






def    a_       (					_A					,      _A     )	->	bool:







				"""simple docstring"""
				snake_case__													=   len(snake_case_     ) + 1
				snake_case__													=   len(snake_case_     ) + 1

				# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
				# length i of input_string matches with prefix string of length j of
				# given pattern.
				# "dp" stands for dynamic programming.
				snake_case__													=   [[0 for i in range(snake_case_     )] for j in range(snake_case_     )]

				# since string of zero length match pattern of zero length
				snake_case__													=   1

				# since pattern of zero length will never match with string of non-zero length
				for i in range(1					,      snake_case_     ):
								snake_case__													=   0

				# since string of zero length will match with pattern where there
				# is at least one * alternatively
				for j in range(1					,      snake_case_     ):
								snake_case__													=   dp[0][j - 2] if pattern[j - 1] == '*' else 0

				# now using bottom-up approach to find for all remaining lengths
				for i in range(1					,      snake_case_     ):
								for j in range(1					,      snake_case_     ):
												if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
																snake_case__													=   dp[i - 1][j - 1]

												elif pattern[j - 1] == "*":
																if dp[i][j - 2] == 1:
																				snake_case__													=   1
																elif pattern[j - 2] in (input_string[i - 1], "."):
																				snake_case__													=   dp[i - 1][j]
																else:
																				snake_case__													=   0
												else:
																snake_case__													=   0

				return bool(dp[-1][-1]     )


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							# inputing the strings
							# input_string = input("input a string :")
							# pattern = input("input a pattern :")

							__UpperCamelCase    :						Optional[int]		     =  """aab"""
							__UpperCamelCase    :						List[str]		     =  """c*a*b"""

							# using function to check whether given string matches the given pattern
							if match_pattern(input_string, pattern):
														print(f'''{input_string} matches the given pattern {pattern}''')
							else:
														print(f'''{input_string} does not match with the given pattern {pattern}''')











import warnings

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

class 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase  ):
      A_						:				List[str]							=    ['image_processor', 'tokenizer']
      A_						:				Optional[Any]							=    'CLIPImageProcessor'
      A_						:				Any							=    ('XLMRobertaTokenizer', 'XLMRobertaTokenizerFast')


      def __init__(self       :      int				,			a__       :      int=None				,			a__       :      Dict=None				,			**a__       :      List[str]      ):





         """simple docstring"""



         __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__       :      Dict=None				,			a__       :      List[str]=None				,			a__       :      Dict=None				,			**a__       :      Tuple      ):





         """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:
            __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    a (self       :      Union[str, Any]				,			*a__       :      int				,			**a__       :      List[Any]      ):





         """simple docstring"""



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


      def    a (self       :      Any				,			*a__       :      List[Any]				,			**a__       :      List[str]      ):





         """simple docstring"""



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


      @property
      def    a (self       :      int      ):





         """simple docstring"""



         __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      )      )

