import numpy as np


def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	):
						snake_case_       =     n_draws * class_counts / class_counts.sum()
						# floored means we don't overshoot n_samples, but probably undershoot
						snake_case_       =     np.floor(__A	)
						# 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(__A	)	)[::-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(__A	)  ,		__A	)
																		snake_case_       =     rng.choice(__A  ,		size=__A  ,		replace=__A	)
																		floored[inds] += 1
																		need_to_add -= add_now
																		if need_to_add == 0:
																								break
						return floored.astype(np.intaa	)


def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__=10	):
						snake_case_,   snake_case_       =     np.unique(__A  ,		return_inverse=__A	)
						snake_case_       =     classes.shape[0]
						snake_case_       =     np.bincount(__A	)
						if np.min(__A	) < 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(__A  ,		kind='''mergesort'''	)  ,		np.cumsum(__A	)[:-1]	)
						for _ in range(__A	):
												snake_case_       =     approximate_mode(__A  ,		__A  ,		__A	)
												snake_case_       =     class_counts - n_i
												snake_case_       =     approximate_mode(__A  ,		__A  ,		__A	)

												snake_case_       =     []
												snake_case_       =     []

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

												yield train, test


def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__	):
						snake_case_       =     5381
						for x in s:
												snake_case_       =     ((hash_value << 5) + hash_value) + ord(SCREAMING_SNAKE_CASE__	)
						return hash_value & 0xFF_FF_FF_FF