# coding=utf-8
# Copyright 2021 The TensorFlow Datasets Authors and the HuggingFace Datasets 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.
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# limitations under the License.

# Lint as: python3
"""AudioData dataset."""


import os
from pathlib import Path

import datasets
from datasets.tasks import AutomaticSpeechRecognition


_CITATION = """\
@inproceedings{
  title={AudioData Speech Corpus},
  author={Your Name},
  year={Year}
}
"""

_DESCRIPTION = """\
The AudioData corpus of reading speech has been developed to provide speech data for acoustic-phonetic research studies
and for the evaluation of automatic speech recognition systems.
More info on AudioData dataset can be understood from the "README" which can be found here:
https://example.com/path/to/readme.txt
"""

# _HOMEPAGE = "https://example.com/path/to/dataset"


class AudioDataConfig(datasets.BuilderConfig):
    """BuilderConfig for AudioData."""

    def __init__(self, **kwargs):
        """
        Args:
          data_dir: `string`, the path to the folder containing the audio files
          citation: `string`, citation for the data set
          url: `string`, url for information about the data set
          **kwargs: keyword arguments forwarded to super.
        """
        super(AudioDataConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)


class AudioData(datasets.GeneratorBasedBuilder):
    """AudioData dataset."""

    BUILDER_CONFIGS = [AudioDataConfig(name="clean", description="'Clean' speech.")]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "folder": datasets.Value("string"),
                    "audio": datasets.Audio(sampling_rate=16_000),
                    "label": datasets.Value("string"),
                }
            ),
            supervised_keys=("folder", "label"),
            # homepage=_HOMEPAGE,
            citation=_CITATION,
            task_templates=[AutomaticSpeechRecognition(audio_column="audio", transcription_column="label")],
        )

    def _split_generators(self, dl_manager):

        data_dir = os.path.abspath(os.path.expanduser(dl_manager.manual_dir))

        if not os.path.exists(data_dir):
            raise FileNotFoundError(
                f"{data_dir} does not exist. Make sure you insert a manual dir via `datasets.load_dataset('timit_asr', data_dir=...)` that includes files unzipped from the TIMIT zip. Manual download instructions: {self.manual_download_instructions}"
            )

        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"split": "train", "data_dir": data_dir}),
            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"split": "test", "data_dir": data_dir}),
        ]


    def _generate_examples(self, split, data_dir):
        """Generate examples from AudioData based on the test/train csv information."""
        # Iterating the contents of the data to extract the relevant information
        wav_paths = sorted(Path(data_dir).glob(f"**/{split}/**/*.wav"))
        for key, wav_path in enumerate(wav_paths):

            # extract transcript
            txt_path = with_case_insensitive_suffix(wav_path, ".txt")
            with txt_path.open(encoding="utf-8") as op:
                transcript = " ".join(op.readlines()[0].split()[2:])  # first two items are sample number

            example = {
                "file": str(wav_path),
                "audio": str(wav_path),
                "text": transcript,
            }

            yield key, example


def with_case_insensitive_suffix(path: Path, suffix: str):
    path = path.with_suffix(suffix.lower())
    path = path if path.exists() else path.with_suffix(suffix.upper())
    return path