import numpy as np
import torch
import torch.utils.data as data_utils
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from PIL import Image
from torch.utils.data import DataLoader, TensorDataset, random_split
from torch.utils.tensorboard import SummaryWriter
import time
from collections import OrderedDict
from collections import namedtuple
from itertools import product

class DoubleConv(nn.Module):
    """(convolution => [BN] => ReLU) * 2"""

    def __init__(self, in_channels, out_channels, mid_channels=None):
        super().__init__()
        if not mid_channels:
            mid_channels = out_channels
        self.double_conv = nn.Sequential(
            nn.Conv2d(in_channels, mid_channels, kernel_size=3, padding=1),
            nn.BatchNorm2d(mid_channels),
            nn.ReLU(inplace=True),
            nn.Conv2d(mid_channels, out_channels, kernel_size=3, padding=1),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True)
        )

    def forward(self, x):
        return self.double_conv(x)
class Down(nn.Module):
    """Downscaling with maxpool then double conv"""

    def __init__(self, in_channels, out_channels):
        super().__init__()
        self.maxpool_conv = nn.Sequential(
            nn.MaxPool2d(2),
            DoubleConv(in_channels, out_channels)
        )

    def forward(self, x):
        return self.maxpool_conv(x)
class Up(nn.Module):
    """Upscaling then double conv"""

    def __init__(self, in_channels, out_channels, bilinear=True):
        super().__init__()

        if bilinear:
            self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
            self.conv = DoubleConv(in_channels, out_channels, in_channels // 2)
        else:
            self.up = nn.ConvTranspose2d(in_channels , in_channels // 2, kernel_size=2, stride=2)
            self.conv = DoubleConv(in_channels, out_channels)


    def forward(self, x1, x2):
        x1 = self.up(x1)
        # input is CHW
        diffY = x2.size()[2] - x1.size()[2]
        diffX = x2.size()[3] - x1.size()[3]

        x1 = F.pad(x1, [diffX // 2, diffX - diffX // 2,
                        diffY // 2, diffY - diffY // 2])
        # if you have padding issues, see
        # https://github.com/HaiyongJiang/U-Net-Pytorch-Unstructured-Buggy/commit/0e854509c2cea854e247a9c615f175f76fbb2e3a
        # https://github.com/xiaopeng-liao/Pytorch-UNet/commit/8ebac70e633bac59fc22bb5195e513d5832fb3bd
        x = torch.cat([x2, x1], dim=1)
        return self.conv(x)
class OutConv(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(OutConv, self).__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1)

    def forward(self, x):
        return self.conv(x)
class UNet(nn.Module):
    def __init__(self, n_channels, n_classes, bilinear=True):
        super(UNet, self).__init__()
        self.n_channels = n_channels
        self.n_classes = n_classes
        self.bilinear = bilinear

        self.inc = DoubleConv(n_channels, 64)
        self.down1 = Down(64, 128)
        self.down2 = Down(128, 256)
        self.down3 = Down(256, 512)
        factor = 2 if bilinear else 1
        self.down4 = Down(512, 1024 // factor)
        self.up1 = Up(1024, 512 // factor, bilinear)
        self.up2 = Up(512, 256 // factor, bilinear)
        self.up3 = Up(256, 128 // factor, bilinear)
        self.up4 = Up(128, 64, bilinear)
        self.outc = OutConv(64, n_classes)

    def forward(self, x):
        x1 = self.inc(x)
        x2 = self.down1(x1)
        x3 = self.down2(x2)
        x4 = self.down3(x3)
        x5 = self.down4(x4)
        x = self.up1(x5, x4)
        x = self.up2(x, x3)
        x = self.up3(x, x2)
        x = self.up4(x, x1)
        logits = self.outc(x)
        return logits
class RunBuilder():
    @staticmethod
    def get_runs(params):

