
import numpy as np



def 	_a  (					__SCREAMING_SNAKE_CASE					:  Dict		,				__SCREAMING_SNAKE_CASE					:  int		,				__SCREAMING_SNAKE_CASE					:  Union[str, Any]    ):






  """simple docstring"""




  _lowerCAmelCase						=		n_draws * class_counts / class_counts.sum()
  # floored means we don't overshoot n_samples, but probably undershoot
  _lowerCAmelCase						=		np.floor(__SCREAMING_SNAKE_CASE    )
  # we add samples according to how much "left over" probability
  # they had, until we arrive at n_samples
  _lowerCAmelCase						=		int(n_draws - floored.sum()    )
  if need_to_add > 0:
    _lowerCAmelCase						=		continuous - floored
    _lowerCAmelCase						=		np.sort(np.unique(__SCREAMING_SNAKE_CASE    )    )[::-1]
    # add according to remainder, but break ties
    # randomly to avoid biases
    for value in values:
      ((_lowerCAmelCase)	,       )						=		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
      _lowerCAmelCase						=		min(len(__SCREAMING_SNAKE_CASE    )		,				__SCREAMING_SNAKE_CASE    )
      _lowerCAmelCase						=		rng.choice(__SCREAMING_SNAKE_CASE		,				size=__SCREAMING_SNAKE_CASE		,				replace=__SCREAMING_SNAKE_CASE    )
      floored[inds] += 1
      need_to_add -= add_now
      if need_to_add == 0:
        break
  return floored.astype(np.intaa    )




def 	_a  (					__SCREAMING_SNAKE_CASE					:  List[str]		,				__SCREAMING_SNAKE_CASE					:  str		,				__SCREAMING_SNAKE_CASE					:  Any		,				__SCREAMING_SNAKE_CASE					:  List[Any]		,				__SCREAMING_SNAKE_CASE					:  int=10    ):






  """simple docstring"""




  _lowerCAmelCase	,       _lowerCAmelCase						=		np.unique(__SCREAMING_SNAKE_CASE		,				return_inverse=__SCREAMING_SNAKE_CASE    )
  _lowerCAmelCase						=		classes.shape[0]
  _lowerCAmelCase						=		np.bincount(__SCREAMING_SNAKE_CASE    )
  if np.min(__SCREAMING_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)    )
  _lowerCAmelCase						=		np.split(np.argsort(__SCREAMING_SNAKE_CASE		,				kind='mergesort'    )		,				np.cumsum(__SCREAMING_SNAKE_CASE    )[:-1]    )
  for _ in range(__SCREAMING_SNAKE_CASE    ):
    _lowerCAmelCase						=		approximate_mode(__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    )
    _lowerCAmelCase						=		class_counts - n_i
    _lowerCAmelCase						=		approximate_mode(__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    )

    _lowerCAmelCase						=		[]
    _lowerCAmelCase						=		[]

    for i in range(__SCREAMING_SNAKE_CASE    ):
      _lowerCAmelCase						=		rng.permutation(class_counts[i]    )
      _lowerCAmelCase						=		class_indices[i].take(__SCREAMING_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]]    )
    _lowerCAmelCase						=		rng.permutation(__SCREAMING_SNAKE_CASE    )
    _lowerCAmelCase						=		rng.permutation(__SCREAMING_SNAKE_CASE    )

    yield train, test


import abc
import copy
import dataclasses
from dataclasses import dataclass
from typing import ClassVar, Dict, Type, TypeVar

from ..features import Features


_UpperCamelCase:					List[Any]     =TypeVar('T', bound='TaskTemplate')





@dataclass(frozen=SCREAMING_SNAKE_CASE   )
class 						__lowercase(     abc.ABC   ):


       """simple docstring"""







       UpperCamelCase_				 =							42
       UpperCamelCase_				 =							42
       UpperCamelCase_				 =							42



       def SCREAMING_SNAKE_CASE_				(     self					:			T  ,      _lowerCAmelCase					:			Features       )					->   T:
         return copy.deepcopy(self       )



       @property
       def SCREAMING_SNAKE_CASE_				(     self					:			str       )					->   Features:
         return Features(**self.input_schema  ,      **self.label_schema       )



       @property
       @abc.abstractmethod
       def SCREAMING_SNAKE_CASE_				(     self					:			Dict       )					->   Dict[str, str]:
         raise NotImplementedError






       @classmethod
       def SCREAMING_SNAKE_CASE_				(     cls					:			Type[T]  ,      _lowerCAmelCase					:			dict       )					->   T:
         _lowerCAmelCase						=		{f.name for f in dataclasses.fields(cls       )}
         return cls(**{k: v for k, v in template_dict.items() if k in field_names}       )
