"""
Pretrained Model Integration
=========================

Download and integrate pretrained models from:
- nnU-Net model zoo
- MONAI model zoo  
- Detectron2 model zoo
- Transfer learning strategies

Author: Tumor Detection Segmentation Team
"""

import logging
import os
import json
import hashlib
from pathlib import Path
from typing import Any, Dict, List, Optional, Tuple, Union
from dataclasses import dataclass

# Optional imports
try:
    import requests
    REQUESTS_AVAILABLE = True
except ImportError:
    REQUESTS_AVAILABLE = False

# Configure logging
logger = logging.getLogger(__name__)


@dataclass
class ModelInfo:
    """Information about a pretrained model."""
    name: str
    framework: str
    url: str
    filename: str
    checksum: str
    description: str
    task_type: str
    input_modalities: List[str]
    target_organs: List[str]


class PretrainedModelManager:
    """
    Manager for downloading and integrating pretrained models from multiple frameworks.
    """

    def __init__(self, 
                 models_dir: str = "models/pretrained",
                 enable_verification: bool = True):
        """
        Initialize pretrained model manager.

        Args:
            models_dir: Directory to store downloaded models
            enable_verification: Whether to verify model checksums
        """
        self.models_dir = Path(models_dir)
        self.models_dir.mkdir(parents=True, exist_ok=True)
        self.enable_verification = enable_verification
        
        # Model registries
        self.nnunet_models = {}
        self.monai_models = {}
        self.detectron2_models = {}
        
        # Initialize model catalogs
        self._initialize_model_catalogs()
        
        logger.info(f"Pretrained model manager initialized at {self.models_dir}")

    def _initialize_model_catalogs(self):
        """Initialize model catalogs for all frameworks."""
        self._setup_nnunet_catalog()
        self._setup_monai_catalog()
        self._setup_detectron2_catalog()

    def _setup_nnunet_catalog(self):
        """Setup nnU-Net model catalog."""
        # nnU-Net pretrained models (example entries)
        self.nnunet_models = {
            "Task001_BrainTumour": ModelInfo(
                name="Task001_BrainTumour",
                framework="nnunet",
                url="https://zenodo.org/record/4003545/files/Task001_BrainTumour.zip",
                filename="Task001_BrainTumour.zip",
                checksum="dummy_checksum_001",
                description="Brain tumor segmentation model",
                task_type="segmentation",
                input_modalities=["FLAIR", "T1w", "T1gd", "T2w"],
                target_organs=["brain"]
            ),
            "Task003_Liver": ModelInfo(
                name="Task003_Liver",
                framework="nnunet",
                url="https://zenodo.org/record/4003545/files/Task003_Liver.zip",
                filename="Task003_Liver.zip",
                checksum="dummy_checksum_003",
                description="Liver and liver tumor segmentation",
                task_type="segmentation",
                input_modalities=["CT"],
                target_organs=["liver"]
            ),
            "Task010_Colon": ModelInfo(
                name="Task010_Colon",
                framework="nnunet",
                url="https://zenodo.org/record/4003545/files/Task010_Colon.zip",
                filename="Task010_Colon.zip",
                checksum="dummy_checksum_010",
                description="Colon cancer segmentation",
                task_type="segmentation",
                input_modalities=["CT"],
                target_organs=["colon"]
            )
        }
        logger.info(f"nnU-Net catalog initialized: {len(self.nnunet_models)} models")

    def _setup_monai_catalog(self):
        """Setup MONAI model catalog."""
        # MONAI model zoo entries
        self.monai_models = {
            "spleen_segmentation_3d": ModelInfo(
                name="spleen_segmentation_3d",
                framework="monai",
                url="https://github.com/Project-MONAI/model-zoo/releases/download/hosting_storage_v1/spleen_ct_segmentation_v0.1.0.zip",
                filename="spleen_ct_segmentation_v0.1.0.zip",
                checksum="dummy_checksum_spleen",
                description="3D spleen segmentation from CT",
                task_type="segmentation",
                input_modalities=["CT"],
                target_organs=["spleen"]
            ),
            "lung_nodule_ct_detection": ModelInfo(
                name="lung_nodule_ct_detection",
                framework="monai",
                url="https://github.com/Project-MONAI/model-zoo/releases/download/hosting_storage_v1/lung_nodule_ct_detection_v0.1.0.zip",
                filename="lung_nodule_ct_detection_v0.1.0.zip",
                checksum="dummy_checksum_lung",
                description="Lung nodule detection from CT",
                task_type="detection",
                input_modalities=["CT"],
                target_organs=["lung"]
            ),
            "cardiac_segmentation_3d": ModelInfo(
                name="cardiac_segmentation_3d",
                framework="monai",
                url="https://github.com/Project-MONAI/model-zoo/releases/download/hosting_storage_v1/cardiac_segmentation_v0.1.0.zip",
                filename="cardiac_segmentation_v0.1.0.zip",
                checksum="dummy_checksum_cardiac",
                description="Cardiac structure segmentation",
                task_type="segmentation",
                input_modalities=["MRI"],
                target_organs=["heart"]
            )
        }
        logger.info(f"MONAI catalog initialized: {len(self.monai_models)} models")

