codingutf8 2022 the huggingface team licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache orglicenseslicense2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license compare the actual values for a slice of last dim coding utf 8 2022 the huggingface team licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache org licenses license 2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license batch_size sequence_length embedding_vector_dim compare the actual values for a slice of last dim
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 FlaxXLMRobertaModelIntegrationTest(unittest.TestCase):
    @slow
    def test_flax_xlm_roberta_base(self):
        model = FlaxXLMRobertaModel.from_pretrained("xlm-roberta-base")
        tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-base")
        text = "The dog is cute and lives in the garden house"
        input_ids = jnp.array([tokenizer.encode(text)])

        expected_output_shape = (1, 12, 768)  
        expected_output_values_last_dim = 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]]
        )

        output = model(input_ids)["last_hidden_state"]
        self.assertEqual(output.shape, expected_output_shape)
        
        self.assertTrue(jnp.allclose(output[:, :, -1], expected_output_values_last_dim, atol=1e-3))