# Copyright 2022 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
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from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, DiagonalGaussianDistribution, Encoder


@dataclass
class AutoencoderKLOutput(BaseOutput):
    """
    Output of AutoencoderKL encoding method.

    Args:
        latent_dist (`DiagonalGaussianDistribution`):
            Encoded outputs of `Encoder` represented as the mean and logvar of `DiagonalGaussianDistribution`.
            `DiagonalGaussianDistribution` allows for sampling latents from the distribution.
    """

    latent_dist: "DiagonalGaussianDistribution"


class AutoencoderKL(ModelMixin, ConfigMixin):
    r"""Variational Autoencoder (VAE) model with KL loss from the paper Auto-Encoding Variational Bayes by Diederik P. Kingma
    and Max Welling.

    This model inherits from [`ModelMixin`]. Check the superclass documentation for the generic methods the library
    implements for all the model (such as downloading or saving, etc.)

    Parameters:
        in_channels (int, *optional*, defaults to 3): Number of channels in the input image.
        out_channels (int,  *optional*, defaults to 3): Number of channels in the output.
        down_block_types (`Tuple[str]`, *optional*, defaults to :
            obj:`("DownEncoderBlock2D",)`): Tuple of downsample block types.
        up_block_types (`Tuple[str]`, *optional*, defaults to :
            obj:`("UpDecoderBlock2D",)`): Tuple of upsample block types.
        block_out_channels (`Tuple[int]`, *optional*, defaults to :
            obj:`(64,)`): Tuple of block output channels.
        act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use.
        latent_channels (`int`, *optional*, defaults to `4`): Number of channels in the latent space.
        sample_size (`int`, *optional*, defaults to `32`): TODO
    """

    @register_to_config
    def __init__(
        self,
        in_channels: int = 3,
        out_channels: int = 3,
        down_block_types: Tuple[str] = ("DownEncoderBlock2D",),
        up_block_types: Tuple[str] = ("UpDecoderBlock2D",),
        block_out_channels: Tuple[int] = (64,),
        layers_per_block: int = 1,
        act_fn: str = "silu",
        latent_channels: int = 4,
        norm_num_groups: int = 32,
        sample_size: int = 32,
    ):
        super().__init__()

        # pass init params to Encoder
        self.encoder = Encoder(
            in_channels=in_channels,
            out_channels=latent_channels,
            down_block_types=down_block_types,
            block_out_channels=block_out_channels,
            layers_per_block=layers_per_block,
            act_fn=act_fn,
            norm_num_groups=norm_num_groups,
            double_z=True,
        )

        # pass init params to Decoder
        self.decoder = Decoder(
            in_channels=latent_channels,
            out_channels=out_channels,
            up_block_types=up_block_types,
            block_out_channels=block_out_channels,
            layers_per_block=layers_per_block,
            norm_num_groups=norm_num_groups,
            act_fn=act_fn,
        )

        self.quant_conv = nn.Conv2d(2 * latent_channels, 2 * latent_channels, 1)
        self.post_quant_conv = nn.Conv2d(latent_channels, latent_channels, 1)
        self.use_slicing = False

    def encode(self, x: torch.FloatTensor, return_dict: bool = True) -> AutoencoderKLOutput:
        h = self.encoder(x)
        moments = self.quant_conv(h)
        posterior = DiagonalGaussianDistribution(moments)

        if not return_dict:
            return (posterior,)

        return AutoencoderKLOutput(latent_dist=posterior)

    def _decode(self, z: torch.FloatTensor, return_dict: bool = True) -> Union[DecoderOutput, torch.FloatTensor]:
        z = self.post_quant_conv(z)
        dec = self.decoder(z)

        if not return_dict:
            return (dec,)

        return DecoderOutput(sample=dec)

    def enable_slicing(self):
        r"""
        Enable sliced VAE decoding.

        When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several
        steps. This is useful to save some memory and allow larger batch sizes.
        """
        self.use_slicing = True

    def disable_slicing(self):
        r"""
        Disable sliced VAE decoding. If `enable_slicing` was previously invoked, this method will go back to computing
        decoding in one step.
        """
        self.use_slicing = False

    def decode(self, z: torch.FloatTensor, return_dict: bool = True) -> Union[DecoderOutput, torch.FloatTensor]:
        if self.use_slicing and z.shape[0] > 1:
            decoded_slices = [self._decode(z_slice).sample for z_slice in z.split(1)]
            decoded = torch.cat(decoded_slices)
        else:
            decoded = self._decode(z).sample

        if not return_dict:
            return (decoded,)

        return DecoderOutput(sample=decoded)

    def forward(
        self,
        sample: torch.FloatTensor,
        sample_posterior: bool = False,
        return_dict: bool = True,
        generator: Optional[torch.Generator] = None,
    ) -> Union[DecoderOutput, torch.FloatTensor]:
        r"""
        Args:
            sample (`torch.FloatTensor`): Input sample.
            sample_posterior (`bool`, *optional*, defaults to `False`):
                Whether to sample from the posterior.
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`DecoderOutput`] instead of a plain tuple.
        """
        x = sample
        posterior = self.encode(x).latent_dist
        if sample_posterior:
            z = posterior.sample(generator=generator)
        else:
            z = posterior.mode()
        dec = self.decode(z).sample

        if not return_dict:
            return (dec,)

        return DecoderOutput(sample=dec)
