





import numpy as np
def   _A (	SCREAMING_SNAKE_CASE  :     Any , SCREAMING_SNAKE_CASE  :     Union[str, Any] , SCREAMING_SNAKE_CASE  :     List[Any]		):





  """simple docstring"""



  a__			:      Optional[int]            =n_draws * class_counts / class_counts.sum()
  # floored means we don't overshoot n_samples, but probably undershoot
  a__			:      Any            =np.floor(SCREAMING_SNAKE_CASE		)
  # we add samples according to how much "left over" probability
  # they had, until we arrive at n_samples
  a__			:      int            =int(n_draws - floored.sum()		)
  if need_to_add > 0:
    a__			:      Any            =continuous - floored
    a__			:      Any            =np.sort(np.unique(SCREAMING_SNAKE_CASE		)		)[::-1]
    # add according to remainder, but break ties
    # randomly to avoid biases
    for value in values:
      ((a__)      ,							)			:      Dict            =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
      a__			:      str            =min(len(SCREAMING_SNAKE_CASE		) , SCREAMING_SNAKE_CASE		)
      a__			:      str            =rng.choice(SCREAMING_SNAKE_CASE , size=SCREAMING_SNAKE_CASE , replace=SCREAMING_SNAKE_CASE		)
      floored[inds] += 1
      need_to_add -= add_now
      if need_to_add == 0:
        break
  return floored.astype(np.intaa		)





def   _A (	SCREAMING_SNAKE_CASE  :     Optional[int] , SCREAMING_SNAKE_CASE  :     Tuple , SCREAMING_SNAKE_CASE  :     List[Any] , SCREAMING_SNAKE_CASE  :     Optional[Any] , SCREAMING_SNAKE_CASE  :     List[str]=10		):





  """simple docstring"""



  a__      ,							a__			:      List[Any]            =np.unique(SCREAMING_SNAKE_CASE , return_inverse=SCREAMING_SNAKE_CASE		)
  a__			:      str            =classes.shape[0]
  a__			:      Any            =np.bincount(SCREAMING_SNAKE_CASE		)
  if np.min(SCREAMING_SNAKE_CASE		) < 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)		)
  a__			:      List[str]            =np.split(np.argsort(SCREAMING_SNAKE_CASE , kind="mergesort"		) , np.cumsum(SCREAMING_SNAKE_CASE		)[:-1]		)
  for _ in range(SCREAMING_SNAKE_CASE		):
    a__			:      Any            =approximate_mode(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE		)
    a__			:      Optional[int]            =class_counts - n_i
    a__			:      Optional[int]            =approximate_mode(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE		)

    a__			:      Tuple            =[]
    a__			:      Optional[Any]            =[]

    for i in range(SCREAMING_SNAKE_CASE		):
      a__			:      List[str]            =rng.permutation(class_counts[i]		)
      a__			:      int            =class_indices[i].take(SCREAMING_SNAKE_CASE , 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]]		)
    a__			:      Dict            =rng.permutation(SCREAMING_SNAKE_CASE		)
    a__			:      Tuple            =rng.permutation(SCREAMING_SNAKE_CASE		)

    yield train, test













def   _A (	SCREAMING_SNAKE_CASE  :     int		):





  """simple docstring"""



  if not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE		) or number < 0:
    raise ValueError("Input must be a non-negative integer"		)

  a__			:      List[str]            =number & ~(number - 1)
  a__			:      Optional[int]            =0
  while intermediate:
    intermediate >>= 1
    index += 1
  return index - 1


if __name__ == "__main__":
     import doctest

     doctest.testmod(verbose=True)






