# 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/zj/czjbomcj7alq7d2rpyq5jovfb4r4f7jmlbscailane6tzp5bclsn.py
# Topologically Sorted Source Nodes: [output, output_1], Original ATen: [aten._native_batch_norm_legit, aten.elu]
# Source node to ATen node mapping:
#   output => var_mean
#   output_1 => expm1, gt, mul_1, mul_3, where
# Graph fragment:
#   %var_mean : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%view, [0, 2, 3]), kwargs = {correction: 0, keepdim: True})
#   %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%view_1, 0), kwargs = {})
#   %mul_1 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_1, 1.0), kwargs = {})
#   %expm1 : [num_users=1] = call_function[target=torch.ops.aten.expm1.default](args = (%mul_1,), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%expm1, 1.0), kwargs = {})
#   %where : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt, %mul_1, %mul_3), kwargs = {})
triton_per_fused__native_batch_norm_legit_elu_0 = async_compile.triton('triton_per_fused__native_batch_norm_legit_elu_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.persistent_reduction(
    size_hints=[16, 16],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__native_batch_norm_legit_elu_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 4, '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__native_batch_norm_legit_elu_0(in_ptr0, out_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    rnumel = 16
    RBLOCK: tl.constexpr = 16
    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)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + (16*x0)), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, 0)
    tmp4 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp6 = tl.where(xmask, tmp4, 0)
    tmp7 = tl.sum(tmp6, 1)[:, None]
    tmp8 = tl.full([XBLOCK, 1], 16, tl.int32)
    tmp9 = tmp8.to(tl.float32)
    tmp10 = tmp7 / tmp9
    tmp11 = tmp1 - tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tl.broadcast_to(tmp12, [XBLOCK, RBLOCK])
    tmp15 = tl.where(xmask, tmp13, 0)
    tmp16 = tl.sum(tmp15, 1)[:, None]
    tmp17 = tmp0 - tmp10
    tmp18 = 16.0
    tmp19 = tmp16 / tmp18
    tmp20 = 1e-05
    tmp21 = tmp19 + tmp20
    tmp22 = libdevice.rsqrt(tmp21)
    tmp23 = tmp17 * tmp22
    tmp24 = 0.0
    tmp25 = tmp23 > tmp24
    tmp26 = 1.0
    tmp27 = tmp23 * tmp26
    tmp28 = libdevice.expm1(tmp27)
    tmp29 = tmp28 * tmp26
    tmp30 = tl.where(tmp25, tmp27, tmp29)
    tl.store(out_ptr2 + (r1 + (16*x0)), tmp30, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/at/catgmbzwhox3nc2xeo6zs6mzasfybxlwxttgaih7j6pjgsp6x4l5.py
# Topologically Sorted Source Nodes: [output_2, output_3, output_4], Original ATen: [aten.convolution, aten._native_batch_norm_legit, aten.elu]
# Source node to ATen node mapping:
#   output_2 => convolution
#   output_3 => add_1, rsqrt_1, var_mean_1
#   output_4 => expm1_1, gt_1, mul_5, mul_7, where_1
# Graph fragment:
#   %convolution : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%where, %primals_2, %primals_3, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %var_mean_1 : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%view_2, [0, 2, 3]), kwargs = {correction: 0, keepdim: True})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem_2, 1e-05), kwargs = {})
#   %rsqrt_1 : [num_users=2] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add_1,), kwargs = {})
#   %gt_1 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%view_3, 0), kwargs = {})
#   %mul_5 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_3, 1.0), kwargs = {})
#   %expm1_1 : [num_users=1] = call_function[target=torch.ops.aten.expm1.default](args = (%mul_5,), kwargs = {})
#   %mul_7 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%expm1_1, 1.0), kwargs = {})
#   %where_1 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_1, %mul_5, %mul_7), kwargs = {})
triton_per_fused__native_batch_norm_legit_convolution_elu_1 = async_compile.triton('triton_per_fused__native_batch_norm_legit_convolution_elu_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.persistent_reduction(
    size_hints=[16, 16],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32', 6: '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, 6), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__native_batch_norm_legit_convolution_elu_1', 'mutated_arg_names': ['in_out_ptr0', 'in_out_ptr1'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 4, '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__native_batch_norm_legit_convolution_elu_1(in_out_ptr0, in_out_ptr1, in_ptr0, out_ptr0, out_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    rnumel = 16
