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import os |
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from shutil import copyfile |
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from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple |
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import sentencepiece as spm |
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from tokenizers import processors |
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from transformers.tokenization_utils import AddedToken, PreTrainedTokenizer |
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from transformers.utils import logging |
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logger = logging.get_logger(__name__) |
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VOCAB_FILES_NAMES = {"vocab_file": "tokenizer.model"} |
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SPIECE_UNDERLINE = "▁" |
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class SEABPETokenizer(PreTrainedTokenizer): |
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""" |
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Construct a Llama tokenizer. Based on byte-level Byte-Pair-Encoding. The default padding token is unset as there is |
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no padding token in the original model. |
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Args: |
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vocab_file (`str`): |
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Path to the vocabulary file. |
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legacy (`bool`, *optional*, defaults to `True`): |
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Whether or not the `legacy` behaviour of the tokenizer should be used. Legacy is before the merge of #24622 |
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which includes fixes to properly handle tokens that appear after special tokens. |
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legacy means we are not modifying existing tokenizers without knowing. (And we need to manually update those core tokenizers) |
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A simple example: |
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- `legacy=True`: |
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```python |
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>>> from transformers import T5Tokenizer |
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>>> tokenizer = T5Tokenizer.from_pretrained("t5-base", legacy=True) |
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>>> tokenizer.encode("Hello <extra_id_0>.") |
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[8774, 32099, 3, 5, 1] |
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``` |
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- `legacy=False`: |
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```python |
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>>> from transformers import T5Tokenizer |
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>>> tokenizer = T5Tokenizer.from_pretrained("t5-base", legacy=False) |
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>>> tokenizer.encode("Hello <extra_id_0>.") # the extra space `[3]` is no longer here |
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[8774, 32099, 5, 1] |
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``` |
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Checkout the pull request and the issue [here](https://github.com/huggingface/transformers/pull/24565) for |
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more details. |
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""" |
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vocab_files_names = VOCAB_FILES_NAMES |
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def __init__( |
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self, |
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vocab_file, |
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unk_token='<|unk|>', |
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bos_token='<|bos|>', |
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eos_token='<|eos|>', |
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pad_token='<|pad|>', |
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mask_token='<|mask|>', |
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sp_model_kwargs: Optional[Dict[str, Any]] = None, |
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add_bos_token=False, |
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add_eos_token=False, |
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clean_up_tokenization_spaces=False, |
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legacy=None, |
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**kwargs, |
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): |
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mask_token = AddedToken(mask_token, lstrip=True, rstrip=True, special=True) if isinstance(mask_token, str) else mask_token |
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self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs |
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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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super().__init__( |
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bos_token=bos_token, |
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eos_token=eos_token, |
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unk_token=unk_token, |
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pad_token=pad_token, |
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mask_token=mask_token, |
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add_bos_token=add_bos_token, |
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add_eos_token=add_eos_token, |
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sp_model_kwargs=self.sp_model_kwargs, |
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clean_up_tokenization_spaces=clean_up_tokenization_spaces, |
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legacy=legacy, |
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**kwargs, |
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) |
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if legacy is None: |
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logger.warning_once( |
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f"You are using the default legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to" |
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" read the related pull request available at https://github.com/huggingface/transformers/pull/24565, and set the legacy attribute accordingly." |
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) |
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legacy = True |
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self.legacy = legacy |
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self.vocab_file = vocab_file |
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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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def __getstate__(self): |
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state = self.__dict__.copy() |
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state["sp_model"] = None |
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state["sp_model_proto"] = self.sp_model.serialized_model_proto() |
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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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self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs) |
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self.sp_model.LoadFromSerializedProto(self.sp_model_proto) |
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def build_inputs_with_special_tokens( |
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self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None |
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) -> List[int]: |
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""" |
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Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and |
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adding special tokens. An sequence has the following format: |
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- single sequence: `<|bos|> X <|eos|>` |
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- pair of sequences: `<|bos|> A <|eos|><|bos|> B <|eos|>` |
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Args: |
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token_ids_0 (`List[int]`): |
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List of IDs to which the special tokens will be added. |
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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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Returns: |
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`List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens. |
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""" |
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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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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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Retrieve 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` method. |
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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 token_ids_1 is None: |
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return [1] + ([0] * len(token_ids_0)) + [1] |
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return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1] |
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def create_token_type_ids_from_sequences( |
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self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None |
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) -> List[int]: |
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""" |
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Create a mask from the two sequences passed to be used in a sequence-pair classification task. A BERT sequence |
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pair mask has the following format: |
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``` |
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0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 |
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| first sequence | second sequence | |
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``` |
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If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s). |
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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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Returns: |
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`List[int]`: List of zeros. |
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""" |
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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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if token_ids_1 is None: |
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return len(bos_token_id + token_ids_0 + eos_token_id) * [0] |
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return len(bos_token_id + token_ids_0 + eos_token_id) * [0] + len(bos_token_id + token_ids_1 + eos_token_id) * [1] |
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@property |
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def vocab_size(self): |
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"""Returns vocab size""" |
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return self.sp_model.get_piece_size() |
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def get_vocab(self): |
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"""Returns vocab as a dict""" |
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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 tokenize(self, text, **kwargs) -> List[str]: |
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if not self.legacy: |
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text = SPIECE_UNDERLINE + text.replace(SPIECE_UNDERLINE, " ") |
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return super().tokenize(text, **kwargs) |
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def _tokenize(self, text): |
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""" |
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Returns a tokenized string. |
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Since the sentencepiece internal model always adds a SPIECE_UNDERLINE, at the beginning of the provided text, |
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we need to remove it by hand when the current text is a subsequence. This happens whenever the `self.tokenize` |
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function is called with specials tokens: the input is split on the special tokens, and each subsequence is |
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passed to `_tokenize`. Thus if a subsequence did not start with a `" "` or SPIECE_UNDERLINE, we have to remove |
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the extra `SPIECE_UNDERLINE` prepended. |
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""" |
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if not self.legacy: |
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is_first = text.startswith(SPIECE_UNDERLINE) |
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if is_first: |
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text = text[1:] |
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tokens = self.sp_model.encode(text, out_type=str) |
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if not self.legacy and not is_first and not text.startswith(" ") and tokens[0].startswith(SPIECE_UNDERLINE): |
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tokens = ([tokens[0][1:]] if len(tokens[0]) > 1 else []) + tokens[1:] |
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return tokens |
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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.IdToPiece(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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current_sub_tokens = [] |
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out_string = "" |
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prev_is_special = False |
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for i, token in enumerate(tokens): |
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if token in self.all_special_tokens: |
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if not prev_is_special and i != 0: |
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out_string += " " |
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out_string += self.sp_model.decode(current_sub_tokens) + token |
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prev_is_special = True |
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current_sub_tokens = [] |
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else: |
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current_sub_tokens.append(token) |
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prev_is_special = False |
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out_string += self.sp_model.decode(current_sub_tokens) |
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return out_string |
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def save_vocabulary(self, save_directory, filename_prefix: Optional[str] = None) -> Tuple[str]: |
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""" |
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Save the vocabulary and special tokens file to a directory. |
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Args: |
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save_directory (`str`): |
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The directory in which to save the vocabulary. |
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Returns: |
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`Tuple(str)`: Paths to the files saved. |
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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) 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,) |
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