#!/usr/bin/env python3
"""Proper NF4 quantization using BnB during loading."""

import torch
from transformers import AutoModelForCausalLM, BitsAndBytesConfig, AutoConfig


def quantize_model(model_path, output_path):
    print(f"Quantizing model from: {model_path}")
    print(f"Output: {output_path}\n")

    bnb_config = BitsAndBytesConfig(
        load_in_4bit=True,
        bnb_4bit_quant_type="nf4",
        bnb_4bit_compute_dtype=torch.float16,
        bnb_4bit_use_double_quant=True,
    )

    print("Loading model with BnB 4-bit to CPU (~70GB bf16 -> ~17GB 4-bit)...")
    print("   This will use CPU RAM for loading\n")

    model = AutoModelForCausalLM.from_pretrained(
        model_path,
        quantization_config=bnb_config,
        device_map="cpu",
        torch_dtype=torch.float16,
        trust_remote_code=True,
        low_cpu_mem_usage=True,
    )

    print("✓ Model loaded and quantized to CPU")
    
    # Count parameters
    total_params = sum(p.numel() for p in model.parameters())
    bnb_params = sum(p.numel() for p in model.parameters() if hasattr(p, 'quant_state') and p.quant_state is not None)
    print(f"   Total: {total_params / 1e9:.2f}B parameters")
    print(f"   Quantized: {bnb_params / 1e9:.2f}B parameters ({bnb_params/total_params*100:.1f}%)\n")

    print(f"Saving quantized model to: {output_path}")
    model.save_pretrained(output_path)
    
    # Save tokenizer
    try:
        tokenizer = AutoConfig.from_pretrained(model_path, trust_remote_code=True)
        # Tokenizer saving would go here if needed
    except:
        pass

    print(f"\n✅ Quantized model saved to: {output_path}")


if __name__ == "__main__":
    import argparse
    parser = argparse.ArgumentParser()
    parser.add_argument("--model-path", type=str, default="/data/models/Ornith-1.0-35B")
    parser.add_argument("--output-path", type=str, default="/data/models/Ornith-1.0-35B-nf4")
    args = parser.parse_args()
    
    quantize_model(args.model_path, args.output_path)