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36434220fc
* Use tokenizers pre-tokenized pipeline * failing pretrokenized test * Fix is_pretokenized in python * add pretokenized tests * style and quality * better tests for batched pretokenized inputs * tokenizers clean up - new padding_strategy - split the files * [HUGE] refactoring tokenizers - padding - truncation - tests * style and quality * bump up requied tokenizers version to 0.8.0-rc1 * switched padding/truncation API - simpler better backward compat * updating tests for custom tokenizers * style and quality - tests on pad * fix QA pipeline * fix backward compatibility for max_length only * style and quality * Various cleans up - add verbose * fix tests * update docstrings * Fix tests * Docs reformatted * __call__ method documented Co-authored-by: Thomas Wolf <thomwolf@users.noreply.github.com> Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>
127 lines
4.6 KiB
Python
127 lines
4.6 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.tokenization_gpt2 import VOCAB_FILES_NAMES, GPT2Tokenizer, GPT2TokenizerFast
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from .test_tokenization_common import TokenizerTesterMixin
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class GPT2TokenizationTest(TokenizerTesterMixin, unittest.TestCase):
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tokenizer_class = GPT2Tokenizer
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test_rust_tokenizer = True
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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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"\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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"er",
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"\u0120lowest",
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"\u0120newer",
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"\u0120wider",
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"<unk>",
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"<|endoftext|>",
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]
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vocab_tokens = dict(zip(vocab, range(len(vocab))))
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merges = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
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self.special_tokens_map = {"unk_token": "<unk>"}
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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", 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 GPT2Tokenizer.from_pretrained(self.tmpdirname, **kwargs)
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def get_rust_tokenizer(self, **kwargs):
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kwargs.update(self.special_tokens_map)
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return GPT2TokenizerFast.from_pretrained(self.tmpdirname, **kwargs)
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def get_input_output_texts(self, tokenizer):
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input_text = "lower newer"
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output_text = "lower newer"
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return input_text, output_text
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def test_full_tokenizer(self):
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tokenizer = GPT2Tokenizer(self.vocab_file, self.merges_file, **self.special_tokens_map)
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text = "lower newer"
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bpe_tokens = ["\u0120low", "er", "\u0120", "n", "e", "w", "er"]
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tokens = tokenizer.tokenize(text, add_prefix_space=True)
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self.assertListEqual(tokens, bpe_tokens)
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input_tokens = tokens + [tokenizer.unk_token]
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input_bpe_tokens = [14, 15, 10, 9, 3, 2, 15, 19]
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self.assertListEqual(tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens)
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def test_rust_and_python_full_tokenizers(self):
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if not self.test_rust_tokenizer:
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return
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tokenizer = self.get_tokenizer()
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rust_tokenizer = self.get_rust_tokenizer(add_prefix_space=True)
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sequence = "lower newer"
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# Testing tokenization
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tokens = tokenizer.tokenize(sequence, add_prefix_space=True)
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rust_tokens = rust_tokenizer.tokenize(sequence)
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self.assertListEqual(tokens, rust_tokens)
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# Testing conversion to ids without special tokens
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ids = tokenizer.encode(sequence, add_special_tokens=False, add_prefix_space=True)
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rust_ids = rust_tokenizer.encode(sequence, add_special_tokens=False)
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self.assertListEqual(ids, rust_ids)
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# Testing conversion to ids with special tokens
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rust_tokenizer = self.get_rust_tokenizer(add_prefix_space=True)
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ids = tokenizer.encode(sequence, add_prefix_space=True)
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rust_ids = rust_tokenizer.encode(sequence)
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self.assertListEqual(ids, rust_ids)
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# Testing the unknown token
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input_tokens = tokens + [rust_tokenizer.unk_token]
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input_bpe_tokens = [14, 15, 10, 9, 3, 2, 15, 19]
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self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens)
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def test_pretokenized_inputs(self, *args, **kwargs):
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# It's very difficult to mix/test pretokenization with byte-level
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# And get both GPT2 and Roberta to work at the same time (mostly an issue of adding a space before the string)
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pass
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