feat: integrate NNUE bot and add Python training pipeline with weight export functionality

modules/bot/python/src/export.py

@@ -0,0 +1,66 @@
+#!/usr/bin/env python3
+"""Export NNUE weights to binary format for runtime loading."""
+
+import torch
+import struct
+import sys
+from pathlib import Path
+
+def export_weights_to_binary(weights_file, output_file):
+    """Load PyTorch weights and export as binary file."""
+
+    if not Path(weights_file).exists():
+        print(f"Error: Weights file not found at {weights_file}")
+        sys.exit(1)
+
+    # Load weights
+    state_dict = torch.load(weights_file, map_location='cpu')
+
+    # Debug: print available layers
+    print(f"Available layers in {weights_file}:")
+    for key in sorted(state_dict.keys()):
+        print(f"  {key}: {state_dict[key].shape}")
+
+    # Create output directory if needed
+    output_path = Path(output_file)
+    output_path.parent.mkdir(parents=True, exist_ok=True)
+
+    with open(output_file, 'wb') as f:
+        # Write magic number and version
+        f.write(b'NNUE')
+        f.write(struct.pack('<I', 1))  # version 1
+
+        # Write each weight tensor in order
+        for layer_name in ['l1.weight', 'l1.bias', 'l2.weight', 'l2.bias', 'l3.weight', 'l3.bias']:
+            if layer_name not in state_dict:
+                print(f"Error: Missing layer {layer_name}")
+                sys.exit(1)
+
+            tensor = state_dict[layer_name]
+            # Convert to float32 and flatten
+            data = tensor.float().flatten().cpu().numpy()
+
+            # Write shape (allows validation on load)
+            shape = list(tensor.shape)
+            f.write(struct.pack('<I', len(shape)))
+            for dim in shape:
+                f.write(struct.pack('<I', dim))
+
+            # Write flattened data as binary floats
+            f.write(struct.pack(f'<{len(data)}f', *data))
+
+            print(f"  {layer_name}: shape {shape}, {len(data)} floats")
+
+    file_size_mb = output_path.stat().st_size / (1024**2)
+    print(f"Weights exported to {output_file} ({file_size_mb:.2f} MB)")
+
+if __name__ == "__main__":
+    weights_file = "nnue_weights.pt"
+    output_file = "../src/main/resources/nnue_weights.bin"
+
+    if len(sys.argv) > 1:
+        weights_file = sys.argv[1]
+    if len(sys.argv) > 2:
+        output_file = sys.argv[2]
+
+    export_weights_to_binary(weights_file, output_file)