


'''simple docstring'''



from .number_theory.prime_numbers import next_prime



class       a							:


     """simple docstring"""






     def __init__(							self	:						int				,					snake_case	:						int				,					snake_case	:						int | None = None				,					snake_case	:						float | None = None				,					)		->				None:
          __UpperCAmelCase      :					str    				=     size_table
          __UpperCAmelCase      :					Optional[Any]    				=     [None] * self.size_table
          __UpperCAmelCase      :					List[Any]    				=     0.75 if lim_charge is None else lim_charge
          __UpperCAmelCase      :					Optional[int]    				=     1 if charge_factor is None else charge_factor
          __UpperCAmelCase      :					list    				=     []
          __UpperCAmelCase      :					dict    				=     {}

     def 					lowerCamelCase__					(							self	:						Union[str, Any]     )		->				Tuple:
          return self._keys

     def 					lowerCamelCase__					(							self	:						int     )		->				List[Any]:
          return sum(1 for slot in self.values if slot is not None     ) / (
              self.size_table * self.charge_factor
          )

     def 					lowerCamelCase__					(							self	:						Tuple				,					snake_case	:						Dict     )		->				Dict:
          return key % self.size_table

     def 					lowerCamelCase__					(							self	:						int				,					snake_case	:						Any     )		->				Dict:
          print(f'step {step_ord}'     )
          print(list(range(len(self.values     )     )     )     )
          print(self.values     )

     def 					lowerCamelCase__					(							self	:						List[str]				,					snake_case	:						str     )		->				List[Any]:
          __UpperCAmelCase      :					Any    				=     1
          __UpperCAmelCase      :					Optional[Any]    				=     values
          for value in values:
               self.insert_data(snake_case     )
               self._step_by_step(snake_case     )
               i += 1

     def 					lowerCamelCase__					(							self	:						List[Any]				,					snake_case	:						List[str]				,					snake_case	:						Any     )		->				Optional[Any]:
          __UpperCAmelCase      :					Dict    				=     data
          __UpperCAmelCase      :					str    				=     data

     def 					lowerCamelCase__					(							self	:						List[Any]				,					snake_case	:						Dict				,					snake_case	:						Any=None     )		->				Tuple:
          __UpperCAmelCase      :					Dict    				=     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      :					Optional[Any]    				=     self.hash_function(new_key + 1     )
               else:
                    __UpperCAmelCase      :					Dict    				=     None
                    break

          return new_key

     def 					lowerCamelCase__					(							self	:						Dict     )		->				List[str]:
          __UpperCAmelCase      :					Optional[Any]    				=     [value for value in self.values if value is not None]
          __UpperCAmelCase      :					Any    				=     next_prime(self.size_table				,					factor=2     )
          self._keys.clear()
          __UpperCAmelCase      :					Union[str, Any]    				=     [None] * self.size_table  # hell's pointers D: don't DRY ;/
          for value in survivor_values:
               self.insert_data(snake_case     )






     def 					lowerCamelCase__					(							self	:						List[str]				,					snake_case	:						str     )		->				List[Any]:
          __UpperCAmelCase      :					str    				=     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      :					Tuple    				=     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     )



'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCAmelCase     :Union[str, Any]												=  {
    "configuration_mpnet": ["MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "MPNetConfig"],
    "tokenization_mpnet": ["MPNetTokenizer"],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase     :Optional[int]												=  ["MPNetTokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase     :Union[str, Any]												=  [
          "MPNET_PRETRAINED_MODEL_ARCHIVE_LIST",
          "MPNetForMaskedLM",
          "MPNetForMultipleChoice",
          "MPNetForQuestionAnswering",
          "MPNetForSequenceClassification",
          "MPNetForTokenClassification",
          "MPNetLayer",
          "MPNetModel",
          "MPNetPreTrainedModel",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase     :Any												=  [
          "TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFMPNetEmbeddings",
          "TFMPNetForMaskedLM",
          "TFMPNetForMultipleChoice",
          "TFMPNetForQuestionAnswering",
          "TFMPNetForSequenceClassification",
          "TFMPNetForTokenClassification",
          "TFMPNetMainLayer",
          "TFMPNetModel",
          "TFMPNetPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
      from .tokenization_mpnet import MPNetTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_mpnet_fast import MPNetTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mpnet import (
                MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                MPNetForMaskedLM,
                MPNetForMultipleChoice,
                MPNetForQuestionAnswering,
                MPNetForSequenceClassification,
                MPNetForTokenClassification,
                MPNetLayer,
                MPNetModel,
                MPNetPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_mpnet import (
                TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFMPNetEmbeddings,
                TFMPNetForMaskedLM,
                TFMPNetForMultipleChoice,
                TFMPNetForQuestionAnswering,
                TFMPNetForSequenceClassification,
                TFMPNetForTokenClassification,
                TFMPNetMainLayer,
                TFMPNetModel,
                TFMPNetPreTrainedModel,
            )

else:
      import sys

      __UpperCAmelCase     :Optional[Any]												=  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)