






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


_UpperCamelCase			:	int								=logging.get_logger(__name__)

_UpperCamelCase			:	Optional[int]								={
    'facebook/levit-128S': 'https://huggingface.co/facebook/levit-128S/resolve/main/config.json',
    # See all LeViT models at https://huggingface.co/models?filter=levit
}






class 						UpperCAmelCase__ (							__snake_case							):
		__snake_case      :       Tuple     =   "levit"







		def __init__(					self       ,A__=224       ,A__=3       ,A__=3       ,A__=2       ,A__=1       ,A__=16       ,A__=[128, 256, 384]       ,A__=[4, 8, 12]       ,A__=[4, 4, 4]       ,A__=[16, 16, 16]       ,A__=0       ,A__=[2, 2, 2]       ,A__=[2, 2, 2]       ,A__=0.02       ,**A__       ,):
						super().__init__(**A__		)
						_A	:				Union[str, Any]   =      image_size
						_A	:				List[str]   =      num_channels
						_A	:				Optional[int]   =      kernel_size
						_A	:				Tuple   =      stride
						_A	:				List[str]   =      padding
						_A	:				int   =      hidden_sizes
						_A	:				Any   =      num_attention_heads
						_A	:				Any   =      depths
						_A	:				str   =      key_dim
						_A	:				Dict   =      drop_path_rate
						_A	:				List[Any]   =      patch_size
						_A	:				Dict   =      attention_ratio
						_A	:				int   =      mlp_ratio
						_A	:				Union[str, Any]   =      initializer_range
						_A	:				Tuple   =      [
						    ['''Subsample''', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
						    ['''Subsample''', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
						]






class 						UpperCAmelCase__ (							__snake_case							):
		__snake_case      :       List[Any]     =   version.parse("1.11"							)







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







		@property
		def  A__      (					self		):
						return 1E-4











def  a__ (__lowercase							:str					,  __lowercase							:str   )		-> float:

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

								return "".join(__lowercase   )

				# matching characters
				_A	:				str   =      get_matched_characters(__lowercase					,  __lowercase   )
				_A	:				Optional[Any]   =      get_matched_characters(__lowercase					,  __lowercase   )
				_A	:				Any   =      len(__lowercase   )

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

				if not match_count:
								_A	:				Optional[int]   =      0.0
				else:
								_A	:				str   =      (
								    1
								    / 3
								    * (
								        match_count / len(__lowercase   )
								        + match_count / len(__lowercase   )
								        + (match_count - transpositions) / match_count
								    )
								)

				# common prefix up to 4 characters
				_A	:				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'))



