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* fix test for bart. Order is correct now let's skip BPEs * ouf * styling * fix bert.... * slow refactoring * current updates * massive refactoring * update * NICE! * update to see where I am at * updates * update * update * revert * updates * updates * start supporting legacy_save * styling * big update * revert some changes * nits * nniiiiiice * small fixes * kinda fix t5 with new behaviour * major update * fixup * fix copies * today's updates * fix byt5 * upfate * update * update * updates * update vocab size test * Barthez does not use not need the fairseq offset ids * super calll must be after * calll super * move all super init * move other super init * fixup * nits * more fixes * nits * more fixes * nits * more fix * remove useless files * ouch all of them are affected * and more! * small imporvements * no more sanitize token * more changes around unique no split tokens * partially fix more things * keep legacy save but add warning * so... more fixes * updates * guess deberta tokenizer could be nuked * fixup * fixup did some bad things * nuke it if it breaks * remove prints and pretrain fast from slow with new format. * fixups * Apply suggestions from code review Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * fiou * nit * by default specials should not be normalized? * update * remove brakpoint * updates * a lot of updates * fixup * fixes revert some changes to match fast * small nits * that makes it cleaner * fix camembert accordingly * update * some lest breaking changes * update * fixup * fix byt5 and whisper mostly * some more fixes, canine's byte vocab * fix gpt2 * fix most of the perceiver tests (4 left) * fix layout lmv3 * fixup * fix copies for gpt2 style * make sure to only warn once * fix perciever and gpt2 tests * some more backward compatibility: also read special tokens map because some ppl use it........////..... * fixup * add else when reading * nits * fresh updates * fix copies * will this make everything faster? * fixes * more fixes * update * more fixes * fixup * is the source of truth right? * sorry camembert for the troubles * current updates * fixup * update led * update * fix regression * fix single word * more model specific fixes * fix t5 tests * fixup * more comments * update * fix nllb * rstrip removed * small fixes * better handle additional_special_tokens and vocab sizes * fixing * styling * fix 4 / 21 * fixup * fix nlbb's tests * some fixes * fix t5 * fixes * style * fix canine tests * damn this is nice * nits * m2m100 nit * fixups * fixes! * fixup * stash * fix merge * revert bad change * fixup * correct order for code Llama * fix speecht5 post merge * styling * revert source of 11 fails * small nits * all changes in one go * fnet hack * fix 2 more tests * update based on main branch of tokenizers * fixup * fix VITS issues * more fixes * fix mgp test * fix camembert issues * oups camembert still has 2 failing tests * mluke fixes * decode fixes * small nits * nits * fix llama and vits * fix camembert * smal nits * more fixes when initialising a fast from a slow and etc * fix one of the last test * fix CPM tokenizer test * fixups * fix pop2piano * fixup * ⚠️ Change tokenizers required version ⚠️ * ⚠️ Change tokenizers required version ⚠️ * "tokenizers>=0.14,<0.15", don't forget smaller than * fix musicgen tests and pretraiendtokenizerfast * fix owlvit and all * update t5 * fix 800 red * fix tests * fix the fix of the fix of t5 * styling * documentation nits * cache _added_tokens_encoder * fixups * Nit * fix red tests * one last nit! * make eveything a lot simpler * Now it's over 😉 * few small nits * Apply suggestions from code review Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * updates that work for now * tests that should no be skipped / changed and fixed next * fixup * i am ashamed * pushe the fix * update * fixups * nits * fix added_tokens_encoder * fix canine test * fix pegasus vocab * fix transfoXL * fixup * whisper needs to be fixed for train new * pegasus nits * more pegasus fixes * minor update * better error message in failed test * fix whisper failing test * fix whisper failing test * fix pegasus * fixup * fix **** pegasus * reset things * remove another file * attempts to fix the strange custome encoder and offset * nits here and there * update * fixup * nit * fix the whisper test * nits nits * Apply suggestions from code review Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * updates based on review * some small update to potentially remove * nits * import rlu cache * Update src/transformers/tokenization_utils_base.py Co-authored-by: Lysandre Debut <hi@lysand.re> * move warning to `from_pretrained` * update tests results now that the special tokens are always added --------- Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> Co-authored-by: Lysandre Debut <hi@lysand.re>
264 lines
14 KiB
Python
264 lines
14 KiB
Python
# coding=utf-8
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# Copyright 2019 Hugging Face inc.