    RBLOCK: tl.constexpr = 16
    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)
    r2 = rindex
    x3 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + (r2 + (16*x3)), xmask, other=0.0)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp5 = tl.where(xmask, tmp3, 0)
    tmp6 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
    tmp8 = tl.where(xmask, tmp6, 0)
    tmp9 = tl.sum(tmp8, 1)[:, None]
    tmp10 = tl.full([XBLOCK, 1], 16, tl.int32)
    tmp11 = tmp10.to(tl.float32)
    tmp12 = tmp9 / tmp11
    tmp13 = tmp3 - tmp12
    tmp14 = tmp13 * tmp13
    tmp15 = tl.broadcast_to(tmp14, [XBLOCK, RBLOCK])
    tmp17 = tl.where(xmask, tmp15, 0)
    tmp18 = tl.sum(tmp17, 1)[:, None]
    tmp19 = 16.0
    tmp20 = tmp18 / tmp19
    tmp21 = 1e-05
    tmp22 = tmp20 + tmp21
    tmp23 = libdevice.rsqrt(tmp22)
    tmp24 = tmp2 - tmp12
    tmp25 = tmp24 * tmp23
    tmp26 = 0.0
    tmp27 = tmp25 > tmp26
    tmp28 = 1.0
    tmp29 = tmp25 * tmp28
    tmp30 = libdevice.expm1(tmp29)
    tmp31 = tmp30 * tmp28
    tmp32 = tl.where(tmp27, tmp29, tmp31)
    tl.store(in_out_ptr0 + (r2 + (16*x3)), tmp2, xmask)
    tl.debug_barrier()
    tl.store(in_out_ptr1 + (x3), tmp23, xmask)
    tl.store(out_ptr1 + (r2 + (16*x3)), tmp32, xmask)
    tl.store(out_ptr0 + (x3), tmp12, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/rb/crbiu4u5ytzqglii4fqbg7tcmfshrvsdjcxspfuroyibunmibr5i.py
# Topologically Sorted Source Nodes: [output_5, add], Original ATen: [aten.convolution, aten.add]
# Source node to ATen node mapping:
#   add => add_2
#   output_5 => convolution_1
# Graph fragment:
#   %convolution_1 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%where_1, %primals_4, %primals_5, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%primals_1, %convolution_1), kwargs = {})
triton_poi_fused_add_convolution_2 = async_compile.triton('triton_poi_fused_add_convolution_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: '*fp32', 3: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_convolution_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 3, '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_convolution_2(in_out_ptr0, in_ptr0, in_ptr1, 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_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp2 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp4 = tmp0 + tmp3
    tl.store(in_out_ptr0 + (x3), tmp4, 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)
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [output, output_1], Original ATen: [aten._native_batch_norm_legit, aten.elu]
        stream0 = get_raw_stream(0)
        triton_per_fused__native_batch_norm_legit_elu_0.run(primals_1, buf3, 16, 16, grid=grid(16), stream=stream0)
        # Topologically Sorted Source Nodes: [output_2], Original ATen: [aten.convolution]
        buf4 = extern_kernels.convolution(buf3, primals_2, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 4, 4, 4), (64, 16, 4, 1))
        buf5 = buf4; del buf4  # reuse
        buf6 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 1, 1), torch.float32)
        buf7 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.float32)
        buf9 = reinterpret_tensor(buf7, (1, 16, 1, 1), (16, 1, 1, 1), 0); del buf7  # reuse
        buf10 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [output_2, output_3, output_4], Original ATen: [aten.convolution, aten._native_batch_norm_legit, aten.elu]
        triton_per_fused__native_batch_norm_legit_convolution_elu_1.run(buf5, buf9, primals_3, buf6, buf10, 16, 16, grid=grid(16), stream=stream0)
        del primals_3
        # Topologically Sorted Source Nodes: [output_5], Original ATen: [aten.convolution]
        buf11 = extern_kernels.convolution(buf10, primals_4, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf11, (4, 4, 4, 4), (64, 16, 4, 1))
        buf12 = buf11; del buf11  # reuse
        # Topologically Sorted Source Nodes: [output_5, add], Original ATen: [aten.convolution, aten.add]
        triton_poi_fused_add_convolution_2.run(buf12, primals_1, primals_5, 256, grid=grid(256), stream=stream0)
        del primals_1
        del primals_5
    return (buf12, primals_2, primals_4, buf3, buf5, buf6, buf9, buf10, )


