import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_pixel_shuffle_0(in_ptr0, out_ptr0, ynumel, xnumel,
    YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 128
    xnumel = 2
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x4 = xindex
    y0 = yindex % 4
    y1 = yindex // 4 % 2
    y2 = yindex // 8 % 4
    y3 = yindex // 32
    y5 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * y2 + 16 * x4 + 32 * y1 + 64 * y3), 
        xmask & ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x4 + 2 * y5), tmp0, xmask & ymask)


def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_2, primals_1, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 4, 4), (64, 16, 4, 1))
        buf1 = empty_strided_cuda((4, 1, 4, 2, 4, 2), (64, 64, 16, 8, 2, 1),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused_pixel_shuffle_0[grid(128, 2)](buf0, buf1, 128, 2,
            XBLOCK=2, YBLOCK=64, num_warps=4, num_stages=1)
        del buf0
    return reinterpret_tensor(buf1, (4, 1, 8, 8), (64, 64, 8, 1), 0
        ), primals_1, primals_2


def conv1x1(in_planes, out_planes, stride=1):
    """1x1 convolution"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride,
        bias=False)


class PixelShuffleICNRNew(nn.Module):

    def __init__(self, in_planes, out_planes, scale=2):
        super().__init__()
        self.conv = conv1x1(in_planes, out_planes)
        self.shuffle = nn.PixelShuffle(scale)
        kernel = self.ICNR(self.conv.weight, upscale_factor=scale)
        self.conv.weight.data.copy_(kernel)

    @staticmethod
    def ICNR(tensor, upscale_factor=2, inizializer=nn.init.kaiming_normal_):
        """Fills the input Tensor or Variable with values according to the method
        described in "Checkerboard artifact free sub-pixel convolution" https://arxiv.org/abs/1707.02937
        Andrew Aitken et al. (2017), this inizialization should be used in the
        last convolutional layer before a PixelShuffle operation
        :param tensor: an n-dimensional torch.Tensor or autograd.Variable
        :param upscale_factor: factor to increase spatial resolution by
        :param inizializer: inizializer to be used for sub_kernel inizialization
        """
        new_shape = [int(tensor.shape[0] / upscale_factor ** 2)] + list(tensor
            .shape[1:])
        sub_kernel = torch.zeros(new_shape)
        sub_kernel = inizializer(sub_kernel)
        sub_kernel = sub_kernel.transpose(0, 1)
        sub_kernel = sub_kernel.contiguous().view(sub_kernel.shape[0],
            sub_kernel.shape[1], -1)
        kernel = sub_kernel.repeat(1, 1, upscale_factor ** 2)
        transposed_shape = [tensor.shape[1]] + [tensor.shape[0]] + list(tensor
            .shape[2:])
        kernel = kernel.contiguous().view(transposed_shape)
        kernel = kernel.transpose(0, 1)
        return kernel

    def forward(self, input_0):
        primals_1 = self.conv.weight
        primals_2 = input_0
        output = call([primals_1, primals_2])
        return output[0]


Measured latency_ms: 0.024801