662 lines
30 KiB
Python
662 lines
30 KiB
Python
# Copyright 2022 The HuggingFace Team. All rights reserved.
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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 unittest
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from transformers import (
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SPIECE_UNDERLINE,
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AddedToken,
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BatchEncoding,
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PreTrainedTokenizerFast,
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SeamlessM4TTokenizer,
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SeamlessM4TTokenizerFast,
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is_torch_available,
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)
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from transformers.testing_utils import (
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get_tests_dir,
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nested_simplify,
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require_sentencepiece,
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require_tokenizers,
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require_torch,
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)
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from ...test_tokenization_common import TokenizerTesterMixin
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SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece.model")
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if is_torch_available():
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from transformers.models.m2m_100.modeling_m2m_100 import shift_tokens_right
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EN_CODE = 256047
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RO_CODE = 256145
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SMALL_TRAINING_CORPUS = [
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["This is the first sentence.", "This is the second one."],
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["This sentence (contains #) over symbols and numbers 12 3.", "But not this one."],
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]
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@require_sentencepiece
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@require_tokenizers
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class SeamlessM4TTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
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from_pretrained_id = "facebook/hf-seamless-m4t-medium"
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tokenizer_class = SeamlessM4TTokenizer
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rust_tokenizer_class = SeamlessM4TTokenizerFast
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test_rust_tokenizer = True
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test_sentencepiece = True
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from_pretrained_kwargs = {}
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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 = SeamlessM4TTokenizer(SAMPLE_VOCAB, keep_accents=True)
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tokenizer.save_pretrained(self.tmpdirname)
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def test_full_tokenizer(self):
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tokenizer = SeamlessM4TTokenizer(SAMPLE_VOCAB, keep_accents=True)
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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(
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tokenizer.convert_tokens_to_ids(tokens),
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[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]],
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)
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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(
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ids,
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[
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value + tokenizer.fairseq_offset
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for value in [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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],
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)
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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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@unittest.skip("This fails currently and is a blocker. No idea why TODO @ylacombe")
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def test_maximum_encoding_length_single_input(self):
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tokenizers = self.get_tokenizers(do_lower_case=False, model_max_length=100)
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for tokenizer in tokenizers:
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with self.subTest(f"{tokenizer.__class__.__name__}"):
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seq_0, ids = self.get_clean_sequence(tokenizer, max_length=20)
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sequence = tokenizer.encode(seq_0, add_special_tokens=False)
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total_length = len(sequence)
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self.assertGreater(
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total_length, 4, "Issue with the testing sequence, please update it, it's too short"
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)
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# Test with max model input length
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model_max_length = tokenizer.model_max_length
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self.assertEqual(model_max_length, 100)
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seq_1 = seq_0 * model_max_length
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sequence1 = tokenizer(seq_1, add_special_tokens=False)
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total_length1 = len(sequence1["input_ids"])
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self.assertGreater(
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total_length1,
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model_max_length,
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"Issue with the testing sequence, please update it, it's too short",
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)
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# Simple
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padding_strategies = (
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[False, True, "longest"] if tokenizer.pad_token and tokenizer.pad_token_id >= 0 else [False]
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)
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for padding_state in padding_strategies:
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with self.subTest(f"Padding: {padding_state}"):
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for truncation_state in [True, "longest_first", "only_first"]:
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with self.subTest(f"Truncation: {truncation_state}"):
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output = tokenizer(seq_1, padding=padding_state, truncation=truncation_state)
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self.assertEqual(len(output["input_ids"]), model_max_length)
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output = tokenizer([seq_1], padding=padding_state, truncation=truncation_state)
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self.assertEqual(len(output["input_ids"][0]), model_max_length)
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# Simple with no truncation
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# Reset warnings
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tokenizer.deprecation_warnings = {}
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with self.assertLogs("transformers", level="WARNING") as cm:
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output = tokenizer(seq_1, padding=padding_state, truncation=False)
