"""Async API
This module contains the API for parallelism in TorchScript, notably:
    * torch.jit.fork
    * torch.jit.wait

This is not intended to be imported directly; please use the exposed
functionalities in `torch.jit`.
"""

import torch

from torch.utils import set_module
from torch.jit._builtins import _register_builtin
from torch._jit_internal import Future

set_module(Future, "torch.jit")


def fork(func, *args, **kwargs):
    """
    Creates an asynchronous task executing `func` and a reference to the value
    of the result of this execution. `fork` will return immediately,
    so the return value of `func` may not have been computed yet. To force completion
    of the task and access the return value invoke `torch.jit.wait` on the Future. `fork` invoked
    with a `func` which returns `T` is typed as `torch.jit.Future[T]`. `fork` calls can be arbitrarily
    nested, and may be invoked with positional and keyword arguments.
    Asynchronous execution will only occur when run in TorchScript. If run in pure python,
    `fork` will not execute in parallel. `fork` will also not execute in parallel when invoked
    while tracing, however the `fork` and `wait` calls will be captured in the exported IR Graph.
    Warning:
        `fork` tasks will execute non-deterministicly. We recommend only spawning
        parallel fork tasks for pure functions that do not modify their inputs,
        module attributes, or global state.
    Arguments:
        func (callable or torch.nn.Module):  A Python function or `torch.nn.Module`
            that will be invoked. If executed in TorchScript, it will execute asynchronously,
            otherwise it will not. Traced invocations of fork will be captured in the IR.
        ``*args``, ``**kwargs``: arguments to invoke `func` with.
    Returns:
        `torch.jit.Future[T]`: a reference to the execution of `func`. The value `T`
        can only be accessed by forcing completion of `func` through `torch.jit.wait`.

    Example (fork a free function):

    .. testcode::
        import torch
        from torch import Tensor
        def foo(a : Tensor, b : int) -> Tensor:
            return a + b
        def bar(a):
            fut : torch.jit.Future[Tensor] = torch.jit.fork(foo, a, b=2)
            return torch.jit.wait(fut)
        script_bar = torch.jit.script(bar)
        input = torch.tensor(2)
        # only the scripted version executes asynchronously
        assert script_bar(input) == bar(input)
        # trace is not run asynchronously, but fork is captured in IR
        graph = torch.jit.trace(bar, (input,)).graph
        assert "fork" in str(graph)

    Example (fork a module method):

    .. testcode::
        import torch
        from torch import Tensor
        class SubMod(torch.nn.Module):
            def forward(self, a: Tensor, b : int):
                return a + b
        class Mod(torch.nn.Module):
            def __init__(self):
                super(self).__init__()
                self.mod = SubMod()
            def forward(self, input):
                fut = torch.jit.fork(self.mod, a, b=2)
                return torch.jit.wait(fut)
        input = torch.tensor(2)
        mod = Mod()
        assert mod(input) == torch.jit.script(mod).forward(input)
    """
    return torch._C.fork(func, *args, **kwargs)


def wait(future):
    """
    Forces completion of a `torch.jit.Future[T]` asynchronous task, returning the
    result of the task. See :func:`~fork` for docs and examples.
    Arguments:
        func (torch.jit.Future[T]): an asynchronous task reference, created through `torch.jit.fork`
    Returns:
        `T`: the return value of the the completed task
    """
    return torch._C.wait(future)


_register_builtin(wait, "aten::wait")
