




'''simple docstring'''







import numpy as np






def 						_lowercase						(      lowercase_		,				lowercase_		,				lowercase_    ):



		'''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 						_lowercase						(      lowercase_		,				lowercase_		,				lowercase_		,				lowercase_		,				lowercase_=10    ):



		'''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  lowerCAmelCase__     (      _UpperCamelCase							:					list       )       ->					list:





						"""simple docstring"""






						if len(_UpperCamelCase       ) <= 1:
												return data
						else:
												return [
												    *quick_sort([e for e in data[1:] if e <= data[0]]       ),
												    data[0],
												    *quick_sort([e for e in data[1:] if e > data[0]]       ),
												]


if __name__ == "__main__":
					import doctest

					doctest.testmod()