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self.assertNotEqual(len(output["input_ids"]), model_max_length)
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self.assertEqual(len(cm.records), 1)
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self.assertTrue(
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cm.records[0].message.startswith(
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"Token indices sequence length is longer than the specified maximum sequence length"
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" for this model"
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)
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)
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tokenizer.deprecation_warnings = {}
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with self.assertLogs("transformers", level="WARNING") as cm:
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output = tokenizer([seq_1], padding=padding_state, truncation=False)
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self.assertNotEqual(len(output["input_ids"][0]), model_max_length)
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self.assertEqual(len(cm.records), 1)
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self.assertTrue(
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cm.records[0].message.startswith(
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"Token indices sequence length is longer than the specified maximum sequence length"
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" for this model"
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)
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)
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# Overflowing tokens
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stride = 2
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# modify padding because it's activated by default in seamlessM4T
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information = tokenizer(
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seq_0,
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max_length=total_length - 2,
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add_special_tokens=False,
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stride=stride,
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truncation="longest_first",
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return_overflowing_tokens=True,
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padding=False,
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# add_prefix_space=False,
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)
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# Overflowing tokens are handled quite differently in slow and fast tokenizers
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if isinstance(tokenizer, PreTrainedTokenizerFast):
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truncated_sequence = information["input_ids"][0]
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overflowing_tokens = information["input_ids"][1]
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self.assertEqual(len(information["input_ids"]), 2)
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self.assertEqual(len(truncated_sequence), total_length - 2)
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self.assertEqual(truncated_sequence, sequence[:-2])
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self.assertEqual(len(overflowing_tokens), 2 + stride)
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self.assertEqual(overflowing_tokens, sequence[-(2 + stride) :])
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else:
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truncated_sequence = information["input_ids"]
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overflowing_tokens = information["overflowing_tokens"]
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self.assertEqual(len(truncated_sequence), total_length - 2)
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self.assertEqual(truncated_sequence, sequence[:-2])
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self.assertEqual(len(overflowing_tokens), 2 + stride)
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self.assertEqual(overflowing_tokens, sequence[-(2 + stride) :])
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@unittest.skip("By defaults, uses pad_to_multiple_of which breaks the test")
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def test_maximum_encoding_length_pair_input(self):
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pass
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def test_padding_to_multiple_of(self):
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tokenizers = self.get_tokenizers()
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for tokenizer in tokenizers:
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with self.subTest(f"{tokenizer.__class__.__name__}"):
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if tokenizer.pad_token is None:
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self.skipTest("No padding token.")
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else:
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empty_tokens = tokenizer("", padding=True, pad_to_multiple_of=8)
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normal_tokens = tokenizer("This is a sample input", padding=True, pad_to_multiple_of=8)
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for key, value in empty_tokens.items():
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self.assertEqual(len(value) % 8, 0, f"BatchEncoding.{key} is not multiple of 8")
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for key, value in normal_tokens.items():
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self.assertEqual(len(value) % 8, 0, f"BatchEncoding.{key} is not multiple of 8")
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# default to padding=True so need to precise which padding is called
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normal_tokens = tokenizer("This", pad_to_multiple_of=8, padding=False)
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for key, value in normal_tokens.items():
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self.assertNotEqual(len(value) % 8, 0, f"BatchEncoding.{key} is not multiple of 8")
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# Should also work with truncation
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normal_tokens = tokenizer("This", padding=True, truncation=True, pad_to_multiple_of=8)
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for key, value in normal_tokens.items():
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self.assertEqual(len(value) % 8, 0, f"BatchEncoding.{key} is not multiple of 8")
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# truncation to something which is not a multiple of pad_to_multiple_of raises an error
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self.assertRaises(
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ValueError,
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tokenizer.__call__,
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"This",
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padding=True,
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truncation=True,
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max_length=12,
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pad_to_multiple_of=8,
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)
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@require_torch
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def test_prepare_seq2seq_batch(self):
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if not self.test_seq2seq:
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return
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tokenizers = self.get_tokenizers()
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for tokenizer in tokenizers:
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with self.subTest(f"{tokenizer.__class__.__name__}"):
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# Longer text that will definitely require truncation.
