-module(environment_modifier).
-behaviour(gen_statem).

-export([start_link/0, start_link/1]).
-export([assess_environment/1, modify_environment/2, adapt_to_changes/1,
         monitor_stability/1, rollback_changes/1, optimize_environment/1,
         set_adaptation_policy/2, get_environment_state/1, trigger_rebalancing/1]).
-export([init/1, callback_mode/0, terminate/3, code_change/4]).
-export([idle/3, environmental_assessment/3, modification_planning/3, 
         change_implementation/3, stability_monitoring/3, adaptation_response/3,
         optimization_phase/3, rollback_execution/3, rebalancing/3, 
         maintenance_mode/3]).

-record(environmental_parameter, {
    name :: atom(),
    type = numeric :: numeric | categorical | boolean | complex,
    current_value :: term(),
    target_value = undefined :: undefined | term(),
    valid_range = unlimited :: unlimited | {min_max, term(), term()} | [term()],
    sensitivity = medium :: low | medium | high | critical,
    dependencies = [] :: [atom()],
    modification_cost = 1.0 :: float(),
    change_latency = 0 :: non_neg_integer(),
    stability_impact = medium :: low | medium | high,
    rollback_support = true :: boolean()
}).

-record(modification_action, {
    id :: term(),
    type = direct :: direct | indirect | cascading | composite,
    target_parameters = [] :: [atom()],
    modification_function :: fun(),
    prerequisites = [] :: [term()],
    side_effects = [] :: [term()],
    execution_order = 1 :: pos_integer(),
    estimated_duration = 1000 :: pos_integer(),
    risk_level = low :: low | medium | high | critical,
    reversibility = full :: full | partial | irreversible,
    validation_function = undefined :: undefined | fun(),
    status = pending :: pending | executing | completed | failed | rolled_back
}).

-record(environment_data, {
    session_id :: term(),
    environment_parameters = #{} :: #{atom() => #environmental_parameter{}},
    parameter_dependencies = digraph:new() :: digraph:graph(),
    current_state = #{} :: #{atom() => term()},
    desired_state = #{} :: #{atom() => term()},
    modification_queue = [] :: [#modification_action{}],
    active_modifications = [] :: [#modification_action{}],
    completed_modifications = [] :: [#modification_action{}],
    rollback_stack = [] :: [#{atom() => term()}],
    adaptation_policy = #{
        auto_adapt => true,
        adaptation_threshold => 0.7,
        stability_timeout => 5000,
        max_concurrent_changes => 3,
        change_validation => true,
        rollback_on_failure => true
    } :: #{atom() => term()},
    environmental_constraints = [] :: [term()],
    stability_metrics = #{
        variance => 0.0,
        drift_rate => 0.0,
        adaptation_speed => 0.0,
        system_resilience => 1.0
    } :: #{atom() => float()},
    monitoring_data = #{} :: #{atom() => [term()]},
    optimization_targets = #{
        performance => maximize,
        stability => maximize,
        resource_efficiency => maximize,
        adaptation_cost => minimize
    } :: #{atom() => maximize | minimize},
    learning_model = #{} :: #{atom() => term()},
    modification_statistics = #{} :: #{atom() => term()},
    observers = [] :: [pid()],
    continuous_monitoring = false :: boolean(),
    monitoring_interval = 2000 :: pos_integer(),
    start_time :: erlang:timestamp()
}).

start_link() ->
    gen_statem:start_link(?MODULE, [], []).

start_link(Options) ->
    gen_statem:start_link(?MODULE, Options, []).

assess_environment(Pid) ->
    gen_statem:call(Pid, assess_environment).

modify_environment(Pid, Modifications) ->
    gen_statem:call(Pid, {modify_environment, Modifications}).

adapt_to_changes(Pid) ->
    gen_statem:call(Pid, adapt_to_changes).

monitor_stability(Pid) ->
    gen_statem:call(Pid, monitor_stability).

rollback_changes(Pid) ->
    gen_statem:call(Pid, rollback_changes).

