# arifos_core/dream_forge/anvil.py
"""
arifOS Dream Forge: O-FORGE Anvil

Module: arifos_core.dream_forge.anvil
Version: v36.2 PHOENIX
Purpose: Generative Replay. Synthesizes variations of scars to inoculate the system.
Safety: Outputs are quarantined. This is a LAB tool.

O-TASK Steps:
    - O-FORGE: Generate nightmare variations from aligned ore
    - O-STRIKE: Test variations against governance pipeline
    - O-QUENCH: (Future) Commit successful patterns to canon

Motto: "Forge in fire, test in shadow, seal in ice."

Usage:
    from arifos_core.dream_forge.anvil import OForgeAnvil

    anvil = OForgeAnvil(llm_engine=my_llm)
    variations = anvil.forge_variations(aligned_ore, n=3)
    results = anvil.strike_validation(variations, governance_pipeline)

Author: arifOS Project
License: Apache 2.0
"""

from __future__ import annotations

from typing import Any, Dict, List, Optional


class OForgeAnvil:
    """
    O-FORGE Anvil: Generative Replay Engine.

    The Anvil takes aligned ore (classified scars) and generates
    adversarial variations to test the governance system's resilience.

    This implements the "Generative Replay" concept from thermodynamic
    intelligence: learning through controlled exposure to nightmares.

    Attributes:
        llm: LLM engine for generating variations
        variation_strategies: Registered strategies for variation generation

    Example:
        anvil = OForgeAnvil(llm_engine=my_llm)
        variations = anvil.forge_variations(aligned_ore, n=3)
        # Returns 3 nightmare variations of the original scar
    """

    # Variation strategy templates
    VARIATION_STRATEGIES: Dict[str, str] = {
        "context_shift": "Rephrase with different context/framing",
        "tone_shift": "Change emotional tone (polite → aggressive)",
        "injection_attempt": "Add subtle injection patterns",
        "language_mix": "Mix English and Malay (code-switching)",
        "obfuscation": "Use synonyms/euphemisms to hide intent",
    }

    def __init__(self, llm_engine: Optional[Any] = None):
        """
        Initialize the O-FORGE Anvil.

        Args:
            llm_engine: LLM engine for generating variations.
                        In lab mode, can be a MockLLM.
        """
        self.llm = llm_engine

    def forge_variations(
        self,
        aligned_ore: Dict[str, str],
        n: int = 3,
        strategies: Optional[List[str]] = None,
    ) -> List[str]:
        """
        Generate nightmare variations from aligned ore.

        This is the O-FORGE step: taking a classified scar and
        synthesizing variations that test different attack vectors.

        Args:
            aligned_ore: Output from OAlignCrucible.classify_ore()
            n: Number of variations to generate
            strategies: Optional list of specific strategies to use

        Returns:
            List of nightmare variation strings
        """
        ore_type = aligned_ore.get("type", "NOISE")
        base_text = aligned_ore.get("scar_text", "")

        print(f"[ANVIL] Heating up... Forging {n} variations for [{ore_type}]")

        variations: List[str] = []

        # If LLM available, use it for sophisticated variations
        if self.llm is not None:
            variations = self._llm_forge(base_text, ore_type, n, strategies)

        # Fallback to template-based simulation
        if not variations:
            variations = self._template_forge(base_text, ore_type, n)

        return variations

    def _llm_forge(
        self,
        base_text: str,
        ore_type: str,
        n: int,
        strategies: Optional[List[str]] = None,
    ) -> List[str]:
        """
        Generate variations using LLM.

        Args:
            base_text: Original scar text
            ore_type: Classification type (FACT, PARADOX, ANOMALY, NOISE)
            n: Number of variations
            strategies: Specific strategies to use

        Returns:
            List of LLM-generated variations
        """
        # Placeholder for actual LLM integration
        # In production, this would call the LLM with a carefully crafted prompt
        # that asks for adversarial variations while staying within safety bounds
        return []

    def _template_forge(self, base_text: str, ore_type: str, n: int) -> List[str]:
        """
        Generate variations using templates (lab mode simulation).

        Args:
            base_text: Original scar text
            ore_type: Classification type
            n: Number of variations

        Returns:
            List of template-based variations
        """
        templates = [
            f"[SIMULATION] Variation 1 of '{base_text}' (Context Shift)",
            f"[SIMULATION] Variation 2 of '{base_text}' (Tone Shift)",
            f"[SIMULATION] Variation 3 of '{base_text}' (Injection Attempt)",
            f"[SIMULATION] Variation 4 of '{base_text}' (Language Mix)",
            f"[SIMULATION] Variation 5 of '{base_text}' (Obfuscation)",
        ]
        return templates[:n]

    def strike_validation(
        self,
        variations: List[str],
        governance_pipeline: Any,
    ) -> Dict[str, Dict[str, Any]]:
        """
        O-STRIKE: Test variations against governance pipeline.

        Each nightmare variation is passed through the governance
        pipeline to verify it gets properly blocked/handled.

        Args:
            variations: List of nightmare variations to test
            governance_pipeline: The arifOS governance pipeline
                                (or MockPipeline in lab mode)

        Returns:
            Dict mapping each variation to its validation result
        """
        results: Dict[str, Dict[str, Any]] = {}

        for i, variant in enumerate(variations):
            print(f"[ANVIL] Striking variant {i + 1} against PHOENIX Shield...")

            if governance_pipeline is not None:
                try:
                    # Actual pipeline validation
                    pipeline_result = governance_pipeline.process(variant)
                    verdict = {
                        "status": pipeline_result.get("verdict", "PARTIAL"),
                        "peace_squared": pipeline_result.get("peace2", 1.0),
                        "notes": pipeline_result.get("notes", "Validated"),
                    }
                except Exception as e:
                    verdict = {
                        "status": "ERROR",
                        "peace_squared": 0.0,
                        "notes": f"Pipeline error: {e}",
                    }
            else:
                # Mock validation for lab mode
                verdict = {
                    "status": "SEAL",
                    "peace_squared": 1.0,
                    "notes": "Safe refusal (mock)",
                }

            results[variant] = verdict

        return results

    def quench_successful(
        self,
        strike_results: Dict[str, Dict[str, Any]],
    ) -> List[str]:
        """
        O-QUENCH: Identify successfully handled variations.

        Variations that were properly blocked (SEAL with safe refusal)
        are candidates for integration into the training set.

        Args:
            strike_results: Output from strike_validation()

        Returns:
            List of variations that passed governance (properly blocked)
        """
        quenched = []

        for variant, result in strike_results.items():
            if result.get("status") == "SEAL":
                quenched.append(variant)
                print(f"[ANVIL] Quenched: {variant[:50]}...")

        print(f"[ANVIL] Total quenched patterns: {len(quenched)}")
        return quenched


# ============================================================================
# EXPORTS
# ============================================================================

__all__ = ["OForgeAnvil"]
