# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Modified from espnet(https://github.com/espnet/espnet)
"""StyleMelGAN Modules."""
import copy
from typing import Any
from typing import Dict
from typing import List

import numpy as np
import paddle
import paddle.nn.functional as F
from paddle import nn

from paddlespeech.t2s.models.melgan import MelGANDiscriminator as BaseDiscriminator
from paddlespeech.t2s.modules.activation import get_activation
from paddlespeech.t2s.modules.nets_utils import initialize
from paddlespeech.t2s.modules.pqmf import PQMF
from paddlespeech.t2s.modules.tade_res_block import TADEResBlock


class StyleMelGANGenerator(nn.Layer):
    """Style MelGAN generator module."""

    def __init__(
            self,
            in_channels: int=128,
            aux_channels: int=80,
            channels: int=64,
            out_channels: int=1,
            kernel_size: int=9,
            dilation: int=2,
            bias: bool=True,
            noise_upsample_scales: List[int]=[11, 2, 2, 2],
            noise_upsample_activation: str="leakyrelu",
            noise_upsample_activation_params: Dict[str,
                                                   Any]={"negative_slope": 0.2},
            upsample_scales: List[int]=[2, 2, 2, 2, 2, 2, 2, 2, 1],
            upsample_mode: str="linear",
            gated_function: str="softmax",
            use_weight_norm: bool=True,
            init_type: str="xavier_uniform", ):
        """Initilize Style MelGAN generator.
        Parameters
        ----------
        in_channels : int
            Number of input noise channels.
        aux_channels : int
            Number of auxiliary input channels.
        channels : int
            Number of channels for conv layer.
        out_channels : int
            Number of output channels.
        kernel_size : int
            Kernel size of conv layers.
        dilation : int
            Dilation factor for conv layers.
        bias : bool
            Whether to add bias parameter in convolution layers.
        noise_upsample_scales : list
            List of noise upsampling scales.
        noise_upsample_activation : str
            Activation function module name for noise upsampling.
        noise_upsample_activation_params : dict
            Hyperparameters for the above activation function.
        upsample_scales : list
            List of upsampling scales.
        upsample_mode : str
            Upsampling mode in TADE layer.
        gated_function : str
            Gated function in TADEResBlock ("softmax" or "sigmoid").
        use_weight_norm : bool
            Whether to use weight norm.
            If set to true, it will be applied to all of the conv layers.
        """
        super().__init__()

        # initialize parameters
        initialize(self, init_type)

        self.in_channels = in_channels
        noise_upsample = []
        in_chs = in_channels
        for noise_upsample_scale in noise_upsample_scales:
            noise_upsample.append(
                nn.Conv1DTranspose(
                    in_chs,
                    channels,
                    noise_upsample_scale * 2,
                    stride=noise_upsample_scale,
                    padding=noise_upsample_scale // 2 + noise_upsample_scale %
                    2,
                    output_padding=noise_upsample_scale % 2,
                    bias_attr=bias, ))
            noise_upsample.append(
                get_activation(noise_upsample_activation, **
                               noise_upsample_activation_params))
            in_chs = channels
        self.noise_upsample = nn.Sequential(*noise_upsample)
        self.noise_upsample_factor = np.prod(noise_upsample_scales)

        self.blocks = nn.LayerList()
        aux_chs = aux_channels
        for upsample_scale in upsample_scales:
            self.blocks.append(
                TADEResBlock(
                    in_channels=channels,
                    aux_channels=aux_chs,
                    kernel_size=kernel_size,
                    dilation=dilation,
                    bias=bias,
                    upsample_factor=upsample_scale,
                    upsample_mode=upsample_mode,
                    gated_function=gated_function, ), )
            aux_chs = channels
        self.upsample_factor = np.prod(upsample_scales)

        self.output_conv = nn.Sequential(
            nn.Conv1D(
                channels,
                out_channels,
                kernel_size,
                1,
                bias_attr=bias,
                padding=(kernel_size - 1) // 2, ),
            nn.Tanh(), )

        nn.initializer.set_global_initializer(None)

