"""
Model Manager for Fine-Tuned Model Deployment and Versioning

Handles loading, deploying, and rolling back fine-tuned models.
"""

import os
import json
import shutil
from typing import Optional, Dict
from datetime import datetime
import logging

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

logger = logging.getLogger(__name__)


class ModelManager:
    """Manage fine-tuned model deployment and versioning"""

    def __init__(self, models_dir: str = None):
        """
        Initialize ModelManager.

        Args:
            models_dir: Base directory for storing fine-tuned models
                       (defaults to MODELS_DIR env var or './models/finetuned')
        """
        if models_dir is None:
            # Use environment variable or /data path for HF Spaces
            models_dir = os.getenv('MODELS_DIR', '/data/models/finetuned')
        
        self.models_dir = models_dir
        self.base_model_name = "facebook/bart-large-mnli"
        
        # Create directory if it doesn't exist
        try:
            os.makedirs(models_dir, exist_ok=True)
        except PermissionError:
            logger.error(f"Permission denied creating models directory: {models_dir}")
            raise

    def get_model_path(self, run_id: int) -> str:
        """Get path to model for a specific training run"""
        return os.path.join(self.models_dir, f"run_{run_id}")

    def load_model(self, run_id: Optional[int] = None):
        """
        Load a fine-tuned model or base model.

        Args:
            run_id: Training run ID (None for base model)

        Returns:
            Tuple of (model, tokenizer)
        """
        if run_id is None:
            logger.info("Loading base model")
            model_name = self.base_model_name
        else:
            model_path = self.get_model_path(run_id)
            if not os.path.exists(model_path):
                raise FileNotFoundError(f"Model not found: {model_path}")
            logger.info(f"Loading fine-tuned model from run {run_id}")
            model_name = model_path

        tokenizer = AutoTokenizer.from_pretrained(model_name)
        model = AutoModelForSequenceClassification.from_pretrained(
            model_name,
            ignore_mismatched_sizes=True
        )

        return model, tokenizer

    def deploy_model(self, run_id: int, db_session) -> Dict:
        """
        Deploy a fine-tuned model (set as active).

        Args:
            run_id: Training run ID to deploy
            db_session: Database session for updating FineTuningRun

        Returns:
            Dict with deployment info
        """
        from app.models.models import FineTuningRun

        logger.info(f"Deploying model from run {run_id}")

        # Verify model exists
        model_path = self.get_model_path(run_id)
        if not os.path.exists(model_path):
            raise FileNotFoundError(f"Model not found: {model_path}")

        # Get the run record
        run = db_session.query(FineTuningRun).filter_by(id=run_id).first()
        if not run:
            raise ValueError(f"Training run {run_id} not found")

        if run.status != 'completed':
            raise ValueError(f"Cannot deploy non-completed run (status: {run.status})")

        # Deactivate all other models
        db_session.query(FineTuningRun).update({'is_active_model': False})

        # Activate this model
        run.is_active_model = True
        db_session.commit()

        logger.info(f"Model from run {run_id} is now active")

        return {
            'run_id': run_id,
            'deployed_at': datetime.utcnow().isoformat(),
            'model_path': model_path
        }

    def rollback_to_baseline(self, db_session) -> Dict:
        """
        Rollback to base model (deactivate all fine-tuned models).

        Args:
            db_session: Database session

        Returns:
            Dict with rollback info
        """
        from app.models.models import FineTuningRun

        logger.info("Rolling back to base model")

        # Deactivate all fine-tuned models
        active_count = db_session.query(FineTuningRun).filter_by(is_active_model=True).count()
        db_session.query(FineTuningRun).update({'is_active_model': False})
        db_session.commit()

        logger.info(f"Deactivated {active_count} fine-tuned model(s)")

        return {
            'rolled_back_at': datetime.utcnow().isoformat(),
            'deactivated_models': active_count,
            'active_model': 'base'
        }

    def get_active_model_info(self, db_session) -> Optional[Dict]:
        """
        Get information about the currently active model.

