"""
Legacy project JSON normalizer.

Goal:
- Convert legacy project JSON (from /opt/gloda-factory/temp/project_*.json)
  into the script_data schema expected by current Streamlit UI (`app.py`)
  and composer (`modules/composer.py`).

Principles:
- Pure rule-based, no AI generation.
- Never drop legacy information: keep full raw doc under `script_data["_legacy"]`
  and per-scene under `scene["_legacy"]`.
"""

from __future__ import annotations

from dataclasses import dataclass
from typing import Any, Dict, List, Optional, Tuple


def _as_str(v: Any) -> str:
    return v if isinstance(v, str) else ""


def _as_dict(v: Any) -> Dict[str, Any]:
    return v if isinstance(v, dict) else {}


def _as_list(v: Any) -> List[Any]:
    return v if isinstance(v, list) else []


def _detect_schema_variant(doc: Dict[str, Any]) -> str:
    scenes = _as_list(doc.get("scenes"))
    if not scenes:
        return "Unknown"
    prompt_keys = {"image_prompt", "visual_prompt", "video_prompt"}
    for s in scenes:
        if isinstance(s, dict) and (set(s.keys()) & prompt_keys):
            return "Schema_A"
    typical_b = {"keyframe", "story_beat", "camera_movement", "image_url"}
    for s in scenes:
        if isinstance(s, dict) and (set(s.keys()) & typical_b):
            return "Schema_B"
    return "Unknown"


def _derive_visual_prompt_from_keyframe(scene: Dict[str, Any]) -> str:
    """
    Build a readable prompt-like summary from keyframe + story_beat.
    This is NOT an AI prompt; it's a structured description to avoid empty fields.
    """
    keyframe = _as_dict(scene.get("keyframe") or scene.get("keyframes"))
    story_beat = _as_str(scene.get("story_beat"))

    parts: List[str] = []
    if keyframe:
        parts.append("[DerivedFromKeyframe]")
        # deterministic ordering for readability
        for k in sorted(keyframe.keys()):
            v = keyframe.get(k)
            if isinstance(v, (str, int, float)) and str(v).strip():
                parts.append(f"{k}: {v}")
            elif isinstance(v, dict) and v:
                # flatten one level
                sub = ", ".join(f"{sk}={sv}" for sk, sv in sorted(v.items()) if str(sv).strip())
                if sub:
                    parts.append(f"{k}: {sub}")
    if story_beat:
        parts.append(f"story_beat: {story_beat}")
    return "\n".join(parts).strip()


def _derive_video_prompt_from_motion(scene: Dict[str, Any]) -> str:
    camera_movement = _as_str(scene.get("camera_movement"))
    rhythm = scene.get("rhythm")
    story_beat = _as_str(scene.get("story_beat"))

    parts: List[str] = []
    parts.append("[DerivedFromMotion]")
    if camera_movement:
        parts.append(f"camera_movement: {camera_movement}")
    if isinstance(rhythm, dict) and rhythm:
        # keep stable keys
        sub = ", ".join(f"{k}={rhythm.get(k)}" for k in sorted(rhythm.keys()))
        parts.append(f"rhythm: {sub}")
    if story_beat:
        parts.append(f"story_beat: {story_beat}")
    return "\n".join(parts).strip()


def _normalize_fancy_text(scene: Dict[str, Any], default_duration: float) -> Dict[str, Any]:
    ft = scene.get("fancy_text")
    if isinstance(ft, dict):
        # Ensure required keys exist
        out = dict(ft)
        out.setdefault("text", "")
        out.setdefault("style", "highlight")
        # support either position dict or string
        if "position" not in out:
            out["position"] = "center"
        out.setdefault("start_time", 0.0)
        out.setdefault("duration", default_duration)
        return out

