
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class    _UpperCamelCase(   SCREAMING_SNAKE_CASE				):
						__A:						Union[str, Any]				   =      ["""image_processor""", """tokenizer"""]
						__A:						Optional[int]				   =      """EfficientNetImageProcessor"""
						__A:						Dict				   =      ("""BertTokenizer""", """BertTokenizerFast""")

						def __init__(					self		:			Union[str, Any]	,						_lowerCamelCase		:			str	,						_lowerCamelCase		:			int			):
													super().__init__(_lowerCamelCase	,						_lowerCamelCase			)

						def __call__(					self		:			str	,						_lowerCamelCase		:			str=None	,						_lowerCamelCase		:			str=None	,						_lowerCamelCase		:			List[str]="max_length"	,						_lowerCamelCase		:			Dict=64	,						_lowerCamelCase		:			Optional[int]=None	,						**_lowerCamelCase		:			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  :			str	       =		self.tokenizer(
																				    _lowerCamelCase	,						padding=_lowerCamelCase	,						max_length=_lowerCamelCase	,						return_tensors=_lowerCamelCase	,						**_lowerCamelCase			)

													if images is not None:
																				_UpperCAmelCase  :			int	       =		self.image_processor(_lowerCamelCase	,						return_tensors=_lowerCamelCase	,						**_lowerCamelCase			)

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

						def 						a__ (					self		:			List[Any]	,						*_lowerCamelCase		:			Tuple	,						**_lowerCamelCase		:			int			):
													return self.tokenizer.batch_decode(*_lowerCamelCase	,						**_lowerCamelCase			)

						def 						a__ (					self		:			List[Any]	,						*_lowerCamelCase		:			Optional[Any]	,						**_lowerCamelCase		:			List[Any]			):
													return self.tokenizer.decode(*_lowerCamelCase	,						**_lowerCamelCase			)






						@property
						def 						a__ (					self		:			str			):
													_UpperCAmelCase  :			Union[str, Any]	       =		self.tokenizer.model_input_names
													_UpperCAmelCase  :			Optional[int]	       =		self.image_processor.model_input_names
													return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)






import math




def 		lowerCAmelCase_     (     _SCREAMING_SNAKE_CASE  )   ->      list[int]:

							"""simple docstring"""






							if not isinstance(_SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE  ):
														_UpperCAmelCase  :			Optional[Any]	       =		F"""Input value of [number={number}] must be an integer"""
														raise TypeError(_SCREAMING_SNAKE_CASE  )
							if number < 1:
														_UpperCAmelCase  :			Dict	       =		F"""Input value of [number={number}] must be a positive integer"""
														raise ValueError(_SCREAMING_SNAKE_CASE  )
							_UpperCAmelCase  :			List[Any]	       =		[number]
							while number != 1:
														if number % 2 == 0:
																					_UpperCAmelCase  :			List[str]	       =		math.floor(math.sqrt(_SCREAMING_SNAKE_CASE  )  )
														else:
																					_UpperCAmelCase  :			Dict	       =		math.floor(
																					    math.sqrt(_SCREAMING_SNAKE_CASE  ) * math.sqrt(_SCREAMING_SNAKE_CASE  ) * math.sqrt(_SCREAMING_SNAKE_CASE  )  )
														sequence.append(_SCREAMING_SNAKE_CASE  )
							return sequence


if __name__ == "__main__":
				import doctest

				doctest.testmod()




