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128 lines
6.0 KiB
128 lines
6.0 KiB
# 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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"""Tokenization class for model PEGASUS."""
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from tokenizers import Regex, Tokenizer, decoders, normalizers, pre_tokenizers, processors
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from tokenizers.models import Unigram
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from ...tokenization_utils_tokenizers import TokenizersBackend
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from ...utils import logging
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logger = logging.get_logger(__name__)
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VOCAB_FILES_NAMES = {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}
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class PegasusTokenizer(TokenizersBackend):
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r"""
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Construct a PEGASUS tokenizer (backed by HuggingFace's *tokenizers* library). Based on
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[Unigram](https://huggingface.co/docs/tokenizers/python/latest/components.html?highlight=unigram#models).
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This tokenizer inherits from [`TokenizersBackend`] which contains most of the main methods. Users should
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refer to this superclass for more information regarding those methods.
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Args:
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vocab_file (`str`, *optional*):
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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://huggingface.co/papers/1912.08777).
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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://huggingface.co/papers/1912.08777).
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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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offset (`int`, *optional*, defaults to 103):
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Offset for additional special tokens.
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vocab (`str` or `list[tuple[str, float]]`, *optional*):
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Custom vocabulary with `(token, score)` tuples. If not provided, a blank vocabulary is initialized.
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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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model = Unigram
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def __init__(
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self,
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vocab: str | list[tuple[str, float]] | None = None,
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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,
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**kwargs,
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):
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self.offset = offset
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if additional_special_tokens is None:
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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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if vocab is None:
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vocab = [(str(unk_token), 0.0), (str(pad_token), 0.0), (str(eos_token), 0.0), (str(mask_token), 0.0)]
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self._vocab = vocab
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self._tokenizer = Tokenizer(Unigram(vocab=vocab, unk_id=self._vocab.index((str(unk_token), 0.0), 1)))
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self._tokenizer.normalizer = normalizers.Sequence(
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[normalizers.Replace(Regex(r"\n"), " "), normalizers.Replace(Regex(r" {2,}"), " ")]
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)
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self._tokenizer.pre_tokenizer = pre_tokenizers.Metaspace(replacement="▁", prepend_scheme="always", split=True)
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self._tokenizer.decoder = decoders.Metaspace(replacement="▁", prepend_scheme="always", split=True)
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super().__init__(
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pad_token=pad_token,
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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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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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**kwargs,
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)
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self._tokenizer.post_processor = processors.TemplateProcessing(
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single=f"$A {eos_token}",
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pair=f"$A $B {eos_token}",
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special_tokens=[(str(eos_token), self.convert_tokens_to_ids(str(eos_token)))],
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)
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__all__ = ["PegasusTokenizer"]
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