defmodule Dspy.LLMCoordinationSystem do
  @moduledoc """
  LLM Coordination System for managing token usage and orchestrating 
  multi-turn autonomous meta-hotswapping processes.
  
  This system:
  1. Tracks and manages LLM token consumption across agent operations
  2. Coordinates multi-turn conversations and decision making
  3. Optimizes token efficiency through intelligent caching and reuse
  4. Provides real-time coordination between autonomous agents
  5. Manages context windows and conversation state
  6. Implements token-based process flow control
  """

  use GenServer
  require Logger

  defstruct [
    :coordination_id,
    :active_conversations,
    :token_budget,
    :token_usage,
    :conversation_cache,
    :coordination_state,
    :agent_registry,
    :llm_clients,
    :optimization_metrics,
    :flow_control
  ]

  @doc """
  Start the LLM coordination system.
  """
  def start_link(opts \\ []) do
    coordination_id = Keyword.get(opts, :coordination_id, generate_coordination_id())
    GenServer.start_link(__MODULE__, %{coordination_id: coordination_id}, name: __MODULE__)
  end

  @doc """
  Begin coordinated multi-turn process with token management.
  """
  def begin_coordinated_process(process_spec, token_budget \\ 10000) do
    GenServer.call(__MODULE__, {:begin_process, process_spec, token_budget})
  end

  @doc """
  Execute a coordinated LLM query with token tracking.
  """
  def coordinated_query(conversation_id, prompt, context \\ %{}) do
    GenServer.call(__MODULE__, {:coordinated_query, conversation_id, prompt, context})
  end

  @doc """
  Get coordination system status and token usage.
  """
  def get_coordination_status do
    GenServer.call(__MODULE__, :get_status)
  end

  @doc """
  Optimize token usage across active processes.
  """
  def optimize_token_usage do
    GenServer.call(__MODULE__, :optimize_tokens)
  end

  # GenServer implementation

  def init(%{coordination_id: coordination_id}) do
    state = %__MODULE__{
      coordination_id: coordination_id,
      active_conversations: %{},
      token_budget: %{total: 10000, used: 0, reserved: 0},
      token_usage: %{queries: 0, responses: 0, efficiency: 1.0},
      conversation_cache: %{},
      coordination_state: :idle,
      agent_registry: %{},
      llm_clients: initialize_llm_clients(),
      optimization_metrics: %{cache_hits: 0, cache_misses: 0, optimization_savings: 0},
      flow_control: %{max_concurrent: 5, current_active: 0, queue: []}
    }
    
    Logger.info("LLM Coordination System #{coordination_id} started")
    {:ok, state}
  end

  def handle_call({:begin_process, process_spec, token_budget}, _from, state) do
    process_id = generate_process_id()
    
    conversation_state = %{
      process_id: process_id,
      spec: process_spec,
      token_budget: token_budget,
      tokens_used: 0,
      turns: [],
      context: %{},
      status: :active,
      start_time: DateTime.utc_now()
    }
    
    updated_state = %{state |
      active_conversations: Map.put(state.active_conversations, process_id, conversation_state),
      token_budget: %{state.token_budget | reserved: state.token_budget.reserved + token_budget},
      coordination_state: :active
    }
    
    Logger.info("Started coordinated process #{process_id} with #{token_budget} token budget")
    {:reply, {:ok, process_id}, updated_state}
  end

  def handle_call({:coordinated_query, conversation_id, prompt, context}, _from, state) do
    case Map.get(state.active_conversations, conversation_id) do
      nil ->
        {:reply, {:error, :conversation_not_found}, state}
        
      conversation ->
        case execute_coordinated_query(state, conversation, prompt, context) do
          {:ok, response, updated_conversation, tokens_used} ->
            updated_conversations = Map.put(state.active_conversations, conversation_id, updated_conversation)
            
            updated_state = %{state |
              active_conversations: updated_conversations,
              token_budget: %{state.token_budget | used: state.token_budget.used + tokens_used},
              token_usage: update_token_usage(state.token_usage, tokens_used)
            }
            
