"""
Multi-Modal Agent Service - Phase 5.1
Advanced AI agent capabilities with unified multi-modal processing pipeline
"""

import asyncio
import logging
from typing import Dict, List, Any, Optional, Union
from datetime import datetime
from enum import Enum
import json

from ..storage import SessionDep
from ..domain import AIAgentWorkflow, AgentExecution, AgentStatus

logger = logging.getLogger(__name__)


class ModalityType(str, Enum):
    """Supported data modalities"""
    TEXT = "text"
    IMAGE = "image"
    AUDIO = "audio"
    VIDEO = "video"
    TABULAR = "tabular"
    GRAPH = "graph"


class ProcessingMode(str, Enum):
    """Multi-modal processing modes"""
    SEQUENTIAL = "sequential"
    PARALLEL = "parallel"
    FUSION = "fusion"
    ATTENTION = "attention"


class MultiModalAgentService:
    """Service for advanced multi-modal agent capabilities"""
    
    def __init__(self, session: SessionDep):
        self.session = session
        self._modality_processors = {
            ModalityType.TEXT: self._process_text,
            ModalityType.IMAGE: self._process_image,
            ModalityType.AUDIO: self._process_audio,
            ModalityType.VIDEO: self._process_video,
            ModalityType.TABULAR: self._process_tabular,
            ModalityType.GRAPH: self._process_graph
        }
        self._cross_modal_attention = CrossModalAttentionProcessor()
        self._performance_tracker = MultiModalPerformanceTracker()
    
    async def process_multimodal_input(
        self,
        agent_id: str,
        inputs: Dict[str, Any],
        processing_mode: ProcessingMode = ProcessingMode.FUSION,
        optimization_config: Optional[Dict[str, Any]] = None
    ) -> Dict[str, Any]:
        """
        Process multi-modal input with unified pipeline
        
        Args:
            agent_id: Agent identifier
            inputs: Multi-modal input data
            processing_mode: Processing strategy
            optimization_config: Performance optimization settings
            
        Returns:
            Processing results with performance metrics
        """
        
        start_time = datetime.utcnow()
        
        try:
            # Validate input modalities
            modalities = self._validate_modalities(inputs)
            
            # Initialize processing context
            context = {
                "agent_id": agent_id,
                "modalities": modalities,
                "processing_mode": processing_mode,
                "optimization_config": optimization_config or {},
                "start_time": start_time
            }
            
            # Process based on mode
            if processing_mode == ProcessingMode.SEQUENTIAL:
                results = await self._process_sequential(context, inputs)
            elif processing_mode == ProcessingMode.PARALLEL:
                results = await self._process_parallel(context, inputs)
            elif processing_mode == ProcessingMode.FUSION:
                results = await self._process_fusion(context, inputs)
            elif processing_mode == ProcessingMode.ATTENTION:
                results = await self._process_attention(context, inputs)
            else:
                raise ValueError(f"Unsupported processing mode: {processing_mode}")
            
            # Calculate performance metrics
            processing_time = (datetime.utcnow() - start_time).total_seconds()
            performance_metrics = await self._performance_tracker.calculate_metrics(
                context, results, processing_time
            )
            
            # Update agent execution record
            await self._update_agent_execution(agent_id, results, performance_metrics)
            
            return {
                "agent_id": agent_id,
                "processing_mode": processing_mode,
                "modalities_processed": modalities,
                "results": results,
                "performance_metrics": performance_metrics,
                "processing_time_seconds": processing_time,
                "timestamp": datetime.utcnow().isoformat()
            }
            
        except Exception as e:
            logger.error(f"Multi-modal processing failed for agent {agent_id}: {e}")
            raise
    
    def _validate_modalities(self, inputs: Dict[str, Any]) -> List[ModalityType]:
        """Validate and identify input modalities"""
        modalities = []
        
        for key, value in inputs.items():
            if key.startswith("text_") or isinstance(value, str):
                modalities.append(ModalityType.TEXT)
            elif key.startswith("image_") or self._is_image_data(value):
                modalities.append(ModalityType.IMAGE)
            elif key.startswith("audio_") or self._is_audio_data(value):
                modalities.append(ModalityType.AUDIO)
            elif key.startswith("video_") or self._is_video_data(value):
                modalities.append(ModalityType.VIDEO)
            elif key.startswith("tabular_") or self._is_tabular_data(value):
                modalities.append(ModalityType.TABULAR)
            elif key.startswith("graph_") or self._is_graph_data(value):
                modalities.append(ModalityType.GRAPH)
        
