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import os |
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from shutil import copyfile |
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from typing import Optional, Tuple |
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from tokenizers import processors |
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from transformers.tokenization_utils_fast import PreTrainedTokenizerFast |
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from transformers.utils import is_sentencepiece_available, logging |
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from transformers.utils.versions import require_version |
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require_version("tokenizers>=0.13.3") |
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logger = logging.get_logger(__name__) |
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VOCAB_FILES_NAMES = {"vocab_file": "tokenizer.model", "tokenizer_file": "tokenizer.json"} |
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DEFAULT_SYSTEM_PROMPT = """You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your \ |
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answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\ |
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that your responses are socially unbiased and positive in nature. |
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If a question does not make any sense, or is not factually coherent, explain why instead of answering something not \ |
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correct. If you don't know the answer to a question, please don't share false information.""" |
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class CrystalCoderTokenizerFast(PreTrainedTokenizerFast): |
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vocab_files_names = VOCAB_FILES_NAMES |
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slow_tokenizer_class = None |
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padding_side = "left" |
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model_input_names = ["input_ids", "attention_mask"] |
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def __init__( |
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self, |
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vocab_file=None, |
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tokenizer_file=None, |
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clean_up_tokenization_spaces=False, |
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unk_token="<|unk|>", |
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bos_token="<|startoftext|>", |
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eos_token="<|endoftext|>", |
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add_bos_token=False, |
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add_eos_token=False, |
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use_default_system_prompt=False, |
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**kwargs, |
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): |
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super().__init__( |
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vocab_file=vocab_file, |
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tokenizer_file=tokenizer_file, |
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clean_up_tokenization_spaces=clean_up_tokenization_spaces, |
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unk_token=unk_token, |
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bos_token=bos_token, |
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eos_token=eos_token, |
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use_default_system_prompt=use_default_system_prompt, |
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**kwargs, |
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) |
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self._add_bos_token = add_bos_token |
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self._add_eos_token = add_eos_token |
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self.update_post_processor() |
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self.use_default_system_prompt = use_default_system_prompt |
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self.vocab_file = vocab_file |
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@property |
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def can_save_slow_tokenizer(self) -> bool: |
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return os.path.isfile(self.vocab_file) if self.vocab_file else False |
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def update_post_processor(self): |
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""" |
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Updates the underlying post processor with the current `bos_token` and `eos_token`. |
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""" |
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bos = self.bos_token |
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bos_token_id = self.bos_token_id |
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if bos is None and self.add_bos_token: |
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raise ValueError("add_bos_token = True but bos_token = None") |
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eos = self.eos_token |
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eos_token_id = self.eos_token_id |
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if eos is None and self.add_eos_token: |
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raise ValueError("add_eos_token = True but eos_token = None") |
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single = f"{(bos+':0 ') if self.add_bos_token else ''}$A:0{(' '+eos+':0') if self.add_eos_token else ''}" |
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pair = f"{single}{(' '+bos+':1') if self.add_bos_token else ''} $B:1{(' '+eos+':1') if self.add_eos_token else ''}" |
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special_tokens = [] |
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if self.add_bos_token: |
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special_tokens.append((bos, bos_token_id)) |
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if self.add_eos_token: |
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special_tokens.append((eos, eos_token_id)) |
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self._tokenizer.post_processor = processors.TemplateProcessing( |
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single=single, pair=pair, special_tokens=special_tokens |
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) |
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@property |
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def add_eos_token(self): |
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return self._add_eos_token |
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@property |
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def add_bos_token(self): |
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return self._add_bos_token |
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@add_eos_token.setter |
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def add_eos_token(self, value): |
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self._add_eos_token = value |
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self.update_post_processor() |
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@add_bos_token.setter |
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def add_bos_token(self, value): |
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self._add_bos_token = value |
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self.update_post_processor() |
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def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: |
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if not self.can_save_slow_tokenizer: |
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raise ValueError( |
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"Your fast tokenizer does not have the necessary information to save the vocabulary for a slow " |
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"tokenizer." |
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) |
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if not os.path.isdir(save_directory): |
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logger.error(f"Vocabulary path ({save_directory}) should be a directory") |
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return |
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out_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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if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file): |
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copyfile(self.vocab_file, out_vocab_file) |
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return (out_vocab_file,) |
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def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None): |
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bos_token_id = [self.bos_token_id] if self.add_bos_token else [] |
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eos_token_id = [self.eos_token_id] if self.add_eos_token else [] |
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output = bos_token_id + token_ids_0 + eos_token_id |
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if token_ids_1 is not None: |
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output = output + bos_token_id + token_ids_1 + eos_token_id |
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return output |
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