

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   str     ,					SCREAMING_SNAKE_CASE_		:   str      ):



					'''simple docstring'''





					lowercase__   :       List[Any]				=	len(SCREAMING_SNAKE_CASE_      ) + 1
					lowercase__   :       List[str]				=	len(SCREAMING_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.
					lowercase__   :       Tuple				=	[[0 for i in range(SCREAMING_SNAKE_CASE_      )] for j in range(SCREAMING_SNAKE_CASE_      )]

					# since string of zero length match pattern of zero length
					lowercase__   :       Tuple				=	1

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

					# since string of zero length will match with pattern where there
					# is at least one * alternatively
					for j in range(1     ,					SCREAMING_SNAKE_CASE_      ):
										lowercase__   :       Any				=	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     ,					SCREAMING_SNAKE_CASE_      ):
										for j in range(1     ,					SCREAMING_SNAKE_CASE_      ):
															if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
																				lowercase__   :       int				=	dp[i - 1][j - 1]

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

							snake_case_        =			'''aab'''
							snake_case_        =			'''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 typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType


class 				SCREAMING_SNAKE_CASE__ (__snake_case					):
    __lowerCamelCase						:	Tuple														=					["""image_processor""", """tokenizer"""]
    __lowerCamelCase						:	int														=					"""ViltImageProcessor"""
    __lowerCamelCase						:	Union[str, Any]														=					("""BertTokenizer""", """BertTokenizerFast""")





    def __init__(				self						,       a=None						,       a=None						,       **a):
         lowercase__   :       Any				=	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						,       )
              lowercase__   :       Union[str, Any]				=	kwargs.pop('feature_extractor')

         lowercase__   :       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)
         lowercase__   :       str				=	self.image_processor





    def __call__(				self						,       a						,       a = None						,       a = True						,       a = False						,       a = None						,       a = None						,       a = 0						,       a = None						,       a = None						,       a = None						,       a = False						,       a = False						,       a = False						,       a = False						,       a = True						,       a = None						,       **a						,       ):
         lowercase__   :       Optional[Any]				=	self.tokenizer(
             text=a						,       add_special_tokens=a						,       padding=a						,       truncation=a						,       max_length=a						,       stride=a						,       pad_to_multiple_of=a						,       return_token_type_ids=a						,       return_attention_mask=a						,       return_overflowing_tokens=a						,       return_special_tokens_mask=a						,       return_offsets_mapping=a						,       return_length=a						,       verbose=a						,       return_tensors=a						,       **a						,       )
         # add pixel_values + pixel_mask
         lowercase__   :       str				=	self.image_processor(a						,       return_tensors=a)
         encoding.update(a)

         return encoding





    def 				snake_case_    (				self						,       *a						,       **a):
         return self.tokenizer.batch_decode(*a						,       **a)





    def 				snake_case_    (				self						,       *a						,       **a):
         return self.tokenizer.decode(*a						,       **a)





    @property
    def 				snake_case_    (				self):
         lowercase__   :       int				=	self.tokenizer.model_input_names
         lowercase__   :       List[str]				=	self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))





    @property
    def 				snake_case_    (				self):
         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):
         warnings.warn(
             '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'						,       a						,       )
         return self.image_processor

