"""simple docstring"""



import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class      UpperCAmelCase_  (						__snake_case							):
    lowercase__						 =				['''image_processor''', '''tokenizer''']
    lowercase__						 =				'''CLIPImageProcessor'''
    lowercase__						 =				('''XLMRobertaTokenizer''', '''XLMRobertaTokenizerFast''')




    def __init__(   self						:	List[str]     ,			snake_case_						:	Tuple=None     ,			snake_case_						:	Tuple=None     ,			**snake_case_						:	Union[str, Any]				)				->							Any:




     '''simple docstring'''

     A__	=    None
     if "feature_extractor" in kwargs:
      warnings.warn(
          "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
          " instead."     ,			a_     ,			)
      A__	=    kwargs.pop("feature_extractor"				)

     A__	=    image_processor if image_processor is not None else feature_extractor
     if image_processor is None:
      raise ValueError("You need to specify an `image_processor`."				)
     if tokenizer is None:
      raise ValueError("You need to specify a `tokenizer`."				)

     super().__init__(a_     ,			a_				)




    def __call__(   self						:	Optional[Any]     ,			snake_case_						:	Union[str, Any]=None     ,			snake_case_						:	Optional[Any]=None     ,			snake_case_						:	Any=None     ,			**snake_case_						:	Optional[Any]				)				->							List[str]:




     '''simple docstring'''

     if text is None and images is None:
      raise ValueError("You have to specify either text or images. Both cannot be none."				)

     if text is not None:
      A__	=    self.tokenizer(a_     ,			return_tensors=a_     ,			**a_				)

     if images is not None:
      A__	=    self.image_processor(a_     ,			return_tensors=a_     ,			**a_				)

     if text is not None and images is not None:
      A__	=    image_features.pixel_values
      return encoding
     elif text is not None:
      return encoding
     else:
      return BatchEncoding(data=dict(**a_				)     ,			tensor_type=a_				)




    def 							__magic_name__							(   self						:	Optional[Any]     ,			*snake_case_						:	Optional[int]     ,			**snake_case_						:	Optional[Any]				)				->							Optional[Any]:




     '''simple docstring'''

     return self.tokenizer.batch_decode(*a_     ,			**a_				)




    def 							__magic_name__							(   self						:	Dict     ,			*snake_case_						:	str     ,			**snake_case_						:	int				)				->							Union[str, Any]:




     '''simple docstring'''

     return self.tokenizer.decode(*a_     ,			**a_				)




    @property
    def 							__magic_name__							(   self						:	List[str]				)				->							Tuple:




     '''simple docstring'''

     A__	=    self.tokenizer.model_input_names
     A__	=    self.image_processor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)











"""simple docstring"""



from __future__ import annotations

SCREAMING_SNAKE_CASE    =	"#"




class      UpperCAmelCase_  :




    def __init__(   self						:	Dict				)				->							None:




     '''simple docstring'''

     A__	=    {}




    def 							__magic_name__							(   self						:	Optional[Any]     ,			snake_case_						:	str				)				->							None:




     '''simple docstring'''

     A__	=    self._trie
     for char in text:
      if char not in trie:
       A__	=    {}
      A__	=    trie[char]
     A__	=    True




    def 							__magic_name__							(   self						:	List[Any]     ,			snake_case_						:	str				)				->							tuple | list:




     '''simple docstring'''

     A__	=    self._trie
     for char in prefix:
      if char in trie:
       A__	=    trie[char]
      else:
       return []
     return self._elements(snake_case_				)




    def 							__magic_name__							(   self						:	Union[str, Any]     ,			snake_case_						:	dict				)				->							tuple:




     '''simple docstring'''

     A__	=    []
     for c, v in d.items():
      A__	=    [" "] if c == END else [(c + s) for s in self._elements(snake_case_				)]
      result.extend(snake_case_				)
     return tuple(snake_case_				)






SCREAMING_SNAKE_CASE    =	Trie()
SCREAMING_SNAKE_CASE    =	("depart", "detergent", "daring", "dog", "deer", "deal")
for word in words:
      trie.insert_word(word)







def   _SCREAMING_SNAKE_CASE       (							lowercase_						)						->		tuple:
 A__	=    trie.find_word(lowercase_						)
 return tuple(string + word for word in suffixes						)







def   _SCREAMING_SNAKE_CASE       (							)						->		None:
 print(autocomplete_using_trie("de"						)						)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      main()





