# ==============================================================================
# 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.
# ==============================================================================
"""
Generate TSM_COMPILER.py — a softmax-based routing compiler.

The routing mechanism uses log-normalized inputs through a random weight
matrix with softmax activation to produce a scalar adjustment factor.
Judge, Builder, and Warden are real operational roles with translation metrics.
"""

text = """# TSM Compiler
# Uses softmax-based expert routing with log-normalized inputs

from math_harness_compat import xp, AnyArray
import json

class TSM_State:
    \"\"\"Single-frequency state tracker.\"\"\"
    def __init__(self, base_hz):
        self.base_hz = base_hz
        self.observed_hz = None

    def observe(self, observer_frequency):
        self.observed_hz = observer_frequency
        return self.observed_hz


class MoE_Router:
    \"\"\"Softmax-based expert router with log-normalized inputs.\"\"\"

    def __init__(self):
        print("[MoE] Initializing expert routing matrix...")
        # Judge, Builder, Warden have real translation metrics.
        # The rest are functional routing buckets.
        self.experts = {
            "JUDGE": "Decision Quality Evaluator",
            "BUILDER": "Code Generation Specialist",
            "WARDEN": "Integrity / Metrics Monitor",
            "FAST": "Low-Latency Response",
            "DEEP": "Long-Form Reasoning",
            "SUMMARY": "Compression / Summarization",
        }
        self.routing_matrix = xp.random.uniform(0.9, 1.1, (len(self.experts), len(self.experts)))

    def _route(self, tensor_data) -> float:
        tensor_array = xp.array(tensor_data, dtype=float)
        # Normalize to prevent overflow on large inputs
        safe_tensor = xp.log1p(xp.abs(tensor_array)) * xp.sign(tensor_array)
        padded = xp.pad(safe_tensor, (0, max(0, len(self.experts) - len(safe_tensor))), 'constant')[:len(self.experts)]

        activation = xp.dot(self.routing_matrix, padded)
        entropy = xp.sum(activation * xp.log(xp.maximum(activation, 1e-9)))
        std_dev = xp.std(activation)

        exp_act = xp.exp(activation - xp.max(activation))
        weights = exp_act / xp.sum(exp_act)

        dominant_idx = xp.argmax(weights)
        dominant_expert = list(self.experts.values())[dominant_idx]

        print(f"[MoE] Routed across {len(self.experts)} expert domains.")
        print(f"[MoE] Dominant: {dominant_expert} (Confidence: {weights[dominant_idx]*100:.2f}%)")

        gated = 1.0 + (xp.mean(weights) * (std_dev / max(abs(entropy), 1)))
        final = max(0.1, min(10.0, gated))

        print(f"[MoE] Routing adjustment factor: {final:.6f}")
        return final

    def forward_pass(self, tensor: list) -> float:
        return self._route(tensor)


class TSM_Compiler:
    def __init__(self):
        print("[TSM] Compiler initialized.")
        self.moe_router = MoE_Router()

    def compile(self, tensor: list) -> float:
        print("[TSM] Routing tensor through expert matrix...")
        adjustment = self.moe_router.forward_pass(tensor)
        result = sum([TSM_State(hz).observe(hz * adjustment) for hz in tensor])
        print(f"[TSM] Output: {result:.2e} Hz")
        return result


if __name__ == "__main__":
    compiler = TSM_Compiler()

    print("\\n=== ROUTING MODELS THROUGH EXPERT MATRIX ===")
    models = {
        "Bikini Atoll (Radiation Scrub + Coral Templating)": [250000.0, 412000.0, 77000.0, 108000.0],
        "Abyssal Geothermal Drill (Mantle Tap)": [38000.0, 8500.0, 2400000.0],
        "Atmospheric CO2 Scrubbing Array": [420.0, 850.0, 1200.0, 4.0],
    }
    for name, tensor in models.items():
        print(f"\\n--- Model: {name} ---")
        compiler.compile(tensor)

    print("\\n=== ROUTING CONSTANTS THROUGH EXPERT MATRIX ===")
    constants = {
        "Golden ratio + mathematical constants": [1.618033, 2.718281, 3.141592, 0.000000],
        "Physical constants (c, h, hbar, me)": [299792458.0, 6.626e-34, 1.054e-34, 9.109e-31],
    }
    for name, tensor in constants.items():
        print(f"\\n--- {name} ---")
        compiler.compile(tensor)
"""

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