





'''simple docstring'''





import warnings

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






class 					lowercase__ (			__SCREAMING_SNAKE_CASE       ):

   '''simple docstring'''


   a		:							Dict     	=	['image_processor', 'tokenizer']
   a		:							Optional[int]     	=	'ChineseCLIPImageProcessor'
   a		:							Union[str, Any]     	=	('BertTokenizer', 'BertTokenizerFast')




   def __init__(					self,	__magic_name__=None,	__magic_name__=None,	**__magic_name__							)				->   Union[str, Any]:






       """simple docstring"""
       UpperCamelCase__							:       int      =   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,	)
           UpperCamelCase__							:       Any      =   kwargs.pop('''feature_extractor'''							)

       UpperCamelCase__							:       str      =   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							)
       UpperCamelCase__							:       Dict      =   self.image_processor




   def __call__(					self,	__magic_name__=None,	__magic_name__=None,	__magic_name__=None,	**__magic_name__							)				->   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:
           UpperCamelCase__							:       Optional[int]      =   self.tokenizer(_a,	return_tensors=_a,	**_a							)

       if images is not None:
           UpperCamelCase__							:       str      =   self.image_processor(_a,	return_tensors=_a,	**_a							)

       if text is not None and images is not None:
           UpperCamelCase__							:       str      =   image_features.pixel_values
           return encoding
       elif text is not None:
           return encoding
       else:
           return BatchEncoding(data=dict(**_a							),	tensor_type=_a							)




   def    UpperCamelCase__						(					self,	*__magic_name__,	**__magic_name__							)				->   Tuple:






       """simple docstring"""
       return self.tokenizer.batch_decode(*_a,	**_a							)




   def    UpperCamelCase__						(					self,	*__magic_name__,	**__magic_name__							)				->   Dict:






       """simple docstring"""
       return self.tokenizer.decode(*_a,	**_a							)




   @property
   def    UpperCamelCase__						(					self							)				->   Tuple:






       """simple docstring"""
       UpperCamelCase__							:       str      =   self.tokenizer.model_input_names
       UpperCamelCase__							:       Optional[int]      =   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.''',	_a,	)
       return self.image_processor_class








from __future__ import annotations




def 	lowerCAmelCase_					(						__UpperCAmelCase:    str	,     __UpperCAmelCase:    str   )   ->			bool:
    UpperCamelCase__							:       List[str]      =   get_failure_array(__UpperCAmelCase   )

    # 2) Step through text searching for pattern
    UpperCamelCase__     ,UpperCamelCase__							:       Dict      =   0, 0  # index into text, pattern
    while i < len(__UpperCAmelCase   ):
        if pattern[j] == text[i]:
            if j == (len(__UpperCAmelCase   ) - 1):
                return True
            j += 1

        # if this is a prefix in our pattern
        # just go back far enough to continue
        elif j > 0:
            UpperCamelCase__							:       Optional[int]      =   failure[j - 1]
            continue
        i += 1
    return False




def 	lowerCAmelCase_					(						__UpperCAmelCase:    str   )   ->			list[int]:
    UpperCamelCase__							:       Union[str, Any]      =   [0]
    UpperCamelCase__							:       Tuple      =   0
    UpperCamelCase__							:       Tuple      =   1
    while j < len(__UpperCAmelCase   ):
        if pattern[i] == pattern[j]:
            i += 1
        elif i > 0:
            UpperCamelCase__							:       str      =   failure[i - 1]
            continue
        j += 1
        failure.append(__UpperCAmelCase   )
    return failure


if __name__ == "__main__":
  # Test 1)
  UpperCAmelCase_  							=				'abc1abc12'
  UpperCAmelCase_  							=				'alskfjaldsabc1abc1abc12k23adsfabcabc'
  UpperCAmelCase_  							=				'alskfjaldsk23adsfabcabc'
  assert kmp(pattern, texta) and not kmp(pattern, texta)

  # Test 2)
  UpperCAmelCase_  							=				'ABABX'
  UpperCAmelCase_  							=				'ABABZABABYABABX'
  assert kmp(pattern, text)

  # Test 3)
  UpperCAmelCase_  							=				'AAAB'
  UpperCAmelCase_  							=				'ABAAAAAB'
  assert kmp(pattern, text)

  # Test 4)
  UpperCAmelCase_  							=				'abcdabcy'
  UpperCAmelCase_  							=				'abcxabcdabxabcdabcdabcy'
  assert kmp(pattern, text)

  # Test 5)
  UpperCAmelCase_  							=				'aabaabaaa'
  assert get_failure_array(pattern) == [0, 1, 0, 1, 2, 3, 4, 5, 2]

