import torch # ; print('Using torch version -', torch.__version__)
if torch.cuda.is_available():
    device = 'cuda'
else:
    device = 'cpu'
from torch.nn import Module, Conv2d, ReLU, ModuleList, MaxPool2d, ConvTranspose2d, BCELoss, BCEWithLogitsLoss, functional as F
from torch.optim import Adam
from torchvision import transforms
from torchvision.transforms import CenterCrop
from torch.utils.data import Dataset, DataLoader
import cv2

height, width = 256, 256
class Block(Module):
    def __init__(self, inChannels, outChannels):
        super().__init__()
        # store the convolution and RELU layers
        self.conv1 = Conv2d(inChannels, outChannels, 3)
        self.relu = ReLU()
        self.conv2 = Conv2d(outChannels, outChannels, 3)
    def forward(self, x):
        # apply CONV => RELU => CONV block to the inputs and return it
        return self.conv2(self.relu(self.conv1(x)))
    
class Encoder(Module):
    def __init__(self, channels=(3, 16, 32, 64)):
        super().__init__()
        # store the encoder blocks and maxpooling layer
        self.encBlocks = ModuleList([Block(channels[i], channels[i + 1]) for i in range(len(channels) - 1)])
        self.pool = MaxPool2d(2)
    def forward(self, x):
        # initialize an empty list to store the intermediate outputs
        blockOutputs = []
        # loop through the encoder blocks
        for block in self.encBlocks:
            # pass the inputs through the current encoder block, store
            # the outputs, and then apply maxpooling on the output
            x = block(x)
            blockOutputs.append(x)
            x = self.pool(x)
        # return the list containing the intermediate outputs
        return blockOutputs
    
class Decoder(Module):
    def __init__(self, channels=(64, 32, 16)):
        super().__init__()
        # initialize the number of channels, upsampler blocks, and
        # decoder blocks
        self.channels = channels
        self.upconvs = ModuleList([ConvTranspose2d(channels[i], channels[i + 1], 2, 2) for i in range(len(channels) - 1)])
        self.dec_blocks = ModuleList([Block(channels[i], channels[i + 1]) for i in range(len(channels) - 1)])
    def forward(self, x, encFeatures):
        # loop through the number of channels
        for i in range(len(self.channels) - 1):
            # pass the inputs through the upsampler blocks
            x = self.upconvs[i](x)
            # crop the current features from the encoder blocks,
            # concatenate them with the current upsampled features,
            # and pass the concatenated output through the current
            # decoder block
            encFeat = self.crop(encFeatures[i], x)
            x = torch.cat([x, encFeat], dim=1)
            x = self.dec_blocks[i](x)
        # return the final decoder output
        return x
    def crop(self, encFeatures, x):
        # grab the dimensions of the inputs, and crop the encoder
        # features to match the dimensions
        (_, _, H, W) = x.shape
        encFeatures = CenterCrop([H, W])(encFeatures)
        # return the cropped features
        return encFeatures
    
class UNet(Module):
    def __init__(self, encChannels=(3, 64, 128, 256, 512, 1024), decChannels=(1024, 512, 256, 128, 64),
                 nbClasses=1, retainDim=True, outSize=(height, width)):
        super().__init__()
        # initialize the encoder and decoder
        self.encoder = Encoder(encChannels)
        self.decoder = Decoder(decChannels)
        # initialize the regression head and store the class variables
        self.head = Conv2d(decChannels[-1], nbClasses, 1)
        self.retainDim = retainDim
        self.outSize = outSize
    def forward(self, x):
        # grab the features from the encoder
        encFeatures = self.encoder(x)
        # pass the encoder features through decoder making sure that
        # their dimensions are suited for concatenation
        decFeatures = self.decoder(encFeatures[::-1][0], encFeatures[::-1][1:])
        # pass the decoder features through the regression head to
        # obtain the segmentation mask
        map_ = self.head(decFeatures)
        # check to see if we are retaining the original output
        # dimensions and if so, then resize the output to match them
        if self.retainDim:
            map_ = F.interpolate(map_, self.outSize)
        # return the segmentation map
        return map_