            {:reply, {:ok, response}, updated_state}
            
          {:error, reason} ->
            {:reply, {:error, reason}, state}
        end
    end
  end

  def handle_call(:get_status, _from, state) do
    status = %{
      coordination_id: state.coordination_id,
      active_conversations: map_size(state.active_conversations),
      token_budget: state.token_budget,
      token_usage: state.token_usage,
      optimization_metrics: state.optimization_metrics,
      flow_control: state.flow_control,
      coordination_state: state.coordination_state
    }
    
    {:reply, status, state}
  end

  def handle_call(:optimize_tokens, _from, state) do
    # The perform_token_optimization always returns {:ok, state, savings}
    # so we don't need to handle {:error, reason} case
    {:ok, optimized_state, savings} = perform_token_optimization(state)
    Logger.info("Token optimization completed, saved #{savings} tokens")
    {:reply, {:ok, savings}, optimized_state}
  end

  # Private coordination functions

  defp execute_coordinated_query(state, conversation, prompt, context) do
    # Check token budget
    estimated_tokens = estimate_token_usage(prompt, context)
    
    if conversation.tokens_used + estimated_tokens > conversation.token_budget do
      {:error, :token_budget_exceeded}
    else
      # Check cache first for efficiency
      cache_key = generate_cache_key(prompt, context)
      
      case check_conversation_cache(state.conversation_cache, cache_key) do
        {:hit, cached_response} ->
          Logger.debug("Cache hit for query")
          
          updated_conversation = add_turn_to_conversation(conversation, prompt, cached_response, 0)
          _updated_metrics = %{state.optimization_metrics | cache_hits: state.optimization_metrics.cache_hits + 1}
          
          {:ok, cached_response, updated_conversation, 0}
          
        :miss ->
          # Execute actual LLM query
          case execute_llm_query(state.llm_clients, prompt, context, conversation) do
            {:ok, response, actual_tokens} ->
              # Update conversation and cache
              updated_conversation = add_turn_to_conversation(conversation, prompt, response, actual_tokens)
              _updated_cache = cache_response(state.conversation_cache, cache_key, response)
              _updated_metrics = %{state.optimization_metrics | cache_misses: state.optimization_metrics.cache_misses + 1}
              
              {:ok, response, updated_conversation, actual_tokens}
              
            {:error, reason} ->
              {:error, reason}
          end
      end
    end
  end

  defp execute_llm_query(llm_clients, prompt, context, conversation) do
    # Enhanced prompt with conversation context
    enhanced_prompt = build_enhanced_prompt(prompt, context, conversation)
    
    # Select best LLM client based on query type and current load
    selected_client = select_optimal_client(llm_clients, enhanced_prompt, conversation)
    
    case query_llm_with_retry(selected_client, enhanced_prompt) do
      {:ok, response} ->
        actual_tokens = calculate_actual_tokens(enhanced_prompt, response)
        {:ok, response, actual_tokens}
        
      {:error, reason} ->
        {:error, reason}
    end
  end

  defp build_enhanced_prompt(prompt, context, conversation) do
    # Build context-aware prompt with conversation history
    conversation_context = build_conversation_context(conversation)
    
    """
    === CONVERSATION CONTEXT ===
    Process ID: #{conversation.process_id}
    Turn Number: #{length(conversation.turns) + 1}
    Previous Context: #{conversation_context}
    Current Context: #{inspect(context)}
    
    === COORDINATION INSTRUCTIONS ===
    This is part of an autonomous meta-hotswapping process.
    Provide structured, actionable responses that can be parsed and executed.
    Focus on the specific phase: #{get_current_phase(conversation)}
    
    === USER PROMPT ===
    #{prompt}
    
    === RESPONSE FORMAT ===
    Provide response in structured format for autonomous processing.
    """
  end

  defp build_conversation_context(conversation) do
    recent_turns = Enum.take(conversation.turns, -3) # Last 3 turns for context
    
