import torch
import os
import numpy as np
import torchaudio
from tqdm import tqdm
# import librosa


def pad_short_audio(audio, min_samples=32000):
    if (audio.size(-1) < min_samples):
        audio = torch.nn.functional.pad(
            audio, (0, min_samples - audio.size(-1)),
            mode='constant',
            value=0.0
        )
    return audio


class MelPairedDataset(torch.utils.data.Dataset):
    def __init__(
        self,
        datadir1,
        datadir2,
        _stft,
        sr=16000,
        fbin_mean=None,
        fbin_std=None,
        augment=False,
        limit_num=None,
    ):
        self.datalist1 = [
            os.path.join(datadir1, x) for x in os.listdir(datadir1)
        ]
        self.datalist1 = sorted(self.datalist1)

        self.datalist2 = [
            os.path.join(datadir2, x) for x in os.listdir(datadir2)
        ]
        self.datalist2 = sorted(self.datalist2)

        if limit_num is not None:
            self.datalist1 = self.datalist1[:limit_num]
            self.datalist2 = self.datalist2[:limit_num]

        self.align_two_file_list()

        self._stft = _stft
        self.sr = sr
        self.augment = augment

        # if fbin_mean is not None:
        #     self.fbin_mean = fbin_mean[..., None]
        #     self.fbin_std = fbin_std[..., None]
        # else:
        #     self.fbin_mean = None
        #     self.fbin_std = None

    def align_two_file_list(self):
        data_dict1 = {os.path.basename(x): x for x in self.datalist1}
        data_dict2 = {os.path.basename(x): x for x in self.datalist2}

        keyset1 = set(data_dict1.keys())
        keyset2 = set(data_dict2.keys())

        intersect_keys = keyset1.intersection(keyset2)

        self.datalist1 = [data_dict1[k] for k in intersect_keys]
        self.datalist2 = [data_dict2[k] for k in intersect_keys]

        print("Two path have %s intersection files" % len(intersect_keys))

    def __getitem__(self, index):
        while True:
            try:
                filename1 = self.datalist1[index]
                filename2 = self.datalist2[index]
                mel1, _, audio1 = self.get_mel_from_file(filename1)
                mel2, _, audio2 = self.get_mel_from_file(filename2)
                break
            except Exception as e:
                print(index, e)
                index = (index + 1) % len(self.datalist)

        # if(self.fbin_mean is not None):
        #     mel = (mel - self.fbin_mean) / self.fbin_std
        min_len = min(mel1.shape[-1], mel2.shape[-1])
        return (
            mel1[..., :min_len],
            mel2[..., :min_len],
            os.path.basename(filename1),
            (audio1, audio2),
        )

    def __len__(self):
        return len(self.datalist1)

    def get_mel_from_file(self, audio_file):
        audio, file_sr = torchaudio.load(audio_file)
        # Only use the first channel
        audio = audio[0:1, ...]
        audio = audio - audio.mean()
        if file_sr != self.sr:
            audio = torchaudio.functional.resample(
                audio, orig_freq=file_sr, new_freq=self.sr
            )

        if self._stft is not None:
            melspec, energy = self.get_mel_from_wav(audio[0, ...])
        else:
            melspec, energy = None, None

        return melspec, energy, audio

    def get_mel_from_wav(self, audio):
        audio = torch.clip(torch.FloatTensor(audio).unsqueeze(0), -1, 1)
        audio = torch.autograd.Variable(audio, requires_grad=False)

        # =========================================================================
        # Following the processing in https://github.com/v-iashin/SpecVQGAN/blob/5bc54f30eb89f82d129aa36ae3f1e90b60e73952/vocoder/mel2wav/extract_mel_spectrogram.py#L141
        melspec, energy = self._stft.mel_spectrogram(
            audio, normalize_fun=torch.log10
        )
        melspec = (melspec * 20) - 20
        melspec = (melspec + 100) / 100
        melspec = torch.clip(melspec, min=0, max=1.0)
        # =========================================================================
        # Augment
        # if(self.augment):
        #     for i in range(1):
        #         random_start = int(torch.rand(1) * 950)
        #         melspec[0,:,random_start:random_start+50] = 0.0
        # =========================================================================
        melspec = torch.squeeze(melspec, 0).numpy().astype(np.float32)
        energy = torch.squeeze(energy, 0).numpy().astype(np.float32)
        return melspec, energy


class WaveDataset(torch.utils.data.Dataset):
    def __init__(
        self,
        datadir,
        sr=16000,
        limit_num=None,
    ):
        self.datalist = [os.path.join(datadir, x) for x in os.listdir(datadir)]
        self.datalist = sorted(self.datalist)
        if limit_num is not None:
            self.datalist = self.datalist[:limit_num]
        self.sr = sr

    def __getitem__(self, index):
        while True:
            try:
                filename = self.datalist[index]
                waveform = self.read_from_file(filename)
                if waveform.size(-1) < 1:
                    raise ValueError("empty file %s" % filename)
                break
            except Exception as e:
                print(index, e)
                index = (index + 1) % len(self.datalist)

        return waveform, os.path.basename(filename)

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

    def read_from_file(self, audio_file):
        audio, file_sr = torchaudio.load(audio_file)
        # Only use the first channel
        audio = audio[0:1, ...]
        audio = audio - audio.mean()

        # if file_sr != self.sr and file_sr == 32000 and self.sr == 16000:
        #     audio = audio[..., ::2]
        # if file_sr != self.sr and file_sr == 48000 and self.sr == 16000:
        #     audio = audio[..., ::3]
        # el

        if file_sr != self.sr:
            audio = torchaudio.functional.resample(
                audio,
                orig_freq=file_sr,
                new_freq=self.sr,  # rolloff=0.95, lowpass_filter_width=16 
            )
            # audio = torch.FloatTensor(librosa.resample(audio.numpy(), file_sr, self.sr))

        audio = pad_short_audio(audio, min_samples=32000)
        return audio


def load_npy_data(loader):
    new_train = []
    for mel, waveform, filename in tqdm(loader):
        batch = batch.float().numpy()
        new_train.append(batch.reshape(-1, ))
    new_train = np.array(new_train)
    return new_train


if __name__ == "__main__":
    path = "/scratch/combined/result/ground/00294 harvest festival rumour 1_mel.npy"
    temp = np.load(path)
    print("temp", temp.shape)
