transformers/tests/models/blenderbot_small/test_tokenization_blenderbot_small.py

#!/usr/bin/env python3
# Copyright 2020 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
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"""Tests for the Blenderbot small tokenizer."""

import json
import os
import shutil
import tempfile
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin


class BlenderbotSmallTokenizerTest(TokenizerTesterMixin, unittest.TestCase):
    from_pretrained_id = "facebook/blenderbot_small-90M"
    tokenizer_class = BlenderbotSmallTokenizer
    test_rust_tokenizer = False

    def test_full_blenderbot_small_tokenizer(self):
        # Create temporary directory for vocab files
        tmpdirname = tempfile.mkdtemp()
        try:
            vocab = ["__start__", "adapt", "act", "ap@@", "te", "__end__", "__unk__"]
            vocab_tokens = dict(zip(vocab, range(len(vocab))))

            merges = ["#version: 0.2", "a p", "t e</w>", "ap t</w>", "a d", "ad apt</w>", "a c", "ac t</w>", ""]
            special_tokens_map = {"unk_token": "__unk__", "bos_token": "__start__", "eos_token": "__end__"}

            vocab_file = os.path.join(tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
            merges_file = os.path.join(tmpdirname, 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 = BlenderbotSmallTokenizer(vocab_file, merges_file, **special_tokens_map)
            text = "adapt act apte"
            bpe_tokens = ["adapt", "act", "ap@@", "te"]
            tokens = tokenizer.tokenize(text)
            self.assertListEqual(tokens, bpe_tokens)

            input_tokens = [tokenizer.bos_token] + tokens + [tokenizer.eos_token]

            input_bpe_tokens = [0, 1, 2, 3, 4, 5]
            self.assertListEqual(tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens)
        finally:
            shutil.rmtree(tmpdirname)

    def test_special_tokens_small_tok(self):
        tok = BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M")
        assert tok("sam").input_ids == [1384]
        src_text = "I am a small frog."
        encoded = tok([src_text], padding=False, truncation=False)["input_ids"]
        decoded = tok.batch_decode(encoded, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
        assert src_text != decoded  # I wish it did!
        assert decoded == "i am a small frog ."

    def test_empty_word_small_tok(self):
        tok = BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M")
        src_text = "I am a small frog ."
        src_text_dot = "."
        encoded = tok(src_text)["input_ids"]
        encoded_dot = tok(src_text_dot)["input_ids"]

        assert encoded[-1] == encoded_dot[0]