

'''simple docstring'''



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, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer



@dataclass
class lowerCamelCase (  A_						):



			'''simple docstring'''






			__snake_case    	=       42



class lowerCamelCase (  A_       ,						A_						):



			'''simple docstring'''






			@register_to_config
			def __init__(      self       :						Optional[Any]		,			lowerCAmelCase_       :						Any = 3		,			lowerCAmelCase_       :						Optional[int] = 3		,			lowerCAmelCase_       :						List[Any] = ("DownEncoderBlock2D",)		,			lowerCAmelCase_       :						Optional[Any] = ("UpDecoderBlock2D",)		,			lowerCAmelCase_       :						int = (64,)		,			lowerCAmelCase_       :						List[Any] = 1		,			lowerCAmelCase_       :						Union[str, Any] = "silu"		,			lowerCAmelCase_       :						Dict = 3		,			lowerCAmelCase_       :						Union[str, Any] = 32		,			lowerCAmelCase_       :						List[str] = 2_56		,			lowerCAmelCase_       :						str = 32		,			lowerCAmelCase_       :						Any = None		,			lowerCAmelCase_       :						List[str] = 0.18215		,			lowerCAmelCase_       :						Optional[Any] = "group"		,			)			->		List[Any]:





									'''simple docstring'''
									super().__init__()

									# pass init params to Encoder
									A__     :   Dict      =Encoder(
									    in_channels=lowerCAmelCase_		,			out_channels=lowerCAmelCase_		,			down_block_types=lowerCAmelCase_		,			block_out_channels=lowerCAmelCase_		,			layers_per_block=lowerCAmelCase_		,			act_fn=lowerCAmelCase_		,			norm_num_groups=lowerCAmelCase_		,			double_z=lowerCAmelCase_		,			)

									A__     :   str      =vq_embed_dim if vq_embed_dim is not None else latent_channels

									A__     :   Optional[Any]      =nn.Convad(lowerCAmelCase_		,			lowerCAmelCase_		,			1 )
									A__     :   int      =VectorQuantizer(lowerCAmelCase_		,			lowerCAmelCase_		,			beta=0.25		,			remap=lowerCAmelCase_		,			sane_index_shape=lowerCAmelCase_ )
									A__     :   List[Any]      =nn.Convad(lowerCAmelCase_		,			lowerCAmelCase_		,			1 )

									# pass init params to Decoder
									A__     :   Union[str, Any]      =Decoder(
									    in_channels=lowerCAmelCase_		,			out_channels=lowerCAmelCase_		,			up_block_types=lowerCAmelCase_		,			block_out_channels=lowerCAmelCase_		,			layers_per_block=lowerCAmelCase_		,			act_fn=lowerCAmelCase_		,			norm_num_groups=lowerCAmelCase_		,			norm_type=lowerCAmelCase_		,			)


			@apply_forward_hook
			def lowercase__						(      self       :						Union[str, Any]		,			lowerCAmelCase_       :						Union[str, Any]		,			lowerCAmelCase_       :						Tuple = True )			->		List[Any]:





									'''simple docstring'''
									A__     :   int      =self.encoder(lowerCAmelCase_ )
									A__     :   Optional[int]      =self.quant_conv(lowerCAmelCase_ )

									if not return_dict:
															return (h,)

									return VQEncoderOutput(latents=lowerCAmelCase_ )


			@apply_forward_hook
			def lowercase__						(      self       :						Any		,			lowerCAmelCase_       :						Tuple		,			lowerCAmelCase_       :						Tuple = False		,			lowerCAmelCase_       :						Optional[int] = True )			->		str:





									'''simple docstring'''
									# also go through quantization layer
									if not force_not_quantize:
															A__    ,						A__    ,						A__     :   List[Any]      =self.quantize(lowerCAmelCase_ )
									else:
															A__     :   Tuple      =h
									A__     :   str      =self.post_quant_conv(lowerCAmelCase_ )
									A__     :   List[str]      =self.decoder(lowerCAmelCase_		,			quant if self.config.norm_type == """spatial""" else None )

									if not return_dict:
															return (dec,)

									return DecoderOutput(sample=lowerCAmelCase_ )



			def lowercase__						(      self       :						Optional[int]		,			lowerCAmelCase_       :						Optional[Any]		,			lowerCAmelCase_       :						Optional[int] = True )			->		Union[str, Any]:





