# Copyright 2023 The HuggingFace Team. 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.
from dataclasses import dataclass
from typing import Optional, Tuple

import numpy as np
import torch
import torch.nn as nn

from diffusers.utils import BaseOutput, is_torch_version
from diffusers.utils.torch_utils import randn_tensor
from .modeling_block import (
    UNetMidBlock2D,
    CausalUNetMidBlock2D,
    get_down_block,
    get_up_block,
    get_input_layer,
    get_output_layer,
)
from .modeling_causal_conv import CausalConv3d, CausalGroupNorm


@dataclass
class DecoderOutput(BaseOutput):
    r"""
    Output of decoding method.

    Args:
        sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            The decoded output sample from the last layer of the model.
    """

    sample: torch.FloatTensor


class CausalVaeEncoder(nn.Module):
    r"""
    The `Encoder` layer of a variational autoencoder that encodes its input into a latent representation.

    Args:
        in_channels (`int`, *optional*, defaults to 3):
            The number of input channels.
        out_channels (`int`, *optional*, defaults to 3):
            The number of output channels.
        down_block_types (`Tuple[str, ...]`, *optional*, defaults to `("DownEncoderBlock2D",)`):
            The types of down blocks to use. See `~diffusers.models.unet_2d_blocks.get_down_block` for available
            options.
        block_out_channels (`Tuple[int, ...]`, *optional*, defaults to `(64,)`):
            The number of output channels for each block.
        layers_per_block (`int`, *optional*, defaults to 2):
            The number of layers per block.
        norm_num_groups (`int`, *optional*, defaults to 32):
            The number of groups for normalization.
        act_fn (`str`, *optional*, defaults to `"silu"`):
            The activation function to use. See `~diffusers.models.activations.get_activation` for available options.
        double_z (`bool`, *optional*, defaults to `True`):
            Whether to double the number of output channels for the last block.
    """

    def __init__(
        self,
        in_channels: int = 3,
        out_channels: int = 3,
        down_block_types: Tuple[str, ...] = ("DownEncoderBlockCausal3D",),
        spatial_down_sample: Tuple[bool, ...] = (True,),
        temporal_down_sample: Tuple[bool, ...] = (False,),
        block_out_channels: Tuple[int, ...] = (64,),
        layers_per_block: Tuple[int, ...] = (2,),
        norm_num_groups: int = 32,
        act_fn: str = "silu",
        double_z: bool = True,
        block_dropout: Tuple[int, ...] = (0.0,),
        mid_block_add_attention=True,
    ):
        super().__init__()
        self.layers_per_block = layers_per_block

        self.conv_in = CausalConv3d(
            in_channels,
            block_out_channels[0],
            kernel_size=3,
            stride=1,
        )

        self.mid_block = None
        self.down_blocks = nn.ModuleList([])

        # down
        output_channel = block_out_channels[0]
        for i, down_block_type in enumerate(down_block_types):
            input_channel = output_channel
            output_channel = block_out_channels[i]

            down_block = get_down_block(
                down_block_type,
                num_layers=self.layers_per_block[i],
                in_channels=input_channel,
                out_channels=output_channel,
                add_spatial_downsample=spatial_down_sample[i],
                add_temporal_downsample=temporal_down_sample[i],
                resnet_eps=1e-6,
                downsample_padding=0,
                resnet_act_fn=act_fn,
                resnet_groups=norm_num_groups,
                attention_head_dim=output_channel,
                temb_channels=None,
                dropout=block_dropout[i],
            )
            self.down_blocks.append(down_block)

        # mid
        self.mid_block = CausalUNetMidBlock2D(
            in_channels=block_out_channels[-1],
            resnet_eps=1e-6,
            resnet_act_fn=act_fn,
            output_scale_factor=1,
            resnet_time_scale_shift="default",
            attention_head_dim=block_out_channels[-1],
            resnet_groups=norm_num_groups,
            temb_channels=None,
            add_attention=mid_block_add_attention,
            dropout=block_dropout[-1],
        )

        # out

        self.conv_norm_out = CausalGroupNorm(num_channels=block_out_channels[-1], num_groups=norm_num_groups, eps=1e-6)
        self.conv_act = nn.SiLU()

        conv_out_channels = 2 * out_channels if double_z else out_channels
        self.conv_out = CausalConv3d(block_out_channels[-1], conv_out_channels, kernel_size=3, stride=1)

        self.gradient_checkpointing = False

    def forward(self, sample: torch.FloatTensor, is_init_image=True, temporal_chunk=False) -> torch.FloatTensor:
        r"""The forward method of the `Encoder` class."""

