import threading
import torch


def get_a_var(obj):
    if isinstance(obj, torch.Tensor):
        return obj

    if isinstance(obj, list) or isinstance(obj, tuple):
        for result in map(get_a_var, obj):
            if isinstance(result, torch.Tensor):
                return result
    if isinstance(obj, dict):
        for result in map(get_a_var, obj.items()):
            if isinstance(result, torch.Tensor):
                return result
    return None


def parallel_apply(modules, inputs, kwargs_tup=None, devices=None):
    assert len(modules) == len(inputs)
    if kwargs_tup is not None:
        assert len(modules) == len(kwargs_tup)
    else:
        kwargs_tup = ({},) * len(modules)
    if devices is not None:
        assert len(modules) == len(devices)
    else:
        devices = [None] * len(modules)

    lock = threading.Lock()
    results = {}
    grad_enabled = torch.is_grad_enabled()

    def _worker(i, module, input, kwargs, device=None):
        torch.set_grad_enabled(grad_enabled)
        if device is None:
            device = get_a_var(input).get_device()
        try:
            with torch.cuda.device(device):
                output = module(*input, **kwargs)
            with lock:
                results[i] = output
        except Exception as e:
            with lock:
                results[i] = e

    if len(modules) > 1:
        threads = [threading.Thread(target=_worker,
                                    args=(i, module, input, kwargs, device))
                   for i, (module, input, kwargs, device) in
                   enumerate(zip(modules, inputs, kwargs_tup, devices))]

        for thread in threads:
            thread.start()
        for thread in threads:
            thread.join()
    else:
        _worker(0, modules[0], inputs[0], kwargs_tup[0], devices[0])

    outputs = []
    for i in range(len(inputs)):
        output = results[i]
        if isinstance(output, Exception):
            raise output
        outputs.append(output)
    return outputs