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https://github.com/huggingface/transformers.git
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9336086ab5
* broken test * batch parity * tests pass * boom boom * boom boom * split out bart tokenizer tests * fix tests * boom boom * Fixed dataset bug * Fix marian * Undo extra * Get marian working * Fix t5 tok tests * Test passing * Cleanup * better assert msg * require torch * Fix mbart tests * undo extra decoder_attn_mask change * Fix import * pegasus tokenizer can ignore src_lang kwargs * unused kwarg test cov * boom boom * add todo for pegasus issue * cover one word translation edge case * Cleanup * doc
69 lines
2.8 KiB
Python
69 lines
2.8 KiB
Python
import unittest
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from pathlib import Path
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from transformers.file_utils import cached_property
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from transformers.testing_utils import require_torch
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from transformers.tokenization_pegasus import PegasusTokenizer
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from .test_tokenization_common import TokenizerTesterMixin
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class PegasusTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
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tokenizer_class = PegasusTokenizer
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def setUp(self):
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super().setUp()
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save_dir = Path(self.tmpdirname)
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spm_file = PegasusTokenizer.vocab_files_names["vocab_file"]
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if not (save_dir / spm_file).exists():
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tokenizer = self.pegasus_large_tokenizer
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tokenizer.save_pretrained(self.tmpdirname)
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@cached_property
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def pegasus_large_tokenizer(self):
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return PegasusTokenizer.from_pretrained("google/pegasus-large")
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@unittest.skip("add_tokens does not work yet")
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def test_swap_special_token(self):
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pass
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def get_tokenizer(self, **kwargs) -> PegasusTokenizer:
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if not kwargs:
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return self.pegasus_large_tokenizer
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else:
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return PegasusTokenizer.from_pretrained(self.tmpdirname, **kwargs)
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def get_input_output_texts(self, tokenizer):
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return ("This is a test", "This is a test")
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def test_pegasus_large_tokenizer_settings(self):
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tokenizer = self.pegasus_large_tokenizer
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# The tracebacks for the following asserts are **better** without messages or self.assertEqual
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assert tokenizer.vocab_size == 96103
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assert tokenizer.pad_token_id == 0
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assert tokenizer.eos_token_id == 1
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assert tokenizer.offset == 103
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assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
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assert tokenizer.unk_token == "<unk>"
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assert tokenizer.mask_token is None
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assert tokenizer.mask_token_id is None
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assert tokenizer.model_max_length == 1024
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raw_input_str = "To ensure a smooth flow of bank resolutions."
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desired_result = [413, 615, 114, 2291, 1971, 113, 1679, 10710, 107, 1]
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ids = tokenizer([raw_input_str], return_tensors=None).input_ids[0]
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self.assertListEqual(desired_result, ids)
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assert tokenizer.convert_ids_to_tokens([0, 1, 2]) == ["<pad>", "</s>", "unk_2"]
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@require_torch
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def test_pegasus_large_seq2seq_truncation(self):
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src_texts = ["This is going to be way too long" * 10000, "short example"]
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tgt_texts = ["not super long but more than 5 tokens", "tiny"]
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batch = self.pegasus_large_tokenizer.prepare_seq2seq_batch(src_texts, tgt_texts=tgt_texts, max_target_length=5)
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assert batch.input_ids.shape == (2, 1024)
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assert batch.attention_mask.shape == (2, 1024)
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assert "labels" in batch # because tgt_texts was specified
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assert batch.labels.shape == (2, 5)
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assert len(batch) == 3 # input_ids, attention_mask, labels. Other things make by BartModel
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