import functools
import time

import numpy as np
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.nn import init
from torch.optim import lr_scheduler

###############################################################################
# Functions
###############################################################################


def weights_init_normal(m):
    classname = m.__class__.__name__
    #print(classname)
    if classname.find('Conv') != -1:
        init.normal(m.weight.data, 0.0, 0.02)
    elif classname.find('Linear') != -1:
        init.normal(m.weight.data, 0.0, 0.02)
    elif classname.find('BatchNorm') != -1:
        init.normal(m.weight.data, 1.0, 0.02)
        init.constant(m.bias.data, 0.0)


def weights_init_xavier(m):
    classname = m.__class__.__name__
    #print(classname)
    if classname.find('Conv') != -1:
        init.xavier_normal(m.weight.data, gain=1)
    elif classname.find('Linear') != -1:
        init.xavier_normal(m.weight.data, gain=1)
    elif classname.find('BatchNorm') != -1:
        init.normal(m.weight.data, 1.0, 0.02)
        init.constant(m.bias.data, 0.0)


def weights_init_kaiming(m):
    classname = m.__class__.__name__
    #print(classname)
    if classname.find('Conv') != -1:
        init.kaiming_normal_(m.weight.data, a=0, mode='fan_in')
    elif classname.find('Linear') != -1:
        init.kaiming_normal_(m.weight.data, a=0, mode='fan_in')
    elif classname.find('BatchNorm') != -1:
        init.normal_(m.weight.data, 1.0, 0.02)
        init.constant_(m.bias.data, 0.0)


def weights_init_orthogonal(m):
    classname = m.__class__.__name__
    #print(classname)
    if classname.find('Conv') != -1:
        init.orthogonal(m.weight.data, gain=1)
    elif classname.find('Linear') != -1:
        init.orthogonal(m.weight.data, gain=1)
    elif classname.find('BatchNorm') != -1:
        init.normal(m.weight.data, 1.0, 0.02)
        init.constant(m.bias.data, 0.0)


def init_weights(net, init_type='normal'):
    #print('initialization method [%s]' % init_type)
    if init_type == 'normal':
        net.apply(weights_init_normal)
    elif init_type == 'xavier':
        net.apply(weights_init_xavier)
    elif init_type == 'kaiming':
        net.apply(weights_init_kaiming)
    elif init_type == 'orthogonal':
        net.apply(weights_init_orthogonal)
    else:
        raise NotImplementedError('initialization method [%s] is not implemented' % init_type)


def get_norm_layer(norm_type='instance'):
    if norm_type == 'batch':
        norm_layer = functools.partial(nn.BatchNorm2d, affine=True)
    elif norm_type == 'instance':
        norm_layer = functools.partial(nn.InstanceNorm2d, affine=False)
    elif norm_type == 'none':
        norm_layer = None
    else:
        raise NotImplementedError('normalization layer [%s] is not found' % norm_type)
    return norm_layer


def adjust_learning_rate(optimizer, lr):
    """Sets the learning rate to a fixed number"""
    for param_group in optimizer.param_groups:
        param_group['lr'] = lr

def get_scheduler(optimizer, opt):
    print('opt.lr_policy = [{}]'.format(opt.lr_policy))
    if opt.lr_policy == 'lambda':
        def lambda_rule(epoch):
            lr_l = 1.0 - max(0, epoch + 1 + opt.epoch_count - opt.niter) / float(opt.niter_decay + 1)
            return lr_l
        scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda_rule)
    elif opt.lr_policy == 'step':
        scheduler = lr_scheduler.StepLR(optimizer, step_size=opt.lr_decay_iters, gamma=0.5)
    elif opt.lr_policy == 'step2':
        scheduler = lr_scheduler.StepLR(optimizer, step_size=opt.lr_decay_iters, gamma=0.1)
    elif opt.lr_policy == 'plateau':
        print('schedular=plateau')
        scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.1, threshold=0.01, patience=5)
    elif opt.lr_policy == 'plateau2':
        scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.2, threshold=0.01, patience=5)
    elif opt.lr_policy == 'step_warmstart':
        def lambda_rule(epoch):
            #print(epoch)
            if epoch < 5:
                lr_l = 0.1
            elif 5 <= epoch < 100:
                lr_l = 1
            elif 100 <= epoch < 200:
                lr_l = 0.1
            elif 200 <= epoch:
                lr_l = 0.01
            return lr_l
        scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda_rule)
    elif opt.lr_policy == 'step_warmstart2':
        def lambda_rule(epoch):
            #print(epoch)
            if epoch < 5:
                lr_l = 0.1
            elif 5 <= epoch < 50:
                lr_l = 1
            elif 50 <= epoch < 100:
                lr_l = 0.1
            elif 100 <= epoch:
                lr_l = 0.01
            return lr_l
        scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda_rule)
    else:

        return NotImplementedError('learning rate policy [%s] is not implemented', opt.lr_policy)
    return scheduler


def define_G(input_nc, output_nc, ngf, which_model_netG, norm='batch', use_dropout=False, init_type='normal', gpu_ids=[]):
    netG = None
    use_gpu = len(gpu_ids) > 0
    norm_layer = get_norm_layer(norm_type=norm)

    if use_gpu:
        assert(torch.cuda.is_available())

    if which_model_netG == 'resnet_9blocks':
        netG = ResnetGenerator(input_nc, output_nc, ngf, norm_layer=norm_layer, use_dropout=use_dropout, n_blocks=9, gpu_ids=gpu_ids)
    elif which_model_netG == 'resnet_6blocks':
        netG = ResnetGenerator(input_nc, output_nc, ngf, norm_layer=norm_layer, use_dropout=use_dropout, n_blocks=6, gpu_ids=gpu_ids)
    elif which_model_netG == 'unet_128':
        netG = UnetGenerator(input_nc, output_nc, 7, ngf, norm_layer=norm_layer, use_dropout=use_dropout, gpu_ids=gpu_ids)
    elif which_model_netG == 'unet_256':
        netG = UnetGenerator(input_nc, output_nc, 8, ngf, norm_layer=norm_layer, use_dropout=use_dropout, gpu_ids=gpu_ids)
    else:
        raise NotImplementedError('Generator model name [%s] is not recognized' % which_model_netG)
    if len(gpu_ids) > 0:
        netG.cuda(gpu_ids[0])
    init_weights(netG, init_type=init_type)
    return netG


def define_D(input_nc, ndf, which_model_netD,
             n_layers_D=3, norm='batch', use_sigmoid=False, init_type='normal', gpu_ids=[]):
    netD = None
    use_gpu = len(gpu_ids) > 0
    norm_layer = get_norm_layer(norm_type=norm)

    if use_gpu:
        assert(torch.cuda.is_available())
    if which_model_netD == 'basic':
        netD = NLayerDiscriminator(input_nc, ndf, n_layers=3, norm_layer=norm_layer, use_sigmoid=use_sigmoid, gpu_ids=gpu_ids)
    elif which_model_netD == 'n_layers':
        netD = NLayerDiscriminator(input_nc, ndf, n_layers_D, norm_layer=norm_layer, use_sigmoid=use_sigmoid, gpu_ids=gpu_ids)
    else:
        raise NotImplementedError('Discriminator model name [%s] is not recognized' %
                                  which_model_netD)
    if use_gpu:
        netD.cuda(gpu_ids[0])
    init_weights(netD, init_type=init_type)
    return netD


def print_network(net):
    num_params = 0
    for param in net.parameters():
        num_params += param.numel()
    print(net)
    print('Total number of parameters: %d' % num_params)


def get_n_parameters(net):
    num_params = 0
    for param in net.parameters():
        num_params += param.numel()
    return num_params


def measure_fp_bp_time(model, x, y):
    # synchronize gpu time and measure fp
    torch.cuda.synchronize()
    t0 = time.time()
    y_pred = model(x)
    torch.cuda.synchronize()
    elapsed_fp = time.time() - t0

    if isinstance(y_pred, tuple):
        y_pred = sum(y_p.sum() for y_p in y_pred)
    else:
        y_pred = y_pred.sum()

