Instructions to use lysandre/tests with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lysandre/tests with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="lysandre/tests")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("lysandre/tests") model = AutoModel.from_pretrained("lysandre/tests", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| # coding=utf-8 | |
| # Copyright 2019 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 tempfile | |
| import unittest | |
| from huggingface_hub import HfApi | |
| from requests.exceptions import HTTPError | |
| from transformers import BertConfig, GPT2Config | |
| from transformers.testing_utils import ENDPOINT_STAGING, PASS, USER, is_staging_test | |
| class ConfigTester(object): | |
| def __init__(self, parent, config_class=None, has_text_modality=True, **kwargs): | |
| self.parent = parent | |
| self.config_class = config_class | |
| self.has_text_modality = has_text_modality | |
| self.inputs_dict = kwargs | |
| def create_and_test_config_common_properties(self): | |
| config = self.config_class(**self.inputs_dict) | |
| if self.has_text_modality: | |
| self.parent.assertTrue(hasattr(config, "vocab_size")) | |
| self.parent.assertTrue(hasattr(config, "hidden_size")) | |
| self.parent.assertTrue(hasattr(config, "num_attention_heads")) | |
| self.parent.assertTrue(hasattr(config, "num_hidden_layers")) | |
| def create_and_test_config_to_json_string(self): | |
| config = self.config_class(**self.inputs_dict) | |
| obj = json.loads(config.to_json_string()) | |
| for key, value in self.inputs_dict.items(): | |
| self.parent.assertEqual(obj[key], value) | |
| def create_and_test_config_to_json_file(self): | |
| config_first = self.config_class(**self.inputs_dict) | |
| with tempfile.TemporaryDirectory() as tmpdirname: | |
| json_file_path = os.path.join(tmpdirname, "config.json") | |
| config_first.to_json_file(json_file_path) | |
| config_second = self.config_class.from_json_file(json_file_path) | |
| self.parent.assertEqual(config_second.to_dict(), config_first.to_dict()) | |
| def create_and_test_config_from_and_save_pretrained(self): | |
| config_first = self.config_class(**self.inputs_dict) | |
| with tempfile.TemporaryDirectory() as tmpdirname: | |
| config_first.save_pretrained(tmpdirname) | |
| config_second = self.config_class.from_pretrained(tmpdirname) | |
| self.parent.assertEqual(config_second.to_dict(), config_first.to_dict()) | |
| def create_and_test_config_with_num_labels(self): | |
| config = self.config_class(**self.inputs_dict, num_labels=5) | |
| self.parent.assertEqual(len(config.id2label), 5) | |
| self.parent.assertEqual(len(config.label2id), 5) | |
| config.num_labels = 3 | |
| self.parent.assertEqual(len(config.id2label), 3) | |
| self.parent.assertEqual(len(config.label2id), 3) | |
| def check_config_can_be_init_without_params(self): | |
| if self.config_class.is_composition: | |
| return | |
| config = self.config_class() | |
| self.parent.assertIsNotNone(config) | |
| def run_common_tests(self): | |
| self.create_and_test_config_common_properties() | |
| self.create_and_test_config_to_json_string() | |
| self.create_and_test_config_to_json_file() | |
| self.create_and_test_config_from_and_save_pretrained() | |
| self.create_and_test_config_with_num_labels() | |
| self.check_config_can_be_init_without_params() | |
| class ConfigPushToHubTester(unittest.TestCase): | |
| def setUpClass(cls): | |
| cls._api = HfApi(endpoint=ENDPOINT_STAGING) | |
| cls._token = cls._api.login(username=USER, password=PASS) | |
| def tearDownClass(cls): | |
| try: | |
| cls._api.delete_repo(token=cls._token, name="test-config") | |
| except HTTPError: | |
| pass | |
| try: | |
| cls._api.delete_repo(token=cls._token, name="test-config-org", organization="valid_org") | |
| except HTTPError: | |
| pass | |
| def test_push_to_hub(self): | |
| config = BertConfig( | |
| vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37 | |
| ) | |
| with tempfile.TemporaryDirectory() as tmp_dir: | |
| config.save_pretrained(tmp_dir, push_to_hub=True, repo_name="test-config", use_auth_token=self._token) | |
| new_config = BertConfig.from_pretrained(f"{USER}/test-config") | |
| for k, v in config.__dict__.items(): | |
| if k != "transformers_version": | |
| self.assertEqual(v, getattr(new_config, k)) | |
| def test_push_to_hub_in_organization(self): | |
| config = BertConfig( | |
| vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37 | |
| ) | |
| with tempfile.TemporaryDirectory() as tmp_dir: | |
| config.save_pretrained( | |
| tmp_dir, | |
| push_to_hub=True, | |
| repo_name="test-config-org", | |
| use_auth_token=self._token, | |
| organization="valid_org", | |
| ) | |
| new_config = BertConfig.from_pretrained("valid_org/test-config-org") | |
| for k, v in config.__dict__.items(): | |
| if k != "transformers_version": | |
| self.assertEqual(v, getattr(new_config, k)) | |
| class ConfigTestUtils(unittest.TestCase): | |
| def test_config_from_string(self): | |
| c = GPT2Config() | |
| # attempt to modify each of int/float/bool/str config records and verify they were updated | |
| n_embd = c.n_embd + 1 # int | |
| resid_pdrop = c.resid_pdrop + 1.0 # float | |
| scale_attn_weights = not c.scale_attn_weights # bool | |
| summary_type = c.summary_type + "foo" # str | |
| c.update_from_string( | |
| f"n_embd={n_embd},resid_pdrop={resid_pdrop},scale_attn_weights={scale_attn_weights},summary_type={summary_type}" | |
| ) | |
| self.assertEqual(n_embd, c.n_embd, "mismatch for key: n_embd") | |
| self.assertEqual(resid_pdrop, c.resid_pdrop, "mismatch for key: resid_pdrop") | |
| self.assertEqual(scale_attn_weights, c.scale_attn_weights, "mismatch for key: scale_attn_weights") | |
| self.assertEqual(summary_type, c.summary_type, "mismatch for key: summary_type") | |