# coding=utf-8
# Copyright 2022 Artem Ploujnikov (HuggingFace adaptation only)
# 
# Original dataset: https://github.com/soerenab/AudioMNIST
#
# If you use this dataset, please cite the following article:
# @ARTICLE{becker2018interpreting,
#   author    = {Becker, S\"oren and Ackermann, Marcel and Lapuschkin, Sebastian and M\"uller, Klaus-Robert and Samek, Wojciech},
#   title     = {Interpreting and Explaining Deep Neural Networks for Classification of Audio Signals},
#   journal   = {CoRR},
#   volume    = {abs/1807.03418},
#   year      = {2018},
#   archivePrefix = {arXiv},
#   eprint    = {1807.03418},
# }


# Lint as: python3
import json
import logging
import re
import os

import datasets

from glob import glob

logger = logging.getLogger(__name__)

_DESCRIPTION = """\
AudioMNIST, a research baseline dataset 
"""

_BASE_URL = "https://huggingface.co/datasets/flexthink/audiomnist/resolve/main"
_HOMEPAGE_URL = "https://huggingface.co/datasets/flexthink/audiomnist"
_SPLITS = ["train", "valid", "test"]
_GENDERS = ["female", "male"]
_ACCENTS = [
    "Arabic",
    "Brasilian",
    "Chinese",
    "Danish",
    "English",
    "French",
    "German",
    "Italian",
    "Levant",
    "Madras",
    "South African",
    "South Korean",
    "Spanish",
    "Tamil",
]
_SAMPLING_RATE = 48000

_ACCENT_MAP = {
    "german": "German",
    "Egyptian_American?": "Arabic",
    "German/Spanish": "German",
}

_META_FILE = "audioMNIST_meta.json"
_RE_FILE_NAME = re.compile("(?P<digit>\d+)_(?P<speaker_id>\d+)_(?P<sample_idx>\d+).wav")


class GraphemeToPhoneme(datasets.GeneratorBasedBuilder):
    def __init__(self, base_url=None, splits=None, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.base_url = base_url or _BASE_URL
        self.splits = splits or _SPLITS

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "file_name": datasets.Value("string"),
                    "audio": datasets.features.Audio(sampling_rate=_SAMPLING_RATE),
                    "speaker_id": datasets.Value("string"),
                    "age": datasets.Value("int8"),
                    "gender": datasets.ClassLabel(names=_GENDERS),
                    "accent": datasets.ClassLabel(names=_ACCENTS),
                    "native_speaker": datasets.Value("bool"),
                    "origin": datasets.Value("string"),
                    "digit": datasets.Value("int8"),
                },
            ),
            supervised_keys=None,
            homepage=_HOMEPAGE_URL,
        )

    def _get_url(self, split):
        return f"{self.base_url}/dataset/{split}.tar.gz"

    def _get_meta_url(self):
        return f"{self.base_url}/meta/{_META_FILE}"

    def _split_generator(self, dl_manager, split):
        archive_url = self._get_url(split)
        archive_path = dl_manager.download_and_extract(archive_url)
        meta_url = self._get_meta_url()
        meta_file = dl_manager.download(meta_url)
        speaker_map = self._get_speaker_map(meta_file)
        return datasets.SplitGenerator(
            name=split,
            gen_kwargs={
                "archive_path": archive_path,
                "speaker_map": speaker_map,
            },
        )

    def _get_speaker_map(self, file_name):
        with open(file_name) as speaker_file:
            result = json.load(speaker_file)
            for entry in result.values():
                entry["accent"] = _ACCENT_MAP.get(
                    entry["accent"], entry["accent"])
            return result

    def _split_generators(self, dl_manager):
        return [self._split_generator(dl_manager, split) for split in self.splits]

    def _map_speaker_info(self, speaker_info):
        result = dict(speaker_info)
        result["native_speaker"] = speaker_info["native speaker"] == "yes"
        del result["native speaker"]
        del result["recordingdate"]
        del result["recordingroom"]
        return result

    def _generate_examples(self, archive_path, speaker_map):
        wav_files = glob(os.path.join(archive_path, 'dataset', '**', '*.wav'))        
        for path in wav_files:
            match = _RE_FILE_NAME.search(path)
            if not match:
                logger.warn(
                    f"File {path} does not match the naming convention"
                )
                continue
            digit, speaker_id = [
                match.group(key) for key in ["digit", "speaker_id"]
            ]
            with open(path, 'rb') as wav_file:
                sample = {
                    "digit": digit,
                    "speaker_id": speaker_id,
                    "file_name": os.path.join(archive_path, path),
                    "audio": {"path": path, "bytes": wav_file.read()},
                }
            if speaker_id not in speaker_map:
                logger.warn(f"Speaker {speaker_id} not found")
            speaker_info = speaker_map[speaker_id]
            sample.update(self._map_speaker_info(speaker_info))
            yield path, sample