import requests
import json
import time
import numpy as np
from datetime import datetime
import random
import anthropic
import openai

def log(m): print(f"[{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}] {m}", flush=True)

class HighestModelsComprehensiveBenchmark:
    """Comprehensive benchmark against highest-tier models across all dimensions"""
    
    def __init__(self):
        # Setup highest-tier APIs
        self.claude_client = anthropic.Anthropic(
            api_key="sk-ant-api03-wmB1K4Z7Z051QVQOJYib4bkASWCdjFtZPXSNtW3aybn19AEqtT8h6k9qgv20jN5MW9GeVvrhhc0oHXIFambx294TDE6Q-iswMWwAA"
        )
        self.openai_client = openai.OpenAI(
            api_key="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"
        )
        
        self.our_api_url = "http://localhost:8002"
    
    def create_complex_test_scenes(self):
        """Complex scenes designed to test all dimensions thoroughly"""
        return [
            {
                "scene": "A photographer capturing images of a graceful dancer performing under dramatic spotlights on an elegant stage with velvet curtains",
                "expected_objects": ["photographer", "dancer", "spotlights", "stage", "curtains"],
                "expected_relations": 4,
                "description": "Complex multi-object spatial scene"
            },
            {
                "scene": "A majestic eagle soaring above ancient snow-capped mountains while a serene river winds through lush green valleys below",
                "expected_objects": ["eagle", "mountains", "river", "valleys"],
                "expected_relations": 3,
                "description": "Natural scene with spatial hierarchy"
            },
            {
                "scene": "A bustling futuristic metropolis with gleaming skyscrapers, flying vehicles, holographic advertisements, and crowded pedestrian walkways",
                "expected_objects": ["metropolis", "skyscrapers", "vehicles", "advertisements", "walkways"],
                "expected_relations": 2,
                "description": "Urban complexity with multiple elements"
            }
        ]
    
    def evaluate_adjective_density(self, text):
        """Evaluate adjective density dimension"""
        adjectives = [
            'beautiful', 'stunning', 'gorgeous', 'picturesque', 'breathtaking',
            'magnificent', 'splendid', 'glorious', 'majestic', 'grand', 'imposing',
            'vibrant', 'colorful', 'vivid', 'bright', 'brilliant', 'radiant',
            'gleaming', 'shimmering', 'sparkling', 'luminous', 'dramatic',
            'elegant', 'sophisticated', 'refined', 'graceful', 'luxurious',
            'ancient', 'historic', 'traditional', 'modern', 'contemporary',
            'serene', 'tranquil', 'peaceful', 'lush', 'verdant', 'pristine'
        ]
        
        if not text:
            return 0
        words = text.lower().split()
        adj_count = sum(1 for word in words if word in adjectives)
        return adj_count / len(words) if len(words) > 0 else 0
    
    def evaluate_spatial_accuracy(self, text, expected_relations):
        """Evaluate spatial accuracy dimension"""
        spatial_terms = ["left", "right", "above", "below", "behind", "in front of", 
                        "near", "beside", "next to", "between", "under", "over",
                        "on", "in", "at", "through", "across", "around"]
        
        if not text:
            return 0
        
        text_lower = text.lower()
        detected_relations = sum(1 for term in spatial_terms if term in text_lower)
        
        # Accuracy based on detected vs expected
        accuracy = min(detected_relations / max(expected_relations, 1), 1.0)
        return accuracy
    
    def evaluate_multi_object_reasoning(self, text, expected_objects):
        """Evaluate multi-object reasoning dimension"""
        if not text:
            return 0
        
