import torch
import torch.nn.functional as F
import torch.utils.data
from librosa.filters import mel as librosa_mel_fn

MAX_WAV_VALUE = 32768.0


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


def spectral_normalize_torch(magnitudes):
    output = dynamic_range_compression_torch(magnitudes)
    return output


def spectral_de_normalize_torch(magnitudes):
    output = dynamic_range_decompression_torch(magnitudes)
    return output


mel_basis = {}
hann_window = {}


def spectrogram_torch(y,
                      n_fft,
                      sampling_rate,
                      hop_size,
                      win_size,
                      center=False):
    if torch.min(y) < -1.:
        print('min value is ', torch.min(y))
    if torch.max(y) > 1.:
        print('max value is ', torch.max(y))

    global hann_window
    dtype_device = str(y.dtype) + '_' + str(y.device)
    wnsize_dtype_device = str(win_size) + '_' + dtype_device
    if wnsize_dtype_device not in hann_window:
        hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(
            dtype=y.dtype, device=y.device)

    y = F.pad(y.unsqueeze(1),
              (int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)),
              mode='reflect')
    y = y.squeeze(1)

    spec = torch.stft(y,
                      n_fft,
                      hop_length=hop_size,
                      win_length=win_size,
                      window=hann_window[wnsize_dtype_device],
                      center=center,
                      pad_mode='reflect',
                      normalized=False,
                      onesided=True)

    spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
    return spec


def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
    global mel_basis
    dtype_device = str(spec.dtype) + '_' + str(spec.device)
    fmax_dtype_device = str(fmax) + '_' + dtype_device
    if fmax_dtype_device not in mel_basis:
        mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
        mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(
            dtype=spec.dtype, device=spec.device)
    spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
    spec = spectral_normalize_torch(spec)
    return spec


def mel_spectrogram_torch(y,
                          n_fft,
                          num_mels,
                          sampling_rate,
                          hop_size,
                          win_size,
                          fmin,
                          fmax,
                          center=False):
    if torch.min(y) < -1.:
        print('min value is ', torch.min(y))
    if torch.max(y) > 1.:
        print('max value is ', torch.max(y))

    global mel_basis, hann_window
    dtype_device = str(y.dtype) + '_' + str(y.device)
    fmax_dtype_device = str(fmax) + '_' + dtype_device
    wnsize_dtype_device = str(win_size) + '_' + dtype_device
    if fmax_dtype_device not in mel_basis:
        mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
        mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(
            dtype=y.dtype, device=y.device)
    if wnsize_dtype_device not in hann_window:
        hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(
            dtype=y.dtype, device=y.device)

    y = F.pad(y.unsqueeze(1),
              (int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)),
              mode='reflect')
    y = y.squeeze(1)

    spec = torch.stft(y,
                      n_fft,
                      hop_length=hop_size,
                      win_length=win_size,
                      window=hann_window[wnsize_dtype_device],
                      center=center,
                      pad_mode='reflect',
                      normalized=False,
                      onesided=True)

    spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)

    spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
    spec = spectral_normalize_torch(spec)

    return spec