



"""simple docstring"""
import warnings

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




class 			snake_case_						(						a_				):
							__lowerCAmelCase    					=					["image_processor", "tokenizer"]
							__lowerCAmelCase    					=					"ChineseCLIPImageProcessor"
							__lowerCAmelCase    					=					("BertTokenizer", "BertTokenizerFast")






							def __init__(   self			,     a_=None			,     a_=None			,     **a_      ):
								a_     :      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."			,     a_			,     )
									a_     :      Optional[Any] 						=     kwargs.pop("feature_extractor"      )

								a_     :      Tuple 						=     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__(a_			,     a_      )
								a_     :      Union[str, Any] 						=     self.image_processor






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

								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_     :      int 						=     self.tokenizer(a_			,     return_tensors=a_			,     **a_      )

								if images is not None:
									a_     :      Any 						=     self.image_processor(a_			,     return_tensors=a_			,     **a_      )

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






							def snake_case_  (   self			,     *a_			,     **a_      ):
								return self.tokenizer.batch_decode(*a_			,     **a_      )






							def snake_case_  (   self			,     *a_			,     **a_      ):
								return self.tokenizer.decode(*a_			,     **a_      )






							@property
							def snake_case_  (   self      ):
								a_     :      Tuple 						=     self.tokenizer.model_input_names
								a_     :      Optional[int] 						=     self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )






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




"""simple docstring"""
def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      float:
		_validate_point(SCREAMING_SNAKE_CASE__	)
		_validate_point(SCREAMING_SNAKE_CASE__	)
		if len(SCREAMING_SNAKE_CASE__	) != len(SCREAMING_SNAKE_CASE__	):
				raise ValueError("Both points must be in the same n-dimensional space"	)

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



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      None:
		if point:
				if isinstance(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	):
						for item in point:
								if not isinstance(SCREAMING_SNAKE_CASE__, (int, float)	):
										a_     :      int 						=     (
										    "Expected a list of numbers as input, found "
										    F"""{type(SCREAMING_SNAKE_CASE__	).__name__}"""
										)
										raise TypeError(SCREAMING_SNAKE_CASE__	)
				else:
						a_     :      Dict 						=     F"""Expected a list of numbers as input, found {type(SCREAMING_SNAKE_CASE__	).__name__}"""
						raise TypeError(SCREAMING_SNAKE_CASE__	)
		else:
				raise ValueError("Missing an input"	)



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      float:
		_validate_point(SCREAMING_SNAKE_CASE__	)
		_validate_point(SCREAMING_SNAKE_CASE__	)
		if len(SCREAMING_SNAKE_CASE__	) != len(SCREAMING_SNAKE_CASE__	):
				raise ValueError("Both points must be in the same n-dimensional space"	)

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


if __name__ == "__main__":
					import doctest

					doctest.testmod()