defmodule Foundation.AI.ModelRegistry do
  @moduledoc """
  Centralized registry for AI model configurations, versions, and lifecycle management.
  """

  def register_model(model_id, config) do
    # Register model with versioning and metadata
    model_config = %{
      id: model_id,
      provider: config.provider,
      model_name: config.model_name,
      version: config.version || "latest",
      capabilities: config.capabilities || [],
      cost_per_1k_tokens: config.cost_per_1k_tokens,
      context_window: config.context_window,
      max_tokens: config.max_tokens,
      performance_tier: config.performance_tier || :standard,
      fallback_models: config.fallback_models || [],
      health_check_endpoint: config.health_check_endpoint,
      registered_at: DateTime.utc_now(),
      status: :available
    }
    
    Foundation.Config.update([:ai, :models, model_id], model_config)
  end

  def get_best_model_for_task(task_type, constraints \\ %{}) do
    # Intelligent model selection based on task requirements
    available_models = get_available_models()
    
    candidates = 
      available_models
      |> filter_by_capabilities(task_type)
      |> filter_by_constraints(constraints)
      |> sort_by_performance_and_cost()
    
    case candidates do
      [best | fallbacks] -> {:ok, best, fallbacks}
      [] -> {:error, :no_suitable_model}
    end
  end

  def health_check_models() do
    # Proactive health checking of registered models
    available_models = get_available_models()
    
    health_results = 
      available_models
      |> Task.async_stream(&check_model_health/1, max_concurrency: 10)
      |> Enum.map(fn {:ok, result} -> result end)
    
    update_model_statuses(health_results)
  end
end

defmodule Foundation.AI.RequestQueue do
  @moduledoc """
  Intelligent request queuing with batching, prioritization, and flow control.
  """
  
  use GenServer
  
  defstruct [
    :queue_name,
    :batching_strategy,
    :priority_queue,
    :batch_timer,
    :current_batch,
    :config
  ]

  def enqueue_request(queue_name, request, priority \\ :normal) do
    enhanced_request = %{
      id: Foundation.Utils.generate_id(),
      request: request,
      priority: priority,
      enqueued_at: System.monotonic_time(),
      deadline: calculate_deadline(request, priority),
      estimated_cost: estimate_request_cost(request),
      from: {self(), make_ref()}
    }
    
    GenServer.cast(via_tuple(queue_name), {:enqueue, enhanced_request})
    
    # Return a handle for tracking
    {:ok, enhanced_request.id}
  end

  def handle_cast({:enqueue, request}, state) do
    # Intelligent queuing based on batching opportunities
    new_queue = PriorityQueue.insert(state.priority_queue, request)
    
    # Check if we should trigger early batching
    if should_create_batch?(new_queue, state.config) do
      send(self(), :process_batch)
    end
    
    {:noreply, %{state | priority_queue: new_queue}}
  end

  def handle_info(:process_batch, state) do
    {batch, remaining_queue} = extract_optimal_batch(state.priority_queue, state.config)
    
    if length(batch) > 0 do
      # Process batch in parallel
      Task.start(fn -> process_batch(batch, state.config) end)
      
      # Emit batching telemetry
      Foundation.Telemetry.emit_histogram(
        [:foundation, :ai, :batch_size], 
        length(batch),
        %{queue: state.queue_name}
      )
    end
    
    # Schedule next batch processing
    schedule_next_batch(state.config)
    
    {:noreply, %{state | priority_queue: remaining_queue}}
  end

  defp extract_optimal_batch(queue, config) do
    # Sophisticated batching logic that considers:
    # - Token count optimization
    # - Request similarity for caching
    # - Deadline urgency
    # - Cost optimization
    
    available_requests = PriorityQueue.to_list(queue)
    
    batch = 
      available_requests
      |> group_by_similarity()
      |> select_optimal_group(config)
      |> Enum.take(config.max_batch_size)
    
    remaining = remove_batch_from_queue(queue, batch)
    
    {batch, remaining}
  end
end

defmodule Foundation.AI.SemanticCache do
  @moduledoc """
  Semantic caching using embeddings for similarity-based cache retrieval.
  """

  def get_or_compute(cache_key, prompt_data, compute_fn, opts \\ []) do
    similarity_threshold = opts[:similarity_threshold] || 0.85
    
    # Generate embedding for the prompt
    case generate_embedding(prompt_data) do
      {:ok, query_embedding} ->
        # Search for semantically similar cached results
        case find_similar_cached_result(query_embedding, similarity_threshold) do
          {:ok, cached_result, similarity_score} ->
            # Found similar result, return it with metadata
            Foundation.Telemetry.emit_counter([:foundation, :ai, :cache, :semantic_hit], %{
              similarity: similarity_score
            })
            
