





from .number_theory.prime_numbers import next_prime

class    __A  :


				def __init__(		self ,			UpperCAmelCase_ ,			UpperCAmelCase_ = None ,			UpperCAmelCase_ = None ,			):
								lowerCamelCase									=size_table
								lowerCamelCase									=[None] * self.size_table
								lowerCamelCase									=0.7_5 if lim_charge is None else lim_charge
								lowerCamelCase									=1 if charge_factor is None else charge_factor
								lowerCamelCase									=[]
								lowerCamelCase									={}


				def _snake_case							(		self				):
								return self._keys


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


				def _snake_case							(		self ,			UpperCAmelCase_				):
								return key % self.size_table


				def _snake_case							(		self ,			UpperCAmelCase_				):
								print(f"""step {step_ord}"""				)
								print(list(range(len(self.values				)				)				)				)
								print(self.values				)


				def _snake_case							(		self ,			UpperCAmelCase_				):
								lowerCamelCase									=1
								lowerCamelCase									=values
								for value in values:
												self.insert_data(UpperCAmelCase_				)
												self._step_by_step(UpperCAmelCase_				)
												i += 1


				def _snake_case							(		self ,			UpperCAmelCase_ ,			UpperCAmelCase_				):
								lowerCamelCase									=data
								lowerCamelCase									=data


				def _snake_case							(		self ,			UpperCAmelCase_ ,			UpperCAmelCase_=None				):
								lowerCamelCase									=self.hash_function(key + 1				)

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

								return new_key


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


				def _snake_case							(		self ,			UpperCAmelCase_				):
								lowerCamelCase									=self.hash_function(UpperCAmelCase_				)

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

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

								else:
												lowerCamelCase									=self._collision_resolution(UpperCAmelCase_ ,			UpperCAmelCase_				)
												if collision_resolution is not None:
																self._set_value(UpperCAmelCase_ ,			UpperCAmelCase_				)
												else:
																self.rehashing()
																self.insert_data(UpperCAmelCase_				)












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


UpperCAmelCase__    :      int       					={
    '''configuration_flaubert''': ['''FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FlaubertConfig''', '''FlaubertOnnxConfig'''],
    '''tokenization_flaubert''': ['''FlaubertTokenizer'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__    :      int       					=[
							    '''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:
							UpperCAmelCase__    :      List[str]       					=[
							    '''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

							UpperCAmelCase__    :      List[Any]       					=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





