"""
Copyright (c) Microsoft Corporation.
Licensed under the MIT license.

A prefetch loader to speedup data loading
Modified from Nvidia Deep Learning Examples
(https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch).
"""
import torch


def move_to_cuda(batch):
    if isinstance(batch, torch.Tensor):
        return batch.cuda(non_blocking=True)
    elif isinstance(batch, list):
        new_batch = [move_to_cuda(t) for t in batch]
    elif isinstance(batch, tuple):
        new_batch = tuple(move_to_cuda(t) for t in batch)
    elif isinstance(batch, dict):
        new_batch = {n: move_to_cuda(t) for n, t in batch.items()}
    else:
        return batch
    return new_batch


def record_cuda_stream(batch):
    if isinstance(batch, torch.Tensor):
        batch.record_stream(torch.cuda.current_stream())
    elif isinstance(batch, list) or isinstance(batch, tuple):
        for t in batch:
            record_cuda_stream(t)
    elif isinstance(batch, dict):
        for t in batch.values():
            record_cuda_stream(t)
    else:
        pass


class PrefetchLoader(object):
    """
    overlap compute and cuda data transfer
    (copied and then modified from nvidia apex)
    """

    def __init__(self, loader):
        self.loader = loader
        self.stream = torch.cuda.Stream()

    def __iter__(self):
        loader_it = iter(self.loader)
        self.preload(loader_it)
        batch = self.next(loader_it)
        while batch is not None:
            yield batch
            batch = self.next(loader_it)

    def __len__(self):
        return len(self.loader)

    def preload(self, it):
        try:
            self.batch = next(it)
        except StopIteration:
            self.batch = None
            return
        # if record_stream() doesn't work, another option is to make sure
        # device inputs are created on the main stream.
        # self.next_input_gpu = torch.empty_like(self.next_input,
        #                                        device='cuda')
        # self.next_target_gpu = torch.empty_like(self.next_target,
        #                                         device='cuda')
        # Need to make sure the memory allocated for next_* is not still in use
        # by the main stream at the time we start copying to next_*:
        # self.stream.wait_stream(torch.cuda.current_stream())
        with torch.cuda.stream(self.stream):
            self.batch = move_to_cuda(self.batch)
            # more code for the alternative if record_stream() doesn't work:
            # copy_ will record the use of the pinned source tensor in this
            # side stream.
            # self.next_input_gpu.copy_(self.next_input, non_blocking=True)
            # self.next_target_gpu.copy_(self.next_target, non_blocking=True)
            # self.next_input = self.next_input_gpu
            # self.next_target = self.next_target_gpu

    def next(self, it):
        torch.cuda.current_stream().wait_stream(self.stream)
        batch = self.batch
        if batch is not None:
            record_cuda_stream(batch)
        self.preload(it)
        return batch

    def __getattr__(self, name):
        method = self.loader.__getattribute__(name)
        return method
