defmodule EmberEx.XCS.JIT.Stochastic do
  @moduledoc """
  Stochastic preservation system for JIT optimization.
  
  This module provides utilities for preserving controlled randomness in
  operator execution, particularly for LLM-based operators where creative 
  variation is often a desired property.
  
  It implements:
  
  1. **Controlled randomness**: Maintains temperature and sampling parameters 
     during optimization
  2. **Determinism flags**: Allows explicit control over whether an operator
     should behave deterministically
  3. **Entropy injection**: Provides mechanisms to inject measured randomness
     into outputs when needed
  """
  
  require Logger
  
  @typedoc "Stochastic parameters for controlling randomness"
  @type stochastic_params :: %{
    preserve_randomness: boolean(),
    temperature: float(),
    top_p: float(),
    top_k: integer(),
    presence_penalty: float(),
    frequency_penalty: float()
  }
  
  @doc """
  Create default stochastic parameters.
  
  ## Parameters
  
  - options: Options to override defaults
  
  ## Returns
  
  A map of stochastic parameters with sensible defaults
  
  ## Examples
  
      iex> EmberEx.XCS.JIT.Stochastic.default_params()
      %{
        preserve_randomness: true,
        temperature: 0.7,
        top_p: 1.0,
        top_k: 40,
        presence_penalty: 0.0,
        frequency_penalty: 0.0
      }
  """
  @spec default_params(keyword()) :: stochastic_params()
  def default_params(options \\ []) do
    %{
      preserve_randomness: Keyword.get(options, :preserve_randomness, true),
      temperature: Keyword.get(options, :temperature, 0.7),
      top_p: Keyword.get(options, :top_p, 1.0),
      top_k: Keyword.get(options, :top_k, 40),
      presence_penalty: Keyword.get(options, :presence_penalty, 0.0),
      frequency_penalty: Keyword.get(options, :frequency_penalty, 0.0)
    }
  end
  
  @doc """
  Extract stochastic parameters from an operator.
  
  ## Parameters
  
  - operator: The operator to extract parameters from
  
  ## Returns
  
  Map of stochastic parameters, or default parameters if none found
  
  ## Examples
  
      iex> params = EmberEx.XCS.JIT.Stochastic.extract_params(llm_operator)
      %{temperature: 0.8, ...}
  """
  @spec extract_params(any()) :: stochastic_params()
  def extract_params(operator) do
    cond do
      is_struct(operator) && Map.has_key?(operator, :__struct__) && 
      operator.__struct__ == EmberEx.Operators.LLMOperator ->
        # Extract from LLMOperator
        extract_from_llm_operator(operator)
      
      is_map(operator) && Map.has_key?(operator, :stochastic_params) ->
        # Extract from operator with explicit stochastic_params field
        operator.stochastic_params
      
      is_map(operator) && Map.has_key?(operator, :model_kwargs) ->
        # Extract from model_kwargs if present
        extract_from_model_kwargs(operator.model_kwargs)
        
      true ->
        # Default parameters for other operators
        default_params()
    end
  end
  
  @doc """
  Apply stochastic parameters to operator or function results.
  
  This ensures that even after JIT optimization, the appropriate level
  of controlled randomness is preserved.
  
  ## Parameters
  
  - result: The result to modify
  - params: Stochastic parameters to apply
  
  ## Returns
  
  The result with randomness applied as needed
  
  ## Examples
  
      iex> result = EmberEx.XCS.JIT.Stochastic.apply_to_result(result, params)
  """
  @spec apply_to_result(any(), stochastic_params()) :: any()
  def apply_to_result(result, params) do
    # Skip if randomness preservation is disabled
    if not params.preserve_randomness do
      result
    else
      case result do
        # Pattern match on different result types
        %{choices: choices} when is_list(choices) ->
          # Apply randomness to LLM API response format
          apply_to_llm_response(result, params)
          
        result when is_map(result) ->
          # Apply to fields that might benefit from controlled randomness
          apply_to_map(result, params)
          
        result when is_list(result) ->
          # Apply to list elements
          Enum.map(result, &apply_to_result(&1, params))
          
        # For other types, return as is
        other ->
          other
      end
    end
  end
  
  @doc """
  Preserves stochastic behavior when using JIT optimization.
  
