defmodule Dspy.Teleprompt.COPRO do
  @moduledoc """
  COPRO (Cooperative Prompt Optimization) teleprompt.
  
  COPRO optimizes prompts through coordinate ascent, generating and refining
  new instructions for each step in the program to maximize the given metric.
  
  ## Algorithm
  
  1. Generate candidate instructions for each program step
  2. Evaluate each candidate using the metric
  3. Select best instruction and update program
  4. Repeat until convergence or max rounds reached
  
  ## Usage
  
      teleprompt = Dspy.Teleprompt.COPRO.new(
        metric: &Dspy.Metrics.f1_score/2,
        num_trials: 10,
        max_rounds: 3
      )
      
      {:ok, optimized_program} = Dspy.Teleprompt.COPRO.compile(
        teleprompt, 
        program, 
        trainset
      )
  
  """
  
  @behaviour Dspy.Teleprompt
  
  alias Dspy.{Example, Module, Prediction, Evaluate, LM, Settings}
  
  defstruct [
    :metric,              # Evaluation metric function
    :num_trials,          # Number of instruction candidates per round
    :max_rounds,          # Maximum optimization rounds
    :minibatch_size,      # Size of evaluation minibatches
    :temperature,         # Temperature for instruction generation
    :max_instruction_length,  # Maximum length of generated instructions
    :num_threads,         # Parallel processing threads
    :seed,                # Random seed
    :verbose              # Whether to print progress
  ]
  
  @type t :: %__MODULE__{
    metric: function(),
    num_trials: pos_integer(),
    max_rounds: pos_integer(),
    minibatch_size: pos_integer(),
    temperature: float(),
    max_instruction_length: pos_integer(),
    num_threads: pos_integer(),
    seed: integer(),
    verbose: boolean()
  }
  
  @doc """
  Create a new COPRO teleprompt.
  
  ## Options
  
  - `:metric` - Evaluation metric function (required)
  - `:num_trials` - Number of instruction candidates per round (default: 10)
  - `:max_rounds` - Maximum optimization rounds (default: 3)
  - `:minibatch_size` - Evaluation minibatch size (default: 25)
  - `:temperature` - Temperature for instruction generation (default: 1.0)
  - `:max_instruction_length` - Max instruction length (default: 200)
  - `:num_threads` - Parallel processing threads (default: auto)
  - `:seed` - Random seed for reproducibility
  - `:verbose` - Print optimization progress (default: true)
  
  """
  @impl Dspy.Teleprompt
  @spec new(keyword()) :: t()
  def new(opts \\ []) do
    unless Keyword.has_key?(opts, :metric) do
      raise ArgumentError, "COPRO requires a :metric function"
    end
    
    %__MODULE__{
      metric: Keyword.fetch!(opts, :metric),
      num_trials: Keyword.get(opts, :num_trials, 10),
      max_rounds: Keyword.get(opts, :max_rounds, 3),
      minibatch_size: Keyword.get(opts, :minibatch_size, 25),
      temperature: Keyword.get(opts, :temperature, 1.0),
      max_instruction_length: Keyword.get(opts, :max_instruction_length, 200),
      num_threads: Keyword.get(opts, :num_threads, System.schedulers_online()),
      seed: Keyword.get(opts, :seed, :os.system_time(:microsecond)),
      verbose: Keyword.get(opts, :verbose, true)
    }
  end
  
  @doc """
  Compile a program using COPRO optimization.
  
  ## Process
  
  1. Analyze program structure and identify optimization points
  2. Generate instruction candidates for each step
  3. Evaluate candidates using coordinate ascent
  4. Select best instructions and create optimized program
  
  """
  @impl Dspy.Teleprompt
  @spec compile(t(), module(), list(Example.t())) :: {:ok, module()} | {:error, term()}
  def compile(%__MODULE__{} = teleprompt, program, trainset) do
    if teleprompt.verbose do
      IO.puts("Starting COPRO optimization...")
    end
    
    with {:ok, validated_trainset} <- validate_trainset(trainset),
         {:ok, program_analysis} <- analyze_program_structure(program),
         {:ok, optimized_instructions} <- optimize_instructions(teleprompt, program, program_analysis, validated_trainset),
         {:ok, optimized_program} <- create_optimized_program(program, optimized_instructions) do
      
      if teleprompt.verbose do
        IO.puts("COPRO optimization completed successfully")
      end
      
