#!/usr/bin/env python3
"""Generate Identity CNN v2 Weights

Creates trivial .bin with 1 layer, 1×1 kernel, identity passthrough.
Output Ch{0,1,2,3} = Input Ch{0,1,2,3} (ignores static features).

With --mix: Output Ch{i} = Input Ch{i} + Input Ch{i+4}
  (p0+p4, p1+p5, p2+p6, p3+p7)

With --p47: Output Ch{i} = Input Ch{i+4} (static features only)
  (p4→ch0, p5→ch1, p6→ch2, p7→ch3)

Usage:
  ./training/gen_identity_weights.py [output.bin]
  ./training/gen_identity_weights.py --mix [output.bin]
  ./training/gen_identity_weights.py --p47 [output.bin]
"""

import argparse
import numpy as np
import struct
from pathlib import Path


def generate_identity_weights(output_path, kernel_size=1, mip_level=0, mix=False, p47=False):
    """Generate identity weights: output = input (ignores static features).

    If mix=True, adds p4→p0, p5→p1, p6→p2, p7→p3 (blends input with static).
    If p47=True, transfers p4→p0, p5→p1, p6→p2, p7→p3 (static features only).

    Binary format:
      Header (20 bytes):
        uint32 magic ('CNN2')
        uint32 version (2)
        uint32 num_layers (1)
        uint32 total_weights (f16 count)
        uint32 mip_level

      LayerInfo (20 bytes):
        uint32 kernel_size
        uint32 in_channels (12)
        uint32 out_channels (4)
        uint32 weight_offset (0)
        uint32 weight_count

      Weights (u32 packed f16):
        Identity matrix for first 4 input channels
        Zeros for static features (channels 4-11) OR
        Mix matrix (p0+p4, p1+p5, p2+p6, p3+p7) if mix=True
    """
    # Identity: 4 output channels, 12 input channels
    # Weight shape: [out_ch, in_ch, kernel_h, kernel_w]
    in_channels = 12  # 4 input + 8 static
    out_channels = 4

    # Identity matrix: diagonal 1.0 for first 4 channels, 0.0 for rest
    weights = np.zeros((out_channels, in_channels, kernel_size, kernel_size), dtype=np.float32)

    # Center position for kernel
    center = kernel_size // 2

    if p47:
        # p47 mode: p4→ch0, p5→ch1, p6→ch2, p7→ch3 (static features only)
        for i in range(out_channels):
            weights[i, i + 4, center, center] = 1.0
    else:
        # Set diagonal to 1.0 (output ch i = input ch i)
        for i in range(out_channels):
            weights[i, i, center, center] = 1.0

        # If mix, add p4→p0, p5→p1, p6→p2, p7→p3
        if mix:
            for i in range(out_channels):
                weights[i, i + 4, center, center] = 1.0

    # Flatten
    weights_flat = weights.flatten()
    weight_count = len(weights_flat)

    mode_name = 'p47' if p47 else ('mix' if mix else 'identity')
    print(f"Generating {mode_name} weights:")
    print(f"  Kernel size: {kernel_size}×{kernel_size}")
    print(f"  Channels: 12D→4D")
    print(f"  Weights: {weight_count}")
    print(f"  Mip level: {mip_level}")
    if mix:
        print(f"  Mode: p0+p4, p1+p5, p2+p6, p3+p7")
    elif p47:
        print(f"  Mode: p4→ch0, p5→ch1, p6→ch2, p7→ch3")

    # Convert to f16
    weights_f16 = np.array(weights_flat, dtype=np.float16)

    # Pad to even count
    if len(weights_f16) % 2 == 1:
        weights_f16 = np.append(weights_f16, np.float16(0.0))

    # Pack f16 pairs into u32
    weights_u32 = weights_f16.view(np.uint32)

    print(f"  Packed: {len(weights_u32)} u32")
    print(f"  Binary size: {20 + 20 + len(weights_u32) * 4} bytes")

    # Write binary
    output_path = Path(output_path)
    output_path.parent.mkdir(parents=True, exist_ok=True)

    with open(output_path, 'wb') as f:
        # Header (20 bytes)
        f.write(struct.pack('<4sIIII',
                           b'CNN2',           # magic
                           2,                 # version
                           1,                 # num_layers
                           len(weights_f16),  # total_weights
                           mip_level))        # mip_level

        # Layer info (20 bytes)
        f.write(struct.pack('<IIIII',
                           kernel_size,       # kernel_size
                           in_channels,       # in_channels
                           out_channels,      # out_channels
                           0,                 # weight_offset
                           weight_count))     # weight_count

        # Weights (u32 packed f16)
        f.write(weights_u32.tobytes())

    print(f"  → {output_path}")

    # Verify
    print("\nVerification:")
    with open(output_path, 'rb') as f:
        data = f.read()
        magic, version, num_layers, total_weights, mip = struct.unpack('<4sIIII', data[:20])
        print(f"  Magic: {magic}")
        print(f"  Version: {version}")
        print(f"  Layers: {num_layers}")
        print(f"  Total weights: {total_weights}")
        print(f"  Mip level: {mip}")
        print(f"  File size: {len(data)} bytes")


def main():
    parser = argparse.ArgumentParser(description='Generate identity CNN v2 weights')
    parser.add_argument('output', type=str, nargs='?',
                       default='workspaces/main/weights/cnn_v2_identity.bin',
                       help='Output .bin file path')
    parser.add_argument('--kernel-size', type=int, default=1,
                       help='Kernel size (default: 1×1)')
    parser.add_argument('--mip-level', type=int, default=0,
                       help='Mip level for p0-p3 features (default: 0)')
    parser.add_argument('--mix', action='store_true',
                       help='Mix mode: p0+p4, p1+p5, p2+p6, p3+p7')
    parser.add_argument('--p47', action='store_true',
                       help='Static features only: p4→ch0, p5→ch1, p6→ch2, p7→ch3')

    args = parser.parse_args()

    print("=== Identity Weight Generator ===\n")
    generate_identity_weights(args.output, args.kernel_size, args.mip_level, args.mix, args.p47)
    print("\nDone!")


if __name__ == '__main__':
    main()