        Run = namedtuple('Run', params.keys())

        runs = []
        for v in product(*params.values()):
            runs.append(Run(*v))
        return runs
class RunManager():
    def __init__(self):

        self.epoch_count = 0
        self.epoch_loss = 0
        self.epoch_num_correct = 0
        self.epoch_start_time = None

        self.run_params = None
        self.run_count = 0
        self.run_data = []
        self.run_start_time = None

        self.network = None
        self.loader = None
        self.tb = None

    def begin_run(self, run, network, loader):
        self.run_start_time = time.time()
        self.run_params = run
        self.run_count += 1
        self.network = network
        self.loader = loader
        self.tb = SummaryWriter(comment=f'-{run}')
        characteristics, labels = next(iter(self.loader))

    def end_run(self):
        self.tb.close()
        self.epoch_count = 0

    def begin_epoch(self):
        self.epoch_start_time = time.time()
        self.epoch_count += 1
        self.epoch_loss = 0
        self.epoch_num_correct = 0

    def end_epoch(self):
        epoch_duration = time.time() - self.epoch_start_time
        run_duration = time.time() - self.run_start_time

        loss = self.epoch_loss / len(self.loader.dataset)
 #       accuracy = self.epoch_num_correct / len(self.loader.dataset)

        self.tb.add_scalar('Loss', loss, self.epoch_count)
 #       self.tb.add_scalar('Accuracy', accuracy, self.epoch_count)

    def _get_num_correct(self, preds, labels):
        return preds.argmax(dim=1).eq(labels).sum().item()

    def track_loss(self, loss, batch):
        self.epoch_loss += loss.item() * batch[0].shape[0]

    def track_num_correct(self, preds, labels):
        self.epoch_num_correct += self._get_num_correct(preds, labels)

    def inform(self, discrete_n):
        if self.epoch_count % discrete_n == 0:
            print(self.epoch_count, ' ', self.run_count)

def data_get(file_name):
    image = Image.open('Actin.tif')
    holdall = []
    cut_size = 64
    step_size = 32
    for I in range(1):
        image.seek(I)
        data_back = np.asarray(image)
        for X in range(0, image.width - cut_size, step_size):
            for Y in range(0, image.height - cut_size, step_size):
                holdall.append(data_back[X:X + cut_size, Y:Y + cut_size])
    newarray = np.dstack(holdall).astype(float)
    newarray = np.swapaxes(newarray, 0, 2)
    noise = np.random.randint(1, 20000, np.shape(newarray))
    train = torch.tensor(newarray + noise).type(torch.float)
    train = torch.unsqueeze(train, 1)
    label = torch.tensor(newarray).type(torch.float)
    label = torch.unsqueeze(label, 1)
    return (train, label, cut_size)

params = OrderedDict(lr=[.01, 0.001], batch_size=[100,200])
m = RunManager()
train, label, cut_size = data_get('Actin.tif')
print(train.shape)
train_tensor = data_utils.TensorDataset(train, label)
criterion = nn.BCELoss()
map = nn.Sigmoid()
for run in RunBuilder.get_runs(params):

    network = UNet(1, 1)
    loader = DataLoader(train_tensor, batch_size=run.batch_size, shuffle=True)
    optimizer = torch.optim.Adam(network.parameters(), lr=run.lr)
    m.begin_run(run, network, loader)

    for epoch in range(1):
        m.begin_epoch()
        count = 0
        for batch in loader:
            print(count)
            count = count + 1
            characteristics, labels = batch
            preds = network(characteristics)  # Pass Batch
            #loss = criterion(preds.sum(), labels.sum()) # loss estimation
            loss = criterion(map(preds), map(labels))
            optimizer.zero_grad()  # Zero Gradients
            loss.backward()  # Calculate Gradients
            optimizer.step()  # Update Weights
            m.track_loss(loss, batch)
       #     m.track_num_correct(preds, labels)
        m.inform(2)
        m.end_epoch()
    m.end_run()