# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream

aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()


# kernel path: runs/run_shard_4/inductor_cache/wi/cwiyl3lwwtancorrifw77xt3aqb4lermdintht45zvkj3bg54nbl.py
# Topologically Sorted Source Nodes: [mul], Original ATen: [aten.mul]
# Source node to ATen node mapping:
#   mul => mul
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_3, 0.5), kwargs = {})
triton_poi_fused_mul_0 = async_compile.triton('triton_poi_fused_mul_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mul_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@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)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/2o/c2oqkq7zaubqmw7vuixxlseb2ff5jzqqbyczicxlmsahuxwdpdyp.py
# Topologically Sorted Source Nodes: [mul_1], Original ATen: [aten.mul]
# Source node to ATen node mapping:
#   mul_1 => mul_1
# Graph fragment:
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_4, 1), kwargs = {})
triton_poi_fused_mul_1 = async_compile.triton('triton_poi_fused_mul_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[4], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mul_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@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)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/ri/criuvsdl3sferb4bb6ci5zaps3wys7xxcpybz7vfo2ba4q7cuq6c.py
# Topologically Sorted Source Nodes: [mul_2, weight, pow_1, sum_1, add, demod, weight_1], Original ATen: [aten.mul, aten.pow, aten.sum, aten.add, aten.rsqrt]
# Source node to ATen node mapping:
#   add => add
#   demod => rsqrt
#   mul_2 => mul_2
#   pow_1 => pow_1
#   sum_1 => sum_1
#   weight => mul_3
#   weight_1 => mul_4
# Graph fragment:
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_5, 0.125), kwargs = {})
#   %mul_3 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_2, %view), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%mul_3, 2), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_1, [2, 3, 4]), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_1, 1e-08), kwargs = {})
#   %rsqrt : [num_users=2] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add,), kwargs = {})
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_3, %view_1), kwargs = {})
triton_per_fused_add_mul_pow_rsqrt_sum_2 = async_compile.triton('triton_per_fused_add_mul_pow_rsqrt_sum_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.persistent_reduction(
    size_hints=[16, 64],
    reduction_hint=ReductionHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_mul_pow_rsqrt_sum_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 1, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@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
    rnumel = 64
    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, :]
    roffset = 0
    rmask = 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)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

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), (4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4, ), (1, ))
    assert_size_stride(primals_5, (1, 4, 4, 4, 4), (256, 64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mul], Original ATen: [aten.mul]
        stream0 = get_raw_stream(0)
        triton_poi_fused_mul_0.run(primals_3, buf0, 16, grid=grid(16), stream=stream0)
        del primals_3
        buf1 = empty_strided_cuda((4, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [mul_1], Original ATen: [aten.mul]
        triton_poi_fused_mul_1.run(primals_4, buf1, 4, grid=grid(4), stream=stream0)
        del primals_4
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mul_1, out], Original ATen: [aten.mul, aten.addmm]
        extern_kernels.addmm(buf1, primals_2, reinterpret_tensor(buf0, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf2)
        del buf1
        buf3 = buf0; del buf0  # reuse
        buf4 = buf3; del buf3  # reuse
        buf5 = empty_strided_cuda((4, 4, 4, 4, 4), (256, 64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mul_2, weight, pow_1, sum_1, add, demod, weight_1], Original ATen: [aten.mul, aten.pow, aten.sum, aten.add, aten.rsqrt]
        triton_per_fused_add_mul_pow_rsqrt_sum_2.run(buf4, primals_5, buf2, buf5, 16, 64, grid=grid(16), stream=stream0)
        # Topologically Sorted Source Nodes: [out_1], Original ATen: [aten.convolution]
        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))
    return (reinterpret_tensor(buf6, (4, 4, 5, 5), (100, 25, 5, 1), 0), primals_2, 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), )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((1, 4, 4, 4, 4), (256, 64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5])
    return print_performance(fn, times=times, repeat=repeat)


if __name__ == "__main__":
    from torch._inductor.wrapper_benchmark import compiled_module_main
    compiled_module_main('None', benchmark_compiled_module)

import math
import torch
import torch.utils.data
import torch
from torch import nn
import torch.nn.functional as F


