






"""simple docstring"""
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments






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


if __name__ == "__main__":
							main()









"""simple docstring"""
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE						):





	"""simple docstring"""




	_A       :				Optional[int]    =				["""image_processor""", """tokenizer"""]
	_A       :				str    =				"""AutoImageProcessor"""
	_A       :				Union[str, Any]    =				"""AutoTokenizer"""





	def __init__(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_							):
		super().__init__(lowerCAmelCase_     ,     lowerCAmelCase_							)
		A_		:						Union[str, Any]	    =   self.image_processor





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

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

		if text is not None:
			A_		:						Optional[int]	    =   self.tokenizer(lowerCAmelCase_     ,     return_tensors=lowerCAmelCase_     ,     **lowerCAmelCase_							)

		if images is not None:
			A_		:						List[Any]	    =   self.image_processor(lowerCAmelCase_     ,     return_tensors=lowerCAmelCase_     ,     **lowerCAmelCase_							)

		if text is not None and images is not None:
			A_		:						int	    =   image_features.pixel_values
			return encoding
		elif text is not None:
			return encoding
		else:
			return BatchEncoding(data=dict(**lowerCAmelCase_							)     ,     tensor_type=lowerCAmelCase_							)





	def    lowerCamelCase(self     ,     *lowerCAmelCase_     ,     **lowerCAmelCase_							):
		return self.tokenizer.batch_decode(*lowerCAmelCase_     ,     **lowerCAmelCase_							)





	def    lowerCamelCase(self     ,     *lowerCAmelCase_     ,     **lowerCAmelCase_							):
		return self.tokenizer.decode(*lowerCAmelCase_     ,     **lowerCAmelCase_							)





	@property
	def    lowerCamelCase(self							):
		return ["input_ids", "attention_mask", "pixel_values"]

