




"""simple docstring"""







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






class  SCREAMING_SNAKE_CASE__   (   UpperCAmelCase__   ):


						def __init__(							self			:     Dict  ,  *lowerCAmelCase			:     str  ,  **lowerCAmelCase			:     Tuple   ):
										super().__init__(*lowerCAmelCase  ,  **lowerCAmelCase   )


						def 					__lowercase	(							self			:     Dict  ,  lowerCAmelCase			:     Any  ,  lowerCAmelCase			:     str   ):
										lowerCAmelCase		=			(
										    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 					__lowercase	(							self			:     List[Any]  ,  lowerCAmelCase			:     List[Any]  ,  lowerCAmelCase			:     List[str]  ,  lowerCAmelCase			:     List[str]   ):
										return (increment * self.__hash_function_a(lowerCAmelCase  ,  lowerCAmelCase   )) % self.size_table




						def 					__lowercase	(							self			:     Tuple  ,  lowerCAmelCase			:     str  ,  lowerCAmelCase			:     Union[str, Any]=None   ):
										lowerCAmelCase		=			1
										lowerCAmelCase		=			self.hash_function(lowerCAmelCase   )

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

										return new_key






"""simple docstring"""







import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class  SCREAMING_SNAKE_CASE__   (   _a   ):
						_a        =  ['image_processor', 'tokenizer']
						_a        =  'VideoMAEImageProcessor'
						_a        =  ('CLIPTokenizer', 'CLIPTokenizerFast')


						def __init__(							self			:     Union[str, Any]  ,  lowerCAmelCase			:     Optional[Any]=None  ,  lowerCAmelCase			:     Tuple=None  ,  **lowerCAmelCase			:     List[str]   ):
										lowerCAmelCase		=			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  ,  )
														lowerCAmelCase		=			kwargs.pop("""feature_extractor"""   )

										lowerCAmelCase		=			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   )
										lowerCAmelCase		=			self.image_processor


						def __call__(							self			:     Union[str, Any]  ,  lowerCAmelCase			:     str=None  ,  lowerCAmelCase			:     Tuple=None  ,  lowerCAmelCase			:     int=None  ,  **lowerCAmelCase			:     str   ):

										if text is None and videos is None:
														raise ValueError("""You have to specify either text or videos. Both cannot be none."""   )

										if text is not None:
														lowerCAmelCase		=			self.tokenizer(lowerCAmelCase  ,  return_tensors=lowerCAmelCase  ,  **lowerCAmelCase   )

										if videos is not None:
														lowerCAmelCase		=			self.image_processor(lowerCAmelCase  ,  return_tensors=lowerCAmelCase  ,  **lowerCAmelCase   )

										if text is not None and videos is not None:
														lowerCAmelCase		=			image_features.pixel_values
														return encoding
										elif text is not None:
														return encoding
										else:
														return BatchEncoding(data=dict(**lowerCAmelCase   )  ,  tensor_type=lowerCAmelCase   )


						def 					__lowercase	(							self			:     str  ,  *lowerCAmelCase			:     List[str]  ,  **lowerCAmelCase			:     int   ):
										return self.tokenizer.batch_decode(*lowerCAmelCase  ,  **lowerCAmelCase   )


						def 					__lowercase	(							self			:     List[str]  ,  *lowerCAmelCase			:     Any  ,  **lowerCAmelCase			:     List[Any]   ):
										return self.tokenizer.decode(*lowerCAmelCase  ,  **lowerCAmelCase   )


						@property
						def 					__lowercase	(							self			:     Dict   ):
										return ["input_ids", "attention_mask", "position_ids", "pixel_values"]


						@property
						def 					__lowercase	(							self			:     List[str]   ):
										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 					__lowercase	(							self			:     Tuple   ):
										warnings.warn(
										    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""  ,  lowerCAmelCase  ,  )
										return self.image_processor





