





import copy
import random

from transformers import CLIPTokenizer



class    A__  (			__snake_case     ):





      def __init__(					self			,				*A_			,				**A_  ):
       '''simple docstring'''




       super().__init__(*A_			,				**A_  )
       UpperCamelCase      :   Any			      =  {}





      def 			__UpperCamelCase(					self			,				A_			,				*A_			,				**A_  ):
       '''simple docstring'''




       UpperCamelCase      :   Dict			      =  super().add_tokens(A_			,				*A_			,				**A_  )
       if num_added_tokens == 0:
        raise ValueError(
            F"""The tokenizer already contains the token {placeholder_token}. Please pass a different"""
            " `placeholder_token` that is not already in the tokenizer."  )





      def 			__UpperCamelCase(					self			,				A_			,				*A_			,				A_=1			,				**A_  ):
       '''simple docstring'''




       UpperCamelCase      :   Optional[Any]			      =  []
       if num_vec_per_token == 1:
        self.try_adding_tokens(A_			,				*A_			,				**A_  )
        output.append(A_  )
       else:
        UpperCamelCase      :   Union[str, Any]			      =  []
        for i in range(A_  ):
         UpperCamelCase      :   Dict			      =  placeholder_token + F"""_{i}"""
         self.try_adding_tokens(A_			,				*A_			,				**A_  )
         output.append(A_  )
        # handle cases where there is a new placeholder token that contains the current placeholder token but is larger
       for token in self.token_map:
        if token in placeholder_token:
         raise ValueError(
             F"""The tokenizer already has placeholder token {token} that can get confused with"""
             F""" {placeholder_token}keep placeholder tokens independent"""  )
       UpperCamelCase      :   Dict			      =  output





      def 			__UpperCamelCase(					self			,				A_			,				A_=False			,				A_=1.0  ):
       '''simple docstring'''




       if isinstance(A_			,				A_  ):
        UpperCamelCase      :   Tuple			      =  []
        for i in range(len(A_  )  ):
         output.append(self.replace_placeholder_tokens_in_text(text[i]			,				vector_shuffle=A_  )  )
        return output
       for placeholder_token in self.token_map:
        if placeholder_token in text:
         UpperCamelCase      :   List[Any]			      =  self.token_map[placeholder_token]
         UpperCamelCase      :   List[Any]			      =  tokens[: 1 + int(len(A_  ) * prop_tokens_to_load  )]
         if vector_shuffle:
          UpperCamelCase      :   Union[str, Any]			      =  copy.copy(A_  )
          random.shuffle(A_  )
         UpperCamelCase      :   Any			      =  text.replace(A_			,				" ".join(A_  )  )
       return text





      def __call__(					self			,				A_			,				*A_			,				A_=False			,				A_=1.0			,				**A_  ):
       '''simple docstring'''




       return super().__call__(
           self.replace_placeholder_tokens_in_text(
               A_			,				vector_shuffle=A_			,				prop_tokens_to_load=A_  )			,				*A_			,				**A_			,				)





      def 			__UpperCamelCase(					self			,				A_			,				*A_			,				A_=False			,				A_=1.0			,				**A_  ):
       '''simple docstring'''




       return super().encode(
           self.replace_placeholder_tokens_in_text(
               A_			,				vector_shuffle=A_			,				prop_tokens_to_load=A_  )			,				*A_			,				**A_			,				)







import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


__lowerCamelCase			:	Optional[Any]					    =	logging.get_logger(__name__)



class    A__  (			__snake_case     ):





      def __init__(					self			,				*A_			,				**A_  ):
       '''simple docstring'''




       warnings.warn(
           "The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
           " use BeitImageProcessor instead."			,				A_			,				)
       super().__init__(*A_			,				**A_  )