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


def upfirdn2d_native(input, kernel, up_x, up_y, down_x, down_y, pad_x0,
    pad_x1, pad_y0, pad_y1):
    _, minor, in_h, in_w = input.shape
    kernel_h, kernel_w = kernel.shape
    out = input.view(-1, minor, in_h, 1, in_w, 1)
    out = F.pad(out, [0, up_x - 1, 0, 0, 0, up_y - 1, 0, 0])
    out = out.view(-1, minor, in_h * up_y, in_w * up_x)
    out = F.pad(out, [max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(
        pad_y1, 0)])
    out = out[:, :, max(-pad_y0, 0):out.shape[2] - max(-pad_y1, 0), max(-
        pad_x0, 0):out.shape[3] - max(-pad_x1, 0)]
    out = out.reshape([-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x +
        pad_x0 + pad_x1])
    w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w)
    out = F.conv2d(out, w)
    out = out.reshape(-1, minor, in_h * up_y + pad_y0 + pad_y1 - kernel_h +
        1, in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1)
    return out[:, :, ::down_y, ::down_x]


def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)):
    return upfirdn2d_native(input, kernel, up, up, down, down, pad[0], pad[
        1], pad[0], pad[1])


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


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 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 = math.sqrt(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:
            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:
            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 = math.sqrt(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))
        if style_dim is not None and style_dim > 0:
            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
        if style is not None:
            style = self.modulation(style).view(batch, 1, in_channel, 1, 1)
        else:
            style = torch.ones(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


def get_inputs():
    return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4])]


def get_init_inputs():
    return [[], {'in_channel': 4, 'out_channel': 4, 'kernel_size': 4,
        'style_dim': 4}]

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 math
import torch.utils.data
import torch
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)


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), (4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4,), (1,))
    assert_size_stride(primals_5, (1, 4, 4, 4, 4), (256, 64, 16, 4, 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_3, buf0, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del primals_3
        buf1 = empty_strided_cuda((4,), (1,), torch.float32)
        triton_poi_fused_mul_1[grid(4)](primals_4, buf1, 4, XBLOCK=4,
            num_warps=1, num_stages=1)
        del primals_4
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(buf1, primals_2, 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))
    return reinterpret_tensor(buf6, (4, 4, 5, 5), (100, 25, 5, 1), 0
        ), primals_2, 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)


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


def upfirdn2d_native(input, kernel, up_x, up_y, down_x, down_y, pad_x0,
    pad_x1, pad_y0, pad_y1):
    _, minor, in_h, in_w = input.shape
    kernel_h, kernel_w = kernel.shape
    out = input.view(-1, minor, in_h, 1, in_w, 1)
    out = F.pad(out, [0, up_x - 1, 0, 0, 0, up_y - 1, 0, 0])
    out = out.view(-1, minor, in_h * up_y, in_w * up_x)
    out = F.pad(out, [max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(
        pad_y1, 0)])
    out = out[:, :, max(-pad_y0, 0):out.shape[2] - max(-pad_y1, 0), max(-
        pad_x0, 0):out.shape[3] - max(-pad_x1, 0)]
    out = out.reshape([-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x +
        pad_x0 + pad_x1])
    w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w)
    out = F.conv2d(out, w)
    out = out.reshape(-1, minor, in_h * up_y + pad_y0 + pad_y1 - kernel_h +
        1, in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1)
    return out[:, :, ::down_y, ::down_x]


def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)):
    return upfirdn2d_native(input, kernel, up, up, down, down, pad[0], pad[
        1], pad[0], pad[1])


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


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 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 = math.sqrt(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 ModulatedConv2dNew(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:
            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:
            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 = math.sqrt(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))
        if style_dim is not None and style_dim > 0:
            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_0, input_1):
        primals_5 = self.weight
        primals_2 = self.modulation.weight
        primals_4 = self.modulation.bias
        primals_1 = input_0
        primals_3 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]
