| """ | |
| Forked from the file src/transformers/models/bert_generation/tokenization_bert_generation.py from the HuggingFace Transformers library. | |
| Permalink: https://github.com/huggingface/transformers/blob/04ab5605fbb4ef207b10bf2772d88c53fc242e83/src/transformers/models/bert_generation/tokenization_bert_generation.py | |
| Tokenizer class for ReplitLM | |
| Class is modified for compatibility with custom vocabulary and to achieve desired encode/decode behavior for Replit Code V1 3B model. | |
| """ | |
| import os | |
| import sentencepiece as spm | |
| from shutil import copyfile | |
| from transformers import PreTrainedTokenizer | |
| from typing import Any, Dict, List, Optional, Tuple | |
| VOCAB_FILES_NAMES = {'vocab_file': 'spiece.model'} | |
| class ReplitLMTokenizer(PreTrainedTokenizer): | |
| """ | |
| Construct a ReplitLMTokenizer tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece). | |
| This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. | |
| Args: | |
| vocab_file (`str`): | |
| [SentencePiece](https://github.com/google/sentencepiece) file (generally has a *.spm* extension) that | |
| contains the vocabulary necessary to instantiate a tokenizer. | |
| eos_token (`str`, *optional*, defaults to `"<|endoftext|>"`): | |
| The end of sequence token. | |
| bos_token (`str`, *optional*, defaults to `None`): | |
| The begin of sequence token. | |
| unk_token (`str`, *optional*, defaults to `"<|unk|>"`): | |
| 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. | |
| pad_token (`str`, *optional*, defaults to `"<|pad|>"`): | |
| The token used for padding, for example when batching sequences of different lengths. | |
| sp_model_kwargs (`dict`, *optional*): | |
| Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for | |
| SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things, | |
| to set: | |
| - `enable_sampling`: Enable subword regularization. | |
| - `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout. | |
| - `nbest_size = {0,1}`: No sampling is performed. | |
| - `nbest_size > 1`: samples from the nbest_size results. | |
| - `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice) | |
| using forward-filtering-and-backward-sampling algorithm. | |
| - `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for | |
| BPE-dropout. | |
| """ | |
| vocab_files_names = VOCAB_FILES_NAMES | |
| prefix_tokens: List[int] = [] | |
| model_input_names = ['input_ids', 'attention_mask'] | |
| def __init__(self, vocab_file, bos_token=None, eos_token='<|endoftext|>', unk_token='<|unk|>', pad_token='<|pad|>', sep_token=None, sp_model_kwargs: Optional[Dict[str, Any]]=None, **kwargs) -> None: | |
| self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs | |
| super().__init__(bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, pad_token=pad_token, sep_token=sep_token, sp_model_kwargs=self.sp_model_kwargs, **kwargs) | |
| self.vocab_file = vocab_file | |
| self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs) | |
| self.sp_model.Load(vocab_file) | |
| def vocab_size(self): | |
| return self.sp_model.get_piece_size() | |
| def get_vocab(self): | |
| vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)} | |
| vocab.update(self.added_tokens_encoder) | |
| return vocab | |
| def __getstate__(self): | |
| state = self.__dict__.copy() | |
| state['sp_model'] = None | |
| return state | |
| def __setstate__(self, d): | |
| self.__dict__ = d | |
| if not hasattr(self, 'sp_model_kwargs'): | |
| self.sp_model_kwargs = {} | |
| self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs) | |
| self.sp_model.load(self.vocab_file) | |
| def _tokenize(self, text: str) -> List[str]: | |
| """Take as input a string and return a list of strings (tokens) for words/sub-words""" | |
| return self.sp_model.encode(text, out_type=str) | |
| def _convert_token_to_id(self, token): | |
| """Converts a token (str) in an id using the vocab.""" | |
| return self.sp_model.piece_to_id(token) | |
| def _convert_id_to_token(self, index): | |
| """Converts an index (integer) in a token (str) using the vocab.""" | |
| token = self.sp_model.id_to_piece(index) | |
| return token | |
| def convert_tokens_to_string(self, tokens): | |
| """Converts a sequence of tokens (string) in a single string.""" | |
| return self.sp_model.decode(tokens) | |
| 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) | |
| elif not os.path.isfile(self.vocab_file): | |
| with open(out_vocab_file, 'wb') as fi: | |
| content_spiece_model = self.sp_model.serialized_model_proto() | |
| fi.write(content_spiece_model) | |
| return (out_vocab_file,) |