| from transformers import RobertaTokenizer, RobertaConfig, RobertaModel | |
| import torch | |
| import sys | |
| import os | |
| from model import Model | |
| def single_tokenize(text, tokenizer, block_size=256): | |
| tokens = tokenizer.tokenize(text)[:block_size - 2] | |
| tokens = [tokenizer.cls_token] + tokens + [tokenizer.sep_token] | |
| ids = tokenizer.convert_tokens_to_ids(tokens) | |
| padding_length = block_size - len(ids) | |
| ids += [tokenizer.pad_token_id] * padding_length | |
| return torch.tensor([ids]) | |
| if __name__ == "__main__": | |
| config =RobertaConfig.from_pretrained("../../../../active_dataset_debugging/base/codebert-base") | |
| config.num_labels = 1 | |
| tokenizer = RobertaTokenizer.from_pretrained("../../../../active_dataset_debugging/base/codebert-base", do_lower_case=True) | |
| model = RobertaModel.from_pretrained("../../../../active_dataset_debugging/base/roberta-base", config=config) | |
| model = Model(model, config, tokenizer, args=None) | |
| model.load_state_dict(torch.load("../model/python/epoch_2/subject_model.pth", map_location=torch.device('cpu'))) | |
| query = "print hello world" | |
| code_1 = """ | |
| import numpy as np | |
| """ | |
| code_2 = """ | |
| a = 'hello world' | |
| """ | |
| code_3 = """ | |
| cout << "hello world" << endl; | |
| """ | |
| code_4 = ''' | |
| print('hello world') | |
| ''' | |
| codes = [] | |
| codes.append(code_1) | |
| codes.append(code_2) | |
| codes.append(code_3) | |
| codes.append(code_4) | |
| scores = [] | |
| nl_inputs = single_tokenize(query, tokenizer) | |
| for code in codes: | |
| code_inputs = single_tokenize(code, tokenizer) | |
| score = model(code_inputs, nl_inputs, return_scores=True) | |
| scores.append(score) | |
| print("Query:", query) | |
| for i in range(len(codes)): | |
| print('------------------------------') | |
| print("Code:", codes[i]) | |
| print("Score:", float(scores[i])) |