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| # coding=utf-8 | |
| # Copyright 2021 The Facebook Inc. and The HuggingFace Inc. team. 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 class for Blenderbot.""" | |
| import json | |
| import os | |
| from functools import lru_cache | |
| from typing import List, Optional, Tuple | |
| import regex as re | |
| from ...tokenization_utils import AddedToken, PreTrainedTokenizer | |
| from ...utils import logging | |
| logger = logging.get_logger(__name__) | |
| VOCAB_FILES_NAMES = { | |
| "vocab_file": "vocab.json", | |
| "merges_file": "merges.txt", | |
| "tokenizer_config_file": "tokenizer_config.json", | |
| } | |
| PRETRAINED_VOCAB_FILES_MAP = { | |
| "vocab_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json"}, | |
| "merges_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt"}, | |
| "tokenizer_config_file": { | |
| "facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json" | |
| }, | |
| } | |
| PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {"facebook/blenderbot-3B": 128} | |
| # Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode | |
| 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 significant 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: | |
| bs.append(b) | |
| cs.append(2**8 + n) | |
| n += 1 | |
| cs = [chr(n) for n in cs] | |
| return dict(zip(bs, cs)) | |
| # Copied from transformers.models.roberta.tokenization_roberta.get_pairs | |
| 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 BlenderbotTokenizer(PreTrainedTokenizer): | |
| """ | |
| Constructs a Blenderbot tokenizer, derived from the GPT-2 tokenizer, using byte-level Byte-Pair-Encoding. | |
| This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will | |
| be encoded differently whether it is at the beginning of the sentence (without space) or not: | |
| ```python | |
| >>> from transformers import BlenderbotTokenizer | |
| >>> tokenizer = BlenderbotTokenizer.from_pretrained("facebook/blenderbot-3B") | |
| >>> tokenizer.add_prefix_space = False | |
| >>> tokenizer("Hello world")["input_ids"] | |
| [47, 921, 86, 1085, 2] | |
| >>> tokenizer(" Hello world")["input_ids"] | |
| [6950, 1085, 2] | |
| ``` | |
| You can get around that behavior by passing `add_prefix_space=True` when instantiating this tokenizer or when you | |
| call it on some text, but since the model was not pretrained this way, it might yield a decrease in performance. | |
| <Tip> | |
| When used with `is_split_into_words=True`, this tokenizer will add a space before each word (even the first one). | |
| </Tip> | |
| 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`): | |
| Path to the merges file. | |
| errors (`str`, *optional*, defaults to `"replace"`): | |
| Paradigm to follow when decoding bytes to UTF-8. See | |
| [bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information. | |
| 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. | |
| cls_token (`str`, *optional*, defaults to `"<s>"`): | |
| The classifier token which is used when doing sequence classification (classification of the whole sequence | |
| instead of per-token classification). It is the first token of the sequence when built with special tokens. | |
| 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. | |
| pad_token (`str`, *optional*, defaults to `"<pad>"`): | |
| The token used for padding, for example when batching sequences of different lengths. | |
| mask_token (`str`, *optional*, defaults to `"<mask>"`): | |
| The token used for masking values. This is the token used when training this model with masked language | |
| modeling. This is the token which the model will try to predict. | |
| add_prefix_space (`bool`, *optional*, defaults to `False`): | |
| Whether or not to add an initial space to the input. This allows to treat the leading word just as any | |
| other word. (Blenderbot tokenizer detect beginning of words by the preceding space). | |
| """ | |
| 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"] | |
| # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.__init__ with Roberta->Blenderbot, RoBERTa->Blenderbot | |
| def __init__( | |
| self, | |
| vocab_file, | |
| merges_file, | |
| errors="replace", | |
| bos_token="<s>", | |
| eos_token="</s>", | |
| sep_token="</s>", | |
| cls_token="<s>", | |
| unk_token="<unk>", | |
| pad_token="<pad>", | |
| mask_token="<mask>", | |
| add_prefix_space=False, | |
| **kwargs, | |
| ): | |
| bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token | |
| pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token | |
| eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token | |
| unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token | |
| sep_token = AddedToken(sep_token, lstrip=False, rstrip=False) if isinstance(sep_token, str) else sep_token | |
| cls_token = AddedToken(cls_token, lstrip=False, rstrip=False) if isinstance(cls_token, str) else cls_token | |
| # Mask token behave like a normal word, i.e. include the space before it | |
| mask_token = ( | |
