# 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 logging import os import json import six from io import open from .file_utils import cached_path logger = logging.getLogger(__name__) SPECIAL_TOKENS_MAP_FILE = 'special_tokens_map.json' ADDED_TOKENS_FILE = 'added_tokens.json' class PreTrainedTokenizer(object): """ An abstract class to handle dowloading and loading pretrained tokenizers and adding tokens to the vocabulary. Derived class can set up a few special tokens to be used in common scripts and internals: bos_token, eos_token, EOP_TOKEN, EOD_TOKEN, unk_token, sep_token, pad_token, cls_token, mask_token additional_special_tokens = [] We defined an added_tokens_encoder to add new tokens to the vocabulary without having to handle the specific vocabulary augmentation methods of the various underlying dictionnary structures (BPE, sentencepiece...). """ vocab_files_names = {} pretrained_vocab_files_map = {} max_model_input_sizes = {} SPECIAL_TOKENS_ATTRIBUTES = ["bos_token", "eos_token", "unk_token", "sep_token", "pad_token", "cls_token", "mask_token", "additional_special_tokens"] @property def bos_token(self): if self._bos_token is None: logger.error("Using bos_token, but it is not set yet.") return self._bos_token @property def eos_token(self): if self._eos_token is None: logger.error("Using eos_token, but it is not set yet.") return self._eos_token @property def unk_token(self): if self._unk_token is None: logger.error("Using unk_token, but it is not set yet.") return self._unk_token @property def sep_token(self): if self._sep_token is None: logger.error("Using sep_token, but it is not set yet.") return self._sep_token @property def pad_token(self): if self._pad_token is None: logger.error("Using pad_token, but it is not set yet.") return self._pad_token @property def cls_token(self): if self._cls_token is None: logger.error("Using cls_token, but it is not set yet.") return self._cls_token @property def mask_token(self): if self._mask_token is None: logger.error("Using mask_token, but it is not set yet.") return self._mask_token @property def additional_special_tokens(self): if self._additional_special_tokens is None: logger.error("Using additional_special_tokens, but it is not set yet.") return self._additional_special_tokens @bos_token.setter def bos_token(self, value): self._bos_token = value @eos_token.setter def eos_token(self, value): self._eos_token = value @unk_token.setter def unk_token(self, value): self._unk_token = value @sep_token.setter def sep_token(self, value): self._sep_token = value @pad_token.setter def pad_token(self, value): self._pad_token = value @cls_token.setter def cls_token(self, value): self._cls_token = value @mask_token.setter def mask_token(self, value): self._mask_token = value @additional_special_tokens.setter def additional_special_tokens(self, value): self._additional_special_tokens = value def __init__(self, max_len=None, **kwargs): self._bos_token = None self._eos_token = None self._unk_token = None self._sep_token = None self._pad_token = None self._cls_token = None self._mask_token = None self._additional_special_tokens = [] self.max_len = max_len if max_len is not None else int(1e12) self.added_tokens_encoder = {} self.added_tokens_decoder = {} for key, value in kwargs.items(): if key in self.SPECIAL_TOKENS_ATTRIBUTES: setattr(self, key, value) @classmethod def from_pretrained(cls, *inputs, **kwargs): return cls._from_pretrained(*inputs, **kwargs) @classmethod def _from_pretrained(cls, pretrained_model_name_or_path, cache_dir=None, *inputs, **kwargs): """ Instantiate a PreTrainedTokenizer from pre-trained vocabulary files. Download and cache the vocabulary files if needed. """ s3_models = list(cls.max_model_input_sizes.keys()) vocab_files = {} if pretrained_model_name_or_path in s3_models: for file_id, map_list in cls.pretrained_vocab_files_map.items(): vocab_files[file_id] = map_list[pretrained_model_name_or_path] else: all_vocab_files_names = {'added_tokens_file': ADDED_TOKENS_FILE, 'special_tokens_map_file': SPECIAL_TOKENS_MAP_FILE} all_vocab_files_names.update(cls.vocab_files_names) for file_id, file_name in all_vocab_files_names.items(): if os.path.isdir(pretrained_model_name_or_path): full_file_name = os.path.join(pretrained_model_name_or_path, file_name) else: full_file_name = pretrained_model_name_or_path if not os.path.exists(full_file_name): logger.info("Didn't find file {}. We won't load it.".format(full_file_name)) full_file_name = None vocab_files[file_id] = full_file_name # Get files from url, cache, or disk depending on the case try: resolved_vocab_files = {} for file_id, file_path in vocab_files.items(): if file_path is None: resolved_vocab_files[file_id] = None else: resolved_vocab_files[file_id] = cached_path(file_path, cache_dir=cache_dir) except