            {:ok, cached_result, %{cache_hit: true, similarity: similarity_score}}
          
          :not_found ->
            # No similar result, compute and cache
            case compute_fn.() do
              {:ok, result} ->
                # Cache the result with its embedding
                cache_with_embedding(cache_key, prompt_data, query_embedding, result)
                {:ok, result, %{cache_hit: false}}
              
              error -> error
            end
        end
      
      {:error, _} ->
        # Fallback to regular caching if embedding generation fails
        Foundation.Cache.get_or_compute(cache_key, compute_fn)
    end
  end

  defp generate_embedding(prompt_data) do
    # Use a fast, local embedding model for cache keys
    embedding_model = get_embedding_model()
    
    text = normalize_for_embedding(prompt_data)
    
    Foundation.AI.generate_embedding(embedding_model, text)
  end

  defp find_similar_cached_result(query_embedding, threshold) do
    # Vector similarity search in cached embeddings
    cached_embeddings = get_cached_embeddings()
    
    similarities = 
      cached_embeddings
      |> Enum.map(fn {cache_key, embedding, result} ->
        similarity = cosine_similarity(query_embedding, embedding)
        {cache_key, similarity, result}
      end)
      |> Enum.filter(fn {_, similarity, _} -> similarity >= threshold end)
      |> Enum.sort_by(fn {_, similarity, _} -> similarity end, :desc)
    
    case similarities do
      [{_key, similarity, result} | _] -> {:ok, result, similarity}
      [] -> :not_found
    end
  end
end

defmodule Foundation.AI.CostManager do
  @moduledoc """
  AI cost tracking, budget management, and optimization.
  """

  def track_request_cost(provider, model, usage_data, metadata \\ %{}) do
    cost_info = %{
      provider: provider,
      model: model,
      input_tokens: usage_data.input_tokens,
      output_tokens: usage_data.output_tokens,
      total_tokens: usage_data.total_tokens,
      cost_usd: calculate_cost(provider, model, usage_data),
      timestamp: DateTime.utc_now(),
      user_id: metadata[:user_id],
      project_id: metadata[:project_id],
      operation_type: metadata[:operation_type]
    }
    
    # Store cost data
    Foundation.Events.new_event(:ai_cost_tracked, cost_info)
    |> Foundation.Events.store()
    
    # Emit cost telemetry
    Foundation.Telemetry.emit_gauge(
      [:foundation, :ai, :cost_usd], 
      cost_info.cost_usd,
      Map.take(cost_info, [:provider, :model, :operation_type])
    )
    
    # Check budget limits
    check_budget_limits(cost_info)
  end

  def get_cost_analysis(time_range, group_by \\ [:provider, :model]) do
    # Comprehensive cost analysis
    {:ok, events} = Foundation.Events.query(%{
      event_type: :ai_cost_tracked,
      time_range: time_range
    })
    
    analysis = 
      events
      |> Enum.map(&extract_cost_data/1)
      |> group_by_dimensions(group_by)
      |> calculate_aggregations()
      |> add_trend_analysis()
      |> add_cost_optimization_recommendations()
    
    {:ok, analysis}
  end

  def suggest_cost_optimizations(usage_patterns) do
    optimizations = []
    
    # Analyze usage patterns for optimization opportunities
    optimizations = 
      optimizations
      |> maybe_suggest_model_downgrade(usage_patterns)
      |> maybe_suggest_batch_optimization(usage_patterns)
      |> maybe_suggest_caching_improvements(usage_patterns)
      |> maybe_suggest_provider_switching(usage_patterns)
    
    optimizations
  end

  defp check_budget_limits(cost_info) do
    # Check various budget limits
    daily_spent = get_daily_cost(cost_info.project_id)
    monthly_spent = get_monthly_cost(cost_info.project_id)
    
    cond do
      exceeds_daily_limit?(daily_spent, cost_info.project_id) ->
        trigger_budget_alert(:daily_limit_exceeded, cost_info)
        
      approaching_monthly_limit?(monthly_spent, cost_info.project_id) ->
        trigger_budget_alert(:monthly_limit_warning, cost_info)
        
      true -> :ok
    end
  end
end

defmodule Foundation.AI.PromptTemplate do
  @moduledoc """
  Advanced prompt templating with inheritance, composition, and optimization.
  """

  defstruct [
    :id,
    :name,
    :template,
    :variables,
    :constraints,
    :optimizations,
    :parent_template,
    :version,
    :metadata
  ]

  def create_template(name, template_string, opts \\ []) do
    template = %__MODULE__{
      id: Foundation.Utils.generate_id(),
      name: name,
      template: parse_template(template_string),
      variables: extract_variables(template_string),
      constraints: opts[:constraints] || %{},
      optimizations: opts[:optimizations] || [],
      parent_template: opts[:inherit_from],
      version: "1.0.0",
      metadata: opts[:metadata] || %{}
    }
    