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
from functools import partial


def ncsn_conv3x3(in_planes, out_planes, stride=1, bias=True, dilation=1,
    init_scale=1.0, padding=1):
    """3x3 convolution with PyTorch initialization. Same as NCSNv1/NCSNv2."""
    init_scale = 1e-10 if init_scale == 0 else init_scale
    conv = nn.Conv2d(in_planes, out_planes, stride=stride, bias=bias,
        dilation=dilation, padding=padding, kernel_size=3)
    conv.weight.data *= init_scale
    if bias:
        conv.bias.data *= init_scale
    return conv


def ncsn_conv1x1(in_planes, out_planes, stride=1, bias=True, dilation=1,
    init_scale=1.0, padding=0):
    """1x1 convolution. Same as NCSNv1/v2."""
    conv = nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride,
        bias=bias, dilation=dilation, padding=padding)
    init_scale = 1e-10 if init_scale == 0 else init_scale
    conv.weight.data *= init_scale
    conv.bias.data *= init_scale
    return conv


class ConvMeanPool(nn.Module):

    def __init__(self, input_dim, output_dim, kernel_size=3, biases=True,
        adjust_padding=False):
        super().__init__()
        if not adjust_padding:
            conv = nn.Conv2d(input_dim, output_dim, kernel_size, stride=1,
                padding=kernel_size // 2, bias=biases)
            self.conv = conv
        else:
            conv = nn.Conv2d(input_dim, output_dim, kernel_size, stride=1,
                padding=kernel_size // 2, bias=biases)
            self.conv = nn.Sequential(nn.ZeroPad2d((1, 0, 1, 0)), conv)

    def forward(self, inputs):
        output = self.conv(inputs)
        output = sum([output[:, :, ::2, ::2], output[:, :, 1::2, ::2],
            output[:, :, ::2, 1::2], output[:, :, 1::2, 1::2]]) / 4.0
        return output


class ResidualBlock(nn.Module):

    def __init__(self, input_dim, output_dim, resample=None, act=nn.ELU(),
        normalization=nn.InstanceNorm2d, adjust_padding=False, dilation=1):
        super().__init__()
        self.non_linearity = act
        self.input_dim = input_dim
        self.output_dim = output_dim
        self.resample = resample
        self.normalization = normalization
        if resample == 'down':
            if dilation > 1:
                self.conv1 = ncsn_conv3x3(input_dim, input_dim, dilation=
                    dilation)
                self.normalize2 = normalization(input_dim)
                self.conv2 = ncsn_conv3x3(input_dim, output_dim, dilation=
                    dilation)
                conv_shortcut = partial(ncsn_conv3x3, dilation=dilation)
            else:
                self.conv1 = ncsn_conv3x3(input_dim, input_dim)
                self.normalize2 = normalization(input_dim)
                self.conv2 = ConvMeanPool(input_dim, output_dim, 3,
                    adjust_padding=adjust_padding)
                conv_shortcut = partial(ConvMeanPool, kernel_size=1,
                    adjust_padding=adjust_padding)
        elif resample is None:
            if dilation > 1:
                conv_shortcut = partial(ncsn_conv3x3, dilation=dilation)
                self.conv1 = ncsn_conv3x3(input_dim, output_dim, dilation=
                    dilation)
                self.normalize2 = normalization(output_dim)
                self.conv2 = ncsn_conv3x3(output_dim, output_dim, dilation=
                    dilation)
            else:
                conv_shortcut = partial(ncsn_conv1x1)
                self.conv1 = ncsn_conv3x3(input_dim, output_dim)
                self.normalize2 = normalization(output_dim)
                self.conv2 = ncsn_conv3x3(output_dim, output_dim)
        else:
            raise Exception('invalid resample value')
        if output_dim != input_dim or resample is not None:
            self.shortcut = conv_shortcut(input_dim, output_dim)
        self.normalize1 = normalization(input_dim)

    def forward(self, x):
        output = self.normalize1(x)
        output = self.non_linearity(output)
        output = self.conv1(output)
        output = self.normalize2(output)
        output = self.non_linearity(output)
        output = self.conv2(output)
        if self.output_dim == self.input_dim and self.resample is None:
            shortcut = x
        else:
            shortcut = self.shortcut(x)
        return shortcut + output


