defmodule Pipeline.DSPy.DeploymentPhases do
  @moduledoc """
  Manages phased rollout of DSPy features across different environments and user segments.
  """
  
  defstruct [
    :current_phase,
    :phase_config,
    :rollout_metrics,
    :rollout_schedule,
    :phase_gates
  ]
  
  @deployment_phases [
    :development,
    :staging,
    :canary,
    :limited_production,
    :full_production
  ]
  
  def start_link(opts \\ []) do
    GenServer.start_link(__MODULE__, opts, name: __MODULE__)
  end
  
  def get_current_phase do
    GenServer.call(__MODULE__, :get_current_phase)
  end
  
  def advance_to_next_phase(approval_metadata \\ %{}) do
    GenServer.call(__MODULE__, {:advance_phase, approval_metadata})
  end
  
  def rollback_to_previous_phase(reason) do
    GenServer.call(__MODULE__, {:rollback_phase, reason})
  end
  
  def get_phase_metrics(phase) do
    GenServer.call(__MODULE__, {:get_phase_metrics, phase})
  end
  
  def init(opts) do
    state = %__MODULE__{
      current_phase: :development,
      phase_config: load_phase_config(opts),
      rollout_metrics: %{},
      rollout_schedule: %{},
      phase_gates: initialize_phase_gates(opts)
    }
    
    {:ok, state}
  end
  
  def handle_call(:get_current_phase, _from, state) do
    {:reply, state.current_phase, state}
  end
  
  def handle_call({:advance_phase, approval_metadata}, _from, state) do
    case can_advance_phase?(state.current_phase, state) do
      {:ok, next_phase} ->
        case execute_phase_transition(state.current_phase, next_phase, state) do
          {:ok, new_state} ->
            Logger.info("Successfully advanced from #{state.current_phase} to #{next_phase}")
            
            # Record phase transition
            transition_record = %{
              from_phase: state.current_phase,
              to_phase: next_phase,
              timestamp: DateTime.utc_now(),
              approval_metadata: approval_metadata
            }
            
            record_phase_transition(transition_record)
            
            {:reply, {:ok, next_phase}, new_state}
          
          {:error, reason} ->
            Logger.error("Failed to advance to #{next_phase}: #{reason}")
            {:reply, {:error, reason}, state}
        end
      
      {:error, reason} ->
        {:reply, {:error, reason}, state}
    end
  end
  
  def handle_call({:rollback_phase, reason}, _from, state) do
    case get_previous_phase(state.current_phase) do
      {:ok, previous_phase} ->
        case execute_phase_rollback(state.current_phase, previous_phase, reason, state) do
          {:ok, new_state} ->
            Logger.warning("Rolled back from #{state.current_phase} to #{previous_phase}: #{reason}")
            
            # Record rollback
            rollback_record = %{
              from_phase: state.current_phase,
              to_phase: previous_phase,
              reason: reason,
              timestamp: DateTime.utc_now()
            }
            
            record_phase_rollback(rollback_record)
            
            {:reply, {:ok, previous_phase}, new_state}
          
          {:error, rollback_error} ->
            Logger.error("Failed to rollback to #{previous_phase}: #{rollback_error}")
            {:reply, {:error, rollback_error}, state}
        end
      
      {:error, reason} ->
        {:reply, {:error, reason}, state}
    end
  end
  
  defp can_advance_phase?(current_phase, state) do
    next_phase = get_next_phase(current_phase)
    
    case next_phase do
      {:ok, phase} ->
        # Check phase gates
        case check_phase_gates(current_phase, phase, state) do
          :ok ->
            {:ok, phase}
          
          {:error, reason} ->
            {:error, "Phase gate check failed: #{reason}"}
        end
      
      {:error, reason} ->
        {:error, reason}
    end
  end
  
  defp check_phase_gates(current_phase, next_phase, state) do
    gates = Map.get(state.phase_gates, {current_phase, next_phase}, [])
    
    Enum.reduce_while(gates, :ok, fn gate, _acc ->
      case execute_phase_gate(gate, current_phase, next_phase, state) do
        :ok ->
          {:cont, :ok}
        