optimize_environment(Pid) ->
    gen_statem:call(Pid, optimize_environment).

set_adaptation_policy(Pid, Policy) ->
    gen_statem:call(Pid, {set_adaptation_policy, Policy}).

get_environment_state(Pid) ->
    gen_statem:call(Pid, get_environment_state).

trigger_rebalancing(Pid) ->
    gen_statem:call(Pid, trigger_rebalancing).

init(Options) ->
    Data = #environment_data{
        session_id = make_ref(),
        continuous_monitoring = proplists:get_value(continuous_monitoring, Options, false),
        monitoring_interval = proplists:get_value(monitoring_interval, Options, 2000),
        start_time = erlang:timestamp(),
        environment_parameters = initialize_environment_parameters(Options),
        parameter_dependencies = build_parameter_dependency_graph(Options),
        adaptation_policy = initialize_adaptation_policy(Options),
        environmental_constraints = initialize_environmental_constraints(Options),
        optimization_targets = initialize_optimization_targets(Options),
        learning_model = initialize_environmental_learning_model(),
        modification_statistics = #{
            assessments_performed => 0,
            modifications_attempted => 0,
            modifications_successful => 0,
            adaptations_triggered => 0,
            rollbacks_executed => 0,
            optimizations_performed => 0,
            stability_violations => 0
        }
    },
    InitialData = capture_initial_environment_state(Data),
    {ok, idle, InitialData}.

callback_mode() -> [state_functions, state_enter].

idle(enter, _OldState, Data) ->
    case Data#environment_data.continuous_monitoring of
        true ->
            {keep_state, Data, [{state_timeout, Data#environment_data.monitoring_interval, continuous_assessment}]};
        false ->
            {keep_state, Data}
    end;
idle({call, From}, assess_environment, Data) ->
    {next_state, environmental_assessment, Data, [{reply, From, ok}]};
idle({call, From}, {modify_environment, Modifications}, Data) ->
    ModificationData = queue_modifications(Modifications, Data),
    {next_state, modification_planning, ModificationData, [{reply, From, ok}]};
idle({call, From}, optimize_environment, Data) ->
    {next_state, optimization_phase, Data, [{reply, From, ok}]};
idle({call, From}, trigger_rebalancing, Data) ->
    {next_state, rebalancing, Data, [{reply, From, ok}]};
idle(state_timeout, continuous_assessment, Data) ->
    {next_state, environmental_assessment, Data};
idle(EventType, Event, Data) ->
    handle_common_events(EventType, Event, Data).

environmental_assessment(enter, _OldState, Data) ->
    AssessmentData = conduct_environmental_assessment(Data),
    UpdatedStats = increment_stat(assessments_performed, Data#environment_data.modification_statistics),
    
    case detect_environmental_issues(AssessmentData) of
        {issues_detected, Issues} ->
            ResponseData = plan_adaptive_response(Issues, AssessmentData),
            {next_state, adaptation_response, ResponseData#environment_data{modification_statistics = UpdatedStats}};
        no_issues ->
            case Data#environment_data.continuous_monitoring of
                true ->
                    {next_state, stability_monitoring, AssessmentData#environment_data{modification_statistics = UpdatedStats}};
                false ->
                    {next_state, idle, AssessmentData#environment_data{modification_statistics = UpdatedStats}}
            end
    end;
environmental_assessment(EventType, Event, Data) ->
    handle_common_events(EventType, Event, Data).

modification_planning(enter, _OldState, Data) ->
    PlanningData = plan_modification_sequence(Data),
    ValidationData = validate_modification_plan(PlanningData),
    
    case check_modification_feasibility(ValidationData) of
        feasible ->
            {next_state, change_implementation, ValidationData};
        {infeasible, Reasons} ->
            AdjustedData = adjust_modification_plan(Reasons, ValidationData),
            {keep_state, AdjustedData};
        requires_assessment ->
            {next_state, environmental_assessment, ValidationData}
    end;
modification_planning(EventType, Event, Data) ->
    handle_common_events(EventType, Event, Data).

