"""Tests for the per-component reward accumulator + EpisodeStats path.

These guard the "watch individual reward function columns" view
(hackathon FAQ Q17, Q43, Q52). If a future change accidentally regresses
the breakdown so only mean reward is logged, the verifier-hack monitoring
loses one of its main inputs and these tests fail loudly.
"""

from __future__ import annotations

import json

import pytest

from server.environment import CERNCollisionEnvironment
from training.training_script import (
    EpisodeContext,
    EpisodeStats,
    RewardComponentAccumulator,
    _stepwise_reward,
    make_reward_fn,
)


def _make_ctx() -> EpisodeContext:
    return EpisodeContext(
        env=CERNCollisionEnvironment(max_steps=8),
        seed=11,
        scenario="easy_diphoton_160",
        difficulty="easy",
    )


def test_episode_stats_populated_when_out_param_given() -> None:
    stats = EpisodeStats()
    ctx = _make_ctx()
    completion = json.dumps({
        "action_type": "configure_beam",
        "parameters": {"beam_energy": "13TeV"},
    })
    reward = _stepwise_reward(
        completion_text=completion,
        ctx=ctx,
        out_stats=stats,
    )
    # The scalar reward must equal the cumulative we recorded (within fp).
    assert pytest.approx(reward, abs=1e-9) == stats.cumulative_reward
    # We did at least one step.
    assert stats.n_steps >= 1
    # Decomposition arithmetic holds: cumulative = step_shaping + terminal.
    assert pytest.approx(stats.cumulative_reward, abs=1e-9) == (
        stats.step_shaping + stats.terminal_reward
    )
    # The completion was a valid action.
    assert stats.parsed_ok is True
    # The env reported a difficulty for the rollout.
    assert stats.difficulty in {"easy", "medium", "hard"}


def test_episode_stats_marks_unparseable_completion() -> None:
    stats = EpisodeStats()
    ctx = _make_ctx()
    _stepwise_reward(
        completion_text="this is not json at all",
        ctx=ctx,
        out_stats=stats,
    )
    assert stats.parsed_ok is False


def test_accumulator_thread_safe_drain_returns_all_appended() -> None:
    acc = RewardComponentAccumulator()
    for i in range(5):
        s = EpisodeStats(cumulative_reward=float(i), parsed_ok=(i % 2 == 0))
        acc.append(s)
    drained = acc.drain()
    assert len(drained) == 5
    # Drain is destructive: subsequent drain returns empty.
    assert acc.drain() == []
    # Order is preserved.
    assert [s.cumulative_reward for s in drained] == [0.0, 1.0, 2.0, 3.0, 4.0]


def test_accumulator_summarise_basic_rates() -> None:
    drained = [
        EpisodeStats(
            cumulative_reward=2.0, terminal_reward=1.5, step_shaping=0.5,
            discovered=True, correct_mass=True, correct_channel=True,
            parsed_ok=True, n_steps=10,
        ),
        EpisodeStats(
            cumulative_reward=-1.0, terminal_reward=-2.0, step_shaping=1.0,
            discovered=False, correct_mass=False, correct_channel=False,
            parsed_ok=False, n_steps=4,
        ),
    ]
    summary = RewardComponentAccumulator.summarise(drained)
    assert summary["n"] == 2
    assert pytest.approx(summary["mean_cumulative"]) == 0.5
    assert pytest.approx(summary["mean_terminal"]) == -0.25
    assert pytest.approx(summary["mean_step_shaping"]) == 0.75
    assert summary["discovered_rate"] == 0.5
    assert summary["mass_correct_rate"] == 0.5
    assert summary["channel_correct_rate"] == 0.5
    assert summary["parsed_rate"] == 0.5
    assert pytest.approx(summary["mean_n_steps"]) == 7.0


def test_accumulator_summarise_empty_returns_zeros() -> None:
    summary = RewardComponentAccumulator.summarise([])
    assert summary["n"] == 0
    assert summary["mean_cumulative"] == 0.0
    assert summary["discovered_rate"] == 0.0


def test_make_reward_fn_writes_to_accumulator() -> None:
    """The production reward path (make_reward_fn) must populate the
    accumulator one entry per completion when one is provided.
    """
    acc = RewardComponentAccumulator()
    ctx = _make_ctx()
    rf = make_reward_fn(ctx, accumulator=acc)
    rewards = rf(
        prompts=["p1", "p2", "p3"],
        completions=[
            json.dumps({"action_type": "configure_beam"}),
            "not-json",
            json.dumps({"action_type": "select_channel", "parameters": {"channel": "diphoton"}}),
        ],
    )
    assert len(rewards) == 3
    drained = acc.drain()
    assert len(drained) == 3
    # Two of the three completions parsed cleanly.
    parsed_count = sum(1 for s in drained if s.parsed_ok)
    assert parsed_count == 2


def test_make_reward_fn_without_accumulator_is_a_noop_for_stats() -> None:
    """When no accumulator is supplied, no per-completion EpisodeStats
    should be allocated (minor perf win for non-monitored runs).
    """
    ctx = _make_ctx()
    rf = make_reward_fn(ctx, accumulator=None)
    rewards = rf(
        prompts=["p1"],
        completions=[json.dumps({"action_type": "configure_beam"})],
    )
    assert len(rewards) == 1
    # Nothing to assert on accumulator here because we passed None;
    # the implicit contract is "doesn't crash".