





"""simple docstring"""


import numpy as np





def 							a__			(  _UpperCAmelCase	,     _UpperCAmelCase	,     _UpperCAmelCase	)    ->  Tuple:

 '''simple docstring'''






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





def 							a__			(  _UpperCAmelCase	,     _UpperCAmelCase	,     _UpperCAmelCase	,     _UpperCAmelCase	,     _UpperCAmelCase=1_0	)    ->  int:

 '''simple docstring'''






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

  snake_case__    							=				[]
  snake_case__    							=				[]

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

  yield train, test








"""simple docstring"""


def 							a__			(  _UpperCAmelCase = 1_0_0_0	)    ->  int:

 '''simple docstring'''






 snake_case__    							=				[]
 snake_case__    							=				[]
 snake_case__    							=				3
 snake_case__    							=				5
 snake_case__    							=				1
 while True:
  snake_case__    							=				z * temp
  if result < n:
   zmulti.append(_UpperCAmelCase	)
   temp += 1
  else:
   snake_case__    							=				1
   break
 while True:
  snake_case__    							=				x * temp
  if result < n:
   xmulti.append(_UpperCAmelCase	)
   temp += 1
  else:
   break
 snake_case__    							=				list(set(xmulti + zmulti	)	)
 return sum(_UpperCAmelCase	)


if __name__ == "__main__":
   print(f'''{solution() = }''')

