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import os
from fitlog import FitLog
from ShuffleNet.model import ShuffleNetV2
from iframe_feeder import iframe_feeder
from torch.utils.data import DataLoader
import torch
from tqdm import tqdm, trange
from torch.optim.lr_scheduler import MultiStepLR
import torch.nn as nn


class ShuffleNetV2Driver:
    def __init__(self, featd, feati, normd, normi, lab):
        self.batchsize = 256
        self.lr = 0.01
        self.momentum = 0.9
        self.decay = 4e-5
        self.gamma = 0.1
        self.schedule = [200, 300]

        self.local_rank = int(os.getenv("LOCAL_RANK", -1))
        self.RANK = int(os.getenv("RANK", -1))

        self.feeder = iframe_feeder(featd, feati, lab, normd, normi)
        self.feeder.set_mode('train')

        if self.local_rank >= 0:
            self.device = torch.device('cuda', self.local_rank)
        else:
            self.device = torch.device('cuda')

        if self.local_rank >= 0:
            self.sampler = torch.utils.data.distributed.DistributedSampler(self.feeder)
            self.loader = DataLoader(self.feeder, batch_size=self.batchsize,sampler=self.sampler, shuffle=False)
        else:
            self.loader = DataLoader(self.feeder, batch_size=self.batchsize, shuffle=True, num_workers=0)





        #self.feeder = iframe_feeder(featd, feati, lab, normd, normi)
        #self.feeder.set_mode('train')

        self.model = ShuffleNetV2(num_classes=2, scale=0.5, SE=True, residual=True)#torchvision.models.ShuffleNetV2(num_classes=2)#
        self.fitlog = FitLog()
        self.detail_log = FitLog(prefix='dt_')
        #self.device = self.get_device() #torch.device('cuda' if torch.cuda.is_available() else 'cpu')
        print("device info: " + str(self.device))

        self.optimizer = torch.optim.SGD(self.model.parameters(),\
            self.lr, momentum=self.momentum, weight_decay=self.decay,\
            nesterov=True)
        self.scheduler = MultiStepLR(self.optimizer, self.schedule, self.gamma)
        self.criterion = torch.nn.CrossEntropyLoss()
        #self.loader = DataLoader(self.feeder, batch_size=self.batchsize, shuffle=True, num_workers=0)

        self.print_interval = 25
        self.n_epoch = 200
        print('self_device:',self.device)
        print('self_local_rank:',self.local_rank)
        self.model.to(self.device)
        self.model = nn.parallel.DistributedDataParallel(self.model, device_ids=[self.local_rank],output_device=self.local_rank, find_unused_parameters=True)

    def get_device(self):
        device_mlu = None
        device_gpu = None
        try:
            # device_mlu = torch.device('mlu')
            device_gpu = torch.device('cuda')
        except Exception as err:
            print(err)
        if device_mlu:
            self.fitlog.append('mlu', True, True)
            return device_mlu
        elif device_gpu:
            self.fitlog.append('cuda', True, True)
            return device_gpu
        else:
            self.fitlog.append('cpu', True, True)
            return torch.device('cpu')

    def train(self, epoch):
        self.feeder.set_mode('train')
        self.model.train()
        nbatch = len(self.loader)
        print('nbatch:',nbatch)
        for batch_idx, (feats, labs) in enumerate(tqdm(self.loader)):
            feats = feats.to(self.device)
            labs = labs.to(self.device)
            self.optimizer.zero_grad()
            res = self.model(feats)
            loss = self.criterion(res, labs)
            loss.backward()
            self.optimizer.step()
            
            if batch_idx % self.print_interval == 0:
                xstr = "Train: epoch: {} batch: {}/{}, loss: {:.6f}".format(epoch, batch_idx, nbatch, loss)
                tqdm.write(xstr)
                self.fitlog.append(xstr, True, True)
                
    def validate(self):
        self.feeder.set_mode('test')
        self.model.eval()
        loss_val = 0
        n_correct = 0
        n_total = 0

        for batch_idx, (feats, labs) in enumerate(tqdm(self.loader)):
            feats = feats.to(self.device)
            labs = labs.to(self.device)
            with torch.no_grad():
                res = self.model(feats)
                loss_val += self.criterion(res, labs).item()
                _, pred = res.max(1)
                n_correct += pred.eq(labs).sum().item()
                n_total += labs.shape[0]
        
        loss_val = loss_val / len(self.loader)
        acc = n_correct / n_total * 100

        self.detail_log.append(str(labs.tolist()))
        self.detail_log.append(str(pred.tolist()))

        xstr = "Validation: avg loss: {:.4f}, avg acc: {:.4f}%".format(loss_val, acc)
        tqdm.write(xstr)
        self.fitlog.append(xstr, True, True)

    def finish(self):
        self.feeder.finish()
        self.fitlog.close()
        self.detail_log.close()

    def run(self):
        for i in range(1, self.n_epoch + 1):
            self.train(i)
            self.validate()
        

if __name__ == "__main__":
    datafolder = "data/"
    torch.distributed.init_process_group(backend="nccl")
    driver = ShuffleNetV2Driver(datafolder + "s2_ftimgd",\
                                datafolder + "s2_ftimgi",\
                                datafolder + "s2_normd",\
                                datafolder + "s2_normi",\
                                datafolder + "s2_label.json")
    driver.run()
    driver.finish()