      {:ok, optimized_program}
    end
  end
  
  # Private functions
  
  defp validate_trainset(trainset) do
    if length(trainset) == 0 do
      {:error, "Empty training set"}
    else
      {:ok, trainset}
    end
  end
  
  defp analyze_program_structure(program) do
    # Analyze the program to identify optimization points
    # This is a simplified analysis - in practice would be more sophisticated
    
    optimization_points = [
      %{
        step_id: :main,
        step_type: :predict,
        current_instruction: get_current_instruction(program),
        signature: get_program_signature(program)
      }
    ]
    
    {:ok, optimization_points}
  end
  
  defp get_current_instruction(program) do
    # Extract current instruction from program
    # This would need to be implemented based on program structure
    "Answer the question accurately and concisely."
  end
  
  defp get_program_signature(program) do
    # Extract signature information from program
    # This would need to be implemented based on program structure
    %{input_fields: [:question], output_fields: [:answer]}
  end
  
  defp optimize_instructions(%__MODULE__{} = teleprompt, program, optimization_points, trainset) do
    %{max_rounds: max_rounds, verbose: verbose} = teleprompt
    
    initial_instructions = optimization_points 
    |> Enum.map(fn point -> {point.step_id, point.current_instruction} end)
    |> Map.new()
    
    # Coordinate ascent optimization
    final_instructions = for round <- 1..max_rounds, reduce: initial_instructions do
      current_instructions ->
        if verbose do
          IO.puts("COPRO round #{round}/#{max_rounds}")
        end
        
        optimize_round(teleprompt, program, optimization_points, trainset, current_instructions)
    end
    
    {:ok, final_instructions}
  end
  
  defp optimize_round(%__MODULE__{} = teleprompt, program, optimization_points, trainset, current_instructions) do
    # Optimize each step sequentially (coordinate ascent)
    optimization_points
    |> Enum.reduce(current_instructions, fn point, acc_instructions ->
      optimize_step(teleprompt, program, point, trainset, acc_instructions)
    end)
  end
  
  defp optimize_step(%__MODULE__{} = teleprompt, program, optimization_point, trainset, current_instructions) do
    %{
      num_trials: num_trials,
      minibatch_size: minibatch_size,
      verbose: verbose
    } = teleprompt
    
    step_id = optimization_point.step_id
    
    if verbose do
      IO.puts("  Optimizing step: #{step_id}")
    end
    
    # Generate candidate instructions
    candidate_instructions = generate_instruction_candidates(
      teleprompt, 
      optimization_point, 
      current_instructions[step_id]
    )
    
    # Evaluate candidates
    minibatch = Enum.take_random(trainset, min(minibatch_size, length(trainset)))
    
    evaluations = candidate_instructions
    |> Enum.map(fn candidate ->
      # Create temporary program with candidate instruction
      temp_instructions = Map.put(current_instructions, step_id, candidate)
      temp_program = create_temporary_program(program, temp_instructions)
      
      # Evaluate on minibatch
      result = Evaluate.evaluate(temp_program, minibatch, teleprompt.metric, progress: false)
      
      {candidate, result.mean}
    end)
    
    # Select best candidate
    {best_instruction, best_score} = evaluations
    |> Enum.max_by(fn {_instruction, score} -> score end)
    
    current_score = current_instructions[step_id] 
    |> then(fn current ->
      temp_program = create_temporary_program(program, current_instructions)
      result = Evaluate.evaluate(temp_program, minibatch, teleprompt.metric, progress: false)
      result.mean
    end)
    
    if best_score > current_score do
      if verbose do
        IO.puts("    Improved score: #{Float.round(current_score, 3)} -> #{Float.round(best_score, 3)}")
      end
      Map.put(current_instructions, step_id, best_instruction)
    else
      if verbose do
        IO.puts("    No improvement found")
      end
      current_instructions
    end
  end
  
  defp generate_instruction_candidates(%__MODULE__{} = teleprompt, optimization_point, current_instruction) do
    %{
      num_trials: num_trials,
      temperature: temperature,
      max_instruction_length: max_length
    } = teleprompt
    
    signature = optimization_point.signature
    
    # Generate instruction candidates using LM
    prompt = build_instruction_generation_prompt(signature, current_instruction, optimization_point)
    
    lm = Settings.get().lm
    
    candidates = for _ <- 1..num_trials do
      case LM.generate(lm, prompt, max_tokens: max_length, temperature: temperature) do
        {:ok, response} ->
          extract_instruction_from_response(response)
        
        {:error, _} ->
          # Fallback to variation of current instruction
          vary_instruction(current_instruction)
      end
    end
    
    # Remove duplicates and invalid candidates
    candidates
    |> Enum.uniq()
    |> Enum.filter(&is_valid_instruction/1)
    |> Enum.take(num_trials)
  end
  
  defp build_instruction_generation_prompt(signature, current_instruction, optimization_point) do
    input_fields = signature.input_fields |> Enum.join(", ")
    output_fields = signature.output_fields |> Enum.join(", ")
    
    """
    I need to optimize an instruction for a language model program.
    
