


from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase__  :int   			=							logging.get_logger(__name__)

lowerCAmelCase__  :List[str]   			=							{
    '''naver-clova-ix/donut-base''': '''https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json''',
    # See all Donut models at https://huggingface.co/models?filter=donut-swin
}

class 		__a      (				snake_case_			):
		_a	:  List[Any]				=	'donut-swin'

		_a	:  Union[str, Any]				=	{
		    'num_attention_heads': 'num_heads',
		    'num_hidden_layers': 'num_layers',
		}
		def __init__(			self			,    _SCREAMING_SNAKE_CASE=224			,    _SCREAMING_SNAKE_CASE=4			,    _SCREAMING_SNAKE_CASE=3			,    _SCREAMING_SNAKE_CASE=96			,    _SCREAMING_SNAKE_CASE=[2, 2, 6, 2]			,    _SCREAMING_SNAKE_CASE=[3, 6, 12, 24]			,    _SCREAMING_SNAKE_CASE=7			,    _SCREAMING_SNAKE_CASE=4.0			,    _SCREAMING_SNAKE_CASE=True			,    _SCREAMING_SNAKE_CASE=0.0			,    _SCREAMING_SNAKE_CASE=0.0			,    _SCREAMING_SNAKE_CASE=0.1			,    _SCREAMING_SNAKE_CASE="gelu"			,    _SCREAMING_SNAKE_CASE=False			,    _SCREAMING_SNAKE_CASE=0.02			,    _SCREAMING_SNAKE_CASE=1e-5			,    **_SCREAMING_SNAKE_CASE			,    ) ->       Optional[int]:




									"""simple docstring"""





									super().__init__(**_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase 		=			image_size
									_UpperCAmelCase 		=			patch_size
									_UpperCAmelCase 		=			num_channels
									_UpperCAmelCase 		=			embed_dim
									_UpperCAmelCase 		=			depths
									_UpperCAmelCase 		=			len(_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase 		=			num_heads
									_UpperCAmelCase 		=			window_size
									_UpperCAmelCase 		=			mlp_ratio
									_UpperCAmelCase 		=			qkv_bias
									_UpperCAmelCase 		=			hidden_dropout_prob
									_UpperCAmelCase 		=			attention_probs_dropout_prob
									_UpperCAmelCase 		=			drop_path_rate
									_UpperCAmelCase 		=			hidden_act
									_UpperCAmelCase 		=			use_absolute_embeddings
									_UpperCAmelCase 		=			layer_norm_eps
									_UpperCAmelCase 		=			initializer_range
									# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
									# this indicates the channel dimension after the last stage of the model
									_UpperCAmelCase 		=			int(embed_dim * 2 ** (len(_SCREAMING_SNAKE_CASE						) - 1)						)

from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments





def    __lowercase     (						):
 UpperCamelCase_					:						Optional[Any] =					HfArgumentParser(lowerCamelCase	)
 UpperCamelCase_					:						Tuple =					parser.parse_args_into_dataclasses()[0]
 UpperCamelCase_					:						Dict =					TensorFlowBenchmark(args=lowerCamelCase	)
 try:
  UpperCamelCase_					:						Any =					parser.parse_args_into_dataclasses()[0]
 except ValueError as e:
  UpperCamelCase_					:						Any =					'Arg --no_{0} is no longer used, please use --no-{0} instead.'
  UpperCamelCase_					:						Optional[int] =					' '.join(str(lowerCamelCase	).split(' '	)[:-1]	)
  UpperCamelCase_					:						Any =					''
  UpperCamelCase_					:						Any =					eval(str(lowerCamelCase	).split(' '	)[-1]	)
  UpperCamelCase_					:						List[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(lowerCamelCase	)
  if len(lowerCamelCase	) > 0:
   UpperCamelCase_					:						List[str] =					full_error_msg + begin_error_msg + str(lowerCamelCase	)
  raise ValueError(lowerCamelCase	)
 benchmark.run()


if __name__ == "__main__":
   main()



