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# 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

# limitations under the License.

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, Encoder, VectorQuantizer

@dataclass

class VQEncoderOutput(BaseOutput):

"""

Output of VQModel encoding method.

Args:

latents (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):

Encoded output sample of the model. Output of the last layer of the model.

"""

latents: torch.FloatTensor

class VQModel(ModelMixin, ConfigMixin):

r"""VQ-VAE model from the paper Neural Discrete Representation Learning by Aaron van den Oord, Oriol Vinyals and Koray

Kavukcuoglu.

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 `3`): Number of channels in the latent space.

sample_size (`int`, *optional*, defaults to `32`): TODO

num_vq_embeddings (`int`, *optional*, defaults to `256`): Number of codebook vectors in the VQ-VAE.

vq_embed_dim (`int`, *optional*): Hidden dim of codebook vectors in the VQ-VAE.

"""

@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 = 3,

sample_size: int = 32,

num_vq_embeddings: int = 256,

norm_num_groups: int = 32,

vq_embed_dim: Optional[int] = None,

):

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=False,

)

vq_embed_dim = vq_embed_dim if vq_embed_dim is not None else latent_channels

self.quant_conv = nn.Conv2d(latent_channels, vq_embed_dim, 1)

self.quantize = VectorQuantizer(num_vq_embeddings, vq_embed_dim, beta=0.25, remap=None, sane_index_shape=False)

self.post_quant_conv = nn.Conv2d(vq_embed_dim, latent_channels, 1)

# 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,

act_fn=act_fn,

norm_num_groups=norm_num_groups,

)

def encode(self, x: torch.FloatTensor, return_dict: bool = True) -> VQEncoderOutput:

h = self.encoder(x)

h = self.quant_conv(h)

if not return_dict:

return (h,)

return VQEncoderOutput(latents=h)

def decode(

self, h: torch.FloatTensor, force_not_quantize: bool = False, return_dict: bool = True

) -> Union[DecoderOutput, torch.FloatTensor]:

# also go through quantization layer

if not force_not_quantize:

quant, emb_loss, info = self.quantize(h)

else:

quant = h

quant = self.post_quant_conv(quant)

dec = self.decoder(quant)

if not return_dict:

return (dec,)

return DecoderOutput(sample=dec)

def forward(self, sample: torch.FloatTensor, return_dict: bool = True) -> Union[DecoderOutput, torch.FloatTensor]:

r"""

Args:

sample (`torch.FloatTensor`): Input sample.

return_dict (`bool`, *optional*, defaults to `True`):

Whether or not to return a [`DecoderOutput`] instead of a plain tuple.

"""

x = sample

h = self.encode(x).latents

dec = self.decode(h).sample

if not return_dict:

return (dec,)

return DecoderOutput(sample=dec)