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  1. qwen.tiktoken +0 -0
  2. tokenization_qwen.py +276 -0
  3. tokenizer_config.json +13 -0
qwen.tiktoken ADDED
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tokenization_qwen.py ADDED
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+ # Copyright (c) Alibaba Cloud.
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+ #
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+ # This source code is licensed under the license found in the
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+ # LICENSE file in the root directory of this source tree.
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+
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+ """Tokenization classes for QWen."""
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+
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+ import base64
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+ import logging
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+ import os
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+ import unicodedata
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+ from typing import Collection, Dict, List, Set, Tuple, Union
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+
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+ import tiktoken
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+ from transformers import PreTrainedTokenizer, AddedToken
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+
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+ logger = logging.getLogger(__name__)
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+
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+
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+ VOCAB_FILES_NAMES = {"vocab_file": "qwen.tiktoken"}
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+
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+ PAT_STR = r"""(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+"""
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+ ENDOFTEXT = "<|endoftext|>"
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+ IMSTART = "<|im_start|>"
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+ IMEND = "<|im_end|>"
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+ # as the default behavior is changed to allow special tokens in
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+ # regular texts, the surface forms of special tokens need to be
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+ # as different as possible to minimize the impact
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+ EXTRAS = tuple((f"<|extra_{i}|>" for i in range(205)))
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+ # changed to use actual index to avoid misconfiguration with vocabulary expansion
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+ SPECIAL_START_ID = 151643
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+ SPECIAL_TOKENS = tuple(
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+ enumerate(
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+ (
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+ (
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+ ENDOFTEXT,
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+ IMSTART,
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+ IMEND,
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+ )
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+ + EXTRAS
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+ ),
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+ start=SPECIAL_START_ID,
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+ )
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+ )
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+ SPECIAL_TOKENS_SET = set(t for i, t in SPECIAL_TOKENS)
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+
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+
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+ def _load_tiktoken_bpe(tiktoken_bpe_file: str) -> Dict[bytes, int]:
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+ with open(tiktoken_bpe_file, "rb") as f:
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+ contents = f.read()
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+ return {
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+ base64.b64decode(token): int(rank)
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+ for token, rank in (line.split() for line in contents.splitlines() if line)
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+ }
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+
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+
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+ class QWenTokenizer(PreTrainedTokenizer):
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+ """QWen tokenizer."""
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+
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+ vocab_files_names = VOCAB_FILES_NAMES
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+
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+ def __init__(
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+ self,
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+ vocab_file,
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+ errors="replace",
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+ extra_vocab_file=None,
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+ **kwargs,
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+ ):
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+ super().__init__(**kwargs)
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+
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+ # how to handle errors in decoding UTF-8 byte sequences
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+ # use ignore if you are in streaming inference
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+ self.errors = errors
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+
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+ self.mergeable_ranks = _load_tiktoken_bpe(vocab_file) # type: Dict[bytes, int]
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+ self.special_tokens = {
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+ token: index
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+ for index, token in SPECIAL_TOKENS
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+ }
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+
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+ # try load extra vocab from file
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+ if extra_vocab_file is not None:
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+ used_ids = set(self.mergeable_ranks.values()) | set(self.special_tokens.values())
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+ extra_mergeable_ranks = _load_tiktoken_bpe(extra_vocab_file)
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+ for token, index in extra_mergeable_ranks.items():
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+ if token in self.mergeable_ranks:
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+ logger.info(f"extra token {token} exists, skipping")
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+ continue
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+ if index in used_ids:
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+ logger.info(f'the index {index} for extra token {token} exists, skipping')
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+ continue
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+ self.mergeable_ranks[token] = index
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+ # the index may be sparse after this, but don't worry tiktoken.Encoding will handle this
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+
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+ enc = tiktoken.Encoding(
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+ "Qwen",
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+ pat_str=PAT_STR,
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+ mergeable_ranks=self.mergeable_ranks,
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+ special_tokens=self.special_tokens,
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+ )
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+ assert (
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+ len(self.mergeable_ranks) + len(self.special_tokens) == enc.n_vocab
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+ ), f"{len(self.mergeable_ranks) + len(self.special_tokens)} != {enc.n_vocab} in encoding"
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+
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+ self.decoder = {
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+ v: k for k, v in self.mergeable_ranks.items()
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+ } # type: dict[int, bytes|str]
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+ self.decoder.update({v: k for k, v in self.special_tokens.items()})
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+
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+ self.tokenizer = enc # type: tiktoken.Encoding
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+
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+ self.eod_id = self.tokenizer.eot_token
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+ self.im_start_id = self.special_tokens[IMSTART]
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+ self.im_end_id = self.special_tokens[IMEND]
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+
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+ def __getstate__(self):
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+ # for pickle lovers
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+ state = self.__dict__.copy()
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+ del state["tokenizer"]
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+ return state
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+
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+ def __setstate__(self, state):
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+ # tokenizer is not python native; don't pass it; rebuild it
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+ self.__dict__.update(state)
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+ enc = tiktoken.Encoding(
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+ "Qwen",
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+ pat_str=PAT_STR,
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+ mergeable_ranks=self.mergeable_ranks,
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+ special_tokens=self.special_tokens,
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+ )
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+ self.tokenizer = enc
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+
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+ def __len__(self) -> int:
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+ return self.tokenizer.n_vocab
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+
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+ def get_vocab(self) -> Dict[bytes, int]:
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+ return self.mergeable_ranks
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+
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+ def convert_tokens_to_ids(
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+ self, tokens: Union[bytes, str, List[Union[bytes, str]]]
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+ ) -> List[int]:
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+ ids = []
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+ if isinstance(tokens, (str, bytes)):
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+ if tokens in self.special_tokens:
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+ return self.special_tokens[tokens]
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+ else:
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+ return self.mergeable_ranks.get(tokens)
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+ for token in tokens:
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+ if token in self.special_tokens:
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+ ids.append(self.special_tokens[token])
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+ else:
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+ ids.append(self.mergeable_ranks.get(token))
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+ return ids
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+
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+ def _add_tokens(
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+ self,
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+ new_tokens: Union[List[str], List[AddedToken]],
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+ special_tokens: bool = False,
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+ ) -> int:
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+ if not special_tokens and new_tokens:
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+ raise ValueError("Adding regular tokens is not supported")
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+ for token in new_tokens:
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+ surface_form = token.content if isinstance(token, AddedToken) else token
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+ if surface_form not in SPECIAL_TOKENS_SET:
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+ raise ValueError("Adding unknown special tokens is not supported")
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+ return 0
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+
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+ def save_vocabulary(self, save_directory: str, **kwargs) -> Tuple[str]:
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+ """
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+ Save only the vocabulary of the tokenizer (vocabulary).
