


import warnings

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



class  UpperCAmelCase_						(				_a):
       '''simple docstring'''
       __UpperCamelCase   :  Tuple				=       ["image_processor", "tokenizer"]
       __UpperCamelCase   :  Any				=       "VideoMAEImageProcessor"
       __UpperCamelCase   :  List[str]				=       ("CLIPTokenizer", "CLIPTokenizerFast")






       def __init__(  self			,       __SCREAMING_SNAKE_CASE=None			,       __SCREAMING_SNAKE_CASE=None			,       **__SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              UpperCamelCase     :						List[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.'''			,       __SCREAMING_SNAKE_CASE			,       )
                     UpperCamelCase     :						Tuple 						=		kwargs.pop('''feature_extractor'''    )

              UpperCamelCase     :						Dict 						=		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__(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						List[str] 						=		self.image_processor






       def __call__(  self			,       __SCREAMING_SNAKE_CASE=None			,       __SCREAMING_SNAKE_CASE=None			,       __SCREAMING_SNAKE_CASE=None			,       **__SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              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     :						Union[str, Any] 						=		self.tokenizer(__SCREAMING_SNAKE_CASE			,       return_tensors=__SCREAMING_SNAKE_CASE			,       **__SCREAMING_SNAKE_CASE    )

              if videos is not None:
                     UpperCamelCase     :						str 						=		self.image_processor(__SCREAMING_SNAKE_CASE			,       return_tensors=__SCREAMING_SNAKE_CASE			,       **__SCREAMING_SNAKE_CASE    )

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






       def 			_lowercase (  self			,       *__SCREAMING_SNAKE_CASE			,       **__SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              return self.tokenizer.batch_decode(*__SCREAMING_SNAKE_CASE			,       **__SCREAMING_SNAKE_CASE    )






       def 			_lowercase (  self			,       *__SCREAMING_SNAKE_CASE			,       **__SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              return self.tokenizer.decode(*__SCREAMING_SNAKE_CASE			,       **__SCREAMING_SNAKE_CASE    )






       @property
       def 			_lowercase (  self    ):




              """simple docstring"""




              return ["input_ids", "attention_mask", "position_ids", "pixel_values"]






       @property
       def 			_lowercase (  self    ):




              """simple docstring"""




              warnings.warn(
                  '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''			,       __SCREAMING_SNAKE_CASE			,       )
              return self.image_processor_class






       @property
       def 			_lowercase (  self    ):




              """simple docstring"""




              warnings.warn(
                  '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''			,       __SCREAMING_SNAKE_CASE			,       )
              return self.image_processor





import sys


def   a						(				SCREAMING_SNAKE_CASE_   :		int				,      SCREAMING_SNAKE_CASE_   :		list[int]							):






       """simple docstring"""





       if not numbers:
              return 0
       if target == 0 and target in numbers:
              return 0
       if not isinstance(SCREAMING_SNAKE_CASE_				,      (list, tuple)							) or not all(
           isinstance(SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_							) for number in numbers							):
              raise ValueError('''numbers must be an iterable of integers'''							)

       UpperCamelCase     :						Dict 						=		0
       UpperCamelCase     :						Tuple 						=		sys.maxsize

       while right < len(SCREAMING_SNAKE_CASE_							):
              curr_sum += numbers[right]
              while curr_sum >= target and left <= right:
                     UpperCamelCase     :						int 						=		min(SCREAMING_SNAKE_CASE_				,      right - left + 1							)
                     curr_sum -= numbers[left]
                     left += 1
              right += 1

       return 0 if min_len == sys.maxsize else min_len

