code: 
  
  
  
  
  
  '''simple docstring'''
  
  
  import numpy as np
  
  
  
  
  def    __a (     UpperCAmelCase       ,			UpperCAmelCase       ,			UpperCAmelCase    )      ->List[Any]:
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	A							  =      n_draws * class_counts / class_counts.sum()
  	# floored means we don't overshoot n_samples, but probably undershoot
  	A							  =      np.floor(UpperCAmelCase    )
  	# we add samples according to how much "left over" probability
  	# they had, until we arrive at n_samples
  	A							  =      int(n_draws - floored.sum()    )
  	if need_to_add > 0:
  		A							  =      continuous - floored
  		A							  =      np.sort(np.unique(UpperCAmelCase    )    )[::-1]
  		# add according to remainder, but break ties
  		# randomly to avoid biases
  		for value in values:
  			((A    )  ,        )							  =      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							  =      min(len(UpperCAmelCase    )       ,			UpperCAmelCase    )
  			A							  =      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    __a (     UpperCAmelCase       ,			UpperCAmelCase       ,			UpperCAmelCase       ,			UpperCAmelCase       ,			UpperCAmelCase=10    )      ->List[Any]:
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	A  ,    A							  =      np.unique(UpperCAmelCase       ,			return_inverse=UpperCAmelCase    )
  	A							  =      classes.shape[0]
  	A							  =      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							  =      np.split(np.argsort(UpperCAmelCase       ,			kind="""mergesort"""    )       ,			np.cumsum(UpperCAmelCase    )[:-1]    )
  	for _ in range(UpperCAmelCase    ):
  		A							  =      approximate_mode(UpperCAmelCase       ,			UpperCAmelCase       ,			UpperCAmelCase    )
  		A							  =      class_counts - n_i
  		A							  =      approximate_mode(UpperCAmelCase       ,			UpperCAmelCase       ,			UpperCAmelCase    )
  
  		A							  =      []
  		A							  =      []
  
  		for i in range(UpperCAmelCase    ):
  			A							  =      rng.permutation(class_counts[i]    )
  			A							  =      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							  =      rng.permutation(UpperCAmelCase    )
  		A							  =      rng.permutation(UpperCAmelCase    )
  
  		yield train, test
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  
  from __future__ import annotations
  
  
  
  class 							__UpperCAmelCase  :
  
  
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		def __init__(self						:      Any      ,      _lowerCAmelCase						:      int      ):
  			A							  =      value
  			A							  =      None
  			A							  =      None
  
  
  
  
  
  
  class 							__UpperCAmelCase  :
  
  
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		__lowerCAmelCase 				=    42
  
  
  
  
  
  
  
  		def __init__(self						:      List[str]      ):
  			A							  =      0
  
  
  
  
  
  
  
  		def        A     (self						:      Dict      ,      _lowerCAmelCase						:      Node | None      ,      _lowerCAmelCase						:      int      ):
  			if node is None:
  				return
  
  			if path_sum == self.target:
  				self.paths += 1
  
  			if node.left:
  				self.depth_first_search(node.left      ,      path_sum + node.left.value      )
  			if node.right:
  				self.depth_first_search(node.right      ,      path_sum + node.right.value      )
  
  
  
  
  
  
  
  		def        A     (self						:      Optional[Any]      ,      _lowerCAmelCase						:      Node | None      ,      _lowerCAmelCase						:      int | None = None      ):
  			if node is None:
  				return 0
  			if target is not None:
  				A							  =      target
  
  			self.depth_first_search(_lowerCAmelCase      ,      node.value      )
  			self.path_sum(node.left      )
  			self.path_sum(node.right      )
  
  			return self.paths
  
  
  if __name__ == "__main__":
  				import doctest
  
  				doctest.testmod()
  
  