code: 
  
  
  '''simple docstring'''
  
  
  
  
  
  import numpy as np
  
  
  
  
  
  
  def   __lowerCamelCase	(       lowerCamelCase__		:	List[Any]     ,       lowerCamelCase__		:	List[Any]     ,       lowerCamelCase__		:	List[str]			):
  
  
  
  
  
  			'''simple docstring'''
  
  
  
  
  			lowerCamelCase								=     n_draws * class_counts / class_counts.sum()
  			# floored means we don't overshoot n_samples, but probably undershoot
  			lowerCamelCase								=     np.floor(lowercase_			)
  			# 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(lowercase_			)			)[::-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(lowercase_			)     ,       lowercase_			)
  									lowerCamelCase								=     rng.choice(lowercase_     ,       size=lowercase_     ,       replace=lowercase_			)
  									floored[inds] += 1
  									need_to_add -= add_now
  									if need_to_add == 0:
  												break
  			return floored.astype(np.intaa			)
  
  
  
  
  
  
  def   __lowerCamelCase	(       lowerCamelCase__		:	Dict     ,       lowerCamelCase__		:	List[str]     ,       lowerCamelCase__		:	Any     ,       lowerCamelCase__		:	Dict     ,       lowerCamelCase__		:	Optional[Any]=10			):
  
  
  
  
  
  			'''simple docstring'''
  
  
  
  
  			lowerCamelCase								=     np.unique(lowercase_     ,       return_inverse=lowercase_			)
  			lowerCamelCase								=     classes.shape[0]
  			lowerCamelCase								=     np.bincount(lowercase_			)
  			if np.min(lowercase_			) < 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(lowercase_     ,       kind="""mergesort"""			)     ,       np.cumsum(lowercase_			)[:-1]			)
  			for _ in range(lowercase_			):
  						lowerCamelCase								=     approximate_mode(lowercase_     ,       lowercase_     ,       lowercase_			)
  						lowerCamelCase								=     class_counts - n_i
  						lowerCamelCase								=     approximate_mode(lowercase_     ,       lowercase_     ,       lowercase_			)
  
  						lowerCamelCase								=     []
  						lowerCamelCase								=     []
  
  						for i in range(lowercase_			):
  									lowerCamelCase								=     rng.permutation(class_counts[i]			)
  									lowerCamelCase								=     class_indices[i].take(lowercase_     ,       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(lowercase_			)
  						lowerCamelCase								=     rng.permutation(lowercase_			)
  
  						yield train, test
  
  
style_context: 
  
  
  import warnings
  
  from .generation import FlaxGenerationMixin
  
  
  
  
  
  
  
  class 					__lowercase					(       a_     ):
  
  
  
  		"""simple docstring"""
  
  		warnings.warn(
  		    "Importing `FlaxGenerationMixin` from `src/transformers/generation_flax_utils.py` is deprecated and will "
  		    "be removed in Transformers v5. Import as `from transformers import FlaxGenerationMixin` instead." ,       a_ ,       )
  
  