"""
Planungs-KI Phase E: Pfad-QA — Lücken erkennen, Brücken vorschlagen, LLM-Prüfung.
"""
from __future__ import annotations

import json
import logging
import re
from typing import Any, Callable, Dict, List, Mapping, Optional, Sequence, Set, Tuple

from ai_prompt_runtime import AiPromptUnavailableError, load_and_render_ai_prompt
from exercise_ai import strip_html_to_plain
from openrouter_chat import (
    effective_openrouter_model_for_prompt_row,
    normalize_openrouter_env,
    openrouter_chat_completion,
)

from planning_exercise_semantics import (
    PlanningSemanticBrief,
    brief_to_summary_dict,
    score_exercise_semantic_relevance,
    step_phase_for_index,
)

_logger = logging.getLogger("shinkan.planning_exercise_path_qa")

_GAP_SKILL_THRESHOLD = 0.10
_GAP_SEMANTIC_THRESHOLD = 0.28
_LARGE_GAP_SCORE = 0.52
_MAX_BRIDGE_INSERTS = 4


def _extract_json_object(text: str) -> Dict[str, Any]:
    s = (text or "").strip()
    if s.startswith("```"):
        s = re.sub(r"^```[a-zA-Z0-9]*\s*", "", s)
        if s.endswith("```"):
            s = s[:-3].strip()
    start = s.find("{")
    end = s.rfind("}")
    if start < 0 or end <= start:
        raise ValueError("Kein JSON-Objekt in LLM-Antwort")
    obj = json.loads(s[start : end + 1])
    if not isinstance(obj, dict):
        raise ValueError("LLM-Antwort ist kein JSON-Objekt")
    return obj


def _skill_jaccard(a: Set[int], b: Set[int]) -> float:
    if not a or not b:
        return 0.0
    inter = len(a & b)
    union = len(a | b)
    return inter / union if union else 0.0


def _load_exercise_skill_ids(cur, exercise_id: int) -> Set[int]:
    cur.execute(
        "SELECT skill_id FROM exercise_skills WHERE exercise_id = %s",
        (int(exercise_id),),
    )
    return {int(r["skill_id"]) for r in cur.fetchall() if r.get("skill_id") is not None}


def _load_exercise_text_bundle(cur, exercise_id: int) -> Dict[str, Any]:
    cur.execute(
        "SELECT id, title, summary, goal FROM exercises WHERE id = %s",
        (int(exercise_id),),
    )
    row = cur.fetchone()
    if not row:
        return {"title": "", "summary": "", "goal": "", "variant_names": []}
    cur.execute(
        """
        SELECT variant_name FROM exercise_variants
        WHERE exercise_id = %s
        ORDER BY sequence_order ASC NULLS LAST, id ASC
        LIMIT 8
        """,
        (int(exercise_id),),
    )
    variants = [str(r.get("variant_name") or "") for r in cur.fetchall()]
    return {
        "title": str(row.get("title") or ""),
        "summary": str(row.get("summary") or ""),
        "goal": str(row.get("goal") or ""),
        "variant_names": variants,
    }


def measure_step_transition_gap(
    cur,
    step_a: Mapping[str, Any],
    step_b: Mapping[str, Any],
    *,
    brief: PlanningSemanticBrief,
    segment_index: int,
    total_segments: int,
) -> Dict[str, Any]:
    eid_a = int(step_a["exercise_id"])
    eid_b = int(step_b["exercise_id"])
    skills_a = _load_exercise_skill_ids(cur, eid_a)
    skills_b = _load_exercise_skill_ids(cur, eid_b)
    skill_sim = _skill_jaccard(skills_a, skills_b)

    bundle_b = _load_exercise_text_bundle(cur, eid_b)
    mid_phase = step_phase_for_index(brief, segment_index + 1, total_segments + 1)
    sem_b, sem_reasons = score_exercise_semantic_relevance(
        title=bundle_b["title"],
        summary=bundle_b["summary"],
        goal=bundle_b["goal"],
        variant_names=bundle_b["variant_names"],
        brief=brief,
        step_phase=mid_phase,
    )

    gap_score = 0.0
    if skill_sim < _GAP_SKILL_THRESHOLD:
        gap_score += 0.45 * (1.0 - skill_sim / max(_GAP_SKILL_THRESHOLD, 0.01))
    if sem_b < _GAP_SEMANTIC_THRESHOLD:
        gap_score += 0.35 * (1.0 - sem_b / max(_GAP_SEMANTIC_THRESHOLD, 0.01))
    if brief.semantic_strength >= 0.5 and sem_b < 0.15:
        gap_score += 0.2

    gap_score = min(1.0, round(gap_score, 4))
    is_large = gap_score >= _LARGE_GAP_SCORE