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+
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+ Returns:
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+ `Tuple(str)`: Paths to the files saved.
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+ """
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+ file_path = os.path.join(save_directory, "qwen.tiktoken")
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+ with open(file_path, "w", encoding="utf8") as w:
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+ for k, v in self.mergeable_ranks.items():
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+ line = base64.b64encode(k).decode("utf8") + " " + str(v) + "\n"
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+ w.write(line)
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+ return (file_path,)
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+
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+ def tokenize(
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+ self,
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+ text: str,
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+ allowed_special: Union[Set, str] = "all",
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+ disallowed_special: Union[Collection, str] = (),
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+ **kwargs,
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+ ) -> List[Union[bytes, str]]:
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+ """
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+ Converts a string in a sequence of tokens.
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+
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+ Args:
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+ text (`str`):
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+ The sequence to be encoded.
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+ allowed_special (`Literal["all"]` or `set`):
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+ The surface forms of the tokens to be encoded as special tokens in regular texts.
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+ Default to "all".
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+ disallowed_special (`Literal["all"]` or `Collection`):
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+ The surface forms of the tokens that should not be in regular texts and trigger errors.
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+ Default to an empty tuple.
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+
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+ kwargs (additional keyword arguments, *optional*):
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+ Will be passed to the underlying model specific encode method.
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+
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+ Returns:
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+ `List[bytes|str]`: The list of tokens.
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+ """
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+ tokens = []
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+ text = unicodedata.normalize("NFC", text)
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+
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+ # this implementation takes a detour: text -> token id -> token surface forms
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+ for t in self.tokenizer.encode(
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+ text, allowed_special=allowed_special, disallowed_special=disallowed_special
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+ ):
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+ tokens.append(self.decoder[t])
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+ return tokens
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+
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+ def convert_tokens_to_string(self, tokens: List[Union[bytes, str]]) -> str:
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+ """
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+ Converts a sequence of tokens in a single string.
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+ """
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+ text = ""
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+ temp = b""
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+ for t in tokens:
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+ if isinstance(t, str):
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+ if temp:
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+ text += temp.decode("utf-8", errors=self.errors)
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+ temp = b""
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+ text += t
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+ elif isinstance(t, bytes):
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+ temp += t
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+ else:
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+ raise TypeError("token should only be of type types or str")
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+ if temp:
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+ text += temp.decode("utf-8", errors=self.errors)
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+ return text
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+
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+ @property
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+ def vocab_size(self):
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+ return self.tokenizer.n_vocab
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+
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+ def _convert_id_to_token(self, index: int) -> Union[bytes, str]:
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+ """Converts an id to a token, special tokens included"""
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+ if index in self.decoder:
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+ return self.decoder[index]
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+ raise ValueError("unknown ids")
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+
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+ def _convert_token_to_id(self, token: Union[bytes, str]) -> int:
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+ """Converts a token to an id using the vocab, special tokens included"""
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+ if token in self.special_tokens:
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+ return self.special_tokens[token]
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+ if token in self.mergeable_ranks:
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+ return self.mergeable_ranks[token]
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+ raise ValueError("unknown token")
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+
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+ def _tokenize(self, text: str, **kwargs):
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+ """
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+ Converts a string in a sequence of tokens (string), using the tokenizer. Split in words for word-based
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+ vocabulary or sub-words for sub-word-based vocabularies (BPE/SentencePieces/WordPieces).
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+
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+ Do NOT take care of added tokens.
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+ """
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+ raise NotImplementedError
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+
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+ def _decode(
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+ self,
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+ token_ids: Union[int, List[int]],
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+ skip_special_tokens: bool = False,
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+ errors: str = None,
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+ **kwargs,
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+ ) -> str:
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+ if isinstance(token_ids, int):
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+ token_ids = [token_ids]
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+ if skip_special_tokens:
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+ token_ids = [i for i in token_ids if i < self.eod_id]
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+ return self.tokenizer.decode(token_ids, errors=errors or self.errors)
tokenizer_config.json ADDED
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+ {
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+ "model_max_length": 32768,
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+ "tokenizer_class": "QWenTokenizer",
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+ "auto_map": {
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+ "AutoTokenizer": [
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+ "tokenization_qwen.QWenTokenizer",
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+ null
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+ ]
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+ },
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+ "bos_token": "<|endoftext|>",
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+ "eos_token": "<|endoftext|>",
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+ "pad_token": "<|extra_204|>"
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+ }