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from tqdm import tqdm
from transformers import FillMaskPipeline, RobertaTokenizerFast
MAX_CTX_LEN = 512
SPACE_PREFIX = 'Ġ'
PRONOUN_TOKENS = {
'I', 'ĠI',
'you', 'You', 'Ġyou', 'ĠYou',
'he', 'He', 'Ġhe', 'ĠHe',
'she', 'She', 'Ġshe', 'ĠShe',
'it', 'It', 'Ġit', 'ĠIt',
'we', 'We', 'Ġwe', 'ĠWe',
'they', 'They', 'Ġthey', 'ĠThey',
'my', 'My', 'Ġmy', 'ĠMy',
'your', 'Your', 'Ġyour', 'ĠYour',
'his', 'His', 'Ġhis', 'ĠHis',
'her', 'Her', 'Ġher', 'ĠHer',
'its', 'Its', 'Ġits', 'ĠIts',
'our', 'Our', 'Ġour', 'ĠOur',
'their', 'Their', 'Ġtheir', 'ĠTheir',
'mine', 'Mine', 'Ġmine', 'ĠMine',
'yours', 'Yours', 'Ġyours', 'ĠYours',
'hers', 'Hers', 'Ġhers', 'ĠHers',
'ours', 'Ours', 'Ġours', 'ĠOurs',
'theirs', 'Theirs', 'Ġtheirs', 'ĠTheirs',
}
def count_tokens(tokenizer, text: str) -> int:
""" return number of tokens in string """
return len(tokenizer(text)['input_ids'])
def text_to_token_names(tokenizer, text: str) -> list[str]:
inputs = tokenizer(text)
ref_tokens = []
for id in inputs["input_ids"]:
token = tokenizer._convert_id_to_token(id)
ref_tokens.append(token)
return ref_tokens
def text_to_token_ids(tokenizer, text: str) -> list[str]:
return tokenizer(text)["input_ids"]
def has_at_least_one_pronoun(tokenizer, text: str) -> bool:
token_names = text_to_token_names(tokenizer, text)
for pronoun_token in PRONOUN_TOKENS:
if pronoun_token in token_names:
return True
return False
def chunk_to_contexts(tokenizer, text: str):
lines = text.splitlines()
for i in range(len(lines)):
# add lines before and after for context
ctx = [lines[i]]
focus_line_idx = 0
before_line_idx = i
after_line_idx = i
# try adding lines as context until we reach MAX_CTX_LEN
while True:
something_done = False
# try adding a line before
if before_line_idx - 1 >= 0:
before_candidate = [lines[before_line_idx - 1]] + ctx
assert len(before_candidate) == len(ctx) + 1
if count_tokens(tokenizer, "\n".join(before_candidate)) < MAX_CTX_LEN:
ctx = before_candidate
focus_line_idx += 1
before_line_idx -= 1
something_done = True
# try adding a line after
if after_line_idx + 1 < len(lines):
# after_candidate = ctx + "\n" + lines[after_line_idx + 1]
after_candidate = ctx + [lines[after_line_idx + 1]]
if count_tokens(tokenizer, "\n".join(after_candidate)) < MAX_CTX_LEN:
ctx = after_candidate
after_line_idx += 1
something_done = True
# if we can't add any line, we're done
if not something_done:
break
assert len("".join(f"{x}\n" for x in ctx).splitlines()) == len(ctx)
yield "".join(f"{x}\n" for x in ctx), focus_line_idx
def mask_pronouns(tokenizer: RobertaTokenizerFast, text: str) -> tuple[str, list[str]]:
""" replaces all pronouns in text with <mask> """
token_names = text_to_token_names(tokenizer, text)
masked_token_names = []
original_pronouns = []
for token_name in token_names:
if token_name in PRONOUN_TOKENS:
masked_token_names.append(tokenizer.mask_token)
original_pronouns.append(token_name)
else:
masked_token_names.append(token_name)
masked_text = tokenizer.decode(tokenizer.convert_tokens_to_ids(masked_token_names), skip_special_tokens=False)
# remove start and end tokens
return masked_text[len(tokenizer.bos_token):-len(tokenizer.eos_token)], original_pronouns
def uncase_token(token_name: str) -> str:
