"""
Next-Generation Prompting Techniques (2024)
Implementing the latest research breakthroughs
"""

import asyncio
import numpy as np
from typing import Dict, List, Any, Optional, Tuple
from dataclasses import dataclass
import networkx as nx
import matplotlib.pyplot as plt
from scipy.optimize import differential_evolution
import json

class QuantumPromptSuperposition:
    """
    Quantum-Inspired Prompt Superposition
    Based on "Quantum Superposition Prompting for Enhanced LLM Reasoning" (2024)
    """
    
    def __init__(self, num_superposition_states: int = 8):
        self.num_states = num_superposition_states
        self.coherence_threshold = 0.85
        
    async def generate_superposition_prompts(
        self,
        base_prompt: str,
        context: Dict[str, Any]
    ) -> List[str]:
        """Generate quantum superposition of prompt states"""
        
        superposition_prompts = []
        
        # Create orthogonal prompt bases
        prompt_bases = await self._generate_orthogonal_bases(base_prompt)
        
        # Apply quantum-inspired transformations
        for i, basis in enumerate(prompt_bases):
            amplitude = np.exp(1j * 2 * np.pi * i / self.num_states)
            
            transformed_prompt = await self._apply_quantum_transformation(
                basis, amplitude, context
            )
            superposition_prompts.append(transformed_prompt)
        
        return superposition_prompts
    
    async def collapse_superposition(
        self,
        superposition_responses: List[str],
        measurement_basis: str = "coherence"
    ) -> str:
        """Collapse quantum superposition to single optimal response"""
        
        # Calculate coherence scores
        coherence_scores = []
        for response in superposition_responses:
            coherence = await self._calculate_quantum_coherence(response)
            coherence_scores.append(coherence)
        
        # Find maximum coherence state
        max_coherence_idx = np.argmax(coherence_scores)
        
        if coherence_scores[max_coherence_idx] > self.coherence_threshold:
            return superposition_responses[max_coherence_idx]
        else:
            # Quantum entanglement - combine states
            return await self._entangle_responses(superposition_responses, coherence_scores)
    
    async def _generate_orthogonal_bases(self, base_prompt: str) -> List[str]:
        """Generate orthogonal prompt basis states"""
        
        transformations = [
            "Think step by step about this problem:",
            "Approach this from multiple perspectives:",
            "Consider the inverse of this problem:",
            "Apply first principles reasoning:",
            "Use analogical thinking for:",
            "Break down systematically:",
            "Challenge assumptions in:",
            "Synthesize insights about:"
        ]
        
        bases = []
        for transform in transformations[:self.num_states]:
            transformed = f"{transform}\n\n{base_prompt}"
            bases.append(transformed)
        
        return bases
    
    async def _apply_quantum_transformation(
        self,
        prompt: str,
        amplitude: complex,
        context: Dict[str, Any]
    ) -> str:
        """Apply quantum-inspired transformation to prompt"""
        
        # Use amplitude phase to modify prompt characteristics
        phase = np.angle(amplitude)
        magnitude = np.abs(amplitude)
        
        if phase < np.pi / 4:
            # High certainty state
            return f"With high confidence, {prompt.lower()}"
        elif phase < np.pi / 2:
            # Exploratory state
            return f"Exploring possibilities, {prompt.lower()}"
        elif phase < 3 * np.pi / 4:
            # Uncertain state
            return f"Considering uncertainties, {prompt.lower()}"
        else:
            # Creative state
            return f"Thinking creatively, {prompt.lower()}"

class NeuralArchitectureSearch:
    """
    Neural Architecture Search for Prompts (NAS-P)
    Automatically discovers optimal prompt architectures
    """
    
    def __init__(self):
        self.search_space = self._define_search_space()
        self.evaluation_cache = {}
        
    def _define_search_space(self) -> Dict[str, List[str]]:
        """Define neural prompt architecture search space"""
        
        return {
            "prefix_patterns": [
                "As an expert in {domain}, ",
                "Thinking step by step, ",
                "Using {reasoning_type} reasoning, ",
                "Considering multiple perspectives, ",
                "Breaking this down systematically, "
            ],
            "structure_patterns": [
                "First, {step1}. Then, {step2}. Finally, {step3}.",
                "1. Analyze: {analysis}\n2. Synthesize: {synthesis}\n3. Conclude: {conclusion}",
                "Question: {question}\nReasoning: {reasoning}\nAnswer: {answer}",
                "Context: {context}\nTask: {task}\nOutput: {output}",
                "Input: {input}\nProcess: {process}\nResult: {result}"
            ],
            "reasoning_types": [
                "deductive", "inductive", "abductive", "analogical", 
                "causal", "counterfactual", "probabilistic"
            ],
            "constraint_patterns": [
                "Ensure accuracy and cite sources.",
                "Be concise but comprehensive.",
                "Consider ethical implications.",
                "Verify logical consistency.",
                "Provide specific examples."
            ]
        }
    
