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import torch
def normalize_abbreviations(text):
text = text.replace(" n't ", "n't ")
text = text.replace(" N'T ", "N'T ")
text = text.replace(" 'll ", "'ll ")
text = text.replace(" 'LL ", "'LL ")
text = text.replace(" 're ", "'re ")
text = text.replace(" 'RE ", "'RE ")
text = text.replace(" 've ", "'ve ")
text = text.replace(" 'VE ", "'VE ")
text = text.replace(" 'm ", "'m ")
text = text.replace(" 'M ", "'M ")
text = text.replace(" 's ", "'s ")
text = text.replace(" 'S ", "'S ")
text = text.replace(" 'd ", "'d ")
text = text.replace(" 'D ", "'D ")
return text
def fix_quotes(text, quote_symbol='"'):
n_quotes = text.count(f" {quote_symbol}") + text.count(f"{quote_symbol} ") - text.count(f" {quote_symbol} ")
if (
n_quotes == 0
or (n_quotes % 2) == 1
or f"{quote_symbol}{quote_symbol}" in text
or f"{quote_symbol} {quote_symbol}" in text
):
return text
i, i_quote, n_changes = 0, 0, 0
while i < len(text):
if text[i] != quote_symbol or (i - 1 >= 0 and text[i - 1] != ' ' and i + 1 < len(text) and text[i + 1] != ' '):
i += 1
continue
if (i_quote % 2) == 0:
if i > 0 and text[i - 1] != ' ':
text = text[:i] + ' ' + text[i:]
i += 1
n_changes += 1
if i + 1 < len(text) and text[i + 1] == ' ':
text = text[:i + 1] + text[i + 2:]
n_changes += 1
else:
if i > 0 and text[i - 1] == ' ':
text = text[:i - 1] + text[i:]
i -= 1
n_changes += 1
if i + 1 < len(text) and text[i + 1].isalnum():
text = text[:i + 1] + ' ' + text[i + 1:]
n_changes += 1
i_quote += 1
i += 1
return text
def detokenize(tokens, compact_dashes=False):
text = ' '.join(tokens)
text = normalize_abbreviations(text)
if compact_dashes:
text = text.replace(' - ', '-')
for i in range(len(text) - 2, -1, -1):
if text[i] == '.' and (text[i + 1].isupper() or text[i + 1] in ['‘', '(', '[', '{']):
text = text[:i+1] + ' ' + text[i+1:]
elif text[i] in ['?', '!', '…', '’'] and (text[i + 1].isalnum() or text[i + 1] in ['‘', '(', '[', '{']):
text = text[:i+1] + ' ' + text[i+1:]
elif i > 2 and text[i] == '.' and text[i - 1] == '.' and text[i - 2] == '.' and text[i + 1] != ' ':
text = text[:i+1] + ' ' + text[i+1:]
elif i > 2 and text[i] == '.' and text[i - 1] == '.' and text[i - 2] == '.' and text[i + 1] != ' ':
text = text[:i+1] + ' ' + text[i+1:]
elif text[i] == ',' and (text[i + 1].isalpha() or text[i + 1] in ['‘', '(', '[', '{']):
text = text[:i+1] + ' ' + text[i+1:]
elif text[i] in [';', ')', ']', '}', '%'] and (text[i + 1].isalnum() or text[i + 1] in ['‘', '(', '[', '{']):
text = text[:i+1] + ' ' + text[i+1:]
elif text[i] == ':' and (text[i + 1] in ['‘', '(', '[', '{'] or (text[i + 1].isalnum() and (not text[i + 1].isnumeric() or i - 1 < 0 or not text[i - 1].isnumeric()))):
text = text[:i+1] + ' ' + text[i+1:]
elif text[i] in ['(', '[', '{'] and text[i + 1] == ' ':
text = text[:i+1] + text[i+2:]
elif text[i] == ' ' and text[i+1] in ['.', ';', ':', '?', '!', '…', ',', '’', ')', ']']:
text = text[:i] + text[i+1:]
elif i > 0 and text[i] == ' ' and text[i - 1] in ['$', '£', '€'] and text[i + 1].isnumeric():
text = text[:i] + text[i+1:]
elif i > 0 and text[i] == ' ' and text[i - 1].isnumeric() and text[i + 1] == '%':
text = text[:i] + text[i+1:]
text = fix_quotes(text, '"')
text = fix_quotes(text, "'")
spans = []
word_offset, char_offset = 0, 0
for i, ch in enumerate(text):
if ch == ' ':
if tokens[word_offset][char_offset] == ' ':
char_offset += 1
continue
assert ch == tokens[word_offset][char_offset], f"{text}\n{' '.join(tokens)}\n{tokens[word_offset]}\n{char_offset} {ch}"
if char_offset == 0:
start = i
if char_offset == len(tokens[word_offset]) - 1:
end = i + 1
spans.append((start, end))
word_offset += 1
char_offset = 0
else:
char_offset += 1
return text, spans
def calculate_spans(original_spans, encoding_offsets):
span_id = 0
subword_spans = [[] for _ in original_spans]
for i, (_, end) in enumerate(encoding_offsets):
subword_spans[span_id].append(i + 1)
while original_spans[span_id][1] <= end:
span_id += 1
if span_id < len(original_spans) and end > original_spans[span_id][0]:
subword_spans[span_id].append(i + 1)
if span_id == len(original_spans):
return subword_spans
return subword_spans
def subtokenize(tokens, tokenizer, compact_dashes=False):
text, spans = detokenize(tokens, compact_dashes=compact_dashes)
encoding = tokenizer(text, return_offsets_mapping=True)
spans = calculate_spans(spans, encoding["offset_mapping"][1:-1])
subwords = encoding["input_ids"]
subword_mask = torch.zeros(len(subwords), len(spans), dtype=torch.bool)
for word_id, subword_ids in enumerate(spans):
for subword_id in subword_ids:
subword_mask[subword_id + 1, word_id] = True
return subwords, subword_mask
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