






from collections import deque

from .hash_table import HashTable







class 				UpperCAmelCase_  (		__lowerCamelCase		):




     def __init__(				self	,						*_lowerCAmelCase	,						**_lowerCAmelCase		):
          super().__init__(*_lowerCAmelCase	,						**_lowerCAmelCase		)




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase		):
          UpperCAmelCase__      :  List[Any]				      =			deque([]		) if self.values[key] is None else self.values[key]
          self.values[key].appendleft(_lowerCAmelCase		)
          UpperCAmelCase__      :  List[str]				      =			self.values[key]




     def   __UpperCAmelCase  (				self		):
          return (
              sum(self.charge_factor - len(_lowerCAmelCase		) for slot in self.values		)
              / self.size_table
              * self.charge_factor
          )




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase=None		):
          if not (
              len(self.values[key]		) == self.charge_factor and self.values.count(_lowerCAmelCase		) == 0
          ):
               return key
          return super()._collision_resolution(_lowerCAmelCase	,						_lowerCAmelCase		)











'''simple docstring'''

import warnings

from ...utils import logging
from .image_processing_deit import DeiTImageProcessor


__lowerCAmelCase		     =  logging.get_logger(__name__)

class       __magic_name__		(       _UpperCamelCase  ):







							def __init__(				self   :			Tuple      ,*_UpperCAmelCase   :			str      ,**_UpperCAmelCase   :			int						):
									warnings.warn(
									    'The class DeiTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
									    ' use DeiTImageProcessor instead.'      ,_UpperCAmelCase      ,)
									super().__init__(*_UpperCAmelCase      ,**_UpperCAmelCase						)




