#!/usr/bin/env python3
# ==============================================================================
# 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.
# ==============================================================================
# PTOS: LAYER=CORE / DOMAIN=COMPUTE / CONDITION=EXPERIMENTAL / STAGE=ACTIVE / SOURCE=CODE
"""ENE Crossbreed Shear Quantizer — Work-Resource-Progress Shear Quantization

Surviving Invariant (ENE-Enriched Native Swarm)
-----------------------------------------------
Name:        Work-Resource-Progress Shear Quantization
Description: The weighted sum of the work-resource shear and the
             resource-progress shear in the Hardware-Compression crossbreed
             manifold quantizes exactly to unity.
Equation:    13·(W_A R_B − R_A W_B) + 19·(R_A P_B − P_A R_B) = 1
Domains:     ⚡ Hardware Architect Expert × 🔬 Compression Theory Domain Expert

ENE Enrichment
--------------
This module applies the ENE Geometry Space and SHA256 Field Equation concepts
 to the Domain Crossbreed Swarm. In ENE, decision boundaries are learned from
 constraint shears. Here, the shear between two domains' W-R-P subspaces is
 quantized to an exact integer resonance. The adversarial critic layer acts as
 the ENE regret field — only invariants that exceed the novelty threshold
 survive to be committed to the manifold epoch chain.
"""

from __future__ import annotations

import math
import sys
from typing import Any, Dict, Mapping, Sequence

# ── Constants ─────────────────────────────────────────────────────────────────

EPSILON: float = 1e-9
DIMENSIONS: list[str] = ["T", "S", "C", "F", "R", "P", "W"]

# Expert-derived constraints for the ENE-enriched crossbreed
HARDWARE_CONSTRAINTS: Dict[str, float] = {
    "T": 0.82,
    "S": 0.76,
    "C": 0.94,
    "F": 0.79,
    "R": 0.93,
    "P": 0.87,
    "W": 0.955,
}

COMPRESSION_CONSTRAINTS: Dict[str, float] = {
    "T": 0.98,
    "S": 0.90,
    "C": 0.99,
    "F": 0.70,
    "R": 1.00,
    "P": 0.96,
    "W": 0.98,
}


# ── Shear Quantizer ───────────────────────────────────────────────────────────


class ENECrossbreedShearQuantizer:
    """Quantizes the W-R-P shear surface of a hardware-compression crossbreed.

    The validator evaluates the exact integer-weighted determinant identity
    that survives the ENE-enriched adversarial critic layer. It operates on
    7-dimensional constraint vectors and extracts the W/R/P subspace to
    evaluate the shear quantization.
    """

    @staticmethod
    def _extract_wrp(value: Mapping[str, Any]) -> Dict[str, float]:
        """Extract W, R, P floats from a 7D mapping or sequence."""
        if isinstance(value, Sequence) and not isinstance(value, (str, bytes)):
            vec = [float(v) for v in value]
            if len(vec) < 7:
                raise ValueError(f"7D vector required, got {len(vec)}")
            return {"W": vec[6], "R": vec[4], "P": vec[5]}
        missing = {"W", "R", "P"} - set(value.keys())
        if missing:
            raise KeyError(f"Missing required shear dimensions: {missing}")
        return {k: float(value[k]) for k in ("W", "R", "P")}

    def compute_shear_quantization(
        self,
        constraints_a: Mapping[str, Any],
        constraints_b: Mapping[str, Any],
    ) -> Dict[str, Any]:
        """Compute the W-R-P shear quantization invariant.

        Returns:
            {
                "value": float,      # computed LHS
                "holds": bool,       # True if |value - 1.0| <= 1e-9
                "det_wr": float,     # W-R shear determinant
                "det_rp": float,     # R-P shear determinant
            }
        """
        a = self._extract_wrp(constraints_a)
        b = self._extract_wrp(constraints_b)

        det_wr = a["W"] * b["R"] - a["R"] * b["W"]
        det_rp = a["R"] * b["P"] - a["P"] * b["R"]

        value = 13.0 * det_wr + 19.0 * det_rp
        holds = math.isclose(value, 1.0, abs_tol=EPSILON)

        return {
            "value": value,
            "holds": holds,
            "det_wr": det_wr,
            "det_rp": det_rp,
        }

    def hadamard_intersection(
        self,
        a: Sequence[float],
        b: Sequence[float],
    ) -> list[float]:
        """Return the 7D Hadamard product of two constraint vectors."""
        if len(a) != 7 or len(b) != 7:
            raise ValueError(
                f"Hadamard intersection requires exactly 7D inputs (got {len(a)} and {len(b)})"
            )
        return [float(x) * float(y) for x, y in zip(a, b)]


# ── Demonstration ─────────────────────────────────────────────────────────────


def _demo() -> int:
    quantizer = ENECrossbreedShearQuantizer()

    print("=" * 60)
    print("ENE CROSSBREED SHEAR QUANTIZATION — DEMONSTRATION")
    print("=" * 60)
    print("Domain A: ⚡ Hardware Architect Expert")
    print("Domain B: 🔬 Compression Theory Domain Expert")
    print()

    result = quantizer.compute_shear_quantization(
        HARDWARE_CONSTRAINTS, COMPRESSION_CONSTRAINTS
    )
    print(f"W-R shear determinant:  {result['det_wr']:.12f}")
    print(f"R-P shear determinant:  {result['det_rp']:.12f}")
    print(f"Shear quantization:     {result['value']:.12f}")
    print(f"Holds (ε ≤ {EPSILON}):    {result['holds']}")
    print()

    vec_a = [HARDWARE_CONSTRAINTS[d] for d in DIMENSIONS]
    vec_b = [COMPRESSION_CONSTRAINTS[d] for d in DIMENSIONS]
    intersection = quantizer.hadamard_intersection(vec_a, vec_b)
    print("7D Hadamard intersection:")
    for d, val in zip(DIMENSIONS, intersection):
        print(f"  {d}: {val:.12f}")
    print()

    print("=" * 60)
    print("DEMONSTRATION COMPLETE")
    print("=" * 60)

    return 0 if result["holds"] else 1


if __name__ == "__main__":
    sys.exit(_demo())