





import numpy as np
def       __UpperCamelCase  (		snake_case				,       snake_case				,       snake_case	)  ->						Dict:


   '''simple docstring'''







   __A      					=   n_draws * class_counts / class_counts.sum()
   # floored means we don't overshoot n_samples, but probably undershoot
   __A      					=   np.floor(a__	)
   # we add samples according to how much "left over" probability
   # they had, until we arrive at n_samples
   __A      					=   int(n_draws - floored.sum()	)
   if need_to_add > 0:
      __A      					=   continuous - floored
      __A      					=   np.sort(np.unique(a__	)	)[::-1]
      # add according to remainder, but break ties
      # randomly to avoid biases
      for value in values:
         ((__A		)					,								)      					=   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
         __A      					=   min(len(a__	)				,       a__	)
         __A      					=   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       __UpperCamelCase  (		snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case=1_0	)  ->						List[str]:


   '''simple docstring'''







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

      __A      					=   []
      __A      					=   []

      for i in range(a__	):
         __A      					=   rng.permutation(class_counts[i]	)
         __A      					=   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]]	)
      __A      					=   rng.permutation(a__	)
      __A      					=   rng.permutation(a__	)

      yield train, test


lowerCamelCase__   = '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

lowerCamelCase__   = [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
lowerCamelCase__   = {
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}




