# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team. # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. # # 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. import copy import io import json import os import unicodedata from shutil import copyfile from typing import Optional from paddle.dataset.common import DATA_HOME from paddle.utils.download import get_path_from_url def convert_to_unicode(text): """ Converts `text` to Unicode (if it's not already), assuming utf-8 input. Args: text (str|bytes): Text to be converted to unicode. Returns: str: converted text. """ if isinstance(text, str): return text elif isinstance(text, bytes): return text.decode("utf-8", "ignore") else: raise ValueError("Unsupported string type: %s" % (type(text))) def whitespace_tokenize(text): """ Runs basic whitespace cleaning and splitting on a peice of text. Args: text (str): Text to be tokened. Returns: list(str): Token list. """ text = text.strip() if not text: return [] tokens = text.split() return tokens def _is_whitespace(char): """ Checks whether `chars` is a whitespace character. """ # \t, \n, and \r are technically contorl characters but we treat them # as whitespace since they are generally considered as such. if char == " " or char == "\t" or char == "\n" or char == "\r": return True cat = unicodedata.category(char) if cat == "Zs": return True return False def _is_control(char): """Checks whether `chars` is a control character.""" # These are technically control characters but we count them as whitespace # characters. if char == "\t" or char == "\n" or char == "\r": return False cat = unicodedata.category(char) if cat.startswith("C"): return True return False def _is_punctuation(char): """Checks whether `chars` is a punctuation character.""" cp = ord(char) # We treat all non-letter/number ASCII as punctuation. # Characters such as "^", "$", and "`" are not in the Unicode # Punctuation class but we treat them as punctuation anyways, for # consistency. if ((cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126)): return True cat = unicodedata.category(char) if cat.startswith("P"): return True return False def is_chinese_char(cp): """Checks whether CP is the codepoint of a CJK character.""" # This defines a "chinese character" as anything in the CJK Unicode block: # https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block) # # Note that the CJK Unicode block is NOT all Japanese and Korean characters, # despite its name. The modern Korean Hangul alphabet is a different block, # as is Japanese Hiragana and Katakana. Those alphabets are used to write # space-separated words, so they are not treated specially and handled # like the all of the other languages. if ((cp >= 0x4E00 and cp <= 0x9FFF) or # (cp >= 0x3400 and cp <= 0x4DBF) or # (cp >= 0x20000 and cp <= 0x2A6DF) or # (cp >= 0x2A700 and cp <= 0x2B73F) or # (cp >= 0x2B740 and cp <= 0x2B81F) or # (cp >= 0x2B820 and cp <= 0x2CEAF) or (cp >= 0xF900 and cp <= 0xFAFF) or # (cp >= 0x2F800 and cp <= 0x2FA1F)): # return True return False def tokenize_chinese_chars(text): """Adds whitespace around any CJK character.""" output = [] buff = "" for char in text: cp = ord(char) if is_chinese_char(cp): if buff != "": output.append(buff) buff = "" output.append(char) else: buff += char if buff != "": output.append(buff) return output class PretrainedTokenizer(object): """ The base class for all pretrained tokenizers. It mainly provides common methods for loading (construction and loading) and saving pretrained tokenizers. Loading and saving also rely on the following class attributes which should be overridden by derived classes accordingly: - **tokenizer_config_file** (str): Represents the file name of tokenizer configuration for configuration saving and loading in local file system. The value is `tokenizer_config.json`. - **resource_files_names** (dict): Represents resources to specific file names mapping for resource saving and loading in local file system. The keys of dict representing resource items should be argument names in tokenizer's `__init__` method, and the values are file names for saving and loading corresponding resources. The mostly used resources here are vocabulary file and sentence-piece model file. - **pretrained_init_configuration** (dict): Provides the tokenizer configurations of built-in pretrained tokenizers (contrasts to tokenizers in local file system). It has pretrained tokenizer names as keys (the same as pretrained model names, such as `bert-base-uncased`), and the values are dict preserving corresponding configuration for tokenizer initialization. - **pretrained_resource_files_map** (dict): Provides resource URLs of built-in pretrained tokenizers (contrasts to tokenizers in local file system). It has the same keys as `resource_files_names`, and the values are also `dict` mapping specific pretrained tokenizer names (such as `bert-base-uncased`) to corresponding resource URLs. Moreover, methods common to tokenizers for tokenization, token/id conversion and encoding as model inputs are also provided here. Besides, metaclass `InitTrackerMeta` is used to create `PretrainedTokenizer`, by which subclasses can track arguments for initialization automatically and expose special tokens initialization used as attributes. """ tokenizer_config_file = "tokenizer_config.json" pretrained_init_configuration = {} resource_files_names = {} # keys are arguments of __init__ pretrained_resource_files_map = {} padding_side = 'right' pad_token_type_id = 0 def __call__(self, text, text_pair=None, max_seq_len: