




'''simple docstring'''


import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin

class 			A    (		_a    ):
							lowercase_							=				['image_processor', 'tokenizer']
							lowercase_							=				'AutoImageProcessor'
							lowercase_							=				'AutoTokenizer'







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


														"""simple docstring"""


														_a	     =				None
														if "feature_extractor" in kwargs:
																					warnings.warn(
																					    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
																					    ''' instead.'''							,			lowerCAmelCase_							,			)
																					_a	     =				kwargs.pop('''feature_extractor'''					)

														_a	     =				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__(lowerCAmelCase_							,			lowerCAmelCase_					)
														_a	     =				self.image_processor
														_a	     =				False







							def __call__(  self       :  Union[str, Any]							,			*lowerCAmelCase_       :  Union[str, Any]							,			**lowerCAmelCase_       :  Any					)			->							Tuple:


														"""simple docstring"""


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

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

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







							def    __lowerCAmelCase			(  self       :  Optional[Any]							,			*lowerCAmelCase_       :  Tuple							,			**lowerCAmelCase_       :  Union[str, Any]					)			->							str:


														"""simple docstring"""


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







							def    __lowerCAmelCase			(  self       :  List[Any]							,			*lowerCAmelCase_       :  List[str]							,			**lowerCAmelCase_       :  List[str]					)			->							str:


														"""simple docstring"""


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







							@contextmanager
							def    __lowerCAmelCase			(  self       :  Dict					)			->							int:


														"""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.'''					)
														_a	     =				True
														_a	     =				self.tokenizer
														yield
														_a	     =				self.image_processor
														_a	     =				False







							@property
							def    __lowerCAmelCase			(  self       :  Dict					)			->							Any:


														"""simple docstring"""


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







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


														"""simple docstring"""


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







'''simple docstring'''


from __future__ import annotations
def 		snake_case_    (UpperCamelCase       :  int      ):





							'''simple docstring'''


							_a	     =				[0] * max_one

							for i in range(0 ,						UpperCamelCase      ):
														_a	     =				i

							for i in range(2 ,						UpperCamelCase      ):
														if totients[i] == i:
																					for j in range(UpperCamelCase ,						UpperCamelCase ,						UpperCamelCase      ):
																												totients[j] -= totients[j] // i

							return totients
def 		snake_case_    (UpperCamelCase       :  int ,						UpperCamelCase       :  int      ):





							'''simple docstring'''


							return sorted(str(UpperCamelCase      )      ) == sorted(str(UpperCamelCase      )      )
def 		snake_case_    (UpperCamelCase       :  int = 1000_0000      ):





							'''simple docstring'''


							_a	     =				1  # i
							_a	     =				0  # φ(i)
							_a	     =				get_totients(max_n + 1      )

							for i in range(2 ,						max_n + 1      ):
														_a	     =				totients[i]

														if i * min_denominator < min_numerator * t and has_same_digits(UpperCamelCase ,						UpperCamelCase      ):
																					_a	     =				i
																					_a	     =				t

							return min_numerator


if __name__ == "__main__":
				print(F'''{solution() = }''')

