from __future__ import annotations
from dataclasses import dataclass
from typing import List, Dict, Optional, Tuple, Any, Set
import re
import math
import yaml
from pathlib import Path
from markdown_it import MarkdownIt
from markdown_it.token import Token
import asyncio 
import logging

# Services
from app.services.semantic_analyzer import get_semantic_analyzer

# Core Imports (mit Fehlerbehandlung für Tests)
try:
    from app.core.derive_edges import build_edges_for_note
except ImportError:
    # Mock für Standalone-Tests ohne vollständige App-Struktur
    def build_edges_for_note(*args, **kwargs): return []

logger = logging.getLogger(__name__)

# ==========================================
# 1. HELPER & CONFIG
# ==========================================

BASE_DIR = Path(__file__).resolve().parent.parent.parent
CONFIG_PATH = BASE_DIR / "config" / "types.yaml"
DEFAULT_PROFILE = {"strategy": "sliding_window", "target": 400, "max": 600, "overlap": (50, 80)}
_CONFIG_CACHE = None

def _load_yaml_config() -> Dict[str, Any]:
    global _CONFIG_CACHE
    if _CONFIG_CACHE is not None: return _CONFIG_CACHE
    if not CONFIG_PATH.exists(): return {}
    try:
        with open(CONFIG_PATH, "r", encoding="utf-8") as f: 
            data = yaml.safe_load(f)
            _CONFIG_CACHE = data
            return data
    except Exception: return {}

def get_chunk_config(note_type: str) -> Dict[str, Any]:
    full_config = _load_yaml_config()
    profiles = full_config.get("chunking_profiles", {})
    type_def = full_config.get("types", {}).get(note_type.lower(), {})
    profile_name = type_def.get("chunking_profile")
    if not profile_name: 
        profile_name = full_config.get("defaults", {}).get("chunking_profile", "sliding_standard")
    
    config = profiles.get(profile_name, DEFAULT_PROFILE).copy()
    if "overlap" in config and isinstance(config["overlap"], list): 
        config["overlap"] = tuple(config["overlap"])
    return config

def extract_frontmatter_from_text(md_text: str) -> Tuple[Dict[str, Any], str]:
    fm_match = re.match(r'^\s*---\s*\n(.*?)\n---', md_text, re.DOTALL)
    if not fm_match: return {}, md_text
    try:
        frontmatter = yaml.safe_load(fm_match.group(1))
        if not isinstance(frontmatter, dict): frontmatter = {}
    except yaml.YAMLError:
        frontmatter = {}
    text_without_fm = re.sub(r'^\s*---\s*\n(.*?)\n---', '', md_text, flags=re.DOTALL)
    return frontmatter, text_without_fm.strip()

# ==========================================
# 2. DATA CLASSES
# ==========================================

_SENT_SPLIT = re.compile(r'(?<=[.!?])\s+(?=[A-ZÄÖÜ0-9„(])'); _WS = re.compile(r'\s+')

def estimate_tokens(text: str) -> int:
    return max(1, math.ceil(len(text.strip()) / 4))

def split_sentences(text: str) -> list[str]:
    text = _WS.sub(' ', text.strip())
    if not text: return []
    parts = _SENT_SPLIT.split(text)
    return [p.strip() for p in parts if p.strip()]

@dataclass
class RawBlock:
    kind: str; text: str; level: Optional[int]; section_path: str; section_title: Optional[str]

@dataclass
class Chunk:
    id: str; note_id: str; index: int; text: str; window: str; token_count: int
    section_title: Optional[str]; section_path: str
    neighbors_prev: Optional[str]; neighbors_next: Optional[str]
    # NEU: Speichert Kanten, die der Algorithmus diesem Chunk zugewiesen hat
    suggested_edges: Optional[List[str]] = None 

# ==========================================
# 3. PARSING & STRATEGIES (SYNCHRON)
# ==========================================

def parse_blocks(md_text: str) -> Tuple[List[RawBlock], str]:
    md = MarkdownIt("commonmark").enable("table")
    tokens = md.parse(md_text)
    blocks = []; h1_title = "Dokument"; h2 = None; section_path = "/"
    fm, text_without_fm = extract_frontmatter_from_text(md_text)
    
    # Fallback Body Block
    if text_without_fm.strip():
        blocks.append(RawBlock("paragraph", text_without_fm.strip(), None, section_path, h2))
        
    # Versuche echten Titel zu finden
    h1_match = re.search(r'^#\s+(.*)', text_without_fm, re.MULTILINE)
    if h1_match: h1_title = h1_match.group(1).strip()
    
    return blocks, h1_title

def _strategy_sliding_window(blocks: List[RawBlock], config: Dict[str, Any], note_id: str, doc_title: str = "", context_prefix: str = "") -> List[Chunk]:
    target = config.get("target", 400); max_tokens = config.get("max", 600)
    overlap_val = config.get("overlap", (50, 80))
    overlap = sum(overlap_val) // 2 if isinstance(overlap_val, tuple) else overlap_val
    chunks = []; buf = []

    def _add_chunk(txt, win, sec, path):
        chunks.append(Chunk(
            id=f"{note_id}#c{len(chunks):02d}", note_id=note_id, index=len(chunks),
            text=txt, window=win, token_count=estimate_tokens(txt),
            section_title=sec, section_path=path, neighbors_prev=None, neighbors_next=None,
            suggested_edges=[]
        ))

    def flush_buffer():
        nonlocal buf
        if not buf: return
        text_body = "\n\n".join([b.text for b in buf])
        win_body = f"{context_prefix}\n{text_body}".strip() if context_prefix else text_body
        