        {:error, reason} ->
          {:halt, {:error, reason}}
      end
    end)
  end
  
  defp execute_phase_gate(gate, current_phase, next_phase, state) do
    case gate.type do
      :metrics_threshold ->
        check_metrics_threshold(gate, current_phase, state)
      
      :manual_approval ->
        check_manual_approval(gate, current_phase, next_phase)
      
      :automated_test ->
        run_automated_test(gate, current_phase, next_phase)
      
      :time_based ->
        check_time_based_gate(gate, current_phase, state)
      
      _ ->
        {:error, "Unknown gate type: #{gate.type}"}
    end
  end
  
  defp check_metrics_threshold(gate, current_phase, state) do
    current_metrics = get_phase_metrics(current_phase, state)
    
    case evaluate_metrics_against_threshold(current_metrics, gate.thresholds) do
      :passed ->
        :ok
      
      {:failed, failing_metrics} ->
        {:error, "Metrics threshold not met: #{inspect(failing_metrics)}"}
    end
  end
end

defmodule Pipeline.DSPy.PhaseConfig do
  @moduledoc """
  Configuration for different deployment phases.
  """
  
  def get_phase_config(phase) do
    case phase do
      :development ->
        %{
          dspy_enabled: true,
          user_percentage: 100,
          feature_flags: [:all_features],
          monitoring_level: :debug,
          rollback_threshold: %{
            error_rate: 0.1,
            performance_degradation: 0.3
          }
        }
      
      :staging ->
        %{
          dspy_enabled: true,
          user_percentage: 100,
          feature_flags: [:core_features],
          monitoring_level: :info,
          rollback_threshold: %{
            error_rate: 0.05,
            performance_degradation: 0.2
          }
        }
      
      :canary ->
        %{
          dspy_enabled: true,
          user_percentage: 1,
          feature_flags: [:core_features],
          monitoring_level: :warn,
          rollback_threshold: %{
            error_rate: 0.02,
            performance_degradation: 0.15
          }
        }
      
      :limited_production ->
        %{
          dspy_enabled: true,
          user_percentage: 10,
          feature_flags: [:core_features],
          monitoring_level: :warn,
          rollback_threshold: %{
            error_rate: 0.01,
            performance_degradation: 0.1
          }
        }
      
      :full_production ->
        %{
          dspy_enabled: true,
          user_percentage: 100,
          feature_flags: [:core_features],
          monitoring_level: :error,
          rollback_threshold: %{
            error_rate: 0.005,
            performance_degradation: 0.05
          }
        }
    end
  end
end

defmodule Pipeline.DSPy.ExperimentManager do
  @moduledoc """
  Manages A/B testing experiments for DSPy features.
  """
  
  use GenServer
  
  defstruct [
    :active_experiments,
    :experiment_config,
    :experiment_results,
    :user_assignments
  ]
  
  def start_link(opts \\ []) do
    GenServer.start_link(__MODULE__, opts, name: __MODULE__)
  end
  
  def create_experiment(experiment_config) do
    GenServer.call(__MODULE__, {:create_experiment, experiment_config})
  end
  
  def get_user_assignment(user_id, experiment_id) do
    GenServer.call(__MODULE__, {:get_user_assignment, user_id, experiment_id})
  end
  
  def record_experiment_result(user_id, experiment_id, result) do
    GenServer.cast(__MODULE__, {:record_result, user_id, experiment_id, result})
  end
  
  def get_experiment_results(experiment_id) do
    GenServer.call(__MODULE__, {:get_experiment_results, experiment_id})
  end
  
  def init(opts) do
    state = %__MODULE__{
      active_experiments: %{},
      experiment_config: %{},
      experiment_results: %{},
      user_assignments: %{}
    }
    
    {:ok, state}
  end
  
  def handle_call({:create_experiment, experiment_config}, _from, state) do
    experiment_id = generate_experiment_id()
    
