






def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			str = "The quick brown fox jumps over the lazy dog"			,				)						->      bool:





    """simple docstring"""

    __A         =	set()

    # Replace all the whitespace in our sentence
    __A         =	input_str.replace(""" """			,				""""""    )
    for alpha in input_str:
        if "a" <= alpha.lower() <= "z":
            frequency.add(alpha.lower()    )
    return len(__lowercase    ) == 2_6



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			str = "The quick brown fox jumps over the lazy dog"			,				)						->      bool:





    """simple docstring"""

    __A         =	[False] * 2_6
    for char in input_str:
        if char.islower():
            __A         =	True
        elif char.isupper():
            __A         =	True
    return all(__lowercase    )



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			str = "The quick brown fox jumps over the lazy dog"			,				)						->      bool:





    """simple docstring"""

    return len({char for char in input_str.lower() if char.isalpha()}    ) == 2_6



def 			_SCREAMING_SNAKE_CASE						( )						->      None:





    """simple docstring"""

    from timeit import timeit

    __A         =	"""from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest"""
    print(timeit("""is_pangram()"""			,				setup=__lowercase    )    )
    print(timeit("""is_pangram_faster()"""			,				setup=__lowercase    )    )
    print(timeit("""is_pangram_fastest()"""			,				setup=__lowercase    )    )
    # 5.348480500048026, 2.6477354579837993, 1.8470395830227062
    # 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  benchmark()












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


class       __lowercase      (	lowercase_  ):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE   						=				["image_processor", "tokenizer"]
     SCREAMING_SNAKE_CASE   						=				"AutoImageProcessor"
     SCREAMING_SNAKE_CASE   						=				"AutoTokenizer"




     def __init__(				self       :			str ,	UpperCamelCase_       :			Any ,	UpperCamelCase_       :			str				):


         """simple docstring"""



         super().__init__(UpperCamelCase_ ,	UpperCamelCase_				)
         __A         =	self.image_processor




     def __call__(				self       :			Optional[Any] ,	UpperCamelCase_       :			Optional[Any]=None ,	UpperCamelCase_       :			Any=None ,	UpperCamelCase_       :			List[Any]=None ,	**UpperCamelCase_       :			int				):


         """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(UpperCamelCase_ ,	return_tensors=UpperCamelCase_ ,	**UpperCamelCase_				)

         if images is not None:
             __A         =	self.image_processor(UpperCamelCase_ ,	return_tensors=UpperCamelCase_ ,	**UpperCamelCase_				)

         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(**UpperCamelCase_				) ,	tensor_type=UpperCamelCase_				)




     def 		lowerCAmelCase_     (				self       :			List[str] ,	*UpperCamelCase_       :			Tuple ,	**UpperCamelCase_       :			List[str]				):


         """simple docstring"""



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




     def 		lowerCAmelCase_     (				self       :			Any ,	*UpperCamelCase_       :			int ,	**UpperCamelCase_       :			Tuple				):


         """simple docstring"""



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





     @property
     def 		lowerCAmelCase_     (				self       :			Optional[Any]				):


         """simple docstring"""



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