Optional[int] = None, stride=0, is_split_into_words=False, pad_to_max_seq_len=False, truncation_strategy="longest_first", return_position_ids=False, return_token_type_ids=True, return_attention_mask=False, return_length=False, return_overflowing_tokens=False, return_special_tokens_mask=False): """ Performs tokenization and uses the tokenized tokens to prepare model inputs. It supports sequence or sequence pair as input, and batch input is allowed. `self.encode()` or `self.batch_encode()` would be called separately for single or batch input depending on input format and `is_split_into_words` argument. Args: text (str, List[str] or List[List[str]]): The sequence or batch of sequences to be processed. One sequence is a string or a list of strings depending on whether it has been pretokenized. If each sequence is provided as a list of strings (pretokenized), you must set `is_split_into_words` as `True` to disambiguate with a batch of sequences. text_pair (str, List[str] or List[List[str]], optional): Same as `text` argument, while it represents for the latter sequence of the sequence pair. max_seq_len (int, optional): If set to a number, will limit the total sequence returned so that it has a maximum length. If there are overflowing tokens, those overflowing tokens will be added to the returned dictionary when `return_overflowing_tokens` is `True`. Defaults to `None`. stride (int, optional): Only available for batch input of sequence pair and mainly for question answering usage. When for QA, `text` represents questions and `text_pair` represents contexts. If `stride` is set to a positive number, the context will be split into multiple spans where `stride` defines the number of (tokenized) tokens to skip from the start of one span to get the next span, thus will produce a bigger batch than inputs to include all spans. Moreover, 'overflow_to_sample' and 'offset_mapping' preserving the original example and position information will be added to the returned dictionary. Defaults to 0. pad_to_max_seq_len (bool, optional): If set to `True`, the returned sequences would be padded up to `max_seq_len` specified length according to padding side (`self.padding_side`) and padding token id. Defaults to `False`. truncation_strategy (str, optional): String selected in the following options: - 'longest_first' (default) Iteratively reduce the inputs sequence until the input is under `max_seq_len` starting from the longest one at each token (when there is a pair of input sequences). - 'only_first': Only truncate the first sequence. - 'only_second': Only truncate the second sequence. - 'do_not_truncate': Do not truncate (raise an error if the input sequence is longer than `max_seq_len`). Defaults to 'longest_first'. return_position_ids (bool, optional): Whether to include tokens position ids in the returned dictionary. Defaults to `False`. return_token_type_ids (bool, optional): Whether to include token type ids in the returned dictionary. Defaults to `True`. return_attention_mask (bool, optional): Whether to include the attention mask in the returned dictionary. Defaults to `False`. return_length (bool, optional): Whether to include the length of each encoded inputs in the returned dictionary. Defaults to `False`. return_overflowing_tokens (bool, optional): Whether to include overflowing token information in the returned dictionary. Defaults to `False`. return_special_tokens_mask (bool, optional): Whether to include special tokens mask information in the returned dictionary. Defaults to `False`. Returns: dict or list[dict] (for batch input): The dict has the following optional items: - **input_ids** (list[int]): List of token ids to be fed to a model. - **position_ids** (list[int], optional): List of token position ids to be fed to a model. Included when `return_position_ids` is `True` - **token_type_ids** (list[int], optional): List of token type ids to be fed to a model. Included when `return_token_type_ids` is `True`. - **attention_mask** (list[int], optional): List of integers valued 0 or 1, where 0 specifies paddings and should not be attended to by the model. Included when `return_attention_mask` is `True`. - **seq_len** (int, optional): The input_ids length. Included when `return_length` is `True`. - **overflowing_tokens** (list[int], optional): List of overflowing tokens. Included when if `max_seq_len` is specified and `return_overflowing_tokens` is True. - **num_truncated_tokens** (int, optional): The number of overflowing tokens. Included when if `max_seq_len` is specified and `return_overflowing_tokens` is True. - **special_tokens_mask** (list[int], optional): List of integers valued 0 or 1, with 0 specifying special added tokens and 1 specifying sequence tokens. Included when `return_special_tokens_mask` is `True`. - **offset_mapping** (list[int], optional): list of pair preserving the index of start and end char in original input for each token. For a special token, the index pair is `(0, 0)`. Included when `stride` works. - **overflow_to_sample** (int, optional): Index of example from which this feature is generated. Included when `stride` works. """ # Input type checking for clearer error assert isinstance(text, str) or ( isinstance(text, (list, tuple)) and (len(text) == 0 or (isinstance(text[0], str) or (isinstance(text[0], (list, tuple)) and (len(text[0]) == 0 or isinstance(text[0][0], str))))) ), ("text input must of type `str` (single example), `List[str]` (batch or single pretokenized example) " "or `List[List[str]]` (batch of pretokenized examples).") assert ( text_pair is None or isinstance(text_pair, str) or (isinstance(text_pair, (list, tuple)) and (len(text_pair) == 0 or (isinstance(text_pair[0], str) or (isinstance(text_pair[0], (list, tuple)) and (len(text_pair[0]) == 0 or isinstance(text_pair[0][0], str)))))) ), ("text_pair