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 """DTLZ2 Optimization with Ax Bayesian Optimizer using JobLib Runner.
 
 This example demonstrates the DAG Executor with Ax optimizer and JobLib scheduler
 running the DTLZ2 problem in two different workflow configurations.
 
 Key Differences from Default Showcase:
 - Uses JobLib scheduler for parallel job execution
 - Configures number of parallel workers (n_jobs)
 - Demonstrates local multi-core parallelism
 
 Configuration:
 - 10 Sobol initialization points
 - 10 Bayesian optimization iterations
 - Batch size of 3 (3 parallel evaluations per iteration)
 - Total evaluations: 10 + (10 * 3) = 40 points
 - JobLib backend: loky (default)
 - Parallel workers: -1 (all available CPUs)
 
 Project: AID2E v0.0.0 - AI assisted Detector Design for EIC
 """
 
 import numpy as np
 import json
 import sys
 from pathlib import Path
 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
 from aid2e.optimizers.base import SearchSpace
 from aid2e.optimizers.ax import AxOptimizer, AxOptimizerConfig
 from aid2e.schedulers.JobLib.config import JobLibRunnerConfig
 
 
 from examples.dtlz2.python_api.dtlz2_custom_optimizer import (
     create_single_branch_workflow,
     create_separate_branches_workflow,
 )
 
 
 # =============================================================================
 # Optimization Functions
 # =============================================================================
 
 def run_case_1_single_branch():
     """Run Case 1: Single branch with Ax optimizer and JobLib scheduler."""
     print("\n" + "="*80)
     print("CASE 1: Single Branch with Ax Bayesian Optimizer + JobLib Scheduler")
     print("="*80)
     
     # Create workflow
     workflow = create_single_branch_workflow(
         name="dtlz2_ax_joblib_single_branch",
         description="DTLZ2 with Ax optimizer and JobLib scheduler - single branch",
     )
     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]}")
     
     # Configure JobLib scheduler
     joblib_config = JobLibRunnerConfig(
         n_jobs=-1,  # Use all available CPUs
         backend="loky",  # Default backend (safe multiprocessing)
         timeout=300,  # 5 minute timeout per job
         verbose=0,  # Silent (increase for debugging)
     )
     
     print(f"\n✓ Scheduler: JobLib")
     print(f"  Workers: {joblib_config.n_jobs} (all CPUs)")
     print(f"  Backend: {joblib_config.backend}")
     print(f"  Timeout: {joblib_config.timeout}s per job")
     
     # Create executor with JobLib scheduler
     executor = DAGExecutor(
         workflow=workflow,
         base_output_dir="/tmp/dtlz2_ax_joblib_optimization/case1",
         log_level="WARNING",
         scheduler_config={
             "runner_type": "JobLibRunner",
             "config": joblib_config,
         },
     )
     
     # Create search space
     search_space = SearchSpace(
         parameters={
             "x1": {"type": "range", "value": 0.5, "bounds": [0.0, 1.0]},
             "x2": {"type": "range", "value": 0.5, "bounds": [0.0, 1.0]},
             "x3": {"type": "range", "value": 0.5, "bounds": [0.0, 1.0]},
         }
     )
     
     # Create Ax optimizer configuration
     ax_config = AxOptimizerConfig(
         initialization_strategy="sobol",
         n_initial_samples=10,
         batch_size=3,
         generator="BOTORCH_MODULAR",
         seed=42,
     )
     
     # Create Ax optimizer
     optimizer = AxOptimizer(
         search_space=search_space,
         config=ax_config,
         objective_names=["f1", "f2"],
         seed=42,
     )
     
     print(f"\n✓ Optimizer: Ax Bayesian Optimizer")
     print(f"  Initialization: {ax_config.initialization_strategy} ({ax_config.n_initial_samples} points)")
     print(f"  Generator: {ax_config.generator}")
     print(f"  Batch Size: {ax_config.batch_size}")
     print(f"  Total Iterations: 10 Bayesian iterations")
     
     # Optimization loop
     print(f"\n{'Iter':<6} {'Batch':<6} {'x1':<10} {'x2':<10} {'x3':<10} {'f1':<12} {'f2':<12} {'Phase':<15}")
     print("-" * 95)
     
     trial_index = 0
     
     # Phase 1: Sobol initialization (10 points in batches of 3)
     n_sobol_batches = int(np.ceil(ax_config.n_initial_samples / ax_config.batch_size))
     for batch in range(n_sobol_batches):
         # Get batch size (might be less than 3 for last batch)
         batch_size = min(ax_config.batch_size, ax_config.n_initial_samples - batch * ax_config.batch_size)
         
