"""
MLX Training Wrapper für Fine-Tuning
Nutzt mlx-lm für LoRA Fine-Tuning
"""

import json
import time
import psutil
from pathlib import Path
from typing import Dict, List, Callable, Optional
from dataclasses import dataclass
import threading
import queue


@dataclass
class TrainingConfig:
    """Training Konfiguration"""
    model_name: str
    learning_rate: float = 1e-5
    epochs: int = 3
    batch_size: int = 4
    lora_rank: int = 8
    lora_alpha: int = 16
    max_seq_length: int = 2048
    val_every: int = 50


class TrainingStatus:
    """Verwaltet den aktuellen Training-Status"""

    def __init__(self):
        self.is_training = False
        self.should_stop = False
        self.current_step = 0
        self.total_steps = 0
        self.current_epoch = 0
        self.train_loss = 0.0
        self.val_loss = 0.0
        self.train_loss_history = []
        self.val_loss_history = []
        self.start_time = None
        self.error = None

    def reset(self):
        """Setzt den Status zurück"""
        self.is_training = False
        self.should_stop = False
        self.current_step = 0
        self.total_steps = 0
        self.current_epoch = 0
        self.train_loss = 0.0
        self.val_loss = 0.0
        self.train_loss_history = []
        self.val_loss_history = []
        self.start_time = None
        self.error = None

    def to_dict(self) -> Dict:
        """Konvertiert zu Dictionary für API"""
        eta = None
        if self.is_training and self.current_step > 0 and self.start_time:
            elapsed = time.time() - self.start_time
            steps_remaining = self.total_steps - self.current_step
            eta = int((elapsed / self.current_step) * steps_remaining)

        memory_usage = psutil.virtual_memory().percent

        return {
            'is_training': self.is_training,
            'current_step': self.current_step,
            'total_steps': self.total_steps,
            'current_epoch': self.current_epoch,
            'train_loss': round(self.train_loss, 4) if self.train_loss else None,
            'val_loss': round(self.val_loss, 4) if self.val_loss else None,
            'train_loss_history': [round(l, 4) for l in self.train_loss_history],
            'val_loss_history': [round(l, 4) for l in self.val_loss_history],
            'eta_seconds': eta,
            'memory_usage_percent': memory_usage,
            'error': self.error
        }


class MLXTrainer:
    """Wrapper für MLX Training"""

    def __init__(self, models_dir: str = "models", output_dir: str = "output"):
        self.models_dir = Path(models_dir)
        self.output_dir = Path(output_dir)
        self.models_dir.mkdir(exist_ok=True)
        self.output_dir.mkdir(exist_ok=True)

        self.status = TrainingStatus()
        self.training_thread = None

    def prepare_training_data(self, train_data: List[Dict],
                            val_data: List[Dict],
                            data_dir: Path) -> tuple[Path, Path]:
        """Konvertiert Daten ins MLX Format (JSONL)"""

        def format_example(item: Dict) -> Dict:
            """Formatiert ein Beispiel im Chat-Format"""
            task_type = item['task_type']
            body = item['body']
            output = item['expected_output']

            # Task-spezifische Prompts
            task_prompts = {
                'Zusammenfassen': 'Fasse folgende E-Mail zusammen:',
                'Antwort schreiben': 'Schreibe eine Antwort auf folgende E-Mail:',
                'Kategorisieren': 'Kategorisiere folgende E-Mail:',
                'Action Items': 'Extrahiere die Action Items aus folgender E-Mail:',
                'Custom': 'Bearbeite folgende E-Mail:'
            }

            instruction = task_prompts.get(task_type, task_prompts['Custom'])

            return {
                'messages': [
                    {
                        'role': 'user',
                        'content': f"{instruction}\n\n{body}"
                    },
                    {
                        'role': 'assistant',
                        'content': output
                    }
                ]
            }

        train_file = data_dir / 'train.jsonl'
        val_file = data_dir / 'val.jsonl'

        # Schreibe Training Data
        with open(train_file, 'w', encoding='utf-8') as f:
            for item in train_data:
                f.write(json.dumps(format_example(item), ensure_ascii=False) + '\n')

        # Schreibe Validation Data
        with open(val_file, 'w', encoding='utf-8') as f:
            for item in val_data:
                f.write(json.dumps(format_example(item), ensure_ascii=False) + '\n')

        return train_file, val_file

    def _run_training(self, config: TrainingConfig,
                     train_file: Path, val_file: Path,
                     output_path: Path):
        """Führt das Training aus (läuft in eigenem Thread)"""
        try:
            # Import hier um MLX nur bei Bedarf zu laden
            from mlx_lm import load, LoRALinear
            from mlx_lm.tuner import train as mlx_train
            import mlx.core as mx
            import mlx.nn as nn
            import mlx.optimizers as optim

            self.status.is_training = True
            self.status.start_time = time.time()
            self.status.error = None

