257 lines
8.8 KiB
Python
257 lines
8.8 KiB
Python
# 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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"""Tokenization classes for QWen."""
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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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import tiktoken
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from transformers import PreTrainedTokenizer, AddedToken
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logger = logging.getLogger(__name__)
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VOCAB_FILES_NAMES = {"vocab_file": "qwen.tiktoken"}
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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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# changed to use actual index to avoid misconfiguration with vocabulary expansion
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def _load_tiktoken_b64(tiktoken_bpe_file: str, startRank=0) -> 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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ll = contents.splitlines()
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return {base64.b64decode(token): int(rank + startRank) for rank, token in enumerate(ll)}
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def _load_tiktoken_char(tiktoken_bpe_file: str, startRank=0) -> 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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ll = contents.splitlines()
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return {token: int(rank + startRank) for rank, token in enumerate(ll)}
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class QWenTokenizer(PreTrainedTokenizer):
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"""QWen tokenizer."""
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vocab_files_names = VOCAB_FILES_NAMES
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def __init__(
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self,
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vocab_file_b64,
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vocab_file_char,
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errors="replace",
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**kwargs,
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):
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super().__init__(**kwargs)
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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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self.mergeable_ranks = _load_tiktoken_b64(vocab_file_b64)
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self.mergeable_ranks.update(_load_tiktoken_char(vocab_file_char, len(self.mergeable_ranks)))
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self.model_max_length = 1024
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special = (
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"user",
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"assistant",
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ENDOFTEXT,
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IMSTART,
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IMEND,
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)
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extras = tuple((f"<|extra_{i}|>" for i in range(4096 - len(self.mergeable_ranks) - len(special))))
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special_tokens = tuple(
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enumerate(
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(special + extras),
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start=len(self.mergeable_ranks),
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)
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)
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self.special_tokens = {token: index for index, token in special_tokens}
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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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self.decoder = {v: k for k, v in self.mergeable_ranks.items()} # 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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self.tokenizer = enc # type: tiktoken.Encoding
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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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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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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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def __len__(self) -> int:
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return self.tokenizer.n_vocab
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def get_vocab(self) -> Dict[bytes, int]:
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return self.mergeable_ranks
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def convert_tokens_to_ids(self, tokens: Union[bytes, str, List[Union[bytes, str]]]) -> 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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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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return 0
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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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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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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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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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kwargs (additional keyword arguments, *optional*):
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Will be passed to the underlying model specific encode method.
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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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# this implementation takes a detour: text -> token id -> token surface forms
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for t in self.tokenizer.encode(text, allowed_special=allowed_special, disallowed_special=disallowed_special):
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tokens.append(self.decoder[t])
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return tokens
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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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@property
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def vocab_size(self):
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return self.tokenizer.n_vocab
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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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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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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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Do NOT take care of added tokens.
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"""
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raise NotImplementedError
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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)
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