



from typing import List

from tokenizers import NormalizedString, PreTokenizedString, normalizers

class    _SCREAMING_SNAKE_CASE						:





     def __init__(							self			:				str  ,	UpperCamelCase			:				Union[str, Any]					)->None:
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						vocab
      __SCREAMING_SNAKE_CASE							:     Dict							=						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."					)
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						rjieba





     def 						__snake_case     (							self			:				Dict  ,	UpperCamelCase			:				int  ,	UpperCamelCase			:				NormalizedString					)->List[NormalizedString]:
      __SCREAMING_SNAKE_CASE							:     List[str]							=						[]

      # 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:
        __SCREAMING_SNAKE_CASE							:     Dict							=						self.normalizers.normalize_str(UpperCamelCase					).split()
        for token in token_list:
         if token:
          __SCREAMING_SNAKE_CASE							:     str							=						start + len(UpperCamelCase					)
          splits.append(normalized_string[start:end]					)
          __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						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 						__snake_case     (							self			:				str  ,	UpperCamelCase			:				PreTokenizedString					)->Dict:
      pretok.split(self.jieba_split					)








"""simple docstring"""






import copy

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


UpperCamelCase_						   =   logging.get_logger(__name__)





class 							snake_case       (     SCREAMING_SNAKE_CASE_		):
   a_					:		Tuple     					=     """hybrid-clip"""
   a_					:		Optional[Any]     					=     True







   def __init__( self       ,							__UpperCAmelCase=5_12       ,							**__UpperCAmelCase)   ->Dict:
      super().__init__(**__UpperCAmelCase)

      if "text_config" not in kwargs:
         raise ValueError("`text_config` can not be `None`.")

      if "vision_config" not in kwargs:
         raise ValueError("`vision_config` can not be `None`.")

      a_						   =     kwargs.pop("text_config")
      a_						   =     kwargs.pop("vision_config")

      a_						   =     text_config.pop("model_type")
      a_						   =     vision_config.pop("model_type")

      from transformers import AutoConfig

      a_						   =     AutoConfig.for_model(__UpperCAmelCase       ,							**__UpperCAmelCase)

      if vision_model_type == "clip":
         a_						   =     AutoConfig.for_model(__UpperCAmelCase       ,							**__UpperCAmelCase).vision_config
      elif vision_model_type == "clip_vision_model":
         from transformers import CLIPVisionConfig

         a_						   =     CLIPVisionConfig(**__UpperCAmelCase)
      else:
         a_						   =     AutoConfig.for_model(__UpperCAmelCase       ,							**__UpperCAmelCase)

      a_						   =     projection_dim
      a_						   =     1.0







   @classmethod
   def    UpperCAmelCase__	( cls       ,							__UpperCAmelCase       ,							__UpperCAmelCase       ,							**__UpperCAmelCase)   ->Optional[int]:
      return cls(text_config=text_config.to_dict()       ,							vision_config=vision_config.to_dict()       ,							**__UpperCAmelCase)







   def    UpperCAmelCase__	( self)   ->str:
      a_						   =     copy.deepcopy(self.__dict__)
      a_						   =     self.text_config.to_dict()
      a_						   =     self.vision_config.to_dict()
      a_						   =     self.__class__.model_type
      return output