									'''simple docstring'''
									A__     :   Optional[int]      =sample
									A__     :   List[Any]      =self.encode(lowerCAmelCase_ ).latents
									A__     :   Optional[int]      =self.decode(lowerCAmelCase_ ).sample

									if not return_dict:
															return (dec,)

									return DecoderOutput(sample=lowerCAmelCase_ )





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, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer





@dataclass
class       A    (   A_ ):
							UpperCamelCase_   :							torch.FloatTensor





class       A    (   A_					,				A_ ):


							@register_to_config
							def __init__(self      ,				lowerCAmelCase = 3      ,				lowerCAmelCase = 3      ,				lowerCAmelCase = ("DownEncoderBlock2D",)      ,				lowerCAmelCase = ("UpDecoderBlock2D",)      ,				lowerCAmelCase = (6_4,)      ,				lowerCAmelCase = 1      ,				lowerCAmelCase = "silu"      ,				lowerCAmelCase = 3      ,				lowerCAmelCase = 3_2      ,				lowerCAmelCase = 2_5_6      ,				lowerCAmelCase = 3_2      ,				lowerCAmelCase = None      ,				lowerCAmelCase = 0.1_82_15      ,				lowerCAmelCase = "group"      ,				):
									super().__init__()

									# pass init params to Encoder
									__lowercase=			Encoder(
									    in_channels=lowerCAmelCase      ,				out_channels=lowerCAmelCase      ,				down_block_types=lowerCAmelCase      ,				block_out_channels=lowerCAmelCase      ,				layers_per_block=lowerCAmelCase      ,				act_fn=lowerCAmelCase      ,				norm_num_groups=lowerCAmelCase      ,				double_z=lowerCAmelCase      ,				)

									__lowercase=			vq_embed_dim if vq_embed_dim is not None else latent_channels

									__lowercase=			nn.Convad(lowerCAmelCase      ,				lowerCAmelCase      ,				1					)
									__lowercase=			VectorQuantizer(lowerCAmelCase      ,				lowerCAmelCase      ,				beta=0.25      ,				remap=lowerCAmelCase      ,				sane_index_shape=lowerCAmelCase					)
									__lowercase=			nn.Convad(lowerCAmelCase      ,				lowerCAmelCase      ,				1					)

									# pass init params to Decoder
									__lowercase=			Decoder(
									    in_channels=lowerCAmelCase      ,				out_channels=lowerCAmelCase      ,				up_block_types=lowerCAmelCase      ,				block_out_channels=lowerCAmelCase      ,				layers_per_block=lowerCAmelCase      ,				act_fn=lowerCAmelCase      ,				norm_num_groups=lowerCAmelCase      ,				norm_type=lowerCAmelCase      ,				)


							@apply_forward_hook
							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase = True					):
									__lowercase=			self.encoder(lowerCAmelCase					)
									__lowercase=			self.quant_conv(lowerCAmelCase					)

									if not return_dict:
											return (h,)

									return VQEncoderOutput(latents=lowerCAmelCase					)


							@apply_forward_hook
							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase = False      ,				lowerCAmelCase = True					):
									# also go through quantization layer
									if not force_not_quantize:
											__lowercase,							__lowercase,							__lowercase=			self.quantize(lowerCAmelCase					)
									else:
											__lowercase=			h
									__lowercase=			self.post_quant_conv(lowerCAmelCase					)
									__lowercase=			self.decoder(lowerCAmelCase      ,				quant if self.config.norm_type == 'spatial' else None					)

									if not return_dict:
											return (dec,)

									return DecoderOutput(sample=lowerCAmelCase					)







							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase = True					):
									__lowercase=			sample
									__lowercase=			self.encode(lowerCAmelCase					).latents
									__lowercase=			self.decode(lowerCAmelCase					).sample

									if not return_dict:
											return (dec,)

									return DecoderOutput(sample=lowerCAmelCase					)






