


'''simple docstring'''





__lowerCamelCase												=						0  # The first color of the flag.
__lowerCamelCase												=						1  # The second color of the flag.
__lowerCamelCase												=						2  # The third color of the flag.
__lowerCamelCase												=						(red, white, blue)


def        a__     (					UpperCamelCase_						:      list						):
   if not sequence:
      return []
   if len(UpperCamelCase_						) == 1:
      return list(UpperCamelCase_						)
   UpperCAmelCase__	:Tuple      =      0
   UpperCAmelCase__	:List[Any]      =      len(UpperCamelCase_						) - 1
   UpperCAmelCase__	:List[str]      =      0
   while mid <= high:
      if sequence[mid] == colors[0]:
         UpperCAmelCase__   ,						UpperCAmelCase__	:str      =      sequence[mid], sequence[low]
         low += 1
         mid += 1
      elif sequence[mid] == colors[1]:
         mid += 1
      elif sequence[mid] == colors[2]:
         UpperCAmelCase__   ,						UpperCAmelCase__	:List[Any]      =      sequence[high], sequence[mid]
         high -= 1
      else:
         UpperCAmelCase__	:Dict      =      F'''The elements inside the sequence must contains only {colors} values'''
         raise ValueError(UpperCamelCase_						)
   return sequence


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    __lowerCamelCase												=						input('''Enter numbers separated by commas:\n''').strip()
    __lowerCamelCase												=						[int(item.strip()) for item in user_input.split(''',''')]
    print(F'''{dutch_national_flag_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 				UpperCAmelCase			(			unittest.TestCase						):


 @slow
 def        __SCREAMING_SNAKE_CASE					(      self							:	List[str] ):
    UpperCAmelCase__	:List[str]      =      TFCamembertModel.from_pretrained('''jplu/tf-camembert-base''' )

    UpperCAmelCase__	:Optional[Any]      =      tf.convert_to_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]]			,       dtype=tf.intaa			,       )  # J'aime le camembert !"

    UpperCAmelCase__	:Any      =      model(__lowerCamelCase )['''last_hidden_state''']
    UpperCAmelCase__	:Dict      =      tf.TensorShape((1, 1_0, 7_6_8) )
    self.assertEqual(output.shape			,       __lowerCamelCase )
    # compare the actual values for a slice.
    UpperCAmelCase__	:Any      =      tf.convert_to_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]]]			,       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 ) )



