

"""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 					a						(       unittest.TestCase				):



    """simple docstring"""



    @slow
    def 				UpperCamelCase	(     self: List[Any]       ):






         """simple docstring"""


         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       )       )









"""simple docstring"""



def  _snake_case     (    UpperCAmelCase_  :					Optional[Any]					,   UpperCAmelCase_  :					Optional[Any]			):
     A__										=  [0 for i in range(r + 1			)]
     # nc0 = 1
     A__										=  1
     for i in range(1					,   n + 1			):
          # to compute current row from previous row.
          A__										=  min(UpperCAmelCase_					,   UpperCAmelCase_			)
          while j > 0:
               c[j] += c[j - 1]
               j -= 1
     return c[r]

print(binomial_coefficient(n=1_0, r=5))