        # apply weight norm
        if use_weight_norm:
            self.apply_weight_norm()

        # reset parameters
        self.reset_parameters()

    def forward(self, c, z=None):
        """Calculate forward propagation.
        Parameters
        ----------
        c : Tensor
            Auxiliary input tensor (B, channels, T).
        z : Tensor
            Input noise tensor (B, in_channels, 1).
        Returns
        ----------
        Tensor
            Output tensor (B, out_channels, T ** prod(upsample_scales)).
        """
        # batch_max_steps(24000) == noise_upsample_factor(80) * upsample_factor(300)
        if z is None:
            z = paddle.randn([paddle.shape(c)[0], self.in_channels, 1])
        # (B, in_channels, noise_upsample_factor).
        x = self.noise_upsample(z)
        for block in self.blocks:
            x, c = block(x, c)
        x = self.output_conv(x)
        return x

    def apply_weight_norm(self):
        """Recursively apply weight normalization to all the Convolution layers
        in the sublayers.
        """

        def _apply_weight_norm(layer):
            if isinstance(layer, (nn.Conv1D, nn.Conv1DTranspose)):
                nn.utils.weight_norm(layer)

        self.apply(_apply_weight_norm)

    def remove_weight_norm(self):
        """Recursively remove weight normalization from all the Convolution 
        layers in the sublayers.
        """

        def _remove_weight_norm(layer):
            try:
                if layer:
                    nn.utils.remove_weight_norm(layer)
            # add AttributeError to bypass https://github.com/PaddlePaddle/Paddle/issues/38532 temporarily
            except (ValueError, AttributeError):
                pass

        self.apply(_remove_weight_norm)

    def reset_parameters(self):
        """Reset parameters.
        This initialization follows official implementation manner.
        https://github.com/descriptinc/melgan-neurips/blob/master/mel2wav/modules.py
        """
        # 定义参数为float的正态分布。
        dist = paddle.distribution.Normal(loc=0.0, scale=0.02)

        def _reset_parameters(m):
            if isinstance(m, nn.Conv1D) or isinstance(m, nn.Conv1DTranspose):
                w = dist.sample(m.weight.shape)
                m.weight.set_value(w)

        self.apply(_reset_parameters)

    def inference(self, c):
        """Perform inference.
        Parameters
        ----------
        c : Tensor
            Input tensor (T, in_channels).
        Returns
        ----------
        Tensor
            Output tensor (T ** prod(upsample_scales), out_channels).
        """
        # (1, in_channels, T)
        c = c.transpose([1, 0]).unsqueeze(0)
        c_shape = paddle.shape(c)
        # prepare noise input
        # there is a bug in Paddle int division, we must convert a int tensor to int here
        noise_T = paddle.cast(
            paddle.ceil(c_shape[2] / int(self.noise_upsample_factor)),
            dtype='int64')
        noise_size = (1, self.in_channels, noise_T)
        # (1, in_channels, T/noise_upsample_factor)
        noise = paddle.randn(noise_size)
        # (1, in_channels, T)
        x = self.noise_upsample(noise)
        x_shape = paddle.shape(x)
        total_length = c_shape[2] * self.upsample_factor
        # Dygraph to Static Graph bug here, 2021.12.15
        c = F.pad(
            c, (0, x_shape[2] - c_shape[2]), "replicate", data_format="NCL")
        # c.shape[2] == x.shape[2] here
        # (1, in_channels, T*prod(upsample_scales))
        for block in self.blocks:
            x, c = block(x, c)
        x = self.output_conv(x)[..., :total_length]
        return x.squeeze(0).transpose([1, 0])


class StyleMelGANDiscriminator(nn.Layer):
    """Style MelGAN disciminator module."""