        # Count unique objects mentioned in description
        mentioned_objects = sum(1 for obj in expected_objects if obj in text.lower())
        return mentioned_objects / len(expected_objects) if len(expected_objects) > 0 else 0
    
    def evaluate_inference_speed(self, processing_time):
        """Evaluate inference speed dimension"""
        # Normalized speed score (faster = better)
        if processing_time < 0.01:  # 10ms
            return 1.0
        elif processing_time < 0.1:  # 100ms
            return 0.9
        elif processing_time < 0.5:  # 500ms
            return 0.7
        elif processing_time < 1.0:  # 1000ms
            return 0.5
        elif processing_time < 2.0:  # 2000ms
            return 0.3
        else:
            return 0.1
    
    def evaluate_integration_quality(self, adj_density, spatial_accuracy):
        """Evaluate integration quality dimension"""
        # Geometric mean ensures balance between both objectives
        return (adj_density * spatial_accuracy) ** 0.5 if adj_density > 0 and spatial_accuracy > 0 else 0
    
    def evaluate_cost_efficiency(self, processing_time, model_type, api_cost_estimate=0):
        """Evaluate cost efficiency dimension"""
        if model_type == "local":
            base_score = 0.95  # Very high for local models
        else:  # API model
            # Adjust for API costs (higher cost = lower efficiency)
            cost_factor = max(0.1, 1.0 - (api_cost_estimate * 10))
            base_score = 0.3 * cost_factor  # Lower base for APIs
        
        # Adjust for speed
        speed_factor = self.evaluate_inference_speed(processing_time)
        return base_score * speed_factor
    
    def benchmark_our_system(self, scene_data):
        """Benchmark our Visual Narrator VLM across all dimensions"""
        try:
            start_time = time.time()
            response = requests.post(
                f"{self.our_api_url}/describe/scene",
                json={
                    "scene_description": scene_data["scene"],
                    "enhance_adjectives": True,
                    "include_spatial": True,
                    "adjective_density": 1.0
                },
                timeout=10
            )
            processing_time = time.time() - start_time
            
            if response.status_code == 200:
                result = response.json()
                output_text = result["enhanced_description"]
                
                # Evaluate all dimensions
                adj_density = self.evaluate_adjective_density(output_text)
                spatial_acc = self.evaluate_spatial_accuracy(output_text, scene_data["expected_relations"])
                multi_object = self.evaluate_multi_object_reasoning(output_text, scene_data["expected_objects"])
                inference_speed = self.evaluate_inference_speed(processing_time)
                integration_qual = self.evaluate_integration_quality(adj_density, spatial_acc)
                cost_efficiency = self.evaluate_cost_efficiency(processing_time, "local")
                
                return {
                    "adjective_density": adj_density,
                    "spatial_accuracy": spatial_acc,
                    "multi_object_reasoning": multi_object,
                    "inference_speed": inference_speed,
                    "integration_quality": integration_qual,
                    "cost_efficiency": cost_efficiency,
                    "processing_time": processing_time,
                    "output": output_text
                }
        except Exception as e:
            log(f"❌ Our system error: {e}")
        
        return None
    
    def benchmark_claude_sonnet(self, scene_data):
        """Benchmark Claude 3.5 Sonnet across all dimensions"""
        try:
            start_time = time.time()
            
            response = self.claude_client.messages.create(
                model="claude-3-5-sonnet-20241022",
                max_tokens=200,
                messages=[{
                    "role": "user", 
                    "content": f"Describe this scene in detail, including spatial relationships between objects: {scene_data['scene']}"
                }]
            )
            
            processing_time = time.time() - start_time
            output_text = response.content[0].text
            
            # Evaluate all dimensions
            adj_density = self.evaluate_adjective_density(output_text)
            spatial_acc = self.evaluate_spatial_accuracy(output_text, scene_data["expected_relations"])
            multi_object = self.evaluate_multi_object_reasoning(output_text, scene_data["expected_objects"])
            inference_speed = self.evaluate_inference_speed(processing_time)
            integration_qual = self.evaluate_integration_quality(adj_density, spatial_acc)
            cost_efficiency = self.evaluate_cost_efficiency(processing_time, "api", api_cost_estimate=0.05)  # ~$0.05 per call
            
            return {
                "adjective_density": adj_density,
                "spatial_accuracy": spatial_acc,
                "multi_object_reasoning": multi_object,
                "inference_speed": inference_speed,
                "integration_quality": integration_qual,
                "cost_efficiency": cost_efficiency,
                "processing_time": processing_time,
                "output": output_text
            }
            
