


"""simple docstring"""

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


UpperCAmelCase__  							=logging.get_logger(__name__)

UpperCAmelCase__  							={
    "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    lowerCamelCase__						(		_a			,   _a    ):
      a :     Optional[Any]											=		"""dinat"""

      a :     Dict											=		{
          """num_attention_heads""": """num_heads""",
          """num_hidden_layers""": """num_layers""",
      }
      def __init__(		self	:	Tuple      ,       A_	:	Optional[Any]=4      ,       A_	:	int=3      ,       A_	:	Tuple=6_4      ,       A_	:	Dict=[3, 4, 6, 5]      ,       A_	:	List[str]=[2, 4, 8, 1_6]      ,       A_	:	Any=7      ,       A_	:	List[str]=[[1, 8, 1], [1, 4, 1, 4], [1, 2, 1, 2, 1, 2], [1, 1, 1, 1, 1]]      ,       A_	:	Tuple=3.0      ,       A_	:	Any=True      ,       A_	:	List[str]=0.0      ,       A_	:	Any=0.0      ,       A_	:	List[str]=0.1      ,       A_	:	str="gelu"      ,       A_	:	Optional[Any]=0.02      ,       A_	:	Any=1e-5      ,       A_	:	List[Any]=0.0      ,       A_	:	int=None      ,       A_	:	Optional[int]=None      ,       **A_	:	str      ,       ):






       '''simple docstring'''


       super().__init__(**A_							)

       __lowercase									=	patch_size
       __lowercase									=	num_channels
       __lowercase									=	embed_dim
       __lowercase									=	depths
       __lowercase									=	len(A_							)
       __lowercase									=	num_heads
       __lowercase									=	kernel_size
       __lowercase									=	dilations
       __lowercase									=	mlp_ratio
       __lowercase									=	qkv_bias
       __lowercase									=	hidden_dropout_prob
       __lowercase									=	attention_probs_dropout_prob
       __lowercase									=	drop_path_rate
       __lowercase									=	hidden_act
       __lowercase									=	layer_norm_eps
       __lowercase									=	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
       __lowercase									=	int(embed_dim * 2 ** (len(A_							) - 1)							)
       __lowercase									=	layer_scale_init_value
       __lowercase									=	["""stem"""] + [F'''stage{idx}''' for idx in range(1      ,       len(A_							) + 1							)]
       __lowercase							,		__lowercase									=	get_aligned_output_features_output_indices(
           out_features=A_      ,       out_indices=A_      ,       stage_names=self.stage_names							)




"""simple docstring"""

def lowerCAmelCase_      (				UpperCamelCase__	: str				,   UpperCamelCase__	: str ):






 """simple docstring"""







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

  return "".join(UpperCamelCase__ )

 # matching characters
 __lowercase									=	get_matched_characters(UpperCamelCase__				,   UpperCamelCase__ )
 __lowercase									=	get_matched_characters(UpperCamelCase__				,   UpperCamelCase__ )
 __lowercase									=	len(UpperCamelCase__ )

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

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

 # common prefix up to 4 characters
 __lowercase									=	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"))
