



import warnings

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



class     lowerCAmelCase_		(  a__							):
       UpperCAmelCase__    : Optional[Any]       					=  ["image_processor", "tokenizer"]
       UpperCAmelCase__    : int       					=  "CLIPImageProcessor"
       UpperCAmelCase__    : str       					=  ("XLMRobertaTokenizer", "XLMRobertaTokenizerFast")
       def __init__(      self,      SCREAMING_SNAKE_CASE_=None,      SCREAMING_SNAKE_CASE_=None,      **SCREAMING_SNAKE_CASE_	)					->		List[str]:
          UpperCamelCase   :  Optional[int]						     =     None
          if "feature_extractor" in kwargs:
             warnings.warn(
                 'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                 ' instead.',      SCREAMING_SNAKE_CASE_,      )
             UpperCamelCase   :  Optional[Any]						     =     kwargs.pop('feature_extractor'	)

          UpperCamelCase   :  str						     =     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__(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)
       def __call__(      self,      SCREAMING_SNAKE_CASE_=None,      SCREAMING_SNAKE_CASE_=None,      SCREAMING_SNAKE_CASE_=None,      **SCREAMING_SNAKE_CASE_	)					->		Tuple:

          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:
             UpperCamelCase   :  Union[str, Any]						     =     self.tokenizer(SCREAMING_SNAKE_CASE_,      return_tensors=SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_	)

          if images is not None:
             UpperCamelCase   :  List[Any]						     =     self.image_processor(SCREAMING_SNAKE_CASE_,      return_tensors=SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_	)

          if text is not None and images is not None:
             UpperCamelCase   :  int						     =     image_features.pixel_values
             return encoding
          elif text is not None:
             return encoding
          else:
             return BatchEncoding(data=dict(**SCREAMING_SNAKE_CASE_	),      tensor_type=SCREAMING_SNAKE_CASE_	)
       def  snake_case_	(      self,      *SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_	)					->		Union[str, Any]:
          return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_	)
       def  snake_case_	(      self,      *SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_	)					->		Optional[Any]:
          return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_	)






       @property
       def  snake_case_	(      self	)					->		int:
          UpperCamelCase   :  Optional[Any]						     =     self.tokenizer.model_input_names
          UpperCamelCase   :  int						     =     self.image_processor.model_input_names
          return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names	)	)








from graphs.minimum_spanning_tree_kruskal import kruskal
def 		UpperCamelCase     (       )     ->					Tuple:
   UpperCamelCase   :  List[str]						     =     9
   UpperCamelCase   :  Optional[Any]						     =     [
       [0, 1, 4],
       [0, 7, 8],
       [1, 2, 8],
       [7, 8, 7],
       [7, 6, 1],
       [2, 8, 2],
       [8, 6, 6],
       [2, 3, 7],
       [2, 5, 4],
       [6, 5, 2],
       [3, 5, 14],
       [3, 4, 9],
       [5, 4, 10],
       [1, 7, 11],
   ]

   UpperCamelCase   :  int						     =     kruskal(snake_case__	,					snake_case__     )

   UpperCamelCase   :  List[str]						     =     [
       [7, 6, 1],
       [2, 8, 2],
       [6, 5, 2],
       [0, 1, 4],
       [2, 5, 4],
       [2, 3, 7],
       [0, 7, 8],
       [3, 4, 9],
   ]

   assert sorted(snake_case__     ) == sorted(snake_case__     )



