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transformers/tests/models/funnel/test_tokenization_funnel.py

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+# Copyright 2020 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 unittest
+
+from transformers import FunnelTokenizer, FunnelTokenizerFast
+from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
+from transformers.testing_utils import require_tokenizers
+
+from ...test_tokenization_common import TokenizerTesterMixin
+
+
+@require_tokenizers
+class FunnelTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
+    from_pretrained_id = "funnel-transformer/small"
+    tokenizer_class = FunnelTokenizer
+    rust_tokenizer_class = FunnelTokenizerFast
+    test_rust_tokenizer = True
+    space_between_special_tokens = True
+
+    @classmethod
+    def setUpClass(cls):
+        super().setUpClass()
+
+        vocab_tokens = [
+            "<unk>",
+            "<cls>",
+            "<sep>",
+            "want",
+            "##want",
+            "##ed",
+            "wa",
+            "un",
+            "runn",
+            "##ing",
+            ",",
+            "low",
+            "lowest",
+        ]
+        cls.vocab_file = os.path.join(cls.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
+        with open(cls.vocab_file, "w", encoding="utf-8") as vocab_writer:
+            vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))
+
+    @classmethod
+    def get_tokenizer(cls, pretrained_name=None, **kwargs):
+        pretrained_name = pretrained_name or cls.tmpdirname
+        return FunnelTokenizer.from_pretrained(pretrained_name, **kwargs)
+
+    @classmethod
+    def get_rust_tokenizer(cls, pretrained_name=None, **kwargs):
+        pretrained_name = pretrained_name or cls.tmpdirname
+        return FunnelTokenizerFast.from_pretrained(pretrained_name, **kwargs)
+
+    def get_input_output_texts(self, tokenizer):
+        input_text = "UNwant\u00e9d,running"
+        output_text = "unwanted, running"
+        return input_text, output_text
+
+    def test_full_tokenizer(self):
+        tokenizer = self.tokenizer_class(self.vocab_file)
+
+        tokens = tokenizer.tokenize("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_token_type_ids(self):
+        tokenizers = self.get_tokenizers(do_lower_case=False)
+        for tokenizer in tokenizers:
+            inputs = tokenizer("UNwant\u00e9d,running")
+            sentence_len = len(inputs["input_ids"]) - 1
+            self.assertListEqual(inputs["token_type_ids"], [2] + [0] * sentence_len)
+
+            inputs = tokenizer("UNwant\u00e9d,running", "UNwant\u00e9d,running")
+            self.assertListEqual(inputs["token_type_ids"], [2] + [0] * sentence_len + [1] * sentence_len)