# 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 os import sys import tempfile import unittest import unittest.mock as mock from pathlib import Path from huggingface_hub import HfFolder, delete_repo from huggingface_hub.file_download import http_get from requests.exceptions import HTTPError from transformers import ( AlbertTokenizer, AutoTokenizer, BertTokenizer, BertTokenizerFast, GPT2TokenizerFast, is_tokenizers_available, ) from transformers.testing_utils import TOKEN, USER, is_staging_test, require_tokenizers from transformers.tokenization_utils import Trie sys.path.append(str(Path(__file__).parent.parent / "utils")) from test_module.custom_tokenization import CustomTokenizer # noqa E402 if is_tokenizers_available(): from test_module.custom_tokenization_fast import CustomTokenizerFast class TokenizerUtilTester(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. _ = BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-bert") # Under the mock environment we get a 500 error when trying to reach the tokenizer. with mock.patch("requests.Session.request", return_value=response_mock) as mock_head: _ = BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-bert") # This check we did call the fake head request mock_head.assert_called() @require_tokenizers def test_cached_files_are_used_when_internet_is_down_missing_files(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. _ = GPT2TokenizerFast.from_pretrained("gpt2") # Under the mock environment we get a 500 error when trying to reach the tokenizer. with mock.patch("requests.Session.request", return_value=response_mock) as mock_head: _ = GPT2TokenizerFast.from_pretrained("gpt2") # This check we did call the fake head request mock_head.assert_called() def test_legacy_load_from_one_file(self): # This test is for deprecated behavior and can be removed in v5 try: tmp_file = tempfile.mktemp() with open(tmp_file, "wb") as f: http_get("https://huggingface.co/albert-base-v1/resolve/main/spiece.model", f) _ = AlbertTokenizer.from_pretrained(tmp_file) finally: os.remove(tmp_file) # Supporting this legacy load introduced a weird bug where the tokenizer would load local files if they are in # the current folder and have the right name. if os.path.isfile("tokenizer.json"): # We skip the test if the user has a `tokenizer.json` in this folder to avoid deleting it. return try: with open("tokenizer.json", "wb") as f: http_get("https://huggingface.co/hf-internal-testing/tiny-random-bert/blob/main/tokenizer.json", f) tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2") # The tiny random BERT has a vocab size of 1024, tiny gpt2 as a vocab size of 1000 self.assertEqual(tokenizer.vocab_size, 1000) # Tokenizer should depend on the remote checkpoint, not the local tokenizer.json file. finally: os.remove("tokenizer.json") def test_legacy_load_from_url(self): # This test is for deprecated behavior and can be removed in v5 _ = AlbertTokenizer.from_pretrained("https://huggingface.co/albert-base-v1/resolve/main/spiece.model") @is_staging_test class TokenizerPushToHubTester(unittest.TestCase): vocab_tokens = ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "bla", "blou"] @classmethod def setUpClass(cls): cls._token = TOKEN HfFolder.save_token(TOKEN) @classmethod def tearDownClass(cls): try: delete_repo(token=cls._token, repo_id="test-tokenizer") except HTTPError: pass try: delete_repo(token=cls._token, repo_id="valid_org/test-tokenizer-org") except HTTPError: pass try: delete_repo(token=cls._token, repo_id="test-dynamic-tokenizer") except HTTPError: pass def test_push_to_hub(self): with tempfile.TemporaryDirectory() as tmp_dir: vocab_file = os.path.join(tmp_dir, "vocab.txt") with open(vocab_file, "w", encoding="utf-8") as vocab_writer: vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens])) tokenizer = BertTokenizer(vocab_file) tokenizer.push_to_hub("test-tokenizer", use_auth_token=self._token) new_tokenizer = BertTokenizer.from_pretrained(f"{USER}/test-tokenizer") self.assertDictEqual(new_tokenizer.vocab, tokenizer.vocab) # Reset repo delete_repo(token=self._token, repo_id="test-tokenizer") # Push to hub via save_pretrained with tempfile.TemporaryDirectory() as tmp_dir: tokenizer.save_pretrained(tmp_dir, repo_id="test-tokenizer", push_to_hub=True, use_auth_token=self._token) new_tokenizer = BertTokenizer.from_pretrained(f"{USER}/test-tokenizer") self.assertDictEqual(new_tokenizer.vocab, tokenizer.vocab) def