"""
Qwen3.8-Max MoE Router Implementation
Simulates the gating network design in sparse mixture-of-experts models
Includes: Top-K selection, load balancing loss, expert capacity constraints, noisy gating
"""

import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Tuple, Optional
import math


class MoERouter(nn.Module):
    """
    Sparse MoE Router - Simulates Qwen3.8-Max's gating network design
    
    Key design elements:
    1. Top-K Gating: Each token selects K most relevant experts
    2. Load Balancing: Auxiliary loss prevents uneven expert utilization
    3. Noisy Gating: Gaussian noise during training promotes expert specialization
    4. Capacity Factor: Limits maximum tokens per expert to prevent overload
    """
    
    def __init__(
        self,
        num_experts: int = 256,
        top_k: int = 8,
        hidden_dim: int = 8192,
        capacity_factor: float = 1.25,
        noise_std: float = 1.0,
        aux_loss_coef: float = 0.01,
    ):
        super().__init__()
        self.num_experts = num_experts
        self.top_k = top_k
        self.hidden_dim = hidden_dim
        self.capacity_factor = capacity_factor
        self.noise_std = noise_std
        self.aux_loss_coef = aux_loss_coef
        
        # Gating network: Linear layer maps hidden_dim to num_experts dimensions
        self.gate = nn.Linear(hidden_dim, num_experts, bias=False)
        
        # Expert embeddings (for noisy gating)
        self.expert_embeddings = nn.Parameter(
            torch.randn(num_experts, hidden_dim) * 0.02
        )
    
    def forward(
        self,
        hidden_states: torch.Tensor,
        training: bool = True
    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
        """
        Forward pass
        
        Args:
            hidden_states: [batch_size * seq_len, hidden_dim]
            training: Whether in training mode
            
        Returns:
            router_weights: [tokens, top_k] Weight for each selected expert per token
            selected_experts: [tokens, top_k] Selected expert indices
            aux_loss: Load balancing auxiliary loss
        """
        # Step 1: Compute gating logits
        logits = self.gate(hidden_states)  # [tokens, num_experts]
        
        # Step 2: Add noise during training to promote expert specialization
        if training:
            noise = torch.randn_like(logits) * self.noise_std
            logits = logits + noise
        
        # Step 3: Softmax normalization
        scores = F.softmax(logits, dim=-1)  # [tokens, num_experts]
        
        # Step 4: Top-K selection
        top_k_weights, top_k_indices = torch.topk(
            scores, self.top_k, dim=-1
        )
        
        # Step 5: Renormalize Top-K weights (sum to 1)
        top_k_weights = top_k_weights / (
            top_k_weights.sum(dim=-1, keepdim=True) + 1e-9
        )
        
        # Step 6: Compute load balancing auxiliary loss
        aux_loss = self._compute_aux_loss(scores, top_k_indices)
        
        return top_k_weights, top_k_indices, aux_loss
    
    def _compute_aux_loss(
        self,
        scores: torch.Tensor,
        selected_experts: torch.Tensor
    ) -> torch.Tensor:
        """
        Compute load balancing auxiliary loss
        
        This loss ensures all experts are used uniformly:
        L_aux = num_experts * sum(f_i * P_i)
        Where f_i = fraction of tokens assigned to expert i
              P_i = average routing probability for expert i
        
        When all experts are used uniformly, L_aux = 1.0
        """
        num_tokens = scores.shape[0]
        
        # Compute fraction of tokens assigned to each expert f_i
        expert_mask = F.one_hot(
            selected_experts.view(-1), 
            num_classes=self.num_experts
        ).float()
        f = expert_mask.sum(dim=0) / (num_tokens * self.top_k)  # [num_experts]
        
        # Compute average routing probability per expert P_i
        P = scores.mean(dim=0)  # [num_experts]
        
        # Auxiliary loss: num_experts * dot(f, P)
        aux_loss = self.num_experts * torch.sum(f * P)
        
        return self.aux_loss_coef * aux_loss
    
    def compute_capacity(self, num_tokens: int) -> int:
        """
        Compute capacity upper bound per expert
        
        capacity = ceil(capacity_factor * num_tokens * top_k / num_experts)
        