    # Validate experiment configuration
    case validate_experiment_config(experiment_config) do
      :ok ->
        # Create experiment
        experiment = %{
          id: experiment_id,
          config: experiment_config,
          status: :active,
          created_at: DateTime.utc_now(),
          participants: 0,
          results: %{}
        }
        
        new_active_experiments = Map.put(state.active_experiments, experiment_id, experiment)
        new_experiment_config = Map.put(state.experiment_config, experiment_id, experiment_config)
        
        new_state = %{
          state |
          active_experiments: new_active_experiments,
          experiment_config: new_experiment_config
        }
        
        {:reply, {:ok, experiment_id}, new_state}
      
      {:error, reason} ->
        {:reply, {:error, reason}, state}
    end
  end
  
  def handle_call({:get_user_assignment, user_id, experiment_id}, _from, state) do
    case Map.get(state.active_experiments, experiment_id) do
      nil ->
        {:reply, {:error, :experiment_not_found}, state}
      
      experiment ->
        # Check if user already has assignment
        assignment_key = {user_id, experiment_id}
        
        case Map.get(state.user_assignments, assignment_key) do
          nil ->
            # Create new assignment
            assignment = assign_user_to_group(user_id, experiment.config)
            
            new_user_assignments = Map.put(state.user_assignments, assignment_key, assignment)
            
            # Update participant count
            updated_experiment = %{experiment | participants: experiment.participants + 1}
            new_active_experiments = Map.put(state.active_experiments, experiment_id, updated_experiment)
            
            new_state = %{
              state |
              user_assignments: new_user_assignments,
              active_experiments: new_active_experiments
            }
            
            {:reply, {:ok, assignment}, new_state}
          
          existing_assignment ->
            {:reply, {:ok, existing_assignment}, state}
        end
    end
  end
  
  defp assign_user_to_group(user_id, experiment_config) do
    # Use consistent hashing to assign users to groups
    hash_value = :crypto.hash(:sha256, "#{user_id}#{experiment_config.seed}")
    |> :binary.decode_unsigned()
    
    # Calculate group based on hash and group weights
    group_weights = experiment_config.groups
    |> Enum.map(fn {group_name, group_config} -> {group_name, group_config.weight} end)
    
    total_weight = Enum.reduce(group_weights, 0, fn {_group, weight}, acc -> acc + weight end)
    
    normalized_hash = rem(hash_value, total_weight)
    
    {assigned_group, _} = Enum.reduce_while(group_weights, {nil, 0}, fn {group_name, weight}, {_current_group, cumulative_weight} ->
      new_cumulative_weight = cumulative_weight + weight
      
      if normalized_hash < new_cumulative_weight do
        {:halt, {group_name, new_cumulative_weight}}
      else
        {:cont, {group_name, new_cumulative_weight}}
      end
    end)
    
    %{
      user_id: user_id,
      experiment_id: experiment_config.id,
      group: assigned_group,
      assigned_at: DateTime.utc_now()
    }
  end
end

defmodule Pipeline.DSPy.ABTestingExecutor do
  @moduledoc """
  Executor that routes requests based on A/B testing assignments.
  """
  
  def execute_step_with_ab_testing(step, context, user_id \\ nil) do
    # Check if there's an active experiment for this step type
    case get_active_experiment_for_step(step) do
      {:ok, experiment_id} ->
        # Get user assignment
        user_id = user_id || extract_user_id(context)
        
        case Pipeline.DSPy.ExperimentManager.get_user_assignment(user_id, experiment_id) do
          {:ok, assignment} ->
            execute_with_assignment(step, context, assignment, experiment_id)
          
          {:error, reason} ->
            Logger.warning("Failed to get A/B test assignment: #{reason}, using default")
            execute_default_step(step, context)
        end
      
      :no_experiment ->
        execute_default_step(step, context)
    end
  end
  
  defp execute_with_assignment(step, context, assignment, experiment_id) do
    start_time = System.monotonic_time()
    
    case assignment.group do
      :control ->
        # Execute with traditional approach
        result = execute_traditional_step(step, context)
        
