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 """DTLZ2 Optimization Showcase with DAG Executor.
 
 This example demonstrates two workflow configurations for the DTLZ2 problem:
 
 Case 1: Single Branch - Both objectives computed in one Python function
 Case 2: Separate Branches - Each objective computed in a separate branch
 
 The DTLZ2 problem is a standard multi-objective test problem with:
 - 3 decision variables (x1, x2, x3) in [0, 1]
 - 2 objectives (f1, f2) to minimize
 - Optimal Pareto front: x1 in [0, 1], x2 = x3 = 0.5
 
 Project: AID2E v0.0.0 - AI assisted Detector Design for EIC
 """
 
 import numpy as np
 import json
 import sys
 from pathlib import Path
 from typing import Dict, Any, List
 
 REPO_ROOT = Path(__file__).resolve().parents[3]
 if str(REPO_ROOT) not in sys.path:
     sys.path.insert(0, str(REPO_ROOT))
 
 from aid2e.utilities.workflows import (
     DAGExecutor,
     WorkflowDefinition,
     BranchDefinition,
     StageDefinition,
     JobDefinition,
 )
 from aid2e.utilities.configurations.objectives import (
     ObjectiveDefinition,
     ObjectiveDirection,
 )
 from examples.dtlz2.dtlz2_evaluator import (
     evaluate_both_objectives_wrapper,
     evaluate_f1_wrapper,
     evaluate_f2_wrapper,
 )
 
 
 # =============================================================================
 # Case 1: Single Branch Workflow
 # =============================================================================
 
 def create_single_branch_workflow(
     *, name: str = "dtlz2_single_branch",
     description: str = "DTLZ2 with both objectives in single branch",
 ) -> WorkflowDefinition:
     """Create workflow with single branch computing both objectives.
     
     Workflow structure:
         Branch: main
             Stage: evaluate
                 Job: compute_objectives (PythonEvaluator)
                     → Computes both f1 and f2 in one function
     """
     # Job using PythonEvaluator to compute both objectives
     compute_job = JobDefinition(
         name="compute_objectives",
         command="python",  # Not used for PythonEvaluator
         payload={
             "evaluator_type": "python",
             "python_callable": evaluate_both_objectives_wrapper,
             "op_args": (),
             "op_kwargs": {},
         },
     )
     
     # Stage containing the job
     eval_stage = StageDefinition(
         name="evaluate",
         jobs=[compute_job],
     )
     
     # Single branch
     main_branch = BranchDefinition(
         name="main",
         stages=[eval_stage],
     )
     
     # Workflow with objectives
     workflow = WorkflowDefinition(
         name=name,
         description=description,
         branches=[main_branch],
         objectives=[
             ObjectiveDefinition(name="f1", direction=ObjectiveDirection.MINIMIZE),
             ObjectiveDefinition(name="f2", direction=ObjectiveDirection.MINIMIZE),
         ],
     )
     
     return workflow
 
 
 # =============================================================================
 # Case 2: Separate Branches Workflow
 # =============================================================================
 
 def create_separate_branches_workflow(
     *, name: str = "dtlz2_separate_branches",
     description: str = "DTLZ2 with separate branches for each objective",
 ) -> WorkflowDefinition:
     """Create workflow with separate branches for each objective.
     
     Workflow structure:
         Branch: f1_branch
             Stage: evaluate_f1
                 Job: compute_f1 (PythonEvaluator)
                     → Computes only f1
         
         Branch: f2_branch
             Stage: evaluate_f2
                 Job: compute_f2 (PythonEvaluator)
                     → Computes only f2
     """
     # Branch 1: Compute f1
     f1_job = JobDefinition(
         name="compute_f1",
         command="python",
         payload={
             "evaluator_type": "python",
             "python_callable": evaluate_f1_wrapper,
             "op_args": (),
             "op_kwargs": {},
         },
     )
     
     f1_stage = StageDefinition(name="evaluate_f1", jobs=[f1_job])
     f1_branch = BranchDefinition(name="f1_branch", stages=[f1_stage])
     
     # Branch 2: Compute f2
     f2_job = JobDefinition(
         name="compute_f2",
         command="python",
         payload={
             "evaluator_type": "python",
             "python_callable": evaluate_f2_wrapper,
             "op_args": (),
             "op_kwargs": {},
         },
     )
     
     f2_stage = StageDefinition(name="evaluate_f2", jobs=[f2_job])
     f2_branch = BranchDefinition(name="f2_branch", stages=[f2_stage])
     
     # Workflow with both branches
     workflow = WorkflowDefinition(
         name=name,
         description=description,
         branches=[f1_branch, f2_branch],
         objectives=[
             ObjectiveDefinition(name="f1", direction=ObjectiveDirection.MINIMIZE),
             ObjectiveDefinition(name="f2", direction=ObjectiveDirection.MINIMIZE),
         ],
     )
     
     return workflow
 
 
 # =============================================================================
 # Simple Random Optimizer (for demonstration)
 # =============================================================================
 
 class SimpleRandomOptimizer:
     """Simple random search optimizer for demonstration.
     
