"""
Evox context builder: co-evolves search algorithms and solutions.
"""

import asyncio
import concurrent.futures
import hashlib
import logging
from pathlib import Path
from typing import Any, Dict, List, Union

from skydiscover.config import Config
from skydiscover.context_builder.default.builder import DefaultContextBuilder
from skydiscover.context_builder.evox import formatters as fmt
from skydiscover.context_builder.utils import TemplateManager, prog_attr
from skydiscover.llm.llm_pool import LLMPool
from skydiscover.search.base_database import Program


def run_async_safely(coro):
    """Run an async coroutine, handling nested event loops."""
    try:
        asyncio.get_running_loop()
    except RuntimeError:
        return asyncio.run(coro)
    else:
        with concurrent.futures.ThreadPoolExecutor(max_workers=1) as executor:
            future = executor.submit(asyncio.run, coro)
            return future.result()


logger = logging.getLogger(__name__)

_DEFAULT_TEMPLATES_DIR = str(Path(__file__).parent.parent / "default" / "templates")
_EVOX_TEMPLATES_DIR = str(Path(__file__).parent / "templates")


class EvoxContextBuilder(DefaultContextBuilder):
    """Context builder for search algorithm discovery."""

    def __init__(
        self,
        config: Config,
        use_llm_stats_insight: bool = True,
    ):
        super().__init__(config)
        self.use_llm_stats_insight = use_llm_stats_insight
        self.template_manager = TemplateManager(_DEFAULT_TEMPLATES_DIR, _EVOX_TEMPLATES_DIR)

        summary_llm_config = config.llm.guide_models
        self.summary_llm: LLMPool = LLMPool(summary_llm_config)
        if summary_llm_config:
            logger.info(
                f"Initialized guide LLM inside EvoxContextBuilder: {summary_llm_config[0].name}"
            )

        self._problem_context_summary_cache: Dict[str, str] = {}

        evox_search_sys_prompt_path = (
            Path(__file__).parent.parent.parent
            / "search"
            / "evox"
            / "config"
            / "evox_search_sys_prompt.txt"
        )
        with open(evox_search_sys_prompt_path, "r") as f:
            self.relevant_task_description_message = f.read()

        batch_prompt = self.template_manager.get_template("batch_summary_prompt")
        self._batch_sections = self._parse_template_sections(batch_prompt)

    @staticmethod
    def _parse_template_sections(text: str) -> Dict[str, str]:
        """Parse a template with ===SECTION=== markers into a dict of section_name -> content."""
        sections: Dict[str, str] = {}
        current_section = None
        current_lines: List[str] = []
        for line in text.splitlines():
            stripped = line.strip()
            if stripped.startswith("===") and stripped.endswith("===") and len(stripped) > 6:
                if current_section is not None:
                    sections[current_section] = "\n".join(current_lines).strip()
                current_section = stripped[3:-3]
                current_lines = []
            else:
                current_lines.append(line)
        if current_section is not None:
            sections[current_section] = "\n".join(current_lines).strip()
        return sections

    async def _generate_stats_insight_async(self, stats_text: str) -> str:
        """Generate stats insight via LLM."""
        if not stats_text:
            return ""
        user_content = f"Population Statistics:\n\n{stats_text}"
        result = await self.summary_llm.generate(
            system_message=self.template_manager.get_template("stats_insight_system_message"),
            messages=[{"role": "user", "content": user_content}],
        )
        return result.text

    async def _generate_problem_context_summary_async(
        self, problem_description: str, evaluator_context: str
    ) -> str:
        """Generate problem context summary via LLM (cached)."""
        cache_key = hashlib.sha256(
            f"{problem_description}|||{evaluator_context}".encode("utf-8")
        ).hexdigest()

        if cache_key in self._problem_context_summary_cache:
            return self._problem_context_summary_cache[cache_key]

        problem_context_input = self.template_manager.get_template("problem_template").format(
            problem_description=problem_description,
            evaluator_context=evaluator_context,
        )

        result = await self.summary_llm.generate(
            system_message=self.template_manager.get_template(
                "problem_context_summary_system_message"
            ),
            messages=[{"role": "user", "content": problem_context_input}],
        )

        self._problem_context_summary_cache[cache_key] = result.text
        return result.text

    async def _generate_batch_summaries_async(self, batch_user_message: str) -> str:
        """Generate batch summaries via LLM."""
        result = await self.summary_llm.generate(
            system_message=self._batch_sections["SYSTEM"],
            messages=[{"role": "user", "content": batch_user_message}],
        )
        return result.text

    def build_prompt(
        self,
        current_program: Union[Program, Dict[str, Program]],
        context: Dict[str, Any] = None,
        **kwargs: Any,
    ) -> Dict[str, str]:
        """Build prompt for search algorithm discovery.

