# Copyright (c) 2024 Alibaba Inc
#
# 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.
import torch
import torch.nn as nn
from einops import pack, rearrange, repeat
from matcha.models.components.decoder import SinusoidalPosEmb, Block1D, ResnetBlock1D, Downsample1D, TimestepEmbedding, Upsample1D
from matcha.models.components.transformer import BasicTransformerBlock

class Transpose(torch.nn.Module):
    def __init__(self, dim0: int, dim1: int):
        super().__init__()
        self.dim0 = dim0
        self.dim1 = dim1

    def forward(self, x: torch.Tensor):
        x = torch.transpose(x, self.dim0, self.dim1)
        return x

class CausalBlock1D(Block1D):
    def __init__(self, dim: int, dim_out: int):
        super(CausalBlock1D, self).__init__(dim, dim_out)
        self.block = torch.nn.Sequential(
            CausalConv1d(dim, dim_out, 3),
            Transpose(1, 2),
            nn.LayerNorm(dim_out),
            Transpose(1, 2),
            nn.Mish(),
        )

    def forward(self, x: torch.Tensor, mask: torch.Tensor):
        output = self.block(x * mask)
        return output * mask


class CausalResnetBlock1D(ResnetBlock1D):
    def __init__(self, dim: int, dim_out: int, time_emb_dim: int, groups: int = 8):
        super(CausalResnetBlock1D, self).__init__(dim, dim_out, time_emb_dim, groups)
        self.block1 = CausalBlock1D(dim, dim_out)
        self.block2 = CausalBlock1D(dim_out, dim_out)

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',
        device=None,
        dtype=None
    ) -> None:
        super(CausalConv1d, 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
        self.causal_padding = (kernel_size - 1, 0)

    def forward(self, x: torch.Tensor):
        x = F.pad(x, self.causal_padding)
        x = super(CausalConv1d, self).forward(x)
        return x

class ConditionalDecoder(nn.Module):
    def __init__(
        self,
        in_channels,
        out_channels,
        channels=(256, 256),
        dropout=0.05,
        attention_head_dim=64,
        n_blocks=1,
        num_mid_blocks=2,
        num_heads=4,
        act_fn="snake",
    ):
        """
        This decoder requires an input with the same shape of the target. So, if your text content
        is shorter or longer than the outputs, please re-sampling it before feeding to the decoder.
        """
        super().__init__()
        channels = tuple(channels)
        self.in_channels = in_channels
        self.out_channels = out_channels

        self.time_embeddings = SinusoidalPosEmb(in_channels)
        time_embed_dim = channels[0] * 4
        self.time_mlp = TimestepEmbedding(
            in_channels=in_channels,
            time_embed_dim=time_embed_dim,
            act_fn="silu",
        )
        self.down_blocks = nn.ModuleList([])
        self.mid_blocks = nn.ModuleList([])
        self.up_blocks = nn.ModuleList([])

        output_channel = in_channels
        for i in range(len(channels)):  # pylint: disable=consider-using-enumerate
            input_channel = output_channel
            output_channel = channels[i]
            is_last = i == len(channels) - 1
            resnet = ResnetBlock1D(dim=input_channel, dim_out=output_channel, time_emb_dim=time_embed_dim)
            transformer_blocks = nn.ModuleList(
                [
                    BasicTransformerBlock(
                        dim=output_channel,
                        num_attention_heads=num_heads,
                        attention_head_dim=attention_head_dim,
                        dropout=dropout,
                        activation_fn=act_fn,
                    )
                    for _ in range(n_blocks)
                ]
            )
            downsample = (
                Downsample1D(output_channel) if not is_last else nn.Conv1d(output_channel, output_channel, 3, padding=1)
            )
            self.down_blocks.append(nn.ModuleList([resnet, transformer_blocks, downsample]))

        for _ in range(num_mid_blocks):
            input_channel = channels[-1]
            out_channels = channels[-1]
            resnet = ResnetBlock1D(dim=input_channel, dim_out=output_channel, time_emb_dim=time_embed_dim)

            transformer_blocks = nn.ModuleList(
                [
                    BasicTransformerBlock(
                        dim=output_channel,
                        num_attention_heads=num_heads,
                        attention_head_dim=attention_head_dim,
                        dropout=dropout,
                        activation_fn=act_fn,
                    )
                    for _ in range(n_blocks)
                ]
            )

            self.mid_blocks.append(nn.ModuleList([resnet, transformer_blocks]))