     Generates random design points within bounds and tracks best results.
     This is a simple optimizer for demonstration - in production, use
     Ax optimizer or other sophisticated algorithms.
     """
     
     def __init__(
         self,
         bounds: Dict[str, tuple],
         n_iterations: int = 10,
         seed: int = 42,
     ):
         """Initialize optimizer.
         
         Args:
             bounds: Parameter bounds, e.g., {"x1": (0, 1), "x2": (0, 1)}
             n_iterations: Number of optimization iterations
             seed: Random seed for reproducibility
         """
         self.bounds = bounds
         self.n_iterations = n_iterations
         self.rng = np.random.RandomState(seed)
         
         # Track results
         self.design_points: List[Dict[str, float]] = []
         self.objectives: List[Dict[str, float]] = []
         self.iteration = 0
     
     def suggest(self) -> Dict[str, float]:
         """Suggest next design point to evaluate.
         
         Returns:
             Design point dictionary
         """
         design_point = {}
         for param, (lower, upper) in self.bounds.items():
             design_point[param] = self.rng.uniform(lower, upper)
         return design_point
     
     def tell(self, design_point: Dict[str, float], objectives: Dict[str, float]):
         """Provide feedback with evaluation results.
         
         Args:
             design_point: Evaluated design point
             objectives: Computed objectives
         """
         self.design_points.append(design_point)
         self.objectives.append(objectives)
         self.iteration += 1
     
     def get_best_pareto_front(self, n_points: int = 5) -> List[Dict[str, Any]]:
         """Get approximate Pareto front points.
         
         Args:
             n_points: Number of points to return
             
         Returns:
             List of dicts with design_point and objectives
         """
         if not self.objectives:
             return []
         
         # Simple Pareto dominance check
         pareto_indices = []
         for i, obj_i in enumerate(self.objectives):
             is_dominated = False
             for j, obj_j in enumerate(self.objectives):
                 if i == j:
                     continue
                 # Check if j dominates i (all objectives worse or equal, at least one worse)
                 if all(obj_j[k] <= obj_i[k] for k in obj_i) and \
                    any(obj_j[k] < obj_i[k] for k in obj_i):
                     is_dominated = True
                     break
             if not is_dominated:
                 pareto_indices.append(i)
         
         # Return Pareto points
         pareto_points = [
             {
                 "design_point": self.design_points[i],
                 "objectives": self.objectives[i],
             }
             for i in pareto_indices[:n_points]
         ]
         
         return pareto_points
 
 
 # =============================================================================
 # Main Optimization Functions
 # =============================================================================
 
 def run_case_1_single_branch():
     """Run Case 1: Single branch with both objectives."""
     print("\n" + "="*80)
     print("CASE 1: Single Branch - Both Objectives in One Python Function")
     print("="*80)
     
     # Create workflow
     workflow = create_single_branch_workflow()
     print(f"\n✓ Workflow: {workflow.name}")
     print(f"  Description: {workflow.description}")
     print(f"  Branches: {len(workflow.branches)}")
     print(f"  Objectives: {[obj.name for obj in workflow.objectives]}")
     
     # Create executor
     executor = DAGExecutor(
         workflow=workflow,
         base_output_dir="/tmp/dtlz2_optimization/case1",
         log_level="WARNING",  # Reduce verbosity
     )
     
     # Create optimizer
     optimizer = SimpleRandomOptimizer(
         bounds={"x1": (0, 1), "x2": (0, 1), "x3": (0, 1)},
         n_iterations=15,
         seed=42,
     )
     
     print(f"\n✓ Optimizer: SimpleRandomOptimizer")
     print(f"  Iterations: {optimizer.n_iterations}")
     print(f"  Bounds: {optimizer.bounds}")
     
     # Optimization loop
     print(f"\n{'Iter':<6} {'x1':<10} {'x2':<10} {'x3':<10} {'f1':<12} {'f2':<12}")
     print("-" * 72)
     
     for i in range(optimizer.n_iterations):
         # Get design point from optimizer
         design_point = optimizer.suggest()
         
         # Execute workflow to evaluate design point
         objectives = executor.execute(design_point)
         
         # Provide feedback to optimizer
         optimizer.tell(design_point, objectives)
         
         # Print results
         print(f"{i+1:<6} {design_point['x1']:<10.4f} {design_point['x2']:<10.4f} "
               f"{design_point['x3']:<10.4f} {objectives.get('f1', 0):<12.6f} "
               f"{objectives.get('f2', 0):<12.6f}")
     
     # Get Pareto front
     pareto_front = optimizer.get_best_pareto_front(n_points=5)
     
     print(f"\n✓ Optimization Complete!")
     print(f"  Total evaluations: {len(optimizer.objectives)}")
     print(f"  Pareto front points: {len(pareto_front)}")
     
     print(f"\nPareto Front (top 5 non-dominated points):")
     print(f"{'x1':<10} {'x2':<10} {'x3':<10} {'f1':<12} {'f2':<12}")
     print("-" * 72)
     for point in pareto_front:
         dp = point['design_point']
         obj = point['objectives']
         print(f"{dp['x1']:<10.4f} {dp['x2']:<10.4f} {dp['x3']:<10.4f} "
               f"{obj.get('f1', 0):<12.6f} {obj.get('f2', 0):<12.6f}")
     
     return optimizer, executor
 
 
 def run_case_2_separate_branches():
     """Run Case 2: Separate branches for each objective."""
     print("\n" + "="*80)
     print("CASE 2: Separate Branches - Each Objective in Different Branch")
     print("="*80)
     
