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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.""" | |
| import json | |
| import os | |
| import re | |
| from typing import Optional, Tuple | |
| from ...tokenization_utils import PreTrainedTokenizer | |
| from ...utils import logging | |
| from ..bert.tokenization_bert import BasicTokenizer | |
| logger = logging.get_logger(__name__) | |
| VOCAB_FILES_NAMES = { | |
| "vocab_file": "vocab.json", | |
| "merges_file": "merges.txt", | |
| } | |
| PRETRAINED_VOCAB_FILES_MAP = { | |
| "vocab_file": {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/vocab.json"}, | |
| "merges_file": {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/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): | |
| """ | |
| Construct a GPT Tokenizer. Based on Byte-Pair-Encoding with the following peculiarities: | |
| - lowercases all inputs, | |
| - uses :obj:`SpaCy` tokenizer and :obj:`ftfy` for pre-BPE tokenization if they are installed, fallback to BERT's | |
| :obj:`BasicTokenizer` if not. | |
| This tokenizer inherits from :class:`~transformers.PreTrainedTokenizer` which contains most of the main methods. | |
| Users should refer to this superclass for more information regarding those methods. | |
| Args: | |
| vocab_file (:obj:`str`): | |
| Path to the vocabulary file. | |
| merges_file (:obj:`str`): | |
| Path to the merges file. | |
| unk_token (:obj:`str`, `optional`, defaults to :obj:`"<unk>"`): | |
| The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this | |
| token instead. | |
| """ | |
| vocab_files_names = VOCAB_FILES_NAMES | |
| pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP | |
| max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES | |
| model_input_names = ["input_ids", "attention_mask"] | |
| def __init__(self, vocab_file, merges_file, unk_token="<unk>", **kwargs): | |
| super().__init__(unk_token=unk_token, **kwargs) | |
| 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 | |
| with open(vocab_file, encoding="utf-8") as vocab_handle: | |
| self.encoder = json.load(vocab_handle) | |
| self.decoder = {v: k for k, v in self.encoder.items()} | |
| with open(merges_file, encoding="utf-8") as merges_handle: | |
| merges = merges_handle.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 do_lower_case(self): | |
| return True | |
| def vocab_size(self): | |
| return len(self.encoder) | |
| def get_vocab(self): | |
| return dict(self.encoder, **self.added_tokens_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) | |
| except ValueError: | |
| new_word.extend(word[i:]) | |
| break | |
| else: | |
| new_word.extend(word[i:j]) | |
| i = j | |
| 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) 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: str, filename_prefix: Optional[str] = None) -> Tuple[str]: | |
| if not os.path.isdir(save_directory): | |
| logger.error(f"Vocabulary path ({save_directory}) should be a directory") | |
| return | |
| vocab_file = os.path.join( | |
| save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] | |
| ) | |
| merge_file = os.path.join( | |
| save_directory, (filename_prefix + "-" if filename_prefix else "") + 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("#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( | |
| f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive." | |
| " Please check that the tokenizer is not corrupted!" | |
| ) | |
| index = token_index | |
| writer.write(" ".join(bpe_tokens) + "\n") | |
| index += 1 | |
| return vocab_file, merge_file | |