        channels = channels[::-1] + (channels[0],)
        for i in range(len(channels) - 1):
            input_channel = channels[i] * 2
            output_channel = channels[i + 1]
            is_last = i == len(channels) - 2
            resnet = ResnetBlock1D(
                dim=input_channel,
                dim_out=output_channel,
                time_emb_dim=time_embed_dim,
            )
            transformer_blocks = nn.ModuleList(
                [
                    BasicTransformerBlock(
                        dim=output_channel,
                        num_attention_heads=num_heads,
                        attention_head_dim=attention_head_dim,
                        dropout=dropout,
                        activation_fn=act_fn,
                    )
                    for _ in range(n_blocks)
                ]
            )
            upsample = (
                Upsample1D(output_channel, use_conv_transpose=True)
                if not is_last
                else nn.Conv1d(output_channel, output_channel, 3, padding=1)
            )
            self.up_blocks.append(nn.ModuleList([resnet, transformer_blocks, upsample]))
        self.final_block = Block1D(channels[-1], channels[-1])
        self.final_proj = nn.Conv1d(channels[-1], self.out_channels, 1)
        self.initialize_weights()

    def initialize_weights(self):
        for m in self.modules():
            if isinstance(m, nn.Conv1d):
                nn.init.kaiming_normal_(m.weight, nonlinearity="relu")
                if m.bias is not None:
                    nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.GroupNorm):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.Linear):
                nn.init.kaiming_normal_(m.weight, nonlinearity="relu")
                if m.bias is not None:
                    nn.init.constant_(m.bias, 0)

    def forward(self, x, mask, mu, t, spks=None, cond=None):
        """Forward pass of the UNet1DConditional model.

        Args:
            x (torch.Tensor): shape (batch_size, in_channels, time)
            mask (_type_): shape (batch_size, 1, time)
            t (_type_): shape (batch_size)
            spks (_type_, optional): shape: (batch_size, condition_channels). Defaults to None.
            cond (_type_, optional): placeholder for future use. Defaults to None.

        Raises:
            ValueError: _description_
            ValueError: _description_

        Returns:
            _type_: _description_
        """

        t = self.time_embeddings(t).to(t.dtype)
        t = self.time_mlp(t)
        x = pack([x, mu], "b * t")[0]
        if spks is not None:
            spks = repeat(spks, "b c -> b c t", t=x.shape[-1])
            x = pack([x, spks], "b * t")[0]
        if cond is not None:
            x = pack([x, cond], "b * t")[0]
        hiddens = []
        masks = [mask]
        for resnet, transformer_blocks, downsample in self.down_blocks:
            mask_down = masks[-1]
            x = resnet(x, mask_down, t)
            x = rearrange(x, "b c t -> b t c").contiguous()
            attn_mask = torch.matmul(mask_down.transpose(1, 2).contiguous(), mask_down)
            for transformer_block in transformer_blocks:
                x = transformer_block(
                    hidden_states=x,
                    attention_mask=attn_mask,
                    timestep=t,
                )
            x = rearrange(x, "b t c -> b c t").contiguous()
            hiddens.append(x)  # Save hidden states for skip connections
            x = downsample(x * mask_down)
            masks.append(mask_down[:, :, ::2])
        masks = masks[:-1]
        mask_mid = masks[-1]

        for resnet, transformer_blocks in self.mid_blocks:
            x = resnet(x, mask_mid, t)
            x = rearrange(x, "b c t -> b t c").contiguous()
            attn_mask = torch.matmul(mask_mid.transpose(1, 2).contiguous(), mask_mid)
            for transformer_block in transformer_blocks:
                x = transformer_block(
                    hidden_states=x,
                    attention_mask=attn_mask,
                    timestep=t,
                )
            x = rearrange(x, "b t c -> b c t").contiguous()

        for resnet, transformer_blocks, upsample in self.up_blocks:
            mask_up = masks.pop()
            skip = hiddens.pop()
            x = pack([x[:, :, :skip.shape[-1]], skip], "b * t")[0]
            x = resnet(x, mask_up, t)
            x = rearrange(x, "b c t -> b t c").contiguous()
            attn_mask = torch.matmul(mask_up.transpose(1, 2).contiguous(), mask_up)
            for transformer_block in transformer_blocks:
                x = transformer_block(
                    hidden_states=x,
                    attention_mask=attn_mask,
                    timestep=t,
                )
            x = rearrange(x, "b t c -> b c t").contiguous()
            x = upsample(x * mask_up)
        x = self.final_block(x, mask_up)
        output = self.final_proj(x * mask_up)
        return output * mask
