"""
Wiederverwendbare Fähigkeiten-Erwartungen für Planungs-KI.

Domänen-Scopes (gleiches Input-/Output-Modell):
- ``progression_stage`` — ein Major Step / stage_spec im Progressionsgraphen
- ``progression_path`` — gesamter Pfad (Ziel + Start/Ziel)
- ``training_section`` — Abschnitt einer Trainingseinheit (Phase G, später)
- ``framework_slot`` — Rahmen-Session-Slot (Phase G, später)

Konsumenten mergen ``skill_weights`` in ``PlanningTargetProfile`` (Retrieval)
oder liefern ``expected_skills`` an Übungs-KI (planning_context).
"""
from __future__ import annotations

from dataclasses import dataclass, field
from typing import Any, Dict, List, Mapping, Optional, Sequence, Tuple

from planning_exercise_profiles import _merge_weight_maps, _normalize_weight_map
from planning_exercise_semantics import PlanningSemanticBrief, resolve_semantic_skill_weights
from planning_exercise_text_signals import _load_skills_for_text_match, _match_skills_in_text

# Scope-Strings — stabil für API/UI und spätere Trainingsplanung
SCOPE_PROGRESSION_STAGE = "progression_stage"
SCOPE_PROGRESSION_PATH = "progression_path"
SCOPE_TRAINING_SECTION = "training_section"
SCOPE_FRAMEWORK_SLOT = "framework_slot"

_LOAD_PROFILE_SKILL_TERMS: Dict[str, Tuple[str, ...]] = {
    "koordination": ("Koordination",),
    "präzision": ("Präzision",),
    "praezision": ("Präzision",),
    "kraft": ("Kraft", "Kime"),
    "geschwindigkeit": ("Geschwindigkeit", "Schnelligkeit"),
    "schnelligkeit": ("Schnelligkeit", "Geschwindigkeit"),
    "timing": ("Timing", "Reaktion"),
    "reaktion": ("Reaktion", "Timing"),
    "distanz": ("Distanz",),
    "raum": ("Raum", "Distanz"),
    "gleichgewicht": ("Gleichgewicht",),
    "kime": ("Kime",),
    "ausdauer": ("Ausdauer",),
    "beweglichkeit": ("Beweglichkeit",),
}


@dataclass
class PlanningSkillExpectationInput:
    scope: str = SCOPE_PROGRESSION_STAGE
    primary_topic: str = ""
    goal_query: str = ""
    start_situation: str = ""
    target_state: str = ""
    stage_learning_goal: str = ""
    roadmap_notes: str = ""
    load_profile: List[str] = field(default_factory=list)
    phase: str = ""
    skill_hints: List[str] = field(default_factory=list)


@dataclass
class PlanningSkillExpectationItem:
    skill_id: int
    skill_name: str
    weight: float
    source: str


@dataclass
class PlanningSkillExpectations:
    scope: str
    skill_weights: Dict[int, float]
    items: List[PlanningSkillExpectationItem]
    sources: List[str]

    def to_api_dict(self) -> Dict[str, Any]:
        return {
            "scope": self.scope,
            "sources": list(self.sources),
            "expected_skills": [
                {
                    "skill_id": it.skill_id,
                    "skill_name": it.skill_name,
                    "weight": round(it.weight, 4),
                    "source": it.source,
                }
                for it in self.items
            ],
        }


def _norm_load_key(s: str) -> str:
    return (s or "").strip().lower().replace("ä", "ae").replace("ö", "oe").replace("ü", "ue")


def _text_blob(inp: PlanningSkillExpectationInput) -> str:
    parts = [
        inp.primary_topic,
        inp.goal_query,
        inp.start_situation,
        inp.target_state,
        inp.stage_learning_goal,
        inp.roadmap_notes,
        inp.phase,
        " ".join(inp.load_profile or []),
        " ".join(inp.skill_hints or []),
    ]
    return "\n".join(p for p in parts if (p or "").strip()).lower()


