


import warnings

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






class    UpperCamelCase    (						_SCREAMING_SNAKE_CASE   ):


   '''simple docstring'''







   lowercase    :		List[str]	  =["image_processor", "tokenizer"]
   lowercase    :		List[str]	  ="VideoMAEImageProcessor"
   lowercase    :		List[Any]	  =("CLIPTokenizer", "CLIPTokenizerFast")
   def __init__(   self      ,							UpperCamelCase_=None      ,							UpperCamelCase_=None      ,							**UpperCamelCase_						):
        lowercase_				:Union[str, Any]       			=				None
        if "feature_extractor" in kwargs:
             warnings.warn(
                 '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                 ''' instead.'''      ,							UpperCamelCase_      ,							)
             lowercase_				:Tuple       			=				kwargs.pop('''feature_extractor'''						)

        lowercase_				:Union[str, 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__(UpperCamelCase_      ,							UpperCamelCase_						)
        lowercase_				:Tuple       			=				self.image_processor
   def __call__(   self      ,							UpperCamelCase_=None      ,							UpperCamelCase_=None      ,							UpperCamelCase_=None      ,							**UpperCamelCase_						):

        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:
             lowercase_				:Optional[Any]       			=				self.tokenizer(UpperCamelCase_      ,							return_tensors=UpperCamelCase_      ,							**UpperCamelCase_						)

        if videos is not None:
             lowercase_				:List[Any]       			=				self.image_processor(UpperCamelCase_      ,							return_tensors=UpperCamelCase_      ,							**UpperCamelCase_						)

        if text is not None and videos is not None:
             lowercase_				:str       			=				image_features.pixel_values
             return encoding
        elif text is not None:
             return encoding
        else:
             return BatchEncoding(data=dict(**UpperCamelCase_						)      ,							tensor_type=UpperCamelCase_						)
   def 						UpperCamelCase     (   self      ,							*UpperCamelCase_      ,							**UpperCamelCase_						):
        return self.tokenizer.batch_decode(*UpperCamelCase_      ,							**UpperCamelCase_						)
   def 						UpperCamelCase     (   self      ,							*UpperCamelCase_      ,							**UpperCamelCase_						):
        return self.tokenizer.decode(*UpperCamelCase_      ,							**UpperCamelCase_						)
   @property
   def 						UpperCamelCase     (   self						):
        return ["input_ids", "attention_mask", "position_ids", "pixel_values"]
   @property
   def 						UpperCamelCase     (   self						):
        warnings.warn(
            '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''      ,							UpperCamelCase_      ,							)
        return self.image_processor_class




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







def     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_					)				->		int:

    """simple docstring"""


    __UpperCamelCase     						=  0
    for ele in arr:
        total_sum += product_sum(lowercase_    ,   depth + 1					) if isinstance(lowercase_    ,   lowercase_					) else ele
    return total_sum * depth




def     __SCREAMING_SNAKE_CASE      (     lowercase_					)				->		int:

    """simple docstring"""


    return product_sum(lowercase_    ,   1					)


if __name__ == "__main__":
 import doctest

 doctest.testmod()






