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

from unittest.mock import AsyncMock, MagicMock, patch

import pytest

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:
    @pytest.mark.asyncio
    async def test_accepts_improvement(
        self,
        seed_prompt: Prompt,
        synthetic_pool: list[SyntheticExample],
        task_description: str,
        mock_llm_port: AsyncMock,
        mock_judge_port: AsyncMock,
        mock_proposer_port: AsyncMock,
    ) -> 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 = AsyncMock(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 = await loop.run(seed_prompt, synthetic_pool, task_description)

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

    @pytest.mark.asyncio
    async def test_rejects_regression(
        self,
        seed_prompt: Prompt,
        synthetic_pool: list[SyntheticExample],
        task_description: str,
        mock_llm_port: AsyncMock,
        mock_judge_port: AsyncMock,
        mock_proposer_port: AsyncMock,
    ) -> 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 = AsyncMock(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 = await loop.run(seed_prompt, synthetic_pool, task_description)

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

    @pytest.mark.asyncio
    async def test_skips_perfect_scores(
        self,
        seed_prompt: Prompt,
        synthetic_pool: list[SyntheticExample],
        task_description: str,
        mock_llm_port: AsyncMock,
        mock_judge_port: AsyncMock,
        mock_proposer_port: AsyncMock,
    ) -> 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 = AsyncMock(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"):
            await loop.run(seed_prompt, synthetic_pool, task_description)

        mock_proposer_port.propose.assert_not_called()