"""Simulation engine for AI PMO decision runtime."""

from __future__ import annotations

from .shared import (
    CanonicalIntent,
    ROUTE_PROJECT_SIGNAL_CANCEL,
    ROUTE_PROJECT_SIGNAL_COMMIT,
    ROUTE_PROJECT_SIGNAL_DISCOVER,
    ROUTE_PROJECT_SIGNAL_REFRESH,
    Any,
    normalize_workload_ratio,
)


class SimulationEngine:
    """执行前仿真。"""

    BUDGET_COST_BY_OPERATION = {
        "query.answer": 1.0,
        "project.create": 120.0,
        "task.create": 14.0,
        "signal.enter": 8.0,
        "signal_source.onboard": 40.0,
        ROUTE_PROJECT_SIGNAL_DISCOVER: 3.0,
        ROUTE_PROJECT_SIGNAL_COMMIT: 8.0,
        ROUTE_PROJECT_SIGNAL_REFRESH: 3.0,
        ROUTE_PROJECT_SIGNAL_CANCEL: 0.2,
    }
    HOURS_BY_OPERATION = {
        "query.answer": 0.2,
        "project.create": 12.0,
        "task.create": 2.5,
        "signal.enter": 1.5,
        "signal_source.onboard": 6.0,
        ROUTE_PROJECT_SIGNAL_DISCOVER: 0.8,
        ROUTE_PROJECT_SIGNAL_COMMIT: 1.5,
        ROUTE_PROJECT_SIGNAL_REFRESH: 0.8,
        ROUTE_PROJECT_SIGNAL_CANCEL: 0.1,
    }

    @classmethod
    def simulate(cls, *, intent: CanonicalIntent, world_model: dict[str, Any], execution_plan: dict[str, Any]) -> dict[str, Any]:
        actions = execution_plan.get("actions") if isinstance(execution_plan.get("actions"), list) else []
        operation_codes = [str(item.get("operation_code") or "").strip() for item in actions if isinstance(item, dict)]
        estimated_budget_delta = sum(cls.BUDGET_COST_BY_OPERATION.get(code, 0.0) for code in operation_codes)
        estimated_capacity_delta = sum(cls.HOURS_BY_OPERATION.get(code, 0.0) for code in operation_codes)

        budget = world_model.get("budget") if isinstance(world_model.get("budget"), dict) else {}
        budget_amount = float(budget.get("budget_amount_usd") or 0.0)
        current_ratio = float(budget.get("usage_ratio") or 0.0)
        projected_budget_ratio = current_ratio + (estimated_budget_delta / budget_amount if budget_amount > 0 else 0.0)

        capacity = world_model.get("capacity") if isinstance(world_model.get("capacity"), list) else []
        current_peak_ratio = max([normalize_workload_ratio(item.get("workload_ratio")) for item in capacity], default=0.0)
        project_member_count = max(1, int(world_model.get("member_count") or len(capacity) or 1))
        projected_capacity_peak_ratio = current_peak_ratio + (estimated_capacity_delta / (40.0 * project_member_count))

        task_summary = world_model.get("task_summary") if isinstance(world_model.get("task_summary"), dict) else {}
        blocked_count = int((task_summary.get("by_status") or {}).get("blocked") or 0)
        overdue_count = int(task_summary.get("overdue_count") or 0)
        missing_skill_tag_ids = task_summary.get("uncovered_skill_tag_ids") if isinstance(task_summary.get("uncovered_skill_tag_ids"), list) else []

        charter = world_model.get("charter") if isinstance(world_model.get("charter"), list) else []
        dependency_pressure = "high" if blocked_count > 0 or overdue_count > 0 else ("medium" if not charter else "low")
        projected_risk_score = min(
            1.0,
            0.12
            + projected_budget_ratio * 0.24
            + projected_capacity_peak_ratio * 0.28
            + blocked_count * 0.08
            + overdue_count * 0.05
            + len(missing_skill_tag_ids) * 0.07,
        )

        return {
            "intent": intent.name,
            "operation_codes": operation_codes,
            "estimated_budget_delta_usd": round(estimated_budget_delta, 2),
            "projected_budget_usage_ratio": round(projected_budget_ratio, 4),
            "estimated_capacity_delta_hours": round(estimated_capacity_delta, 2),
            "projected_capacity_peak_ratio": round(projected_capacity_peak_ratio, 4),
            "blocked_task_count": blocked_count,
            "overdue_task_count": overdue_count,
            "dependency_pressure": dependency_pressure,
            "missing_skill_tag_ids": missing_skill_tag_ids[:20],
            "missing_skill_count": len(missing_skill_tag_ids),
            "projected_risk_score": round(projected_risk_score, 4),
        }