
"""simple docstring"""






from .number_theory.prime_numbers import next_prime




class      _SCREAMING_SNAKE_CASE   :






			def __init__(   self			,  __A			,  __A = None			,  __A = None			,  )     ->	List[str]:
						lowerCAmelCase_       :Tuple     	=    size_table
						lowerCAmelCase_       :List[str]     	=    [None] * self.size_table
						lowerCAmelCase_       :Union[str, Any]     	=    0.7_5 if lim_charge is None else lim_charge
						lowerCAmelCase_       :Dict     	=    1 if charge_factor is None else charge_factor
						lowerCAmelCase_       :list     	=    []
						lowerCAmelCase_       :dict     	=    {}






			def 					__lowerCAmelCase					(   self			)     ->	Any:
						return self._keys






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






			def 					__lowerCAmelCase					(   self			,  __A			)     ->	Optional[Any]:
						return key % self.size_table






			def 					__lowerCAmelCase					(   self			,  __A			)     ->	List[str]:
						print(f"""step {step_ord}"""			)
						print(list(range(len(self.values			)			)			)			)
						print(self.values			)






			def 					__lowerCAmelCase					(   self			,  __A			)     ->	Optional[int]:
						lowerCAmelCase_       :Optional[Any]     	=    1
						lowerCAmelCase_       :Dict     	=    values
						for value in values:
									self.insert_data(snake_case_			)
									self._step_by_step(snake_case_			)
									i += 1






			def 					__lowerCAmelCase					(   self			,  __A			,  __A			)     ->	int:
						lowerCAmelCase_       :int     	=    data
						lowerCAmelCase_       :int     	=    data






			def 					__lowerCAmelCase					(   self			,  __A			,  __A=None			)     ->	List[Any]:
						lowerCAmelCase_       :Optional[Any]     	=    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:
												lowerCAmelCase_       :Dict     	=    self.hash_function(new_key + 1			)
									else:
												lowerCAmelCase_       :List[str]     	=    None
												break

						return new_key






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






			def 					__lowerCAmelCase					(   self			,  __A			)     ->	Dict:
						lowerCAmelCase_       :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:
									lowerCAmelCase_       :Dict     	=    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_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase_	:		Union[str, Any]         =   {
    'configuration_camembert': ['CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CamembertConfig', 'CamembertOnnxConfig'],
}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_	:		Any         =   ['CamembertTokenizer']

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_	:		int         =   ['CamembertTokenizerFast']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_	:		Optional[Any]         =   [
       'CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'CamembertForCausalLM',
       'CamembertForMaskedLM',
       'CamembertForMultipleChoice',
       'CamembertForQuestionAnswering',
       'CamembertForSequenceClassification',
       'CamembertForTokenClassification',
       'CamembertModel',
       'CamembertPreTrainedModel',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_	:		int         =   [
       'TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFCamembertForCausalLM',
       'TFCamembertForMaskedLM',
       'TFCamembertForMultipleChoice',
       'TFCamembertForQuestionAnswering',
       'TFCamembertForSequenceClassification',
       'TFCamembertForTokenClassification',
       'TFCamembertModel',
       'TFCamembertPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_camembert import CamembertTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_camembert_fast import CamembertTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_camembert import (
          CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          CamembertForCausalLM,
          CamembertForMaskedLM,
          CamembertForMultipleChoice,
          CamembertForQuestionAnswering,
          CamembertForSequenceClassification,
          CamembertForTokenClassification,
          CamembertModel,
          CamembertPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_camembert import (
          TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFCamembertForCausalLM,
          TFCamembertForMaskedLM,
          TFCamembertForMultipleChoice,
          TFCamembertForQuestionAnswering,
          TFCamembertForSequenceClassification,
          TFCamembertForTokenClassification,
          TFCamembertModel,
          TFCamembertPreTrainedModel,
      )

else:
   import sys

   lowerCamelCase_	:		int         =   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)