



from ...configuration_utils import PretrainedConfig
from ...utils import logging


A_		:  List[str]										=  logging.get_logger(__name__)

A_		:  int										=  {
    '''microsoft/mpnet-base''': '''https://huggingface.co/microsoft/mpnet-base/resolve/main/config.json''',
}



class 		_lowercase		(							UpperCAmelCase__						):
   _UpperCAmelCase  						=      '''mpnet'''

   def __init__(   self      :							List[Any]				, __lowerCAmelCase      :							Optional[Any]=3_0527				, __lowerCAmelCase      :							List[Any]=768				, __lowerCAmelCase      :							int=12				, __lowerCAmelCase      :							int=12				, __lowerCAmelCase      :							Dict=3072				, __lowerCAmelCase      :							Optional[int]="gelu"				, __lowerCAmelCase      :							Any=0.1				, __lowerCAmelCase      :							Dict=0.1				, __lowerCAmelCase      :							int=512				, __lowerCAmelCase      :							List[str]=0.0_2				, __lowerCAmelCase      :							Optional[Any]=1E-12				, __lowerCAmelCase      :							str=32				, __lowerCAmelCase      :							Union[str, Any]=1				, __lowerCAmelCase      :							Optional[int]=0				, __lowerCAmelCase      :							Tuple=2				, **__lowerCAmelCase      :							Union[str, Any]				, )							->					Dict:


    """simple docstring"""






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

    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							=		initializer_range
    a							=		layer_norm_eps
    a							=		relative_attention_num_buckets




"""simple docstring"""



from typing import List

from tokenizers import NormalizedString, PreTokenizedString, normalizers



class        a    :



    """simple docstring"""



    def __init__(  self:			List[Any]     ,    UpperCamelCase:			str							):






         """simple docstring"""


         A__				      =	vocab
         A__				      =	normalizers.BertNormalizer(
             clean_text=UpperCamelCase     ,    handle_chinese_chars=UpperCamelCase     ,    strip_accents=UpperCamelCase     ,    lowercase=UpperCamelCase     ,    )
         try:
              import rjieba
         except ImportError:
              raise ImportError(
                  """You need to install rjieba to use RoFormerTokenizer. """
                  """See https://pypi.org/project/rjieba/ for installation."""							)
         A__				      =	rjieba

    def 					UpperCamelCase						(  self:			Optional[Any]     ,    UpperCamelCase:			int     ,    UpperCamelCase:			NormalizedString							):






         """simple docstring"""


         A__				      =	[]

         # this code slice normalized_string is too slow (6s) but test_alignement_methods can pass
         for token, start, end in self.jieba.tokenize(str(UpperCamelCase							)     ,    hmm=UpperCamelCase							):
              if token in self.vocab:
                   splits.append(normalized_string[start:end]							)
              else:
                   A__				      =	self.normalizers.normalize_str(UpperCamelCase							).split()
                   for token in token_list:
                        if token:
                             A__				      =	start + len(UpperCamelCase							)
                             splits.append(normalized_string[start:end]							)
                             A__				      =	end

        # this code test_alignement_methods can't pass but fast (300ms)
        # for token in self.jieba.cut(str(normalized_string), False):
        #     if token in self.vocab:
        #         splits.append(NormalizedString(token))
        #     else:
        #         token_list = self.normalizers.normalize_str(token).split()
        #         for token in token_list:
        #             if token:
        #                 splits.append(NormalizedString(token))

         return splits





    def 					UpperCamelCase						(  self:			Tuple     ,    UpperCamelCase:			PreTokenizedString							):






         """simple docstring"""


         pretok.split(self.jieba_split							)






