# What does this PR do?

This PR fixes the last batch of models that are too big for common tests. After it, only two classes override the common test to skip it:
- timm-backbone
- layoutlmv2
In the first case, it's because there is no timm model small enough to work (I did switch to the smallest resnet though) and in the second detectron2 does not let us configure a smaller backbone (I did try with the constant exposed by they don't seem to have any effect).
Fix last models for common tests that are too big.. # What does this PR do?

This PR fixes the last batch of models that are too big for common tests. After it, only two classes override the common test to skip it:
- timm-backbone
- layoutlmv2
In the first case, it's because there is no timm model small enough to work (I did switch to the smallest resnet though) and in the second detectron2 does not let us configure a smaller backbone (I did try with the constant exposed by they don't seem to have any effect).
# 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")

# 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")
