




def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			assert x is not None
			assert y is not None

			SCREAMING_SNAKE_CASE           =					len(SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE           =					len(SCREAMING_SNAKE_CASE   )

			# declaring the array for storing the dp values
			SCREAMING_SNAKE_CASE           =					[[0] * (n + 1) for _ in range(m + 1   )]  # noqa: E741

			for i in range(1					,	m + 1   ):
						for j in range(1					,	n + 1   ):
									SCREAMING_SNAKE_CASE           =					1 if x[i - 1] == y[j - 1] else 0

									SCREAMING_SNAKE_CASE           =					max(l[i - 1][j]					,	l[i][j - 1]					,	l[i - 1][j - 1] + match   )

			SCREAMING_SNAKE_CASE           =					""""""
			SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					m, n
			while i > 0 and j > 0:
						SCREAMING_SNAKE_CASE           =					1 if x[i - 1] == y[j - 1] else 0

						if l[i][j] == l[i - 1][j - 1] + match:
									if match == 1:
												SCREAMING_SNAKE_CASE           =					x[i - 1] + seq
									i -= 1
									j -= 1
						elif l[i][j] == l[i - 1][j]:
									i -= 1
						else:
									j -= 1

			return l[m][n], seq


if __name__ == "__main__":
		__A						:							Dict							=  """AGGTAB"""
		__A						:							Optional[int]							=  """GXTXAYB"""
		__A						:							Union[str, Any]							=  4
		__A						:							Any							=  """GTAB"""

		__A	,     __A						:							Optional[Any]							=  longest_common_subsequence(a, b)
		print("""len =""", ln, """, sub-sequence =""", subseq)
		import doctest

		doctest.testmod()












import warnings

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




class 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							a__						  =     ['''image_processor''', '''tokenizer''']
							a__						  =     '''ViTImageProcessor'''
							a__						  =     ('''CLIPTokenizer''', '''CLIPTokenizerFast''')





							def __init__(					self       :	List[str]       ,     a       :	Dict=None       ,     a       :	List[str]=None       ,     **a       :	List[str]			)			->					Optional[Any]:
										SCREAMING_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       ,     )
													SCREAMING_SNAKE_CASE           =					kwargs.pop("""feature_extractor"""			)

										SCREAMING_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       :	List[Any]       ,     a       :	int=None       ,     a       :	Dict=None       ,     a       :	int=None       ,     a       :	Tuple=None       ,     **a       :	Any			)			->					Any:
										if text is None and visual_prompt is None and images is None:
													raise ValueError("""You have to specify either text, visual prompt or images."""			)

										if text is not None and visual_prompt is not None:
													raise ValueError("""You have to specify exactly one type of prompt. Either text or visual prompt."""			)

										if text is not None:
													SCREAMING_SNAKE_CASE           =					self.tokenizer(a       ,     return_tensors=a       ,     **a			)

										if visual_prompt is not None:
													SCREAMING_SNAKE_CASE           =					self.image_processor(a       ,     return_tensors=a       ,     **a			)

										if images is not None:
													SCREAMING_SNAKE_CASE           =					self.image_processor(a       ,     return_tensors=a       ,     **a			)

										if visual_prompt is not None and images is not None:
													SCREAMING_SNAKE_CASE           =					{
													    """pixel_values""": image_features.pixel_values,
													    """conditional_pixel_values""": prompt_features.pixel_values,
													}
													return encoding
										elif text is not None and images is not None:
													SCREAMING_SNAKE_CASE           =					image_features.pixel_values
													return encoding
										elif text is not None:
													return encoding
										elif visual_prompt is not None:
													SCREAMING_SNAKE_CASE           =					{
													    """conditional_pixel_values""": prompt_features.pixel_values,
													}
													return encoding
										else:
													return BatchEncoding(data=dict(**a			)       ,     tensor_type=a			)





							def    _UpperCAmelCase     (					self       :	Dict       ,     *a       :	Dict       ,     **a       :	Union[str, Any]			)			->					Any:
										return self.tokenizer.batch_decode(*a       ,     **a			)





							def    _UpperCAmelCase     (					self       :	Tuple       ,     *a       :	List[Any]       ,     **a       :	Union[str, Any]			)			->					Dict:
										return self.tokenizer.decode(*a       ,     **a			)





							@property
							def    _UpperCAmelCase     (					self       :	Any			)			->					Optional[Any]:
										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    _UpperCAmelCase     (					self       :	Optional[int]			)			->					str:
										warnings.warn(
										    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""       ,     a       ,     )
										return self.image_processor






