defmodule EmberEx.XCS.JIT.Documentation do
  @moduledoc """
  Comprehensive documentation and examples for the EmberEx JIT optimization system.
  
  This module contains detailed documentation, examples, and best practices for
  leveraging the Just-In-Time (JIT) optimization system in EmberEx.
  """
  
  @doc """
  # JIT Optimization Guide
  
  ## Overview
  
  The Just-In-Time (JIT) optimization system in EmberEx provides several ways
  to improve the performance of operators:
  
  1. **Automatic optimization** - Using `jit/2` on any operator
  2. **Strategy selection** - Specifying optimization strategies
  3. **Custom JIT integration** - Creating JIT-friendly operators
  4. **Stochastic preservation** - Maintaining controlled randomness in LLM operations
  
  ## Basic Usage
  
  ```elixir
  # Create an operator
  operator = EmberEx.Operators.MapOperator.new(fn x -> x * 2 end)
  
  # Apply JIT optimization
  optimized = EmberEx.XCS.JIT.Core.jit(operator)
  
  # Use the optimized operator
  result = EmberEx.Operators.Operator.call(optimized, 10)
  ```
  
  ## Strategy Selection
  
  EmberEx offers multiple JIT strategies, each with different optimization techniques:
  
  - **Structural**: Analyzes operator structure without execution
  - **Trace**: Records execution paths for optimization
  - **Enhanced**: Combines structural and trace analysis
  - **LLM**: Specialized for language model operators with prompt caching
  
  You can select a specific strategy:
  
  ```elixir
  # Use trace strategy
  optimized = EmberEx.XCS.JIT.Core.jit(operator, mode: :trace)
  
  # Use LLM-specific strategy
  optimized = EmberEx.XCS.JIT.Core.jit(llm_operator, mode: :llm)
  ```
  
  ## JIT with LLM Operators
  
  For LLM operators, you can use the specialized LLM strategy or stochastic preservation:
  
  ```elixir
  # Create an LLM operator
  specification = EmberEx.Specifications.EctoSpecification.new(
    "Summarize the following text: {text}",
    nil,
    output_schema
  )
  model = EmberEx.Models.create_model_callable("openai/gpt-4")
  llm_operator = EmberEx.Operators.LLMOperator.new(specification, model)
  
  # Option 1: Use JIT with LLM strategy
  optimized = EmberEx.XCS.JIT.Core.jit(llm_operator, mode: :llm)
  
  # Option 2: Use stochastic preservation
  optimized = EmberEx.XCS.JIT.Stochastic.jit_stochastic(llm_operator, 
    stochastic_params: %{temperature: 0.7, preserve_randomness: true})
  ```
  
  ## Making an Operator JIT-Friendly
  
  To make your custom operator work well with JIT optimization:
  
  1. Implement clear, deterministic behavior
  2. Provide hints about execution patterns
  3. Consider adding a custom JIT strategy
  
  Example of a JIT-friendly operator:
  
  ```elixir
  defmodule MyJITFriendlyOperator do
    use EmberEx.Operators.BaseOperator
    
    # Include a field to track execution patterns
    defstruct [:function, :execution_patterns]
    
    def new(function) do
      %__MODULE__{
        function: function,
        execution_patterns: []
      }
    end
    
    # Store execution pattern for JIT analysis
    def record_execution_pattern(operator, pattern) do
      updated_patterns = [pattern | operator.execution_patterns]
      %{operator | execution_patterns: updated_patterns}
    end
    
    @impl true
    def forward(operator, inputs) do
      # Apply the function to the input
      result = operator.function.(inputs)
      
      # Record the execution pattern for future JIT optimization
      pattern = %{input_type: get_type(inputs), output_type: get_type(result)}
      updated_operator = record_execution_pattern(operator, pattern)
      
      # Return the result
      result
    end
    
    defp get_type(value) do
      cond do
        is_map(value) -> :map
        is_list(value) -> :list
        is_binary(value) -> :string
        is_integer(value) -> :integer
        is_float(value) -> :float
        true -> :unknown
      end
    end
  end
  ```
  
  ## Creating a Custom JIT Strategy
  
  For operators with specific optimization opportunities, create a custom strategy:
  
  ```elixir
  defmodule MyCustomStrategy do
    use EmberEx.XCS.JIT.Strategies.BaseStrategy
    
    defstruct []
    
    def new do
      %__MODULE__{}
    end
    
    @impl true
    def compile(strategy, target, options) do
      # Analyze target and create an optimized function
      fn inputs ->
        # Optimized implementation
        # ...
      end
    end
    
    @impl true
    def score_target(strategy, target, options) do
      # Determine if this strategy is suitable for the target
      # Return a score and rationale
      if is_struct(target) && target.__struct__ == MyCustomOperator do
        {100, "This strategy is designed specifically for MyCustomOperator"}
      else
        {0, "This strategy only works with MyCustomOperator"}
      end
    end
  end
  ```
  
  ## Performance Benchmarking
  
  Use the benchmark suite to measure optimization impact:
  
  ```elixir
  # Benchmark a specific operator
  result = EmberEx.Benchmarks.JITBenchmarks.run_benchmark(
    operator,
    %{value: 10},
    runs: 1000
  )
  
  # Compare different JIT strategies
  results = EmberEx.Benchmarks.JITBenchmarks.compare_strategies(
    operator,
    %{value: 10}
  )
  
  # Generate a performance report
  report = EmberEx.Benchmarks.JITBenchmarks.generate_report(results)
  IO.puts(report)
  ```
  
  ## Metrics Collection
  
  Monitor JIT optimization performance with Prometheus:
  
  ```elixir
  # Start the metrics exporter
  {:ok, _} = EmberEx.Metrics.Exporters.Prometheus.start_link()
  
  # Set up metrics
  :ok = EmberEx.Metrics.Exporters.Prometheus.setup_metrics()
  
  # Record JIT optimization metrics
  :ok = EmberEx.Metrics.Exporters.Prometheus.record_jit_optimization(
    "MapOperator", 
    "structural", 
    0.025
  )
  ```
  
  ## Best Practices
  
  1. **Start Simple**: Begin with automatic optimization before trying specific strategies
  2. **Benchmark**: Always measure performance improvements to validate optimizations
  3. **Monitor Cache Size**: Watch JIT cache size for memory consumption
  4. **Consider Stochasticity**: For LLM operators, use stochastic preservation as needed
  5. **Pre-optimize**: For critical paths, pre-optimize operators during application startup
  """
  def get_documentation do
    # This function exists to provide access to the documentation
    # while avoiding the warning about @doc with no definition following
    "JIT Optimization Guide"
  end
end
