"""
Pose estimation service for WiFi-DensePose API
"""

import logging
import asyncio
from typing import Dict, List, Optional, Any
from datetime import datetime, timedelta

import numpy as np
import torch

from src.config.settings import Settings
from src.config.domains import DomainConfig
from src.core.csi_processor import CSIProcessor
from src.core.phase_sanitizer import PhaseSanitizer
from src.models.densepose_head import DensePoseHead
from src.models.modality_translation import ModalityTranslationNetwork

logger = logging.getLogger(__name__)


class PoseService:
    """Service for pose estimation operations."""
    
    def __init__(self, settings: Settings, domain_config: DomainConfig):
        """Initialize pose service."""
        self.settings = settings
        self.domain_config = domain_config
        self.logger = logging.getLogger(__name__)
        
        # Initialize components
        self.csi_processor = None
        self.phase_sanitizer = None
        self.densepose_model = None
        self.modality_translator = None
        
        # Service state
        self.is_initialized = False
        self.is_running = False
        self.last_error = None
        
        # Processing statistics
        self.stats = {
            "total_processed": 0,
            "successful_detections": 0,
            "failed_detections": 0,
            "average_confidence": 0.0,
            "processing_time_ms": 0.0
        }
    
    async def initialize(self):
        """Initialize the pose service."""
        try:
            self.logger.info("Initializing pose service...")
            
            # Initialize CSI processor
            csi_config = {
                'buffer_size': self.settings.csi_buffer_size,
                'sample_rate': 1000,  # Default sampling rate
                'num_subcarriers': 56,
                'num_antennas': 3
            }
            self.csi_processor = CSIProcessor(config=csi_config)
            
            # Initialize phase sanitizer
            self.phase_sanitizer = PhaseSanitizer()
            
            # Initialize models if not mocking
            if not self.settings.mock_pose_data:
                await self._initialize_models()
            else:
                self.logger.info("Using mock pose data for development")
            
            self.is_initialized = True
            self.logger.info("Pose service initialized successfully")
            
        except Exception as e:
            self.last_error = str(e)
            self.logger.error(f"Failed to initialize pose service: {e}")
            raise
    
    async def _initialize_models(self):
        """Initialize neural network models."""
        try:
            # Initialize DensePose model
            if self.settings.pose_model_path:
                self.densepose_model = DensePoseHead()
                # Load model weights if path is provided
                # model_state = torch.load(self.settings.pose_model_path)
                # self.densepose_model.load_state_dict(model_state)
                self.logger.info("DensePose model loaded")
            else:
                self.logger.warning("No pose model path provided, using default model")
                self.densepose_model = DensePoseHead()
            
            # Initialize modality translation
            config = {
                'input_channels': 64,  # CSI data channels
                'hidden_channels': [128, 256, 512],
                'output_channels': 256,  # Visual feature channels
                'use_attention': True
            }
            self.modality_translator = ModalityTranslationNetwork(config)
            
            # Set models to evaluation mode
            self.densepose_model.eval()
            self.modality_translator.eval()
            
        except Exception as e:
            self.logger.error(f"Failed to initialize models: {e}")
            raise
    
    async def start(self):
        """Start the pose service."""
        if not self.is_initialized:
            await self.initialize()
        
        self.is_running = True
        self.logger.info("Pose service started")
    
    async def stop(self):
        """Stop the pose service."""
        self.is_running = False
        self.logger.info("Pose service stopped")
    
    async def process_csi_data(self, csi_data: np.ndarray, metadata: Dict[str, Any]) -> Dict[str, Any]:
        """Process CSI data and estimate poses."""
        if not self.is_running:
            raise RuntimeError("Pose service is not running")
        
        start_time = datetime.now()
        
        try:
            # Process CSI data
            processed_csi = await self._process_csi(csi_data, metadata)
            
            # Estimate poses
            poses = await self._estimate_poses(processed_csi, metadata)
            
            # Update statistics
            processing_time = (datetime.now() - start_time).total_seconds() * 1000
            self._update_stats(poses, processing_time)
            
            return {
                "timestamp": start_time.isoformat(),
                "poses": poses,
                "metadata": metadata,
                "processing_time_ms": processing_time,
                "confidence_scores": [pose.get("confidence", 0.0) for pose in poses]
            }
            
        except Exception as e:
            self.last_error = str(e)
            self.stats["failed_detections"] += 1
            self.logger.error(f"Error processing CSI data: {e}")
            raise
    
    async def _process_csi(self, csi_data: np.ndarray, metadata: Dict[str, Any]) -> np.ndarray:
        """Process raw CSI data."""
        # Add CSI data to processor
        self.csi_processor.add_data(csi_data, metadata.get("timestamp", datetime.now()))
        
