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- # 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 AutoModel
-
-
- @require_torch
- @require_sentencepiece
- @require_tokenizers
- class BortIntegrationTest(unittest.TestCase):
- @slow
- def test_output_embeds_base_model(self):
- model = AutoModel.from_pretrained("amazon/bort")
- model.to(torch_device)
-
- input_ids = torch.tensor(
- [[0, 18077, 4082, 7804, 8606, 6195, 2457, 3321, 11, 10489, 16, 269, 2579, 328, 2]],
- device=torch_device,
- dtype=torch.long,
- ) # Schloß Nymphenburg in Munich is really nice!
- output = model(input_ids)["last_hidden_state"]
- expected_shape = torch.Size((1, 15, 1024))
- self.assertEqual(output.shape, expected_shape)
- # compare the actual values for a slice.
- expected_slice = torch.tensor(
- [[[-0.0349, 0.0436, -1.8654], [-0.6964, 0.0835, -1.7393], [-0.9819, 0.2956, -0.2868]]],
- device=torch_device,
- dtype=torch.float,
- )
- self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))
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