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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 DebertaV2Tokenizer, DebertaV2TokenizerFast
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from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
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from ...test_tokenization_common import TokenizerTesterMixin
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SAMPLE_VOCAB = get_tests_dir("fixtures/spiece.model")
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@require_sentencepiece
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@require_tokenizers
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class DebertaV2TokenizationTest(TokenizerTesterMixin, unittest.TestCase):
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tokenizer_class = DebertaV2Tokenizer
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rust_tokenizer_class = DebertaV2TokenizerFast
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test_sentencepiece = True
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test_sentencepiece_ignore_case = True
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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 = DebertaV2Tokenizer(SAMPLE_VOCAB, unk_token="<unk>")
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tokenizer.save_pretrained(self.tmpdirname)
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def get_input_output_texts(self, tokenizer):
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input_text = "this is a test"
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output_text = "this is a test"
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return input_text, output_text
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def test_convert_token_and_id(self):
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"""Test ``_convert_token_to_id`` and ``_convert_id_to_token``."""
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token = "<pad>"
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token_id = 0
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self.assertEqual(self.get_tokenizer()._convert_token_to_id(token), token_id)
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self.assertEqual(self.get_tokenizer()._convert_id_to_token(token_id), token)
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def test_get_vocab(self):
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vocab_keys = list(self.get_tokenizer().get_vocab().keys())
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self.assertEqual(vocab_keys[0], "<pad>")
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self.assertEqual(vocab_keys[1], "<unk>")
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self.assertEqual(vocab_keys[-1], "[PAD]")
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self.assertEqual(len(vocab_keys), 30_001)
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def test_vocab_size(self):
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self.assertEqual(self.get_tokenizer().vocab_size, 30_000)
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def test_do_lower_case(self):
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# fmt: off
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sequence = " \tHeLLo!how \n Are yoU? "
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tokens_target = ["▁hello", "!", "how", "▁are", "▁you", "?"]
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# fmt: on
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tokenizer = DebertaV2Tokenizer(SAMPLE_VOCAB, unk_token="<unk>", do_lower_case=True)
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tokens = tokenizer.convert_ids_to_tokens(tokenizer.encode(sequence, add_special_tokens=False))
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self.assertListEqual(tokens, tokens_target)
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rust_tokenizer = DebertaV2TokenizerFast(SAMPLE_VOCAB, unk_token="<unk>", do_lower_case=True)
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rust_tokens = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(sequence, add_special_tokens=False))
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self.assertListEqual(rust_tokens, tokens_target)
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@unittest.skip("There is an inconsistency between slow and fast tokenizer due to a bug in the fast one.")
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def test_sentencepiece_tokenize_and_convert_tokens_to_string(self):
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pass
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@unittest.skip("There is an inconsistency between slow and fast tokenizer due to a bug in the fast one.")
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def test_sentencepiece_tokenize_and_decode(self):
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pass
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def test_split_by_punct(self):
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# fmt: off
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sequence = "I was born in 92000, and this is falsé."
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tokens_target = ["▁", "<unk>", "▁was", "▁born", "▁in", "▁9", "2000", "▁", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "▁", ".", ]
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# fmt: on
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tokenizer = DebertaV2Tokenizer(SAMPLE_VOCAB, unk_token="<unk>", split_by_punct=True)
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tokens = tokenizer.convert_ids_to_tokens(tokenizer.encode(sequence, add_special_tokens=False))
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self.assertListEqual(tokens, tokens_target)
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rust_tokenizer = DebertaV2TokenizerFast(SAMPLE_VOCAB, unk_token="<unk>", split_by_punct=True)
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rust_tokens = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(sequence, add_special_tokens=False))
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self.assertListEqual(rust_tokens, tokens_target)
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def test_do_lower_case_split_by_punct(self):
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# fmt: off
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sequence = "I was born in 92000, and this is falsé."
