"""LibriAdapt Dataset"""

import os
from pathlib import Path

import datasets
import csv

_CITATION = """\
@inproceedings{mathur20,
	doi = {10.1109/icassp40776.2020.9053074},
	url = {https://doi.org/10.1109\%2Ficassp40776.2020.9053074},
	year = 2020,
	month = {may},
	publisher = {{IEEE}},
	author = {Akhil Mathur and Fahim Kawsar and Nadia Berthouze and Nicholas D. Lane},
	title = {Libri-Adapt: a New Speech Dataset for Unsupervised Domain Adaptation},
	booktitle = {{ICASSP} 2020 - 2020 {IEEE} International Conference on Acoustics, Speech and Signal Processing ({ICASSP})}
}
"""

_DESCRIPTION = """\
LibriAdapt (For more information refer to the original paper at https://doi.org/10.1109%2Ficassp40776.2020.9053074)
"""

_HOMEPAGE = "https://github.com/akhilmathurs/libriadapt"


class LRL_TranserConfig(datasets.BuilderConfig):
    """BuilderConfig for TimitASR."""

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


class LRL_Transfer(datasets.GeneratorBasedBuilder):
    """LRL Transfer dataset."""

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

    @property
    def manual_download_instructions(self):
        return (
            "If you want to use noise, you can download the dataset and script here: https://github.com/akhilmathurs/libriadapt"
        )

    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"),
                    "accent": datasets.Value("string"),
                    "microphone": datasets.Value("string"),
                    "id": datasets.Value("string"),
                }
            ),
            supervised_keys=("file", "text"),
            homepage=_HOMEPAGE,
            citation=_CITATION,
        )

    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 dataset 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"))
        wav_paths = wav_paths if wav_paths else sorted(
            Path(data_dir).glob(f"**/{split.upper()}/**/*.WAV"))
        for key, wav_path in enumerate(wav_paths):
            # extract microphone
            mic = str(wav_path).split("clean")[-1].split("/")[1]

            # extract accent
            accent = str(wav_path).split("en-")[-1][0:2]

            # extract transcript
            num = str(wav_path).split("/")[-1].split(".")[0]
            csv_path = str(wav_path).split("clean")[
                          0] + "clean/" + split + "_files_" + mic + ".csv"
            with open(csv_path) as csvfile:
                reader = csv.reader(csvfile, delimiter=',')
                for row in reader:
                    if row[0].split("/")[-1].split(".")[0] == num:
                        transcript = row[2]

            id_ = wav_path.stem

            example = {
                "file": str(wav_path),
                "audio": str(wav_path),
                "text": transcript,
                "accent": accent,
                "microphone": mic,
                "id": id_,
            }

            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