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import os
import json
import re
from word2number.w2n import word_to_num as english_word2int
import torch
from transformers import T5ForConditionalGeneration, T5Config, T5Tokenizer
from src.models.base import BaseModel
from src.enum import T5DomainClassTypes, T5PriceSubclassTypes
from src.misc.schemas import (
PriceExtractionSchema,
ProductNamedEntityExtractionSchema,
SpecsExtractionSchema,
SpecificationSchema,
)
def arabic_word2int(textnum, numwords={}):
if not numwords:
units = [
"",
"واحد",
"اثنان",
"ثلاثة",
"أربعة",
"خمسة",
"ستة",
"سبعة",
"ثمانية",
"تسعة",
"عشرة",
"أحد عشر",
"اثنا عشر",
"ثلاثة عشر",
"أربعة عشر",
"خمسة عشر",
"ستة عشر",
"سبعة عشر",
"ثمانية عشر",
"تسعة عشر",
]
tens = [
"عشرون",
"ثلاثون",
"أربعون",
"خمسون",
"ستون",
"سبعون",
"ثمانون",
"تسعون",
]
scales = ["مية", "الف", "مليون", "مليار", "ترليون"]
numwords["و"] = (1, 0)
for idx, word in enumerate(units):
numwords[word] = (1, idx)
for idx, word in enumerate(tens):
numwords[word] = (1, (idx + 2) * 10)
for idx, word in enumerate(scales):
numwords[word] = (10 ** (idx * 3 or 2), 0)
current = result = 0
for word in textnum.split():
if word not in numwords:
raise Exception("Illegal word: " + word)
scale, increment = numwords[word]
current = current * scale + increment
if scale > 100:
result += current
current = 0
return result + current
ARABIC_TEXT_PATTERN = r"[\u0600-\u06ff]|[\u0750-\u077f]|[\ufb50-\ufc3f]|[\ufe70-\ufefc]"
class T5NERModel(BaseModel):
def __init__(
self,
model_ckpt_dir: str = os.path.join("ckpt", "ner_model"),
device: Literal["cpu", "cuda"] = "cpu",
) -> None:
super().__init__()
self.domains = [
line.replace("\n", "")
for line in open(
os.path.join(model_ckpt_dir, "domain_special_tokens.txt"), "r"
).readlines()
if "[" in line and "]" in line
]
self.slots = [
line.replace("\n", "")
for line in open(
os.path.join(model_ckpt_dir, "slot_special_tokens.txt"), "r"
).readlines()
if "[" not in line or "]" not in line
]
self.device = device
self.model = T5ForConditionalGeneration.from_pretrained(model_ckpt_dir)
self.model.to(device)
self.model.eval()
self.tokenizer = T5Tokenizer.from_pretrained(model_ckpt_dir)
self.t5config = T5Config.from_pretrained(model_ckpt_dir)
self.sos_context_token_id = self.tokenizer.convert_tokens_to_ids(
["<sos_context>"]
)[0]
self.eos_context_token_id = self.tokenizer.convert_tokens_to_ids(
["<eos_context>"]
)[0]
bos_token_id = self.t5config.decoder_start_token_id
eos_token_id = self.tokenizer.eos_token_id
# bos_token = self.tokenizer.convert_ids_to_tokens([bos_token_id])[0]
# eos_token = self.tokenizer.convert_ids_to_tokens([eos_token_id])[0]
# pad_token_id = self.tokenizer.convert_tokens_to_ids(["<_PAD_>"])[0]
all_sos_token_list = ["<sos_b>", "<sos_a>", "<sos_r>"]
all_eos_token_list = ["<eos_b>", "<eos_a>", "<eos_r>"]
# print(pipe)
self.special_token_list = [
"<_PAD_>",
"<go_r>",
"<go_b>",
"<go_a>",
"<eos_u>",
"<eos_r>",
"<eos_b>",
"<eos_a>",
"<go_d>",
"<eos_d>",
"<sos_u>",
"<sos_r>",
"<sos_b>",
"<sos_a>",
"<sos_d>",
"<sos_db>",
"<eos_db>",
"<sos_context>",
"<eos_context>",
]
all_sos_token_id_list = []
for token in all_sos_token_list:
one_id = self.tokenizer.convert_tokens_to_ids([token])[0]
all_sos_token_id_list.append(one_id)
all_eos_token_id_list = []
for token in all_eos_token_list:
one_id = self.tokenizer.convert_tokens_to_ids([token])[0]
all_eos_token_id_list.append(one_id)
self.bs_prefix_id = self.tokenizer.convert_tokens_to_ids(
self.tokenizer.tokenize("translate dialogue to belief state:")
)
def predict(
self, input_query, *args: Any, **kwds: Any
) -> ProductNamedEntityExtractionSchema:
extraction_results = ProductNamedEntityExtractionSchema()
parsed_entities_pred = self.pipe(input_query)
print(parsed_entities_pred)
if len(parsed_entities_pred.keys()):
if T5DomainClassTypes.BRAND.value in parsed_entities_pred.keys():
extraction_results.brand_extraction = parsed_entities_pred[
T5DomainClassTypes.BRAND.value
]
if T5DomainClassTypes.PRICE.value in parsed_entities_pred.keys():
