wehub-resource-sync
168 lines
6.8 KiB
Python
168 lines
6.8 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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# Copyright 2020 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import json
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import os
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import unittest
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from paddlenlp.transformers import BlenderbotSmallTokenizer
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from ..test_tokenizer_common import TokenizerTesterMixin
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VOCAB_FILES_NAMES = {
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"vocab_file": "vocab.json",
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"merges_file": "merges.txt",
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}
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class TestTokenizationBlenderbotSmall(TokenizerTesterMixin, unittest.TestCase):
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tokenizer_class = BlenderbotSmallTokenizer
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test_rust_tokenizer = False
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test_offsets = False
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def setUp(self):
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super().setUp()
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vocab = [
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"l@@",
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"o@@",
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"w@@",
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"e@@",
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"s@@",
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"t@@",
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"t",
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"\u0120",
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"\u0120l",
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"\u0120n",
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"\u0120lo",
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"\u0120low",
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"\u0120lowest",
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"__start__",
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"__end__",
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"__unk__",
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"__null__",
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"__newln__",
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".",
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]
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vocab_tokens = dict(zip(vocab, range(len(vocab))))
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merges = ["#version: 0.2", "low er", ""]
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self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
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self.merges_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["merges_file"])
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self.special_tokens_map = {
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"bos_token": "__start__",
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"eos_token": "__end__",
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"unk_token": "__unk__",
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"pad_token": "__null__",
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"eol_token": "__newln__",
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}
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with open(self.vocab_file, "w", encoding="utf-8") as fp:
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fp.write(json.dumps(vocab_tokens) + "\n")
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with open(self.merges_file, "w", encoding="utf-8") as fp:
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fp.write("\n".join(merges))
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def get_tokenizer(self, **kwargs):
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kwargs.update(self.special_tokens_map)
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return self.tokenizer_class.from_pretrained(self.tmpdirname, **kwargs)
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def test_add_tokens_tokenizer(self):
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tokenizers = self.get_tokenizers(do_lower_case=False)
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for tokenizer in tokenizers:
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with self.subTest(f"{tokenizer.__class__.__name__}"):
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vocab_size = tokenizer.vocab_size
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all_size = len(tokenizer)
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self.assertNotEqual(vocab_size, 0)
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# We usually have added tokens from the start in tests because our vocab fixtures are
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# smaller than the original vocabs - let's not assert this
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# self.assertEqual(vocab_size, all_size)
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new_toks = ["aaaaa bbbbbb", "cccccccccdddddddd"]
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added_toks = tokenizer.add_tokens(new_toks)
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vocab_size_2 = tokenizer.vocab_size
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all_size_2 = len(tokenizer)
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self.assertNotEqual(vocab_size_2, 0)
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self.assertEqual(vocab_size, vocab_size_2)
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self.assertEqual(added_toks, len(new_toks))
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self.assertEqual(all_size_2, all_size + len(new_toks))
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tokens = tokenizer.encode(
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"aaaaa bbbbbb low cccccccccdddddddd l", return_token_type_ids=None, add_special_tokens=False
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)["input_ids"]
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self.assertGreaterEqual(len(tokens), 4)
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self.assertGreater(tokens[0], tokenizer.vocab_size - 1)
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self.assertGreater(tokens[-2], tokenizer.vocab_size - 1)
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new_toks_2 = {"eos_token": ">>>>|||<||<<|<<", "pad_token": "<<<<<|||>|>>>>|>"}
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added_toks_2 = tokenizer.add_special_tokens(new_toks_2)
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vocab_size_3 = tokenizer.vocab_size
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all_size_3 = len(tokenizer)
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self.assertNotEqual(vocab_size_3, 0)
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self.assertEqual(vocab_size, vocab_size_3)
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self.assertEqual(added_toks_2, len(new_toks_2))
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self.assertEqual(all_size_3, all_size_2 + len(new_toks_2))
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tokens = tokenizer.encode(
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">>>>|||<||<<|<< aaaaa bbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l",
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return_token_type_ids=None,
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add_special_tokens=False,
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)["input_ids"]
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self.assertGreaterEqual(len(tokens), 6)
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self.assertGreater(tokens[0], tokenizer.vocab_size - 1)
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self.assertGreater(tokens[0], tokens[1])
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self.assertGreater(tokens[-2], tokenizer.vocab_size - 1)
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self.assertGreater(tokens[-2], tokens[-3])
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self.assertEqual(tokens[0], tokenizer.eos_token_id)
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self.assertEqual(tokens[-2], tokenizer.pad_token_id)
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def test_consecutive_unk_string(self):
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tokenizers = self.get_tokenizers(fast=True, do_lower_case=True)
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for tokenizer in tokenizers:
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tokens = [tokenizer.unk_token for _ in range(2)]
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string = tokenizer.convert_tokens_to_string(tokens)
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encoding = tokenizer(
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text=string,
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runcation=True,
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return_offsets_mapping=True,
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)
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self.assertEqual(len(encoding["input_ids"]), 2)
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self.assertEqual(len(encoding["offset_mapping"]), 2)
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def test_special_tokens_mask_input_pairs(self):
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tokenizers = self.get_tokenizers(do_lower_case=False)
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for tokenizer in tokenizers:
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with self.subTest(f"{tokenizer.__class__.__name__}"):
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sequence_0 = "Encode this."
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sequence_1 = "This one too please."
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encoded_sequence = tokenizer.encode(sequence_0, return_token_type_ids=None, add_special_tokens=False)[
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"input_ids"
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]
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encoded_sequence += tokenizer.encode(sequence_1, return_token_type_ids=None, add_special_tokens=False)[
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"input_ids"
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]
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encoded_sequence_dict = tokenizer.encode(
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sequence_0,
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sequence_1,
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add_special_tokens=True,
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return_special_tokens_mask=True,
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# add_prefix_space=False,
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)
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encoded_sequence_w_special = encoded_sequence_dict["input_ids"]
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special_tokens_mask = encoded_sequence_dict["special_tokens_mask"]
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self.assertEqual(len(special_tokens_mask), len(encoded_sequence_w_special))
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