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tokens_target = ["▁i", "▁was", "▁born", "▁in", "▁9", "2000", "▁", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "▁", ".", ]
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# fmt: on
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tokenizer = DebertaV2Tokenizer(SAMPLE_VOCAB, unk_token="<unk>", do_lower_case=True, split_by_punct=True)
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tokens = tokenizer.convert_ids_to_tokens(tokenizer.encode(sequence, add_special_tokens=False))
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self.assertListEqual(tokens, tokens_target)
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rust_tokenizer = DebertaV2TokenizerFast(
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SAMPLE_VOCAB, unk_token="<unk>", do_lower_case=True, split_by_punct=True
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)
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rust_tokens = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(sequence, add_special_tokens=False))
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self.assertListEqual(rust_tokens, tokens_target)
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def test_do_lower_case_split_by_punct_false(self):
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# fmt: off
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sequence = "I was born in 92000, and this is falsé."
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tokens_target = ["▁i", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", ".", ]
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# fmt: on
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tokenizer = DebertaV2Tokenizer(SAMPLE_VOCAB, unk_token="<unk>", do_lower_case=True, split_by_punct=False)
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tokens = tokenizer.convert_ids_to_tokens(tokenizer.encode(sequence, add_special_tokens=False))
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self.assertListEqual(tokens, tokens_target)
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rust_tokenizer = DebertaV2TokenizerFast(
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SAMPLE_VOCAB, unk_token="<unk>", do_lower_case=True, split_by_punct=False
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)
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rust_tokens = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(sequence, add_special_tokens=False))
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self.assertListEqual(rust_tokens, tokens_target)
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def test_do_lower_case_false_split_by_punct(self):
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# fmt: off
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sequence = "I was born in 92000, and this is falsé."
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tokens_target = ["▁", "<unk>", "▁was", "▁born", "▁in", "▁9", "2000", "▁", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "▁", ".", ]
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# fmt: on
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tokenizer = DebertaV2Tokenizer(SAMPLE_VOCAB, unk_token="<unk>", do_lower_case=False, split_by_punct=True)
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tokens = tokenizer.convert_ids_to_tokens(tokenizer.encode(sequence, add_special_tokens=False))
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self.assertListEqual(tokens, tokens_target)
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rust_tokenizer = DebertaV2TokenizerFast(
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SAMPLE_VOCAB, unk_token="<unk>", do_lower_case=False, split_by_punct=True
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)
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rust_tokens = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(sequence, add_special_tokens=False))
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self.assertListEqual(rust_tokens, tokens_target)
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def test_do_lower_case_false_split_by_punct_false(self):
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# fmt: off
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sequence = " \tHeLLo!how \n Are yoU? "
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tokens_target = ["▁", "<unk>", "e", "<unk>", "o", "!", "how", "▁", "<unk>", "re", "▁yo", "<unk>", "?"]
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# fmt: on
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tokenizer = DebertaV2Tokenizer(SAMPLE_VOCAB, unk_token="<unk>", do_lower_case=False, split_by_punct=False)
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tokens = tokenizer.convert_ids_to_tokens(tokenizer.encode(sequence, add_special_tokens=False))
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self.assertListEqual(tokens, tokens_target)
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rust_tokenizer = DebertaV2TokenizerFast(
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SAMPLE_VOCAB, unk_token="<unk>", do_lower_case=False, split_by_punct=False
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)
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rust_tokens = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(sequence, add_special_tokens=False))
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self.assertListEqual(rust_tokens, tokens_target)
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def test_rust_and_python_full_tokenizers(self):
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tokenizer = self.get_tokenizer()
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rust_tokenizer = self.get_rust_tokenizer()
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sequence = "I was born in 92000, and this is falsé."
