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| # coding=utf-8 | |
| # Copyright 2022 The HuggingFace Team and Microsoft Research AI4Science. 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. | |
| """Tokenization classes for BioGPT.""" | |
| import json | |
| import os | |
| from typing import List, Optional, Tuple | |
| from ...tokenization_utils import PreTrainedTokenizer | |
| from ...utils import logging | |
| logger = logging.get_logger(__name__) | |
| VOCAB_FILES_NAMES = { | |
| "vocab_file": "vocab.json", | |
| "merges_file": "merges.txt", | |
| } | |
| PRETRAINED_VOCAB_FILES_MAP = { | |
| "vocab_file": { | |
| "microsoft/biogpt": "https://huggingface.co/microsoft/biogpt/resolve/main/vocab.json", | |
| }, | |
| "merges_file": {"microsoft/biogpt": "https://huggingface.co/microsoft/biogpt/resolve/main/merges.txt"}, | |
| } | |
| PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { | |
| "microsoft/biogpt": 1024, | |
| } | |
| 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 | |
| class BioGptTokenizer(PreTrainedTokenizer): | |
| """ | |
| Construct an FAIRSEQ Transformer tokenizer. Moses tokenization followed by Byte-Pair Encoding. | |
| This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to | |
| this superclass for more information regarding those methods. | |
| Args: | |
| vocab_file (`str`): | |
| Path to the vocabulary file. | |
| merges_file (`str`): | |
| Merges file. | |
| unk_token (`str`, *optional*, defaults to `"<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. | |
| bos_token (`str`, *optional*, defaults to `"<s>"`): | |
| The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token. | |
| <Tip> | |
| When building a sequence using special tokens, this is not the token that is used for the beginning of | |
| sequence. The token used is the `cls_token`. | |
| </Tip> | |
| eos_token (`str`, *optional*, defaults to `"</s>"`): | |
| The end of sequence token. | |
| <Tip> | |
| When building a sequence using special tokens, this is not the token that is used for the end of sequence. | |
| The token used is the `sep_token`. | |
| </Tip> | |
| sep_token (`str`, *optional*, defaults to `"</s>"`): | |
| The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for | |
| sequence classification or for a text and a question for question answering. It is also used as the last | |
| token of a sequence built with special tokens. | |
| pad_token (`str`, *optional*, defaults to `"<pad>"`): | |
| The token used for padding, for example when batching sequences of different lengths. | |
| """ | |
| 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>", | |
| bos_token="<s>", | |
| eos_token="</s>", | |
| sep_token="</s>", | |
| pad_token="<pad>", | |
| **kwargs, | |
| ): | |
| try: | |
| import sacremoses | |
| except ImportError: | |
| raise ImportError( | |
| "You need to install sacremoses to use BioGptTokenizer. " | |
| "See https://pypi.org/project/sacremoses/ for installation." | |
| ) | |
| self.lang = "en" | |
| self.sm = sacremoses | |
| # cache of sm.MosesTokenizer instance | |
| self.cache_moses_tokenizer = {} | |
| self.cache_moses_detokenizer = {} | |
| """ Initialisation""" | |
| 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 = merges_handle.read().split("\n")[:-1] | |
| merges = [tuple(merge.split()[:2]) for merge in merges] | |
| self.bpe_ranks = dict(zip(merges, range(len(merges)))) | |
| self.cache = {} | |
| super().__init__( | |
| bos_token=bos_token, | |
| eos_token=eos_token, | |
| sep_token=sep_token, | |
| unk_token=unk_token, | |
| pad_token=pad_token, | |
| **kwargs, | |
| ) | |
| def vocab_size(self): | |
| """Returns vocab size""" | |
| return len(self.encoder) | |
| def get_vocab(self): | |
| return dict(self.encoder, **self.added_tokens_encoder) | |
| def moses_tokenize(self, text, lang): | |
| if lang not in self.cache_moses_tokenizer: | |
| moses_tokenizer = self.sm.MosesTokenizer(lang=lang) | |
| self.cache_moses_tokenizer[lang] = moses_tokenizer | |
| return self.cache_moses_tokenizer[lang].tokenize( | |
| text, aggressive_dash_splits=True, return_str=False, escape=True | |
| ) | |
| def moses_detokenize(self, tokens, lang): | |
| if lang not in self.cache_moses_detokenizer: | |
