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Model: torphix/stablelm-2-glados-v1 Source: Original Platform
2026-05-25 13:13:32 +08:00
commit 2002db5b39
12 changed files with 101744 additions and 0 deletions
--- a/tokenization_arcade100k.py
+++ b/tokenization_arcade100k.py
@@ -0,0 +1,292 @@
+# coding=utf-8
+# Copyright (c) 2023 Alibaba Cloud & Stability AI.
+#
+# Tongyi Qianwen LICENSE AGREEMENT:
+# https://github.com/QwenLM/Qwen/blob/5aa84bdfd3237b37f01bc88cd49b3279b9a71d0b/Tongyi%20Qianwen%20LICENSE%20AGREEMENT
+"""Tokenization classes for Arcade100k."""
+
+import base64
+import os
+import unicodedata
+from typing import Collection, Dict, List, Set, Tuple, Union
+
+import tiktoken
+from transformers.utils import logging
+from transformers import PreTrainedTokenizer, AddedToken
+
+logger = logging.get_logger(__name__)
+
+VOCAB_FILES_NAMES = {"vocab_file": "arcade100k.tiktoken"}
+NAME = "arcade100k"
+
+
+def _load_tiktoken_bpe(tiktoken_bpe_file: str) -> Dict[bytes, int]:
+    with open(tiktoken_bpe_file, "rb") as f:
+        contents = f.read()
+    return {
+        base64.b64decode(token): int(rank)
+        for token, rank in (line.split() for line in contents.splitlines() if line)
+    }
+
+
+ENDOFTEXT = "<|endoftext|>"
+FIM = [
+    "<|fim_prefix|>",
+    "<|fim_middle|>",
+    "<|fim_suffix|>",
+    "<|fim_pad|>",
+]
+# `StarCoder` Tokens
+CODE = [
+    "<gh_stars>",
+    "<filename>",
+    "<issue_start>",
+    "<issue_comment>",
+    "<issue_closed>",
+    "<jupyter_start>",
+    "<jupyter_text>",
+    "<jupyter_code>",
+    "<jupyter_output>",
+    "<empty_output>",
+    "<commit_before>",
+    "<commit_msg>",
+    "<commit_after>",
+    "<reponame>",
+]
+CHAT = [
+    "<|im_start|>",  # Chat: Input message start
+    "<|im_end|>",  # Chat: Input message end
+]
+PAUSE = "<|pause|>"  # Think before you speak (https://arxiv.org/abs/2310.02226)
+REGISTERS = [
+    f"<|reg{i}|>" for i in range(0, 8)
+]  # Register 0 sink token (https://arxiv.org/abs/2309.17453)
+ENDOFPROMPT = "<|endofprompt|>"
+SPECIAL_TOKENS_NAMES = (
+    [ENDOFTEXT]
+    + FIM
+    + CODE
+    + [ENDOFPROMPT]
+    + CHAT
+    + [PAUSE]
+    + REGISTERS
+    + ["<|extra0|>"]
+)
+START_ID = 100257
+SPECIAL_TOKENS = {t: START_ID + i for i, t in enumerate(SPECIAL_TOKENS_NAMES)}
+
+
+def _arcade100k(vocab_file: str):
+    mergeable_ranks = _load_tiktoken_bpe(vocab_file)
+
+    return {
+        "name": NAME,
+        "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+""",
+        "mergeable_ranks": mergeable_ranks,
+        "special_tokens": SPECIAL_TOKENS,
+    }
+
+
+class Arcade100kTokenizer(PreTrainedTokenizer):
+    """
+    Construct a Arcade100k tokenizer backed by `tiktoken`.
+
+    Args:
+        vocab_file (`str`):
+            Path to the vocabulary file.
+        errors (`str`, *optional*, defaults to `"replace"`):
+            How to handle errors in decoding UTF-8 byte sequences.
+            WARNING: the default behaviour of this function is lossy, since decoded bytes are not
+            guaranteed to be valid UTF-8. You can control this behaviour using the `errors` parameter,
+            for instance, setting `errors=strict`.
