# Generated by SnakeBridge v0.13.0 - DO NOT EDIT MANUALLY
# Regenerate with: mix compile
# Library: dspy 3.1.2
# Python module: dspy.clients.databricks
# Python class: DatabricksProvider

defmodule Dspy.Clients.Databricks.DatabricksProvider do
  @moduledoc """
  Wrapper for Python class DatabricksProvider.
  """
  def __snakebridge_python_name__, do: "dspy.clients.databricks"
  def __snakebridge_python_class__, do: "DatabricksProvider"
  def __snakebridge_library__, do: "dspy"
  @opaque t :: SnakeBridge.Ref.t()

  @doc """
  Initialize self.  See help(type(self)) for accurate signature.
  """
  @spec new(keyword()) :: {:ok, SnakeBridge.Ref.t()} | {:error, Snakepit.Error.t()}
  def new(opts \\ []) do
    SnakeBridge.Runtime.call_class(__MODULE__, :__init__, [], opts)
  end

  @doc """
  Python method `DatabricksProvider.deploy_finetuned_model`.

  ## Parameters

  - `model` (String.t())
  - `data_format` (term() default: None)
  - `databricks_host` (term() default: None)
  - `databricks_token` (term() default: None)
  - `deploy_timeout` (integer() default: 900)

  ## Returns

  - `term()`
  """
  @spec deploy_finetuned_model(SnakeBridge.Ref.t(), String.t(), list(term()), keyword()) ::
          {:ok, term()} | {:error, Snakepit.Error.t()}
  def deploy_finetuned_model(ref, model, args, opts \\ []) do
    {args, opts} = SnakeBridge.Runtime.normalize_args_opts(args, opts)

    SnakeBridge.Runtime.call_method(
      ref,
      :deploy_finetuned_model,
      [model] ++ List.wrap(args),
      opts
    )
  end

  @doc """
  Python method `DatabricksProvider.finetune`.

  ## Parameters

  - `job` (Dspy.Clients.Databricks.TrainingJobDatabricks.t())
  - `model` (String.t())
  - `train_data` (list(%{optional(String.t()) => term()}))
  - `train_data_format` (term() default: 'chat')
  - `train_kwargs` (term() default: None)

  ## Returns

  - `String.t()`
  """
  @spec finetune(
          SnakeBridge.Ref.t(),
          Dspy.Clients.Databricks.TrainingJobDatabricks.t(),
          String.t(),
          list(%{optional(String.t()) => term()}),
          list(term()),
          keyword()
        ) :: {:ok, String.t()} | {:error, Snakepit.Error.t()}
  def finetune(ref, job, model, train_data, args, opts \\ []) do
    {args, opts} = SnakeBridge.Runtime.normalize_args_opts(args, opts)

    SnakeBridge.Runtime.call_method(
      ref,
      :finetune,
      [job, model, train_data] ++ List.wrap(args),
      opts
    )
  end

  @doc """
  Python method `DatabricksProvider.is_provider_model`.

  ## Parameters

  - `model` (String.t())

  ## Returns

  - `boolean()`
  """
  @spec is_provider_model(SnakeBridge.Ref.t(), String.t(), keyword()) ::
          {:ok, boolean()} | {:error, Snakepit.Error.t()}
  def is_provider_model(ref, model, opts \\ []) do
    SnakeBridge.Runtime.call_method(ref, :is_provider_model, [model], opts)
  end

  @doc """
  Python method `DatabricksProvider.kill`.

  ## Parameters

  - `lm` (term())
  - `launch_kwargs` (term() default: None)

  ## Returns

  - `term()`
  """
  @spec kill(SnakeBridge.Ref.t(), term(), list(term()), keyword()) ::
          {:ok, term()} | {:error, Snakepit.Error.t()}
  def kill(ref, lm, args, opts \\ []) do
    {args, opts} = SnakeBridge.Runtime.normalize_args_opts(args, opts)
    SnakeBridge.Runtime.call_method(ref, :kill, [lm] ++ List.wrap(args), opts)
  end

  @doc """
  Python method `DatabricksProvider.launch`.

  ## Parameters

  - `lm` (term())
  - `launch_kwargs` (term() default: None)

  ## Returns

  - `term()`
  """
  @spec launch(SnakeBridge.Ref.t(), term(), list(term()), keyword()) ::
          {:ok, term()} | {:error, Snakepit.Error.t()}
  def launch(ref, lm, args, opts \\ []) do
    {args, opts} = SnakeBridge.Runtime.normalize_args_opts(args, opts)
    SnakeBridge.Runtime.call_method(ref, :launch, [lm] ++ List.wrap(args), opts)
  end

  @doc """
  Represents the result of an asynchronous computation.

  ## Parameters

  - `finetuning_run` (term() default: None)
  - `args` (term())
  - `kwargs` (term())

  ## Returns

  - `term()`
  """
  @spec training_job(SnakeBridge.Ref.t(), list(term()), keyword()) ::
          {:ok, term()} | {:error, Snakepit.Error.t()}
  def training_job(ref, args, opts \\ []) do
    {args, opts} = SnakeBridge.Runtime.normalize_args_opts(args, opts)
    SnakeBridge.Runtime.call_method(ref, "TrainingJob", [] ++ List.wrap(args), opts)
  end

  @doc """
  Python method `DatabricksProvider.upload_data`.

  ## Parameters

  - `train_data` (list(%{optional(String.t()) => term()}))
  - `databricks_unity_catalog_path` (String.t())
  - `data_format` (Dspy.Clients.UtilsFinetune.TrainDataFormat.t())

  ## Returns

  - `term()`
  """
  @spec upload_data(
          SnakeBridge.Ref.t(),
          list(%{optional(String.t()) => term()}),
          String.t(),
          Dspy.Clients.UtilsFinetune.TrainDataFormat.t(),
          keyword()
        ) :: {:ok, term()} | {:error, Snakepit.Error.t()}
  def upload_data(ref, train_data, databricks_unity_catalog_path, data_format, opts \\ []) do
    SnakeBridge.Runtime.call_method(
      ref,
      :upload_data,
      [train_data, databricks_unity_catalog_path, data_format],
      opts
    )
  end

  @spec finetunable(SnakeBridge.Ref.t()) :: {:ok, term()} | {:error, Snakepit.Error.t()}
  def finetunable(ref) do
    SnakeBridge.Runtime.get_attr(ref, :finetunable)
  end
end