import warnings

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







class      SCREAMING_SNAKE_CASE__					(  __UpperCamelCase    ):
       A							:	Optional[Any]          =	["image_processor", "tokenizer"]
       A							:	Dict          =	"VideoMAEImageProcessor"
       A							:	Tuple          =	("CLIPTokenizer", "CLIPTokenizerFast")

       def __init__(							self					:     str			,			_lowerCAmelCase					:     Tuple=None			,			_lowerCAmelCase					:     Any=None			,			**_lowerCAmelCase					:     Union[str, Any]			):
           __snake_case       :				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."""			,			_lowerCAmelCase			,			)
               __snake_case       :				Optional[Any] 			=    kwargs.pop("""feature_extractor"""			)

           __snake_case       :				int 			=    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			)
           __snake_case       :				Optional[Any] 			=    self.image_processor

       def __call__(							self					:     str			,			_lowerCAmelCase					:     int=None			,			_lowerCAmelCase					:     Dict=None			,			_lowerCAmelCase					:     Optional[int]=None			,			**_lowerCAmelCase					:     List[str]			):

           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:
               __snake_case       :				Optional[Any] 			=    self.tokenizer(_lowerCAmelCase			,			return_tensors=_lowerCAmelCase			,			**_lowerCAmelCase			)

           if videos is not None:
               __snake_case       :				Optional[int] 			=    self.image_processor(_lowerCAmelCase			,			return_tensors=_lowerCAmelCase			,			**_lowerCAmelCase			)

           if text is not None and videos is not None:
               __snake_case       :				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 			snake_case__    (							self					:     Tuple			,			*_lowerCAmelCase					:     str			,			**_lowerCAmelCase					:     List[Any]			):
           return self.tokenizer.batch_decode(*_lowerCAmelCase			,			**_lowerCAmelCase			)

       def 			snake_case__    (							self					:     Tuple			,			*_lowerCAmelCase					:     List[Any]			,			**_lowerCAmelCase					:     Dict			):
           return self.tokenizer.decode(*_lowerCAmelCase			,			**_lowerCAmelCase			)

       @property
       def 			snake_case__    (							self					:     str			):
           return ["input_ids", "attention_mask", "position_ids", "pixel_values"]

       @property
       def 			snake_case__    (							self					:     str			):
           warnings.warn(
               """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""			,			_lowerCAmelCase			,			)
           return self.image_processor_class





       @property
       def 			snake_case__    (							self					:     Union[str, Any]			):
           warnings.warn(
               """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""			,			_lowerCAmelCase			,			)
           return self.image_processor




import math
from timeit import timeit







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					int   ):







    '''simple docstring'''
    __snake_case       :				Optional[Any] 			=    0
    __snake_case       :				Union[str, Any] 			=    abs(__SCREAMING_SNAKE_CASE   )
    while True:
        __snake_case       :				List[str] 			=    n // 1_0
        digits += 1
        if n == 0:
            break
    return digits







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					int   ):







    '''simple docstring'''
    return 1 if n == 0 else math.floor(math.log(abs(__SCREAMING_SNAKE_CASE   )		,							1_0   ) + 1   )







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					int   ):







    '''simple docstring'''
    return len(str(abs(__SCREAMING_SNAKE_CASE   )   )   )







def 					__lowerCAmelCase				(		):







    '''simple docstring'''
    from collections.abc import Callable

    def benchmark_a_function(__SCREAMING_SNAKE_CASE       :					Callable		,							__SCREAMING_SNAKE_CASE       :					int   ) -> None:
        __snake_case       :				Tuple 			=    F'''{func.__name__}({value})'''
        __snake_case       :				Union[str, Any] 			=    timeit(F'''__main__.{call}'''		,							setup="""import __main__"""   )
        print(F'''{call}: {func(__SCREAMING_SNAKE_CASE   )} -- {timing} seconds'''   )

    for value in (2_6_2_1_4_4, 1_1_2_5_8_9_9_9_0_6_8_4_2_6_2_4, 1_2_6_7_6_5_0_6_0_0_2_2_8_2_2_9_4_0_1_4_9_6_7_0_3_2_0_5_3_7_6):
        for func in (num_digits, num_digits_fast, num_digits_faster):
            benchmark_a_function(__SCREAMING_SNAKE_CASE		,							__SCREAMING_SNAKE_CASE   )
        print()


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     benchmark()



