defmodule Ortex do
  @moduledoc """
  Documentation for `Ortex`.

  `Ortex` is an Elixir wrapper around [ONNX Runtime](https://onnxruntime.ai/) using
  [Rustler](https://hexdocs.pm/rustler) and [ORT](https://github.com/pykeio/ort).
  """

  @doc """
  Load an `Ortex.Model` from disk. Optionally pass the execution providers as a list
  of descending priority and graph optimization level 1-3. Any graph optimization level
  beyond the range of 1-3 will disable graph optimization.

  ## Examples

      iex> Ortex.load("./models/resnet50.onnx")
      iex> Ortex.load("./models/resnet50.onnx", [:cuda, :cpu])
      iex> Ortex.load("./models/resnet50.onnx", [:cpu], 0)

  """
  defdelegate load(path, eps \\ [:cpu], opt \\ 3), to: Ortex.Model

  @doc """
  Run a forward pass through a model.

  This takes a model and tuple of `Nx.Tensors`,
  optionally transfers them to the `Ortex.Backend` if they aren't there already,
  and runs a forward pass through the model. This will return a tuple of `Ortex.Backend`
  tensors, it's up to the user to transfer these back to another backend if additional
  ops are required.

  If there is only one input you can optionally pass a bare tensor rather than a tuple.

  ## Examples

      iex> model = Ortex.load("./models/resnet50.onnx")
      iex> {%Nx.Tensor{shape: {1, 1000}}} = Ortex.run(
      ...>    model, Nx.broadcast(0.0, {1, 3, 224, 224}))


      iex> model = Ortex.load("./models/tinymodel.onnx")
      iex> {%Nx.Tensor{shape: {1, 10}},
      ...>  %Nx.Tensor{shape: {1, 10}},
      ...>  %Nx.Tensor{shape: {1, 10}}} = Ortex.run(
      ...>    model, {
      ...>      Nx.broadcast(0, {1, 100}) |> Nx.as_type(:s32),
      ...>      Nx.broadcast(0, {1, 100}) |> Nx.as_type(:f32)
      ...>    })
  """
  defdelegate run(model, tensors), to: Ortex.Model
end