    async def search_optimal_architecture(
        self,
        base_prompt: str,
        evaluation_function: callable,
        search_iterations: int = 50
    ) -> Dict[str, Any]:
        """Search for optimal prompt architecture using evolutionary algorithms"""
        
        def objective_function(genome):
            """Convert genome to prompt architecture and evaluate"""
            
            architecture = self._genome_to_architecture(genome)
            prompt = self._architecture_to_prompt(architecture, base_prompt)
            
            # Use cached evaluation if available
            prompt_hash = hash(prompt)
            if prompt_hash in self.evaluation_cache:
                return self.evaluation_cache[prompt_hash]
            
            # Evaluate architecture (simulate async call)
            score = asyncio.run(evaluation_function(prompt))
            self.evaluation_cache[prompt_hash] = score
            
            return -score  # Minimize negative score (maximize original score)
        
        # Define search bounds (genome represents architecture choices)
        bounds = [
            (0, len(self.search_space["prefix_patterns"]) - 1),
            (0, len(self.search_space["structure_patterns"]) - 1),
            (0, len(self.search_space["reasoning_types"]) - 1),
            (0, len(self.search_space["constraint_patterns"]) - 1),
            (0.0, 1.0),  # Temperature parameter
            (0.0, 1.0),  # Complexity weight
        ]
        
        # Run differential evolution
        result = differential_evolution(
            objective_function,
            bounds,
            maxiter=search_iterations,
            popsize=15,
            seed=42
        )
        
        # Convert best genome back to architecture
        best_architecture = self._genome_to_architecture(result.x)
        best_prompt = self._architecture_to_prompt(best_architecture, base_prompt)
        
        return {
            "optimal_architecture": best_architecture,
            "optimal_prompt": best_prompt,
            "performance_score": -result.fun,
            "search_iterations": result.nit,
            "evaluation_count": result.nfev
        }
    
    def _genome_to_architecture(self, genome: np.ndarray) -> Dict[str, Any]:
        """Convert numerical genome to prompt architecture"""
        
        return {
            "prefix": self.search_space["prefix_patterns"][int(genome[0])],
            "structure": self.search_space["structure_patterns"][int(genome[1])],
            "reasoning_type": self.search_space["reasoning_types"][int(genome[2])],
            "constraints": self.search_space["constraint_patterns"][int(genome[3])],
            "temperature": float(genome[4]),
            "complexity_weight": float(genome[5])
        }
    
    def _architecture_to_prompt(
        self,
        architecture: Dict[str, Any],
        base_prompt: str
    ) -> str:
        """Convert architecture specification to actual prompt"""
        
        # Build prompt from architecture components
        prefix = architecture["prefix"].format(
            domain="AI and reasoning",
            reasoning_type=architecture["reasoning_type"]
        )
        
        # Apply structure pattern
        structured_prompt = f"{prefix}{base_prompt}\n\n{architecture['constraints']}"
        
        return structured_prompt

class HyperParameterOptimizedPrompting:
    """
    Hyperparameter Optimization for Prompting (HOP)
    Automatically tune prompt hyperparameters using Bayesian optimization
    """
    
    def __init__(self):
        self.optimization_history = []
        self.hyperparameter_space = self._define_hyperparameter_space()
        
    def _define_hyperparameter_space(self) -> Dict[str, Tuple[float, float]]:
        """Define hyperparameter optimization space"""
        
        return {
            "temperature": (0.1, 2.0),
            "top_p": (0.1, 1.0),
            "frequency_penalty": (-2.0, 2.0),
            "presence_penalty": (-2.0, 2.0),
            "prompt_complexity": (0.1, 1.0),
            "context_window_usage": (0.1, 1.0),
            "reasoning_depth": (1, 10),
            "example_count": (0, 10)
        }
    
    async def optimize_hyperparameters(
        self,
        base_prompt: str,
        evaluation_function: callable,
        optimization_steps: int = 30
    ) -> Dict[str, Any]:
        """Optimize prompt hyperparameters using Bayesian optimization"""
        
        from skopt import gp_minimize
        from skopt.space import Real, Integer
        from skopt.utils import use_named_args
        
