Script/Model/CodeT5+/new-target-completion/run_completion.py

# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Fine-tuning the library models for language modeling on a text file (GPT, GPT-2, BERT, RoBERTa).
GPT and GPT-2 are fine-tuned using a causal language modeling (CLM) loss while BERT and RoBERTa are fine-tuned
using a masked language modeling (MLM) loss.
"""

from __future__ import absolute_import
import os
import sys
import pickle
import torch
import json
import random
import logging
import argparse
import numpy as np
from io import open
from itertools import cycle
import torch.nn as nn
from tqdm import tqdm, trange
from torch.nn.utils.rnn import pad_sequence
from torch.utils.data import DataLoader, Dataset, SequentialSampler, RandomSampler,TensorDataset
from torch.utils.data.distributed import DistributedSampler
from tqdm import tqdm
from fuzzywuzzy import fuzz
import re
import multiprocessing
from transformers import (WEIGHTS_NAME, AdamW, get_linear_schedule_with_warmup, T5ForConditionalGeneration, AutoTokenizer)

divide_number = 2
cpu_cont = 16
logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s -   %(message)s',
                    datefmt = '%m/%d/%Y %H:%M:%S',
                    level = logging.INFO)
logger = logging.getLogger(__name__)

# 


class Example(object):
    """A single training/test example."""
    def __init__(self,
                 idx,
                 source,
                 target,
                 comp_type,
                 tar_type
                 ):
        self.idx = idx
        self.source = source
        self.target = target
        self.comp_type = comp_type
        self.tar_type = tar_type


def read_examples(filename):
    """Read examples from filename."""
    examples=[]
    
    with open(filename,encoding="utf-8") as f:
        max_src_len = 0
        max_tar_len = 0
        for idx, line in enumerate(f):
            js=json.loads(line)
            inputs = " ".join(js["Template_token"][1:])

            # print(inputs)
            if "ground_truth" in js:
                outputs = " ".join(js["ground_truth"])
            else:
                outputs = inputs
            if 'Idx' in js:
                idx = js['Idx']  
            

            comp_type = js["Compiler_Type"]
            tar_type = js["Target"]
            examples.append(
                Example(
                        idx = idx,
                        source = inputs,
                        target = outputs,
                        comp_type = comp_type,
                        tar_type = tar_type
                ) 
            )
    return examples


class InputFeatures(object):
    """A single training/test features for a example."""
    def __init__(self,
                 example_id,
                 source_ids, source_mask,
                 target_ids, target_mask,
                 comp_type, tar_type
    ):
        self.example_id = example_id
        self.source_ids = source_ids
        self.source_mask = source_mask
        self.target_ids = target_ids
        self.target_mask = target_mask
        self.comp_type = comp_type
        self.tar_type = tar_type    
        
def convert_examples_to_features(examples, tokenizer, args,stage=None):
    features = []
    for example_index, example in enumerate(examples):
        #source
        source_ids = torch.LongTensor(tokenizer.encode(example.source, 
                add_special_tokens=True, max_length=args.max_source_length, truncation=True))
        # print(tokenizer.encode(example.source, 
        #         add_special_tokens=True, max_length=args.max_source_length, truncation=True))
        source_mask = torch.ones_like(source_ids)   
        #target
        if stage=="test":
            target = "None"
        else:
            target = example.target

        target_ids = torch.LongTensor(tokenizer.encode(target, 
                add_special_tokens=True, max_length=args.max_target_length, truncation=True))
        target_mask = torch.ones_like(target_ids) 

       
        features.append(
            InputFeatures(
                 example_index,
                 source_ids, source_mask,
                 target_ids, target_mask,
                 example.comp_type, example.tar_type
            )
        )
    return features



def set_seed(seed=20240124):
    random.seed(seed)
    os.environ['PYHTONHASHSEED'] = str(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)
    torch.backends.cudnn.deterministic = True
        
        
def main():
    parser = argparse.ArgumentParser()

