
import copy
import random

from transformers import CLIPTokenizer


class 						__lowerCAmelCase	(	lowerCAmelCase_      ):



      """simple docstring"""







      def __init__(      self		:       Any				,       *_snake_case		:       Optional[Any]				,       **_snake_case		:       Union[str, Any]				):
          super().__init__(*_snake_case				,       **_snake_case				)
          __lowercase		:			Dict	    =       {}




      def 		snake_case_			(      self		:       str				,       _snake_case		:       Optional[Any]				,       *_snake_case		:       Tuple				,       **_snake_case		:       List[str]				):
          __lowercase		:			List[str]	    =       super().add_tokens(_snake_case				,       *_snake_case				,       **_snake_case				)
          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 		snake_case_			(      self		:       int				,       _snake_case		:       Dict				,       *_snake_case		:       Tuple				,       _snake_case		:       Optional[Any]=1				,       **_snake_case		:       Optional[int]				):
          __lowercase		:			List[Any]	    =       []
          if num_vec_per_token == 1:
              self.try_adding_tokens(_snake_case				,       *_snake_case				,       **_snake_case				)
              output.append(_snake_case				)
          else:
              __lowercase		:			Union[str, Any]	    =       []
              for i in range(_snake_case				):
                  __lowercase		:			List[str]	    =       placeholder_token + F'_{i}'
                  self.try_adding_tokens(_snake_case				,       *_snake_case				,       **_snake_case				)
                  output.append(_snake_case				)
        # 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'				)
          __lowercase		:			str	    =       output




      def 		snake_case_			(      self		:       Optional[int]				,       _snake_case		:       Tuple				,       _snake_case		:       List[Any]=False				,       _snake_case		:       Optional[Any]=1.0				):
          if isinstance(_snake_case				,       _snake_case				):
              __lowercase		:			int	    =       []
              for i in range(len(_snake_case				)				):
                  output.append(self.replace_placeholder_tokens_in_text(text[i]				,       vector_shuffle=_snake_case				)				)
              return output
          for placeholder_token in self.token_map:
              if placeholder_token in text:
                  __lowercase		:			int	    =       self.token_map[placeholder_token]
                  __lowercase		:			int	    =       tokens[: 1 + int(len(_snake_case				) * prop_tokens_to_load				)]
                  if vector_shuffle:
                      __lowercase		:			Dict	    =       copy.copy(_snake_case				)
                      random.shuffle(_snake_case				)
                  __lowercase		:			int	    =       text.replace(_snake_case				,       ''' '''.join(_snake_case				)				)
          return text




      def __call__(      self		:       str				,       _snake_case		:       Union[str, Any]				,       *_snake_case		:       int				,       _snake_case		:       str=False				,       _snake_case		:       str=1.0				,       **_snake_case		:       str				):
          return super().__call__(
              self.replace_placeholder_tokens_in_text(
                  _snake_case				,       vector_shuffle=_snake_case				,       prop_tokens_to_load=_snake_case				)				,       *_snake_case				,       **_snake_case				,       )




      def 		snake_case_			(      self		:       Any				,       _snake_case		:       Dict				,       *_snake_case		:       str				,       _snake_case		:       Dict=False				,       _snake_case		:       Optional[int]=1.0				,       **_snake_case		:       Dict				):
          return super().encode(
              self.replace_placeholder_tokens_in_text(
                  _snake_case				,       vector_shuffle=_snake_case				,       prop_tokens_to_load=_snake_case				)				,       *_snake_case				,       **_snake_case				,       )




__lowerCAmelCase					:			Dict			      =		"\n# Installazione di Transformers\n! pip install transformers datasets\n# Per installare dalla fonte invece dell'ultima versione rilasciata, commenta il comando sopra e\n# rimuovi la modalità commento al comando seguente.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

__lowerCAmelCase					:			Tuple			      =		[{"type": "code", "content": INSTALL_CONTENT}]
__lowerCAmelCase					:			List[Any]			      =		{
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}


