# Copyright (c) 2020 Mobvoi Inc. (authors: Binbin Zhang, Di Wu)
#               2024 Alibaba Inc (Xiang Lyu)
#
# 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)
"""ConvolutionModule definition."""

from typing import Tuple

import torch
from torch import nn
import torch.nn.functional as F


class ConvolutionModule(nn.Module):
    """ConvolutionModule in Conformer model."""

    def __init__(self,
                 channels: int,
                 kernel_size: int = 15,
                 activation: nn.Module = nn.ReLU(),
                 norm: str = "batch_norm",
                 causal: bool = False,
                 bias: bool = True):
        """Construct an ConvolutionModule object.
        Args:
            channels (int): The number of channels of conv layers.
            kernel_size (int): Kernel size of conv layers.
            causal (int): Whether use causal convolution or not
        """
        super().__init__()

        self.pointwise_conv1 = nn.Conv1d(
            channels,
            2 * channels,
            kernel_size=1,
            stride=1,
            padding=0,
            bias=bias,
        )
        # self.lorder is used to distinguish if it's a causal convolution,
        # if self.lorder > 0: it's a causal convolution, the input will be
        #    padded with self.lorder frames on the left in forward.
        # else: it's a symmetrical convolution
        if causal:
            padding = 0
            self.lorder = kernel_size - 1
        else:
            # kernel_size should be an odd number for none causal convolution
            assert (kernel_size - 1) % 2 == 0
            padding = (kernel_size - 1) // 2
            self.lorder = 0
        self.depthwise_conv = nn.Conv1d(
            channels,
            channels,
            kernel_size,
            stride=1,
            padding=padding,
            groups=channels,
            bias=bias,
        )

        assert norm in ['batch_norm', 'layer_norm']
        if norm == "batch_norm":
            self.use_layer_norm = False
            self.norm = nn.BatchNorm1d(channels)
        else:
            self.use_layer_norm = True
            self.norm = nn.LayerNorm(channels)

        self.pointwise_conv2 = nn.Conv1d(
            channels,
            channels,
            kernel_size=1,
            stride=1,
            padding=0,
            bias=bias,
        )
        self.activation = activation

    def forward(
        self,
        x: torch.Tensor,
        mask_pad: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
        cache: torch.Tensor = torch.zeros((0, 0, 0)),
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        """Compute convolution module.
        Args:
            x (torch.Tensor): Input tensor (#batch, time, channels).
            mask_pad (torch.Tensor): used for batch padding (#batch, 1, time),
                (0, 0, 0) means fake mask.
            cache (torch.Tensor): left context cache, it is only
                used in causal convolution (#batch, channels, cache_t),
                (0, 0, 0) meas fake cache.
        Returns:
            torch.Tensor: Output tensor (#batch, time, channels).
        """
        # exchange the temporal dimension and the feature dimension
        x = x.transpose(1, 2)  # (#batch, channels, time)

        # mask batch padding
        if mask_pad.size(2) > 0:  # time > 0
            x.masked_fill_(~mask_pad, 0.0)

        if self.lorder > 0:
            if cache.size(2) == 0:  # cache_t == 0
                x = nn.functional.pad(x, (self.lorder, 0), 'constant', 0.0)
            else:
                assert cache.size(0) == x.size(0)  # equal batch
                assert cache.size(1) == x.size(1)  # equal channel
                x = torch.cat((cache, x), dim=2)
            assert (x.size(2) > self.lorder)
            new_cache = x[:, :, -self.lorder:]
        else:
            # It's better we just return None if no cache is required,
            # However, for JIT export, here we just fake one tensor instead of
            # None.
            new_cache = torch.zeros((0, 0, 0), dtype=x.dtype, device=x.device)

        # GLU mechanism
        x = self.pointwise_conv1(x)  # (batch, 2*channel, dim)
        x = nn.functional.glu(x, dim=1)  # (batch, channel, dim)