         # Suggest candidates
         candidates = optimizer.suggest_candidates(n_candidates=batch_size)
         
         # Evaluate each candidate (JobLib handles parallelism internally)
         for i, design_point in enumerate(candidates):
             objectives = executor.execute(design_point)
             optimizer.update_with_results(trial_index, design_point, objectives)
             
             print(f"{trial_index+1:<6} {batch+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} {'Sobol Init':<15}")
             
             trial_index += 1
     
     # Phase 2: Bayesian optimization (10 iterations x 3 points = 30 points)
     n_bayesian_iterations = 10
     for iteration in range(n_bayesian_iterations):
         # Suggest batch of candidates
         candidates = optimizer.suggest_candidates(n_candidates=ax_config.batch_size)
         
         # Evaluate each candidate
         for i, design_point in enumerate(candidates):
             objectives = executor.execute(design_point)
             optimizer.update_with_results(trial_index, design_point, objectives)
             
             print(f"{trial_index+1:<6} {iteration+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} {'Bayesian':<15}")
             
             trial_index += 1
     
     # Get Pareto front
     pareto_front = optimizer.get_pareto_front()
     
     print(f"\n✓ Optimization Complete!")
     print(f"  Total evaluations: {trial_index}")
     print(f"  Sobol initialization: {ax_config.n_initial_samples}")
     print(f"  Bayesian iterations: {n_bayesian_iterations}")
     print(f"  Pareto front points: {len(pareto_front)}")
     
     print(f"\nPareto Front (non-dominated points):")
     print(f"{'Trial':<8} {'x1':<10} {'x2':<10} {'x3':<10} {'f1':<12} {'f2':<12}")
     print("-" * 72)
     for trial in pareto_front[:10]:  # Show top 10
         dp = trial.parameters
         obj = trial.metrics if trial.metrics else {}
         print(f"{trial.index:<8} {dp.get('x1', 0):<10.4f} {dp.get('x2', 0):<10.4f} {dp.get('x3', 0):<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 with Ax optimizer and JobLib scheduler."""
     print("\n" + "="*80)
     print("CASE 2: Separate Branches with Ax Bayesian Optimizer + JobLib Scheduler")
     print("="*80)
     
     # Create workflow
     workflow = create_separate_branches_workflow(
         name="dtlz2_ax_joblib_separate_branches",
         description="DTLZ2 with Ax optimizer and JobLib scheduler - separate branches",
     )
     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]}")
     
     # Configure JobLib scheduler
     joblib_config = JobLibRunnerConfig(
         n_jobs=-1,  # Use all available CPUs
         backend="loky",
         timeout=300,
         verbose=0,
     )
     
     print(f"\n✓ Scheduler: JobLib")
     print(f"  Workers: {joblib_config.n_jobs} (all CPUs)")
     print(f"  Backend: {joblib_config.backend}")
     print(f"  Timeout: {joblib_config.timeout}s per job")
     
     # Create executor with JobLib scheduler
     executor = DAGExecutor(
         workflow=workflow,
         base_output_dir="/tmp/dtlz2_ax_joblib_optimization/case2",
         log_level="WARNING",
         scheduler_config={
             "runner_type": "JobLibRunner",
             "config": joblib_config,
         },
     )
     
     # Create search space
     search_space = SearchSpace(
         parameters={
             "x1": {"type": "range", "value": 0.5, "bounds": [0.0, 1.0]},
             "x2": {"type": "range", "value": 0.5, "bounds": [0.0, 1.0]},
             "x3": {"type": "range", "value": 0.5, "bounds": [0.0, 1.0]},
         }
     )
     
     # Create Ax optimizer configuration (same as Case 1)
     ax_config = AxOptimizerConfig(
         initialization_strategy="sobol",
         n_initial_samples=10,
         batch_size=3,
         generator="BOTORCH_MODULAR",
         seed=42,  # Same seed for reproducibility
     )
     
     # Create Ax optimizer
     optimizer = AxOptimizer(
         search_space=search_space,
         config=ax_config,
         objective_names=["f1", "f2"],
         seed=42,
     )
     
     print(f"\n✓ Optimizer: Ax Bayesian Optimizer")
     print(f"  Initialization: {ax_config.initialization_strategy} ({ax_config.n_initial_samples} points)")
     print(f"  Generator: {ax_config.generator}")
     print(f"  Batch Size: {ax_config.batch_size}")
     print(f"  Total Iterations: 10 Bayesian iterations")
     
