# coding=utf-8
# Copyright 2024 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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 pandas as pd
import numpy as np

import datasets

_CITATION = """
@inproceedings{defferrard2016fma,
  title={FMA: A Dataset for Music Analysis},
  author={Defferrard, Micha{\"e}l and Benzi, Kirell and Vandergheynst, Pierre and Bresson, Xavier},
  booktitle={18th International Society for Music Information Retrieval Conference},
  year={2017},
}
"""

_DESCRIPTION = """
The Free Music Archive (FMA) is an open and easily accessible dataset of music collections.
"""

_HOMEPAGE = "https://github.com/mdeff/fma"

_LICENSE = "Creative Commons Attribution 4.0 International License"

_URLs = {
    "small": "https://os.unil.cloud.switch.ch/fma/fma_small.zip",
    "metadata": "https://os.unil.cloud.switch.ch/fma/fma_metadata.zip",
}

class FMADataset(datasets.GeneratorBasedBuilder):
    """FMA small dataset."""

    VERSION = datasets.Version("1.0.0")

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(name="small", version=VERSION, description="The small subset of FMA dataset"),
    ]

    def _info(self):
        features = datasets.Features(
            {
                "track_id": datasets.Value("int32"),
                "title": datasets.Value("string"),
                "artist": datasets.Value("string"),
                "genre": datasets.Value("string"),
                "audio": datasets.Audio(sampling_rate=44100),
            }
        )
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        data_dir = dl_manager.download_and_extract(_URLs)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "filepath": os.path.join(data_dir["small"], "fma_small"),
                    "metadata_path": os.path.join(data_dir["metadata"], "fma_metadata"),
                },
            ),
        ]

    def _generate_examples(self, filepath, metadata_path):
        """Yields examples."""
        # Load metadata
        tracks = pd.read_csv(os.path.join(metadata_path, "tracks.csv"), index_col=0, header=[0, 1])
        
        # Iterate through audio files
        for root, _, files in os.walk(filepath):
            for file in files:
                if file.endswith('.mp3'):
                    track_id = int(file.split('.')[0])
                    audio_path = os.path.join(root, file)
                    
                    # Get metadata
                    title = tracks.loc[track_id, ('track', 'title')]
                    artist = tracks.loc[track_id, ('artist', 'name')]
                    genre = tracks.loc[track_id, ('track', 'genre_top')]
                    
                    yield track_id, {
                        "track_id": track_id,
                        "title": title,
                        "artist": artist,
                        "genre": genre,
                        "audio": audio_path,
                    }

    @property
    def manual_download_instructions(self):
        return """
        To use the FMA dataset, you need to download it manually. Please follow these steps:

        1. Go to https://github.com/mdeff/fma
        2. Download the 'fma_small.zip' and 'fma_metadata.zip' files
        3. Extract both zip files
        4. Copy the 'fma_small' folder and the 'fma_metadata' folder to the root of this dataset repository

        Once you have completed these steps, the dataset will be ready to use.
        """ coding=utf-8
# Copyright 2024 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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 pandas as pd
import numpy as np

import datasets

_CITATION = """
@inproceedings{defferrard2016fma,
  title={FMA: A Dataset for Music Analysis},
  author={Defferrard, Micha{\"e}l and Benzi, Kirell and Vandergheynst, Pierre and Bresson, Xavier},
  booktitle={18th International Society for Music Information Retrieval Conference},
  year={2017},
}
"""

_DESCRIPTION = """
The Free Music Archive (FMA) is an open and easily accessible dataset of music collections.
"""

_HOMEPAGE = "https://github.com/mdeff/fma"

_LICENSE = "Creative Commons Attribution 4.0 International License"

_URLs = {
    "small": "https://os.unil.cloud.switch.ch/fma/fma_small.zip",
    "metadata": "https://os.unil.cloud.switch.ch/fma/fma_metadata.zip",
}


class FMADataset(datasets.GeneratorBasedBuilder):
    """FMA small dataset."""

    VERSION = datasets.Version("1.0.0")

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(name="small", version=VERSION, description="The small subset of FMA dataset"),
    ]

    def _info(self):
        features = datasets.Features(
            {
                "track_id": datasets.Value("int32"),
                "title": datasets.Value("string"),
                "artist": datasets.Value("string"),
                "genre": datasets.Value("string"),
                "audio": datasets.Audio(sampling_rate=44100),
            }
        )
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        data_dir = dl_manager.download_and_extract(_URLs)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "filepath": os.path.join(data_dir["small"], "fma_small"),
                    "metadata_path": os.path.join(data_dir["metadata"], "fma_metadata"),
                },
            ),
        ]

    def _generate_examples(self, filepath, metadata_path):
        """Yields examples."""
        # Load metadata
        tracks = pd.read_csv(os.path.join(metadata_path, "tracks.csv"), index_col=0, header=[0, 1])
        
        # Iterate through audio files
        for root, _, files in os.walk(filepath):
            for file in files:
                if file.endswith('.mp3'):
                    track_id = int(file.split('.')[0])
                    audio_path = os.path.join(root, file)
                    
                    # Get metadata
                    title = tracks.loc[track_id, ('track', 'title')]
                    artist = tracks.loc[track_id, ('artist', 'name')]
                    genre = tracks.loc[track_id, ('track', 'genre_top')]
                    
                    yield track_id, {
                        "track_id": track_id,
                        "title": title,
                        "artist": artist,
                        "genre": genre,
                        "audio": audio_path,
                    }