        Args:
            current_program: Program or {label: Program} to evolve from.
            context: optional dict with program_metrics, other_context_programs,
                previous_programs, db_stats, search_stats, etc.

        Returns:
            {"system": str, "user": str} ready for LLM.generate().
        """
        context = context or {}

        program_metrics = context.get("program_metrics", {})
        other_context_programs = context.get("other_context_programs", {})
        previous_programs = context.get("previous_programs", [])
        language = self.config.language or "python"

        user_template_key = self.user_template_override or "search_evolution_user_message"
        user_template = self.template_manager.get_template(user_template_key)
        system_message = self._get_system_message()

        search_window_context = fmt.format_search_window_context(context)

        db_stats = context.get("db_stats", {})

        actual_program = (
            list(current_program.values())[0]
            if isinstance(current_program, dict) and current_program
            else current_program
        )
        horizon = 0
        if actual_program:
            metrics = prog_attr(actual_program, "metrics", {})
            horizon = int(metrics.get("search_window_horizon") or 0)

        if horizon > 0 and db_stats:
            db_stats = fmt.filter_db_stats_by_horizon(db_stats, horizon)

        search_stats = context.get("search_stats") or {}

        stats_insight_data = None
        population_state = ""
        if self.use_llm_stats_insight and db_stats:
            stats_text = fmt.format_population_state(db_stats)
            if stats_text:
                stats_insight_data = stats_text
        else:
            population_state = fmt.format_population_state(db_stats) if db_stats else ""

        problem_description = fmt.format_problem_description(
            search_stats.get("problem_description")
        )
        evaluator_context = fmt.format_evaluator_context(search_stats.get("evaluator_context"))

        problem_context_data = self.template_manager.get_template("problem_template").format(
            problem_description=problem_description,
            evaluator_context=evaluator_context,
        )

        all_programs_data = fmt.prepare_search_algorithms_data(other_context_programs)
        batch_summary_data = None
        if all_programs_data:
            per_program_tpl = self._batch_sections["PER_PROGRAM"]
            combined_content_parts = []
            for prog in all_programs_data:
                part = (
                    f"=== PROGRAM {prog['program_num']} (score={prog['combined_score']:.4f}, improvement={prog['improvement']:.4f}) ===\n"
                    + per_program_tpl.format(
                        task_description=self.relevant_task_description_message,
                        solution=prog["solution"],
                        db_stats_text=prog["db_stats_text"],
                    )
                )
                combined_content_parts.append(part)
            batch_summary_data = self._batch_sections["INSTRUCTIONS"].format(
                num_programs=len(all_programs_data),
                combined_content="\n".join(combined_content_parts),
            )

        async def gather_llm_calls():
            tasks = []

            if stats_insight_data:
                tasks.append(
                    ("stats_insight", self._generate_stats_insight_async(stats_insight_data))
                )

            has_meaningful_data = (
                problem_description
                and problem_description.strip()
                and evaluator_context
                and evaluator_context.strip()
                and not (
                    problem_description.startswith("(No ") and evaluator_context.startswith("(No ")
                )
            )
            if has_meaningful_data:
                tasks.append(
                    (
                        "problem_context",
                        self._generate_problem_context_summary_async(
                            problem_description, evaluator_context
                        ),
                    )
                )

            if batch_summary_data:
                tasks.append(
                    ("batch_summaries", self._generate_batch_summaries_async(batch_summary_data))
                )

            if not tasks:
                return {}

            results = await asyncio.gather(*[task for _, task in tasks], return_exceptions=True)

            result_dict = {}
            for (name, _), result in zip(tasks, results):
                if isinstance(result, Exception):
                    result_dict[name] = ""
                else:
                    result_dict[name] = result

            return result_dict

        llm_results = run_async_safely(gather_llm_calls())

        if self.use_llm_stats_insight:
            population_state = llm_results.get("stats_insight", population_state)

        problem_context_summary = llm_results.get("problem_context", "")
        problem_template = (
            problem_context_summary.strip()
            if problem_context_summary
            else (problem_context_data or "")
        )

        summaries_by_num = fmt.parse_batch_summaries(
            llm_results.get("batch_summaries", ""), all_programs_data
        )

        simplification_threshold = self.context_config.suggest_simplification_after_chars
        improvement_areas = fmt.identify_search_improvement_areas(
            actual_program, program_metrics, previous_programs, simplification_threshold
        )
        other_context_program_section = fmt.format_search_algorithms(
            other_context_programs, language, summaries_by_num=summaries_by_num
        )

        current_program_section = fmt.format_current_program(
            current_program,
            language,
            improvement_areas=improvement_areas,
        )

        user_message = user_template.format(
            current_program=current_program_section,
            other_context_programs=other_context_program_section,
            language=language,
            search_window_context=search_window_context,
            population_state=population_state,
            problem_template=problem_template,
            **kwargs,
        )

        return {
            "system": system_message,
            "user": user_message,
        }