price_extraction_result: PriceExtractionSchema = PriceExtractionSchema()
extraction_results.price_extraction = price_extraction_result
price_entities_pred = parsed_entities_pred[
T5DomainClassTypes.PRICE.value
]
is_upper_parsed = True
is_lower_parsed = True
if T5PriceSubclassTypes.CURRENCY.value in price_entities_pred.keys():
price_extraction_result.unit = price_entities_pred[
T5PriceSubclassTypes.CURRENCY.value
]
if (
T5PriceSubclassTypes.GE.value in price_entities_pred.keys()
and T5PriceSubclassTypes.LE.value in price_entities_pred.keys()
):
upper_range = price_entities_pred[T5PriceSubclassTypes.LE.value]
lower_range = price_entities_pred[T5PriceSubclassTypes.GE.value]
try:
upper_range = float(upper_range)
except:
try:
if re.match(
pattern=ARABIC_TEXT_PATTERN, string=input_query
):
upper_range = float(arabic_word2int(upper_range))
else:
upper_range = float(english_word2int(upper_range))
except:
is_upper_parsed = False
try:
lower_range = float(lower_range)
except:
try:
if re.match(
pattern=ARABIC_TEXT_PATTERN, string=input_query
):
lower_range = float(arabic_word2int(lower_range))
else:
lower_range = float(english_word2int(lower_range))
except:
is_lower_parsed = False
if is_lower_parsed and is_upper_parsed:
price_extraction_result.upper_range = max(
upper_range, lower_range
)
price_extraction_result.lower_range = min(
lower_range, upper_range
)
elif is_upper_parsed:
price_extraction_result.upper_range = upper_range
elif is_lower_parsed:
price_extraction_result.lower_range = lower_range
elif T5PriceSubclassTypes.GE.value in price_entities_pred.keys():
lower_range = price_entities_pred[T5PriceSubclassTypes.GE.value]
try:
lower_range = float(lower_range)
except:
try:
if re.match(
pattern=ARABIC_TEXT_PATTERN, string=input_query
):
lower_range = float(arabic_word2int(lower_range))
else:
lower_range = float(english_word2int(lower_range))
except:
is_lower_parsed = False
if is_lower_parsed:
price_extraction_result.lower_range = lower_range
elif T5PriceSubclassTypes.LE.value in price_entities_pred.keys():
upper_range = price_entities_pred[T5PriceSubclassTypes.LE.value]
try:
upper_range = float(upper_range)
except:
try:
if re.match(
pattern=ARABIC_TEXT_PATTERN, string=input_query
):
upper_range = float(arabic_word2int(upper_range))
else:
upper_range = float(english_word2int(upper_range))
except:
is_upper_parsed = False
if is_upper_parsed:
price_extraction_result.upper_range = upper_range
elif T5PriceSubclassTypes.EQ.value in price_entities_pred.keys():
exact_range = price_entities_pred[T5PriceSubclassTypes.EQ.value]
try:
exact_range = float(exact_range)
except:
try:
if re.match(
pattern=ARABIC_TEXT_PATTERN, string=input_query
):
exact_range = float(arabic_word2int(exact_range))
else:
exact_range = float(english_word2int(exact_range))
except:
is_lower_parsed = False
if is_lower_parsed:
price_extraction_result.upper_range = exact_range
price_extraction_result.lower_range = exact_range
if T5DomainClassTypes.SPECS.value in parsed_entities_pred.keys():
specs_extraction_result: SpecsExtractionSchema = SpecsExtractionSchema()
extraction_results.specs_extraction = specs_extraction_result
specs_entities_pred = parsed_entities_pred[
T5DomainClassTypes.SPECS.value
]
for spec_key, spec_val in specs_entities_pred.items():
specs_extraction_result.specs.append(
SpecificationSchema(spec_name=spec_key, spec_val=spec_val)
)
if T5DomainClassTypes.RATE.value in parsed_entities_pred.keys():
extraction_results.rate_extraction = parsed_entities_pred[
T5DomainClassTypes.RATE.value
]
if T5DomainClassTypes.SUBCATEGORY.value in parsed_entities_pred.keys():
extraction_results.sub_category_extraction = parsed_entities_pred[
T5DomainClassTypes.SUBCATEGORY.value
]
if T5DomainClassTypes.SUPERCATEGORY.value in parsed_entities_pred.keys():
extraction_results.super_category_extraction = parsed_entities_pred[
T5DomainClassTypes.SUPERCATEGORY.value
]
return extraction_results
def parse_bs(self, sent) -> Dict[str, Union[Dict[str, str], str]]:
