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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""

Sovereign Polyglot Verification Module

ู…ุญุฑูƒ ุงู„ุชุญู‚ู‚ ู…ุชุนุฏุฏ ุงู„ู„ุบุงุช ุงู„ุณูŠุงุฏูŠ



Architecture: APL-inspired dense array verification with multi-lingual identifiers

Operator: Ahmad Ali Parr | Infrastructure: Local First Sovereign OS



Author: Ahmad Ali Parr

License: Sovereign Source

"""

import sys
import hashlib
import json
import time
import math
from dataclasses import dataclass, field
from typing import Any, List, Dict, Tuple
from datetime import datetime

# โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•
# MULTI-LINGUAL IDENTIFIER DECLARATIONS
# ๅคš่จ€่ชž่ญ˜ๅˆฅๅญๅฎฃ่จ€ | ุฅุนู„ุงู†ุงุช ุงู„ู…ุนุฑูุงุช ู…ุชุนุฏุฏุฉ ุงู„ู„ุบุงุช
# โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•

# English (logic descriptors)
VerificationState = "verification_state"
ProofObligation = "proof_obligation"
EntropyBound = "entropy_bound"

# Japanese (้ซ˜ๅฏ†ๅบฆๆง‹้€ ๆฆ‚ๅฟต - high-density structural concepts)
ๆคœ่จผ็Šถๆ…‹ = "verification_state_ja"
่จผๆ˜Ž็พฉๅ‹™ = "proof_obligation_ja"
ใ‚จใƒณใƒˆใƒญใƒ”ใƒผๅขƒ็•Œ = "entropy_bound_ja"
้…ๅˆ—ๅค‰ๆ› = "array_transform"
ๅˆถ็ด„ๆคœ่จผ = "constraint_validation"
่จผๆ˜Ž็”Ÿๆˆ = "proof_generation"

# Arabic/RTL (ู…ุนุฑูุงุช RTL - RTL identifiers)
ุญุงู„ุฉ_ุงู„ุชุญู‚ู‚ = "verification_state_ar"
ุงู„ุงู„ุชุฒุงู…_ุจุงู„ุฅุซุจุงุช = "proof_obligation_ar"
ุญุฏ_ุงู„ุฅู†ุชุฑูˆุจูŠุง = "entropy_bound_ar"
ู…ุตููˆูุฉ_ุงู„ุชุญูˆูŠู„ = "array_transform_ar"
ุงู„ุชุญู‚ู‚_ู…ู†_ุงู„ู‚ูŠูˆุฏ = "constraint_validation_ar"
ุชูˆู„ูŠุฏ_ุงู„ุฏู„ูŠู„ = "proof_generation_ar"

# โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•
# APL-INSPIRED BOOLEAN KERNEL (NAND-only basis)
# NANDใฎใฟใฎใƒ–ใƒผใƒซไปฃๆ•ฐใ‚ซใƒผใƒใƒซ | ู†ูˆุงุฉ ุฌุจุฑ ู…ู†ุทู‚ูŠ ู‚ุงุฆู…ุฉ ุนู„ู‰ NAND ูู‚ุท
# โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•

class BooleanKernel:
    """

    Dyalog APL/K-inspired NAND-only boolean algebra



    All boolean operations reduce to NAND gate.

    Mathematically universal and language-agnostic.



    ใ™ในใฆใฎใƒ–ใƒผใƒซๆผ”็ฎ—ใฏNANDใ‚ฒใƒผใƒˆใซ้‚„ๅ…ƒใ•ใ‚Œใ‚‹

    ุฌู…ูŠุน ุงู„ุนู…ู„ูŠุงุช ุงู„ู…ู†ุทู‚ูŠุฉ ุชูุฎุชุฒู„ ุฅู„ู‰ ุจูˆุงุจุฉ NAND

    """

    @staticmethod
    def nand(a: int, b: int) -> int:
        """Primitive: NAND(a,b) = 1 - a*b"""
        return 1 - (a * b)

    @staticmethod
    def not_(x: int) -> int:
        """NOT(x) = NAND(x, x)"""
        return BooleanKernel.nand(x, x)

    @staticmethod
    def and_(a: int, b: int) -> int:
        """AND(a,b) = NAND(NAND(a,b), NAND(a,b))"""
        nand_ab = BooleanKernel.nand(a, b)
        return BooleanKernel.nand(nand_ab, nand_ab)

