chore: import upstream snapshot with attribution

tests/models/bartpho/test_tokenization_bartpho.py

@@ -0,0 +1,78 @@
+# Copyright 2021 HuggingFace Inc. team.
+# 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.
+
+
+import os
+import tempfile
+import unittest
+
+from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
+from transformers.testing_utils import get_tests_dir
+
+from ...test_tokenization_common import TokenizerTesterMixin
+
+
+SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece_bpe.model")
+
+
+class BartphoTokenizerTest(TokenizerTesterMixin, unittest.TestCase):
+    from_pretrained_id = "vinai/bartpho-syllable"
+    tokenizer_class = BartphoTokenizer
+    test_rust_tokenizer = False
+    test_sentencepiece = True
+
+    @classmethod
+    def setUpClass(cls):
+        super().setUpClass()
+        cls.special_tokens_map = {"unk_token": "<unk>"}
+
+    @classmethod
+    def get_tokenizer(cls, pretrained_name=None, **kwargs):
+        """Create a fresh tokenizer for each test instead of loading from saved."""
+        kwargs.update(cls.special_tokens_map)
+
+        # Create a temporary directory for this tokenizer
+        tmpdir = tempfile.mkdtemp()
+        vocab = ["▁This", "▁is", "▁a", "▁t", "est"]
+        vocab_tokens = dict(zip(vocab, range(len(vocab))))
+
+        monolingual_vocab_file = os.path.join(tmpdir, VOCAB_FILES_NAMES["monolingual_vocab_file"])
+        with open(monolingual_vocab_file, "w", encoding="utf-8") as fp:
+            fp.writelines(f"{token} {vocab_tokens[token]}\n" for token in vocab_tokens)
+
+        return BartphoTokenizer(SAMPLE_VOCAB, monolingual_vocab_file, **kwargs)
+
+    def get_input_output_texts(self, tokenizer):
+        input_text = "This is a là test"
+        output_text = "This is a<unk><unk> test"
+        return input_text, output_text
+
+    def test_full_tokenizer(self):
+        vocab = ["▁This", "▁is", "▁a", "▁t", "est"]
+        vocab_tokens = dict(zip(vocab, range(len(vocab))))
+        special_tokens_map = {"unk_token": "<unk>"}
+
+        with tempfile.TemporaryDirectory() as tmpdir:
+            monolingual_vocab_file = os.path.join(tmpdir, VOCAB_FILES_NAMES["monolingual_vocab_file"])
+            with open(monolingual_vocab_file, "w", encoding="utf-8") as fp:
+                fp.writelines(f"{token} {vocab_tokens[token]}\n" for token in vocab_tokens)
+            tokenizer = BartphoTokenizer(SAMPLE_VOCAB, monolingual_vocab_file, **special_tokens_map)
+
+            text = "This is a là test"
+            bpe_tokens = "▁This ▁is ▁a ▁l à ▁t est".split()
+            tokens = tokenizer.tokenize(text)
+            self.assertListEqual(tokens, bpe_tokens)
+
+            input_tokens = tokens + [tokenizer.unk_token]
+            input_bpe_tokens = [4, 5, 6, 3, 3, 7, 8, 3]
+            self.assertListEqual(tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens)