# Generated by SnakeBridge v0.15.0 - DO NOT EDIT MANUALLY
# Regenerate with: mix compile
# Library: dspy 3.1.2
# Python module: dspy.datasets
# Python class: DataLoader

defmodule Dspy.Datasets.DataLoader do
  @moduledoc """
  Wrapper for Python class DataLoader.
  """
  def __snakebridge_python_name__, do: "dspy.datasets"
  def __snakebridge_python_class__, do: "DataLoader"
  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 `DataLoader._shuffle_and_sample`.

  ## Parameters

  - `split` (String.t())
  - `data` (term())
  - `size` (term())
  - `seed` (integer() default: 0)

  ## Returns

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

    SnakeBridge.Runtime.call_method(
      ref,
      :_shuffle_and_sample,
      [split, data, size] ++ List.wrap(args),
      opts
    )
  end

  @doc """
  Python method `DataLoader.from_csv`.

  ## Parameters

  - `file_path` (String.t())
  - `fields` (term() default: None)
  - `input_keys` ({String.t()} default: ())

  ## Returns

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

  @doc """
  Python method `DataLoader.from_huggingface`.

  ## Parameters

  - `dataset_name` (String.t())
  - `args` (term())
  - `input_keys` ({String.t()} keyword-only default: ())
  - `fields` (term() keyword-only default: None)
  - `kwargs` (term())

  ## Returns

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

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

  @doc """
  Python method `DataLoader.from_json`.

  ## Parameters

  - `file_path` (String.t())
  - `fields` (term() default: None)
  - `input_keys` ({String.t()} default: ())

  ## Returns

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

  @doc """
  Python method `DataLoader.from_pandas`.

  ## Parameters

  - `df` (term())
  - `fields` (term() default: None)
  - `input_keys` ({String.t()} default: ())

  ## Returns

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

  @doc """
  Python method `DataLoader.from_parquet`.

  ## Parameters

  - `file_path` (String.t())
  - `fields` (term() default: None)
  - `input_keys` ({String.t()} default: ())

  ## Returns

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

  @doc """
  Python method `DataLoader.from_rm`.

  ## Parameters

  - `num_samples` (integer())
  - `fields` (list(String.t()))
  - `input_keys` (list(String.t()))

  ## Returns

  - `list(term())`
  """
  @spec from_rm(SnakeBridge.Ref.t(), integer(), list(String.t()), list(String.t()), keyword()) ::
          {:ok, list(term())} | {:error, Snakepit.Error.t()}
  def from_rm(ref, num_samples, fields, input_keys, opts \\ []) do
    SnakeBridge.Runtime.call_method(ref, :from_rm, [num_samples, fields, input_keys], opts)
  end

  @doc """
  Python method `DataLoader.prepare_by_seed`.

  ## Parameters

  - `train_seeds` (term() default: None)
  - `train_size` (integer() default: 16)
  - `dev_size` (integer() default: 1000)
  - `divide_eval_per_seed` (boolean() default: True)
  - `eval_seed` (integer() default: 2023)
  - `kwargs` (term())

  ## Returns

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

  @doc """
  Python method `DataLoader.reset_seeds`.

  ## Parameters

  - `train_seed` (term() default: None)
  - `train_size` (term() default: None)
  - `eval_seed` (term() default: None)
  - `dev_size` (term() default: None)
  - `test_size` (term() default: None)

  ## Returns

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

  @doc """
  Python method `DataLoader.sample`.

  ## Parameters

  - `dataset` (list(term()))
  - `n` (integer())
  - `args` (term())
  - `kwargs` (term())

  ## Returns

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

  @doc """
  Python method `DataLoader.train_test_split`.

  ## Parameters

  - `dataset` (list(term()))
  - `train_size` (term() default: 0.75)
  - `test_size` (term() default: None)
  - `random_state` (term() default: None)

  ## Returns

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

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

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

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