






"""simple docstring"""





from .number_theory.prime_numbers import next_prime






class 					a						:






    def __init__(	self					:							Tuple	,						__SCREAMING_SNAKE_CASE					:							int	,						__SCREAMING_SNAKE_CASE					:							int | None = None	,						__SCREAMING_SNAKE_CASE					:							float | None = None	,						)					->		None:
      lowerCamelCase_   					=						size_table
      lowerCamelCase_   					=						[None] * self.size_table
      lowerCamelCase_   					=						0.75 if lim_charge is None else lim_charge
      lowerCamelCase_   					=						1 if charge_factor is None else charge_factor
      lowerCamelCase_   					=						[]
      lowerCamelCase_   					=						{}






    def        UpperCamelCase	(	self					:							Tuple )					->		Dict:
      return self._keys






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






    def        UpperCamelCase	(	self					:							str	,						__SCREAMING_SNAKE_CASE					:							Optional[int] )					->		List[Any]:
      return key % self.size_table






    def        UpperCamelCase	(	self					:							Optional[int]	,						__SCREAMING_SNAKE_CASE					:							str )					->		int:
      print(F'''step {step_ord}''' )
      print(list(range(len(self.values ) ) ) )
      print(self.values )






    def        UpperCamelCase	(	self					:							Optional[int]	,						__SCREAMING_SNAKE_CASE					:							Tuple )					->		Tuple:
      lowerCamelCase_   					=						1
      lowerCamelCase_   					=						values
      for value in values:
        self.insert_data(__SCREAMING_SNAKE_CASE )
        self._step_by_step(__SCREAMING_SNAKE_CASE )
        i += 1






    def        UpperCamelCase	(	self					:							Optional[Any]	,						__SCREAMING_SNAKE_CASE					:							Union[str, Any]	,						__SCREAMING_SNAKE_CASE					:							Optional[Any] )					->		List[Any]:
      lowerCamelCase_   					=						data
      lowerCamelCase_   					=						data






    def        UpperCamelCase	(	self					:							Tuple	,						__SCREAMING_SNAKE_CASE					:							Optional[Any]	,						__SCREAMING_SNAKE_CASE					:							Union[str, Any]=None )					->		Dict:
      lowerCamelCase_   					=						self.hash_function(key + 1 )

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

      return new_key






    def        UpperCamelCase	(	self					:							Any )					->		Any:
      lowerCamelCase_   					=						[value for value in self.values if value is not None]
      lowerCamelCase_   					=						next_prime(self.size_table	,						factor=2 )
      self._keys.clear()
      lowerCamelCase_   					=						[None] * self.size_table  # hell's pointers D: don't DRY ;/
      for value in survivor_values:
        self.insert_data(__SCREAMING_SNAKE_CASE )






    def        UpperCamelCase	(	self					:							Any	,						__SCREAMING_SNAKE_CASE					:							Optional[int] )					->		List[str]:
      lowerCamelCase_   					=						self.hash_function(__SCREAMING_SNAKE_CASE )

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

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

      else:
        lowerCamelCase_   					=						self._collision_resolution(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE )
        if collision_resolution is not None:
          self._set_value(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE )
        else:
          self.rehashing()
          self.insert_data(__SCREAMING_SNAKE_CASE )












"""simple docstring"""





import numpy as np







def 					lowerCamelCase__	(							_lowerCamelCase	:		Dict		,    _lowerCamelCase	:		Any		,    _lowerCamelCase	:		Tuple			)				->    str:
  lowerCamelCase_   					=						n_draws * class_counts / class_counts.sum()
  # floored means we don't overshoot n_samples, but probably undershoot
  lowerCamelCase_   					=						np.floor(_lowerCamelCase			)
  # we add samples according to how much "left over" probability
  # they had, until we arrive at n_samples
  lowerCamelCase_   					=						int(n_draws - floored.sum()			)
  if need_to_add > 0:
    lowerCamelCase_   					=						continuous - floored
    lowerCamelCase_   					=						np.sort(np.unique(_lowerCamelCase			)			)[::-1]
    # add according to remainder, but break ties
    # randomly to avoid biases
    for value in values:
      ((lowerCamelCase_)					,     )   					=						np.where(remainder == value			)
      # if we need_to_add less than what's in inds
      # we draw randomly from them.
      # if we need to add more, we add them all and
      # go to the next value
      lowerCamelCase_   					=						min(len(_lowerCamelCase			)		,    _lowerCamelCase			)
      lowerCamelCase_   					=						rng.choice(_lowerCamelCase		,    size=_lowerCamelCase		,    replace=_lowerCamelCase			)
      floored[inds] += 1
      need_to_add -= add_now
      if need_to_add == 0:
        break
  return floored.astype(np.intaa			)







def 					lowerCamelCase__	(							_lowerCamelCase	:		Any		,    _lowerCamelCase	:		int		,    _lowerCamelCase	:		Optional[Any]		,    _lowerCamelCase	:		List[str]		,    _lowerCamelCase	:		Optional[int]=10			)				->    Any:
  lowerCamelCase_					,     lowerCamelCase_   					=						np.unique(_lowerCamelCase		,    return_inverse=_lowerCamelCase			)
  lowerCamelCase_   					=						classes.shape[0]
  lowerCamelCase_   					=						np.bincount(_lowerCamelCase			)
  if np.min(_lowerCamelCase			) < 2:
    raise ValueError('Minimum class count error'			)
  if n_train < n_classes:
    raise ValueError(
        'The train_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_train, n_classes)			)
  if n_test < n_classes:
    raise ValueError(
        'The test_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_test, n_classes)			)
  lowerCamelCase_   					=						np.split(np.argsort(_lowerCamelCase		,    kind='mergesort'			)		,    np.cumsum(_lowerCamelCase			)[:-1]			)
  for _ in range(_lowerCamelCase			):
    lowerCamelCase_   					=						approximate_mode(_lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase			)
    lowerCamelCase_   					=						class_counts - n_i
    lowerCamelCase_   					=						approximate_mode(_lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase			)

    lowerCamelCase_   					=						[]
    lowerCamelCase_   					=						[]

    for i in range(_lowerCamelCase			):
      lowerCamelCase_   					=						rng.permutation(class_counts[i]			)
      lowerCamelCase_   					=						class_indices[i].take(_lowerCamelCase		,    mode='clip'			)
      train.extend(perm_indices_class_i[: n_i[i]]			)
      test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]			)
    lowerCamelCase_   					=						rng.permutation(_lowerCamelCase			)
    lowerCamelCase_   					=						rng.permutation(_lowerCamelCase			)

    yield train, test




