Source code for transformers.models.openai.tokenization_openai

# 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# 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_file": "vocab.json",
    "merges_file": "merges.txt",

    "vocab_file": {"openai-gpt": ""},
    "merges_file": {"openai-gpt": ""},

    "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
    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("โ€”", "-")
    text = text.replace("โ€“", "-")
    text = text.replace("โ€•", "-")
    text = text.replace("โ€ฆ", "...")
    text = text.replace("ยด", "'")
    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()

[docs]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 ="\n")[1:-1] merges = [tuple(merge.split()) for merge in merges] self.bpe_ranks = dict(zip(merges, range(len(merges)))) self.cache = {} @property def do_lower_case(self): return True @property 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
[docs] 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