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#%%
from utils.load_model import load_ner
from utils.input_process import make_ner_input
from utils.ner_utils import make_name_list, show_name_list, combine_similar_names
import torch
from utils.train_model import KCSN
from utils.arguments import get_train_args
args = get_train_args()
path ='model/model.ckpt'
model = KCSN(args)
checkpoint = torch.load(path)
model.load_state_dict(checkpoint['model'])
# model = checkpoint['model']
# %%
with open('test/test.txt', "r", encoding="utf-8") as f:
file_content = f.read()
content = make_ner_input(file_content)
name_list, time, place = make_name_list(content, checkpoint)
name_dic = show_name_list(name_list)
similar_name = combine_similar_names(name_dic)
for i in similar_name:
print(i)
# %% CSN 모델
import torch
from utils.fs_utils import get_alias2id, find_speak
from utils.ner_utils import make_name_list
from utils.input_process import make_ner_input, make_instance_list, input_data_loader
checkpoint = torch.load('./model/final.pth')
model = checkpoint['model']
model.to('cpu')
tokenizer = checkpoint['tokenizer']
check_name = './data/name.txt'
alias2id = get_alias2id(check_name)
with open('test/KoCSN_test.txt', "r", encoding="utf-8") as f:
file_content = f.read()
instances, instance_num = make_instance_list(file_content)
inputs = input_data_loader(instances, alias2id)
output = find_speak(model, inputs, tokenizer, alias2id)
def make_script(texts, instance_num, output):
script = []
for idx, text in enumerate(texts):
if idx in instance_num
#%%
n = int(input())
num = list(map(int, input().split()))
ans = []
for i, j in enumerate(num):
print(i, j)
if len(ans) == 0:
ans.append(i+1)
else:
ans.insert(len(ans)-j, i+1)
print(ans)
# %%
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