    return {
        "from_exercise_id": eid_a,
        "to_exercise_id": eid_b,
        "from_title": step_a.get("title"),
        "to_title": step_b.get("title"),
        "skill_similarity": round(skill_sim, 4),
        "semantic_score_to": sem_b,
        "gap_score": gap_score,
        "is_large_gap": is_large,
        "expected_phase": mid_phase,
        "reasons": sem_reasons,
    }


def detect_path_gaps(
    cur,
    steps: Sequence[Mapping[str, Any]],
    *,
    brief: PlanningSemanticBrief,
) -> List[Dict[str, Any]]:
    if len(steps) < 2:
        return []
    gaps: List[Dict[str, Any]] = []
    total_segments = len(steps) - 1
    for i in range(total_segments):
        gap = measure_step_transition_gap(
            cur,
            steps[i],
            steps[i + 1],
            brief=brief,
            segment_index=i,
            total_segments=total_segments,
        )
        if gap.get("is_large_gap"):
            gaps.append(gap)
    return gaps


def _pick_bridge_hit(
    hits: Sequence[Mapping[str, Any]],
    *,
    used_ids: Set[int],
    step_a_id: int,
    step_b_id: int,
) -> Optional[Dict[str, Any]]:
    for hit in hits:
        eid = int(hit["id"])
        if eid in used_ids or eid in {step_a_id, step_b_id}:
            continue
        return dict(hit)
    return None


def insert_bridge_exercises(
    cur,
    steps: List[Dict[str, Any]],
    gaps: Sequence[Mapping[str, Any]],
    *,
    brief: PlanningSemanticBrief,
    bridge_search_fn: Callable[..., List[Dict[str, Any]]],
    max_inserts: int = _MAX_BRIDGE_INSERTS,
) -> Tuple[List[Dict[str, Any]], List[Dict[str, Any]], List[Dict[str, Any]]]:
    """
    Fügt zwischen großen Lücken Brücken-Übungen ein.
    bridge_search_fn(from_step, to_step, gap) -> hits
    Returns: (steps, bridge_inserts, unfilled_gaps)
    """
    if not gaps:
        return steps, [], []

    used_ids = {int(s["exercise_id"]) for s in steps if s.get("exercise_id") is not None}
    inserts: List[Dict[str, Any]] = []
    unfilled: List[Dict[str, Any]] = []
    out = list(steps)

    gap_by_pair = {
        (int(g["from_exercise_id"]), int(g["to_exercise_id"])): g for g in gaps
    }

    i = 0
    while i < len(out) - 1 and len(inserts) < max_inserts:
        a = out[i]
        b = out[i + 1]
        if a.get("exercise_id") is None or b.get("exercise_id") is None:
            i += 1
            continue
        key = (int(a["exercise_id"]), int(b["exercise_id"]))
        gap = gap_by_pair.get(key)
        if not gap:
            i += 1
            continue

        hits = bridge_search_fn(a, b, gap)
        bridge_hit = _pick_bridge_hit(
            hits,
            used_ids=used_ids,
            step_a_id=int(a["exercise_id"]),
            step_b_id=int(b["exercise_id"]),
        )
        if not bridge_hit:
            unfilled.append(gap)
            i += 1
            continue

        bridge_step = {
            "exercise_id": int(bridge_hit["id"]),
            "variant_id": bridge_hit.get("suggested_variant_id"),
            "title": bridge_hit.get("title"),
            "summary": bridge_hit.get("summary"),
            "score": bridge_hit.get("score"),
            "reasons": list(bridge_hit.get("reasons") or []) + ["Brücken-Übung (Lückenfüller)"],
            "variants": bridge_hit.get("variants") or [],
            "suggested_variant_id": bridge_hit.get("suggested_variant_id"),
            "suggested_variant_name": bridge_hit.get("suggested_variant_name"),
            "is_bridge": True,
            "bridge_for_gap": {
                "from_exercise_id": int(a["exercise_id"]),
                "to_exercise_id": int(b["exercise_id"]),
                "gap_score": gap.get("gap_score"),
            },
        }
        out.insert(i + 1, bridge_step)
        used_ids.add(int(bridge_step["exercise_id"]))
        inserts.append(
            {
                "inserted_after_index": i,
                "bridge_exercise_id": int(bridge_step["exercise_id"]),
                "bridge_title": bridge_step.get("title"),
                "gap": gap,
            }
        )
        i += 2

    return out, inserts, unfilled


def try_llm_qa_progression_path(
    cur,
    *,
    goal_query: str,
    brief: PlanningSemanticBrief,
    steps: Sequence[Mapping[str, Any]],
    gaps: Sequence[Mapping[str, Any]],
    bridge_inserts: Sequence[Mapping[str, Any]],
) -> Tuple[Optional[Dict[str, Any]], bool]:
    api_key, _ = normalize_openrouter_env()
    if not api_key or len(steps) < 2:
        return None, False

