transformers/tests/models/byt5/test_tokenization_byt5.py

# Copyright 2020 Google T5 Authors and HuggingFace Inc. team.
#
# 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 re
import shutil
import tempfile
import unittest
from functools import cached_property

from transformers import BatchEncoding, ByT5Tokenizer

from ...test_tokenization_common import TokenizerTesterMixin


class ByT5TokenizationTest(TokenizerTesterMixin, unittest.TestCase):
    tokenizer_class = ByT5Tokenizer
    from_pretrained_id = "google/byt5-small"
    test_rust_tokenizer = False

    @classmethod
    def setUpClass(cls):
        super().setUpClass()
        tokenizer = ByT5Tokenizer()
        tokenizer.save_pretrained(cls.tmpdirname)

    @cached_property
    def t5_base_tokenizer(self):
        return ByT5Tokenizer.from_pretrained("google/byt5-small")

    @classmethod
    def get_tokenizer(cls, pretrained_name=None, **kwargs) -> ByT5Tokenizer:
        pretrained_name = pretrained_name or cls.tmpdirname
        return cls.tokenizer_class.from_pretrained(pretrained_name, **kwargs)

    def get_clean_sequence(self, tokenizer, with_prefix_space=False, max_length=20, min_length=5) -> tuple[str, list]:
        # XXX The default common tokenizer tests assume that every ID is decodable on its own.
        # This assumption is invalid for ByT5 because single bytes might not be
        # valid utf-8 (byte 128 for instance).
        # Here we're overriding the smallest possible method to provide
        # a clean sequence without making the same assumption.

        toks = []
        for i in range(len(tokenizer)):
            try:
                tok = tokenizer.decode([i], clean_up_tokenization_spaces=False)
            except UnicodeDecodeError:
                pass
            toks.append((i, tok))

        toks = list(filter(lambda t: re.match(r"^[ a-zA-Z]+$", t[1]), toks))
        toks = list(filter(lambda t: [t[0]] == tokenizer.encode(t[1], add_special_tokens=False), toks))
        if max_length is not None and len(toks) > max_length:
            toks = toks[:max_length]
        if min_length is not None and len(toks) < min_length and len(toks) > 0:
            while len(toks) < min_length:
                toks = toks + toks
        # toks_str = [t[1] for t in toks]
        toks_ids = [t[0] for t in toks]

        # Ensure consistency
        output_txt = tokenizer.decode(toks_ids, clean_up_tokenization_spaces=False)
        if " " not in output_txt and len(toks_ids) > 1:
            output_txt = (
                tokenizer.decode([toks_ids[0]], clean_up_tokenization_spaces=False)
                + " "
                + tokenizer.decode(toks_ids[1:], clean_up_tokenization_spaces=False)
            )
        if with_prefix_space:
            output_txt = " " + output_txt
        output_ids = tokenizer.encode(output_txt, add_special_tokens=False)
        return output_txt, output_ids

    def test_eos_treatment(self):
        tokenizer = self.t5_base_tokenizer
        batch_with_eos_added = tokenizer(["hi</s>", "I went to the gym</s>", "</s>"])
        batch_without_eos_added = tokenizer(["hi", "I went to the gym", ""])
        self.assertListEqual(batch_with_eos_added["input_ids"], batch_without_eos_added["input_ids"])

    def test_multibytes_char(self):
        tokenizer = self.t5_base_tokenizer
        src_text = "Unicode €."
        encoded = tokenizer(src_text)
        encoded_ids = [88, 113, 108, 102, 114, 103, 104, 35, 229, 133, 175, 49, 1]
        self.assertEqual(encoded["input_ids"], encoded_ids)

        # decoding
        decoded = tokenizer.decode(encoded_ids)
        self.assertEqual(decoded, "Unicode €.</s>")

        encoded = tokenizer("e è é ê ë")
        encoded_ids = [104, 35, 198, 171, 35, 198, 172, 35, 198, 173, 35, 198, 174, 1]
        self.assertEqual(encoded["input_ids"], encoded_ids)
        # decoding
        decoded = tokenizer.decode(encoded_ids)
        self.assertEqual(decoded, "e è é ê ë</s>")

        # encode/decode, but with `encode` instead of `__call__`
        self.assertEqual(tokenizer.decode(tokenizer.encode("e è é ê ë")), "e è é ê ë</s>")

    def test_prepare_batch_integration(self):
        tokenizer = self.t5_base_tokenizer
        src_text = ["A long paragraph for summarization.", "Another paragraph for summarization."]
        expected_src_tokens = [68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 1, 0]  # fmt: skip
        batch = tokenizer(src_text, padding=True, return_tensors="pt")
        self.assertIsInstance(batch, BatchEncoding)

        result = list(batch.input_ids.numpy()[0])

        self.assertListEqual(expected_src_tokens, result)

        self.assertEqual((2, 37), batch.input_ids.shape)
        self.assertEqual((2, 37), batch.attention_mask.shape)

    def test_empty_target_text(self):
        tokenizer = self.t5_base_tokenizer
        src_text = ["A long paragraph for summarization.", "Another paragraph for summarization."]
        batch = tokenizer(src_text, padding=True, return_tensors="pt")
        # check if input_ids are returned and no decoder_input_ids
        self.assertIn("input_ids", batch)
        self.assertIn("attention_mask", batch)
        self.assertNotIn("decoder_input_ids", batch)
        self.assertNotIn("decoder_attention_mask", batch)