        Under uniform token distribution, each expert expects:
        num_tokens * top_k / num_experts tokens
        capacity_factor provides additional buffer space
        """
        expected_tokens_per_expert = (
            num_tokens * self.top_k / self.num_experts
        )
        capacity = math.ceil(
            self.capacity_factor * expected_tokens_per_expert
        )
        return capacity


class ExpertFFN(nn.Module):
    """
    Single Expert Feed-Forward Network (FFN)
    
    Each expert is an independent FFN layer with the same structure
    as the shared layer but completely independent parameters
    """
    
    def __init__(self, hidden_dim: int, intermediate_dim: int):
        super().__init__()
        self.w1 = nn.Linear(hidden_dim, intermediate_dim, bias=False)
        self.w2 = nn.Linear(intermediate_dim, hidden_dim, bias=False)
        self.w3 = nn.Linear(hidden_dim, intermediate_dim, bias=False)
        self.act = nn.SiLU()  # Swish activation function
    
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """SwiGLU activation: output = W2(SiLU(W1(x)) * W3(x))"""
        return self.w2(self.act(self.w1(x)) * self.w3(x))


class SparseMoELayer(nn.Module):
    """
    Complete Sparse MoE Layer - Simulates Qwen3.8-Max's single-layer MoE FFN
    
    Integrates Router + Expert Pool + Weighted Sum
    Implements the full token dispatch and aggregation pipeline
    """
    
    def __init__(
        self,
        num_experts: int = 256,
        top_k: int = 8,
        hidden_dim: int = 8192,
        intermediate_dim: int = 29568,  # Inferred for 95B active params
        capacity_factor: float = 1.25,
    ):
        super().__init__()
        self.num_experts = num_experts
        self.top_k = top_k
        
        # Router
        self.router = MoERouter(
            num_experts=num_experts,
            top_k=top_k,
            hidden_dim=hidden_dim,
            capacity_factor=capacity_factor,
        )
        
        # Expert Pool
        self.experts = nn.ModuleList([
            ExpertFFN(hidden_dim, intermediate_dim)
            for _ in range(num_experts)
        ])
    
    def forward(self, hidden_states: torch.Tensor, training: bool = True):
        """
        MoE layer forward pass
        
        Pipeline:
        1. Router selects Top-K experts for each token
        2. Tokens dispatched to corresponding experts for processing
        3. Weighted sum of expert outputs produces final result
        
        Computational efficiency: Only top_k/num_experts ≈ 8/256 ≈ 3.1% parameters activated
        """
        batch_seq_len, hidden_dim = hidden_states.shape
        
        # Step 1: Routing decision
        router_weights, selected_experts, aux_loss = self.router(
            hidden_states, training=training
        )
        
        # Step 2: Group tokens by expert
        flat_experts = selected_experts.view(-1)  # [tokens * top_k]
        flat_weights = router_weights.view(-1)    # [tokens * top_k]
        
        # Replicate hidden_states to match top_k copies
        expanded_hidden = hidden_states.unsqueeze(1).expand(
            -1, self.top_k, -1
        ).reshape(-1, hidden_dim)  # [tokens * top_k, hidden_dim]
        
        # Step 3: Execute FFN per expert group (batched in production)
        output = torch.zeros_like(expanded_hidden)
        
        for expert_idx in range(self.num_experts):
            mask = (flat_experts == expert_idx)
            if mask.any():
                expert_tokens = expanded_hidden[mask]
                expert_output = self.experts[expert_idx](expert_tokens)
                output[mask] = expert_output
        
        # Step 4: Weighted sum
        output = output * flat_weights.unsqueeze(-1)
        output = output.view(batch_seq_len, self.top_k, hidden_dim)
        output = output.sum(dim=1)  # [tokens, hidden_dim]
        
        return output, aux_loss


def demonstrate_moe_routing():
    """
    Demonstrate the complete MoE routing pipeline
    
    Simulates Qwen3.8-Max's single-layer MoE forward pass
    """
    torch.manual_seed(42)
    
