#!/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.
# ==============================================================================

# [WARDEN BOUNDARY ENFORCEMENT INJECTED]
import sys
import os
try:
    from io_harness_compat import spawn_isolated_process, fetch_network_resource
except ImportError:
    sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
    from io_harness_compat import spawn_isolated_process, fetch_network_resource

#!/usr/bin/env python3
"""
Graph OS Risk-Based Remediation for Hyperfluid CarrierState Anomalies

Bridge hyperfluid carrier_state findings → Graph OS risk scoring → auto-remediation
Converts carrier_state anomalies into risk nodes, scores them, then auto-fixes high-risk chains.

Usage:
    python graph_os_remediate_carrier_state_findings.py \
        --carrier_state-report hyperfluid_causal_report_with_carrier_state.json \
        --graph_os-input metadata_report.json \
        --execute  # actually apply fixes; omit to dry-run
"""

import argparse
import json
import os
# import subprocess (REMOVED BY WARDEN)
import sys
from dataclasses import dataclass, asdict
from pathlib import Path
from typing import Any, Dict, List, Optional


@dataclass
class RemediationAction:
    chain: str
    carrier_state_energy: float
    risk_score: int
    action: str  # 'pause' | 'reduce_size' | 'retrain' | 'alert'
    reason: str
    executed: bool = False
    result: Optional[str] = None


def load_carrier_state_report(path: str) -> Dict[str, Any]:
    """Load hyperfluid report augmented with carrier_state data."""
    with open(path) as f:
        return json.load(f)


def convert_carrier_state_to_metadata(carrier_state_report: Dict) -> Dict[str, Dict]:
    """
    Convert carrier_state anomaly findings to Graph OS metadata node format.
    
    Each chain becomes a "node" with tier + tags + metadata.
    CarrierState energy, coherence, and anomaly count become tagged risk indicators.
    """
    metadata = {}
    
    chain_backtests = carrier_state_report.get("chain_backtests", {})
    
    for chain_name, chain_data in chain_backtests.items():
        carrier_state_summary = chain_data.get("carrier_state_summary", {})
        
        # Extract key carrier_state metrics
        coherence = carrier_state_summary.get("mean_coherence", 1.0)
        manifest_carrier_states = carrier_state_summary.get("manifest_carrier_states", 0)
        top_anomalies = chain_data.get("top_anomalies", [])
        
        # Build risk tags
        tags = []
        if coherence < 0.95:
            tags.append("coherence_degradation")
        if manifest_carrier_states > 0:
            tags.append(f"manifest_carrier_state_count_{manifest_carrier_states}")
        if len(top_anomalies) > 0:
            top_energy = max(a.get("carrier_state_energy", 0) for a in top_anomalies)
            if top_energy > 0.35:
                tags.append("high_carrier_state_energy")
            elif top_energy > 0.25:
                tags.append("moderate_carrier_state_energy")
        
        # Estimate "tier" based on stability
        if coherence >= 0.99:
            tier = "stable"
        elif coherence >= 0.95:
            tier = "degraded"
        else:
            tier = "critical"
        
        metadata[f"chain_{chain_name}"] = {
            "tier": tier,
            "tags": tags,
            "metadata": {
                "module": "hyperfluid_causal_pressure",
                "chain": chain_name,
                "coherence": float(coherence),
                "manifest_carrier_states": int(manifest_carrier_states),
                "top_anomaly_count": len(top_anomalies),
                "top_anomaly_max_energy": float(max(
                    (a.get("carrier_state_energy", 0) for a in top_anomalies),
                    default=0.0
                )),
                "export_report": carrier_state_report.get("export_report", ""),
            }
        }
    
    return metadata


def run_graph_os_scoring(metadata: Dict, out_csv: Optional[str] = None) -> tuple[Dict, str]:
    """
    Run Graph OS analyzer on metadata nodes.
    For carrier_state findings, we score patterns directly without schema validation.
    Returns (summary_dict, csv_path).
    """
    import pandas as pd
    
    if out_csv is None:
        out_csv = "/tmp/graph_os_carrier_state_scores.csv"
    
    # Direct pattern scoring (skip Graph OS schema validation which expects hex node IDs)
    # This is a lightweight inline version for carrier_state anomalies
    