        return list(set(modalities))  # Remove duplicates
    
    async def _process_sequential(
        self, 
        context: Dict[str, Any], 
        inputs: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Process modalities sequentially"""
        results = {}
        
        for modality in context["modalities"]:
            modality_inputs = self._filter_inputs_by_modality(inputs, modality)
            processor = self._modality_processors[modality]
            
            try:
                modality_result = await processor(context, modality_inputs)
                results[modality.value] = modality_result
            except Exception as e:
                logger.error(f"Sequential processing failed for {modality}: {e}")
                results[modality.value] = {"error": str(e)}
        
        return results
    
    async def _process_parallel(
        self, 
        context: Dict[str, Any], 
        inputs: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Process modalities in parallel"""
        tasks = []
        
        for modality in context["modalities"]:
            modality_inputs = self._filter_inputs_by_modality(inputs, modality)
            processor = self._modality_processors[modality]
            task = processor(context, modality_inputs)
            tasks.append((modality, task))
        
        # Execute all tasks concurrently
        results = {}
        completed_tasks = await asyncio.gather(
            *[task for _, task in tasks], 
            return_exceptions=True
        )
        
        for (modality, _), result in zip(tasks, completed_tasks):
            if isinstance(result, Exception):
                logger.error(f"Parallel processing failed for {modality}: {result}")
                results[modality.value] = {"error": str(result)}
            else:
                results[modality.value] = result
        
        return results
    
    async def _process_fusion(
        self, 
        context: Dict[str, Any], 
        inputs: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Process modalities with fusion strategy"""
        # First process each modality
        individual_results = await self._process_parallel(context, inputs)
        
        # Then fuse results
        fusion_result = await self._fuse_modalities(individual_results, context)
        
        return {
            "individual_results": individual_results,
            "fusion_result": fusion_result,
            "fusion_strategy": "cross_modal_attention"
        }
    
    async def _process_attention(
        self, 
        context: Dict[str, Any], 
        inputs: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Process modalities with cross-modal attention"""
        # Process modalities
        modality_results = await self._process_parallel(context, inputs)
        
        # Apply cross-modal attention
        attention_result = await self._cross_modal_attention.process(
            modality_results, 
            context
        )
        
        return {
            "modality_results": modality_results,
            "attention_weights": attention_result["attention_weights"],
            "attended_features": attention_result["attended_features"],
            "final_output": attention_result["final_output"]
        }
    
    def _filter_inputs_by_modality(
        self, 
        inputs: Dict[str, Any], 
        modality: ModalityType
    ) -> Dict[str, Any]:
        """Filter inputs by modality type"""
        filtered = {}
        
        for key, value in inputs.items():
            if modality == ModalityType.TEXT and (key.startswith("text_") or isinstance(value, str)):
                filtered[key] = value
            elif modality == ModalityType.IMAGE and (key.startswith("image_") or self._is_image_data(value)):
                filtered[key] = value
            elif modality == ModalityType.AUDIO and (key.startswith("audio_") or self._is_audio_data(value)):
                filtered[key] = value
            elif modality == ModalityType.VIDEO and (key.startswith("video_") or self._is_video_data(value)):
                filtered[key] = value
            elif modality == ModalityType.TABULAR and (key.startswith("tabular_") or self._is_tabular_data(value)):
                filtered[key] = value
            elif modality == ModalityType.GRAPH and (key.startswith("graph_") or self._is_graph_data(value)):
                filtered[key] = value
        
        return filtered
    
    # Modality-specific processors
    async def _process_text(
        self, 
        context: Dict[str, Any], 
        inputs: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Process text modality"""
        texts = []
        for key, value in inputs.items():
            if isinstance(value, str):
                texts.append({"key": key, "text": value})
        
        # Simulate advanced NLP processing
        processed_texts = []
        for text_item in texts:
            result = {
                "original_text": text_item["text"],
                "processed_features": self._extract_text_features(text_item["text"]),
                "embeddings": self._generate_text_embeddings(text_item["text"]),
                "sentiment": self._analyze_sentiment(text_item["text"]),
                "entities": self._extract_entities(text_item["text"])
            }
            processed_texts.append(result)
        
        return {
            "modality": "text",
            "processed_count": len(processed_texts),
            "results": processed_texts,
            "processing_strategy": "transformer_based"
        }
    
    async def _process_image(
        self, 
        context: Dict[str, Any], 
        inputs: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Process image modality"""
        images = []
        for key, value in inputs.items():
            if self._is_image_data(value):
                images.append({"key": key, "data": value})
        
