import torch
from typing import Tuple
from core.embedding import PositionalEncoding



class SepConv1d(torch.nn.Module):
    def __init__(self,
                 in_channels,
                 out_channels,
                 kernel_size,
                 stride=1,
                 padding=0,
                 dilation=1,):
        super(SepConv1d, self).__init__()
        self.depthwise = torch.nn.Conv1d(in_channels,
                                         in_channels,
                                         kernel_size=kernel_size,
                                         stride=stride,
                                         padding=padding,
                                         dilation=dilation,
                                         groups=in_channels)
        self.pointwise = torch.nn.Conv1d(in_channels, out_channels, kernel_size=1)

    def forward(self, x):
        x = self.depthwise(x)
        x = self.pointwise(x)
        return x

class Conv(torch.nn.Module):
    """
    Convolution Module
    """

    def __init__(self,
                 in_channels,
                 out_channels,
                 kernel_size=1,
                 stride=1,
                 padding=0,
                 dilation=1,
                 bias=True,):
        """
        :param in_channels: dimension of input
        :param out_channels: dimension of output
        :param kernel_size: size of kernel
        :param stride: size of stride
        :param padding: size of padding
        :param dilation: dilation rate
        :param bias: boolean. if True, bias is included.
        :param w_init: str. weight inits with xavier initialization.
        """
        super(Conv, self).__init__()

        self.conv = torch.nn.Conv1d(in_channels,
                              out_channels,
                              kernel_size=kernel_size,
                              stride=stride,
                              padding=padding,
                              dilation=dilation,
                              bias=bias)

    def forward(self, x):
        x = x.contiguous().transpose(1, 2)
        x = self.conv(x)
        x = x.contiguous().transpose(1, 2)

        return x


def initialize(model, init_type="pytorch"):
    """Initialize Transformer module

    :param torch.nn.Module model: core instance
    :param str init_type: initialization type
    """
    if init_type == "pytorch":
        return

    # weight init
    for p in model.parameters():
        if p.dim() > 1:
            if init_type == "xavier_uniform":
                torch.nn.init.xavier_uniform_(p.data)
            elif init_type == "xavier_normal":
                torch.nn.init.xavier_normal_(p.data)
            elif init_type == "kaiming_uniform":
                torch.nn.init.kaiming_uniform_(p.data, nonlinearity="relu")
            elif init_type == "kaiming_normal":
                torch.nn.init.kaiming_normal_(p.data, nonlinearity="relu")
            else:
                raise ValueError("Unknown initialization: " + init_type)
    # bias init
    for p in model.parameters():
        if p.dim() == 1:
            p.data.zero_()

    # reset some loss with default init
    for m in model.modules():
        if isinstance(m, (torch.nn.Embedding, torch.nn.LayerNorm)):
            m.reset_parameters()


class MultiSequential(torch.nn.Sequential):
    """Multi-input multi-output torch.nn.Sequential"""

    def forward(self, *args):
        for m in self:
            args = m(*args)
        return args


def repeat(N, fn):
    """repeat module N times

    :param int N: repeat time
    :param function fn: function to generate module
    :return: repeated loss
    :rtype: MultiSequential
    """
    return MultiSequential(*[fn() for _ in range(N)])


# def layer_norm(x: torch.Tensor, dim):
#     if dim == -1:
#         return torch.nn.LayerNorm(x)
#     else:
#         out = torch.nn.LayerNorm(x.transpose(1, -1))
#         return out.transpose(1, -1)


class LayerNorm(torch.nn.Module):

    def __init__(self, nout: int):
        super(LayerNorm, self).__init__()
        self.layer_norm = torch.nn.LayerNorm(nout, eps=1e-12)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = self.layer_norm(x.transpose(1, -1))
        x = x.transpose(1, -1)
        return x

# class LayerNorm(torch.nn.LayerNorm):
#     """Layer normalization module
#
#     :param int nout: output dim size
#     :param int dim: dimension to be normalized
#     """
#
#     def __init__(self, nout: int, dim: int=-1):
#         super(LayerNorm, self).__init__(nout, eps=1e-12)
#         self.dim = dim
#
#     def forward(self, x: torch.Tensor) -> torch.Tensor:
#         """Apply layer normalization
#
#         :param torch.Tensor x: input tensor
#         :return: layer normalized tensor
#         :rtype torch.Tensor
#         """
#         if self.dim == -1:
#             return super(LayerNorm, self).forward(x)
#         return super(LayerNorm, self).forward(x.transpose(1, -1)).transpose(1, -1)


class Conv2dSubsampling(torch.nn.Module):
    """Convolutional 2D subsampling (to 1/4 length)