    ## Required parameters  
    parser.add_argument("--model_name_or_path", default=None, type=str, required=True,
                        help="Path to pre-trained model: e.g. roberta-base" )   
    parser.add_argument("--output_dir", default=None, type=str, required=True,
                        help="The output directory where the model predictions and checkpoints will be written.")   
    parser.add_argument("--load_model_path", default=None, type=str, 
                        help="Path to trained model: Should contain the .bin files" )    
    ## Other parameters
    parser.add_argument("--task", default=None, type=str, required=True,
                        help="Task Type: statement_level, next_statement" )

    parser.add_argument("--train_filename", default="../../Dataset/", type=str, 
                        help="The train filename. Should contain the .jsonl files for this task.")
    parser.add_argument("--dev_filename", default="../../Dataset/", type=str, 
                        help="The dev filename. Should contain the .jsonl files for this task.")
    parser.add_argument("--test_filename", default="../../Dataset/", type=str, 
                        help="The test filename. Should contain the .jsonl files for this task.")  
    
    parser.add_argument("--config_name", default="", type=str,
                        help="Pretrained config name or path if not the same as model_name")
    parser.add_argument("--tokenizer_name", default="", type=str,
                        help="Pretrained tokenizer name or path if not the same as model_name") 
    # parser.add_argument("--max_source_length", default=64, type=int,
    #                     help="The maximum total source sequence length after tokenization. Sequences longer "
    #                          "than this will be truncated, sequences shorter will be padded.")
    # parser.add_argument("--max_target_length", default=32, type=int,
    #                     help="The maximum total target sequence length after tokenization. Sequences longer "
    #                          "than this will be truncated, sequences shorter will be padded.")
    
    parser.add_argument("--do_train", action='store_true',
                        help="Whether to run training.")
    
    parser.add_argument("--do_eval", action='store_true',
                        help="Whether to run eval on the dev set.")
    parser.add_argument("--do_test", action='store_true',
                        help="Whether to run eval on the dev set.")
    parser.add_argument("--test_org", action='store_true',
                        help="Whether to run eval on org model.")
    parser.add_argument("--do_lower_case", action='store_true',
                        help="Set this flag if you are using an uncased model.")
    parser.add_argument("--no_cuda", action='store_true',
                        help="Avoid using CUDA when available") 
    parser.add_argument("--do_cpuonly", action='store_true',
                        help="Whether CPU only training.")
    parser.add_argument("--do_itr", action='store_true',
                        help="Whether to itr training.")
    parser.add_argument("--train_batch_size", default=8, type=int,
                        help="Batch size per GPU/CPU for training.")
    parser.add_argument("--eval_batch_size", default=8, type=int,
                        help="Batch size per GPU/CPU for evaluation.")
    parser.add_argument('--gradient_accumulation_steps', type=int, default=1,
                        help="Number of updates steps to accumulate before performing a backward/update pass.")
    parser.add_argument("--learning_rate", default=5e-5, type=float,
                        help="The initial learning rate for Adam.")
    parser.add_argument("--beam_size", default=10, type=int,
                        help="beam size for beam search")    
    parser.add_argument("--weight_decay", default=0.0, type=float,
                        help="Weight deay if we apply some.")
    parser.add_argument("--adam_epsilon", default=1e-8, type=float,
                        help="Epsilon for Adam optimizer.")
    parser.add_argument("--max_grad_norm", default=1.0, type=float,
                        help="Max gradient norm.")
    parser.add_argument("--num_train_epochs", default=3, type=int,
                        help="Total number of training epochs to perform.")
    parser.add_argument("--max_steps", default=-1, type=int,
                        help="If > 0: set total number of training steps to perform. Override num_train_epochs.")
    parser.add_argument("--eval_steps", default=-1, type=int,
                        help="")
    parser.add_argument("--max_target_length", default=128, type=int,
                        help="")
    parser.add_argument("--max_source_length", default=512, type=int,
                        help="")
    parser.add_argument("--train_steps", default=-1, type=int,
                        help="")
    parser.add_argument("--warmup_steps", default=0, type=int,
                        help="Linear warmup over warmup_steps.")
    parser.add_argument("--local_rank", type=int, default=-1,
                        help="For distributed training: local_rank")   
    parser.add_argument('--seed', type=int, default=20240124,
                        help="random seed for initialization")
    # print arguments
    args = parser.parse_args()
    # set log
    logging.basicConfig(format='%(asctime)s - %(levelname)s - %(name)s -   %(message)s',
                    datefmt='%m/%d/%Y %H:%M:%S',level=logging.INFO )
    # set device
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    args.n_gpu = torch.cuda.device_count()
    args.device = device
    logger.info("device: %s, n_gpu: %s",device, args.n_gpu)
    
