




'''simple docstring'''


import warnings

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






class 						lowerCAmelCase_	(       UpperCamelCase_							):







  '''simple docstring'''

  UpperCamelCase_			:      Union[str, Any]										=				["""image_processor""", """tokenizer"""]
  UpperCamelCase_			:      Optional[Any]										=				"""VideoMAEImageProcessor"""
  UpperCamelCase_			:      Dict										=				("""CLIPTokenizer""", """CLIPTokenizerFast""")



  def __init__( self				:							Optional[Any]					,  SCREAMING_SNAKE_CASE_				:							Any=None					,  SCREAMING_SNAKE_CASE_				:							int=None					,  **SCREAMING_SNAKE_CASE_				:							str	)			->		Any:







       '''simple docstring'''



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

       A:		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	)
       A:		Tuple          =		self.image_processor



  def __call__( self				:							Optional[Any]					,  SCREAMING_SNAKE_CASE_				:							List[str]=None					,  SCREAMING_SNAKE_CASE_				:							Optional[Any]=None					,  SCREAMING_SNAKE_CASE_				:							Union[str, Any]=None					,  **SCREAMING_SNAKE_CASE_				:							Any	)			->		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:
            A:		Tuple          =		self.tokenizer(__a					,  return_tensors=__a					,  **__a	)

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

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



  def 					_snake_case     ( self				:							Tuple					,  *SCREAMING_SNAKE_CASE_				:							Any					,  **SCREAMING_SNAKE_CASE_				:							int	)			->		List[Any]:







       '''simple docstring'''



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



  def 					_snake_case     ( self				:							Optional[int]					,  *SCREAMING_SNAKE_CASE_				:							str					,  **SCREAMING_SNAKE_CASE_				:							Union[str, Any]	)			->		Optional[int]:







       '''simple docstring'''



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



  @property
  def 					_snake_case     ( self				:							int	)			->		int:







       '''simple docstring'''



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



  @property
  def 					_snake_case     ( self				:							List[str]	)			->		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 					_snake_case     ( self				:							Optional[Any]	)			->		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() = }''')