'''simple docstring'''




import warnings

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





class        __magic_name__		(       _UpperCAmelCase):
			UpperCamelCase__							=			['''image_processor''', '''tokenizer''']
			UpperCamelCase__							=			'''VideoMAEImageProcessor'''
			UpperCamelCase__							=			('''CLIPTokenizer''', '''CLIPTokenizerFast''')







			def __init__(      self							:    Any							,     lowercase_							:    int=None							,     lowercase_							:    List[Any]=None							,     **lowercase_							:    Any       ):
										lowercase_   :     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."""							,     lowercase_							,     )
																	lowercase_   :     str									=			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__(lowercase_							,     lowercase_       )
										lowercase_   :     List[str]									=			self.image_processor







			def __call__(      self							:    Tuple							,     lowercase_							:    List[str]=None							,     lowercase_							:    Tuple=None							,     lowercase_							:    Any=None							,     **lowercase_							:    Dict       ):

										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[int]									=			self.tokenizer(lowercase_							,     return_tensors=lowercase_							,     **lowercase_       )

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

										if text is not None and videos is not None:
																	lowercase_   :     Union[str, Any]									=			image_features.pixel_values
																	return encoding
										elif text is not None:
																	return encoding
										else:
																	return BatchEncoding(data=dict(**lowercase_       )							,     tensor_type=lowercase_       )







			def       SCREAMING_SNAKE_CASE_	(      self							:    List[str]							,     *lowercase_							:    Optional[int]							,     **lowercase_							:    str       ):
										return self.tokenizer.batch_decode(*lowercase_							,     **lowercase_       )







			def       SCREAMING_SNAKE_CASE_	(      self							:    List[Any]							,     *lowercase_							:    Optional[int]							,     **lowercase_							:    Dict       ):
										return self.tokenizer.decode(*lowercase_							,     **lowercase_       )







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







			@property
			def       SCREAMING_SNAKE_CASE_	(      self							:    Tuple       ):
										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       SCREAMING_SNAKE_CASE_	(      self							:    List[str]       ):
										warnings.warn(
										    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""							,     lowercase_							,     )
										return self.image_processor






'''simple docstring'''




from transformers import HfArgumentParser, PyTorchBenchmark, PyTorchBenchmarkArguments




def 				lowerCamelCase      ( )     -> List[Any]:
							lowercase_   :     Tuple									=			HfArgumentParser(UpperCAmelCase__					)
							try:
														lowercase_   :     List[Any]									=			parser.parse_args_into_dataclasses()[0]
							except ValueError as e:
														lowercase_   :     int									=			"""Arg --no_{0} is no longer used, please use --no-{0} instead."""
														lowercase_   :     Any									=			""" """.join(str(UpperCAmelCase__					).split(""" """					)[:-1]					)
														lowercase_   :     Optional[Any]									=			""""""
														lowercase_   :     List[str]									=			eval(str(UpperCAmelCase__					).split(""" """					)[-1]					)
														lowercase_   :     Dict									=			[]
														for arg in depreciated_args:
																					# arg[2:] removes '--'
																					if arg[2:] in PyTorchBenchmarkArguments.deprecated_args:
																												# arg[5:] removes '--no_'
																												full_error_msg += arg_error_msg.format(arg[5:]					)
																					else:
																												wrong_args.append(UpperCAmelCase__					)
														if len(UpperCAmelCase__					) > 0:
																					lowercase_   :     Dict									=			full_error_msg + begin_error_msg + str(UpperCAmelCase__					)
														raise ValueError(UpperCAmelCase__					)

							lowercase_   :     Optional[int]									=			PyTorchBenchmark(args=UpperCAmelCase__					)
							benchmark.run()


if __name__ == "__main__":
						main()





