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
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_convolution_relu_0(in_ptr0, in_ptr1, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 61504
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 3844 % 4
    x0 = xindex % 3844
    x4 = xindex // 3844
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(out_ptr0 + (x0 + 3872 * x4), tmp4, xmask)


@triton.jit
def triton_poi_fused_avg_pool2d_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 6400
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 20
    x1 = xindex // 20 % 20
    x2 = xindex // 400
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (3 * x0 + 186 * x1 + 3872 * x2), xmask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 3 * x0 + 186 * x1 + 3872 * x2), xmask,
        eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + 3 * x0 + 186 * x1 + 3872 * x2), xmask,
        eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + 3 * x0 + 186 * x1 + 3872 * x2), xmask,
        eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (4 + 3 * x0 + 186 * x1 + 3872 * x2), xmask,
        eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (62 + 3 * x0 + 186 * x1 + 3872 * x2), xmask,
        eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (63 + 3 * x0 + 186 * x1 + 3872 * x2), xmask,
        eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr0 + (64 + 3 * x0 + 186 * x1 + 3872 * x2), xmask,
        eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr0 + (65 + 3 * x0 + 186 * x1 + 3872 * x2), xmask,
        eviction_policy='evict_last')
    tmp17 = tl.load(in_ptr0 + (66 + 3 * x0 + 186 * x1 + 3872 * x2), xmask,
        eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr0 + (124 + 3 * x0 + 186 * x1 + 3872 * x2), xmask,
        eviction_policy='evict_last')
    tmp21 = tl.load(in_ptr0 + (125 + 3 * x0 + 186 * x1 + 3872 * x2), xmask,
        eviction_policy='evict_last')
    tmp23 = tl.load(in_ptr0 + (126 + 3 * x0 + 186 * x1 + 3872 * x2), xmask,
        eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr0 + (127 + 3 * x0 + 186 * x1 + 3872 * x2), xmask,
        eviction_policy='evict_last')
    tmp27 = tl.load(in_ptr0 + (128 + 3 * x0 + 186 * x1 + 3872 * x2), xmask,
        eviction_policy='evict_last')
    tmp29 = tl.load(in_ptr0 + (186 + 3 * x0 + 186 * x1 + 3872 * x2), xmask,
        eviction_policy='evict_last')
    tmp31 = tl.load(in_ptr0 + (187 + 3 * x0 + 186 * x1 + 3872 * x2), xmask,
        eviction_policy='evict_last')
    tmp33 = tl.load(in_ptr0 + (188 + 3 * x0 + 186 * x1 + 3872 * x2), xmask,
        eviction_policy='evict_last')
    tmp35 = tl.load(in_ptr0 + (189 + 3 * x0 + 186 * x1 + 3872 * x2), xmask,
        eviction_policy='evict_last')
    tmp37 = tl.load(in_ptr0 + (190 + 3 * x0 + 186 * x1 + 3872 * x2), xmask,
        eviction_policy='evict_last')
    tmp39 = tl.load(in_ptr0 + (248 + 3 * x0 + 186 * x1 + 3872 * x2), xmask,
        eviction_policy='evict_last')
    tmp41 = tl.load(in_ptr0 + (249 + 3 * x0 + 186 * x1 + 3872 * x2), xmask,
        eviction_policy='evict_last')
    tmp43 = tl.load(in_ptr0 + (250 + 3 * x0 + 186 * x1 + 3872 * x2), xmask,
        eviction_policy='evict_last')
    tmp45 = tl.load(in_ptr0 + (251 + 3 * x0 + 186 * x1 + 3872 * x2), xmask,
        eviction_policy='evict_last')
    tmp47 = tl.load(in_ptr0 + (252 + 3 * x0 + 186 * x1 + 3872 * x2), xmask,
        eviction_policy='evict_last')
    tmp2 = tmp1 + tmp0
    tmp4 = tmp3 + tmp2
    tmp6 = tmp5 + tmp4
    tmp8 = tmp7 + tmp6
    tmp10 = tmp9 + tmp8
    tmp12 = tmp11 + tmp10
    tmp14 = tmp13 + tmp12
    tmp16 = tmp15 + tmp14
    tmp18 = tmp17 + tmp16
    tmp20 = tmp19 + tmp18
    tmp22 = tmp21 + tmp20
    tmp24 = tmp23 + tmp22
    tmp26 = tmp25 + tmp24
    tmp28 = tmp27 + tmp26
    tmp30 = tmp29 + tmp28
    tmp32 = tmp31 + tmp30
    tmp34 = tmp33 + tmp32
    tmp36 = tmp35 + tmp34
    tmp38 = tmp37 + tmp36
    tmp40 = tmp39 + tmp38
    tmp42 = tmp41 + tmp40
    tmp44 = tmp43 + tmp42
    tmp46 = tmp45 + tmp44
    tmp48 = tmp47 + tmp46
    tmp49 = 0.04
    tmp50 = tmp48 * tmp49
    tl.store(out_ptr0 + x3, tmp50, 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,), (1,))
    assert_size_stride(primals_3, (4, 4, 64, 64), (16384, 4096, 64, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 62, 62), (15376, 3844, 62, 1))
        buf1 = empty_strided_cuda((4, 4, 62, 62), (15488, 3872, 62, 1),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused_convolution_relu_0[grid(61504)](buf0, primals_2,
            buf1, 61504, XBLOCK=256, num_warps=4, num_stages=1)
        del buf0
        del primals_2
        buf2 = empty_strided_cuda((4, 4, 20, 20), (1600, 400, 20, 1), torch
            .float32)
        triton_poi_fused_avg_pool2d_1[grid(6400)](buf1, buf2, 6400, XBLOCK=
            256, num_warps=4, num_stages=1)
    return buf2, primals_1, primals_3, buf1


class CNN64x3New(nn.Module):

    def __init__(self, input_channels, output_channels):
        super(CNN64x3New, self).__init__()
        self.conv = nn.Conv2d(in_channels=input_channels, kernel_size=3,
            out_channels=output_channels)
        self.relu = nn.ReLU()
        self.pool = nn.AvgPool2d(5, stride=3, padding=0)

    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: 0.03587