from ..utils import DummyObject, requires_backends
class SCREAMING_SNAKE_CASE_		(			metaclass=__lowerCAmelCase		):
   __lowerCAmelCase											=							["""torch""", """transformers""", """invisible_watermark"""]







   def __init__(    self	:	Tuple      ,      *lowerCamelCase_	:	Dict      ,      **lowerCamelCase_	:	int							):





    """simple docstring"""


    requires_backends(self      ,      ["""torch""", """transformers""", """invisible_watermark"""]							)







   @classmethod
   def 	lowerCamelCase_  (    cls	:	int      ,      *lowerCamelCase_	:	Optional[Any]      ,      **lowerCamelCase_	:	Union[str, Any]							):





    """simple docstring"""


    requires_backends(cls      ,      ["""torch""", """transformers""", """invisible_watermark"""]							)







   @classmethod
   def 	lowerCamelCase_  (    cls	:	List[str]      ,      *lowerCamelCase_	:	List[str]      ,      **lowerCamelCase_	:	List[str]							):





    """simple docstring"""


    requires_backends(cls      ,      ["""torch""", """transformers""", """invisible_watermark"""]							)





class SCREAMING_SNAKE_CASE_		(			metaclass=__lowerCAmelCase		):
   __lowerCAmelCase											=							["""torch""", """transformers""", """invisible_watermark"""]







   def __init__(    self	:	List[Any]      ,      *lowerCamelCase_	:	Any      ,      **lowerCamelCase_	:	int							):





    """simple docstring"""


    requires_backends(self      ,      ["""torch""", """transformers""", """invisible_watermark"""]							)







   @classmethod
   def 	lowerCamelCase_  (    cls	:	Dict      ,      *lowerCamelCase_	:	Optional[Any]      ,      **lowerCamelCase_	:	Optional[int]							):





    """simple docstring"""


    requires_backends(cls      ,      ["""torch""", """transformers""", """invisible_watermark"""]							)







   @classmethod
   def 	lowerCamelCase_  (    cls	:	List[str]      ,      *lowerCamelCase_	:	List[Any]      ,      **lowerCamelCase_	:	Union[str, Any]							):





    """simple docstring"""


    requires_backends(cls      ,      ["""torch""", """transformers""", """invisible_watermark"""]							)

import numpy as np





def  lowercase(  UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_					)		->  List[str]:







 '''simple docstring'''
 UpperCamelCase      =			n_draws * class_counts / class_counts.sum()
 # floored means we don't overshoot n_samples, but probably undershoot
 UpperCamelCase      =			np.floor(UpperCamelCase_					)
 # we add samples according to how much "left over" probability
 # they had, until we arrive at n_samples
 UpperCamelCase      =			int(n_draws - floored.sum()					)
 if need_to_add > 0:
  UpperCamelCase      =			continuous - floored
  UpperCamelCase      =			np.sort(np.unique(UpperCamelCase_					)					)[::-1]
  # add according to remainder, but break ties
  # randomly to avoid biases
  for value in values:
   ((UpperCamelCase)     ,    )      =			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      =			min(len(UpperCamelCase_					)       ,				UpperCamelCase_					)
   UpperCamelCase      =			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  lowercase(  UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_=10					)		->  Dict:







 '''simple docstring'''
 UpperCamelCase     ,    UpperCamelCase      =			np.unique(UpperCamelCase_       ,				return_inverse=UpperCamelCase_					)
 UpperCamelCase      =			classes.shape[0]
 UpperCamelCase      =			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)					)
 UpperCamelCase      =			np.split(np.argsort(UpperCamelCase_       ,				kind="""mergesort"""					)       ,				np.cumsum(UpperCamelCase_					)[:-1]					)
 for _ in range(UpperCamelCase_					):
  UpperCamelCase      =			approximate_mode(UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_					)
  UpperCamelCase      =			class_counts - n_i
  UpperCamelCase      =			approximate_mode(UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_					)

  UpperCamelCase      =			[]
  UpperCamelCase      =			[]

  for i in range(UpperCamelCase_					):
   UpperCamelCase      =			rng.permutation(class_counts[i]					)
   UpperCamelCase      =			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]]					)
  UpperCamelCase      =			rng.permutation(UpperCamelCase_					)
  UpperCamelCase      =			rng.permutation(UpperCamelCase_					)

  yield train, test