    # Set seed
    set_seed(args.seed)

    # make dir if output_dir not exist
    if os.path.exists(args.output_dir) is False:
        os.makedirs(args.output_dir)

    # build model
    tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path)
    is_trust = False 
    if "codet5p-220m" in args.model_name_or_path:
        is_trust = False
    else:
        is_trust = True
    model = T5ForConditionalGeneration.from_pretrained(args.model_name_or_path) 

    
    logger.info("Training/evaluation parameters %s", args)

    if args.load_model_path is not None:
        model_save_name = "Existing_Types/pytorch_model.bin"
        if args.do_itr:
            model_save_name = "pytorch_model.bin"
        if args.do_cpuonly:
            model_save_name = "New_Types/pytorch_model.bin"        
        if args.task == "statement_level":
            logger.info("reload model from {}".format(args.load_model_path + "/statement_level/"+model_save_name))
            model.load_state_dict(torch.load(args.load_model_path + "/statement_level/"+model_save_name))
        else:
            logger.info("reload model from {}".format(args.load_model_path + "/next_statement/"+model_save_name))
            model.load_state_dict(torch.load(args.load_model_path + "/next_statement/"+model_save_name))
            
    # model.eval()        
    model.to(args.device)   
    
    if args.n_gpu > 1:
        # multi-gpu training
        model = torch.nn.DataParallel(model)

    if args.do_train:
        # Prepare training data loader

        file_name_pre = "New_Target_Completion"
        file_name_post = "Existing_Types/train.jsonl"
        if args.do_itr:
            file_name_pre = "Iterative_Expansion_Completion"
            file_name_post = "train.jsonl"
        if args.do_cpuonly and not args.do_itr:
            file_name_pre = "New_Target_Completion"
            file_name_post = "New_Types/train.jsonl"
        if args.task == "statement_level":
            train_examples = read_examples(args.train_filename + file_name_pre +'/statement_level/'+file_name_post)
        else:
            train_examples = read_examples(args.train_filename + file_name_pre +'/statement_level/'+file_name_post)

        train_features = convert_examples_to_features(train_examples, tokenizer,args,stage='train')
        all_source_ids = pad_sequence([f.source_ids for f in train_features], batch_first=True, padding_value=tokenizer.pad_token_id)
        all_source_mask = pad_sequence([f.source_mask for f in train_features], batch_first=True, padding_value=0)
        all_target_ids = pad_sequence([f.target_ids for f in train_features], batch_first=True, padding_value=tokenizer.pad_token_id)
        all_target_mask = pad_sequence([f.target_mask for f in train_features], batch_first=True, padding_value=0)
        train_data = TensorDataset(all_source_ids,all_source_mask,all_target_ids,all_target_mask)
        train_sampler = RandomSampler(train_data)
        train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=args.train_batch_size // args.gradient_accumulation_steps)


        # Prepare optimizer and schedule (linear warmup and decay)
        no_decay = ['bias', 'LayerNorm.weight']
        optimizer_grouped_parameters = [
            {'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
             'weight_decay': args.weight_decay},
            {'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}
        ]
        optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)
        scheduler = get_linear_schedule_with_warmup(optimizer, 
                                                    num_warmup_steps=int(len(train_dataloader)*args.num_train_epochs*0.1),
                                                    num_training_steps=len(train_dataloader)*args.num_train_epochs)
    
        #Start training
        logger.info("***** Running training *****")
        logger.info("  Num examples = %d", len(train_examples))
        logger.info("  Batch size = %d", args.train_batch_size * args.gradient_accumulation_steps)
        logger.info("  Num epoch = %d", args.num_train_epochs)
        

        model.train()
        patience, best_score, losses, dev_dataset = 0, 0, [], {}
        for epoch in range(args.num_train_epochs):
            for idx,batch in enumerate(train_dataloader):
                batch = tuple(t.to(device) for t in batch)
                source_ids,source_mask,target_ids,target_mask = batch
                # loss,_,_ = model(source_ids=source_ids,target_ids=target_ids)

                loss = model(input_ids=source_ids, attention_mask=source_mask.gt(0), 
                                  labels=target_ids, decoder_attention_mask=target_mask.gt(0)).loss
                
