


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      ):
 @slow
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Optional[int]:




        '''simple docstring'''
        lowercase__:   int  			=   TFCamembertModel.from_pretrained('jplu/tf-camembert-base' )

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

        lowercase__:   str  			=   model(lowerCAmelCase__ )['last_hidden_state']
        lowercase__:   List[str]  			=   tf.TensorShape((1, 10, 768) )
        self.assertEqual(output.shape				, lowerCAmelCase__ )
        # compare the actual values for a slice.
        lowercase__:   Any  			=   tf.convert_to_tensor(
            [[[-0.0_2_5_4, 0.0_2_3_5, 0.1_0_2_7], [0.0_6_0_6, -0.1_8_1_1, -0.0_4_1_8], [-0.1_5_6_1, -0.1_1_2_7, 0.2_6_8_7]]]				, 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 ) )






import math




def    snake_case_     (  snake_case			,							snake_case     )  ->				float:
       return math.pow(snake_case			,							2     ) - a




def    snake_case_     (  snake_case     )  ->				float:
       return 2 * x




def    snake_case_     (  snake_case     )  ->				float:
       lowercase__:   Dict  			=   2.0

       while start <= a:
              lowercase__:   str  			=   math.pow(snake_case			,							2     )

       return start




def    snake_case_     (  snake_case			,							snake_case = 99_99			,							snake_case = 0.0_0_0_0_0_0_0_0_0_0_0_0_0_1     )  ->				float:

       if a < 0:
              raise ValueError('math domain error'     )

       lowercase__:   Tuple  			=   get_initial_point(snake_case     )

       for _ in range(snake_case     ):
              lowercase__:   List[Any]  			=   value
              lowercase__:   Any  			=   value - fx(snake_case			,							snake_case     ) / fx_derivative(snake_case     )
              if abs(prev_value - value     ) < tolerance:
                     return value

       return value


if __name__ == "__main__":
 from doctest import testmod

 testmod()


