import os
from glob import glob

import librosa
import torch
import torchaudio
import numpy as np
from scipy.io.wavfile import read

from tortoise.utils.stft import STFT


def load_wav_to_torch(full_path):
    sampling_rate, data = read(full_path)
    if data.dtype == np.int32:
        norm_fix = 2 ** 31
    elif data.dtype == np.int16:
        norm_fix = 2 ** 15
    elif data.dtype == np.float16 or data.dtype == np.float32:
        norm_fix = 1.
    else:
        raise NotImplemented(f"Provided data dtype not supported: {data.dtype}")
    return (torch.FloatTensor(data.astype(np.float32)) / norm_fix, sampling_rate)


def load_audio(audiopath, sampling_rate):
    if audiopath[-4:] == '.wav':
        audio, lsr = load_wav_to_torch(audiopath)
    elif audiopath[-4:] == '.mp3':
        audio, lsr = librosa.load(audiopath, sr=sampling_rate)
        audio = torch.FloatTensor(audio)

    # Remove any channel data.
    if len(audio.shape) > 1:
        if audio.shape[0] < 5:
            audio = audio[0]
        else:
            assert audio.shape[1] < 5
            audio = audio[:, 0]

    if lsr != sampling_rate:
        audio = torchaudio.functional.resample(audio, lsr, sampling_rate)

    # Check some assumptions about audio range. This should be automatically fixed in load_wav_to_torch, but might not be in some edge cases, where we should squawk.
    # '2' is arbitrarily chosen since it seems like audio will often "overdrive" the [-1,1] bounds.
    if torch.any(audio > 2) or not torch.any(audio < 0):
        print(f"Error with {audiopath}. Max={audio.max()} min={audio.min()}")
    audio.clip_(-1, 1)

    return audio.unsqueeze(0)


TACOTRON_MEL_MAX = 2.3143386840820312
TACOTRON_MEL_MIN = -11.512925148010254


def denormalize_tacotron_mel(norm_mel):
    return ((norm_mel+1)/2)*(TACOTRON_MEL_MAX-TACOTRON_MEL_MIN)+TACOTRON_MEL_MIN


def normalize_tacotron_mel(mel):
    return 2 * ((mel - TACOTRON_MEL_MIN) / (TACOTRON_MEL_MAX - TACOTRON_MEL_MIN)) - 1


def dynamic_range_compression(x, C=1, clip_val=1e-5):
    """
    PARAMS
    ------
    C: compression factor
    """
    return torch.log(torch.clamp(x, min=clip_val) * C)


def dynamic_range_decompression(x, C=1):
    """
    PARAMS
    ------
    C: compression factor used to compress
    """
    return torch.exp(x) / C


def get_voices():
    subs = os.listdir('tortoise/voices')
    voices = {}
    for sub in subs:
        subj = os.path.join('tortoise/voices', sub)
        if os.path.isdir(subj):
            voices[sub] = list(glob(f'{subj}/*.wav')) + list(glob(f'{subj}/*.mp3')) + list(glob(f'{subj}/*.pth'))
    return voices


def load_voice(voice):
    if voice == 'random':
        return None, None

    voices = get_voices()
    paths = voices[voice]
    if len(paths) == 1 and paths[0].endswith('.pth'):
        return None, torch.load(paths[0])
    else:
        conds = []
        for cond_path in paths:
            c = load_audio(cond_path, 22050)
            conds.append(c)
        return conds, None


def load_voices(voices):
    latents = []
    clips = []
    for voice in voices:
        if voice == 'random':
            print("Cannot combine a random voice with a non-random voice. Just using a random voice.")
            return None, None
        clip, latent = load_voice(voice)
        if latent is None:
            assert len(latents) == 0, "Can only combine raw audio voices or latent voices, not both. Do it yourself if you want this."
            clips.extend(clip)
        elif voice is None:
            assert len(voices) == 0, "Can only combine raw audio voices or latent voices, not both. Do it yourself if you want this."
            latents.append(latent)
    if len(latents) == 0:
        return clips, None
    else:
        latents = torch.stack(latents, dim=0)
        return None, latents.mean(dim=0)


class TacotronSTFT(torch.nn.Module):
    def __init__(self, filter_length=1024, hop_length=256, win_length=1024,
                 n_mel_channels=80, sampling_rate=22050, mel_fmin=0.0,
                 mel_fmax=8000.0):
        super(TacotronSTFT, self).__init__()
        self.n_mel_channels = n_mel_channels
        self.sampling_rate = sampling_rate
        self.stft_fn = STFT(filter_length, hop_length, win_length)
        from librosa.filters import mel as librosa_mel_fn
        mel_basis = librosa_mel_fn(
            sampling_rate, filter_length, n_mel_channels, mel_fmin, mel_fmax)
        mel_basis = torch.from_numpy(mel_basis).float()
        self.register_buffer('mel_basis', mel_basis)

    def spectral_normalize(self, magnitudes):
        output = dynamic_range_compression(magnitudes)
        return output

    def spectral_de_normalize(self, magnitudes):
        output = dynamic_range_decompression(magnitudes)
        return output

    def mel_spectrogram(self, y):
        """Computes mel-spectrograms from a batch of waves
        PARAMS
        ------
        y: Variable(torch.FloatTensor) with shape (B, T) in range [-1, 1]

        RETURNS
        -------
        mel_output: torch.FloatTensor of shape (B, n_mel_channels, T)
        """
        assert(torch.min(y.data) >= -10)
        assert(torch.max(y.data) <= 10)
        y = torch.clip(y, min=-1, max=1)

        magnitudes, phases = self.stft_fn.transform(y)
        magnitudes = magnitudes.data
        mel_output = torch.matmul(self.mel_basis, magnitudes)
        mel_output = self.spectral_normalize(mel_output)
        return mel_output


def wav_to_univnet_mel(wav, do_normalization=False):
    stft = TacotronSTFT(1024, 256, 1024, 100, 24000, 0, 12000)
    stft = stft.cuda()
    mel = stft.mel_spectrogram(wav)
    if do_normalization:
        mel = normalize_tacotron_mel(mel)
    return mel