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tests/utils/test_tokenization_utils.py

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+# 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
+
+import httpx
+
+from transformers import AutoTokenizer, BertTokenizer, BertTokenizerFast, GPT2TokenizerFast, is_tokenizers_available
+from transformers.testing_utils import TOKEN, TemporaryHubRepo, is_staging_test, require_tokenizers
+from transformers.tokenization_python import ExtensionsTrie, Trie
+
+
+sys.path.append(str(Path(__file__).parent.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 = httpx.HTTPStatusError(
+            "failed", request=mock.Mock(), response=mock.Mock()
+        )
+        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("httpx.Client.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 = httpx.HTTPStatusError(
+            "failed", request=mock.Mock(), response=mock.Mock()
+        )
+        response_mock.json.return_value = {}
+
+        # Download this model to make sure it's in the cache.
+        _ = GPT2TokenizerFast.from_pretrained("openai-community/gpt2")
+
+        # Under the mock environment we get a 500 error when trying to reach the tokenizer.
+        with mock.patch("httpx.Client.request", return_value=response_mock) as mock_head:
+            _ = GPT2TokenizerFast.from_pretrained("openai-community/gpt2")
+            # This check we did call the fake head request
+            mock_head.assert_called()
+
+
+@is_staging_test
+class TokenizerPushToHubTester(unittest.TestCase):
+    vocab_tokens = ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "bla", "blou"]
+
+    @classmethod
+    def setUpClass(cls):
+        cls._token = TOKEN
+
+    def test_push_to_hub(self):
+        with TemporaryHubRepo(token=self._token) as tmp_repo:
+            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(tmp_repo.repo_id, token=self._token)
+            new_tokenizer = BertTokenizer.from_pretrained(tmp_repo.repo_id)
+            self.assertDictEqual(new_tokenizer.vocab, tokenizer.vocab)
+
+    def test_push_to_hub_chat_templates(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.chat_template = "test template"
+            with TemporaryHubRepo(token=self._token) as tmp_repo:
+                tokenizer.save_pretrained(tmp_repo.repo_id, token=self._token, push_to_hub=True)
+                reloaded_tokenizer = BertTokenizer.from_pretrained(tmp_repo.repo_id)
+                self.assertEqual(tokenizer.chat_template, reloaded_tokenizer.chat_template)
+
+            with TemporaryHubRepo(token=self._token) as tmp_repo:
+                tokenizer.chat_template = {"default": "a", "secondary": "b"}
+                tokenizer.save_pretrained(tmp_repo.repo_id, token=self._token, push_to_hub=True)
+                reloaded_tokenizer = BertTokenizer.from_pretrained(tmp_repo.repo_id)
+                self.assertEqual(tokenizer.chat_template, reloaded_tokenizer.chat_template)
+
+    def test_push_to_hub_via_save_pretrained(self):
+        with TemporaryHubRepo(token=self._token) as tmp_repo:
+            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)
+
+                # Push to hub via save_pretrained
+                tokenizer.save_pretrained(tmp_dir, repo_id=tmp_repo.repo_id, push_to_hub=True, token=self._token)
+
+            new_tokenizer = BertTokenizer.from_pretrained(tmp_repo.repo_id)
+            self.assertDictEqual(new_tokenizer.vocab, tokenizer.vocab)
+
+    def test_push_to_hub_in_organization(self):
+        with TemporaryHubRepo(namespace="valid_org", token=self._token) as tmp_repo:
+            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(tmp_repo.repo_id, token=self._token)
+            new_tokenizer = BertTokenizer.from_pretrained(tmp_repo.repo_id)
+            self.assertDictEqual(new_tokenizer.vocab, tokenizer.vocab)
+
+    def test_push_to_hub_in_organization_via_save_pretrained(self):
+        with TemporaryHubRepo(namespace="valid_org", token=self._token) as tmp_repo:
+            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)
+
+                # Push to hub via save_pretrained
+                tokenizer.save_pretrained(tmp_dir, repo_id=tmp_repo.repo_id, push_to_hub=True, token=self._token)
+
+            new_tokenizer = BertTokenizer.from_pretrained(tmp_repo.repo_id)
+            self.assertDictEqual(new_tokenizer.vocab, tokenizer.vocab)
+
+    @require_tokenizers
+    def test_push_to_hub_dynamic_tokenizer(self):
+        with TemporaryHubRepo(token=self._token) as tmp_repo:
+            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(tmp_repo.repo_id, token=self._token)
+
+            tokenizer = AutoTokenizer.from_pretrained(tmp_repo.repo_id, 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")
+
+    @require_tokenizers
+    def test_push_to_hub_dynamic_tokenizer_with_both_slow_and_fast_classes(self):
+        with TemporaryHubRepo(token=self._token) as tmp_repo:
+            CustomTokenizer.register_for_auto_class()
+
+            # 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(tmp_repo.repo_id, token=self._token)
+
+            tokenizer = AutoTokenizer.from_pretrained(tmp_repo.repo_id, 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(tmp_repo.repo_id, 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__, "CustomTokenizerFast")
+
+
+@require_tokenizers
+class TokenizersBackendTest(unittest.TestCase):
+    def test_clean_up_tokenization_spaces(self):
+        tokenizer = GPT2TokenizerFast.from_pretrained("openai-community/gpt2")
+
+        # GPT-2 is a BPE tokenizer — clean_up_tokenization is skipped because it
+        # was designed for WordPiece and is destructive for BPE (strips legitimate
+        # spaces before punctuation).
+        # Use text with spaces before punctuation that cleanup would strip if applied.
+        text = "x != y"
+        token_ids = tokenizer.encode(text)
+
+        decoded_no_cleanup = tokenizer.decode(token_ids, clean_up_tokenization_spaces=False)
+        self.assertEqual(decoded_no_cleanup, text)
+
+        # With BPE guard, cleanup=True also preserves the text
+        decoded_with_cleanup = tokenizer.decode(token_ids, clean_up_tokenization_spaces=True)
+        self.assertEqual(decoded_with_cleanup, text)
+
+
+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")
+        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"])
+
+
+class ExtensionsTrieTest(unittest.TestCase):
+    def test_extensions(self):
+        # Test searching by prefix
+        trie = ExtensionsTrie()
+        trie.add("foo")
+        trie.add("food")
+        trie.add("foodie")
+        trie.add("helium")
+        self.assertEqual(trie.extensions("foo"), ["foo", "food", "foodie"])
+        self.assertEqual(trie.extensions("helium"), ["helium"])
+
+    def test_empty_prefix(self):
+        trie = ExtensionsTrie()
+        # Test searching with an empty prefix returns all values
+        trie.add("hello")
+        trie.add("bye")
+        self.assertEqual(trie.extensions(""), ["hello", "bye"])
+
+    def test_no_extension_match(self):
+        trie = ExtensionsTrie()
+        # Test searching for a prefix that doesn't match any key
+        values = trie.extensions("unknown")
+
+        self.assertEqual(len(values), 0)
+
+    def test_update_value(self):
+        trie = ExtensionsTrie()
+        # Test updating the value of an existing key
+        trie.add("hi")
+        trie.add("hi")
+        self.assertEqual(trie.extensions("hi"), ["hi"])