131 строка
4.9 KiB
Python
131 строка
4.9 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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import logging
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import unittest
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from transformers import is_tf_available
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from .utils import DUMMY_UNKWOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, require_tf, slow
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if is_tf_available():
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from transformers import (
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AutoConfig,
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BertConfig,
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TFAutoModel,
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TFBertModel,
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TFAutoModelForPreTraining,
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TFBertForPreTraining,
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TFAutoModelWithLMHead,
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TFBertForMaskedLM,
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TFRobertaForMaskedLM,
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TFAutoModelForSequenceClassification,
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TFBertForSequenceClassification,
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TFAutoModelForQuestionAnswering,
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TFBertForQuestionAnswering,
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)
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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 TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
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for model_name in ["bert-base-uncased"]:
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config = AutoConfig.from_pretrained(model_name)
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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)
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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_model_for_pretraining_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 TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
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for model_name in ["bert-base-uncased"]:
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config = AutoConfig.from_pretrained(model_name)
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self.assertIsNotNone(config)
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self.assertIsInstance(config, BertConfig)
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model = TFAutoModelForPreTraining.from_pretrained(model_name)
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self.assertIsNotNone(model)
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self.assertIsInstance(model, TFBertForPreTraining)
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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 TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
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for model_name in ["bert-base-uncased"]:
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config = AutoConfig.from_pretrained(model_name)
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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)
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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 TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
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for model_name in ["bert-base-uncased"]:
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config = AutoConfig.from_pretrained(model_name)
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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)
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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 TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
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for model_name in ["bert-base-uncased"]:
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config = AutoConfig.from_pretrained(model_name)
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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)
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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)
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self.assertIsInstance(model, TFBertForMaskedLM)
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self.assertEqual(model.num_parameters(), 14830)
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self.assertEqual(model.num_parameters(only_trainable=True), 14830)
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def test_from_identifier_from_model_type(self):
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logging.basicConfig(level=logging.INFO)
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model = TFAutoModelWithLMHead.from_pretrained(DUMMY_UNKWOWN_IDENTIFIER)
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self.assertIsInstance(model, TFRobertaForMaskedLM)
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self.assertEqual(model.num_parameters(), 14830)
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self.assertEqual(model.num_parameters(only_trainable=True), 14830)
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