51 lines
1.8 KiB
Python
51 lines
1.8 KiB
Python
# coding=utf-8
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# Copyright 2018 The Hugging Face Inc. Team
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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 logging
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import unittest
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from transformers import is_torch_available
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from .utils import require_torch, slow
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if is_torch_available():
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from transformers import BertModel, BertForMaskedLM, Model2Model
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from transformers.modeling_bert import BERT_PRETRAINED_MODEL_ARCHIVE_MAP
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@require_torch
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class EncoderDecoderModelTest(unittest.TestCase):
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@slow
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def test_model2model_from_pretrained(self):
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logging.basicConfig(level=logging.INFO)
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for model_name in list(BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
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model = Model2Model.from_pretrained(model_name)
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self.assertIsInstance(model.encoder, BertModel)
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self.assertIsInstance(model.decoder, BertForMaskedLM)
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self.assertEqual(model.decoder.config.is_decoder, True)
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self.assertEqual(model.encoder.config.is_decoder, False)
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def test_model2model_from_pretrained_not_bert(self):
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logging.basicConfig(level=logging.INFO)
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with self.assertRaises(ValueError):
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_ = Model2Model.from_pretrained("roberta")
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with self.assertRaises(ValueError):
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_ = Model2Model.from_pretrained("distilbert")
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with self.assertRaises(ValueError):
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_ = Model2Model.from_pretrained("does-not-exist")
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