        # Get processed data
        processed_data = self.csi_processor.get_processed_data()
        
        # Apply phase sanitization
        if processed_data is not None:
            sanitized_data = self.phase_sanitizer.sanitize(processed_data)
            return sanitized_data
        
        return csi_data
    
    async def _estimate_poses(self, csi_data: np.ndarray, metadata: Dict[str, Any]) -> List[Dict[str, Any]]:
        """Estimate poses from processed CSI data."""
        if self.settings.mock_pose_data:
            return self._generate_mock_poses()
        
        try:
            # Convert CSI data to tensor
            csi_tensor = torch.from_numpy(csi_data).float()
            
            # Add batch dimension if needed
            if len(csi_tensor.shape) == 2:
                csi_tensor = csi_tensor.unsqueeze(0)
            
            # Translate modality (CSI to visual-like features)
            with torch.no_grad():
                visual_features = self.modality_translator(csi_tensor)
                
                # Estimate poses using DensePose
                pose_outputs = self.densepose_model(visual_features)
            
            # Convert outputs to pose detections
            poses = self._parse_pose_outputs(pose_outputs)
            
            # Filter by confidence threshold
            filtered_poses = [
                pose for pose in poses 
                if pose.get("confidence", 0.0) >= self.settings.pose_confidence_threshold
            ]
            
            # Limit number of persons
            if len(filtered_poses) > self.settings.pose_max_persons:
                filtered_poses = sorted(
                    filtered_poses, 
                    key=lambda x: x.get("confidence", 0.0), 
                    reverse=True
                )[:self.settings.pose_max_persons]
            
            return filtered_poses
            
        except Exception as e:
            self.logger.error(f"Error in pose estimation: {e}")
            return []
    
    def _parse_pose_outputs(self, outputs: torch.Tensor) -> List[Dict[str, Any]]:
        """Parse neural network outputs into pose detections."""
        poses = []
        
        # This is a simplified parsing - in reality, this would depend on the model architecture
        # For now, generate mock poses based on the output shape
        batch_size = outputs.shape[0]
        
        for i in range(batch_size):
            # Extract pose information (mock implementation)
            confidence = float(torch.sigmoid(outputs[i, 0]).item()) if outputs.shape[1] > 0 else 0.5
            
            pose = {
                "person_id": i,
                "confidence": confidence,
                "keypoints": self._generate_keypoints(),
                "bounding_box": self._generate_bounding_box(),
                "activity": self._classify_activity(outputs[i] if len(outputs.shape) > 1 else outputs),
                "timestamp": datetime.now().isoformat()
            }
            
            poses.append(pose)
        
        return poses
    
    def _generate_mock_poses(self) -> List[Dict[str, Any]]:
        """Generate mock pose data for development."""
        import random
        
        num_persons = random.randint(1, min(3, self.settings.pose_max_persons))
        poses = []
        
        for i in range(num_persons):
            confidence = random.uniform(0.3, 0.95)
            
            pose = {
                "person_id": i,
                "confidence": confidence,
                "keypoints": self._generate_keypoints(),
                "bounding_box": self._generate_bounding_box(),
                "activity": random.choice(["standing", "sitting", "walking", "lying"]),
                "timestamp": datetime.now().isoformat()
            }
            
            poses.append(pose)
        
        return poses
    
    def _generate_keypoints(self) -> List[Dict[str, Any]]:
        """Generate keypoints for a person."""
        import random
        
        keypoint_names = [
            "nose", "left_eye", "right_eye", "left_ear", "right_ear",
            "left_shoulder", "right_shoulder", "left_elbow", "right_elbow",
            "left_wrist", "right_wrist", "left_hip", "right_hip",
            "left_knee", "right_knee", "left_ankle", "right_ankle"
        ]
        
        keypoints = []
        for name in keypoint_names:
            keypoints.append({
                "name": name,
                "x": random.uniform(0.1, 0.9),
                "y": random.uniform(0.1, 0.9),
                "confidence": random.uniform(0.5, 0.95)
            })
        
        return keypoints
    
    def _generate_bounding_box(self) -> Dict[str, float]:
        """Generate bounding box for a person."""
        import random
        
