#!/usr/bin/env python
"""Demo app for https://github.com/ziqihuangg/ReVersion.
The code in this repo is partly adapted from the following repository:
https://huggingface.co/spaces/hysts/LoRA-SD-training

S-Lab License 1.0

Copyright 2022 S-Lab
Redistribution and use for non-commercial purpose in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
4. In the event that redistribution and/or use for commercial purpose in source or binary forms, with or without modification is required, please contact the contributor(s) of the work.
"""

from __future__ import annotations
import sys
import os
import pathlib

import argparse
import gradio as gr
import torch

from inference import inference_fn
# from inference_custom_diffusion import InferencePipeline
# from trainer import Trainer
# from uploader import upload


def parse_args() -> argparse.Namespace:
    parser = argparse.ArgumentParser()
    parser.add_argument('--device', type=str, default='cpu')
    parser.add_argument('--theme', type=str)
    parser.add_argument('--share', action='store_true')
    parser.add_argument('--port', type=int)
    parser.add_argument('--disable-queue',
                        dest='enable_queue',
                        action='store_false')
    return parser.parse_args()


TITLE = '# ReVersion'
DESCRIPTION = '''This is a demo for [https://github.com/ziqihuangg/ReVersion](https://github.com/ziqihuangg/ReVersion).
It is recommended to upgrade to GPU in Settings after duplicating this space to use it.
<a href="https://huggingface.co/spaces/Ziqi/ReVersion?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>
'''
DETAILDESCRIPTION='''
ReVersion
'''
# DETAILDESCRIPTION='''
# Custom Diffusion allows you to fine-tune text-to-image diffusion models, such as Stable Diffusion, given a few images of a new concept (~4-20).
# We fine-tune only a subset of model parameters, namely key and value projection matrices, in the cross-attention layers and the modifier token used to represent the object.
# This also reduces the extra storage for each additional concept to 75MB. Our method also allows you to use a combination of concepts. There's still limitations on which compositions work. For more analysis please refer to our [website](https://www.cs.cmu.edu/~custom-diffusion/).
# <center>
# <img src="https://huggingface.co/spaces/nupurkmr9/custom-diffusion/resolve/main/method.jpg" width="600" align="center" >
# </center>
# '''

ORIGINAL_SPACE_ID = 'Ziqi/ReVersion'
SPACE_ID = os.getenv('SPACE_ID', ORIGINAL_SPACE_ID)
# SHARED_UI_WARNING = f'''# Attention - This Space doesn't work in this shared UI. You can duplicate and use it with a paid private T4 GPU.
# <center><a class="duplicate-button" style="display:inline-block" target="_blank" href="https://huggingface.co/spaces/{SPACE_ID}?duplicate=true"><img src="https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14" alt="Duplicate Space"></a></center>
# '''
if os.getenv('SYSTEM') == 'spaces' and SPACE_ID != ORIGINAL_SPACE_ID:
    SETTINGS = f'<a href="https://huggingface.co/spaces/{SPACE_ID}/settings">Settings</a>'

else:
    SETTINGS = 'Settings'
CUDA_NOT_AVAILABLE_WARNING = f'''# Attention - Running on CPU.
<center>
You can assign a GPU in the {SETTINGS} tab if you are running this on HF Spaces.
"T4 small" is sufficient to run this demo.
</center>
'''

os.system("git clone https://github.com/ziqihuangg/ReVersion")
sys.path.append("ReVersion")

def show_warning(warning_text: str) -> gr.Blocks:
    with gr.Blocks() as demo:
        with gr.Box():
            gr.Markdown(warning_text)
    return demo


def update_output_files() -> dict:
    paths = sorted(pathlib.Path('results').glob('*.bin'))
    paths = [path.as_posix() for path in paths]  # type: ignore
    return gr.update(value=paths or None)

def find_weight_files() -> list[str]:
    curr_dir = pathlib.Path(__file__).parent
    paths = sorted(curr_dir.rglob('*.bin'))
    paths = [path for path in paths if '.lfs' not in str(path)]
    return [path.relative_to(curr_dir).as_posix() for path in paths]


def reload_custom_diffusion_weight_list() -> dict:
    return gr.update(choices=find_weight_files())


def create_inference_demo(func: inference_fn) -> gr.Blocks:
    with gr.Blocks() as demo:
        with gr.Row():
            with gr.Column():
                model_id = gr.Dropdown(
                    choices=['experiments/painted_on'],
                    value='experiments/painted_on',
                    label='Relation',
                    visible=True)
                # reload_button = gr.Button('Reload Weight List')
                prompt = gr.Textbox(
                    label='Prompt',
                    max_lines=1,
                    placeholder='Example: "cat <R> stone"')
                placeholder_string = gr.Textbox(
                    label='Placeholder String',
                    max_lines=1,
                    placeholder='Example: "<R>"')

                with gr.Accordion('Other Parameters', open=False):
                    guidance_scale = gr.Slider(label='Classifier-Free Guidance Scale',
                                               minimum=0,
                                               maximum=50,
                                               step=0.1,
                                               value=7.5)
                    num_samples = gr.Slider(label='Batch Size',
                                               minimum=0,
                                               maximum=10.,
                                               step=1,
                                               value=10)

                run_button = gr.Button('Generate')

                # gr.Markdown('''
                # - Models with names starting with "custom-diffusion-models/" are the pretrained models provided in the [original repo](https://github.com/adobe-research/custom-diffusion), and the ones with names starting with "results/delta.bin" are your trained models.
                # - After training, you can press "Reload Weight List" button to load your trained model names.
                # - Increase number of steps in Other parameters for better samples qualitatively.
                # ''')
            with gr.Column():
                result = gr.Image(label='Result')

        # reload_button.click(fn=reload_custom_diffusion_weight_list,
        #                     inputs=None,
        #                     outputs=weight_name)
        prompt.submit(fn=func,
                      inputs=[
                          model_id,
                          prompt,
                          placeholder_string,
                          num_samples,
                          guidance_scale
                      ],
                      outputs=result,
                      queue=False)
        run_button.click(fn=func,
                        inputs=[
                            model_id,
                            prompt,
                            placeholder_string,
                            num_samples,
                            guidance_scale
                        ],
                         outputs=result,
                         queue=False)
    return demo


args = parse_args()
args.device = 'cuda' if torch.cuda.is_available() else 'cpu'
print('*** Now using %s.'%(args.device))

with gr.Blocks(css='style.css') as demo:
    # if os.getenv('IS_SHARED_UI'):
    #     show_warning(SHARED_UI_WARNING)
    if not torch.cuda.is_available():
        show_warning(CUDA_NOT_AVAILABLE_WARNING)

    gr.Markdown(TITLE)
    gr.Markdown(DESCRIPTION)
    gr.Markdown(DETAILDESCRIPTION)

    with gr.Tabs():
        with gr.TabItem('Test'):
            create_inference_demo(inference_fn)

demo.launch(
    enable_queue=args.enable_queue,
    server_port=args.port,
    share=args.share,
)
# demo.queue(default_enabled=False).launch(server_port=args.port, share=args.share)