    # zero gradients, synchronize time and measure
    model.zero_grad()
    t0 = time.time()
    #y_pred.backward(y)
    y_pred.backward()
    torch.cuda.synchronize()
    elapsed_bp = time.time() - t0
    return elapsed_fp, elapsed_bp


def benchmark_fp_bp_time(model, x, y, n_trial=1000):
    # transfer the model on GPU
    model.cuda()

    # DRY RUNS
    for i in range(10):
        _, _ = measure_fp_bp_time(model, x, y)

    print('DONE WITH DRY RUNS, NOW BENCHMARKING')
    
    # START BENCHMARKING
    t_forward = []
    t_backward = []
    
    print('trial: {}'.format(n_trial))
    for i in range(n_trial):
        t_fp, t_bp = measure_fp_bp_time(model, x, y)
        t_forward.append(t_fp)
        t_backward.append(t_bp)

    # free memory
    del model

    return np.mean(t_forward), np.mean(t_backward)

##############################################################################
# Classes
##############################################################################


# Defines the GAN loss which uses either LSGAN or the regular GAN.
# When LSGAN is used, it is basically same as MSELoss,
# but it abstracts away the need to create the target label tensor
# that has the same size as the input
class GANLoss(nn.Module):
    def __init__(self, use_lsgan=True, target_real_label=1.0, target_fake_label=0.0,
                 tensor=torch.FloatTensor):
        super(GANLoss, self).__init__()
        self.real_label = target_real_label
        self.fake_label = target_fake_label
        self.real_label_var = None
        self.fake_label_var = None
        self.Tensor = tensor
        if use_lsgan:
            self.loss = nn.MSELoss()
        else:
            self.loss = nn.BCELoss()

    def get_target_tensor(self, input, target_is_real):
        target_tensor = None
        if target_is_real:
            create_label = ((self.real_label_var is None) or
                            (self.real_label_var.numel() != input.numel()))
            if create_label:
                real_tensor = self.Tensor(input.size()).fill_(self.real_label)
                self.real_label_var = Variable(real_tensor, requires_grad=False)
            target_tensor = self.real_label_var
        else:
            create_label = ((self.fake_label_var is None) or
                            (self.fake_label_var.numel() != input.numel()))
            if create_label:
                fake_tensor = self.Tensor(input.size()).fill_(self.fake_label)
                self.fake_label_var = Variable(fake_tensor, requires_grad=False)
            target_tensor = self.fake_label_var
        return target_tensor

    def __call__(self, input, target_is_real):
        target_tensor = self.get_target_tensor(input, target_is_real)
        return self.loss(input, target_tensor)


# Defines the generator that consists of Resnet blocks between a few
# downsampling/upsampling operations.
# Code and idea originally from Justin Johnson's architecture.
# https://github.com/jcjohnson/fast-neural-style/
class ResnetGenerator(nn.Module):
    def __init__(self, input_nc, output_nc, ngf=64, norm_layer=nn.BatchNorm2d, use_dropout=False, n_blocks=6, gpu_ids=[], padding_type='reflect'):
        assert(n_blocks >= 0)
        super(ResnetGenerator, self).__init__()
        self.input_nc = input_nc
        self.output_nc = output_nc
        self.ngf = ngf
        self.gpu_ids = gpu_ids
        if type(norm_layer) == functools.partial:
            use_bias = norm_layer.func == nn.InstanceNorm2d
        else:
            use_bias = norm_layer == nn.InstanceNorm2d

        model = [nn.ReflectionPad2d(3),
                 nn.Conv2d(input_nc, ngf, kernel_size=7, padding=0,
                           bias=use_bias),
                 norm_layer(ngf),
                 nn.ReLU(True)]

        n_downsampling = 2
        for i in range(n_downsampling):
            mult = 2**i
            model += [nn.Conv2d(ngf * mult, ngf * mult * 2, kernel_size=3,
                                stride=2, padding=1, bias=use_bias),
                      norm_layer(ngf * mult * 2),
                      nn.ReLU(True)]

