tokenization_opencua.py

import os
import tiktoken

from logging import getLogger
from pathlib import Path
from typing import (
    cast,
    Tuple,
    Dict,
    Iterator,
    List,
    Union,
    Optional,
)
from shutil import copyfile
from tiktoken.load import load_tiktoken_bpe
from tokenizers import AddedToken
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers.models.gpt2.tokenization_gpt2 import bytes_to_unicode



logger = getLogger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "tiktoken.model"}

class TikTokenTokenizer(PreTrainedTokenizer):
    """
    Tokenizing and encoding/decoding text using the Tiktoken tokenizer. See megatron/tokenizer/tiktoken_tokenizer.py.

    This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
    this superclass for more information regarding those methods.

    Args:
        vocab_file (`str`):
            The path to the Tiktoken model file.
        bos_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<|begin_of_text|>",`):
            The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
        eos_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<|end_of_text|>"`):
            The end of sequence token.
        unk_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<|reserved_special_token_249|>"`):
            The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
            token instead. The second to last item in special_tokens.
        pad_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<|reserved_special_token_250|>"`):
            The token used for padding, for example when batching sequences of different lengths.
        additional_special_tokens (list of `str`, *optional*):
            A tuple or a list of additional tokens, which will be marked as `special`, meaning that they will be
            skipped when decoding if `skip_special_tokens` is set to `True`.
    """

    vocab_files_names = VOCAB_FILES_NAMES

    model_input_names = ["input_ids", "attention_mask"]

    special_tokens: Dict[str, int]

    num_reserved_special_tokens = 256

    pat_str = "|".join(
        [
            r"""[\p{Han}]+""",
            r"""[^\r\n\p{L}\p{N}]?[\p{Lu}\p{Lt}\p{Lm}\p{Lo}\p{M}&&[^\p{Han}]]*[\p{Ll}\p{Lm}\p{Lo}\p{M}&&[^\p{Han}]]+(?i:'s|'t|'re|'ve|'m|'ll|'d)?""",
            r"""[^\r\n\p{L}\p{N}]?[\p{Lu}\p{Lt}\p{Lm}\p{Lo}\p{M}&&[^\p{Han}]]+[\p{Ll}\p{Lm}\p{Lo}\p{M}&&[^\p{Han}]]*(?i:'s|'t|'re|'ve|'m|'ll|'d)?""",
            r"""\p{N}{1,3}""",
            r""" ?[^\s\p{L}\p{N}]+[\r\n]*""",
            r"""\s*[\r\n]+""",
            r"""\s+(?!\S)""",
            r"""\s+""",
        ]
    )

    def __init__(
        self,
        vocab_file,
        bos_token: Union[str, AddedToken]="[BOS]",
        eos_token: Union[str, AddedToken]="[EOS]",
        unk_token: Union[str, AddedToken, None]=None,
        pad_token: Union[str, AddedToken, None]=None,
        additional_special_tokens: List[str]=None,
        added_tokens_decoder: Optional[dict] = None,
        **kwargs,
    ):
        assert os.path.isfile(vocab_file), vocab_file

        if additional_special_tokens is None:
            # dumping mode
            used_special_tokens  = [
                "<|im_end|>", 
                "<|im_user|>", 
                "<|im_assistant|>", 
                "<|reserved_token_0|>",
                "<|start_header_id|>", 
                "<|end_header_id|>", 
                "<|reserved_token_1|>", 
                "[EOT]",  
                "<|im_system|>", 
                "<|reserved_token_2|>",
                "<|reserved_token_3|>",
                "<|reserved_token_4|>",
                "<|reserved_token_5|>",
                "<|reserved_token_6|>", 
                "<|reserved_token_7|>",
                "<|im_middle|>", 
                "<|media_begin|>",
                "<|media_content|>", 
                "<|media_end|>", 
                "<|media_placeholder|>", 
            ] 
            used_reserved_tokens = 8
            last_reserved_token_id = self.num_reserved_special_tokens - 4 - len(used_special_tokens)  + used_reserved_tokens - 1
            additional_special_tokens = used_special_tokens + [
                f"<|reserved_token_{i}|>"
                for i in range(used_reserved_tokens, last_reserved_token_id + 1)
            ]
            # num_reserved_special_tokens = additional_special_tokens + BOS + EOS + unk_token + pad_token
            assert len(additional_special_tokens) + 4 == self.num_reserved_special_tokens, f"additional_special_tokens num: {len(additional_special_tokens)} is not correct"
            # we assume that the instance is under initialization and unk_token and pad_token should be automatically inferred
            if unk_token is not None:
                raise ValueError("unk_token should not be set in dumping mode when additional_special_tokens is None")
            if pad_token is not None:
                raise ValueError("pad_token should not be set in dumping mode when additional_special_tokens is None")
            # last two reserved tokens
            unk_token = f"[UNK]"
            pad_token = f"[PAD]"

