import torch
import torch.nn as nn
import torch.nn.utils.prune as prune

def prune_conv_channels(model, amount=0.3):
    """Prune conv2d filters by L1 norm (structured)."""
    for name, module in model.named_modules():
        if isinstance(module, nn.Conv2d):
            prune.ln_structured(module, name="weight", amount=amount, n=1, dim=0)
            prune.remove(module, "weight")  # Make pruning permanent
    return model

def prune_transformer_heads(model, heads_to_prune):
    """Prune attention heads from a transformer.

    Args:
        heads_to_prune: dict of {layer_idx: [head_indices]}
            e.g., {0: [0, 3], 2: [1, 5, 7]}
    """
    for layer_idx, heads in heads_to_prune.items():
        layer = model.encoder.layer[layer_idx]
        attention = layer.attention.self

        num_heads = attention.num_attention_heads
        head_size = attention.attention_head_size

        # Build index of heads to keep
        keep_heads = sorted(set(range(num_heads)) - set(heads))
        keep_indices = torch.cat([
            torch.arange(h * head_size, (h + 1) * head_size) for h in keep_heads
        ])

        # Slice query, key, value projections
        for proj in [attention.query, attention.key, attention.value]:
            proj.weight = nn.Parameter(proj.weight.index_select(0, keep_indices))
            proj.bias = nn.Parameter(proj.bias.index_select(0, keep_indices))

        # Update output projection
        attention.output.dense.weight = nn.Parameter(
            attention.output.dense.weight.index_select(1, keep_indices)
        )
        attention.num_attention_heads = len(keep_heads)
        attention.all_head_size = len(keep_heads) * head_size

    return model

import torch.nn.functional as F

def distillation_loss(student_logits, teacher_logits, labels, temperature=4.0, alpha=0.5):
    """Combined hard-label and soft-label distillation loss."""
    hard_loss = F.cross_entropy(student_logits, labels)
    soft_loss = F.kl_div(
        F.log_softmax(student_logits / temperature, dim=-1),
        F.softmax(teacher_logits / temperature, dim=-1),
        reduction="batchmean",
    ) * (temperature ** 2)
    return alpha * soft_loss + (1 - alpha) * hard_loss

def train_distillation(teacher, student, train_loader, optimizer, epochs=10, device="cuda"):
    """Standard distillation training loop."""
    teacher.eval().to(device)
    student.train().to(device)

    for epoch in range(epochs):
        total_loss = 0
        for batch in train_loader:
            inputs = batch["input_ids"].to(device)
            attention_mask = batch["attention_mask"].to(device)
            labels = batch["labels"].to(device)

            with torch.no_grad():
                teacher_out = teacher(inputs, attention_mask=attention_mask)

            student_out = student(inputs, attention_mask=attention_mask)

            loss = distillation_loss(
                student_logits=student_out.logits,
                teacher_logits=teacher_out.logits,
                labels=labels,
                temperature=4.0,
                alpha=0.7,
            )

            optimizer.zero_grad()
            loss.backward()
            torch.nn.utils.clip_grad_norm_(student.parameters(), 1.0)
            optimizer.step()
            total_loss += loss.item()

        print(f"Epoch {epoch+1}/{epochs} - Loss: {total_loss / len(train_loader):.4f}")

import torch.quantization as quant

def setup_qat(model, backend="x86"):
    """Prepare model for quantization-aware training."""
    model.train()
    model.qconfig = quant.get_default_qat_qconfig(backend)

    # Fuse common patterns before QAT
    fuse_modules = []
    for name, module in model.named_modules():
        if isinstance(module, nn.Sequential):
            children = list(module.named_children())
            for i in range(len(children) - 1):
                n1, m1 = children[i]
                n2, m2 = children[i + 1]
                if isinstance(m1, nn.Conv2d) and isinstance(m2, nn.BatchNorm2d):
                    fuse_modules.append([f"{name}.{n1}", f"{name}.{n2}"])

    if fuse_modules:
        torch.quantization.fuse_modules(model, fuse_modules, inplace=True)

    quant.prepare_qat(model, inplace=True)
    return model

def convert_and_export(model, sample_input, output_path="model_quantized.pt"):
    """Convert QAT model to quantized and export."""
    model.eval()
    quantized = quant.convert(model.cpu(), inplace=False)
    traced = torch.jit.trace(quantized, sample_input.cpu())
    traced.save(output_path)
    return quantized

import torch
import onnx
from onnxruntime.quantization import quantize_dynamic, QuantType

def export_to_onnx(model, sample_input, path="model.onnx", opset=17):
    """Export PyTorch model to ONNX format."""
    model.eval()
    torch.onnx.export(
        model,
        sample_input,
        path,
        opset_version=opset,
        input_names=["input"],
        output_names=["output"],
        dynamic_axes={"input": {0: "batch"}, "output": {0: "batch"}},
    )
    # Validate
    onnx_model = onnx.load(path)
    onnx.checker.check_model(onnx_model)
    return path

def quantize_onnx_dynamic(input_path, output_path="model_quant.onnx"):
    """Apply dynamic INT8 quantization to ONNX model."""
    quantize_dynamic(
        input_path,
        output_path,
        weight_type=QuantType.QInt8,
    )
    return output_path