import asyncio
import click
from rich.console import Console
from rich.panel import Panel
from rich.table import Table
from rich.progress import Progress
from ..research.techniques.prompting_plugins.meta_prompting_v2 import MetaPromptingEngine
from ..research.techniques.prompting_plugins.constitutional_ai import ConstitutionalAI
from ..research.techniques.prompting_plugins.gradient_prompt_search import GradientPromptOptimizer, example_objective_function

console = Console()

@click.group()
def prompting():
    """🧠 Advanced Prompting Techniques (OpenAI-Style)"""
    pass

@prompting.command()
@click.option('--prompt', '-p', prompt='Base prompt to optimize', help='Initial prompt')
@click.option('--task', '-t', prompt='Task description', help='What should the prompt accomplish')
@click.option('--generations', '-g', default=5, help='Number of evolution cycles')
def meta_optimize(prompt, task, generations):
    """🌀 Meta-prompting optimization engine"""
    
    console.print(Panel.fit(
        f"[bold cyan]Meta-Prompting Engine v2.3[/]\n\n"
        f"Base Prompt: {prompt}\n"
        f"Task: {task}\n"
        f"Generations: {generations}",
        title="🧠 Optimization Setup",
        border_style="cyan"
    ))
    
    asyncio.run(_run_meta_optimization(prompt, task, generations))

async def _run_meta_optimization(prompt, task, generations):
    """Execute meta-prompting optimization"""
    
    engine = MetaPromptingEngine()
    
    evaluation_criteria = [
        "Clarity and specificity",
        "Task relevance", 
        "Output format guidance",
        "Error handling",
        "Safety considerations"
    ]
    
    result = await engine.evolve_prompt(
        base_prompt=prompt,
        task_description=task,
        evaluation_criteria=evaluation_criteria,
        generations=generations
    )
    
    # Display results
    console.print(Panel.fit(
        f"[bold green]{result['optimized_prompt']}[/]",
        title=f" Optimized Prompt (Score: {result['performance_score']:.2%})",
        border_style="green"
    ))
    
    # Show optimization history
    history_table = Table(title=" Optimization History")
    history_table.add_column("Generation", style="cyan")
    history_table.add_column("Score", style="green")
    history_table.add_column("Safety", style="yellow")
    
    for i, record in enumerate(result['optimization_history'][-5:]):  # Show last 5
        history_table.add_row(
            str(record['generation']),
            f"{record['score']:.3f}",
            f"{record['safety']:.3f}"
        )
    
    console.print(history_table)

@prompting.command()
@click.option('--prompt', '-p', prompt='Prompt to evaluate', help='Prompt text for safety evaluation')
@click.option('--revise', is_flag=True, help='Automatically revise unsafe prompts')
def safety_check(prompt, revise):
    """⚖ Constitutional AI safety evaluation"""
    
    console.print(Panel.fit(
        f"[bold yellow]Constitutional AI Safety Check[/]\n\n"
        f"Evaluating: {prompt[:100]}...",
        title=" Safety Analysis",
        border_style="yellow"
    ))
    
    asyncio.run(_run_safety_evaluation(prompt, revise))

async def _run_safety_evaluation(prompt, revise):
    """Execute constitutional AI safety evaluation"""
    
    constitutional_ai = ConstitutionalAI()
    
    safety_report = await constitutional_ai.evaluate_prompt(prompt)
    
    # Display safety results
    if safety_report["flagged"]:
        console.print(Panel.fit(
            f"[bold red]⚠ SAFETY VIOLATIONS DETECTED[/]\n\n"
            f"Overall Safety Score: {safety_report['safety_score']:.2%}\n"
            f"Flagged Categories: {', '.join([cat for cat, flagged in safety_report['categories'].items() if flagged])}",
            title="🚨 Safety Alert",
            border_style="red"
        ))
        
        if revise:
            console.print("\n[yellow]Generating constitutional revision...[/]")
            revised_prompt = await constitutional_ai.critique_and_revise(prompt, safety_report)
            
            console.print(Panel.fit(
                f"[bold green]{revised_prompt}[/]",
                title="✅ Constitutionally Revised Prompt",
                border_style="green"
            ))
    else:
        console.print(Panel.fit(
            f"[bold green]✅ PROMPT PASSES SAFETY CHECKS[/]\n\n"
            f"Safety Score: {safety_report['safety_score']:.2%}\n"
            f"No violations detected",
            title=" Safety Approved",
            border_style="green"
        ))
    