        # 1D Depthwise Conv
        x = self.depthwise_conv(x)
        if self.use_layer_norm:
            x = x.transpose(1, 2)
        x = self.activation(self.norm(x))
        if self.use_layer_norm:
            x = x.transpose(1, 2)
        x = self.pointwise_conv2(x)
        # mask batch padding
        if mask_pad.size(2) > 0:  # time > 0
            x.masked_fill_(~mask_pad, 0.0)

        return x.transpose(1, 2), new_cache


# NOTE(Xiang Lyu) causal conv module used in convolution-based vocoder
class CausalConv1d(torch.nn.Conv1d):
    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        kernel_size: int,
        stride: int = 1,
        dilation: int = 1,
        groups: int = 1,
        bias: bool = True,
        padding_mode: str = 'zeros',
        causal_type: str = 'left',
        device=None,
        dtype=None
    ) -> None:
        super(CausalConv1d, self).__init__(in_channels, out_channels,
                                           kernel_size, stride=1,
                                           padding=0, dilation=dilation,
                                           groups=groups, bias=bias,
                                           padding_mode=padding_mode,
                                           device=device, dtype=dtype)
        assert stride == 1
        self.causal_padding = int((kernel_size * dilation - dilation) / 2) * 2 + (kernel_size + 1) % 2
        assert causal_type in ['left', 'right']
        self.causal_type = causal_type

    def forward(self, x: torch.Tensor, cache: torch.Tensor = torch.zeros(0, 0, 0)) -> Tuple[torch.Tensor]:
        input_timestep = x.shape[2]
        if cache.size(2) == 0:
            cache = torch.zeros(x.shape[0], x.shape[1], self.causal_padding).to(x)
        assert cache.size(2) == self.causal_padding
        if self.causal_type == 'left':
            x = torch.concat([cache, x], dim=2)
        else:
            x = torch.concat([x, cache], dim=2)
        x = super(CausalConv1d, self).forward(x)
        assert x.shape[2] == input_timestep
        return x


class CausalConv1dDownSample(torch.nn.Conv1d):
    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        kernel_size: int,
        stride: int = 1,
        dilation: int = 1,
        groups: int = 1,
        bias: bool = True,
        padding_mode: str = 'zeros',
        device=None,
        dtype=None
    ) -> None:
        super(CausalConv1dDownSample, self).__init__(in_channels, out_channels,
                                                     kernel_size, stride,
                                                     padding=0, dilation=dilation,
                                                     groups=groups, bias=bias,
                                                     padding_mode=padding_mode,
                                                     device=device, dtype=dtype)
        assert stride != 1 and dilation == 1
        assert kernel_size % stride == 0
        self.causal_padding = stride - 1

    def forward(self, x: torch.Tensor, cache: torch.Tensor = torch.zeros(0, 0, 0)) -> Tuple[torch.Tensor, torch.Tensor]:
        if cache.size(2) == 0:
            x = F.pad(x, (self.causal_padding, 0), value=0.0)
        else:
            assert cache.size(2) == self.causal_padding
            x = torch.concat([cache, x], dim=2)
        x = super(CausalConv1dDownSample, self).forward(x)
        return x


class CausalConv1dUpsample(torch.nn.Conv1d):
    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        kernel_size: int,
        stride: int = 1,
        dilation: int = 1,
        groups: int = 1,
        bias: bool = True,
        padding_mode: str = 'zeros',
        device=None,
        dtype=None
    ) -> None:
        super(CausalConv1dUpsample, self).__init__(in_channels, out_channels,
                                                   kernel_size, 1,
                                                   padding=0, dilation=dilation,
                                                   groups=groups, bias=bias,
                                                   padding_mode=padding_mode,
                                                   device=device, dtype=dtype)
        assert dilation == 1
        self.causal_padding = kernel_size - 1
        self.upsample = torch.nn.Upsample(scale_factor=stride, mode='nearest')

    def forward(self, x: torch.Tensor, cache: torch.Tensor = torch.zeros(0, 0, 0)) -> Tuple[torch.Tensor, torch.Tensor]:
        x = self.upsample(x)
        input_timestep = x.shape[2]
        if cache.size(2) == 0:
            x = F.pad(x, (self.causal_padding, 0), value=0.0)
        else:
            assert cache.size(2) == self.causal_padding
            x = torch.concat([cache, x], dim=2)
        x = super(CausalConv1dUpsample, self).forward(x)
        assert input_timestep == x.shape[2]
        return x