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src_text = [
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" UN Chief Says There Is No Military Solution in Syria",
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" Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for"
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" Syria is that 'there is no military solution' to the nearly five-year conflict and more weapons"
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" will only worsen the violence and misery for millions of people.",
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]
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tgt_text = [
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"Şeful ONU declară că nu există o soluţie militară în Siria",
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"Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al"
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' Rusiei pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi'
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" că noi arme nu vor face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.",
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]
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try:
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batch = tokenizer.prepare_seq2seq_batch(
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src_texts=src_text,
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tgt_texts=tgt_text,
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max_length=3,
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max_target_length=10,
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return_tensors="pt",
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src_lang="eng",
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tgt_lang="ron",
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pad_to_multiple_of=None,
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)
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except NotImplementedError:
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return
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self.assertEqual(batch.input_ids.shape[1], 3)
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self.assertEqual(batch.labels.shape[1], 10)
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# TODO: not working for tgt_text
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# max_target_length will default to max_length if not specified
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batch = tokenizer.prepare_seq2seq_batch(
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src_texts=src_text,
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tgt_texts=tgt_text,
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max_length=4,
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return_tensors="pt",
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pad_to_multiple_of=None,
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)
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self.assertEqual(batch.input_ids.shape[1], 4)
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self.assertEqual(batch.labels.shape[1], 4)
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batch_encoder_only = tokenizer.prepare_seq2seq_batch(
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src_texts=src_text,
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max_length=4,
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max_target_length=10,
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return_tensors="pt",
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pad_to_multiple_of=None,
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)
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self.assertEqual(batch_encoder_only.input_ids.shape[1], 4)
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self.assertEqual(batch_encoder_only.attention_mask.shape[1], 4)
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self.assertNotIn("decoder_input_ids", batch_encoder_only)
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@unittest.skip("Unfortunately way too slow to build a BPE with SentencePiece.")
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def test_save_slow_from_fast_and_reload_fast(self):
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pass
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# Copied from tests.models.nllb.test_tokenization_nllb.NllbTokenizationTest.test_special_tokens_initialization
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def test_special_tokens_initialization(self):
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for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
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with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
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added_tokens = [AddedToken("<special>", lstrip=True)]
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tokenizer_r = self.rust_tokenizer_class.from_pretrained(
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pretrained_name, additional_special_tokens=added_tokens, **kwargs
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)
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r_output = tokenizer_r.encode("Hey this is a <special> token")
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special_token_id = tokenizer_r.encode("<special>", add_special_tokens=False)[0]
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self.assertTrue(special_token_id in r_output)
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if self.test_slow_tokenizer:
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tokenizer_cr = self.rust_tokenizer_class.from_pretrained(
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pretrained_name,
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additional_special_tokens=added_tokens,
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**kwargs, # , from_slow=True <- unfortunately too slow to convert
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)
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tokenizer_p = self.tokenizer_class.from_pretrained(
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pretrained_name, additional_special_tokens=added_tokens, **kwargs
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)
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p_output = tokenizer_p.encode("Hey this is a <special> token")
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cr_output = tokenizer_cr.encode("Hey this is a <special> token")
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self.assertEqual(p_output, r_output)
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self.assertEqual(cr_output, r_output)
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self.assertTrue(special_token_id in p_output)
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self.assertTrue(special_token_id in cr_output)
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@unittest.skip(
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"encode_plus and batch_encode_plus are deprecated and __call__ do some processing, so we expect different results."
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)
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def test_call(self):
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pass
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def test_training_new_tokenizer(self):
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# This feature only exists for fast tokenizers
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if not self.test_rust_tokenizer:
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return
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tokenizer = self.get_rust_tokenizer()
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new_tokenizer = tokenizer.train_new_from_iterator(SMALL_TRAINING_CORPUS, 100)
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# Test we can use the new tokenizer with something not seen during training
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inputs = new_tokenizer(["This is the first sentence", "This sentence is different 🤗."])
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self.assertEqual(len(inputs["input_ids"]), 2)
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decoded_input = new_tokenizer.decode(inputs["input_ids"][0], skip_special_tokens=True)
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expected_result = "This is the first sentence"
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if tokenizer.backend_tokenizer.normalizer is not None:
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expected_result = tokenizer.backend_tokenizer.normalizer.normalize_str(expected_result)
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self.assertEqual(expected_result, decoded_input)
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# We check that the parameters of the tokenizer remained the same
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# Check we have the same number of added_tokens for both pair and non-pair inputs.
