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104 lines
4.3 KiB
Python
104 lines
4.3 KiB
Python
# coding=utf-8
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# Copyright 2018 The Google AI Language Team Authors.
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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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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import unittest
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import shutil
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import logging
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from transformers import is_tf_available
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from .utils import require_tf, slow, SMALL_MODEL_IDENTIFIER
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if is_tf_available():
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from transformers import (AutoConfig, BertConfig,
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TFAutoModel, TFBertModel,
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TFAutoModelWithLMHead, TFBertForMaskedLM,
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TFAutoModelForSequenceClassification, TFBertForSequenceClassification,
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TFAutoModelForQuestionAnswering, TFBertForQuestionAnswering)
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from transformers.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP
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from .modeling_common_test import (CommonTestCases, ids_tensor)
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from .configuration_common_test import ConfigTester
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@require_tf
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class TFAutoModelTest(unittest.TestCase):
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@slow
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def test_model_from_pretrained(self):
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import h5py
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self.assertTrue(h5py.version.hdf5_version.startswith("1.10"))
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logging.basicConfig(level=logging.INFO)
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# for model_name in list(TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
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for model_name in ['bert-base-uncased']:
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config = AutoConfig.from_pretrained(model_name, force_download=True)
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self.assertIsNotNone(config)
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self.assertIsInstance(config, BertConfig)
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model = TFAutoModel.from_pretrained(model_name, force_download=True)
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self.assertIsNotNone(model)
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self.assertIsInstance(model, TFBertModel)
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@slow
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def test_lmhead_model_from_pretrained(self):
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logging.basicConfig(level=logging.INFO)
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# for model_name in list(TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
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for model_name in ['bert-base-uncased']:
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config = AutoConfig.from_pretrained(model_name, force_download=True)
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self.assertIsNotNone(config)
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self.assertIsInstance(config, BertConfig)
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model = TFAutoModelWithLMHead.from_pretrained(model_name, force_download=True)
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self.assertIsNotNone(model)
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self.assertIsInstance(model, TFBertForMaskedLM)
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@slow
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def test_sequence_classification_model_from_pretrained(self):
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logging.basicConfig(level=logging.INFO)
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# for model_name in list(TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
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for model_name in ['bert-base-uncased']:
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config = AutoConfig.from_pretrained(model_name, force_download=True)
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self.assertIsNotNone(config)
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self.assertIsInstance(config, BertConfig)
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model = TFAutoModelForSequenceClassification.from_pretrained(model_name, force_download=True)
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self.assertIsNotNone(model)
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self.assertIsInstance(model, TFBertForSequenceClassification)
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@slow
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def test_question_answering_model_from_pretrained(self):
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logging.basicConfig(level=logging.INFO)
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# for model_name in list(TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
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for model_name in ['bert-base-uncased']:
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config = AutoConfig.from_pretrained(model_name, force_download=True)
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self.assertIsNotNone(config)
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self.assertIsInstance(config, BertConfig)
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model = TFAutoModelForQuestionAnswering.from_pretrained(model_name, force_download=True)
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self.assertIsNotNone(model)
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self.assertIsInstance(model, TFBertForQuestionAnswering)
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def test_from_pretrained_identifier(self):
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logging.basicConfig(level=logging.INFO)
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model = TFAutoModelWithLMHead.from_pretrained(SMALL_MODEL_IDENTIFIER, force_download=True)
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self.assertIsInstance(model, TFBertForMaskedLM)
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if __name__ == "__main__":
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unittest.main()
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