        x = random.uniform(0.1, 0.6)
        y = random.uniform(0.1, 0.6)
        width = random.uniform(0.2, 0.4)
        height = random.uniform(0.3, 0.5)
        
        return {
            "x": x,
            "y": y,
            "width": width,
            "height": height
        }
    
    def _classify_activity(self, features: torch.Tensor) -> str:
        """Classify activity from features."""
        # Simple mock classification
        import random
        activities = ["standing", "sitting", "walking", "lying", "unknown"]
        return random.choice(activities)
    
    def _update_stats(self, poses: List[Dict[str, Any]], processing_time: float):
        """Update processing statistics."""
        self.stats["total_processed"] += 1
        
        if poses:
            self.stats["successful_detections"] += 1
            confidences = [pose.get("confidence", 0.0) for pose in poses]
            avg_confidence = sum(confidences) / len(confidences)
            
            # Update running average
            total = self.stats["successful_detections"]
            current_avg = self.stats["average_confidence"]
            self.stats["average_confidence"] = (current_avg * (total - 1) + avg_confidence) / total
        else:
            self.stats["failed_detections"] += 1
        
        # Update processing time (running average)
        total = self.stats["total_processed"]
        current_avg = self.stats["processing_time_ms"]
        self.stats["processing_time_ms"] = (current_avg * (total - 1) + processing_time) / total
    
    async def get_status(self) -> Dict[str, Any]:
        """Get service status."""
        return {
            "status": "healthy" if self.is_running and not self.last_error else "unhealthy",
            "initialized": self.is_initialized,
            "running": self.is_running,
            "last_error": self.last_error,
            "statistics": self.stats.copy(),
            "configuration": {
                "mock_data": self.settings.mock_pose_data,
                "confidence_threshold": self.settings.pose_confidence_threshold,
                "max_persons": self.settings.pose_max_persons,
                "batch_size": self.settings.pose_processing_batch_size
            }
        }
    
    async def get_metrics(self) -> Dict[str, Any]:
        """Get service metrics."""
        return {
            "pose_service": {
                "total_processed": self.stats["total_processed"],
                "successful_detections": self.stats["successful_detections"],
                "failed_detections": self.stats["failed_detections"],
                "success_rate": (
                    self.stats["successful_detections"] / max(1, self.stats["total_processed"])
                ),
                "average_confidence": self.stats["average_confidence"],
                "average_processing_time_ms": self.stats["processing_time_ms"]
            }
        }
    
    async def reset(self):
        """Reset service state."""
        self.stats = {
            "total_processed": 0,
            "successful_detections": 0,
            "failed_detections": 0,
            "average_confidence": 0.0,
            "processing_time_ms": 0.0
        }
        self.last_error = None
        self.logger.info("Pose service reset")
    
    # API endpoint methods
    async def estimate_poses(self, zone_ids=None, confidence_threshold=None, max_persons=None,
                           include_keypoints=True, include_segmentation=False):
        """Estimate poses with API parameters."""
        try:
            # Generate mock CSI data for estimation
            mock_csi = np.random.randn(64, 56, 3)  # Mock CSI data
            metadata = {
                "timestamp": datetime.now(),
                "zone_ids": zone_ids or ["zone_1"],
                "confidence_threshold": confidence_threshold or self.settings.pose_confidence_threshold,
                "max_persons": max_persons or self.settings.pose_max_persons
            }
            
            # Process the data
            result = await self.process_csi_data(mock_csi, metadata)
            
            # Format for API response
            persons = []
            for i, pose in enumerate(result["poses"]):
                person = {
                    "person_id": str(pose["person_id"]),
                    "confidence": pose["confidence"],
                    "bounding_box": pose["bounding_box"],
                    "zone_id": zone_ids[0] if zone_ids else "zone_1",
                    "activity": pose["activity"],
                    "timestamp": datetime.fromisoformat(pose["timestamp"])
                }
                
                if include_keypoints:
                    person["keypoints"] = pose["keypoints"]
                
                if include_segmentation:
                    person["segmentation"] = {"mask": "mock_segmentation_data"}
                
                persons.append(person)
            
