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import string
from random import random
from random import sample
from utilities_language_general.esp_constants import nlp
from utilities_language_general.esp_constants import PHRASES
from utilities_language_general.esp_utils import check_token_bert
from utilities_language_general.esp_utils import fix_irregular_lemma
from utilities_language_general.esp_constants import BAD_USER_TARGET_WORDS
from utilities_language_general.esp_utils import get_distractors_from_model_bert
class SENTENCE:
def __init__(self, original: str, n_sentence: int, max_num_distractors):
self.original = original
self.n_sentence = n_sentence
self.max_num_distractors = max_num_distractors
self.parsed = nlp(self.original)
self.sentence_lemma_pos = []
self.sentence_phrases = []
self.target_words = []
self.text_with_masked_task = ''
def lemmatize_sentence(self):
for token in self.parsed:
lemma_pos = f'{token.lemma_}_{token.pos_}'
if token.pos_ in ('AUX', 'VERB', 'ADJ'):
lemma_pos = fix_irregular_lemma(lemma=lemma_pos)
self.sentence_lemma_pos.append((lemma_pos, token))
def bind_phrases(self):
previous_was_phrase = False
for i in range(len(self.sentence_lemma_pos) - 1):
phrase_candidate = f'{self.sentence_lemma_pos[i][0]}_{self.sentence_lemma_pos[i + 1][0]}'
if phrase_candidate in PHRASES and not previous_was_phrase:
# phrase is {phrase: {original_token1: spacy.token, original_token2: spacy.token}}
phrase = [
f'{self.sentence_lemma_pos[i][0]}_{self.sentence_lemma_pos[i + 1][0]}',
{
'original_token1': self.sentence_lemma_pos[i][1],
'original_token2': self.sentence_lemma_pos[i + 1][1]
}
]
self.sentence_phrases.append(phrase)
previous_was_phrase = True
else:
if not previous_was_phrase:
self.sentence_phrases.append(self.sentence_lemma_pos[i][1])
previous_was_phrase = False
def search_target_words_automatically(self, target_minimum: set, frequency_dict: dict = None):
for token in self.sentence_phrases:
if isinstance(token, list): # if token is a phrase
original_token1 = token[1]['original_token1']
original_token2 = token[1]['original_token2']
original_token1_tags = original_token1.morph.to_dict()
original_token2_tags = original_token2.morph.to_dict()
tags = dict()
if ('haber_AUX' == f'{original_token1.lemma_}_{original_token1.pos_}'
and original_token2.pos_ in ('VERB', 'ADJ', 'AUX')):
tags['VerbForm'] = 'Compuesto'
tags['Mood'] = original_token1_tags.get('Mood')
tags['Tense'] = original_token1_tags.get('Tense')
tags['Person'] = original_token1_tags.get('Person')
tags['Number'] = original_token1_tags.get('Number')
tags['Gender'] = None
else:
tags = original_token1_tags | original_token2_tags
not_ner = True if (original_token1.ent_type == 0 and original_token2.ent_type == 0) else False
target_word = {
'masked_sentence': self.original.replace(f'{original_token1.text} {original_token2.text}',
'[MASK]'),
'sentence_number': self.n_sentence,
'sentence_text': self.original,
'original_text': f'{original_token1.text} {original_token2.text}',
'lemma': token[0],
'pos': 'phrase',
'gender': tags.get('Gender'),
'tags': tags,
'position_in_sentence': self.original.find(original_token1.text),
'not_named_entity': not_ner,
'frequency_in_text': 0
}
self.target_words.append(target_word)
else: # if token is just a spacy.nlp token
if check_token_bert(token=token, current_minimum=target_minimum):
tags = token.morph.to_dict()
target_word = {
'masked_sentence': self.original.replace(token.text, '[MASK]'),
'sentence_number': self.n_sentence,
'sentence_text': self.original,
'original_text': token.text,
'lemma': token.lemma_,
'pos': ('simple', token.pos_),
'gender': tags.get('Gender'),
'number_children': len([child for child in token.children]),
'tags': tags,
'position_in_sentence': self.original.find(token.text),
'not_named_entity': True if token.ent_type == 0 else False,
'frequency_in_text': frequency_dict.get(token.lemma_, 1),
}
self.target_words.append(target_word)
def search_user_target_words(self, user_target_words: set = None, frequency_dict: dict = None):
for _utw in user_target_words:
if _utw in self.original:
parse_utw = nlp(_utw)
if ' ' in _utw:
tags = dict()
if ('haber_AUX' == f'{parse_utw[0].lemma_}_{parse_utw[0].pos_}'
and parse_utw[1].pos_ in ('VERB', 'ADJ', 'AUX')):
tags['VerbForm'] = 'Compuesto'
tags['Mood'] = parse_utw[0].morph.to_dict().get('Mood')
tags['Tense'] = parse_utw[0].morph.to_dict().get('Tense')
tags['Person'] = parse_utw[0].morph.to_dict().get('Person')
tags['Number'] = parse_utw[0].morph.to_dict().get('Number')
tags['Gender'] = None
else:
tags = parse_utw[0].morph.to_dict() | parse_utw[1].morph.to_dict()
user_target_word_lemma = '_'.join([f'{token.lemma_}_{token.pos_}' for token in parse_utw])
