





import warnings

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

class 					snake_case					(   __lowerCamelCase	):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_     : Tuple      		=["image_processor", "tokenizer"]
 SCREAMING_SNAKE_CASE_     : Optional[Any]      		="VideoMAEImageProcessor"
 SCREAMING_SNAKE_CASE_     : str      		=("CLIPTokenizer", "CLIPTokenizerFast")






 def __init__(	self	:							Dict							,     __A	:							Tuple=None							,     __A	:							List[str]=None							,     **__A	:							List[str]     ):
        __UpperCamelCase	=  None
        if "feature_extractor" in kwargs:
               warnings.warn(
                   'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                   ' instead.'							,     lowercase__							,     )
               __UpperCamelCase	=  kwargs.pop('feature_extractor'     )

        __UpperCamelCase	=  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__(lowercase__							,     lowercase__     )
        __UpperCamelCase	=  self.image_processor






 def __call__(	self	:							List[Any]							,     __A	:							Dict=None							,     __A	:							int=None							,     __A	:							Any=None							,     **__A	:							Any     ):







        if text is None and videos is None:
               raise ValueError('You have to specify either text or videos. Both cannot be none.'     )

        if text is not None:
               __UpperCamelCase	=  self.tokenizer(lowercase__							,     return_tensors=lowercase__							,     **lowercase__     )

        if videos is not None:
               __UpperCamelCase	=  self.image_processor(lowercase__							,     return_tensors=lowercase__							,     **lowercase__     )

        if text is not None and videos is not None:
               __UpperCamelCase	=  image_features.pixel_values
               return encoding
        elif text is not None:
               return encoding
        else:
               return BatchEncoding(data=dict(**lowercase__     )							,     tensor_type=lowercase__     )






 def 	_lowerCamelCase (	self	:							Any							,     *__A	:							Optional[Any]							,     **__A	:							int     ):
        return self.tokenizer.batch_decode(*lowercase__							,     **lowercase__     )






 def 	_lowerCamelCase (	self	:							Tuple							,     *__A	:							Tuple							,     **__A	:							Optional[int]     ):
        return self.tokenizer.decode(*lowercase__							,     **lowercase__     )






 @property
 def 	_lowerCamelCase (	self	:							Optional[int]     ):
        return ["input_ids", "attention_mask", "position_ids", "pixel_values"]






 @property
 def 	_lowerCamelCase (	self	:							Any     ):
        warnings.warn(
            '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'							,     lowercase__							,     )
        return self.image_processor_class






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







'''simple docstring'''
def     lowercase__       (       __lowercase						:  str    ,  __lowercase						:  Tuple					)			->				str:



       """simple docstring"""






       sequence.append(__lowercase					)

       __UpperCamelCase	=  0
       while sequence[index] != target:
              index += 1

       sequence.pop()

       if index == len(__lowercase					):
              return None

       return index


if __name__ == "__main__":
    a__							:							Any			=input('''Enter numbers separated by comma:\n''').strip()
    a__							:							Any			=[int(item) for item in user_input.split(''',''')]

    a__							:							Tuple			=input('''Enter a single number to be found in the list:\n''')
    a__							:							List[Any]			=int(target_input)
    a__							:							List[Any]			=sentinel_linear_search(sequence, target)
    if result is not None:
        print(f'{target} found at positions: {result}')
    else:
        print('''Not found''')