input must of type `str` (single example), `List[str]` (batch or single pretokenized example) " "or `List[List[str]]` (batch of pretokenized examples).") is_batched = bool( (not is_split_into_words and isinstance(text, (list, tuple))) or (is_split_into_words and isinstance(text, (list, tuple)) and text and isinstance(text[0], (list, tuple)))) if is_batched: batch_text_or_text_pairs = list(zip( text, text_pair)) if text_pair is not None else text return self.batch_encode( batch_text_or_text_pairs=batch_text_or_text_pairs, max_seq_len=max_seq_len, stride=stride, is_split_into_words=is_split_into_words, pad_to_max_seq_len=pad_to_max_seq_len, truncation_strategy="longest_first", return_position_ids=return_position_ids, return_token_type_ids=return_token_type_ids, return_attention_mask=return_attention_mask, return_length=return_length, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask) else: return self.encode( text=text, text_pair=text_pair, max_seq_len=max_seq_len, pad_to_max_seq_len=pad_to_max_seq_len, truncation_strategy="longest_first", return_position_ids=return_position_ids, return_token_type_ids=return_token_type_ids, return_attention_mask=return_attention_mask, return_length=return_length, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask) @property def all_special_tokens(self): """ list: All the special tokens ('', ''...) corresponding to special token arguments in `__init__` (arguments end with '_end'). """ all_toks = [] set_attr = self.special_tokens_map for attr_value in set_attr.values(): all_toks = all_toks + (list(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: All the token ids corresponding to all the special tokens. """ all_toks = self.all_special_tokens all_ids = self.convert_tokens_to_ids(all_toks) return all_ids def convert_tokens_to_ids(self, tokens): """ Converts a sequence of tokens into ids using the `vocab` attribute (an instance of `Vocab`). Override it if needed. Args： tokens (list[int]): List of token ids. Returns: list: Converted id list. """ if isinstance(tokens, list): token_ids = [] for token in tokens: token_id = self.vocab.get(token, self.unk_token_id) token_ids.append(token_id) return token_ids elif isinstance(tokens, str): token_id = self.vocab.get(tokens, self.unk_token_id) token_ids.append(token_id) return token_ids @classmethod def from_pretrained(cls, pretrained_model_name_or_path, *args, **kwargs): """ Creates an instance of `PretrainedTokenizer`. Related resources are loaded by specifying name of a built-in pretrained model, or a community-contributed pretrained model, or a local file directory path. Args: pretrained_model_name_or_path (str): Name of pretrained model or dir path to load from. The string can be: - Name of built-in pretrained model - Name of a community-contributed pretrained model. - Local directory path which contains tokenizer related resources and tokenizer config file ("tokenizer_config.json"). *args (tuple): position arguments for model `__init__`. If provided, use these as position argument values for tokenizer initialization. **kwargs (dict): keyword arguments for model `__init__`. If provided, use these to update pre-defined keyword argument values for tokenizer initialization. Returns: PretrainedTokenizer: An instance of `PretrainedTokenizer`. Example: .. code-block:: from paddlenlp.transformers import BertTokenizer # Name of built-in pretrained model tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') # Name of community-contributed pretrained model tokenizer = BertTokenizer.from_pretrained('yingyibiao/bert-base-uncased-sst-2-finetuned') # Load from local directory path tokenizer = BertTokenizer.from_pretrained('./my_bert/') """ pretrained_models = list(cls.pretrained_init_configuration.keys()) vocab_files = {} init_configuration = {} # From built-in pretrained models if pretrained_model_name_or_path in pretrained_models: for file_id, map_list in cls.pretrained_resource_files_map.items(): vocab_files[file_id] = map_list[pretrained_model_name_or_path] init_configuration = copy.deepcopy( cls.pretrained_init_configuration[pretrained_model_name_or_path] ) # From local dir path elif os.path.isdir(pretrained_model_name_or_path): for file_id, file_name in cls.resource_files_names.items(): full_file_name = os.path.join(pretrained_model_name_or_path, file_name) vocab_files[file_id] = full_file_name vocab_files["tokenizer_config_file"] = os.path.join( pretrained_model_name_or_path, cls.tokenizer_config_file) default_root = os.path.join(DATA_HOME, pretrained_model_name_or_path) resolved_vocab_files = {} for file_id, file_path in vocab_files.items(): if file_path is None or os.path.isfile(file_path): resolved_vocab_files[file_id] = file_path continue path = os.path.join(default_root, file_path.split('/')[-1]) if os.path.exists(path): print("Already cached %s" % path) resolved_vocab_files[file_id] = path else: print("Downloading %s and saved to %s" % (file_path, default_root)) try: resolved_vocab_files[file_id] = get_path_from_url( file_path, default_root) except RuntimeError as err: print(err) raise RuntimeError( f"Can't load tokenizer for '{pretrained_model_name_or_path}'.\n" f"Please make sure that '{pretrained_model_name_or_path}' is:\n" "- a correct model-identifier of built-in pretrained models,\n" "- or a correct model-identifier of community-contributed pretrained models,\n" "- or the correct path to a directory containing relevant tokenizer files.