from __future__ import print_function
from __future__ import division
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import torchvision
from torchvision import datasets, models, transforms
import matplotlib.pyplot as plt
import time
import os
import copy

from torchsampler import ImbalancedDatasetSampler
from ignite.engine import Engine, create_supervised_evaluator
from ignite.metrics import ConfusionMatrix
import seaborn as sns


def train_model(model, dataloaders, criterion, optimizer, device, num_epochs=25, is_inception=False):
    since = time.time()

    val_acc_history = []

    best_model_wts = copy.deepcopy(model.state_dict())
    best_acc = 0.0

    for epoch in range(num_epochs):
        print('Epoch {}/{}'.format(epoch, num_epochs - 1))
        print('-' * 10)

        # Each epoch has a training and validation phase
        for phase in ['train', 'val']:
            if phase == 'train':
                model.train()  # Set model to training mode
            else:
                model.eval()   # Set model to evaluate mode

            running_loss = 0.0
            running_corrects = 0

            # Iterate over data.
            for inputs, labels in dataloaders[phase]:
                inputs = inputs.to(device)
                labels = labels.to(device)

                # zero the parameter gradients
                optimizer.zero_grad()

                # forward
                # track history if only in train
                with torch.set_grad_enabled(phase == 'train'):
                    # Get model outputs and calculate loss
                    # Special case for inception because in training it has an auxiliary output. In train
                    #   mode we calculate the loss by summing the final output and the auxiliary output
                    #   but in testing we only consider the final output.
                    if is_inception and phase == 'train':
                        # From https://discuss.pytorch.org/t/how-to-optimize-inception-model-with-auxiliary-classifiers/7958
                        outputs, aux_outputs = model(inputs)
                        loss1 = criterion(outputs, labels)
                        loss2 = criterion(aux_outputs, labels)
                        loss = loss1 + 0.4*loss2
                    else:
                        outputs = model(inputs)
                        loss = criterion(outputs, labels)

                    _, preds = torch.max(outputs, 1)

                    # backward + optimize only if in training phase
                    if phase == 'train':
                        loss.backward()
                        optimizer.step()

                # statistics
                running_loss += loss.item() * inputs.size(0)
                running_corrects += torch.sum(preds == labels.data)

            epoch_loss = running_loss / len(dataloaders[phase].dataset)
            epoch_acc = running_corrects.double() / len(dataloaders[phase].dataset)

            print('{} Loss: {:.4f} Acc: {:.4f}'.format(phase, epoch_loss, epoch_acc))

            # deep copy the model
            if phase == 'val' and epoch_acc > best_acc:
                best_acc = epoch_acc
                best_model_wts = copy.deepcopy(model.state_dict())
            if phase == 'val':
                val_acc_history.append(epoch_acc)

        print()

    time_elapsed = time.time() - since
    print('Training complete in {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60))
    print('Best val Acc: {:4f}'.format(best_acc))

    # load best model weights
    model.load_state_dict(best_model_wts)
    return model, val_acc_history

def set_parameter_requires_grad(model, feature_extracting):
    if feature_extracting:
        for param in model.parameters():
            param.requires_grad = False

def get_input_size(model_name):
    input_size = 0

    if model_name == "resnet":
        """ Resnet18
        """
        input_size = 224

    elif model_name == "alexnet":
        """ Alexnet
        """
        input_size = 224

    elif model_name == "vgg":
        """ VGG11_bn
        """
        input_size = 224

    elif model_name == "squeezenet":
        """ Squeezenet
        """
        input_size = 224

    elif model_name == "densenet":
        """ Densenet
        """
        input_size = 224

    elif model_name == "inception":
        """ Inception v3
        Be careful, expects (299,299) sized images and has auxiliary output
        """
        input_size = 299

    else:
        raise ValueError("Invalid model name, exiting...")


    return input_size


def initialize_model(model_name, num_classes, feature_extract, use_pretrained=True):
    # Initialize these variables which will be set in this if statement. Each of these
    #   variables is model specific.
    model_ft = None
    input_size = 0

    if model_name == "resnet":
        """ Resnet18
        """
        model_ft = models.resnet18(pretrained=use_pretrained)
        set_parameter_requires_grad(model_ft, feature_extract)
        num_ftrs = model_ft.fc.in_features
        model_ft.fc = nn.Linear(num_ftrs, num_classes)
        input_size = 224

    elif model_name == "alexnet":
        """ Alexnet
        """
        model_ft = models.alexnet(pretrained=use_pretrained)
        set_parameter_requires_grad(model_ft, feature_extract)
        num_ftrs = model_ft.classifier[6].in_features
        model_ft.classifier[6] = nn.Linear(num_ftrs,num_classes)
        input_size = 224