change_implementation(enter, _OldState, Data) ->
    ImplementationData = begin_modification_implementation(Data),
    UpdatedStats = increment_stat(modifications_attempted, Data#environment_data.modification_statistics),
    
    case execute_next_modification(ImplementationData) of
        {modification_completed, CompletedData} ->
            SuccessStats = increment_stat(modifications_successful, CompletedData#environment_data.modification_statistics),
            {next_state, stability_monitoring, CompletedData#environment_data{modification_statistics = SuccessStats}};
        {modification_in_progress, ProgressData} ->
            {keep_state, ProgressData#environment_data{modification_statistics = UpdatedStats}, 
             [{state_timeout, 100, continue_implementation}]};
        {modification_failed, FailureData} ->
            case should_rollback_on_failure(FailureData) of
                true ->
                    {next_state, rollback_execution, FailureData#environment_data{modification_statistics = UpdatedStats}};
                false ->
                    {next_state, adaptation_response, FailureData#environment_data{modification_statistics = UpdatedStats}}
            end
    end;
change_implementation(state_timeout, continue_implementation, Data) ->
    case execute_next_modification(Data) of
        {modification_completed, CompletedData} ->
            SuccessStats = increment_stat(modifications_successful, CompletedData#environment_data.modification_statistics),
            {next_state, stability_monitoring, CompletedData#environment_data{modification_statistics = SuccessStats}};
        {modification_in_progress, ProgressData} ->
            {keep_state, ProgressData, [{state_timeout, 100, continue_implementation}]};
        {modification_failed, FailureData} ->
            {next_state, rollback_execution, FailureData}
    end;
change_implementation(EventType, Event, Data) ->
    handle_common_events(EventType, Event, Data).

stability_monitoring(enter, _OldState, Data) ->
    MonitoringData = initiate_stability_monitoring(Data),
    StabilityTimeout = maps:get(stability_timeout, Data#environment_data.adaptation_policy, 5000),
    {keep_state, MonitoringData, [{state_timeout, StabilityTimeout, stability_check}]};
stability_monitoring(state_timeout, stability_check, Data) ->
    StabilityAnalysis = analyze_system_stability(Data),
    
    case evaluate_stability_status(StabilityAnalysis) of
        stable ->
            case Data#environment_data.continuous_monitoring of
                true ->
                    {next_state, idle, StabilityAnalysis};
                false ->
                    {next_state, maintenance_mode, StabilityAnalysis}
            end;
        unstable ->
            UpdatedStats = increment_stat(stability_violations, Data#environment_data.modification_statistics),
            {next_state, adaptation_response, StabilityAnalysis#environment_data{modification_statistics = UpdatedStats}};
        stabilizing ->
            {keep_state, StabilityAnalysis, [{state_timeout, 2000, stability_check}]}
    end;
stability_monitoring({call, From}, monitor_stability, Data) ->
    StabilityReport = generate_stability_report(Data),
    {keep_state, Data, [{reply, From, {ok, StabilityReport}}]};
stability_monitoring(EventType, Event, Data) ->
    handle_common_events(EventType, Event, Data).

adaptation_response(enter, _OldState, Data) ->
    AdaptationData = formulate_adaptation_strategy(Data),
    UpdatedStats = increment_stat(adaptations_triggered, Data#environment_data.modification_statistics),
    
    case determine_adaptation_type(AdaptationData) of
        corrective_action ->
            {next_state, change_implementation, AdaptationData#environment_data{modification_statistics = UpdatedStats}};
        parameter_tuning ->
            TuningData = apply_parameter_tuning(AdaptationData),
            {next_state, stability_monitoring, TuningData#environment_data{modification_statistics = UpdatedStats}};
        system_rebalancing ->
            {next_state, rebalancing, AdaptationData#environment_data{modification_statistics = UpdatedStats}};
        rollback_required ->
            {next_state, rollback_execution, AdaptationData#environment_data{modification_statistics = UpdatedStats}}
    end;
adaptation_response({call, From}, adapt_to_changes, Data) ->
    AdaptedData = execute_immediate_adaptation(Data),
    {keep_state, AdaptedData, [{reply, From, ok}]};
adaptation_response(EventType, Event, Data) ->
    handle_common_events(EventType, Event, Data).

