"""
Simulation Handler for Orchestrator.

Enables multi-turn social simulation and scenario evolution
using the Swarm Colony.
"""

from core.observability.logging import get_logger
from typing import Any, Dict, List
from core.orchestration.handlers.swarm_handler import SwarmHandler

logger = get_logger(__name__)


class SimulationHandler(SwarmHandler):
    """
    Handler for 'scenario_simulation' intent.

    Extends SwarmHandler to support multi-round evolution where agent
    outcomes from round N affect the world state and memory context
    for round N+1.
    """

    async def handle_simulation(
        self, query: str, context: Dict[str, Any], rounds: int = 3
    ) -> Dict[str, Any]:
        """
        Handle a multi-round simulation.
        """
        logger.info(f"Starting multi-round simulation: {query} ({rounds} rounds)")

        current_state = query
        all_round_results = []

        for r in range(1, rounds + 1):
            logger.info(f"Starting Simulation Round {r}")

            # 1. Decompose current state into tasks
            sub_tasks = await self._decompose_task(current_state, context)
            if not sub_tasks:
                break

            # 2. Execute sub-tasks
            sub_results = await self._execute_subtasks(sub_tasks, current_state)

            # 3. Synthesize round outcome
            round_synthesis = await self._synthesize_results(
                current_state, sub_results, context
            )

            all_round_results.append(
                {"round": r, "sub_results": sub_results, "synthesis": round_synthesis}
            )

            # 4. Update memory with round outcome (World State update)
            if self._colony.memory_manager:
                from core.memory.types import MemoryType

                await self._colony.memory_manager.add_memory(
                    content=f"Outcome of Round {r} for simulation '{query}': {round_synthesis}",
                    memory_type=MemoryType.EPISODIC,
                    metadata={
                        "simulation": query,
                        "round": r,
                        "type": "simulation_outcome",
                    },
                )

            # 5. Update current state for next round
            current_state = (
                f"Original Goal: {query}\nPrevious Round Outcome: {round_synthesis}"
            )

        # Final synthesis of the entire simulation
        final_report = await self._generate_final_simulation_report(
            query, all_round_results
        )

        return {
            "response": final_report,
            "rounds": all_round_results,
            "metadata": {"total_rounds": rounds, "approach": "swarm_simulation"},
        }

    async def _generate_final_simulation_report(
        self, original_query: str, history: List[Dict[str, Any]]
    ) -> str:
        """
        Final synthesis of all simulation rounds.
        """
        if not self.llm_service:
            return "Simulation completed. (Synthesis unavailable without LLM)"

        history_text = "\n\n".join(
            f"### Round {h['round']}\n{h['synthesis']}" for h in history
        )

        prompt = f"""You are a Simulation Analysis Agent. Analyze the following multi-round simulation history and provide a final predictive report.

Original Scenario: {original_query}

Simulation History:
{history_text}

Provide a final report that:
1. Summarizes the evolution of the scenario.
2. Identifies key inflection points or emergent behaviors.
3. Provides a final prediction or recommendation based on the simulation outcome.
"""
        return await self.llm_service.generate_response(prompt)
