
import numpy as np



def      __UpperCAmelCase    (  snake_case_							:		int				,					snake_case_							:		Union[str, Any]				,					snake_case_							:		Any				):



	'''simple docstring'''



	UpperCAmelCase:					List[Any] 						=     n_draws * class_counts / class_counts.sum()
	# floored means we don't overshoot n_samples, but probably undershoot
	UpperCAmelCase:					Dict 						=     np.floor(snake_case_				)
	# we add samples according to how much "left over" probability
	# they had, until we arrive at n_samples
	UpperCAmelCase:					Union[str, Any] 						=     int(n_draws - floored.sum()				)
	if need_to_add > 0:
		UpperCAmelCase:					Optional[Any] 						=     continuous - floored
		UpperCAmelCase:					Tuple 						=     np.sort(np.unique(snake_case_				)				)[::-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:					str 						=     min(len(snake_case_				)				,					snake_case_				)
			UpperCAmelCase:					str 						=     rng.choice(snake_case_				,					size=snake_case_				,					replace=snake_case_				)
			floored[inds] += 1
			need_to_add -= add_now
			if need_to_add == 0:
				break
	return floored.astype(np.intaa				)



def      __UpperCAmelCase    (  snake_case_							:		Union[str, Any]				,					snake_case_							:		Optional[int]				,					snake_case_							:		List[Any]				,					snake_case_							:		List[Any]				,					snake_case_							:		List[Any]=1_0				):



	'''simple docstring'''



	UpperCAmelCase:					Optional[int] 						=     np.unique(snake_case_				,					return_inverse=snake_case_				)
	UpperCAmelCase:					str 						=     classes.shape[0]
	UpperCAmelCase:					List[Any] 						=     np.bincount(snake_case_				)
	if np.min(snake_case_				) < 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:					Optional[int] 						=     np.split(np.argsort(snake_case_				,					kind="mergesort"				)				,					np.cumsum(snake_case_				)[:-1]				)
	for _ in range(snake_case_				):
		UpperCAmelCase:					Tuple 						=     approximate_mode(snake_case_				,					snake_case_				,					snake_case_				)
		UpperCAmelCase:					List[str] 						=     class_counts - n_i
		UpperCAmelCase:					Union[str, Any] 						=     approximate_mode(snake_case_				,					snake_case_				,					snake_case_				)

		UpperCAmelCase:					Optional[int] 						=     []
		UpperCAmelCase:					Dict 						=     []

		for i in range(snake_case_				):
			UpperCAmelCase:					Optional[int] 						=     rng.permutation(class_counts[i]				)
			UpperCAmelCase:					int 						=     class_indices[i].take(snake_case_				,					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:					List[Any] 						=     rng.permutation(snake_case_				)
		UpperCAmelCase:					Any 						=     rng.permutation(snake_case_				)

		yield train, test




from hashlib import mda, shaaaa

__UpperCamelCase							:							Dict    							=					(shaaaa, mda)

class    __magic_name__						:


     def __init__(   self				:  Optional[Any]				,						lowerCamelCase__				:  int = 8      )	->   None:





      '''simple docstring'''






      UpperCamelCase__       :     int 							= 0b0
      UpperCamelCase__       :     Optional[int] 							= size


     def   UpperCAmelCase__						(   self				:  Tuple				,						lowerCamelCase__				:  str      )	->   None:





      '''simple docstring'''






      UpperCamelCase__       :     Union[str, Any] 							= self.hash_(lowerCamelCase__      )
      self.bitarray |= h


     def   UpperCAmelCase__						(   self				:  Any				,						lowerCamelCase__				:  str      )	->   bool:





      '''simple docstring'''






      UpperCamelCase__       :     Any 							= self.hash_(lowerCamelCase__      )
      return (h & self.bitarray) == h


     def __contains__(   self				:  Any				,						lowerCamelCase__				:  str      )	->   bool:





      '''simple docstring'''






      return self.exists(lowerCamelCase__      )


     def   UpperCAmelCase__						(   self				:  Union[str, Any]				,						lowerCamelCase__				:  int      )	->   str:





      '''simple docstring'''






      UpperCamelCase__       :     Optional[int] 							= bin(lowerCamelCase__      )[2:]
      return res.zfill(self.size      )


     @property
     def   UpperCAmelCase__						(   self				:  Tuple      )	->   str:





      '''simple docstring'''






      return self.format_bin(self.bitarray      )


     def   UpperCAmelCase__						(   self				:  int				,						lowerCamelCase__				:  str      )	->   int:





      '''simple docstring'''






      UpperCamelCase__       :     Union[str, Any] 							= 0b0
      for func in HASH_FUNCTIONS:
       UpperCamelCase__       :     Optional[Any] 							= (
           int.from_bytes(func(value.encode()      ).digest()				,						'''little'''      ) % self.size
       )
       res |= 2**position
      return res


     def   UpperCAmelCase__						(   self				:  Union[str, Any]				,						lowerCamelCase__				:  str      )	->   str:





      '''simple docstring'''






      return self.format_bin(self.hash_(lowerCamelCase__      )      )


     @property
     def   UpperCAmelCase__						(   self				:  str      )	->   float:





      '''simple docstring'''






      UpperCamelCase__       :     Tuple 							= bin(self.bitarray      ).count('''1'''      )
      return (n_ones / self.size) ** len(lowerCamelCase__      )


