File size: 6,160 Bytes
ee6e328
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
# 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 sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoFeatureExtractor, Wav2Vec2FeatureExtractor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / "utils"))

from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402


SAMPLE_FEATURE_EXTRACTION_CONFIG_DIR = get_tests_dir("fixtures")


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
        response_mock.json.return_value = {}

        # 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("requests.Session.request", 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()

    def test_legacy_load_from_url(self):
        # This test is for deprecated behavior and can be removed in v5
        _ = Wav2Vec2FeatureExtractor.from_pretrained(
            "https://huggingface.co./hf-internal-testing/tiny-random-wav2vec2/resolve/main/preprocessor_config.json"
        )


@is_staging_test
class FeatureExtractorPushToHubTester(unittest.TestCase):
    @classmethod
    def setUpClass(cls):
        cls._token = TOKEN
        HfFolder.save_token(TOKEN)

    @classmethod
    def tearDownClass(cls):
        try:
            delete_repo(token=cls._token, repo_id="test-feature-extractor")
        except HTTPError:
            pass

        try:
            delete_repo(token=cls._token, repo_id="valid_org/test-feature-extractor-org")
        except HTTPError:
            pass

        try:
            delete_repo(token=cls._token, repo_id="test-dynamic-feature-extractor")
        except HTTPError:
            pass

    def test_push_to_hub(self):
        feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(SAMPLE_FEATURE_EXTRACTION_CONFIG_DIR)
        feature_extractor.push_to_hub("test-feature-extractor", 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))

        # Reset repo
        delete_repo(token=self._token, repo_id="test-feature-extractor")

        # Push to hub via save_pretrained
        with tempfile.TemporaryDirectory() as tmp_dir:
            feature_extractor.save_pretrained(
                tmp_dir, repo_id="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)
        feature_extractor.push_to_hub("valid_org/test-feature-extractor", use_auth_token=self._token)

        new_feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("valid_org/test-feature-extractor")
        for k, v in feature_extractor.__dict__.items():
            self.assertEqual(v, getattr(new_feature_extractor, k))

        # Reset repo
        delete_repo(token=self._token, repo_id="valid_org/test-feature-extractor")

        # Push to hub via save_pretrained
        with tempfile.TemporaryDirectory() as tmp_dir:
            feature_extractor.save_pretrained(
                tmp_dir, repo_id="valid_org/test-feature-extractor-org", push_to_hub=True, use_auth_token=self._token
            )

        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)

        feature_extractor.push_to_hub("test-dynamic-feature-extractor", use_auth_token=self._token)

        # This has added the proper auto_map field to the config
        self.assertDictEqual(
            feature_extractor.auto_map,
            {"AutoFeatureExtractor": "custom_feature_extraction.CustomFeatureExtractor"},
        )

        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")