




import warnings

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


class      lowercase__     (			__snake_case	):
	a_         =["""image_processor""", """tokenizer"""]
	a_         ="""ChineseCLIPImageProcessor"""
	a_         =("""BertTokenizer""", """BertTokenizerFast""")

	def __init__(							self		,    __UpperCAmelCase=None		,    __UpperCAmelCase=None		,    **__UpperCAmelCase       )->					List[str]:







							'''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."		,    _lowercase		,    )
													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__(_lowercase		,    _lowercase       )
							lowerCAmelCase__       =						self.image_processor

	def __call__(							self		,    __UpperCAmelCase=None		,    __UpperCAmelCase=None		,    __UpperCAmelCase=None		,    **__UpperCAmelCase       )->					List[Any]:







							'''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(_lowercase		,    return_tensors=_lowercase		,    **_lowercase       )

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

							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(**_lowercase       )		,    tensor_type=_lowercase       )

	def 		UpperCAmelCase  (							self		,    *__UpperCAmelCase		,    **__UpperCAmelCase       )->					Union[str, Any]:







							'''simple docstring'''





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

	def 		UpperCAmelCase  (							self		,    *__UpperCAmelCase		,    **__UpperCAmelCase       )->					Optional[Any]:







							'''simple docstring'''





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

	@property
	def 		UpperCAmelCase  (							self       )->					Dict:







							'''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 		UpperCAmelCase  (							self       )->					List[str]:







							'''simple docstring'''





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














def        _a  (     UpperCamelCase_					:				list    ,   UpperCamelCase_					:				list   ) ->   float:
						"""simple docstring"""







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

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


def        _a  (     UpperCamelCase_					:				list[float]   ) ->   None:
						"""simple docstring"""







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


def        _a  (     UpperCamelCase_					:				list    ,   UpperCamelCase_					:				list   ) ->   float:
						"""simple docstring"""







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

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


if __name__ == "__main__":
	import doctest

	doctest.testmod()






