codingutf8 2020 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 coding utf 8 2020 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 my dog is cute compare the actual values for a slice
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 TFXLMRobertaModel


@require_tf
@require_sentencepiece
@require_tokenizers
class TFFlaubertModelIntegrationTest(unittest.TestCase):
    @slow
    def test_output_embeds_base_model(self):
        model = TFXLMRobertaModel.from_pretrained("jplu/tf-xlm-roberta-base")

        features = {
            "input_ids": tf.convert_to_tensor([[0, 2646, 10269, 83, 99942, 2]], dtype=tf.int32),  
            "attention_mask": tf.convert_to_tensor([[1, 1, 1, 1, 1, 1]], dtype=tf.int32),
        }

        output = model(features)["last_hidden_state"]
        expected_shape = tf.TensorShape((1, 6, 768))
        self.assertEqual(output.shape, expected_shape)
        
        expected_slice = tf.convert_to_tensor(
            [
                [
                    [0.0681762, 0.10894451, 0.06772504],
                    [-0.06423668, 0.02366615, 0.04329344],
                    [-0.06057295, 0.09974135, -0.00070584],
                ]
            ],
            dtype=tf.float32,
        )

        self.assertTrue(np.allclose(output[:, :3, :3].numpy(), expected_slice.numpy(), atol=1e-4))