


import warnings
from typing import List, Optional, Union

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






class _lowerCamelCase(						_a   ):
       lowercase_ :	Dict     =			["""image_processor""", """tokenizer"""]
       lowercase_ :	Optional[Any]     =			"""FlavaImageProcessor"""
       lowercase_ :	List[str]     =			("""BertTokenizer""", """BertTokenizerFast""")
       def __init__(					self,      lowerCamelCase=None,      lowerCamelCase=None,      **lowerCamelCase)       ->   List[str]:




           """simple docstring"""

           _lowercase       :							Dict      		=		None
           if "feature_extractor" in kwargs:
               warnings.warn(
                   'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                   ' instead.',      lowerCamelCase,      )
               _lowercase       :							List[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__(lowerCamelCase,      lowerCamelCase)
           _lowercase       :							int      		=		self.image_processor
       def __call__(					self,      lowerCamelCase = None,      lowerCamelCase = None,      lowerCamelCase = True,      lowerCamelCase = False,      lowerCamelCase = False,      lowerCamelCase = None,      lowerCamelCase = 0,      lowerCamelCase = None,      lowerCamelCase = None,      lowerCamelCase = None,      lowerCamelCase = None,      lowerCamelCase = None,      lowerCamelCase = False,      lowerCamelCase = False,      lowerCamelCase = False,      lowerCamelCase = False,      lowerCamelCase = True,      lowerCamelCase = None,      **lowerCamelCase,      )       ->   Dict:




           """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:
               _lowercase       :							Optional[int]      		=		self.tokenizer(
                   text=lowerCamelCase,      add_special_tokens=lowerCamelCase,      padding=lowerCamelCase,      truncation=lowerCamelCase,      max_length=lowerCamelCase,      stride=lowerCamelCase,      pad_to_multiple_of=lowerCamelCase,      return_token_type_ids=lowerCamelCase,      return_attention_mask=lowerCamelCase,      return_overflowing_tokens=lowerCamelCase,      return_special_tokens_mask=lowerCamelCase,      return_offsets_mapping=lowerCamelCase,      return_length=lowerCamelCase,      verbose=lowerCamelCase,      return_tensors=lowerCamelCase,      **lowerCamelCase,      )
           if images is not None:
               _lowercase       :							str      		=		self.image_processor(
                   lowerCamelCase,      return_image_mask=lowerCamelCase,      return_codebook_pixels=lowerCamelCase,      return_tensors=lowerCamelCase,      **lowerCamelCase,      )

           if text is not None and images is not None:
               encoding.update(lowerCamelCase)
               return encoding
           elif text is not None:
               return encoding
           else:
               return BatchEncoding(data=dict(**lowerCamelCase),      tensor_type=lowerCamelCase)
       def 			UpperCamelCase     (					self,      *lowerCamelCase,      **lowerCamelCase)       ->   str:




           """simple docstring"""

           return self.tokenizer.batch_decode(*lowerCamelCase,      **lowerCamelCase)
       def 			UpperCamelCase     (					self,      *lowerCamelCase,      **lowerCamelCase)       ->   List[str]:




           """simple docstring"""

           return self.tokenizer.decode(*lowerCamelCase,      **lowerCamelCase)
       @property
       def 			UpperCamelCase     (					self)       ->   Optional[Any]:




           """simple docstring"""

           _lowercase       :							Dict      		=		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 			UpperCamelCase     (					self)       ->   Optional[int]:




           """simple docstring"""

           warnings.warn(
               '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.',      lowerCamelCase,      )
           return self.image_processor_class




       @property
       def 			UpperCamelCase     (					self)       ->   int:




           """simple docstring"""

           warnings.warn(
               '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.',      lowerCamelCase,      )
           return self.image_processor








'''simple docstring'''







def 						snake_case_							(_a	:							str    ,   _a	:							str     ):
    UpperCAmelCase			       =		len(_a     ) + 1
    UpperCAmelCase			       =		len(_a     ) + 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.
    UpperCAmelCase			       =		[[0 for i in range(_a     )] for j in range(_a     )]

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

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

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

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

   A     ='aab'
   A     ='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}""")




