"""Rakeffet dataset."""


import os

import datasets
from datasets import load_dataset
from datasets.tasks import AutomaticSpeechRecognition


_CITATION = """\
@inproceedings{Zandie2021RakeffetAC,
  title={Rakeffet AI},
  author={Yisroel Lazerson},
  booktitle={Cooolio},
  year={2022}
}
"""

_DESCRIPTION = "Rakeffet is cool."
_URL = "google.com"
_NAME = "rakeffet"

_DL_URLS = {
    "dev": "https://huggingface.co/datasets/izzy-lazerson/rakeffet/resolve/main/data/dev.tar.gz",
    "test": "https://huggingface.co/datasets/izzy-lazerson/rakeffet/resolve/main/data/test.tar.gz",
    "train": "https://huggingface.co/datasets/izzy-lazerson/rakeffet/resolve/main/data/train.tar.gz"
}


class RakeffetConfig(datasets.BuilderConfig):
    """BuilderConfig for Rakeffet."""

    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.
        """
        super(
            RakeffetConfig,
            self
        ).__init__(
            version=datasets.Version("1.0.0", ""),
            **kwargs
        )


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

    BUILDER_CONFIGS = [
        RakeffetConfig(
            name=_NAME,
        )
    ]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "id": datasets.Value("string"),
                    "audio": datasets.Audio(sampling_rate=16_000),
                    "text": datasets.Value("string"),
                }
            ),
            supervised_keys=("id", "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 {}

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

        return train_splits + dev_splits + test_splits

    def _generate_examples(self, files, local_extracted_archive):
        """Generate examples from a Rakeffet archive_path."""
        audio_data = {}
        transcripts = {}
        paths = {}
        for path, f in files:
            if path.endswith(".mp3"):
                id_ = path.split("/")[-1][: -len(".mp3")]
                audio_data[id_] = f.read()
                paths[id_] = os.path.join(local_extracted_archive, path)
            elif path.endswith(".csv"):
                for line in f:
                    line_fields = line.decode("utf-8").split(',')
                    id_ = line_fields[0]
                    transcripts[id_] = line_fields[1].strip()

        for key, id_ in enumerate(transcripts):
            yield key, {"audio": {"bytes": audio_data[id_],
                                  "path": paths[id_]},
                        "text": transcripts[id_],
                        "id": id_}