
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
class      A__       (						lowerCAmelCase__    ):
							lowerCAmelCase__		:						Union[str, Any]         =							["image_processor", "tokenizer"]
							lowerCAmelCase__		:						int         =							"AutoImageProcessor"
							lowerCAmelCase__		:						int         =							"AutoTokenizer"





							def __init__(			self  :       List[Any]					,			_UpperCAmelCase  :       Optional[Any]=None					,			_UpperCAmelCase  :       str=None					,			**_UpperCAmelCase  :       int    )						->		str:



											"""simple docstring"""


											__lowercase								= 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					,			)
															__lowercase								= kwargs.pop('feature_extractor'    )

											__lowercase								= 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    )
											__lowercase								= self.image_processor
											__lowercase								= False





							def __call__(			self  :       Dict					,			*_UpperCAmelCase  :       List[Any]					,			**_UpperCAmelCase  :       int    )						->		str:



											"""simple docstring"""


											if self._in_target_context_manager:
															return self.current_processor(*_UpperCAmelCase					,			**_UpperCAmelCase    )

											__lowercase								= kwargs.pop('images'					,			_UpperCAmelCase    )
											__lowercase								= kwargs.pop('text'					,			_UpperCAmelCase    )
											if len(_UpperCAmelCase    ) > 0:
															__lowercase								= args[0]
															__lowercase								= 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:
															__lowercase								= self.image_processor(_UpperCAmelCase					,			*_UpperCAmelCase					,			**_UpperCAmelCase    )
											if text is not None:
															__lowercase								= self.tokenizer(_UpperCAmelCase					,			**_UpperCAmelCase    )

											if text is None:
															return inputs
											elif images is None:
															return encodings
											else:
															__lowercase								= encodings['input_ids']
															return inputs





							def       a__			(			self  :       int					,			*_UpperCAmelCase  :       Tuple					,			**_UpperCAmelCase  :       Any    )						->		List[Any]:



											"""simple docstring"""


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





							def       a__			(			self  :       List[str]					,			*_UpperCAmelCase  :       Dict					,			**_UpperCAmelCase  :       Dict    )						->		Optional[Any]:



											"""simple docstring"""


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





							@contextmanager
							def       a__			(			self  :       Any    )						->		Tuple:



											"""simple docstring"""


											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.'    )
											__lowercase								= True
											__lowercase								= self.tokenizer
											yield
											__lowercase								= self.image_processor
											__lowercase								= False





							@property
							def       a__			(			self  :       List[Any]    )						->		Optional[Any]:



											"""simple docstring"""


											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  :       Dict    )						->		List[Any]:



											"""simple docstring"""


											warnings.warn(
											    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'					,			_UpperCAmelCase					,			)
											return self.image_processor







from math import pi




def 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				float      )					->						float:
				return degree / (180 / pi)


if __name__ == "__main__":
							from doctest import testmod

							testmod()