"""Convert compacted bs span to triple list
Ex:
"""
sent = sent.strip("</s>")
sent = sent.split()
belief_state = {}
domain_idx = [idx for idx, token in enumerate(sent) if token in self.domains]
for i, d_idx in enumerate(domain_idx):
next_d_idx = len(sent) if i + 1 == len(domain_idx) else domain_idx[i + 1]
domain = sent[d_idx]
sub_span = sent[d_idx + 1 : next_d_idx]
sub_s_idx = [
idx for idx, token in enumerate(sub_span) if token in self.slots
]
if len(sub_s_idx) == 0:
belief_state[domain] = " ".join(sub_span)
for j, s_idx in enumerate(sub_s_idx):
next_s_idx = (
len(sub_span) if j == len(sub_s_idx) - 1 else sub_s_idx[j + 1]
)
slot = sub_span[s_idx]
value = " ".join(sub_span[s_idx + 1 : next_s_idx])
bs = " ".join([value])
if domain not in belief_state.keys():
belief_state[domain] = {}
belief_state[domain][slot] = bs
return belief_state
def pipe(self, text):
text = f"<sos_u> {text} <eos_u>"
# print(tokenizer.tokenize(sent))
max_decode_len = 120
(
pad_token_id,
start_token_id,
end_token_id,
) = self.tokenizer.convert_tokens_to_ids(
[
"<_PAD_>",
"<sos_b>",
"<eos_b>",
]
)
src_input = torch.LongTensor(
[
self.bs_prefix_id
+ [self.sos_context_token_id]
+ self.tokenizer.convert_tokens_to_ids(self.tokenizer.tokenize(text))
+ [self.eos_context_token_id]
]
)
src_mask = torch.ones_like(src_input)
src_mask = src_mask.masked_fill(src_input.eq(pad_token_id), 0.0).type(
torch.FloatTensor
)
src_input = src_input.to(self.device)
src_mask = src_mask.to(self.device)
start_token, end_token = "<sos_b>", "<eos_b>"
outputs = self.model.generate(
input_ids=src_input,
attention_mask=src_mask,
decoder_start_token_id=start_token_id,
pad_token_id=pad_token_id,
eos_token_id=end_token_id,
max_length=max_decode_len,
)
one_res_text = self.tokenized_decode(outputs[0])
one_res_text = one_res_text.split(start_token)[-1].split(end_token)[0].strip()
final_res_list = []
for token in one_res_text.split():
if token == "<_PAD_>":
continue
else:
final_res_list.append(token)
one_res_text = " ".join(final_res_list).strip()
return self.parse_bs(one_res_text)
def tokenized_decode(self, token_id_list):
pred_tokens = self.tokenizer.convert_ids_to_tokens(token_id_list)
res_text = ""
curr_list = []
for token in pred_tokens:
if token in self.special_token_list + ["<s>", "</s>", "<pad>"]:
if len(curr_list) == 0:
res_text += " " + token + " "
else:
curr_res = self.tokenizer.convert_tokens_to_string(curr_list)
res_text = res_text + " " + curr_res + " " + token + " "
curr_list = []
else:
curr_list.append(token)
if len(curr_list) > 0:
curr_res = self.tokenizer.convert_tokens_to_string(curr_list)
res_text = res_text + " " + curr_res + " "
res_text_list = res_text.strip().split()
res_text = " ".join(res_text_list).strip()
return res_text
if __name__ == "__main__":
model = T5NERModel(model_ckpt_dir=r"ckpt\ner_model")
print(
json.dumps(
model(input_query="يساوي 12 ريال و عشرون ريال").model_dump(),
indent=3,
ensure_ascii=False,
)
)
print("=====================")
print(
json.dumps(
model(input_query="من 12 ريال").model_dump(), indent=3, ensure_ascii=False
)
)
print("=====================")
print(
json.dumps(
model(input_query="أقل 12 ريال").model_dump(), indent=3, ensure_ascii=False
)
)
print("=====================")
print(
json.dumps(
model(input_query="أكثر من 12 ريال").model_dump(),
indent=3,
ensure_ascii=False,
)
)
print("=====================")
print(
json.dumps(
model(input_query="أكثر من 12 إلى 20 ريال").model_dump(),
indent=3,
ensure_ascii=False,
)
)
print("=====================")
print(
json.dumps(
model(input_query="عطر ديور ارخص من ٢٠٠").model_dump(),
indent=3,
ensure_ascii=False,
)
)
print("=====================")
print(
json.dumps(
model(
input_query="عطر ديور ارخص من ٢٠٠ حجمه 12 ميليلتر و تقيمه 13"
).model_dump(),
indent=3,
ensure_ascii=False,
)
)
print("======================")
print(
json.dumps(
model(input_query="Backpack Herschel, Black - 70 SAR").model_dump(),
indent=3,
ensure_ascii=False,
)
)
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