"""
Main use case — high-level orchestration.

Entry point for business logic. Coordinates bootstrap → evolution → result.
Contains no technical logic, only orchestration.
"""
from __future__ import annotations

from prometheus.application.bootstrap import SyntheticBootstrap
from prometheus.application.dto import OptimizationConfig, OptimizationResult
from prometheus.application.evaluator import PromptEvaluator
from prometheus.application.evolution import EvolutionLoop
from prometheus.domain.entities import Prompt
from prometheus.domain.ports import ProposerPort


class OptimizePromptUseCase:
    """Single MVP use case.

    Injects dependencies via constructor (dependency injection).
    """

    def __init__(
        self,
        evaluator: PromptEvaluator,
        proposer: ProposerPort,
        bootstrap: SyntheticBootstrap,
    ):
        self._evaluator = evaluator
        self._proposer = proposer
        self._bootstrap = bootstrap

    def execute(self, config: OptimizationConfig) -> OptimizationResult:
        """Full pipeline:
        1. Bootstrap → generate synthetic inputs
        2. Evolution → optimization loop
        3. Return result
        """
        # Phase 0: Bootstrap
        synthetic_pool = self._bootstrap.run(
            task_description=config.task_description,
            n_examples=config.n_synthetic_inputs,
        )

        # Phase 1: Evolution
        loop = EvolutionLoop(
            evaluator=self._evaluator,
            proposer=self._proposer,
            bootstrap=self._bootstrap,
            max_iterations=config.max_iterations,
            minibatch_size=config.minibatch_size,
            perfect_score=config.perfect_score,
            verbose=config.verbose,
            circuit_breaker_threshold=config.circuit_breaker_threshold,
            error_strategy=config.error_strategy,
        )
        seed_prompt = Prompt(text=config.seed_prompt)
        state = loop.run(seed_prompt, synthetic_pool, config.task_description)

        # Phase 2: Result
        initial_score = (
            state.candidates[0].best_score if state.candidates else 0.0
        )
        final_score = state.best_candidate.best_score if state.best_candidate else 0.0

        return OptimizationResult(
            optimized_prompt=(
                state.best_candidate.prompt.text
                if state.best_candidate
                else config.seed_prompt
            ),
            initial_prompt=config.seed_prompt,
            iterations_used=state.iteration,
            total_llm_calls=state.total_llm_calls + 1,  # +1 for bootstrap
            initial_score=initial_score,
            final_score=final_score,
            improvement=final_score - initial_score,
            history=state.history,
        )