    # legacy doesn't have fancy_text
    return {
        "text": "",
        "style": "highlight",
        "position": "center",
        "start_time": 0.0,
        "duration": default_duration,
    }


def _build_voiceover_timeline_from_scenes(normalized_scenes: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    timeline: List[Dict[str, Any]] = []
    t = 0.0
    for idx, s in enumerate(normalized_scenes):
        dur = float(s.get("duration") or 0) or 0.0
        legacy = _as_dict(s.get("_legacy"))
        vo = _as_str(legacy.get("voiceover") or s.get("voiceover") or "")
        if vo.strip():
            timeline.append(
                {
                    "id": idx + 1,
                    "text": vo,
                    "subtitle": vo,
                    "start_time": t,
                    "duration": dur if dur > 0 else 3.0,
                }
            )
        t += dur if dur > 0 else 0.0
    return timeline


def normalize_legacy_project(doc: Dict[str, Any]) -> Dict[str, Any]:
    """
    Return a script_data dict compatible with current UI.
    """
    schema = _detect_schema_variant(doc)

    scenes_in = _as_list(doc.get("scenes"))
    normalized_scenes: List[Dict[str, Any]] = []

    for s in scenes_in:
        if not isinstance(s, dict):
            continue

        scene_id = int(s.get("id") or (len(normalized_scenes) + 1))
        duration = float(s.get("duration") or 0) or 0.0
        if duration <= 0:
            duration = 3.0

        # visual prompt
        visual_prompt = ""
        if schema == "Schema_A":
            # legacy key is usually image_prompt
            visual_prompt = _as_str(s.get("visual_prompt") or s.get("image_prompt") or "")
        elif schema == "Schema_B":
            visual_prompt = _derive_visual_prompt_from_keyframe(s)
        else:
            visual_prompt = _as_str(s.get("visual_prompt") or s.get("image_prompt") or "")

        if not visual_prompt and s.get("keyframe"):
            visual_prompt = _derive_visual_prompt_from_keyframe(s)

        # video prompt
        video_prompt = _as_str(s.get("video_prompt") or "")
        if not video_prompt:
            video_prompt = _derive_video_prompt_from_motion(s)

        # fancy text (default safe)
        fancy_text = _normalize_fancy_text(s, default_duration=duration)

        normalized_scene: Dict[str, Any] = {
            "id": scene_id,
            "duration": duration,
            "visual_prompt": visual_prompt,
            "video_prompt": video_prompt,
            "fancy_text": fancy_text,
            # keep optional fields if present
            "timeline": s.get("timeline", ""),
        }

        # Attach per-scene legacy snapshot (do not mutate the original)
        normalized_scene["_legacy"] = {
            "schema": schema,
            "image_url": s.get("image_url"),
            "keyframe": s.get("keyframe") or s.get("keyframes"),
            "camera_movement": s.get("camera_movement"),
            "story_beat": s.get("story_beat"),
            "rhythm": s.get("rhythm"),
            "sound_design": s.get("sound_design"),
            "voiceover": s.get("voiceover"),
        }

        normalized_scenes.append(normalized_scene)

    # voiceover timeline: normalize existing if present, else derive from scenes voiceover
    vtl = doc.get("voiceover_timeline")
    voiceover_timeline: List[Dict[str, Any]] = []
    if isinstance(vtl, list) and vtl:
        for idx, it in enumerate(vtl):
            if not isinstance(it, dict):
                continue
            # unify field names
            text = _as_str(it.get("text") or it.get("voiceover") or "")
            subtitle = _as_str(it.get("subtitle") or text)
            start_time = float(it.get("start_time") or 0.0)
            duration = float(it.get("duration") or 3.0)
            voiceover_timeline.append(
                {
                    "id": int(it.get("id") or (idx + 1)),
                    "text": text,
                    "subtitle": subtitle,
                    "start_time": start_time,
                    "duration": duration,
                }
            )
    else:
        voiceover_timeline = _build_voiceover_timeline_from_scenes(normalized_scenes)

    # script_data expected by UI
    script_data: Dict[str, Any] = {
        "hook": doc.get("hook", ""),
        "selling_points": doc.get("selling_points", []) or [],
        "target_audience": doc.get("target_audience", "") or "",
        "video_style": doc.get("video_style", "") or "",
        "bgm_style": doc.get("bgm_style", "") or "",
        "voiceover_timeline": voiceover_timeline,
        "scenes": normalized_scenes,
        "cta": doc.get("cta", ""),
        # Keep analysis for UI fallback display
        "analysis": doc.get("analysis", ""),
        # Preserve original
        "_legacy": doc,
        "_legacy_schema": schema,
    }

    return script_data