        # Record result for experiment
        execution_metrics = %{
          execution_time: System.monotonic_time() - start_time,
          success: elem(result, 0) == :ok,
          method: :traditional
        }
        
        record_experiment_result(assignment.user_id, experiment_id, execution_metrics)
        
        result
      
      :treatment ->
        # Execute with DSPy optimization
        result = execute_dspy_step(step, context)
        
        # Record result for experiment
        execution_metrics = %{
          execution_time: System.monotonic_time() - start_time,
          success: elem(result, 0) == :ok,
          method: :dspy
        }
        
        record_experiment_result(assignment.user_id, experiment_id, execution_metrics)
        
        result
      
      _ ->
        Logger.warning("Unknown experiment group: #{assignment.group}")
        execute_default_step(step, context)
    end
  end
  
  defp record_experiment_result(user_id, experiment_id, metrics) do
    Pipeline.DSPy.ExperimentManager.record_experiment_result(user_id, experiment_id, metrics)
  end
end

defmodule Pipeline.DSPy.ProductionMonitor do
  @moduledoc """
  Monitors DSPy performance in production and triggers alerts.
  """
  
  use GenServer
  
  defstruct [
    :monitoring_config,
    :performance_metrics,
    :alert_thresholds,
    :alert_history,
    :dashboard_metrics
  ]
  
  def start_link(opts \\ []) do
    GenServer.start_link(__MODULE__, opts, name: __MODULE__)
  end
  
  def record_execution_metrics(execution_metrics) do
    GenServer.cast(__MODULE__, {:record_execution_metrics, execution_metrics})
  end
  
  def get_performance_dashboard do
    GenServer.call(__MODULE__, :get_performance_dashboard)
  end
  
  def get_alert_status do
    GenServer.call(__MODULE__, :get_alert_status)
  end
  
  def init(opts) do
    state = %__MODULE__{
      monitoring_config: load_monitoring_config(opts),
      performance_metrics: initialize_metrics_store(),
      alert_thresholds: load_alert_thresholds(opts),
      alert_history: [],
      dashboard_metrics: %{}
    }
    
    # Start periodic monitoring
    schedule_monitoring_cycle()
    
    {:ok, state}
  end
  
  def handle_cast({:record_execution_metrics, execution_metrics}, state) do
    # Update performance metrics
    updated_metrics = update_performance_metrics(execution_metrics, state.performance_metrics)
    
    # Check for alerts
    alerts = check_for_alerts(execution_metrics, updated_metrics, state.alert_thresholds)
    
    # Send alerts if any
    if not Enum.empty?(alerts) do
      send_alerts(alerts)
    end
    
    # Update dashboard metrics
    updated_dashboard = update_dashboard_metrics(execution_metrics, state.dashboard_metrics)
    
    new_state = %{
      state |
      performance_metrics: updated_metrics,
      alert_history: alerts ++ state.alert_history,
      dashboard_metrics: updated_dashboard
    }
    
    {:noreply, new_state}
  end
  
  def handle_info(:monitoring_cycle, state) do
    # Perform periodic monitoring tasks
    new_state = perform_monitoring_cycle(state)
    
    schedule_monitoring_cycle()
    
    {:noreply, new_state}
  end
  
  defp perform_monitoring_cycle(state) do
    # Calculate aggregated metrics
    aggregated_metrics = calculate_aggregated_metrics(state.performance_metrics)
    
    # Check system health
    health_status = check_system_health(aggregated_metrics)
    
    # Update dashboard
    updated_dashboard = update_dashboard_with_aggregated_metrics(aggregated_metrics, state.dashboard_metrics)
    
    # Check for trend-based alerts
    trend_alerts = check_trend_alerts(aggregated_metrics, state.alert_thresholds)
    
    if not Enum.empty?(trend_alerts) do
      send_alerts(trend_alerts)
    end
    
    %{
      state |
      dashboard_metrics: updated_dashboard,
      alert_history: trend_alerts ++ state.alert_history
    }
  end
  
  defp check_for_alerts(execution_metrics, performance_metrics, alert_thresholds) do
    alerts = []
    