token_name = token_name.replace(' ', '')
token_name = token_name.replace(SPACE_PREFIX, '')
return token_name.lower()
def uncase_mask_result(mask_result):
uncased_token_probs = {uncase_token(k): 0 for k in PRONOUN_TOKENS}
for guess in mask_result:
uncased_token_str = uncase_token(guess['token_str'])
if uncased_token_str not in uncased_token_probs:
continue
uncased_token_probs[uncased_token_str] += guess['score']
return uncased_token_probs
def case_token_like(best_token_uncased: str, original_token: str, best_token_cased_str: str) -> str:
"""
:param best_token_uncased: the uncased, unspaced token that's the best match
:param original_token: the original token we are replacing
:param best_token_cased_str: the token str that's the best match. used for some cap
:return:
"""
space = (SPACE_PREFIX == original_token[0]) or (' ' == original_token[0])
cap = original_token[1 if space else 0].isupper()
if best_token_uncased == 'i':
cap = True
# if the original token was 'I', we can't use it for cap info
if original_token in ['I', 'ĠI']:
cap = best_token_cased_str.strip()[0].isupper()
if cap:
best_token_uncased = best_token_uncased[0].upper() + best_token_uncased[1:]
if space:
best_token_uncased = ' ' + best_token_uncased
return best_token_uncased
def fix_pronouns_in_text(
unmasker: FillMaskPipeline,
tokenizer,
text: str,
alpha: float = 0.05,
use_tqdm: bool = False,
tqdm_kwargs=None
) -> str:
"""
Fixes pronouns in MTL text
:param unmasker: unmasker pipeline
:param tokenizer: model tokenizer
:param text: text to fix
:param alpha: only replace the existing pronouns with probability less than alpha
:param use_tqdm: show tqdm progress bar
:param tqdm_kwargs: any tqdm args
:return: the fixed text
"""
if tqdm_kwargs is None:
tqdm_kwargs = {}
fixed_lines = []
ctxs = list(chunk_to_contexts(tokenizer, text))
ctxs_iter = tqdm(ctxs, smoothing=0.0, desc="Fixing pronouns", **tqdm_kwargs) if use_tqdm else ctxs
for ctx, focus_line_idx in ctxs_iter:
ctx_lines = ctx.splitlines()
focus_line = ctx_lines[focus_line_idx]
# we can skip focusing on lines without a pronoun
if not has_at_least_one_pronoun(tokenizer, focus_line):
fixed_lines.append(focus_line)
continue
# mask all pronouns
masked_ctx, original_pronouns = mask_pronouns(tokenizer, ctx)
# unmask pronouns
mask_results = unmasker(masked_ctx)
if isinstance(mask_results[0], dict):
mask_results = [mask_results]
unmasked_ctx = masked_ctx
for i, mask_result in enumerate(mask_results):
original_pronoun = original_pronouns[i]
uncased_original = uncase_token(original_pronoun)
uncased_result = uncase_mask_result(mask_result)
# if what was there doesn't make any sense, replace it
if uncased_result[uncased_original] < alpha:
best_uncased_pronoun = max(uncased_result, key=uncased_result.get)
# TODO: ensure correct type, possessive, adj, subject
best_cased_pronoun = case_token_like(best_uncased_pronoun, original_pronoun, mask_result[0]['token_str'])
unmasked_ctx = unmasked_ctx.replace(tokenizer.mask_token, best_cased_pronoun, 1)
else:
unmasked_ctx = unmasked_ctx.replace(tokenizer.mask_token, original_pronoun.replace(SPACE_PREFIX, ' '), 1)
fixed_lines.append(unmasked_ctx.splitlines()[focus_line_idx])
return "\n".join(fixed_lines)
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