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


def get_init_inputs():
    return [[], {'input_dim': 4, 'output_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 torch.nn as nn
from functools import partial
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_per_fused__native_batch_norm_legit_elu_0(in_ptr0, out_ptr2,
    xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    RBLOCK: tl.constexpr = 16
    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)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 16 * x0), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tl.where(xmask, tmp1, 0)
    tmp4 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp6 = tl.where(xmask, tmp4, 0)
    tmp7 = tl.sum(tmp6, 1)[:, None]
    tmp8 = tl.full([XBLOCK, 1], 16, tl.int32)
    tmp9 = tmp8.to(tl.float32)
    tmp10 = tmp7 / tmp9
    tmp11 = tmp1 - tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tl.broadcast_to(tmp12, [XBLOCK, RBLOCK])
    tmp15 = tl.where(xmask, tmp13, 0)
    tmp16 = tl.sum(tmp15, 1)[:, None]
    tmp17 = tmp0 - tmp10
    tmp18 = 16.0
    tmp19 = tmp16 / tmp18
    tmp20 = 1e-05
    tmp21 = tmp19 + tmp20
    tmp22 = libdevice.rsqrt(tmp21)
    tmp23 = tmp17 * tmp22
    tmp24 = 0.0
    tmp25 = tmp23 > tmp24
    tmp26 = 1.0
    tmp27 = tmp23 * tmp26
    tmp28 = libdevice.expm1(tmp27)
    tmp29 = tmp28 * tmp26
    tmp30 = tl.where(tmp25, tmp27, tmp29)
    tl.store(out_ptr2 + (r1 + 16 * x0), tmp30, xmask)


@triton.jit
def triton_per_fused__native_batch_norm_legit_convolution_elu_1(in_out_ptr0,
    in_out_ptr1, in_ptr0, out_ptr0, out_ptr1, xnumel, rnumel, XBLOCK: tl.
    constexpr):
    xnumel = 16
    RBLOCK: tl.constexpr = 16
    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)
    r2 = rindex
    x3 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + (r2 + 16 * x3), xmask, other=0.0)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tl.where(xmask, tmp3, 0)
    tmp6 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
    tmp8 = tl.where(xmask, tmp6, 0)
    tmp9 = tl.sum(tmp8, 1)[:, None]
    tmp10 = tl.full([XBLOCK, 1], 16, tl.int32)
    tmp11 = tmp10.to(tl.float32)
    tmp12 = tmp9 / tmp11
    tmp13 = tmp3 - tmp12
    tmp14 = tmp13 * tmp13
    tmp15 = tl.broadcast_to(tmp14, [XBLOCK, RBLOCK])
    tmp17 = tl.where(xmask, tmp15, 0)
    tmp18 = tl.sum(tmp17, 1)[:, None]
    tmp19 = 16.0
    tmp20 = tmp18 / tmp19
    tmp21 = 1e-05
    tmp22 = tmp20 + tmp21
    tmp23 = libdevice.rsqrt(tmp22)
    tmp24 = tmp2 - tmp12
    tmp25 = tmp24 * tmp23
    tmp26 = 0.0
    tmp27 = tmp25 > tmp26
    tmp28 = 1.0
    tmp29 = tmp25 * tmp28
    tmp30 = libdevice.expm1(tmp29)
    tmp31 = tmp30 * tmp28
    tmp32 = tl.where(tmp27, tmp29, tmp31)
    tl.store(in_out_ptr0 + (r2 + 16 * x3), tmp2, xmask)
    tl.debug_barrier()
    tl.store(in_out_ptr1 + x3, tmp23, xmask)
    tl.store(out_ptr1 + (r2 + 16 * x3), tmp32, xmask)
    tl.store(out_ptr0 + x3, tmp12, xmask)


@triton.jit
def triton_poi_fused_add_convolution_2(in_out_ptr0, in_ptr0, in_ptr1,
    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_ptr0 + x3, xmask)
    tmp1 = tl.load(in_out_ptr0 + x3, xmask)
    tmp2 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp4 = tmp0 + tmp3
    tl.store(in_out_ptr0 + x3, tmp4, 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)
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_per_fused__native_batch_norm_legit_elu_0[grid(16)](primals_1,
            buf3, 16, 16, XBLOCK=8, num_warps=2, num_stages=1)
        buf4 = extern_kernels.convolution(buf3, primals_2, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 4, 4, 4), (64, 16, 4, 1))
        buf5 = buf4
        del buf4
        buf6 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 1, 1), torch.float32)
        buf7 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.float32
            )
        buf9 = reinterpret_tensor(buf7, (1, 16, 1, 1), (16, 1, 1, 1), 0)
        del buf7
        buf10 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_per_fused__native_batch_norm_legit_convolution_elu_1[grid(16)](
            buf5, buf9, primals_3, buf6, buf10, 16, 16, XBLOCK=1, num_warps
            =2, num_stages=1)
        del primals_3
        buf11 = extern_kernels.convolution(buf10, primals_4, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf11, (4, 4, 4, 4), (64, 16, 4, 1))
        buf12 = buf11
        del buf11
        triton_poi_fused_add_convolution_2[grid(256)](buf12, primals_1,
            primals_5, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_1
        del primals_5
    return buf12, primals_2, primals_4, buf3, buf5, buf6, buf9, buf10