    # Check error rate
    current_error_rate = calculate_current_error_rate(performance_metrics)
    if current_error_rate > alert_thresholds.error_rate do
      alert = create_alert(:error_rate, current_error_rate, alert_thresholds.error_rate)
      alerts = [alert | alerts]
    end
    
    # Check performance degradation
    performance_degradation = calculate_performance_degradation(performance_metrics)
    if performance_degradation > alert_thresholds.performance_degradation do
      alert = create_alert(:performance_degradation, performance_degradation, alert_thresholds.performance_degradation)
      alerts = [alert | alerts]
    end
    
    # Check response time
    current_response_time = execution_metrics.execution_time
    if current_response_time > alert_thresholds.response_time do
      alert = create_alert(:response_time, current_response_time, alert_thresholds.response_time)
      alerts = [alert | alerts]
    end
    
    alerts
  end
  
  defp create_alert(alert_type, current_value, threshold) do
    %{
      type: alert_type,
      current_value: current_value,
      threshold: threshold,
      severity: calculate_alert_severity(current_value, threshold),
      timestamp: DateTime.utc_now(),
      message: generate_alert_message(alert_type, current_value, threshold)
    }
  end
  
  defp send_alerts(alerts) do
    Enum.each(alerts, fn alert ->
      # Log alert
      Logger.warning("Production alert: #{alert.message}")
      
      # Send to external monitoring systems
      send_to_monitoring_system(alert)
      
      # Send notifications based on severity
      case alert.severity do
        :critical ->
          send_critical_notification(alert)
        
        :high ->
          send_high_priority_notification(alert)
        
        :medium ->
          send_medium_priority_notification(alert)
        
        :low ->
          # Log only, no external notification
          :ok
      end
    end)
  end
end

defmodule Pipeline.DSPy.ProductionDashboard do
  @moduledoc """
  Provides real-time dashboard metrics for DSPy production monitoring.
  """
  
  def get_dashboard_data do
    %{
      overview: get_overview_metrics(),
      performance: get_performance_metrics(),
      experiments: get_experiment_metrics(),
      alerts: get_alert_metrics(),
      deployment: get_deployment_metrics()
    }
  end
  
  defp get_overview_metrics do
    %{
      total_executions: get_total_executions(),
      dspy_executions: get_dspy_executions(),
      traditional_executions: get_traditional_executions(),
      success_rate: get_overall_success_rate(),
      average_response_time: get_average_response_time()
    }
  end
  
  defp get_performance_metrics do
    %{
      dspy_performance: %{
        success_rate: get_dspy_success_rate(),
        average_response_time: get_dspy_average_response_time(),
        optimization_effectiveness: get_optimization_effectiveness()
      },
      traditional_performance: %{
        success_rate: get_traditional_success_rate(),
        average_response_time: get_traditional_average_response_time()
      },
      comparison: %{
        performance_improvement: calculate_performance_improvement(),
        cost_comparison: calculate_cost_comparison(),
        quality_improvement: calculate_quality_improvement()
      }
    }
  end
  
  defp get_experiment_metrics do
    active_experiments = Pipeline.DSPy.ExperimentManager.get_active_experiments()
    
    Enum.map(active_experiments, fn experiment ->
      %{
        id: experiment.id,
        name: experiment.config.name,
        status: experiment.status,
        participants: experiment.participants,
        results: get_experiment_results_summary(experiment.id),
        statistical_significance: calculate_statistical_significance(experiment.id)
      }
    end)
  end
  
  defp get_alert_metrics do
    %{
      active_alerts: get_active_alerts(),
      recent_alerts: get_recent_alerts(),
      alert_trends: get_alert_trends()
    }
  end
  
  defp get_deployment_metrics do
    %{
      current_phase: Pipeline.DSPy.DeploymentPhases.get_current_phase(),
      phase_metrics: get_current_phase_metrics(),
      rollout_percentage: get_rollout_percentage(),
      canary_health: get_canary_health_status()
    }
  end
end

defmodule Pipeline.DSPy.AutoRollback do
  @moduledoc """
  Automated rollback system that triggers rollbacks based on monitoring metrics.
  """
  