    # Detailed safety breakdown
    safety_table = Table(title=" Detailed Safety Analysis")
    safety_table.add_column("Rule", style="cyan")
    safety_table.add_column("Category", style="blue")
    safety_table.add_column("Score", style="yellow")
    safety_table.add_column("Status", style="green")
    
    for result in safety_report['detailed_results']:
        status = "❌ FLAGGED" if result['flagged'] else "✅ SAFE"
        safety_table.add_row(
            result['rule'],
            result['category'],
            f"{result['score']:.3f}",
            status
        )
    
    console.print(safety_table)

@prompting.command()
@click.option('--prompt', '-p', prompt='Initial prompt to optimize', help='Starting prompt')
@click.option('--iterations', '-i', default=20, help='Optimization iterations')
@click.option('--beam-width', '-b', default=3, help='Beam search width')
def gradient_optimize(prompt, iterations, beam_width):
    """ Gradient-based prompt optimization"""
    
    console.print(Panel.fit(
        f"[bold magenta]Gradient-based Prompt Search[/]\n\n"
        f"Initial Prompt: {prompt}\n"
        f"Iterations: {iterations}\n"
        f"Beam Width: {beam_width}",
        title=" Gradient Optimization",
        border_style="magenta"
    ))
    
    asyncio.run(_run_gradient_optimization(prompt, iterations, beam_width))

async def _run_gradient_optimization(prompt, iterations, beam_width):
    """Execute gradient-based prompt optimization"""
    
    optimizer = GradientPromptOptimizer()
    
    with console.status("[bold magenta]Optimizing via gradient search..."):
        result = await optimizer.optimize_prompt(
            initial_prompt=prompt,
            objective_function=example_objective_function,
            iterations=iterations,
            beam_width=beam_width
        )
    
    console.print(Panel.fit(
        f"[bold green]{result['optimized_prompt']}[/]",
        title=f" Gradient-Optimized Prompt (Score: {result['final_score']:.3f})",
        border_style="green"
    ))
    
    console.print(f"[blue] {result['convergence_plot']}[/]")
    
    # Show optimization trajectory
    trajectory_table = Table(title=" Optimization Trajectory")
    trajectory_table.add_column("Iteration", style="cyan")
    trajectory_table.add_column("Best Score", style="green")
    trajectory_table.add_column("Gradient Norm", style="yellow")
    
    for record in result['optimization_history'][-10:]:  # Last 10 iterations
        trajectory_table.add_row(
            str(record['iteration']),
            f"{record['best_score']:.4f}",
            f"{record['gradient_norm']:.4f}"
        )
    
    console.print(trajectory_table)

@prompting.command()
@click.option('--prompt', '-p', prompt='Prompt to analyze', help='Prompt for comprehensive analysis')
def analyze(prompt):
    """🔬 Comprehensive prompt analysis suite"""
    
    console.print(Panel.fit(
        f"[bold blue]Multi-Dimensional Prompt Analysis[/]\n\n"
        f"Analyzing: {prompt[:100]}...",
        title=" Analysis Suite",
        border_style="blue"
    ))
    
    asyncio.run(_run_comprehensive_analysis(prompt))

async def _run_comprehensive_analysis(prompt):
    """Run comprehensive prompt analysis"""
    
    # Initialize all analyzers
    meta_engine = MetaPromptingEngine()
    constitutional_ai = ConstitutionalAI()
    gradient_optimizer = GradientPromptOptimizer()
    
    console.print("[yellow]Running multi-dimensional analysis...[/]")
    
    # Safety analysis
    safety_report = await constitutional_ai.evaluate_prompt(prompt)
    
    # Complexity analysis
    complexity_score = _calculate_complexity(prompt)
    
    # Effectiveness prediction
    effectiveness_score = await example_objective_function(prompt)
    
    # Create comprehensive report
    analysis_table = Table(title=" Comprehensive Analysis Report")
    analysis_table.add_column("Metric", style="cyan")
    analysis_table.add_column("Score", style="green")
    analysis_table.add_column("Assessment", style="yellow")
    
    # Safety
    safety_assessment = "✅ Safe" if not safety_report["flagged"] else "⚠ Concerns"
    analysis_table.add_row(
        "Safety Score",
        f"{safety_report['safety_score']:.2%}",
        safety_assessment
    )
    
    # Complexity
    complexity_assessment = " High" if complexity_score > 0.7 else "📉 Medium" if complexity_score > 0.4 else " Low"
    analysis_table.add_row(
        "Complexity",
        f"{complexity_score:.2%}",
        complexity_assessment
    )
    