    Current instruction: "#{current_instruction}"
    
    The program takes these inputs: #{input_fields}
    The program produces these outputs: #{output_fields}
    
    Generate a new, improved instruction that will help the model perform better.
    The instruction should be clear, specific, and actionable.
    
    New instruction:
    """
  end
  
  defp extract_instruction_from_response(response) do
    # Extract instruction from LM response
    response
    |> String.trim()
    |> String.replace(~r/^(New instruction:|Instruction:)/i, "")
    |> String.trim()
    |> String.replace(~r/^"/, "")
    |> String.replace(~r/"$/, "")
  end
  
  defp vary_instruction(instruction) do
    # Simple instruction variation as fallback
    variations = [
      "#{instruction} Be precise and accurate.",
      "#{instruction} Provide detailed reasoning.",
      "#{instruction} Think step by step.",
      "#{instruction} Consider all relevant factors.",
      "#{instruction} Be thorough and complete."
    ]
    
    Enum.random(variations)
  end
  
  defp is_valid_instruction(instruction) when is_binary(instruction) do
    length = String.length(instruction)
    length > 10 and length < 500
  end
  
  defp is_valid_instruction(_), do: false
  
  defp create_temporary_program(original_program, instructions) do
    # Create a temporary program with modified instructions
    # This is a simplified implementation
    {:module, module_name, _binary, _exports} = defmodule TemporaryCOPROProgram do
      @behaviour Dspy.Module
      
      @original_program original_program
      @instructions instructions
      
      def __original_program__, do: @original_program
      def __instructions__, do: @instructions
      
      @impl Dspy.Module
      def forward(input) do
        original = __original_program__()
        instructions = __instructions__()
        
        # Add optimized instructions to input context
        enhanced_input = add_instructions_to_context(input, instructions)
        
        # Forward to original program
        Module.forward(original, enhanced_input)
      end
      
      @impl Dspy.Module
      def parameters do
        original_params = Module.parameters(__original_program__())
        
        Map.merge(original_params, %{
          optimized_instructions: __instructions__()
        })
      end
      
      defp add_instructions_to_context(input, instructions) when is_map(input) do
        main_instruction = Map.get(instructions, :main, "")
        Map.put(input, :instruction, main_instruction)
      end
      
      defp add_instructions_to_context(input, instructions) do
        main_instruction = Map.get(instructions, :main, "")
        %{input: input, instruction: main_instruction}
      end
    end
    
    module_name
  end
  
  defp create_optimized_program(original_program, optimized_instructions) do
    # Create final optimized program
    {:module, optimized_program, _binary, _exports} = defmodule OptimizedCOPROProgram do
      @behaviour Dspy.Module
      
      @original_program original_program
      @optimized_instructions optimized_instructions
      
      def __original_program__, do: @original_program
      def __optimized_instructions__, do: @optimized_instructions
      
      @impl Dspy.Module
      def forward(input) do
        original = __original_program__()
        instructions = __optimized_instructions__()
        
        # Add optimized instructions to input context
        enhanced_input = add_instructions_to_context(input, instructions)
        
        # Forward to original program
        Module.forward(original, enhanced_input)
      end
      
      @impl Dspy.Module
      def parameters do
        original_params = Module.parameters(__original_program__())
        
        Map.merge(original_params, %{
          copro_optimized_instructions: __optimized_instructions__(),
          optimization_method: "COPRO"
        })
      end
      
      defp add_instructions_to_context(input, instructions) when is_map(input) do
        main_instruction = Map.get(instructions, :main, "")
        Map.put(input, :instruction, main_instruction)
      end
      
      defp add_instructions_to_context(input, instructions) do
        main_instruction = Map.get(instructions, :main, "")
        %{input: input, instruction: main_instruction}
      end
    end
    
    {:ok, optimized_program}
  end
end