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_cat_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex // 16 % 8
    x0 = xindex % 4
    x1 = xindex // 4 % 4
    x3 = xindex // 128
    x4 = xindex % 16
    x5 = xindex
    tmp0 = x2
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (20 + x0 + 9 * x1 + 81 * x2 + 324 * x3), tmp4 &
        xmask, other=0.0)
    tmp6 = tl.load(in_ptr1 + x2, tmp4 & xmask, eviction_policy='evict_last',
        other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full(tmp7.shape, 0.0, tmp7.dtype)
    tmp9 = tl.where(tmp4, tmp7, tmp8)
    tmp10 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp13 = tl.load(in_ptr2 + (x4 + 16 * (-4 + x2) + 64 * x3), tmp10 &
        xmask, other=0.0)
    tmp14 = tl.where(tmp4, tmp9, tmp13)
    tl.store(out_ptr0 + x5, tmp14, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(2, 
            2), padding=(0, 0), dilation=(1, 1), transposed=True,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 9, 9), (324, 81, 9, 1))
        buf1 = empty_strided_cuda((4, 8, 4, 4), (128, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(512)](buf0, primals_2, primals_4, buf1,
            512, XBLOCK=256, num_warps=4, num_stages=1)
        del buf0
        del primals_2
        del primals_4
    return buf1, primals_1, primals_3


def center_crop(layer, max_height, max_width):
    _, _, h, w = layer.size()
    xy1 = (w - max_width) // 2
    xy2 = (h - max_height) // 2
    return layer[:, :, xy2:xy2 + max_height, xy1:xy1 + max_width]


class TransitionUpNew(nn.Module):
    """
    Scale the resolution up by transposed convolution
    """

    def __init__(self, in_channels: 'int', out_channels: 'int', scale: 'int'=2
        ):
        super().__init__()
        if scale == 2:
            self.convTrans = nn.ConvTranspose2d(in_channels=in_channels,
                out_channels=out_channels, kernel_size=3, stride=2, padding
                =0, bias=True)
        elif scale == 4:
            self.convTrans = nn.Sequential(nn.ConvTranspose2d(in_channels=
                in_channels, out_channels=out_channels, kernel_size=3,
                stride=2, padding=0, bias=False), nn.BatchNorm2d(
                out_channels), nn.ConvTranspose2d(in_channels=out_channels,
                out_channels=out_channels, kernel_size=3, stride=2, padding
                =0, bias=True))

    def forward(self, input_0, input_1):
        primals_1 = self.convTrans.weight
        primals_2 = self.convTrans.bias
        primals_3 = input_0
        primals_4 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]


Measured latency_ms: 0.025808