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| """Tokenization classes for Salesforce CTRL.""" |
|
|
| import json |
| import os |
| from typing import Optional, Tuple |
|
|
| import regex as re |
|
|
| 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", |
| } |
|
|
|
|
| CONTROL_CODES = { |
| "Pregnancy": 168629, |
| "Christianity": 7675, |
| "Explain": 106423, |
| "Fitness": 63440, |
| "Saving": 63163, |
| "Ask": 27171, |
| "Ass": 95985, |
| "Joke": 163509, |
| "Questions": 45622, |
| "Thoughts": 49605, |
| "Retail": 52342, |
| "Feminism": 164338, |
| "Writing": 11992, |
| "Atheism": 192263, |
| "Netflix": 48616, |
| "Computing": 39639, |
| "Opinion": 43213, |
| "Alone": 44967, |
| "Funny": 58917, |
| "Gaming": 40358, |
| "Human": 4088, |
| "India": 1331, |
| "Joker": 77138, |
| "Diet": 36206, |
| "Legal": 11859, |
| "Norman": 4939, |
| "Tip": 72689, |
| "Weight": 52343, |
| "Movies": 46273, |
| "Running": 23425, |
| "Science": 2090, |
| "Horror": 37793, |
| "Confession": 60572, |
| "Finance": 12250, |
| "Politics": 16360, |
| "Scary": 191985, |
| "Support": 12654, |
| "Technologies": 32516, |
| "Teenage": 66160, |
| "Event": 32769, |
| "Learned": 67460, |
| "Notion": 182770, |
| "Wikipedia": 37583, |
| "Books": 6665, |
| "Extract": 76050, |
| "Confessions": 102701, |
| "Conspiracy": 75932, |
| "Links": 63674, |
| "Narcissus": 150425, |
| "Relationship": 54766, |
| "Relationships": 134796, |
| "Reviews": 41671, |
| "News": 4256, |
| "Translation": 26820, |
| "multilingual": 128406, |
| } |
|
|
|
|
| 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 |
|
|
| pairs = set(pairs) |
| return pairs |
|
|
|
|
| class CTRLTokenizer(PreTrainedTokenizer): |
| """ |
| Construct a CTRL tokenizer. Based on 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`): |
| Path to the 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. |
| """ |
|
|
| vocab_files_names = VOCAB_FILES_NAMES |
| control_codes = CONTROL_CODES |
|
|
| def __init__(self, vocab_file, merges_file, unk_token="<unk>", **kwargs): |
| 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:-1] |
| merges = [tuple(merge.split()) for merge in merges] |
| self.bpe_ranks = dict(zip(merges, range(len(merges)))) |
| self.cache = {} |
| super().__init__(unk_token=unk_token, **kwargs) |
|
|
| @property |
| 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) |
| word = tuple(list(word[:-1]) + [word[-1] + "</w>"]) |
| 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) |
| word = word[:-4] |
| self.cache[token] = word |
| return word |
|
|
| def _tokenize(self, text): |
| """Tokenize a string.""" |
| split_tokens = [] |
|
|
| words = re.findall(r"\S+\n?", text) |
|
|
| for token in words: |
| 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.""" |
| out_string = " ".join(tokens).replace("@@ ", "").strip() |
| return out_string |
|
|
| 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 |
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