"""Candidate rewrite for Builder entries (light STAR optimization)."""

from __future__ import annotations

from copy import deepcopy
import re
from typing import Any

from .state import _dedupe_strings
from ..experience_optimizer import _fact_text_is_preserved, split_description_parts


def _candidate_rewrite(
    agent: Any, profile: dict[str, Any], entry: dict[str, Any], section: str, *, instruction: str | None = None,
    ensure_facts: bool = False,
) -> dict[str, Any]:
    try:
        proposal = agent.expander.expand(
            deepcopy(entry),
            context={
                "job_type": profile.get("job_type"),
                "target_position": profile.get("target_position"),
                "entry_type": section,
                "instruction": instruction,
            },
        )
    except Exception:
        proposal = {}
    original = str(entry.get("description") or "").strip()
    optimized = str(proposal.get("optimized_description") or "").strip() or original
    if ensure_facts:
        # Explicit user-requested revision: still-missing material facts are folded
        # back in (the user asked for them; this is not a silent auto-append).
        missing = _uncovered_material_facts(optimized, original)
        if missing:
            if "• " in optimized:
                optimized = optimized + "".join(f"\n• {fact}" for fact in missing)
            else:
                optimized = f"{optimized.rstrip('。')}；{'；'.join(missing)}。"
    return {
        "optimized_description": optimized,
        "changes": proposal.get("changes") or [],
        "source": proposal.get("source") or "ai_expanded",
        "uncovered_facts": _uncovered_material_facts(optimized, original),
        **({"generation_source": proposal["generation_source"]} if proposal.get("generation_source") else {}),
    }


def _uncovered_material_facts(candidate: str, original: str) -> list[str]:
    """Material user facts the candidate dropped. Reported, never auto-appended."""
    uncovered = [fact for fact in _material_fact_fragments(original) if not _fact_is_preserved(fact, candidate)]
    fragments = split_description_parts(original)
    if len(fragments) >= 2:
        # Structured descriptions (feature lists, tech stack, outcomes) are checked
        # fragment by fragment, so a dropped feature module is reported even when the
        # tech stack survived. Single-sentence descriptions keep the regex-only path.
        ledger = [
            {"id": f"fragment_{index}", "source": "user_form", "field": "description_part", "text": fragment}
            for index, fragment in enumerate(fragments, start=1)
        ]
        uncovered.extend(
            fragment
            for index, fragment in enumerate(fragments, start=1)
            if not _fact_text_is_preserved(f"fragment_{index}", ledger, candidate)
        )
    return _dedupe_strings(uncovered)


def _material_fact_fragments(text: str) -> list[str]:
    facts: list[str] = []
    patterns = (
        r"gpa\s*[:：]?\s*\d+(?:\.\d+)?\s*/\s*\d+(?:\.\d+)?",
        r"(?:排名\s*)?(?:前\s*百分之\s*\d+(?:\.\d+)?|前\s*\d+(?:\.\d+)?\s*%|top\s*\d+(?:\.\d+)?\s*%)",
        r"(?:专业|年级)?(?:排名)?前(?:十|二十|三十|五十)",
        r"(?:获得|荣获|获评|获奖|取得)[^。；;\n]{0,30}(?:奖学金|奖项|荣誉|一等奖|二等奖|三等奖|优秀[^。；;\n]{0,12})",
        r"(?:完成|参与|负责|主导|开发|设计|实现|搭建|推进|开展)[^。；;\n]{0,40}(?:课程项目|课程设计|项目|竞赛|实验室|实践|实训|研究|论文)",
        r"(?:服务|覆盖|面向|参与|支持|管理|处理|完成|交付|提升|降低|增长)[^。；;\n]{0,20}?\d+(?:\.\d+)?\s*(?:%|人|名(?:学生|用户|客户|参与者)?|次|天|周|月|小时|万元|万|千|个|项|篇|场)",
    )
    for pattern in patterns:
        facts.extend(match.group(0).strip(" \t,，") for match in re.finditer(pattern, text, flags=re.IGNORECASE))
    tool_pattern = r"\b(?:python|sql|java|javascript|typescript|vue|react|excel|power\s*bi|tableau|pandas|tensorflow|pytorch|docker|git|linux)\b"
    facts.extend(match.group(0).strip() for match in re.finditer(tool_pattern, text, flags=re.IGNORECASE))
    return _dedupe_strings([fact for fact in facts if fact])


def _fact_is_preserved(fact: str, candidate: str) -> bool:
    normalized_fact = _normalize_material_fact(fact)
    normalized_candidate = _normalize_material_fact(candidate)
    return bool(normalized_fact) and normalized_fact in normalized_candidate


def _normalize_material_fact(value: str) -> str:
    normalized = value.casefold().replace("百分之", "%")
    normalized = re.sub(r"(?:排名|专业排名|年级排名)?前\s*(\d+(?:\.\d+)?)\s*%?", r"top\1", normalized)
    normalized = re.sub(r"top\s*(\d+(?:\.\d+)?)\s*%?", r"top\1", normalized)
    return re.sub(r"[\s,，。；;：:]", "", normalized)