'''simple docstring'''


from .number_theory.prime_numbers import next_prime






class 	__SCREAMING_SNAKE_CASE		:







  '''simple docstring'''






  def __init__(					self ,						snake_case_ ,						snake_case_ = None ,						snake_case_ = None ,						):




       '''simple docstring'''

       UpperCAmelCase_				:  Any  = size_table
       UpperCAmelCase_				:  List[Any]  = [None] * self.size_table
       UpperCAmelCase_				:  int  = 0.75 if lim_charge is None else lim_charge
       UpperCAmelCase_				:  Optional[Any]  = 1 if charge_factor is None else charge_factor
       UpperCAmelCase_				:  list  = []
       UpperCAmelCase_				:  dict  = {}


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       return self._keys


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       return sum(1 for slot in self.values if slot is not None		) / (
           self.size_table * self.charge_factor
       )


  def _UpperCamelCase	(					self ,						snake_case_		):




       '''simple docstring'''

       return key % self.size_table


  def _UpperCamelCase	(					self ,						snake_case_		):




       '''simple docstring'''

       print(F'''step {step_ord}'''		)
       print(list(range(len(self.values		)		)		)		)
       print(self.values		)


  def _UpperCamelCase	(					self ,						snake_case_		):




       '''simple docstring'''

       UpperCAmelCase_				:  Optional[int]  = 1
       UpperCAmelCase_				:  Any  = values
       for value in values:
            self.insert_data(snake_case_		)
            self._step_by_step(snake_case_		)
            i += 1


  def _UpperCamelCase	(					self ,						snake_case_ ,						snake_case_		):




       '''simple docstring'''

       UpperCAmelCase_				:  Optional[int]  = data
       UpperCAmelCase_				:  Any  = data


  def _UpperCamelCase	(					self ,						snake_case_ ,						snake_case_=None		):




       '''simple docstring'''

       UpperCAmelCase_				:  Tuple  = self.hash_function(key + 1		)

       while self.values[new_key] is not None and self.values[new_key] != key:
            if self.values.count(snake_case_		) > 0:
                 UpperCAmelCase_				:  Dict  = self.hash_function(new_key + 1		)
            else:
                 UpperCAmelCase_				:  Optional[Any]  = None
                 break

       return new_key


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Dict  = [value for value in self.values if value is not None]
       UpperCAmelCase_				:  List[Any]  = next_prime(self.size_table ,						factor=2		)
       self._keys.clear()
       UpperCAmelCase_				:  Any  = [None] * self.size_table  # hell's pointers D: don't DRY ;/
       for value in survivor_values:
            self.insert_data(snake_case_		)






  def _UpperCamelCase	(					self ,						snake_case_		):




       '''simple docstring'''

       UpperCAmelCase_				:  List[Any]  = self.hash_function(snake_case_		)

       if self.values[key] is None:
            self._set_value(snake_case_ ,						snake_case_		)

       elif self.values[key] == data:
            pass

       else:
            UpperCAmelCase_				:  Optional[Any]  = self._collision_resolution(snake_case_ ,						snake_case_		)
            if collision_resolution is not None:
                 self._set_value(snake_case_ ,						snake_case_		)
            else:
                 self.rehashing()
                 self.insert_data(snake_case_		)











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_			      =  {'''configuration_qdqbert''': ['''QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''QDQBertConfig''']}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCAmelCase_			      =  [
						    '''QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''QDQBertForMaskedLM''',
						    '''QDQBertForMultipleChoice''',
						    '''QDQBertForNextSentencePrediction''',
						    '''QDQBertForQuestionAnswering''',
						    '''QDQBertForSequenceClassification''',
						    '''QDQBertForTokenClassification''',
						    '''QDQBertLayer''',
						    '''QDQBertLMHeadModel''',
						    '''QDQBertModel''',
						    '''QDQBertPreTrainedModel''',
						    '''load_tf_weights_in_qdqbert''',
						]


if TYPE_CHECKING:
						from .configuration_qdqbert import QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, QDQBertConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_qdqbert import (
												    QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    QDQBertForMaskedLM,
												    QDQBertForMultipleChoice,
												    QDQBertForNextSentencePrediction,
												    QDQBertForQuestionAnswering,
												    QDQBertForSequenceClassification,
												    QDQBertForTokenClassification,
												    QDQBertLayer,
												    QDQBertLMHeadModel,
												    QDQBertModel,
												    QDQBertPreTrainedModel,
												    load_tf_weights_in_qdqbert,
												)


else:
						import sys

						UpperCAmelCase_			      =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





