hinditokenizer.py

import regex as re
from base_tokenizer import Tokenizer, get_stats, merge

DEVANAGARI_SPLIT_PATTERN = r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{N}+| ?(?:[\u0904-\u0939\u093d-\u093d\u0950-\u0950\u0958-\u0961\u0970-\u097f\ua8f2-\ua8fe\U00011b00-\U00011b09\u1cd3-\u1cd3\u1ce9-\u1cec\u1cee-\u1cf3\u1cf5-\u1cf6\u1cfa-\u1cfa][\u0900-\u0903\u093a-\u093c\u093e-\u094f\u0951-\u0957\u0962-\u0963\ua8e0-\ua8f1\ua8ff-\ua8ff\u1cd0-\u1cd2\u1cd4-\u1ce8\u1ced-\u1ced\u1cf4-\u1cf4\u1cf7-\u1cf9]*)+| ?\p{L}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""


class DevanagariTokenizer(Tokenizer):

    def __init__(self, pattern=None):
        """
        - pattern: optional string to override the default (GPT-4 split pattern)
        - special_tokens: str -> int dictionary of special tokens
          example: {'<|endoftext|>': 100257}
        """
        super().__init__()
        self.pattern = DEVANAGARI_SPLIT_PATTERN if pattern is None else pattern
        self.compiled_pattern = re.compile(self.pattern)
        self.special_tokens = {}
        self.inverse_special_tokens = {}

    def train(self, text, vocab_size, verbose=False):
        assert vocab_size >= 256
        num_merges = vocab_size - 256

        # split the text up into text chunks
        text_chunks = re.findall(self.compiled_pattern, text)

        # input text preprocessing
        ids = [list(ch.encode("utf-8")) for ch in text_chunks]

        # iteratively merge the most common pairs to create new tokens
        merges = {}  # (int, int) -> int
        vocab = {idx: bytes([idx]) for idx in range(256)}  # idx -> bytes
        input_len = 0
        for chunk_ids in ids:
            # calculate length of tokens for compression ratio.
            # total token length is sum of all token length in each chunk.
            input_len += len(chunk_ids)
        for i in range(num_merges):
            # count the number of times every consecutive pair appears
            stats = {}
            for chunk_ids in ids:
                # passing in stats will update it in place, adding up counts
                get_stats(chunk_ids, stats)
            # find the pair with the highest count
            pair = max(stats, key=stats.get)
            # mint a new token: assign it the next available id
            idx = 256 + i
            # replace all occurrences of pair in ids with idx
            ids = [merge(chunk_ids, pair, idx) for chunk_ids in ids]
            # save the merge
            merges[pair] = idx
            vocab[idx] = vocab[pair[0]] + vocab[pair[1]]
            # prints
            if verbose:
                print(f"merge {i + 1}/{num_merges}: {pair} -> {idx} ({vocab[idx]}) had {stats[pair]} occurrences")
        output_len = 0
        for chunk_ids in ids:
            output_len += len(chunk_ids)
        print(f"input_len: {input_len}, output_len: {output_len} compression ratio: {input_len / output_len:.2f}X")

        # save class variables
        self.merges = merges  # used in encode()
        self.vocab = vocab  # used in decode()

    def register_special_tokens(self, special_tokens):
        # special_tokens is a dictionary of str -> int
        # example: {"<|endoftext|>": 100257}
        self.special_tokens = special_tokens
        self.inverse_special_tokens = {v: k for k, v in special_tokens.items()}

    def decode(self, ids):
        # given ids (list of integers), return Python string
        part_bytes = []
        # get the byte for the corresponding token from vocab
        for idx in ids:
            if idx in self.vocab:
                part_bytes.append(self.vocab[idx])
            elif idx in self.inverse_special_tokens:
                part_bytes.append(self.inverse_special_tokens[idx].encode("utf-8"))
            else:
                raise ValueError(f"invalid token id: {idx}")
        text_bytes = b"".join(part_bytes)
        text = text_bytes.decode("utf-8", errors="replace")
        return text

    def _encode_chunk(self, text_bytes):
        # return the token ids
        # let's begin. first, convert all bytes to integers in range 0..255
        ids = list(text_bytes)
        while len(ids) >= 2:
            # find the pair with the lowest merge index
            stats = get_stats(ids)
            pair = min(stats, key=lambda p: self.merges.get(p, float("inf")))
            # subtle: if there are no more merges available, the key will
            # result in an inf for every single pair, and the min will be
            # just the first pair in the list, arbitrarily
            # we can detect this terminating case by a membership check
            if pair not in self.merges:
                break  # nothing else can be merged anymore
            # otherwise let's merge the best pair (lowest merge index)
            idx = self.merges[pair]
            ids = merge(ids, pair, idx)
        return ids

    def encode_ordinary(self, text):
        """Encoding that ignores any special tokens."""
        # split text into chunks of text by categories defined in regex pattern
        text_chunks = re.findall(self.compiled_pattern, text)
        # all chunks of text are encoded separately, then results are joined
        ids = []
        for chunk in text_chunks:
            chunk_bytes = chunk.encode("utf-8")  # raw bytes
            chunk_ids = self._encode_chunk(chunk_bytes)
            ids.extend(chunk_ids)
        return ids

    def encode(self, text, allowed_special="none_raise"):
        """
        Unlike encode_ordinary, this function handles special tokens.
        allowed_special: can be "all"|"none"|"none_raise" or a custom set of special tokens
        if none_raise, then an error is raised if any special token is encountered in text
        this is the default tiktoken behavior right now as well
        any other behavior is either annoying, or a major footgun
        """
        # decode the user desire w.r.t. handling of special tokens
        special = None
        if allowed_special == "all":
            special = self.special_tokens
        elif allowed_special == "none":
            special = {}
        elif allowed_special == "none_raise":
            special = {}
            assert all(token not in text for token in self.special_tokens)
        elif isinstance(allowed_special, set):
            special = {k: v for k, v in self.special_tokens.items() if k in allowed_special}
        else:
            raise ValueError(f"allowed_special={allowed_special} not understood")
        if not special:
            # shortcut: if no special tokens, just use the ordinary encoding
            return self.encode_ordinary(text)
        # otherwise, we have to be careful with potential special tokens in text
        # we handle special tokens by splitting the text
        # based on the occurrence of any exact match with any of the special tokens
        # we can use re.split for this. note that surrounding the pattern with ()
        # makes it into a capturing group, so the special tokens will be included
        special_pattern = "(" + "|".join(re.escape(k) for k in special) + ")"
        special_chunks = re.split(special_pattern, text)
        # now all the special characters are separated from the rest of the text
        # all chunks of text are encoded separately, then results are joined
        ids = []
        for part in special_chunks:
            if part in special:
                # this is a special token, encode it separately as a special case
                ids.append(special[part])
            else:
                # this is an ordinary sequence, encode it normally
                ids.extend(self.encode_ordinary(part))
        return ids