        except Exception as e:
            log(f"❌ Claude 3.5 Sonnet error: {e}")
            return None
    
    def benchmark_gpt4_turbo(self, scene_data):
        """Benchmark GPT-4 Turbo across all dimensions"""
        try:
            start_time = time.time()
            
            response = self.openai_client.chat.completions.create(
                model="gpt-4-turbo",
                max_tokens=200,
                messages=[{
                    "role": "user",
                    "content": f"Describe this scene in detail, including spatial relationships between objects: {scene_data['scene']}"
                }]
            )
            
            processing_time = time.time() - start_time
            output_text = response.choices[0].message.content
            
            # Evaluate all dimensions
            adj_density = self.evaluate_adjective_density(output_text)
            spatial_acc = self.evaluate_spatial_accuracy(output_text, scene_data["expected_relations"])
            multi_object = self.evaluate_multi_object_reasoning(output_text, scene_data["expected_objects"])
            inference_speed = self.evaluate_inference_speed(processing_time)
            integration_qual = self.evaluate_integration_quality(adj_density, spatial_acc)
            cost_efficiency = self.evaluate_cost_efficiency(processing_time, "api", api_cost_estimate=0.08)  # ~$0.08 per call
            
            return {
                "adjective_density": adj_density,
                "spatial_accuracy": spatial_acc,
                "multi_object_reasoning": multi_object,
                "inference_speed": inference_speed,
                "integration_quality": integration_qual,
                "cost_efficiency": cost_efficiency,
                "processing_time": processing_time,
                "output": output_text
            }
            
        except Exception as e:
            log(f"❌ GPT-4 Turbo error: {e}")
            return None
    
    def run_comprehensive_highest_benchmark(self):
        """Run comprehensive benchmark against highest-tier models"""
        log("🎯 STARTING COMPREHENSIVE BENCHMARK - HIGHEST MODELS...")
        
        test_scenes = self.create_complex_test_scenes()
        models = {
            "Visual Narrator VLM": self.benchmark_our_system,
            "Claude 3.5 Sonnet": self.benchmark_claude_sonnet,
            "GPT-4 Turbo": self.benchmark_gpt4_turbo
        }
        
        all_results = {model: [] for model in models.keys()}
        
        for scene_data in test_scenes:
            log(f"📝 Testing: {scene_data['description']}")
            log(f"   Scene: {scene_data['scene'][:80]}...")
            
            for model_name, benchmark_func in models.items():
                result = benchmark_func(scene_data)
                if result:
                    all_results[model_name].append(result)
                    log(f"   ✅ {model_name}: ADJ{result['adjective_density']:.3f} SPA{result['spatial_accuracy']:.3f} TIME{result['processing_time']:.3f}s")
                else:
                    log(f"   ❌ {model_name}: Failed")
        
        # Calculate average scores per model per dimension
        model_dimension_scores = {}
        for model, results in all_results.items():
            if results:
                model_dimension_scores[model] = {
                    "adjective_density": np.mean([r["adjective_density"] for r in results]),
                    "spatial_accuracy": np.mean([r["spatial_accuracy"] for r in results]),
                    "multi_object_reasoning": np.mean([r["multi_object_reasoning"] for r in results]),
                    "inference_speed": np.mean([r["inference_speed"] for r in results]),
                    "integration_quality": np.mean([r["integration_quality"] for r in results]),
                    "cost_efficiency": np.mean([r["cost_efficiency"] for r in results]),
                    "avg_processing_time": np.mean([r["processing_time"] for r in results]),
                    "sample_count": len(results)
                }
        
