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