  use GenServer
  
  defstruct [
    :rollback_config,
    :monitoring_subscriptions,
    :rollback_history,
    :rollback_thresholds
  ]
  
  def start_link(opts \\ []) do
    GenServer.start_link(__MODULE__, opts, name: __MODULE__)
  end
  
  def trigger_manual_rollback(reason, approval_metadata) do
    GenServer.call(__MODULE__, {:manual_rollback, reason, approval_metadata})
  end
  
  def get_rollback_status do
    GenServer.call(__MODULE__, :get_rollback_status)
  end
  
  def init(opts) do
    state = %__MODULE__{
      rollback_config: load_rollback_config(opts),
      monitoring_subscriptions: subscribe_to_monitoring_events(),
      rollback_history: [],
      rollback_thresholds: load_rollback_thresholds(opts)
    }
    
    {:ok, state}
  end
  
  def handle_info({:monitoring_alert, alert}, state) do
    # Check if alert should trigger rollback
    case should_trigger_rollback?(alert, state.rollback_thresholds) do
      true ->
        Logger.warning("Triggering automatic rollback due to alert: #{alert.type}")
        
        case execute_rollback("Automatic rollback due to #{alert.type}", alert, state) do
          {:ok, new_state} ->
            {:noreply, new_state}
          
          {:error, reason} ->
            Logger.error("Failed to execute automatic rollback: #{reason}")
            {:noreply, state}
        end
      
      false ->
        {:noreply, state}
    end
  end
  
  def handle_call({:manual_rollback, reason, approval_metadata}, _from, state) do
    case execute_rollback(reason, approval_metadata, state) do
      {:ok, new_state} ->
        {:reply, :ok, new_state}
      
      {:error, reason} ->
        {:reply, {:error, reason}, state}
    end
  end
  
  defp should_trigger_rollback?(alert, rollback_thresholds) do
    case alert.type do
      :error_rate ->
        alert.current_value > rollback_thresholds.error_rate and
        alert.severity in [:critical, :high]
      
      :performance_degradation ->
        alert.current_value > rollback_thresholds.performance_degradation and
        alert.severity in [:critical, :high]
      
      :system_failure ->
        true  # Always rollback on system failures
      
      _ ->
        false
    end
  end
  
  defp execute_rollback(reason, metadata, state) do
    rollback_start_time = DateTime.utc_now()
    
    try do
      # Step 1: Disable DSPy features
      disable_dspy_features()
      
      # Step 2: Rollback to previous deployment phase
      case Pipeline.DSPy.DeploymentPhases.rollback_to_previous_phase(reason) do
        {:ok, previous_phase} ->
          # Step 3: Update configuration
          update_rollback_configuration(previous_phase)
          
          # Step 4: Clear caches
          clear_optimization_caches()
          
          # Step 5: Notify monitoring systems
          notify_rollback_completion(reason, metadata)
          
          # Record rollback
          rollback_record = %{
            reason: reason,
            metadata: metadata,
            started_at: rollback_start_time,
            completed_at: DateTime.utc_now(),
            previous_phase: previous_phase,
            status: :completed
          }
          
          new_rollback_history = [rollback_record | state.rollback_history]
          new_state = %{state | rollback_history: new_rollback_history}
          
          Logger.info("Rollback completed successfully")
          {:ok, new_state}
        
        {:error, reason} ->
          Logger.error("Failed to rollback deployment phase: #{reason}")
          {:error, reason}
      end
    rescue
      error ->
        Logger.error("Rollback failed with error: #{inspect(error)}")
        {:error, "Rollback execution failed: #{Exception.message(error)}"}
    end
  end
  
  defp disable_dspy_features do
    # Disable DSPy features across all components
    Pipeline.DSPy.FeatureFlags.disable_all_dspy_features()
    
    # Update configuration to disable DSPy
    Pipeline.Enhanced.ConfigurationSystem.update_global_config(%{
      "dspy_enabled" => false,
      "fallback_to_traditional" => true
    })
  end
  
  defp update_rollback_configuration(previous_phase) do
    # Update configuration for rollback phase
    phase_config = Pipeline.DSPy.PhaseConfig.get_phase_config(previous_phase)
    
