"""functions for parallel"""
import torch
from torch.nn.parallel._functions import _get_stream


def scatter(input, devices, streams=None):
    """Scatters tensor across multiple GPUs.
    """
    if streams is None:
        streams = [None] * len(devices)

    if isinstance(input, list):
        chunk_size = (len(input) - 1) // len(devices) + 1
        outputs = [
            scatter(input[i], [devices[i // chunk_size]],
                    [streams[i // chunk_size]]) for i in range(len(input))
        ]
        return outputs
    elif isinstance(input, torch.Tensor):
        output = input.contiguous()
        # TODO: copy to a pinned buffer first (if copying from CPU)
        stream = streams[0] if output.numel() > 0 else None
        with torch.cuda.device(devices[0]), torch.cuda.stream(stream):
            output = output.cuda(devices[0], non_blocking=True)
        return output
    else:
        raise Exception('Unknown type {}.'.format(type(input)))


def synchronize_stream(output, devices, streams):
    """[summary]

    Args:
        output ([type]): [description]
        devices ([type]): [description]
        streams ([type]): [description]

    Raises:
        Exception: [description]
    """
    if isinstance(output, list):
        chunk_size = len(output) // len(devices)
        for i in range(len(devices)):
            for j in range(chunk_size):
                synchronize_stream(output[i * chunk_size + j], [devices[i]],
                                   [streams[i]])
    elif isinstance(output, torch.Tensor):
        if output.numel() != 0:
            with torch.cuda.device(devices[0]):
                main_stream = torch.cuda.current_stream()
                main_stream.wait_stream(streams[0])
                output.record_stream(main_stream)
    else:
        raise Exception('Unknown type {}.'.format(type(output)))


def get_input_device(input):
    """[summary]

    Args:
        input ([type]): [description]

    Raises:
        Exception: [description]

    Returns:
        [type]: [description]
    """
    if isinstance(input, list):
        for item in input:
            input_device = get_input_device(item)
            if input_device != -1:
                return input_device
        return -1
    elif isinstance(input, torch.Tensor):
        return input.get_device() if input.is_cuda else -1
    else:
        raise Exception('Unknown type {}.'.format(type(input)))


class Scatter(object):
    """[summary]

    Args:
        object ([type]): [description]

    Returns:
        [type]: [description]
    """

    @staticmethod
    def forward(target_gpus, input):
        """[summary]

        Args:
            target_gpus ([type]): [description]
            input ([type]): [description]

        Returns:
            [type]: [description]
        """
        input_device = get_input_device(input)
        streams = None
        if input_device == -1:
            # Perform CPU to GPU copies in a background stream
            streams = [_get_stream(device) for device in target_gpus]

        outputs = scatter(input, target_gpus, streams)
        # Synchronize with the copy stream
        if streams is not None:
            synchronize_stream(outputs, target_gpus, streams)

        return tuple(outputs)
