from __future__ import annotations

from decimal import Decimal, ROUND_HALF_UP

from .models import EconomicsSnapshot, PricingModel

TWOPLACES = Decimal("0.01")
OVERAGE_RATE = Decimal("0.002")  # EUR per token above allowance (observatory estimate)


def _money(value: Decimal) -> Decimal:
    return value.quantize(TWOPLACES, rounding=ROUND_HALF_UP)


def _simulate_model(
    model: PricingModel,
    snapshot: EconomicsSnapshot,
    ai_cost_per_member: Decimal,
    included_tokens: int = 100_000,
    actual_tokens: int = 120_000,
) -> dict:
    members = snapshot.active_members or 1
    subscription_revenue = model.access_fee_amount * members
    overage_revenue = Decimal("0")
    if model.model_type == "hybrid_subscription_usage" and actual_tokens > included_tokens:
        overage_tokens = actual_tokens - included_tokens
        overage_revenue = OVERAGE_RATE * overage_tokens * members

    gross_revenue = subscription_revenue + overage_revenue
    platform_cost = snapshot.monthly_total_platform_cost + (ai_cost_per_member * members)
    margin = gross_revenue - platform_cost
    margin_pct = (margin / gross_revenue * Decimal("100")) if gross_revenue else Decimal("0")

    return {
        "model_id": model.id,
        "model_name": model.name,
        "model_type": model.model_type,
        "status": model.status,
        "access_fee_eur": model.access_fee_amount,
        "projected_revenue_eur": _money(gross_revenue),
        "projected_overage_eur": _money(overage_revenue),
        "projected_platform_cost_eur": _money(platform_cost),
        "projected_margin_eur": _money(margin),
        "projected_margin_pct": _money(margin_pct),
        "assumed_tokens_per_member": actual_tokens,
        "included_tokens": included_tokens if model.model_type != "flat_subscription" else None,
    }


def build_pricing_simulations(
    snapshot: EconomicsSnapshot,
    models: list[PricingModel],
    ai_cost_per_member: Decimal,
) -> dict:
    scenarios = [
        _simulate_model(model, snapshot, ai_cost_per_member)
        for model in models
        if model.status in ("active", "candidate")
    ]
    active = next((item for item in scenarios if item["status"] == "active"), scenarios[0])
    best_margin = max(scenarios, key=lambda item: item["projected_margin_eur"])

    return {
        "period": snapshot.period,
        "currency": snapshot.currency,
        "active_scenario_id": active["model_id"],
        "best_margin_scenario_id": best_margin["model_id"],
        "scenarios": scenarios,
        "notes": "Projections hold member count and infrastructure cost constant; overage uses observatory token estimate.",
    }