


from collections import OrderedDict
from typing import Mapping

from packaging import version

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


lowerCAmelCase__			:      List[Any]								=logging.get_logger(__name__)

lowerCAmelCase__			:      List[Any]								={
    'microsoft/resnet-50': 'https://huggingface.co/microsoft/resnet-50/blob/main/config.json',
}







class 	__lowercase    (__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE  ):





							"""simple docstring"""







							_UpperCAmelCase      =						"""resnet"""
							_UpperCAmelCase      =						["""basic""", """bottleneck"""]






							def __init__(      self			,						lowerCAmelCase__=3			,						lowerCAmelCase__=6_4			,						lowerCAmelCase__=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]			,						lowerCAmelCase__=[3, 4, 6, 3]			,						lowerCAmelCase__="bottleneck"			,						lowerCAmelCase__="relu"			,						lowerCAmelCase__=False			,						lowerCAmelCase__=None			,						lowerCAmelCase__=None			,						**lowerCAmelCase__			,						):





										"""simple docstring"""




										super().__init__(**lowerCAmelCase__       )
										if layer_type not in self.layer_types:
													raise ValueError(F'''layer_type={layer_type} is not one of {','.join(self.layer_types       )}'''       )
										SCREAMING_SNAKE_CASE_		:     Any					=					num_channels
										SCREAMING_SNAKE_CASE_		:     str					=					embedding_size
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					hidden_sizes
										SCREAMING_SNAKE_CASE_		:     Any					=					depths
										SCREAMING_SNAKE_CASE_		:     int					=					layer_type
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					hidden_act
										SCREAMING_SNAKE_CASE_		:     Tuple					=					downsample_in_first_stage
										SCREAMING_SNAKE_CASE_		:     int					=					['stem'] + [F'''stage{idx}''' for idx in range(1			,						len(lowerCAmelCase__       ) + 1       )]
										SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     str					=					get_aligned_output_features_output_indices(
										    out_features=lowerCAmelCase__			,						out_indices=lowerCAmelCase__			,						stage_names=self.stage_names       )







class 	__lowercase    (__SCREAMING_SNAKE_CASE  ):





							"""simple docstring"""







							_UpperCAmelCase      =						version.parse("""1.11"""  )






							@property
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										return OrderedDict(
										    [
										        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
										    ]       )






							@property
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										return 1E-3









def 						a__			(				A__,      A__				):

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

						return "".join(A__				)

			# matching characters
			SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					get_matched_characters(A__,      A__				)
			SCREAMING_SNAKE_CASE_		:     int					=					get_matched_characters(A__,      A__				)
			SCREAMING_SNAKE_CASE_		:     Any					=					len(A__				)

			# transposition
			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					(
			    len([(ca, ca) for ca, ca in zip(A__,      A__				) if ca != ca]				) // 2
			)

			if not match_count:
						SCREAMING_SNAKE_CASE_		:     Dict					=					0.0
			else:
						SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					(
						    1
						    / 3
						    * (
						        match_count / len(A__				)
						        + match_count / len(A__				)
						        + (match_count - transpositions) / match_count
						    )
						)

			# common prefix up to 4 characters
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					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'))