EnvironmentError: if pretrained_model_name_or_path in s3_models: logger.error("Couldn't reach server to download vocabulary.") else: logger.error( "Model name '{}' was not found in model name list ({}). " "We assumed '{}' was a path or url but couldn't find files {} " "at this path or url.".format( pretrained_model_name_or_path, ', '.join(s3_models), pretrained_model_name_or_path, str(vocab_files.keys()))) return None for file_id, file_path in vocab_files.items(): if file_path == resolved_vocab_files[file_id]: logger.info("loading file {}".format(file_path)) else: logger.info("loading file {} from cache at {}".format( file_path, resolved_vocab_files[file_id])) # Set max length if needed if pretrained_model_name_or_path in cls.max_model_input_sizes: # if we're using a pretrained model, ensure the tokenizer # wont index sequences longer than the number of positional embeddings max_len = cls.max_model_input_sizes[pretrained_model_name_or_path] kwargs['max_len'] = min(kwargs.get('max_len', int(1e12)), max_len) # Merge resolved_vocab_files arguments in kwargs. added_tokens_file = resolved_vocab_files.pop('added_tokens_file', None) special_tokens_map_file = resolved_vocab_files.pop('special_tokens_map_file', None) for args_name, file_path in resolved_vocab_files.items(): if args_name not in kwargs: kwargs[args_name] = file_path if special_tokens_map_file is not None: special_tokens_map = json.load(open(special_tokens_map_file, encoding="utf-8")) for key, value in special_tokens_map.items(): if key not in kwargs: kwargs[key] = value # Instantiate tokenizer. tokenizer = cls(*inputs, **kwargs) # Add supplementary tokens. if added_tokens_file is not None: added_tok_encoder = json.load(open(added_tokens_file, encoding="utf-8")) added_tok_decoder = {v:k for k, v in added_tok_encoder.items()} tokenizer.added_tokens_encoder.update(added_tok_encoder) tokenizer.added_tokens_decoder.update(added_tok_decoder) return tokenizer def save_pretrained(self, save_directory): """ Save the tokenizer vocabulary files (with added tokens) and the special-tokens-to-class-attributes-mapping to a directory, so that it can be re-loaded using the `from_pretrained(save_directory)` class method. """ if not os.path.isdir(save_directory): logger.error("Saving directory ({}) should be a directory".format(save_directory)) return special_tokens_map_file = os.path.join(save_directory, SPECIAL_TOKENS_MAP_FILE) added_tokens_file = os.path.join(save_directory, ADDED_TOKENS_FILE) with open(special_tokens_map_file, 'w', encoding='utf-8') as f: f.write(json.dumps(self.special_tokens_map, ensure_ascii=False)) with open(added_tokens_file, 'w', encoding='utf-8') as f: if self.added_tokens_encoder: out_str = json.dumps(self.added_tokens_decoder, ensure_ascii=False) else: out_str = u"{}" f.write(out_str) vocab_files = self.save_vocabulary(save_directory) return vocab_files + (special_tokens_map_file, added_tokens_file) def save_vocabulary(self, save_directory): """ Save the tokenizer vocabulary to a directory. This method doesn't save added tokens and special token mappings. Please use `save_pretrained()` to save the full Tokenizer state so that it can be reloaded using the `from_pretrained(save_directory)` class method. """ raise NotImplementedError def vocab_size(self): raise NotImplementedError def __len__(self): return self.vocab_size + len(self.added_tokens_encoder) def add_tokens(self, new_tokens): """ Add a list of new tokens to the tokenizer class. If the new tokens are not in the vocabulary, they are added to the added_tokens_encoder with indices starting from the last index of the current vocabulary. Returns: Number of tokens added to the vocabulary which can be used to correspondingly increase the size of the associated model embedding matrices. """ if not new_tokens: return 0 to_add_tokens = [] for token in new_tokens: if self.convert_tokens_to_ids(token) == self.convert_tokens_to_ids(self.unk_token): to_add_tokens.append(token) logger.info("Adding %s to the vocabulary", token) added_tok_encoder = dict((tok, len(self) + i) for i, tok in enumerate(to_add_tokens)) added_tok_decoder = {v:k for k, v in added_tok_encoder.items()} self.added_tokens_encoder.update(added_tok_encoder) self.added_tokens_decoder.update(added_tok_decoder) return len(to_add_tokens) def add_special_tokens(self, special_tokens_dict): """ Add a dictionnary of special tokens (eos, pad, cls...) to the encoder and link them to class attributes. If the special tokens are not in the vocabulary, they are added to it and indexed starting from the last index of the current vocabulary. Returns: Number of tokens added to the vocabulary which can be used to correspondingly increase the size of the associated model embedding matrices. """ if not special_tokens_dict: return 0 