


"""simple docstring"""


from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments



def   snake_case						(				)->		Dict:
      '''simple docstring'''


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


if __name__ == "__main__":
   main()







"""simple docstring"""


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


_snake_case          =							logging.get_logger(__name__)

_snake_case          =							{
    "microsoft/swinv2-tiny-patch4-window8-256": (
        "https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json"
    ),
}



class   _a      (      SCREAMING_SNAKE_CASE_ ):
    a_    :				Union[str, Any]             =    'swinv2'

    a_    :				Optional[int]             =    {
        'num_attention_heads': 'num_heads',
        'num_hidden_layers': 'num_layers',
    }
    def __init__( self							:					List[str]							,					SCREAMING_SNAKE_CASE__							:					int=2_24							,					SCREAMING_SNAKE_CASE__							:					str=4							,					SCREAMING_SNAKE_CASE__							:					List[Any]=3							,					SCREAMING_SNAKE_CASE__							:					str=96							,					SCREAMING_SNAKE_CASE__							:					Dict=[2, 2, 6, 2]							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]=[3, 6, 12, 24]							,					SCREAMING_SNAKE_CASE__							:					Tuple=7							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]=4.0							,					SCREAMING_SNAKE_CASE__							:					List[str]=True							,					SCREAMING_SNAKE_CASE__							:					List[Any]=0.0							,					SCREAMING_SNAKE_CASE__							:					Optional[int]=0.0							,					SCREAMING_SNAKE_CASE__							:					Dict=0.1							,					SCREAMING_SNAKE_CASE__							:					Tuple="gelu"							,					SCREAMING_SNAKE_CASE__							:					Tuple=False							,					SCREAMING_SNAKE_CASE__							:					Any=0.02							,					SCREAMING_SNAKE_CASE__							:					List[Any]=1e-5							,					SCREAMING_SNAKE_CASE__							:					int=32							,					**SCREAMING_SNAKE_CASE__							:					List[str]							,					):
          super().__init__(**SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		image_size
          lowerCamelCase__           =		patch_size
          lowerCamelCase__           =		num_channels
          lowerCamelCase__           =		embed_dim
          lowerCamelCase__           =		depths
          lowerCamelCase__           =		len(SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		num_heads
          lowerCamelCase__           =		window_size
          lowerCamelCase__           =		mlp_ratio
          lowerCamelCase__           =		qkv_bias
          lowerCamelCase__           =		hidden_dropout_prob
          lowerCamelCase__           =		attention_probs_dropout_prob
          lowerCamelCase__           =		drop_path_rate
          lowerCamelCase__           =		hidden_act
          lowerCamelCase__           =		use_absolute_embeddings
          lowerCamelCase__           =		layer_norm_eps
          lowerCamelCase__           =		initializer_range
          lowerCamelCase__           =		encoder_stride
          # we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
          # this indicates the channel dimension after the last stage of the model
          lowerCamelCase__           =		int(embed_dim * 2 ** (len(SCREAMING_SNAKE_CASE__				) - 1)				)
          lowerCamelCase__           =		(0, 0, 0, 0)



