




def      __lowerCAmelCase			(					UpperCamelCase      )     ->	Optional[int]:

  '''simple docstring'''





  if len(A__      ) <= 1:
    return [tuple(A__      )]

  lowerCAmelCase__				:  List[Any]		       =     []

  def generate(UpperCamelCase	,      UpperCamelCase      ):
    if k == 1:
      res.append(tuple(arr[:]      )      )
      return

    generate(k - 1	,      A__      )

    for i in range(k - 1      ):
      if k % 2 == 0:  # k is even
        lowerCAmelCase__       ,			lowerCAmelCase__				:  Optional[int]		       =     arr[k - 1], arr[i]
      else:  # k is odd
        lowerCAmelCase__       ,			lowerCAmelCase__				:  Optional[Any]		       =     arr[k - 1], arr[0]
      generate(k - 1	,      A__      )

  generate(len(A__      )	,      A__      )
  return res


if __name__ == "__main__":
     lowerCAmelCase_					    = input("""Enter numbers separated by a comma:\n""").strip()
     lowerCAmelCase_					    = [int(item) for item in user_input.split(""",""")]
     print(heaps(arr))







from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
     import numpy as np
     import tensorflow as tf

     from transformers import TFCamembertModel






@require_tf
@require_sentencepiece
@require_tokenizers
class  _lowerCAmelCase     (  unittest.TestCase	):

      @slow
      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  Optional[Any]		       =     TFCamembertModel.from_pretrained('''jplu/tf-camembert-base'''		)

        lowerCAmelCase__				:  List[str]		       =     tf.convert_to_tensor(
            [[5, 121, 11, 660, 16, 730, 2_5543, 110, 83, 6]]      ,				dtype=tf.intaa      ,				)  # J'aime le camembert !"

        lowerCAmelCase__				:  str		       =     model(__UpperCAmelCase		)['''last_hidden_state''']
        lowerCAmelCase__				:  List[Any]		       =     tf.TensorShape((1, 10, 768)		)
        self.assertEqual(output.shape      ,				__UpperCAmelCase		)
        # compare the actual values for a slice.
        lowerCAmelCase__				:  int		       =     tf.convert_to_tensor(
            [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]]      ,				dtype=tf.floataa      ,				)
        # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
        # camembert.eval()
        # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

        self.assertTrue(np.allclose(output[:, :3, :3].numpy()      ,				expected_slice.numpy()      ,				atol=1e-4		)		)