optimization_phase(enter, _OldState, Data) ->
    OptimizationData = analyze_optimization_opportunities(Data),
    UpdatedStats = increment_stat(optimizations_performed, Data#environment_data.modification_statistics),
    
    case identify_optimization_actions(OptimizationData) of
        {optimizations_available, Actions} ->
            OptimizedData = apply_optimization_actions(Actions, OptimizationData),
            {next_state, stability_monitoring, OptimizedData#environment_data{modification_statistics = UpdatedStats}};
        no_optimizations_needed ->
            {next_state, idle, OptimizationData#environment_data{modification_statistics = UpdatedStats}}
    end;
optimization_phase(EventType, Event, Data) ->
    handle_common_events(EventType, Event, Data).

rollback_execution(enter, _OldState, Data) ->
    RollbackData = initiate_rollback_sequence(Data),
    UpdatedStats = increment_stat(rollbacks_executed, Data#environment_data.modification_statistics),
    
    case execute_rollback_actions(RollbackData) of
        {rollback_completed, RestoredData} ->
            {next_state, stability_monitoring, RestoredData#environment_data{modification_statistics = UpdatedStats}};
        {rollback_failed, FailureData} ->
            CriticalData = handle_critical_rollback_failure(FailureData),
            {next_state, maintenance_mode, CriticalData#environment_data{modification_statistics = UpdatedStats}}
    end;
rollback_execution({call, From}, rollback_changes, Data) ->
    RollbackData = execute_manual_rollback(Data),
    {keep_state, RollbackData, [{reply, From, ok}]};
rollback_execution(EventType, Event, Data) ->
    handle_common_events(EventType, Event, Data).

rebalancing(enter, _OldState, Data) ->
    RebalancingData = analyze_system_imbalances(Data),
    
    case formulate_rebalancing_strategy(RebalancingData) of
        {rebalancing_plan, Plan} ->
            ExecutionData = execute_rebalancing_plan(Plan, RebalancingData),
            {next_state, stability_monitoring, ExecutionData};
        no_rebalancing_needed ->
            {next_state, idle, RebalancingData}
    end;
rebalancing(EventType, Event, Data) ->
    handle_common_events(EventType, Event, Data).

maintenance_mode(enter, _OldState, Data) ->
    MaintenanceData = enter_maintenance_mode(Data),
    {keep_state, MaintenanceData};
maintenance_mode({call, From}, assess_environment, Data) ->
    {next_state, environmental_assessment, Data, [{reply, From, ok}]};
maintenance_mode({call, From}, get_environment_state, Data) ->
    State = compile_environment_state(Data),
    {keep_state, Data, [{reply, From, {ok, State}}]};
maintenance_mode(EventType, Event, Data) ->
    handle_common_events(EventType, Event, Data).

terminate(_Reason, _State, Data) ->
    cleanup_environment_resources(Data),
    ok.

code_change(_Vsn, State, Data, _Extra) ->
    {ok, State, Data}.

capture_initial_environment_state(Data) ->
    Parameters = Data#environment_data.environment_parameters,
    CurrentState = maps:map(fun(_Name, Param) -> 
        Param#environmental_parameter.current_value 
    end, Parameters),
    Data#environment_data{current_state = CurrentState}.

conduct_environmental_assessment(Data) ->
    Parameters = Data#environment_data.environment_parameters,
    Constraints = Data#environment_data.environmental_constraints,
    
    ParameterAnalysis = analyze_parameter_states(Parameters),
    ConstraintAnalysis = evaluate_constraint_satisfaction(Constraints, Data#environment_data.current_state),
    DependencyAnalysis = analyze_parameter_dependencies(Data#environment_data.parameter_dependencies),
    TrendAnalysis = analyze_environmental_trends(Data#environment_data.monitoring_data),
    
    UpdatedMetrics = calculate_stability_metrics(ParameterAnalysis, Data#environment_data.stability_metrics),
    