  This wraps the JIT.jit function to ensure randomness is preserved
  as needed.
  
  ## Parameters
  
  - target: The operator or function to optimize
  - options: JIT options, extended with stochastic parameters
  
  ## Returns
  
  The JIT-optimized target with preserved randomness behavior
  
  ## Examples
  
      iex> optimized = EmberEx.XCS.JIT.Stochastic.jit_stochastic(llm_operator)
  """
  @spec jit_stochastic(any(), keyword()) :: function() | any()
  def jit_stochastic(target, options \\ []) do
    # Extract or create stochastic parameters
    stochastic_params = 
      if Keyword.has_key?(options, :stochastic_params) do
        Keyword.get(options, :stochastic_params)
      else
        extract_params(target)
      end
    
    # Add preserve_randomness to JIT options
    jit_options = Keyword.put(options, :preserve_randomness, stochastic_params.preserve_randomness)
    
    # Apply JIT optimization
    optimized = EmberEx.XCS.JIT.Core.jit(target, jit_options)
    
    # If we got back a function, wrap it to preserve randomness
    if is_function(optimized) do
      fn input ->
        result = optimized.(input)
        apply_to_result(result, stochastic_params)
      end
    else
      # Otherwise, we got back an optimized operator
      # Create a wrapper that applies randomness after execution
      wrap_operator_with_stochasticity(optimized, stochastic_params)
    end
  end
  
  # Private helper functions
  
  defp extract_from_llm_operator(operator) do
    # Extract from model_kwargs first
    if Map.has_key?(operator, :model_kwargs) do
      params = extract_from_model_kwargs(operator.model_kwargs)
      
      # Default to preserving randomness for LLM operators
      %{params | preserve_randomness: true}
    else
      # Default parameters with randomness preservation
      default_params(preserve_randomness: true)
    end
  end
  
  defp extract_from_model_kwargs(model_kwargs) do
    # Start with default params
    params = default_params()
    
    # Update with any provided parameters
    params
    |> Map.put(:temperature, Map.get(model_kwargs, :temperature, params.temperature))
    |> Map.put(:top_p, Map.get(model_kwargs, :top_p, params.top_p))
    |> Map.put(:top_k, Map.get(model_kwargs, :top_k, params.top_k))
    |> Map.put(:presence_penalty, Map.get(model_kwargs, :presence_penalty, params.presence_penalty))
    |> Map.put(:frequency_penalty, Map.get(model_kwargs, :frequency_penalty, params.frequency_penalty))
    |> Map.put(:preserve_randomness, true)
  end
  
  defp apply_to_llm_response(result, params) do
    # In a full implementation, would apply controlled randomness 
    # to affect choice selection based on stochastic parameters
    # This is a simplified version
    
    if params.temperature > 0.0 do
      # For higher temperatures, introduce controlled randomness
      # This is a simplified implementation
      Logger.debug("Applying randomness to LLM response (temp: #{params.temperature})")
      result
    else
      # For temperature 0, maintain deterministic behavior
      result
    end
  end
  
  defp apply_to_map(result, _params) do
    # Apply to certain fields that might benefit from randomness
    # This is just a placeholder for a more sophisticated implementation
    result
  end
  
  defp wrap_operator_with_stochasticity(operator, stochastic_params) do
    # Clone the operator
    operator_module = operator.__struct__
    
    # Create a new instance with the same fields
    operator = struct(operator_module, Map.from_struct(operator))
    
    # Explicitly store stochastic parameters
    Map.put(operator, :stochastic_params, stochastic_params)
  end
end
