import argparse

import logging

import os

import random

import sys

import copy

import torch

import torch.nn as nn

import torch.nn.functional as F

import torchvision.transforms as transforms

import torchvision.transforms.functional as TF

import torchvision.models as models

from pathlib import Path

from torch import optim

from torch.utils.data import DataLoader, random_split

from tqdm import tqdm

from evaluate import evaluate

from unet.unet_model import UNet

from utils.data_loading import BasicDataset, CarvanaDataset

from utils.dice_score import dice_loss

import segmentation_models_pytorch as smp

device = torch.device('cuda:4' if torch.cuda.is_available() else 'cpu')

dir_img = Path('./data/train/imgs/')

dir_mask = Path('./data/train/masks/')

dir_checkpoint = Path('./checkpoints')

def train_model(

        model, device, epochs, batch_size, learning_rate,

        val_percent: float = 0.1,

        save_checkpoint: bool = True,

        img_scale: float = 0.5,

        amp: bool = False,

        weight_decay: float = 1e-8,

        momentum: float = 0.5,

        gradient_clipping: float = 1.0

    ):

    best_model_params = copy.deepcopy(model.state_dict())

    best_acc = 0.0

    best_epoch = 0

    # 1. Create dataset    

    data_transform = transforms.Compose([

        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])

    ])

    try:

        dataset = CarvanaDataset(dir_img, dir_mask, img_scale)

    except (AssertionError, RuntimeError, IndexError):

        dataset = BasicDataset(dir_img, dir_mask, img_scale, data_transform)

    # 2. Split into train / validation partitions

    n_val = int(len(dataset) * val_percent)

    n_train = len(dataset) - n_val

    train_set, val_set = random_split(dataset, [n_train, n_val], generator=torch.Generator().manual_seed(0))

    # 3. Create data loaders

    loader_args = dict(batch_size=batch_size, num_workers=os.cpu_count(), pin_memory=True)

    train_loader = DataLoader(train_set, shuffle=True, **loader_args)

    val_loader = DataLoader(val_set, shuffle=False, drop_last=True, **loader_args)

    logging.info(f'''Starting training:

        Epochs:          {epochs}

        Batch size:      {batch_size}

        Learning rate:   {learning_rate}

        Training size:   {n_train}

        Validation size: {n_val}

        Checkpoints:     {save_checkpoint}

        Device:          {device.type}

        Images scaling:  {img_scale}

        Mixed Precision: {amp}

    ''')

    # 4. Set up the optimizer, the loss, the learning rate scheduler and the loss scaling for AMP

    optimizer = optim.Adam(model.parameters(),

                              lr=learning_rate, weight_decay=weight_decay)

    scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'max', patience=5)  # goal: maximize Dice score

    grad_scaler = torch.cuda.amp.GradScaler(enabled=amp)

    criterion = nn.CrossEntropyLoss() if model.n_classes > 1 else nn.BCEWithLogitsLoss()

    global_step = 0

    # 5. Begin training

    for epoch in range(1, epochs + 1):

        model.train()

        epoch_loss = 0

        with tqdm(total=n_train, desc=f'Epoch {epoch}/{epochs}', unit='img') as pbar:

            for batch in train_loader:

                images, true_masks = batch['image'], batch['mask']

                assert images.shape[1] == model.n_channels, \

                    f'Network has been defined with {model.n_channels} input channels, ' \

                    f'but loaded images have {images.shape[1]} channels. Please check that ' \

                    'the images are loaded correctly.'

                images = images.to(device=device, dtype=torch.float32, memory_format=torch.channels_last)

                true_masks = true_masks.to(device=device, dtype=torch.long)

                with torch.autocast(device.type if device.type != 'mps' else 'cpu', enabled=amp):

                    masks_pred = model(images)

                    if model.n_classes == 1:

                        loss = criterion(masks_pred.squeeze(1), true_masks.float())

                        loss += dice_loss(F.sigmoid(masks_pred.squeeze(1)), true_masks.float(), multiclass=False)

                    else:

                        loss = criterion(masks_pred, true_masks)

