
import numpy as np

def a__   (	A_,					A_,					A_ ):






  '''simple docstring'''





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





def a__   (	A_,					A_,					A_,					A_,					A_=10 ):






  '''simple docstring'''





  __magic_name__					,   __magic_name__    					=	np.unique(snake_case__,					return_inverse=snake_case__ )
  __magic_name__    					=	classes.shape[0]
  __magic_name__    					=	np.bincount(snake_case__ )
  if np.min(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) )
  __magic_name__    					=	np.split(np.argsort(snake_case__,					kind="""mergesort""" ),					np.cumsum(snake_case__ )[:-1] )
  for _ in range(snake_case__ ):
    __magic_name__    					=	approximate_mode(snake_case__,					snake_case__,					snake_case__ )
    __magic_name__    					=	class_counts - n_i
    __magic_name__    					=	approximate_mode(snake_case__,					snake_case__,					snake_case__ )

    __magic_name__    					=	[]
    __magic_name__    					=	[]

    for i in range(snake_case__ ):
      __magic_name__    					=	rng.permutation(class_counts[i] )
      __magic_name__    					=	class_indices[i].take(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]] )
    __magic_name__    					=	rng.permutation(snake_case__ )
    __magic_name__    					=	rng.permutation(snake_case__ )

    yield train, test



class 	UpperCAmelCase_      :


  '''simple docstring'''







  def __init__(			self      :			Tuple      ,							UpperCamelCase__      :			Dict=None   )      ->     List[str]:







    """simple docstring"""






    __magic_name__    					=	data
    __magic_name__    					=	None





  def __repr__(			self      :			Optional[int]   )      ->     Optional[int]:







    """simple docstring"""






    __magic_name__    					=	""""""
    __magic_name__    					=	self
    while temp:
      string_rep += F'''<{temp.data}> ---> '''
      __magic_name__    					=	temp.next
    string_rep += "<END>"
    return string_rep







def a__   (	A_ ):






  '''simple docstring'''





  if not elements_list:
    raise Exception("""The Elements List is empty""" )

  # Set first element as Head
  __magic_name__    					=	Node(elements_list[0] )
  __magic_name__    					=	head
  # Loop through elements from position 1
  for data in elements_list[1:]:
    __magic_name__    					=	Node(A_ )
    __magic_name__    					=	current.next
  return head


__lowerCAmelCase      :					Union[str, Any]   	=       [1, 3, 5, 32, 44, 12, 43]
print(F'''List: {list_data}''')
print('Creating Linked List from List.')
__lowerCAmelCase      :					Optional[Any]   	=       make_linked_list(list_data)
print('Linked List:')





print(linked_list)