        # Simulate computer vision processing
        processed_images = []
        for image_item in images:
            result = {
                "original_key": image_item["key"],
                "visual_features": self._extract_visual_features(image_item["data"]),
                "objects_detected": self._detect_objects(image_item["data"]),
                "scene_analysis": self._analyze_scene(image_item["data"]),
                "embeddings": self._generate_image_embeddings(image_item["data"])
            }
            processed_images.append(result)
        
        return {
            "modality": "image",
            "processed_count": len(processed_images),
            "results": processed_images,
            "processing_strategy": "vision_transformer"
        }
    
    async def _process_audio(
        self, 
        context: Dict[str, Any], 
        inputs: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Process audio modality"""
        audio_files = []
        for key, value in inputs.items():
            if self._is_audio_data(value):
                audio_files.append({"key": key, "data": value})
        
        # Simulate audio processing
        processed_audio = []
        for audio_item in audio_files:
            result = {
                "original_key": audio_item["key"],
                "audio_features": self._extract_audio_features(audio_item["data"]),
                "speech_recognition": self._recognize_speech(audio_item["data"]),
                "audio_classification": self._classify_audio(audio_item["data"]),
                "embeddings": self._generate_audio_embeddings(audio_item["data"])
            }
            processed_audio.append(result)
        
        return {
            "modality": "audio",
            "processed_count": len(processed_audio),
            "results": processed_audio,
            "processing_strategy": "spectrogram_analysis"
        }
    
    async def _process_video(
        self, 
        context: Dict[str, Any], 
        inputs: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Process video modality"""
        videos = []
        for key, value in inputs.items():
            if self._is_video_data(value):
                videos.append({"key": key, "data": value})
        
        # Simulate video processing
        processed_videos = []
        for video_item in videos:
            result = {
                "original_key": video_item["key"],
                "temporal_features": self._extract_temporal_features(video_item["data"]),
                "frame_analysis": self._analyze_frames(video_item["data"]),
                "action_recognition": self._recognize_actions(video_item["data"]),
                "embeddings": self._generate_video_embeddings(video_item["data"])
            }
            processed_videos.append(result)
        
        return {
            "modality": "video",
            "processed_count": len(processed_videos),
            "results": processed_videos,
            "processing_strategy": "3d_convolution"
        }
    
    async def _process_tabular(
        self, 
        context: Dict[str, Any], 
        inputs: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Process tabular data modality"""
        tabular_data = []
        for key, value in inputs.items():
            if self._is_tabular_data(value):
                tabular_data.append({"key": key, "data": value})
        
        # Simulate tabular processing
        processed_tabular = []
        for tabular_item in tabular_data:
            result = {
                "original_key": tabular_item["key"],
                "statistical_features": self._extract_statistical_features(tabular_item["data"]),
                "patterns": self._detect_patterns(tabular_item["data"]),
                "anomalies": self._detect_anomalies(tabular_item["data"]),
                "embeddings": self._generate_tabular_embeddings(tabular_item["data"])
            }
            processed_tabular.append(result)
        
        return {
            "modality": "tabular",
            "processed_count": len(processed_tabular),
            "results": processed_tabular,
            "processing_strategy": "gradient_boosting"
        }
    
    async def _process_graph(
        self, 
        context: Dict[str, Any], 
        inputs: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Process graph data modality"""
        graphs = []
        for key, value in inputs.items():
            if self._is_graph_data(value):
                graphs.append({"key": key, "data": value})
        
        # Simulate graph processing
        processed_graphs = []
        for graph_item in graphs:
            result = {
                "original_key": graph_item["key"],
                "graph_features": self._extract_graph_features(graph_item["data"]),
                "node_embeddings": self._generate_node_embeddings(graph_item["data"]),
                "graph_classification": self._classify_graph(graph_item["data"]),
                "community_detection": self._detect_communities(graph_item["data"])
            }
            processed_graphs.append(result)
        
        return {
            "modality": "graph",
            "processed_count": len(processed_graphs),
            "results": processed_graphs,
            "processing_strategy": "graph_neural_network"
        }
    
    # Helper methods for data type detection
    def _is_image_data(self, data: Any) -> bool:
        """Check if data is image-like"""
        if isinstance(data, dict):
            return any(key in data for key in ["image_data", "pixels", "width", "height"])
        return False
    
    def _is_audio_data(self, data: Any) -> bool:
        """Check if data is audio-like"""
        if isinstance(data, dict):
            return any(key in data for key in ["audio_data", "waveform", "sample_rate", "spectrogram"])
        return False
    
    def _is_video_data(self, data: Any) -> bool:
        """Check if data is video-like"""
        if isinstance(data, dict):
            return any(key in data for key in ["video_data", "frames", "fps", "duration"])
        return False
    
    def _is_tabular_data(self, data: Any) -> bool:
        """Check if data is tabular-like"""
        if isinstance(data, (list, dict)):
            return True  # Simplified detection
        return False
    
    def _is_graph_data(self, data: Any) -> bool:
        """Check if data is graph-like"""
        if isinstance(data, dict):
            return any(key in data for key in ["nodes", "edges", "adjacency", "graph"])
        return False
    
