tests/test_tokenization_utils.py

# 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("openai-community/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("openai-community/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/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 openai-community/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")


@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", 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, 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", 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, 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", 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", 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"])