


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):
     SCREAMING_SNAKE_CASE__      			=			["""image_processor""", """tokenizer"""]
     SCREAMING_SNAKE_CASE__      			=			"""BlipImageProcessor"""
     SCREAMING_SNAKE_CASE__      			=			("""BertTokenizer""", """BertTokenizerFast""")





     def __init__(self		,     UpperCAmelCase		,     UpperCAmelCase       )     ->   Any:
      _lowercase							      =False
      super().__init__(UpperCAmelCase		,     UpperCAmelCase       )
      _lowercase							      =self.image_processor





     def __call__(self		,     UpperCAmelCase = None		,     UpperCAmelCase = None		,     UpperCAmelCase = True		,     UpperCAmelCase = False		,     UpperCAmelCase = None		,     UpperCAmelCase = None		,     UpperCAmelCase = 0		,     UpperCAmelCase = None		,     UpperCAmelCase = None		,     UpperCAmelCase = False		,     UpperCAmelCase = False		,     UpperCAmelCase = False		,     UpperCAmelCase = False		,     UpperCAmelCase = False		,     UpperCAmelCase = True		,     UpperCAmelCase = None		,     **UpperCAmelCase		,     )     ->   Optional[int]:

      if images is None and text is None:
       raise ValueError('''You have to specify either images or text.'''       )

      # Get only text
      if images is None:
       _lowercase							      =self.tokenizer
       _lowercase							      =self.tokenizer(
           text=UpperCAmelCase		,     add_special_tokens=UpperCAmelCase		,     padding=UpperCAmelCase		,     truncation=UpperCAmelCase		,     max_length=UpperCAmelCase		,     stride=UpperCAmelCase		,     pad_to_multiple_of=UpperCAmelCase		,     return_attention_mask=UpperCAmelCase		,     return_overflowing_tokens=UpperCAmelCase		,     return_special_tokens_mask=UpperCAmelCase		,     return_offsets_mapping=UpperCAmelCase		,     return_token_type_ids=UpperCAmelCase		,     return_length=UpperCAmelCase		,     verbose=UpperCAmelCase		,     return_tensors=UpperCAmelCase		,     **UpperCAmelCase		,     )
       return text_encoding

      # add pixel_values
      _lowercase							      =self.image_processor(UpperCAmelCase		,     return_tensors=UpperCAmelCase       )

      if text is not None:
       _lowercase							      =self.tokenizer(
           text=UpperCAmelCase		,     add_special_tokens=UpperCAmelCase		,     padding=UpperCAmelCase		,     truncation=UpperCAmelCase		,     max_length=UpperCAmelCase		,     stride=UpperCAmelCase		,     pad_to_multiple_of=UpperCAmelCase		,     return_attention_mask=UpperCAmelCase		,     return_overflowing_tokens=UpperCAmelCase		,     return_special_tokens_mask=UpperCAmelCase		,     return_offsets_mapping=UpperCAmelCase		,     return_token_type_ids=UpperCAmelCase		,     return_length=UpperCAmelCase		,     verbose=UpperCAmelCase		,     return_tensors=UpperCAmelCase		,     **UpperCAmelCase		,     )
      else:
       _lowercase							      =None

      if text_encoding is not None:
       encoding_image_processor.update(UpperCAmelCase       )

      return encoding_image_processor





     def      __A					(self		,     *UpperCAmelCase		,     **UpperCAmelCase       )     ->   int:
      return self.tokenizer.batch_decode(*UpperCAmelCase		,     **UpperCAmelCase       )





     def      __A					(self		,     *UpperCAmelCase		,     **UpperCAmelCase       )     ->   List[Any]:
      return self.tokenizer.decode(*UpperCAmelCase		,     **UpperCAmelCase       )





     @property
     def      __A					(self       )     ->   str:
      _lowercase							      =self.tokenizer.model_input_names
      _lowercase							      =self.image_processor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )







'''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}""")