\n" ) # Prepare tokenizer initialization kwargs # Did we saved some inputs and kwargs to reload ? tokenizer_config_file = resolved_vocab_files.pop( "tokenizer_config_file", None) if tokenizer_config_file is not None: with io.open(tokenizer_config_file, encoding="utf-8") as f: init_kwargs = json.load(f) else: init_kwargs = init_configuration # position args are stored in kwargs, maybe better not include init_args = init_kwargs.pop("init_args", ()) init_kwargs.pop("init_class", None) # Update with newly provided args and kwargs init_args = init_args if not args else args init_kwargs.update(kwargs) # Merge resolved_vocab_files arguments in init_kwargs if not including. # Maybe need more ways to load resources. for args_name, file_path in resolved_vocab_files.items(): # when `pretrained_model_name_or_path` is a pretrained model name, # use pretrained_init_configuration as `init_kwargs` to init which # does not include the vocab file in it, thus add vocab file into # args. if args_name not in init_kwargs: init_kwargs[args_name] = file_path # when `pretrained_model_name_or_path` is a pretrained model dir, # use tokenizer_config_file.json as `init_kwargs` to init which # does include a vocab file path in it. However, if the vocab file # path included in json does not exist, such as was deleted, to make # it still work, use the vocab file under this dir. elif not os.path.isfile( init_kwargs[args_name]) and os.path.isfile(file_path): init_kwargs[args_name] = file_path # TODO(guosheng): avoid reduplication of position args and key word args tokenizer = cls(*init_args, **init_kwargs) return tokenizer def save_pretrained(self, save_directory): """ Save tokenizer configuration and related resources to files under `save_directory`. The tokenizer configuration would be saved into `tokenizer_config_file` indicating file (thus `tokenizer_config.json`), and resources would be saved into `resource_files_names` indicating files by using `self.save_resources(save_directory)`. The `save_directory` can be used in `from_pretrained` as argument value of `pretrained_model_name_or_path` to re-load the tokenizer. Args: save_directory (str): Directory to save files into. Example: .. code-block:: from paddlenlp.transformers import BertTokenizer tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') tokenizer.save_pretrained('trained_model') # reload from save_directory tokenizer = BertTokenizer.from_pretrained('trained_model') """ assert not os.path.isfile( save_directory ), "Saving directory ({}) should be a directory, not a file".format( save_directory) os.makedirs(save_directory, exist_ok=True) tokenizer_config_file = os.path.join(save_directory, self.tokenizer_config_file) # init_config is set in metaclass created `__init__`, tokenizer_config = self.init_config with io.open(tokenizer_config_file, "w", encoding="utf-8") as f: f.write(json.dumps(tokenizer_config, ensure_ascii=False)) self.save_resources(save_directory) def save_resources(self, save_directory): """ Save tokenizer related resources to `resource_files_names` indicating files under `save_directory` by copying directly. Override it if necessary. Args: save_directory (str): Directory to save files into. """ for name, file_name in self.resource_files_names.items(): src_path = self.init_config[name] dst_path = os.path.join(save_directory, file_name) if os.path.abspath(src_path) != os.path.abspath(dst_path): copyfile(src_path, dst_path) @staticmethod def load_vocabulary(filepath, unk_token=None, pad_token=None, bos_token=None, eos_token=None, **kwargs): """ Instantiate an instance of `Vocab` from a file reserving all tokens by using `Vocab.from_dict`. The file contains a token per line, and the line number would be the index of corresponding token. Args: filepath (str): path of file to construct vocabulary. unk_token (str): special token for unknown token. If no need, it also could be `None`. Defaults to `None`. pad_token (str): special token for padding token. If no need, it also could be `None`. Defaults to `None`. bos_token (str): special token for bos token. If no need, it also could be `None`. Defaults to `None`. eos_token (str): special token for eos token. If no need, it also could be `None`. Defaults to `None`. **kwargs (dict): keyword arguments for `Vocab.from_dict`. Returns: Vocab: An instance of `Vocab`. """ token_to_idx = {} with io.open(filepath, 'r', encoding='utf-8') as f: for index, line in enumerate(f): token = line.rstrip('\n') token_to_idx[token] = int(index) return token_to_idx def __getattr__(self, name): if name.endswith('_token'): return self.special_tokens_map[name] elif name.endswith('_token_id'): return self.vocab[self.special_tokens_map[name[:-3]]] raise AttributeError("'{}' object has no attribute '{}'".format( type(self).__name__, name)) def truncate_sequences(self, ids, pair_ids=None, num_tokens_to_remove=0, truncation_strategy='longest_first', stride=0): """ Truncates a sequence pair in place to the maximum length. Args: ids: list of tokenized input ids. Can be obtained from a string by chaining the `tokenize` and `convert_tokens_to_ids` methods. pair_ids: Optional second list of input ids. Can be obtained from a string by chaining the `tokenize` and `convert_tokens_to_ids` methods. num_tokens_to_remove (:obj:`int`, `optional`, defaults to ``0``): number of tokens to remove using the truncation strategy truncation_strategy: string selected in the following options: - 'longest_first' (default) Iteratively reduce the inputs sequence until the input is under max_seq_len starting from the longest one at each token (when there is a pair of input sequences). Overflowing tokens only contains overflow from the first sequence. - 'only_first': Only truncate the first sequence. raise an error if the first sequence is shorter or equal to than num_tokens_to_remove. - 'only_second': Only truncate the second sequence - 'do_not_truncate': Does not truncate (raise