code: 
  
  
  
  
  
  
  """simple docstring"""
  
  
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
  
  
  A_			  =    {
      "configuration_flaubert": ["FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "FlaubertConfig", "FlaubertOnnxConfig"],
      "tokenization_flaubert": ["FlaubertTokenizer"],
  }
  
  try:
  				if not is_torch_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				A_			  =    [
  				    "FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
  				    "FlaubertForMultipleChoice",
  				    "FlaubertForQuestionAnswering",
  				    "FlaubertForQuestionAnsweringSimple",
  				    "FlaubertForSequenceClassification",
  				    "FlaubertForTokenClassification",
  				    "FlaubertModel",
  				    "FlaubertWithLMHeadModel",
  				    "FlaubertPreTrainedModel",
  				]
  
  try:
  				if not is_tf_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				A_			  =    [
  				    "TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
  				    "TFFlaubertForMultipleChoice",
  				    "TFFlaubertForQuestionAnsweringSimple",
  				    "TFFlaubertForSequenceClassification",
  				    "TFFlaubertForTokenClassification",
  				    "TFFlaubertModel",
  				    "TFFlaubertPreTrainedModel",
  				    "TFFlaubertWithLMHeadModel",
  				]
  
  
  if TYPE_CHECKING:
  				from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
  				from .tokenization_flaubert import FlaubertTokenizer
  
  				try:
  								if not is_torch_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .modeling_flaubert import (
  								    FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
  								    FlaubertForMultipleChoice,
  								    FlaubertForQuestionAnswering,
  								    FlaubertForQuestionAnsweringSimple,
  								    FlaubertForSequenceClassification,
  								    FlaubertForTokenClassification,
  								    FlaubertModel,
  								    FlaubertPreTrainedModel,
  								    FlaubertWithLMHeadModel,
  								)
  
  				try:
  								if not is_tf_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .modeling_tf_flaubert import (
  								    TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
  								    TFFlaubertForMultipleChoice,
  								    TFFlaubertForQuestionAnsweringSimple,
  								    TFFlaubertForSequenceClassification,
  								    TFFlaubertForTokenClassification,
  								    TFFlaubertModel,
  								    TFFlaubertPreTrainedModel,
  								    TFFlaubertWithLMHeadModel,
  								)
  
  else:
  				import sys
  
  				A_			  =    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
style_context: 
  
  
  
  
  
  
  """simple docstring"""
  
  
  
  
  
  
  from .hash_table import HashTable
  from .number_theory.prime_numbers import is_prime, next_prime
  class 						__lowerCAmelCase   (			UpperCAmelCase       ):
  
  
  
  
  
  
  
    '''simple docstring'''
  
  
    def __init__(      self:		List[Any]				,			*UpperCamelCase_:		Dict				,			**UpperCamelCase_:		Optional[int]					):
       super().__init__(*UpperCamelCase_				,			**UpperCamelCase_					)
    def 					UpperCamelCase__   (      self:		Any				,			UpperCamelCase_:		Optional[int]				,			UpperCamelCase_:		Optional[int]					):
       UpperCamelCase_     =(
           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 					UpperCamelCase__   (      self:		str				,			UpperCamelCase_:		List[str]				,			UpperCamelCase_:		str				,			UpperCamelCase_:		Tuple					):
       return (increment * self.__hash_function_a(UpperCamelCase_				,			UpperCamelCase_					)) % self.size_table
  
  
  
  
  
    def 					UpperCamelCase__   (      self:		str				,			UpperCamelCase_:		int				,			UpperCamelCase_:		str=None					):
       UpperCamelCase_     =1
       UpperCamelCase_     =self.hash_function(UpperCamelCase_					)
  
       while self.values[new_key] is not None and self.values[new_key] != key:
          UpperCamelCase_     =(
              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
  