wehub-resource-sync
187 lines
9.1 KiB
Python
187 lines
9.1 KiB
Python
# coding=utf-8
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# Copyright 2019 HuggingFace 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 paddlenlp.transformers import PretrainedTokenizerFast
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from tests.testing_utils import require_package
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from tests.transformers.test_tokenizer_common import TokenizerTesterMixin
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@require_package("tokenizers")
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class PreTrainedTokenizationFastTest(TokenizerTesterMixin, unittest.TestCase):
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rust_tokenizer_class = PretrainedTokenizerFast
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tokenizer_class = PretrainedTokenizerFast
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test_slow_tokenizer = False
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test_rust_tokenizer = True
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from_pretrained_vocab_key = "vocab_file"
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def setUp(self):
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self.test_rust_tokenizer = False # because we don't have pretrained_vocab_files_map
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super().setUp()
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self.test_rust_tokenizer = True
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model_paths = ["__internal_testing__/tiny-random-llama-fast"]
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# self.bytelevel_bpe_model_name = "SaulLu/dummy-tokenizer-bytelevel-bpe"
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# Inclusion of 2 tokenizers to test different types of models (Unigram and WordLevel for the moment)
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self.tokenizers_list = [(PretrainedTokenizerFast, model_path, {}) for model_path in model_paths]
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tokenizer = PretrainedTokenizerFast.from_pretrained(model_paths[0])
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tokenizer.save_pretrained(self.tmpdirname)
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@unittest.skip(
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"We disable this test for PretrainedTokenizerFast because it is the only tokenizer that is not linked to any model"
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)
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def test_tokenizer_mismatch_warning(self):
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pass
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@unittest.skip(
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"We disable this test for PretrainedTokenizerFast because it is the only tokenizer that is not linked to any model"
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)
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def test_encode_decode_with_spaces(self):
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pass
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@unittest.skip(
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"We disable this test for PretrainedTokenizerFast because it is the only tokenizer that is not linked to any model"
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)
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def test_added_tokens_serialization(self):
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pass
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@unittest.skip(
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"We disable this test for PretrainedTokenizerFast because it is the only tokenizer that is not linked to any model"
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)
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def test_additional_special_tokens_serialization(self):
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pass
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@unittest.skip(reason="PretrainedTokenizerFast is the only tokenizer that is not linked to any model")
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def test_prepare_for_model(self):
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pass
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@unittest.skip(reason="PretrainedTokenizerFast doesn't have tokenizer_file in its signature")
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def test_rust_tokenizer_signature(self):
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pass
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@unittest.skip(reason="PretrainedTokenizerFast passes error cases temporarily")
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def test_maximum_encoding_length_single_input(self):
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pass
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@unittest.skip(reason="PretrainedTokenizerFast passes error cases temporarily")
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def test_offsets_mapping_with_unk(self):
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pass
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@unittest.skip(reason="PretrainedTokenizerFast passes error cases temporarily")
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def test_maximum_encoding_length_pair_input(self):
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pass
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@unittest.skip(reason="PretrainedTokenizerFast passes error cases temporarily")
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def test_pretokenized_inputs(self):
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pass
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@unittest.skip(reason="PretrainedTokenizerFast passes error cases temporarily")
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def test_pretrained_model_lists(self):
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pass
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# def test_training_new_tokenizer(self):
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# tmpdirname_orig = self.tmpdirname
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# # Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
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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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# try:
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# self.tmpdirname = tempfile.mkdtemp()
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# tokenizer = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
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# tokenizer.save_pretrained(self.tmpdirname)
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# super().test_training_new_tokenizer()
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# finally:
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# # Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
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# # is restored
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# shutil.rmtree(self.tmpdirname)
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# self.tmpdirname = tmpdirname_orig
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# def test_training_new_tokenizer_with_special_tokens_change(self):
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# tmpdirname_orig = self.tmpdirname
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# # Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
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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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# try:
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# self.tmpdirname = tempfile.mkdtemp()
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# tokenizer = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
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# tokenizer.save_pretrained(self.tmpdirname)
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# super().test_training_new_tokenizer_with_special_tokens_change()
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# finally:
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# # Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
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# # is restored
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# shutil.rmtree(self.tmpdirname)
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# self.tmpdirname = tmpdirname_orig
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# def test_training_new_tokenizer_with_bytelevel(self):
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# tokenizer = self.rust_tokenizer_class.from_pretrained(self.bytelevel_bpe_model_name)
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# toy_text_iterator = ("a" for _ in range(1000))
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# new_tokenizer = tokenizer.train_new_from_iterator(text_iterator=toy_text_iterator, length=1000, vocab_size=50)
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# encoding_ids = new_tokenizer.encode("a🤗")
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# self.assertEqual(encoding_ids, [64, 172, 253, 97, 245])
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# def test_init_from_tokenizers_model(self):
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# from tokenizers import Tokenizer
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# sentences = ["Hello, y'all!", "How are you 😁 ? There should not be any issue right?"]
