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._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.optim
import torch.utils.data
import torch.backends.cudnn
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_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    xnumel = 9
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 64
    y1 = yindex // 64
    tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last'
        )
    tl.store(out_ptr0 + (y0 + 64 * x2 + 576 * y1), tmp0, xmask)


@triton.jit
def triton_poi_fused_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 256
    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]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    x2 = xindex
    y3 = yindex
    y0 = yindex % 64
    y1 = yindex // 64
    tmp0 = tl.load(in_ptr0 + (x2 + 4096 * y3), ymask, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + (y0 + 64 * x2 + 262144 * y1), tmp0, ymask)


@triton.jit
def triton_poi_fused_relu_threshold_backward_2(in_ptr0, in_ptr1, out_ptr0,
    out_ptr1, ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 256
    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]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    x2 = xindex % 128
    x3 = xindex // 128
    y0 = yindex % 64
    y1 = yindex // 64
    x4 = xindex
    y5 = yindex
    tmp0 = tl.load(in_ptr0 + (2 * (x3 % 2) + 4 * y0 + 256 * (x2 // 2) + 
        16384 * (x3 // 2) + 1048576 * y1 + x2 % 2), ymask, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr1 + (2 * (x3 % 2) + 4 * y0 + x2 % 2), ymask,
        eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1, 1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + (x4 + 16384 * y5), tmp4, ymask)
    tl.store(out_ptr1 + (y0 + 64 * x4 + 1048576 * y1), tmp6, ymask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (256, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_2, (256,), (1,))
    assert_size_stride(primals_3, (4, 64, 64, 64), (262144, 4096, 64, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((256, 64, 3, 3), (576, 1, 192, 64), torch
            .float32)
        get_raw_stream(0)
        triton_poi_fused_0[grid(16384, 9)](primals_1, buf0, 16384, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((4, 64, 64, 64), (262144, 1, 4096, 64),
            torch.float32)
        triton_poi_fused_1[grid(256, 4096)](primals_3, buf1, 256, 4096,
            XBLOCK=32, YBLOCK=32, num_warps=4, num_stages=1)
        del primals_3
        buf2 = extern_kernels.convolution(buf1, buf0, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 256, 64, 64), (1048576, 1, 16384, 256))
        buf3 = empty_strided_cuda((4, 64, 128, 128), (1048576, 16384, 128, 
            1), torch.float32)
        buf4 = empty_strided_cuda((4, 64, 128, 128), (1048576, 1, 8192, 64),
            torch.bool)
        triton_poi_fused_relu_threshold_backward_2[grid(256, 16384)](buf2,
            primals_2, buf3, buf4, 256, 16384, XBLOCK=16, YBLOCK=256,
            num_warps=8, num_stages=1)
        del buf2
        del primals_2
    return buf3, buf0, buf1, buf4


class UpsampleBlockNew(nn.Module):

    def __init__(self):
        super().__init__()
        self.conv = nn.Conv2d(64, 256, 3, 1, 1)
        self.shuffle = nn.PixelShuffle(2)
        self.relu = nn.ReLU()

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


Measured latency_ms: 0.192554