import csv
import os
from pathlib import Path
from typing import Dict, List, Tuple, Union

import torchaudio
from torch import Tensor
from torch.utils.data import Dataset


def load_commonvoice_item(
    line: List[str], header: List[str], path: str, folder_audio: str, ext_audio: str
) -> Tuple[Tensor, int, Dict[str, str]]:
    # Each line as the following data:
    # client_id, path, sentence, up_votes, down_votes, age, gender, accent

    if header[1] != "path":
        raise ValueError(f"expect `header[1]` to be 'path', but got {header[1]}")
    fileid = line[1]
    filename = os.path.join(path, folder_audio, fileid)
    if not filename.endswith(ext_audio):
        filename += ext_audio
    waveform, sample_rate = torchaudio.load(filename)

    dic = dict(zip(header, line))

    return waveform, sample_rate, dic


class COMMONVOICE(Dataset):
    """*CommonVoice* :cite:`ardila2020common` dataset.

    Args:
        root (str or Path): Path to the directory where the dataset is located.
             (Where the ``tsv`` file is present.)
        tsv (str, optional):
            The name of the tsv file used to construct the metadata, such as
            ``"train.tsv"``, ``"test.tsv"``, ``"dev.tsv"``, ``"invalidated.tsv"``,
            ``"validated.tsv"`` and ``"other.tsv"``. (default: ``"train.tsv"``)
    """

    _ext_txt = ".txt"
    _ext_audio = ".mp3"
    _folder_audio = "clips"

    def __init__(self, root: Union[str, Path], tsv: str = "train.tsv") -> None:

        # Get string representation of 'root' in case Path object is passed
        self._path = os.fspath(root)
        self._tsv = os.path.join(self._path, tsv)

        with open(self._tsv, "r") as tsv_:
            walker = csv.reader(tsv_, delimiter="\t")
            self._header = next(walker)
            self._walker = list(walker)

    def __getitem__(self, n: int) -> Tuple[Tensor, int, Dict[str, str]]:
        """Load the n-th sample from the dataset.

        Args:
            n (int): The index of the sample to be loaded

        Returns:
            Tuple of the following items;

            Tensor:
                Waveform
            int:
                Sample rate
            Dict[str, str]:
                Dictionary containing the following items from the corresponding TSV file;

                * ``"client_id"``
                * ``"path"``
                * ``"sentence"``
                * ``"up_votes"``
                * ``"down_votes"``
                * ``"age"``
                * ``"gender"``
                * ``"accent"``
        """
        line = self._walker[n]
        return load_commonvoice_item(line, self._header, self._path, self._folder_audio, self._ext_audio)

    def __len__(self) -> int:
        return len(self._walker)