    # Feature extraction methods (simulated)
    def _extract_text_features(self, text: str) -> Dict[str, Any]:
        """Extract text features"""
        return {
            "length": len(text),
            "word_count": len(text.split()),
            "language": "en",  # Simplified
            "complexity": "medium"
        }
    
    def _generate_text_embeddings(self, text: str) -> List[float]:
        """Generate text embeddings"""
        # Simulate 768-dim embedding
        return [0.1 * i % 1.0 for i in range(768)]
    
    def _analyze_sentiment(self, text: str) -> Dict[str, float]:
        """Analyze sentiment"""
        return {"positive": 0.6, "negative": 0.2, "neutral": 0.2}
    
    def _extract_entities(self, text: str) -> List[str]:
        """Extract named entities"""
        return ["PERSON", "ORG", "LOC"]  # Simplified
    
    def _extract_visual_features(self, image_data: Any) -> Dict[str, Any]:
        """Extract visual features"""
        return {
            "color_histogram": [0.1, 0.2, 0.3, 0.4],
            "texture_features": [0.5, 0.6, 0.7],
            "shape_features": [0.8, 0.9, 1.0]
        }
    
    def _detect_objects(self, image_data: Any) -> List[str]:
        """Detect objects in image"""
        return ["person", "car", "building"]
    
    def _analyze_scene(self, image_data: Any) -> str:
        """Analyze scene"""
        return "urban_street"
    
    def _generate_image_embeddings(self, image_data: Any) -> List[float]:
        """Generate image embeddings"""
        return [0.2 * i % 1.0 for i in range(512)]
    
    def _extract_audio_features(self, audio_data: Any) -> Dict[str, Any]:
        """Extract audio features"""
        return {
            "mfcc": [0.1, 0.2, 0.3, 0.4, 0.5],
            "spectral_centroid": 0.6,
            "zero_crossing_rate": 0.1
        }
    
    def _recognize_speech(self, audio_data: Any) -> str:
        """Recognize speech"""
        return "hello world"
    
    def _classify_audio(self, audio_data: Any) -> str:
        """Classify audio"""
        return "speech"
    
    def _generate_audio_embeddings(self, audio_data: Any) -> List[float]:
        """Generate audio embeddings"""
        return [0.3 * i % 1.0 for i in range(256)]
    
    def _extract_temporal_features(self, video_data: Any) -> Dict[str, Any]:
        """Extract temporal features"""
        return {
            "motion_vectors": [0.1, 0.2, 0.3],
            "temporal_consistency": 0.8,
            "action_potential": 0.7
        }
    
    def _analyze_frames(self, video_data: Any) -> List[Dict[str, Any]]:
        """Analyze video frames"""
        return [{"frame_id": i, "features": [0.1, 0.2, 0.3]} for i in range(10)]
    
    def _recognize_actions(self, video_data: Any) -> List[str]:
        """Recognize actions"""
        return ["walking", "running", "sitting"]
    
    def _generate_video_embeddings(self, video_data: Any) -> List[float]:
        """Generate video embeddings"""
        return [0.4 * i % 1.0 for i in range(1024)]
    
    def _extract_statistical_features(self, tabular_data: Any) -> Dict[str, float]:
        """Extract statistical features"""
        return {
            "mean": 0.5,
            "std": 0.2,
            "min": 0.0,
            "max": 1.0,
            "median": 0.5
        }
    
    def _detect_patterns(self, tabular_data: Any) -> List[str]:
        """Detect patterns"""
        return ["trend_up", "seasonal", "outlier"]
    
    def _detect_anomalies(self, tabular_data: Any) -> List[int]:
        """Detect anomalies"""
        return [1, 5, 10]  # Indices of anomalous rows
    
    def _generate_tabular_embeddings(self, tabular_data: Any) -> List[float]:
        """Generate tabular embeddings"""
        return [0.5 * i % 1.0 for i in range(128)]
    
    def _extract_graph_features(self, graph_data: Any) -> Dict[str, Any]:
        """Extract graph features"""
        return {
            "node_count": 100,
            "edge_count": 200,
            "density": 0.04,
            "clustering_coefficient": 0.3
        }
    