     # Optimization loop
     print(f"\n{'Iter':<6} {'Batch':<6} {'x1':<10} {'x2':<10} {'x3':<10} {'f1':<12} {'f2':<12} {'Phase':<15}")
     print("-" * 95)
     
     trial_index = 0
     
     # Phase 1: Sobol initialization
     n_sobol_batches = int(np.ceil(ax_config.n_initial_samples / ax_config.batch_size))
     for batch in range(n_sobol_batches):
         batch_size = min(ax_config.batch_size, ax_config.n_initial_samples - batch * ax_config.batch_size)
         candidates = optimizer.suggest_candidates(n_candidates=batch_size)
         
         for i, design_point in enumerate(candidates):
             objectives = executor.execute(design_point)
             optimizer.update_with_results(trial_index, design_point, objectives)
             
             print(f"{trial_index+1:<6} {batch+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} {'Sobol Init':<15}")
             
             trial_index += 1
     
     # Phase 2: Bayesian optimization
     n_bayesian_iterations = 10
     for iteration in range(n_bayesian_iterations):
         candidates = optimizer.suggest_candidates(n_candidates=ax_config.batch_size)
         
         for i, design_point in enumerate(candidates):
             objectives = executor.execute(design_point)
             optimizer.update_with_results(trial_index, design_point, objectives)
             
             print(f"{trial_index+1:<6} {iteration+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} {'Bayesian':<15}")
             
             trial_index += 1
     
     # Get Pareto front
     pareto_front = optimizer.get_pareto_front()
     
     print(f"\n✓ Optimization Complete!")
     print(f"  Total evaluations: {trial_index}")
     print(f"  Sobol initialization: {ax_config.n_initial_samples}")
     print(f"  Bayesian iterations: {n_bayesian_iterations}")
     print(f"  Pareto front points: {len(pareto_front)}")
     
     print(f"\nPareto Front (non-dominated points):")
     print(f"{'Trial':<8} {'x1':<10} {'x2':<10} {'x3':<10} {'f1':<12} {'f2':<12}")
     print("-" * 72)
     for trial in pareto_front[:10]:  # Show top 10
         dp = trial.parameters
         obj = trial.metrics if trial.metrics else {}
         print(f"{trial.index:<8} {dp.get('x1', 0):<10.4f} {dp.get('x2', 0):<10.4f} {dp.get('x3', 0):<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 Bayesian Optimization with Ax + JobLib Scheduler")
     print("="*80)
     print("\nConfiguration:")
     print("  • 10 Sobol initialization points")
     print("  • 10 Bayesian optimization iterations")
     print("  • Batch size of 3 (parallel evaluations)")
     print("  • Total evaluations: 10 + (10 × 3) = 40 points")
     print("\nScheduler:")
     print("  • JobLib for local parallel execution")
     print("  • Backend: loky (safe multiprocessing)")
     print("  • Workers: all available CPUs")
     print("\nWorkflow 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")
     
     try:
         # 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"  Total evaluations: {len(optimizer1.get_trials())}")
         print(f"  Pareto front size: {len(optimizer1.get_pareto_front())}")
         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"  Total evaluations: {len(optimizer2.get_trials())}")
         print(f"  Pareto front size: {len(optimizer2.get_pareto_front())}")
         print(f"  Output directory: {executor2.output_dir}")
         
         print("\n" + "="*80)
         print("✅ Ax + JobLib Optimization Showcase Complete!")
         print("="*80)
         print("\nKey Results:")
         print("  • Both workflow configurations use identical Ax optimizer")
         print("  • Bayesian optimization with SAASBO surrogate model")
         print("  • qNEHVI acquisition for multi-objective optimization")
         print("  • Batch optimization with 3 parallel evaluations per iteration")
         print("  • Sobol initialization ensures good space exploration")
         print("  • JobLib scheduler provides local multi-core parallelism")
         
     except ImportError as e:
         print("\n" + "="*80)
         print("⚠️  ERROR: Missing Dependencies")
         print("="*80)
         print(f"\n{e}")
         print("\nTo run this showcase, install required packages:")
         print("  pip install ax-platform joblib")
         print("\nAlternatively, use the simple random optimizer showcase:")
         print("  python examples/dtlz2/python_api/dtlz2_custom_optimizer.py")

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