import os
import random

import torchaudio
import torchcodec.decoders as decoders
import tqdm
from torch.utils.data import Dataset

import AudioUtils


class AudioDataset(Dataset):
    audio_sample_rates = [11025]

    def __init__(self, input_dir, clip_length: int = 8000, normalize: bool = True):
        self.clip_length = clip_length
        self.normalize = normalize

        input_files = [
            os.path.join(input_dir, f)
            for f in os.listdir(input_dir)
            if os.path.isfile(os.path.join(input_dir, f))
            and f.lower().endswith((".wav", ".mp3", ".flac"))
        ]

        data = []
        for audio_clip in tqdm.tqdm(
            input_files, desc=f"Processing {len(input_files)} audio file(s)"
        ):
            decoder = decoders.AudioDecoder(audio_clip)
            decoded_samples = decoder.get_all_samples()

            audio = decoded_samples.data.float()  # ensure float32
            original_sample_rate = decoded_samples.sample_rate

            audio = AudioUtils.stereo_tensor_to_mono(audio)
            if normalize:
                audio = AudioUtils.normalize(audio)

            mangled_sample_rate = random.choice(self.audio_sample_rates)
            resample_transform_low = torchaudio.transforms.Resample(
                original_sample_rate, mangled_sample_rate
            )
            resample_transform_high = torchaudio.transforms.Resample(
                mangled_sample_rate, original_sample_rate
            )

            low_audio = resample_transform_high(resample_transform_low(audio))

            splitted_high_quality_audio = AudioUtils.split_audio(audio, clip_length)
            splitted_low_quality_audio = AudioUtils.split_audio(low_audio, clip_length)

            if not splitted_high_quality_audio or not splitted_low_quality_audio:
                continue  # skip empty or invalid clips

            splitted_high_quality_audio[-1] = AudioUtils.pad_tensor(
                splitted_high_quality_audio[-1], clip_length
            )
            splitted_low_quality_audio[-1] = AudioUtils.pad_tensor(
                splitted_low_quality_audio[-1], clip_length
            )

            for high_quality_data, low_quality_data in zip(
                splitted_high_quality_audio, splitted_low_quality_audio
            ):
                data.append(
                    (
                        (high_quality_data, low_quality_data),
                        (original_sample_rate, mangled_sample_rate),
                    )
                )

        self.audio_data = data

    def __len__(self):
        return len(self.audio_data)

    def __getitem__(self, idx):
        return self.audio_data[idx]