                
                if args.n_gpu > 1:
                    loss = loss.mean() # mean() to average on multi-gpu.

                if args.gradient_accumulation_steps > 1:
                    loss = loss / args.gradient_accumulation_steps
                    
                losses.append(loss.item())
                loss.backward()
                if len(losses) % args.gradient_accumulation_steps == 0:
                    #Update parameters
                    optimizer.step()
                    optimizer.zero_grad()
                    scheduler.step()
                    if len(losses) // args.gradient_accumulation_steps % 100 == 0:
                        logger.info("epoch {} step {} loss {}".format(epoch,
                                                     len(losses)//args.gradient_accumulation_steps,
                                                     round(np.mean(losses[-100*args.gradient_accumulation_steps:]),4)))
            if args.do_eval:
                #Eval model with dev dataset   
                                
                if 'dev_loss' in dev_dataset:
                    eval_examples,eval_data = dev_dataset['dev_loss']
                else:
                    file_name_pre = "New_Target_Completion"
                    file_name_post = "Existing_Types/valid.jsonl"
                    if args.do_itr:
                        file_name_pre = "Iterative_Expansion_Completion"
                        file_name_post = "valid.jsonl"
                    if args.do_cpuonly and not args.do_itr:
                        file_name_pre = "New_Target_Completion"
                        file_name_post = "New_Types/valid.jsonl"
                    if args.task == "statement_level":
                        eval_examples = read_examples(args.dev_filename + file_name_pre +'/statement_level/'+file_name_post)
                    else:
                        eval_examples = read_examples(args.dev_filename + file_name_pre +'/statement_level/'+file_name_post)

                    
                    eval_features = convert_examples_to_features(eval_examples, tokenizer, args,stage='dev')
                    all_source_ids = pad_sequence([f.source_ids for f in eval_features], batch_first=True, padding_value=tokenizer.pad_token_id)
                    all_source_mask = pad_sequence([f.source_mask for f in eval_features], batch_first=True, padding_value=0)
                    all_target_ids = pad_sequence([f.target_ids for f in eval_features], batch_first=True, padding_value=tokenizer.pad_token_id)
                    all_target_mask = pad_sequence([f.target_mask for f in eval_features], batch_first=True, padding_value=0)
                    eval_data = TensorDataset(all_source_ids,all_source_mask,all_target_ids,all_target_mask)
                    dev_dataset['dev_loss' ]= eval_examples,eval_data
                eval_sampler = SequentialSampler(eval_data)
                eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size)
                res_list = []
                logger.info("\n***** Running evaluation *****")
                logger.info("  Num examples = %d", len(eval_examples))
                logger.info("  Batch size = %d", args.eval_batch_size)

                #Start Evaling model
                model.eval()
                p=[]
                eval_loss,tokens_num = 0,0
                for batch in eval_dataloader:
                    batch = tuple(t.to(device) for t in batch)
                    source_ids,source_mask,target_ids,target_mask = batch
                    with torch.no_grad():
                        loss = model(input_ids=source_ids, attention_mask=source_mask, 
                                    labels=target_ids, decoder_attention_mask=target_mask).loss
                        preds = model.module.generate(source_ids, attention_mask=source_mask, use_cache=True, 
                                        num_beams=args.beam_size, max_new_tokens =args.max_target_length) # module. for multi GPU
                            
                        # convert ids to text
                        for pred in preds:
                            # print(pred)
                            text = tokenizer.decode(pred, skip_special_tokens=True, clean_up_tokenization_spaces=False)
                            p.append(text)
                        if args.n_gpu > 1:
                            loss = loss.mean() # mean() to average on multi-gpu.

