'''
 * Copyright (c) 2022, salesforce.com, inc.
 * All rights reserved.
 * SPDX-License-Identifier: BSD-3-Clause
 * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause
 * By Junnan Li
'''
import argparse
import os
# import ruamel_yaml as yaml
try:
    import ruamel_yaml as yaml
except ModuleNotFoundError:
    import ruamel.yaml as yaml
import numpy as np
import random
import time
import datetime
import json
from pathlib import Path

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import sys
sys.path.append("..") 
from models.blip_vqa import blip_vqa
import utils
from utils import cosine_lr_schedule
from data import create_dataset, create_sampler, create_loader
from data.vqa_dataset import vqa_collate_fn
from data.utils import save_result


def train(model, data_loader, optimizer, epoch, device):
    # train
    model.train()

    metric_logger = utils.MetricLogger(delimiter="  ")
    metric_logger.add_meter('lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))
    metric_logger.add_meter('loss', utils.SmoothedValue(window_size=1, fmt='{value:.4f}'))

    header = 'Train Epoch: [{}]'.format(epoch)
    print_freq = 50

    for i,(image, question, answer, weights, n) in enumerate(metric_logger.log_every(data_loader, print_freq, header)):
        image, weights = image.to(device,non_blocking=True), weights.to(device,non_blocking=True)

        loss = model(image, question, answer, train=True, n=n, weights=weights)

        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        metric_logger.update(loss=loss.item())
        metric_logger.update(lr=optimizer.param_groups[0]["lr"])

    # gather the stats from all processes
    metric_logger.synchronize_between_processes()
    print("Averaged stats:", metric_logger.global_avg())
    return {k: "{:.3f}".format(meter.global_avg) for k, meter in metric_logger.meters.items()}


@torch.no_grad()
def evaluation(model, data_loader, device, config) :
    # test
    model.eval()

    metric_logger = utils.MetricLogger(delimiter="  ")
    header = 'Generate VQA test result:'
    print_freq = 50

    result = []

    if config['inference']=='rank':
        answer_list = data_loader.dataset.answer_list
        answer_candidates = model.tokenizer(answer_list, padding='longest', return_tensors='pt').to(device)
        answer_candidates.input_ids[:,0] = model.tokenizer.bos_token_id

    for n, (image, question, question_id) in enumerate(metric_logger.log_every(data_loader, print_freq, header)):
        image = image.to(device,non_blocking=True)

        if config['inference']=='generate':
            answers = model(image, question, train=False, inference='generate')

            for answer, ques_id in zip(answers, question_id):
                ques_id = int(ques_id.item())
                result.append({"question_id":ques_id, "answer":answer})

        elif config['inference']=='rank':
            answer_ids = model(image, question, answer_candidates, train=False, inference='rank', k_test=config['k_test'])

            for ques_id, answer_id in zip(question_id, answer_ids):
                result.append({"question_id":int(ques_id.item()), "answer":answer_list[answer_id]})

        elif config['inference'] == 'vqa_prob': #pred yes probability
            probs = model(image, question, train=False, inference="vqa_prob")
            for prob, ques_id in zip(probs, question_id):
                result.append({"question_id":int(ques_id.item()), "answer":prob})


    return result


def VQA(evaluate, device, seed, distributed, config, result_dir, output_dir):
    # utils.init_distributed_mode(args)

    device = torch.device(device)

    # fix the seed for reproducibility
    seed = seed + utils.get_rank()
    torch.manual_seed(seed)
    np.random.seed(seed)
    random.seed(seed)
    cudnn.benchmark = True

    #### Dataset ####
    print("Creating vqa datasets")
    datasets = create_dataset('vqa', config)

    if distributed:
        num_tasks = utils.get_world_size()
        global_rank = utils.get_rank()
        samplers = create_sampler(datasets, [True, False], num_tasks, global_rank)
    else:
        samplers = [None, None]

    train_loader, test_loader = create_loader(datasets,samplers,
                                              batch_size=[config['batch_size_train'],config['batch_size_test']],
                                              # num_workers=[4,4],is_trains=[True, False],
                                              num_workers=[1,1],is_trains=[True, False],
                                              collate_fns=[vqa_collate_fn,None])
    #### Model ####
    print("Creating model")
    model = blip_vqa(pretrained=config['pretrained'], image_size=config['image_size'],
                       vit=config['vit'], vit_grad_ckpt=config['vit_grad_ckpt'], vit_ckpt_layer=config['vit_ckpt_layer'])

    model = model.to(device)

    model_without_ddp = model
    if distributed:
        model = torch.nn.parallel.DistributedDataParallel(model)
        model_without_ddp = model.module

    optimizer = torch.optim.AdamW(params=model.parameters(), lr=config['init_lr'], weight_decay=config['weight_decay'])

    best = 0
    best_epoch = 0

    print("Start training")
    start_time = time.time()
    for epoch in range(0, config['max_epoch']):
        if not evaluate:
            if distributed:
                train_loader.sampler.set_epoch(epoch)

            cosine_lr_schedule(optimizer, epoch, config['max_epoch'], config['init_lr'], config['min_lr'])

            train_stats = train(model, train_loader, optimizer, epoch, device)

        else:
            break

        if utils.is_main_process():
            log_stats = {**{f'train_{k}': v for k, v in train_stats.items()},
                         'epoch': epoch,
                        }
            with open(os.path.join(output_dir, "log.txt"),"a") as f:
                f.write(json.dumps(log_stats) + "\n")

            save_obj = {
                'model': model_without_ddp.state_dict(),
                'optimizer': optimizer.state_dict(),
                'config': config,
                'epoch': epoch,
            }
            torch.save(save_obj, os.path.join(output_dir, 'checkpoint_%02d.pth'%epoch))

        dist.barrier()

    vqa_result = evaluation(model_without_ddp, test_loader, device, config)
    result_file = save_result(vqa_result, result_dir, 'vqa_result')

    total_time = time.time() - start_time
    total_time_str = str(datetime.timedelta(seconds=int(total_time)))
    print('Training time {}'.format(total_time_str))
    return vqa_result


def VQA_main(ann_root,output_dir,inference='vqa_prob'): #annotation path, output path
    config = 'configs/vqa.yaml' #todo config file
    evaluate = True
    device = 'cuda'
    seed = 42
    distributed = False

    config = yaml.load(open(config, 'r'), Loader=yaml.Loader)

    config['ann_root']=ann_root
    config['inference'] = inference

    result_dir = os.path.join(output_dir, 'result')

    Path(output_dir).mkdir(parents=True, exist_ok=True)
    Path(result_dir).mkdir(parents=True, exist_ok=True)

    yaml.dump(config, open(os.path.join(output_dir, 'config.yaml'), 'w'))

    result = VQA(evaluate, device, seed, distributed, config, result_dir, output_dir)
    return result #list("question_id":0,"answer":yes)
