import os
import datasets
import typing as tp
from pathlib import Path

SAMPLE_RATE = 44_100
EXTENSION = '.wav'

ESC50_LABELS = [
    "airplane",
    "breathing",
    "brushing_teeth",
    "can_opening",
    "car_horn",
    "cat",
    "chainsaw",
    "chirping_birds",
    "church_bells",
    "clapping",
    "clock_alarm",
    "clock_tick",
    "coughing",
    "cow",
    "crackling_fire",
    "crickets",
    "crow",
    "crying_baby",
    "dog",
    "door_wood_creaks",
    "door_wood_knock",
    "drinking_sipping",
    "engine",
    "fireworks",
    "footsteps",
    "frog",
    "glass_breaking",
    "hand_saw",
    "helicopter",
    "hen",
    "insects",
    "keyboard_typing",
    "laughing",
    "mouse_click",
    "pig",
    "pouring_water",
    "rain",
    "rooster",
    "sea_waves",
    "sheep",
    "siren",
    "sneezing",
    "snoring",
    "thunderstorm",
    "toilet_flush",
    "train",
    "vacuum_cleaner",
    "washing_machine",
    "water_drops",
    "wind",
]

ESC50_LABEL2ID = {
    "dog": 0,
    "rooster": 1,
    "pig": 2,
    "cow": 3,
    "frog": 4,
    "cat": 5,
    "hen": 6,
    "insects": 7,
    "sheep": 8,
    "crow": 9,
    "rain": 10,
    "sea_waves": 11,
    "crackling_fire": 12,
    "crickets": 13,
    "chirping_birds": 14,
    "water_drops": 15,
    "wind": 16,
    "pouring_water": 17,
    "toilet_flush": 18,
    "thunderstorm": 19,
    "crying_baby": 20,
    "sneezing": 21,
    "clapping": 22,
    "breathing": 23,
    "coughing": 24,
    "footsteps": 25,
    "laughing": 26,
    "brushing_teeth": 27,
    "snoring": 28,
    "drinking_sipping": 29,
    "door_wood_knock": 30,
    "mouse_click": 31,
    "keyboard_typing": 32,
    "door_wood_creaks": 33,
    "can_opening": 34,
    "washing_machine": 35,
    "vacuum_cleaner": 36,
    "clock_alarm": 37,
    "clock_tick": 38,
    "glass_breaking": 39,
    "helicopter": 40,
    "chainsaw": 41,
    "siren": 42,
    "car_horn": 43,
    "engine": 44,
    "train": 45,
    "church_bells": 46,
    "airplane": 47,
    "fireworks": 48,
    "hand_saw": 49
}
ESC50_ID2LABEL = {v:k for k, v in ESC50_LABEL2ID.items()}


class Esc50Config(datasets.BuilderConfig):
    """BuilderConfig for ESC50."""
    
    def __init__(self, features, **kwargs):
        super(Esc50Config, self).__init__(version=datasets.Version("0.0.1", ""), **kwargs)
        self.features = features


class ESC50(datasets.GeneratorBasedBuilder):
    """
    The ESC50 dataset
    """

    BUILDER_CONFIGS = [
        Esc50Config(
            features=datasets.Features(
                {
                    "file": datasets.Value("string"),
                    "audio": datasets.Audio(sampling_rate=SAMPLE_RATE),
                    "sound": datasets.Value("string"),
                    "label": datasets.ClassLabel(names=ESC50_LABELS),
                }
            ),
            name=f"fold{f}", 
            description='',
        ) for f in range(1, 6) 
    ]
    
    def _info(self):
        return datasets.DatasetInfo(
            description='Environmental sound classification dataset',
            features=self.config.features,
        )

    def _split_generators(self, dl_manager):
        data_dir = dl_manager.download_and_extract("ESC-50-master.zip")
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN, 
                gen_kwargs={
                    "data_dir": data_dir, 
                    "split": 'train'
                }, 
            ), 
            datasets.SplitGenerator(
                name=datasets.Split.TEST, 
                gen_kwargs={
                    "data_dir": data_dir, 
                    "split": 'test'
                }, 
            ), 
        ]

    def _generate_examples(self, data_dir, split):
        _, _walker = fast_scandir(data_dir, [EXTENSION], recursive=True)

        for idx, fileid in enumerate(_walker):
            fold = default_find_fold(fileid)
            test_fold = self.config.name
            train_fold = [f'fold{f}' for f in range(1, 11)]
            train_fold.remove(test_fold)
            
            if split == 'train' and f'fold{fold}' in train_fold:
                yield idx, {
                    'file': fileid, 
                    'audio': fileid, 
                    'sound': default_find_classes(fileid), 
                    'label': default_find_classes(fileid)
                }
            elif split == 'test' and f'fold{fold}' in test_fold:
                yield idx, {
                    'file': fileid, 
                    'audio': fileid, 
                    'sound': default_find_classes(fileid), 
                    'label': default_find_classes(fileid)
                }


def default_find_classes(audio_path):
    id_ = Path(audio_path).stem.split('-')[-1]
    return ESC50_ID2LABEL.get(int(id_))


def default_find_fold(audio_path):
    fold = Path(audio_path).stem.split('-')[0]
    return int(fold)


def fast_scandir(path: str, exts: tp.List[str], recursive: bool = False):
    # Scan files recursively faster than glob
    # From github.com/drscotthawley/aeiou/blob/main/aeiou/core.py
    subfolders, files = [], []

    try:  # hope to avoid 'permission denied' by this try
        for f in os.scandir(path):
            try:  # 'hope to avoid too many levels of symbolic links' error
                if f.is_dir():
                    subfolders.append(f.path)
                elif f.is_file():
                    if os.path.splitext(f.name)[1].lower() in exts:
                        files.append(f.path)
            except Exception:
                pass
    except Exception:
        pass

    if recursive:
        for path in list(subfolders):
            sf, f = fast_scandir(path, exts, recursive=recursive)
            subfolders.extend(sf)
            files.extend(f)  # type: ignore

    return subfolders, files