#!/usr/bin/env python3
"""
Quick chimera assembler: Copy source GGUF header/metadata intact,
replace tensor data from organ directory.
Signature 935
"""
import struct, sys, os, json

def main():
    if len(sys.argv) < 4:
        print("Usage: quick_chimera.py <source.gguf> <organs_dir> <output.gguf>")
        sys.exit(1)
    
    source_gguf = sys.argv[1]
    organs_dir = sys.argv[2]
    output_gguf = sys.argv[3]
    
    f = open(source_gguf, "rb")
    magic = struct.unpack("<I", f.read(4))[0]
    version = struct.unpack("<I", f.read(4))[0]
    n_tensors = struct.unpack("<Q", f.read(8))[0]
    n_metadata = struct.unpack("<Q", f.read(8))[0]
    
    print(f"Source: {os.path.basename(source_gguf)}")
    print(f"  Version: {version}, Tensors: {n_tensors}, Metadata: {n_metadata}")
    
    def read_string():
        slen = struct.unpack("<Q", f.read(8))[0]
        return f.read(slen).decode("utf-8")
    
    def skip_value(vtype):
        sizes = {0:1, 1:1, 2:2, 3:2, 4:4, 5:4, 6:4, 7:1, 10:8, 11:8, 12:8}
        if vtype in sizes:
            f.read(sizes[vtype])
        elif vtype == 8:
            read_string()
        elif vtype == 9:
            arr_type = struct.unpack("<I", f.read(4))[0]
            arr_len = struct.unpack("<Q", f.read(8))[0]
            for _ in range(arr_len):
                skip_value(arr_type)
        else:
            raise ValueError(f"Unknown type: {vtype}")
    
    for _ in range(n_metadata):
        read_string()
        vtype = struct.unpack("<I", f.read(4))[0]
        skip_value(vtype)
    
    tensor_info = []
    for _ in range(n_tensors):
        name = read_string()
        n_dims = struct.unpack("<I", f.read(4))[0]
        dims = [struct.unpack("<Q", f.read(8))[0] for _ in range(n_dims)]
        dtype = struct.unpack("<I", f.read(4))[0]
        offset = struct.unpack("<Q", f.read(8))[0]
        tensor_info.append({"name": name, "dims": dims, "dtype": dtype, "offset": offset})
    
    pos = f.tell()
    padding = (32 - (pos % 32)) % 32
    f.read(padding)
    data_start = f.tell()
    
    print(f"  Header: {data_start} bytes, Data start: {data_start}")
    
    # Copy full header
    f.seek(0)
    header = f.read(data_start)
    
    # Build organ map
    organ_map = {}
    for category in ["skeleton", "organs", "embed", "norm", "adapters", "unknown"]:
        cat_dir = os.path.join(organs_dir, category)
        if os.path.isdir(cat_dir):
            for fname in os.listdir(cat_dir):
                if fname.endswith(".bin"):
                    tname = fname[:-4]
                    organ_map[tname] = os.path.join(cat_dir, fname)
    
    print(f"  Organ files: {len(organ_map)}")
    
    # Write output
    out = open(output_gguf, "wb")
    out.write(header)
    
    written = 0
    fallback = 0
    for ti in tensor_info:
        name = ti["name"]
        safe_name = name.replace(".", "_")
        organ_path = organ_map.get(safe_name)
        
        if organ_path and os.path.exists(organ_path):
            data = open(organ_path, "rb").read()
            out.write(data)
            written += 1
        else:
            # Fallback: read from source GGUF
            # Calculate tensor size
            next_idx = tensor_info.index(ti) + 1
            if next_idx < len(tensor_info):
                size = tensor_info[next_idx]["offset"] - ti["offset"]
            else:
                f.seek(0, 2)
                file_end = f.tell()
                size = file_end - (data_start + ti["offset"])
            
            f.seek(data_start + ti["offset"])
            data = f.read(size)
            out.write(data)
            fallback += 1
    
    out.close()
    f.close()
    
    final_size = os.path.getsize(output_gguf)
    print(f"\n  Output: {output_gguf}")
    print(f"  Size: {final_size / (1024**3):.2f} GB")
    print(f"  From organs: {written}, From source: {fallback}, Total: {written+fallback}/{n_tensors}")
    print(f"  Signature: 935")

if __name__ == "__main__":
    main()