    def _generate_node_embeddings(self, graph_data: Any) -> List[List[float]]:
        """Generate node embeddings"""
        return [[0.6 * i % 1.0 for i in range(64)] for _ in range(100)]
    
    def _classify_graph(self, graph_data: Any) -> str:
        """Classify graph type"""
        return "social_network"
    
    def _detect_communities(self, graph_data: Any) -> List[List[int]]:
        """Detect communities"""
        return [[0, 1, 2], [3, 4, 5], [6, 7, 8]]
    
    async def _fuse_modalities(
        self, 
        individual_results: Dict[str, Any], 
        context: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Fuse results from different modalities"""
        # Simulate fusion using weighted combination
        fused_features = []
        fusion_weights = context.get("optimization_config", {}).get("fusion_weights", {})
        
        for modality, result in individual_results.items():
            if "error" not in result:
                weight = fusion_weights.get(modality, 1.0)
                # Simulate feature fusion
                modality_features = [weight * 0.1 * i % 1.0 for i in range(256)]
                fused_features.extend(modality_features)
        
        return {
            "fused_features": fused_features,
            "fusion_method": "weighted_concatenation",
            "modality_contributions": list(individual_results.keys())
        }
    
    async def _update_agent_execution(
        self, 
        agent_id: str, 
        results: Dict[str, Any], 
        performance_metrics: Dict[str, Any]
    ) -> None:
        """Update agent execution record"""
        try:
            # Find existing execution or create new one
            execution = self.session.query(AgentExecution).filter(
                AgentExecution.agent_id == agent_id,
                AgentExecution.status == AgentStatus.RUNNING
            ).first()
            
            if execution:
                execution.results = results
                execution.performance_metrics = performance_metrics
                execution.updated_at = datetime.utcnow()
                self.session.commit()
        except Exception as e:
            logger.error(f"Failed to update agent execution: {e}")


class CrossModalAttentionProcessor:
    """Cross-modal attention mechanism processor"""
    
    async def process(
        self, 
        modality_results: Dict[str, Any], 
        context: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Process cross-modal attention"""
        
        # Simulate attention weight calculation
        modalities = list(modality_results.keys())
        num_modalities = len(modalities)
        
        # Generate attention weights (simplified)
        attention_weights = {}
        total_weight = 0.0
        
        for i, modality in enumerate(modalities):
            weight = 1.0 / num_modalities  # Equal attention initially
            attention_weights[modality] = weight
            total_weight += weight
        
        # Normalize weights
        for modality in attention_weights:
            attention_weights[modality] /= total_weight
        
        # Generate attended features
        attended_features = []
        for modality, weight in attention_weights.items():
            if "error" not in modality_results[modality]:
                # Simulate attended feature generation
                features = [weight * 0.2 * i % 1.0 for i in range(512)]
                attended_features.extend(features)
        
        # Generate final output
        final_output = {
            "representation": attended_features,
            "attention_summary": attention_weights,
            "dominant_modality": max(attention_weights, key=attention_weights.get)
        }
        
        return {
            "attention_weights": attention_weights,
            "attended_features": attended_features,
            "final_output": final_output
        }


class MultiModalPerformanceTracker:
    """Performance tracking for multi-modal operations"""
    
    async def calculate_metrics(
        self, 
        context: Dict[str, Any], 
        results: Dict[str, Any], 
        processing_time: float
    ) -> Dict[str, Any]:
        """Calculate performance metrics"""
        
        modalities = context["modalities"]
        processing_mode = context["processing_mode"]
        
        # Calculate throughput
        total_inputs = sum(1 for _ in results.values() if "error" not in _)
        throughput = total_inputs / processing_time if processing_time > 0 else 0
        
        # Calculate accuracy (simulated)
        accuracy = 0.95  # 95% accuracy target
        
        # Calculate efficiency based on processing mode
        mode_efficiency = {
            ProcessingMode.SEQUENTIAL: 0.7,
            ProcessingMode.PARALLEL: 0.9,
            ProcessingMode.FUSION: 0.85,
            ProcessingMode.ATTENTION: 0.8
        }
        
        efficiency = mode_efficiency.get(processing_mode, 0.8)
        
        # Calculate GPU utilization (simulated)
        gpu_utilization = 0.8  # 80% GPU utilization
        
        return {
            "processing_time_seconds": processing_time,
            "throughput_inputs_per_second": throughput,
            "accuracy_percentage": accuracy * 100,
            "efficiency_score": efficiency,
            "gpu_utilization_percentage": gpu_utilization * 100,
            "modalities_processed": len(modalities),
            "processing_mode": processing_mode,
            "performance_score": (accuracy + efficiency + gpu_utilization) / 3 * 100
        }
