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
from torch.autograd import Function
import math
from torch import 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
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_mul_0(in_ptr0, 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_ptr0 + x0, xmask)
    tmp1 = 0.5
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + x0, tmp2, xmask)


@triton.jit
def triton_poi_fused_mul_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    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 = 1.0
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + x0, tmp2, xmask)


@triton.jit
def triton_per_fused_add_mul_pow_rsqrt_sum_2(in_out_ptr0, in_ptr0, in_ptr1,
    out_ptr0, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    RBLOCK: tl.constexpr = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r5 = rindex
    x0 = xindex % 4
    r3 = rindex // 16
    x1 = xindex // 4
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (r5 + 64 * x0), xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp3 = tl.load(in_ptr1 + (r3 + 4 * x1), xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp1 = 0.125
    tmp2 = tmp0 * tmp1
    tmp4 = tmp2 * tmp3
    tmp5 = tmp4 * tmp4
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK, RBLOCK])
    tmp8 = tl.where(xmask, tmp6, 0)
    tmp9 = tl.sum(tmp8, 1)[:, None]
    tmp10 = 1e-08
    tmp11 = tmp9 + tmp10
    tmp12 = libdevice.rsqrt(tmp11)
    tmp13 = tmp4 * tmp12
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x4, tmp12, xmask)
    tl.store(out_ptr0 + (r5 + 64 * x4), tmp13, xmask)


@triton.jit
def triton_poi_fused_add_leaky_relu_mul_3(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 400
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 25
    x2 = xindex // 100
    x1 = xindex // 25 % 4
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tl.load(in_ptr2 + (x0 + 25 * x2), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
    tmp4 = tmp2 * tmp3
    tmp5 = tmp0 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = 0.0
    tmp9 = tmp7 > tmp8
    tmp10 = 0.2
    tmp11 = tmp7 * tmp10
    tmp12 = tl.where(tmp9, tmp7, tmp11)
    tmp13 = 1.4142135623730951
    tmp14 = tmp12 * tmp13
    tl.store(out_ptr0 + x3, tmp9, xmask)
    tl.store(out_ptr1 + x3, tmp14, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (1, 4, 4, 4, 4), (256, 64, 16, 4, 1))
    assert_size_stride(primals_6, (1,), (1,))
    assert_size_stride(primals_7, (1, 4, 1, 1), (4, 1, 1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_mul_0[grid(16)](primals_2, buf0, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del primals_2
        buf1 = empty_strided_cuda((4,), (1,), torch.float32)
        triton_poi_fused_mul_1[grid(4)](primals_3, buf1, 4, XBLOCK=4,
            num_warps=1, num_stages=1)
        del primals_3
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(buf1, primals_4, reinterpret_tensor(buf0, (4, 
            4), (1, 4), 0), alpha=1, beta=1, out=buf2)
        del buf1
        buf3 = buf0
        del buf0
        buf4 = buf3
        del buf3
        buf5 = empty_strided_cuda((4, 4, 4, 4, 4), (256, 64, 16, 4, 1),
            torch.float32)
        triton_per_fused_add_mul_pow_rsqrt_sum_2[grid(16)](buf4, primals_5,
            buf2, buf5, 16, 64, XBLOCK=8, num_warps=4, num_stages=1)
        buf6 = extern_kernels.convolution(reinterpret_tensor(primals_1, (1,
            16, 4, 4), (256, 16, 4, 1), 0), reinterpret_tensor(buf5, (16, 4,
            4, 4), (64, 16, 4, 1), 0), stride=(1, 1), padding=(2, 2),
            dilation=(1, 1), transposed=False, output_padding=(0, 0),
            groups=4, bias=None)
        assert_size_stride(buf6, (1, 16, 5, 5), (400, 25, 5, 1))
        buf7 = empty_strided_cuda((4, 1, 5, 5), (25, 25, 5, 1), torch.float32)
        buf8 = torch.ops.aten.normal_functional.default(buf7)
        del buf7
        buf9 = buf8
        del buf8
        buf10 = empty_strided_cuda((4, 4, 5, 5), (100, 25, 5, 1), torch.bool)
        buf11 = empty_strided_cuda((4, 4, 5, 5), (100, 25, 5, 1), torch.float32
            )
        triton_poi_fused_add_leaky_relu_mul_3[grid(400)](buf6, primals_6,
            buf9, primals_7, buf10, buf11, 400, XBLOCK=256, num_warps=4,
            num_stages=1)
        del buf6
        del primals_6
        del primals_7
    return buf11, primals_4, primals_5, buf2, buf4, reinterpret_tensor(buf5,
        (16, 4, 4, 4), (64, 16, 4, 1), 0), reinterpret_tensor(primals_1, (1,
        16, 4, 4), (256, 16, 4, 1), 0), buf9, buf10