+    """
+
+    vocab_files_names = VOCAB_FILES_NAMES
+    model_input_names = ["input_ids", "attention_mask"]
+
+    def __init__(
+        self,
+        vocab_file: str,
+        errors: str = "replace",
+        **kwargs,
+    ):
+        super().__init__(errors=errors, **kwargs)
+        self.errors = errors
+
+        self._tiktoken_config = _arcade100k(vocab_file)
+        self.tokenizer = tiktoken.Encoding(**self._tiktoken_config)
+
+        # TODO: Remove this assertion
+        assert (
+            len(self.tokenizer._mergeable_ranks)
+            + len(self.tokenizer._special_tokens)
+            + 1
+            == self.tokenizer.n_vocab
+        ), f"{len(self.tokenizer._mergeable_ranks) + len(self.tokenizer._special_tokens)} != {self.tokenizer.n_vocab} in encoding"
+
+        self.decoder = {i: n for n, i in self.tokenizer._mergeable_ranks.items()}
+        self.decoder.update({i: n for n, i in self.tokenizer._special_tokens.items()})
+        # Provide default `eos_token` and `pad_token`
+        if self.eos_token is None:
+            self.eos_token = self.decoder[self.tokenizer.eot_token]
+        if self.pad_token is None:
+            self.pad_token = self.decoder[self.tokenizer.pad_token]
+
+        # Expose for convenience
+        self.mergeable_ranks = self.tokenizer._mergeable_ranks
+        self.special_tokens = self.tokenizer._special_tokens
+
+    def __len__(self):
+        return self.tokenizer.n_vocab
+
+    def __getstate__(self):
+        # Required for `pickle` support
+        state = self.__dict__.copy()
+        del state["tokenizer"]
+        return state
+
+    def __setstate__(self, state):
+        self.__dict__.update(state)
+        self.tokenizer = tiktoken.Encoding(**self._tiktoken_config)
+
+    @property
+    def vocab_size(self):
+        return self.tokenizer.n_vocab
+
+    def get_vocab(self) -> Dict[bytes, int]:
+        return self.tokenizer._mergeable_ranks
+
+    def convert_tokens_to_ids(
+        self, tokens: Union[bytes, str, List[Union[bytes, str]]]
+    ) -> List[int]:
+        ids = []
+        if isinstance(tokens, (str, bytes)):
+            if tokens in self.tokenizer._special_tokens:
+                return self.tokenizer._special_tokens[tokens]
+            else:
+                return self.tokenizer._mergeable_ranks.get(tokens)
+        for token in tokens:
+            if token in self.tokenizer._special_tokens:
+                ids.append(self.tokenizer._special_tokens[token])
+            else:
+                ids.append(self.tokenizer._mergeable_ranks.get(token))
+        return ids
+
+    def _add_tokens(
+        self,
+        new_tokens: Union[List[str], List[AddedToken]],
+        special_tokens: bool = False,
+    ) -> int:
+        if not special_tokens and new_tokens:
+            raise ValueError("Adding regular tokens is not supported")
+        for token in new_tokens:
+            surface_form = token.content if isinstance(token, AddedToken) else token
+            if surface_form not in SPECIAL_TOKENS:
+                raise ValueError("Adding unknown special tokens is not supported")
+        return 0
+
+    def save_vocabulary(self, save_directory: str, **kwargs) -> Tuple[str]:
+        """
+        Save only the vocabulary of the tokenizer (vocabulary).
+
+        Returns:
+            `Tuple(str)`: Paths to the files saved.
+        """
+        file_path = os.path.join(save_directory, "arcade100k.tiktoken")
+        with open(file_path, "w", encoding="utf8") as w:
+            for k, v in self.tokenizer._mergeable_ranks.items():
+                line = base64.b64encode(k).decode("utf8") + " " + str(v) + "\n"
+                w.write(line)
+        return (file_path,)
+
+    def tokenize(
+        self,
+        text: str,
+        allowed_special: Union[Set, str] = "all",
+        disallowed_special: Union[Collection, str] = (),
+        **kwargs,
+    ) -> List[Union[bytes, str]]:
+        """
+        Converts a string in a sequence of tokens.
+
+        Args:
+            text (`str`):
+                The sequence to be encoded.
+            allowed_special (`Literal["all"]` or `set`):
+                The surface forms of the tokens to be encoded as special tokens in regular texts.
+                Default to "all".
+            disallowed_special (`Literal["all"]` or `Collection`):
+                The surface forms of the tokens that should not be in regular texts and trigger errors.
+                Default to an empty tuple.
+
+            kwargs (additional keyword arguments, *optional*):
+                Will be passed to the underlying model specific encode method.
+
+        Returns:
+            `List[bytes|str]`: The list of tokens.
+        """
+        tokens = []
+        text = unicodedata.normalize("NFC", text)
+
+        # this implementation takes a detour: text -> token id -> token surface forms
+        for t in self.tokenizer.encode(
+            text, allowed_special=allowed_special, disallowed_special=disallowed_special
+        ):
+            tokens.append(self.decoder[t])
+        return tokens
+
+    def convert_tokens_to_string(self, tokens: List[Union[bytes, str]]) -> str:
+        """
+        Converts a sequence of tokens in a single string.
+        """
+        text = ""
+        temp = b""
+        for t in tokens:
+            if isinstance(t, str):
+                if temp:
+                    text += temp.decode("utf-8", errors=self.errors)
+                    temp = b""
+                text += t
+            elif isinstance(t, bytes):
+                temp += t
+            else:
+                raise TypeError("token should only be of type types or str")
+        if temp:
+            text += temp.decode("utf-8", errors=self.errors)
+        return text
+
+    def _convert_id_to_token(self, index: int) -> Union[bytes, str]:
+        """Converts an id to a token, special tokens included"""
+        if index in self.decoder:
+            return self.decoder[index]
+        raise ValueError("unknown ids")
+
+    def _convert_token_to_id(self, token: Union[bytes, str]) -> int:
+        """Converts a token to an id using the vocab, special tokens included"""
+        if token in self.tokenizer._special_tokens:
+            return self.tokenizer._special_tokens[token]
+        if token in self.tokenizer._mergeable_ranks:
+            return self.tokenizer._mergeable_ranks[token]
+        raise ValueError("unknown token")
+
+    def _tokenize(self, text: str, **kwargs):
+        """
+        Converts a string in a sequence of tokens (string), using the tokenizer. Split in words for word-based
+        vocabulary or sub-words for sub-word-based vocabularies (BPE/SentencePieces/WordPieces).
+
+        Do NOT take care of added tokens.
+        """
+        raise NotImplementedError
+
+    def _decode(
+        self,
+        token_ids: Union[int, List[int]],
+        skip_special_tokens: bool = False,
+        errors: str = None,
+        **kwargs,
+    ) -> str:
+        if isinstance(token_ids, int):
+            token_ids = [token_ids]
+        if skip_special_tokens:
+            token_ids = [i for i in token_ids if i < self.tokenizer.eot_token]
+        return self.tokenizer.decode(token_ids)