        # Define optimization space
        space = [
            Real(self.hyperparameter_space["temperature"][0], 
                 self.hyperparameter_space["temperature"][1], name="temperature"),
            Real(self.hyperparameter_space["top_p"][0], 
                 self.hyperparameter_space["top_p"][1], name="top_p"),
            Real(self.hyperparameter_space["frequency_penalty"][0], 
                 self.hyperparameter_space["frequency_penalty"][1], name="frequency_penalty"),
            Real(self.hyperparameter_space["presence_penalty"][0], 
                 self.hyperparameter_space["presence_penalty"][1], name="presence_penalty"),
            Real(self.hyperparameter_space["prompt_complexity"][0], 
                 self.hyperparameter_space["prompt_complexity"][1], name="prompt_complexity"),
            Integer(int(self.hyperparameter_space["reasoning_depth"][0]), 
                   int(self.hyperparameter_space["reasoning_depth"][1]), name="reasoning_depth"),
            Integer(int(self.hyperparameter_space["example_count"][0]), 
                   int(self.hyperparameter_space["example_count"][1]), name="example_count")
        ]
        
        @use_named_args(space)
        async def objective(**params):
            """Objective function for hyperparameter optimization"""
            
            # Generate optimized prompt with hyperparameters
            optimized_prompt = self._apply_hyperparameters(base_prompt, params)
            
            # Evaluate prompt performance
            score = await evaluation_function(optimized_prompt, params)
            
            # Log optimization step
            self.optimization_history.append({
                "parameters": params,
                "score": score,
                "prompt": optimized_prompt
            })
            
            return -score  # Minimize negative score
        
        # Run Bayesian optimization
        result = gp_minimize(
            func=lambda x: asyncio.run(objective(*x)),
            dimensions=space,
            n_calls=optimization_steps,
            n_initial_points=5,
            acq_func="gp_hedge",
            random_state=42
        )
        
        # Extract optimal hyperparameters
        optimal_params = dict(zip([dim.name for dim in space], result.x))
        optimal_prompt = self._apply_hyperparameters(base_prompt, optimal_params)
        
        return {
            "optimal_hyperparameters": optimal_params,
            "optimal_prompt": optimal_prompt,
            "best_score": -result.fun,
            "optimization_history": self.optimization_history,
            "convergence_plot": self._plot_convergence()
        }
    
    def _apply_hyperparameters(
        self,
        base_prompt: str,
        hyperparameters: Dict[str, float]
    ) -> str:
        """Apply hyperparameters to modify prompt structure"""
        
        complexity = hyperparameters.get("prompt_complexity", 0.5)
        reasoning_depth = int(hyperparameters.get("reasoning_depth", 3))
        example_count = int(hyperparameters.get("example_count", 2))
        
        # Adjust prompt complexity
        if complexity > 0.7:
            prefix = "Using advanced reasoning and comprehensive analysis, "
        elif complexity > 0.4:
            prefix = "Thinking carefully about this problem, "
        else:
            prefix = "Simply put, "
        
        # Add reasoning steps
        reasoning_steps = []
        for i in range(reasoning_depth):
            reasoning_steps.append(f"Step {i+1}: Consider {['context', 'implications', 'alternatives', 'evidence', 'conclusions', 'verification', 'applications', 'limitations', 'improvements', 'synthesis'][i % 10]}")
        
        reasoning_section = "\n".join(reasoning_steps) if reasoning_depth > 1 else ""
        
        # Add examples if requested
        example_section = ""
        if example_count > 0:
            example_section = f"\n\nProvide {example_count} specific examples to illustrate your reasoning."
        
        return f"{prefix}{base_prompt}\n\n{reasoning_section}{example_section}"

class MetaCognitivePrompting:
    """
    Meta-Cognitive Prompting Framework
    Self-aware prompting with reflection and adaptation
    """
    
    def __init__(self):
        self.metacognitive_strategies = [
            "planning", "monitoring", "evaluating", "reflecting", "adapting"
        ]
        self.reflection_history = []
        
    async def metacognitive_process(
        self,
        initial_prompt: str,
        task_context: Dict[str, Any],
        max_iterations: int = 5
    ) -> Dict[str, Any]:
        """Execute metacognitive prompting process with self-reflection"""
        
        current_prompt = initial_prompt
        iteration_results = []
        
        for iteration in range(max_iterations):
            # Planning phase
            planning_result = await self._planning_phase(current_prompt, task_context)