import torch
import torch.nn as nn
from models import CNNBase, CAM, ResNet50, BloodNet3D, CellMovementEncoder
from weakly_supervised_localization import WSLoss
from self_supervised_distillation import DistillKL
from adversarial_robustness import FGSM

# Assuming you have defined CNNBase, CAM, ResNet50, and other necessary components

# 1. Weakly supervised localization
cnn = CNNBase()
cam = CAM(cnn)

loss_fn = nn.BCEWithLogitsLoss()
ws_loss_fn = WSLoss(loss_fn)

optimizer = torch.optim.Adam(cnn.parameters())

for images, labels in loader:
    cnn.train()  # Set to train mode
    optimizer.zero_grad()  # Clear gradients

    preds = cnn(images)  
    cam_maps = cam(images)

    loss = ws_loss_fn(preds, cam_maps, labels)
    loss.backward()

    optimizer.step()

# 2. Self-supervised data distillation
teacher = ResNet50()  # Pretrained on 100x more data
student = CNNBase()

distill_loss = DistillKL(teacher, student)

optimizer_student = torch.optim.Adam(student.parameters())  # Separate optimizer for the student

for images in unsup_blood_cells:
    student.train()  # Set to train mode
    optimizer_student.zero_grad()  # Clear gradients

    t_preds = teacher(images) 
    s_preds = student(images)

    loss = distill_loss(s_preds, t_preds)
    loss.backward()

    optimizer_student.step()

# 3. Adversarial Training 
adv_steps = 5
epsilon = 0.1

model = CNNBase()  # Assuming you need a model instance for FGSM
loss_fn = nn.BCEWithLogitsLoss()
attacker = FGSM(model, loss_fn, epsilon)

optimizer_adv = torch.optim.Adam(model.parameters())  # Separate optimizer for the adversarial model

for images, labels in loader:  # Assuming you have a loader for this as well
    model.train()  # Set to train mode
    optimizer_adv.zero_grad()  # Clear gradients

    for i in range(adv_steps):
        images = attacker.attack(images)

    preds = model(images)

    loss = loss_fn(preds, labels)
    loss.backward()

    optimizer_adv.step()

# 4. 3D Convolutions
bloodnet_3d = BloodNet3D()
optimizer_3d = torch.optim.Adam(bloodnet_3d.parameters())  # Separate optimizer for 3D Convolutions

bloodnet_3d.train()  # Set to train mode
optimizer_3d.zero_grad()  # Clear gradients

# Add code for 3D Convolutions if needed

optimizer_3d.step()

# 5. Video self-supervision
frame_order_loss = nn.MSELoss()

for blood_cell_video in videos:
    bloodnet_video = CellMovementEncoder()  
    optimizer_video = torch.optim.Adam(bloodnet_video.parameters())  # Separate optimizer for video

    frames = shuffleFrames(blood_cell_video)

    bloodnet_video.train()  # Set to train mode
    optimizer_video.zero_grad()  # Clear gradients

    pred_order = bloodnet_video(frames)  # Assuming frames are the input to CellMovementEncoder
    true_order = torch.arange(len(frames))

    loss = frame_order_loss(pred_order, true_order)
    loss.backward()

    optimizer_video.step()