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# make sure it has the same prefix tokens first
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new_tokenizer.tgt_lang = tokenizer.tgt_lang
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tokenizer.tgt_lang = tokenizer.tgt_lang
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self.assertEqual(tokenizer.num_special_tokens_to_add(False), new_tokenizer.num_special_tokens_to_add(False))
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self.assertEqual(tokenizer.num_special_tokens_to_add(True), new_tokenizer.num_special_tokens_to_add(True))
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# Check we have the correct max_length for both pair and non-pair inputs.
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self.assertEqual(tokenizer.max_len_single_sentence, new_tokenizer.max_len_single_sentence)
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self.assertEqual(tokenizer.max_len_sentences_pair, new_tokenizer.max_len_sentences_pair)
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# Assert the set of special tokens match as we didn't ask to change them
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self.assertSequenceEqual(
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tokenizer.all_special_tokens_extended,
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new_tokenizer.all_special_tokens_extended,
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)
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self.assertDictEqual(tokenizer.special_tokens_map, new_tokenizer.special_tokens_map)
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@unittest.skip("Fails because of the hack of adding <unk> in _tokenize")
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def test_pickle_subword_regularization_tokenizer(self):
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pass
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@unittest.skip("Fails because of the hack of adding <unk> in _tokenize")
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def test_subword_regularization_tokenizer(self):
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pass
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@require_torch
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@require_sentencepiece
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@require_tokenizers
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class SeamlessM4TDistilledIntegrationTest(unittest.TestCase):
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checkpoint_name = "facebook/hf-seamless-m4t-medium"
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src_text = [
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" UN Chief Says There Is No Military Solution in Syria",
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""" 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.""",
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]
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tgt_text = [
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"Şeful ONU declară că nu există o soluţie militară în Siria",
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"Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"
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' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'
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" face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.",
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]
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expected_src_tokens = [256047, 16297, 134408, 8165, 248066, 14734, 950, 1135, 105721, 3573, 83, 27352, 108, 49486, 3] # fmt: skip
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@classmethod
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def setUpClass(cls):
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cls.tokenizer: SeamlessM4TTokenizer = SeamlessM4TTokenizer.from_pretrained(