| AddedToken(mask_token, lstrip=True, rstrip=False, normalized=False) | |
| if isinstance(mask_token, str) | |
| else mask_token | |
| ) | |
| # these special tokens are not part of the vocab.json, let's add them in the correct order | |
| 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 = merges_handle.read().split("\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 = {} | |
| self.add_prefix_space = add_prefix_space | |
| # Should have 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+""") | |
| super().__init__( | |
| errors=errors, | |
| bos_token=bos_token, | |
| eos_token=eos_token, | |
| unk_token=unk_token, | |
| sep_token=sep_token, | |
| cls_token=cls_token, | |
| pad_token=pad_token, | |
| mask_token=mask_token, | |
| add_prefix_space=add_prefix_space, | |
| **kwargs, | |
| ) | |
| # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot | |
| def vocab_size(self): | |
| return len(self.encoder) | |
| # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.get_vocab with Roberta->Blenderbot, RoBERTa->Blenderbot | |
| def get_vocab(self): | |
| vocab = dict(self.encoder).copy() | |
| vocab.update(self.added_tokens_encoder) | |
| return vocab | |
| # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.bpe with Roberta->Blenderbot, RoBERTa->Blenderbot | |
| 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 | |
| # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer._tokenize with Roberta->Blenderbot, RoBERTa->Blenderbot | |
| 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 control tokens of the BPE (spaces in our case) | |
| bpe_tokens.extend(bpe_token for bpe_token in self.bpe(token).split(" ")) | |
| return bpe_tokens | |
| # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer._convert_token_to_id with Roberta->Blenderbot, RoBERTa->Blenderbot | |
| 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)) | |
| # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer._convert_id_to_token with Roberta->Blenderbot, RoBERTa->Blenderbot | |
| def _convert_id_to_token(self, index): | |
| """Converts an index (integer) in a token (str) using the vocab.""" | |
| return self.decoder.get(index) | |
| # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.convert_tokens_to_string with Roberta->Blenderbot, RoBERTa->Blenderbot | |
| 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 | |
| # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.save_vocabulary with Roberta->Blenderbot, RoBERTa->Blenderbot | |
| 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: | |
| 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( | |
| 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 | |
| # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.get_special_tokens_mask with Roberta->Blenderbot, RoBERTa->Blenderbot | |
| 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 | |
| ) | |
| if token_ids_1 is None: | |
| return [1] + ([0] * len(token_ids_0)) + [1] | |
| return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1] | |
| # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.create_token_type_ids_from_sequences with Roberta->Blenderbot, RoBERTa->Blenderbot | |
| 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. Blenderbot does | |
| not make use of token type ids, therefore a list of zeros is returned. | |
| 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 zeros. | |
| """ | |
| sep = [self.sep_token_id] | |
| cls = [self.cls_token_id] | |
| if token_ids_1 is None: | |
| return len(cls + token_ids_0 + sep) * [0] | |
| return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0] | |
| # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.prepare_for_tokenization with Roberta->Blenderbot, RoBERTa->Blenderbot | |
| def prepare_for_tokenization(self, text, is_split_into_words=False, **kwargs): | |
| add_prefix_space = kwargs.pop("add_prefix_space", self.add_prefix_space) | |
| if (is_split_into_words or add_prefix_space) and (len(text) > 0 and not text[0].isspace()): | |
| text = " " + text | |
| return (text, kwargs) | |
| def build_inputs_with_special_tokens(self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None): | |
| """ | |
| Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and | |
| adding special tokens. A Blenderbot sequence has the following format: | |
| - single sequence: ` X </s>` | |
| Args: | |
| token_ids_0 (`List[int]`): | |
| List of IDs to which the special tokens will be added | |
| token_ids_1 (`List[int]`, *optional*): | |
| Will be ignored | |
| Returns: | |
| `List[int]`: list of [input IDs](../glossary#input-ids) with the appropriate special tokens. | |
| """ | |
| return token_ids_0 + [self.eos_token_id] | |
| def default_chat_template(self): | |
| """ | |
| A very simple chat template that just adds whitespace between messages. | |
| """ | |
| return ( | |
| "{% for message in messages %}" | |
| "{% if message['role'] == 'user' %}{{ ' ' }}{% endif %}" | |
| "{{ message['content'] }}" | |
| "{% if not loop.last %}{{ ' ' }}{% endif %}" | |
| "{% endfor %}" | |
| "{{ eos_token }}" | |
| ) | |