"""Unit tests for the evolution loop — with full mocking."""
from __future__ import annotations

from unittest.mock import MagicMock, patch

from prometheus.application.bootstrap import SyntheticBootstrap
from prometheus.application.evaluator import PromptEvaluator
from prometheus.application.evolution import EvolutionLoop
from prometheus.domain.entities import EvalResult, Prompt, SyntheticExample, Trajectory


class TestEvolutionLoop:
    def test_accepts_improvement(
        self,
        seed_prompt: Prompt,
        synthetic_pool: list[SyntheticExample],
        task_description: str,
        mock_llm_port: MagicMock,
        mock_judge_port: MagicMock,
        mock_proposer_port: MagicMock,
    ) -> None:
        """When the new prompt improves the score, the best candidate is updated."""
        evaluator = PromptEvaluator(mock_llm_port, mock_judge_port)
        bootstrap = MagicMock(spec=SyntheticBootstrap)
        bootstrap.sample_minibatch.return_value = synthetic_pool[:5]

        initial_eval = EvalResult(
            scores=[0.3, 0.4, 0.3, 0.5, 0.2],
            feedbacks=["bad"] * 5,
            trajectories=[
                Trajectory(f"input{i}", f"output{i}", s, "bad", "prompt")
                for i, s in enumerate([0.3, 0.4, 0.3, 0.5, 0.2])
            ],
        )
        old_eval = EvalResult(
            scores=[0.3, 0.4, 0.3, 0.5, 0.2],
            feedbacks=["bad"] * 5,
            trajectories=[
                Trajectory(f"input{i}", f"output{i}", s, "bad", "prompt")
                for i, s in enumerate([0.3, 0.4, 0.3, 0.5, 0.2])
            ],
        )
        new_eval = EvalResult(
            scores=[0.8, 0.9, 0.7, 0.8, 0.9],
            feedbacks=["good"] * 5,
            trajectories=[],
        )
        evaluator.evaluate = MagicMock(side_effect=[initial_eval, old_eval, new_eval])

        loop = EvolutionLoop(
            evaluator=evaluator,
            proposer=mock_proposer_port,
            bootstrap=bootstrap,
            max_iterations=1,
            minibatch_size=5,
        )
        with patch.object(loop, "_log"):
            state = loop.run(seed_prompt, synthetic_pool, task_description)

        assert state.best_candidate is not None
        assert state.best_candidate.best_score > 0

    def test_rejects_regression(
        self,
        seed_prompt: Prompt,
        synthetic_pool: list[SyntheticExample],
        task_description: str,
        mock_llm_port: MagicMock,
        mock_judge_port: MagicMock,
        mock_proposer_port: MagicMock,
    ) -> None:
        """When the new prompt degrades the score, the best candidate stays unchanged."""
        evaluator = PromptEvaluator(mock_llm_port, mock_judge_port)
        bootstrap = MagicMock(spec=SyntheticBootstrap)
        bootstrap.sample_minibatch.return_value = synthetic_pool[:5]

        initial_eval = EvalResult(
            scores=[0.7, 0.8, 0.7, 0.8, 0.9],
            feedbacks=["ok"] * 5,
            trajectories=[
                Trajectory(f"input{i}", f"output{i}", s, "ok", "prompt")
                for i, s in enumerate([0.7, 0.8, 0.7, 0.8, 0.9])
            ],
        )
        old_eval = EvalResult(
            scores=[0.7, 0.8, 0.7, 0.8, 0.9],
            feedbacks=["ok"] * 5,
            trajectories=[
                Trajectory(f"input{i}", f"output{i}", s, "ok", "prompt")
                for i, s in enumerate([0.7, 0.8, 0.7, 0.8, 0.9])
            ],
        )
        new_eval = EvalResult(
            scores=[0.2, 0.1, 0.3, 0.2, 0.1],
            feedbacks=["bad"] * 5,
            trajectories=[],
        )
        evaluator.evaluate = MagicMock(side_effect=[initial_eval, old_eval, new_eval])

        loop = EvolutionLoop(
            evaluator=evaluator,
            proposer=mock_proposer_port,
            bootstrap=bootstrap,
            max_iterations=1,
            minibatch_size=5,
        )
        with patch.object(loop, "_log"):
            state = loop.run(seed_prompt, synthetic_pool, task_description)

        assert state.best_candidate is not None
        assert state.best_candidate.prompt.text == seed_prompt.text

    def test_skips_perfect_scores(
        self,
        seed_prompt: Prompt,
        synthetic_pool: list[SyntheticExample],
        task_description: str,
        mock_llm_port: MagicMock,
        mock_judge_port: MagicMock,
        mock_proposer_port: MagicMock,
    ) -> None:
        """When all scores are perfect, no proposition is made."""
        evaluator = PromptEvaluator(mock_llm_port, mock_judge_port)
        bootstrap = MagicMock(spec=SyntheticBootstrap)
        bootstrap.sample_minibatch.return_value = synthetic_pool[:5]

        perfect_eval = EvalResult(
            scores=[1.0, 1.0, 1.0, 1.0, 1.0],
            feedbacks=["perfect"] * 5,
            trajectories=[
                Trajectory(f"input{i}", f"output{i}", 1.0, "perfect", "prompt")
                for i in range(5)
            ],
        )
        evaluator.evaluate = MagicMock(return_value=perfect_eval)

        loop = EvolutionLoop(
            evaluator=evaluator,
            proposer=mock_proposer_port,
            bootstrap=bootstrap,
            max_iterations=3,
            minibatch_size=5,
        )
        with patch.object(loop, "_log"):
            loop.run(seed_prompt, synthetic_pool, task_description)

        mock_proposer_port.propose.assert_not_called()