import warnings

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

class        UpperCamelCase						(				__a				):
						a__		:str      =			['''image_processor''', '''tokenizer''']
						a__		:int      =			'''ChineseCLIPImageProcessor'''
						a__		:Optional[Any]      =			('''BertTokenizer''', '''BertTokenizerFast''')





						def __init__(self			,     __UpperCamelCase=None			,     __UpperCamelCase=None			,     **__UpperCamelCase						)				->				List[str]:
												UpperCamelCase_		:							Union[str, 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."""			,     __UpperCamelCase			,     )
																		UpperCamelCase_		:							Optional[Any]  =		kwargs.pop("""feature_extractor"""						)

												UpperCamelCase_		:							int  =		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__(__UpperCamelCase			,     __UpperCamelCase						)
												UpperCamelCase_		:							Any  =		self.image_processor





						def __call__(self			,     __UpperCamelCase=None			,     __UpperCamelCase=None			,     __UpperCamelCase=None			,     **__UpperCamelCase						)				->				Optional[Any]:

												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:
																		UpperCamelCase_		:							Dict  =		self.tokenizer(__UpperCamelCase			,     return_tensors=__UpperCamelCase			,     **__UpperCamelCase						)

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

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





						def        A_							(self			,     *__UpperCamelCase			,     **__UpperCamelCase						)				->				List[Any]:
												return self.tokenizer.batch_decode(*__UpperCamelCase			,     **__UpperCamelCase						)





						def        A_							(self			,     *__UpperCamelCase			,     **__UpperCamelCase						)				->				List[Any]:
												return self.tokenizer.decode(*__UpperCamelCase			,     **__UpperCamelCase						)





						@property
						def        A_							(self						)				->				List[str]:
												UpperCamelCase_		:							int  =		self.tokenizer.model_input_names
												UpperCamelCase_		:							str  =		self.image_processor.model_input_names
												return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names						)						)





						@property
						def        A_							(self						)				->				Tuple:
												warnings.warn(
												    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""			,     __UpperCamelCase			,     )
												return self.image_processor_class







from __future__ import annotations






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				float		, _SCREAMING_SNAKE_CASE     :				float		, _SCREAMING_SNAKE_CASE     :				float		, ):
						if (electron_conc, hole_conc, intrinsic_conc).count(0			) != 1:
												raise ValueError("""You cannot supply more or less than 2 values"""			)
						elif electron_conc < 0:
												raise ValueError("""Electron concentration cannot be negative in a semiconductor"""			)
						elif hole_conc < 0:
												raise ValueError("""Hole concentration cannot be negative in a semiconductor"""			)
						elif intrinsic_conc < 0:
												raise ValueError(
												    """Intrinsic concentration cannot be negative in a semiconductor"""			)
						elif electron_conc == 0:
												return (
												    "electron_conc",
												    intrinsic_conc**2 / hole_conc,
												)
						elif hole_conc == 0:
												return (
												    "hole_conc",
												    intrinsic_conc**2 / electron_conc,
												)
						elif intrinsic_conc == 0:
												return (
												    "intrinsic_conc",
												    (electron_conc * hole_conc) ** 0.5,
												)
						else:
												return (-1, -1)


if __name__ == "__main__":
	import doctest

	doctest.testmod()






