





_A   :       Optional[Any]    					=    2_56
# Modulus to hash a string
_A   :       Optional[Any]    					=    1_00_00_03



def 			_a		(       UpperCAmelCase     ,    UpperCAmelCase	)							->							Dict:







   """simple docstring"""
   lowerCamelCase__  :		Tuple     =				len(UpperCAmelCase	)
   lowerCamelCase__  :		Any     =				len(UpperCAmelCase	)
   if p_len > t_len:
      return False

   lowerCamelCase__  :		List[str]     =				0
   lowerCamelCase__  :		Dict     =				0
   lowerCamelCase__  :		Any     =				1

   # Calculating the hash of pattern and substring of text
   for i in range(UpperCAmelCase	):
      lowerCamelCase__  :		Any     =				(ord(pattern[i]	) + p_hash * alphabet_size) % modulus
      lowerCamelCase__  :		str     =				(ord(text[i]	) + text_hash * alphabet_size) % modulus
      if i == p_len - 1:
         continue
      lowerCamelCase__  :		Any     =				(modulus_power * alphabet_size) % modulus

   for i in range(0     ,    t_len - p_len + 1	):
      if text_hash == p_hash and text[i : i + p_len] == pattern:
         return True
      if i == t_len - p_len:
         continue
      # Calculate the https://en.wikipedia.org/wiki/Rolling_hash
      lowerCamelCase__  :		Tuple     =				(
          (text_hash - ord(text[i]	) * modulus_power) * alphabet_size
          + ord(text[i + p_len]	)
      ) % modulus
   return False



def 			_a		(       )							->							List[Any]:







   """simple docstring"""
   lowerCamelCase__  :		Any     =				'''abc1abc12'''
   lowerCamelCase__  :		Any     =				'''alskfjaldsabc1abc1abc12k23adsfabcabc'''
   lowerCamelCase__  :		Optional[Any]     =				'''alskfjaldsk23adsfabcabc'''
   assert rabin_karp(UpperCAmelCase     ,    UpperCAmelCase	) and not rabin_karp(UpperCAmelCase     ,    UpperCAmelCase	)

   # Test 2)
   lowerCamelCase__  :		Optional[Any]     =				'''ABABX'''
   lowerCamelCase__  :		Tuple     =				'''ABABZABABYABABX'''
   assert rabin_karp(UpperCAmelCase     ,    UpperCAmelCase	)

   # Test 3)
   lowerCamelCase__  :		List[Any]     =				'''AAAB'''
   lowerCamelCase__  :		str     =				'''ABAAAAAB'''
   assert rabin_karp(UpperCAmelCase     ,    UpperCAmelCase	)

   # Test 4)
   lowerCamelCase__  :		Tuple     =				'''abcdabcy'''
   lowerCamelCase__  :		List[str]     =				'''abcxabcdabxabcdabcdabcy'''
   assert rabin_karp(UpperCAmelCase     ,    UpperCAmelCase	)

   # Test 5)
   lowerCamelCase__  :		Dict     =				'''Lü'''
   lowerCamelCase__  :		Tuple     =				'''Lüsai'''
   assert rabin_karp(UpperCAmelCase     ,    UpperCAmelCase	)
   lowerCamelCase__  :		Optional[Any]     =				'''Lue'''
   assert not rabin_karp(UpperCAmelCase     ,    UpperCAmelCase	)
   print('''Success.'''	)


if __name__ == "__main__":
       test_rabin_karp()











import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class     lowerCamelCase_					(   lowerCamelCase			):
				a__       							=	['''image_processor''', '''tokenizer''']
				a__       							=	'''ChineseCLIPImageProcessor'''
				a__       							=	('''BertTokenizer''', '''BertTokenizerFast''')







				def __init__(    self							,       __lowerCAmelCase=None							,       __lowerCAmelCase=None							,       **__lowerCAmelCase  ):
								"""simple docstring"""
								__magic_name__		:Tuple   =   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							,       )
												__magic_name__		:Optional[Any]   =   kwargs.pop('''feature_extractor'''  )

								__magic_name__		:Tuple   =   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  )
								__magic_name__		:List[Any]   =   self.image_processor







				def __call__(    self							,       __lowerCAmelCase=None							,       __lowerCAmelCase=None							,       __lowerCAmelCase=None							,       **__lowerCAmelCase  ):
								"""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:
												__magic_name__		:int   =   self.tokenizer(__lowerCAmelCase							,       return_tensors=__lowerCAmelCase							,       **__lowerCAmelCase  )

								if images is not None:
												__magic_name__		:Dict   =   self.image_processor(__lowerCAmelCase							,       return_tensors=__lowerCAmelCase							,       **__lowerCAmelCase  )

								if text is not None and images is not None:
												__magic_name__		:Union[str, Any]   =   image_features.pixel_values
												return encoding
								elif text is not None:
												return encoding
								else:
												return BatchEncoding(data=dict(**__lowerCAmelCase  )							,       tensor_type=__lowerCAmelCase  )







				def       A						(    self							,       *__lowerCAmelCase							,       **__lowerCAmelCase  ):
								"""simple docstring"""
								return self.tokenizer.batch_decode(*__lowerCAmelCase							,       **__lowerCAmelCase  )







				def       A						(    self							,       *__lowerCAmelCase							,       **__lowerCAmelCase  ):
								"""simple docstring"""
								return self.tokenizer.decode(*__lowerCAmelCase							,       **__lowerCAmelCase  )







				@property
				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:List[Any]   =   self.tokenizer.model_input_names
								__magic_name__		:Any   =   self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )







				@property
				def       A						(    self  ):
								"""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






