#
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# to you 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
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# 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.
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from singa import singa_wrap as singa
from singa import device
from singa import tensor
from singa import opt
import numpy as np
import time
import argparse
from PIL import Image
from data import cardiovascular
from model import cardionet

np_dtype = {"float16": np.float16, "float32": np.float32}

singa_dtype = {"float16": tensor.float16, "float32": tensor.float32}


# Data augmentation
def augmentation(x, batch_size):
    xpad = np.pad(x, [[0, 0], [0, 0], [4, 4], [4, 4]], 'symmetric')
    for data_num in range(0, batch_size):
        offset = np.random.randint(8, size=2)
        x[data_num, :, :, :] = xpad[data_num, :,
                               offset[0]:offset[0] + x.shape[2],
                               offset[1]:offset[1] + x.shape[2]]
        if_flip = np.random.randint(2)
        if (if_flip):
            x[data_num, :, :, :] = x[data_num, :, :, ::-1]
    return x


# Calculate accuracy
def accuracy(pred, target):
    # y is network output to be compared with ground truth (int)
    y = np.argmax(pred, axis=1)
    a = y == target
    correct = np.array(a, "int").sum()
    return correct


# Data partition according to the rank
def partition(global_rank, world_size, train_x, train_y, val_x, val_y):
    # Partition training data
    data_per_rank = train_x.shape[0] // world_size
    idx_start = global_rank * data_per_rank
    idx_end = (global_rank + 1) * data_per_rank
    train_x = train_x[idx_start:idx_end]
    train_y = train_y[idx_start:idx_end]

    # Partition evaluation data
    data_per_rank = val_x.shape[0] // world_size
    idx_start = global_rank * data_per_rank
    idx_end = (global_rank + 1) * data_per_rank
    val_x = val_x[idx_start:idx_end]
    val_y = val_y[idx_start:idx_end]
    return train_x, train_y, val_x, val_y


# Function to all reduce NUMPY accuracy and loss from multiple devices
def reduce_variable(variable, dist_opt, reducer):
    reducer.copy_from_numpy(variable)
    dist_opt.all_reduce(reducer.data)
    dist_opt.wait()
    output = tensor.to_numpy(reducer)
    return output


def resize_dataset(x, image_size):
    num_data = x.shape[0]
    dim = x.shape[1]
    X = np.zeros(shape=(num_data, dim, image_size, image_size),
                 dtype=np.float32)
    for n in range(0, num_data):
        for d in range(0, dim):
            X[n, d, :, :] = np.array(Image.fromarray(x[n, d, :, :]).resize(
                (image_size, image_size), Image.BILINEAR),
                dtype=np.float32)
    return X


def run(global_rank,
        world_size,
        local_rank,
        max_epoch,
        batch_size,
        model,
        sgd,
        graph,
        verbosity,
        path,
        dist_option='plain',
        spars=None,
        precision='float32'):
    dev = device.get_default_device()
    #dev = device.create_cuda_gpu_on(local_rank)
    # need to change to CPU device for CPU-only machines
    dev.SetRandSeed(0)
    np.random.seed(0)

   
    train_x, train_y, val_x, val_y = cardiovascular.load(path)


    num_channels = 1
    image_size = 1
    data_size = train_x.shape[1]

    num_classes = 2

    if model == 'resnet':
        from model import resnet
        model = resnet.resnet50(num_channels=num_channels,
                                num_classes=num_classes)
    elif model == 'xceptionnet':
        from model import xceptionnet
        model = xceptionnet.create_model(num_channels=num_channels,
                                         num_classes=num_classes)
    elif model == 'cnn':
        from model import cnn
        model = cnn.create_model(num_channels=num_channels,
                                 num_classes=num_classes)
    elif model == 'alexnet':
        from model import alexnet
        model = alexnet.create_model(num_channels=num_channels,
                                     num_classes=num_classes)
    elif model == 'cardionet':
        import os, sys, inspect
        current = os.path.dirname(
            os.path.abspath(inspect.getfile(inspect.currentframe())))
        parent = os.path.dirname(current)
        sys.path.insert(0, parent)
        model = cardionet.create_model(data_size=data_size, perceptron_size=1000, num_classes=num_classes)

    # For distributed training, sequential has better performance
    if hasattr(sgd, "communicator"):
        DIST = True
        sequential = True
    else:
        DIST = False
        sequential = False

    if DIST:
        train_x, train_y, val_x, val_y = partition(global_rank, world_size,
                                                   train_x, train_y, val_x,
                                                   val_y)

    if model.dimension == 4:
        tx = tensor.Tensor(
            (batch_size, num_channels, model.input_size, model.input_size), dev,
            singa_dtype[precision])
    elif model.dimension == 2:
        tx = tensor.Tensor((batch_size, data_size), dev, singa_dtype[precision])
        # np.reshape(train_x, (train_x.shape[0], -1))
        # np.reshape(val_x, (val_x.shape[0], -1))

    ty = tensor.Tensor((batch_size,), dev, tensor.int32)
    num_train_batch = train_x.shape[0] // batch_size
    num_val_batch = val_x.shape[0] // batch_size
    idx = np.arange(train_x.shape[0], dtype=np.int32)