    # Configuration (simplified for demonstration)
    config = {
        "num_experts": 64,       # Simplified to 64 (actual inferred as 256)
        "top_k": 8,
        "hidden_dim": 512,       # Simplified for demo
        "intermediate_dim": 2048,
        "batch_size": 4,
        "seq_len": 128,
    }
    
    print("=" * 70)
    print("Qwen3.8-Max MoE Routing Demonstration")
    print("=" * 70)
    print(f"Total Experts: {config['num_experts']}")
    print(f"Top-K: {config['top_k']}")
    print(f"Activation Rate: {config['top_k']/config['num_experts']*100:.1f}%")
    print(f"Batch: {config['batch_size']}, SeqLen: {config['seq_len']}")
    print("-" * 70)
    
    # Build MoE layer
    moe_layer = SparseMoELayer(**{k: v for k, v in config.items() 
                                   if k not in ['batch_size', 'seq_len']})
    
    # Construct input
    hidden_states = torch.randn(
        config['batch_size'] * config['seq_len'], 
        config['hidden_dim']
    )
    
    # Forward pass
    output, aux_loss = moe_layer(hidden_states, training=True)
    
    print(f"\nInput shape: {hidden_states.shape}")
    print(f"Output shape: {output.shape}")
    print(f"Auxiliary Loss (Load Balancing): {aux_loss.item():.4f}")
    
    # Analyze expert usage distribution
    with torch.no_grad():
        scores = F.softmax(moe_layer.router.gate(hidden_states), dim=-1)
        _, selected = torch.topk(scores, config['top_k'], dim=-1)
        
        expert_counts = torch.bincount(
            selected.view(-1), 
            minlength=config['num_experts']
        )
        
        print(f"\nExpert Usage Distribution:")
        print(f"  Active Experts: {(expert_counts > 0).sum().item()}")
        print(f"  Busiest Expert: Tokens={expert_counts.max().item()}, "
              f"Index={expert_counts.argmax().item()}")
        print(f"  Least Used Expert: Tokens={expert_counts.min().item()}")
        print(f"  Usage Std Dev: {expert_counts.float().std().item():.2f}")
        print(f"  Ideal Uniform Distribution: "
              f"{config['batch_size'] * config['seq_len'] * config['top_k'] / config['num_experts']:.1f} tokens/expert")
    
    # Parameter count analysis
    total_params = sum(p.numel() for p in moe_layer.parameters())
    active_params = config['hidden_dim'] * config['intermediate_dim'] * 3 * config['top_k']
    
    print(f"\nParameter Analysis:")
    print(f"  Total Parameters: {total_params:,}")
    print(f"  Per-Pass Active Parameters: {active_params:,}")
    print(f"  Activation Rate: {active_params/total_params*100:.2f}%")
    print("=" * 70)


if __name__ == "__main__":
    demonstrate_moe_routing()

"""
Qwen3.8 Agent Adaptive Closed-Loop Learning Framework
Simulates the "execute-feedback-iterate" adaptive cycle in long-horizon tasks
"""

import asyncio
import json
import time
from dataclasses import dataclass, field
from typing import List, Dict, Optional, Callable, Any
from enum import Enum
from collections import deque


class TaskStatus(Enum):
    PENDING = "pending"
    EXECUTING = "executing"
    WAITING_FEEDBACK = "waiting_feedback"
    COMPLETED = "completed"
    FAILED = "failed"
    ADAPTING = "adapting"


@dataclass
class TaskStep:
    """Single task step"""
    step_id: int
    description: str
    action: str
    status: TaskStatus = TaskStatus.PENDING
    result: Optional[Dict] = None
    feedback: Optional[str] = None
    iterations: int = 0
    max_iterations: int = 10


@dataclass 
class AgentMemory:
    """Agent working memory - Simulates Qwen3.8's long-horizon memory system"""
    short_term: deque = field(default_factory=lambda: deque(maxlen=100))
    long_term: Dict[str, Any] = field(default_factory=dict)
    execution_log: List[Dict] = field(default_factory=list)
    strategy_history: List[str] = field(default_factory=list)
    
    def add_short_term(self, item: Dict):
        self.short_term.append(item)
    
    def add_long_term(self, key: str, value: Any):
        self.long_term[key] = value
    
