PegasusForConditionalGeneration (torch version) (#6340)

Co-authored-by: Jingqing  Zhang <jingqing.zhang15@imperial.ac.uk>

tests/test_tokenization_pegasus.py

@@ -0,0 +1,69 @@
+import unittest
+from pathlib import Path
+
+from transformers.file_utils import cached_property
+from transformers.testing_utils import require_torch
+from transformers.tokenization_pegasus import PegasusTokenizer
+
+from .test_tokenization_common import TokenizerTesterMixin
+
+
+class PegasusTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
+
+    tokenizer_class = PegasusTokenizer
+
+    def setUp(self):
+        super().setUp()
+
+        save_dir = Path(self.tmpdirname)
+        spm_file = PegasusTokenizer.vocab_files_names["vocab_file"]
+        if not (save_dir / spm_file).exists():
+            tokenizer = self.pegasus_large_tokenizer
+            tokenizer.save_pretrained(self.tmpdirname)
+
+    @cached_property
+    def pegasus_large_tokenizer(self):
+        return PegasusTokenizer.from_pretrained("google/pegasus-large")
+
+    @unittest.skip("add_tokens does not work yet")
+    def test_swap_special_token(self):
+        pass
+
+    def get_tokenizer(self, **kwargs) -> PegasusTokenizer:
+        if not kwargs:
+            return self.pegasus_large_tokenizer
+        else:
+            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_pegasus_large_tokenizer_settings(self):
+        tokenizer = self.pegasus_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.mask_token is None
+        assert tokenizer.mask_token_id is None
+        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]) == ["<pad>", "</s>", "unk_2"]
+
+    @require_torch
+    def test_pegasus_large_seq2seq_truncation(self):
+        src_texts = ["This is going to be way too long" * 10000, "short example"]
+        tgt_texts = ["not super long but more than 5 tokens", "tiny"]
+        batch = self.pegasus_large_tokenizer.prepare_seq2seq_batch(src_texts, tgt_texts=tgt_texts, max_target_length=5)
+        assert batch.input_ids.shape == (2, 1024)
+        assert batch.attention_mask.shape == (2, 1024)
+        assert "decoder_input_ids" in batch  # because tgt_texts was specified
+        assert batch.decoder_input_ids.shape == (2, 5)
+        assert batch.decoder_attention_mask.shape == (2, 5)
+        assert len(batch) == 4  # no extra keys