





'''simple docstring'''


import numpy as np




def  __a			(  UpperCAmelCase							,      UpperCAmelCase							,      UpperCAmelCase     )   ->Optional[int]:



	"""simple docstring"""







	A					=   n_draws * class_counts / class_counts.sum()
	# floored means we don't overshoot n_samples, but probably undershoot
	A					=   np.floor(_lowerCamelCase     )
	# 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(_lowerCamelCase     )     )[::-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(_lowerCamelCase     )							,      _lowerCamelCase     )
			A					=   rng.choice(_lowerCamelCase							,      size=_lowerCamelCase							,      replace=_lowerCamelCase     )
			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     )   ->str:



	"""simple docstring"""







	A					=   np.unique(_lowerCamelCase							,      return_inverse=_lowerCamelCase     )
	A					=   classes.shape[0]
	A					=   np.bincount(_lowerCamelCase     )
	if np.min(_lowerCamelCase     ) < 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(_lowerCamelCase							,      kind="""mergesort"""     )							,      np.cumsum(_lowerCamelCase     )[:-1]     )
	for _ in range(_lowerCamelCase     ):
		A					=   approximate_mode(_lowerCamelCase							,      _lowerCamelCase							,      _lowerCamelCase     )
		A					=   class_counts - n_i
		A					=   approximate_mode(_lowerCamelCase							,      _lowerCamelCase							,      _lowerCamelCase     )

		A					=   []
		A					=   []

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

		yield train, test


from transformers import PretrainedConfig



class     snake_case_			(						__A			):
   __A      :						List[Any]   =							"custom"






   def __init__( self							:	Any      ,		lowercase_							:	Optional[int]=1      ,		**lowercase_							:	Any    )   ->		int:
       lowercase__					:  List[Any]          =							attribute
       super().__init__(**lowercase_    )



class     snake_case_			(						__A			):
   __A      :						Optional[int]   =							"custom"






   def __init__( self							:	Tuple      ,		lowercase_							:	str=1      ,		**lowercase_							:	List[Any]    )   ->		Dict:
       lowercase__					:  Union[str, Any]          =							attribute



