






'''simple docstring'''






import warnings

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




class a_     (     UpperCamelCase_				):
   _snake_case	   =				["""image_processor""", """tokenizer"""]
   _snake_case	   =				"""VideoMAEImageProcessor"""
   _snake_case	   =				("""CLIPTokenizer""", """CLIPTokenizerFast""")







   def __init__(self       , __a=None       , __a=None       , **__a)      ->   Any:
      """simple docstring"""






      __snake_case					:   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.'       , __a       , )
         __snake_case					:   Optional[int]  				=			kwargs.pop('feature_extractor')

      __snake_case					:   List[Any]  				=			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)
      __snake_case					:   Tuple  				=			self.image_processor







   def __call__(self       , __a=None       , __a=None       , __a=None       , **__a)      ->   Any:
      """simple docstring"""






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

      if text is not None:
         __snake_case					:   Tuple  				=			self.tokenizer(__a       , return_tensors=__a       , **__a)

      if videos is not None:
         __snake_case					:   int  				=			self.image_processor(__a       , return_tensors=__a       , **__a)

      if text is not None and videos is not None:
         __snake_case					:   int  				=			image_features.pixel_values
         return encoding
      elif text is not None:
         return encoding
      else:
         return BatchEncoding(data=dict(**__a)       , tensor_type=__a)







   def      SCREAMING_SNAKE_CASE__       (self       , *__a       , **__a)      ->   List[Any]:
      """simple docstring"""






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







   def      SCREAMING_SNAKE_CASE__       (self       , *__a       , **__a)      ->   Optional[int]:
      """simple docstring"""






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







   @property
   def      SCREAMING_SNAKE_CASE__       (self)      ->   int:
      """simple docstring"""






      return ["input_ids", "attention_mask", "position_ids", "pixel_values"]







   @property
   def      SCREAMING_SNAKE_CASE__       (self)      ->   Dict:
      """simple docstring"""






      warnings.warn(
          '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'       , __a       , )
      return self.image_processor_class







   @property
   def      SCREAMING_SNAKE_CASE__       (self)      ->   Union[str, Any]:
      """simple docstring"""






      warnings.warn(
          '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'       , __a       , )
      return self.image_processor







'''simple docstring'''






def 							_SCREAMING_SNAKE_CASE	(					)     ->			int:







   """simple docstring"""

   for a in range(3_00	):
      for b in range(a + 1	,		4_00	):
         for c in range(b + 1	,		5_00	):
            if (a + b + c) == 10_00 and (a**2) + (b**2) == (c**2):
               return a * b * c

   return -1




def 							_SCREAMING_SNAKE_CASE	(					)     ->			int:







   """simple docstring"""

   for a in range(3_00	):
      for b in range(4_00	):
         __snake_case					:   Optional[Any]  				=			10_00 - a - b
         if a < b < c and (a**2) + (b**2) == (c**2):
            return a * b * c

   return -1




def 							_SCREAMING_SNAKE_CASE	(					)     ->			None:







   """simple docstring"""
   import timeit

   print(
       timeit.timeit('solution()'	,		setup='from __main__ import solution'	,		number=10_00	)	)
   print(
       timeit.timeit(
           'solution_fast()'	,		setup='from __main__ import solution_fast'	,		number=10_00	)	)


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