

"""simple docstring"""




import numpy as np

def 				A_					(							a   ,				a   ,				a					):

      """simple docstring"""



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



def 				A_					(							a   ,				a   ,				a   ,				a   ,				a=1_0					):

      """simple docstring"""



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

            SCREAMING_SNAKE_CASE_			:					Dict          =       []
            SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       []

            for i in range(a					):
                  SCREAMING_SNAKE_CASE_			:					List[str]          =       rng.permutation(class_counts[i]					)
                  SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       class_indices[i].take(a   ,				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]]					)
            SCREAMING_SNAKE_CASE_			:					List[str]          =       rng.permutation(a					)
            SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       rng.permutation(a					)

            yield train, test









from typing import TYPE_CHECKING

from ...utils import _LazyModule


lowerCAmelCase       :				Tuple         =					{'tokenization_phobert': ['PhobertTokenizer']}


if TYPE_CHECKING:
    from .tokenization_phobert import PhobertTokenizer

else:
    import sys

    lowerCAmelCase       :				Optional[int]         =					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






