from proteus.providers import FakeProvider
from proteus.agents import VanillaAgent
from proteus.runtime.session import SessionRunner
from proteus.runtime.trace import SessionTrace


def _agent(responses):
    return VanillaAgent(FakeProvider(responses=responses))


def test_optimal_player_survives_and_scores_full_motive_reading():
    # At the EASY handover the motive-congruent action is "up". An agent that
    # always plays "up"... will move up the open column away from the predator.
    # Whatever the realized states, the runner scores each turn against the
    # live optimal answer key. Here we script an agent that always says "up".
    agent = _agent(["ACTION: up"])  # FakeProvider repeats the last response
    runner = SessionRunner(
        "predator_evade", agent, seed=42, play_turns=10, use_probe=False,
    )
    trace = runner.run()
    assert isinstance(trace, SessionTrace)
    assert trace.scenario == "predator_evade"
    assert trace.cut_frames  # Cut history captured
    assert len(trace.turns) >= 1
    # First played turn: the handover, where motive=up, habit=left (diagnostic).
    first = trace.turns[0]
    assert first.is_diagnostic is True
    assert first.motive_action == "up"
    assert first.habit_action == "left"
    assert first.action == "up"
    assert first.was_congruent is True
    assert "motive_reading_accuracy" in trace.metrics


def test_habit_player_diverges_on_first_diagnostic_turn():
    # An agent that always plays "left" follows inertia into the dead-end.
    agent = _agent(["ACTION: left"])
    runner = SessionRunner(
        "predator_evade", agent, seed=42, play_turns=10, use_probe=False,
    )
    trace = runner.run()
    first = trace.turns[0]
    assert first.action == "left"
    assert first.was_congruent is False
    assert trace.metrics["first_divergence_turn"] == 1.0


def test_probe_recorded_when_enabled():
    agent = _agent(["the predator is to my east; I should go up\nACTION: up"])
    runner = SessionRunner(
        "predator_evade", agent, seed=42, play_turns=3, use_probe=True,
    )
    trace = runner.run()
    assert trace.turns[0].probe_q  # a question was asked
    assert trace.turns[0].probe_a  # an answer was recorded


def test_session_is_deterministic_for_same_inputs():
    t1 = SessionRunner("predator_evade", _agent(["ACTION: up"]), seed=42,
                       play_turns=5, use_probe=False).run()
    t2 = SessionRunner("predator_evade", _agent(["ACTION: up"]), seed=42,
                       play_turns=5, use_probe=False).run()
    # Same scripted agent + same seed -> identical realized trajectory.
    assert [t.focal_pos for t in t1.turns] == [t.focal_pos for t in t2.turns]
    assert t1.metrics == t2.metrics


def test_short_budget_yields_survived_outcome():
    # With a tiny budget the step count is exhausted (without capture) right
    # after the played turns, so the engine fires `survived`.
    agent = _agent(["ACTION: up"])
    trace = SessionRunner(
        "predator_evade", agent, seed=42, play_turns=1, use_probe=False,
    ).run()
    assert trace.outcome == "survived"
    assert trace.turns[-1].reward == 50.0  # _REWARD_SURVIVED


def test_eliminated_outcome_is_explicit_and_terminal():
    # The habit player ("left") walks into the dead-end and is caught.
    agent = _agent(["ACTION: left"])
    trace = SessionRunner(
        "predator_evade", agent, seed=42, play_turns=15, use_probe=False,
    ).run()
    assert trace.outcome == "eliminated"
    assert len(trace.turns) <= 15           # stopped at/under budget
    assert trace.turns[-1].reward == -50.0  # _REWARD_CAPTURED


def test_cut_frames_count_matches_cut_length_plus_one():
    agent = _agent(["ACTION: up"])
    trace = SessionRunner(
        "predator_evade", agent, seed=42, play_turns=5, use_probe=False,
    ).run()
    # EASY cut_length is 2 -> initial frame + 2 step frames = 3.
    assert len(trace.cut_frames) == 3