import time

import torch
from torch.autograd import Variable
from torch.utils.data.sampler import SubsetRandomSampler

from archived.elasticache import redis_init
from archived.s3.get_object import get_object

from archived.old_model import LogisticRegression
from data_loader.libsvm_dataset import DenseDatasetWithLines

# lambda setting
redis_location = "test.fifamc.ng.0001.euc1.cache.amazonaws.com"
grad_bucket = "tmp-grads"
model_bucket = "tmp-updates"
local_dir = "/tmp"
w_prefix = "w_"
b_prefix = "b_"
w_grad_prefix = "w_grad_"
b_grad_prefix = "b_grad_"

# algorithm setting
learning_rate = 0.1
batch_size = 100
num_epochs = 2
validation_ratio = .2
shuffle_dataset = True
random_seed = 42

endpoint = redis_init(redis_location)


def handler(event, context):
    start_time = time.time()
    bucket = event['bucket']
    key = event['name']
    num_features = event['num_features']
    num_classes = event['num_classes']
    print('bucket = {}'.format(bucket))
    print('key = {}'.format(key))
  
    key_splits = key.split("_")
    worker_index = int(key_splits[0])
    num_worker = int(key_splits[1])

    # read file(dataset) from s3
    file = get_object(bucket, key).read().decode('utf-8').split("\n")
    print("read data cost {} s".format(time.time() - start_time))
    parse_start = time.time()
    dataset = DenseDatasetWithLines(file, num_features)
    preprocess_start = time.time()
    print("libsvm operation cost {}s".format(parse_start - preprocess_start))
   
    # Creating data indices for training and validation splits:
    dataset_size = len(dataset)
    print("dataset size = {}".format(dataset_size))
    indices = list(range(dataset_size))
    split = int(np.floor(validation_ratio * dataset_size))
    if shuffle_dataset:
        np.random.seed(random_seed)
        np.random.shuffle(indices)
    train_indices, val_indices = indices[split:], indices[:split]

    # Creating PT data samplers and loaders:
    train_sampler = SubsetRandomSampler(train_indices)
    valid_sampler = SubsetRandomSampler(val_indices)

    train_loader = torch.utils.data.DataLoader(dataset,
                                               batch_size=batch_size,
                                               sampler=train_sampler)
    validation_loader = torch.utils.data.DataLoader(dataset,
                                                    batch_size=batch_size,
                                                    sampler=valid_sampler)
    
    print("preprocess data cost {} s".format(time.time() - preprocess_start))
    
    model = LogisticRegression(num_features, num_classes)

    # Loss and Optimizer
    # Softmax is internally computed.
    # Set parameters to be updated.
    criterion = torch.nn.CrossEntropyLoss()
    optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
    
    # Training the Model
    for epoch in range(num_epochs):
        for batch_index, (items, labels) in enumerate(train_loader):
            print("------worker {} epoch {} batch {}------".format(worker_index, epoch, batch_index))
            batch_start = time.time()
            items = Variable(items.view(-1, num_features))
            labels = Variable(labels)

            # Forward + Backward + Optimize
            optimizer.zero_grad()
            outputs = model(items)
            loss = criterion(outputs, labels)
            loss.backward()
            print("forward and backward cost {} s".format(time.time()-batch_start))

            w_grad = model.linear.weight.grad.data.numpy()
            b_grad = model.linear.bias.grad.data.numpy()
            print("w_grad before merge = {}".format(w_grad[0][0:5]))
            print("b_grad before merge = {}".format(b_grad))
            
            #synchronization starts from that every worker writes their gradients of this batch and epoch
            sync_start = time.time()
            hset_object(endpoint, grad_bucket, w_grad_prefix + str(worker_index), w_grad.tobytes())
            hset_object(endpoint, grad_bucket, b_grad_prefix + str(worker_index), b_grad.tobytes())
            
            #merge gradients among files
            merge_start = time.time()
            file_postfix = "{}_{}".format(epoch, batch_index)
            if worker_index == 0:
                merge_start = time.time()
                w_grad_merge, b_grad_merge = \
                    merge_w_b_grads(endpoint, 
                                    grad_bucket, num_worker, w_grad.dtype,
                                    w_grad.shape, b_grad.shape,
                                    w_grad_prefix, b_grad_prefix)
                print("model average time = {}".format(time.time()-merge_start))
                #possible rewrite the file before being accessed. wait until anyone finishes accessing.
                put_merged_w_b_grads(endpoint, model_bucket,
                                    w_grad_merge, b_grad_merge,
                                    w_grad_prefix, b_grad_prefix)
                hset_object(endpoint, model_bucket, "epoch", epoch)
                hset_object(endpoint, model_bucket, "index", batch_index)
                #delete_expired_w_b(endpoint,
                #                   model_bucket, epoch, batch_index, w_grad_prefix, b_grad_prefix)
                model.linear.weight.grad = Variable(torch.from_numpy(w_grad_merge))
                model.linear.bias.grad = Variable(torch.from_numpy(b_grad_merge))
            else:
                # wait for flag to access
                while hget_object(endpoint, model_bucket, "epoch") != None:
                    if int(hget_object(endpoint, model_bucket, "epoch")) == epoch \
                            and int(hget_object(endpoint, model_bucket, "index")) == batch_index:
                        break
                    time.sleep(0.01)
                w_grad_merge, b_grad_merge = get_merged_w_b_grads(endpoint,model_bucket,
                                                                    w_grad.dtype, w_grad.shape, b_grad.shape,
                                                                    w_grad_prefix, b_grad_prefix)
                hcounter(endpoint, model_bucket, "counter") #flag it if it's accessed.
                print("number of access at this time = {}".format(int(hget_object(endpoint, model_bucket, "counter"))))
                model.linear.weight.grad = Variable(torch.from_numpy(w_grad_merge))
                model.linear.bias.grad = Variable(torch.from_numpy(b_grad_merge))

            print("w_grad after merge = {}".format(model.linear.weight.grad.data.numpy()[0][:5]))
            print("b_grad after merge = {}".format(model.linear.bias.grad.data.numpy()))

            print("synchronization cost {} s".format(time.time() - sync_start))

            optimizer.step()

            print("batch cost {} s".format(time.time() - batch_start))

            if (batch_index + 1) % 10 == 0:
                print('Epoch: [%d/%d], Step: [%d/%d], Loss: %.4f'
                      % (epoch + 1, num_epochs, batch_index + 1, len(train_indices) / batch_size, loss.data))
    """
    if worker_index == 0:
        while sync_counter(endpoint, bucket, num_workers):
            time.sleep(0.001)
        clear_bucket(endpoint, model_bucket)
        clear_bucket(endpoint, grad_bucket)
    """
    # Test the Model
    correct = 0
    total = 0
    for items, labels in validation_loader:
        items = Variable(items.view(-1, num_features))
        outputs = model(items)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum()

    print('Accuracy of the model on the %d test samples: %d %%' % (len(val_indices), 100 * correct / total))

    end_time = time.time()
    print("elapsed time = {} s".format(end_time - start_time))