user_target_word_pos = 'phrase'
user_target_word_tags = tags
not_ner = True if (parse_utw[0].ent_type == 0 and parse_utw[1].ent_type == 0) else False
else:
user_target_word_lemma = f'{parse_utw[0].lemma_}_{parse_utw[0].pos_}'
user_target_word_pos = ('simple', parse_utw[0].pos_)
user_target_word_tags = parse_utw[0].morph.to_dict()
not_ner = parse_utw[0].ent_type == 0
target_word = {
'masked_sentence': self.original.replace(_utw, '[MASK]'),
'sentence_number': self.n_sentence,
'sentence_text': self.original,
'original_text': _utw,
'lemma': user_target_word_lemma,
'pos': user_target_word_pos,
'gender': user_target_word_tags.get('Gender'),
'tags': user_target_word_tags,
'position_in_sentence': self.original.find(_utw),
'not_named_entity': not_ner,
'frequency_in_text': frequency_dict.get(user_target_word_lemma, 1)
}
self.target_words.append(target_word)
def search_target_words(self, target_words_automatic_mode: bool, target_minimum,
user_target_words: set = None,
frequency_dict: dict = None):
if target_words_automatic_mode:
self.search_target_words_automatically(target_minimum=target_minimum,
frequency_dict=frequency_dict)
else:
self.search_user_target_words(user_target_words=user_target_words,
frequency_dict=frequency_dict)
def filter_target_words(self, target_words_automatic_mode):
c_position = 0
bad_target_words = []
for target_word in self.target_words:
position_difference = 3 if target_words_automatic_mode else 0
if not (target_word['position_in_sentence'] == 0
or abs(target_word['position_in_sentence'] - c_position) >= position_difference):
bad_target_words.append(target_word)
for btw in bad_target_words:
BAD_USER_TARGET_WORDS.append(btw['original_text'])
self.target_words.remove(btw)
class TASK:
def __init__(self, task_data, max_num_distractors):
self.task_data = task_data
self.distractors = None
self.distractors_number = 0
self.bad_target_word = False
self.pos = task_data['pos']
self.lemma = task_data['lemma']
self.gender = task_data['gender']
self.max_num_distractors = max_num_distractors
self.original_text = task_data['original_text']
self.sentence_text = task_data['sentence_text']
self.sentence_number = task_data['sentence_number']
self.masked_sentence = task_data['masked_sentence']
self.frequency_in_text = task_data['frequency_in_text']
self.position_in_sentence = task_data['position_in_sentence']
self.text_with_masked_task = task_data['text_with_masked_task']
self.result = ''
self.variants = []
def __repr__(self):
return '\n'.join([f'{key}\t=\t{value}' for key, value in self.__dict__.items()])
def attach_distractors_to_target_word(self, model, global_distractors, distractor_minimum,
level_name, max_frequency):
pos = self.pos[0] if self.pos[0] == 'phrase' else self.pos[1]
# distractors_full_text = get_distractors_from_model_bert(model=model, lemma=self.lemma, pos=pos,
# gender=self.gender, level_name=level_name,
# text_with_masked_task=self.text_with_masked_task,
# global_distractors=global_distractors,
# distractor_minimum=distractor_minimum,
# max_num_distractors=self.max_num_distractors)
distractors_sentence = get_distractors_from_model_bert(model=model, lemma=self.lemma, pos=pos,
gender=self.gender, level_name=level_name,
text_with_masked_task=self.masked_sentence,
global_distractors=global_distractors,
distractor_minimum=distractor_minimum,
max_num_distractors=self.max_num_distractors)
if distractors_sentence is None or self.frequency_in_text > max_frequency:
self.bad_target_word = True
self.distractors = None
else:
self.distractors = [d[0] for i, d in enumerate(distractors_sentence) if i < 15]
self.distractors_number = len(distractors_sentence) if distractors_sentence is not None else 0
def sample_distractors(self, num_distractors):
if not self.bad_target_word:
num_distractors = min(self.distractors_number, num_distractors) if num_distractors >= 4 else num_distractors
self.distractors = sample(self.distractors[:min(self.distractors_number, 10)], num_distractors)
def compile_task(self, max_num_distractors):
len_distractors = len(self.distractors)
len_variants = min(len_distractors, max_num_distractors) if max_num_distractors > 4 \
else max_num_distractors
letters = (f'({letter})' for letter in string.ascii_lowercase[:len_variants + 1])
try:
distractors = sample(self.distractors, len_variants) + [self.original_text, ]
except ValueError:
distractors = self.distractors + [self.original_text, ]
tmp_vars = [f'{item[0]} {item[1].replace("_", " ")}'
for item in zip(letters, sorted(distractors, key=lambda _: random()))]
self.variants.append((self.original_text, tmp_vars))
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