        mult = 2**n_downsampling
        for i in range(n_blocks):
            model += [ResnetBlock(ngf * mult, padding_type=padding_type, norm_layer=norm_layer, use_dropout=use_dropout, use_bias=use_bias)]

        for i in range(n_downsampling):
            mult = 2**(n_downsampling - i)
            model += [nn.ConvTranspose2d(ngf * mult, int(ngf * mult / 2),
                                         kernel_size=3, stride=2,
                                         padding=1, output_padding=1,
                                         bias=use_bias),
                      norm_layer(int(ngf * mult / 2)),
                      nn.ReLU(True)]
        model += [nn.ReflectionPad2d(3)]
        model += [nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)]
        model += [nn.Tanh()]

        self.model = nn.Sequential(*model)

    def forward(self, input):
        if self.gpu_ids and isinstance(input.data, torch.cuda.FloatTensor):
            return nn.parallel.data_parallel(self.model, input, self.gpu_ids)
        else:
            return self.model(input)


# Define a resnet block
class ResnetBlock(nn.Module):
    def __init__(self, dim, padding_type, norm_layer, use_dropout, use_bias):
        super(ResnetBlock, self).__init__()
        self.conv_block = self.build_conv_block(dim, padding_type, norm_layer, use_dropout, use_bias)

    def build_conv_block(self, dim, padding_type, norm_layer, use_dropout, use_bias):
        conv_block = []
        p = 0
        if padding_type == 'reflect':
            conv_block += [nn.ReflectionPad2d(1)]
        elif padding_type == 'replicate':
            conv_block += [nn.ReplicationPad2d(1)]
        elif padding_type == 'zero':
            p = 1
        else:
            raise NotImplementedError('padding [%s] is not implemented' % padding_type)

        conv_block += [nn.Conv2d(dim, dim, kernel_size=3, padding=p, bias=use_bias),
                       norm_layer(dim),
                       nn.ReLU(True)]
        if use_dropout:
            conv_block += [nn.Dropout(0.5)]

        p = 0
        if padding_type == 'reflect':
            conv_block += [nn.ReflectionPad2d(1)]
        elif padding_type == 'replicate':
            conv_block += [nn.ReplicationPad2d(1)]
        elif padding_type == 'zero':
            p = 1
        else:
            raise NotImplementedError('padding [%s] is not implemented' % padding_type)
        conv_block += [nn.Conv2d(dim, dim, kernel_size=3, padding=p, bias=use_bias),
                       norm_layer(dim)]

        return nn.Sequential(*conv_block)

    def forward(self, x):
        out = x + self.conv_block(x)
        return out


# Defines the Unet generator.
# |num_downs|: number of downsamplings in UNet. For example,
# if |num_downs| == 7, image of size 128x128 will become of size 1x1
# at the bottleneck
class UnetGenerator(nn.Module):
    def __init__(self, input_nc, output_nc, num_downs, ngf=64,
                 norm_layer=nn.BatchNorm2d, use_dropout=False, gpu_ids=[]):
        super(UnetGenerator, self).__init__()
        self.gpu_ids = gpu_ids

        # construct unet structure
        unet_block = UnetSkipConnectionBlock(ngf * 8, ngf * 8, input_nc=None, submodule=None, norm_layer=norm_layer, innermost=True)
        for i in range(num_downs - 5):
            unet_block = UnetSkipConnectionBlock(ngf * 8, ngf * 8, input_nc=None, submodule=unet_block, norm_layer=norm_layer, use_dropout=use_dropout)
        unet_block = UnetSkipConnectionBlock(ngf * 4, ngf * 8, input_nc=None, submodule=unet_block, norm_layer=norm_layer)
        unet_block = UnetSkipConnectionBlock(ngf * 2, ngf * 4, input_nc=None, submodule=unet_block, norm_layer=norm_layer)
        unet_block = UnetSkipConnectionBlock(ngf, ngf * 2, input_nc=None, submodule=unet_block, norm_layer=norm_layer)
        unet_block = UnetSkipConnectionBlock(output_nc, ngf, input_nc=input_nc, submodule=unet_block, outermost=True, norm_layer=norm_layer)

        self.model = unet_block

    def forward(self, input):
        if self.gpu_ids and isinstance(input.data, torch.cuda.FloatTensor):
            return nn.parallel.data_parallel(self.model, input, self.gpu_ids)
        else:
            return self.model(input)


