


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


_lowercase							:   str						=						logging.get_logger(__name__)

_lowercase							:   Optional[Any]						=						{
    "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    _UpperCamelCase     (       __snake_case					):

				"""simple docstring"""







				lowerCAmelCase					=			'donut-swin'

				lowerCAmelCase					=			{
				    'num_attention_heads': 'num_heads',
				    'num_hidden_layers': 'num_layers',
				}

				def __init__(    self					, a__=224					, a__=4					, a__=3					, a__=96					, a__=[2, 2, 6, 2]					, a__=[3, 6, 12, 24]					, a__=7					, a__=4.0					, a__=True					, a__=0.0					, a__=0.0					, a__=0.1					, a__="gelu"					, a__=False					, a__=0.02					, a__=1e-5					, **a__					, )						->					str:
					super().__init__(**a__ )

					A             =					image_size
					A             =					patch_size
					A             =					num_channels
					A             =					embed_dim
					A             =					depths
					A             =					len(a__ )
					A             =					num_heads
					A             =					window_size
					A             =					mlp_ratio
					A             =					qkv_bias
					A             =					hidden_dropout_prob
					A             =					attention_probs_dropout_prob
					A             =					drop_path_rate
					A             =					hidden_act
					A             =					use_absolute_embeddings
					A             =					layer_norm_eps
					A             =					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
					A             =					int(embed_dim * 2 ** (len(a__ ) - 1) )







from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments





def 	_lowerCAmelCase					( )			-> str:





	"""simple docstring"""







	A             =					HfArgumentParser(UpperCamelCase__    )
	A             =					parser.parse_args_into_dataclasses()[0]
	A             =					TensorFlowBenchmark(args=UpperCamelCase__    )
	try:
		A             =					parser.parse_args_into_dataclasses()[0]
	except ValueError as e:
		A             =					"""Arg --no_{0} is no longer used, please use --no-{0} instead."""
		A             =					""" """.join(str(UpperCamelCase__    ).split(""" """    )[:-1]    )
		A             =					""""""
		A             =					eval(str(UpperCamelCase__    ).split(""" """    )[-1]    )
		A             =					[]
		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(UpperCamelCase__    )
		if len(UpperCamelCase__    ) > 0:
			A             =					full_error_msg + begin_error_msg + str(UpperCamelCase__    )
		raise ValueError(UpperCamelCase__    )
	benchmark.run()


if __name__ == "__main__":
				main()



