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"""Tokenization classes for Molformer.""" |
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import collections |
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import json |
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import os |
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import re |
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from typing import List, Optional, Tuple |
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from transformers.tokenization_utils import 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": "vocab.json"} |
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PRETRAINED_VOCAB_FILES_MAP = { |
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"vocab_file": { |
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"ibm/MoLFormer-XL-both-10pct": "https://huggingface.co/ibm/MoLFormer-XL-both-10pct/resolve/main/vocab.json", |
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} |
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} |
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PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { |
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"ibm/MoLFormer-XL-both-10pct": 202, |
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} |
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class MolformerTokenizer(PreTrainedTokenizer): |
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r""" |
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Construct a Molformer tokenizer. Based on regex. |
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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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File containing the vocabulary. |
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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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sep_token (`str`, *optional*, defaults to `"<eos>"`): |
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The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for |
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sequence classification or for a text and a question for question answering. It is also used as the last |
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token of a sequence built with special tokens. |
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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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cls_token (`str`, *optional*, defaults to `"<bos>"`): |
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The classifier token which is used when doing sequence classification (classification of the whole sequence |
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instead of per-token classification). It is the first token of the sequence when built with special tokens. |
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mask_token (`str`, *optional*, defaults to `"<mask>"`): |
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The token used for masking values. This is the token used when training this model with masked language |
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modeling. This is the token which the model will try to predict. |
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""" |
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vocab_files_names = VOCAB_FILES_NAMES |
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pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP |
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max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES |
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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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unk_token="<unk>", |
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sep_token="<eos>", |
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pad_token="<pad>", |
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cls_token="<bos>", |
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mask_token="<mask>", |
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**kwargs, |
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): |
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if not os.path.isfile(vocab_file): |
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raise ValueError( |
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f"Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from an IBM pretrained" |
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" model use `tokenizer = AutoTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`" |
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) |
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with open(vocab_file, encoding="utf-8") as vocab_handle: |
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self.vocab = json.load(vocab_handle) |
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self.ids_to_tokens = collections.OrderedDict([(ids, tok) for tok, ids in self.vocab.items()]) |
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self.pattern = ( |
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r"(\[[^\]]+]|Br?|Cl?|N|O|S|P|F|I|b|c|n|o|s|p|\(|\)|\.|=|#|-|\+|\\|\/|:|~|@|\?|>|\*|\$|\%[0-9]{2}|[0-9])" |
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) |
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self.regex_tokenizer = re.compile(self.pattern) |
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super().__init__( |
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unk_token=unk_token, |
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sep_token=sep_token, |
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pad_token=pad_token, |
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cls_token=cls_token, |
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mask_token=mask_token, |
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**kwargs, |
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) |
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@property |
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def vocab_size(self): |
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return len(self.vocab) |
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def get_vocab(self): |
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return dict(self.vocab, **self.added_tokens_encoder) |
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def _tokenize(self, text): |
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split_tokens = self.regex_tokenizer.findall(text) |
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return split_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.vocab.get(token, self.vocab.get(self.unk_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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return self.ids_to_tokens.get(index, self.unk_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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out_string = "".join(tokens).strip() |
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return out_string |
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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. A BERT sequence has the following format: |
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- single sequence: `[CLS] X [SEP]` |
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- pair of sequences: `[CLS] A [SEP] B [SEP]` |
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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 [self.cls_token_id] + token_ids_0 + [self.sep_token_id] |
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cls = [self.cls_token_id] |
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sep = [self.sep_token_id] |
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return cls + token_ids_0 + sep + token_ids_1 + sep |
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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 not None: |
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return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1] |
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return [1] + ([0] * len(token_ids_0)) + [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 [token type IDs](../glossary#token-type-ids) according to the given sequence(s). |
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""" |
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sep = [self.sep_token_id] |
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cls = [self.cls_token_id] |
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if token_ids_1 is None: |
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return len(cls + token_ids_0 + sep) * [0] |
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return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1] |
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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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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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with open(vocab_file, "w", encoding="utf-8") as f: |
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f.write(json.dumps(self.vocab, indent=2, sort_keys=True, ensure_ascii=False) + "\n") |
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return (vocab_file,) |
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