# coding=utf-8 # Copyright 2020 The HuggingFace Team. All rights reserved. # # 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. import unittest from transformers import is_torch_available from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device if is_torch_available(): import torch from transformers import CamembertModel @require_torch @require_sentencepiece @require_tokenizers class CamembertModelIntegrationTest(unittest.TestCase): @slow def test_output_embeds_base_model(self): model = CamembertModel.from_pretrained("camembert-base") model.to(torch_device) input_ids = torch.tensor( [[5, 121, 11, 660, 16, 730, 25543, 110, 83, 6]], device=torch_device, dtype=torch.long, ) # J'aime le camembert ! with torch.no_grad(): output = model(input_ids)["last_hidden_state"] expected_shape = torch.Size((1, 10, 768)) self.assertEqual(output.shape, expected_shape) # compare the actual values for a slice. expected_slice = torch.tensor( [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]], device=torch_device, dtype=torch.float, ) # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0') # camembert.eval() # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach() self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))