mirror of
https://github.com/huggingface/transformers.git
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251 lines
9.8 KiB
Python
251 lines
9.8 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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from __future__ import absolute_import, division, print_function, unicode_literals
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import os
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import sys
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from io import open
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import tempfile
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import shutil
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import unittest
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if sys.version_info[0] == 2:
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import cPickle as pickle
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class TemporaryDirectory(object):
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"""Context manager for tempfile.mkdtemp() so it's usable with "with" statement."""
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def __enter__(self):
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self.name = tempfile.mkdtemp()
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return self.name
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def __exit__(self, exc_type, exc_value, traceback):
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shutil.rmtree(self.name)
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else:
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import pickle
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TemporaryDirectory = tempfile.TemporaryDirectory
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unicode = str
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class CommonTestCases:
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class CommonTokenizerTester(unittest.TestCase):
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tokenizer_class = None
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def setUp(self):
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self.tmpdirname = tempfile.mkdtemp()
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def tearDown(self):
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shutil.rmtree(self.tmpdirname)
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def get_tokenizer(self, **kwargs):
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raise NotImplementedError
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def get_input_output_texts(self):
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raise NotImplementedError
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def test_tokenizers_common_properties(self):
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tokenizer = self.get_tokenizer()
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attributes_list = ["bos_token", "eos_token", "unk_token", "sep_token",
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"pad_token", "cls_token", "mask_token"]
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for attr in attributes_list:
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self.assertTrue(hasattr(tokenizer, attr))
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self.assertTrue(hasattr(tokenizer, attr + "_id"))
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self.assertTrue(hasattr(tokenizer, "additional_special_tokens"))
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self.assertTrue(hasattr(tokenizer, 'additional_special_tokens_ids'))
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attributes_list = ["max_len", "init_inputs", "init_kwargs", "added_tokens_encoder",
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"added_tokens_decoder"]
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for attr in attributes_list:
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self.assertTrue(hasattr(tokenizer, attr))
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def test_save_and_load_tokenizer(self):
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# safety check on max_len default value so we are sure the test works
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tokenizer = self.get_tokenizer()
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self.assertNotEqual(tokenizer.max_len, 42)
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# Now let's start the test
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tokenizer = self.get_tokenizer(max_len=42)
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before_tokens = tokenizer.encode(u"He is very happy, UNwant\u00E9d,running")
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with TemporaryDirectory() as tmpdirname:
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tokenizer.save_pretrained(tmpdirname)
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tokenizer = self.tokenizer_class.from_pretrained(tmpdirname)
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after_tokens = tokenizer.encode(u"He is very happy, UNwant\u00E9d,running")
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self.assertListEqual(before_tokens, after_tokens)
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self.assertEqual(tokenizer.max_len, 42)
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tokenizer = self.tokenizer_class.from_pretrained(tmpdirname, max_len=43)
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self.assertEqual(tokenizer.max_len, 43)
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def test_pickle_tokenizer(self):
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tokenizer = self.get_tokenizer()
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self.assertIsNotNone(tokenizer)
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text = u"Munich and Berlin are nice cities"
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subwords = tokenizer.tokenize(text)
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with TemporaryDirectory() as tmpdirname:
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filename = os.path.join(tmpdirname, u"tokenizer.bin")
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pickle.dump(tokenizer, open(filename, "wb"))
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tokenizer_new = pickle.load(open(filename, "rb"))
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subwords_loaded = tokenizer_new.tokenize(text)
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self.assertListEqual(subwords, subwords_loaded)
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def test_add_tokens_tokenizer(self):
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tokenizer = self.get_tokenizer()
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vocab_size = tokenizer.vocab_size
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all_size = len(tokenizer)
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self.assertNotEqual(vocab_size, 0)
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self.assertEqual(vocab_size, all_size)
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new_toks = ["aaaaa bbbbbb", "cccccccccdddddddd"]
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added_toks = tokenizer.add_tokens(new_toks)
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vocab_size_2 = tokenizer.vocab_size
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all_size_2 = len(tokenizer)
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self.assertNotEqual(vocab_size_2, 0)
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self.assertEqual(vocab_size, vocab_size_2)
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self.assertEqual(added_toks, len(new_toks))
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self.assertEqual(all_size_2, all_size + len(new_toks))
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tokens = tokenizer.encode("aaaaa bbbbbb low cccccccccdddddddd l")
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out_string = tokenizer.decode(tokens)
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self.assertGreaterEqual(len(tokens), 4)
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self.assertGreater(tokens[0], tokenizer.vocab_size - 1)
