286 lines
13 KiB
Python
286 lines
13 KiB
Python
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# coding=utf-8
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# Copyright 2020 Google and The HuggingFace Inc. team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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from shutil import copyfile
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from typing import Any, Dict, List, Optional, Tuple
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import sentencepiece as spm
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from ...tokenization_utils import AddedToken, PreTrainedTokenizer
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from ...utils import logging
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SPIECE_UNDERLINE = "▁"
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VOCAB_FILES_NAMES = {"vocab_file": "spiece.model"}
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logger = logging.get_logger(__name__)
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# TODO ArthurZ refactor this to only use the added_tokens_encoder
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class PegasusTokenizer(PreTrainedTokenizer):
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r"""
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Construct a PEGASUS 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. Users should refer to
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this superclass for more information regarding those 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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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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eos_token (`str`, *optional*, defaults to `"</s>"`):
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The end of sequence token.
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<Tip>
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When building a sequence using special tokens, this is not the token that is used for the end of sequence.
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The token used is the `sep_token`.
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</Tip>
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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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mask_token (`str`, *optional*, defaults to `"<mask_2>"`):
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The token used for masking single token values. This is the token used when training this model with masked
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language modeling (MLM). This is the token that the PEGASUS encoder will try to predict during pretraining.
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It corresponds to *[MASK2]* in [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive
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Summarization](https://arxiv.org/pdf/1912.08777.pdf).
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mask_token_sent (`str`, *optional*, defaults to `"<mask_1>"`):
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The token used for masking whole target sentences. This is the token used when training this model with gap
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sentences generation (GSG). This is the sentence that the PEGASUS decoder will try to predict during
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pretraining. It corresponds to *[MASK1]* in [PEGASUS: Pre-training with Extracted Gap-sentences for
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Abstractive Summarization](https://arxiv.org/pdf/1912.08777.pdf).
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additional_special_tokens (`List[str]`, *optional*):
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Additional special tokens used by the tokenizer. If no additional_special_tokens are provided <mask_2> and
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<unk_2, ..., unk_102> are used as additional special tokens corresponding to the [original PEGASUS
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tokenizer](https://github.com/google-research/pegasus/blob/939830367bcf411193d2b5eca2f2f90f3f9260ca/pegasus/ops/pretrain_parsing_ops.cc#L66)
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that uses the tokens 2 - 104 only for pretraining
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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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model_input_names = ["input_ids", "attention_mask"]
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def __init__(
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self,
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vocab_file,
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pad_token="<pad>",
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eos_token="</s>",
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unk_token="<unk>",
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mask_token="<mask_2>",
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mask_token_sent="<mask_1>",
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additional_special_tokens=None,
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offset=103, # entries 2 - 104 are only used for pretraining
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sp_model_kwargs: Optional[Dict[str, Any]] = None,
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**kwargs,
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) -> None:
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self.offset = offset
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if additional_special_tokens is not None:
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if not isinstance(additional_special_tokens, list):
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raise TypeError(
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f"additional_special_tokens should be of type {type(list)}, but is"
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f" {type(additional_special_tokens)}"
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)
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additional_special_tokens_extended = (
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([mask_token_sent] + additional_special_tokens)
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if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
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else additional_special_tokens
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)
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# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
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additional_special_tokens_extended += [
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f"<unk_{i}>" for i in range(len(additional_special_tokens_extended), self.offset - 1)
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]
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if len(set(additional_special_tokens_extended)) != len(additional_special_tokens_extended):
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raise ValueError(
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"Please make sure that the provided additional_special_tokens do not contain an incorrectly"
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f" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}."
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)
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additional_special_tokens = additional_special_tokens_extended
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else:
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additional_special_tokens_extended = []
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additional_special_tokens = [mask_token_sent] if mask_token_sent is not None else []
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additional_special_tokens += [f"<unk_{i}>" for i in range(2, self.offset)]
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self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
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self.mask_token_sent = mask_token_sent
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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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_added_tokens_decoder = {
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0: AddedToken(str(pad_token), special=True),
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1: AddedToken(str(eos_token), special=True),
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}
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if self.mask_token_sent is not None:
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_added_tokens_decoder[2] = AddedToken(mask_token_sent, special=True)
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_added_tokens_decoder[3] = AddedToken(str(mask_token), special=True)
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for i in range(2, self.offset):
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_added_tokens_decoder[len(_added_tokens_decoder)] = AddedToken(f"<unk_{i}>", special=True)
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# Force update as we want to make sure vocab is enforced (same as fast)
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self._added_tokens_decoder = kwargs.pop("added_tokens_decoder", {})
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self._added_tokens_decoder.update(_added_tokens_decoder)
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super().__init__(
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eos_token=eos_token,
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unk_token=unk_token,
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mask_token=mask_token,
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pad_token=pad_token,
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mask_token_sent=mask_token_sent,
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offset=offset,
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additional_special_tokens=additional_special_tokens,
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sp_model_kwargs=self.sp_model_kwargs,
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**kwargs,
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)
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@property
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def vocab_size(self) -> int:
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return len(self.sp_model) + self.offset
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def get_vocab(self) -> Dict[str, int]:
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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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# for backward compatibility
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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: str) -> int:
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"""Converts a token (str) to an id using the vocab."""
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sp_id = self.sp_model.piece_to_id(token)
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return sp_id + self.offset
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def _convert_id_to_token(self, index: int) -> str:
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"""Converts an index (integer) to a token (str) using the vocab."""
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if index < self.offset:
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return self.sp_model.IdToPiece(index)
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token = self.sp_model.IdToPiece(index - self.offset)
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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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for token in tokens:
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# make sure that special tokens are not decoded using sentencepiece model
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if token in self.all_special_tokens:
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out_string += self.sp_model.decode(current_sub_tokens) + token
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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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out_string += self.sp_model.decode(current_sub_tokens)
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return out_string.strip()
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def num_special_tokens_to_add(self, pair=False):
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"""Just EOS"""
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return 1
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def _special_token_mask(self, seq):
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all_special_ids = set(self.all_special_ids) # call it once instead of inside list comp
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all_special_ids.remove(self.unk_token_id) # <unk> is only sometimes special
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return [1 if x in all_special_ids else 0 for x in seq]
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def get_special_tokens_mask(
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self, token_ids_0: List, token_ids_1: Optional[List] = None, already_has_special_tokens: bool = False
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) -> List[int]:
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"""Get list where entries are [1] if a token is [eos] or [pad] else 0."""
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if already_has_special_tokens:
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return self._special_token_mask(token_ids_0)
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elif token_ids_1 is None:
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return self._special_token_mask(token_ids_0) + [1]
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else:
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return self._special_token_mask(token_ids_0 + token_ids_1) + [1]
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def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None) -> List[int]:
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"""
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Build model inputs from a sequence or a pair of sequences for sequence classification tasks by concatenating
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and adding special tokens. A PEGASUS sequence has the following format, where `X` represents the sequence:
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- single sequence: `X </s>`
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- pair of sequences: `A B </s>` (not intended use)
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BOS is never used. Pairs of sequences are not the expected use case, but they will be handled without a
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separator.
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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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if token_ids_1 is None:
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return token_ids_0 + [self.eos_token_id]
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# We don't expect to process pairs, but leave the pair logic for API consistency
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return token_ids_0 + token_ids_1 + [self.eos_token_id]
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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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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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