






"""simple docstring"""


from itertools import permutations




def 			snake_case  (A_      :int = 5      ):







  '''simple docstring'''

  if gon_side < 3 or gon_side > 5:
    raise ValueError('gon_side must be in the range [3, 5]'      )

  # Since it's 16, we know 10 is on the outer ring
  # Put the big numbers at the end so that they are never the first number
  a     :			List[str]				=   list(range(gon_side + 1	,			0	,			-1      )      )
  a     :			str				=   list(range(gon_side + 2	,			gon_side * 2 + 1      )      )

  for perm in permutations(small_numbers + big_numbers      ):
    a     :			List[str]				=   generate_gon_ring(A_	,			list(A_      )      )
    if is_magic_gon(A_      ):
      return int(''.join(str(A_      ) for n in numbers      )      )

  a     :			Union[str, Any]				=   f'''Magic {gon_side}-gon ring is impossible'''
  raise ValueError(A_      )




def 			snake_case  (A_      :int	,			A_      :list[int]      ):







  '''simple docstring'''

  a     :			Any				=   [0] * (gon_side * 3)
  a     :			List[Any]				=   perm[0:3]
  perm.append(perm[1]      )

  a     :			Tuple				=   1 if gon_side < 5 else 2

  for i in range(1	,			len(A_      ) // 3 + magic_number      ):
    a     :			str				=   perm[2 * i + 1]
    a     :			Union[str, Any]				=   result[3 * i - 1]
    a     :			int				=   perm[2 * i + 2]

  return result




def 			snake_case  (A_      :list[int]      ):







  '''simple docstring'''

  if len(A_      ) % 3 != 0:
    raise ValueError('a gon ring should have a length that is a multiple of 3'      )

  if min(numbers[::3]      ) != numbers[0]:
    return False

  a     :			int				=   sum(numbers[:3]      )

  return all(sum(numbers[i : i + 3]      ) == total for i in range(3	,			len(A_      )	,			3      )      )


if __name__ == "__main__":
   print(solution())












"""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 						snake_case  (  unittest.TestCase       ):





    @slow
    def 				lowerCamelCase__      (							self	:					str			):


      '''simple docstring'''




      a     :			int				=   CamembertModel.from_pretrained('camembert-base'			)
      model.to(A			)

      a     :			List[Any]				=   torch.tensor(
          [[5, 1_2_1, 1_1, 6_6_0, 1_6, 7_3_0, 2_5_5_4_3, 1_1_0, 8_3, 6]]				,		device=A				,		dtype=torch.long				,		)  # J'aime le camembert !
      with torch.no_grad():
        a     :			int				=   model(A			)['last_hidden_state']
      a     :			Union[str, Any]				=   torch.Size((1, 1_0, 7_6_8)			)
      self.assertEqual(output.shape				,		A			)
      # compare the actual values for a slice.
      a     :			List[str]				=   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=A				,		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]				,		A				,		atol=1E-4			)			)




