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.triton_helpers import libdevice
import torch.nn as nn
import torch.utils.data
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_mul_0(in_ptr0, 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_ptr0 + x0, xmask)
    tmp1 = 0.23570226039551584
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + x0, tmp2, xmask)


@triton.jit
def triton_poi_fused_convolution_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 16 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, xmask)


@triton.jit
def triton_poi_fused_add_div_leaky_relu_mean_mul_pow_sqrt_2(in_out_ptr0,
    in_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 16
    x2 = xindex // 64
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp6 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp11 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp17 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp23 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 0.2
    tmp4 = tmp0 * tmp3
    tmp5 = tl.where(tmp2, tmp0, tmp4)
    tmp7 = tmp6 > tmp1
    tmp8 = tmp6 * tmp3
    tmp9 = tl.where(tmp7, tmp6, tmp8)
    tmp10 = tmp9 * tmp9
    tmp12 = tmp11 > tmp1
    tmp13 = tmp11 * tmp3
    tmp14 = tl.where(tmp12, tmp11, tmp13)
    tmp15 = tmp14 * tmp14
    tmp16 = tmp10 + tmp15
    tmp18 = tmp17 > tmp1
    tmp19 = tmp17 * tmp3
    tmp20 = tl.where(tmp18, tmp17, tmp19)
    tmp21 = tmp20 * tmp20
    tmp22 = tmp16 + tmp21
    tmp24 = tmp23 > tmp1
    tmp25 = tmp23 * tmp3
    tmp26 = tl.where(tmp24, tmp23, tmp25)
    tmp27 = tmp26 * tmp26
    tmp28 = tmp22 + tmp27
    tmp29 = 4.0
    tmp30 = tmp28 / tmp29
    tmp31 = 1e-08
    tmp32 = tmp30 + tmp31
    tmp33 = libdevice.sqrt(tmp32)
    tmp34 = tmp5 / tmp33
    tmp35 = 0.23570226039551584
    tmp36 = tmp34 * tmp35
    tl.store(in_out_ptr0 + x3, tmp36, xmask)


@triton.jit
def triton_poi_fused_add_div_leaky_relu_mean_pow_sqrt_3(in_ptr0, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 16
    x2 = xindex // 64
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp6 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp11 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp17 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp23 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 0.2
    tmp4 = tmp0 * tmp3
    tmp5 = tl.where(tmp2, tmp0, tmp4)
    tmp7 = tmp6 > tmp1
    tmp8 = tmp6 * tmp3
    tmp9 = tl.where(tmp7, tmp6, tmp8)
    tmp10 = tmp9 * tmp9
    tmp12 = tmp11 > tmp1
    tmp13 = tmp11 * tmp3
    tmp14 = tl.where(tmp12, tmp11, tmp13)
    tmp15 = tmp14 * tmp14
    tmp16 = tmp10 + tmp15
    tmp18 = tmp17 > tmp1
    tmp19 = tmp17 * tmp3
    tmp20 = tl.where(tmp18, tmp17, tmp19)
    tmp21 = tmp20 * tmp20
    tmp22 = tmp16 + tmp21
    tmp24 = tmp23 > tmp1
    tmp25 = tmp23 * tmp3
    tmp26 = tl.where(tmp24, tmp23, tmp25)
    tmp27 = tmp26 * tmp26
    tmp28 = tmp22 + tmp27
    tmp29 = 4.0
    tmp30 = tmp28 / tmp29
    tmp31 = 1e-08
    tmp32 = tmp30 + tmp31
    tmp33 = libdevice.sqrt(tmp32)
    tmp34 = tmp5 / tmp33
    tl.store(out_ptr0 + x3, tmp34, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = 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,), (1,))
    assert_size_stride(primals_4, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_5, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_mul_0[grid(256)](primals_1, buf0, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        del primals_1
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 4, 4), (64, 16, 4, 1))
        buf2 = buf1
        del buf1
        triton_poi_fused_convolution_1[grid(256)](buf2, primals_3, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_3
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf4 = buf3
        del buf3
        triton_poi_fused_add_div_leaky_relu_mean_mul_pow_sqrt_2[grid(256)](buf4
            , buf2, 256, XBLOCK=128, num_warps=4, num_stages=1)
        buf5 = extern_kernels.convolution(buf4, primals_4, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf5, (4, 4, 4, 4), (64, 16, 4, 1))
        buf6 = buf5
        del buf5
        triton_poi_fused_convolution_1[grid(256)](buf6, primals_5, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_5
        buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_add_div_leaky_relu_mean_pow_sqrt_3[grid(256)](buf6,
            buf7, 256, XBLOCK=256, num_warps=4, num_stages=1)
    return buf7, primals_2, primals_4, buf0, buf2, buf4, buf6


class WSConv2d(nn.Module):
    """
    Weight scaled Conv2d (Equalized Learning Rate)
    Note that input is multiplied rather than changing weights
    this will have the same result.

    Inspired by:
    https://github.com/nvnbny/progressive_growing_of_gans/blob/master/modelUtils.py
    """

    def __init__(self, in_channels, out_channels, kernel_size=3, stride=1,
        padding=1, gain=2):
        super(WSConv2d, self).__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size,
            stride, padding)
        self.scale = (gain / self.conv.weight[0].numel()) ** 0.5
        nn.init.normal_(self.conv.weight)
        nn.init.zeros_(self.conv.bias)

    def forward(self, x):
        return self.conv(x * self.scale)


class PixelNorm(nn.Module):

    def __init__(self):
        super(PixelNorm, self).__init__()
        self.epsilon = 1e-08

    def forward(self, x):
        return x / torch.sqrt(torch.mean(x ** 2, dim=1, keepdim=True) +
            self.epsilon)


class ConvBlockNew(nn.Module):

    def __init__(self, in_channels, out_channels, use_pixelnorm=True):
        super(ConvBlockNew, self).__init__()
        self.use_pn = use_pixelnorm
        self.conv1 = WSConv2d(in_channels, out_channels)
        self.conv2 = WSConv2d(out_channels, out_channels)
        self.leaky = nn.LeakyReLU(0.2)
        self.pn = PixelNorm()

    def forward(self, input_0):
        primals_2 = self.conv1.conv.weight
        primals_3 = self.conv1.conv.bias
        primals_4 = self.conv2.conv.weight
        primals_5 = self.conv2.conv.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]


Measured latency_ms: 0.077431