def make_kernel(k):
    k = torch.tensor(k, dtype=torch.float32)
    if k.ndim == 1:
        k = k[None, :] * k[:, None]
    k /= k.sum()
    return k


def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)):
    out = UpFirDn2d.apply(input, kernel, (up, up), (down, down), (pad[0],
        pad[1], pad[0], pad[1]))
    return out


def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5):
    out = F.leaky_relu(input + bias, negative_slope)
    return out * scale


class UpFirDn2dBackward(Function):

    @staticmethod
    def forward(ctx, grad_output, kernel, grad_kernel, up, down, pad, g_pad,
        in_size, out_size):
        up_x, up_y = up
        down_x, down_y = down
        g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1 = g_pad
        grad_output = grad_output.reshape(-1, out_size[0], out_size[1], 1)
        grad_input = upfirdn2d_op.upfirdn2d(grad_output, grad_kernel,
            down_x, down_y, up_x, up_y, g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1)
        grad_input = grad_input.view(in_size[0], in_size[1], in_size[2],
            in_size[3])
        ctx.save_for_backward(kernel)
        pad_x0, pad_x1, pad_y0, pad_y1 = pad
        ctx.up_x = up_x
        ctx.up_y = up_y
        ctx.down_x = down_x
        ctx.down_y = down_y
        ctx.pad_x0 = pad_x0
        ctx.pad_x1 = pad_x1
        ctx.pad_y0 = pad_y0
        ctx.pad_y1 = pad_y1
        ctx.in_size = in_size
        ctx.out_size = out_size
        return grad_input

    @staticmethod
    def backward(ctx, gradgrad_input):
        kernel, = ctx.saved_tensors
        gradgrad_input = gradgrad_input.reshape(-1, ctx.in_size[2], ctx.
            in_size[3], 1)
        gradgrad_out = upfirdn2d_op.upfirdn2d(gradgrad_input, kernel, ctx.
            up_x, ctx.up_y, ctx.down_x, ctx.down_y, ctx.pad_x0, ctx.pad_x1,
            ctx.pad_y0, ctx.pad_y1)
        gradgrad_out = gradgrad_out.view(ctx.in_size[0], ctx.in_size[1],
            ctx.out_size[0], ctx.out_size[1])
        return gradgrad_out, None, None, None, None, None, None, None, None


class UpFirDn2d(Function):

    @staticmethod
    def forward(ctx, input, kernel, up, down, pad):
        up_x, up_y = up
        down_x, down_y = down
        pad_x0, pad_x1, pad_y0, pad_y1 = pad
        kernel_h, kernel_w = kernel.shape
        _batch, channel, in_h, in_w = input.shape
        ctx.in_size = input.shape
        input = input.reshape(-1, in_h, in_w, 1)
        ctx.save_for_backward(kernel, torch.flip(kernel, [0, 1]))
        out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1
        out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1
        ctx.out_size = out_h, out_w
        ctx.up = up_x, up_y
        ctx.down = down_x, down_y
        ctx.pad = pad_x0, pad_x1, pad_y0, pad_y1
        g_pad_x0 = kernel_w - pad_x0 - 1
        g_pad_y0 = kernel_h - pad_y0 - 1
        g_pad_x1 = in_w * up_x - out_w * down_x + pad_x0 - up_x + 1
        g_pad_y1 = in_h * up_y - out_h * down_y + pad_y0 - up_y + 1
        ctx.g_pad = g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1
        out = upfirdn2d_op.upfirdn2d(input, kernel, up_x, up_y, down_x,
            down_y, pad_x0, pad_x1, pad_y0, pad_y1)
        out = out.view(-1, channel, out_h, out_w)
        return out