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cls.checkpoint_name, src_lang="eng", tgt_lang="ron"
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)
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# cls.pad_token_id = 1
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return cls
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|
def test_language_codes(self):
|
|
self.assertEqual(self.tokenizer.convert_tokens_to_ids("__ace_Latn__"), 256002)
|
|
self.assertEqual(self.tokenizer.convert_tokens_to_ids("__shn__"), 256152)
|
|
self.assertEqual(self.tokenizer.convert_tokens_to_ids("__eng__"), 256047)
|
|
self.assertEqual(self.tokenizer.convert_tokens_to_ids("__fra__"), 256057)
|
|
self.assertEqual(self.tokenizer.convert_tokens_to_ids("__quy__"), 256144)
|
|
|
|
def test_tokenizer_tgt_lang(self):
|
|
ids = self.tokenizer(self.src_text, src_lang="fra").input_ids[0]
|
|
self.assertListEqual(self.expected_src_tokens[1:], ids[1 : len(self.expected_src_tokens)])
|
|
self.assertEqual(256057, ids[0])
|
|
|
|
rest_ids = ids[len(self.expected_src_tokens) :]
|
|
self.assertListEqual([0] * len(rest_ids), rest_ids)
|
|
|
|
ids = self.tokenizer(self.src_text, src_lang="__shn__").input_ids[0]
|
|
self.assertListEqual(self.expected_src_tokens[1:], ids[1 : len(self.expected_src_tokens)])
|
|
self.assertEqual(256152, ids[0])
|
|
|
|
# Copied from tests.models.nllb.test_tokenization_nllb.NllbDistilledIntegrationTest.test_enro_tokenizer_decode_ignores_language_codes
|
|
def test_enro_tokenizer_decode_ignores_language_codes(self):
|
|
self.assertIn(RO_CODE, self.tokenizer.all_special_ids)
|
|
generated_ids = [RO_CODE, 4254, 98068, 112923, 39072, 3909, 713, 102767, 26, 17314, 35642, 14683, 33118, 2022, 66987, 2, 256047] # fmt: skip
|
|
|
|
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_enro_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[-1], 3)
|
|
self.assertEqual(ids[0], EN_CODE)
|
|
self.assertEqual(len(ids), desired_max_length)
|
|
|
|
@require_torch
|
|
def test_enro_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),
|
|
pad_to_multiple_of=None,
|
|
return_tensors="pt",
|
|
)
|
|
batch["decoder_input_ids"] = shift_tokens_right(
|
|
batch["labels"], self.tokenizer.pad_token_id, self.tokenizer.convert_tokens_to_ids("__ron__")
|
|
)
|
|
|
|
self.assertIsInstance(batch, BatchEncoding)
|
|
|
|
self.assertEqual((2, 15), batch.input_ids.shape)
|
|
self.assertEqual((2, 15), batch.attention_mask.shape)
|
|
result = batch.input_ids.tolist()[0]
|
|
self.assertListEqual(self.expected_src_tokens, result)
|
|
self.assertEqual(RO_CODE, batch.decoder_input_ids[0, 0]) # EOS
|
|
# 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_length(self):
|
|
batch = self.tokenizer(
|
|
self.src_text, padding=True, truncation=True, max_length=3, return_tensors="pt", pad_to_multiple_of=None
|
|
)
|
|
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,
|
|
decoder_start_token_id=self.tokenizer.convert_tokens_to_ids(self.tokenizer.tgt_lang),
|
|
)
|
|
|
|
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="eng", tgt_lang="fra"
|
|
)
|
|
|
|
self.assertEqual(
|
|
nested_simplify(inputs),
|
|
{
|
|
# A, test, EOS, en_XX
|
|
"input_ids": [[256047, 70, 7356, 3]],
|
|
"attention_mask": [[1, 1, 1, 1]],
|
|
# ar_AR
|
|
"forced_bos_token_id": 256057,
|
|
},
|
|
)
|
|
|
|
|
|
@require_sentencepiece
|
|
@require_tokenizers
|
|
class CommonSpmIntegrationTests(unittest.TestCase):
|
|
"""
|
|
A class that regroups important test to make sure that we properly handle the special tokens.
|
|
"""
|
|
|
|
@classmethod
|
|
def setUpClass(cls):
|
|
tokenizer = SeamlessM4TTokenizer(SAMPLE_VOCAB, extra_ids=0, add_bos_token=False, legacy=False)
|
|
tokenizer.add_special_tokens({"additional_special_tokens": [AddedToken("<s>", rstrip=False, lstrip=False)]})
|
|
cls.tokenizer = tokenizer
|
|
return cls
|
|
|
|
def test_add_dummy_prefix(self):
|
|
# make sure `'▁'` is prepended, and outputs match sp_model's
|
|
# `sentencepiece.NormalizerSpec.add_dummy_prefix` attribute
|
|
input_ids = self.tokenizer.encode(". Hello")