| moses_detokenizer = self.sm.MosesDetokenizer(lang=lang) | |
| self.cache_moses_detokenizer[lang] = moses_detokenizer | |
| return self.cache_moses_detokenizer[lang].detokenize(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) | |
| 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, bypass_tokenizer=False): | |
| """Returns a tokenized string.""" | |
| if bypass_tokenizer: | |
| text = text.split() | |
| else: | |
| text = self.moses_tokenize(text, self.lang) | |
| split_tokens = [] | |
| for token in text: | |
| if token: | |
| split_tokens.extend(list(self.bpe(token).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 index (integer) in a token (str) 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.""" | |
| # remove BPE | |
| tokens = [t.replace(" ", "").replace("</w>", " ") for t in tokens] | |
| tokens = "".join(tokens).split() | |
| # detokenize | |
| text = self.moses_detokenize(tokens, self.lang) | |
| return text | |
| def build_inputs_with_special_tokens( | |
| self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None | |
| ) -> List[int]: | |
| """ | |
| Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and | |
| adding special tokens. A BioGPT sequence has the following format: | |
| - single sequence: `</s> X ` | |
| - pair of sequences: `</s> A </s> B ` | |
| Args: | |
| token_ids_0 (`List[int]`): | |
| List of IDs to which the special tokens will be added. | |
| token_ids_1 (`List[int]`, *optional*): | |
| Optional second list of IDs for sequence pairs. | |
| Returns: | |
| `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens. | |
| """ | |
| if token_ids_1 is None: | |
| return [self.sep_token_id] + token_ids_0 | |
| sep = [self.sep_token_id] | |
| return sep + token_ids_0 + sep + token_ids_1 | |
| def get_special_tokens_mask( | |
| self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False | |
| ) -> List[int]: | |
| """ | |
| Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding | |
| special tokens using the tokenizer `prepare_for_model` method. | |
| Args: | |
| token_ids_0 (`List[int]`): | |
| List of IDs. | |
| token_ids_1 (`List[int]`, *optional*): | |
| Optional second list of IDs for sequence pairs. | |
| already_has_special_tokens (`bool`, *optional*, defaults to `False`): | |
| Whether or not the token list is already formatted with special tokens for the model. | |
| Returns: | |
| `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. | |
| """ | |
| if already_has_special_tokens: | |
| return super().get_special_tokens_mask( | |
| token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True | |
| ) | |
| # no bos used in fairseq | |
| if token_ids_1 is not None: | |
| return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) | |
| return [1] + ([0] * len(token_ids_0)) | |
| def create_token_type_ids_from_sequences( | |
| self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None | |
| ) -> List[int]: | |
| """ | |
| Create a mask from the two sequences passed to be used in a sequence-pair classification task. A FAIRSEQ | |
| Transformer sequence pair mask has the following format: | |
| ``` | |
| 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 | |
| | first sequence | second sequence | | |
| ``` | |
| 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 IDs](../glossary#token-type-ids) according to the given sequence(s). | |
| """ | |
| sep = [self.sep_token_id] | |
| # no bos used in fairseq | |
| if token_ids_1 is None: | |
| return len(token_ids_0 + sep) * [0] | |
| return len(token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1] | |
| 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, indent=2, sort_keys=True, ensure_ascii=False) + "\n") | |
| index = 0 | |
| with open(merge_file, "w", encoding="utf-8") as writer: | |
| 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 | |
| def __getstate__(self): | |
| state = self.__dict__.copy() | |
| state["sm"] = None | |
| return state | |
| def __setstate__(self, d): | |
| self.__dict__ = d | |
| try: | |
| import sacremoses | |
| except ImportError: | |
| raise ImportError( | |
| "You need to install sacremoses to use XLMTokenizer. " | |
| "See https://pypi.org/project/sacremoses/ for installation." | |
| ) | |
| self.sm = sacremoses | |