



"""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      ):





     '''simple docstring'''



     @slow
     def  __lowerCAmelCase	(					self		)							->					int:
         _UpperCAmelCase							:  str											=    TFCamembertModel.from_pretrained('''jplu/tf-camembert-base'''		)

         _UpperCAmelCase							:  Tuple											=    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							:  Dict											=    model(A		)['''last_hidden_state''']
         _UpperCAmelCase							:  int											=    tf.TensorShape((1, 1_0, 7_6_8)		)
         self.assertEqual(output.shape       ,	A		)
         # compare the actual values for a slice.
         _UpperCAmelCase							:  Optional[Any]											=    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		)		)







"""simple docstring"""
def  lowerCamelCase_						(UpperCamelCase__		:		int							):
    if not isinstance(UpperCamelCase__     ,       UpperCamelCase__							) or number < 0:
        raise ValueError('''Input must be a non-negative integer'''							)

    _UpperCAmelCase							:  str											=    0
    while number:
        # This way we arrive at next set bit (next 1) instead of looping
        # through each bit and checking for 1s hence the
        # loop won't run 32 times it will only run the number of `1` times
        number &= number - 1
        count += 1
    return count


if __name__ == "__main__":
  import doctest

  doctest.testmod()


