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sam3/model/tokenizer_ve.py
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253
sam3/model/tokenizer_ve.py
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# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
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"""
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Text Tokenizer.
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Copied and lightly adapted from VE repo, which in turn copied
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from open_clip and openAI CLIP.
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"""
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import gzip
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import html
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import io
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import os
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import string
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from functools import lru_cache
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from typing import List, Optional, Union
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import ftfy
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import regex as re
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import torch
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from iopath.common.file_io import g_pathmgr
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# https://stackoverflow.com/q/62691279
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os.environ["TOKENIZERS_PARALLELISM"] = "false"
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DEFAULT_CONTEXT_LENGTH = 77
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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 significant 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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def _clean_canonicalize(x):
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# basic, remove whitespace, remove punctuation, lower case
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return canonicalize_text(basic_clean(x))
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def _clean_lower(x):
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# basic, remove whitespace, lower case
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return whitespace_clean(basic_clean(x)).lower()
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def _clean_whitespace(x):
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# basic, remove whitespace
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return whitespace_clean(basic_clean(x))
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def get_clean_fn(type: str):
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if type == "canonicalize":
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return _clean_canonicalize
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elif type == "lower":
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return _clean_lower
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elif type == "whitespace":
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return _clean_whitespace
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else:
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assert False, f"Invalid clean function ({type})."
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def canonicalize_text(text, *, keep_punctuation_exact_string=None):
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"""Returns canonicalized `text` (lowercase and punctuation removed).
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From: https://github.com/google-research/big_vision/blob/53f18caf27a9419231bbf08d3388b07671616d3d/big_vision/evaluators/proj/image_text/prompt_engineering.py#L94
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Args:
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text: string to be canonicalized.
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keep_punctuation_exact_string: If provided, then this exact string kept.
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For example providing '{}' will keep any occurrences of '{}' (but will
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still remove '{' and '}' that appear separately).
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"""
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text = text.replace("_", " ")
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if keep_punctuation_exact_string:
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text = keep_punctuation_exact_string.join(
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part.translate(str.maketrans("", "", string.punctuation))
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for part in text.split(keep_punctuation_exact_string)
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)
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else:
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text = text.translate(str.maketrans("", "", string.punctuation))
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text = text.lower()
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text = re.sub(r"\s+", " ", text)
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return text.strip()
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class SimpleTokenizer(object):
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def __init__(
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self,
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bpe_path: Union[str, os.PathLike],
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additional_special_tokens: Optional[List[str]] = None,
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context_length: Optional[int] = DEFAULT_CONTEXT_LENGTH,
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clean: str = "lower",
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):
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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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with g_pathmgr.open(bpe_path, "rb") as fh:
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bpe_bytes = io.BytesIO(fh.read())
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merges = gzip.open(bpe_bytes).read().decode("utf-8").split("\n")
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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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special_tokens = ["<start_of_text>", "<end_of_text>"]
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if additional_special_tokens:
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special_tokens += additional_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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self.sot_token_id = self.all_special_ids[0]
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self.eot_token_id = self.all_special_ids[1]
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self.context_length = context_length
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self.clean_fn = get_clean_fn(clean)
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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 = self.clean_fn(text)
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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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def __call__(
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self, texts: Union[str, List[str]], context_length: Optional[int] = None
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) -> torch.LongTensor:
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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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context_length = context_length or self.context_length
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assert context_length, "Please set a valid context length"
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all_tokens = [
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[self.sot_token_id] + self.encode(text) + [self.eot_token_id]
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for text in texts
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]
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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] # Truncate
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tokens[-1] = self.eot_token_id
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result[i, : len(tokens)] = torch.tensor(tokens)
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return result
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