import torch.nn as nn
from libraries import LoRAInjector

# Any pretrained model (ResNet, GPT-2, BERT, custom MLP, etc.)
model = load_your_pretrained_model()

# Inject LoRA into all linear layers (rank 8, alpha 16)
count = LoRAInjector.inject(model, target_modules=None, rank=8, alpha=16.0)
print(f"Injected LoRA into {count} layers")

# Freeze the backbone, keep LoRA + task head trainable
LoRAInjector.freeze_non_lora(model)
params = (
    LoRAInjector.get_lora_parameters(model)
    + LoRAInjector.get_non_lora_trainable_parameters(model)
)
optimizer = torch.optim.AdamW(params, lr=1e-4)
print(f"LoRA params: {LoRAInjector.count_lora_params(model):,}")

# Optional: LoRA+ uses a larger LR for B than A
# param_groups = LoRAInjector.get_lora_plus_param_groups(model, base_lr=1e-4, lr_ratio=16.0)
# optimizer = torch.optim.AdamW(param_groups)

# ... train ...

# Merge for zero-overhead inference
LoRAInjector.merge_all(model)

# Save tiny checkpoint (LoRA weights only)
torch.save(LoRAInjector.lora_state_dict(model), "lora_weights.pt")

from libraries import BaseModel, TransferScheduler

# Wrap any model
base = BaseModel(pretrained_model)
base.freeze_all()
base.replace_head(num_classes=10)  # auto-detects fc/classifier/head

# Set up progressive unfreezing (ULMFiT-style)
groups = base.get_layer_groups()
scheduler = TransferScheduler(groups, base_lr=1e-3, decay=2.6)
optimizer = scheduler.build_optimizer()

for epoch in range(num_epochs):
    scheduler.step(epoch)  # unfreezes next layer group each epoch
    train_epoch(base, train_loader, criterion, optimizer)

from libraries import compute_fisher_diagonal, EWCLoss

# After training on source task:
fisher = compute_fisher_diagonal(source_model, source_loader, criterion)

# Create EWC penalty (Bayesian prior from source)
ewc = EWCLoss(source_model, fisher, lambda_=1000.0)

# Fine-tune on target — EWC prevents catastrophic forgetting
for x, y in target_loader:
    loss = criterion(model(x), y) + ewc(model)
    loss.backward()
    optimizer.step()

from libraries import compute_cka, extract_representations, NegativeTransferMonitor

# Compare layer representations between source and target
reps_source = extract_representations(model, source_loader, layer_name="layer3")
reps_target = extract_representations(model, target_loader, layer_name="layer3")
similarity = compute_cka(reps_source, reps_target)
print(f"CKA similarity: {similarity:.4f}")  # 1.0 = identical, 0.0 = orthogonal

# Online monitoring during training
monitor = NegativeTransferMonitor(reference_model=pretrained_model, patience=3)
for epoch in range(epochs):
    val_loss = evaluate(model, val_loader, criterion)["loss"]
    warning = monitor.check(epoch, val_loss, model)
    if warning:
        print(f"WARNING: {warning}")
        break

from libraries import GPUCarbonTracker
from libraries.carbon import compare_emissions

# Track GPU energy via NVML (auto-falls back to CPU estimation)
tracker = GPUCarbonTracker("lora_finetune",
                            carbon_intensity_kg_kwh=0.45,  # US average
                            pue=1.1)                        # data center PUE
tracker.start()
# ... GPU training ...
result = tracker.stop()
print(f"Energy: {result['kwh']:.6f} kWh, CO2: {result['co2_kg']:.6f} kg")

# Compare full fine-tuning vs LoRA
summary = compare_emissions([full_ft_result, lora_result])
print(f"LoRA saved {summary['comparisons'][0]['co2_saved_pct']:.1f}% CO2")

from libraries import (
    compute_task_vector, task_arithmetic_merge,
    ties_merge, dare_merge, slerp_merge,
    merge_lora_adapters, task_vector_similarity,
)

# Compute task vectors (what each fine-tuning learned)
tv_math = compute_task_vector(base.state_dict(), model_math.state_dict())
tv_code = compute_task_vector(base.state_dict(), model_code.state_dict())

# Check compatibility before merging
sim = task_vector_similarity(tv_math, tv_code)
print(f"Task similarity: {sim:.4f}")  # higher = less interference

# Task Arithmetic: additive composition
merged_sd = task_arithmetic_merge(base.state_dict(), [tv_math, tv_code],
                                   scalings=[0.5, 0.5])

# TIES: resolves sign conflicts for better merging
merged_sd = ties_merge(base.state_dict(), [tv_math, tv_code],
                        density=0.2, scaling=1.0)

# DARE + TIES: random drop + rescale + TIES
merged_sd = dare_merge(base.state_dict(), [tv_math, tv_code],
                        drop_rate=0.9, use_ties=True, seed=42)

# SLERP: spherical interpolation (2 models)
merged_sd = slerp_merge(model_a.state_dict(), model_b.state_dict(), t=0.5)

# LoRA Soups: merge multiple LoRA adapters
merged_lora = merge_lora_adapters([lora_sd_math, lora_sd_code])

from libraries import fine_tune, GPUCarbonTracker, EWCLoss

history = fine_tune(
    model, train_loader, val_loader,
    epochs=10,
    optimizer=optimizer,
    criterion=nn.CrossEntropyLoss(),
    scheduler=transfer_scheduler,       # progressive unfreezing
    ewc_loss=ewc_penalty,               # Bayesian regularization
    carbon_tracker=GPUCarbonTracker("experiment"),  # CO2 tracking
    device="cuda",
)
# history = {'train_loss': [...], 'val_loss': [...], 'val_accuracy': [...], 'co2_result': {...}}

import torch
from libraries import (
    fit_linear_sgd, regularized_transfer_linear,
    bayesian_transfer_linear, LoRAAdapterVector,
    should_transfer, CarbonTracker, set_seed,
)

set_seed(42)

# 1. Train source model (mini-batch SGD)
w_src, b_src = fit_linear_sgd(X_source, y_source,
                               torch.zeros(d), torch.zeros(1),
                               epochs=30, lr=0.01, batch_size=64)

# 2. Check for negative transfer
decision = should_transfer(X_source_np, X_target_np, verbose=True)

# 3. Transfer to target domain (closed-form — zero gradient steps!)
tracker = CarbonTracker("regularized_transfer")
tracker.start()
w_tgt, b_tgt = regularized_transfer_linear(X_target, y_target,
                                            w_src, b_src, lam=1.0)
result = tracker.stop()
print(f"CO2: {result['co2_kg']:.2e} kg")

from libraries import should_transfer, compute_cka

# Classical
decision = should_transfer(X_source, X_target, verbose=True)
# MMD2 = 0.0312  (threshold: 0.5)  -> TRANSFER

# Deep learning
similarity = compute_cka(source_representations, target_representations)
# CKA = 0.85 -> high similarity, transfer likely safe