crucible_framework

defmodule Crucible.Harness.TinkexRunner do
  @moduledoc """
  Runs ML experiments using Tinkex backend.

  This module implements the Runner behaviour for executing ML training experiments
  through Tinkex. It manages training sessions, checkpointing, and result collection.

  ## Example

      {:ok, experiment} = MLExperiment.new(name: "test", ...)
      {:ok, runner} = TinkexRunner.init(experiment, [])

      {:ok, runner} = TinkexRunner.run_stage(runner, :train, %{
        type: :train,
        config: %{epochs: 5, batch_size: 8}
      })

      results = TinkexRunner.get_results(runner)

  """

  require Logger

  alias Crucible.Harness.MLExperiment

  @type t :: %__MODULE__{
          experiment: MLExperiment.t(),
          session: pid() | atom() | nil,
          trainer: pid() | nil,
          checkpoints: [String.t()],
          results: map(),
          status: :initialized | :running | :completed | :failed,
          output_dir: String.t() | nil,
          resume_from: String.t() | nil
        }

  defstruct [
    :experiment,
    :session,
    :trainer,
    :output_dir,
    :resume_from,
    checkpoints: [],
    results: %{},
    status: :initialized
  ]

  @doc """
  Initializes a runner with the given experiment configuration.

  ## Options

    * `:session` - Training session pid
    * `:output_dir` - Output directory for results
    * `:resume_from` - Checkpoint name to resume from

  ## Examples

      {:ok, runner} = TinkexRunner.init(experiment, session: my_session)

  """
  @spec init(MLExperiment.t(), keyword()) :: {:ok, t()} | {:error, term()}
  def init(%MLExperiment{} = experiment, opts) when is_list(opts) do
    runner = %__MODULE__{
      experiment: experiment,
      session: Keyword.get(opts, :session),
      output_dir: Keyword.get(opts, :output_dir),
      resume_from: Keyword.get(opts, :resume_from),
      checkpoints: [],
      results: %{},
      status: :initialized
    }

    {:ok, runner}
  end

  @doc """
  Runs a specific stage of the experiment.

  ## Stage Types

    * `:train` - Training stage using Tinkex
    * `:eval` - Evaluation stage
    * `:analysis` - Statistical analysis stage

  ## Examples

      {:ok, runner} = TinkexRunner.run_stage(runner, :train, %{
        type: :train,
        config: %{epochs: 5}
      })

  """
  @spec run_stage(t(), atom(), map()) :: {:ok, t()} | {:error, term()}
  def run_stage(%__MODULE__{} = runner, stage_name, stage_config) do
    # Check for forced errors (for testing)
    if get_in(stage_config, [:config, :force_error]) do
      {:error, "Forced error for testing"}
    else
      # Emit start telemetry
      emit_stage_start(runner, stage_name)

      runner = %{runner | status: :running}

      # Run the appropriate stage
      result =
        case stage_config.type do
          :train ->
            run_train_stage(runner, stage_config.config)

          :eval ->
            run_eval_stage(runner, stage_config.config)

          :analysis ->
            run_analysis_stage(runner, stage_config.config)

          _ ->
            {:ok, %{}}
        end

      case result do
        {:ok, stage_result} ->
          # Emit stop telemetry
          emit_stage_stop(runner, stage_name, stage_result)

          # Update results
          updated_results = Map.put(runner.results, stage_name, stage_result)

          # Update checkpoints if any
          new_checkpoints =
            case stage_result do
              %{checkpoints: cps} -> runner.checkpoints ++ cps
              _ -> runner.checkpoints
            end

          {:ok,
           %{
             runner
             | results: updated_results,
               checkpoints: new_checkpoints
           }}

        {:error, reason} = error ->
          emit_stage_error(runner, stage_name, reason)
          error
      end
    end
  end

  @doc """
  Gets the current results from the runner.

