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""" CLIP tokenizer |
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Copied from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI. |
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""" |
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import gzip |
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import html |
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import os |
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from functools import lru_cache |
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from typing import Union, List |
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import ftfy |
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import regex as re |
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import torch |
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@lru_cache() |
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def default_bpe(): |
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return os.path.join( |
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os.path.dirname(os.path.abspath(__file__)), "bpe_simple_vocab_16e6.txt.gz" |
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) |
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@lru_cache() |
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def bytes_to_unicode(): |
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""" |
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Returns list of utf-8 byte and a corresponding list of unicode strings. |
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The reversible bpe codes work on unicode strings. |
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This means you need a large # of unicode characters in your vocab if you want to avoid UNKs. |
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When you're at something like a 10B token dataset you end up needing around 5K for decent coverage. |
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This is a signficant percentage of your normal, say, 32K bpe vocab. |
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To avoid that, we want lookup tables between utf-8 bytes and unicode strings. |
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And avoids mapping to whitespace/control characters the bpe code barfs on. |
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""" |
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bs = ( |
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list(range(ord("!"), ord("~") + 1)) |
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+ list(range(ord("¡"), ord("¬") + 1)) |
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+ list(range(ord("®"), ord("ÿ") + 1)) |
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) |
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cs = bs[:] |
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n = 0 |
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for b in range(2**8): |
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if b not in bs: |
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bs.append(b) |
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cs.append(2**8 + n) |
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n += 1 |
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cs = [chr(n) for n in cs] |
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return dict(zip(bs, cs)) |
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def get_pairs(word): |
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"""Return set of symbol pairs in a word. |
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Word is represented as tuple of symbols (symbols being variable-length strings). |
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""" |
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pairs = set() |
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prev_char = word[0] |
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for char in word[1:]: |
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pairs.add((prev_char, char)) |
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prev_char = char |
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return pairs |
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def basic_clean(text): |
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text = ftfy.fix_text(text) |
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text = html.unescape(html.unescape(text)) |
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return text.strip() |
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def whitespace_clean(text): |
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text = re.sub(r"\s+", " ", text) |
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text = text.strip() |
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return text |
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class SimpleTokenizer(object): |
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def __init__(self, bpe_path: str = default_bpe(), special_tokens=None): |
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self.byte_encoder = bytes_to_unicode() |
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self.byte_decoder = {v: k for k, v in self.byte_encoder.items()} |
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merges = gzip.open(bpe_path).read().decode("utf-8").split("\n") |
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merges = merges[1 : 49152 - 256 - 2 + 1] |
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merges = [tuple(merge.split()) for merge in merges] |
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vocab = list(bytes_to_unicode().values()) |
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vocab = vocab + [v + "</w>" for v in vocab] |
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for merge in merges: |
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vocab.append("".join(merge)) |
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if not special_tokens: |
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special_tokens = ["<start_of_text>", "<end_of_text>"] |
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else: |
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special_tokens = ["<start_of_text>", "<end_of_text>"] + special_tokens |
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vocab.extend(special_tokens) |
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self.encoder = dict(zip(vocab, range(len(vocab)))) |
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self.decoder = {v: k for k, v in self.encoder.items()} |
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self.bpe_ranks = dict(zip(merges, range(len(merges)))) |
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self.cache = {t: t for t in special_tokens} |
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special = "|".join(special_tokens) |
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self.pat = re.compile( |
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special + r"""|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""", |
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re.IGNORECASE, |
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) |
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self.vocab_size = len(self.encoder) |
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self.all_special_ids = [self.encoder[t] for t in special_tokens] |
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def bpe(self, token): |
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if token in self.cache: |
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return self.cache[token] |
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word = tuple(token[:-1]) + (token[-1] + "</w>",) |
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pairs = get_pairs(word) |
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if not pairs: |
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return token + "</w>" |
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while True: |
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bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf"))) |
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if bigram not in self.bpe_ranks: |
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break |
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first, second = bigram |
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new_word = [] |
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i = 0 |
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while i < len(word): |
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try: |
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j = word.index(first, i) |
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new_word.extend(word[i:j]) |
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i = j |
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except: |
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new_word.extend(word[i:]) |
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break |
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if word[i] == first and i < len(word) - 1 and word[i + 1] == second: |
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new_word.append(first + second) |
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i += 2 |
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else: |
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new_word.append(word[i]) |
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i += 1 |
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new_word = tuple(new_word) |
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word = new_word |
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if len(word) == 1: |
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break |
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else: |
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pairs = get_pairs(word) |
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word = " ".join(word) |
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self.cache[token] = word |
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return word |
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def encode(self, text): |
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bpe_tokens = [] |
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text = whitespace_clean(basic_clean(text)).lower() |
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for token in re.findall(self.pat, text): |
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token = "".join(self.byte_encoder[b] for b in token.encode("utf-8")) |
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bpe_tokens.extend( |
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self.encoder[bpe_token] for bpe_token in self.bpe(token).split(" ") |
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) |
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return bpe_tokens |
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def decode(self, tokens): |
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text = "".join([self.decoder[token] for token in tokens]) |
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text = ( |
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bytearray([self.byte_decoder[c] for c in text]) |
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.decode("utf-8", errors="replace") |
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.replace("</w>", " ") |
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) |
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return text |
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_tokenizer = SimpleTokenizer() |
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def tokenize( |
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texts: Union[str, List[str]], context_length: int = 77 |
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) -> torch.LongTensor: |
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""" |
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Returns the tokenized representation of given input string(s) |
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Parameters |
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---------- |
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texts : Union[str, List[str]] |
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An input string or a list of input strings to tokenize |
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context_length : int |
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The context length to use; all CLIP models use 77 as the context length |
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Returns |
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------- |
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A two-dimensional tensor containing the resulting tokens, shape = [number of input strings, context_length] |
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""" |
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if isinstance(texts, str): |
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texts = [texts] |
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sot_token = _tokenizer.encoder["<start_of_text>"] |
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eot_token = _tokenizer.encoder["<end_of_text>"] |
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all_tokens = [[sot_token] + _tokenizer.encode(text) + [eot_token] for text in texts] |
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result = torch.zeros(len(all_tokens), context_length, dtype=torch.long) |
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for i, tokens in enumerate(all_tokens): |
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if len(tokens) > context_length: |
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tokens = tokens[:context_length] |
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result[i, : len(tokens)] = torch.tensor(tokens) |
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return result |
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