


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


snake_case							:       Tuple     			=      logging.get_logger(__name__)

snake_case							:       str     			=      {
    '''facebook/convnextv2-tiny-1k-224''': '''https://huggingface.co/facebook/convnextv2-tiny-1k-224/resolve/main/config.json''',
}







class        snake_case_   (lowerCamelCase_	,						lowerCamelCase_      ):
					UpperCAmelCase__					:    Union[str, Any]          =			'''convnextv2'''
					def __init__(      self   :List[str]			,__snake_case   :Optional[int]=3			,__snake_case   :List[str]=4			,__snake_case   :List[str]=4			,__snake_case   :str=None			,__snake_case   :List[str]=None			,__snake_case   :Any="gelu"			,__snake_case   :str=0.02			,__snake_case   :str=1E-12			,__snake_case   :Union[str, Any]=0.0			,__snake_case   :Optional[int]=2_24			,__snake_case   :Optional[int]=None			,__snake_case   :Tuple=None			,**__snake_case   :Union[str, Any]			,)					->    Optional[Any]:
												super().__init__(**__snake_case      )

												a__             =     num_channels
												a__             =     patch_size
												a__             =     num_stages
												a__             =     [96, 1_92, 3_84, 7_68] if hidden_sizes is None else hidden_sizes
												a__             =     [3, 3, 9, 3] if depths is None else depths
												a__             =     hidden_act
												a__             =     initializer_range
												a__             =     layer_norm_eps
												a__             =     drop_path_rate
												a__             =     image_size
												a__             =     ['stem'] + [F'stage{idx}' for idx in range(1			,len(self.depths      ) + 1      )]
												a__      ,					a__             =     get_aligned_output_features_output_indices(
												    out_features=__snake_case			,out_indices=__snake_case			,stage_names=self.stage_names      )








def 							__lowercase   ( __lowerCAmelCase							:				str					,    __lowerCAmelCase							:				str   ):

							def get_matched_characters(__lowerCAmelCase							:				str					,    __lowerCAmelCase							:				str   ) -> str:
														a__             =     []
														a__             =     min(len(_stra   )					,    len(_stra   )   ) // 2
														for i, l in enumerate(_stra   ):
																					a__             =     int(max(0					,    i - limit   )   )
																					a__             =     int(min(i + limit + 1					,    len(_stra   )   )   )
																					if l in _stra[left:right]:
																												matched.append(__lowerCAmelCase   )
																												a__             =     F'{_stra[0:_stra.index(__lowerCAmelCase   )]} {_stra[_stra.index(__lowerCAmelCase   ) + 1:]}'

														return "".join(__lowerCAmelCase   )

							# matching characters
							a__             =     get_matched_characters(__lowerCAmelCase					,    __lowerCAmelCase   )
							a__             =     get_matched_characters(__lowerCAmelCase					,    __lowerCAmelCase   )
							a__             =     len(__lowerCAmelCase   )

							# transposition
							a__             =     (
							    len([(ca, ca) for ca, ca in zip(__lowerCAmelCase					,    __lowerCAmelCase   ) if ca != ca]   ) // 2
							)

							if not match_count:
														a__             =     0.0
							else:
														a__             =     (
														    1
														    / 3
														    * (
														        match_count / len(__lowerCAmelCase   )
														        + match_count / len(__lowerCAmelCase   )
														        + (match_count - transpositions) / match_count
														    )
														)

							# common prefix up to 4 characters
							a__             =     0
							for ca, ca in zip(stra[:4]					,    stra[:4]   ):
														if ca == ca:
																					prefix_len += 1
														else:
																					break

							return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						print(jaro_winkler('''hello''', '''world'''))




