131 lines
4.5 KiB
Python
131 lines
4.5 KiB
Python
# coding=utf-8
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# Copyright 2018 The Google AI Language Team Authors.
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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 transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
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from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
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from transformers.testing_utils import require_tokenizers
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from .test_tokenization_common import TokenizerTesterMixin
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@require_tokenizers
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class OpenAIGPTTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
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tokenizer_class = OpenAIGPTTokenizer
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rust_tokenizer_class = OpenAIGPTTokenizerFast
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test_rust_tokenizer = True
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test_seq2seq = False
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def setUp(self):
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super().setUp()
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# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
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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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"r",
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"s",
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"t",
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"i",
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"d",
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"n",
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"w</w>",
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"r</w>",
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"t</w>",
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"lo",
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"low",
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"er</w>",
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"low</w>",
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"lowest</w>",
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"newer</w>",
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"wider</w>",
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"<unk>",
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]
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vocab_tokens = dict(zip(vocab, range(len(vocab))))
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merges = ["#version: 0.2", "l o", "lo w", "e r</w>", ""]
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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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with open(self.vocab_file, "w") as fp:
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fp.write(json.dumps(vocab_tokens))
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with open(self.merges_file, "w") as fp:
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fp.write("\n".join(merges))
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def get_input_output_texts(self, tokenizer):
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return "lower newer", "lower newer"
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def test_full_tokenizer(self):
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tokenizer = OpenAIGPTTokenizer(self.vocab_file, self.merges_file)
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text = "lower"
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bpe_tokens = ["low", "er</w>"]
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tokens = tokenizer.tokenize(text)
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self.assertListEqual(tokens, bpe_tokens)
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input_tokens = tokens + ["<unk>"]
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input_bpe_tokens = [14, 15, 20]
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self.assertListEqual(tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens)
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def test_padding(self, max_length=15):
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for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
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with self.subTest("{} ({})".format(tokenizer.__class__.__name__, pretrained_name)):
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tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
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# Simple input
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s = "This is a simple input"
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s2 = ["This is a simple input 1", "This is a simple input 2"]
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p = ("This is a simple input", "This is a pair")
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p2 = [
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("This is a simple input 1", "This is a simple input 2"),
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("This is a simple pair 1", "This is a simple pair 2"),
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]
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# Simple input tests
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self.assertRaises(ValueError, tokenizer_r.encode, s, max_length=max_length, padding="max_length")
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# Simple input
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self.assertRaises(ValueError, tokenizer_r.encode_plus, s, max_length=max_length, padding="max_length")
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# Simple input
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self.assertRaises(
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ValueError,
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tokenizer_r.batch_encode_plus,
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s2,
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max_length=max_length,
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padding="max_length",
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)
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# Pair input
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self.assertRaises(ValueError, tokenizer_r.encode, p, max_length=max_length, padding="max_length")
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# Pair input
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self.assertRaises(ValueError, tokenizer_r.encode_plus, p, max_length=max_length, padding="max_length")
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# Pair input
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self.assertRaises(
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ValueError,
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tokenizer_r.batch_encode_plus,
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p2,
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max_length=max_length,
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padding="max_length",
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)
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