# 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_1/inductor_cache/ss/cssyjjl3mw4nt7jmyffj4insp57ypdwdrjatqjapjpniogymqic5.py
# Topologically Sorted Source Nodes: [pow_1, mean, add, sqrt, x], Original ATen: [aten.pow, aten.mean, aten.add, aten.sqrt, aten.div]
# Source node to ATen node mapping:
#   add => add
#   mean => mean
#   pow_1 => pow_1
#   sqrt => sqrt
#   x => div
# Graph fragment:
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%primals_1, 2), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%pow_1, [1], True), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mean, 1e-08), kwargs = {})
#   %sqrt : [num_users=1] = call_function[target=torch.ops.aten.sqrt.default](args = (%add,), kwargs = {})
#   %div : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%primals_1, %sqrt), kwargs = {})
triton_poi_fused_add_div_mean_pow_sqrt_0 = async_compile.triton('triton_poi_fused_add_div_mean_pow_sqrt_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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_div_mean_pow_sqrt_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_add_div_mean_pow_sqrt_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
    x3 = xindex
    x0 = xindex % 16
    x2 = (xindex // 64)
    tmp0 = tl.load(in_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (16 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (32 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (48 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp2 = tmp1 * tmp1
    tmp4 = tmp3 * tmp3
    tmp5 = tmp2 + tmp4
    tmp7 = tmp6 * tmp6
    tmp8 = tmp5 + tmp7
    tmp10 = tmp9 * tmp9
    tmp11 = tmp8 + tmp10
    tmp12 = 4.0
    tmp13 = tmp11 / tmp12
    tmp14 = 1e-08
    tmp15 = tmp13 + tmp14
    tmp16 = libdevice.sqrt(tmp15)
    tmp17 = tmp0 / tmp16
    tl.store(out_ptr0 + (x3), tmp17, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/th/cthvui4kx4mjrhbi6ifvibomn5ynzklvrd2dyw7cvbog2ii5zlcw.py
# Topologically Sorted Source Nodes: [mul, x_2, x_3], Original ATen: [aten.mul, aten.add, aten.leaky_relu]
# Source node to ATen node mapping:
#   mul => mul
#   x_2 => add_1
#   x_3 => gt, mul_1, where
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution, %primals_3), kwargs = {})
#   %add_1 : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %expand), kwargs = {})
#   %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%add_1, 0), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_1, 0.2), kwargs = {})
#   %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt, %add_1, %mul_1), kwargs = {})
triton_poi_fused_add_leaky_relu_mul_1 = async_compile.triton('triton_poi_fused_add_leaky_relu_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=[2048], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_leaky_relu_mul_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_add_leaky_relu_mul_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1296
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 81) % 4
    tmp0 = tl.load(in_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr1 + (0))
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp4 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last')
    tmp3 = tmp0 * tmp2
    tmp5 = tmp3 + tmp4
    tmp6 = 0.0
    tmp7 = tmp5 > tmp6
    tmp8 = 0.2
    tmp9 = tmp5 * tmp8
    tmp10 = tl.where(tmp7, tmp5, tmp9)
    tl.store(out_ptr0 + (x3), tmp10, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (1, ), (1, ))
    assert_size_stride(primals_4, (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)
        # Topologically Sorted Source Nodes: [pow_1, mean, add, sqrt, x], Original ATen: [aten.pow, aten.mean, aten.add, aten.sqrt, aten.div]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_div_mean_pow_sqrt_0.run(primals_1, buf0, 256, grid=grid(256), stream=stream0)
        del primals_1
        # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.convolution]
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1), padding=(4, 4), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 9, 9), (324, 81, 9, 1))
        buf2 = empty_strided_cuda((4, 4, 9, 9), (324, 81, 9, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mul, x_2, x_3], Original ATen: [aten.mul, aten.add, aten.leaky_relu]
        triton_poi_fused_add_leaky_relu_mul_1.run(buf1, primals_3, primals_4, buf2, 1296, grid=grid(1296), stream=stream0)
    return (buf2, primals_2, primals_3, primals_4, buf0, buf1, )


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, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4])
    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 torch
import torch.nn as nn
import torch.nn.functional as F


class PixelNormLayer(nn.Module):

