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# coding=utf-8 | |
# Copyright 2020 The Facebook AI Research 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. | |
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"} | |
# See all BART models at https://huggingface.co/models?filter=bart | |
PRETRAINED_VOCAB_FILES_MAP = { | |
"vocab_file": { | |
"facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/vocab.json", | |
"facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/vocab.json", | |
"facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json", | |
"facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json", | |
"facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json", | |
"yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json", | |
}, | |
"merges_file": { | |
"facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/merges.txt", | |
"facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/merges.txt", | |
"facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt", | |
"facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt", | |
"facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt", | |
"yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt", | |
}, | |
} | |
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { | |
"facebook/bart-base": 1024, | |
"facebook/bart-large": 1024, | |
"facebook/bart-large-mnli": 1024, | |
"facebook/bart-large-cnn": 1024, | |
"facebook/bart-large-xsum": 1024, | |
"yjernite/bart_eli5": 1024, | |
} | |
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)) | |
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 BartTokenizer(PreTrainedTokenizer): | |
""" | |
Constructs a BART tokenizer, which is smilar to the ROBERTa 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 BartTokenizer | |
>>> tokenizer = BartTokenizer.from_pretrained("facebook/bart-base") | |
>>> tokenizer("Hello world")["input_ids"] | |
[0, 31414, 232, 2] | |
>>> tokenizer(" Hello world")["input_ids"] | |
[0, 20920, 232, 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. (BART 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"] | |
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 | |
eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_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 | |
unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token | |
pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token | |
# Mask token behave like a normal word, i.e. include the space before it | |
# TODO seems like both slow and fast actually don't strip left and right soooooooo yeah. See `test_embeded_special_tokens` | |
# Also this not only will strip the spaces but any punctuation | |
mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token | |
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, | |
) | |
def vocab_size(self): | |
return len(self.encoder) | |
def get_vocab(self): | |
return dict(self.encoder, **self.added_tokens_encoder) | |
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 | |
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 | |
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) | |
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 | |
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 | |
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 BART sequence has the following format: | |
- single sequence: `<s> X </s>` | |
- pair of sequences: `<s> A </s></s> B </s>` | |
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.cls_token_id] + token_ids_0 + [self.sep_token_id] | |
cls = [self.cls_token_id] | |
sep = [self.sep_token_id] | |
return cls + token_ids_0 + sep + sep + token_ids_1 + sep | |
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] | |
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. BART 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] | |
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) | |