        Args:
            db_session: Database session

        Returns:
            Dict with active model info, or None if base model is active
        """
        from app.models.models import FineTuningRun

        active_run = db_session.query(FineTuningRun).filter_by(is_active_model=True).first()

        if not active_run:
            return None

        return {
            'run_id': active_run.id,
            'model_path': self.get_model_path(active_run.id),
            'created_at': active_run.created_at.isoformat() if active_run.created_at else None,
            'results': active_run.get_results(),
            'config': active_run.get_config()
        }

    def export_model(self, run_id: int, export_path: str) -> str:
        """
        Export model for backup or sharing.

        Args:
            run_id: Training run ID
            export_path: Destination path for export

        Returns:
            Path to exported model
        """
        logger.info(f"Exporting model from run {run_id}")

        model_path = self.get_model_path(run_id)
        if not os.path.exists(model_path):
            raise FileNotFoundError(f"Model not found: {model_path}")

        # Create export directory
        os.makedirs(export_path, exist_ok=True)

        # Copy all model files
        export_model_path = os.path.join(export_path, f"model_run_{run_id}")
        shutil.copytree(model_path, export_model_path, dirs_exist_ok=True)

        # Create model card
        model_card = {
            'run_id': run_id,
            'export_date': datetime.utcnow().isoformat(),
            'base_model': self.base_model_name,
            'model_type': 'BART with LoRA fine-tuning',
            'task': 'Multi-class text classification',
            'categories': ['Vision', 'Problem', 'Objectives', 'Directives', 'Values', 'Actions']
        }

        with open(os.path.join(export_model_path, 'model_card.json'), 'w') as f:
            json.dump(model_card, f, indent=2)

        logger.info(f"Model exported to {export_model_path}")

        return export_model_path

    def import_model(self, import_path: str, run_id: int) -> str:
        """
        Import a previously exported model.

        Args:
            import_path: Path to imported model directory
            run_id: Training run ID to assign

        Returns:
            Path to imported model in models directory
        """
        logger.info(f"Importing model to run {run_id}")

        if not os.path.exists(import_path):
            raise FileNotFoundError(f"Import path not found: {import_path}")

        # Verify it's a valid model directory
        required_files = ['config.json', 'pytorch_model.bin']  # or adapter_model.bin for LoRA
        has_required = any(os.path.exists(os.path.join(import_path, f)) for f in required_files)

        if not has_required:
            raise ValueError(f"Import path does not contain a valid model")

        # Copy to models directory
        model_path = self.get_model_path(run_id)
        shutil.copytree(import_path, model_path, dirs_exist_ok=True)

        logger.info(f"Model imported to {model_path}")

        return model_path

    def delete_model(self, run_id: int) -> None:
        """
        Delete a fine-tuned model from disk.

        Args:
            run_id: Training run ID
        """
        logger.info(f"Deleting model from run {run_id}")

        model_path = self.get_model_path(run_id)
        if os.path.exists(model_path):
            shutil.rmtree(model_path)
            logger.info(f"Model deleted: {model_path}")
        else:
            logger.warning(f"Model not found: {model_path}")

    def get_model_size(self, run_id: int) -> Dict:
        """
        Get size information for a model.

        Args:
            run_id: Training run ID

        Returns:
            Dict with size info
        """
        model_path = self.get_model_path(run_id)

        if not os.path.exists(model_path):
            return {'exists': False}

        # Calculate directory size
        total_size = 0
        file_count = 0

        for dirpath, dirnames, filenames in os.walk(model_path):
            for filename in filenames:
                filepath = os.path.join(dirpath, filename)
                total_size += os.path.getsize(filepath)
                file_count += 1

        return {
            'exists': True,
            'total_size_bytes': total_size,
            'total_size_mb': round(total_size / (1024 * 1024), 2),
            'file_count': file_count,
            'path': model_path
        }

    def list_available_models(self, db_session) -> list:
        """
        List all available fine-tuned models.

        Args:
            db_session: Database session

        Returns:
            List of dicts with model info
        """
        from app.models.models import FineTuningRun

        runs = db_session.query(FineTuningRun).filter_by(status='completed').all()

        models = []
        for run in runs:
            model_path = self.get_model_path(run.id)
            size_info = self.get_model_size(run.id)

            models.append({
                'run_id': run.id,
                'created_at': run.created_at.isoformat() if run.created_at else None,
                'is_active': run.is_active_model,
                'results': run.get_results(),
                'model_exists': size_info.get('exists', False),
                'size_mb': size_info.get('total_size_mb', 0)
            })

        return models