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"""Tokenization classes for RWKV5.""" |
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import os |
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import re |
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from typing import TYPE_CHECKING, List, Optional, Tuple |
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from transformers.tokenization_utils import AddedToken, PreTrainedTokenizer |
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from transformers.utils import logging |
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if TYPE_CHECKING: |
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pass |
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logger = logging.get_logger(__name__) |
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VOCAB_FILES_NAMES = { |
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"vocab_file": "vocab.txt", |
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} |
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PRETRAINED_VOCAB_FILES_MAP = { |
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"vocab_file": { |
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"ArthurZ/rwkv-5-utf": "https://huggingface.co/ArthurZ/rwkv-5-utf/blob/main/vocab.txt", |
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}, |
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} |
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def whitespace_tokenize(text): |
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"""Runs basic whitespace cleaning and splitting on a piece of text. |
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The separators are kept |
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""" |
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text = text.strip() |
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if not text: |
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return [] |
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tokens = re.split(b"(?= )", text) |
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return tokens |
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class WordpieceTokenizer(object): |
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"""Runs WordPiece tokenization.""" |
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def __init__(self, vocab, unk_token): |
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self.vocab = vocab |
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self.unk_token = unk_token |
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def tokenize(self, text): |
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""" |
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Tokenizes a piece of text into its word pieces. This uses a greedy longest-match-first algorithm to perform |
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tokenization using the given vocabulary. |
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For example, `input = "unaffable"` wil return as output `["un", "##aff", "##able"]`. |
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Args: |
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text: A single token or whitespace separated tokens. This should have |
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already been passed through *BasicTokenizer*. |
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Returns: |
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A list of wordpiece tokens. |
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""" |
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output_tokens = [] |
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for token in whitespace_tokenize(text): |
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chars = list(token) |
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is_bad = False |
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start = 0 |
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sub_tokens = [] |
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while start < len(chars): |
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end = len(chars) |
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cur_substr = None |
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while start < end: |
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substr = bytes(chars[start:end]) |
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if substr in self.vocab: |
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cur_substr = substr |
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break |
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end -= 1 |
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if cur_substr is None: |
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is_bad = True |
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break |
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try: |
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cur_substr = cur_substr.decode() |
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except UnicodeDecodeError: |
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cur_substr = str(cur_substr) |
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sub_tokens.append(cur_substr) |
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start = end |
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if is_bad: |
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output_tokens.append(self.unk_token) |
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else: |
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output_tokens.extend(sub_tokens) |
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return output_tokens |
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class Rwkv5Tokenizer(PreTrainedTokenizer): |
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vocab_files_names = VOCAB_FILES_NAMES |
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pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP |
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max_model_input_sizes = {"ArthurZ/rwkv-5-utf": 2048} |
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model_input_names = ["input_ids", "attention_mask"] |
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def __init__(self, vocab_file, bos_token="<s>", eos_token="<s>", unk_token="<s>", **kwargs): |
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if not os.path.isfile(vocab_file): |
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raise ValueError( |
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f"Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained" |
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" model use `tokenizer = BertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`" |
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) |
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with open(vocab_file, "r") as reader: |
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tokens = reader.readlines() |
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vocab = {} |
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for index, token in enumerate(tokens): |
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token = eval(token.rstrip("\n")) |
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vocab[token] = index |
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self.add_bos_token = True |
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self.encoder = vocab |
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self.decoder = {v: k for k, v in vocab.items()} |
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self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.encoder, unk_token=str(unk_token)) |
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self._added_tokens_decoder = {0: AddedToken(str(bos_token))} |
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super().__init__(bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, **kwargs) |
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@property |
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def vocab_size(self): |
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return len(self.encoder) |
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def get_vocab(self): |
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vocab = {str(self.convert_ids_to_tokens(i)): i for i in range(self.vocab_size)} |
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vocab.update(self.added_tokens_encoder) |
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return vocab |
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def _tokenize(self, text, split_special_tokens=False): |
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return self.wordpiece_tokenizer.tokenize(text.encode("utf-8")) |
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def _convert_token_to_id(self, token): |
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"""Converts a token (byte) to an id using the vocab.""" |
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if token.startswith("b'\\"): |
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token = eval(token) |
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elif not isinstance(token, bytes): |
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token = token.encode("utf-8", errors="replace") |
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return self.encoder.get(token, self.unk_token_id) |
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def _convert_id_to_token(self, index): |
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"""Converts an index (integer) in a token (byte) using the vocab.""" |
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token = self.decoder.get(index, self.unk_token) |
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if isinstance(token, (bytes)): |
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token = token.decode("utf-8", errors="replace") |
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return token |
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def convert_tokens_to_string(self, tokens): |
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"""Converts a sequence of tokens (bytes) in a single string. Additional tokens are encoded to bytes""" |
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out_string = b"".join([k.encode(errors="replace") if isinstance(k, str) else k for k in tokens]).decode( |
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"utf-8" |
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) |
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return out_string |
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def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: |
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index = 0 |
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if os.path.isdir(save_directory): |
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vocab_file = os.path.join( |
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save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] |
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) |
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else: |
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vocab_file = (filename_prefix + "-" if filename_prefix else "") + save_directory |
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with open(vocab_file, "w") as writer: |
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for token, token_index in sorted(self.encoder.items(), key=lambda kv: kv[1]): |
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if index != token_index: |
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logger.warning( |
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f"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive." |
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" Please check that the vocabulary is not corrupted!" |
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) |
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index = token_index |
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writer.write(str(token) + "\n") |
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index += 1 |
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return (vocab_file,) |
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def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None): |
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if self.add_bos_token: |
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bos_token_ids = [self.bos_token_id] |
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else: |
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bos_token_ids = [] |
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output = bos_token_ids + token_ids_0 |
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if token_ids_1 is None: |
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return output |
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return output + bos_token_ids + token_ids_1 |
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def get_special_tokens_mask( |
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self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False |
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) -> List[int]: |
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""" |
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Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding |
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special tokens using the tokenizer `prepare_for_model` or `encode_plus` methods. |
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Args: |
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token_ids_0 (`List[int]`): |
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List of IDs. |
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token_ids_1 (`List[int]`, *optional*): |
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Optional second list of IDs for sequence pairs. |
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already_has_special_tokens (`bool`, *optional*, defaults to `False`): |
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Whether or not the token list is already formatted with special tokens for the model. |
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Returns: |
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`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. |
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""" |
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if already_has_special_tokens: |
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return super().get_special_tokens_mask( |
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token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True |
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) |
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if not self.add_bos_token: |
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return super().get_special_tokens_mask( |
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token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=False |
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) |
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if token_ids_1 is None: |
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return [1] + ([0] * len(token_ids_0)) |
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return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) |
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