def ncsn_conv3x3(in_planes, out_planes, stride=1, bias=True, dilation=1,
    init_scale=1.0, padding=1):
    """3x3 convolution with PyTorch initialization. Same as NCSNv1/NCSNv2."""
    init_scale = 1e-10 if init_scale == 0 else init_scale
    conv = nn.Conv2d(in_planes, out_planes, stride=stride, bias=bias,
        dilation=dilation, padding=padding, kernel_size=3)
    conv.weight.data *= init_scale
    if bias:
        conv.bias.data *= init_scale
    return conv


def ncsn_conv1x1(in_planes, out_planes, stride=1, bias=True, dilation=1,
    init_scale=1.0, padding=0):
    """1x1 convolution. Same as NCSNv1/v2."""
    conv = nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride,
        bias=bias, dilation=dilation, padding=padding)
    init_scale = 1e-10 if init_scale == 0 else init_scale
    conv.weight.data *= init_scale
    conv.bias.data *= init_scale
    return conv


class ConvMeanPool(nn.Module):

    def __init__(self, input_dim, output_dim, kernel_size=3, biases=True,
        adjust_padding=False):
        super().__init__()
        if not adjust_padding:
            conv = nn.Conv2d(input_dim, output_dim, kernel_size, stride=1,
                padding=kernel_size // 2, bias=biases)
            self.conv = conv
        else:
            conv = nn.Conv2d(input_dim, output_dim, kernel_size, stride=1,
                padding=kernel_size // 2, bias=biases)
            self.conv = nn.Sequential(nn.ZeroPad2d((1, 0, 1, 0)), conv)

    def forward(self, inputs):
        output = self.conv(inputs)
        output = sum([output[:, :, ::2, ::2], output[:, :, 1::2, ::2],
            output[:, :, ::2, 1::2], output[:, :, 1::2, 1::2]]) / 4.0
        return output


class ResidualBlockNew(nn.Module):

    def __init__(self, input_dim, output_dim, resample=None, act=nn.ELU(),
        normalization=nn.InstanceNorm2d, adjust_padding=False, dilation=1):
        super().__init__()
        self.non_linearity = act
        self.input_dim = input_dim
        self.output_dim = output_dim
        self.resample = resample
        self.normalization = normalization
        if resample == 'down':
            if dilation > 1:
                self.conv1 = ncsn_conv3x3(input_dim, input_dim, dilation=
                    dilation)
                self.normalize2 = normalization(input_dim)
                self.conv2 = ncsn_conv3x3(input_dim, output_dim, dilation=
                    dilation)
                conv_shortcut = partial(ncsn_conv3x3, dilation=dilation)
            else:
                self.conv1 = ncsn_conv3x3(input_dim, input_dim)
                self.normalize2 = normalization(input_dim)
                self.conv2 = ConvMeanPool(input_dim, output_dim, 3,
                    adjust_padding=adjust_padding)
                conv_shortcut = partial(ConvMeanPool, kernel_size=1,
                    adjust_padding=adjust_padding)
        elif resample is None:
            if dilation > 1:
                conv_shortcut = partial(ncsn_conv3x3, dilation=dilation)
                self.conv1 = ncsn_conv3x3(input_dim, output_dim, dilation=
                    dilation)
                self.normalize2 = normalization(output_dim)
                self.conv2 = ncsn_conv3x3(output_dim, output_dim, dilation=
                    dilation)
            else:
                conv_shortcut = partial(ncsn_conv1x1)
                self.conv1 = ncsn_conv3x3(input_dim, output_dim)
                self.normalize2 = normalization(output_dim)
                self.conv2 = ncsn_conv3x3(output_dim, output_dim)
        else:
            raise Exception('invalid resample value')
        if output_dim != input_dim or resample is not None:
            self.shortcut = conv_shortcut(input_dim, output_dim)
        self.normalize1 = normalization(input_dim)

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