    def get_context_summary(self) -> str:
        """Generate current context summary for decision-making"""
        recent = list(self.short_term)[-10:]
        summary_parts = []
        for item in recent:
            summary_parts.append(
                f"- [{item.get('type', 'info')}] {item.get('content', '')}"
            )
        return "\n".join(summary_parts)


class QwenAgentOrchestrator:
    """
    Qwen3.8 Agent Orchestrator
    
    Simulates QwenWork's multi-Agent collaborative scheduling architecture
    Core capabilities:
    1. Task decomposition and sub-Agent scheduling
    2. Adaptive feedback loops
    3. Long-horizon memory management
    4. Tool calling and execution
    """
    
    def __init__(self, model_name: str = "qwen3.8-max"):
        self.model_name = model_name
        self.memory = AgentMemory()
        self.sub_agents: Dict[str, Any] = {}
        self.tool_registry: Dict[str, Callable] = {}
        self.max_concurrent_agents = 330  # Reference: quant investment scenario
        self.execution_history: List[Dict] = []
    
    def register_tool(self, name: str, tool_fn: Callable, description: str):
        """Register callable tools"""
        self.tool_registry[name] = {
            "fn": tool_fn,
            "description": description,
        }
    
    async def plan(self, task: str) -> List[TaskStep]:
        """
        Task planning phase
        Decompose complex tasks into executable sub-steps
        """
        self.memory.add_short_term({
            "type": "plan",
            "content": f"Planning task: {task}",
            "timestamp": time.time()
        })
        
        steps = [
            TaskStep(
                step_id=1,
                description="Analyze task requirements and constraints",
                action="analyze_requirements",
            ),
            TaskStep(
                step_id=2,
                description="Design solution architecture",
                action="design_solution",
            ),
            TaskStep(
                step_id=3,
                description="Execute core implementation",
                action="implement_core",
            ),
            TaskStep(
                step_id=4,
                description="Validation and testing",
                action="validate_and_test",
            ),
            TaskStep(
                step_id=5,
                description="Iterative optimization",
                action="iterate_optimize",
                max_iterations=50,
            ),
        ]
        
        self.memory.strategy_history.append("decompose_execute_iterate")
        return steps
    
    async def execute_step(
        self, 
        step: TaskStep,
        environment: Optional[Dict] = None
    ) -> TaskStep:
        """Execute single step with adaptive feedback loop"""
        step.status = TaskStatus.EXECUTING
        
        for iteration in range(step.max_iterations):
            step.iterations = iteration + 1
            
            # Execute action
            result = await self._execute_action(step.action, environment)
            step.result = result
            
            # Get environment feedback
            feedback = await self._get_feedback(result, step)
            step.feedback = feedback
            
            # Record to working memory
            self.memory.add_short_term({
                "type": "execution",
                "content": f"Step {step.step_id}, "
                          f"Iteration {iteration + 1}: {feedback}",
                "step_result": result,
            })
            
            # Determine whether to continue iterating
            if self._should_terminate(result, feedback):
                step.status = TaskStatus.COMPLETED
                break
            
            # Adaptive strategy adjustment
            step.status = TaskStatus.ADAPTING
            step.action = await self._adapt_strategy(step, feedback)
            step.status = TaskStatus.EXECUTING
        
        return step
    
    async def _execute_action(
        self, action: str, environment: Optional[Dict]
    ) -> Dict:
        """Execute action and return results"""
        if action in self.tool_registry:
            result = self.tool_registry[action]["fn"](environment)
            return {"status": "success", "data": result}
        
        return {
            "status": "success",
            "data": {
                "action": action,
                "timestamp": time.time(),
                "output": f"Executed {action} via {self.model_name}"
            }
        }
    
    async def _get_feedback(self, result: Dict, step: TaskStep) -> str:
        """Get environment feedback signal"""
        if result.get("status") == "success":
            return "Action completed successfully"
        return f"Action failed: {result.get('error', 'unknown')}"
    
    def _should_terminate(self, result: Dict, feedback: str) -> bool:
        """Determine whether iteration should terminate"""
        return result.get("status") == "success"
    
    async def _adapt_strategy(self, step: TaskStep, feedback: str) -> str:
        """
        Adapt strategy based on feedback
        This is the core of Qwen3.8's long-horizon task capability
        """
        context = self.memory.get_context_summary()
        
        if "failed" in feedback.lower():
            adapted = f"{step.action}_retry_with_modification"
            self.memory.strategy_history.append(f"adapt: {adapted}")
        else:
            adapted = step.action
        
        return adapted
    
    async def run_task(self, task: str, environment: Dict = None) -> Dict:
        """
        Complete task execution pipeline
        Simulates Qwen3.8's end-to-end task delivery capability
        """
        start_time = time.time()
        