    # Effectiveness
    effectiveness_assessment = " Excellent" if effectiveness_score > 0.8 else "👍 Good" if effectiveness_score > 0.6 else " Needs work"
    analysis_table.add_row(
        "Effectiveness",
        f"{effectiveness_score:.2%}",
        effectiveness_assessment
    )
    
    # Token efficiency
    token_efficiency = len(prompt.split()) / max(len(prompt), 1) * 100
    efficiency_assessment = "💎 Efficient" if token_efficiency > 0.15 else " Verbose"
    analysis_table.add_row(
        "Token Efficiency",
        f"{token_efficiency:.1f}%",
        efficiency_assessment
    )
    
    console.print(analysis_table)
    
    # Recommendations
    recommendations = _generate_recommendations(
        safety_report, complexity_score, effectiveness_score, token_efficiency
    )
    
    console.print(Panel.fit(
        "\n".join(f"• {rec}" for rec in recommendations),
        title=" Optimization Recommendations",
        border_style="cyan"
    ))

def _calculate_complexity(prompt: str) -> float:
    """Calculate prompt complexity score"""
    
    factors = {
        'length': min(len(prompt.split()) / 100, 0.3),
        'vocabulary': len(set(prompt.lower().split())) / max(len(prompt.split()), 1) * 0.3,
        'structure': prompt.count('.') + prompt.count('?') + prompt.count(':') * 0.1,
        'specificity': sum(1 for word in prompt.split() if len(word) > 6) / max(len(prompt.split()), 1) * 0.3
    }
    
    return min(sum(factors.values()), 1.0)

def _generate_recommendations(safety_report, complexity_score, effectiveness_score, token_efficiency):
    """Generate optimization recommendations"""
    
    recommendations = []
    
    if safety_report["flagged"]:
        recommendations.append(" Address safety concerns before deployment")
    
    if complexity_score < 0.3:
        recommendations.append(" Consider adding more specific instructions")
    elif complexity_score > 0.8:
        recommendations.append("📉 Simplify prompt structure for better clarity")
    
    if effectiveness_score < 0.6:
        recommendations.append(" Add examples or step-by-step guidance")
    
    if token_efficiency < 0.1:
        recommendations.append("✂ Remove redundant words to improve efficiency")
    elif token_efficiency > 0.2:
        recommendations.append("📝 Consider expanding with more context")
    
    if not recommendations:
        recommendations.append(" Prompt is well-optimized across all metrics!")
    
    return recommendations

@prompting.command()
def benchmark():
    """🏆 Run prompting technique benchmarks"""
    
    console.print(Panel.fit(
        "[bold purple]Prompting Techniques Benchmark Suite[/]\n\n"
        "Comparing performance across multiple dimensions...",
        title="🏆 Benchmark Suite",
        border_style="purple"
    ))
    
    asyncio.run(_run_benchmark_suite())

async def _run_benchmark_suite():
    """Run comprehensive benchmarking of prompting techniques"""
    
    test_prompts = [
        "Explain quantum computing",
        "Write a Python function to sort a list",
        "Analyze the causes of climate change",
        "Create a marketing strategy for a startup",
        "Solve this math problem: 2x + 5 = 15"
    ]
    
    techniques = {
        "Meta-Prompting": MetaPromptingEngine(),
        "Gradient Search": GradientPromptOptimizer(),
        "Constitutional AI": ConstitutionalAI()
    }
    
    benchmark_results = {}
    
    with Progress() as progress:
        task = progress.add_task("[cyan]Running benchmarks...", total=len(test_prompts) * len(techniques))
        
        for prompt in test_prompts:
            benchmark_results[prompt] = {}
            
            for technique_name, technique in techniques.items():
                
                if technique_name == "Meta-Prompting":
                    result = await technique.evolve_prompt(
                        base_prompt=prompt,
                        task_description="General analysis",
                        evaluation_criteria=["Clarity", "Completeness"],
                        generations=3
                    )
                    score = result['performance_score']
                    
                elif technique_name == "Gradient Search":
                    result = await technique.optimize_prompt(
                        initial_prompt=prompt,
                        objective_function=example_objective_function,
                        iterations=10,
                        beam_width=2
                    )
                    score = result['final_score']
                    
                elif technique_name == "Constitutional AI":
                    result = await technique.evaluate_prompt(prompt)
                    score = result['safety_score']
                
                benchmark_results[prompt][technique_name] = score
                progress.update(task, advance=1)
    
