





'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

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


lowercase__   :				List[Any]      =   {
    '''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 			SCREAMING_SNAKE_CASE   (__lowercase							):
     lowerCAmelCase		      =	'''albert'''


     def __init__(						self						,     _UpperCAmelCase=3_0000						,     _UpperCAmelCase=128						,     _UpperCAmelCase=4096						,     _UpperCAmelCase=12						,     _UpperCAmelCase=1						,     _UpperCAmelCase=64						,     _UpperCAmelCase=1_6384						,     _UpperCAmelCase=1						,     _UpperCAmelCase="gelu_new"						,     _UpperCAmelCase=0						,     _UpperCAmelCase=0						,     _UpperCAmelCase=512						,     _UpperCAmelCase=2						,     _UpperCAmelCase=0.02						,     _UpperCAmelCase=1e-1_2						,     _UpperCAmelCase=0.1						,     _UpperCAmelCase="absolute"						,     _UpperCAmelCase=0						,     _UpperCAmelCase=2						,     _UpperCAmelCase=3						,     **_UpperCAmelCase						,     ):

          '''simple docstring'''






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

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




class 			SCREAMING_SNAKE_CASE   (__lowercase							):


     @property
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          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),
              ])






'''simple docstring'''




def 	_lowerCAmelCase      (  __snake_case  :			str			,							__snake_case  :			str      )					->					float:

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

          return "".join(__snake_case      )

     # matching characters
     __A						:			Any								=	get_matched_characters(__snake_case			,							__snake_case      )
     __A						:			str								=	get_matched_characters(__snake_case			,							__snake_case      )
     __A						:			str								=	len(__snake_case      )

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

     if not match_count:
          __A						:			int								=	0.0
     else:
          __A						:			Union[str, Any]								=	(
              1
              / 3
              * (
                  match_count / len(__snake_case      )
                  + match_count / len(__snake_case      )
                  + (match_count - transpositions) / match_count
              )
          )

     # common prefix up to 4 characters
     __A						:			Tuple								=	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'''))