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| """ CLIP tokenizer | |
| Copied from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI. | |
| """ | |
| import gzip | |
| import html | |
| import os | |
| from functools import lru_cache | |
| from typing import Union, List | |
| import ftfy | |
| import regex as re | |
| import torch | |
| # https://stackoverflow.com/q/62691279 | |
| import os | |
| os.environ["TOKENIZERS_PARALLELISM"] = "false" | |
| def default_bpe(): | |
| return os.path.join(os.path.dirname(os.path.abspath(__file__)), "bpe_simple_vocab_16e6.txt.gz") | |
| def bytes_to_unicode(): | |
| """ | |
| Returns list of utf-8 byte and a corresponding list of unicode strings. | |
| 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. | |
| And avoids mapping to whitespace/control characters the bpe code barfs on. | |
| """ | |
| 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 | |
| def basic_clean(text): | |
| text = ftfy.fix_text(text) | |
| text = html.unescape(html.unescape(text)) | |
| return text.strip() | |
| def whitespace_clean(text): | |
| text = re.sub(r'\s+', ' ', text) | |
| text = text.strip() | |
| return text | |
| class SimpleTokenizer(object): | |
| def __init__(self, bpe_path: str = default_bpe(), special_tokens=None): | |
| self.byte_encoder = bytes_to_unicode() | |
| self.byte_decoder = {v: k for k, v in self.byte_encoder.items()} | |
| merges = gzip.open(bpe_path).read().decode("utf-8").split('\n') | |
| merges = merges[1:49152-256-2+1] | |
| merges = [tuple(merge.split()) for merge in merges] | |
| vocab = list(bytes_to_unicode().values()) | |
| vocab = vocab + [v+'</w>' for v in vocab] | |
| for merge in merges: | |
| vocab.append(''.join(merge)) | |
| if not special_tokens: | |
| special_tokens = ['<start_of_text>', '<end_of_text>'] | |
| else: | |
| special_tokens = ['<start_of_text>', '<end_of_text>'] + special_tokens | |
| vocab.extend(special_tokens) | |
| self.encoder = dict(zip(vocab, range(len(vocab)))) | |
| self.decoder = {v: k for k, v in self.encoder.items()} | |
| self.bpe_ranks = dict(zip(merges, range(len(merges)))) | |
| self.cache = {t:t for t in special_tokens} | |
| special = "|".join(special_tokens) | |
| self.pat = re.compile(special + r"""|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""", re.IGNORECASE) | |
| self.vocab_size = len(self.encoder) | |
| self.all_special_ids = [self.encoder[t] for t in special_tokens] | |
| def bpe(self, token): | |
| if token in self.cache: | |
| return self.cache[token] | |
| word = tuple(token[:-1]) + ( token[-1] + '</w>',) | |
| 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) | |
| new_word.extend(word[i:j]) | |
| i = j | |
| except: | |
| new_word.extend(word[i:]) | |
| break | |
| 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 encode(self, text): | |
| bpe_tokens = [] | |
| text = whitespace_clean(basic_clean(text)).lower() | |
| for token in re.findall(self.pat, text): | |
| token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8')) | |
| bpe_tokens.extend(self.encoder[bpe_token] for bpe_token in self.bpe(token).split(' ')) | |
| return bpe_tokens | |
| def decode(self, tokens): | |
| text = ''.join([self.decoder[token] for token in tokens]) | |
| text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors="replace").replace('</w>', ' ') | |
| return text | |
| _tokenizer = SimpleTokenizer() | |
| def decode(output_ids: torch.Tensor): | |
| output_ids = output_ids.cpu().numpy() | |
| return _tokenizer.decode(output_ids) | |
| def tokenize(texts: Union[str, List[str]], context_length: int = 77) -> torch.LongTensor: | |
| """ | |
| Returns the tokenized representation of given input string(s) | |
| Parameters | |
| ---------- | |
| texts : Union[str, List[str]] | |
| An input string or a list of input strings to tokenize | |
| context_length : int | |
| The context length to use; all CLIP models use 77 as the context length | |
| Returns | |
| ------- | |
| A two-dimensional tensor containing the resulting tokens, shape = [number of input strings, context_length] | |
| """ | |
| if isinstance(texts, str): | |
| texts = [texts] | |
| sot_token = _tokenizer.encoder["<start_of_text>"] | |
| eot_token = _tokenizer.encoder["<end_of_text>"] | |
| all_tokens = [[sot_token] + _tokenizer.encode(text) + [eot_token] for text in texts] | |
| result = torch.zeros(len(all_tokens), context_length, dtype=torch.long) | |
| for i, tokens in enumerate(all_tokens): | |
| if len(tokens) > context_length: | |
| tokens = tokens[:context_length] # Truncate | |
| tokens[-1] = eot_token | |
| result[i, :len(tokens)] = torch.tensor(tokens) | |
| return result | |
| class HFTokenizer: | |
| """HuggingFace tokenizer wrapper""" | |
| def __init__(self, tokenizer_name: str): | |
| from transformers import AutoTokenizer | |
| self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_name) | |
| def save_pretrained(self, dest): | |
| self.tokenizer.save_pretrained(dest) | |
| def __call__(self, texts: Union[str, List[str]], context_length: int = 77) -> torch.Tensor: | |
| # same cleaning as for default tokenizer, except lowercasing | |
| # adding lower (for case-sensitive tokenizers) will make it more robust but less sensitive to nuance | |
| if isinstance(texts, str): | |
| texts = [texts] | |
| texts = [whitespace_clean(basic_clean(text)) for text in texts] | |
| input_ids = self.tokenizer( | |
| texts, | |
| return_tensors='pt', | |
| max_length=context_length, | |
| padding='max_length', | |
| truncation=True, | |
| ).input_ids | |
| return input_ids | |