



from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


UpperCAmelCase_							:      Dict      =  logging.get_logger(__name__)

UpperCAmelCase_							:      Union[str, Any]      =  {
    '''shi-labs/dinat-mini-in1k-224''': '''https://huggingface.co/shi-labs/dinat-mini-in1k-224/resolve/main/config.json''',
    # See all Dinat models at https://huggingface.co/models?filter=dinat
}






class      UpperCamelCase    (						a_       ,    a_     ):
	lowerCAmelCase	:   int				=	"""dinat"""

	lowerCAmelCase	:   Optional[Any]				=	{
	    """num_attention_heads""": """num_heads""",
	    """num_hidden_layers""": """num_layers""",
	}



	def __init__(       self			,	UpperCAmelCase__=4			,	UpperCAmelCase__=3			,	UpperCAmelCase__=64			,	UpperCAmelCase__=[3, 4, 6, 5]			,	UpperCAmelCase__=[2, 4, 8, 16]			,	UpperCAmelCase__=7			,	UpperCAmelCase__=[[1, 8, 1], [1, 4, 1, 4], [1, 2, 1, 2, 1, 2], [1, 1, 1, 1, 1]]			,	UpperCAmelCase__=3.0			,	UpperCAmelCase__=True			,	UpperCAmelCase__=0.0			,	UpperCAmelCase__=0.0			,	UpperCAmelCase__=0.1			,	UpperCAmelCase__="gelu"			,	UpperCAmelCase__=0.02			,	UpperCAmelCase__=1e-5			,	UpperCAmelCase__=0.0			,	UpperCAmelCase__=None			,	UpperCAmelCase__=None			,	**UpperCAmelCase__			,	):
							super().__init__(**lowercase_		)

							A__							=       patch_size
							A__							=       num_channels
							A__							=       embed_dim
							A__							=       depths
							A__							=       len(lowercase_		)
							A__							=       num_heads
							A__							=       kernel_size
							A__							=       dilations
							A__							=       mlp_ratio
							A__							=       qkv_bias
							A__							=       hidden_dropout_prob
							A__							=       attention_probs_dropout_prob
							A__							=       drop_path_rate
							A__							=       hidden_act
							A__							=       layer_norm_eps
							A__							=       initializer_range
							# we set the hidden_size attribute in order to make Dinat work with VisionEncoderDecoderModel
							# this indicates the channel dimension after the last stage of the model
							A__							=       int(embed_dim * 2 ** (len(lowercase_		) - 1)		)
							A__							=       layer_scale_init_value
							A__							=       ['''stem'''] + [F"""stage{idx}""" for idx in range(1			,	len(lowercase_		) + 1		)]
							A__							=       get_aligned_output_features_output_indices(
							    out_features=lowercase_			,	out_indices=lowercase_			,	stage_names=self.stage_names		)










def  UpperCamelCase						(       _A			:				str			,							_A			:				str    )->       str:





						"""simple docstring"""





						A__							=       len(_A    )
						A__							=       len(_A    )
						A__							=       (
						    first_str_length if first_str_length > second_str_length else second_str_length
						)
						A__							=       []
						for char_count in range(_A    ):
												if char_count < first_str_length:
																		output_list.append(first_str[char_count]    )
												if char_count < second_str_length:
																		output_list.append(second_str[char_count]    )
						return "".join(_A    )


if __name__ == "__main__":
						print(alternative_string_arrange("AB", "XYZ"), end=" ")