        sample = self.conv_in(sample, is_init_image=is_init_image, temporal_chunk=temporal_chunk)

        if self.training and self.gradient_checkpointing:

            def create_custom_forward(module):
                def custom_forward(*inputs):
                    return module(*inputs)

                return custom_forward

            # down
            if is_torch_version(">=", "1.11.0"):
                for down_block in self.down_blocks:
                    sample = torch.utils.checkpoint.checkpoint(
                        create_custom_forward(down_block), sample, is_init_image, 
                            temporal_chunk, use_reentrant=False
                    )
                # middle
                sample = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(self.mid_block), sample, is_init_image, 
                        temporal_chunk, use_reentrant=False
                )
            else:
                for down_block in self.down_blocks:
                    sample = torch.utils.checkpoint.checkpoint(create_custom_forward(down_block), sample, is_init_image, temporal_chunk)
                # middle
                sample = torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block), sample, is_init_image, temporal_chunk)

        else:
            # down
            for down_block in self.down_blocks:
                sample = down_block(sample, is_init_image=is_init_image, temporal_chunk=temporal_chunk)

            # middle
            sample = self.mid_block(sample, is_init_image=is_init_image, temporal_chunk=temporal_chunk)

        # post-process
        sample = self.conv_norm_out(sample)
        sample = self.conv_act(sample)
        sample = self.conv_out(sample, is_init_image=is_init_image, temporal_chunk=temporal_chunk)

        return sample


class CausalVaeDecoder(nn.Module):
    r"""
    The `Decoder` layer of a variational autoencoder that decodes its latent representation into an output sample.

    Args:
        in_channels (`int`, *optional*, defaults to 3):
            The number of input channels.
        out_channels (`int`, *optional*, defaults to 3):
            The number of output channels.
        up_block_types (`Tuple[str, ...]`, *optional*, defaults to `("UpDecoderBlock2D",)`):
            The types of up blocks to use. See `~diffusers.models.unet_2d_blocks.get_up_block` for available options.
        block_out_channels (`Tuple[int, ...]`, *optional*, defaults to `(64,)`):
            The number of output channels for each block.
        layers_per_block (`int`, *optional*, defaults to 2):
            The number of layers per block.
        norm_num_groups (`int`, *optional*, defaults to 32):
            The number of groups for normalization.
        act_fn (`str`, *optional*, defaults to `"silu"`):
            The activation function to use. See `~diffusers.models.activations.get_activation` for available options.
        norm_type (`str`, *optional*, defaults to `"group"`):
            The normalization type to use. Can be either `"group"` or `"spatial"`.
    """

    def __init__(
        self,
        in_channels: int = 3,
        out_channels: int = 3,
        up_block_types: Tuple[str, ...] = ("UpDecoderBlockCausal3D",),
        spatial_up_sample: Tuple[bool, ...] = (True,),
        temporal_up_sample: Tuple[bool, ...] = (False,),
        block_out_channels: Tuple[int, ...] = (64,),
        layers_per_block: Tuple[int, ...] = (2,),
        norm_num_groups: int = 32,
        act_fn: str = "silu",
        mid_block_add_attention=True,
        interpolate: bool = True,
        block_dropout: Tuple[int, ...] = (0.0,),
    ):
        super().__init__()
        self.layers_per_block = layers_per_block

        self.conv_in = CausalConv3d(
            in_channels,
            block_out_channels[-1],
            kernel_size=3,
            stride=1,
        )

        self.mid_block = None
        self.up_blocks = nn.ModuleList([])

        # mid
        self.mid_block = CausalUNetMidBlock2D(
            in_channels=block_out_channels[-1],
            resnet_eps=1e-6,
            resnet_act_fn=act_fn,
            output_scale_factor=1,
            resnet_time_scale_shift="default",
            attention_head_dim=block_out_channels[-1],
            resnet_groups=norm_num_groups,
            temb_channels=None,
            add_attention=mid_block_add_attention,
            dropout=block_dropout[-1],
        )

        # up
        reversed_block_out_channels = list(reversed(block_out_channels))
        output_channel = reversed_block_out_channels[0]
        for i, up_block_type in enumerate(up_block_types):
            prev_output_channel = output_channel
            output_channel = reversed_block_out_channels[i]

            is_final_block = i == len(block_out_channels) - 1

            up_block = get_up_block(
                up_block_type,
                num_layers=self.layers_per_block[i],
                in_channels=prev_output_channel,
                out_channels=output_channel,
                prev_output_channel=None,
                add_spatial_upsample=spatial_up_sample[i],
                add_temporal_upsample=temporal_up_sample[i],
                resnet_eps=1e-6,
                resnet_act_fn=act_fn,
                resnet_groups=norm_num_groups,
                attention_head_dim=output_channel,
                temb_channels=None,
                resnet_time_scale_shift='default',
                interpolate=interpolate,
                dropout=block_dropout[i],
            )
            self.up_blocks.append(up_block)
            prev_output_channel = output_channel