                        if args.gradient_accumulation_steps > 1:
                            loss = loss / args.gradient_accumulation_steps
                        eval_loss += loss.item()
                        tokens_num += 1
                        

                #Pring loss of dev dataset    
                model.train()
                eval_loss = eval_loss / tokens_num
                result = {'eval_ppl': round(np.exp(eval_loss),5)}
                for key in sorted(result.keys()):
                    logger.info("  %s = %s", key, str(result[key]))
                logger.info("  "+"*"*20)   

                EM = 0.0
                edit_sim = 0.0    
                total = len(p)
                token_accuracy = 0
                for ref,gold in zip(p,eval_examples):
                    pred = ref.strip()
                    gt = gold.target
                    edit_sim += fuzz.ratio(pred, gt)
                    if pred.split() == gt.split():
                        EM += 1
                    res_list.append([pred,gt])
                dev_acc = round(EM/total*100, 2)
                # logger.info("  %s = %s "%("loss",round(np.mean(dev_losses),4)))
                logger.info("  %s = %s "%("Epoch",str(epoch)))
                logger.info("  %s = %s "%("EM Acc",str(dev_acc)))
                logger.info("  %s = %s "%("Edit Distance",str(round(edit_sim/total, 2))))
                logger.info("  "+"*"*20)

                if dev_acc > best_score:
                    best_score = dev_acc
                    # Save best checkpoint for best bleu
                    if args.task == "statement_level":
                        output_dir = os.path.join(args.output_dir, 'statement_level/')
                    else:
                        output_dir = os.path.join(args.output_dir, 'next_statement/')
                    if not os.path.exists(output_dir):
                        os.makedirs(output_dir)
                    model_to_save = model.module if hasattr(model, 'module') else model  # Only save the model it-self
                    model_save_name = "Exitsing_Types/pytorch_model.bin"
                    if args.do_itr:
                        model_save_name = "pytorch_model.bin"
                    if args.do_cpuonly:
                        model_save_name = "New_Types/pytorch_model.bin"
                    output_model_file = os.path.join(output_dir, model_save_name)
                    torch.save(model_to_save.state_dict(), output_model_file)
                    patience = 0
                else:
                    patience += 1
                    if patience == 3:
                        break
                logger.info("  Best score:%s",best_score)
                logger.info("  "+"*"*20)

                if args.task == "statement_level":
                    output_dir = os.path.join(args.output_dir, 'statement_level/')
                else:
                    output_dir = os.path.join(args.output_dir, 'next_statement/')

    if args.do_test:
        res_list = []
        output_dir2 = ""
        
        if args.load_model_path is not None:
            model_to_load = model.module if hasattr(model, 'module') else model
            # print(output_dir)
            # odel_to_load.load_state_dict(torch.load(output_dir))
            model_save_name = "Existing_Types/pytorch_model.bin"
            if args.do_itr and not args.do_cpuonly:
                model_save_name = "pytorch_model.bin"
            if args.do_itr and args.do_cpuonly:
                args.load_model_path = "../../../../Saved_Models/CodeT5+/New_Target_Completion"
                model_save_name = "New_Types/pytorch_model.bin"
            if args.do_cpuonly:
                model_save_name = "New_Types/pytorch_model.bin"          
            if args.task == "statement_level":
                logger.info("reload model from {}".format(args.load_model_path + "/statement_level/"+model_save_name))
                model_to_load.load_state_dict(torch.load(args.load_model_path + "/statement_level/"+model_save_name))
            else:
                logger.info("reload model from {}".format(args.load_model_path + "/next_statement/"+model_save_name))
                model_to_load.load_state_dict(torch.load(args.load_model_path + "/next_statement/"+model_save_name))
        
        file_name_pre = "New_Target_Completion"
        file_name_post = "Existing_Types/test.jsonl"
        if args.do_itr:
            file_name_pre = "Iterative_Expansion_Completion"
            file_name_post = "test.jsonl"
        if args.do_cpuonly and not args.do_itr:
            file_name_pre = "New_Target_Completion"
            file_name_post = "New_Types/test.jsonl"
        if args.task == "statement_level":
            args.test_filename = os.path.join(args.test_filename, file_name_pre +'/statement_level/'+file_name_post)
        else:
            args.test_filename = os.path.join(args.test_filename, file_name_pre +'/next_statement/'+file_name_post)
        eval_examples = read_examples(args.test_filename)
        eval_features = convert_examples_to_features(eval_examples, tokenizer, args,stage='test')
        all_source_ids = pad_sequence([f.source_ids for f in eval_features], batch_first=True, padding_value=tokenizer.pad_token_id)
        all_source_mask = pad_sequence([f.source_mask for f in eval_features], batch_first=True, padding_value=0)
        all_target_ids = pad_sequence([f.target_ids for f in eval_features], batch_first=True, padding_value=tokenizer.pad_token_id)
        all_target_mask = pad_sequence([f.target_mask for f in eval_features], batch_first=True, padding_value=0)
        eval_data = TensorDataset(all_source_ids,all_source_mask,all_target_ids,all_target_mask)

