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 		UpperCamelCase_						(					self						) ->				Dict:






           """simple docstring"""
           _lowerCamelCase			    =				TFCamembertModel.from_pretrained('''jplu/tf-camembert-base'''						)

           _lowerCamelCase			    =				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 !"

           _lowerCamelCase			    =				model(A_						)['''last_hidden_state''']
           _lowerCamelCase			    =				tf.TensorShape((1, 10, 7_68)						)
           self.assertEqual(output.shape				,				A_						)
           # compare the actual values for a slice.
           _lowerCamelCase			    =				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						)						)
import os
import tempfile
import unittest

from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property



@unittest.skipUnless(os.path.exists(__lowercase )   ,  'Tatoeba directory does not exist.' )
class  UpperCamelCase				( unittest.TestCase ):

     '''simple docstring'''

     @cached_property
     def 		UpperCamelCase_						(					self						) ->				int:






           """simple docstring"""
           _lowerCamelCase			    =				tempfile.mkdtemp()
           return TatoebaConverter(save_dir=A_						)



     @slow
     def 		UpperCamelCase_						(					self						) ->				Any:






           """simple docstring"""
           self.resolver.convert_models(['''heb-eng''']						)





     @slow
     def 		UpperCamelCase_						(					self						) ->				Tuple:






           """simple docstring"""
           _lowerCamelCase    ,					_lowerCamelCase			    =				self.resolver.write_model_card('''opus-mt-he-en'''				,				dry_run=A_						)
           assert mmeta["long_pair"] == "heb-eng"