test_tokenization_pegasus.py

import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.file_utils import cached_property
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch

from .test_tokenization_common import TokenizerTesterMixin


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


@require_sentencepiece
@require_tokenizers
class PegasusTokenizationTest(TokenizerTesterMixin, unittest.TestCase):

    tokenizer_class = PegasusTokenizer
    rust_tokenizer_class = PegasusTokenizerFast
    test_rust_tokenizer = True

    def setUp(self):
        super().setUp()

        # We have a SentencePiece fixture for testing
        tokenizer = PegasusTokenizer(SAMPLE_VOCAB)
        tokenizer.save_pretrained(self.tmpdirname)

    @cached_property
    def _large_tokenizer(self):
        return PegasusTokenizer.from_pretrained("google/pegasus-large")

    def get_tokenizer(self, **kwargs) -> PegasusTokenizer:
        return PegasusTokenizer.from_pretrained(self.tmpdirname, **kwargs)

    def get_input_output_texts(self, tokenizer):
        return ("This is a test", "This is a test")

    def test_mask_tokens_rust_pegasus(self):
        rust_tokenizer = self.rust_tokenizer_class.from_pretrained(self.tmpdirname)
        py_tokenizer = self.tokenizer_class.from_pretrained(self.tmpdirname)
        raw_input_str = "Let's see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important </s> <pad> <pad> <pad>"
        rust_ids = rust_tokenizer([raw_input_str], return_tensors=None, add_special_tokens=False).input_ids[0]
        py_ids = py_tokenizer([raw_input_str], return_tensors=None, add_special_tokens=False).input_ids[0]
        # TODO: (Thom, Patrick) - this fails because the rust tokenizer does not know about the <mask_1>, <mask_2>, and those <unk_token_x> yet
        self.assertListEqual(py_ids, rust_ids)

    def test_large_mask_tokens(self):
        tokenizer = self._large_tokenizer
        # <mask_1> masks whole sentence while <mask_2> masks single word
        raw_input_str = "<mask_1> To ensure a <mask_2> flow of bank resolutions."
        desired_result = [2, 413, 615, 114, 3, 1971, 113, 1679, 10710, 107, 1]
        ids = tokenizer([raw_input_str], return_tensors=None).input_ids[0]
        self.assertListEqual(desired_result, ids)

    def test_large_tokenizer_settings(self):
        tokenizer = self._large_tokenizer
        # The tracebacks for the following asserts are **better** without messages or self.assertEqual
        assert tokenizer.vocab_size == 96103
        assert tokenizer.pad_token_id == 0
        assert tokenizer.eos_token_id == 1
        assert tokenizer.offset == 103
        assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
        assert tokenizer.unk_token == "<unk>"
        assert tokenizer.model_max_length == 1024
        raw_input_str = "To ensure a smooth flow of bank resolutions."
        desired_result = [413, 615, 114, 2291, 1971, 113, 1679, 10710, 107, 1]
        ids = tokenizer([raw_input_str], return_tensors=None).input_ids[0]
        self.assertListEqual(desired_result, ids)
        assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]

    @require_torch
    def test_large_seq2seq_truncation(self):
        src_texts = ["This is going to be way too long." * 150, "short example"]
        tgt_texts = ["not super long but more than 5 tokens", "tiny"]
        batch = self._large_tokenizer.prepare_seq2seq_batch(
            src_texts, tgt_texts=tgt_texts, max_target_length=5, return_tensors="pt"
        )
        assert batch.input_ids.shape == (2, 1024)
        assert batch.attention_mask.shape == (2, 1024)
        assert "labels" in batch  # because tgt_texts was specified
        assert batch.labels.shape == (2, 5)
        assert len(batch) == 3  # input_ids, attention_mask, labels. Other things make by BartModel