    # Attach model to graph
    model.set_optimizer(sgd)
    model.compile([tx], is_train=True, use_graph=graph, sequential=sequential)
    dev.SetVerbosity(verbosity)

    # Training and evaluation loop
    for epoch in range(max_epoch):
        start_time = time.time()
        np.random.shuffle(idx)

        if global_rank == 0:
            print('Starting Epoch %d:' % (epoch))


        # Training phase
        train_correct = np.zeros(shape=[1], dtype=np.float32)
        test_correct = np.zeros(shape=[1], dtype=np.float32)
        train_loss = np.zeros(shape=[1], dtype=np.float32)

        model.train()
        for b in range(num_train_batch):
            # if b % 100 == 0:
            #     print ("b: \n", b)
            # Generate the patch data in this iteration
            x = train_x[idx[b * batch_size:(b + 1) * batch_size]]
            if model.dimension == 4:
                x = augmentation(x, batch_size)
                if (image_size != model.input_size):
                    x = resize_dataset(x, model.input_size)
            x = x.astype(np_dtype[precision])
            y = train_y[idx[b * batch_size:(b + 1) * batch_size]]

            # Copy the patch data into input tensors
            tx.copy_from_numpy(x)
            ty.copy_from_numpy(y)

            # Train the model
            out, loss = model(tx, ty, dist_option, spars)
            train_correct += accuracy(tensor.to_numpy(out), y)
            train_loss += tensor.to_numpy(loss)[0]

        if DIST:
            # Reduce the evaluation accuracy and loss from multiple devices
            reducer = tensor.Tensor((1,), dev, tensor.float32)
            train_correct = reduce_variable(train_correct, sgd, reducer)
            train_loss = reduce_variable(train_loss, sgd, reducer)

        if global_rank == 0:
            print('Training loss = %f, training accuracy = %f' %
                  (train_loss, train_correct /
                   (num_train_batch * batch_size * world_size)),
                  flush=True)

        # Evaluation phase
        model.eval()
        for b in range(num_val_batch):
            x = val_x[b * batch_size:(b + 1) * batch_size]
            if model.dimension == 4:
                if (image_size != model.input_size):
                    x = resize_dataset(x, model.input_size)
            x = x.astype(np_dtype[precision])
            y = val_y[b * batch_size:(b + 1) * batch_size]
            tx.copy_from_numpy(x)
            ty.copy_from_numpy(y)
            out_test = model(tx)
            test_correct += accuracy(tensor.to_numpy(out_test), y)

        if DIST:
            # Reduce the evaulation accuracy from multiple devices
            test_correct = reduce_variable(test_correct, sgd, reducer)

        # Output the evaluation accuracy
        if global_rank == 0:
            print('Evaluation accuracy = %f, Elapsed Time = %fs' %
                  (test_correct / (num_val_batch * batch_size * world_size),
                   time.time() - start_time),
                  flush=True)

    dev.PrintTimeProfiling()


if __name__ == '__main__':
    # Use argparse to get command config: max_epoch, model, data, etc., for single gpu training
    parser = argparse.ArgumentParser(
        description='Training using the autograd and graph.')
    parser.add_argument(
        'model',
        choices=['cnn', 'resnet', 'xceptionnet', 'cardionet', 'alexnet'],
        default='cardionet')
    parser.add_argument('-p',
                        choices=['float32', 'float16'],
                        default='float32',
                        dest='precision')
    parser.add_argument('-m',
                        '--max-epoch',
                        default=100,
                        type=int,
                        help='maximum epochs',
                        dest='max_epoch')
    parser.add_argument('-b',
                        '--batch-size',
                        default=64,
                        type=int,
                        help='batch size',
                        dest='batch_size')
    parser.add_argument('-l',
                        '--learning-rate',
                        default=0.001,
                        type=float,
                        help='initial learning rate',
                        dest='lr')
    # Determine which gpu to use
    parser.add_argument('-i',
                        '--device-id',
                        default=0,
                        type=int,
                        help='which GPU to use',
                        dest='device_id')
    parser.add_argument('-g',
                        '--disable-graph',
                        default='True',
                        action='store_false',
                        help='disable graph',
                        dest='graph')
    parser.add_argument('-v',
                        '--log-verbosity',
                        default=0,
                        type=int,
                        help='logging verbosity',
                        dest='verbosity')
    parser.add_argument('-dir',
                        '--path-to-dataset',
                        default=None,
                        help='path to dataset',
                        dest='path')                    
                        

    args = parser.parse_args()

    sgd = opt.SGD(lr=args.lr, momentum=0.9, weight_decay=1e-5, dtype=singa_dtype[args.precision])
    run(0,
        1,
        args.device_id,
        args.max_epoch,
        args.batch_size,
        args.model,
        sgd,
        args.graph,
        args.verbosity,
        args.path,
        precision=args.precision)
