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tests/models/flaubert/test_tokenization_flaubert.py

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+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# 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.
+"""Testing suite for the FlauBERT tokenizer."""
+
+import json
+import os
+import tempfile
+import unittest
+
+from transformers import FlaubertTokenizer
+from transformers.models.flaubert.tokenization_flaubert import VOCAB_FILES_NAMES
+from transformers.testing_utils import slow
+
+from ...test_tokenization_common import TokenizerTesterMixin
+
+
+class FlaubertTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
+    from_pretrained_id = "flaubert/flaubert_base_cased"
+    tokenizer_class = FlaubertTokenizer
+    test_rust_tokenizer = False
+
+    # Copied from transformers.tests.models.xlm.test_tokenization_xlm.XLMTokenizationTest.test_full_tokenizer
+    def test_full_tokenizer(self):
+        """Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt"""
+
+        vocab = ["l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "w</w>", "r</w>", "t</w>", "i</w>", "lo", "low", "ne", "new", "er</w>", "low</w>", "lowest</w>", "new</w>", "newer</w>", "wider</w>", "<unk>"]  # fmt: skip
+
+        vocab_tokens = dict(zip(vocab, range(len(vocab))))
+        merges = ["n e 300", "ne w 301", "e r</w> 302", ""]
+
+        with tempfile.TemporaryDirectory() as tmpdir:
+            vocab_file = os.path.join(tmpdir, VOCAB_FILES_NAMES["vocab_file"])
+            merges_file = os.path.join(tmpdir, VOCAB_FILES_NAMES["merges_file"])
+            with open(vocab_file, "w", encoding="utf-8") as fp:
+                fp.write(json.dumps(vocab_tokens) + "\n")
+            with open(merges_file, "w", encoding="utf-8") as fp:
+                fp.write("\n".join(merges))
+            tokenizer = FlaubertTokenizer(vocab_file, merges_file)
+
+        text = "lower newer"
+        bpe_tokens = ["l", "o", "w", "er</w>", "new", "er</w>"]
+        tokens = tokenizer.tokenize(text)
+        self.assertListEqual(tokens, bpe_tokens)
+
+        input_tokens = tokens + [tokenizer.unk_token]
+        input_bpe_tokens = [0, 1, 2, 18, 17, 18, 24]
+        self.assertListEqual(tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens)
+
+    @slow
+    # Copied from transformers.tests.models.xlm.test_tokenization_xlm.XLMTokenizationTest.test_sequence_builders
+    def test_sequence_builders(self):
+        tokenizer = FlaubertTokenizer.from_pretrained("flaubert/flaubert_base_cased")
+
+        text = tokenizer.encode("sequence builders", add_special_tokens=False)
+        text_2 = tokenizer.encode("multi-sequence build", add_special_tokens=False)
+
+        encoded_sentence = tokenizer.build_inputs_with_special_tokens(text)
+        encoded_pair = tokenizer.build_inputs_with_special_tokens(text, text_2)
+        print(encoded_sentence)
+        print(encoded_sentence)
+
+        assert encoded_sentence == [0] + text + [1]
+        assert encoded_pair == [0] + text + [1] + text_2 + [1]