        # Phase 1: Planning
        steps = await self.plan(task)
        
        # Phase 2: Step-by-step execution (with adaptive loops)
        results = []
        for step in steps:
            result = await self.execute_step(step, environment)
            results.append({
                "step_id": result.step_id,
                "description": result.description,
                "status": result.status.value,
                "iterations": result.iterations,
            })
        
        # Phase 3: Summary
        total_time = time.time() - start_time
        self.memory.add_long_term("last_task_summary", {
            "task": task,
            "total_steps": len(steps),
            "total_iterations": sum(r["iterations"] for r in results),
            "elapsed_time": total_time,
        })
        
        return {
            "task": task,
            "model": self.model_name,
            "steps_completed": len(steps),
            "results": results,
            "total_iterations": sum(r["iterations"] for r in results),
            "elapsed_time": total_time,
            "strategies_used": self.memory.strategy_history,
        }


async def demonstrate_agent_orchestration():
    """Demonstrate the complete Agent orchestrator workflow"""
    
    agent = QwenAgentOrchestrator(model_name="qwen3.8-max")
    
    # Register tools
    agent.register_tool(
        "analyze_requirements",
        lambda env: {
            "requirements": ["performance", "cost control", "reliability"],
            "complexity": "high"
        },
        "Analyze task requirements"
    )
    agent.register_tool(
        "design_solution",
        lambda env: {"architecture": "microservices", "components": 5},
        "Design solution"
    )
    agent.register_tool(
        "implement_core",
        lambda env: {"files_created": 23, "lines_of_code": 3500},
        "Implement core functionality"
    )
    agent.register_tool(
        "validate_and_test",
        lambda env: {
            "tests_passed": 156, "tests_total": 160, "coverage": 0.94
        },
        "Validate and test"
    )
    agent.register_tool(
        "iterate_optimize",
        lambda env: {"improvements": 8, "performance_gain": "12%"},
        "Iterative optimization"
    )
    
    task = "Build a self-evolving agent harness framework"
    result = await agent.run_task(task)
    
    print("=" * 70)
    print("Qwen3.8 Agent Task Execution Report")
    print("=" * 70)
    print(f"Task: {result['task']}")
    print(f"Model: {result['model']}")
    print(f"Steps Completed: {result['steps_completed']}")
    print(f"Total Iterations: {result['total_iterations']}")
    print(f"Elapsed Time: {result['elapsed_time']:.4f}s")
    print(f"\nStrategy History: {' -> '.join(result['strategies_used'])}")
    print("\nStep Details:")
    for step in result['results']:
        print(f"  Step {step['step_id']}: [{step['status']}] "
              f"{step['description']} ({step['iterations']} iterations)")
    print("=" * 70)


if __name__ == "__main__":
    asyncio.run(demonstrate_agent_orchestration())

/*
Qwen3.8 Hybrid Agent Implementation (Go)
Simulates visual feedback-driven iterative programming Agent
Supports: Code Generation → Render & Observe → Visual Feedback → Code Modification loop
*/

package main

import (
	"fmt"
	"math/rand"
	"strings"
	"time"
)

// HybridAgent combines programming and visual understanding capabilities
type HybridAgent struct {
	ModelName       string
	Iteration       int
	MaxIterations   int
	CurrentCode     string
	VisualFeedback  string
	TaskDescription string
	ExecutionLog    []ExecutionRecord
}

// ExecutionRecord stores execution history
type ExecutionRecord struct {
	Iteration int
	Action    string
	Code      string
	Feedback  string
	Score     float64
	Timestamp time.Time
}

// VisualFeedbackResult contains visual analysis results
type VisualFeedbackResult struct {
	LayoutScore  float64  // Layout score 0-1
	ColorMatch   float64  // Color match score 0-1
	FunctionalOK bool     // Whether functionality works
	Issues       []string // Issues discovered
	Suggestion   string   // Improvement suggestion
}

