


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig


lowerCAmelCase__      		= {
    'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/config.json',
    'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/config.json',
    'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/config.json',
    'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json',
    'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/config.json',
    'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/config.json',
    'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/config.json',
    'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json',
}







class      lowerCAmelCase__       (				a):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE  			= "albert"


     def __init__(						self ,						__lowerCamelCase=3_0_0_0_0 ,						__lowerCamelCase=1_2_8 ,						__lowerCamelCase=4_0_9_6 ,						__lowerCamelCase=1_2 ,						__lowerCamelCase=1 ,						__lowerCamelCase=6_4 ,						__lowerCamelCase=1_6_3_8_4 ,						__lowerCamelCase=1 ,						__lowerCamelCase="gelu_new" ,						__lowerCamelCase=0 ,						__lowerCamelCase=0 ,						__lowerCamelCase=5_1_2 ,						__lowerCamelCase=2 ,						__lowerCamelCase=0.0_2 ,						__lowerCamelCase=1e-12 ,						__lowerCamelCase=0.1 ,						__lowerCamelCase="absolute" ,						__lowerCamelCase=0 ,						__lowerCamelCase=2 ,						__lowerCamelCase=3 ,						**__lowerCamelCase ,						)			->				Tuple:
            super().__init__(pad_token_id=__lowerCamelCase ,						bos_token_id=__lowerCamelCase ,						eos_token_id=__lowerCamelCase ,						**__lowerCamelCase)

            _A      :       Optional[Any]				    = vocab_size
            _A      :       int				    = embedding_size
            _A      :       int				    = hidden_size
            _A      :       List[Any]				    = num_hidden_layers
            _A      :       Dict				    = num_hidden_groups
            _A      :       Optional[int]				    = num_attention_heads
            _A      :       int				    = inner_group_num
            _A      :       List[Any]				    = hidden_act
            _A      :       List[Any]				    = intermediate_size
            _A      :       Tuple				    = hidden_dropout_prob
            _A      :       Tuple				    = attention_probs_dropout_prob
            _A      :       Optional[Any]				    = max_position_embeddings
            _A      :       List[Any]				    = type_vocab_size
            _A      :       List[str]				    = initializer_range
            _A      :       List[Any]				    = layer_norm_eps
            _A      :       str				    = classifier_dropout_prob
            _A      :       Optional[Any]				    = position_embedding_type







class      lowerCAmelCase__       (				a):




     '''simple docstring'''


     @property
     def  _lowerCamelCase (						self)			->				Mapping[str, Mapping[int, str]]:
            if self.task == "multiple-choice":
                   _A      :       int				    = {0: "batch", 1: "choice", 2: "sequence"}
            else:
                   _A      :       int				    = {0: "batch", 1: "sequence"}
            return OrderedDict(
                [
                    ("input_ids", dynamic_axis),
                    ("attention_mask", dynamic_axis),
                    ("token_type_ids", dynamic_axis),
                ])

def    lowerCAmelCase_ (     __A,  __A							)     ->				float:







      '''simple docstring'''


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

            return "".join(__A							)

      # matching characters
      UpperCAmelCase__								=  get_matched_characters(__A,  __A							)
      UpperCAmelCase__								=  get_matched_characters(__A,  __A							)
      UpperCAmelCase__								=  len(__A							)

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

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

      # common prefix up to 4 characters
      UpperCAmelCase__								=  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'))



