
import warnings

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







class   lowerCamelCase	(						A_						):
     UpperCAmelCase__       :							Dict     =	["image_processor", "tokenizer"]
     UpperCAmelCase__       :							Tuple     =	"CLIPImageProcessor"
     UpperCAmelCase__       :							List[Any]     =	("XLMRobertaTokenizer", "XLMRobertaTokenizerFast")





     def __init__(self   :					Union[str, Any]       ,					_A   :					List[Any]=None       ,					_A   :					Optional[int]=None       ,					**_A   :					List[str]     )		->		str:
         snake_case				  =      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       ,					)
             snake_case				  =      kwargs.pop("feature_extractor"     )

         snake_case				  =      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   :					Any       ,					_A   :					int=None       ,					_A   :					Optional[int]=None       ,					_A   :					List[Any]=None       ,					**_A   :					List[Any]     )		->		Any:

         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:
             snake_case				  =      self.tokenizer(_A       ,					return_tensors=_A       ,					**_A     )

         if images is not None:
             snake_case				  =      self.image_processor(_A       ,					return_tensors=_A       ,					**_A     )

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





     def 	UpperCAmelCase(self   :					Dict       ,					*_A   :					str       ,					**_A   :					Dict     )		->		int:
         return self.tokenizer.batch_decode(*_A       ,					**_A     )





     def 	UpperCAmelCase(self   :					Optional[int]       ,					*_A   :					int       ,					**_A   :					List[str]     )		->		List[str]:
         return self.tokenizer.decode(*_A       ,					**_A     )





     @property
     def 	UpperCAmelCase(self   :					str     )		->		List[Any]:
         snake_case				  =      self.tokenizer.model_input_names
         snake_case				  =      self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )



def 		lowercase_    (     A__ = 1000	)							->							int:
    """simple docstring"""

    snake_case				  =      -1
    snake_case				  =      0
    for a in range(1				,	n // 3	):
        # Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
        snake_case				  =      (n * n - 2 * a * n) // (2 * n - 2 * a)
        snake_case				  =      n - a - b
        if c * c == (a * a + b * b):
            snake_case				  =      a * b * c
            if candidate >= product:
                snake_case				  =      candidate
    return product


if __name__ == "__main__":
   print(f"{solution() = }")