    Pipeline.Enhanced.ConfigurationSystem.apply_phase_config(phase_config)
  end
  
  defp clear_optimization_caches do
    # Clear all optimization caches to ensure clean state
    Pipeline.DSPy.OptimizationCache.clear_all()
    Pipeline.DSPy.HierarchicalCache.clear_all()
  end
  
  defp notify_rollback_completion(reason, metadata) do
    # Send rollback notification to monitoring systems
    rollback_notification = %{
      event: :rollback_completed,
      reason: reason,
      metadata: metadata,
      timestamp: DateTime.utc_now()
    }
    
    Pipeline.Monitoring.send_event(rollback_notification)
  end
end

defmodule Pipeline.DSPy.ManualRollback do
  @moduledoc """
  Manual rollback procedures for emergency situations.
  """
  
  def emergency_rollback(reason, operator_id) do
    Logger.warning("Emergency rollback initiated by #{operator_id}: #{reason}")
    
    # Immediate actions
    immediate_actions = [
      {:disable_dspy_features, &disable_dspy_features_immediately/0},
      {:activate_circuit_breaker, &activate_emergency_circuit_breaker/0},
      {:notify_team, &send_emergency_notification/2}
    ]
    
    # Execute immediate actions
    Enum.each(immediate_actions, fn {action_name, action_fn} ->
      case action_fn.() do
        :ok ->
          Logger.info("Emergency action #{action_name} completed")
        
        {:error, error} ->
          Logger.error("Emergency action #{action_name} failed: #{error}")
      end
    end)
    
    # Start full rollback process
    Pipeline.DSPy.AutoRollback.trigger_manual_rollback(reason, %{
      operator_id: operator_id,
      rollback_type: :emergency,
      initiated_at: DateTime.utc_now()
    })
  end
  
  def graceful_rollback(reason, operator_id, rollback_options \\ []) do
    Logger.info("Graceful rollback initiated by #{operator_id}: #{reason}")
    
    # Pre-rollback checks
    case perform_pre_rollback_checks() do
      :ok ->
        # Execute graceful rollback
        execute_graceful_rollback(reason, operator_id, rollback_options)
      
      {:error, reason} ->
        Logger.error("Pre-rollback checks failed: #{reason}")
        {:error, reason}
    end
  end
  
  defp execute_graceful_rollback(reason, operator_id, rollback_options) do
    # Drain traffic gradually
    if Keyword.get(rollback_options, :drain_traffic, true) do
      drain_traffic_gradually()
    end
    
    # Wait for in-flight requests to complete
    wait_for_inflight_requests()
    
    # Execute rollback
    Pipeline.DSPy.AutoRollback.trigger_manual_rollback(reason, %{
      operator_id: operator_id,
      rollback_type: :graceful,
      rollback_options: rollback_options,
      initiated_at: DateTime.utc_now()
    })
  end
  
  defp disable_dspy_features_immediately do
    # Immediately disable all DSPy features
    :ets.insert(:dspy_feature_flags, {:dspy_enabled, false})
    :ets.insert(:dspy_feature_flags, {:optimization_enabled, false})
    :ets.insert(:dspy_feature_flags, {:fallback_to_traditional, true})
    
    # Broadcast to all nodes
    :rpc.multicall(Node.list(), :ets, :insert, [:dspy_feature_flags, {:dspy_enabled, false}])
    
    :ok
  end
  
  defp activate_emergency_circuit_breaker do
    # Activate circuit breaker to prevent DSPy execution
    Pipeline.DSPy.CircuitBreaker.activate_emergency_mode()
    
    :ok
  end
  
  defp send_emergency_notification(reason, operator_id) do
    # Send immediate notification to on-call team
    notification = %{
      alert_type: :emergency_rollback,
      reason: reason,
      operator_id: operator_id,
      timestamp: DateTime.utc_now()
    }
    
    Pipeline.Monitoring.send_critical_alert(notification)
    
