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

defmodule Dspy.Utils.Saving do
  @moduledoc """
  Submodule bindings for `dspy.utils.saving`.

  ## Version

  - Requested: 3.1.2
  - Observed at generation: 3.1.2

  ## Runtime Options

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

      Dspy.Utils.Saving.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.Utils.Saving.predict(data, __runtime__: [timeout_profile: :ml_inference])

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

      # Route to a pool and enforce strict affinity
      Dspy.Utils.Saving.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.utils.saving"
  @doc false
  def __snakebridge_library__, do: "dspy"

  @doc """
  Python binding for `dspy.utils.saving.get_dependency_versions`.

  ## Returns

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

  @doc """
  Load saved DSPy model.

  This method is used to load a saved DSPy model with `save_program=True`, i.e., the model is saved with cloudpickle.

  ## Parameters

  - `path` - Path to the saved model. (type: `String.t()`)
  - `allow_pickle` - Whether to allow loading the model with pickle. This is dangerous and should only be used if you are sure you trust the source of the model. (type: `boolean()`)

  ## Returns

  - `term()`
  """
  @spec load(String.t()) :: {:ok, term()} | {:error, Snakepit.Error.t()}
  @spec load(String.t(), keyword()) :: {:ok, term()} | {:error, Snakepit.Error.t()}
  @spec load(String.t(), boolean()) :: {:ok, term()} | {:error, Snakepit.Error.t()}
  @spec load(String.t(), boolean(), keyword()) :: {:ok, term()} | {:error, Snakepit.Error.t()}
  def load(path) do
    SnakeBridge.Runtime.call(__MODULE__, :load, [path], [])
  end

  def load(path, 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__, :load, [path], opts)
  end

  def load(path, allow_pickle) do
    SnakeBridge.Runtime.call(__MODULE__, :load, [path, allow_pickle], [])
  end

  def load(path, allow_pickle, 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__, :load, [path, allow_pickle], opts)
  end
end