168 lines
6.3 KiB
Python
168 lines
6.3 KiB
Python
# coding=utf-8
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# Copyright 2018 The Google AI Language Team Authors.
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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 json
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import os
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import unittest
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from transformers.file_utils import cached_property
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from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES, FSMTTokenizer
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from transformers.testing_utils import slow
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from .test_tokenization_common import TokenizerTesterMixin
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# using a different tiny model than the one used for default params defined in init to ensure proper testing
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FSMT_TINY2 = "stas/tiny-wmt19-en-ru"
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class FSMTTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
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tokenizer_class = FSMTTokenizer
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def setUp(self):
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super().setUp()
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# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
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vocab = [
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"l",
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"o",
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"w",
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"e",
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"r",
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"s",
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"t",
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"i",
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"d",
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"n",
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"w</w>",
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"r</w>",
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"t</w>",
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"lo",
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"low",
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"er</w>",
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"low</w>",
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"lowest</w>",
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"newer</w>",
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"wider</w>",
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"<unk>",
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]
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vocab_tokens = dict(zip(vocab, range(len(vocab))))
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merges = ["l o 123", "lo w 1456", "e r</w> 1789", ""]
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self.langs = ["en", "ru"]
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config = {
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"langs": self.langs,
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"src_vocab_size": 10,
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"tgt_vocab_size": 20,
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}
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self.src_vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["src_vocab_file"])
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self.tgt_vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["tgt_vocab_file"])
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config_file = os.path.join(self.tmpdirname, "tokenizer_config.json")
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self.merges_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["merges_file"])
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with open(self.src_vocab_file, "w") as fp:
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fp.write(json.dumps(vocab_tokens))
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with open(self.tgt_vocab_file, "w") as fp:
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fp.write(json.dumps(vocab_tokens))
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with open(self.merges_file, "w") as fp:
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fp.write("\n".join(merges))
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with open(config_file, "w") as fp:
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fp.write(json.dumps(config))
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@cached_property
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def tokenizer_ru_en(self):
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return FSMTTokenizer.from_pretrained("facebook/wmt19-ru-en")
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@cached_property
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def tokenizer_en_ru(self):
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return FSMTTokenizer.from_pretrained("facebook/wmt19-en-ru")
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def test_online_tokenizer_config(self):
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"""this just tests that the online tokenizer files get correctly fetched and
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loaded via its tokenizer_config.json and it's not slow so it's run by normal CI
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"""
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tokenizer = FSMTTokenizer.from_pretrained(FSMT_TINY2)
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self.assertListEqual([tokenizer.src_lang, tokenizer.tgt_lang], ["en", "ru"])
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self.assertEqual(tokenizer.src_vocab_size, 21)
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self.assertEqual(tokenizer.tgt_vocab_size, 21)
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def test_full_tokenizer(self):
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""" Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt """
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tokenizer = FSMTTokenizer(self.langs, self.src_vocab_file, self.tgt_vocab_file, self.merges_file)
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text = "lower"
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bpe_tokens = ["low", "er</w>"]
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tokens = tokenizer.tokenize(text)
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self.assertListEqual(tokens, bpe_tokens)
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input_tokens = tokens + ["<unk>"]
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input_bpe_tokens = [14, 15, 20]
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self.assertListEqual(tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens)
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@slow
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def test_sequence_builders(self):
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tokenizer = self.tokenizer_ru_en
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text = tokenizer.encode("sequence builders", add_special_tokens=False)
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text_2 = tokenizer.encode("multi-sequence build", add_special_tokens=False)
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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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assert encoded_sentence == text + [2]
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assert encoded_pair == text + [2] + text_2 + [2]
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@slow
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def test_match_encode_decode(self):
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tokenizer_enc = self.tokenizer_en_ru
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tokenizer_dec = self.tokenizer_ru_en
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targets = [
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[
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"Here's a little song I wrote. Don't worry, be happy.",
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[2470, 39, 11, 2349, 7222, 70, 5979, 7, 8450, 1050, 13160, 5, 26, 6445, 7, 2],
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],
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["This is it. No more. I'm done!", [132, 21, 37, 7, 1434, 86, 7, 70, 6476, 1305, 427, 2]],
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]
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# if data needs to be recreated or added, run:
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# import torch
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# model = torch.hub.load("pytorch/fairseq", "transformer.wmt19.en-ru", checkpoint_file="model4.pt", tokenizer="moses", bpe="fastbpe")
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# for src_text, _ in targets: print(f"""[\n"{src_text}",\n {model.encode(src_text).tolist()}\n],""")
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for src_text, tgt_input_ids in targets:
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encoded_ids = tokenizer_enc.encode(src_text, return_tensors=None)
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self.assertListEqual(encoded_ids, tgt_input_ids)
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# and decode backward, using the reversed languages model
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decoded_text = tokenizer_dec.decode(encoded_ids, skip_special_tokens=True)
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self.assertEqual(decoded_text, src_text)
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@slow
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def test_tokenizer_lower(self):
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tokenizer = FSMTTokenizer.from_pretrained("facebook/wmt19-ru-en", do_lower_case=True)
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tokens = tokenizer.tokenize("USA is United States of America")
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expected = ["us", "a</w>", "is</w>", "un", "i", "ted</w>", "st", "ates</w>", "of</w>", "am", "er", "ica</w>"]
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self.assertListEqual(tokens, expected)
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@unittest.skip("FSMTConfig.__init__ requires non-optional args")
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def test_torch_encode_plus_sent_to_model(self):
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pass
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@unittest.skip("FSMTConfig.__init__ requires non-optional args")
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def test_np_encode_plus_sent_to_model(self):
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pass
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