
"""simple docstring"""


import numpy as np



def     _lowerCamelCase(__UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase       )				-> List[Any]:
	_lowerCAmelCase								=n_draws * class_counts / class_counts.sum()
	# floored means we don't overshoot n_samples, but probably undershoot
	_lowerCAmelCase								=np.floor(__UpperCamelCase       )
	# we add samples according to how much "left over" probability
	# they had, until we arrive at n_samples
	_lowerCAmelCase								=int(n_draws - floored.sum()       )
	if need_to_add > 0:
		_lowerCAmelCase								=continuous - floored
		_lowerCAmelCase								=np.sort(np.unique(__UpperCamelCase       )       )[::-1]
		# add according to remainder, but break ties
		# randomly to avoid biases
		for value in values:
			((_lowerCAmelCase)						,							)								=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
			_lowerCAmelCase								=min(len(__UpperCamelCase       )    , __UpperCamelCase       )
			_lowerCAmelCase								=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     _lowerCamelCase(__UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase=10       )				-> Dict:
	_lowerCAmelCase						,							_lowerCAmelCase								=np.unique(__UpperCamelCase    , return_inverse=__UpperCamelCase       )
	_lowerCAmelCase								=classes.shape[0]
	_lowerCAmelCase								=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)       )
	_lowerCAmelCase								=np.split(np.argsort(__UpperCamelCase    , kind="""mergesort"""       )    , np.cumsum(__UpperCamelCase       )[:-1]       )
	for _ in range(__UpperCamelCase       ):
		_lowerCAmelCase								=approximate_mode(__UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase       )
		_lowerCAmelCase								=class_counts - n_i
		_lowerCAmelCase								=approximate_mode(__UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase       )

		_lowerCAmelCase								=[]
		_lowerCAmelCase								=[]

		for i in range(__UpperCamelCase       ):
			_lowerCAmelCase								=rng.permutation(class_counts[i]       )
			_lowerCAmelCase								=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]]       )
		_lowerCAmelCase								=rng.permutation(__UpperCamelCase       )
		_lowerCAmelCase								=rng.permutation(__UpperCamelCase       )

		yield train, test





"""simple docstring"""


def     _lowerCamelCase(__UpperCamelCase       )				-> list:
	_lowerCAmelCase								=[True for i in range(n + 1       )]
	_lowerCAmelCase								=[i - 1 for i in range(n + 1       )]
	_lowerCAmelCase								=[]
	for i in range(2    , n + 1       ):
		if is_prime[i]:
			primes.append(__UpperCamelCase       )
		for j in range(0    , len(__UpperCamelCase       )       ):
			if i * primes[j] >= n:
				break
			_lowerCAmelCase								=False

			if i % primes[j] == 0:
				_lowerCAmelCase								=totients[i] * primes[j]
				break

			_lowerCAmelCase								=totients[i] * (primes[j] - 1)

	return totients


if __name__ == "__main__":
			import doctest

			doctest.testmod()



