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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 | |
| import sys | |
| from io import open | |
| from tqdm import tqdm | |
| from .file_utils import cached_path | |
| from .tokenization import BasicTokenizer | |
| logger = logging.getLogger(__name__) | |
| PRETRAINED_VOCAB_ARCHIVE_MAP = { | |
| 'openai-gpt': "https://s3.amazonaws.com/models.huggingface.co/bert/openai-gpt-vocab.json", | |
| } | |
| PRETRAINED_MERGES_ARCHIVE_MAP = { | |
| 'openai-gpt': "https://s3.amazonaws.com/models.huggingface.co/bert/openai-gpt-merges.txt", | |
| } | |
| PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP = { | |
| 'openai-gpt': 512, | |
| } | |
| VOCAB_NAME = 'vocab.json' | |
| MERGES_NAME = 'merges.txt' | |
| SPECIAL_TOKENS_NAME = 'special_tokens.txt' | |
| 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(object): | |
| """ | |
| 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. | |
| - argument special_tokens and function set_special_tokens: | |
| can be used to add additional symbols (ex: "__classify__") to a vocabulary. | |
| """ | |
| def from_pretrained(cls, pretrained_model_name_or_path, cache_dir=None, *inputs, **kwargs): | |
| """ | |
| Instantiate a PreTrainedBertModel from a pre-trained model file. | |
| Download and cache the pre-trained model file if needed. | |
| """ | |
| if pretrained_model_name_or_path in PRETRAINED_VOCAB_ARCHIVE_MAP: | |
| vocab_file = PRETRAINED_VOCAB_ARCHIVE_MAP[pretrained_model_name_or_path] | |
| merges_file = PRETRAINED_MERGES_ARCHIVE_MAP[pretrained_model_name_or_path] | |
| special_tokens_file = None | |
| else: | |
| vocab_file = os.path.join(pretrained_model_name_or_path, VOCAB_NAME) | |
| merges_file = os.path.join(pretrained_model_name_or_path, MERGES_NAME) | |
| special_tokens_file = os.path.join(pretrained_model_name_or_path, SPECIAL_TOKENS_NAME) | |
| if not os.path.exists(special_tokens_file): | |
| special_tokens_file = None | |
| else: | |
| logger.info("loading special tokens file {}".format(special_tokens_file)) | |
| # redirect to the cache, if necessary | |
| try: | |
| resolved_vocab_file = cached_path(vocab_file, cache_dir=cache_dir) | |
| resolved_merges_file = cached_path(merges_file, cache_dir=cache_dir) | |
| except EnvironmentError: | |
| logger.error( | |
| "Model name '{}' was not found in model name list ({}). " | |
| "We assumed '{}' was a path or url but couldn't find files {} and {} " | |
| "at this path or url.".format( | |
| pretrained_model_name_or_path, | |
| ', '.join(PRETRAINED_VOCAB_ARCHIVE_MAP.keys()), | |
| pretrained_model_name_or_path, | |
| vocab_file, merges_file)) | |
| return None | |
| if resolved_vocab_file == vocab_file and resolved_merges_file == merges_file: | |
| logger.info("loading vocabulary file {}".format(vocab_file)) | |
| logger.info("loading merges file {}".format(merges_file)) | |
| else: | |
| logger.info("loading vocabulary file {} from cache at {}".format( | |
| vocab_file, resolved_vocab_file)) | |
| logger.info("loading merges file {} from cache at {}".format( | |
| merges_file, resolved_merges_file)) | |
| if pretrained_model_name_or_path in PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP: | |
| # if we're using a pretrained model, ensure the tokenizer wont index sequences longer | |
| # than the number of positional embeddings | |
| max_len = PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP[pretrained_model_name_or_path] | |
| kwargs['max_len'] = min(kwargs.get('max_len', int(1e12)), max_len) | |
| # Instantiate tokenizer. | |
| if special_tokens_file and 'special_tokens' not in kwargs: | |
| special_tokens = open(special_tokens_file, encoding='utf-8').read().split('\n')[:-1] | |
| else: | |
| special_tokens = kwargs.pop('special_tokens', []) | |
| tokenizer = cls(resolved_vocab_file, resolved_merges_file, special_tokens=special_tokens, *inputs, **kwargs) | |
| return tokenizer | |
| def __init__(self, vocab_file, merges_file, special_tokens=None, max_len=None): | |
| try: | |
| import ftfy | |
| import spacy | |
| self.nlp = spacy.load('en', disable=['parser', 'tagger', 'ner', 'textcat']) | |
| 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, | |
| never_split=special_tokens if special_tokens is not None else []) | |