    def test_max_length_integration(self):
        tokenizer = self.t5_base_tokenizer
        tgt_text = [
            "Summary of the text.",
            "Another summary.",
        ]
        targets = tokenizer(
            text_target=tgt_text, max_length=32, padding="max_length", truncation=True, return_tensors="pt"
        )
        self.assertEqual(32, targets["input_ids"].shape[1])

    def test_eos_in_input(self):
        tokenizer = self.t5_base_tokenizer
        src_text = ["A long paragraph for summarization. </s>"]
        tgt_text = ["Summary of the text. </s>"]
        expected_src_tokens = [68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 35, 1]  # fmt: skip
        expected_tgt_tokens = [86, 120, 112, 112, 100, 117, 124, 35, 114, 105, 35, 119, 107, 104, 35, 119, 104, 123, 119, 49, 35, 1]  # fmt: skip

        batch = tokenizer(src_text, text_target=tgt_text)

        self.assertEqual(expected_src_tokens, batch["input_ids"][0])
        self.assertEqual(expected_tgt_tokens, batch["labels"][0])

    # cannot use default save_and_load_tokenizer test method because tokenizer has no vocab
    def test_save_and_load_tokenizer(self):
        # safety check on max_len default value so we are sure the test works
        tokenizers = self.get_tokenizers()
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                self.assertNotEqual(tokenizer.model_max_length, 42)

        # Now let's start the test
        tokenizers = self.get_tokenizers()
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                # Isolate this from the other tests because we save additional tokens/etc
                tmpdirname = tempfile.mkdtemp()

                sample_text = " He is very happy, UNwant\u00e9d,running"
                before_tokens = tokenizer.encode(sample_text, add_special_tokens=False)
                tokenizer.save_pretrained(tmpdirname)

                after_tokenizer = tokenizer.__class__.from_pretrained(tmpdirname)
                after_tokens = after_tokenizer.encode(sample_text, add_special_tokens=False)
                self.assertListEqual(before_tokens, after_tokens)

                shutil.rmtree(tmpdirname)

        tokenizers = self.get_tokenizers(model_max_length=42)
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                # Isolate this from the other tests because we save additional tokens/etc
                tmpdirname = tempfile.mkdtemp()

                sample_text = " He is very happy, UNwant\u00e9d,running"
                tokenizer.add_tokens(["bim", "bambam"])
                extra_special_tokens = tokenizer.extra_special_tokens
                extra_special_tokens.append("new_extra_special_token")
                tokenizer.add_special_tokens(
                    {"extra_special_tokens": extra_special_tokens}, replace_extra_special_tokens=False
                )
                before_tokens = tokenizer.encode(sample_text, add_special_tokens=False)
                tokenizer.save_pretrained(tmpdirname)

                after_tokenizer = tokenizer.__class__.from_pretrained(tmpdirname)
                after_tokens = after_tokenizer.encode(sample_text, add_special_tokens=False)
                self.assertListEqual(before_tokens, after_tokens)
                self.assertIn("new_extra_special_token", after_tokenizer.extra_special_tokens)
                self.assertEqual(after_tokenizer.model_max_length, 42)

                tokenizer = tokenizer.__class__.from_pretrained(tmpdirname, model_max_length=43)
                self.assertEqual(tokenizer.model_max_length, 43)

                shutil.rmtree(tmpdirname)

    def test_decode_single_bytes(self):
        tokenizer_list = []
        if self.test_rust_tokenizer:
            tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()))

        for tokenizer_class, tokenizer_utils in tokenizer_list:
            with tempfile.TemporaryDirectory() as tmp_dir:
                tokenizer_utils.save_pretrained(tmp_dir)

                tokenizer = tokenizer_class.from_pretrained(tmp_dir)

                self.assertTrue(tokenizer.decode([255]) == "")

    @unittest.skip(reason="ByT5Tokenizer does not have a vocabulary")
    def test_get_vocab(self):
        pass

    @unittest.skip(reason="inputs cannot be pretokenized as ids depend on whole input string")
    def test_pretokenized_inputs(self):
        pass

    @unittest.skip(reason="ByT5Tokenizer does not have a vocabulary")
    def test_conversion_reversible(self):
        pass

    def test_convert_tokens_to_string_format(self):
        # The default common tokenizer tests uses invalid tokens for ByT5 that can only accept one-character strings
        # and special added tokens as tokens
        tokenizers = self.get_tokenizers(fast=True, do_lower_case=True)
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                tokens = ["t", "h", "i", "s", " ", "i", "s", " ", "a", " ", "t", "e", "x", "t", "</s>"]
                string = tokenizer.convert_tokens_to_string(tokens)

                self.assertIsInstance(string, str)

    # We need a different implementation of the test of the same name defined in TokenizerTesterMixin because this tokenizer
    # doesn't have a vocab
    def test_tokenizers_common_ids_setters(self):
        tokenizers = self.get_tokenizers()
        for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}"):
                attributes_list = [
                    "bos_token",
                    "eos_token",
                    "unk_token",
                    "sep_token",
                    "pad_token",
                    "cls_token",
                    "mask_token",
                ]

                token_id_to_test_setters = 0
                token_to_test_setters = tokenizer.convert_ids_to_tokens(
                    token_id_to_test_setters, skip_special_tokens=False
                )

                for attr in attributes_list:
                    setattr(tokenizer, attr + "_id", None)
                    self.assertEqual(getattr(tokenizer, attr), None)
                    self.assertEqual(getattr(tokenizer, attr + "_id"), None)

                    setattr(tokenizer, attr + "_id", token_id_to_test_setters)
                    self.assertEqual(getattr(tokenizer, attr), token_to_test_setters)
                    self.assertEqual(getattr(tokenizer, attr + "_id"), token_id_to_test_setters)