from __future__ import annotations

from typing import Any, Dict, List, Tuple

from catalog_text_templates import (
    RECOMMENDATION_TEMPLATES,
    STRENGTH_TEMPLATES,
    VERDICT_LEVELS,
    WEAKNESS_TEMPLATES,
)


def clamp(v: float, lo: float, hi: float) -> float:
    return max(lo, min(hi, v))


def pick_top_unique(items: List[str], limit: int = 3) -> List[str]:
    out: List[str] = []
    seen = set()
    for item in items:
        if item and item not in seen:
            out.append(item)
            seen.add(item)
        if len(out) >= limit:
            break
    return out


def build_strength_keys(
    official_scores: Dict[str, float],
    diagnostic_metrics: Dict[str, int],
) -> List[str]:
    keys: List[str] = []

    if official_scores.get("composition", 0.0) >= 4.2:
        keys.append("strong_composition")
    if diagnostic_metrics.get("hierarchy_strength", 0) >= 4:
        keys.append("clear_hierarchy")
    if diagnostic_metrics.get("focal_clarity", 0) >= 4:
        keys.append("clear_focal_point")
    if diagnostic_metrics.get("palette_control", 0) >= 4:
        keys.append("controlled_palette")
    if diagnostic_metrics.get("visual_impact", 0) >= 4:
        keys.append("strong_impact")
    if diagnostic_metrics.get("technical_quality", 0) >= 4:
        keys.append("good_technical_quality")
    if diagnostic_metrics.get("visual_clutter", 0) <= 2:
        keys.append("clean_presentation")
    if official_scores.get("typography", 0.0) >= 4.0:
        keys.append("readable_typography")
    if official_scores.get("message_clarity", 0.0) >= 4.0:
        keys.append("good_message_clarity")

    return pick_top_unique(keys, 3)


def build_weakness_keys(
    official_scores: Dict[str, float],
    diagnostic_metrics: Dict[str, int],
) -> List[str]:
    keys: List[str] = []

    if official_scores.get("composition", 0.0) <= 2.8:
        keys.append("weak_composition")
    if diagnostic_metrics.get("hierarchy_strength", 0) <= 2:
        keys.append("weak_hierarchy")
    if diagnostic_metrics.get("focal_clarity", 0) <= 2:
        keys.append("weak_focal_point")
    if diagnostic_metrics.get("palette_control", 0) <= 2:
        keys.append("weak_palette_control")
    if diagnostic_metrics.get("visual_impact", 0) <= 2:
        keys.append("low_visual_impact")
    if diagnostic_metrics.get("technical_quality", 0) <= 2:
        keys.append("low_technical_quality")
    if diagnostic_metrics.get("visual_clutter", 0) >= 4:
        keys.append("high_clutter")
    if official_scores.get("typography", 0.0) <= 3.0:
        keys.append("weak_typography")
    if official_scores.get("message_clarity", 0.0) <= 3.0:
        keys.append("weak_message_clarity")

    return pick_top_unique(keys, 3)


def build_recommendation_keys(
    official_scores: Dict[str, float],
    diagnostic_metrics: Dict[str, int],
    pins: List[str],
) -> List[str]:
    keys: List[str] = []

    if diagnostic_metrics.get("hierarchy_strength", 0) <= 3:
        keys.append("improve_hierarchy")
    if diagnostic_metrics.get("visual_clutter", 0) >= 4:
        keys.append("reduce_clutter")
    if diagnostic_metrics.get("focal_clarity", 0) <= 3:
        keys.append("improve_focal_point")
    if official_scores.get("typography", 0.0) <= 3.3:
        keys.append("improve_typography")
    if official_scores.get("color", 0.0) <= 3.2:
        keys.append("improve_palette")
    if official_scores.get("message_clarity", 0.0) <= 3.5:
        keys.append("improve_message_clarity")
    if official_scores.get("quality", 0.0) <= 3.3:
        keys.append("improve_technical_quality")
    if "credits_box" in pins or "small_footer_info" in pins:
        keys.append("improve_footer_block")

    return pick_top_unique(keys, 3)