     # Create workflow
     workflow = create_separate_branches_workflow()
     print(f"\n✓ Workflow: {workflow.name}")
     print(f"  Description: {workflow.description}")
     print(f"  Branches: {len(workflow.branches)} ({[b.name for b in workflow.branches]})")
     print(f"  Objectives: {[obj.name for obj in workflow.objectives]}")
     
     # Create executor
     executor = DAGExecutor(
         workflow=workflow,
         base_output_dir="/tmp/dtlz2_optimization/case2",
         log_level="WARNING",  # Reduce verbosity
     )
     
     # Create optimizer
     optimizer = SimpleRandomOptimizer(
         bounds={"x1": (0, 1), "x2": (0, 1), "x3": (0, 1)},
         n_iterations=15,
         seed=42,  # Same seed for reproducibility
     )
     
     print(f"\n✓ Optimizer: SimpleRandomOptimizer")
     print(f"  Iterations: {optimizer.n_iterations}")
     print(f"  Bounds: {optimizer.bounds}")
     
     # Optimization loop
     print(f"\n{'Iter':<6} {'x1':<10} {'x2':<10} {'x3':<10} {'f1':<12} {'f2':<12}")
     print("-" * 72)
     
     for i in range(optimizer.n_iterations):
         # Get design point from optimizer
         design_point = optimizer.suggest()
         
         # Execute workflow (will run both branches)
         objectives = executor.execute(design_point)
         
         # Provide feedback to optimizer
         optimizer.tell(design_point, objectives)
         
         # Print results
         print(f"{i+1:<6} {design_point['x1']:<10.4f} {design_point['x2']:<10.4f} "
               f"{design_point['x3']:<10.4f} {objectives.get('f1', 0):<12.6f} "
               f"{objectives.get('f2', 0):<12.6f}")
     
     # Get Pareto front
     pareto_front = optimizer.get_best_pareto_front(n_points=5)
     
     print(f"\n✓ Optimization Complete!")
     print(f"  Total evaluations: {len(optimizer.objectives)}")
     print(f"  Pareto front points: {len(pareto_front)}")
     
     print(f"\nPareto Front (top 5 non-dominated points):")
     print(f"{'x1':<10} {'x2':<10} {'x3':<10} {'f1':<12} {'f2':<12}")
     print("-" * 72)
     for point in pareto_front:
         dp = point['design_point']
         obj = point['objectives']
         print(f"{dp['x1']:<10.4f} {dp['x2']:<10.4f} {dp['x3']:<10.4f} "
               f"{obj.get('f1', 0):<12.6f} {obj.get('f2', 0):<12.6f}")
     
     return optimizer, executor
 
 
 # =============================================================================
 # Main Entry Point
 # =============================================================================
 
 if __name__ == "__main__":
     print("\n" + "="*80)
     print("DTLZ2 Multi-Objective Optimization with DAG Executor")
     print("="*80)
     print("\nThis showcase demonstrates two workflow configurations:")
     print("  1. Single branch - both objectives in one Python function")
     print("  2. Separate branches - each objective in different branch")
     print("\nDTLZ2 Problem:")
     print("  Variables: x1, x2, x3 in [0, 1]")
     print("  Objectives: f1, f2 (minimize both)")
     print("  Optimal Pareto front: x1 in [0, 1], x2 = x3 = 0.5")
     
     # Run both cases
     optimizer1, executor1 = run_case_1_single_branch()
     optimizer2, executor2 = run_case_2_separate_branches()
     
     # Summary
     print("\n" + "="*80)
     print("COMPARISON SUMMARY")
     print("="*80)
     
     print("\nCase 1 (Single Branch):")
     print(f"  Workflow: {executor1.workflow.name}")
     print(f"  Branches: {len(executor1.workflow.branches)}")
     print(f"  Jobs executed: {len(executor1.global_xcom)}")
     print(f"  Output directory: {executor1.output_dir}")
     
     print("\nCase 2 (Separate Branches):")
     print(f"  Workflow: {executor2.workflow.name}")
     print(f"  Branches: {len(executor2.workflow.branches)}")
     print(f"  Jobs executed: {len(executor2.global_xcom)}")
     print(f"  Output directory: {executor2.output_dir}")
     
     print("\n" + "="*80)
     print("✅ Showcase Complete!")
     print("="*80)
     print("\nKey Takeaways:")
     print("  • Both workflow configurations produce the same results")
     print("  • Single branch is simpler for tightly coupled objectives")
     print("  • Separate branches allow independent objective computation")
     print("  • DAG Executor handles both cases seamlessly")
     print("  • Optimizer integration is identical for both cases")

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