dspex

# Generated by SnakeBridge v0.14.0 - DO NOT EDIT MANUALLY
# Regenerate with: mix compile
# Library: dspy 3.1.2
# Python module: dspy.teleprompt.simba_utils

defmodule Dspy.Teleprompt.SimbaUtils do
  @moduledoc """
  Submodule bindings for `dspy.teleprompt.simba_utils`.

  ## Version

  - Requested: 3.1.2
  - Observed at generation: 3.1.2

  ## Runtime Options

  All functions accept a `__runtime__` option for controlling execution behavior:

      Dspy.Teleprompt.SimbaUtils.some_function(args, __runtime__: [timeout: 120_000])

  ### Supported runtime options

  - `:timeout` - Call timeout in milliseconds (default: 120,000ms / 2 minutes)
  - `:timeout_profile` - Use a named profile (`:default`, `:ml_inference`, `:batch_job`, `:streaming`)
  - `:stream_timeout` - Timeout for streaming operations (default: 1,800,000ms / 30 minutes)
  - `:session_id` - Override the session ID for this call
  - `:pool_name` - Target a specific Snakepit pool (multi-pool setups)
  - `:affinity` - Override session affinity (`:hint`, `:strict_queue`, `:strict_fail_fast`)

  ### Timeout Profiles

  - `:default` - 2 minute timeout for regular calls
  - `:ml_inference` - 10 minute timeout for ML/LLM workloads
  - `:batch_job` - Unlimited timeout for long-running jobs
  - `:streaming` - 2 minute timeout, 30 minute stream_timeout

  ### Example with timeout override

      # For a long-running ML inference call
      Dspy.Teleprompt.SimbaUtils.predict(data, __runtime__: [timeout_profile: :ml_inference])

      # Or explicit timeout
      Dspy.Teleprompt.SimbaUtils.predict(data, __runtime__: [timeout: 600_000])

      # Route to a pool and enforce strict affinity
      Dspy.Teleprompt.SimbaUtils.predict(data, __runtime__: [pool_name: :strict_pool, affinity: :strict_queue])

  See `SnakeBridge.Defaults` for global timeout configuration.

  """

  @doc false
  def __snakebridge_python_name__, do: "dspy.teleprompt.simba_utils"
  @doc false
  def __snakebridge_library__, do: "dspy"

  @doc """
  Python binding for `dspy.teleprompt.simba_utils.append_a_demo`.

  ## Parameters

  - `demo_input_field_maxlen` (term())

  ## Returns

  - `term()`
  """
  @spec append_a_demo(term(), keyword()) :: {:ok, term()} | {:error, Snakepit.Error.t()}
  def append_a_demo(demo_input_field_maxlen, opts \\ []) do
    SnakeBridge.Runtime.call(__MODULE__, :append_a_demo, [demo_input_field_maxlen], opts)
  end

  @doc """
  Python binding for `dspy.teleprompt.simba_utils.append_a_rule`.

  ## Parameters

  - `bucket` (term())
  - `system` (term())
  - `kwargs` (term())

  ## Returns

  - `term()`
  """
  @spec append_a_rule(term(), term(), keyword()) :: {:ok, term()} | {:error, Snakepit.Error.t()}
  def append_a_rule(bucket, system, opts \\ []) do
    SnakeBridge.Runtime.call(__MODULE__, :append_a_rule, [bucket, system], opts)
  end

  @doc """
  Python binding for `dspy.teleprompt.simba_utils.inspect_modules`.

  ## Parameters

  - `program` (term())

  ## Returns

  - `term()`
  """
  @spec inspect_modules(term(), keyword()) :: {:ok, term()} | {:error, Snakepit.Error.t()}
  def inspect_modules(program, opts \\ []) do
    SnakeBridge.Runtime.call(__MODULE__, :inspect_modules, [program], opts)
  end

  @doc """
  Python binding for `dspy.teleprompt.simba_utils.prepare_models_for_resampling`.

  ## Parameters

  - `program` (Dspy.Primitives.ModuleClass3.t())
  - `n` (integer())
  - `teacher_settings` (term() default: None)

  ## Returns

  - `term()`
  """
  @spec prepare_models_for_resampling(Dspy.Primitives.ModuleClass3.t(), integer()) ::
          {:ok, term()} | {:error, Snakepit.Error.t()}
  @spec prepare_models_for_resampling(Dspy.Primitives.ModuleClass3.t(), integer(), keyword()) ::
          {:ok, term()} | {:error, Snakepit.Error.t()}
  @spec prepare_models_for_resampling(Dspy.Primitives.ModuleClass3.t(), integer(), term()) ::
          {:ok, term()} | {:error, Snakepit.Error.t()}
  @spec prepare_models_for_resampling(
          Dspy.Primitives.ModuleClass3.t(),
          integer(),
          term(),
          keyword()
        ) :: {:ok, term()} | {:error, Snakepit.Error.t()}
  def prepare_models_for_resampling(program, n) do
    SnakeBridge.Runtime.call(__MODULE__, :prepare_models_for_resampling, [program, n], [])
  end

  def prepare_models_for_resampling(program, n, opts)
      when is_list(opts) and
             (opts == [] or
                (is_tuple(hd(opts)) and tuple_size(hd(opts)) == 2 and is_atom(elem(hd(opts), 0)))) do
    SnakeBridge.Runtime.call(__MODULE__, :prepare_models_for_resampling, [program, n], opts)
  end

  def prepare_models_for_resampling(program, n, teacher_settings) do
    SnakeBridge.Runtime.call(
      __MODULE__,
      :prepare_models_for_resampling,
      [program, n, teacher_settings],
      []
    )
  end

  def prepare_models_for_resampling(program, n, teacher_settings, opts)
      when is_list(opts) and
             (opts == [] or
                (is_tuple(hd(opts)) and tuple_size(hd(opts)) == 2 and is_atom(elem(hd(opts), 0)))) do
    SnakeBridge.Runtime.call(
      __MODULE__,
      :prepare_models_for_resampling,
      [program, n, teacher_settings],
      opts
    )
  end

  @doc """
  Python binding for `dspy.teleprompt.simba_utils.recursive_mask`.

  ## Parameters

  - `o` (term())

  ## Returns

  - `term()`
  """
  @spec recursive_mask(term(), keyword()) :: {:ok, term()} | {:error, Snakepit.Error.t()}
  def recursive_mask(o, opts \\ []) do
    SnakeBridge.Runtime.call(__MODULE__, :recursive_mask, [o], opts)
  end

  @doc """
  Python binding for `dspy.teleprompt.simba_utils.wrap_program`.

  ## Parameters

  - `program` (Dspy.Primitives.ModuleClass3.t())
  - `metric` (term())

  ## Returns

  - `term()`
  """
  @spec wrap_program(Dspy.Primitives.ModuleClass3.t(), term(), keyword()) ::
          {:ok, term()} | {:error, Snakepit.Error.t()}
  def wrap_program(program, metric, opts \\ []) do
    SnakeBridge.Runtime.call(__MODULE__, :wrap_program, [program, metric], opts)
  end
end