transformers/tests/models/mbart50/test_tokenization_mbart50.py

# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# 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 tempfile
import unittest

from transformers import BatchEncoding, MBart50Tokenizer, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece.model")

if is_torch_available():
    from transformers.models.mbart.modeling_mbart import shift_tokens_right

EN_CODE = 250004
RO_CODE = 250020


@require_sentencepiece
@require_tokenizers
class MBart50TokenizationTest(TokenizerTesterMixin, unittest.TestCase):
    from_pretrained_id = "facebook/mbart-large-50-one-to-many-mmt"
    tokenizer_class = MBart50Tokenizer

    integration_expected_tokens = ['▁This', '▁is', '▁a', '▁test', '▁', '😊', '▁I', '▁was', '▁born', '▁in', '▁9', '2000', ',', '▁and', '▁this', '▁is', '▁fals', 'é', '.', '▁', '生活的', '真', '谛', '是', '▁Hi', '▁Hello', '▁Hi', '▁Hello', '▁Hello','▁', '<s>', '▁hi', '<s>', '▁there', '▁The', '▁following', '▁string', '▁should', '▁be', '▁properly', '▁en', 'code', 'd', ':', '▁Hello', '.', '▁But', '▁ir', 'd', '▁and', '▁ปี', '▁ir', 'd', '▁ด', '▁Hey', '▁how', '▁are', '▁you', '▁doing']  # fmt: skip
    integration_expected_token_ids = [3293, 83, 10, 3034, 6, 82803, 87, 509, 103122, 23, 483, 13821, 4, 136, 903, 83, 84047, 446, 5, 6, 62668, 5364, 245875, 354, 2673, 35378, 2673, 35378, 35378,6, 0, 1274, 0, 2685, 581, 25632, 79315, 5608, 186, 155965, 22, 40899, 71, 12, 35378, 5, 4966, 193, 71, 136, 10249, 193, 71, 48229, 28240, 3642, 621, 398, 20594]  # fmt: skip
    expected_tokens_from_ids = ['▁This', '▁is', '▁a', '▁test', '▁', '😊', '▁I', '▁was', '▁born', '▁in', '▁9', '2000', ',', '▁and', '▁this', '▁is', '▁fals', 'é', '.', '▁', '生活的', '真', '谛', '是', '▁Hi', '▁Hello', '▁Hi', '▁Hello', '▁Hello','▁', '<s>', '▁hi', '<s>', '▁there', '▁The', '▁following', '▁string', '▁should', '▁be', '▁properly', '▁en', 'code', 'd', ':', '▁Hello', '.', '▁But', '▁ir', 'd', '▁and', '▁ปี', '▁ir', 'd', '▁ด', '▁Hey', '▁how', '▁are', '▁you', '▁doing']  # fmt: skip
    integration_expected_decoded_text = "This is a test 😊 I was born in 92000, and this is falsé. 生活的真谛是 Hi Hello Hi Hello Hello <s> hi<s> there The following string should be properly encoded: Hello. But ird and ปี ird ด Hey how are you doing"


@require_torch
@require_sentencepiece
@require_tokenizers
class MBart50OneToManyIntegrationTest(unittest.TestCase):
    checkpoint_name = "facebook/mbart-large-50-one-to-many-mmt"
    src_text = [
        " UN Chief Says There Is No Military Solution in Syria",
        """ Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.""",
    ]
    tgt_text = [
        "Şeful ONU declară că nu există o soluţie militară în Siria",
        "Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"
        ' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'
        " face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.",
    ]
    expected_src_tokens = [EN_CODE, 8274, 127873, 25916, 7, 8622, 2071, 438, 67485, 53, 187895, 23, 51712, 2]

