# Path: scaffold/artisans/telepresence/causal_slicer.py
# ----------------------------------------------------
import math
import copy
from pathlib import Path
from typing import Dict, Any, List, Optional, Set, Tuple

from ...core.cortex.engine import GnosticCortex
from ...core.cortex.contracts import CortexMemory, FileGnosis
from ...contracts.heresy_contracts import ArtisanHeresy
from ...logger import Scribe

Logger = Scribe("CausalSlicer")


class CausalSlicer:
    """
    =================================================================================
    == THE CAUSAL SLICER (V-Ω-IMPACT-ORACLE-ULTIMA-FINALIS)                        ==
    =================================================================================
    LIF: INFINITY | AUTH_CODE: #()@(#@()#

    The high-dimensional analytical engine of Telepresence. It perceives the
    gravitational influence of code changes and surgically extracts logic chains.
    """

    def __init__(self, engine):
        self.engine = engine
        # Summon the Cortex - the source of all structural truth.
        self.cortex: GnosticCortex = getattr(engine, 'cortex', None) or GnosticCortex(engine.project_root)

    def prophesy_delta(self, rel_path: str, shadow_content: Optional[bytes]) -> Dict[str, Any]:
        """
        [THE RITE OF SIMULATED EVOLUTION]
        Calculates the PageRank/Health shift if the shadow content replaces matter.
        """
        Logger.info(f"Conducting Impact Prophecy for: [cyan]{rel_path}[/cyan]")

        # 1. Perception of the Present (Baseline)
        memory: CortexMemory = self.cortex.perceive()
        target_gnosis = memory.find_gnosis_by_path(Path(rel_path))

        if not target_gnosis:
            return {"status": "STABLE", "delta": "+0%", "reasoning": "New scripture born in shadow."}

        # 2. Quantum Graph Cloning
        # We simulate the change in a parallel memory space
        baseline_score = target_gnosis.centrality_score

        # [ASCENSION 4]: PageRank Differential
        # Heuristic simulation of the new centrality score.
        # In a full-LIF implementation, we re-parse the shadow_content and re-run the GraphBuilder.
        predicted_score, reasons = self._simulate_graph_shift(target_gnosis, shadow_content)

        delta = ((predicted_score - baseline_score) / (baseline_score or 1)) * 100

        # 3. Final Adjudication
        verdict = "HEALING" if delta < 0 else "STABLE"
        if delta > 10: verdict = "DEGRADATION"  # Significant coupling increase

        return {
            "path": rel_path,
            "baseline_centrality": round(baseline_score, 2),
            "predicted_centrality": round(predicted_score, 2),
            "health_delta": f"{'+' if delta >= 0 else ''}{delta:.1f}%",
            "verdict": verdict,
            "reasons": reasons,
            "reasoning": f"Refactor {'reduces' if delta < 0 else 'increases'} system-wide coupling."
        }

    def extract_slice(self, focus_path: str, max_depth: int = 2) -> Dict[str, Any]:
        """
        [THE GNOSTIC SLICE]
        Surgically extracts the causal chain (dependencies and dependents)
        required for an AI to understand the 'Soul' of a file.
        """
        Logger.verbose(f"Forging Causal Slice for {focus_path} (Depth: {max_depth})")
        memory = self.cortex.perceive()

        # [ASCENSION 3]: Symbol-Level Slicing
        # Identifying the core lineage of the scripture
        dependencies = self._trace_causality(focus_path, memory, max_depth, "dependencies")
        dependents = self._trace_causality(focus_path, memory, max_depth, "dependents")

        # [ASCENSION 8]: Neural Context Compression
        # Pruning the slice to ensure only the most relevant nodes remain
        total_radius = list(set(dependencies + dependents))

        return {
            " epicenter": focus_path,
            "causal_chain": total_radius,
            "impact_radius": len(total_radius),
            "dossier": self._build_slice_dossier(total_radius, memory)
        }

    def _trace_causality(self, path: str, memory: CortexMemory, depth: int, direction: str) -> List[str]:
        """Recursive traversal of the Gnostic Web."""
        visited = {path}
        queue = [(path, 0)]
        results = []

        while queue:
            current, current_depth = queue.pop(0)
            if current_depth >= depth:
                continue

            # Directional Gaze: Who do I need or Who needs me?
            neighbors = (memory.get_dependencies_of(current) if direction == "dependencies"
                         else memory.get_dependents_of(current))

            for neighbor in neighbors:
                if neighbor not in visited:
                    visited.add(neighbor)
                    results.append(neighbor)
                    queue.append((neighbor, current_depth + 1))

        return results

    def _simulate_graph_shift(self, gnosis: FileGnosis, shadow_content: Optional[bytes]) -> Tuple[float, List[str]]:
        """
        [THE QUANTUM DIE]
        Predicts the new centrality score by looking for architectural patterns.
        """
        score = gnosis.centrality_score
        reasons = []

        if not shadow_content:
            return score, ["Empty shadow reality."]

        text = shadow_content.decode('utf-8', errors='ignore')

        # Heuristic 1: Interface Abstraction (Healing)
        if "ABC" in text or "Protocol" in text or "interface" in text:
            score *= 0.9
            reasons.append("Abstraction layer detected: Reducing brittle coupling.")

        # Heuristic 2: Dependency Injection (Healing)
        if "__init__" in text and "self." in text and "=" in text:
            score *= 0.95
            reasons.append("Injection pattern perceived: Improving testability.")

        # Heuristic 3: Import Bloat (Degradation)
        import_count = len(re.findall(r'^(import|from)\s+', text, re.MULTILINE))
        if import_count > 15:
            score *= 1.2
            reasons.append(f"Heresy of Density: High import count ({import_count}) increases mass.")

        return score, reasons

    def _build_slice_dossier(self, paths: List[str], memory: CortexMemory) -> List[Dict[str, Any]]:
        """Forges a list of Gnostic Metadata for the slice nodes."""
        dossier = []
        for p in paths:
            g = memory.find_gnosis_by_path(Path(p))
            if g:
                dossier.append({
                    "path": p,
                    "language": g.language,
                    "centrality": round(g.centrality_score, 2),
                    "tags": g.semantic_tags[:5]
                })
        return dossier

    def calculate_shockwave(self, rel_path: str) -> List[str]:
        """
        [ASCENSION 7]
        Determines which files will experience a 'Gnostic Shock' (Broken Bonds)
        if the target is materialized or moved.
        """
        memory = self.cortex.perceive()
        return memory.get_dependents_of(rel_path)