    :param int idim: input dim
    :param int odim: output dim
    :param flaot dropout_rate: dropout rate
    """

    def __init__(self, idim: int, odim: int, dropout_rate: float):
        super(Conv2dSubsampling, self).__init__()
        self.conv = torch.nn.Sequential(
            torch.nn.Conv2d(1, odim, 3, 2),
            torch.nn.ReLU(),
            torch.nn.Conv2d(odim, odim, 3, 2),
            torch.nn.ReLU()
        )
        self.out = torch.nn.Sequential(
            torch.nn.Linear(odim * (((idim - 1) // 2 - 1) // 2), odim),
            PositionalEncoding(odim, dropout_rate)
        )

    def forward(self, x: torch.Tensor, x_mask: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
        """Subsample x

        :param torch.Tensor x: input tensor
        :param torch.Tensor x_mask: input mask
        :return: subsampled x and mask
        :rtype Tuple[torch.Tensor, torch.Tensor]
        """
        x = x.unsqueeze(1)  # (b, c, t, f)
        x = self.conv(x)
        b, c, t, f = x.size()
        x = self.out(x.transpose(1, 2).contiguous().view(b, t, c * f))
        if x_mask is None:
            return x, None
        return x, x_mask[:, :, :-2:2][:, :, :-2:2]


class PositionwiseFeedForward(torch.nn.Module):
    """Positionwise feed forward

    :param int idim: input dimenstion
    :param int hidden_units: number of hidden units
    :param float dropout_rate: dropout rate
    """

    def __init__(self, idim: int, hidden_units: int, dropout_rate: float):
        super(PositionwiseFeedForward, self).__init__()
        self.w_1 = torch.nn.Linear(idim, hidden_units)
        self.w_2 = torch.nn.Linear(hidden_units, idim)
        self.dropout = torch.nn.Dropout(dropout_rate)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return self.w_2(self.dropout(torch.relu(self.w_1(x))))


class MultiLayeredConv1d(torch.nn.Module):
    """Multi-layered conv1d for Transformer block.

    This is a module of multi-leyered conv1d designed to replace positionwise feed-forward network
    in Transforner block, which is introduced in `FastSpeech: Fast, Robust and Controllable Text to Speech`_.

    Args:
        in_chans (int): Number of input channels.
        hidden_chans (int): Number of hidden channels.
        kernel_size (int): Kernel size of conv1d.
        dropout_rate (float): Dropout rate.

    .. _`FastSpeech: Fast, Robust and Controllable Text to Speech`:
        https://arxiv.org/pdf/1905.09263.pdf

    """

    def __init__(self, in_chans: int, hidden_chans: int, kernel_size:int, dropout_rate: float):
        super(MultiLayeredConv1d, self).__init__()
        self.w_1 = torch.nn.Conv1d(in_chans, hidden_chans, kernel_size,
                                   stride=1, padding=(kernel_size - 1) // 2)
        self.w_2 = torch.nn.Conv1d(hidden_chans, in_chans, 1,
                                   stride=1, padding=(1 - 1) // 2)
        self.dropout = torch.nn.Dropout(dropout_rate)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """Calculate forward propagation.

        Args:
            x (Tensor): Batch of input tensors (B, *, in_chans).

        Returns:
            Tensor: Batch of output tensors (B, *, hidden_chans)

        """
        x = torch.relu(self.w_1(x.transpose(-1, 1))).transpose(-1, 1)
        return self.w_2(self.dropout(x).transpose(-1, 1)).transpose(-1, 1)


class MultiLayeredSepConv1d(torch.nn.Module):
    """Multi-layered conv1d for Transformer block.

    This is a module of multi-leyered conv1d designed to replace positionwise feed-forward network
    in Transforner block, which is introduced in `FastSpeech: Fast, Robust and Controllable Text to Speech`_.

    Args:
        in_chans (int): Number of input channels.
        hidden_chans (int): Number of hidden channels.
        kernel_size (int): Kernel size of conv1d.
        dropout_rate (float): Dropout rate.