def _resolve_skills_by_name_terms(
    cur,
    terms: Sequence[str],
    *,
    weight: float = 0.9,
    source: str,
    weights: Dict[int, float],
    items: Dict[int, PlanningSkillExpectationItem],
) -> bool:
    found = False
    for name in terms:
        if not name:
            continue
        cur.execute(
            """
            SELECT id, name FROM skills
            WHERE (status IS NULL OR status = 'active')
              AND LOWER(name) LIKE %s
            ORDER BY CASE WHEN LOWER(name) = %s THEN 0 WHEN LOWER(name) LIKE %s THEN 1 ELSE 2 END,
                     LENGTH(name) ASC
            LIMIT 1
            """,
            (f"%{name.lower()}%", name.lower(), f"{name.lower()}%"),
        )
        row = cur.fetchone()
        if not row:
            continue
        sid = int(row["id"])
        w = max(weights.get(sid, 0.0), weight)
        weights[sid] = w
        items[sid] = PlanningSkillExpectationItem(
            skill_id=sid,
            skill_name=str(row.get("name") or "").strip(),
            weight=w,
            source=source,
        )
        found = True
    return found


def _merge_weights_into(
    weights: Dict[int, float],
    items: Dict[int, PlanningSkillExpectationItem],
    incoming: Dict[int, float],
    *,
    source: str,
    skill_rows: Sequence[Tuple[int, str, int]],
) -> None:
    name_by_id = {sid: name for sid, name, _ in skill_rows}
    for sid, w in incoming.items():
        if w <= 0:
            continue
        merged = max(weights.get(sid, 0.0), float(w))
        weights[sid] = merged
        items[sid] = PlanningSkillExpectationItem(
            skill_id=sid,
            skill_name=name_by_id.get(sid, f"Skill #{sid}"),
            weight=merged,
            source=source,
        )


def build_planning_skill_expectations(
    cur,
    inp: PlanningSkillExpectationInput,
    *,
    semantic_brief: Optional[PlanningSemanticBrief] = None,
) -> PlanningSkillExpectations:
    """Deterministisch: Thema + Text + load_profile → skill_weights."""
    weights: Dict[int, float] = {}
    items: Dict[int, PlanningSkillExpectationItem] = {}
    sources: List[str] = []

    skill_rows = _load_skills_for_text_match(cur)

    if semantic_brief is not None:
        topic_weights = resolve_semantic_skill_weights(cur, semantic_brief)
        if topic_weights:
            sources.append("semantic_topic")
            _merge_weights_into(
                weights, items, topic_weights, source="semantic_topic", skill_rows=skill_rows
            )

    blob = _text_blob(inp)
    if blob:
        text_weights = _match_skills_in_text(blob, skill_rows)
        if text_weights:
            sources.append("text_match")
            _merge_weights_into(
                weights, items, text_weights, source="text_match", skill_rows=skill_rows
            )

    load_found = False
    for raw in inp.load_profile or []:
        key = _norm_load_key(str(raw))
        terms = _LOAD_PROFILE_SKILL_TERMS.get(key, (str(raw).strip(),) if key else ())
        if _resolve_skills_by_name_terms(
            cur, terms, weight=0.88, source="load_profile", weights=weights, items=items
        ):
            load_found = True
    if load_found and "load_profile" not in sources:
        sources.append("load_profile")

    normalized = _normalize_weight_map(weights)
    out_items = sorted(
        [
            PlanningSkillExpectationItem(
                skill_id=sid,
                skill_name=items[sid].skill_name,
                weight=normalized[sid],
                source=items[sid].source,
            )
            for sid in normalized
        ],
        key=lambda x: (-x.weight, x.skill_name.lower()),
    )[:8]

    return PlanningSkillExpectations(
        scope=inp.scope or SCOPE_PROGRESSION_STAGE,
        skill_weights=normalized,
        items=out_items,
        sources=sources,
    )