test_push_to_hub_in_organization(self): with tempfile.TemporaryDirectory() as tmp_dir: vocab_file = os.path.join(tmp_dir, "vocab.txt") with open(vocab_file, "w", encoding="utf-8") as vocab_writer: vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens])) tokenizer = BertTokenizer(vocab_file) tokenizer.push_to_hub("valid_org/test-tokenizer-org", use_auth_token=self._token) new_tokenizer = BertTokenizer.from_pretrained("valid_org/test-tokenizer-org") self.assertDictEqual(new_tokenizer.vocab, tokenizer.vocab) # Reset repo delete_repo(token=self._token, repo_id="valid_org/test-tokenizer-org") # Push to hub via save_pretrained with tempfile.TemporaryDirectory() as tmp_dir: tokenizer.save_pretrained( tmp_dir, repo_id="valid_org/test-tokenizer-org", push_to_hub=True, use_auth_token=self._token ) new_tokenizer = BertTokenizer.from_pretrained("valid_org/test-tokenizer-org") self.assertDictEqual(new_tokenizer.vocab, tokenizer.vocab) @require_tokenizers def test_push_to_hub_dynamic_tokenizer(self): CustomTokenizer.register_for_auto_class() with tempfile.TemporaryDirectory() as tmp_dir: vocab_file = os.path.join(tmp_dir, "vocab.txt") with open(vocab_file, "w", encoding="utf-8") as vocab_writer: vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens])) tokenizer = CustomTokenizer(vocab_file) # No fast custom tokenizer tokenizer.push_to_hub("test-dynamic-tokenizer", use_auth_token=self._token) tokenizer = AutoTokenizer.from_pretrained(f"{USER}/test-dynamic-tokenizer", trust_remote_code=True) # Can't make an isinstance check because the new_model.config is from the CustomTokenizer class of a dynamic module self.assertEqual(tokenizer.__class__.__name__, "CustomTokenizer") # Fast and slow custom tokenizer CustomTokenizerFast.register_for_auto_class() with tempfile.TemporaryDirectory() as tmp_dir: vocab_file = os.path.join(tmp_dir, "vocab.txt") with open(vocab_file, "w", encoding="utf-8") as vocab_writer: vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens])) bert_tokenizer = BertTokenizerFast.from_pretrained(tmp_dir) bert_tokenizer.save_pretrained(tmp_dir) tokenizer = CustomTokenizerFast.from_pretrained(tmp_dir) tokenizer.push_to_hub("test-dynamic-tokenizer", use_auth_token=self._token) tokenizer = AutoTokenizer.from_pretrained(f"{USER}/test-dynamic-tokenizer", trust_remote_code=True) # Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module self.assertEqual(tokenizer.__class__.__name__, "CustomTokenizerFast") tokenizer = AutoTokenizer.from_pretrained( f"{USER}/test-dynamic-tokenizer", use_fast=False, trust_remote_code=True ) # Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module self.assertEqual(tokenizer.__class__.__name__, "CustomTokenizer") class TrieTest(unittest.TestCase): def test_trie(self): trie = Trie() trie.add("Hello 友達") self.assertEqual(trie.data, {"H": {"e": {"l": {"l": {"o": {" ": {"友": {"達": {"": 1}}}}}}}}}) trie.add("Hello") trie.data self.assertEqual(trie.data, {"H": {"e": {"l": {"l": {"o": {"": 1, " ": {"友": {"達": {"": 1}}}}}}}}}) def test_trie_split(self): trie = Trie() self.assertEqual(trie.split("[CLS] This is a extra_id_100"), ["[CLS] This is a extra_id_100"]) trie.add("[CLS]") trie.add("extra_id_1") trie.add("extra_id_100") self.assertEqual(trie.split("[CLS] This is a extra_id_100"), ["[CLS]", " This is a ", "extra_id_100"]) def test_trie_single(self): trie = Trie() trie.add("A") self.assertEqual(trie.split("ABC"), ["A", "BC"]) self.assertEqual(trie.split("BCA"), ["BC", "A"]) def test_trie_final(self): trie = Trie() trie.add("TOKEN]") trie.add("[SPECIAL_TOKEN]") self.assertEqual(trie.split("This is something [SPECIAL_TOKEN]"), ["This is something ", "[SPECIAL_TOKEN]"]) def test_trie_subtokens(self): trie = Trie() trie.add("A") trie.add("P") trie.add("[SPECIAL_TOKEN]") self.assertEqual(trie.split("This is something [SPECIAL_TOKEN]"), ["This is something ", "[SPECIAL_TOKEN]"]) def test_trie_suffix_tokens(self): trie = Trie() trie.add("AB") trie.add("B") trie.add("C") self.assertEqual(trie.split("ABC"), ["AB", "C"]) def test_trie_skip(self): trie = Trie() trie.add("ABC") trie.add("B") trie.add("CD") self.assertEqual(trie.split("ABCD"), ["ABC", "D"]) def test_cut_text_hardening(self): # Even if the offsets are wrong, we necessarily output correct string # parts. trie = Trie() parts = trie.cut_text("ABC", [0, 0, 2, 1, 2, 3]) self.assertEqual(parts, ["AB", "C"])