mirror of
https://github.com/huggingface/transformers.git
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45f56580a7
* Allow custom code for Processors * Add more test * Test all auto_map configs are properly set
222 lines
9.4 KiB
Python
222 lines
9.4 KiB
Python
# coding=utf-8
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# Copyright 2021 the HuggingFace 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 json
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import os
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import sys
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import tempfile
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import unittest
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from pathlib import Path
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from shutil import copyfile
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from huggingface_hub import Repository, delete_repo, login
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from requests.exceptions import HTTPError
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from transformers import AutoProcessor, AutoTokenizer, Wav2Vec2Config, Wav2Vec2FeatureExtractor, Wav2Vec2Processor
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from transformers.file_utils import FEATURE_EXTRACTOR_NAME, is_tokenizers_available
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from transformers.testing_utils import PASS, USER, is_staging_test
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from transformers.tokenization_utils import TOKENIZER_CONFIG_FILE
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sys.path.append(str(Path(__file__).parent.parent / "utils"))
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from test_module.custom_feature_extraction import CustomFeatureExtractor # noqa E402
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from test_module.custom_processing import CustomProcessor # noqa E402
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from test_module.custom_tokenization import CustomTokenizer # noqa E402
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SAMPLE_PROCESSOR_CONFIG = os.path.join(
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os.path.dirname(os.path.abspath(__file__)), "fixtures/dummy_feature_extractor_config.json"
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)
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SAMPLE_VOCAB = os.path.join(os.path.dirname(os.path.abspath(__file__)), "fixtures/vocab.json")
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SAMPLE_FEATURE_EXTRACTION_CONFIG_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "fixtures")
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class AutoFeatureExtractorTest(unittest.TestCase):
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def test_processor_from_model_shortcut(self):
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processor = AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h")
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self.assertIsInstance(processor, Wav2Vec2Processor)
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def test_processor_from_local_directory_from_repo(self):
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with tempfile.TemporaryDirectory() as tmpdirname:
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model_config = Wav2Vec2Config()
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processor = AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h")
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# save in new folder
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model_config.save_pretrained(tmpdirname)
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processor.save_pretrained(tmpdirname)
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processor = AutoProcessor.from_pretrained(tmpdirname)
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self.assertIsInstance(processor, Wav2Vec2Processor)
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def test_processor_from_local_directory_from_extractor_config(self):
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with tempfile.TemporaryDirectory() as tmpdirname:
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# copy relevant files
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copyfile(SAMPLE_PROCESSOR_CONFIG, os.path.join(tmpdirname, FEATURE_EXTRACTOR_NAME))
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copyfile(SAMPLE_VOCAB, os.path.join(tmpdirname, "vocab.json"))
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processor = AutoProcessor.from_pretrained(tmpdirname)
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self.assertIsInstance(processor, Wav2Vec2Processor)
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def test_processor_from_feat_extr_processor_class(self):
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with tempfile.TemporaryDirectory() as tmpdirname:
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feature_extractor = Wav2Vec2FeatureExtractor()
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tokenizer = AutoTokenizer.from_pretrained("facebook/wav2vec2-base-960h")
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processor = Wav2Vec2Processor(feature_extractor, tokenizer)
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# save in new folder
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processor.save_pretrained(tmpdirname)
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# drop `processor_class` in tokenizer
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with open(os.path.join(tmpdirname, TOKENIZER_CONFIG_FILE), "r") as f:
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config_dict = json.load(f)
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config_dict.pop("processor_class")
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with open(os.path.join(tmpdirname, TOKENIZER_CONFIG_FILE), "w") as f:
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f.write(json.dumps(config_dict))
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processor = AutoProcessor.from_pretrained(tmpdirname)
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self.assertIsInstance(processor, Wav2Vec2Processor)
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def test_processor_from_tokenizer_processor_class(self):
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with tempfile.TemporaryDirectory() as tmpdirname:
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feature_extractor = Wav2Vec2FeatureExtractor()
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tokenizer = AutoTokenizer.from_pretrained("facebook/wav2vec2-base-960h")
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processor = Wav2Vec2Processor(feature_extractor, tokenizer)
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# save in new folder
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processor.save_pretrained(tmpdirname)
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# drop `processor_class` in feature extractor
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with open(os.path.join(tmpdirname, FEATURE_EXTRACTOR_NAME), "r") as f:
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config_dict = json.load(f)
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config_dict.pop("processor_class")
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with open(os.path.join(tmpdirname, FEATURE_EXTRACTOR_NAME), "w") as f:
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f.write(json.dumps(config_dict))
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processor = AutoProcessor.from_pretrained(tmpdirname)
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self.assertIsInstance(processor, Wav2Vec2Processor)
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def test_processor_from_local_directory_from_model_config(self):
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with tempfile.TemporaryDirectory() as tmpdirname:
