#!/usr/bin/env python3
"""Streaming NF4 quantization using accelerate device_map."""

import argparse
import gc
import torch
from pathlib import Path
from transformers import AutoModelForCausalLM, BitsAndBytesConfig, AutoConfig


def quantize_weight_nf4(weight: torch.Tensor):
    """Quantize a single weight tensor to NF4 using Linear4bit."""
    if weight.dim() != 2:
        return weight, None

    # Create Linear4bit and quantize
    in_features = weight.size(1)
    out_features = weight.size(0)
    linear = Linear4bit(in_features, out_features, bias=False, compute_dtype=torch.float16, quant_type="nf4")
    
    with torch.no_grad():
        linear.weight = nn.Parameter(weight.clone())
        # Force quantization
        _ = linear.weight.quant_state
    
    # Return quantized weight
    return linear.weight, None


def streaming_quantize(model_path: str, output_path: str):
    print(f"Streaming NF4 quantization: {model_path}")
    output_path = Path(output_path)
    output_path.mkdir(parents=True, exist_ok=True)

    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 (shards streamed to GPUs)...")
    print("   This will distribute across both GPUs\n")

    model = AutoModelForCausalLM.from_pretrained(
        model_path,
        quantization_config=bnb_config,
        device_map="auto",
        max_memory={0: "28GiB", 1: "28GiB", "cpu": "120GiB"},
        low_cpu_mem_usage=True,
        trust_remote_code=True,
    )

    # Count quantized 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"✓ Model loaded and quantized")
    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)
    
    print(f"\n✅ Quantized model saved to: {output_path}")


if __name__ == "__main__":
    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()

    streaming_quantize(args.model_path, args.output_path)