[BIG] pytorch-transformers => transformers

transformers/tests/tokenization_bert_test.py

@@ -0,0 +1,141 @@
+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import os
+import unittest
+from io import open
+
+from transformers.tokenization_bert import (BasicTokenizer,
+                                                    BertTokenizer,
+                                                    WordpieceTokenizer,
+                                                    _is_control, _is_punctuation,
+                                                    _is_whitespace, VOCAB_FILES_NAMES)
+
+from .tokenization_tests_commons import CommonTestCases
+
+class BertTokenizationTest(CommonTestCases.CommonTokenizerTester):
+
+    tokenizer_class = BertTokenizer
+
+    def setUp(self):
+        super(BertTokenizationTest, self).setUp()
+
+        vocab_tokens = [
+            "[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn",
+            "##ing", ",", "low", "lowest",
+        ]
+        self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES['vocab_file'])
+        with open(self.vocab_file, "w", encoding='utf-8') as vocab_writer:
+            vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))
+
+    def get_tokenizer(self, **kwargs):
+        return BertTokenizer.from_pretrained(self.tmpdirname, **kwargs)
+
+    def get_input_output_texts(self):
+        input_text = u"UNwant\u00E9d,running"
+        output_text = u"unwanted, running"
+        return input_text, output_text
+
+    def test_full_tokenizer(self):
+        tokenizer = self.tokenizer_class(self.vocab_file)
+
+        tokens = tokenizer.tokenize(u"UNwant\u00E9d,running")
+        self.assertListEqual(tokens, ["un", "##want", "##ed", ",", "runn", "##ing"])
+        self.assertListEqual(tokenizer.convert_tokens_to_ids(tokens), [7, 4, 5, 10, 8, 9])
+
+    def test_chinese(self):
+        tokenizer = BasicTokenizer()
+
+        self.assertListEqual(
+            tokenizer.tokenize(u"ah\u535A\u63A8zz"),
+            [u"ah", u"\u535A", u"\u63A8", u"zz"])
+
+    def test_basic_tokenizer_lower(self):
+        tokenizer = BasicTokenizer(do_lower_case=True)
+
+        self.assertListEqual(
+            tokenizer.tokenize(u" \tHeLLo!how  \n Are yoU?  "),
+            ["hello", "!", "how", "are", "you", "?"])
+        self.assertListEqual(tokenizer.tokenize(u"H\u00E9llo"), ["hello"])
+
+    def test_basic_tokenizer_no_lower(self):
+        tokenizer = BasicTokenizer(do_lower_case=False)
+
+        self.assertListEqual(
+            tokenizer.tokenize(u" \tHeLLo!how  \n Are yoU?  "),
+            ["HeLLo", "!", "how", "Are", "yoU", "?"])
+
+    def test_wordpiece_tokenizer(self):
+        vocab_tokens = [
+            "[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn",
+            "##ing"
+        ]
+
+        vocab = {}
+        for (i, token) in enumerate(vocab_tokens):
+            vocab[token] = i
+        tokenizer = WordpieceTokenizer(vocab=vocab, unk_token="[UNK]")
+
+        self.assertListEqual(tokenizer.tokenize(""), [])
+
+        self.assertListEqual(
+            tokenizer.tokenize("unwanted running"),
+            ["un", "##want", "##ed", "runn", "##ing"])
+
+        self.assertListEqual(
+            tokenizer.tokenize("unwantedX running"), ["[UNK]", "runn", "##ing"])
+
+    def test_is_whitespace(self):
+        self.assertTrue(_is_whitespace(u" "))
+        self.assertTrue(_is_whitespace(u"\t"))
+        self.assertTrue(_is_whitespace(u"\r"))
+        self.assertTrue(_is_whitespace(u"\n"))
+        self.assertTrue(_is_whitespace(u"\u00A0"))
+
+        self.assertFalse(_is_whitespace(u"A"))
+        self.assertFalse(_is_whitespace(u"-"))
+
+    def test_is_control(self):
+        self.assertTrue(_is_control(u"\u0005"))
+
+        self.assertFalse(_is_control(u"A"))
+        self.assertFalse(_is_control(u" "))
+        self.assertFalse(_is_control(u"\t"))
+        self.assertFalse(_is_control(u"\r"))
+
+    def test_is_punctuation(self):
+        self.assertTrue(_is_punctuation(u"-"))
+        self.assertTrue(_is_punctuation(u"$"))
+        self.assertTrue(_is_punctuation(u"`"))
+        self.assertTrue(_is_punctuation(u"."))
+
+        self.assertFalse(_is_punctuation(u"A"))
+        self.assertFalse(_is_punctuation(u" "))
+
+    def test_sequence_builders(self):
+        tokenizer = self.tokenizer_class.from_pretrained("bert-base-uncased")
+
+        text = tokenizer.encode("sequence builders")
+        text_2 = tokenizer.encode("multi-sequence build")
+
+        encoded_sentence = tokenizer.add_special_tokens_single_sequence(text)
+        encoded_pair = tokenizer.add_special_tokens_sequence_pair(text, text_2)
+
+        assert encoded_sentence == [101] + text + [102]
+        assert encoded_pair == [101] + text + [102] + text_2 + [102]
+
+if __name__ == '__main__':
+    unittest.main()