lite 🔗

def tflite_model_delete(self, resource: Resource) -> None:
    """Performs the Tensorflow Lite model deletion operation.

    Wraps the `vaccel_tflite_session_delete()` C operation.

    Args:
        resource: A resource with the model to unload.

    Returns:
        The status of the operation execution.

    Raises:
        FFIError: If the C operation fails.
    """
    ret = lib.vaccel_tflite_session_delete(
        self._c_ptr_or_raise, resource._c_ptr
    )
    if ret != 0:
        raise FFIError(ret, "Tensorflow Lite model delete failed")

def tflite_model_load(self, resource: Resource) -> None:
    """Performs the Tensorflow Lite model loading operation.

    Wraps the `vaccel_tflite_session_load()` C operation.

    Args:
        resource: A resource with the model to load.

    Raises:
        FFIError: If the C operation fails.
    """
    ret = lib.vaccel_tflite_session_load(
        self._c_ptr_or_raise, resource._c_ptr
    )
    if ret != 0:
        raise FFIError(ret, "Tensorflow Lite model loading failed")

def tflite_model_run(
    self,
    resource: Resource,
    in_tensors: list[Tensor],
    nr_out_tensors: int = 1,
) -> (list[Tensor], int):
    """Performs the Tensorflow Lite model run operation.

    Wraps the `vaccel_tflite_session_run()` C operation.

    Args:
        resource: A resource with the model to run.
        in_tensors: The input tensors for the inference.
        nr_out_tensors: The number of output tensors. Defaults to 1.

    Returns:
        A tuple containing:
            - The output tensors
            - The status of the operation execution.

    Raises:
        FFIError: If the C operation fails.
    """
    c_in_tensors = CList.from_ptrs(in_tensors)
    c_out_tensors = CList.from_ptrs([Tensor.empty()] * nr_out_tensors)
    status = CInt(0, "uint8_t")

    ret = lib.vaccel_tflite_session_run(
        self._c_ptr_or_raise,
        resource._c_ptr,
        c_in_tensors._c_ptr,
        len(c_in_tensors),
        c_out_tensors._c_ptr,
        len(c_out_tensors),
        status._c_ptr,
    )
    if ret != 0:
        raise FFIError(ret, "Tensorflow Lite model run failed")

    out_tensors = [Tensor.from_c_obj(t) for t in c_out_tensors.value]
    return (out_tensors, status.value)

def as_bytelike(self) -> bytes | bytearray | memoryview:
    """Returns the tensor data buffer as a byte-like object.

    Returns:
        The data of the tensor as a byte-like object.
    """
    if self._c_data is not None:
        if isinstance(self._c_data, CBytes):
            return self._c_data.value
        return self._c_data.as_memoryview()
    return CBytes.from_c_obj(
        self._c_ptr_or_raise.data, self._c_ptr_or_raise.size
    ).value

def as_memoryview(self) -> memoryview:
    """Returns the tensor data buffer as memoryview.

    Returns:
        The data of the tensor as memoryview.
    """
    data = self.as_bytelike()
    return data if isinstance(data, memoryview) else memoryview(data)

def as_numpy(self) -> "np.ndarray":
    """Returns the tensor data buffer as a NumPy array.

    Returns:
        The data of the tensor as a NumPy array.
    """
    if not HAS_NUMPY:
        msg = "NumPy is not available"
        raise NotImplementedError(msg)

    if isinstance(self._c_data, CNumpyArray):
        return self._c_data.value

    dtype = TensorTypeMapper.type_to_numpy(self.data_type)
    return CNumpyArray.from_c_obj(
        self._c_ptr_or_raise.data,
        self._c_ptr_or_raise.size,
        self.shape,
        dtype,
    ).value

@classmethod
def empty(cls) -> "Tensor":
    """Initializes a new empty `Tensor` object.

    The object has a NULL pointer in place of the C struct.

    Returns:
        A new `Tensor` object
    """
    inst = cls.__new__(cls)
    inst._dims = None
    inst._data = None
    inst._data_type = None
    inst._c_data = None
    inst._c_obj_ptr = ffi.new("struct vaccel_tflite_tensor **")
    inst._c_obj = inst._c_obj_ptr[0]
    inst._c_obj_ptr[0] = ffi.NULL
    inst._c_size = ffi.sizeof("struct vaccel_tflite_tensor *")
    return inst

@classmethod
def from_buffer(
    cls,
    dims: list[int],
    data_type: TensorType,
    data: bytes | bytearray | memoryview,
) -> "Tensor":
    """Initializes a new `Tensor` object from byte-like data.

    Args:
        dims: The dims to be passed to the C struct.
        data_type: The data_type to be passed to the C struct.
        data: The data to be passed to the C struct.

    Returns:
        A new `Tensor` object
    """
    inst = cls.__new__(cls)
    inst._dims = dims
    inst._data = data
    inst._data_type = data_type
    inst._c_data = CBytes(inst._data)
    inst._c_obj_ptr = ffi.NULL
    inst._c_obj_data = inst._c_data._c_ptr
    super().__init__(inst)
    return inst

@classmethod
def from_c_obj(cls, c_obj: ffi.CData) -> "Tensor":
    """Initializes a new `Tensor` object from an existing C struct.

    Args:
        c_obj: A pointer to a `struct vaccel_tflite_tensor` C object.

    Returns:
        A new `Tensor` object
    """
    type_str = ffi.getctype(ffi.typeof(c_obj))
    if type_str != "struct vaccel_tflite_tensor *":
        msg = f"Expected 'struct vaccel_tflite_tensor *', got '{type_str}'"
        raise TypeError(msg)

    inst = cls.__new__(cls)
    inst._dims = None
    inst._data = None
    inst._data_type = None
    inst._c_data = None
    inst._c_obj_ptr = ffi.NULL
    inst._c_obj_data = ffi.NULL
    inst._c_obj = c_obj
    inst._c_size = ffi.sizeof(inst._c_obj)
    return inst

@classmethod
def from_numpy(cls, data: "np.ndarray") -> "Tensor":
    """Initializes a new `Tensor` object from a NumPy array.

    Args:
        data: The NumPy array containing the tensor data.

    Returns:
        A new `Tensor` object

    Raises:
        NotImplementedError: If NumPy is not installed.
    """
    if not HAS_NUMPY:
        msg = "NumPy is not available"
        raise NotImplementedError(msg)

    inst = cls.__new__(cls)
    inst._dims = list(data.shape)
    inst._data = data
    inst._data_type = TensorTypeMapper.type_from_numpy(inst._data.dtype)
    inst._c_data = CNumpyArray(inst._data)
    inst._c_obj_ptr = ffi.NULL
    inst._c_obj_data = inst._c_data._c_ptr
    super().__init__(inst)
    return inst

def to_bytes(self) -> bytes:
    """Returns the tensor data buffer as bytes.

    Returns:
        The data of the tensor as bytes.
    """
    data = self.as_bytelike()
    return data if isinstance(data, bytes) else bytes(data)