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self.assertGreater(tokens[-2], tokenizer.vocab_size - 1)
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new_toks_2 = {'eos_token': ">>>>|||<||<<|<<",
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'pad_token': "<<<<<|||>|>>>>|>"}
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added_toks_2 = tokenizer.add_special_tokens(new_toks_2)
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vocab_size_3 = tokenizer.vocab_size
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all_size_3 = len(tokenizer)
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self.assertNotEqual(vocab_size_3, 0)
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self.assertEqual(vocab_size, vocab_size_3)
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self.assertEqual(added_toks_2, len(new_toks_2))
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self.assertEqual(all_size_3, all_size_2 + len(new_toks_2))
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tokens = tokenizer.encode(">>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l")
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out_string = tokenizer.decode(tokens)
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self.assertGreaterEqual(len(tokens), 6)
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self.assertGreater(tokens[0], tokenizer.vocab_size - 1)
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self.assertGreater(tokens[0], tokens[1])
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self.assertGreater(tokens[-2], tokenizer.vocab_size - 1)
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self.assertGreater(tokens[-2], tokens[-3])
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self.assertEqual(tokens[0], tokenizer.eos_token_id)
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self.assertEqual(tokens[-2], tokenizer.pad_token_id)
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def test_required_methods_tokenizer(self):
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tokenizer = self.get_tokenizer()
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input_text, output_text = self.get_input_output_texts()
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tokens = tokenizer.tokenize(input_text)
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ids = tokenizer.convert_tokens_to_ids(tokens)
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ids_2 = tokenizer.encode(input_text)
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self.assertListEqual(ids, ids_2)
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tokens_2 = tokenizer.convert_ids_to_tokens(ids)
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text_2 = tokenizer.decode(ids)
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self.assertEqual(text_2, output_text)
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self.assertNotEqual(len(tokens_2), 0)
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self.assertIsInstance(text_2, (str, unicode))
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def test_pretrained_model_lists(self):
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weights_list = list(self.tokenizer_class.max_model_input_sizes.keys())
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weights_lists_2 = []
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for file_id, map_list in self.tokenizer_class.pretrained_vocab_files_map.items():
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weights_lists_2.append(list(map_list.keys()))
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for weights_list_2 in weights_lists_2:
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self.assertListEqual(weights_list, weights_list_2)
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def test_mask_output(self):
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if sys.version_info <= (3, 0):
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return
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tokenizer = self.get_tokenizer()
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if tokenizer.add_special_tokens_sentences_pair.__qualname__.split('.')[0] != "PreTrainedTokenizer":
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seq_0 = "Test this method."
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seq_1 = "With these inputs."
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information = tokenizer.encode_plus(seq_0, seq_1, add_special_tokens=True, output_mask=True)
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sequences, mask = information["sequence"], information["mask"]
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assert len(sequences) == len(mask)
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def test_number_of_added_tokens(self):
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tokenizer = self.get_tokenizer()
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seq_0 = "Test this method."
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seq_1 = "With these inputs."
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sequences = tokenizer.encode(seq_0, seq_1)
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attached_sequences = tokenizer.encode(seq_0, seq_1, add_special_tokens=True)
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# Method is implemented (e.g. not GPT-2)
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if len(attached_sequences) != 2:
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assert tokenizer.num_added_tokens(pair=True) == len(attached_sequences) - len(sequences)
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def test_maximum_encoding_length_single_input(self):
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tokenizer = self.get_tokenizer()
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seq_0 = "This is a sentence to be encoded."
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sequence = tokenizer.encode(seq_0)
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num_added_tokens = tokenizer.num_added_tokens()
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total_length = len(sequence) + num_added_tokens
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information = tokenizer.encode_plus(seq_0, max_length=total_length - 2, add_special_tokens=True)
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truncated_sequence = information["sequence"]
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overflowing_tokens = information["overflowing_tokens"]
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assert len(overflowing_tokens) == 2
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assert len(truncated_sequence) == total_length - 2
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assert truncated_sequence == tokenizer.add_special_tokens_single_sentence(sequence[:-2])
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def test_maximum_encoding_length_pair_input(self):
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tokenizer = self.get_tokenizer()
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seq_0 = "This is a sentence to be encoded."
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seq_1 = "This is another sentence to be encoded."
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sequence = tokenizer.encode(seq_0, seq_1, add_special_tokens=True)
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truncated_second_sequence = tokenizer.add_special_tokens_sentences_pair(
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tokenizer.encode(seq_0),
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tokenizer.encode(seq_1)[:-2]
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)
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information = tokenizer.encode_plus(seq_0, seq_1, max_length=len(sequence) - 2, add_special_tokens=True)
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truncated_sequence = information["sequence"]
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overflowing_tokens = information["overflowing_tokens"]
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assert len(truncated_sequence) == len(sequence) - 2
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assert truncated_sequence == truncated_second_sequence
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