// NewHybridAgent creates a new hybrid agent instance
func NewHybridAgent(modelName, task string, maxIter int) *HybridAgent {
	return &HybridAgent{
		ModelName:       modelName,
		MaxIterations:   maxIter,
		TaskDescription: task,
		ExecutionLog:    make([]ExecutionRecord, 0),
	}
}

// AnalyzeScreenshot analyzes current render (simulates visual understanding)
func (a *HybridAgent) AnalyzeScreenshot(screenshot string) VisualFeedbackResult {
	a.Iteration++
	
	baseScore := 0.5 + float64(a.Iteration)*0.08
	
	return VisualFeedbackResult{
		LayoutScore:  minFloat(baseScore+rand.Float64()*0.1, 1.0),
		ColorMatch:   minFloat(baseScore+rand.Float64()*0.05, 1.0),
		FunctionalOK: baseScore > 0.7,
		Issues:       generateIssues(a.Iteration),
		Suggestion:   generateSuggestion(a.Iteration),
	}
}

// GenerateCode generates/modifies code based on task and visual feedback
func (a *HybridAgent) GenerateCode(feedback VisualFeedbackResult) string {
	if a.Iteration == 1 {
		return generateInitialCode(a.TaskDescription)
	}
	return modifyCode(a.CurrentCode, feedback)
}

// RenderCode renders code to visual interface
func (a *HybridAgent) RenderCode(code string) string {
	return fmt.Sprintf("[Rendered UI] Lines: %d, Components: %d",
		strings.Count(code, "\n"),
		strings.Count(code, "Component"))
}

// Run executes the complete Hybrid Agent loop
func (a *HybridAgent) Run() ExecutionRecord {
	fmt.Println(strings.Repeat("=", 70))
	fmt.Printf("Qwen3.8 Hybrid Agent Launched\n")
	fmt.Printf("Model: %s\n", a.ModelName)
	fmt.Printf("Task: %s\n", a.TaskDescription)
	fmt.Println(strings.Repeat("=", 70))
	
	var finalRecord ExecutionRecord
	
	for i := 0; i < a.MaxIterations; i++ {
		code := a.GenerateCode(VisualFeedbackResult{})
		a.CurrentCode = code
		screenshot := a.RenderCode(code)
		feedback := a.AnalyzeScreenshot(screenshot)
		
		record := ExecutionRecord{
			Iteration: a.Iteration,
			Action:    "code_generate_and_refine",
			Code:      code[:minInt(len(code), 80)] + "...",
			Feedback:  feedback.Suggestion,
			Score:     (feedback.LayoutScore + feedback.ColorMatch) / 2,
			Timestamp: time.Now(),
		}
		a.ExecutionLog = append(a.ExecutionLog, record)
		
		fmt.Printf("\n[Iteration %d/%d]\n", a.Iteration, a.MaxIterations)
		fmt.Printf("  Layout: %.3f | Color: %.3f | Functional: %v\n",
			feedback.LayoutScore, feedback.ColorMatch, feedback.FunctionalOK)
		if len(feedback.Issues) > 0 {
			fmt.Printf("  Issues: %s\n", strings.Join(feedback.Issues, "; "))
		}
		fmt.Printf("  Combined Score: %.3f\n", record.Score)
		fmt.Printf("  Suggestion: %s\n", feedback.Suggestion)
		
		finalRecord = record
		
		if feedback.LayoutScore > 0.95 && feedback.ColorMatch > 0.95 &&
			feedback.FunctionalOK {
			fmt.Printf("\n✓ Target achieved at iteration %d\n", a.Iteration)
			break
		}
	}
	
	fmt.Println(strings.Repeat("=", 70))
	fmt.Printf("Complete: %d iterations, Final score: %.3f\n",
		len(a.ExecutionLog), finalRecord.Score)
	fmt.Println(strings.Repeat("=", 70))
	
	return finalRecord
}

// ===== Helper Functions =====

func generateInitialCode(task string) string {
	return fmt.Sprintf(`// Auto-generated by Qwen3.8 Hybrid Agent
// Task: %s
import React, { useState } from 'react';

