"""Model export functionality for SDXL Model Merger."""

import torch
from safetensors.torch import save_file

from . import config
from .config import SCRIPT_DIR
from .gpu_decorator import GPU


def _quantize_model(model, qtype: str):
    """Apply torchao quantization to a model using quantize_."""
    from torchao.quantization import quantize_

    if qtype == "int8":
        from torchao.quantization import Int8WeightOnlyConfig

        print("  ⚙️ Quantizing with int8_weight_only...")
        config = Int8WeightOnlyConfig()
        quantize_(model, config)

    elif qtype == "int4":
        from torchao.quantization import Int4WeightOnlyConfig

        print("  ⚙️ Quantizing with int4_weight_only (group_size=32)...")
        config = Int4WeightOnlyConfig(group_size=32)
        quantize_(model, config)

    elif qtype == "float8":
        from torchao.quantization import Float8DynamicActivationFloat8WeightConfig

        print("  ⚙️ Quantizing with float8_dynamic_activation_float8_weight...")
        config = Float8DynamicActivationFloat8WeightConfig()
        quantize_(model, config)

    else:
        raise ValueError(f"Unsupported qtype: {qtype}. Must be one of: int8, int4, float8")


@GPU(duration=180)
def _extract_and_save(pipe, include_lora, quantize, qtype, save_format):
    """GPU-decorated helper that extracts weights and saves the model."""
    if include_lora:
        try:
            pipe.unload_lora_weights()
        except Exception as e:
            print(f"  ℹ️ Could not unload LoRAs: {e}")

    # Quantize components in-place before extracting state dicts
    if quantize and qtype != "none":
        _quantize_model(pipe.unet, qtype)
        # torchao quantized tensors cannot be saved with safetensors, use torch.save instead
        # Don't dequantize - keep the quantized format for smaller file size

    merged_state_dict = {}

    # Extract UNet weights
    for k, v in pipe.unet.state_dict().items():
        # For quantized tensors, save directly; otherwise convert to half
        if hasattr(v, 'dequantize'):
            # Keep quantized tensor as-is for smaller file size
            merged_state_dict[f"unet.{k}"] = v
        else:
            merged_state_dict[f"unet.{k}"] = v.contiguous().half()

    # Extract text encoder weights
    if pipe.text_encoder is not None:
        for k, v in pipe.text_encoder.state_dict().items():
            if hasattr(v, 'dequantize'):
                merged_state_dict[f"text_encoder.{k}"] = v
            else:
                merged_state_dict[f"text_encoder.{k}"] = v.contiguous().half()
    if pipe.text_encoder_2 is not None:
        for k, v in pipe.text_encoder_2.state_dict().items():
            if hasattr(v, 'dequantize'):
                merged_state_dict[f"text_encoder_2.{k}"] = v
            else:
                merged_state_dict[f"text_encoder_2.{k}"] = v.contiguous().half()

    # Extract VAE weights
    if pipe.vae is not None:
        for k, v in pipe.vae.state_dict().items():
            if hasattr(v, 'dequantize'):
                merged_state_dict[f"first_stage_model.{k}"] = v
            else:
                merged_state_dict[f"first_stage_model.{k}"] = v.contiguous().half()

    # Save model
    ext = ".bin" if save_format == "bin" else ".safetensors"
    prefix = f"{qtype}_" if quantize and qtype != "none" else ""
    out_path = SCRIPT_DIR / f"merged_{prefix}checkpoint{ext}"

    if quantize and qtype != "none":
        # torchao quantized tensors are not compatible with safetensors
        # Use torch.save instead which preserves the quantization format
        ext = ".pt"
        out_path = SCRIPT_DIR / f"merged_{qtype}_checkpoint.pt"
        torch.save(merged_state_dict, str(out_path))
    elif ext == ".bin":
        torch.save(merged_state_dict, str(out_path))
    else:
        save_file(merged_state_dict, str(out_path))

    return out_path


def export_merged_model(
    include_lora: bool,
    quantize: bool,
    qtype: str,
    save_format: str = "safetensors",
):
    """
    Export the merged pipeline model with optional LoRA baking and quantization.

    Args:
        include_lora: Whether to include fused LoRAs in export
        quantize: Whether to apply quantization
        qtype: Quantization type - 'none', 'int8', 'int4', or 'float8'
        save_format: Output format - 'safetensors' or 'bin'

    Returns:
        Tuple of (output_path or None, status message)
    """
    # Fetch the pipeline at call time — avoids the stale import-by-value problem.
    pipe = config.get_pipe()

    if not pipe:
        return None, "⚠️ Please load a pipeline first."

    try:
        # Validate quantization type
        valid_qtypes = ("none", "int8", "int4", "float8")
        if qtype not in valid_qtypes:
            return None, f"❌ Invalid quantization type: {qtype}. Must be one of: {valid_qtypes}"

        out_path = _extract_and_save(pipe, include_lora, quantize, qtype, save_format)

        size_gb = out_path.stat().st_size / 1024**3

        if quantize and qtype != "none":
            msg = f"✅ Quantized checkpoint saved: `{out_path}` ({size_gb:.2f} GB)"
        else:
            msg = f"✅ Merged checkpoint saved: `{out_path}` ({size_gb:.2f} GB)"

        return str(out_path), msg

    except ImportError as e:
        return None, f"❌ Missing dependency: {str(e)}"
    except Exception as e:
        import traceback
        print(traceback.format_exc())
        return None, f"❌ Export failed: {str(e)}"


def get_export_status() -> str:
    """Get current export capability status."""
    try:
        from torchao.quantization import quantize_, Int4WeightOnlyConfig, Int8WeightOnlyConfig, Float8DynamicActivationFloat8WeightConfig
        return "✅ torchao available for quantization"
    except ImportError:
        return "ℹ️ Install torchao for quantization support: pip install torchao"