Source code for transformers.tokenization_gpt2

# 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 logging
import os
from functools import lru_cache

import regex as re
import tokenizers as tk

from .tokenization_utils import PreTrainedTokenizer, PreTrainedTokenizerFast

logger = logging.getLogger(__name__)

    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",

    "vocab_file": {
        "gpt2": "",
        "gpt2-medium": "",
        "gpt2-large": "",
        "gpt2-xl": "",
        "distilgpt2": "",
    "merges_file": {
        "gpt2": "",
        "gpt2-medium": "",
        "gpt2-large": "",
        "gpt2-xl": "",
        "distilgpt2": "",

    "gpt2": 1024,
    "gpt2-medium": 1024,
    "gpt2-large": 1024,
    "gpt2-xl": 1024,
    "distilgpt2": 1024,

def bytes_to_unicode():
    Returns list of utf-8 byte and a mapping to unicode strings.
    We specifically avoids mapping to whitespace/control characters the bpe code barfs on.

    The reversible bpe codes work on unicode strings.
    This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
    When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
    This is a signficant percentage of your normal, say, 32K bpe vocab.
    To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
    bs = (
        list(range(ord("!"), ord("~") + 1)) + list(range(ord("¡"), ord("¬") + 1)) + list(range(ord("®"), ord("ÿ") + 1))
    cs = bs[:]
    n = 0
    for b in range(2 ** 8):
        if b not in bs:
            cs.append(2 ** 8 + n)
            n += 1
    cs = [chr(n) for n in cs]
    return dict(zip(bs, cs))

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

[docs]class GPT2Tokenizer(PreTrainedTokenizer): """ GPT-2 BPE tokenizer. Peculiarities: - Byte-level Byte-Pair-Encoding - Requires a space to start the input string => the encoding and tokenize methods should be called with the ``add_prefix_space`` flag set to ``True``. Otherwise, this tokenizer's ``encode``, ``decode``, and ``tokenize`` methods will not conserve the spaces at the beginning of a string: `tokenizer.decode(tokenizer.encode(" Hello")) = "Hello"` """ 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, errors="replace", unk_token="<|endoftext|>", bos_token="<|endoftext|>", eos_token="<|endoftext|>", **kwargs ): super().__init__(bos_token=bos_token, eos_token=eos_token, 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 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()} self.errors = errors # how to handle errors in decoding self.byte_encoder = bytes_to_unicode() self.byte_decoder = {v: k for k, v in self.byte_encoder.items()} with open(merges_file, encoding="utf-8") as merges_handle: bpe_merges ="\n")[1:-1] bpe_merges = [tuple(merge.split()) for merge in bpe_merges] self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges)))) self.cache = {} # Should haved added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions self.pat = re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""") @property def vocab_size(self): return len(self.encoder) def bpe(self, token): if token in self.cache: return self.cache[token] word = tuple(token) pairs = get_pairs(word) if not pairs: return token 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) self.cache[token] = word return word def _tokenize(self, text): """ Tokenize a string. """ bpe_tokens = [] for token in re.findall(self.pat, text): token = "".join( self.byte_encoder[b] for b in token.encode("utf-8") ) # Maps all our bytes to unicode strings, avoiding controle tokens of the BPE (spaces in our case) bpe_tokens.extend(bpe_token for bpe_token in self.bpe(token).split(" ")) return bpe_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 index (integer) in a token (str) using the vocab.""" return self.decoder.get(index)
[docs] def convert_tokens_to_string(self, tokens): """ Converts a sequence of tokens (string) in a single string. """ text = "".join(tokens) text = bytearray([self.byte_decoder[c] for c in text]).decode("utf-8", errors=self.errors) return text
[docs] 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("#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) + "\n") index += 1 return vocab_file, merge_file
[docs] def prepare_for_tokenization(self, text, **kwargs): if "add_prefix_space" in kwargs and kwargs["add_prefix_space"]: return " " + text return text
class GPT2TokenizerFast(PreTrainedTokenizerFast): 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="<|endoftext|>", bos_token="<|endoftext|>", eos_token="<|endoftext|>", pad_to_max_length=False, add_prefix_space=False, max_length=None, stride=0, truncation_strategy="longest_first", **kwargs ): super().__init__(bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, **kwargs) self._tokenizer = tk.Tokenizer(tk.models.BPE.from_files(vocab_file, merges_file)) self._update_special_tokens() self._tokenizer.with_pre_tokenizer( self._tokenizer.with_decoder( if max_length: self._tokenizer.with_truncation(max_length, stride=stride, strategy=truncation_strategy) self._tokenizer.with_padding( max_length=max_length if pad_to_max_length else None, direction=self.padding_side, pad_id=self.pad_token_id if self.pad_token_id is not None else 0, pad_type_id=self.pad_token_type_id, pad_token=self.pad_token if self.pad_token is not None else "", ) self._decoder =