def expectation_input_from_progression_stage(
    *,
    goal_query: str,
    goal_analysis: Optional[Mapping[str, Any]] = None,
    resolved_structured: Optional[Mapping[str, Any]] = None,
    stage_spec: Optional[Mapping[str, Any]] = None,
    semantic_brief_summary: Optional[Mapping[str, Any]] = None,
    major_step: Optional[Mapping[str, Any]] = None,
) -> PlanningSkillExpectationInput:
    """Roadmap-Stufe → PlanningSkillExpectationInput (Progressionsgraph)."""
    ga = dict(goal_analysis or {})
    rs = dict(resolved_structured or {})
    spec = dict(stage_spec or {})
    brief = dict(semantic_brief_summary or {})
    major = dict(major_step or {})

    skill_hints: List[str] = []
    for item in (brief.get("must_phrases") or [])[:4]:
        s = str(item or "").strip()
        if s:
            skill_hints.append(s)

    return PlanningSkillExpectationInput(
        scope=SCOPE_PROGRESSION_STAGE,
        primary_topic=str(ga.get("primary_topic") or brief.get("primary_topic") or "").strip(),
        goal_query=(goal_query or "").strip(),
        start_situation=str(rs.get("start_situation") or ga.get("start_assumption") or "").strip(),
        target_state=str(rs.get("target_state") or ga.get("target_state") or "").strip(),
        stage_learning_goal=str(
            spec.get("learning_goal") or major.get("learning_goal") or ""
        ).strip(),
        roadmap_notes=str(rs.get("roadmap_notes") or "").strip(),
        load_profile=[str(x).strip() for x in (spec.get("load_profile") or []) if str(x).strip()],
        phase=str(spec.get("phase") or major.get("phase") or "").strip(),
        skill_hints=skill_hints,
    )


def expectation_input_from_progression_path(
    *,
    goal_query: str,
    goal_analysis: Optional[Mapping[str, Any]] = None,
    resolved_structured: Optional[Mapping[str, Any]] = None,
    semantic_brief_summary: Optional[Mapping[str, Any]] = None,
) -> PlanningSkillExpectationInput:
    """Gesamtpfad-Kontext (z. B. einmaliges Pfad-Profil)."""
    ga = dict(goal_analysis or {})
    rs = dict(resolved_structured or {})
    brief = dict(semantic_brief_summary or {})
    skill_hints: List[str] = []
    for item in (brief.get("must_phrases") or [])[:4]:
        s = str(item or "").strip()
        if s:
            skill_hints.append(s)
    return PlanningSkillExpectationInput(
        scope=SCOPE_PROGRESSION_PATH,
        primary_topic=str(ga.get("primary_topic") or brief.get("primary_topic") or "").strip(),
        goal_query=(goal_query or "").strip(),
        start_situation=str(rs.get("start_situation") or ga.get("start_assumption") or "").strip(),
        target_state=str(rs.get("target_state") or ga.get("target_state") or "").strip(),
        roadmap_notes=str(rs.get("roadmap_notes") or "").strip(),
        skill_hints=skill_hints,
    )


def apply_expectations_to_target(target, expectations: PlanningSkillExpectations):
    """Merge Erwartungs-Skills in PlanningTargetProfile (Retrieval)."""
    from planning_exercise_semantics import enrich_target_with_semantic_expectations

    if not expectations.skill_weights:
        return target
    return enrich_target_with_semantic_expectations(
        target, skill_weights=dict(expectations.skill_weights)
    )


def merge_expectation_skill_weights(
    base: Dict[int, float],
    extra: Dict[int, float],
    *,
    extra_scale: float = 1.0,
) -> Dict[int, float]:
    merged = _merge_weight_maps(base, extra, scale=extra_scale)
    return _normalize_weight_map(merged)


__all__ = [
    "SCOPE_FRAMEWORK_SLOT",
    "SCOPE_PROGRESSION_PATH",
    "SCOPE_PROGRESSION_STAGE",
    "SCOPE_TRAINING_SECTION",
    "PlanningSkillExpectationInput",
    "PlanningSkillExpectationItem",
    "PlanningSkillExpectations",
    "apply_expectations_to_target",
    "build_planning_skill_expectations",
    "expectation_input_from_progression_path",
    "expectation_input_from_progression_stage",
    "merge_expectation_skill_weights",
]