function App() {
  const [data, setData] = useState([]);
  
  return (
    <div className="app-container">
      <header>Dashboard</header>
      <main>
        <Component type="chart" data={data} />
        <Component type="table" data={data} />
      </main>
    </div>
  );
}
export default App;`, task)
}

func modifyCode(currentCode string, feedback VisualFeedbackResult) string {
	modifications := []string{
		"// Refined layout based on visual feedback",
		"// Adjusted color scheme for better contrast",
		"// Fixed responsive breakpoints",
		"// Optimized component rendering",
	}
	idx := rand.Intn(len(modifications))
	return currentCode + "\n" + modifications[idx]
}

func generateIssues(iteration int) []string {
	if iteration <= 2 {
		return []string{"Layout spacing inconsistent", "Color contrast below threshold"}
	} else if iteration <= 4 {
		return []string{"Minor alignment issue on mobile viewport"}
	}
	return nil
}

func generateSuggestion(iteration int) string {
	suggestions := []string{
		"Increase padding between components by 8px",
		"Use design system color tokens for consistency",
		"Add responsive breakpoints for tablet viewport",
		"Optimize re-render performance with memo",
		"Fine-tune animation timing to 300ms ease-out",
		"All quality metrics within acceptable range",
	}
	return suggestions[minInt(iteration-1, len(suggestions)-1)]
}

func minFloat(a, b float64) float64 {
	if a < b { return a }
	return b
}

func minInt(a, b int) int {
	if a < b { return a }
	return b
}

func main() {
	rand.Seed(time.Now().UnixNano())
	
	agent := NewHybridAgent(
		"Qwen3.8-Max",
		"Rebuild complete frontend project from single UI screenshot",
		6,
	)
	
	agent.Run()
}

/*
QwenWork Multi-Agent Collaborative Scheduling Framework (Go)
Simulates quantitative investment scenario: 330 sub-Agents running factor backtests in parallel
*/

package main

import (
	"fmt"
	"math/rand"
	"strings"
	"sync"
	"time"
)

// SubAgent represents a specialized agent
type SubAgent struct {
	ID        int
	AgentType string // "factor_analyst", "risk_manager", "executor"
	Status    string
	Results   []BacktestResult
	ParentID  int
}

// BacktestResult contains backtest metrics
type BacktestResult struct {
	FactorName   string
	SharpeRatio  float64
	MaxDrawdown  float64
	AnnualReturn float64
	WinRate      float64
}

// MultiAgentCoordinator orchestrates parallel agent execution
type MultiAgentCoordinator struct {
	Orchestrator   *SubAgent
	SubAgents      []*SubAgent
	MaxConcurrency int
	TaskQueue      chan Task
	ResultsChannel chan BacktestResult
	WaitGroup      sync.WaitGroup
	Mutex          sync.Mutex
}

// Task represents a scheduling task
type Task struct {
	ID         int
	FactorName string
	Params     map[string]float64
	Priority   int
}

// NewCoordinator creates a new coordinator instance
func NewCoordinator(maxConcurrency int) *MultiAgentCoordinator {
	return &MultiAgentCoordinator{
		SubAgents:      make([]*SubAgent, 0, maxConcurrency),
		MaxConcurrency: maxConcurrency,
		TaskQueue:      make(chan Task, 1000),
		ResultsChannel: make(chan BacktestResult, 6000),
	}
}

// InitializeSubAgents sets up the agent pool
func (c *MultiAgentCoordinator) InitializeSubAgents(count int) {
	c.Orchestrator = &SubAgent{
		ID:        0,
		AgentType: "orchestrator",
		Status:    "active",
	}

	agentTypes := []string{
		"factor_analyst",
		"backtest_runner",
		"risk_evaluator",
		"portfolio_builder",
		"performance_monitor",
	}

	for i := 1; i <= count; i++ {
		agent := &SubAgent{
			ID:        i,
			AgentType: agentTypes[rand.Intn(len(agentTypes))],
			Status:    "idle",
			ParentID:  0,
		}
		c.SubAgents = append(c.SubAgents, agent)
	}