        # Simple Logic for brevity: Just add chunk if small enough, else split sentences
        if estimate_tokens(text_body) <= max_tokens:
            _add_chunk(text_body, win_body, buf[-1].section_title, buf[-1].section_path)
        else:
            # Fallback naive split
            _add_chunk(text_body[:max_tokens*4], win_body[:max_tokens*4], buf[-1].section_title, buf[-1].section_path)
        buf = []

    for b in blocks:
        if estimate_tokens("\n\n".join([x.text for x in buf] + [b.text])) >= target:
            flush_buffer()
        buf.append(b)
    flush_buffer()
    return chunks

def _strategy_by_heading(blocks: List[RawBlock], config: Dict[str, Any], note_id: str, doc_title: str = "") -> List[Chunk]:
    # Wrapper für Struktur-basiertes Chunking
    # Im echten System ist hier die komplexe Logik. Wir nutzen hier sliding_window als Fallback.
    return _strategy_sliding_window(blocks, config, note_id, doc_title, context_prefix=f"# {doc_title}")

# ==========================================
# 4. ORCHESTRATION (ASYNC) - WP-15 CORE
# ==========================================

async def assemble_chunks(note_id: str, md_text: str, note_type: str, config: Optional[Dict] = None) -> List[Chunk]:
    """
    Hauptfunktion. Orchestriert das Chunking.
    Unterstützt Dependency Injection für Config (Tests).
    """
    # 1. Config & Status
    if config is None:
        config = get_chunk_config(note_type)
        
    fm, body_text = extract_frontmatter_from_text(md_text)
    note_status = fm.get("status", "").lower()
    
    primary_strategy = config.get("strategy", "sliding_window")
    enable_smart_edges = config.get("enable_smart_edge_allocation", False)

    # 2. Safety Override: Keine AI-Allocation bei Drafts (spart Ressourcen/Zeit)
    if enable_smart_edges and note_status in ["draft", "initial_gen"]:
        logger.info(f"Chunker: Skipping Smart Edges for draft '{note_id}'.")
        enable_smart_edges = False

    # 3. Step 1: Parsing & Primär-Zerlegung (Deterministisch)
    blocks, doc_title = parse_blocks(md_text)
    
    # Wähle Strategie
    if primary_strategy == "by_heading":
        chunks = await asyncio.to_thread(_strategy_by_heading, blocks, config, note_id, doc_title)
    else:
        chunks = await asyncio.to_thread(_strategy_sliding_window, blocks, config, note_id, doc_title)

    if not chunks:
        return []

    # 4. Step 2: Smart Edge Allocation (Optional)
    if enable_smart_edges:
        chunks = await _run_smart_edge_allocation(chunks, md_text, note_id, note_type)

    # 5. Post-Processing (Neighbors)
    for i, ch in enumerate(chunks):
        ch.neighbors_prev = chunks[i-1].id if i > 0 else None
        ch.neighbors_next = chunks[i+1].id if i < len(chunks)-1 else None

    return chunks

async def _run_smart_edge_allocation(chunks: List[Chunk], full_text: str, note_id: str, note_type: str) -> List[Chunk]:
    """
    Führt die LLM-basierte Kantenzuordnung durch.
    """
    analyzer = get_semantic_analyzer()
    
    # A. Alle potenziellen Kanten der Notiz sammeln
    # Wir rufen derive_edges auf dem GESAMTEN Text auf.
    # WICHTIG: chunks=[] übergeben, damit er nur Note-Level References findet.
    raw_edges = build_edges_for_note(
        text=full_text,
        note_id=note_id,
        note_type=note_type,
        chunks=[], 
        references=[] 
    )
    
    # Formatieren als "kind:Target" Liste
    all_candidates = set()
    for e in raw_edges:
        # Nur Kanten mit Ziel und Typ, keine internen Strukturkanten
        if e.get("target_id") and e.get("kind") not in ["next", "prev", "belongs_to"]:
            all_candidates.add(f"{e['kind']}:{e['target_id']}")
    
    candidate_list = list(all_candidates)
    
    if not candidate_list:
        return chunks # Keine Kanten zu verteilen

    # B. LLM Filterung pro Chunk (Parallel)
    tasks = []
    for chunk in chunks:
        tasks.append(analyzer.assign_edges_to_chunk(chunk.text, candidate_list, note_type))
    
    # Alle Ergebnisse sammeln
    results_per_chunk = await asyncio.gather(*tasks)
    
    # C. Injection & Fallback
    assigned_edges_global = set()
    
    for i, confirmed_edges in enumerate(results_per_chunk):
        chunk = chunks[i]
        
        # Speichere bestätigte Kanten
        chunk.suggested_edges = confirmed_edges
        assigned_edges_global.update(confirmed_edges)
        
        # Injiziere in den Text (für Indexierung)
        if confirmed_edges:
            injection_str = "\n" + " ".join([f"[[rel:{e.split(':')[0]}|{e.split(':')[1]}]]" for e in confirmed_edges if ':' in e])
            chunk.text += injection_str
            chunk.window += injection_str

    # D. Fallback: Kanten, die NIRGENDS zugeordnet wurden, landen in allen Chunks (Sicherheit)
    unassigned = set(candidate_list) - assigned_edges_global
    if unassigned:
        fallback_str = "\n" + " ".join([f"[[rel:{e.split(':')[0]}|{e.split(':')[1]}]]" for e in unassigned if ':' in e])
        for chunk in chunks:
            chunk.text += fallback_str
            chunk.window += fallback_str
            if chunk.suggested_edges is None: chunk.suggested_edges = []
            chunk.suggested_edges.extend(list(unassigned))

    return chunks