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| # coding=utf-8 | |
| # Copyright 2018 The Open AI Team Authors and The 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. | |
| """Tokenization classes for OpenAI GPT.""" | |
| from __future__ import (absolute_import, division, print_function, | |
| unicode_literals) | |
| import json | |
| import logging | |
| import os | |
| import re | |
| from io import open | |
| from .tokenization_utils import PreTrainedTokenizer | |
| from .tokenization_bert import BasicTokenizer | |
| logger = logging.getLogger(__name__) | |
| VOCAB_FILES_NAMES = { | |
| 'vocab_file': 'vocab.json', | |
| 'merges_file': 'merges.txt', | |
| } | |
| PRETRAINED_VOCAB_FILES_MAP = { | |
| 'vocab_file': | |
| { | |
| 'openai-gpt': "https://s3.amazonaws.com/models.huggingface.co/bert/openai-gpt-vocab.json", | |
| }, | |
| 'merges_file': | |
| { | |
| 'openai-gpt': "https://s3.amazonaws.com/models.huggingface.co/bert/openai-gpt-merges.txt", | |
| }, | |
| } | |
| PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { | |
| 'openai-gpt': 512, | |
| } | |
| def get_pairs(word): | |
| """ | |
| Return set of symbol pairs in a word. | |
| word is represented as tuple of symbols (symbols being variable-length strings) | |
| """ | |
| pairs = set() | |
| prev_char = word[0] | |
| for char in word[1:]: | |
| pairs.add((prev_char, char)) | |
| prev_char = char | |
| return pairs | |
| def text_standardize(text): | |
| """ | |
| fixes some issues the spacy tokenizer had on books corpus | |
| also does some whitespace standardization | |
| """ | |
| text = text.replace('—', '-') | |
| text = text.replace('–', '-') | |
| text = text.replace('―', '-') | |
| text = text.replace('…', '...') | |
| text = text.replace('´', "'") | |
| text = re.sub(r'''(-+|~+|!+|"+|;+|\?+|\++|,+|\)+|\(+|\\+|\/+|\*+|\[+|\]+|}+|{+|\|+|_+)''', r' \1 ', text) | |
| text = re.sub(r'\s*\n\s*', ' \n ', text) | |
| text = re.sub(r'[^\S\n]+', ' ', text) | |
| return text.strip() | |
| class OpenAIGPTTokenizer(PreTrainedTokenizer): | |
| """ | |
| BPE tokenizer. Peculiarities: | |
| - lower case all inputs | |
| - uses SpaCy tokenizer and ftfy for pre-BPE tokenization if they are installed, fallback to BERT's BasicTokenizer if not. | |
| """ | |
| vocab_files_names = VOCAB_FILES_NAMES | |
| pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP | |
| max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES | |
| def __init__(self, vocab_file, merges_file, unk_token="<unk>", **kwargs): | |
| super(OpenAIGPTTokenizer, self).__init__(unk_token=unk_token, **kwargs) | |
| self.max_len_single_sentence = self.max_len # no default special tokens - you can update this value if you add special tokens | |
| self.max_len_sentences_pair = self.max_len # no default special tokens - you can update this value if you add special tokens | |
| try: | |
| import ftfy | |
| from spacy.lang.en import English | |
| _nlp = English() | |
| self.nlp = _nlp.Defaults.create_tokenizer(_nlp) | |
| self.fix_text = ftfy.fix_text | |
| except ImportError: | |
| logger.warning("ftfy or spacy is not installed using BERT BasicTokenizer instead of SpaCy & ftfy.") | |
| self.nlp = BasicTokenizer(do_lower_case=True) | |
| self.fix_text = None | |
| self.encoder = json.load(open(vocab_file, encoding="utf-8")) | |
| self.decoder = {v:k for k,v in self.encoder.items()} | |
| merges = open(merges_file, encoding='utf-8').read().split('\n')[1:-1] | |
| merges = [tuple(merge.split()) for merge in merges] | |
| self.bpe_ranks = dict(zip(merges, range(len(merges)))) | |
| self.cache = {} | |
| def vocab_size(self): | |
| return len(self.encoder) | |
| def bpe(self, token): | |
| word = tuple(token[:-1]) + (token[-1] + '</w>',) | |
| if token in self.cache: | |
| return self.cache[token] | |
| pairs = get_pairs(word) | |
| if not pairs: | |
| return token+'</w>' | |
| while True: | |
| bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float('inf'))) | |
| if bigram not in self.bpe_ranks: | |
| break | |
| first, second = bigram | |
| new_word = [] | |
| i = 0 | |
| while i < len(word): | |
| try: | |
| j = word.index(first, i) | |
| new_word.extend(word[i:j]) | |
| i = j | |
| except: | |
| new_word.extend(word[i:]) | |
| break | |
| if word[i] == first and i < len(word)-1 and word[i+1] == second: | |
| new_word.append(first+second) | |
| i += 2 | |
| else: | |
| new_word.append(word[i]) | |
| i += 1 | |
| new_word = tuple(new_word) | |
| word = new_word | |
| if len(word) == 1: | |
| break | |
| else: | |
| pairs = get_pairs(word) | |
| word = ' '.join(word) | |
| if word == '\n </w>': | |
| word = '\n</w>' | |
| self.cache[token] = word | |
| return word | |
| def _tokenize(self, text): | |
| """ Tokenize a string. """ | |
| split_tokens = [] | |
| if self.fix_text is None: | |
| # Using BERT's BasicTokenizer | |
| text = self.nlp.tokenize(text) | |
| for token in text: | |
| split_tokens.extend([t for t in self.bpe(token).split(' ')]) | |
| else: | |
| # Using SpaCy & ftfy (original tokenization process of OpenAI GPT) | |
| text = self.nlp(text_standardize(self.fix_text(text))) | |
| for token in text: | |
| split_tokens.extend([t for t in self.bpe(token.text.lower()).split(' ')]) | |
| return split_tokens | |
| def _convert_token_to_id(self, token): | |
| """ Converts a token (str/unicode) in an id using the vocab. """ | |
| return self.encoder.get(token, self.encoder.get(self.unk_token)) | |
| def _convert_id_to_token(self, index): | |
| """Converts an id in a token (BPE) using the vocab.""" | |
| return self.decoder.get(index, self.unk_token) | |
| def convert_tokens_to_string(self, tokens): | |
| """ Converts a sequence of tokens (string) in a single string. """ | |
| out_string = ''.join(tokens).replace('</w>', ' ').strip() | |
| return out_string | |
| def save_vocabulary(self, save_directory): | |
| """Save the tokenizer vocabulary and merge files to a directory.""" | |
| if not os.path.isdir(save_directory): | |
| logger.error("Vocabulary path ({}) should be a directory".format(save_directory)) | |
| return | |
| vocab_file = os.path.join(save_directory, VOCAB_FILES_NAMES['vocab_file']) | |
| merge_file = os.path.join(save_directory, VOCAB_FILES_NAMES['merges_file']) | |
| with open(vocab_file, 'w', encoding='utf-8') as f: | |
| f.write(json.dumps(self.encoder, ensure_ascii=False)) | |
| index = 0 | |
| with open(merge_file, "w", encoding="utf-8") as writer: | |
| writer.write(u'#version: 0.2\n') | |
| for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]): | |
| if index != token_index: | |
| logger.warning("Saving vocabulary to {}: BPE merge indices are not consecutive." | |
| " Please check that the tokenizer is not corrupted!".format(merge_file)) | |
| index = token_index | |
| writer.write(' '.join(bpe_tokens) + u'\n') | |
| index += 1 | |
| return vocab_file, merge_file | |