    Enum.map_join(recent_turns, "\n", fn turn ->
      "Turn #{turn.turn_number}: #{String.slice(turn.prompt, 0, 100)}... -> #{String.slice(turn.response, 0, 100)}..."
    end)
  end

  defp get_current_phase(conversation) do
    turn_count = length(conversation.turns)
    
    case turn_count do
      0 -> :analysis_and_selection
      1 -> :design_and_planning
      2 -> :implementation
      3 -> :testing
      4 -> :validation
      _ -> :finalization
    end
  end

  defp select_optimal_client(llm_clients, prompt, conversation) do
    # Select client based on complexity, conversation phase, and current load
    phase = get_current_phase(conversation)
    complexity = estimate_query_complexity(prompt)
    
    case {phase, complexity} do
      {:analysis_and_selection, :high} -> llm_clients.primary_reasoning
      {:design_and_planning, _} -> llm_clients.code_specialist
      {:implementation, _} -> llm_clients.code_specialist
      {:testing, _} -> llm_clients.testing_specialist
      {:validation, _} -> llm_clients.validation_specialist
      _ -> llm_clients.general_purpose
    end
  end

  defp query_llm_with_retry(client, prompt, retries \\ 3) do
    try do
      case execute_single_llm_query(client, prompt) do
        {:ok, response} ->
          {:ok, response}
          
        {:error, :rate_limit} when retries > 0 ->
          backoff_time = exponential_backoff(3 - retries)
          Logger.info("Rate limited, backing off for #{backoff_time}ms")
          Process.sleep(backoff_time)
          query_llm_with_retry(client, prompt, retries - 1)
          
        {:error, :timeout} when retries > 0 ->
          Logger.warning("LLM query timed out, retrying")
          query_llm_with_retry(client, prompt, retries - 1)
          
        {:error, reason} when retries > 0 ->
          Logger.warning("LLM query failed, retrying: #{inspect(reason)}")
          Process.sleep(1000)
          query_llm_with_retry(client, prompt, retries - 1)
          
        {:error, reason} ->
          Logger.error("LLM query failed after all retries: #{inspect(reason)}")
          {:error, reason}
      end
    rescue
      error ->
        Logger.error("Exception in LLM query: #{inspect(error)}")
        {:error, {:exception, error}}
    end
  end

  defp execute_single_llm_query(client, prompt) do
    case call_llm_api(client, prompt) do
      {:ok, response} when is_binary(response) ->
        {:ok, response}
      {:ok, response} ->
        {:ok, inspect(response)}
      {:error, reason} ->
        {:error, reason}
      result ->
        # Fallback for unexpected formats - ensures we always return {:ok, binary()}
        {:ok, inspect(result)}
    end
  end

  defp call_llm_api(_client, prompt) do
    # Enhanced mock with realistic error scenarios for testing
    cond do
      String.contains?(prompt, "error_test") ->
        {:error, :api_error}
      String.contains?(prompt, "timeout_test") ->
        {:error, :timeout}
      String.contains?(prompt, "rate_limit_test") ->
        {:error, :rate_limit}
      true ->
        simulated_response = generate_simulated_response(prompt)
        {:ok, simulated_response}
    end
  end

  defp generate_simulated_response(prompt) do
    cond do
      String.contains?(prompt, "analysis") ->
        """
        {
          "problem_decomposition": "Analyzed problem into core components",
          "required_capabilities": ["module_creation", "testing", "validation"],
          "implementation_strategy": "Create modular solution with testing",
          "complexity_estimate": "medium",
          "success_criteria": "Working module with passing tests"
        }
        """
        
      String.contains?(prompt, "design") ->
        """
        {
          "module_architecture": "Single module with core functions",
          "function_specifications": ["main_function/1", "helper_function/2"],
          "data_flow": "Input -> Process -> Output",
          "testing_strategy": "Unit tests with ExUnit",
          "file_structure": "/lib/generated_module.ex",
          "hotswap_points": ["main_function integration"]
        }
        """
        
      String.contains?(prompt, "implementation") ->
        """
        defmodule GeneratedModule do
          @moduledoc "Autonomously generated module"
          
          def main_function(input) do
            result = process_input(input)
            {:ok, result}
          end
          
          defp process_input(input) do
            "Processed: " <> to_string(input)
          end
        end
        """
        
      String.contains?(prompt, "test") ->
        """
        defmodule GeneratedModuleTest do
          use ExUnit.Case
          
          test "main function works correctly" do
            assert {:ok, result} = GeneratedModule.main_function("test")
            assert String.contains?(result, "Processed: test")
          end
        end
        """
        
