import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import math

from anode import odesolver_adjoint as odesolver

#from torchdiffeq import odeint_adjoint as odeint
'''
from torchdiffeq import odeint
class ODEBlock(nn.Module):

    def __init__(self, odefunc):
        super(ODEBlock, self).__init__()
        self.odefunc = odefunc
        self.integration_time = torch.tensor([0, 1]).float()

    def forward(self, x):
        self.integration_time = self.integration_time.type_as(x)
        out = odeint(self.odefunc, x, self.integration_time, rtol=1e-3, atol=1e-3)
        return out[1]

    @property
    def nfe(self):
        return self.odefunc.nfe

    @nfe.setter
    def nfe(self, value):
        self.odefunc.nfe = value
'''
class ODEBlock(nn.Module):

    def __init__(self, odefunc,NT):
        super(ODEBlock, self).__init__()
        self.odefunc = odefunc
        self.options = {}
        self.options.update({'Nt':int(NT)})
        self.options.update({'method':'RK4'})
        print(self.options)

    def forward(self, x):
        out = odesolver(self.odefunc, x, self.options)
        return out

    @property
    def nfe(self):
        return self.odefunc.nfe

    @nfe.setter
    def nfe(self, value):
        self.odefunc.nfe = value

class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, in_planes, planes, stride=1):
        super(BasicBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)

        self.shortcut = nn.Sequential()
        if stride != 1 or in_planes != self.expansion*planes:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(self.expansion*planes)
            )

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.bn2(self.conv2(out))
        out += self.shortcut(x)
        out = F.relu(out)
        return out

class BasicBlock2(nn.Module):
    expansion = 1

    def __init__(self, dim):
        super(BasicBlock2, self).__init__()
        in_planes = dim
        planes = dim
        stride = 1
        self.nfe = 0
        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)

        self.shortcut = nn.Sequential()

    def forward(self,t, x):
        self.nfe += 1
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.bn2(self.conv2(out))
        #out += self.shortcut(x)
        out = F.relu(out)
        return out

class ResNet(nn.Module):
    def __init__(self, block, num_blocks, NT=2,num_classes = 100, ODEBlock_ = None):
        super(ResNet, self).__init__()
        self.in_planes = 64
        self.ODEBlock = ODEBlock_
        self.NT = NT #Added
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.layer1_1 = self._make_layer(64, 1, stride=1)
        self.layer1_2 = self._make_layer2(64, num_blocks[0]-1,NT[0], stride=1)

        self.layer2_1 = self._make_layer(128, 1, stride=2)
        self.layer2_2 = self._make_layer2(128, num_blocks[1]-1, NT[1],stride=1)

        self.layer3_1 = self._make_layer(256, 1, stride=2)
        self.layer3_2 = self._make_layer2(256, num_blocks[2]-1, NT[2],stride=1)

        self.layer4_1 = self._make_layer(512, 1, stride=2)
        self.layer4_2 = self._make_layer2(512, num_blocks[3]-1, NT[3],stride=1)
        #chnages ARE here..
        #self.features = nn.Sequential
        #self.linear = nn.Linear(512 * block.expansion, num_classes)

    def _make_layer(self, planes, num_blocks, stride):
        strides = [stride] + [1] * (num_blocks-1)
        layers = []
        for stride in strides:
            layers.append(BasicBlock(self.in_planes, planes, stride))
            self.in_planes = planes * BasicBlock.expansion
        return nn.Sequential(*layers)

    def _make_layer2(self, planes, num_blocks, NT,stride):
        strides = [stride] + [1]*(num_blocks-1)
        layers = []
        for stride in strides:
            layers.append(self.ODEBlock(BasicBlock2(self.in_planes),NT))
        return nn.Sequential(*layers)

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.layer1_1(out)
        out = self.layer1_2(out)
        out = self.layer2_1(out)
        out = self.layer2_2(out)
        out = self.layer3_1(out)
        out = self.layer3_2(out)
        out = self.layer4_1(out)
        out = self.layer4_2(out)
        #print(out.shape)
        #out = F.avg_pool2d(out, 4)
        #out = out.view(out.size(0), -1)
        #out = self.linear(out)
        return out

class ResNet18(nn.Module):
    def __init__(self,Nt):
        super(ResNet18,self).__init__()
        self.avgpool_size = 4
        #feature_layers = [ODEBlock(ODEfunc(64))] if is_odenet else [ResBlock(64, 64) for _ in range(6)]
        #print("hjvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv")

        #fc_layers = [norm(64), nn.ReLU(inplace=True), nn.AdaptiveAvgPool2d((1, 1)), Flatten(), nn.Linear(64, 10)]
    
        #self.features = nn.Sequential(*downsampling_layers, *feature_layers)#,*fc_layers).to(device)
        self.features = ResNet(BasicBlock, [2,2,2,2], NT =Nt ,ODEBlock_ = ODEBlock)

'''
def ResNet18(ODEBlock):
      print("Here...................")
      return ResNet(BasicBlock, [2,2,2,2], ODEBlock_ = ODEBlock)
'''
def lr_schedule(lr, epoch):
    optim_factor = 0
    if epoch > 250:
        optim_factor = 2
    elif epoch > 150:
        optim_factor = 1

    return lr / math.pow(10, (optim_factor))