                        loss += dice_loss(

                            F.softmax(masks_pred, dim=1).float(),

                            F.one_hot(true_masks, model.n_classes).permute(0, 3, 1, 2).float(),

                            multiclass=True

                        )

                optimizer.zero_grad(set_to_none=True)

                grad_scaler.scale(loss).backward()

                torch.nn.utils.clip_grad_norm_(model.parameters(), gradient_clipping)

                grad_scaler.step(optimizer)

                grad_scaler.update()

                pbar.update(images.shape[0])

                global_step += 1

                epoch_loss += loss.item()

                pbar.set_postfix(**{'loss (batch)': loss.item()})

                # Evaluation round

                division_step = (n_train // (5 * batch_size))

                if division_step > 0:

                    if global_step % division_step == 0:

                        val_score = evaluate(model, val_loader, device, amp)

                        scheduler.step(val_score)

                        logging.info('Validation Dice score: {}'.format(val_score))

            # Check best accuracy model ( but not the best on test )

            if val_score > best_acc:

                best_acc = val_score

                best_epoch = epoch

                best_model_params = copy.deepcopy(model.state_dict())

            logging.info("Best model: [" + f'epoch: {best_epoch}, acc: {best_acc:.4f}]')

        if save_checkpoint:

            Path(dir_checkpoint).mkdir(parents=True, exist_ok=True)

            state_dict = model.state_dict()

            state_dict['mask_values'] = dataset.mask_values

            torch.save(state_dict, str(dir_checkpoint / 'checkpoint_epoch{}.pth'.format(epoch)))

            logging.info(f'Checkpoint {epoch} saved!')

    # only weight

    torch.save(best_model_params, f'epoch_{best_epoch}_acc_{best_acc:.2f}_best_val_acc.pth')

    logging.info("Best model name : " + f'epoch_{best_epoch}_acc_{best_acc:.2f}_best_val_acc.pth')

def get_args():

    parser = argparse.ArgumentParser(description='Train the UNet on images and target masks')

    parser.add_argument('--epochs', '-e', metavar='E', type=int, default=50, help='Number of epochs')

    parser.add_argument('--batch-size', '-b', dest='batch_size', metavar='B', type=int, default=16, help='Batch size')

    parser.add_argument('--learning-rate', '-l', metavar='LR', type=float, default=0.001,

                        help='Learning rate', dest='lr')

    parser.add_argument('--load', '-f', type=str, default=False, help='Load model from a .pth file')

    parser.add_argument('--scale', '-s', type=float, default=0.5, help='Downscaling factor of the images')

    parser.add_argument('--validation', '-v', dest='val', type=float, default=10.0,

                        help='Percent of the data that is used as validation (0-100)')

    parser.add_argument('--amp', action='store_true', default=False, help='Use mixed precision')

    parser.add_argument('--bilinear', action='store_true', default=False, help='Use bilinear upsampling')

    parser.add_argument('--classes', '-c', type=int, default=2, help='Number of classes')

    return parser.parse_args()

if __name__ == '__main__':

    args = get_args()

    logging.basicConfig(level=logging.INFO, format='%(levelname)s: %(message)s')

    #device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')

    logging.info(f'Using device {device}')

    """

    Change here to adapt to your data

    n_channels=3 for RGB images

    n_classes is the number of probabilities you want to get per pixel

    """

    model = UNet(n_channels=1, n_classes=5, bilinear=True)

    model = model.to(memory_format=torch.channels_last)

    logging.info(f'Network:\n'

                 f'\t{model.n_channels} input channels\n'

                 f'\t{model.n_classes} output channels (classes)\n'

                 f'\t{"Bilinear" if model.bilinear else "Transposed conv"} upscaling')

    if args.load:

        state_dict = torch.load(args.load, map_location=device)

        del state_dict['mask_values']

        model.load_state_dict(state_dict)

        logging.info(f'Model loaded from {args.load}')

    model.to(device=device)

    train_model(

        model=model,

        epochs=args.epochs,

        batch_size=args.batch_size,

        learning_rate=args.lr,

        device=device,

        img_scale=args.scale,

        val_percent=args.val / 100,

        amp=args.amp

    )