'''simple docstring'''






def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Any				):
     _A    		=  len(__snake_case				)
     while cur > 1:
          # Find the maximum number in arr
          _A    		=  arr.index(max(arr[0:cur]				)				)
          # Reverse from 0 to mi
          _A    		=  arr[mi::-1] + arr[mi + 1 : len(__snake_case				)]
          # Reverse whole list
          _A    		=  arr[cur - 1 :: -1] + arr[cur : len(__snake_case				)]
          cur -= 1
     return arr


if __name__ == "__main__":
   _UpperCAmelCase    :     Tuple         =       input('''Enter numbers separated by a comma:\n''').strip()
   _UpperCAmelCase    :     Optional[int]         =       [int(item) for item in user_input.split(''',''')]
   print(pancake_sort(unsorted))




'''simple docstring'''






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 lowercase_       (		unittest.TestCase			):







   """simple docstring"""

   @slow
   def 	__UpperCAmelCase   (      self      :			List[str]     )  ->			Any:
        _A    		=  TFCamembertModel.from_pretrained('jplu/tf-camembert-base'     )

        _A    		=  tf.convert_to_tensor(
            [[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]], dtype=tf.intaa, )  # J'aime le camembert !"

        _A    		=  model(UpperCamelCase__     )['last_hidden_state']
        _A    		=  tf.TensorShape((1, 10, 7_68)     )
        self.assertEqual(output.shape, UpperCamelCase__     )
        # compare the actual values for a slice.
        _A    		=  tf.convert_to_tensor(
            [[[-0.0_254, 0.0_235, 0.1_027], [0.0_606, -0.1_811, -0.0_418], [-0.1_561, -0.1_127, 0.2_687]]], 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     )     )