            logger.info(f"adding unk_token: {unk_token} and pad_token: {pad_token}")
            self.additional_special_tokens = additional_special_tokens
            special_tokens = [str(bos_token), str(eos_token)] + additional_special_tokens + [str(unk_token), str(pad_token)]

            self.vocab_file = vocab_file
            mergeable_ranks = load_tiktoken_bpe(vocab_file)
            num_base_tokens = len(mergeable_ranks)
            self.special_tokens = {
                token: num_base_tokens + i for i, token in enumerate(special_tokens)
            }
        else:
            self.additional_special_tokens = additional_special_tokens
            special_tokens_mapping = {
            i: added_tokens_decoder[i].content for i in added_tokens_decoder
        }

            self.vocab_file = vocab_file
            mergeable_ranks = load_tiktoken_bpe(vocab_file)
            num_base_tokens = len(mergeable_ranks)
            self.special_tokens = {
                special_tokens_mapping.get(i, f"<|reserved_token_{i}|>"): i
                for i in range(
                    num_base_tokens, num_base_tokens + self.num_reserved_special_tokens + 2
                )
            }



        self.model = tiktoken.Encoding(
            name=Path(vocab_file).name,
            pat_str=self.pat_str,
            mergeable_ranks=mergeable_ranks,
            special_tokens=self.special_tokens,
        )
        logger.info(f"Reloaded tiktoken model from {vocab_file}")

        self.n_words: int = self.model.n_vocab
        # BOS / EOS token IDs
        self.bos_id: int = self.special_tokens[str(bos_token)]
        self.eos_id: int = self.special_tokens[str(eos_token)]
        
        logger.info(
            f"#words: {self.n_words} - BOS ID: {self.bos_id} - EOS ID: {self.eos_id}"
        )

        self.pad_id: int = self.special_tokens[str(pad_token)]
        self.unk_id: int = self.special_tokens[str(unk_token)]
        self.byte_encoder = bytes_to_unicode()
        self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}

        self.decoder = {}
        for i in range(self.n_words):
            # Taken from https://gist.github.com/xenova/a452a6474428de0182b17605a98631ee
            decoding = ''.join([
                self.byte_encoder[ord(char)] for char in
                self.model.decode_single_token_bytes(i).decode('latin-1')
            ])
            self.decoder[i] = decoding

        self.encoder = {}
        for i in range(self.n_words):
            if i in self.decoder:
                self.encoder[self.decoder[i]] = i

        super().__init__(
            bos_token=bos_token,
            eos_token=eos_token,
            unk_token=unk_token,
            pad_token=pad_token,
            additional_special_tokens=self.additional_special_tokens,
            **kwargs,
        )
        self.all_special_ids_set = set(self.all_special_ids)

    def encode(
        self,
        text: str,
        allow_special_tokens = True,
        **kwargs
    ) -> List[int]:
        """
        Encodes a string into a list of token IDs.

        Args:
            text (str): The input string to be encoded.