an error if the input sequence is longer than max_seq_len) stride (:obj:`int`, `optional`, defaults to ``0``): If set to a number along with max_seq_len, the overflowing tokens returned will contain some tokens from the main sequence returned. The value of this argument defines the number of additional tokens. """ if num_tokens_to_remove <= 0: return ids, pair_ids, [] if truncation_strategy == 'longest_first': overflowing_tokens = [] for _ in range(num_tokens_to_remove): if pair_ids is None or len(ids) > len(pair_ids): overflowing_tokens = [ids[-1]] + overflowing_tokens ids = ids[:-1] else: pair_ids = pair_ids[:-1] window_len = min(len(ids), stride) if window_len > 0: overflowing_tokens = ids[-window_len:] + overflowing_tokens elif truncation_strategy == 'only_first': assert len(ids) > num_tokens_to_remove window_len = min(len(ids), stride + num_tokens_to_remove) overflowing_tokens = ids[-window_len:] ids = ids[:-num_tokens_to_remove] elif truncation_strategy == 'only_second': assert pair_ids is not None and len(pair_ids) > num_tokens_to_remove window_len = min(len(pair_ids), stride + num_tokens_to_remove) overflowing_tokens = pair_ids[-window_len:] pair_ids = pair_ids[:-num_tokens_to_remove] elif truncation_strategy == 'do_not_truncate': raise ValueError( "Input sequence are too long for max_length. Please select a truncation strategy." ) else: raise ValueError( "Truncation_strategy should be selected in ['longest_first', 'only_first', 'only_second', 'do_not_truncate']" ) return (ids, pair_ids, overflowing_tokens) def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None): """ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. Should be overridden in a subclass if the model has a special way of building those. Args: token_ids_0 (:obj:`List[int]`): List of IDs to which the special tokens will be added. token_ids_1 (:obj:`List[int]`, `optional`): Optional second list of IDs for sequence pairs. Returns: List[int]: List of input_id with the appropriate special tokens. """ if token_ids_1 is None: return token_ids_0 return token_ids_0 + token_ids_1 def build_offset_mapping_with_special_tokens(self, offset_mapping_0, offset_mapping_1=None): """ Build offset map from a pair of offset map by concatenating and adding offsets of special tokens. Should be overridden in a subclass if the model has a special way of building those. Args: offset_mapping_0 (List[tuple]): List of char offsets to which the special tokens will be added. offset_mapping_1 (List[tuple], optional): Optional second list of char offsets for offset mapping pairs. Returns: List[tuple]: List of char offsets with the appropriate offsets of special tokens. """ if offset_mapping_1 is None: return offset_mapping_0 return offset_mapping_0 + offset_mapping_1 def get_special_tokens_mask(self, token_ids_0, token_ids_1=None, already_has_special_tokens=False): """ Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding special tokens using the tokenizer ``encode`` methods. Args: token_ids_0 (List[int]): List of ids of the first sequence. token_ids_1 (List[int], optional): List of ids of the second sequence. already_has_special_tokens (bool, optional): Whether or not the token list is already formatted with special tokens for the model. Defaults to None. Returns: results (List[int]): The list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. """ return [0] * ( (len(token_ids_1) if token_ids_1 else 0) + len(token_ids_0)) def create_token_type_ids_from_sequences(self, token_ids_0, token_ids_1=None): """ Create a mask from the two sequences passed to be used in a sequence-pair classification task. Should be overridden in a subclass if the model has a special way of building those. If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s). Args: token_ids_0 (List[int]): List of IDs. token_ids_1 (List[int], optional): Optional second list of IDs for sequence pairs. Returns: List[int]: List of token_type_id according to the given sequence(s). """ if token_ids_1 is None: return len(token_ids_0) * [0] return [0] * len(token_ids_0) + [1] * len(token_ids_1) def num_special_tokens_to_add(self, pair): """ Returns the number of added tokens when encoding a sequence with special tokens. Args: pair (bool, optional): Whether the number of added tokens should be computed in the case of a sequence pair or a single sequence. Defaults to `False`. Returns: int: Number of special tokens added to sequences. """ token_ids_0 = [] token_ids_1 = [] return len( self.build_inputs_with_special_tokens( token_ids_0, token_ids_1 if pair else None)) def encode(self, text, text_pair=None, max_seq_len=512, pad_to_max_seq_len=False, truncation_strategy="longest_first", return_position_ids=False, return_token_type_ids=True, return_attention_mask=False, return_length=False, return_overflowing_tokens=False, return_special_tokens_mask=False): """ Performs tokenization and uses the tokenized tokens to prepare model inputs. It supports sequence or sequence pair as input, and batch input is not allowed. Args: text (str, List[str] or List[int]): The sequence to be processed. One sequence is a string, a list of strings, or a list of integers depending on whether it has been pretokenized and converted to ids. text_pair (str, List[str] or List[List[str]]): Same as `text` argument, while it represents for the latter sequence of the sequence pair. max_seq_len (int, optional): If set to a number, will limit the total sequence returned so that it has a maximum length. If there are overflowing tokens, those overflowing tokens will be added to the returned dictionary when `return_overflowing_tokens` is `True`. Defaults to `None`. stride (int, optional): Only available for batch