    .. _`FastSpeech: Fast, Robust and Controllable Text to Speech`:
        https://arxiv.org/pdf/1905.09263.pdf

    """

    def __init__(self, in_chans: int, hidden_chans: int, kernel_size:int, dropout_rate: float):
        super(MultiLayeredSepConv1d, self).__init__()
        self.w_1 = SepConv1d(in_chans, hidden_chans, kernel_size,
                                   stride=1, padding=(kernel_size - 1) // 2)
        self.w_2 = SepConv1d(hidden_chans, in_chans, 1,
                                   stride=1, padding=(1 - 1) // 2)
        self.dropout = torch.nn.Dropout(dropout_rate)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """Calculate forward propagation.

        Args:
            x (Tensor): Batch of input tensors (B, *, in_chans).

        Returns:
            Tensor: Batch of output tensors (B, *, hidden_chans)

        """
        x = torch.relu(self.w_1(x.transpose(-1, 1))).transpose(-1, 1)
        return self.w_2(self.dropout(x).transpose(-1, 1)).transpose(-1, 1)



class Postnet(torch.nn.Module):
    """Postnet module for Spectrogram prediction network.
    This is a module of Postnet in Spectrogram prediction network,
    which described in `Natural TTS Synthesis by
    Conditioning WaveNet on Mel Spectrogram Predictions`_.
    The Postnet predicts refines the predicted
    Mel-filterbank of the decoder,
    which helps to compensate the detail sturcture of spectrogram.
    .. _`Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions`:
       https://arxiv.org/abs/1712.05884
    """

    def __init__(
        self,
        idim: int,
        odim: int,
        n_layers: int=5,
        n_chans: int=512,
        n_filts: int=5,
        dropout_rate: float=0.5,
        use_batch_norm: bool=True,
    ):
        """Initialize postnet module.
        Args:
            idim (int): Dimension of the inputs.
            odim (int): Dimension of the outputs.
            n_layers (int, optional): The number of layers.
            n_filts (int, optional): The number of filter size.
            n_units (int, optional): The number of filter channels.
            use_batch_norm (bool, optional): Whether to use batch normalization..
            dropout_rate (float, optional): Dropout rate..
        """
        super(Postnet, self).__init__()
        self.postnet = torch.nn.ModuleList()
        for layer in range(n_layers - 1):
            ichans = odim if layer == 0 else n_chans
            ochans = odim if layer == n_layers - 1 else n_chans
            if use_batch_norm:
                self.postnet += [
                    torch.nn.Sequential(
                        torch.nn.Conv1d(
                            ichans,
                            ochans,
                            n_filts,
                            stride=1,
                            padding=(n_filts - 1) // 2,
                            bias=False,
                        ),
                        torch.nn.BatchNorm1d(ochans),
                        torch.nn.Tanh(),
                        torch.nn.Dropout(dropout_rate),
                    )
                ]
            else:
                self.postnet += [
                    torch.nn.Sequential(
                        torch.nn.Conv1d(
                            ichans,
                            ochans,
                            n_filts,
                            stride=1,
                            padding=(n_filts - 1) // 2,
                            bias=False,
                        ),
                        torch.nn.Tanh(),
                        torch.nn.Dropout(dropout_rate),
                    )
                ]
        ichans = n_chans if n_layers != 1 else odim
        if use_batch_norm:
            self.postnet += [
                torch.nn.Sequential(
                    torch.nn.Conv1d(
                        ichans,
                        odim,
                        n_filts,
                        stride=1,
                        padding=(n_filts - 1) // 2,
                        bias=False,
                    ),
                    torch.nn.BatchNorm1d(odim),
                    torch.nn.Dropout(dropout_rate),
                )
            ]
        else:
            self.postnet += [
                torch.nn.Sequential(
                    torch.nn.Conv1d(
                        ichans,
                        odim,
                        n_filts,
                        stride=1,
                        padding=(n_filts - 1) // 2,
                        bias=False,
                    ),
                    torch.nn.Dropout(dropout_rate),
                )
            ]

    def forward(self, xs):
        """Calculate forward propagation.
        Args:
            xs (Tensor): Batch of the sequences of padded input tensors (B, idim, Tmax).
        Returns:
            Tensor: Batch of padded output tensor. (B, odim, Tmax).
        """
        for postnet in self.postnet:
            xs = postnet(xs)
        return xs