# 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_6/inductor_cache/of/cofwklo5nuhcnwyox2iqrwvifqkquaz5nm5gngbpzzb6xu4usmqd.py
# Topologically Sorted Source Nodes: [mul], Original ATen: [aten.mul]
# Source node to ATen node mapping:
#   mul => mul
# Graph fragment:
#   %mul : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_1, 0.23570226039551584), 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=[256], 
    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 = 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)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/32/c32v7egt4mupqssam3gmac2qgv3ujprjybthsgweflmot256qqw7.py
# Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   conv2d => convolution
# Graph fragment:
#   %convolution : [num_users=4] = call_function[target=torch.ops.aten.convolution.default](args = (%mul, %primals_2, %primals_3, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_1 = async_compile.triton('triton_poi_fused_convolution_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=[256], 
    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_convolution_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, '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_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)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/4b/c4biasj4wzw4qmo6u767lcafgh6e6kct7c433onys3pjjjwfxxqj.py
# Topologically Sorted Source Nodes: [x, pow_1, mean, add, sqrt, x_1, mul_1], Original ATen: [aten.leaky_relu, aten.pow, aten.mean, aten.add, aten.sqrt, aten.div, aten.mul]
# Source node to ATen node mapping:
#   add => add
#   mean => mean
#   mul_1 => mul_2
#   pow_1 => pow_1
#   sqrt => sqrt
#   x => gt, mul_1, where
#   x_1 => div
# Graph fragment:
#   %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution, 0), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution, 0.2), kwargs = {})
#   %where : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt, %convolution, %mul_1), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%where, 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=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%where, %sqrt), kwargs = {})
#   %mul_2 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%div, 0.23570226039551584), kwargs = {})
triton_poi_fused_add_div_leaky_relu_mean_mul_pow_sqrt_2 = async_compile.triton('triton_poi_fused_add_div_leaky_relu_mean_mul_pow_sqrt_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.pointwise(
    size_hints=[256], 
    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_add_div_leaky_relu_mean_mul_pow_sqrt_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 5, '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_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)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/7v/c7vm65ktpe4n23v24el4vlbfc7rafxngxnpi7ekdsaph6dnzyef4.py
# Topologically Sorted Source Nodes: [x_2, pow_2, mean_1, add_1, sqrt_1, x_3], Original ATen: [aten.leaky_relu, aten.pow, aten.mean, aten.add, aten.sqrt, aten.div]
# Source node to ATen node mapping:
#   add_1 => add_1
#   mean_1 => mean_1
#   pow_2 => pow_2
#   sqrt_1 => sqrt_1
#   x_2 => gt_1, mul_3, where_1
#   x_3 => div_1
# Graph fragment:
#   %gt_1 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_1, 0), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_1, 0.2), kwargs = {})
#   %where_1 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_1, %convolution_1, %mul_3), kwargs = {})
#   %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%where_1, 2), kwargs = {})
#   %mean_1 : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%pow_2, [1], True), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mean_1, 1e-08), kwargs = {})
#   %sqrt_1 : [num_users=1] = call_function[target=torch.ops.aten.sqrt.default](args = (%add_1,), kwargs = {})
#   %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%where_1, %sqrt_1), kwargs = {})
triton_poi_fused_add_div_leaky_relu_mean_pow_sqrt_3 = async_compile.triton('triton_poi_fused_add_div_leaky_relu_mean_pow_sqrt_3', '''
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=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_add_div_leaky_relu_mean_pow_sqrt_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, '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_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)
''', 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, 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)
        # Topologically Sorted Source Nodes: [mul], Original ATen: [aten.mul]
        stream0 = get_raw_stream(0)
        triton_poi_fused_mul_0.run(primals_1, buf0, 256, grid=grid(256), stream=stream0)
        del primals_1
        # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
        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  # reuse
        # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_1.run(buf2, primals_3, 256, grid=grid(256), stream=stream0)
        del primals_3
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf4 = buf3; del buf3  # reuse
        # Topologically Sorted Source Nodes: [x, pow_1, mean, add, sqrt, x_1, mul_1], Original ATen: [aten.leaky_relu, aten.pow, aten.mean, aten.add, aten.sqrt, aten.div, aten.mul]
        triton_poi_fused_add_div_leaky_relu_mean_mul_pow_sqrt_2.run(buf4, buf2, 256, grid=grid(256), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution]
        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  # reuse
        # Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_1.run(buf6, primals_5, 256, grid=grid(256), stream=stream0)
        del primals_5
        buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_2, pow_2, mean_1, add_1, sqrt_1, x_3], Original ATen: [aten.leaky_relu, aten.pow, aten.mean, aten.add, aten.sqrt, aten.div]
        triton_poi_fused_add_div_leaky_relu_mean_pow_sqrt_3.run(buf6, buf7, 256, grid=grid(256), stream=stream0)
    return (buf7, primals_2, primals_4, buf0, buf2, buf4, buf6, )


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, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((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 torch
import torch.nn as nn
import torch.utils.data


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 ConvBlock(nn.Module):

    def __init__(self, in_channels, out_channels, use_pixelnorm=True):
        super(ConvBlock, 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, x):
        x = self.leaky(self.conv1(x))
        x = self.pn(x) if self.use_pn else x
        x = self.leaky(self.conv2(x))
        x = self.pn(x) if self.use_pn else x
        return x


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


def get_init_inputs():
    return [[], {'in_channels': 4, 'out_channels': 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
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=256, 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=256, 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=256, 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=128, 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]