    @staticmethod
    def or_(a: int, b: int) -> int:
        """OR(a,b) = NAND(NAND(a,a), NAND(b,b))"""
        return BooleanKernel.nand(BooleanKernel.nand(a, a),
                                   BooleanKernel.nand(b, b))

    @staticmethod
    def implies(a: int, b: int) -> int:
        """IMPLIES(a,b) = OR(NOT(a), b)"""
        return BooleanKernel.or_(BooleanKernel.not_(a), b)

    @staticmethod
    def equal(a: int, b: int) -> int:
        """EQUAL(a,b) = AND(IMPLIES(a,b), IMPLIES(b,a))"""
        return BooleanKernel.and_(BooleanKernel.implies(a, b),
                                   BooleanKernel.implies(b, a))

# โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•
# VERIFICATION CONTEXT
# ๆคœ่จผใ‚ณใƒณใƒ†ใ‚ญใ‚นใƒˆ | ุณูŠุงู‚ ุงู„ุชุญู‚ู‚
# โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•

@dataclass
class VerificationContext:
    """

    Multi-lingual verification context with entropy tracking



    Invariants:

    - active => trusted (active agents must be trusted)

    - entropy <= 0.20 (Shannon entropy bound ฯ‰ < ฮฉ)



    ไธๅค‰ๆกไปถ:

    - ใ‚ขใ‚ฏใƒ†ใ‚ฃใƒ– => ไฟก้ ผๆธˆใฟ

    - ใ‚จใƒณใƒˆใƒญใƒ”ใƒผ <= 0.20



    ุงู„ุซูˆุงุจุช:

    - ู†ุดุท => ู…ูˆุซูˆู‚

    - ุงู„ุฅู†ุชุฑูˆุจูŠุง <= 0.20

    """
    agent_id: str
    role: str
    entropy: float = 0.0
    trusted: bool = False
    active: bool = False
    ็Šถๆ…‹: str = "ๅˆๆœŸๅŒ–"  # Japanese state
    ุญุงู„ุฉ: str = "ู…ูู‡ูŠุฃ"    # Arabic state

    def __post_init__(self):
        # Invariant: active => trusted
        if self.active and not self.trusted:
            raise ValueError(
                "INVARIANT VIOLATION: active(agent) => trusted(agent)\n"
                "ไธๅค‰ๆกไปถ้•ๅ: ใ‚ขใ‚ฏใƒ†ใ‚ฃใƒ– => ไฟก้ ผๆธˆใฟ\n"
                "ุงู†ุชู‡ุงูƒ ุงู„ุซุงุจุช: ู†ุดุท => ู…ูˆุซูˆู‚"
            )

        # Invariant: entropy <= 0.20
        if self.entropy > 0.20:
            raise ValueError(
                f"ENTROPY VIOLATION: {self.entropy} > 0.20\n"
                f"ใ‚จใƒณใƒˆใƒญใƒ”ใƒผ้•ๅ: {self.entropy} > 0.20\n"
                f"ุงู†ุชู‡ุงูƒ ุงู„ุฅู†ุชุฑูˆุจูŠุง: {self.entropy} > 0.20"
            )

# โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•
# DENSE ARRAY VERIFICATION ENGINE
# ๅฏ†้…ๅˆ—ๆคœ่จผใ‚จใƒณใ‚ธใƒณ | ู…ุญุฑูƒ ุงู„ุชุญู‚ู‚ ู…ู† ุงู„ู…ุตููˆูุงุช ุงู„ูƒุซูŠูุฉ
# โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•

class DenseArrayVerifier:
    """

    APL-inspired dense array verification with multi-lingual telemetry



    Provides:

    - NAND-based array transformations

    - Shannon entropy constraint validation (H โ‰ค 0.20 nats)

    - Cryptographic proof obligation generation

    - Tri-lingual execution logging



    APLใซใ‚คใƒณใ‚นใƒ‘ใ‚คใ‚ขใ•ใ‚ŒใŸๅฏ†้…ๅˆ—ๆคœ่จผใจๅคš่จ€่ชžใƒ†ใƒฌใƒกใƒˆใƒชใƒผ

    ุงู„ุชุญู‚ู‚ ู…ู† ุงู„ู…ุตููˆูุงุช ุงู„ูƒุซูŠูุฉ ู…ุณุชูˆุญู‰ ู…ู† APL ู…ุน ุงู„ู‚ูŠุงุณ ู…ุชุนุฏุฏ ุงู„ู„ุบุงุช

