wehub-resource-sync
127 lines
6.3 KiB
Python
127 lines
6.3 KiB
Python
# Copyright (c) 2023 PaddlePaddle Authors. 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 paddlenlp.transformers import (
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AutoTokenizer,
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BertTokenizer,
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CLIPTokenizer,
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T5Tokenizer,
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)
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from paddlenlp.utils.log import logger
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from tests.testing_utils import slow
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@unittest.skip("skipping due to connection error!")
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class TokenizerLoadTester(unittest.TestCase):
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@slow
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def test_bert_load(self):
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logger.info("Download model from PaddleNLP BOS")
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bert_tokenizer = BertTokenizer.from_pretrained("bert-base-uncased", from_hf_hub=False)
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bert_tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased", from_hf_hub=False)
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logger.info("Download model from local")
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bert_tokenizer.save_pretrained("./paddlenlp-test-model/bert-base-uncased")
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bert_tokenizer = BertTokenizer.from_pretrained("./paddlenlp-test-model/bert-base-uncased")
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bert_tokenizer = AutoTokenizer.from_pretrained("./paddlenlp-test-model/bert-base-uncased")
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bert_tokenizer = BertTokenizer.from_pretrained("./paddlenlp-test-model/", subfolder="bert-base-uncased")
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bert_tokenizer = AutoTokenizer.from_pretrained("./paddlenlp-test-model/", subfolder="bert-base-uncased")
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logger.info("Download model from PaddleNLP BOS with subfolder")
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bert_tokenizer = BertTokenizer.from_pretrained(
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"baicai/paddlenlp-test-model", subfolder="bert-base-uncased", from_hf_hub=False
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)
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bert_tokenizer = AutoTokenizer.from_pretrained(
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"baicai/paddlenlp-test-model", subfolder="bert-base-uncased", from_hf_hub=False
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)
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logger.info("Download model from aistudio")
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bert_tokenizer = BertTokenizer.from_pretrained("aistudio/bert-base-uncased", from_aistudio=True)
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bert_tokenizer = AutoTokenizer.from_pretrained("aistudio/bert-base-uncased", from_aistudio=True)
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logger.info("Download model from aistudio with subfolder")
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bert_tokenizer = BertTokenizer.from_pretrained(
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"aistudio/paddlenlp-test-model", subfolder="bert-base-uncased", from_aistudio=True
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)
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bert_tokenizer = AutoTokenizer.from_pretrained(
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"aistudio/paddlenlp-test-model", subfolder="bert-base-uncased", from_aistudio=True
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)
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@slow
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def test_clip_load(self):
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logger.info("Download model from PaddleNLP BOS")
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clip_tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32", from_hf_hub=False)
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clip_tokenizer = AutoTokenizer.from_pretrained("openai/clip-vit-base-patch32", from_hf_hub=False)
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logger.info("Download model from local")
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clip_tokenizer.save_pretrained("./paddlenlp-test-model/clip-vit-base-patch32")
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clip_tokenizer = CLIPTokenizer.from_pretrained("./paddlenlp-test-model/clip-vit-base-patch32")
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clip_tokenizer = AutoTokenizer.from_pretrained("./paddlenlp-test-model/clip-vit-base-patch32")
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clip_tokenizer = CLIPTokenizer.from_pretrained("./paddlenlp-test-model/", subfolder="clip-vit-base-patch32")
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clip_tokenizer = AutoTokenizer.from_pretrained("./paddlenlp-test-model/", subfolder="clip-vit-base-patch32")
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logger.info("Download model from PaddleNLP BOS with subfolder")
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clip_tokenizer = CLIPTokenizer.from_pretrained(
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"baicai/paddlenlp-test-model", subfolder="clip-vit-base-patch32", from_hf_hub=False
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)
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clip_tokenizer = AutoTokenizer.from_pretrained(
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"baicai/paddlenlp-test-model", subfolder="clip-vit-base-patch32", from_hf_hub=False
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)
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logger.info("Download model from aistudio")
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clip_tokenizer = CLIPTokenizer.from_pretrained("aistudio/clip-vit-base-patch32", from_aistudio=True)
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clip_tokenizer = AutoTokenizer.from_pretrained("aistudio/clip-vit-base-patch32", from_aistudio=True)
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logger.info("Download model from aistudio with subfolder")
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clip_tokenizer = CLIPTokenizer.from_pretrained(
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"aistudio/paddlenlp-test-model", subfolder="clip-vit-base-patch32", from_aistudio=True
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)
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clip_tokenizer = AutoTokenizer.from_pretrained(
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"aistudio/paddlenlp-test-model", subfolder="clip-vit-base-patch32", from_aistudio=True
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)
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@slow
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def test_t5_load(self):
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logger.info("Download model from PaddleNLP BOS")
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t5_tokenizer = T5Tokenizer.from_pretrained("t5-small", from_hf_hub=False)
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t5_tokenizer = AutoTokenizer.from_pretrained("t5-small", from_hf_hub=False)
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logger.info("Download model from local")
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t5_tokenizer.save_pretrained("./paddlenlp-test-model/t5-small")
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t5_tokenizer = T5Tokenizer.from_pretrained("./paddlenlp-test-model/t5-small")
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t5_tokenizer = AutoTokenizer.from_pretrained("./paddlenlp-test-model/t5-small")
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t5_tokenizer = T5Tokenizer.from_pretrained("./paddlenlp-test-model/", subfolder="t5-small")
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t5_tokenizer = AutoTokenizer.from_pretrained("./paddlenlp-test-model/", subfolder="t5-small")
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logger.info("Download model from PaddleNLP BOS with subfolder")
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t5_tokenizer = T5Tokenizer.from_pretrained(
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"baicai/paddlenlp-test-model", subfolder="t5-small", from_hf_hub=False
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)
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t5_tokenizer = AutoTokenizer.from_pretrained(
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"baicai/paddlenlp-test-model", subfolder="t5-small", from_hf_hub=False
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)
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logger.info("Download model from aistudio")
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t5_tokenizer = T5Tokenizer.from_pretrained("aistudio/t5-small", from_aistudio=True)
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t5_tokenizer = AutoTokenizer.from_pretrained("aistudio/t5-small", from_aistudio=True)
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t5_tokenizer = T5Tokenizer.from_pretrained(
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"aistudio/paddlenlp-test-model", subfolder="t5-small", from_aistudio=True
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)
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t5_tokenizer = AutoTokenizer.from_pretrained(
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"aistudio/paddlenlp-test-model", subfolder="t5-small", from_aistudio=True
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)
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