        # Calculate bleu
        eval_sampler = SequentialSampler(eval_data)
        eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size)

        model.eval() 
        p=[]
        for batch in tqdm(eval_dataloader,total=len(eval_dataloader)):
            batch = tuple(t.to(device) for t in batch)
            source_ids, source_mask, _, _  = batch 
            with torch.no_grad():
                preds = model.module.generate(source_ids, attention_mask=source_mask, use_cache=True, 
                                        num_beams=args.beam_size, max_new_tokens =args.max_target_length) # module. for multi GPU
            for pred in preds:
                # print(pred)
                text = tokenizer.decode(pred, skip_special_tokens=True, clean_up_tokenization_spaces=False)
                p.append(text)
        model.train()
        # edit_sim = 0.0
        # EM = 0.0
        # total = len(p)
        gcc_dic = {"riscv":[0,0,0], "nvptx":[0,0,0], "arc":[0,0,0]}
        llvm_dic = {"RISCV":[0,0,0], "NVPTX":[0,0,0], "ARC":[0,0,0],"RI5CY":[0,0,0]}
        for ref,gold in zip(p,eval_examples):
            pred = ref.strip()
            gt = gold.target
            if gold.comp_type == "GCC":
                gcc_dic[gold.tar_type][1] += fuzz.ratio(pred, gt)
                gcc_dic[gold.tar_type][2] += 1
                if pred.split() == gt.split():
                    gcc_dic[gold.tar_type][0] += 1
            if gold.comp_type == "LLVM":
                llvm_dic[gold.tar_type][1] += fuzz.ratio(pred, gt)
                llvm_dic[gold.tar_type][2] += 1
                if pred.split() == gt.split():
                    llvm_dic[gold.tar_type][0] += 1
            res_list.append([pred,gt])
        # dev_acc = round(edit_sim/total, 2)
        # dev_em = round(EM/total, 4)

        for k in gcc_dic.keys():
            if gcc_dic[k][2] > 0:
                dev_acc = round(1.0*gcc_dic[k][1] / gcc_dic[k][2], 2)
                dev_em = round(100.0*gcc_dic[k][0] / gcc_dic[k][2], 4)
                logger.info(" "+"#"*20)
                logger.info("GCC %s:  %s = %s "%(k, "Edit Distance", str(dev_acc)))
                logger.info("GCC %s:  %s = %s "%(k, "Exact Match Rate", str(dev_em)))
                logger.info("  "+"*"*20)

        for k in llvm_dic.keys():
            if llvm_dic[k][2] > 0:
                dev_acc = round(1.0*llvm_dic[k][1] / llvm_dic[k][2], 2)
                dev_em = round(100.0*llvm_dic[k][0] / llvm_dic[k][2], 4)
                logger.info(" "+"#"*20)
                logger.info("LLVM %s:  %s = %s "%(k, "Edit Distance", str(dev_acc)))
                logger.info("LLVM %s:  %s = %s "%(k, "Exact Match Rate", str(dev_em)))
                logger.info("  "+"*"*20)


        # if args.test_org:
        #     output_dir = args.output_dir
        # else:
        #     if args.task == "statement_level":
        #         output_dir = os.path.join(args.output_dir, 'statement_level/')
        #     else:
        #         output_dir = os.path.join(args.output_dir, 'next_statement/')
        # result_file_name = "/test_result.jsonl"
        # if args.do_itr:
        #     result_file_name = "/test_result_itr.jsonl"
        # if args.do_cpuonly:
        #     result_file_name = "/test_result_cpu.jsonl"
        # with open(output_dir + result_file_name, 'w') as wf:
        #     for line in res_list:
        #         dic = {}
        #         dic["Pred"] = line[0]
        #         dic["GT"] = line[1]
        #         wf.write(json.dumps(dic))
        #         wf.write("\n")

    
                
                
if __name__ == "__main__":
    main()