193 lines
6.8 KiB
Python
193 lines
6.8 KiB
Python
# coding=utf-8
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# Copyright 2018 Google T5 Authors and HuggingFace Inc. team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import unittest
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from transformers import BatchEncoding
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from transformers.file_utils import cached_property
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from transformers.testing_utils import _torch_available
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from transformers.tokenization_t5 import T5Tokenizer
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from .test_tokenization_common import TokenizerTesterMixin
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SPIECE_UNDERLINE = "▁"
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SAMPLE_VOCAB = os.path.join(os.path.dirname(os.path.abspath(__file__)), "fixtures/test_sentencepiece.model")
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FRAMEWORK = "pt" if _torch_available else "tf"
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class T5TokenizationTest(TokenizerTesterMixin, unittest.TestCase):
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tokenizer_class = T5Tokenizer
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def setUp(self):
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super().setUp()
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# We have a SentencePiece fixture for testing
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tokenizer = T5Tokenizer(SAMPLE_VOCAB)
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tokenizer.save_pretrained(self.tmpdirname)
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def test_full_tokenizer(self):
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tokenizer = T5Tokenizer(SAMPLE_VOCAB)
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tokens = tokenizer.tokenize("This is a test")
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self.assertListEqual(tokens, ["▁This", "▁is", "▁a", "▁t", "est"])
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self.assertListEqual(tokenizer.convert_tokens_to_ids(tokens), [285, 46, 10, 170, 382])
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tokens = tokenizer.tokenize("I was born in 92000, and this is falsé.")
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self.assertListEqual(
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tokens,
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[
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SPIECE_UNDERLINE + "I",
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SPIECE_UNDERLINE + "was",
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SPIECE_UNDERLINE + "b",
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"or",
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"n",
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SPIECE_UNDERLINE + "in",
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SPIECE_UNDERLINE + "",
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"9",
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"2",
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"0",
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"0",
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"0",
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",",
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SPIECE_UNDERLINE + "and",
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SPIECE_UNDERLINE + "this",
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SPIECE_UNDERLINE + "is",
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SPIECE_UNDERLINE + "f",
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"al",
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"s",
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"é",
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".",
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],
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)
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ids = tokenizer.convert_tokens_to_ids(tokens)
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self.assertListEqual(ids, [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4])
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back_tokens = tokenizer.convert_ids_to_tokens(ids)
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self.assertListEqual(
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back_tokens,
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[
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SPIECE_UNDERLINE + "I",
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SPIECE_UNDERLINE + "was",
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SPIECE_UNDERLINE + "b",
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"or",
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"n",
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SPIECE_UNDERLINE + "in",
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SPIECE_UNDERLINE + "",
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"<unk>",
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"2",
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"0",
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"0",
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"0",
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",",
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SPIECE_UNDERLINE + "and",
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SPIECE_UNDERLINE + "this",
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SPIECE_UNDERLINE + "is",
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SPIECE_UNDERLINE + "f",
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"al",
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"s",
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"<unk>",
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".",
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],
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)
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@cached_property
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def t5_base_tokenizer(self):
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return T5Tokenizer.from_pretrained("t5-base")
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def test_eos_treatment(self):
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tokenizer = self.t5_base_tokenizer
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batch_with_eos_added = tokenizer(["hi</s>", "I went to the gym</s>", "</s>"])
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batch_without_eos_added = tokenizer(["hi", "I went to the gym", ""])
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self.assertListEqual(batch_with_eos_added["input_ids"], batch_without_eos_added["input_ids"])
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def test_prepare_seq2seq_batch(self):
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tokenizer = self.t5_base_tokenizer
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src_text = ["A long paragraph for summarization.", "Another paragraph for summarization."]
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tgt_text = [
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"Summary of the text.",
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"Another summary.",
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]
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expected_src_tokens = [71, 307, 8986, 21, 4505, 1635, 1707, 5, tokenizer.eos_token_id]
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batch = tokenizer.prepare_seq2seq_batch(
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src_text,
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tgt_texts=tgt_text,
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return_tensors=FRAMEWORK,
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)
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self.assertIsInstance(batch, BatchEncoding)
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result = list(batch.input_ids.numpy()[0])
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self.assertListEqual(expected_src_tokens, result)
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self.assertEqual((2, 9), batch.input_ids.shape)
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self.assertEqual((2, 9), batch.attention_mask.shape)
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def test_empty_target_text(self):
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tokenizer = self.t5_base_tokenizer
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src_text = ["A long paragraph for summarization.", "Another paragraph for summarization."]
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batch = tokenizer.prepare_seq2seq_batch(src_text, return_tensors=FRAMEWORK)
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# check if input_ids are returned and no decoder_input_ids
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self.assertIn("input_ids", batch)
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self.assertIn("attention_mask", batch)
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self.assertNotIn("decoder_input_ids", batch)
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self.assertNotIn("decoder_attention_mask", batch)
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def test_max_target_length(self):
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tokenizer = self.t5_base_tokenizer
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src_text = ["A short paragraph for summarization.", "Another short paragraph for summarization."]
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tgt_text = [
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"Summary of the text.",
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"Another summary.",
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]
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batch = tokenizer.prepare_seq2seq_batch(
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src_text, tgt_texts=tgt_text, max_target_length=32, padding="max_length", return_tensors=FRAMEWORK
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)
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self.assertEqual(32, batch["labels"].shape[1])
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# test None max_target_length
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batch = tokenizer.prepare_seq2seq_batch(
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src_text, tgt_texts=tgt_text, max_length=32, padding="max_length", return_tensors=FRAMEWORK
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)
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self.assertEqual(32, batch["labels"].shape[1])
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def test_outputs_not_longer_than_maxlen(self):
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tokenizer = self.t5_base_tokenizer
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batch = tokenizer.prepare_seq2seq_batch(
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["I am a small frog" * 1000, "I am a small frog"], return_tensors=FRAMEWORK
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)
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self.assertIsInstance(batch, BatchEncoding)
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self.assertEqual(batch.input_ids.shape, (2, 512))
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def test_eos_in_input(self):
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tokenizer = self.t5_base_tokenizer
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src_text = ["A long paragraph for summarization. </s>"]
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tgt_text = ["Summary of the text. </s>"]
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expected_src_tokens = [71, 307, 8986, 21, 4505, 1635, 1707, 5, 1]
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expected_tgt_tokens = [20698, 13, 8, 1499, 5, 1]
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batch = tokenizer.prepare_seq2seq_batch(src_text, tgt_texts=tgt_text, return_tensors=FRAMEWORK)
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src_ids = list(batch.input_ids.numpy()[0])
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tgt_ids = list(batch.labels.numpy()[0])
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self.assertEqual(expected_src_tokens, src_ids)
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self.assertEqual(expected_tgt_tokens, tgt_ids)
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