| self.fix_text = None | |
| self.max_len = max_len if max_len is not None else int(1e12) | |
| 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 = {} | |
| self.special_tokens = {} | |
| self.special_tokens_decoder = {} | |
| self.set_special_tokens(special_tokens) | |
| def __len__(self): | |
| return len(self.encoder) + len(self.special_tokens) | |
| def set_special_tokens(self, special_tokens): | |
| """ Add a list of additional tokens to the encoder. | |
| The additional tokens are indexed starting from the last index of the | |
| current vocabulary in the order of the `special_tokens` list. | |
| """ | |
| if not special_tokens: | |
| self.special_tokens = {} | |
| self.special_tokens_decoder = {} | |
| return | |
| self.special_tokens = dict((tok, len(self.encoder) + i) for i, tok in enumerate(special_tokens)) | |
| self.special_tokens_decoder = {v:k for k, v in self.special_tokens.items()} | |
| if self.fix_text is None: | |
| # Using BERT's BasicTokenizer: we can update the tokenizer | |
| self.nlp.never_split = special_tokens | |
| logger.info("Special tokens {}".format(self.special_tokens)) | |
| 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_tokens_to_ids(self, tokens): | |
| """ Converts a sequence of tokens into ids using the vocab. """ | |
| ids = [] | |
| if isinstance(tokens, str) or (sys.version_info[0] == 2 and isinstance(tokens, unicode)): | |
| if tokens in self.special_tokens: | |
| return self.special_tokens[tokens] | |
| else: | |
| return self.encoder.get(tokens, 0) | |
| for token in tokens: | |
| if token in self.special_tokens: | |
| ids.append(self.special_tokens[token]) | |
| else: | |
| ids.append(self.encoder.get(token, 0)) | |
| if len(ids) > self.max_len: | |
| logger.warning( | |
| "Token indices sequence length is longer than the specified maximum " | |
| " sequence length for this OpenAI GPT model ({} > {}). Running this" | |
| " sequence through the model will result in indexing errors".format(len(ids), self.max_len) | |
| ) | |
| return ids | |
| def convert_ids_to_tokens(self, ids, skip_special_tokens=False): | |
| """Converts a sequence of ids in BPE tokens using the vocab.""" | |
| tokens = [] | |
| for i in ids: | |
| if i in self.special_tokens_decoder: | |
| if not skip_special_tokens: | |
| tokens.append(self.special_tokens_decoder[i]) | |
| else: | |
| tokens.append(self.decoder[i]) | |
| return tokens | |
| def encode(self, text): | |
| return self.convert_tokens_to_ids(self.tokenize(text)) | |
| def decode(self, ids, skip_special_tokens=False, clean_up_tokenization_spaces=True): | |
| """Converts a sequence of ids in a string.""" | |
| tokens = self.convert_ids_to_tokens(ids, skip_special_tokens=skip_special_tokens) | |
| out_string = ''.join(tokens).replace('</w>', ' ').strip() | |
| if clean_up_tokenization_spaces: | |
| out_string = out_string.replace('<unk>', '') | |
| out_string = out_string.replace(' .', '.').replace(' ?', '?').replace(' !', '!').replace(' ,', ',').replace(' ,', ',' | |
| ).replace(" ' ", "'").replace(" n't", "n't").replace(" 'm", "'m").replace(" do not", " don't" | |
| ).replace(" 's", "'s").replace(" 've", "'ve").replace(" 're", "'re") | |
| return out_string | |
| def save_vocabulary(self, vocab_path): | |
| """Save the tokenizer vocabulary and merge files to a directory.""" | |
| if not os.path.isdir(vocab_path): | |
| logger.error("Vocabulary path ({}) should be a directory".format(vocab_path)) | |
| return | |
| vocab_file = os.path.join(vocab_path, VOCAB_NAME) | |
| merge_file = os.path.join(vocab_path, MERGES_NAME) | |
| special_tokens_file = os.path.join(vocab_path, SPECIAL_TOKENS_NAME) | |
| 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 | |
| index = len(self.encoder) | |
| with open(special_tokens_file, 'w', encoding='utf-8') as writer: | |
| for token, token_index in sorted(self.special_tokens.items(), key=lambda kv: kv[1]): | |
| if index != token_index: | |
| logger.warning("Saving special tokens vocabulary to {}: BPE indices are not consecutive." | |
| " Please check that the tokenizer is not corrupted!".format(special_tokens_file)) | |
| index = token_index | |
| writer.write(token + u'\n') | |
| index += 1 | |
| return vocab_file, merge_file, special_tokens_file | |