    def _setup_detectron2_catalog(self):
        """Setup Detectron2 model catalog."""
        # Detectron2 model zoo entries (adapted for medical imaging)
        self.detectron2_models = {
            "faster_rcnn_medical": ModelInfo(
                name="faster_rcnn_medical",
                framework="detectron2",
                url="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_50_FPN_3x/137849458/model_final_280758.pkl",
                filename="faster_rcnn_R_50_FPN_3x.pkl",
                checksum="dummy_checksum_faster",
                description="Faster R-CNN for medical object detection",
                task_type="detection",
                input_modalities=["CT", "MRI", "X-ray"],
                target_organs=["general"]
            ),
            "mask_rcnn_medical": ModelInfo(
                name="mask_rcnn_medical",
                framework="detectron2",
                url="https://dl.fbaipublicfiles.com/detectron2/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/model_final_f10217.pkl",
                filename="mask_rcnn_R_50_FPN_3x.pkl",
                checksum="dummy_checksum_mask",
                description="Mask R-CNN for medical instance segmentation",
                task_type="instance_segmentation",
                input_modalities=["CT", "MRI", "X-ray"],
                target_organs=["general"]
            )
        }
        logger.info(f"Detectron2 catalog initialized: {len(self.detectron2_models)} models")

    def list_available_models(self, 
                            framework: Optional[str] = None,
                            task_type: Optional[str] = None,
                            modality: Optional[str] = None) -> List[ModelInfo]:
        """
        List available pretrained models with optional filtering.

        Args:
            framework: Filter by framework (nnunet, monai, detectron2)
            task_type: Filter by task type (segmentation, detection, classification)
            modality: Filter by input modality (CT, MRI, X-ray, etc.)

        Returns:
            List of matching model information
        """
        all_models = []
        
        # Collect models from all frameworks
        if framework is None or framework == "nnunet":
            all_models.extend(self.nnunet_models.values())
        if framework is None or framework == "monai":
            all_models.extend(self.monai_models.values())
        if framework is None or framework == "detectron2":
            all_models.extend(self.detectron2_models.values())
        
        # Apply filters
        filtered_models = all_models
        
        if task_type:
            filtered_models = [m for m in filtered_models if m.task_type == task_type]
        
        if modality:
            filtered_models = [m for m in filtered_models if modality in m.input_modalities]
        
        return filtered_models

    def load_model_config(self, model_name: str) -> Optional[Dict[str, Any]]:
        """
        Load model configuration for transfer learning.

        Args:
            model_name: Name of the model

        Returns:
            Model configuration dictionary
        """
        # Find model info
        model_info = None
        for catalog in [self.nnunet_models, self.monai_models, self.detectron2_models]:
            if model_name in catalog:
                model_info = catalog[model_name]
                break
        
        if model_info is None:
            logger.error(f"Model {model_name} not found")
            return None
        
        # Framework-specific configuration loading
        if model_info.framework == "nnunet":
            return self._load_nnunet_config(model_name)
        elif model_info.framework == "monai":
            return self._load_monai_config(model_name)
        elif model_info.framework == "detectron2":
            return self._load_detectron2_config(model_name)
        
        return None

    def _load_nnunet_config(self, model_name: str) -> Dict[str, Any]:
        """Load nnU-Net model configuration."""
        config = {
            "framework": "nnunet",
            "model_name": model_name,
            "architecture": "3D_fullres",
            "trainer": "nnUNetTrainerV2",
            "preprocessing": {
                "normalization": "z_score",
                "resampling": "third_order",
                "cropping": "foreground"
            }
        }
        return config

    def _load_monai_config(self, model_name: str) -> Dict[str, Any]:
        """Load MONAI model configuration."""
        config = {
            "framework": "monai",
            "model_name": model_name,
            "architecture": "UNet",
            "spatial_dims": 3,
            "in_channels": 1,
            "out_channels": 2,
            "loss_function": "DiceLoss"
        }
        return config

    def _load_detectron2_config(self, model_name: str) -> Dict[str, Any]:
        """Load Detectron2 model configuration."""
        config = {
            "framework": "detectron2",
            "model_name": model_name,
            "architecture": "FasterRCNN",
            "backbone": "ResNet50",
            "num_classes": 2
        }
        return config

    def get_model_summary(self) -> Dict[str, Any]:
        """Get summary of all available models."""
        summary = {
            "total_models": len(self.nnunet_models) + len(self.monai_models) + len(self.detectron2_models),
            "frameworks": {
                "nnunet": len(self.nnunet_models),
                "monai": len(self.monai_models),
                "detectron2": len(self.detectron2_models)
            },
            "task_types": {},
            "modalities": {},
            "target_organs": {}
        }
        
        # Analyze all models
        all_models = self.list_available_models()
        
        for model in all_models:
            # Count task types
            task_type = model.task_type
            summary["task_types"][task_type] = summary["task_types"].get(task_type, 0) + 1
            
            # Count modalities
            for modality in model.input_modalities:
                summary["modalities"][modality] = summary["modalities"].get(modality, 0) + 1
            
            # Count target organs
            for organ in model.target_organs:
                summary["target_organs"][organ] = summary["target_organs"].get(organ, 0) + 1
        
        return summary
