

'''simple docstring'''





import unittest

from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow


if is_flax_available():
  import jax.numpy as jnp

  from transformers import FlaxXLMRobertaModel





@require_sentencepiece
@require_tokenizers
@require_flax
class 				__lowerCAmelCase  (      unittest.TestCase							):




  @slow
  def 						A__  (       self      )						->				Any:
        '''simple docstring'''






        _lowercase			 =FlaxXLMRobertaModel.from_pretrained('xlm-roberta-base'      )
        _lowercase			 =AutoTokenizer.from_pretrained('xlm-roberta-base'      )
        _lowercase			 ='The dog is cute and lives in the garden house'
        _lowercase			 =jnp.array([tokenizer.encode(lowerCAmelCase      )]      )

        _lowercase			 =(1, 12, 768)  # batch_size, sequence_length, embedding_vector_dim
        _lowercase			 =jnp.array(
            [[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]]      )

        _lowercase			 =model(lowerCAmelCase      )['last_hidden_state']
        self.assertEqual(output.shape							,     lowerCAmelCase      )
        # compare the actual values for a slice of last dim
        self.assertTrue(jnp.allclose(output[:, :, -1]							,     lowerCAmelCase							,     atol=1e-3      )      )





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 				__lowerCAmelCase  (      unittest.TestCase							):




  @slow
  def 						A__  (       self      )						->				str:
        '''simple docstring'''






        _lowercase			 =TFCamembertModel.from_pretrained('jplu/tf-camembert-base'      )

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

        _lowercase			 =model(lowerCAmelCase      )['last_hidden_state']
        _lowercase			 =tf.TensorShape((1, 10, 768)      )
        self.assertEqual(output.shape							,     lowerCAmelCase      )
        # compare the actual values for a slice.
        _lowercase			 =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      )      )