    # Validate template
    case validate_template(template) do
      :ok -> {:ok, template}
      error -> error
    end
  end

  def render(template, variables, opts \\ []) do
    context = %{
      variables: variables,
      constraints: template.constraints,
      render_opts: opts
    }
    
    # Apply inheritance if present
    resolved_template = resolve_inheritance(template)
    
    # Render with optimizations
    rendered = 
      resolved_template
      |> apply_pre_render_optimizations(context)
      |> render_template_string(context)
      |> apply_post_render_optimizations(context)
    
    # Validate constraints
    case validate_rendered_output(rendered, template.constraints) do
      :ok -> {:ok, rendered}
      error -> error
    end
  end

  def optimize_template(template, optimization_data) do
    # Use historical performance data to optimize templates
    optimizations = [
      maybe_add_few_shot_examples(template, optimization_data),
      maybe_adjust_instruction_order(template, optimization_data),
      maybe_simplify_language(template, optimization_data),
      maybe_add_constraints(template, optimization_data)
    ]
    
    optimized_template = Enum.reduce(optimizations, template, &apply_optimization/2)
    
    # Version the optimized template
    %{optimized_template | 
      version: increment_version(template.version),
      metadata: Map.put(template.metadata, :optimization_source, optimization_data.source)
    }
  end

  defp resolve_inheritance(%{parent_template: nil} = template), do: template
  defp resolve_inheritance(%{parent_template: parent_id} = template) do
    case get_template(parent_id) do
      {:ok, parent} ->
        resolved_parent = resolve_inheritance(parent)
        merge_templates(resolved_parent, template)
      
      {:error, _} -> template
    end
  end
end

defmodule Foundation.AI.MultiModal do
  @moduledoc """
  Multi-modal request processing and pipeline management.
  """

  def process_multi_modal_request(request, opts \\ []) do
    # Analyze request modalities
    modalities = detect_modalities(request)
    
    # Create processing pipeline based on modalities
    pipeline = create_multi_modal_pipeline(modalities, opts)
    
    # Execute with specialized handling
    Foundation.Workflow.run(pipeline, %{request: request, modalities: modalities}, 
      correlation_id: opts[:correlation_id]
    )
  end

  defp detect_modalities(request) do
    modalities = []
    
    modalities = if has_text?(request), do: [:text | modalities], else: modalities
    modalities = if has_images?(request), do: [:image | modalities], else: modalities
    modalities = if has_audio?(request), do: [:audio | modalities], else: modalities
    modalities = if has_video?(request), do: [:video | modalities], else: modalities
    
    modalities
  end

  defp create_multi_modal_pipeline(modalities, opts) do
    base_pipeline = [
      {:validate_inputs, &validate_multi_modal_inputs/1},
      {:preprocess_modalities, &preprocess_modalities/1}
    ]
    
    # Add modality-specific processing steps
    modality_pipeline = 
      modalities
      |> Enum.flat_map(&get_modality_processing_steps/1)
    
    fusion_pipeline = [
      {:fuse_modalities, &fuse_modality_results/1},
      {:generate_response, &generate_multi_modal_response/1},
      {:post_process, &post_process_response/1}
    ]
    
    base_pipeline ++ modality_pipeline ++ fusion_pipeline
  end

  defp process_text_modality(acc) do
    text_data = extract_text_data(acc.request)
    
    # Process text with appropriate model
    case Foundation.AI.process_text(text_data, acc.processing_opts) do
      {:ok, processed_text} ->
        {:ok, put_in(acc, [:results, :text], processed_text)}
      error -> error
    end
  end

  defp process_image_modality(acc) do
    image_data = extract_image_data(acc.request)
    
    # Process images with vision model
    case Foundation.AI.process_images(image_data, acc.processing_opts) do
      {:ok, processed_images} ->
        {:ok, put_in(acc, [:results, :images], processed_images)}
      error -> error
    end
  end
end

defmodule Foundation.AI.DistributedTraining do
  @moduledoc """
  Coordination of distributed training and optimization tasks.
  """

  def start_distributed_optimization(optimization_spec) do
    # Create distributed optimization coordinator
    coordinator_id = Foundation.Utils.generate_id()
    
