# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

import typing as tp

import numpy as np
from torchaudio.transforms import MelSpectrogram
import torch
from torch import nn
from torch.nn import functional as F

from ..modules import pad_for_conv1d


class MelSpectrogramWrapper(nn.Module):
    """Wrapper around MelSpectrogram torchaudio transform providing proper padding
    and additional post-processing including log scaling.

    Args:
        n_mels (int): Number of mel bins.
        n_fft (int): Number of fft.
        hop_length (int): Hop size.
        win_length (int): Window length.
        n_mels (int): Number of mel bins.
        sample_rate (int): Sample rate.
        f_min (float or None): Minimum frequency.
        f_max (float or None): Maximum frequency.
        log (bool): Whether to scale with log.
        normalized (bool): Whether to normalize the melspectrogram.
        floor_level (float): Floor level based on human perception (default=1e-5).
    """
    def __init__(self, n_fft: int = 1024, hop_length: int = 256, win_length: tp.Optional[int] = None,
                 n_mels: int = 80, sample_rate: float = 22050, f_min: float = 0.0, f_max: tp.Optional[float] = None,
                 log: bool = True, normalized: bool = False, floor_level: float = 1e-5):
        super().__init__()
        self.n_fft = n_fft
        hop_length = int(hop_length)
        self.hop_length = hop_length
        self.mel_transform = MelSpectrogram(n_mels=n_mels, sample_rate=sample_rate, n_fft=n_fft, hop_length=hop_length,
                                            win_length=win_length, f_min=f_min, f_max=f_max, normalized=normalized,
                                            window_fn=torch.hann_window, center=False)
        self.floor_level = floor_level
        self.log = log

    def forward(self, x):
        p = int((self.n_fft - self.hop_length) // 2)
        if len(x.shape) == 2:
            x = x.unsqueeze(1)
        x = F.pad(x, (p, p), "reflect")
        # Make sure that all the frames are full.
        # The combination of `pad_for_conv1d` and the above padding
        # will make the output of size ceil(T / hop).
        x = pad_for_conv1d(x, self.n_fft, self.hop_length)
        self.mel_transform.to(x.device)
        mel_spec = self.mel_transform(x)
        B, C, freqs, frame = mel_spec.shape
        if self.log:
            mel_spec = torch.log10(self.floor_level + mel_spec)
        return mel_spec.reshape(B, C * freqs, frame)


class MelSpectrogramL1Loss(torch.nn.Module):
    """L1 Loss on MelSpectrogram.

    Args:
        sample_rate (int): Sample rate.
        n_fft (int): Number of fft.
        hop_length (int): Hop size.
        win_length (int): Window length.
        n_mels (int): Number of mel bins.
        f_min (float or None): Minimum frequency.
        f_max (float or None): Maximum frequency.
        log (bool): Whether to scale with log.
        normalized (bool): Whether to normalize the melspectrogram.
        floor_level (float): Floor level value based on human perception (default=1e-5).
    """
    def __init__(self, sample_rate: int, n_fft: int = 1024, hop_length: int = 256, win_length: int = 1024,
                 n_mels: int = 80, f_min: float = 0.0, f_max: tp.Optional[float] = None,
                 log: bool = True, normalized: bool = False, floor_level: float = 1e-5):
        super().__init__()
        self.l1 = torch.nn.L1Loss()
        self.melspec = MelSpectrogramWrapper(n_fft=n_fft, hop_length=hop_length, win_length=win_length,
                                             n_mels=n_mels, sample_rate=sample_rate, f_min=f_min, f_max=f_max,
                                             log=log, normalized=normalized, floor_level=floor_level)

    def forward(self, x, y):
        self.melspec.to(x.device)
        s_x = self.melspec(x)
        s_y = self.melspec(y)
        return self.l1(s_x, s_y)


class MultiScaleMelSpectrogramLoss(nn.Module):
    """Multi-Scale spectrogram loss (msspec).

    Args:
        sample_rate (int): Sample rate.
        range_start (int): Power of 2 to use for the first scale.
        range_stop (int): Power of 2 to use for the last scale.
        n_mels (int): Number of mel bins.
        f_min (float): Minimum frequency.
        f_max (float or None): Maximum frequency.
        normalized (bool): Whether to normalize the melspectrogram.
        alphas (bool): Whether to use alphas as coefficients or not.
        floor_level (float): Floor level value based on human perception (default=1e-5).
    """
    def __init__(self, sample_rate: int, range_start: int = 6, range_end: int = 11,
                 n_mels: int = 64, f_min: float = 0.0, f_max: tp.Optional[float] = None,
                 normalized: bool = False, alphas: bool = True, floor_level: float = 1e-5):
        super().__init__()
        l1s = list()
        l2s = list()
        self.alphas = list()
        self.total = 0
        self.normalized = normalized
        for i in range(range_start, range_end):
            l1s.append(
                MelSpectrogramWrapper(n_fft=2 ** i, hop_length=(2 ** i) / 4, win_length=2 ** i,
                                      n_mels=n_mels, sample_rate=sample_rate, f_min=f_min, f_max=f_max,
                                      log=False, normalized=normalized, floor_level=floor_level))
            l2s.append(
                MelSpectrogramWrapper(n_fft=2 ** i, hop_length=(2 ** i) / 4, win_length=2 ** i,
                                      n_mels=n_mels, sample_rate=sample_rate, f_min=f_min, f_max=f_max,
                                      log=True, normalized=normalized, floor_level=floor_level))
            if alphas:
                self.alphas.append(np.sqrt(2 ** i - 1))
            else:
                self.alphas.append(1)
            self.total += self.alphas[-1] + 1

        self.l1s = nn.ModuleList(l1s)
        self.l2s = nn.ModuleList(l2s)

    def forward(self, x, y):
        loss = 0.0
        self.l1s.to(x.device)
        self.l2s.to(x.device)
        for i in range(len(self.alphas)):
            s_x_1 = self.l1s[i](x)
            s_y_1 = self.l1s[i](y)
            s_x_2 = self.l2s[i](x)
            s_y_2 = self.l2s[i](y)
            loss += F.l1_loss(s_x_1, s_y_1) + self.alphas[i] * F.mse_loss(s_x_2, s_y_2)
        if self.normalized:
            loss = loss / self.total
        return loss