




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowerCamelCase			:	str							 =     logging.get_logger(__name__)

_lowerCamelCase			:	Optional[int]							 =     {
    """facebook/data2vec-text-base""": """https://huggingface.co/data2vec/resolve/main/config.json""",
}


class        UpperCamelCase_					(				__lowercase  ):

    '''simple docstring'''






    UpperCAmelCase__													=   'data2vec-text'


    def __init__(    self   :			Dict      ,				UpperCAmelCase__   :			Optional[int]=30_522      ,				UpperCAmelCase__   :			Tuple=768      ,				UpperCAmelCase__   :			Optional[int]=12      ,				UpperCAmelCase__   :			Any=12      ,				UpperCAmelCase__   :			Tuple=3_072      ,				UpperCAmelCase__   :			str="gelu"      ,				UpperCAmelCase__   :			str=0.1      ,				UpperCAmelCase__   :			Optional[int]=0.1      ,				UpperCAmelCase__   :			Optional[int]=512      ,				UpperCAmelCase__   :			Dict=2      ,				UpperCAmelCase__   :			Tuple=0.02      ,				UpperCAmelCase__   :			List[str]=1e-12      ,				UpperCAmelCase__   :			Any=1      ,				UpperCAmelCase__   :			str=0      ,				UpperCAmelCase__   :			str=2      ,				UpperCAmelCase__   :			int="absolute"      ,				UpperCAmelCase__   :			Tuple=True      ,				UpperCAmelCase__   :			Union[str, Any]=None      ,				**UpperCAmelCase__   :			Tuple      ,				)		->List[Any]:





           '''simple docstring'''

           super().__init__(pad_token_id=__A      ,				bos_token_id=__A      ,				eos_token_id=__A      ,				**__A)

           A__   				=					vocab_size
           A__   				=					hidden_size
           A__   				=					num_hidden_layers
           A__   				=					num_attention_heads
           A__   				=					hidden_act
           A__   				=					intermediate_size
           A__   				=					hidden_dropout_prob
           A__   				=					attention_probs_dropout_prob
           A__   				=					max_position_embeddings
           A__   				=					type_vocab_size
           A__   				=					initializer_range
           A__   				=					layer_norm_eps
           A__   				=					position_embedding_type
           A__   				=					use_cache
           A__   				=					classifier_dropout


class        UpperCamelCase_					(				__lowercase  ):

    '''simple docstring'''






    @property
    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[int])		->Mapping[str, Mapping[int, str]]:





           '''simple docstring'''

           if self.task == "multiple-choice":
                  A__   				=					{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
           else:
                  A__   				=					{0: '''batch''', 1: '''sequence'''}
           return OrderedDict(
               [
                   ('''input_ids''', dynamic_axis),
                   ('''attention_mask''', dynamic_axis),
               ])








def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_		)     -> float:



       """simple docstring"""

       def get_matched_characters(lowercase_				,       lowercase_		) -> 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(lowercase_		)
                            A__   				=					f"""{_stra[0:_stra.index(lowercase_		)]} {_stra[_stra.index(lowercase_		) + 1:]}"""

              return "".join(lowercase_		)

       # matching characters
       A__   				=					get_matched_characters(lowercase_				,       lowercase_		)
       A__   				=					get_matched_characters(lowercase_				,       lowercase_		)
       A__   				=					len(lowercase_		)

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

       if not match_count:
              A__   				=					0.0
       else:
              A__   				=					(
                  1
                  / 3
                  * (
                      match_count / len(lowercase_		)
                      + match_count / len(lowercase_		)
                      + (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"""))


