




def 				lowerCAmelCase_	(     snake_case_,snake_case_     ):
      while b:
            _A			,   _A							:     List[str]			=						b, a % b
      return a







def 				lowerCAmelCase_	(     snake_case_,snake_case_     ):
      return a if b == 0 else euclidean_gcd_recursive(snake_case_,a % b     )







def 				lowerCAmelCase_	(     ):
      print(f'''euclidean_gcd(3, 5) = {euclidean_gcd(3,5     )}'''     )
      print(f'''euclidean_gcd(5, 3) = {euclidean_gcd(5,3     )}'''     )
      print(f'''euclidean_gcd(1, 3) = {euclidean_gcd(1,3     )}'''     )
      print(f'''euclidean_gcd(3, 6) = {euclidean_gcd(3,6     )}'''     )
      print(f'''euclidean_gcd(6, 3) = {euclidean_gcd(6,3     )}'''     )

      print(f'''euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3,5     )}'''     )
      print(f'''euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5,3     )}'''     )
      print(f'''euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1,3     )}'''     )
      print(f'''euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3,6     )}'''     )
      print(f'''euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6,3     )}'''     )


if __name__ == "__main__":
  main()







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		):

     @slow
     def 					a__	(  self				)						->							Any:
           _A							:     Tuple			=						TFCamembertModel.from_pretrained("""jplu/tf-camembert-base"""				)

           _A							:     List[Any]			=						tf.convert_to_tensor(
               [[5, 121, 11, 660, 16, 730, 2_5543, 110, 83, 6]]		,     dtype=tf.intaa		,     )  # J'aime le camembert !"

           _A							:     List[str]			=						model(_a				)["""last_hidden_state"""]
           _A							:     Union[str, Any]			=						tf.TensorShape((1, 10, 768)				)
           self.assertEqual(output.shape		,     _a				)
           # compare the actual values for a slice.
           _A							:     List[Any]			=						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				)				)

