# coding=utf-8 # Copyright 2021 HuggingFace Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import json import os import sys import tempfile import unittest import unittest.mock as mock from pathlib import Path from huggingface_hub import Repository, delete_repo, login from requests.exceptions import HTTPError from transformers import AutoFeatureExtractor, Wav2Vec2FeatureExtractor from transformers.testing_utils import PASS, USER, check_json_file_has_correct_format, get_tests_dir, is_staging_test from transformers.utils import is_torch_available, is_vision_available sys.path.append(str(Path(__file__).parent.parent / "utils")) from test_module.custom_feature_extraction import CustomFeatureExtractor # noqa E402 if is_torch_available(): import numpy as np import torch if is_vision_available(): from PIL import Image SAMPLE_FEATURE_EXTRACTION_CONFIG_DIR = get_tests_dir("fixtures") def prepare_image_inputs(feature_extract_tester, equal_resolution=False, numpify=False, torchify=False): """This function prepares a list of PIL images, or a list of numpy arrays if one specifies numpify=True, or a list of PyTorch tensors if one specifies torchify=True. """ assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time" if equal_resolution: image_inputs = [] for i in range(feature_extract_tester.batch_size): image_inputs.append( np.random.randint( 255, size=( feature_extract_tester.num_channels, feature_extract_tester.max_resolution, feature_extract_tester.max_resolution, ), dtype=np.uint8, ) ) else: image_inputs = [] for i in range(feature_extract_tester.batch_size): width, height = np.random.choice( np.arange(feature_extract_tester.min_resolution, feature_extract_tester.max_resolution), 2 ) image_inputs.append( np.random.randint(255, size=(feature_extract_tester.num_channels, width, height), dtype=np.uint8) ) if not numpify and not torchify: # PIL expects the channel dimension as last dimension image_inputs = [Image.fromarray(np.moveaxis(x, 0, -1)) for x in image_inputs] if torchify: image_inputs = [torch.from_numpy(x) for x in image_inputs] return image_inputs class FeatureExtractionSavingTestMixin: def test_feat_extract_to_json_string(self): feat_extract = self.feature_extraction_class(**self.feat_extract_dict) obj = json.loads(feat_extract.to_json_string()) for key, value in self.feat_extract_dict.items(): self.assertEqual(obj[key], value) def test_feat_extract_to_json_file(self): feat_extract_first = self.feature_extraction_class(**self.feat_extract_dict) with tempfile.TemporaryDirectory() as tmpdirname: json_file_path = os.path.join(tmpdirname, "feat_extract.json") feat_extract_first.to_json_file(json_file_path) feat_extract_second = self.feature_extraction_class.from_json_file(json_file_path) self.assertEqual(feat_extract_second.to_dict(), feat_extract_first.to_dict()) def test_feat_extract_from_and_save_pretrained(self): feat_extract_first = self.feature_extraction_class(**self.feat_extract_dict) with tempfile.TemporaryDirectory() as tmpdirname: saved_file = feat_extract_first.save_pretrained(tmpdirname)[0] check_json_file_has_correct_format(saved_file) feat_extract_second = self.feature_extraction_class.from_pretrained(tmpdirname) self.assertEqual(feat_extract_second.to_dict(), feat_extract_first.to_dict()) def test_init_without_params(self): feat_extract = self.feature_extraction_class() self.assertIsNotNone(feat_extract) class FeatureExtractorUtilTester(unittest.TestCase): def test_cached_files_are_used_when_internet_is_down(self): # A mock response for an HTTP head request to emulate server down response_mock = mock.Mock() response_mock.status_code = 500 response_mock.headers = [] response_mock.raise_for_status.side_effect = HTTPError # Download this model to make sure it's in the cache. _ = Wav2Vec2FeatureExtractor.from_pretrained("hf-internal-testing/tiny-random-wav2vec2") # Under the mock environment we get a 500 error when trying to reach the model. with mock.patch("transformers.utils.hub.requests.head", return_value=response_mock) as mock_head: _ = Wav2Vec2FeatureExtractor.from_pretrained("hf-internal-testing/tiny-random-wav2vec2") # This check we did call the fake head request mock_head.assert_called() @is_staging_test class FeatureExtractorPushToHubTester(unittest.TestCase): @classmethod def setUpClass(cls): cls._token = login(username=USER, password=PASS) @classmethod def tearDownClass(cls): try: delete_repo(token=cls._token, name="test-feature-extractor") except HTTPError: pass try: delete_repo(token=cls._token, name="test-feature-extractor-org", organization="valid_org") except HTTPError: pass try: delete_repo(token=cls._token, name="test-dynamic-feature-extractor") except HTTPError: pass def test_push_to_hub(self): feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(SAMPLE_FEATURE_EXTRACTION_CONFIG_DIR) with tempfile.TemporaryDirectory() as tmp_dir: feature_extractor.save_pretrained( os.path.join(tmp_dir, "test-feature-extractor"), push_to_hub=True, use_auth_token=self._token ) new_feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(f"{USER}/test-feature-extractor") for k, v in feature_extractor.__dict__.items(): self.assertEqual(v, getattr(new_feature_extractor, k)) def test_push_to_hub_in_organization(self): feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(SAMPLE_FEATURE_EXTRACTION_CONFIG_DIR) with tempfile.TemporaryDirectory() as tmp_dir: feature_extractor.save_pretrained( os.path.join(tmp_dir, "test-feature-extractor-org"), push_to_hub=True, use_auth_token=self._token, organization="valid_org", ) new_feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("valid_org/test-feature-extractor-org") for k, v in feature_extractor.__dict__.items(): self.assertEqual(v, getattr(new_feature_extractor, k)) def test_push_to_hub_dynamic_feature_extractor(self): CustomFeatureExtractor.register_for_auto_class() feature_extractor = CustomFeatureExtractor.from_pretrained(SAMPLE_FEATURE_EXTRACTION_CONFIG_DIR) with tempfile.TemporaryDirectory() as tmp_dir: repo = Repository(tmp_dir, clone_from=f"{USER}/test-dynamic-feature-extractor", use_auth_token=self._token) feature_extractor.save_pretrained(tmp_dir) # This has added the proper auto_map field to the config self.assertDictEqual( feature_extractor.auto_map, {"AutoFeatureExtractor": "custom_feature_extraction.CustomFeatureExtractor"}, ) # The code has been copied from fixtures self.assertTrue(os.path.isfile(os.path.join(tmp_dir, "custom_feature_extraction.py"))) repo.push_to_hub() new_feature_extractor = AutoFeatureExtractor.from_pretrained( f"{USER}/test-dynamic-feature-extractor", trust_remote_code=True ) # Can't make an isinstance check because the new_feature_extractor is from the CustomFeatureExtractor class of a dynamic module self.assertEqual(new_feature_extractor.__class__.__name__, "CustomFeatureExtractor")