    # Analyze cluster resources
    {:ok, cluster_resources} = analyze_cluster_resources()
    
    # Create execution plan
    execution_plan = create_execution_plan(optimization_spec, cluster_resources)
    
    # Start coordinator process
    {:ok, coordinator_pid} = Foundation.AI.OptimizationCoordinator.start_link(
      id: coordinator_id,
      spec: optimization_spec,
      plan: execution_plan
    )
    
    # Distribute work across nodes
    distribute_work_across_cluster(coordinator_pid, execution_plan)
    
    {:ok, coordinator_id}
  end

  defp create_execution_plan(spec, resources) do
    # Sophisticated planning algorithm
    %{
      total_work_units: spec.total_evaluations,
      node_assignments: distribute_work_by_capability(spec, resources),
      checkpointing_strategy: determine_checkpointing_strategy(spec),
      fault_tolerance_level: spec.fault_tolerance || :standard,
      communication_pattern: determine_communication_pattern(spec, resources),
      resource_scaling: %{
        scale_up_triggers: define_scale_up_conditions(spec),
        scale_down_triggers: define_scale_down_conditions(spec),
        max_nodes: spec.max_nodes || length(resources.available_nodes)
      }
    }
  end

  defp distribute_work_by_capability(spec, resources) do
    # Match work requirements to node capabilities
    available_nodes = resources.available_nodes
    
    work_assignments = 
      available_nodes
      |> Enum.map(fn node ->
        capability_score = calculate_node_capability(node, spec)
        work_allocation = calculate_work_allocation(capability_score, spec)
        
        %{
          node: node,
          work_units: work_allocation.units,
          estimated_completion: work_allocation.estimated_time,
          backup_nodes: select_backup_nodes(node, available_nodes)
        }
      end)
    
    work_assignments
  end
end

defmodule Foundation.AI.PerformanceAnalytics do
  @moduledoc """
  Advanced analytics for AI model performance monitoring and optimization.
  """

  def track_model_performance(model_id, request_data, response_data, metrics) do
    performance_record = %{
      model_id: model_id,
      timestamp: DateTime.utc_now(),
      request_metadata: extract_request_metadata(request_data),
      response_quality: calculate_response_quality(response_data, metrics),
      latency_ms: metrics.latency_ms,
      token_efficiency: calculate_token_efficiency(request_data, response_data),
      cost_effectiveness: calculate_cost_effectiveness(metrics),
      user_satisfaction: metrics[:user_satisfaction],
      task_success_rate: metrics[:task_success_rate]
    }
    
    # Store performance data
    Foundation.Events.new_event(:model_performance_tracked, performance_record)
    |> Foundation.Events.store()
    
    # Real-time anomaly detection
    check_for_performance_anomalies(model_id, performance_record)
    
    # Update rolling averages
    update_performance_baselines(model_id, performance_record)
  end

  def analyze_performance_trends(model_id, time_range, analysis_type \\ :comprehensive) do
    # Retrieve performance data
    {:ok, performance_events} = Foundation.Events.query(%{
      event_type: :model_performance_tracked,
      data_filter: %{model_id: model_id},
      time_range: time_range
    })
    
    case analysis_type do
      :comprehensive -> comprehensive_analysis(performance_events)
      :degradation_detection -> detect_performance_degradation(performance_events)
      :optimization_opportunities -> identify_optimization_opportunities(performance_events)
      :comparative -> comparative_analysis(performance_events)
    end
  end

  def generate_performance_recommendations(model_id, analysis_results) do
    recommendations = []
    
    # Analyze different aspects of performance
    recommendations = 
      recommendations
      |> maybe_recommend_model_tuning(analysis_results)
      |> maybe_recommend_prompt_optimization(analysis_results)
      |> maybe_recommend_caching_strategy(analysis_results)
      |> maybe_recommend_infrastructure_changes(analysis_results)
      |> maybe_recommend_cost_optimizations(analysis_results)
    
    # Prioritize recommendations by impact and effort
    prioritized_recommendations = prioritize_recommendations(recommendations)
    
    {:ok, prioritized_recommendations}
  end

  defp check_for_performance_anomalies(model_id, current_performance) do
    # Get baseline performance metrics
    baseline = get_performance_baseline(model_id)
    
    anomalies = detect_anomalies(current_performance, baseline)
    
    if length(anomalies) > 0 do
      trigger_performance_alerts(model_id, anomalies)
    end
  end
end

defmodule Foundation.AI.AdaptiveLearning do
  @moduledoc """
  Adaptive learning system that automatically optimizes AI operations.
  """

  def start_adaptive_optimization(target_system) do
    # Initialize learning agent
    agent_config = %{
      target: target_system,
      learning_rate: 0.01,
      exploration_rate: 0.1,
      optimization_frequency: :daily,
      performance_metrics: [:accuracy, :latency, :cost],
      constraints: get_system_constraints(target_system)
    }
    
