
'''simple docstring'''

import warnings

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






class  A  (						SCREAMING_SNAKE_CASE__     ):
	snake_case__   :Tuple   =  ['image_processor', 'tokenizer']
	snake_case__   :List[Any]   =  'ChineseCLIPImageProcessor'
	snake_case__   :Optional[int]   =  ('BertTokenizer', 'BertTokenizerFast')





	def __init__(       self      :							Dict   ,				__magic_name__      :							List[str]=None   ,				__magic_name__      :							List[Any]=None   ,				**__magic_name__      :							Optional[Any]				):


				"""simple docstring"""

				lowerCAmelCase__										=				None
				if "feature_extractor" in kwargs:
							warnings.warn(
							    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
							    " instead."   ,				__magic_name__   ,				)
							lowerCAmelCase__										=				kwargs.pop("feature_extractor"				)

				lowerCAmelCase__										=				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__(__magic_name__   ,				__magic_name__				)
				lowerCAmelCase__										=				self.image_processor





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


				"""simple docstring"""

				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:
							lowerCAmelCase__										=				self.tokenizer(__magic_name__   ,				return_tensors=__magic_name__   ,				**__magic_name__				)

				if images is not None:
							lowerCAmelCase__										=				self.image_processor(__magic_name__   ,				return_tensors=__magic_name__   ,				**__magic_name__				)

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





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Tuple   ,				*__magic_name__      :							Union[str, Any]   ,				**__magic_name__      :							List[str]				):


				"""simple docstring"""

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





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Any   ,				*__magic_name__      :							str   ,				**__magic_name__      :							Union[str, Any]				):


				"""simple docstring"""

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





	@property
	def 				__SCREAMING_SNAKE_CASE	(       self      :							str				):


				"""simple docstring"""

				lowerCAmelCase__										=				self.tokenizer.model_input_names
				lowerCAmelCase__										=				self.image_processor.model_input_names
				return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)







	@property
	def 				__SCREAMING_SNAKE_CASE	(       self      :							Tuple				):


				"""simple docstring"""

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











def 		lowerCamelCase__       (						_A	,      _A				):







	'''simple docstring'''





	_validate_point(_A				)
	_validate_point(_A				)
	if len(_A				) != len(_A				):
		raise ValueError("Both points must be in the same n-dimensional space"				)

	return float(sum(abs(a - b				) for a, b in zip(_A	,      _A				)				)				)


def 		lowerCamelCase__       (						_A				):







	'''simple docstring'''





	if point:
		if isinstance(_A	,      _A				):
			for item in point:
				if not isinstance(_A	,      (int, float)				):
					snake_case_						       =	(
					    "Expected a list of numbers as input, found "
					    f"{type(_A				).__name__}"
					)
					raise TypeError(_A				)
		else:
			snake_case_						       =	f"Expected a list of numbers as input, found {type(_A				).__name__}"
			raise TypeError(_A				)
	else:
		raise ValueError("Missing an input"				)


def 		lowerCamelCase__       (						_A	,      _A				):







	'''simple docstring'''





	_validate_point(_A				)
	_validate_point(_A				)
	if len(_A				) != len(_A				):
		raise ValueError("Both points must be in the same n-dimensional space"				)

	return float(sum(abs(x - y				) for x, y in zip(_A	,      _A				)				)				)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