    @classmethod
    def setUpClass(cls):
        cls.tokenizer: MBart50Tokenizer = MBart50Tokenizer.from_pretrained(
            cls.checkpoint_name, src_lang="en_XX", tgt_lang="ro_RO"
        )
        cls.pad_token_id = 1
        return cls

    def check_language_codes(self):
        self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ar_AR"], 250001)
        self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["en_EN"], 250004)
        self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ro_RO"], 250020)
        self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["mr_IN"], 250038)

    def test_tokenizer_batch_encode_plus(self):
        ids = self.tokenizer(self.src_text).input_ids[0]
        self.assertListEqual(self.expected_src_tokens, ids)

    def test_tokenizer_decode_ignores_language_codes(self):
        self.assertIn(RO_CODE, self.tokenizer.all_special_ids)
        generated_ids = [RO_CODE, 884, 9019, 96, 9, 916, 86792, 36, 18743, 15596, 5, 2]
        result = self.tokenizer.decode(generated_ids, skip_special_tokens=True)
        expected_romanian = self.tokenizer.decode(generated_ids[1:], skip_special_tokens=True)
        self.assertEqual(result, expected_romanian)
        self.assertNotIn(self.tokenizer.eos_token, result)

    def test_tokenizer_truncation(self):
        src_text = ["this is gunna be a long sentence " * 20]
        assert isinstance(src_text[0], str)
        desired_max_length = 10
        ids = self.tokenizer(src_text, max_length=desired_max_length, truncation=True).input_ids[0]
        self.assertEqual(ids[0], EN_CODE)
        self.assertEqual(ids[-1], 2)
        self.assertEqual(len(ids), desired_max_length)

    def test_mask_token(self):
        self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]), [250053, 250001])

    def test_special_tokens_unaffacted_by_save_load(self):
        tmpdirname = tempfile.mkdtemp()
        original_special_tokens = self.tokenizer.fairseq_tokens_to_ids
        self.tokenizer.save_pretrained(tmpdirname)
        new_tok = MBart50Tokenizer.from_pretrained(tmpdirname)
        self.assertDictEqual(new_tok.fairseq_tokens_to_ids, original_special_tokens)

    @require_torch
    def test_batch_fairseq_parity(self):
        batch = self.tokenizer(self.src_text, text_target=self.tgt_text, padding=True, return_tensors="pt")
        batch["decoder_input_ids"] = shift_tokens_right(batch["labels"], self.tokenizer.pad_token_id)

        # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
        assert batch.input_ids[1][0] == EN_CODE
        assert batch.input_ids[1][-1] == 2
        assert batch.labels[1][0] == RO_CODE
        assert batch.labels[1][-1] == 2
        assert batch.decoder_input_ids[1][:2].tolist() == [2, RO_CODE]

    @require_torch
    def test_tokenizer_prepare_batch(self):
        batch = self.tokenizer(
            self.src_text,
            text_target=self.tgt_text,
            padding=True,
            truncation=True,
            max_length=len(self.expected_src_tokens),
            return_tensors="pt",
        )
        batch["decoder_input_ids"] = shift_tokens_right(batch["labels"], self.tokenizer.pad_token_id)

        self.assertIsInstance(batch, BatchEncoding)

        self.assertEqual((2, 14), batch.input_ids.shape)
        self.assertEqual((2, 14), batch.attention_mask.shape)
        result = batch.input_ids.tolist()[0]
        self.assertListEqual(self.expected_src_tokens, result)
        self.assertEqual(2, batch.decoder_input_ids[0, 0])  # decoder_start_token_id
        # Test that special tokens are reset
        self.assertEqual(self.tokenizer.prefix_tokens, [EN_CODE])
        self.assertEqual(self.tokenizer.suffix_tokens, [self.tokenizer.eos_token_id])

    def test_seq2seq_max_target_length(self):
        batch = self.tokenizer(self.src_text, padding=True, truncation=True, max_length=3, return_tensors="pt")
        targets = self.tokenizer(
            text_target=self.tgt_text, padding=True, truncation=True, max_length=10, return_tensors="pt"
        )
        labels = targets["input_ids"]
        batch["decoder_input_ids"] = shift_tokens_right(labels, self.tokenizer.pad_token_id)

        self.assertEqual(batch.input_ids.shape[1], 3)
        self.assertEqual(batch.decoder_input_ids.shape[1], 10)

    @require_torch
    def test_tokenizer_translation(self):
        inputs = self.tokenizer._build_translation_inputs(
            "A test", return_tensors="pt", src_lang="en_XX", tgt_lang="ar_AR"
        )

        self.assertEqual(
            nested_simplify(inputs),
            {
                # en_XX, A, test, EOS
                "input_ids": [[250004, 62, 3034, 2]],
                "attention_mask": [[1, 1, 1, 1]],
                # ar_AR
                "forced_bos_token_id": 250001,
            },
        )