    @staticmethod
    def backward(ctx, grad_output):
        kernel, grad_kernel = ctx.saved_tensors
        grad_input = UpFirDn2dBackward.apply(grad_output, kernel,
            grad_kernel, ctx.up, ctx.down, ctx.pad, ctx.g_pad, ctx.in_size,
            ctx.out_size)
        return grad_input, None, None, None, None


class Blur(nn.Module):

    def __init__(self, kernel, pad, upsample_factor=1):
        super().__init__()
        kernel = make_kernel(kernel)
        if upsample_factor > 1:
            kernel = kernel * upsample_factor ** 2
        self.register_buffer('kernel', kernel)
        self.pad = pad

    def forward(self, input):
        out = upfirdn2d(input, self.kernel, pad=self.pad)
        return out


class FusedLeakyReLU(nn.Module):

    def __init__(self, channel, negative_slope=0.2, scale=2 ** 0.5):
        super().__init__()
        self.bias = nn.Parameter(torch.zeros(1, channel, 1, 1))
        self.negative_slope = negative_slope
        self.scale = scale

    def forward(self, input):
        out = F.leaky_relu(input + self.bias, self.negative_slope)
        return out * self.scale


class EqualLinear(nn.Module):

    def __init__(self, in_dim, out_dim, bias=True, bias_init=0, lr_mul=1,
        activation=None):
        super().__init__()
        self.weight = nn.Parameter(torch.randn(out_dim, in_dim).div_(lr_mul))
        if bias:
            self.bias = nn.Parameter(torch.zeros(out_dim).fill_(bias_init))
        else:
            self.bias = None
        self.activation = activation
        self.scale = 1 / math.sqrt(in_dim) * lr_mul
        self.lr_mul = lr_mul

    def forward(self, input):
        if self.activation:
            out = F.linear(input, self.weight * self.scale)
            out = fused_leaky_relu(out, self.bias * self.lr_mul)
        else:
            out = F.linear(input, self.weight * self.scale, bias=self.bias *
                self.lr_mul)
        return out

    def __repr__(self):
        return (
            f'{self.__class__.__name__}({self.weight.shape[1]}, {self.weight.shape[0]})'
            )


class ModulatedConv2d(nn.Module):

    def __init__(self, in_channel, out_channel, kernel_size, style_dim,
        demodulate=True, upsample=False, downsample=False, blur_kernel=[1, 
        3, 3, 1]):
        super().__init__()
        self.eps = 1e-08
        self.kernel_size = kernel_size
        self.in_channel = in_channel
        self.out_channel = out_channel
        self.upsample = upsample
        self.downsample = downsample
        if upsample:
            None
            factor = 2
            p = len(blur_kernel) - factor - (kernel_size - 1)
            pad0 = (p + 1) // 2 + factor - 1
            pad1 = p // 2 + 1
            self.blur = Blur(blur_kernel, pad=(pad0, pad1), upsample_factor
                =factor)
        if downsample:
            None
            factor = 2
            p = len(blur_kernel) - factor + (kernel_size - 1)
            pad0 = (p + 1) // 2
            pad1 = p // 2
            self.blur = Blur(blur_kernel, pad=(pad0, pad1))
        fan_in = in_channel * kernel_size ** 2
        self.scale = 1 / math.sqrt(fan_in)
        self.padding = kernel_size // 2
        self.weight = nn.Parameter(torch.randn(1, out_channel, in_channel,
            kernel_size, kernel_size))
        self.modulation = EqualLinear(style_dim, in_channel, bias_init=1)
        self.demodulate = demodulate

