




"""simple docstring"""

import numpy as np

def 		lowercase_   (		_UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase						):




	"""simple docstring"""






	A_	:					Optional[int]						 =		n_draws * class_counts / class_counts.sum()
	# floored means we don't overshoot n_samples, but probably undershoot
	A_	:					int						 =		np.floor(_UpperCAmelCase						)
	# we add samples according to how much "left over" probability
	# they had, until we arrive at n_samples
	A_	:					List[Any]						 =		int(n_draws - floored.sum()						)
	if need_to_add > 0:
		A_	:					Any						 =		continuous - floored
		A_	:					int						 =		np.sort(np.unique(_UpperCAmelCase						)						)[::-1]
		# add according to remainder, but break ties
		# randomly to avoid biases
		for value in values:
			((A_) ,			)	:					int						 =		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_	:					List[str]						 =		min(len(_UpperCAmelCase						)	,   _UpperCAmelCase						)
			A_	:					Dict						 =		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 		lowercase_   (		_UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase=10						):




	"""simple docstring"""






	A_ ,			A_	:					int						 =		np.unique(_UpperCAmelCase	,   return_inverse=_UpperCAmelCase						)
	A_	:					int						 =		classes.shape[0]
	A_	:					List[str]						 =		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)						)
	A_	:					List[Any]						 =		np.split(np.argsort(_UpperCAmelCase	,   kind='''mergesort'''						)	,   np.cumsum(_UpperCAmelCase						)[:-1]						)
	for _ in range(_UpperCAmelCase						):
		A_	:					int						 =		approximate_mode(_UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase						)
		A_	:					int						 =		class_counts - n_i
		A_	:					Optional[int]						 =		approximate_mode(_UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase						)

		A_	:					Any						 =		[]
		A_	:					Optional[int]						 =		[]

		for i in range(_UpperCAmelCase						):
			A_	:					Optional[int]						 =		rng.permutation(class_counts[i]						)
			A_	:					List[Any]						 =		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]]						)
		A_	:					Union[str, Any]						 =		rng.permutation(_UpperCAmelCase						)
		A_	:					int						 =		rng.permutation(_UpperCAmelCase						)

		yield train, test











"""simple docstring"""

class 							lowercase   :







				def __init__(				self :       Union[str, Any]   ,  _lowerCamelCase :       int							):



					"""simple docstring"""

					A_	:					Optional[int]						 =		data
					A_	:					int
					A_	:					Node

def 		lowercase_   (		_UpperCAmelCase						):




	"""simple docstring"""






	A_	:					List[str]						 =		0
	A_	:					List[str]						 =		x

def 		lowercase_   (		_UpperCAmelCase	,   _UpperCAmelCase						):




	"""simple docstring"""






	A_ ,			A_	:					Any						 =		find_set(_UpperCAmelCase						), find_set(_UpperCAmelCase						)
	if x == y:
		return

	elif x.rank > y.rank:
		A_	:					Optional[int]						 =		x
	else:
		A_	:					Optional[int]						 =		y
		if x.rank == y.rank:
			y.rank += 1

def 		lowercase_   (		_UpperCAmelCase						):




	"""simple docstring"""






	if x != x.parent:
		A_	:					Union[str, Any]						 =		find_set(x.parent						)
	return x.parent

def 		lowercase_   (		_UpperCAmelCase						):




	"""simple docstring"""






	A_	:					str						 =		({0, 1, 2}, {3, 4, 5})
	for s in sets:
		if node.data in s:
			return s
	A_	:					Optional[int]						 =		f"""{node.data} is not in {sets}"""
	raise ValueError(_UpperCAmelCase						)

def 		lowercase_   (		):




	"""simple docstring"""






	A_	:					Dict						 =		[Node(_UpperCAmelCase						) for i in range(6						)]
	for v in vertex:
		make_set(_UpperCAmelCase						)

	union_set(vertex[0]	,   vertex[1]						)
	union_set(vertex[1]	,   vertex[2]						)
	union_set(vertex[3]	,   vertex[4]						)
	union_set(vertex[3]	,   vertex[5]						)

	for nodea in vertex:
		for nodea in vertex:
			if find_python_set(_UpperCAmelCase						).isdisjoint(find_python_set(_UpperCAmelCase						)						):
				assert find_set(_UpperCAmelCase						) != find_set(_UpperCAmelCase						)
			else:
				assert find_set(_UpperCAmelCase						) == find_set(_UpperCAmelCase						)


if __name__ == "__main__":
		test_disjoint_set()