    {:ok, agent_pid} = Foundation.AI.LearningAgent.start_link(agent_config)
    
    # Start monitoring and learning loop
    schedule_learning_cycle(agent_pid)
    
    {:ok, agent_pid}
  end

  def observe_system_performance(system_id, performance_data) do
    # Feed performance data to the learning system
    observation = %{
      system_id: system_id,
      timestamp: DateTime.utc_now(),
      performance: performance_data,
      context: extract_context_features(performance_data)
    }
    
    Foundation.AI.LearningAgent.observe(system_id, observation)
  end

  def suggest_optimizations(system_id) do
    # Get current system state
    current_state = get_system_state(system_id)
    
    # Use learned patterns to suggest optimizations
    case Foundation.AI.LearningAgent.generate_suggestions(system_id, current_state) do
      {:ok, suggestions} ->
        # Validate suggestions against constraints
        validated_suggestions = validate_suggestions(suggestions, system_id)
        
        # Rank by expected impact
        ranked_suggestions = rank_by_expected_impact(validated_suggestions)
        
        {:ok, ranked_suggestions}
      
      error -> error
    end
  end

  def apply_optimization(system_id, optimization) do
    # Safely apply optimization with rollback capability
    case create_optimization_checkpoint(system_id) do
      {:ok, checkpoint} ->
        try do
          result = execute_optimization(system_id, optimization)
          monitor_optimization_impact(system_id, optimization, checkpoint)
          result
        rescue
          error ->
            rollback_optimization(system_id, checkpoint)
            {:error, {:optimization_failed, error}}
        end
      
      error -> error
    end
  end
end

defmodule Foundation.AI.Security do
  @moduledoc """
  Comprehensive security and privacy framework for AI operations.
  """

  def secure_request_processing(request, security_config) do
    # Multi-layered security processing
    pipeline = [
      {:validate_request, &validate_request_security/1},
      {:sanitize_inputs, &sanitize_sensitive_data/1},
      {:apply_privacy_controls, &apply_privacy_controls/1},
      {:check_access_permissions, &check_access_permissions/1},
      {:audit_request, &audit_security_event/1}
    ]
    
    case Foundation.Workflow.run(pipeline, %{request: request, config: security_config}) do
      {:ok, %{processed_request: processed_request}} ->
        {:ok, processed_request}
      error -> error
    end
  end

  def detect_security_threats(request_data, model_response) do
    threat_detectors = [
      &detect_prompt_injection/2,
      &detect_data_extraction_attempts/2,
      &detect_model_manipulation/2,
      &detect_privacy_violations/2,
      &detect_adversarial_inputs/2
    ]
    
    threats = 
      threat_detectors
      |> Enum.flat_map(fn detector -> 
        detector.(request_data, model_response) 
      end)
      |> Enum.uniq()
    
    if length(threats) > 0 do
      trigger_security_alerts(threats)
      {:threats_detected, threats}
    else
      :secure
    end
  end

  def apply_differential_privacy(data, privacy_budget) do
    # Apply differential privacy mechanisms
    case privacy_budget.mechanism do
      :laplace ->
        apply_laplace_noise(data, privacy_budget)
      
      :gaussian ->
        apply_gaussian_noise(data, privacy_budget)
      
      :exponential ->
        apply_exponential_mechanism(data, privacy_budget)
    end
  end

  def encrypt_sensitive_data(data, encryption_config) do
    # Multi-layer encryption for different sensitivity levels
    case encryption_config.level do
      :standard ->
        Foundation.Crypto.encrypt(data, encryption_config.key)
      
      :high_security ->
        data
        |> Foundation.Crypto.encrypt(encryption_config.primary_key)
        |> Foundation.Crypto.encrypt(encryption_config.secondary_key)
      
      :zero_knowledge ->
        Foundation.Crypto.zero_knowledge_encrypt(data, encryption_config)
    end
  end

  defp detect_prompt_injection(request, _response) do
    # Sophisticated prompt injection detection
    injection_patterns = get_injection_patterns()
    
    detected_patterns = 
      injection_patterns
      |> Enum.filter(fn pattern ->
        String.contains?(request.prompt, pattern) or
        Regex.match?(pattern.regex, request.prompt)
      end)
    
    if length(detected_patterns) > 0 do
      [%{type: :prompt_injection, patterns: detected_patterns}]
    else
      []
    end
  end
end