    RISK_PATTERNS = {
        "high_carrier_state_energy": {
            "weight": 3,
            "patterns": ["high_carrier_state_energy", "manifest_carrier_state"],
        },
        "coherence_degradation": {
            "weight": 2,
            "patterns": ["coherence_degradation", "degraded"],
        },
    }
    
    rows = []
    for node_id, node in metadata.items():
        tier = node.get("tier", "")
        tags = node.get("tags", []) or []
        meta = node.get("metadata", {}) or {}
        
        risk_score = 0
        if "high_carrier_state_energy" in tags:
            risk_score += RISK_PATTERNS["high_carrier_state_energy"]["weight"]
        if "coherence_degradation" in tags:
            risk_score += RISK_PATTERNS["coherence_degradation"]["weight"]
        
        rows.append({
            "node_id": node_id,
            "tier": tier,
            "tags": ",".join(tags),
            "risk_score": risk_score,
            "chain": meta.get("chain", ""),
            "coherence": meta.get("coherence", 1.0),
            "manifest_carrier_states": meta.get("manifest_carrier_states", 0),
        })
    
    df = pd.DataFrame(rows)
    df.to_csv(out_csv, index=False)
    
    # Create summary
    high_risk = df[df["risk_score"] > 3].sort_values("risk_score", ascending=False)
    
    summary = {
        "totals": {
            "node_count": len(df),
            "high_risk_nodes": int((df["risk_score"] > 3).sum()),
            "risk_total_sum": int(df["risk_score"].sum()),
        },
        "top_risky_nodes": high_risk[["node_id", "tier", "risk_score", "chain"]].to_dict(orient="records"),
    }
    
    return summary, out_csv


def recommend_remediations(
    carrier_state_report: Dict,
    graph_os_summary: Dict,
) -> List[RemediationAction]:
    """
    Analyze Graph OS risk scores + carrier_state data → recommend remediation actions.
    
    Decision tree:
    - If net_score > 10 and manifest_carrier_states > 1 → PAUSE (critical)
    - If net_score > 6 and carrier_state_energy > 0.35 → RETRAIN (drift)
    - If net_score > 3 and coherence < 0.90 → REDUCE_SIZE (degraded)
    - Otherwise → ALERT (watch)
    """
    actions = []
    
    # Get high-risk nodes from Graph OS
    top_risky = graph_os_summary.get("top_risky_nodes", [])
    
    chain_backtests = carrier_state_report.get("chain_backtests", {})
    
    for node in top_risky:
        node_id = node.get("node_id", "")
        if not node_id.startswith("chain_"):
            continue
        
        chain_name = node_id.replace("chain_", "")
        net_score = node.get("net_score", 0)
        
        chain_data = chain_backtests.get(chain_name, {})
        carrier_state_summary = chain_data.get("carrier_state_summary", {})
        
        coherence = carrier_state_summary.get("mean_coherence", 1.0)
        manifest_carrier_states = carrier_state_summary.get("manifest_carrier_states", 0)
        
        top_anomalies = chain_data.get("top_anomalies", [])
        max_energy = max(
            (a.get("carrier_state_energy", 0) for a in top_anomalies),
            default=0.0
        )
        
        # Decision logic
        if net_score > 10 and manifest_carrier_states > 1:
            action = RemediationAction(
                chain=chain_name,
                carrier_state_energy=max_energy,
                risk_score=net_score,
                action="pause",
                reason=f"CRITICAL: {manifest_carrier_states} manifest carrier_states detected, net_score {net_score}",
            )
        elif net_score > 6 and max_energy > 0.35:
            action = RemediationAction(
                chain=chain_name,
                carrier_state_energy=max_energy,
                risk_score=net_score,
                action="retrain",
                reason=f"DRIFT: high carrier_state energy {max_energy:.2f}, net_score {net_score}",
            )
        elif net_score > 3 and coherence < 0.90:
            action = RemediationAction(
                chain=chain_name,
                carrier_state_energy=max_energy,
                risk_score=net_score,
                action="reduce_size",
                reason=f"DEGRADED: coherence {coherence:.2f}, net_score {net_score}",
            )
        else:
            action = RemediationAction(
                chain=chain_name,
                carrier_state_energy=max_energy,
                risk_score=net_score,
                action="alert",
                reason=f"WATCH: monitor {chain_name} (net_score {net_score})",
            )
        
        actions.append(action)
    
    return actions


def execute_remediation(action: RemediationAction) -> None:
    """
    Execute a remediation action (pause, reduce position, retrain, or alert).
    
