import nni
from nni.distiller import ModelSpeedup, DynamicLayerwiseDistiller
from nni.quantization import QATQuantizer, Quantizer
from nni.pruning import TaylorPruner, AGPPruner
from torchvision.models import resnet18
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transforms
import torchvision.datasets as datasets

# Fine-tune ResNet18 on Cifar10 dataset
model = resnet18()
# Define optimizer and loss function
optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)
criterion = nn.CrossEntropyLoss()
# Train the model for 30 epochs
for epoch in range(30):
    # Training loop
    for data, target in train_loader:
        optimizer.zero_grad()
        output = model(data)
        loss = criterion(output, target)
        loss.backward()
        optimizer.step()
    # Evaluate accuracy
    accuracy = evaluate_accuracy(model, test_loader)

# Create teacher model by duplicating fine-tuned model
teacher_model = nn.Sequential(*list(model.children()))

# Create pruner
pruner = TaylorPruner(model, AGPPruner, config_list=[('Conv2d', 0.5)], trainer=trainer, optimizer=optimizer)
pruner.compress(train_loader, 100, 30)

# Create quantizer
quantizer = QATQuantizer(model, config_list=[('Conv2d', 'BatchNorm2d', 'int8')], start_step=100)

# Create distiller
distiller = DynamicLayerwiseDistiller(model, teacher_model, config_list=[('Conv2d', 'mean_squared_error')])
distiller.compress(60, 100)

# Speed up model
speedup = ModelSpeedup(model, pruner.masks)
speedup.speedup_model()

# Evaluate accuracy of compressed model
compressed_accuracy = evaluate_accuracy(model, test_loader)

# Simulate quantization
quantizer.update_calibration_config()

# Evaluate accuracy of compressed and quantized model
quantized_accuracy = evaluate_accuracy(model, test_loader)