    elif model_name == "vgg":
        """ VGG11_bn
        """
        model_ft = models.vgg11_bn(pretrained=use_pretrained)
        set_parameter_requires_grad(model_ft, feature_extract)
        num_ftrs = model_ft.classifier[6].in_features
        model_ft.classifier[6] = nn.Linear(num_ftrs,num_classes)
        input_size = 224

    elif model_name == "squeezenet":
        """ Squeezenet
        """
        model_ft = models.squeezenet1_0(pretrained=use_pretrained)
        set_parameter_requires_grad(model_ft, feature_extract)
        model_ft.classifier[1] = nn.Conv2d(512, num_classes, kernel_size=(1,1), stride=(1,1))
        model_ft.num_classes = num_classes
        input_size = 224

    elif model_name == "densenet":
        """ Densenet
        """
        model_ft = models.densenet121(pretrained=use_pretrained)
        set_parameter_requires_grad(model_ft, feature_extract)
        num_ftrs = model_ft.classifier.in_features
        model_ft.classifier = nn.Linear(num_ftrs, num_classes)
        input_size = 224

    elif model_name == "inception":
        """ Inception v3
        Be careful, expects (299,299) sized images and has auxiliary output
        """
        model_ft = models.inception_v3(pretrained=use_pretrained)
        set_parameter_requires_grad(model_ft, feature_extract)
        # Handle the auxilary net
        num_ftrs = model_ft.AuxLogits.fc.in_features
        model_ft.AuxLogits.fc = nn.Linear(num_ftrs, num_classes)
        # Handle the primary net
        num_ftrs = model_ft.fc.in_features
        model_ft.fc = nn.Linear(num_ftrs,num_classes)
        input_size = 299

    else:
        raise ValueError("Invalid model name, exiting...")


    return model_ft, input_size


def get_data_transforms(input_size):
    return {
        'train': transforms.Compose([
            transforms.RandomResizedCrop(input_size),
            transforms.RandomHorizontalFlip(),
            transforms.ToTensor(),
            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
        ]),
        'val': transforms.Compose([
            transforms.Resize(input_size),
            transforms.CenterCrop(input_size),
            transforms.ToTensor(),
            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
        ]),
    }

def get_data_sets(input_size, data_dir, batch_size):
    data_transforms = get_data_transforms(input_size)

    image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x]) for x in ['train', 'val']}
    # Create training and validation dataloaders
    dataloaders_dict = {
        x: torch.utils.data.DataLoader(
            image_datasets[x], 
            batch_size=batch_size, 
            shuffle=True, 
            #sampler=ImbalancedDatasetSampler(image_datasets[x]),
            num_workers=4) for x in
        ['train', 'val']}
    return dataloaders_dict


def set_training_mode(model_ft, feature_extract, device):

    # Send the model to GPU
    model_ft = model_ft.to(device)

    # Gather the parameters to be optimized/updated in this run. If we are
    #  finetuning we will be updating all parameters. However, if we are
    #  doing feature extract method, we will only update the parameters
    #  that we have just initialized, i.e. the parameters with requires_grad
    #  is True.
    params_to_update = model_ft.parameters()
    print("Params to learn:")
    if feature_extract:
        params_to_update = []
        for name, param in model_ft.named_parameters():
            if param.requires_grad == True:
                params_to_update.append(param)
                print("\t", name)
    else:
        for name, param in model_ft.named_parameters():
            if param.requires_grad == True:
                print("\t", name)

    # Observe that all parameters are being optimized
    optimizer_ft = optim.SGD(params_to_update, lr=0.001, momentum=0.9)
    return optimizer_ft


def display_confusion_matrix(model_ft, device, dataloaders_dict):


    val_evaluator = create_supervised_evaluator(model_ft, metrics={"cm": ConfusionMatrix(num_classes=11)},
                                                device=device)

    val_evaluator.run(dataloaders_dict["val"])


    metrics = val_evaluator.state.metrics
    cm = metrics['cm']
    cm = cm.numpy()
    cm = cm.astype(int)
    classes = list(range(11))
    fig, ax = plt.subplots(figsize=(10, 10))
    ax = plt.subplot()
    sns.heatmap(cm, annot=True, ax=ax, fmt="d")
    # labels, title and ticks
    ax.set_xlabel('Predicted labels')
    ax.set_ylabel('True labels')
    ax.set_title('Confusion Matrix')
    ax.xaxis.set_ticklabels(classes, rotation=90)
    ax.yaxis.set_ticklabels(classes, rotation=0)