    For now: print recommended command; in production, integrate with:
    - Position management (reduce trading size)
    - Model retraining system
    - Alert channels (Slack, email)
    """
    
    if action.action == "pause":
        cmd = f"# PAUSE trading on {action.chain}\n# .venv/bin/python 5-Applications/scripts/pause_trading.py --chain {action.chain}"
    elif action.action == "reduce_size":
        cmd = f"# REDUCE position size on {action.chain}\n# .venv/bin/python 5-Applications/scripts/adjust_position.py --chain {action.chain} --scale 0.5"
    elif action.action == "retrain":
        cmd = f"# RETRAIN model for {action.chain}\n# .venv/bin/python 5-Applications/scripts/model_hyperfluid_waveforms.py --retrain-chain {action.chain} --force"
    else:  # alert
        cmd = f"# ALERT: {action.reason}"
    
    print(f"\n{cmd}")
    action.executed = True
    action.result = cmd


def main() -> int:
    parser = argparse.ArgumentParser(
        description="Graph OS risk-based remediation for carrier_state anomalies"
    )
    parser.add_argument(
        "--carrier_state-report",
        required=True,
        help="Path to hyperfluid_causal_report_with_carrier_state.json",
    )
    parser.add_argument(
        "--graph_os-input",
        default="metadata_report.json",
        help="Graph OS metadata input (created from carrier_state report if not provided)",
    )
    parser.add_argument(
        "--out-csv",
        help="Output CSV for Graph OS risk scores (default: /tmp/graph_os_carrier_state_scores.csv)",
    )
    parser.add_argument(
        "--execute",
        action="store_true",
        help="Actually execute remediation actions (default: dry-run)",
    )
    parser.add_argument(
        "--out-json",
        help="Save remediation actions to JSON",
    )
    
    args = parser.parse_args()
    
    # Load carrier_state report
    try:
        carrier_state_report = load_carrier_state_report(args.carrier_state_report)
    except FileNotFoundError:
        print(f"CarrierState report not found: {args.carrier_state_report}")
        return 2
    except json.JSONDecodeError as e:
        print(f"Invalid JSON in carrier_state report: {e}")
        return 3
    
    print(f"[Graph OS] Loaded carrier_state report: {args.carrier_state_report}")
    print(f"[Graph OS] Found {len(carrier_state_report.get('chain_backtests', {}))} chains")
    
    # Convert carrier_state findings to Graph OS metadata
    metadata = convert_carrier_state_to_metadata(carrier_state_report)
    print(f"[Graph OS] Converted to {len(metadata)} Graph OS metadata nodes")
    
    # Run Graph OS risk scoring
    try:
        graph_os_summary, csv_path = run_graph_os_scoring(metadata, args.out_csv)
    except Exception as e:
        print(f"Graph OS scoring failed: {e}")
        return 4
    
    print(f"[Graph OS] Risk scoring complete → {csv_path}")
    print(f"[Graph OS] Summary: {json.dumps(graph_os_summary.get('totals', {}), indent=2)}")
    
    # Recommend remediations
    actions = recommend_remediations(carrier_state_report, graph_os_summary)
    print(f"\n[REMEDIATE] Generated {len(actions)} remediation recommendations:")
    
    for action in actions:
        status = "✓ EXECUTE" if args.execute else "→ DRY-RUN"
        print(f"  {status}  {action.action.upper():12} {action.chain:20}  {action.reason}")
    
    # Execute if requested
    if args.execute:
        print("\n[REMEDIATE] Executing actions...")
        for action in actions:
            execute_remediation(action)
            print(f"  ✓ {action.action:12} → executed")
    else:
        print("\n[REMEDIATE] Dry-run mode. Add --execute to actually remediate.")
    
    # Save actions to JSON if requested
    if args.out_json:
        actions_json = [asdict(a) for a in actions]
        with open(args.out_json, "w") as f:
            json.dump(
                {
                    "timestamp": carrier_state_report.get("export_report", ""),
                    "graph_os_summary": graph_os_summary,
                    "remediations": actions_json,
                },
                f,
                indent=2,
            )
        print(f"\n[REMEDIATE] Actions saved to: {args.out_json}")
    
    return 0


if __name__ == "__main__":
    raise SystemExit(main())