"""
In-Line Lunge rubric scorer — pure function, no model calls.

FMS In-Line Lunge Criteria (bilateral):
- Score 3: dowel contacts maintained, no torso movement, knee touches behind heel.
- Score 2: movement completed with compensation (trunk lean, loss of balance).
- Score 1: loss of balance, inability to maintain foot contact or posture.
- Score 0: PAIN — never auto-scored.
"""
from __future__ import annotations

from formscout.types import BiomechFeatures, ScoreResult


def score_inline_lunge(features: BiomechFeatures) -> ScoreResult:
    """Pure rubric scorer for in-line lunge."""
    angles = features.angles
    alignments = features.alignments

    has_knee = "front_knee_flexion_deg" in angles
    if not has_knee:
        return ScoreResult(
            score=1, rationale="Insufficient data: knee flexion not measurable",
            confidence=0.3, notes="missing key measurements",
        )

    knee_flex = angles.get("front_knee_flexion_deg", 180)
    trunk_upright = alignments.get("trunk_upright", False)
    knee_over_ankle = alignments.get("knee_over_ankle", False)

    rationale_parts = []

    # Good lunge: knee flexion < 90° (deep), trunk upright, knee aligned
    deep_enough = knee_flex < 100
    if deep_enough and trunk_upright and knee_over_ankle:
        score = 3
        rationale_parts.append("Deep lunge with trunk upright and knee aligned")
    elif deep_enough or (trunk_upright and knee_over_ankle):
        score = 2
        if not trunk_upright:
            rationale_parts.append(f"trunk lean {angles.get('trunk_lean_from_vertical_deg', '?')}°")
        if not knee_over_ankle:
            rationale_parts.append("knee drifts past ankle")
        if not deep_enough:
            rationale_parts.append(f"knee flexion {knee_flex:.0f}° (insufficient depth)")
        rationale_parts.insert(0, "Completed with compensation")
    else:
        score = 1
        rationale_parts.append("Unable to complete lunge pattern")
        if not deep_enough:
            rationale_parts.append(f"knee flexion only {knee_flex:.0f}°")

    confidence = features.confidence * 0.85

    return ScoreResult(
        score=score, rationale="; ".join(rationale_parts),
        confidence=confidence, notes="",
    )