

import numpy as np
def 					a				(      A__     :       Dict				,       A__     :       Optional[Any]				,       A__     :       Tuple					)   ->				Dict:





      """simple docstring"""



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





      """simple docstring"""



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

            _lowercase     	=[]
            _lowercase     	=[]

            for i in range(A__					):
                  _lowercase     	=rng.permutation(class_counts[i]					)
                  _lowercase     	=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]]					)
            _lowercase     	=rng.permutation(A__					)
            _lowercase     	=rng.permutation(A__					)

            yield train, test





import warnings

from ...utils import logging
from .image_processing_vit import ViTImageProcessor


lowercase_     =       logging.get_logger(__name__)





class 			__lowerCAmelCase					( SCREAMING_SNAKE_CASE							):




  def __init__(    self							,       *lowerCAmelCase							,       **lowerCAmelCase )  ->			None:
        '''simple docstring'''






        warnings.warn(
            'The class ViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
            ' use ViTImageProcessor instead.'							,       lowerCAmelCase							,       )
        super().__init__(*lowerCAmelCase							,       **lowerCAmelCase )


