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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_poi_fused__unsafe_index_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 8
    x1 = xindex // 8
    x2 = xindex
    tmp0 = x0
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 * tmp2
    tmp4 = tmp3.to(tl.int32)
    tmp5 = tl.load(in_ptr0 + (tmp4 + 4 * x1), xmask, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + x2, tmp5, xmask)


@triton.jit
def triton_poi_fused_convolution_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 8 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 1), (4, 1, 1))
    assert_size_stride(primals_3, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 8), (32, 8, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__unsafe_index_0[grid(128)](primals_1, buf0, 128,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_1
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1,),
            padding=(0,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 8), (32, 8, 1))
        buf2 = buf1
        del buf1
        triton_poi_fused_convolution_1[grid(128)](buf2, primals_3, 128,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_3
    return buf2, primals_2, buf0


class UpsampleBlockNew(nn.Module):
    """
    Defines upsampling block performed using bilinear
    or nearest-neigbor interpolation followed by 1-by-1 convolution
    (the latter can be used to reduce a number of feature channels)

    Args:
        ndim:
            Data dimensionality (1D or 2D)
        input_channels:
            Number of input channels for the block
        output_channels:
            Number of the output channels for the block
        scale_factor:
            Scale factor for upsampling
        mode:
            Upsampling mode. Select between "bilinear" and "nearest"
    """

    def __init__(self, ndim: 'int', input_channels: 'int', output_channels:
        'int', scale_factor: 'int'=2, mode: 'str'='bilinear') ->None:
        """
        Initializes module parameters
        """
        super(UpsampleBlockNew, self).__init__()
        if not any([mode == 'bilinear', mode == 'nearest']):
            raise NotImplementedError(
                "use 'bilinear' or 'nearest' for upsampling mode")
        if not 0 < ndim < 3:
            raise AssertionError('ndim must be equal to 1 or 2')
        conv = nn.Conv2d if ndim == 2 else nn.Conv1d
        self.scale_factor = scale_factor
        self.mode = mode if ndim == 2 else 'nearest'
        self.conv = conv(input_channels, output_channels, kernel_size=1,
            stride=1, padding=0)

    def forward(self, input_0):
        primals_2 = self.conv.weight
        primals_3 = self.conv.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]


Measured latency_ms: 0.014851