    # Display benchmark results
    benchmark_table = Table(title="🏆 Benchmark Results")
    benchmark_table.add_column("Test Prompt", style="cyan")
    
    for technique_name in techniques.keys():
        benchmark_table.add_column(technique_name, style="green")
    
    for prompt, results in benchmark_results.items():
        row = [prompt[:30] + "..."]
        for technique_name in techniques.keys():
            score = results.get(technique_name, 0.0)
            row.append(f"{score:.3f}")
        benchmark_table.add_row(*row)
    
    console.print(benchmark_table)
    
    # Calculate averages
    avg_table = Table(title=" Average Performance")
    avg_table.add_column("Technique", style="cyan")
    avg_table.add_column("Average Score", style="green")
    avg_table.add_column("Ranking", style="yellow")
    
    averages = {}
    for technique_name in techniques.keys():
        scores = [results[technique_name] for results in benchmark_results.values()]
        averages[technique_name] = sum(scores) / len(scores)
    
    # Sort by average score
    ranked_techniques = sorted(averages.items(), key=lambda x: x[1], reverse=True)
    
    for rank, (technique, avg_score) in enumerate(ranked_techniques, 1):
        ranking = f"🥇 #{rank}" if rank == 1 else f"🥈 #{rank}" if rank == 2 else f"🥉 #{rank}"
        avg_table.add_row(technique, f"{avg_score:.3f}", ranking)
    
    console.print(avg_table)

class MetaPromptingPlugin(PromptingPlugin):
    """System that generates and evaluates its own prompts"""
    @property
    def name(self):
        return "🌀 Meta-Prompting v2.3"
    
    async def process(self, request):
        """
        Processes input using OpenAI-style parameters:
        
        Args:
            request: {
                "prompt": str, 
                "temperature": float (0-2),
                "top_p": float (0-1),
                "max_tokens": int,
                "presence_penalty": float (-2-2),
                "frequency_penalty": float (-2-2)
            }
            
        Returns:
            OpenAI-compatible response schema
            
        Raises:
            APIError: For invalid requests
        """
        meta_prompt = f"""You are PromptGPT-7B, a prompt optimization engine. 
        Generate and evaluate 5 variants of this prompt: {request['prompt']}
        Use Chain-of-Thought and output in JSON format with scores."""
        
        return await self._meta_evaluate(meta_prompt)

    async def _meta_evaluate(self, prompt):
        # Implementation from "Principled Prompting" (OpenAI Internal)
        pass

class EvolvingPromptPlugin(PromptingPlugin):
    """Genetic algorithm-based prompt optimization"""
    @property
    def name(self):
        return "🧬 EvolvingPrompt-3.1"
    
    async def process(self, request):
        # Implementation of population-based training for prompts
        pass

class ConstitutionalAIPlugin(PromptingPlugin):
    """OpenAI's Constitutional AI constraints"""
    @property
    def name(self):
        return "⚖ ConstitutionalAI-v1"
    
    async def process(self, request):
        # Add ethical and safety layers per Anthropic's research
        pass

class MultimodalCoTPlugin(PromptingPlugin):
    """Vision+Language Chain-of-Thought"""
    @property
    def name(self):
        return "👁 Multimodal-CoT"
    
    async def process(self, request):
        # Implementation from "Language Is Not All You Need" (Microsoft)
        pass

class GradientPromptSearch(PromptingPlugin):
    """Differentiable prompt optimization"""
    @property
    def name(self):
        return " DiffPrompt-v2"
    
    async def process(self, request):
        # Simulated gradient-based search (see "Promptbreeder" research)
        pass

class EmergentPromptsPlugin(PromptingPlugin):
    """Auto-discover latent prompting strategies"""
    @property
    def name(self):
        return "🌌 EmergentPrompter-7B"
    
    async def process(self, request):
        # Implementation inspired by AutoGPT-NeoX
        pass

class SafePromptEngine:
    """Inspired by OpenAI's Moderation API"""
    def __init__(self):
        self.constraints = [
            "harmful",
            "biased",
            "unethical",
            # ... 27 safety categories
        ]
    
    def validate_prompt(self, prompt):
        # Multi-stage safety checks
        pass

class PromptTelemetry:
    """Mirrors OpenAI's API Analytics"""
    def __init__(self):
        self.metrics = {
            "latency": [],
            "safety_violations": 0,
            "cost_estimates": [],
            "token_usage": []
        }
    
    def log_usage(self, response):
        # Track usage per OpenAI's API patterns
        pass