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 math
import torch.nn as nn
import torch.utils.data
import torch.nn.parallel
import torch.optim
import torch.utils.data.distributed
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_relu_0(in_out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, xmask)
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tl.store(in_out_ptr0 + x0, tmp2, xmask)


@triton.jit
def triton_poi_fused_add_relu_threshold_backward_1(in_ptr0, in_ptr1,
    out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + x2, xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr1 + x2, tmp6, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_2, (4, 4, 2, 2), (16, 4, 2, 1))
    assert_size_stride(primals_3, (4, 4, 3, 3), (36, 9, 3, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_2, primals_1, stride=(2, 
            2), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 1, 1), (4, 1, 1, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_relu_0[grid(16)](buf1, 16, XBLOCK=16, num_warps=1,
            num_stages=1)
        buf2 = extern_kernels.convolution(buf1, primals_3, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 4, 1, 1), (4, 1, 1, 1))
        buf3 = empty_strided_cuda((4, 4, 2, 2), (16, 4, 2, 1), torch.float32)
        buf4 = empty_strided_cuda((4, 4, 2, 2), (16, 4, 2, 1), torch.bool)
        triton_poi_fused_add_relu_threshold_backward_1[grid(64)](buf2,
            primals_2, buf3, buf4, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del buf2
    return buf3, primals_1, primals_2, primals_3, buf1, buf4


def init_layer(L):
    if isinstance(L, nn.Conv2d):
        n = L.kernel_size[0] * L.kernel_size[1] * L.out_channels
        L.weight.data.normal_(0, math.sqrt(2.0 / float(n)))
    elif isinstance(L, nn.BatchNorm2d):
        L.weight.data.fill_(1)
        L.bias.data.fill_(0)


class SimpleBlockNew(nn.Module):
    maml = False

    def __init__(self, indim, outdim, half_res):
        super(SimpleBlockNew, self).__init__()
        self.indim = indim
        self.outdim = outdim
        self.C1 = nn.Conv2d(indim, outdim, kernel_size=3, stride=2 if
            half_res else 1, padding=1, bias=False)
        self.BN1 = nn.Identity()
        self.C2 = nn.Conv2d(outdim, outdim, kernel_size=3, padding=1, bias=
            False)
        self.BN2 = nn.Identity()
        self.relu1 = nn.ReLU(inplace=True)
        self.relu2 = nn.ReLU(inplace=True)
        self.parametrized_layers = [self.C1, self.C2, self.BN1, self.BN2]
        self.half_res = half_res
        if indim != outdim:
            self.shortcut = nn.Conv2d(indim, outdim, 1, 2 if half_res else 
                1, bias=False)
            self.BNshortcut = nn.Identity()
            self.parametrized_layers.append(self.shortcut)
            self.parametrized_layers.append(self.BNshortcut)
            self.shortcut_type = '1x1'
        else:
            self.shortcut_type = 'identity'
        for layer in self.parametrized_layers:
            init_layer(layer)

    def forward(self, input_0):
        primals_1 = self.C1.weight
        primals_3 = self.C2.weight
        primals_2 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]


Measured latency_ms: 0.067891