# 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
#
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# 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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# Lint as: python3
"""MAGICDATA Mandarin Chinese Read Speech Corpus."""


import os

import datasets
from datasets.tasks import AutomaticSpeechRecognition


_CITATION = """\
 @misc{magicdata_2019, 
 title={MAGICDATA Mandarin Chinese Read Speech Corpus}, 
 url={https://openslr.org/68/}, 
 publisher={Magic Data Technology Co., Ltd.},  
 year={2019}, 
 month={May}} 
"""

_DESCRIPTION = """\
The corpus by Magic Data Technology Co., Ltd. , containing 755 hours of scripted read speech data 
from 1080 native speakers of the Mandarin Chinese spoken in mainland China. 
The sentence transcription accuracy is higher than 98%.
"""

_URL = "https://openslr.org/68/"
_DL_URL = "http://www.openslr.org/resources/68/"


_DL_URLS = {
  "train": _DL_URL + "train_set.tar.gz",
  "dev": _DL_URL + "dev_set.tar.gz",
  "test": _DL_URL + "test_set.tar.gz",
}


class MMCRSCConfig(datasets.BuilderConfig):
  """BuilderConfig for MMCRSC."""

  def __init__(self, **kwargs):
    """
    Args:
      data_dir: `string`, the path to the folder containing the files in the
        downloaded .tar
      citation: `string`, citation for the data set
      url: `string`, url for information about the data set
      **kwargs: keyword arguments forwarded to super.
    """
    # version history
    # 0.1.0: First release on Huggingface
    super(MMCRSCConfig, self).__init__(version=datasets.Version("0.1.0", ""), **kwargs)


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

  DEFAULT_WRITER_BATCH_SIZE = 256
  DEFAULT_CONFIG_NAME = "all"

  def _info(self):
    return datasets.DatasetInfo(
      description=_DESCRIPTION,
      features=datasets.Features(
        {
          "file": datasets.Value("string"),
          "audio": datasets.Audio(sampling_rate=16_000),
          "text": datasets.Value("string"),
          "speaker_id": datasets.Value("int64"),
          "id": datasets.Value("string"),
        }
      ),
      supervised_keys=("file", "text"),
      homepage=_URL,
      citation=_CITATION,
      task_templates=[AutomaticSpeechRecognition(audio_column="audio", transcription_column="text")],
    )

  def _split_generators(self, dl_manager):
    archive_path = dl_manager.download(_DL_URLS)
    # (Optional) In non-streaming mode, we can extract the archive locally to have actual local audio files:
    local_extracted_archive = dl_manager.extract(archive_path) if not dl_manager.is_streaming else {}

    return  [
      datasets.SplitGenerator(
        name=datasets.Split.TRAIN,
        gen_kwargs={
          "local_extracted_archive": local_extracted_archive.get("train"),
          "files": dl_manager.iter_archive(archive_path["train"]),
        },
      ),
      datasets.SplitGenerator(
        name=datasets.Split.VALIDATION,
        gen_kwargs={
          "local_extracted_archive": local_extracted_archive.get("dev"),
          "files": dl_manager.iter_archive(archive_path["dev"]),
        },
      ),
      datasets.SplitGenerator(
        name=datasets.Split.TEST,
        gen_kwargs={
          "local_extracted_archive": local_extracted_archive.get("test"),
          "files": dl_manager.iter_archive(archive_path["test"]),
        },
      ),
    ]

  def _generate_examples(self, files, local_extracted_archive):
    """Generate examples from a LibriSpeech archive_path."""
    audio_data = {}
    transcripts = []
    for path, f in files:
      if path.endswith(".wav"):
        id_ = path.split("/")[-1]
        audio_data[id_] = f.read()
      elif path.endswith("TRANS.txt"):
        for line in f:
          if line and (b'.wav' in line):
            line = line.decode("utf-8").strip()
            id_, speaker_id, transcript = line.split("\t")
            audio_file = id_
            audio_file = (
              os.path.join(local_extracted_archive, audio_file)
              if local_extracted_archive
              else audio_file
            )
            transcripts.append(
              {
                "id": id_,
                "speaker_id": speaker_id,
                "file": audio_file,
                "text": transcript,
              }
            )
    if audio_data:
      for key, transcript in enumerate(transcripts):
        if transcript["id"] in audio_data:
          audio = {"path": transcript["file"], "bytes": audio_data[transcript["id"]]}
          yield key, {"audio": audio, **transcript}
      audio_data = {}
      transcripts = []