# 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.
"""
@classmethod
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