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.nn.functional as F
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 = 256
    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_avg_pool2d_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 2
    x1 = xindex // 2
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (2 * x0 + 8 * x1), xmask, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 8 * x1), xmask, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr0 + (4 + 2 * x0 + 8 * x1), xmask, eviction_policy=
        'evict_last')
    tmp5 = tl.load(in_ptr0 + (5 + 2 * x0 + 8 * x1), xmask, eviction_policy=
        'evict_last')
    tmp2 = tmp1 + tmp0
    tmp4 = tmp3 + tmp2
    tmp6 = tmp5 + tmp4
    tmp7 = 0.25
    tmp8 = tmp6 * tmp7
    tl.store(out_ptr0 + x2, tmp8, xmask)


@triton.jit
def triton_poi_fused_add_avg_pool2d_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 2
    x1 = xindex // 2
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (2 * x0 + 8 * x1), xmask, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 8 * x1), xmask, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr0 + (4 + 2 * x0 + 8 * x1), xmask, eviction_policy=
        'evict_last')
    tmp5 = tl.load(in_ptr0 + (5 + 2 * x0 + 8 * x1), xmask, eviction_policy=
        'evict_last')
    tmp9 = tl.load(in_out_ptr0 + x2, xmask)
    tmp2 = tmp1 + tmp0
    tmp4 = tmp3 + tmp2
    tmp6 = tmp5 + tmp4
    tmp7 = 0.25
    tmp8 = tmp6 * tmp7
    tmp10 = tmp8 + tmp9
    tl.store(in_out_ptr0 + x2, tmp10, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_3, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_4, (4, 4, 1, 1), (4, 1, 1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(1, 
            1), padding=(1, 1), 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 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_relu_0[grid(256)](buf1, 256, XBLOCK=128, num_warps
            =4, 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, 4, 4), (64, 16, 4, 1))
        buf3 = empty_strided_cuda((4, 4, 2, 2), (16, 4, 2, 1), torch.float32)
        triton_poi_fused_avg_pool2d_1[grid(64)](primals_1, buf3, 64, XBLOCK
            =64, num_warps=1, num_stages=1)
        buf4 = extern_kernels.convolution(buf3, primals_4, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 4, 2, 2), (16, 4, 2, 1))
        buf5 = buf4
        del buf4
        triton_poi_fused_add_avg_pool2d_2[grid(64)](buf5, buf2, 64, XBLOCK=
            64, num_warps=1, num_stages=1)
    return buf5, primals_1, primals_2, primals_3, primals_4, buf1, buf2, buf3


def _downsample(x):
    return F.avg_pool2d(x, 2)


class OptimizedBlockNew(nn.Module):

    def __init__(self, in_channels, out_channels, ksize=3, pad=1,
        activation=F.relu, bn=False):
        super(OptimizedBlockNew, self).__init__()
        self.activation = activation
        self.bn = bn
        self.c1 = nn.Conv2d(in_channels, out_channels, ksize, 1, pad, bias=
            False)
        nn.init.xavier_uniform_(self.c1.weight, gain=math.sqrt(2.0))
        self.c2 = nn.Conv2d(out_channels, out_channels, ksize, 1, pad, bias
            =False)
        nn.init.xavier_uniform_(self.c2.weight, gain=math.sqrt(2.0))
        self.c_sc = nn.Conv2d(in_channels, out_channels, 1, 1, 0, bias=False)
        nn.init.xavier_uniform_(self.c_sc.weight, gain=1.0)
        if self.bn:
            self.b1 = nn.BatchNorm2d(out_channels)
            nn.init.constant_(self.b1.weight, 1.0)
            self.b2 = nn.BatchNorm2d(out_channels)
            nn.init.constant_(self.b2.weight, 1.0)
            self.b_sc = nn.BatchNorm2d(out_channels)
            nn.init.constant_(self.b_sc.weight, 1.0)

    def residual(self, x):
        h = x
        h = self.b1(self.c1(h)) if self.bn else self.c1(h)
        h = self.activation(h)
        h = self.b2(self.c2(h)) if self.bn else self.c2(h)
        h = _downsample(h)
        return h

    def shortcut(self, x):
        return self.b_sc(self.c_sc(_downsample(x))) if self.bn else self.c_sc(
            _downsample(x))

    def forward(self, input_0):
        primals_2 = self.c1.weight
        primals_3 = self.c2.weight
        primals_4 = self.c_sc.weight
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]


Measured latency_ms: 0.061776