Input module

@synalinks_export(["synalinks.modules.AudioInput", "synalinks.AudioInput"])
def AudioInput(
    optional=False,
    name=None,
):
    """Instantiate a symbolic multimodal (audio) input for a `Program`.

    The audio sibling of `ImageInput`: it declares a `ChatMessages` input
    branch into which you put a user `ChatMessage` whose content mixes the
    instruction text with one or more `synalinks.Audio` parts.

    Args:
        optional (bool): Whether the input is optional or not.
            An optional input can accept `None` values.
        name (string): Optional name string for the module.
            Should be unique in a program (do not reuse the same name twice).
            It will be autogenerated if it isn't provided.

    Returns:
        (SymbolicDataModel): A `ChatMessages`-shaped symbolic data model.
    """
    return Input(data_model=ChatMessages, optional=optional, name=name)

@synalinks_export(["synalinks.modules.ImageInput", "synalinks.ImageInput"])
def ImageInput(
    optional=False,
    name=None,
):
    """Instantiate a symbolic multimodal (image) input for a `Program`.

    A modality is a content part of a `Message`, so an image input is just a
    chat input: this declares a `ChatMessages` input branch into which you put
    a user `ChatMessage` whose content mixes the question text with one or more
    `synalinks.Image` parts.

    Example:

    ```python
    import synalinks

    inputs = synalinks.ImageInput()
    outputs = await synalinks.Generator(
        data_model=Answer,
        language_model=language_model,
    )(inputs)
    program = synalinks.Program(inputs=inputs, outputs=outputs)

    result = await program(
        synalinks.ChatMessages(
            messages=[
                synalinks.ChatMessage(
                    role="user",
                    content=[
                        "What is in this picture?",
                        synalinks.Image(url="https://example.com/cat.png"),
                    ],
                )
            ]
        )
    )
    ```

    Args:
        optional (bool): Whether the input is optional or not.
            An optional input can accept `None` values.
        name (string): Optional name string for the module.
            Should be unique in a program (do not reuse the same name twice).
            It will be autogenerated if it isn't provided.

    Returns:
        (SymbolicDataModel): A `ChatMessages`-shaped symbolic data model.
    """
    return Input(data_model=ChatMessages, optional=optional, name=name)

@synalinks_export(["synalinks.modules.Input", "synalinks.Input"])
def Input(
    schema=None,
    data_model=None,
    optional=False,
    name=None,
):
    """Used to instantiate a `SymbolicDataModel`.

    A `SymbolicDataModel` is a symbolic data model-like object, which we augment with
    certain attributes that allow us to build a Synalinks `Program` just by knowing the
    inputs and outputs of the program (similar to Keras symbolic tensor).

    Example:

    ```python
    import synalinks

    class Query(synalinks.DataModel):
        query: str

    inputs = synalinks.Input(data_model=Query)

    # You can also create it using a JSON schema like this:

    inputs = synalinks.Input(schema=Query.get_schema())

    # Or using a symbolic datamodel:

    inputs = synalinks.Input(data_model=Query.to_symbolic_data_model())
    ```

    Args:
        schema (dict): A Json schema of the data_model.
            If not provided uses the `data_model` argument.
        data_model (DataModel): Optional existing data model to wrap into
            the `Input` layer. If set, the module will use this data_model rather
            than creating a new placeholder data model.
        optional (bool): Whether the input is optional or not.
            An optional input can accept `None` values.
        name (string): Optional name string for the module.
            Should be unique in a program (do not reuse the same name twice).
            It will be autogenerated if it isn't provided.

    Returns:
        (SymbolicDataModel): The symbolic data model corresponding to
            the given data model/schema.
    """
    module = InputModule(
        schema=schema,
        input_data_model=data_model.to_symbolic_data_model() if data_model else None,
        optional=optional,
        name=name,
    )
    return module.output