            # Lade Modell
            model_path = self.models_dir / config.model_name
            if not model_path.exists():
                raise FileNotFoundError(f"Model not found: {model_path}")

            # Training durchführen mit mlx-lm
            # Dies ist ein vereinfachtes Beispiel - mlx-lm hat eigene Trainer
            # In der Praxis würde man mlx_lm.tuner verwenden

            # Lade Training Config
            train_config = {
                'model': str(model_path),
                'data': str(train_file),
                'val_data': str(val_file),
                'train': True,
                'iters': config.epochs * 100,  # Approximation
                'val_batches': 10,
                'learning_rate': config.learning_rate,
                'batch_size': config.batch_size,
                'lora_layers': config.lora_rank,
                'adapter_file': str(output_path / 'adapters.npz'),
                'save_every': 50,
                'val_every': config.val_every,
            }

            # Callback für Progress-Updates
            def training_callback(step: int, loss: float, val_loss: Optional[float] = None):
                if self.status.should_stop:
                    return False  # Stop training

                self.status.current_step = step
                self.status.train_loss = loss
                self.status.train_loss_history.append(loss)

                if val_loss is not None:
                    self.status.val_loss = val_loss
                    self.status.val_loss_history.append(val_loss)

                return True

            # Hinweis: Dies ist ein Platzhalter für echtes MLX Training
            # In der Praxis würde man mlx_lm.tuner.train() oder eine
            # eigene Training Loop mit mlx nutzen

            # Simuliere Training für Demo (MUSS durch echtes MLX Training ersetzt werden)
            total_steps = config.epochs * (len(list(open(train_file))) // config.batch_size)
            self.status.total_steps = total_steps

            for epoch in range(config.epochs):
                self.status.current_epoch = epoch + 1

                for step in range(total_steps // config.epochs):
                    if self.status.should_stop:
                        break

                    # Simuliere Training Step
                    self.status.current_step = epoch * (total_steps // config.epochs) + step
                    fake_loss = 2.0 - (self.status.current_step / total_steps) * 1.5
                    self.status.train_loss = fake_loss
                    self.status.train_loss_history.append(fake_loss)

                    # Validation alle N Steps
                    if step % config.val_every == 0:
                        fake_val_loss = 2.2 - (self.status.current_step / total_steps) * 1.4
                        self.status.val_loss = fake_val_loss
                        self.status.val_loss_history.append(fake_val_loss)

                    time.sleep(0.1)  # Simuliere Rechenzeit

                if self.status.should_stop:
                    break

            # Speichere finale Adapter
            # output_path / 'adapters.npz' würde die LoRA Weights enthalten

            self.status.is_training = False

        except Exception as e:
            self.status.error = str(e)
            self.status.is_training = False

    def start_training(self, config: TrainingConfig,
                      train_data: List[Dict],
                      val_data: List[Dict]) -> bool:
        """Startet das Training"""

        if self.status.is_training:
            return False

        # Bereite Daten vor
        data_dir = self.output_dir / f"training_{int(time.time())}"
        data_dir.mkdir(exist_ok=True)

        train_file, val_file = self.prepare_training_data(
            train_data, val_data, data_dir
        )

        # Output-Pfad
        output_path = self.output_dir / f"run_{int(time.time())}"
        output_path.mkdir(exist_ok=True)

        # Reset Status
        self.status.reset()

        # Starte Training in eigenem Thread
        self.training_thread = threading.Thread(
            target=self._run_training,
            args=(config, train_file, val_file, output_path),
            daemon=True
        )
        self.training_thread.start()

        return True

    def stop_training(self) -> bool:
        """Stoppt das laufende Training"""
        if not self.status.is_training:
            return False

        self.status.should_stop = True

        # Warte max 5 Sekunden auf Thread
        if self.training_thread:
            self.training_thread.join(timeout=5)

        return True

    def get_status(self) -> Dict:
        """Gibt aktuellen Status zurück"""
        return self.status.to_dict()

    def list_available_models(self) -> List[str]:
        """Listet verfügbare Modelle auf"""
        if not self.models_dir.exists():
            return []

        models = []
        for path in self.models_dir.iterdir():
            if path.is_dir():
                models.append(path.name)

        return models

    def download_model(self, model_name: str) -> bool:
        """
        Lädt ein Modell herunter
        In der Praxis würde man hier huggingface_hub nutzen
        """
        # Placeholder - würde huggingface_hub.snapshot_download nutzen
        # und dann mit mlx_lm.convert konvertieren

        # Beispiel:
        # from huggingface_hub import snapshot_download
        # from mlx_lm.convert import convert
        #
        # hf_path = snapshot_download(model_name)
        # mlx_path = self.models_dir / model_name
        # convert(hf_path, mlx_path)

        return False  # Nicht implementiert in diesem Beispiel