



from .number_theory.prime_numbers import next_prime






class  lowerCamelCase_   :
  """simple docstring"""




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




  def 						_lowercase					(			self				:     int       ) -> List[Any]:
    return self._keys




  def 						_lowercase					(			self				:     int       ) -> Tuple:
    return sum(1 for slot in self.values if slot is not None       ) / (
        self.size_table * self.charge_factor
    )




  def 						_lowercase					(			self				:     Dict      ,     _a				:     Union[str, Any]       ) -> int:
    return key % self.size_table




  def 						_lowercase					(			self				:     Optional[int]      ,     _a				:     List[str]       ) -> Any:
    print(f'step {step_ord}'       )
    print(list(range(len(self.values       )       )       )       )
    print(self.values       )




  def 						_lowercase					(			self				:     str      ,     _a				:     str       ) -> Optional[Any]:
    __lowerCamelCase	:	Tuple   	=			1
    __lowerCamelCase	:	Optional[int]   	=			values
    for value in values:
      self.insert_data(_a       )
      self._step_by_step(_a       )
      i += 1




  def 						_lowercase					(			self				:     Optional[Any]      ,     _a				:     Optional[Any]      ,     _a				:     Optional[int]       ) -> str:
    __lowerCamelCase	:	Any   	=			data
    __lowerCamelCase	:	List[Any]   	=			data




  def 						_lowercase					(			self				:     int      ,     _a				:     Optional[Any]      ,     _a				:     Tuple=None       ) -> Optional[Any]:
    __lowerCamelCase	:	List[Any]   	=			self.hash_function(key + 1       )

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

    return new_key




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




  def 						_lowercase					(			self				:     Optional[Any]      ,     _a				:     Any       ) -> Optional[Any]:
    __lowerCamelCase	:	str   	=			self.hash_function(_a       )

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

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

    else:
      __lowerCamelCase	:	Tuple   	=			self._collision_resolution(_a      ,     _a       )
      if collision_resolution is not None:
        self._set_value(_a      ,     _a       )
      else:
        self.rehashing()
        self.insert_data(_a       )











'''simple docstring'''





from typing import TYPE_CHECKING

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


_UpperCamelCase							    =						{
    'configuration_imagegpt': ['IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ImageGPTConfig', 'ImageGPTOnnxConfig']
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase							    =						['ImageGPTFeatureExtractor']
   _UpperCamelCase							    =						['ImageGPTImageProcessor']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase							    =						[
       'IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'ImageGPTForCausalImageModeling',
       'ImageGPTForImageClassification',
       'ImageGPTModel',
       'ImageGPTPreTrainedModel',
       'load_tf_weights_in_imagegpt',
   ]


if TYPE_CHECKING:
   from .configuration_imagegpt import IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, ImageGPTConfig, ImageGPTOnnxConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_imagegpt import ImageGPTFeatureExtractor
      from .image_processing_imagegpt import ImageGPTImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_imagegpt import (
          IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          ImageGPTForCausalImageModeling,
          ImageGPTForImageClassification,
          ImageGPTModel,
          ImageGPTPreTrainedModel,
          load_tf_weights_in_imagegpt,
      )

else:
   import sys

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