    step_payload = []
    for idx, step in enumerate(steps):
        if step.get("is_ai_proposal") or step.get("exercise_id") is None:
            step_payload.append(
                {
                    "index": idx + 1,
                    "proposal_key": step.get("proposal_key"),
                    "title": step.get("title"),
                    "summary": strip_html_to_plain(step.get("summary"), max_len=400),
                    "is_bridge": bool(step.get("is_bridge")),
                    "is_ai_proposal": True,
                    "reasons": list(step.get("reasons") or [])[:3],
                }
            )
            continue
        bundle = _load_exercise_text_bundle(cur, int(step["exercise_id"]))
        step_payload.append(
            {
                "index": idx + 1,
                "exercise_id": int(step["exercise_id"]),
                "proposal_key": step.get("proposal_key"),
                "title": step.get("title") or bundle["title"],
                "goal": strip_html_to_plain(bundle["goal"], max_len=400),
                "is_bridge": bool(step.get("is_bridge")),
                "is_ai_proposal": False,
                "reasons": list(step.get("reasons") or [])[:3],
            }
        )

    variables = {
        "goal_query": goal_query or "",
        "semantic_brief_json": json.dumps(brief_to_summary_dict(brief), ensure_ascii=False),
        "steps_json": json.dumps(step_payload, ensure_ascii=False),
        "gaps_json": json.dumps(list(gaps), ensure_ascii=False),
        "bridge_inserts_json": json.dumps(list(bridge_inserts), ensure_ascii=False),
    }

    try:
        prow, rendered = load_and_render_ai_prompt(cur, "planning_exercise_path_qa", variables)
        model = effective_openrouter_model_for_prompt_row(prow)
        raw = openrouter_chat_completion(api_key=api_key, model=model, user_content=rendered.text)
        obj = _extract_json_object(raw)
        return obj, True
    except AiPromptUnavailableError:
        return None, False
    except Exception as exc:
        _logger.warning("Pfad-QA-LLM fehlgeschlagen: %s", exc)
        return None, False


def apply_llm_path_reorder(
    steps: List[Dict[str, Any]],
    llm_qa: Mapping[str, Any],
) -> Tuple[List[Dict[str, Any]], bool, List[str]]:
    """
    Wendet LLM-Neuordnung an (ordered_step_indices = Permutation der aktuellen Indizes).
    """
    raw = llm_qa.get("ordered_step_indices")
    if not isinstance(raw, list) or len(raw) != len(steps):
        return steps, False, []

    try:
        indices = [int(x) for x in raw]
    except (TypeError, ValueError):
        return steps, False, ["Neuordnung: ungültige Indizes"]

    if sorted(indices) != list(range(len(steps))):
        return steps, False, ["Neuordnung: keine gültige Permutation — ignoriert"]

    if indices == list(range(len(steps))):
        return steps, False, []

    notes = [str(n) for n in (llm_qa.get("sequence_notes") or []) if str(n).strip()]
    return [steps[i] for i in indices], True, notes


def build_path_qa_summary(
    *,
    gaps: Sequence[Mapping[str, Any]],
    bridge_inserts: Sequence[Mapping[str, Any]],
    ai_proposals: Sequence[Mapping[str, Any]],
    llm_qa: Optional[Mapping[str, Any]],
    llm_applied: bool,
    reorder_applied: bool = False,
    reorder_notes: Optional[Sequence[str]] = None,
) -> Dict[str, Any]:
    summary: Dict[str, Any] = {
        "gap_count": len(gaps),
        "large_gaps": list(gaps),
        "bridge_insert_count": len(bridge_inserts),
        "bridge_inserts": list(bridge_inserts),
        "ai_proposal_count": len(ai_proposals),
        "ai_proposals": list(ai_proposals),
        "llm_qa_applied": llm_applied,
        "reorder_applied": reorder_applied,
        "reorder_notes": list(reorder_notes or []),
    }
    if llm_qa:
        summary["overall_ok"] = bool(llm_qa.get("overall_ok", True))
        summary["quality_score"] = llm_qa.get("quality_score")
        summary["issues"] = list(llm_qa.get("issues") or [])
        summary["sequence_notes"] = list(llm_qa.get("sequence_notes") or [])
        summary["topic_coverage"] = llm_qa.get("topic_coverage")
        summary["recommendations"] = list(llm_qa.get("recommendations") or [])
    else:
        summary["overall_ok"] = len(gaps) == 0
        summary["issues"] = [
            f"Lücke zwischen „{g.get('from_title')}“ und „{g.get('to_title')}“ (Score {g.get('gap_score')})"
            for g in gaps
        ] if gaps else []
    return summary


__all__ = [
    "apply_llm_path_reorder",
    "build_path_qa_summary",
    "detect_path_gaps",
    "insert_bridge_exercises",
    "measure_step_transition_gap",
    "try_llm_qa_progression_path",
]