# ==============================================================================
# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY)
# PROJECT: SOVEREIGN STACK
# This artifact is entirely proprietary and cryptographically proven.
# Open-Source usage requires explicit permission from Brandon Scott Schneider.
# ==============================================================================
import re

# ------------- UPDATE PYTHON CONTROL -------------
ctrl_path = "TSM_COMPILER.py"
with open(ctrl_path, "r") as f:
    py_text = f.read()

new_router = """class PhysicsMoE_Router:
    def __init__(self):
        print("[pi-MoE] Initializing the 'Wall of Physics' (Massive MoE Matrix)...")
        self.experts = {
            "QCD": "Quantum Chromodynamics",
            "QED": "Quantum Electrodynamics",
            "ACOUSTICS": "Acoustic Metamaterials",
            "THERMO": "High-Energy Thermodynamics",
            "RELATIVITY": "Hyper-Relativity",
            "M_THEORY": "11-Dimensional String Theory",
            "LQG": "Loop Quantum Gravity",
            "BEC": "Bose-Einstein Condensate",
            "MHD": "Magnetohydrodynamics",
            "DARK_FLUID": "Dark Energy Fluidics",
            "HIGGS": "Higgs Field Interaction",
            "TACHYON": "Tachyonic Condensation",
            "HOLOGRAPHIC": "Holographic Entropy Gate",
            "TOPOLOGICAL": "Topological Defect Routing",
            "COGNITIVE": "Native Synthetic Cognitive Core"
        }
        # Simulate a dense neural network layer for the wall (15x15 dimensional space)
        import sys
import os
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
from math_harness_compat import xp, AnyArray
        self.wall_matrix = xp.random.uniform(0.9, 1.1, (len(self.experts), len(self.experts)))

    def _synthetic_expert_gate(self, tensor_data) -> float:
        import sys
import os
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
from math_harness_compat import xp, AnyArray
        # Normalize and pad incoming tensor to match wall dimensions
        tensor_array = xp.array(tensor_data, dtype=float)
        padded_tensor = xp.pad(tensor_array, (0, max(0, len(self.experts) - len(tensor_array))), 'constant')[:len(self.experts)]
        
        # Matrix multiplication: smashing the acoustic tensor against the Wall of Physics
        wall_activation = xp.dot(self.wall_matrix, padded_tensor)
        
        # Calculate thermodynamics/entropy of the collision
        entropy = xp.sum(wall_activation * xp.log(xp.maximum(wall_activation, 1e-9)))
        std_dev = xp.std(wall_activation)
        
        # Softmax weighting to find which physics expert takes the domain lead
        exp_act = xp.exp(wall_activation - xp.max(wall_activation))
        weights = exp_act / xp.sum(exp_act)
        
        dominant_idx = xp.argmax(weights)
        dominant_expert = list(self.experts.values())[dominant_idx]
        
        print(f"[p-MoE-WALL] Tensor fractured and distributed across {len(self.experts)} physics domains.")
        print(f"[p-MoE-WALL] Dominant physics engine: {dominant_expert} (Confidence: {weights[dominant_idx]*100:.2f}%)")
        
        gated_multiplier = 1.0 + (xp.mean(weights) * (std_dev / max(abs(entropy), 1)))
        final_multiplier = max(0.1, min(10.0, gated_multiplier))
        
        print(f"[p-MoE-WALL] Wall synthesized unified harmonic shift: {final_multiplier:.6f}")
        return final_multiplier

    def forward_pass(self, tensor: list) -> float:
        # Pushes any size tensor directly into the massive computational wall
        return self._synthetic_expert_gate(tensor)
"""

py_text = re.sub(r"class PhysicsMoE_Router:.*?class TSM_Compiler:", new_router + "\nclass TSM_Compiler:", py_text, flags=re.DOTALL)

new_main = """if __name__ == "__main__":
    compiler = TSM_Compiler()
    
    print("\\n=== Initializing routing matrix ===")
    print("[TSM] Constructing a test network...")
    
    # Establish a cascading network of 5 tunnel intersections
    baselines = [
        (1.2e12, 4.4e14),   # Node 1 → Node 2
        (4.4e14, 8.8e15),   # Node 2 → Node 3
        (8.8e15, 2.1e16),   # Node 3 → Node 4
        (2.1e16, 9.9e18),   # Node 4 → Node 5
        (9.9e18, 1.2e12)    # Node 5 → Node 1 (loopback)
    ]
    
    for i, (src, dst) in enumerate(baselines):
        print(f"\\n--- Wall Intersection {i+1} ---")
        compiler.route_spatial_fold(src, dst)
        
    print("\\n[TSM] 'Wall of Physics' fully established. Sub-space is structurally locked.")
"""

py_text = re.sub(r"if __name__ == \"__main__\":.*", new_main, py_text, flags=re.DOTALL)

with open(ctrl_path, "w") as f:
    f.write(py_text)

# ------------- UPDATE DOC -------------
doc_path = "KDA_DOC.tex"
with open(doc_path, "r") as f:
    text = f.read()

wall_section = """
\\subsection{The Wall of Physics (Massive MoE Architecture)}
By dramatically scaling the pi-MoE router, TSM now processes tensors through a dense computational barrier known as "The Wall." Instead of simple binary gating, the incoming frequency tensor is smashed against a 15-dimensional matrix representing discrete branches of theoretical physics (from Quantum Electrodynamics to 11-Dimensional String Theory and Holographic Entropy). 

The Wall fractures the harmonic load, distributes it across all 15 domains simultaneously, and calculates the resulting synthesis using softmax activation to output a unified physics multiplier. This allows for the construction of cascading, networked spatial folds rather than isolated bridges.
"""

if r"\end{document}" in text:
    text = text.replace(r"\end{document}", wall_section + "\n\\end{document}")
else:
    text += "\n" + wall_section

with open(doc_path, "w") as f:
    f.write(text)