



import numpy as np


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

								_UpperCAmelCase						:  Union[str, Any]     					=	[]
								_UpperCAmelCase						:  List[str]     					=	[]

								for i in range(a__			):
												_UpperCAmelCase						:  Dict     					=	rng.permutation(class_counts[i]			)
												_UpperCAmelCase						:  Tuple     					=	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]]			)
								_UpperCAmelCase						:  Optional[Any]     					=	rng.permutation(a__			)
								_UpperCAmelCase						:  Tuple     					=	rng.permutation(a__			)

								yield train, test


'''simple docstring'''







from ..utils import DummyObject, requires_backends







class 		snake_case_       (						metaclass=A__						):



   """simple docstring"""





   __lowerCAmelCase	:							List[str]								=['''torch''', '''librosa''']







   def __init__( self			,   *UpperCamelCase			,   **UpperCamelCase):
          requires_backends(self			,   ["torch", "librosa"])







   @classmethod
   def     __UpperCAmelCase      ( cls			,   *UpperCamelCase			,   **UpperCamelCase):
          requires_backends(cls			,   ["torch", "librosa"])







   @classmethod
   def     __UpperCAmelCase      ( cls			,   *UpperCamelCase			,   **UpperCamelCase):
          requires_backends(cls			,   ["torch", "librosa"])







class 		snake_case_       (						metaclass=A__						):



   """simple docstring"""





   __lowerCAmelCase	:							Union[str, Any]								=['''torch''', '''librosa''']







   def __init__( self			,   *UpperCamelCase			,   **UpperCamelCase):
          requires_backends(self			,   ["torch", "librosa"])







   @classmethod
   def     __UpperCAmelCase      ( cls			,   *UpperCamelCase			,   **UpperCamelCase):
          requires_backends(cls			,   ["torch", "librosa"])







   @classmethod
   def     __UpperCAmelCase      ( cls			,   *UpperCamelCase			,   **UpperCamelCase):
          requires_backends(cls			,   ["torch", "librosa"])