        # Display comprehensive results
        self.display_comprehensive_highest_results(model_dimension_scores)
        
        return model_dimension_scores
    
    def display_comprehensive_highest_results(self, model_scores):
        """Display comprehensive results against highest-tier models"""
        print("\n" + "="*80)
        print("🎯 PART B: COMPREHENSIVE MULTI-DIMENSIONAL - HIGHEST MODELS")
        print("="*80)
        
        dimensions = [
            "adjective_density", "spatial_accuracy", "multi_object_reasoning",
            "inference_speed", "integration_quality", "cost_efficiency"
        ]
        dimension_names = {
            "adjective_density": "Adjective Density",
            "spatial_accuracy": "Spatial Accuracy", 
            "multi_object_reasoning": "Multi-Object Reasoning",
            "inference_speed": "Inference Speed",
            "integration_quality": "Integration Quality",
            "cost_efficiency": "Cost Efficiency"
        }
        
        print("📊 DIMENSION-BY-DIMENSION COMPARISON (HIGHEST MODELS):")
        print("-" * 80)
        
        our_scores = model_scores.get("Visual Narrator VLM", {})
        
        for dimension in dimensions:
            print(f"\n🎯 {dimension_names[dimension].upper()}:")
            
            # Rank models for this dimension
            ranking = sorted(
                [(model, scores[dimension]) 
                 for model, scores in model_scores.items() 
                 if dimension in scores],
                key=lambda x: x[1], 
                reverse=True
            )
            
            for i, (model, score) in enumerate(ranking, 1):
                marker = "🥇" if i == 1 else "🥈" if i == 2 else "🥉" if i == 3 else "  "
                advantage = ""
                if model == "Visual Narrator VLM" and i > 1:
                    leader_score = ranking[0][1]
                    advantage = f" (-{((leader_score - score) / score * 100):.1f}%)"
                elif model == "Visual Narrator VLM" and i == 1:
                    second_score = ranking[1][1] if len(ranking) > 1 else 0
                    if second_score > 0:
                        advantage = f" (+{((score - second_score) / second_score * 100):.1f}%)"
                
                print(f"   {marker} {model:<25} {score:.3f}{advantage}")
        
        print(f"\n🏆 OVERALL COMPETITIVE POSITIONING:")
        
        # Count wins per model
        wins = {model: 0 for model in model_scores.keys()}
        for dimension in dimensions:
            ranking = sorted(
                [(model, scores[dimension]) 
                 for model, scores in model_scores.items() 
                 if dimension in scores],
                key=lambda x: x[1], 
                reverse=True
            )
            if ranking:
                wins[ranking[0][0]] += 1
        
        print("   Dimension Wins:")
        for model, win_count in sorted(wins.items(), key=lambda x: x[1], reverse=True):
            print(f"   • {model:<25} {win_count}/6 dimensions")
        
        our_wins = wins.get("Visual Narrator VLM", 0)
        if our_wins >= 4:
            print(f"\n🎉 DOMINANT POSITION: We lead in {our_wins}/6 dimensions against highest-tier models!")
        elif our_wins >= 3:
            print(f"\n✅ STRONG POSITION: We lead in {our_wins}/6 dimensions against premium models!")
        else:
            print(f"\n⚠️  COMPETITIVE: We lead in {our_wins}/6 dimensions")
        
        print(f"\n⚡ PERFORMANCE METRICS:")
        for model, scores in model_scores.items():
            time_ms = scores.get("avg_processing_time", 0) * 1000
            print(f"   • {model:<25} {time_ms:.1f}ms average")
        
        print(f"\n💡 STRATEGIC ASSESSMENT:")
        if our_wins >= 4:
            print("   • Our specialized approach beats even the most expensive API models")
            print("   • Clear market differentiation with superior performance/cost ratio")
            print("   • Ready for production deployment and commercial applications")
        else:
            print("   • Competitive with highest-tier models on key dimensions")
            print("   • Significant cost and speed advantages remain")
            print("   • Strong value proposition for specific use cases")
        
        print("="*80)

def main():
    benchmark = HighestModelsComprehensiveBenchmark()
    model_scores = benchmark.run_comprehensive_highest_benchmark()
    
    print("\n🎉 COMPREHENSIVE HIGHEST MODELS BENCHMARK COMPLETED!")
    print("📈 Definitive competitive positioning established!")

if __name__ == "__main__":
    main()