  ## Examples

      results = TinkexRunner.get_results(runner)
      # => %{train: %{...}, eval: %{...}}

  """
  @spec get_results(t()) :: map()
  def get_results(%__MODULE__{results: results}) do
    results
  end

  @doc """
  Cleans up runner resources.

  ## Examples

      :ok = TinkexRunner.cleanup(runner)

  """
  @spec cleanup(t()) :: :ok
  def cleanup(%__MODULE__{} = _runner) do
    # Clean up any resources
    # In a real implementation, this would clean up Tinkex sessions, etc.
    :ok
  end

  # Private functions

  defp run_train_stage(runner, config) do
    Logger.info("Running training stage for experiment: #{runner.experiment.name}")

    # Get training parameters
    epochs = Map.get(config, :epochs, 3)
    batch_size = Map.get(config, :batch_size, 8)
    checkpoint_every = Map.get(config, :checkpoint_every, 100)

    # In a real implementation, this would:
    # 1. Create a Tinkex training session if not exists
    # 2. Load the dataset
    # 3. Run the training loop
    # 4. Save checkpoints

    # Mock training result for now
    # Assuming 10 batches per epoch
    total_steps = epochs * 10

    checkpoints =
      for step <- 1..total_steps, rem(step, checkpoint_every) == 0 do
        "step-#{step}"
      end

    result = %{
      total_steps: total_steps,
      epochs_completed: epochs,
      batch_size: batch_size,
      metrics: %{
        avg_loss: :rand.uniform() * 0.5 + 0.2,
        final_loss: :rand.uniform() * 0.3 + 0.1
      },
      checkpoints: checkpoints
    }

    {:ok, result}
  end

  defp run_eval_stage(_runner, config) do
    Logger.info("Running evaluation stage")

    # Check for forced errors (for testing)
    if Map.get(config, :force_error) do
      {:error, "Forced evaluation error for testing"}
    else
      # Get evaluation parameters
      _test_data = Map.get(config, :test_data, "default_test")
      metrics = Map.get(config, :metrics, [:accuracy])

      # In a real implementation, this would:
      # 1. Load the test dataset
      # 2. Run inference on test samples
      # 3. Compute metrics

      # Mock evaluation result
      result =
        Enum.reduce(metrics, %{}, fn metric, acc ->
          Map.put(acc, metric, :rand.uniform())
        end)
        |> Map.merge(%{
          schema_compliance: 0.98,
          citation_accuracy: 0.96,
          mean_entailment: 0.55,
          overall_pass_rate: 0.48
        })

      {:ok, result}
    end
  end

  defp run_analysis_stage(_runner, config) do
    Logger.info("Running analysis stage")

    # Get analysis parameters
    tests = Map.get(config, :tests, [:t_test])

    # In a real implementation, this would:
    # 1. Load results from previous stages
    # 2. Run statistical tests using Crucible.Bench
    # 3. Generate analysis results

    # Mock analysis result
    result = %{
      tests_run: tests,
      p_values: %{},
      effect_sizes: %{},
      significant: false
    }

    {:ok, result}
  end

  defp emit_stage_start(runner, stage_name) do
    :telemetry.execute(
      [:crucible, :harness, :stage_start],
      %{system_time: System.system_time()},
      %{
        stage: stage_name,
        experiment_id: runner.experiment.id,
        experiment_name: runner.experiment.name
      }
    )
  end

  defp emit_stage_stop(runner, stage_name, result) do
    :telemetry.execute(
      [:crucible, :harness, :stage_stop],
      %{
        system_time: System.system_time(),
        result_count: map_size(result)
      },
      %{
        stage: stage_name,
        experiment_id: runner.experiment.id,
        experiment_name: runner.experiment.name
      }
    )
  end

  defp emit_stage_error(runner, stage_name, reason) do
    :telemetry.execute(
      [:crucible, :harness, :stage_error],
      %{system_time: System.system_time()},
      %{
        stage: stage_name,
        experiment_id: runner.experiment.id,
        error: reason
      }
    )
  end
end