added_special_tokens = self.add_tokens(special_tokens_dict.values()) for key, value in special_tokens_dict.items(): logger.info("Assigning %s to the %s key of the tokenizer", value, key) setattr(self, key, value) return added_special_tokens def tokenize(self, text, **kwargs): """ Converts a string in a sequence of tokens (string), using the tokenizer. Split in words for word-based vocabulary or sub-words for sub-word-based vocabularies (BPE/SentencePieces/WordPieces). Take care of added tokens. """ def split_on_tokens(tok_list, text): if not text: return [] if not tok_list: return self._tokenize(text, **kwargs) tok = tok_list[0] split_text = text.split(tok) return sum((split_on_tokens(tok_list[1:], sub_text.strip()) + [tok] \ for sub_text in split_text), [])[:-1] added_tokens = list(self.added_tokens_encoder.keys()) tokenized_text = split_on_tokens(added_tokens, text) return tokenized_text def _tokenize(self, text, **kwargs): """ Converts a string in a sequence of tokens (string), using the tokenizer. Split in words for word-based vocabulary or sub-words for sub-word-based vocabularies (BPE/SentencePieces/WordPieces). Don't take care of added tokens. """ raise NotImplementedError def convert_tokens_to_ids(self, tokens): """ Converts a single token or a sequence of tokens (str/unicode) in a integer id (resp.) a sequence of ids, using the vocabulary. """ if isinstance(tokens, str) or (six.PY2 and isinstance(tokens, unicode)): return self.convert_token_to_id_with_added_voc(tokens) ids = [] for token in tokens: ids.append(self.convert_token_to_id_with_added_voc(token)) if len(ids) > self.max_len: logger.warning("Token indices sequence length is longer than the specified maximum sequence length " "for this model ({} > {}). Running this sequence through the model will result in " "indexing errors".format(len(ids), self.max_len)) return ids def convert_token_to_id_with_added_voc(self, token): if token in self.added_tokens_encoder: return self.added_tokens_encoder[token] return self._convert_token_to_id(token) def _convert_token_to_id(self, token): raise NotImplementedError def convert_ids_to_tokens(self, ids, skip_special_tokens=False): """ Converts a single index or a sequence of indices (integers) in a token " (resp.) a sequence of tokens (str/unicode), using the vocabulary and added tokens. Args: skip_special_tokens: Don't decode special tokens (self.all_special_tokens). Default: False """ if isinstance(ids, int): return self.convert_id_to_token(ids) tokens = [] for index in ids: if index in self.all_special_ids and skip_special_tokens: continue if index in self.added_tokens_decoder: tokens.append(self.added_tokens_decoder[index]) else: tokens.append(self._convert_id_to_token(index)) return tokens def _convert_id_to_token(self, index): raise NotImplementedError def encode(self, text): """ Converts a string in a sequence of ids (integer), using the tokenizer and vocabulary. same as self.convert_tokens_to_ids(self.tokenize(text)). """ return self.convert_tokens_to_ids(self.tokenize(text)) def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True): """ Converts a sequence of ids (integer) in a string, using the tokenizer and vocabulary with options to remove special tokens and clean up tokenization spaces. """ filtered_tokens = self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens) text = self._convert_ids_to_string(filtered_tokens) if clean_up_tokenization_spaces: text = clean_up_tokenization(text) return text def _convert_ids_to_string(self, tokens_ids): """ Converts a sequence of ids (integer) in a string, using the tokenizer and vocabulary. roughtly same as ' '.join(self.convert_ids_to_tokens(token_ids)). """ return ' '.join(self.convert_ids_to_tokens(tokens_ids)) @property def special_tokens_map(self): """ A dictionary mapping special token class attribute (cls_token, unk_token...) to their values ('', ''...) """ set_attr = {} for attr in self.SPECIAL_TOKENS_ATTRIBUTES: attr_value = getattr(self, "_" + attr) if attr_value: set_attr[attr] = attr_value return set_attr @property def all_special_tokens(self): """ List all the special tokens ('', ''...) mapped to class attributes (cls_token, unk_token...). """ all_toks = [] set_attr = self.special_tokens_map for attr_value in set_attr.values(): all_toks = all_toks + (attr_value if isinstance(attr_value, (list, tuple)) else [attr_value]) all_toks = list(set(all_toks)) return all_toks @property def all_special_ids(self): """ List the vocabulary indices of the special tokens ('', ''...) mapped to class attributes (cls_token, unk_token...). """ all_toks = self.all_special_tokens all_ids = list(self.convert_tokens_to_ids(t) for t in all_toks) return all_ids def clean_up_tokenization(out_string): out_string.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