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tokens = tokenizer.convert_ids_to_tokens(tokenizer.encode(sequence, add_special_tokens=False))
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rust_tokens = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(sequence, add_special_tokens=False))
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self.assertListEqual(tokens, rust_tokens)
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ids = tokenizer.encode(sequence, add_special_tokens=False)
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rust_ids = rust_tokenizer.encode(sequence, add_special_tokens=False)
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self.assertListEqual(ids, rust_ids)
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rust_tokenizer = self.get_rust_tokenizer()
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ids = tokenizer.encode(sequence)
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rust_ids = rust_tokenizer.encode(sequence)
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self.assertListEqual(ids, rust_ids)
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def test_full_tokenizer(self):
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sequence = "This is a test"
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ids_target = [13, 1, 4398, 25, 21, 1289]
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tokens_target = ["▁", "T", "his", "▁is", "▁a", "▁test"]
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back_tokens_target = ["▁", "<unk>", "his", "▁is", "▁a", "▁test"]
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tokenizer = DebertaV2Tokenizer(SAMPLE_VOCAB, unk_token="<unk>", keep_accents=True)
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rust_tokenizer = DebertaV2TokenizerFast(SAMPLE_VOCAB, unk_token="<unk>", keep_accents=True)
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ids = tokenizer.encode(sequence, add_special_tokens=False)
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self.assertListEqual(ids, ids_target)
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tokens = tokenizer.tokenize(sequence)
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self.assertListEqual(tokens, tokens_target)
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back_tokens = tokenizer.convert_ids_to_tokens(ids)
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self.assertListEqual(back_tokens, back_tokens_target)
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rust_ids = rust_tokenizer.encode(sequence, add_special_tokens=False)
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self.assertListEqual(rust_ids, ids_target)
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rust_tokens = rust_tokenizer.tokenize(sequence)
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self.assertListEqual(rust_tokens, tokens_target)
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rust_back_tokens = rust_tokenizer.convert_ids_to_tokens(rust_ids)
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self.assertListEqual(rust_back_tokens, back_tokens_target)
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# fmt: off
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sequence = "I was born in 92000, and this is falsé."
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ids_target = [13, 1, 23, 386, 19, 561, 3050, 15, 17, 48, 25, 8256, 18, 1, 9]
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tokens_target = ["▁", "I", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "é", ".", ]
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back_tokens_target = ["▁", "<unk>", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", ".", ]
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# fmt: on
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ids = tokenizer.encode(sequence, add_special_tokens=False)
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self.assertListEqual(ids, ids_target)
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tokens = tokenizer.tokenize(sequence)
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self.assertListEqual(tokens, tokens_target)
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back_tokens = tokenizer.convert_ids_to_tokens(ids)
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self.assertListEqual(back_tokens, back_tokens_target)
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rust_ids = rust_tokenizer.encode(sequence, add_special_tokens=False)
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self.assertListEqual(rust_ids, ids_target)
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rust_tokens = rust_tokenizer.tokenize(sequence)
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self.assertListEqual(rust_tokens, tokens_target)
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rust_back_tokens = rust_tokenizer.convert_ids_to_tokens(rust_ids)
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self.assertListEqual(rust_back_tokens, back_tokens_target)
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def test_sequence_builders(self):
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tokenizer = DebertaV2Tokenizer(SAMPLE_VOCAB)
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text = tokenizer.encode("sequence builders")
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text_2 = tokenizer.encode("multi-sequence build")
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encoded_sentence = tokenizer.build_inputs_with_special_tokens(text)
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encoded_pair = tokenizer.build_inputs_with_special_tokens(text, text_2)
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self.assertEqual([tokenizer.cls_token_id] + text + [tokenizer.sep_token_id], encoded_sentence)
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self.assertEqual(
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[tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_2 + [tokenizer.sep_token_id],
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encoded_pair,
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)
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@slow
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def test_tokenizer_integration(self):
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# fmt: off
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expected_encoding = {'input_ids': [[1, 39867, 36, 19390, 486, 27, 35052, 81436, 18, 60685, 1225, 7, 35052, 81436, 18, 9367, 16899, 18, 15937, 53, 594, 773, 18, 16287, 30465, 36, 15937, 6, 41139, 38, 36979, 60763, 191, 6, 34132, 99, 6, 50538, 390, 43230, 6, 34132, 2779, 20850, 14, 699, 1072, 1194, 36, 382, 10901, 53, 7, 699, 1072, 2084, 36, 20422, 630, 53, 19, 105, 3049, 1896, 1053, 16899, 1506, 11, 37978, 4243, 7, 1237, 31869, 200, 16566, 654, 6, 35052, 81436, 7, 55630, 13593, 4, 2], [1, 26, 15011, 13, 667, 8, 1053, 18, 23611, 1237, 72356, 12820, 34, 104134, 1209, 35, 13313, 6627, 21, 202, 347, 7, 164, 2399, 11, 46, 4485, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 5, 1232, 2864, 15785, 14951, 105, 5, 8581, 1250, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
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# fmt: on
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self.tokenizer_integration_test_util(
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expected_encoding=expected_encoding,
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model_name="microsoft/deberta-v2-xlarge",
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revision="ad6e42c1532ddf3a15c39246b63f5559d558b670",
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)
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