| 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])) |