        # out
        self.conv_norm_out = CausalGroupNorm(num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=1e-6)
        self.conv_act = nn.SiLU()
        self.conv_out = CausalConv3d(block_out_channels[0], out_channels, kernel_size=3, stride=1)

        self.gradient_checkpointing = False

    def forward(
        self,
        sample: torch.FloatTensor,
        is_init_image=True, 
        temporal_chunk=False,
    ) -> torch.FloatTensor:
        r"""The forward method of the `Decoder` class."""

        sample = self.conv_in(sample, is_init_image=is_init_image, temporal_chunk=temporal_chunk)

        upscale_dtype = next(iter(self.up_blocks.parameters())).dtype
        if self.training and self.gradient_checkpointing:

            def create_custom_forward(module):
                def custom_forward(*inputs):
                    return module(*inputs)

                return custom_forward

            if is_torch_version(">=", "1.11.0"):
                # middle
                sample = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(self.mid_block),
                    sample,
                    is_init_image=is_init_image,
                    temporal_chunk=temporal_chunk,
                    use_reentrant=False,
                )
                sample = sample.to(upscale_dtype)

                # up
                for up_block in self.up_blocks:
                    sample = torch.utils.checkpoint.checkpoint(
                        create_custom_forward(up_block),
                        sample,
                        is_init_image=is_init_image,
                        temporal_chunk=temporal_chunk,
                        use_reentrant=False,
                    )
            else:
                # middle
                sample = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(self.mid_block), sample, is_init_image=is_init_image, temporal_chunk=temporal_chunk,
                )
                sample = sample.to(upscale_dtype)

                # up
                for up_block in self.up_blocks:
                    sample = torch.utils.checkpoint.checkpoint(create_custom_forward(up_block), sample, 
                        is_init_image=is_init_image, temporal_chunk=temporal_chunk,)
        else:
            # middle
            sample = self.mid_block(sample, is_init_image=is_init_image, temporal_chunk=temporal_chunk)
            sample = sample.to(upscale_dtype)
            
            # up
            for up_block in self.up_blocks:
                sample = up_block(sample, is_init_image=is_init_image, temporal_chunk=temporal_chunk,)

        # post-process
        sample = self.conv_norm_out(sample)
        sample = self.conv_act(sample)
        sample = self.conv_out(sample, is_init_image=is_init_image, temporal_chunk=temporal_chunk)

        return sample


class DiagonalGaussianDistribution(object):
    def __init__(self, parameters: torch.Tensor, deterministic: bool = False):
        self.parameters = parameters
        self.mean, self.logvar = torch.chunk(parameters, 2, dim=1)
        self.logvar = torch.clamp(self.logvar, -30.0, 20.0)
        self.deterministic = deterministic
        self.std = torch.exp(0.5 * self.logvar)
        self.var = torch.exp(self.logvar)
        if self.deterministic:
            self.var = self.std = torch.zeros_like(
                self.mean, device=self.parameters.device, dtype=self.parameters.dtype
            )

    def sample(self, generator: Optional[torch.Generator] = None) -> torch.FloatTensor:
        # make sure sample is on the same device as the parameters and has same dtype
        sample = randn_tensor(
            self.mean.shape,
            generator=generator,
            device=self.parameters.device,
            dtype=self.parameters.dtype,
        )
        x = self.mean + self.std * sample
        return x

    def kl(self, other: "DiagonalGaussianDistribution" = None) -> torch.Tensor:
        if self.deterministic:
            return torch.Tensor([0.0])
        else:
            if other is None:
                return 0.5 * torch.sum(
                    torch.pow(self.mean, 2) + self.var - 1.0 - self.logvar,
                    dim=[2, 3, 4],
                )
            else:
                return 0.5 * torch.sum(
                    torch.pow(self.mean - other.mean, 2) / other.var
                    + self.var / other.var
                    - 1.0
                    - self.logvar
                    + other.logvar,
                    dim=[2, 3, 4],
                )

    def nll(self, sample: torch.Tensor, dims: Tuple[int, ...] = [1, 2, 3]) -> torch.Tensor:
        if self.deterministic:
            return torch.Tensor([0.0])
        logtwopi = np.log(2.0 * np.pi)
        return 0.5 * torch.sum(
            logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var,
            dim=dims,
        )

    def mode(self) -> torch.Tensor:
        return self.mean