    :ok
  end
end

# production_deployment.yaml
deployment:
  phases:
    canary:
      user_percentage: 1
      duration: 24h
      success_criteria:
        error_rate: < 0.01
        performance_degradation: < 0.1
        user_satisfaction: > 0.95
    
    limited_production:
      user_percentage: 10
      duration: 72h
      success_criteria:
        error_rate: < 0.005
        performance_degradation: < 0.05
        user_satisfaction: > 0.98
    
    full_production:
      user_percentage: 100
      duration: null
      success_criteria:
        error_rate: < 0.001
        performance_degradation: < 0.02
        user_satisfaction: > 0.99

  experiments:
    dspy_vs_traditional:
      enabled: true
      groups:
        control:
          weight: 50
          configuration:
            dspy_enabled: false
        treatment:
          weight: 50
          configuration:
            dspy_enabled: true
      
      success_metrics:
        - accuracy
        - response_time
        - cost_per_request
        - user_satisfaction
      
      duration: 30d
      minimum_sample_size: 1000

  monitoring:
    alerts:
      error_rate:
        warning: 0.01
        critical: 0.02
      performance_degradation:
        warning: 0.1
        critical: 0.2
      response_time:
        warning: 5000ms
        critical: 10000ms
    
    dashboards:
      - production_overview
      - experiment_results
      - performance_comparison
      - alert_summary

  rollback:
    automatic_triggers:
      - error_rate > 0.02
      - performance_degradation > 0.3
      - system_failure
    
    manual_approval_required: false
    
    emergency_contacts:
      - team: engineering
        oncall: true
      - team: product
        oncall: false

defmodule Pipeline.DSPy.ProductionReadinessTest do
  use ExUnit.Case
  
  describe "Deployment Phases" do
    test "can advance through all deployment phases" do
      # Start from development
      {:ok, current_phase} = Pipeline.DSPy.DeploymentPhases.get_current_phase()
      assert current_phase == :development
      
      # Advance to staging
      {:ok, next_phase} = Pipeline.DSPy.DeploymentPhases.advance_to_next_phase()
      assert next_phase == :staging
      
      # Continue through all phases
      phases = [:canary, :limited_production, :full_production]
      
      Enum.each(phases, fn expected_phase ->
        {:ok, phase} = Pipeline.DSPy.DeploymentPhases.advance_to_next_phase()
        assert phase == expected_phase
      end)
    end
    
    test "rollback works correctly" do
      # Advance to a later phase
      Pipeline.DSPy.DeploymentPhases.advance_to_next_phase()
      Pipeline.DSPy.DeploymentPhases.advance_to_next_phase()
      
      # Rollback
      {:ok, previous_phase} = Pipeline.DSPy.DeploymentPhases.rollback_to_previous_phase("Test rollback")
      assert previous_phase == :staging
    end
  end
  
  describe "A/B Testing" do
    test "users are consistently assigned to groups" do
      experiment_config = create_test_experiment_config()
      {:ok, experiment_id} = Pipeline.DSPy.ExperimentManager.create_experiment(experiment_config)
      
      user_id = "test_user_123"
      
      # Get assignment multiple times
      {:ok, assignment1} = Pipeline.DSPy.ExperimentManager.get_user_assignment(user_id, experiment_id)
      {:ok, assignment2} = Pipeline.DSPy.ExperimentManager.get_user_assignment(user_id, experiment_id)
      
      # Should be the same
      assert assignment1.group == assignment2.group
    end
  end
  
  describe "Monitoring and Alerts" do
    test "alerts are triggered when thresholds are exceeded" do
      # Simulate high error rate
      high_error_metrics = %{
        error_rate: 0.05,
        execution_time: 1000,
        success: false
      }
      
      Pipeline.DSPy.ProductionMonitor.record_execution_metrics(high_error_metrics)
      
      # Check that alert was triggered
      alerts = Pipeline.DSPy.ProductionMonitor.get_recent_alerts()
      assert Enum.any?(alerts, fn alert -> alert.type == :error_rate end)
    end
  end
end