# Defines the submodule with skip connection.
# X -------------------identity---------------------- X
#   |-- downsampling -- |submodule| -- upsampling --|
class UnetSkipConnectionBlock(nn.Module):
    def __init__(self, outer_nc, inner_nc, input_nc=None,
                 submodule=None, outermost=False, innermost=False, norm_layer=nn.BatchNorm2d, use_dropout=False):
        super(UnetSkipConnectionBlock, self).__init__()
        self.outermost = outermost
        if type(norm_layer) == functools.partial:
            use_bias = norm_layer.func == nn.InstanceNorm2d
        else:
            use_bias = norm_layer == nn.InstanceNorm2d
        if input_nc is None:
            input_nc = outer_nc
        downconv = nn.Conv2d(input_nc, inner_nc, kernel_size=4,
                             stride=2, padding=1, bias=use_bias)
        downrelu = nn.LeakyReLU(0.2, True)
        downnorm = norm_layer(inner_nc)
        uprelu = nn.ReLU(True)
        upnorm = norm_layer(outer_nc)

        if outermost:
            upconv = nn.ConvTranspose2d(inner_nc * 2, outer_nc,
                                        kernel_size=4, stride=2,
                                        padding=1)
            down = [downconv]
            up = [uprelu, upconv, nn.Tanh()]
            model = down + [submodule] + up
        elif innermost:
            upconv = nn.ConvTranspose2d(inner_nc, outer_nc,
                                        kernel_size=4, stride=2,
                                        padding=1, bias=use_bias)
            down = [downrelu, downconv]
            up = [uprelu, upconv, upnorm]
            model = down + up
        else:
            upconv = nn.ConvTranspose2d(inner_nc * 2, outer_nc,
                                        kernel_size=4, stride=2,
                                        padding=1, bias=use_bias)
            down = [downrelu, downconv, downnorm]
            up = [uprelu, upconv, upnorm]

            if use_dropout:
                model = down + [submodule] + up + [nn.Dropout(0.5)]
            else:
                model = down + [submodule] + up

        self.model = nn.Sequential(*model)

    def forward(self, x):
        if self.outermost:
            return self.model(x)
        else:
            return torch.cat([x, self.model(x)], 1)


# Defines the PatchGAN discriminator with the specified arguments.
class NLayerDiscriminator(nn.Module):
    def __init__(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, use_sigmoid=False, gpu_ids=[]):
        super(NLayerDiscriminator, self).__init__()
        self.gpu_ids = gpu_ids
        if type(norm_layer) == functools.partial:
            use_bias = norm_layer.func == nn.InstanceNorm2d
        else:
            use_bias = norm_layer == nn.InstanceNorm2d

        kw = 4
        padw = 1
        sequence = [
            nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw),
            nn.LeakyReLU(0.2, True)
        ]

        nf_mult = 1
        nf_mult_prev = 1
        for n in range(1, n_layers):
            nf_mult_prev = nf_mult
            nf_mult = min(2**n, 8)
            sequence += [
                nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult,
                          kernel_size=kw, stride=2, padding=padw, bias=use_bias),
                norm_layer(ndf * nf_mult),
                nn.LeakyReLU(0.2, True)
            ]

        nf_mult_prev = nf_mult
        nf_mult = min(2**n_layers, 8)
        sequence += [
            nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult,
                      kernel_size=kw, stride=1, padding=padw, bias=use_bias),
            norm_layer(ndf * nf_mult),
            nn.LeakyReLU(0.2, True)
        ]

        sequence += [nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)]

        if use_sigmoid:
            sequence += [nn.Sigmoid()]

        self.model = nn.Sequential(*sequence)

    def forward(self, input):
        if len(self.gpu_ids) and isinstance(input.data, torch.cuda.FloatTensor):
            return nn.parallel.data_parallel(self.model, input, self.gpu_ids)
        else:
            return self.model(input)
