"""Benefit engine."""

from __future__ import annotations

import pytest

from tokencalc.benefit_model import (
    calculate_acw_summarization_benefit,
    calculate_email_ai_benefit,
    calculate_total_benefit,
)
from tokencalc.defaults import CTM_DEFAULT_FEATURE_SCOPES, CTM_DEFAULT_SITES
from tokencalc.inputs import WORKING_SECONDS_PER_YEAR, FeatureScope, SiteInput

ALL_SITES = [s.site_name for s in CTM_DEFAULT_SITES]


def _small_site() -> SiteInput:
    return SiteInput(
        "Small", "US", agents=10, supervisors=1,
        voice_volume_monthly=10_000, email_volume_monthly=1_000,
        chat_volume_monthly=0, sms_volume_monthly=0,
        voice_aht_seconds=300, email_aht_seconds=600,
        chat_aht_seconds=480, voice_acw_seconds=60,
        fully_loaded_agent_cost_annual=74_880,  # → $0.01/second exactly
        fully_loaded_supervisor_cost_annual=95_000,
    )


def test_acw_benefit_hand_check():
    """10,000 calls × 12 × 70% eligible × 60s ACW × 40% reduction ×
    50% Y1 realization × $0.01/s = $10,080."""
    site = _small_site()
    assert site.agent_cost_per_second == pytest.approx(0.01)
    df = calculate_acw_summarization_benefit(
        [site], FeatureScope("Agent Copilot", ["Small"]), "realistic", year=1,
    )
    expected = 10_000 * 12 * 0.70 * 60 * 0.40 * 0.50 * 0.01
    assert df["annual_value"].sum() == pytest.approx(expected)


def test_email_benefit_split():
    site = _small_site()
    df = calculate_email_ai_benefit(
        [site], FeatureScope("Email AI (Auto-Respond)", ["Small"]),
        "realistic", year=1,
    )
    lines = set(df["benefit_line"])
    assert lines == {
        "Email Auto-Respond (displaced handling)",
        "Email Auto-Suggest (drafting time)",
    }
    # auto-respond: 1,000×12 × 20% × 600s × 50% × $0.01 = $7,200
    respond = df[df["benefit_line"].str.contains("Respond")]["annual_value"].sum()
    assert respond == pytest.approx(7_200)


def test_scenarios_produce_distinct_benefits():
    totals = {
        name: calculate_total_benefit(
            CTM_DEFAULT_SITES, CTM_DEFAULT_FEATURE_SCOPES, name, year=2
        )["annual_value"].sum()
        for name in ("floor", "realistic", "stretch")
    }
    assert totals["floor"] < totals["realistic"] < totals["stretch"]


def test_claim_exceeds_realistic():
    realistic = calculate_total_benefit(
        CTM_DEFAULT_SITES, CTM_DEFAULT_FEATURE_SCOPES, "realistic", year=1,
        params="realistic",
    )["annual_value"].sum()
    claim = calculate_total_benefit(
        CTM_DEFAULT_SITES, CTM_DEFAULT_FEATURE_SCOPES, "realistic", year=1,
        params="claim",
    )["annual_value"].sum()
    assert claim > realistic


def test_benefits_ramp_by_year():
    by_year = [
        calculate_total_benefit(
            CTM_DEFAULT_SITES, CTM_DEFAULT_FEATURE_SCOPES, "realistic", year=y
        )["annual_value"].sum()
        for y in (1, 2, 3)
    ]
    assert by_year[0] < by_year[1] < by_year[2]


def test_zero_volume_site_is_safe():
    site = SiteInput(
        "Empty", "US", agents=0, supervisors=0,
        voice_volume_monthly=0, email_volume_monthly=0,
        chat_volume_monthly=0, sms_volume_monthly=0,
        voice_aht_seconds=300, email_aht_seconds=600,
        chat_aht_seconds=480, voice_acw_seconds=0,
        fully_loaded_agent_cost_annual=0,
        fully_loaded_supervisor_cost_annual=0,
    )
    df = calculate_total_benefit(
        [site], [FeatureScope("Agent Copilot", ["Empty"])], "realistic", year=1,
    )
    assert df["annual_value"].sum() == 0


def test_working_seconds_constant():
    assert WORKING_SECONDS_PER_YEAR == 2_080 * 3_600