def render_texts_from_keys(
    keys: List[str],
    template_map: Dict[str, str],
) -> List[str]:
    return [template_map[k] for k in keys if k in template_map]


def compute_verdict_level(
    primary_label: str,
    primary_confidence: str,
    diagnostic_score: float,
) -> str:
    if primary_label == "good" and diagnostic_score >= 4.3:
        return "strong"
    if primary_label == "good" and diagnostic_score >= 3.5:
        return "good"
    if primary_label == "uncertain":
        return "uncertain"
    if primary_label == "bad" and diagnostic_score <= 2.7:
        return "weak"
    return "mixed"


def build_verdict_summary(
    *,
    verdict_level: str,
    primary_label: str,
    diagnostic_score: float,
    tags: List[str],
    strengths: List[str],
    weaknesses: List[str],
) -> str:
    level_text = VERDICT_LEVELS.get(verdict_level, "Смешанный")

    tone_bits: List[str] = []
    if "minimal" in tags:
        tone_bits.append("минималистичной подачей")
    if "clean" in tags:
        tone_bits.append("чистой подачей")
    if "cinematic" in tags:
        tone_bits.append("атмосферной подачей")
    if "editorial" in tags:
        tone_bits.append("редакционной структурой")

    tone_text = ""
    if tone_bits:
        tone_text = " с " + ", ".join(tone_bits[:2])

    if primary_label == "good":
        return f"{level_text} постер{tone_text}, который в целом производит положительное визуальное впечатление."
    if primary_label == "bad":
        return f"{level_text} постер, которому пока не хватает ясности и собранности восприятия."
    return f"{level_text} постер: в нем есть сильные стороны, но общий сигнал пока неоднозначен."


def build_verdict_takeaway(
    strengths: List[str],
    weaknesses: List[str],
    recommendations: List[str],
) -> str:
    if recommendations:
        first = recommendations[0]
        if first.endswith("."):
            return first
        return first + "."
    if weaknesses:
        return "Основной резерв роста связан с улучшением читаемости и структуры."
    if strengths:
        return "Текущую визуальную логику стоит сохранить и доработать точечно."
    return "Нужна дополнительная проверка композиции, ясности сообщения и визуальной иерархии."


def build_structured_report(
    *,
    official_scores: Dict[str, float],
    diagnostic_metrics: Dict[str, int],
    tags: List[str],
    pins: List[str],
    primary_label: str,
    primary_confidence: str,
    diagnostic_score: float,
) -> Dict[str, Any]:
    strength_keys = build_strength_keys(official_scores, diagnostic_metrics)
    weakness_keys = build_weakness_keys(official_scores, diagnostic_metrics)
    recommendation_keys = build_recommendation_keys(official_scores, diagnostic_metrics, pins)

    strengths = render_texts_from_keys(strength_keys, STRENGTH_TEMPLATES)
    weaknesses = render_texts_from_keys(weakness_keys, WEAKNESS_TEMPLATES)
    recommendations = render_texts_from_keys(recommendation_keys, RECOMMENDATION_TEMPLATES)

    verdict_level = compute_verdict_level(
        primary_label=primary_label,
        primary_confidence=primary_confidence,
        diagnostic_score=diagnostic_score,
    )
    verdict_summary = build_verdict_summary(
        verdict_level=verdict_level,
        primary_label=primary_label,
        diagnostic_score=diagnostic_score,
        tags=tags,
        strengths=strengths,
        weaknesses=weaknesses,
    )
    verdict_takeaway = build_verdict_takeaway(
        strengths=strengths,
        weaknesses=weaknesses,
        recommendations=recommendations,
    )

    return {
        "tags": tags,
        "pins": pins,
        "strengths": strengths,
        "weaknesses": weaknesses,
        "recommendations": recommendations,
        "verdict": {
            "level": verdict_level,
            "summary": verdict_summary,
            "takeaway": verdict_takeaway,
        },
        "debug_keys": {
            "strength_keys": strength_keys,
            "weakness_keys": weakness_keys,
            "recommendation_keys": recommendation_keys,
        },
    }