      String.contains?(prompt, "validation") ->
        """
        {
          "correctness": "All functions work as expected",
          "goal_achievement": "Successfully implemented requested functionality",
          "code_quality": "High quality, well-structured code",
          "performance": "Efficient implementation",
          "safety": "No security concerns identified",
          "legitimacy_score": 0.95
        }
        """
        
      true ->
        "Coordinated response for: #{String.slice(prompt, 0, 50)}..."
    end
  end

  defp add_turn_to_conversation(conversation, prompt, response, tokens_used) do
    turn = %{
      turn_number: length(conversation.turns) + 1,
      prompt: prompt,
      response: response,
      tokens_used: tokens_used,
      timestamp: DateTime.utc_now()
    }
    
    %{conversation |
      turns: conversation.turns ++ [turn],
      tokens_used: conversation.tokens_used + tokens_used
    }
  end

  defp check_conversation_cache(cache, cache_key) do
    case Map.get(cache, cache_key) do
      nil -> :miss
      cached_response -> {:hit, cached_response}
    end
  end

  defp cache_response(cache, cache_key, response) do
    # Simple cache - in production would implement LRU or TTL
    Map.put(cache, cache_key, response)
  end

  defp generate_cache_key(prompt, context) do
    content = prompt <> inspect(context)
    :crypto.hash(:sha256, content) |> Base.encode16()
  end

  defp estimate_token_usage(prompt, context) do
    # Rough estimation: ~4 characters per token
    content_length = String.length(prompt) + String.length(inspect(context))
    div(content_length, 4)
  end

  defp estimate_query_complexity(prompt) do
    cond do
      String.length(prompt) > 1000 -> :high
      String.length(prompt) > 500 -> :medium
      true -> :low
    end
  end

  defp calculate_actual_tokens(prompt, response) do
    # Simplified token calculation
    total_content = String.length(prompt) + String.length(response)
    div(total_content, 4)
  end

  defp update_token_usage(usage, tokens_used) do
    %{usage |
      queries: usage.queries + 1,
      responses: usage.responses + 1,
      efficiency: calculate_efficiency(usage, tokens_used)
    }
  end

  defp calculate_efficiency(usage, tokens_used) do
    # Simple efficiency metric based on token usage trends
    if usage.queries > 0 do
      avg_tokens_per_query = tokens_used / usage.queries
      max(0.1, min(1.0, 100 / avg_tokens_per_query))
    else
      1.0
    end
  end

  defp perform_token_optimization(state) do
    # Implement token optimization strategies
    _optimization_savings = 0
    
    # 1. Cache optimization
    cache_savings = optimize_cache(state.conversation_cache)
    
    # 2. Context compression
    context_savings = compress_contexts(state.active_conversations)
    
    # 3. Query deduplication
    dedup_savings = deduplicate_queries(state.active_conversations)
    
    total_savings = cache_savings + context_savings + dedup_savings
    
    optimized_state = %{state |
      optimization_metrics: %{state.optimization_metrics |
        optimization_savings: state.optimization_metrics.optimization_savings + total_savings
      }
    }
    
    {:ok, optimized_state, total_savings}
  end

  defp optimize_cache(_cache) do
    # Cache optimization logic
    50 # Simulated savings
  end

  defp compress_contexts(_conversations) do
    # Context compression logic  
    30 # Simulated savings
  end

  defp deduplicate_queries(_conversations) do
    # Query deduplication logic
    20 # Simulated savings
  end

  defp initialize_llm_clients do
    %{
      primary_reasoning: %{type: :gpt4, endpoint: "reasoning_endpoint"},
      code_specialist: %{type: :codex, endpoint: "code_endpoint"},
      testing_specialist: %{type: :gpt35, endpoint: "testing_endpoint"},
      validation_specialist: %{type: :gpt4, endpoint: "validation_endpoint"},
      general_purpose: %{type: :gpt35, endpoint: "general_endpoint"}
    }
  end

  defp generate_coordination_id do
    "coord_" <> (:crypto.strong_rand_bytes(8) |> Base.encode16(case: :lower))
  end

  defp generate_process_id do
    "proc_" <> (:crypto.strong_rand_bytes(6) |> Base.encode16(case: :lower))
  end

  defp exponential_backoff(attempt) do
    base_delay = 1000
    jitter = :rand.uniform(500)
    max_delay = 30_000  # Maximum 30 seconds
    
    calculated_delay = round(base_delay * :math.pow(2, attempt) + jitter)
    min(calculated_delay, max_delay)
  end
end