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 the dog is cute and lives in the garden house xlmr torch hub load pytorchfairseq xlmr base xlmr eval expectedoutputvalueslastdim xlmr extractfeaturesinputids0 1 compare the actual values for a slice of last dim the dog is cute and lives in the garden house xlmr torch hub load pytorchfairseq xlmr large xlmr eval expectedoutputvalueslastdim xlmr extractfeaturesinputids0 1 compare the actual values for a slice of last dim 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 the dog is cute and lives in the garden house batch_size sequence_length embedding_vector_dim xlmr torch hub load pytorch fairseq xlmr base xlmr eval expected_output_values_last_dim xlmr extract_features input_ids 0 1 compare the actual values for a slice of last dim the dog is cute and lives in the garden house batch_size sequence_length embedding_vector_dim xlmr torch hub load pytorch fairseq xlmr large xlmr eval expected_output_values_last_dim xlmr extract_features input_ids 0 1 compare the actual values for a slice of last dim
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
    import torch

    from transformers import XLMRobertaModel


@require_sentencepiece
@require_tokenizers
@require_torch
class XLMRobertaModelIntegrationTest(unittest.TestCase):
    @slow
    def test_xlm_roberta_base(self):
        model = XLMRobertaModel.from_pretrained("xlm-roberta-base")
        input_ids = torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]])
        

        expected_output_shape = torch.Size((1, 12, 768))  
        expected_output_values_last_dim = torch.tensor(
            [[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]]
        )
        
        
        
        with torch.no_grad():
            output = model(input_ids)["last_hidden_state"].detach()
        self.assertEqual(output.shape, expected_output_shape)
        
        self.assertTrue(torch.allclose(output[:, :, -1], expected_output_values_last_dim, atol=1e-3))

    @slow
    def test_xlm_roberta_large(self):
        model = XLMRobertaModel.from_pretrained("xlm-roberta-large")
        input_ids = torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]])
        

        expected_output_shape = torch.Size((1, 12, 1024))  
        expected_output_values_last_dim = torch.tensor(
            [[-0.0699, -0.0318, 0.0705, -0.1241, 0.0999, -0.0520, 0.1004, -0.1838, -0.4704, 0.1437, 0.0821, 0.0126]]
        )
        
        
        
        with torch.no_grad():
            output = model(input_ids)["last_hidden_state"].detach()
        self.assertEqual(output.shape, expected_output_shape)
        
        self.assertTrue(torch.allclose(output[:, :, -1], expected_output_values_last_dim, atol=1e-3))