	fmt.Printf("Initialized %d sub-Agents (types: %d)\n", count, len(agentTypes))
}

// GenerateTasks populates the task queue
func (c *MultiAgentCoordinator) GenerateTasks(numTasks int) {
	factorCategories := []string{
		"momentum", "value", "quality", "volatility",
		"liquidity", "growth", "sentiment", "technical",
	}

	for i := 0; i < numTasks; i++ {
		task := Task{
			ID:         i,
			FactorName: fmt.Sprintf("%s_factor_%d",
				factorCategories[rand.Intn(len(factorCategories))], i),
			Params: map[string]float64{
				"lookback":  float64(5 + rand.Intn(60)),
				"rebalance": float64(1 + rand.Intn(20)),
				"threshold": rand.Float64() * 2,
			},
			Priority: rand.Intn(5),
		}
		c.TaskQueue <- task
	}
	close(c.TaskQueue)
	fmt.Printf("Generated %d backtest tasks\n", numTasks)
}

// ExecuteBacktest runs a single factor backtest
func (c *MultiAgentCoordinator) ExecuteBacktest(task Task, agent *SubAgent) BacktestResult {
	agent.Status = "executing"
	time.Sleep(time.Microsecond * 50)
	
	result := BacktestResult{
		FactorName:   task.FactorName,
		SharpeRatio:  rand.Float64()*3 - 0.5,
		MaxDrawdown:  -rand.Float64() * 30,
		AnnualReturn: rand.Float64()*40 - 10,
		WinRate:      0.4 + rand.Float64()*0.3,
	}
	
	agent.Status = "idle"
	return result
}

// Run starts the parallel multi-agent backtest
func (c *MultiAgentCoordinator) Run() []BacktestResult {
	startTime := time.Now()
	
	var allResults []BacktestResult
	var mu sync.Mutex
	sem := make(chan struct{}, c.MaxConcurrency)
	
	for task := range c.TaskQueue {
		agent := c.findIdleAgent()
		if agent == nil {
			sem <- struct{}{}
			agent = c.findIdleAgent()
		}
		
		c.WaitGroup.Add(1)
		go func(t Task, a *SubAgent) {
			defer c.WaitGroup.Done()
			defer func() { <-sem }()
			
			result := c.ExecuteBacktest(t, a)
			mu.Lock()
			allResults = append(allResults, result)
			mu.Unlock()
		}(task, agent)
	}
	
	c.WaitGroup.Wait()
	elapsed := time.Since(startTime)
	
	fmt.Printf("\nBacktest complete: %d tasks, elapsed %v\n", len(allResults), elapsed)
	fmt.Printf("Average per task: %v\n", elapsed/time.Duration(len(allResults)))
	
	return allResults
}

func (c *MultiAgentCoordinator) findIdleAgent() *SubAgent {
	c.Mutex.Lock()
	defer c.Mutex.Unlock()
	for _, agent := range c.SubAgents {
		if agent.Status == "idle" {
			return agent
		}
	}
	return nil
}

func main() {
	rand.Seed(time.Now().UnixNano())
	
	fmt.Println(strings.Repeat("=", 70))
	fmt.Println("QwenWork Multi-Agent Collaborative Backtest System")
	fmt.Println("Model: Qwen3.8-Max | Sub-Agents: 330 | Tasks: 6000")
	fmt.Println(strings.Repeat("=", 70))
	
	coordinator := NewCoordinator(330)
	coordinator.InitializeSubAgents(330)
	coordinator.GenerateTasks(6000)
	
	results := coordinator.Run()
	
	topSharpe := 0.0
	for _, r := range results {
		if r.SharpeRatio > topSharpe {
			topSharpe = r.SharpeRatio
		}
	}
	
	fmt.Printf("\nResults Summary:\n")
	fmt.Printf("  Total backtests: %d\n", len(results))
	fmt.Printf("  Highest Sharpe: %.3f\n", topSharpe)
	fmt.Printf("  Avg Annual Return: %.2f%%\n", avgReturn(results))
	fmt.Println(strings.Repeat("=", 70))
}

func avgReturn(results []BacktestResult) float64 {
	if len(results) == 0 { return 0 }
	sum := 0.0
	for _, r := range results {
		sum += r.AnnualReturn
	}
	return sum / float64(len(results))
}