52 lines
1.8 KiB
Python
52 lines
1.8 KiB
Python
# coding=utf-8
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# Copyright 2020 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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from transformers import is_tf_available
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from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
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if is_tf_available():
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import numpy as np
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import tensorflow as tf
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from transformers import TFAutoModel
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@require_tf
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@require_sentencepiece
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@require_tokenizers
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class TFBortIntegrationTest(unittest.TestCase):
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@slow
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def test_output_embeds_base_model(self):
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model = TFAutoModel.from_pretrained("amazon/bort")
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input_ids = tf.convert_to_tensor(
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[[0, 18077, 4082, 7804, 8606, 6195, 2457, 3321, 11, 10489, 16, 269, 2579, 328, 2]],
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dtype=tf.int32,
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) # Schloß Nymphenburg in Munich is really nice!
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output = model(input_ids)["last_hidden_state"]
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expected_shape = tf.TensorShape((1, 15, 1024))
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self.assertEqual(output.shape, expected_shape)
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# compare the actual values for a slice.
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expected_slice = tf.convert_to_tensor(
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[[[-0.0349, 0.0436, -1.8654], [-0.6964, 0.0835, -1.7393], [-0.9819, 0.2956, -0.2868]]],
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dtype=tf.float32,
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)
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self.assertTrue(np.allclose(output[:, :3, :3].numpy(), expected_slice.numpy(), atol=1e-4))
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