            # Zone summary
            zone_summary = {}
            for zone_id in (zone_ids or ["zone_1"]):
                zone_summary[zone_id] = len([p for p in persons if p.get("zone_id") == zone_id])
            
            return {
                "timestamp": datetime.now(),
                "frame_id": f"frame_{int(datetime.now().timestamp())}",
                "persons": persons,
                "zone_summary": zone_summary,
                "processing_time_ms": result["processing_time_ms"],
                "metadata": {"mock_data": self.settings.mock_pose_data}
            }
            
        except Exception as e:
            self.logger.error(f"Error in estimate_poses: {e}")
            raise
    
    async def analyze_with_params(self, zone_ids=None, confidence_threshold=None, max_persons=None,
                                include_keypoints=True, include_segmentation=False):
        """Analyze pose data with custom parameters."""
        return await self.estimate_poses(zone_ids, confidence_threshold, max_persons,
                                       include_keypoints, include_segmentation)
    
    async def get_zone_occupancy(self, zone_id: str):
        """Get current occupancy for a specific zone."""
        try:
            # Mock occupancy data
            import random
            count = random.randint(0, 5)
            persons = []
            
            for i in range(count):
                persons.append({
                    "person_id": f"person_{i}",
                    "confidence": random.uniform(0.7, 0.95),
                    "activity": random.choice(["standing", "sitting", "walking"])
                })
            
            return {
                "count": count,
                "max_occupancy": 10,
                "persons": persons,
                "timestamp": datetime.now()
            }
            
        except Exception as e:
            self.logger.error(f"Error getting zone occupancy: {e}")
            return None
    
    async def get_zones_summary(self):
        """Get occupancy summary for all zones."""
        try:
            import random
            zones = ["zone_1", "zone_2", "zone_3", "zone_4"]
            zone_data = {}
            total_persons = 0
            active_zones = 0
            
            for zone_id in zones:
                count = random.randint(0, 3)
                zone_data[zone_id] = {
                    "occupancy": count,
                    "max_occupancy": 10,
                    "status": "active" if count > 0 else "inactive"
                }
                total_persons += count
                if count > 0:
                    active_zones += 1
            
            return {
                "total_persons": total_persons,
                "zones": zone_data,
                "active_zones": active_zones
            }
            
        except Exception as e:
            self.logger.error(f"Error getting zones summary: {e}")
            raise
    
    async def get_historical_data(self, start_time, end_time, zone_ids=None,
                                aggregation_interval=300, include_raw_data=False):
        """Get historical pose estimation data."""
        try:
            # Mock historical data
            import random
            from datetime import timedelta
            
            current_time = start_time
            aggregated_data = []
            raw_data = [] if include_raw_data else None
            
            while current_time < end_time:
                # Generate aggregated data point
                data_point = {
                    "timestamp": current_time,
                    "total_persons": random.randint(0, 8),
                    "zones": {}
                }
                
                for zone_id in (zone_ids or ["zone_1", "zone_2", "zone_3"]):
                    data_point["zones"][zone_id] = {
                        "occupancy": random.randint(0, 3),
                        "avg_confidence": random.uniform(0.7, 0.95)
                    }
                
                aggregated_data.append(data_point)
                
                # Generate raw data if requested
                if include_raw_data:
                    for _ in range(random.randint(0, 5)):
                        raw_data.append({
                            "timestamp": current_time + timedelta(seconds=random.randint(0, aggregation_interval)),
                            "person_id": f"person_{random.randint(1, 10)}",
                            "zone_id": random.choice(zone_ids or ["zone_1", "zone_2", "zone_3"]),
                            "confidence": random.uniform(0.5, 0.95),
                            "activity": random.choice(["standing", "sitting", "walking"])
                        })
                
                current_time += timedelta(seconds=aggregation_interval)
            
            return {
                "aggregated_data": aggregated_data,
                "raw_data": raw_data,
                "total_records": len(aggregated_data)
            }
            
        except Exception as e:
            self.logger.error(f"Error getting historical data: {e}")
            raise
    
    async def get_recent_activities(self, zone_id=None, limit=10):
        """Get recently detected activities."""
        try:
            import random
            activities = []
            
            for i in range(limit):
                activity = {
                    "activity_id": f"activity_{i}",
                    "person_id": f"person_{random.randint(1, 5)}",
                    "zone_id": zone_id or random.choice(["zone_1", "zone_2", "zone_3"]),
                    "activity": random.choice(["standing", "sitting", "walking", "lying"]),
                    "confidence": random.uniform(0.6, 0.95),
                    "timestamp": datetime.now() - timedelta(minutes=random.randint(0, 60)),
                    "duration_seconds": random.randint(10, 300)
                }
                activities.append(activity)
            
            return activities
            
        except Exception as e:
            self.logger.error(f"Error getting recent activities: {e}")
            raise
    
