
'''simple docstring'''

from typing import TYPE_CHECKING

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


UpperCamelCase_   	=				{
    """configuration_squeezebert""": [
        """SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """SqueezeBertConfig""",
        """SqueezeBertOnnxConfig""",
    ],
    """tokenization_squeezebert""": ["""SqueezeBertTokenizer"""],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase_   	=				["""SqueezeBertTokenizerFast"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase_   	=				[
         """SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """SqueezeBertForMaskedLM""",
         """SqueezeBertForMultipleChoice""",
         """SqueezeBertForQuestionAnswering""",
         """SqueezeBertForSequenceClassification""",
         """SqueezeBertForTokenClassification""",
         """SqueezeBertModel""",
         """SqueezeBertModule""",
         """SqueezeBertPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_squeezebert import (
         SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
         SqueezeBertConfig,
         SqueezeBertOnnxConfig,
     )
     from .tokenization_squeezebert import SqueezeBertTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_squeezebert import (
              SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              SqueezeBertForMaskedLM,
              SqueezeBertForMultipleChoice,
              SqueezeBertForQuestionAnswering,
              SqueezeBertForSequenceClassification,
              SqueezeBertForTokenClassification,
              SqueezeBertModel,
              SqueezeBertModule,
              SqueezeBertPreTrainedModel,
          )

else:
     import sys

     UpperCamelCase_   	=				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





from collections import deque

from .hash_table import HashTable







class   __lowercase      (_UpperCAmelCase ):
 def __init__(							self      ,							*A_      ,							**A_					)					->int:



       '''simple docstring'''





       super().__init__(*A_      ,							**A_					)
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_					)					->Optional[int]:



       '''simple docstring'''





       __lowerCAmelCase					:						Dict					=					deque([]					) if self.values[key] is None else self.values[key]
       self.values[key].appendleft(A_					)
       __lowerCAmelCase					:						int					=					self.values[key]
 def 	UpperCamelCase__  (							self					)					->Tuple:



       '''simple docstring'''





       return (
           sum(self.charge_factor - len(A_					) for slot in self.values					)
           / self.size_table
           * self.charge_factor
       )





 def 	UpperCamelCase__  (							self      ,							A_      ,							A_=None					)					->str:



       '''simple docstring'''





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


