# coding=utf-8
# Copyright 2021 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,
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""" Common Voice Dataset"""

from datasets import AutomaticSpeechRecognition


import datasets
import os
import pandas as pd


_CITATION = """\
@misc{cahyawijaya2023crosslingual,
    title={Cross-Lingual Cross-Age Group Adaptation for Low-Resource Elderly Speech Emotion Recognition}, 
    author={Samuel Cahyawijaya and Holy Lovenia and Willy Chung and Rita Frieske and Zihan Liu and Pascale Fung},
    year={2023},
    eprint={2306.14517},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
"""

_DESCRIPTION = """\
YueMotion is a Cantonese speech emotion dataset.
"""

_HOMEPAGE = "https://huggingface.co/datasets/CAiRE/YueMotion"

_URL = "https://huggingface.co/datasets/CAiRE/YueMotion/raw/main/"
_URLS = {
    "train": _URL + "train_metadata.csv",
    "test": _URL + "test_metadata.csv",
    "validation": _URL + "validation_metadata.csv",
    "waves": "https://huggingface.co/datasets/CAiRE/YueMotion/resolve/main/data.tar.bz2",
}


class YueMotionConfig(datasets.BuilderConfig):
    """BuilderConfig for YueMotion."""

    def __init__(self, name="main", **kwargs):
        """
        Args:
          **kwargs: keyword arguments forwarded to super.
        """
        super(YueMotionConfig, self).__init__(name, **kwargs)


class YueMotion(datasets.GeneratorBasedBuilder):
    """YueMotion: Cantonese speech emotion recognition for both adults and elderly. Snapshot date: 28 June 2023."""

    BUILDER_CONFIGS = [
        YueMotionConfig(
            name="main",
            version=datasets.Version("1.0.0", ""),
            description=_DESCRIPTION,
        )
    ]

    def _info(self):
        features = datasets.Features(
            {
                "split": datasets.Value("string"),
                "speaker_id": datasets.Value("string"),
                "path": datasets.Value("string"),
                "audio": datasets.Audio(sampling_rate=16_000),
                "gender": datasets.Value("string"),
                "age": datasets.Value("int64"),
                "sentence_id": datasets.Value("string"),
                "label_id": datasets.Value("int64"),
                "label": datasets.Value("string"),
            }
        )
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            supervised_keys=None,
            homepage=_HOMEPAGE,
            citation=_CITATION,
            task_templates=[AutomaticSpeechRecognition(audio_column="audio", transcription_column="transcription")],
        )

    def _split_generators(self, dl_manager):
        downloaded_files = dl_manager.download_and_extract(_URLS)

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "metadata_path": downloaded_files["train"],
                    "wave_path": downloaded_files["waves"],
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "metadata_path": downloaded_files["test"],
                    "wave_path": downloaded_files["waves"],
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    "metadata_path": downloaded_files["validation"],
                    "wave_path": downloaded_files["waves"],
                },
            ),
        ]

    def _generate_examples(self, metadata_path, wave_path):
        print(metadata_path)
        metadata_df = pd.read_csv(metadata_path)

        for index, row in metadata_df.iterrows():
            example = {
                "split": row["split"],
                "speaker_id": row["speaker_id"],
                "path": os.path.join(wave_path, row["file_name"]),
                "audio": os.path.join(wave_path, row["file_name"]),
                "gender": row["gender"],
                "age": row["age"],
                "sentence_id": row["sentence_id"],
                "label_id": row["label_id"],
                "label": row["label"],
            }
            yield index, example