        Returns:
            list[int]: A list of token IDs.
        """
        # If there are other args, we should call super().encode because there are a lot of code
        # to handle those args. supper().encode finally will call _tokenize and _convert_token_to_id.
        # NOTE: our encode method is not compatible with the super().encode method,
        #   e.g. split_special_tokens' default is True in our encode method.
        if len(kwargs) > 0:
            logger.warning( f"Calling super().encode with {kwargs}" )
            return super().encode(text, **kwargs)

        assert type(text) is str

        # The tiktoken tokenizer can handle <=400k chars without
        # pyo3_runtime.PanicException.
        TIKTOKEN_MAX_ENCODE_CHARS = 400_000

        # https://github.com/openai/tiktoken/issues/195
        # Here we iterate over subsequences and split if we exceed the limit
        # of max consecutive non-whitespace or whitespace characters.
        MAX_NO_WHITESPACES_CHARS = 25_000

        texts = self.pre_tokenizer_process(text)

        all_substrs = []
        for text in texts:
            substrs = (
                substr
                for i in range(0, len(text), TIKTOKEN_MAX_ENCODE_CHARS)
                for substr in self._split_whitespaces_or_nonwhitespaces(
                    text[i: i + TIKTOKEN_MAX_ENCODE_CHARS], MAX_NO_WHITESPACES_CHARS
                )
            )
            all_substrs.extend(substrs)

        t: List[int] = []
        for substr in all_substrs:
            if allow_special_tokens:
                t.extend(
                    self.model.encode(
                        substr,
                        allowed_special="all",
                    )
                )
            else:
                t.extend(
                    self.model.encode(
                        substr,
                        disallowed_special=(),
                    )
                )

        return t

    def decode(
        self,
        token_ids: Union[int, List[int]],
        **kwargs
    ) -> str:
        """
        Decodes a list of token IDs into a string.

        Args:
            token_ids (List[int]): The list of token IDs to be decoded.

        Returns:
            str: The decoded string.
        """
        # If there are other args, we should call super().decode because there are a lot of code
        # to handle those args. supper().encode finally will call convert_tokens_to_string and _convert_id_to_token.
        if len(kwargs) > 0:
            return super().decode(token_ids, **kwargs)

        if type(token_ids) is int:
            token_ids = [token_ids]

        return self.model.decode(cast(List[int], token_ids))

    @staticmethod
    def _split_whitespaces_or_nonwhitespaces(
        s: str, max_consecutive_slice_len: int
    ) -> Iterator[str]:
        """
        Splits the string `s` so that each substring contains no more than `max_consecutive_slice_len`
        consecutive whitespaces or consecutive non-whitespaces.
        """
        current_slice_len = 0
        current_slice_is_space = s[0].isspace() if len(s) > 0 else False
        slice_start = 0

        for i in range(len(s)):
            is_now_space = s[i].isspace()

            if current_slice_is_space ^ is_now_space:
                current_slice_len = 1
                current_slice_is_space = is_now_space
            else:
                current_slice_len += 1
                if current_slice_len > max_consecutive_slice_len:
                    yield s[slice_start:i]
                    slice_start = i
                    current_slice_len = 1
        yield s[slice_start:]

    def pre_tokenizer_process(self, text: str) -> List[str]:
        """
        pre-tokenizes the input text into a list of tokens.
        This method is used to split the input text into smaller chunks for internal processing.
        """
        return [text]


    """ ----- Below are the abstract methods required by PreTrainedTokenizer ----- """
    @property
    def vocab_size(self) -> int:
        return self.n_words

    def get_vocab(self) -> Dict[str, int]:
        return self.encoder

    def _tokenize(self, text: str, **kwargs) -> List[str]:
        return [
            self.decoder[t]
            for t in self.encode(text)
        ]

    def _convert_token_to_id(self, token: str) -> int:
        return self.encoder.get(token, self.unk_id)

    def _convert_id_to_token(self, index: int) -> str:
        return self.decoder.get(index)

    @staticmethod
    def clean_up_tokenization(out_string: str) -> str:
        return out_string

    def convert_tokens_to_string(self, tokens: List[str]) -> str:
        text = ''.join(tokens)
        text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', 'replace')
        return text

    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
        if not os.path.isdir(save_directory):
            raise ValueError(f"vocabulary path ({save_directory}) should be a directory")
        out_vocab_file = os.path.join(
            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
        )

        if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
            copyfile(self.vocab_file, out_vocab_file)

        return (out_vocab_file,)


class TikTokenV3(TikTokenTokenizer):
    num_reserved_special_tokens = 293 + 128
    pat_str = "(?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+"