher_model_name_or_path "GalaktischeGurke/swhisper_large_8552" \
+  --train_dataset_name "GalaktischeGurke/merge_all_OS_v3" \
+  --train_dataset_config_name "default" \
+  --train_split_name "train" \
+  --text_column_name "sentence" \
+  --eval_text_column_name "sentence" \
+  --audio_column_name "audio" \
+  --eval_dataset_name "GalaktischeGurke/merge_all_OS_v3" \
+  --eval_dataset_config_name "default" \
+  --eval_split_name "test" \
+  --eval_steps 5000 \
+  --save_steps 5000 \
+  --warmup_steps 500 \
+  --learning_rate 1e-4 \
+  --lr_scheduler_type "linear" \
+  --logging_steps 25 \
+  --save_total_limit 5 \
+  --max_steps 100000 \
+  --per_device_train_batch_size 4 \
+  --per_device_eval_batch_size 4 \
+  --dataloader_num_workers 130 \
+  --preprocessing_num_workers 130 \
+  --ddp_timeout 7200 \
+  --dtype "bfloat16" \
+  --output_dir "./" \
+  --use_pseudo_labels "false" \
+  --condition_on_prev_probability "0.0" \
+  --do_train \
+  --do_eval \
+  --gradient_checkpointing \
+  --overwrite_output_dir \
+  --predict_with_generate \
+  --freeze_encoder \
+  --streaming false \
+  --push_to_hub \
+  --report_to "wandb"accelerate launch --mixed_precision=bf16 run_distillation.py \
+  --model_name_or_path "./distil-large-v3-init" \
+  --teacher_model_name_or_path "GalaktischeGurke/swhisper_large_8552" \
+  --train_dataset_name "GalaktischeGurke/merge_all_OS_v3" \
+  --train_dataset_config_name "default" \
+  --train_split_name "train" \
+  --text_column_name "sentence" \
+  --eval_text_column_name "sentence" \
+  --audio_column_name "audio" \
+  --eval_dataset_name "GalaktischeGurke/merge_all_OS_v3" \
+  --eval_dataset_config_name "default" \
+  --eval_split_name "test" \
+  --eval_steps 5000 \
+  --save_steps 5000 \
+  --warmup_steps 500 \
+  --learning_rate 1e-4 \
+  --lr_scheduler_type "linear" \
+  --logging_steps 25 \
+  --save_total_limit 5 \
+  --max_steps 100000 \
+  --per_device_train_batch_size 4 \
+  --per_device_eval_batch_size 4 \
+  --dataloader_num_workers 130 \
+  --preprocessing_num_workers 130 \
+  --ddp_timeout 7200 \
+  --dtype "bfloat16" \
+  --output_dir "./" \
+  --use_pseudo_labels "false" \
+  --condition_on_prev_probability "0.0" \
+  --do_train \
+  --do_eval \
+  --gradient_checkpointing \
+  --overwrite_output_dir \
+  --predict_with_generate \
+  --freeze_encoder \
+  --streaming false \
+  --push_to_hub \
+  --report_to "wandb"
diff --git a/vocab.json b/vocab.json
new file mode 100644
index 0000000000000000000000000000000000000000..b45bc0e97a5511c917c112c42231cdbf455e200d
--- /dev/null
+++ b/vocab.json
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