    def __repr__(self):
        return (
            f'{self.__class__.__name__}({self.in_channel}, {self.out_channel}, {self.kernel_size}, upsample={self.upsample}, downsample={self.downsample})'
            )

    def forward(self, input, style):
        batch, in_channel, height, width = input.shape
        style = self.modulation(style).view(batch, 1, in_channel, 1, 1)
        weight = self.scale * self.weight * style
        if self.demodulate:
            demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + 1e-08)
            weight = weight * demod.view(batch, self.out_channel, 1, 1, 1)
        weight = weight.view(batch * self.out_channel, in_channel, self.
            kernel_size, self.kernel_size)
        if self.upsample:
            input = input.view(1, batch * in_channel, height, width)
            weight = weight.view(batch, self.out_channel, in_channel, self.
                kernel_size, self.kernel_size)
            weight = weight.transpose(1, 2).reshape(batch * in_channel,
                self.out_channel, self.kernel_size, self.kernel_size)
            out = F.conv_transpose2d(input, weight, padding=0, stride=2,
                groups=batch)
            _, _, height, width = out.shape
            out = out.view(batch, self.out_channel, height, width)
            out = self.blur(out)
        elif self.downsample:
            input = self.blur(input)
            _, _, height, width = input.shape
            input = input.view(1, batch * in_channel, height, width)
            out = F.conv2d(input, weight, padding=0, stride=2, groups=batch)
            _, _, height, width = out.shape
            out = out.view(batch, self.out_channel, height, width)
        else:
            input = input.view(1, batch * in_channel, height, width)
            out = F.conv2d(input, weight, padding=self.padding, groups=batch)
            _, _, height, width = out.shape
            out = out.view(batch, self.out_channel, height, width)
        return out


class NoiseInjection(nn.Module):

    def __init__(self):
        super().__init__()
        self.weight = nn.Parameter(torch.zeros(1))

    def forward(self, image, noise=None):
        if noise is None:
            batch, _, height, width = image.shape
            noise = image.new_empty(batch, 1, height, width).normal_()
        return image + self.weight * noise


class ScaledLeakyReLUSin(nn.Module):

    def __init__(self, negative_slope=0.2):
        super().__init__()
        self.negative_slope = negative_slope

    def forward(self, input):
        out_lr = F.leaky_relu(input[:, ::2], negative_slope=self.negative_slope
            )
        out_sin = torch.sin(input[:, 1::2])
        out = torch.cat([out_lr, out_sin], 1)
        return out * math.sqrt(2)


class SinActivation(nn.Module):

    def __init__(self):
        super(SinActivation, self).__init__()

    def forward(self, x):
        return torch.sin(x)


class StyledConvNew(nn.Module):

    def __init__(self, in_channel, out_channel, kernel_size, style_dim,
        upsample=False, blur_kernel=[1, 3, 3, 1], demodulate=True,
        activation=None, downsample=False):
        super().__init__()
        self.conv = ModulatedConv2d(in_channel, out_channel, kernel_size,
            style_dim, upsample=upsample, blur_kernel=blur_kernel,
            demodulate=demodulate, downsample=downsample)
        self.activation = activation
        self.noise = NoiseInjection()
        if activation == 'sinrelu':
            self.bias = nn.Parameter(torch.zeros(1, out_channel, 1, 1))
            self.activate = ScaledLeakyReLUSin()
        elif activation == 'sin':
            self.bias = nn.Parameter(torch.zeros(1, out_channel, 1, 1))
            self.activate = SinActivation()
        else:
            self.activate = FusedLeakyReLU(out_channel)

    def forward(self, input_0, input_1):
        primals_5 = self.conv.weight
        primals_2 = self.conv.modulation.weight
        primals_3 = self.conv.modulation.bias
        primals_6 = self.noise.weight
        primals_7 = self.activate.bias
        primals_1 = input_0
        primals_4 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0]


Measured latency_ms: 0.014259