input of sequence pair and mainly for question answering usage. When for QA, `text` represents questions and `text_pair` represents contexts. If `stride` is set to a positive number, the context will be split into multiple spans where `stride` defines the number of (tokenized) tokens to skip from the start of one span to get the next span, thus will produce a bigger batch than inputs to include all spans. Moreover, 'overflow_to_sample' and 'offset_mapping' preserving the original example and position information will be added to the returned dictionary. Defaults to 0. pad_to_max_seq_len (bool, optional): If set to `True`, the returned sequences would be padded up to `max_seq_len` specified length according to padding side (`self.padding_side`) and padding token id. Defaults to `False`. truncation_strategy (str, optional): String selected in the following options: - 'longest_first' (default) Iteratively reduce the inputs sequence until the input is under `max_seq_len` starting from the longest one at each token (when there is a pair of input sequences). - 'only_first': Only truncate the first sequence. - 'only_second': Only truncate the second sequence. - 'do_not_truncate': Do not truncate (raise an error if the input sequence is longer than `max_seq_len`). Defaults to 'longest_first'. return_position_ids (bool, optional): Whether to include tokens position ids in the returned dictionary. Defaults to `False`. return_token_type_ids (bool, optional): Whether to include token type ids in the returned dictionary. Defaults to `True`. return_attention_mask (bool, optional): Whether to include the attention mask in the returned dictionary. Defaults to `False`. return_length (bool, optional): Whether to include the length of each encoded inputs in the returned dictionary. Defaults to `False`. return_overflowing_tokens (bool, optional): Whether to include overflowing token information in the returned dictionary. Defaults to `False`. return_special_tokens_mask (bool, optional): Whether to include special tokens mask information in the returned dictionary. Defaults to `False`. Returns: dict: The dict has the following optional items: - **input_ids** (list[int]): List of token ids to be fed to a model. - **position_ids** (list[int], optional): List of token position ids to be fed to a model. Included when `return_position_ids` is `True` - **token_type_ids** (list[int], optional): List of token type ids to be fed to a model. Included when `return_token_type_ids` is `True`. - **attention_mask** (list[int], optional): List of integers valued 0 or 1, where 0 specifies paddings and should not be attended to by the model. Included when `return_attention_mask` is `True`. - **seq_len** (int, optional): The input_ids length. Included when `return_length` is `True`. - **overflowing_tokens** (list[int], optional): List of overflowing tokens. Included when if `max_seq_len` is specified and `return_overflowing_tokens` is True. - **num_truncated_tokens** (int, optional): The number of overflowing tokens. Included when if `max_seq_len` is specified and `return_overflowing_tokens` is True. - **special_tokens_mask** (list[int], optional): List of integers valued 0 or 1, with 0 specifying special added tokens and 1 specifying sequence tokens. Included when `return_special_tokens_mask` is `True`. """ def get_input_ids(text): if isinstance(text, str): tokens = self._tokenize(text) return self.convert_tokens_to_ids(tokens) elif isinstance(text, (list, tuple)) and len(text) > 0 and isinstance( text[0], str): return self.convert_tokens_to_ids(text) elif isinstance(text, (list, tuple)) and len(text) > 0 and isinstance( text[0], int): return text else: raise ValueError( "Input is not valid. Should be a string, a list/tuple of strings or a list/tuple of integers." ) ids = get_input_ids(text) pair_ids = get_input_ids(text_pair) if text_pair is not None else None pair = bool(pair_ids is not None) len_ids = len(ids) len_pair_ids = len(pair_ids) if pair else 0 encoded_inputs = {} # Truncation: Handle max sequence length total_len = len_ids + len_pair_ids + (self.num_special_tokens_to_add( pair=pair)) if max_seq_len and total_len > max_seq_len: ids, pair_ids, overflowing_tokens = self.truncate_sequences( ids, pair_ids=pair_ids, num_tokens_to_remove=total_len - max_seq_len, truncation_strategy=truncation_strategy, ) if return_overflowing_tokens: encoded_inputs["overflowing_tokens"] = overflowing_tokens encoded_inputs["num_truncated_tokens"] = total_len - max_seq_len # Add special tokens sequence = self.build_inputs_with_special_tokens(ids, pair_ids) token_type_ids = self.create_token_type_ids_from_sequences( ids, pair_ids) # Build output dictionnary encoded_inputs["input_ids"] = sequence if return_token_type_ids: encoded_inputs["token_type_ids"] = token_type_ids if return_special_tokens_mask: encoded_inputs[ "special_tokens_mask"] = self.get_special_tokens_mask( ids, pair_ids) if return_length: encoded_inputs["seq_len"] = len(encoded_inputs["input_ids"]) # Check lengths assert max_seq_len is None or len( encoded_inputs["input_ids"]) <= max_seq_len # Padding needs_to_be_padded = pad_to_max_seq_len and \ max_seq_len and len(encoded_inputs["input_ids"]) < max_seq_len if needs_to_be_padded: difference = max_seq_len - len(encoded_inputs["input_ids"]) if self.padding_side == 'right': if return_attention_mask: encoded_inputs["attention_mask"] = [1] * len( encoded_inputs["input_ids"]) + [0] * difference if return_token_type_ids: encoded_inputs["token_type_ids"] = ( encoded_inputs["token_type_ids"] + [self.pad_token_type_id] * difference) if return_special_tokens_mask: encoded_inputs["special_tokens_mask"] = encoded_inputs[ "special_tokens_mask"] + [1] * difference encoded_inputs["input_ids"] = encoded_inputs["input_ids"] + [ self.pad_token_id ] * difference elif self.padding_side == 'left': if return_attention_mask: encoded_inputs["attention_mask"] = [0] * difference + [ 1 ] * len(encoded_inputs["input_ids"]) if return_token_type_ids: encoded_inputs["token_type_ids"] = ( [self.pad_token_type_id] * difference + encoded_inputs["token_type_ids"]) if return_special_tokens_mask: encoded_inputs["special_tokens_mask"] = [ 1 ] * difference + encoded_inputs["special_tokens_mask"] encoded_inputs["input_ids"] = [ self.pad_token_id ] * difference + encoded_inputs["input_ids"] else: if return_attention_mask: encoded_inputs["attention_mask"] = [1] * len( encoded_inputs["input_ids"]) if return_position_ids: encoded_inputs["position_ids"] = list( range(len(encoded_inputs["input_ids"]))) return encoded_inputs def batch_encode(self, batch_text_or_text_pairs, max_seq_len=512, pad_to_max_seq_len=False, stride=0, is_split_into_words=False, truncation_strategy="longest_first", return_position_ids=False, return_token_type_ids=True, return_attention_mask=False, return_length=False, return_overflowing_tokens=False, return_special_tokens_mask=False): """ Performs tokenization and uses the tokenized tokens to prepare model inputs. It supports batch inputs of sequence or sequence pair. Args: batch_text_or_text_pairs (list): The element of list can be sequence or sequence pair, and the sequence is a string or a list of strings depending on whether it has been pretokenized. If each sequence is provided as a list of strings (pretokenized), you must set `is_split_into_words` as `True` to disambiguate with a sequence pair. max_seq_len (int, optional): If set to a number, will limit the total sequence returned so that it has a maximum length. If there are overflowing tokens, those overflowing tokens will be added to the returned dictionary when `return_overflowing_tokens` is `True`. Defaults to `None`. stride (int, optional): Only available for batch input of sequence pair and mainly for question answering usage. When for QA, `text` represents questions and `text_pair` represents contexts. If `stride` is set to a positive number, the context will be split into multiple spans where `stride` defines the number of (tokenized) tokens to skip from the start of one span to get the next span, thus will produce a bigger batch than inputs to include all spans. Moreover, 'overflow_to_sample' and 'offset_mapping' preserving the original example and position information will be added to the returned dictionary. Defaults to 0. pad_to_max_seq_len (bool, optional): If set to `True`, the returned sequences would be padded up to `max_seq_len` specified length according to padding side (`self.padding_side`) and padding token id. Defaults to `False`. truncation_strategy (str, optional): String selected in the following options: - 'longest_first' (default) Iteratively reduce the inputs sequence until the input is under `max_seq_len` starting from the longest one at each token (when there is a pair of input sequences). - 'only_first': Only truncate the first sequence. - 'only_second': Only truncate the second sequence. - 'do_not_truncate': Do not truncate (raise an error if the input sequence is longer than `max_seq_len`). Defaults to 'longest_first'. return_position_ids (bool, optional): Whether to include tokens position ids in the returned dictionary. Defaults to `False`. return_token_type_ids (bool, optional): Whether to include token type ids in the returned dictionary. Defaults to `True`. return_attention_mask (bool, optional): Whether to include the attention mask in the returned dictionary. Defaults to `False`. return_length (bool, optional): Whether to include the length of each encoded inputs in the returned dictionary. Defaults to `False`. return_overflowing_tokens (bool, optional): Whether to include overflowing token information in the returned dictionary. Defaults to `False`. return_special_tokens_mask (bool, optional): Whether to include special tokens mask information in the returned dictionary. Defaults to `False`. Returns: list[dict]: The dict has the following optional items: - **input_ids** (list[int]): List of token ids to be fed to a model. - **position_ids** (list[int], optional): List of token position ids to be fed to a model. Included when `return_position_ids` is `True` - **token_type_ids** (list[int], optional): List of token type ids to be fed to a model. Included when `return_token_type_ids` is `True`. - **attention_mask** (list[int], optional): List of integers valued 0 or 1, where 0 specifies paddings and should not be attended to by the model. Included when `return_attention_mask` is `True`. - **seq_len** (int, optional): The input_ids length. Included when `return_length` is `True`. - **overflowing_tokens** (list[int], optional): List of overflowing tokens. Included when if `max_seq_len` is specified and `return_overflowing_tokens` is True. - **num_truncated_tokens** (int, optional): The number of overflowing tokens. Included when if `max_seq_len` is specified and `return_overflowing_tokens` is True. - **special_tokens_mask** (list[int], optional): List of integers valued 0 or 1, with 0 specifying special added tokens and 1 specifying sequence tokens. Included when `return_special_tokens_mask` is `True`. - **offset_mapping** (list[int], optional): list of pair preserving the index of start and end char in original input for each token. For a sqecial token, the index pair is `(0, 0)`. Included when `stride` works. - **overflow_to_sample** (int, optional): Index of example from which this feature is generated. Included when `stride` works. """ def get_input_ids(text): if isinstance(text, str): tokens = self._tokenize(text) return self.convert_tokens_to_ids(tokens) elif isinstance(text, (list, tuple)) and len(text) > 0 and isinstance( text[0], str): return self.convert_tokens_to_ids(text) elif isinstance(text, (list, tuple)) and len(text) > 0 and isinstance( text[0], int): return text else: raise ValueError( "Input is not valid. Should be a string, a list/tuple of strings or a list/tuple of integers." ) batch_encode_inputs = [] for example_id, tokens_or_pair_tokens in enumerate( batch_text_or_text_pairs): if not isinstance(tokens_or_pair_tokens, (list, tuple)): text, text_pair = tokens_or_pair_tokens, None elif is_split_into_words and not isinstance( tokens_or_pair_tokens[0], (list, tuple)): text, text_pair = tokens_or_pair_tokens, None else: text, text_pair = tokens_or_pair_tokens first_ids = get_input_ids(text) second_ids = get_input_ids( text_pair) if text_pair is not None else None if stride > 0 and second_ids is not None: max_len_for_pair = max_seq_len - len( first_ids) - self.num_special_tokens_to_add(pair=True) token_offset_mapping = self.get_offset_mapping(text) token_pair_offset_mapping = self.get_offset_mapping(text_pair) offset = 0 while offset < len(second_ids): encoded_inputs = {} length = len(second_ids) - offset if length > max_len_for_pair: length = max_len_for_pair ids = first_ids pair_ids = second_ids[offset:offset + length] mapping = token_offset_mapping pair_mapping = token_pair_offset_mapping[offset:offset + length] offset_mapping = self.build_offset_mapping_with_special_tokens( mapping, pair_mapping) sequence = self.build_inputs_with_special_tokens( ids, pair_ids) token_type_ids = self.create_token_type_ids_from_sequences( ids, pair_ids) # Build output dictionnary encoded_inputs["input_ids"] = sequence if return_token_type_ids: encoded_inputs["token_type_ids"] = token_type_ids if return_special_tokens_mask: encoded_inputs[ "special_tokens_mask"] = self.get_special_tokens_mask( ids, pair_ids) if return_length: encoded_inputs["seq_len"] = len( encoded_inputs["input_ids"]) # Check lengths assert max_seq_len is None or len( encoded_inputs["input_ids"]) <= max_seq_len # Padding needs_to_be_padded = pad_to_max_seq_len and \ max_seq_len and len(encoded_inputs["input_ids"]) < max_seq_len encoded_inputs['offset_mapping'] = offset_mapping if needs_to_be_padded: difference = max_seq_len - len( encoded_inputs["input_ids"]) if self.padding_side == 'right': if return_attention_mask: encoded_inputs["attention_mask"] = [1] * len( encoded_inputs["input_ids"] ) + [0] * difference if return_token_type_ids: # 0 for padding token mask encoded_inputs["token_type_ids"] = ( encoded_inputs["token_type_ids"] + [self.pad_token_type_id] * difference) if return_special_tokens_mask: encoded_inputs[ "special_tokens_mask"] = encoded_inputs[ "special_tokens_mask"] + [1 ] * difference encoded_inputs["input_ids"] = encoded_inputs[ "input_ids"] + [self.pad_token_id] * difference encoded_inputs['offset_mapping'] = encoded_inputs[ 'offset_mapping'] + [(0, 0)] * difference elif self.padding_side == 'left': if return_attention_mask: encoded_inputs["attention_mask"] = [ 0 ] * difference + [1] * len( encoded_inputs["input_ids"]) if return_token_type_ids: # 0 for padding token mask encoded_inputs["token_type_ids"] = ( [self.pad_token_type_id] * difference + encoded_inputs["token_type_ids"]) if return_special_tokens_mask: encoded_inputs["special_tokens_mask"] = [ 1 ] * difference + encoded_inputs[ "special_tokens_mask"] encoded_inputs["input_ids"] = [ self.pad_token_id ] * difference + encoded_inputs["input_ids"] encoded_inputs['offset_mapping'] = [ (0, 0) ] * difference + encoded_inputs['offset_mapping'] else: if return_attention_mask: encoded_inputs["attention_mask"] = [1] * len( encoded_inputs["input_ids"]) if return_position_ids: encoded_inputs["position_ids"] = list( range(len(encoded_inputs["input_ids"]))) encoded_inputs['overflow_to_sample'] = example_id batch_encode_inputs.append(encoded_inputs) if offset + length == len(second_ids): break offset += min(length, stride) else: batch_encode_inputs.append( self.encode( first_ids, second_ids, max_seq_len=max_seq_len, pad_to_max_seq_len=pad_to_max_seq_len, truncation_strategy=truncation_strategy, return_position_ids=return_position_ids, return_token_type_ids=return_token_type_ids, return_attention_mask=return_attention_mask, return_length=return_length, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask)) return batch_encode_inputs def get_offset_mapping(self, text): """ Returns the map of tokens and the start and end index of their start and end character. Modified from https://github.com/bojone/bert4keras/blob/master/bert4keras/tokenizers.py#L372 Args: text (str): Input text. Returns: list: The offset map of input text. """ split_tokens = [] for token in self.basic_tokenizer.tokenize(text): for sub_token in self.wordpiece_tokenizer.tokenize(token): split_tokens.append( sub_token if sub_token != self.unk_token else token) normalized_text, char_mapping = '', [] for i, ch in enumerate(text): if self.basic_tokenizer.do_lower_case: ch = ch.lower() ch = unicodedata.normalize('NFD', ch) ch = ''.join([c for c in ch if unicodedata.category(c) != 'Mn']) ch = ''.join([ c for c in ch if not (ord(c) == 0 or ord(c) == 0xfffd or _is_control(c)) ]) normalized_text += ch char_mapping.extend([i] * len(ch)) text, token_mapping, offset = normalized_text, [], 0 for token in split_tokens: if token[:2] == '##': token = token[2:] start = text[offset:].index(token) + offset end = start + len(token) token_mapping.append( (char_mapping[start], char_mapping[end - 1] + 1)) offset = end return token_mapping