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

    def forward(self, x):
        return x / torch.sqrt(torch.mean(x ** 2, dim=1, keepdim=True) + 1e-08)


class WScaleLayer(nn.Module):

    def __init__(self, size):
        super(WScaleLayer, self).__init__()
        self.scale = nn.Parameter(torch.randn([1]))
        self.b = nn.Parameter(torch.randn(size))
        self.size = size

    def forward(self, x):
        x_size = x.size()
        x = x * self.scale + self.b.view(1, -1, 1, 1).expand(x_size[0],
            self.size, x_size[2], x_size[3])
        return x


class NormConvBlock(nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size, padding):
        super(NormConvBlock, self).__init__()
        self.norm = PixelNormLayer()
        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, 1,
            padding, bias=False)
        self.wscale = WScaleLayer(out_channels)

    def forward(self, x):
        x = self.norm(x)
        x = self.conv(x)
        x = F.leaky_relu(self.wscale(x), negative_slope=0.2)
        return x


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


def get_init_inputs():
    return [[], {'in_channels': 4, 'out_channels': 4, 'kernel_size': 4,
        'padding': 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 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_add_div_mean_pow_sqrt_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
    x3 = xindex
    x0 = xindex % 16
    x2 = xindex // 64
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp9 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tmp1 * tmp1
    tmp4 = tmp3 * tmp3
    tmp5 = tmp2 + tmp4
    tmp7 = tmp6 * tmp6
    tmp8 = tmp5 + tmp7
    tmp10 = tmp9 * tmp9
    tmp11 = tmp8 + tmp10
    tmp12 = 4.0
    tmp13 = tmp11 / tmp12
    tmp14 = 1e-08
    tmp15 = tmp13 + tmp14
    tmp16 = libdevice.sqrt(tmp15)
    tmp17 = tmp0 / tmp16
    tl.store(out_ptr0 + x3, tmp17, xmask)


@triton.jit
def triton_poi_fused_add_leaky_relu_mul_1(in_ptr0, in_ptr1, in_ptr2,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 1296
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 81 % 4
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp4 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tmp0 * tmp2
    tmp5 = tmp3 + tmp4
    tmp6 = 0.0
    tmp7 = tmp5 > tmp6
    tmp8 = 0.2
    tmp9 = tmp5 * tmp8
    tmp10 = tl.where(tmp7, tmp5, tmp9)
    tl.store(out_ptr0 + x3, tmp10, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (1,), (1,))
    assert_size_stride(primals_4, (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_add_div_mean_pow_sqrt_0[grid(256)](primals_1, buf0,
            256, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_1
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1),
            padding=(4, 4), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 9, 9), (324, 81, 9, 1))
        buf2 = empty_strided_cuda((4, 4, 9, 9), (324, 81, 9, 1), torch.float32)
        triton_poi_fused_add_leaky_relu_mul_1[grid(1296)](buf1, primals_3,
            primals_4, buf2, 1296, XBLOCK=256, num_warps=4, num_stages=1)
    return buf2, primals_2, primals_3, primals_4, buf0, buf1


class PixelNormLayer(nn.Module):

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

    def forward(self, x):
        return x / torch.sqrt(torch.mean(x ** 2, dim=1, keepdim=True) + 1e-08)


class WScaleLayer(nn.Module):

    def __init__(self, size):
        super(WScaleLayer, self).__init__()
        self.scale = nn.Parameter(torch.randn([1]))
        self.b = nn.Parameter(torch.randn(size))
        self.size = size

    def forward(self, x):
        x_size = x.size()
        x = x * self.scale + self.b.view(1, -1, 1, 1).expand(x_size[0],
            self.size, x_size[2], x_size[3])
        return x


class NormConvBlockNew(nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size, padding):
        super(NormConvBlockNew, self).__init__()
        self.norm = PixelNormLayer()
        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, 1,
            padding, bias=False)
        self.wscale = WScaleLayer(out_channels)

    def forward(self, input_0):
        primals_1 = self.conv.weight
        primals_3 = self.wscale.scale
        primals_4 = self.wscale.b
        primals_2 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]