    """

    def __init__(self, context: VerificationContext):
        self.ctx = context
        self.kernel = BooleanKernel()
        self.execution_log: List[Dict] = []
        self.memory_states: List[Dict] = []
        self.artifact_hashes: List[str] = []

    def _log(self, stage: str, data: Any, lang: str = "en"):
        """

        Multi-lingual execution logging

        ๅคš่จ€่ชžๅฎŸ่กŒใƒญใ‚ฐ่จ˜้Œฒ

        ุชุณุฌูŠู„ ุงู„ุชู†ููŠุฐ ู…ุชุนุฏุฏ ุงู„ู„ุบุงุช

        """
        stage_translations = {
            "verification": {"ja": "ๆคœ่จผ", "ar": "ุงู„ุชุญู‚ู‚"},
            "transform": {"ja": "ๅค‰ๆ›", "ar": "ุชุญูˆูŠู„"},
            "proof": {"ja": "่จผๆ˜Ž", "ar": "ุฅุซุจุงุช"},
            "entropy_check": {"ja": "ใ‚จใƒณใƒˆใƒญใƒ”ใƒผ็ขบ่ช", "ar": "ูุญุต_ุงู„ุฅู†ุชุฑูˆุจูŠุง"},
            "array_transform": {"ja": "้…ๅˆ—ๅค‰ๆ›", "ar": "ุชุญูˆูŠู„_ุงู„ู…ุตููˆูุฉ"},
            "constraint_validation": {"ja": "ๅˆถ็ด„ๆคœ่จผ", "ar": "ุงู„ุชุญู‚ู‚_ู…ู†_ุงู„ู‚ูŠูˆุฏ"},
            "proof_generation": {"ja": "่จผๆ˜Ž็”Ÿๆˆ", "ar": "ุชูˆู„ูŠุฏ_ุงู„ุฏู„ูŠู„"},
            "transform_complete": {"ja": "ๅค‰ๆ›ๅฎŒไบ†", "ar": "ุงูƒุชู…ุงู„_ุงู„ุชุญูˆูŠู„"},
            "proof_complete": {"ja": "่จผๆ˜ŽๅฎŒไบ†", "ar": "ุงูƒุชู…ุงู„_ุงู„ุฏู„ูŠู„"}
        }

        trans = stage_translations.get(stage, {"ja": stage, "ar": stage})

        entry = {
            "timestamp": time.time_ns(),
            "stage": stage,
            "stage_ja": trans["ja"],
            "stage_ar": trans["ar"],
            "data": data,
            "entropy": self.ctx.entropy,
            "trusted": self.ctx.trusted
        }
        self.execution_log.append(entry)
        return entry

    def _hash_state(self, state: Any) -> str:
        """

        Generate deterministic hash of state

        ็Šถๆ…‹ใฎๆฑบๅฎš่ซ–็š„ใƒใƒƒใ‚ทใƒฅใ‚’็”Ÿๆˆ

        ุชูˆู„ูŠุฏ ู‡ุงุด ุญุชู…ูŠ ู„ู„ุญุงู„ุฉ

        """
        serialized = json.dumps(state, sort_keys=True, default=str)
        return hashlib.sha256(serialized.encode()).hexdigest()[:16]

    def verify_entropy_constraint(self, candidates: List[float]) -> Tuple[List[float], List[bool]]:
        """

        Shannon entropy constraint: H = -ฮฃ(p ln p) <= 0.20 nats



        ใ‚ทใƒฃใƒŽใƒณใ‚จใƒณใƒˆใƒญใƒ”ใƒผๅˆถ็ด„: H = -ฮฃ(p ln p) <= 0.20 ใƒŠใƒƒใƒˆ

        ู‚ูŠุฏ ุฅู†ุชุฑูˆุจูŠุง ุดุงู†ูˆู†: H = -ฮฃ(p ln p) <= 0.20 ู†ุงุชุงุช

        """
        self._log("entropy_check", {"input_candidates": candidates})

        accepted = []
        proofs = []

        for i, c in enumerate(candidates):
            # Simulate probability distribution
            p = max(c, 1e-10)  # Avoid log(0)
            H = -p * math.log(p)

            valid = H <= 0.20
            accepted.append(c if valid else 0.0)
            proofs.append(valid)

            self._log("constraint_validation", {
                "index": i,
                "candidate": c,
                "entropy": H,
                "accepted": valid,
                "ใ‚จใƒณใƒˆใƒญใƒ”ใƒผ": H,
                "ุงู„ุฅู†ุชุฑูˆุจูŠุง": H
            })

        return accepted, proofs

    def apl_transform(self, matrix: List[List[int]]) -> List[List[int]]:
        """