    def __init__(
            self,
            repeats: int=2,
            window_sizes: List[int]=[512, 1024, 2048, 4096],
            pqmf_params: List[List[int]]=[
                [1, None, None, None],
                [2, 62, 0.26700, 9.0],
                [4, 62, 0.14200, 9.0],
                [8, 62, 0.07949, 9.0],
            ],
            discriminator_params: Dict[str, Any]={
                "out_channels": 1,
                "kernel_sizes": [5, 3],
                "channels": 16,
                "max_downsample_channels": 512,
                "bias": True,
                "downsample_scales": [4, 4, 4, 1],
                "nonlinear_activation": "leakyrelu",
                "nonlinear_activation_params": {
                    "negative_slope": 0.2
                },
                "pad": "Pad1D",
                "pad_params": {
                    "mode": "reflect"
                },
            },
            use_weight_norm: bool=True,
            init_type: str="xavier_uniform", ):
        """Initilize Style MelGAN discriminator.
        Parameters
        ----------
        repeats : int
            Number of repititons to apply RWD.
        window_sizes : list
            List of random window sizes.
        pqmf_params : list
            List of list of Parameters for PQMF modules
        discriminator_params : dict
            Parameters for base discriminator module.
        use_weight_nom : bool
            Whether to apply weight normalization.
        """
        super().__init__()

        # initialize parameters
        initialize(self, init_type)

        # window size check
        assert len(window_sizes) == len(pqmf_params)
        sizes = [ws // p[0] for ws, p in zip(window_sizes, pqmf_params)]
        assert len(window_sizes) == sum([sizes[0] == size for size in sizes])

        self.repeats = repeats
        self.window_sizes = window_sizes
        self.pqmfs = nn.LayerList()
        self.discriminators = nn.LayerList()
        for pqmf_param in pqmf_params:
            d_params = copy.deepcopy(discriminator_params)
            d_params["in_channels"] = pqmf_param[0]
            if pqmf_param[0] == 1:
                self.pqmfs.append(nn.Identity())
            else:
                self.pqmfs.append(PQMF(*pqmf_param))
            self.discriminators.append(BaseDiscriminator(**d_params))

        nn.initializer.set_global_initializer(None)

        # apply weight norm
        if use_weight_norm:
            self.apply_weight_norm()

        # reset parameters
        self.reset_parameters()

    def forward(self, x):
        """Calculate forward propagation.
        Parameters
        ----------
        x : Tensor
            Input tensor (B, 1, T).
        Returns
        ----------
        List
            List of discriminator outputs, #items in the list will be
            equal to repeats * #discriminators.
        """
        outs = []
        for _ in range(self.repeats):
            outs += self._forward(x)
        return outs

    def _forward(self, x):
        outs = []
        for idx, (ws, pqmf, disc) in enumerate(
                zip(self.window_sizes, self.pqmfs, self.discriminators)):
            start_idx = int(np.random.randint(paddle.shape(x)[-1] - ws))
            x_ = x[:, :, start_idx:start_idx + ws]
            if idx == 0:
                # nn.Identity()
                x_ = pqmf(x_)
            else:
                x_ = pqmf.analysis(x_)
            outs += [disc(x_)]
        return outs

    def apply_weight_norm(self):
        """Recursively apply weight normalization to all the Convolution layers
        in the sublayers.
        """

        def _apply_weight_norm(layer):
            if isinstance(layer, (nn.Conv1D, nn.Conv1DTranspose)):
                nn.utils.weight_norm(layer)

        self.apply(_apply_weight_norm)

    def remove_weight_norm(self):
        """Recursively remove weight normalization from all the Convolution 
        layers in the sublayers.
        """

        def _remove_weight_norm(layer):
            try:
                nn.utils.remove_weight_norm(layer)
            except ValueError:
                pass

        self.apply(_remove_weight_norm)

    def reset_parameters(self):
        """Reset parameters.
        This initialization follows official implementation manner.
        https://github.com/descriptinc/melgan-neurips/blob/master/mel2wav/modules.py
        """
        # 定义参数为float的正态分布。
        dist = paddle.distribution.Normal(loc=0.0, scale=0.02)

        def _reset_parameters(m):
            if isinstance(m, nn.Conv1D) or isinstance(m, nn.Conv1DTranspose):
                w = dist.sample(m.weight.shape)
                m.weight.set_value(w)

        self.apply(_reset_parameters)


class StyleMelGANInference(nn.Layer):
    def __init__(self, normalizer, style_melgan_generator):
        super().__init__()
        self.normalizer = normalizer
        self.style_melgan_generator = style_melgan_generator

    def forward(self, logmel):
        normalized_mel = self.normalizer(logmel)
        wav = self.style_melgan_generator.inference(normalized_mel)
        return wav
