import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin

class        UpperCamelCase						(				__a				):
						a__		:Union[str, Any]      =			['''image_processor''', '''tokenizer''']
						a__		:Optional[int]      =			'''AutoImageProcessor'''
						a__		:Any      =			'''AutoTokenizer'''





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

												UpperCamelCase_		:							Optional[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_		:							List[str]  =		self.image_processor
												UpperCamelCase_		:							str  =		False





						def __call__(self			,     *__UpperCamelCase			,     **__UpperCamelCase						)				->				Union[str, Any]:
												# For backward compatibility
												if self._in_target_context_manager:
																		return self.current_processor(*__UpperCamelCase			,     **__UpperCamelCase						)

												UpperCamelCase_		:							List[Any]  =		kwargs.pop("""images"""			,     __UpperCamelCase						)
												UpperCamelCase_		:							str  =		kwargs.pop("""text"""			,     __UpperCamelCase						)
												if len(__UpperCamelCase						) > 0:
																		UpperCamelCase_		:							str  =		args[0]
																		UpperCamelCase_		:							List[Any]  =		args[1:]

												if images is None and text is None:
																		raise ValueError("""You need to specify either an `images` or `text` input to process."""						)

												if images is not None:
																		UpperCamelCase_		:							List[Any]  =		self.image_processor(__UpperCamelCase			,     *__UpperCamelCase			,     **__UpperCamelCase						)
												if text is not None:
																		UpperCamelCase_		:							Optional[Any]  =		self.tokenizer(__UpperCamelCase			,     **__UpperCamelCase						)

												if text is None:
																		return inputs
												elif images is None:
																		return encodings
												else:
																		UpperCamelCase_		:							List[Any]  =		encodings["""input_ids"""]
																		return inputs





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





						def        A_							(self			,     *__UpperCamelCase			,     **__UpperCamelCase						)				->				Tuple:
												return self.tokenizer.decode(*__UpperCamelCase			,     **__UpperCamelCase						)





						@contextmanager
						def        A_							(self						)				->				str:
												warnings.warn(
												    """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
												    """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
												    """your images inputs, or in a separate call."""						)
												UpperCamelCase_		:							Optional[int]  =		True
												UpperCamelCase_		:							List[Any]  =		self.tokenizer
												yield
												UpperCamelCase_		:							Any  =		self.image_processor
												UpperCamelCase_		:							Dict  =		False





						def        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase=False			,     __UpperCamelCase=None						)				->				Union[str, Any]:
												if added_vocab is None:
																		UpperCamelCase_		:							Tuple  =		self.tokenizer.get_added_vocab()

												UpperCamelCase_		:							Any  =		{}

												while tokens:
																		UpperCamelCase_		:							Optional[Any]  =		re.search(R"""<s_(.*?)>"""			,     __UpperCamelCase			,     re.IGNORECASE						)
																		if start_token is None:
																								break
																		UpperCamelCase_		:							int  =		start_token.group(1						)
																		UpperCamelCase_		:							Union[str, Any]  =		re.search(Rf'''</s_{key}>'''			,     __UpperCamelCase			,     re.IGNORECASE						)
																		UpperCamelCase_		:							List[str]  =		start_token.group()
																		if end_token is None:
																								UpperCamelCase_		:							List[Any]  =		tokens.replace(__UpperCamelCase			,     """"""						)
																		else:
																								UpperCamelCase_		:							Tuple  =		end_token.group()
																								UpperCamelCase_		:							Dict  =		re.escape(__UpperCamelCase						)
																								UpperCamelCase_		:							List[str]  =		re.escape(__UpperCamelCase						)
																								UpperCamelCase_		:							List[str]  =		re.search(f'''{start_token_escaped}(.*?){end_token_escaped}'''			,     __UpperCamelCase			,     re.IGNORECASE						)
																								if content is not None:
																														UpperCamelCase_		:							List[Any]  =		content.group(1						).strip()
																														if r"<s_" in content and r"</s_" in content:  # non-leaf node
																																				UpperCamelCase_		:							Optional[int]  =		self.tokenajson(__UpperCamelCase			,     is_inner_value=__UpperCamelCase			,     added_vocab=__UpperCamelCase						)
																																				if value:
																																										if len(__UpperCamelCase						) == 1:
																																																UpperCamelCase_		:							Any  =		value[0]
																																										UpperCamelCase_		:							int  =		value
																														else:  # leaf nodes
																																				UpperCamelCase_		:							Dict  =		[]
																																				for leaf in content.split(R"""<sep/>"""						):
																																										UpperCamelCase_		:							Tuple  =		leaf.strip()
																																										if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
																																																UpperCamelCase_		:							Union[str, Any]  =		leaf[1:-2]  # for categorical special tokens
																																										output[key].append(__UpperCamelCase						)
																																				if len(output[key]						) == 1:
																																										UpperCamelCase_		:							Optional[int]  =		output[key][0]

																								UpperCamelCase_		:							Optional[int]  =		tokens[tokens.find(__UpperCamelCase						) + len(__UpperCamelCase						) :].strip()
																								if tokens[:6] == r"<sep/>":  # non-leaf nodes
																														return [output] + self.tokenajson(tokens[6:]			,     is_inner_value=__UpperCamelCase			,     added_vocab=__UpperCamelCase						)

												if len(__UpperCamelCase						):
																		return [output] if is_inner_value else output
												else:
																		return [] if is_inner_value else {"text_sequence": tokens}





						@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





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







from __future__ import annotations

SCREAMING_SNAKE_CASE		:     Tuple         =	1.6_021E-19  # units = C






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				float		, _SCREAMING_SNAKE_CASE     :				float		, _SCREAMING_SNAKE_CASE     :				float		, ):
						if (conductivity, electron_conc, mobility).count(0			) != 1:
												raise ValueError("""You cannot supply more or less than 2 values"""			)
						elif conductivity < 0:
												raise ValueError("""Conductivity cannot be negative"""			)
						elif electron_conc < 0:
												raise ValueError("""Electron concentration cannot be negative"""			)
						elif mobility < 0:
												raise ValueError("""mobility cannot be negative"""			)
						elif conductivity == 0:
												return (
												    "conductivity",
												    mobility * electron_conc * ELECTRON_CHARGE,
												)
						elif electron_conc == 0:
												return (
												    "electron_conc",
												    conductivity / (mobility * ELECTRON_CHARGE),
												)
						else:
												return (
												    "mobility",
												    conductivity / (electron_conc * ELECTRON_CHARGE),
												)


if __name__ == "__main__":
	import doctest

	doctest.testmod()