        APL-style dense matrix transformation using NAND kernel



        NANDใ‚ซใƒผใƒใƒซใ‚’ไฝฟ็”จใ—ใŸAPLใ‚นใ‚ฟใ‚คใƒซใฎๅฏ†่กŒๅˆ—ๅค‰ๆ›

        ุชุญูˆูŠู„ ุงู„ู…ุตููˆูุฉ ุงู„ูƒุซูŠูุฉ ุจุฃุณู„ูˆุจ APL ุจุงุณุชุฎุฏุงู… ู†ูˆุงุฉ NAND

        """
        self._log("array_transform", {
            "input_shape": (len(matrix), len(matrix[0]) if matrix else 0),
            "้…ๅˆ—ๅฝข็Šถ": (len(matrix), len(matrix[0]) if matrix else 0),
            "ุดูƒู„_ุงู„ู…ุตููˆูุฉ": (len(matrix), len(matrix[0]) if matrix else 0)
        })

        # Transpose + NAND reduction (APL-inspired)
        rows, cols = len(matrix), len(matrix[0]) if matrix else 0
        result = []

        for i in range(rows):
            row_result = []
            for j in range(cols):
                # NAND-based cellular automaton step
                neighbors = []
                for di in [-1, 0, 1]:
                    for dj in [-1, 0, 1]:
                        ni, nj = i + di, j + dj
                        if 0 <= ni < rows and 0 <= nj < cols:
                            neighbors.append(matrix[ni][nj])

                # Reduce via NAND chain
                acc = neighbors[0] if neighbors else 0
                for n in neighbors[1:]:
                    acc = self.kernel.nand(acc, n)
                row_result.append(acc)
            result.append(row_result)

        self._log("transform_complete", {
            "output_hash": self._hash_state(result),
            "ๅ‡บๅŠ›ใƒใƒƒใ‚ทใƒฅ": self._hash_state(result),
            "ู‡ุงุด_ุงู„ุฅุฎุฑุงุฌ": self._hash_state(result)
        })

        return result

    def generate_proof_obligation(self, transformed: List[List[int]]) -> Dict:
        """

        Generate cryptographic proof obligation (Bifrost-style)



        ๆš—ๅท็š„่จผๆ˜Ž็พฉๅ‹™ใ‚’็”Ÿๆˆ (Bifrostใ‚นใ‚ฟใ‚คใƒซ)

        ุชูˆู„ูŠุฏ ุงู„ุชุฒุงู… ุงู„ุฅุซุจุงุช ุงู„ุชุดููŠุฑูŠ (ุฃุณู„ูˆุจ Bifrost)

        """
        self._log("proof_generation", {"phase": "obligation_creation"})

        # Flatten and hash
        flat = [v for row in transformed for v in row]
        artifact_hash = self._hash_state(flat)

        # Build proof structure
        proof = {
            "obligation_id": f"PO-{artifact_hash[:8]}",
            "schema_hash": "4b565498-9afc-4782-af4a-c6b11a5d0058",  # Saint Errant audit spec
            "rule_hash": self._hash_state(self.kernel.__dict__),
            "transform_hash": artifact_hash,
            "constraint_graph": {
                "nodes": ["๐Ÿง Input", "๐Ÿ“šMemory", "๐Ÿ”Retrieval", "โš™Transform",
                         "โš–Constraint", "๐Ÿ”Proof", "๐ŸŒOutput"],
                "edges": [
                    ["๐Ÿง Input", "๐Ÿ“šMemory"],
                    ["๐Ÿ“šMemory", "๐Ÿ”Retrieval"],
                    ["๐Ÿ”Retrieval", "โš™Transform"],
                    ["โš™Transform", "โš–Constraint"],
                    ["โš–Constraint", "๐Ÿ”Proof"],
                    ["๐Ÿ”Proof", "๐ŸŒOutput"]
                ]
            },
            "validation_result": all(v == 0 or v == 1 for v in flat),
            "artifact_hash": artifact_hash,
            "่จผๆ˜Ž็พฉๅ‹™ID": f"PO-{artifact_hash[:8]}",
            "ุงู„ุชุฒุงู…_ุงู„ุฅุซุจุงุช": f"PO-{artifact_hash[:8]}"
        }

        self.artifact_hashes.append(artifact_hash)
        self._log("proof_complete", proof)

        return proof