

'''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 		__SCREAMING_SNAKE_CASE       ( lowercase__			):
				lowerCamelCase_							=						42





class 		__SCREAMING_SNAKE_CASE       ( lowercase__	,       lowercase__			):







				@register_to_config
				def __init__(					self    :		Union[str, Any]		,				UpperCAmelCase__    :		int = 3		,				UpperCAmelCase__    :		int = 3		,				UpperCAmelCase__    :		Tuple[str] = ("DownEncoderBlock2D",)		,				UpperCAmelCase__    :		Tuple[str] = ("UpDecoderBlock2D",)		,				UpperCAmelCase__    :		Tuple[int] = (64,)		,				UpperCAmelCase__    :		int = 1		,				UpperCAmelCase__    :		str = "silu"		,				UpperCAmelCase__    :		int = 3		,				UpperCAmelCase__    :		int = 32		,				UpperCAmelCase__    :		int = 256		,				UpperCAmelCase__    :		int = 32		,				UpperCAmelCase__    :		Optional[int] = None		,				UpperCAmelCase__    :		float = 0.1_82_15		,				UpperCAmelCase__    :		str = "group"		,				):
										'''simple docstring'''
										super().__init__()

										# pass init params to Encoder
										lowercase   :       str								=Encoder(
										    in_channels=UpperCAmelCase__		,				out_channels=UpperCAmelCase__		,				down_block_types=UpperCAmelCase__		,				block_out_channels=UpperCAmelCase__		,				layers_per_block=UpperCAmelCase__		,				act_fn=UpperCAmelCase__		,				norm_num_groups=UpperCAmelCase__		,				double_z=UpperCAmelCase__		,				)

										lowercase   :       List[str]								=vq_embed_dim if vq_embed_dim is not None else latent_channels

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

										# pass init params to Decoder
										lowercase   :       str								=Decoder(
										    in_channels=UpperCAmelCase__		,				out_channels=UpperCAmelCase__		,				up_block_types=UpperCAmelCase__		,				block_out_channels=UpperCAmelCase__		,				layers_per_block=UpperCAmelCase__		,				act_fn=UpperCAmelCase__		,				norm_num_groups=UpperCAmelCase__		,				norm_type=UpperCAmelCase__		,				)







				@apply_forward_hook
				def    lowerCamelCase_  (					self    :		List[Any]		,				UpperCAmelCase__    :		torch.FloatTensor		,				UpperCAmelCase__    :		bool = True      ):
										'''simple docstring'''
										lowercase   :       Any								=self.encoder(UpperCAmelCase__      )
										lowercase   :       Tuple								=self.quant_conv(UpperCAmelCase__      )

										if not return_dict:
																return (h,)

										return VQEncoderOutput(latents=UpperCAmelCase__      )







				@apply_forward_hook
				def    lowerCamelCase_  (					self    :		Tuple		,				UpperCAmelCase__    :		torch.FloatTensor		,				UpperCAmelCase__    :		bool = False		,				UpperCAmelCase__    :		bool = True      ):
										'''simple docstring'''
										# also go through quantization layer
										if not force_not_quantize:
																lowercase		,  lowercase		,  lowercase   :       Union[str, Any]								=self.quantize(UpperCAmelCase__      )
										else:
																lowercase   :       List[str]								=h
										lowercase   :       Union[str, Any]								=self.post_quant_conv(UpperCAmelCase__      )
										lowercase   :       int								=self.decoder(UpperCAmelCase__		,				quant if self.config.norm_type == '''spatial''' else None      )

										if not return_dict:
																return (dec,)

										return DecoderOutput(sample=UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		Dict		,				UpperCAmelCase__    :		torch.FloatTensor		,				UpperCAmelCase__    :		bool = True      ):
										'''simple docstring'''
										lowercase   :       List[str]								=sample
										lowercase   :       Optional[Any]								=self.encode(UpperCAmelCase__      ).latents
										lowercase   :       Tuple								=self.decode(UpperCAmelCase__      ).sample

										if not return_dict:
																return (dec,)

										return DecoderOutput(sample=UpperCAmelCase__      )








'''simple docstring'''





from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments






def 							_lowerCAmelCase (      )							->     List[Any]:
						lowercase   :       Tuple								=HfArgumentParser(__magic_name__	)
						lowercase   :       Union[str, Any]								=parser.parse_args_into_dataclasses()[0]
						lowercase   :       Any								=TensorFlowBenchmark(args=__magic_name__	)
						try:
												lowercase   :       List[Any]								=parser.parse_args_into_dataclasses()[0]
						except ValueError as e:
												lowercase   :       List[Any]								='''Arg --no_{0} is no longer used, please use --no-{0} instead.'''
												lowercase   :       Any								=''' '''.join(str(__magic_name__	).split(''' '''	)[:-1]	)
												lowercase   :       Optional[Any]								=''''''
												lowercase   :       List[str]								=eval(str(__magic_name__	).split(''' '''	)[-1]	)
												lowercase   :       Optional[Any]								=[]
												for arg in depreciated_args:
																		# arg[2:] removes '--'
																		if arg[2:] in TensorFlowBenchmark.deprecated_args:
																								# arg[5:] removes '--no_'
																								full_error_msg += arg_error_msg.format(arg[5:]	)
																		else:
																								wrong_args.append(__magic_name__	)
												if len(__magic_name__	) > 0:
																		lowercase   :       int								=full_error_msg + begin_error_msg + str(__magic_name__	)
												raise ValueError(__magic_name__	)
						benchmark.run()


if __name__ == "__main__":
							main()





