






from typing import TYPE_CHECKING

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


lowerCAmelCase_           = {
    'configuration_roc_bert': ['ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoCBertConfig'],
    'tokenization_roc_bert': ['RoCBertTokenizer'],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					pass

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_           = [
					    'ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'RoCBertForCausalLM',
					    'RoCBertForMaskedLM',
					    'RoCBertForMultipleChoice',
					    'RoCBertForPreTraining',
					    'RoCBertForQuestionAnswering',
					    'RoCBertForSequenceClassification',
					    'RoCBertForTokenClassification',
					    'RoCBertLayer',
					    'RoCBertModel',
					    'RoCBertPreTrainedModel',
					    'load_tf_weights_in_roc_bert',
					]

if TYPE_CHECKING:
					from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
					from .tokenization_roc_bert import RoCBertTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										raise OptionalDependencyNotAvailable()

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_roc_bert import (
										    ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    RoCBertForCausalLM,
										    RoCBertForMaskedLM,
										    RoCBertForMultipleChoice,
										    RoCBertForPreTraining,
										    RoCBertForQuestionAnswering,
										    RoCBertForSequenceClassification,
										    RoCBertForTokenClassification,
										    RoCBertLayer,
										    RoCBertModel,
										    RoCBertPreTrainedModel,
										    load_tf_weights_in_roc_bert,
										)


else:
					import sys

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














from collections import deque

from .hash_table import HashTable
class      UpperCamelCase (				snake_case__ ):







		"""simple docstring"""



		def __init__(  self		:       Any						,*_SCREAMING_SNAKE_CASE		:       Optional[int]						,**_SCREAMING_SNAKE_CASE		:       Optional[Any]		)    ->   int:


						'''simple docstring'''
						super().__init__(*_SCREAMING_SNAKE_CASE						,**_SCREAMING_SNAKE_CASE		)
		def       A(  self		:       Optional[int]						,_SCREAMING_SNAKE_CASE		:       Optional[Any]						,_SCREAMING_SNAKE_CASE		:       Tuple		)    ->   List[str]:


						'''simple docstring'''
						A	 =      deque([]		) if self.values[key] is None else self.values[key]
						self.values[key].appendleft(_SCREAMING_SNAKE_CASE		)
						A	 =      self.values[key]
		def       A(  self		:       Optional[Any]		)    ->   Union[str, Any]:


						'''simple docstring'''
						return (
						    sum(self.charge_factor - len(_SCREAMING_SNAKE_CASE		) for slot in self.values		)
						    / self.size_table
						    * self.charge_factor
						)







		def       A(  self		:       Dict						,_SCREAMING_SNAKE_CASE		:       Optional[int]						,_SCREAMING_SNAKE_CASE		:       str=None		)    ->   Union[str, Any]:


						'''simple docstring'''
						if not (
						    len(self.values[key]		) == self.charge_factor and self.values.count(_SCREAMING_SNAKE_CASE		) == 0
						):
										return key
						return super()._collision_resolution(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		)