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model_config = Wav2Vec2Config(processor_class="Wav2Vec2Processor")
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model_config.save_pretrained(tmpdirname)
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# copy relevant files
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copyfile(SAMPLE_VOCAB, os.path.join(tmpdirname, "vocab.json"))
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# create emtpy sample processor
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with open(os.path.join(tmpdirname, FEATURE_EXTRACTOR_NAME), "w") as f:
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f.write("{}")
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processor = AutoProcessor.from_pretrained(tmpdirname)
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self.assertIsInstance(processor, Wav2Vec2Processor)
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def test_from_pretrained_dynamic_processor(self):
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processor = AutoProcessor.from_pretrained("hf-internal-testing/test_dynamic_processor", trust_remote_code=True)
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self.assertTrue(processor.special_attribute_present)
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self.assertEqual(processor.__class__.__name__, "NewProcessor")
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feature_extractor = processor.feature_extractor
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self.assertTrue(feature_extractor.special_attribute_present)
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self.assertEqual(feature_extractor.__class__.__name__, "NewFeatureExtractor")
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tokenizer = processor.tokenizer
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self.assertTrue(tokenizer.special_attribute_present)
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if is_tokenizers_available():
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self.assertEqual(tokenizer.__class__.__name__, "NewTokenizerFast")
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# Test we can also load the slow version
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processor = AutoProcessor.from_pretrained(
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"hf-internal-testing/test_dynamic_processor", trust_remote_code=True, use_fast=False
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)
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tokenizer = processor.tokenizer
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self.assertTrue(tokenizer.special_attribute_present)
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self.assertEqual(tokenizer.__class__.__name__, "NewTokenizer")
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else:
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self.assertEqual(tokenizer.__class__.__name__, "NewTokenizer")
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@is_staging_test
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class ProcessorPushToHubTester(unittest.TestCase):
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vocab_tokens = ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "bla", "blou"]
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@classmethod
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def setUpClass(cls):
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cls._token = login(username=USER, password=PASS)
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@classmethod
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def tearDownClass(cls):
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try:
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delete_repo(token=cls._token, name="test-dynamic-processor")
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except HTTPError:
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pass
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def test_push_to_hub_dynamic_processor(self):
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CustomFeatureExtractor.register_for_auto_class()
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CustomTokenizer.register_for_auto_class()
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CustomProcessor.register_for_auto_class()
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feature_extractor = CustomFeatureExtractor.from_pretrained(SAMPLE_FEATURE_EXTRACTION_CONFIG_DIR)
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with tempfile.TemporaryDirectory() as tmp_dir:
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vocab_file = os.path.join(tmp_dir, "vocab.txt")
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with open(vocab_file, "w", encoding="utf-8") as vocab_writer:
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vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]))
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tokenizer = CustomTokenizer(vocab_file)
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processor = CustomProcessor(feature_extractor, tokenizer)
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with tempfile.TemporaryDirectory() as tmp_dir:
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repo = Repository(tmp_dir, clone_from=f"{USER}/test-dynamic-processor", use_auth_token=self._token)
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processor.save_pretrained(tmp_dir)
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# This has added the proper auto_map field to the feature extractor config
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self.assertDictEqual(
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processor.feature_extractor.auto_map,
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{
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"AutoFeatureExtractor": "custom_feature_extraction.CustomFeatureExtractor",
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"AutoProcessor": "custom_processing.CustomProcessor",
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},
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)
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# This has added the proper auto_map field to the tokenizer config
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with open(os.path.join(tmp_dir, "tokenizer_config.json")) as f:
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tokenizer_config = json.load(f)
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self.assertDictEqual(
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tokenizer_config["auto_map"],
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{
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"AutoTokenizer": ["custom_tokenization.CustomTokenizer", None],
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"AutoProcessor": "custom_processing.CustomProcessor",
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},
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)
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# The code has been copied from fixtures
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self.assertTrue(os.path.isfile(os.path.join(tmp_dir, "custom_feature_extraction.py")))
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self.assertTrue(os.path.isfile(os.path.join(tmp_dir, "custom_tokenization.py")))
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self.assertTrue(os.path.isfile(os.path.join(tmp_dir, "custom_processing.py")))
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repo.push_to_hub()
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new_processor = AutoProcessor.from_pretrained(f"{USER}/test-dynamic-processor", trust_remote_code=True)
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# Can't make an isinstance check because the new_processor is from the CustomProcessor class of a dynamic module
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self.assertEqual(new_processor.__class__.__name__, "CustomProcessor")
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