



'''simple docstring'''






import random




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_		)->		str:





       '''simple docstring'''


       _UpperCAmelCase  :						List[Any]			     = a[left_index]
       _UpperCAmelCase  :						str			     = left_index + 1
       for j in range(left_index + 1       ,   lowerCAmelCase_		):
              if a[j] < pivot:
                     _UpperCAmelCase     ,_UpperCAmelCase  :						List[Any]			     = a[i], a[j]
                     i += 1
       _UpperCAmelCase     ,_UpperCAmelCase  :						Any			     = a[i - 1], a[left_index]
       return i - 1




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_		)->		Dict:





       '''simple docstring'''


       if left < right:
              _UpperCAmelCase  :						List[Any]			     = random.randint(lowerCAmelCase_       ,   right - 1		)
              _UpperCAmelCase     ,_UpperCAmelCase  :						str			     = (
                  a[left],
                  a[pivot],
              )  # switches the pivot with the left most bound
              _UpperCAmelCase  :						Tuple			     = partition(lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_		)
              quick_sort_random(
                  lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_		)  # recursive quicksort to the left of the pivot point
              quick_sort_random(
                  lowerCAmelCase_       ,   pivot_index + 1       ,   lowerCAmelCase_		)  # recursive quicksort to the right of the pivot point




def     snake_case_					(     )->		Optional[Any]:





       '''simple docstring'''


       _UpperCAmelCase  :						Union[str, Any]			     = input("""Enter numbers separated by a comma:\n"""		).strip()
       _UpperCAmelCase  :						Union[str, Any]			     = [int(lowerCAmelCase_		) for item in user_input.split(""","""		)]

       quick_sort_random(lowerCAmelCase_       ,   0       ,   len(lowerCAmelCase_		)		)

       print(lowerCAmelCase_		)


if __name__ == "__main__":
   main()








'''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 _snake_case (							self							)		->   Any:
        _UpperCAmelCase  :						Dict			     = TFCamembertModel.from_pretrained("""jplu/tf-camembert-base"""							)

        _UpperCAmelCase  :						Optional[int]			     = tf.convert_to_tensor(
            [[5, 121, 11, 660, 16, 730, 25_543, 110, 83, 6]]  ,dtype=tf.intaa  ,)  # J'aime le camembert !"

        _UpperCAmelCase  :						Dict			     = model(a_							)["""last_hidden_state"""]
        _UpperCAmelCase  :						Dict			     = tf.TensorShape((1, 10, 768)							)
        self.assertEqual(output.shape  ,a_							)
        # compare the actual values for a slice.
        _UpperCAmelCase  :						Tuple			     = 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							)							)



