


import numpy as np

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   Optional[Any]     ,	SCREAMING_SNAKE_CASE__     :   List[str]     ,	SCREAMING_SNAKE_CASE__     :   Union[str, Any]							):
     __UpperCamelCase       				=n_draws * class_counts / class_counts.sum()
     # floored means we don't overshoot n_samples, but probably undershoot
     __UpperCamelCase       				=np.floor(SCREAMING_SNAKE_CASE__							)
     # 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(SCREAMING_SNAKE_CASE__							)							)[::-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(SCREAMING_SNAKE_CASE__							)     ,	SCREAMING_SNAKE_CASE__							)
               __UpperCamelCase       				=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 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   str     ,	SCREAMING_SNAKE_CASE__     :   int     ,	SCREAMING_SNAKE_CASE__     :   List[Any]     ,	SCREAMING_SNAKE_CASE__     :   List[Any]     ,	SCREAMING_SNAKE_CASE__     :   int=10							):
     __UpperCamelCase				, __UpperCamelCase       				=np.unique(SCREAMING_SNAKE_CASE__     ,	return_inverse=SCREAMING_SNAKE_CASE__							)
     __UpperCamelCase       				=classes.shape[0]
     __UpperCamelCase       				=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)							)
     __UpperCamelCase       				=np.split(np.argsort(SCREAMING_SNAKE_CASE__     ,	kind='mergesort'							)     ,	np.cumsum(SCREAMING_SNAKE_CASE__							)[:-1]							)
     for _ in range(SCREAMING_SNAKE_CASE__							):
          __UpperCamelCase       				=approximate_mode(SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__							)
          __UpperCamelCase       				=class_counts - n_i
          __UpperCamelCase       				=approximate_mode(SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__							)

          __UpperCamelCase       				=[]
          __UpperCamelCase       				=[]

          for i in range(SCREAMING_SNAKE_CASE__							):
               __UpperCamelCase       				=rng.permutation(class_counts[i]							)
               __UpperCamelCase       				=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]]							)
          __UpperCamelCase       				=rng.permutation(SCREAMING_SNAKE_CASE__							)
          __UpperCamelCase       				=rng.permutation(SCREAMING_SNAKE_CASE__							)

          yield train, test









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_A									=		{
    'configuration_flaubert': ['FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FlaubertConfig', 'FlaubertOnnxConfig'],
    'tokenization_flaubert': ['FlaubertTokenizer'],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A									=		[
       'FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'FlaubertForMultipleChoice',
       'FlaubertForQuestionAnswering',
       'FlaubertForQuestionAnsweringSimple',
       'FlaubertForSequenceClassification',
       'FlaubertForTokenClassification',
       'FlaubertModel',
       'FlaubertWithLMHeadModel',
       'FlaubertPreTrainedModel',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A									=		[
       'TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFFlaubertForMultipleChoice',
       'TFFlaubertForQuestionAnsweringSimple',
       'TFFlaubertForSequenceClassification',
       'TFFlaubertForTokenClassification',
       'TFFlaubertModel',
       'TFFlaubertPreTrainedModel',
       'TFFlaubertWithLMHeadModel',
   ]


if TYPE_CHECKING:
   from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
   from .tokenization_flaubert import FlaubertTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flaubert import (
          FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          FlaubertForMultipleChoice,
          FlaubertForQuestionAnswering,
          FlaubertForQuestionAnsweringSimple,
          FlaubertForSequenceClassification,
          FlaubertForTokenClassification,
          FlaubertModel,
          FlaubertPreTrainedModel,
          FlaubertWithLMHeadModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_flaubert import (
          TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFFlaubertForMultipleChoice,
          TFFlaubertForQuestionAnsweringSimple,
          TFFlaubertForSequenceClassification,
          TFFlaubertForTokenClassification,
          TFFlaubertModel,
          TFFlaubertPreTrainedModel,
          TFFlaubertWithLMHeadModel,
      )

else:
   import sys

   _A									=		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