    Data#environment_data{
        stability_metrics = UpdatedMetrics,
        monitoring_data = update_monitoring_data(Data#environment_data.monitoring_data, ParameterAnalysis)
    }.

detect_environmental_issues(Data) ->
    StabilityMetrics = Data#environment_data.stability_metrics,
    AdaptationThreshold = maps:get(adaptation_threshold, Data#environment_data.adaptation_policy, 0.7),
    
    SystemResilience = maps:get(system_resilience, StabilityMetrics, 1.0),
    Variance = maps:get(variance, StabilityMetrics, 0.0),
    
    Issues = [],
    Issues1 = case SystemResilience < AdaptationThreshold of
        true -> [low_resilience | Issues];
        false -> Issues
    end,
    Issues2 = case Variance > 0.3 of
        true -> [high_variance | Issues1];
        false -> Issues1
    end,
    
    case Issues2 of
        [] -> no_issues;
        DetectedIssues -> {issues_detected, DetectedIssues}
    end.

plan_adaptive_response(Issues, Data) ->
    AdaptiveActions = generate_adaptive_actions(Issues, Data),
    Data#environment_data{modification_queue = AdaptiveActions}.

queue_modifications(Modifications, Data) ->
    ModificationActions = convert_to_modification_actions(Modifications),
    CurrentQueue = Data#environment_data.modification_queue,
    Data#environment_data{modification_queue = CurrentQueue ++ ModificationActions}.

plan_modification_sequence(Data) ->
    Queue = Data#environment_data.modification_queue,
    Dependencies = Data#environment_data.parameter_dependencies,
    
    OptimizedSequence = optimize_modification_order(Queue, Dependencies),
    Data#environment_data{modification_queue = OptimizedSequence}.

validate_modification_plan(Data) ->
    Queue = Data#environment_data.modification_queue,
    Constraints = Data#environment_data.environmental_constraints,
    Policy = Data#environment_data.adaptation_policy,
    
    ValidationResults = validate_against_constraints(Queue, Constraints),
    PolicyCompliance = check_policy_compliance(Queue, Policy),
    
    Data#environment_data{
        modification_queue = apply_validation_results(Queue, ValidationResults, PolicyCompliance)
    }.

check_modification_feasibility(Data) ->
    Queue = Data#environment_data.modification_queue,
    case length(Queue) of
        0 -> {infeasible, no_modifications};
        _ ->
            case all_modifications_valid(Queue) of
                true -> feasible;
                false -> requires_assessment
            end
    end.

begin_modification_implementation(Data) ->
    Queue = Data#environment_data.modification_queue,
    CurrentState = Data#environment_data.current_state,
    
    RollbackState = CurrentState,
    RollbackStack = [RollbackState | Data#environment_data.rollback_stack],
    
    Data#environment_data{rollback_stack = RollbackStack}.

execute_next_modification(Data) ->
    case Data#environment_data.modification_queue of
        [] ->
            {modification_completed, Data};
        [Action | RestQueue] ->
            case execute_modification_action(Action, Data) of
                {success, UpdatedData} ->
                    CompletedActions = [Action#modification_action{status = completed} | Data#environment_data.completed_modifications],
                    NewData = UpdatedData#environment_data{
                        modification_queue = RestQueue,
                        completed_modifications = CompletedActions
                    },
                    case RestQueue of
                        [] -> {modification_completed, NewData};
                        _ -> {modification_in_progress, NewData}
                    end;
                {failure, FailureData} ->
                    FailedAction = Action#modification_action{status = failed},
                    FailureData2 = FailureData#environment_data{
                        completed_modifications = [FailedAction | Data#environment_data.completed_modifications]
                    },
                    {modification_failed, FailureData2}
            end
    end.

execute_modification_action(Action, Data) ->
    ModificationFunction = Action#modification_action.modification_function,
    TargetParameters = Action#modification_action.target_parameters,
    
    try
        UpdatedState = ModificationFunction(Data#environment_data.current_state, TargetParameters),
        UpdatedData = Data#environment_data{current_state = UpdatedState},
        {success, UpdatedData}
    catch
        _:Reason ->
            {failure, Data}
    end.