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# tokenizer = Tokenizer.from_pretrained("google-t5/t5-base")
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# # Enable padding
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# tokenizer.enable_padding(pad_id=0, pad_token="<pad>", length=512, pad_to_multiple_of=8)
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# self.assertEqual(
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# tokenizer.padding,
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# {
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# "length": 512,
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# "pad_to_multiple_of": 8,
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# "pad_id": 0,
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# "pad_token": "<pad>",
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# "pad_type_id": 0,
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# "direction": "right",
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# },
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# )
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# fast_tokenizer = PretrainedTokenizerFast(tokenizer_object=tokenizer)
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# tmpdirname = tempfile.mkdtemp()
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# fast_tokenizer.save_pretrained(tmpdirname)
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# fast_from_saved = PretrainedTokenizerFast.from_pretrained(tmpdirname)
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# for tok in [fast_tokenizer, fast_from_saved]:
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# self.assertEqual(tok.pad_token_id, 0)
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# self.assertEqual(tok.padding_side, "right")
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# self.assertEqual(tok.pad_token, "<pad>")
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# self.assertEqual(tok.init_kwargs["max_length"], 512)
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# self.assertEqual(tok.init_kwargs["pad_to_multiple_of"], 8)
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# self.assertEqual(tok(sentences, padding = True), {'input_ids': [[8774, 6, 3, 63, 31, 1748, 55, 1, 0, 0, 0, 0,0, 0, 0, 0],[ 571, 33, 25, 3, 2, 3, 58, 290, 225, 59, 36, 136, 962, 269, 58, 1]], '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]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}) # fmt: skip
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# tokenizer.enable_truncation(8, stride=0, strategy="longest_first", direction="right")
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# self.assertEqual(
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# tokenizer.truncation, {"max_length": 8, "stride": 0, "strategy": "longest_first", "direction": "right"}
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# )
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# fast_tokenizer = PretrainedTokenizerFast(tokenizer_object=tokenizer)
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# tmpdirname = tempfile.mkdtemp()
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# fast_tokenizer.save_pretrained(tmpdirname)
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# fast_from_saved = PretrainedTokenizerFast.from_pretrained(tmpdirname)
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# for tok in [fast_tokenizer, fast_from_saved]:
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# self.assertEqual(tok.truncation_side, "right")
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# self.assertEqual(tok.init_kwargs["truncation_strategy"], "longest_first")
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# self.assertEqual(tok.init_kwargs["max_length"], 8)
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# self.assertEqual(tok.init_kwargs["stride"], 0)
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# # NOTE even if the model has a default max_length, it is not used...
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# # thus tok(sentences, truncation = True) does nothing and does not warn either
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# self.assertEqual(tok(sentences, truncation = True, max_length = 8), {'input_ids': [[8774, 6, 3, 63, 31, 1748, 55, 1],[ 571, 33, 25, 3, 2, 3, 58, 1]], 'token_type_ids': [[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]]}) # fmt: skip
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