chore: import upstream snapshot with attribution
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# Copyright (c) Facebook, Inc. and its affiliates.
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#
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# This source code is licensed under the MIT license found in the
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# LICENSE file in the root directory of this source tree.
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import torch
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from fairseq.data import encoders
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def get_whole_word_mask(args, dictionary):
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bpe = encoders.build_bpe(args)
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if bpe is not None:
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def is_beginning_of_word(i):
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if i < dictionary.nspecial:
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# special elements are always considered beginnings
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return True
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tok = dictionary[i]
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if tok.startswith("madeupword"):
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return True
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try:
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return bpe.is_beginning_of_word(tok)
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except ValueError:
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return True
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mask_whole_words = torch.ByteTensor(
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list(map(is_beginning_of_word, range(len(dictionary))))
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)
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return mask_whole_words
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return None
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