should_rollback_on_failure(Data) ->
    maps:get(rollback_on_failure, Data#environment_data.adaptation_policy, true).

initiate_stability_monitoring(Data) ->
    MonitoringData = Data#environment_data.monitoring_data,
    CurrentState = Data#environment_data.current_state,
    
    UpdatedMonitoring = record_monitoring_snapshot(CurrentState, MonitoringData),
    Data#environment_data{monitoring_data = UpdatedMonitoring}.

analyze_system_stability(Data) ->
    MonitoringData = Data#environment_data.monitoring_data,
    CurrentMetrics = Data#environment_data.stability_metrics,
    
    StabilityAnalysis = perform_stability_analysis(MonitoringData),
    UpdatedMetrics = update_stability_metrics(StabilityAnalysis, CurrentMetrics),
    
    Data#environment_data{stability_metrics = UpdatedMetrics}.

evaluate_stability_status(Data) ->
    Metrics = Data#environment_data.stability_metrics,
    Variance = maps:get(variance, Metrics, 0.0),
    DriftRate = maps:get(drift_rate, Metrics, 0.0),
    
    case {Variance < 0.1, DriftRate < 0.05} of
        {true, true} -> stable;
        {false, _} -> unstable;
        {_, false} -> stabilizing
    end.

formulate_adaptation_strategy(Data) ->
    Issues = extract_current_issues(Data),
    AdaptationPolicy = Data#environment_data.adaptation_policy,
    
    Strategy = select_adaptation_strategy(Issues, AdaptationPolicy),
    AdaptationActions = generate_adaptation_actions(Strategy, Data),
    
    Data#environment_data{modification_queue = AdaptationActions}.

determine_adaptation_type(Data) ->
    Queue = Data#environment_data.modification_queue,
    case analyze_modification_types(Queue) of
        mostly_corrective -> corrective_action;
        mostly_tuning -> parameter_tuning;
        complex_changes -> system_rebalancing;
        critical_issues -> rollback_required
    end.

analyze_optimization_opportunities(Data) ->
    CurrentState = Data#environment_data.current_state,
    OptimizationTargets = Data#environment_data.optimization_targets,
    PerformanceMetrics = Data#environment_data.stability_metrics,
    
    Opportunities = identify_optimization_gaps(CurrentState, OptimizationTargets, PerformanceMetrics),
    Data#environment_data{modification_queue = Opportunities}.

identify_optimization_actions(Data) ->
    Queue = Data#environment_data.modification_queue,
    case length(Queue) of
        0 -> no_optimizations_needed;
        _ -> {optimizations_available, Queue}
    end.

initiate_rollback_sequence(Data) ->
    RollbackStack = Data#environment_data.rollback_stack,
    case RollbackStack of
        [] ->
            Data;
        [PreviousState | RestStack] ->
            Data#environment_data{
                desired_state = PreviousState,
                rollback_stack = RestStack
            }
    end.

execute_rollback_actions(Data) ->
    DesiredState = Data#environment_data.desired_state,
    CurrentState = Data#environment_data.current_state,
    
    case apply_state_rollback(CurrentState, DesiredState) of
        {success, RestoredState} ->
            RestoredData = Data#environment_data{current_state = RestoredState},
            {rollback_completed, RestoredData};
        {failure, _Reason} ->
            {rollback_failed, Data}
    end.

analyze_system_imbalances(Data) ->
    CurrentState = Data#environment_data.current_state,
    Dependencies = Data#environment_data.parameter_dependencies,
    
    ImbalanceAnalysis = detect_parameter_imbalances(CurrentState, Dependencies),
    Data#environment_data{monitoring_data = 
        maps:put(imbalance_analysis, ImbalanceAnalysis, Data#environment_data.monitoring_data)}.

