





'''simple docstring'''






import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin



class  lowerCamelCase__				(						snake_case_						):


	"""simple docstring"""





	__magic_name__							   =		["""image_processor""", """tokenizer"""]
	__magic_name__							   =		"""AutoImageProcessor"""
	__magic_name__							   =		"""AutoTokenizer"""




	def __init__(					self   ,     UpperCAmelCase__=None   ,     UpperCAmelCase__=None   ,     **UpperCAmelCase__			)       ->				List[str]:
					_A :				List[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__   ,     )
									_A :				Optional[Any]				=  kwargs.pop('''feature_extractor'''			)

					_A :				List[Any]				=  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__			)
					_A :				Union[str, Any]				=  self.image_processor
					_A :				List[str]				=  False




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

					_A :				Dict				=  kwargs.pop('''images'''   ,     UpperCAmelCase__			)
					_A :				Any				=  kwargs.pop('''text'''   ,     UpperCAmelCase__			)
					if len(UpperCAmelCase__			) > 0:
									_A :				Optional[int]				=  args[0]
									_A :				int				=  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 :				str				=  self.image_processor(UpperCAmelCase__   ,     *UpperCAmelCase__   ,     **UpperCAmelCase__			)
					if text is not None:
									_A :				Optional[Any]				=  self.tokenizer(UpperCAmelCase__   ,     **UpperCAmelCase__			)

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




	def 				_lowerCamelCase				(					self   ,     *UpperCAmelCase__   ,     **UpperCAmelCase__			)       ->				int:
					return self.tokenizer.batch_decode(*UpperCAmelCase__   ,     **UpperCAmelCase__			)




	def 				_lowerCamelCase				(					self   ,     *UpperCAmelCase__   ,     **UpperCAmelCase__			)       ->				Any:
					return self.tokenizer.decode(*UpperCAmelCase__   ,     **UpperCAmelCase__			)




	@contextmanager
	def 				_lowerCamelCase				(					self			)       ->				Dict:
					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 :				Optional[int]				=  True
					_A :				str				=  self.tokenizer
					yield
					_A :				List[str]				=  self.image_processor
					_A :				int				=  False




	@property
	def 				_lowerCamelCase				(					self			)       ->				Optional[int]:
					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 				_lowerCamelCase				(					self			)       ->				List[Any]:
					warnings.warn(
					    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''   ,     UpperCAmelCase__   ,     )
					return self.image_processor







'''simple docstring'''






from .hash_table import HashTable
from .number_theory.prime_numbers import is_prime, next_prime



class  lowerCamelCase__				(						snake_case_						):


	"""simple docstring"""





	def __init__(					self   ,     *UpperCAmelCase__   ,     **UpperCAmelCase__			)       ->				Optional[int]:
					super().__init__(*UpperCAmelCase__   ,     **UpperCAmelCase__			)




	def 				_lowerCamelCase				(					self   ,     UpperCAmelCase__   ,     UpperCAmelCase__			)       ->				Optional[int]:
					_A :				Union[str, Any]				=  (
					    next_prime(value % self.size_table			)
					    if not is_prime(value % self.size_table			)
					    else value % self.size_table
					)  # gt = bigger than
					return next_prime_gt - (data % next_prime_gt)




	def 				_lowerCamelCase				(					self   ,     UpperCAmelCase__   ,     UpperCAmelCase__   ,     UpperCAmelCase__			)       ->				Union[str, Any]:
					return (increment * self.__hash_function_a(UpperCAmelCase__   ,     UpperCAmelCase__			)) % self.size_table




	def 				_lowerCamelCase				(					self   ,     UpperCAmelCase__   ,     UpperCAmelCase__=None			)       ->				List[Any]:
					_A :				Optional[int]				=  1
					_A :				Optional[int]				=  self.hash_function(UpperCAmelCase__			)

					while self.values[new_key] is not None and self.values[new_key] != key:
									_A :				int				=  (
									    self.__hash_double_function(UpperCAmelCase__   ,     UpperCAmelCase__   ,     UpperCAmelCase__			)
									    if self.balanced_factor() >= self.lim_charge
									    else None
									)
									if new_key is None:
													break
									else:
													i += 1

					return new_key