|
|
self.assertEqual(input_ids, [3, 1, 8, 5, 157, 87, 21, 3])
|
|
sp_encode = self.tokenizer.sp_model.encode(". Hello")
|
|
|
|
# [bos, lang_id, _] + offset_sp_encode
|
|
self.assertEqual(input_ids[:-1], [3, 1, 8] + [i + self.tokenizer.fairseq_offset for i in sp_encode])
|
|
tokens = self.tokenizer.tokenize(". Hello")
|
|
self.assertEqual(tokens, ["▁", ".", "▁He", "ll", "o"])
|
|
|
|
tokens = self.tokenizer.tokenize("")
|
|
self.assertEqual(tokens, [])
|
|
self.assertEqual(tokens, self.tokenizer.sp_model.encode("", out_type=str))
|
|
|
|
tokens = self.tokenizer.tokenize(" ")
|
|
self.assertEqual(tokens, [])
|
|
self.assertEqual(tokens, self.tokenizer.sp_model.encode(" ", out_type=str))
|
|
|
|
tokens = self.tokenizer.tokenize("▁")
|
|
self.assertEqual(tokens, [])
|
|
self.assertEqual(tokens, self.tokenizer.sp_model.encode("▁", out_type=str))
|
|
|
|
def test_remove_extra_whitespaces(self):
|
|
# make sure the extra spaces are eaten. Since the sample vocab does not have
|
|
# `______`. sentencepiece.NormalizerSpec.remove_extra_whitespaces attribute is set to False
|
|
|
|
input_ids = self.tokenizer.encode(" . Hello")
|
|
self.assertEqual(input_ids, [3, 1, 8, 5, 157, 87, 21, 3])
|
|
sp_encode = self.tokenizer.sp_model.encode(" . Hello")
|
|
self.assertEqual([i - self.tokenizer.fairseq_offset for i in input_ids[2:-1]], [7] + sp_encode)
|
|
tokens = self.tokenizer.tokenize(" . Hello")
|
|
self.assertEqual(tokens, ["▁", ".", "▁He", "ll", "o"])
|
|
|
|
# `'▁'` is also a whitespace
|
|
input_ids = self.tokenizer.encode("▁He is not")
|
|
self.assertEqual(input_ids, [3, 1, 157, 47, 45, 3])
|
|
tokens = self.tokenizer.tokenize("▁He is not")
|
|
sp_encode = [
|
|
self.tokenizer.sp_model.piece_to_id("▁He"),
|
|
self.tokenizer.sp_model.piece_to_id("▁is"),
|
|
self.tokenizer.sp_model.piece_to_id("▁not"),
|
|
]
|
|
self.assertEqual([i - self.tokenizer.fairseq_offset for i in input_ids[2:-1]], sp_encode)
|
|
self.assertEqual(tokens, ["▁He", "▁is", "▁not"]) # no extra space added
|
|
|
|
input_ids = self.tokenizer.encode("▁He is not<s> ▁He")
|
|
self.assertEqual(input_ids, [3, 1, 157, 47, 45, 2, 157, 3])
|
|
tokens = self.tokenizer.tokenize("▁He is not<s> ▁He")
|
|
self.assertEqual(tokens, ["▁He", "▁is", "▁not", "<s>", "▁He"]) # spaces are eaten by spm + our strip
|
|
# make sure that the output after the extra id is the same as if
|
|
# extra_id was not there
|
|
input_ids = self.tokenizer.encode("▁He is not ▁He")
|
|
self.assertEqual(input_ids, [3, 1, 157, 47, 45, 157, 3])
|
|
tokens = self.tokenizer.tokenize("▁He is not ▁He")
|
|
self.assertEqual(tokens, ["▁He", "▁is", "▁not", "▁He"]) # spaces are eaten by spm even if not start
|
|
|
|
def test_character_after_special_token(self):
|
|
# Make sure that `tokenizer.tokenize` is similar to
|
|
# adding the equivalent special token to the vocab
|
|
input_ids = self.tokenizer.encode("Hey <s>I")
|
|
self.assertEqual(input_ids, [3, 1, 157, 31, 2, 101, 3])
|
|
sp_encode = self.tokenizer.sp_model.encode("Hey .I")
|
|
|
|
# the last token besides eos should be 100 offset
|
|
self.assertEqual(input_ids[-2] - self.tokenizer.fairseq_offset, sp_encode[-1])
|
|
tokens = self.tokenizer.tokenize("<s>I")
|
|
self.assertEqual(tokens, ["<s>", "I"])
|
|
|
|
input_ids = self.tokenizer.encode("Hello, <s>,")
|
|
self.assertEqual(input_ids, [3, 1, 157, 87, 21, 4, 2, 4, 3])
|
|
tokens = self.tokenizer.tokenize("Hello, <s>,")
|
|
self.assertEqual(tokens, ["▁He", "ll", "o", ",", "<s>", ","])
|
|
|
|
def test_special_tokens_strip(self):
|
|
input_ids = self.tokenizer.encode(" <s> ,")
|
|
self.assertEqual(input_ids, [3, 1, 2, 8, 4, 3])
|
|
tokens = self.tokenizer.tokenize(" <s> ,")
|
|
# spaces are eaten by rstrip / lstrip + spm sp_model.encode(" ") = []
|
|
self.assertEqual(tokens, ["<s>", "▁", ","])
|
|
|
|
input_ids = self.tokenizer.encode("No <s> ▁He")
|
|
self.assertEqual(input_ids, [3, 1, 285, 2, 157, 3])
|
|
tokens = self.tokenizer.tokenize("No <s> ▁He")
|
|
self.assertEqual(tokens, ["▁No", "<s>", "▁He"]) # spaces are eaten by rstrip / lstrip
|