formulate_rebalancing_strategy(Data) ->
    ImbalanceAnalysis = maps:get(imbalance_analysis, Data#environment_data.monitoring_data, []),
    case ImbalanceAnalysis of
        [] -> no_rebalancing_needed;
        Imbalances -> 
            Plan = create_rebalancing_plan(Imbalances, Data),
            {rebalancing_plan, Plan}
    end.

compile_environment_state(Data) ->
    #{
        session_id => Data#environment_data.session_id,
        current_state => Data#environment_data.current_state,
        environment_parameters => Data#environment_data.environment_parameters,
        stability_metrics => Data#environment_data.stability_metrics,
        modification_statistics => Data#environment_data.modification_statistics,
        adaptation_policy => Data#environment_data.adaptation_policy
    }.

generate_stability_report(Data) ->
    #{
        stability_metrics => Data#environment_data.stability_metrics,
        monitoring_data => Data#environment_data.monitoring_data,
        recent_modifications => lists:sublist(Data#environment_data.completed_modifications, 5)
    }.

increment_stat(Stat, Stats) ->
    maps:update_with(Stat, fun(X) -> X + 1 end, 1, Stats).

handle_common_events({call, From}, get_environment_state, Data) ->
    State = compile_environment_state(Data),
    {keep_state, Data, [{reply, From, {ok, State}}]};
handle_common_events({call, From}, {set_adaptation_policy, Policy}, Data) ->
    UpdatedPolicy = maps:merge(Data#environment_data.adaptation_policy, Policy),
    {keep_state, Data#environment_data{adaptation_policy = UpdatedPolicy}, [{reply, From, ok}]};
handle_common_events(_EventType, _Event, _Data) ->
    {keep_state_and_data, [postpone]}.

initialize_environment_parameters(_Options) -> #{}.
build_parameter_dependency_graph(_Options) -> digraph:new().
initialize_adaptation_policy(_Options) -> #{}.
initialize_environmental_constraints(_Options) -> [].
initialize_optimization_targets(_Options) -> #{}.
initialize_environmental_learning_model() -> #{}.
adjust_modification_plan(_Reasons, Data) -> Data.
generate_adaptive_actions(_Issues, _Data) -> [].
convert_to_modification_actions(_Modifications) -> [].
optimize_modification_order(Queue, _Dependencies) -> Queue.
validate_against_constraints(_Queue, _Constraints) -> [].
check_policy_compliance(_Queue, _Policy) -> ok.
apply_validation_results(Queue, _ValidationResults, _PolicyCompliance) -> Queue.
all_modifications_valid(_Queue) -> true.
record_monitoring_snapshot(_State, MonitoringData) -> MonitoringData.
perform_stability_analysis(_MonitoringData) -> #{}.
update_stability_metrics(_Analysis, Metrics) -> Metrics.
extract_current_issues(_Data) -> [].
select_adaptation_strategy(_Issues, _Policy) -> corrective.
generate_adaptation_actions(_Strategy, _Data) -> [].
analyze_modification_types(_Queue) -> mostly_corrective.
apply_parameter_tuning(Data) -> Data.
execute_immediate_adaptation(Data) -> Data.
apply_optimization_actions(_Actions, Data) -> Data.
execute_manual_rollback(Data) -> Data.
handle_critical_rollback_failure(Data) -> Data.
execute_rebalancing_plan(_Plan, Data) -> Data.
enter_maintenance_mode(Data) -> Data.
cleanup_environment_resources(_Data) -> ok.
analyze_parameter_states(_Parameters) -> #{}.
evaluate_constraint_satisfaction(_Constraints, _State) -> ok.
analyze_parameter_dependencies(_Dependencies) -> #{}.
analyze_environmental_trends(_MonitoringData) -> #{}.
calculate_stability_metrics(_Analysis, Metrics) -> Metrics.
update_monitoring_data(MonitoringData, _Analysis) -> MonitoringData.
identify_optimization_gaps(_State, _Targets, _Metrics) -> [].
apply_state_rollback(_Current, Desired) -> {success, Desired}.
detect_parameter_imbalances(_State, _Dependencies) -> [].
create_rebalancing_plan(_Imbalances, _Data) -> [].