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_constant_pad_nd_0(in_ptr0, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 300
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 5 % 5
    x0 = xindex % 5
    x2 = xindex // 25
    x3 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 4, tl.int64)
    tmp2 = tmp0 < tmp1
    tmp3 = x0
    tmp4 = tmp3 < tmp1
    tmp5 = tmp2 & tmp4
    tmp6 = tl.load(in_ptr0 + (x0 + 4 * x1 + 16 * x2), tmp5 & xmask, other=0.0)
    tl.store(out_ptr0 + x3, tmp6, xmask)


@triton.jit
def triton_poi_fused_leaky_relu_1(in_ptr0, out_ptr0, out_ptr1, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 192
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 0.2
    tmp4 = tmp0 * tmp3
    tmp5 = tl.where(tmp2, tmp0, tmp4)
    tl.store(out_ptr0 + x0, tmp2, xmask)
    tl.store(out_ptr1 + x0, tmp5, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (3, 3, 16, 16), (768, 256, 16, 1))
    assert_size_stride(primals_2, (4, 3, 4, 4), (48, 16, 4, 1))
    assert_size_stride(primals_3, (3, 3, 3, 3), (27, 9, 3, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 3, 5, 5), (75, 25, 5, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_constant_pad_nd_0[grid(300)](primals_2, buf0, 300,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_2
        buf1 = extern_kernels.convolution(buf0, primals_1, stride=(1, 1),
            padding=(7, 7), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 3, 4, 4), (48, 16, 4, 1))
        buf2 = empty_strided_cuda((4, 3, 4, 4), (48, 16, 4, 1), torch.bool)
        buf3 = empty_strided_cuda((4, 3, 4, 4), (48, 16, 4, 1), torch.float32)
        triton_poi_fused_leaky_relu_1[grid(192)](buf1, buf2, buf3, 192,
            XBLOCK=256, num_warps=4, num_stages=1)
        del buf1
        buf4 = extern_kernels.convolution(buf3, primals_3, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 3, 4, 4), (48, 16, 4, 1))
    return buf4, primals_1, primals_3, buf0, buf2, buf3


class ATTANew(nn.Module):

    def __init__(self):
        super(ATTANew, self).__init__()
        self.conv1 = nn.Conv2d(3, 3, 16, padding='same', groups=1, bias=False)
        self.lr = nn.LeakyReLU(0.2)
        self.conv2 = nn.Conv2d(3, 3, 3, padding='same', groups=1, bias=False)
        torch.nn.init.dirac_(self.conv1.weight, 1)
        torch.nn.init.dirac_(self.conv2.weight, 1)
        self.conv1.weight.data += torch.randn_like(self.conv1.weight.data
            ) * 0.01
        self.conv2.weight.data += torch.randn_like(self.conv2.weight.data
            ) * 0.01

    def forward(self, input_0):
        primals_1 = self.conv1.weight
        primals_3 = self.conv2.weight
        primals_2 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]


Measured latency_ms: 0.044299