    async def is_calibrating(self):
        """Check if calibration is in progress."""
        return False  # Mock implementation
    
    async def start_calibration(self):
        """Start calibration process."""
        import uuid
        calibration_id = str(uuid.uuid4())
        self.logger.info(f"Started calibration: {calibration_id}")
        return calibration_id
    
    async def run_calibration(self, calibration_id):
        """Run calibration process."""
        self.logger.info(f"Running calibration: {calibration_id}")
        # Mock calibration process
        await asyncio.sleep(5)
        self.logger.info(f"Calibration completed: {calibration_id}")
    
    async def get_calibration_status(self):
        """Get current calibration status."""
        return {
            "is_calibrating": False,
            "calibration_id": None,
            "progress_percent": 100,
            "current_step": "completed",
            "estimated_remaining_minutes": 0,
            "last_calibration": datetime.now() - timedelta(hours=1)
        }
    
    async def get_statistics(self, start_time, end_time):
        """Get pose estimation statistics."""
        try:
            import random
            
            # Mock statistics
            total_detections = random.randint(100, 1000)
            successful_detections = int(total_detections * random.uniform(0.8, 0.95))
            
            return {
                "total_detections": total_detections,
                "successful_detections": successful_detections,
                "failed_detections": total_detections - successful_detections,
                "success_rate": successful_detections / total_detections,
                "average_confidence": random.uniform(0.75, 0.90),
                "average_processing_time_ms": random.uniform(50, 200),
                "unique_persons": random.randint(5, 20),
                "most_active_zone": random.choice(["zone_1", "zone_2", "zone_3"]),
                "activity_distribution": {
                    "standing": random.uniform(0.3, 0.5),
                    "sitting": random.uniform(0.2, 0.4),
                    "walking": random.uniform(0.1, 0.3),
                    "lying": random.uniform(0.0, 0.1)
                }
            }
            
        except Exception as e:
            self.logger.error(f"Error getting statistics: {e}")
            raise
    
    async def process_segmentation_data(self, frame_id):
        """Process segmentation data in background."""
        self.logger.info(f"Processing segmentation data for frame: {frame_id}")
        # Mock background processing
        await asyncio.sleep(2)
        self.logger.info(f"Segmentation processing completed for frame: {frame_id}")
    
    # WebSocket streaming methods
    async def get_current_pose_data(self):
        """Get current pose data for streaming."""
        try:
            # Generate current pose data
            result = await self.estimate_poses()
            
            # Format data by zones for WebSocket streaming
            zone_data = {}
            
            # Group persons by zone
            for person in result["persons"]:
                zone_id = person.get("zone_id", "zone_1")
                
                if zone_id not in zone_data:
                    zone_data[zone_id] = {
                        "pose": {
                            "persons": [],
                            "count": 0
                        },
                        "confidence": 0.0,
                        "activity": None,
                        "metadata": {
                            "frame_id": result["frame_id"],
                            "processing_time_ms": result["processing_time_ms"]
                        }
                    }
                
                zone_data[zone_id]["pose"]["persons"].append(person)
                zone_data[zone_id]["pose"]["count"] += 1
                
                # Update zone confidence (average)
                current_confidence = zone_data[zone_id]["confidence"]
                person_confidence = person.get("confidence", 0.0)
                zone_data[zone_id]["confidence"] = (current_confidence + person_confidence) / 2
                
                # Set activity if not already set
                if not zone_data[zone_id]["activity"] and person.get("activity"):
                    zone_data[zone_id]["activity"] = person["activity"]
            
            return zone_data
            
        except Exception as e:
            self.logger.error(f"Error getting current pose data: {e}")
            # Return empty zone data on error
            return {}
    
    # Health check methods
    async def health_check(self):
        """Perform health check."""
        try:
            status = "healthy" if self.is_running and not self.last_error else "unhealthy"
            
            return {
                "status": status,
                "message": self.last_error if self.last_error else "Service is running normally",
                "uptime_seconds": 0.0,  # TODO: Implement actual uptime tracking
                "metrics": {
                    "total_processed": self.stats["total_processed"],
                    "success_rate": (
                        self.stats["successful_detections"] / max(1, self.stats["total_processed"])
                    ),
                    "average_processing_time_ms": self.stats["processing_time_ms"]
                }
            }
            
        except Exception as e:
            return {
                "status": "unhealthy",
                "message": f"Health check failed: {str(e)}"
            }
    
    async def is_ready(self):
        """Check if service is ready."""
        return self.is_initialized and self.is_running