






'''simple docstring'''







def  lowerCAmelCase__		(					a_  :     list			)   -> list:
       UpperCAmelCase__	:   List[str]												=	len(a_			)
       for cycle_start in range(0			,						array_len - 1			):
              UpperCAmelCase__	:   Union[str, Any]												=	array[cycle_start]

              UpperCAmelCase__	:   Optional[int]												=	cycle_start
              for i in range(cycle_start + 1			,						a_			):
                     if array[i] < item:
                            pos += 1

              if pos == cycle_start:
                     continue

              while item == array[pos]:
                     pos += 1

              UpperCAmelCase__							,							UpperCAmelCase__	:   Optional[Any]												=	item, array[pos]
              while pos != cycle_start:
                     UpperCAmelCase__	:   str												=	cycle_start
                     for i in range(cycle_start + 1			,						a_			):
                            if array[i] < item:
                                   pos += 1

                     while item == array[pos]:
                            pos += 1

                     UpperCAmelCase__							,							UpperCAmelCase__	:   Union[str, Any]												=	item, array[pos]

       return array


if __name__ == "__main__":
  assert cycle_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
  assert cycle_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15]







'''simple docstring'''







import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
  import torch

  from transformers import CamembertModel




@require_torch
@require_sentencepiece
@require_tokenizers
class 					__UpperCAmelCase (		unittest.TestCase	):

      '''simple docstring'''





      @slow
      def 							lowerCamelCase     (					self						):
             UpperCAmelCase__	:   Optional[int]												=	CamembertModel.from_pretrained('''camembert-base'''						)
             model.to(_UpperCAmelCase						)

             UpperCAmelCase__	:   Optional[int]												=	torch.tensor(
                 [[5, 121, 11, 660, 16, 730, 2_5543, 110, 83, 6]]  ,		device=_UpperCAmelCase  ,		dtype=torch.long  ,		)  # J'aime le camembert !
             with torch.no_grad():
                    UpperCAmelCase__	:   Tuple												=	model(_UpperCAmelCase						)['''last_hidden_state''']
             UpperCAmelCase__	:   List[str]												=	torch.Size((1, 10, 768)						)
             self.assertEqual(output.shape  ,		_UpperCAmelCase						)
             # compare the actual values for a slice.
             UpperCAmelCase__	:   List[Any]												=	torch.tensor(
                 [[[-0.02_54, 0.02_35, 0.10_27], [0.06_06, -0.18_11, -0.04_18], [-0.15_61, -0.11_27, 0.26_87]]]  ,		device=_UpperCAmelCase  ,		dtype=torch.float  ,		)
             # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
             # camembert.eval()
             # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

             self.assertTrue(torch.allclose(output[:, :3, :3]  ,		_UpperCAmelCase  ,		atol=1E-4						)						)