"""End-to-end pipeline test with mocked LLM calls."""
from __future__ import annotations

from unittest.mock import MagicMock

from prometheus.application.bootstrap import SyntheticBootstrap
from prometheus.application.dto import OptimizationConfig
from prometheus.application.evaluator import PromptEvaluator
from prometheus.application.use_cases import OptimizePromptUseCase
from prometheus.domain.entities import EvalResult, Prompt, SyntheticExample, Trajectory
from prometheus.domain.ports import JudgePort, LLMPort, ProposerPort


def _make_eval(scores: list[float]) -> EvalResult:
    return EvalResult(
        scores=scores,
        feedbacks=["feedback"] * len(scores),
        trajectories=[
            Trajectory(f"in{i}", f"out{i}", s, "feedback", "prompt")
            for i, s in enumerate(scores)
        ],
    )


class TestFullPipeline:
    def test_pipeline_produces_result(self) -> None:
        """Full pipeline with mocked ports produces an OptimizationResult."""
        mock_llm = MagicMock(spec=LLMPort)
        mock_llm.execute.return_value = "mock response"

        mock_judge = MagicMock(spec=JudgePort)
        # Initial eval (low), then alternating current/new evals per iteration
        eval_sequence = [
            _make_eval([0.3, 0.3, 0.3, 0.3, 0.3]),  # initial seed eval
        ]
        for _ in range(5):  # 5 iterations
            eval_sequence.append(_make_eval([0.4, 0.4, 0.4, 0.4, 0.4]))  # current eval
            eval_sequence.append(_make_eval([0.6, 0.6, 0.6, 0.6, 0.6]))  # new eval (accepted)
        mock_judge.judge_batch.return_value = [(0.5, "ok")] * 5

        mock_proposer = MagicMock(spec=ProposerPort)
        mock_proposer.propose.return_value = Prompt(text="Improved prompt")

        evaluator = PromptEvaluator(mock_llm, mock_judge)
        evaluator.evaluate = MagicMock(side_effect=eval_sequence)

        mock_gen = MagicMock()
        mock_gen.generate_inputs.return_value = [
            SyntheticExample(input_text=f"synth input {i}", id=i) for i in range(20)
        ]
        bootstrap = SyntheticBootstrap(generator=mock_gen, seed=42)

        use_case = OptimizePromptUseCase(
            evaluator=evaluator,
            proposer=mock_proposer,
            bootstrap=bootstrap,
        )

        config = OptimizationConfig(
            seed_prompt="Answer questions.",
            task_description="Answer questions accurately.",
            max_iterations=5,
            n_synthetic_inputs=20,
            minibatch_size=5,
            seed=42,
        )

        result = use_case.execute(config)

        assert result.initial_prompt == "Answer questions."
        assert result.optimized_prompt == "Improved prompt"
        assert result.iterations_used == 5
        assert result.total_llm_calls > 0
        assert result.final_score > result.initial_score