# 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/sv/csvlv6wvjrp53f4ahzh3lmzvhxwlged7xkff5xqrl6jfc64skj6o.py
# Topologically Sorted Source Nodes: [conv2d, output], Original ATen: [aten.convolution, aten.native_group_norm]
# Source node to ATen node mapping:
#   conv2d => convolution
#   output => add, add_1, mul_2, rsqrt, var_mean
# Graph fragment:
#   %convolution : [num_users=4] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %var_mean : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%view_1, [2, 3]), kwargs = {correction: 0, keepdim: True})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem, 1e-08), kwargs = {})
#   %rsqrt : [num_users=2] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add,), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_2, %unsqueeze_5), kwargs = {})
#   %add_1 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_2, %unsqueeze_2), kwargs = {})
triton_per_fused_convolution_native_group_norm_0 = async_compile.triton('triton_per_fused_convolution_native_group_norm_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=[4, 64],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: '*fp32', 8: 'i32', 9: '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, 7, 9), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_convolution_native_group_norm_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 5, '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_convolution_native_group_norm_0(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, out_ptr2, out_ptr3, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    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)
    r3 = rindex
    x0 = xindex
    r2 = (rindex // 16)
    tmp0 = tl.load(in_out_ptr0 + (r3 + (64*x0)), xmask, other=0.0)
    tmp1 = tl.load(in_ptr0 + (r2), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + (0))
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK, RBLOCK])
    tmp32 = tl.load(in_ptr2 + (r2), None, eviction_policy='evict_last')
    tmp34 = tl.load(in_ptr3 + (r2), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp7 = tmp6 * tmp2
    tmp8 = tl.where(tmp4, tmp2, tmp7)
    tmp9 = tl.broadcast_to(tmp8, [XBLOCK, RBLOCK])
    tmp11 = tl.where(xmask, tmp9, 0)
    tmp12 = tl.broadcast_to(tmp9, [XBLOCK, RBLOCK])
    tmp14 = tl.where(xmask, tmp12, 0)
    tmp15 = tl.sum(tmp14, 1)[:, None]
    tmp16 = tl.full([XBLOCK, 1], 64, tl.int32)
    tmp17 = tmp16.to(tl.float32)
    tmp18 = tmp15 / tmp17
    tmp19 = tmp9 - tmp18
    tmp20 = tmp19 * tmp19
    tmp21 = tl.broadcast_to(tmp20, [XBLOCK, RBLOCK])
    tmp23 = tl.where(xmask, tmp21, 0)
    tmp24 = tl.sum(tmp23, 1)[:, None]
    tmp25 = tmp8 - tmp18
    tmp26 = 64.0
    tmp27 = tmp24 / tmp26
    tmp28 = 1e-08
    tmp29 = tmp27 + tmp28
    tmp30 = libdevice.rsqrt(tmp29)
    tmp31 = tmp25 * tmp30
    tmp33 = tmp31 * tmp32
    tmp35 = tmp33 + tmp34
    tl.store(in_out_ptr0 + (r3 + (64*x0)), tmp2, xmask)
    tl.store(out_ptr2 + (r3 + (64*x0)), tmp35, xmask)
    tl.store(out_ptr3 + (x0), tmp30, xmask)
    tl.store(out_ptr0 + (x0), tmp18, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/wc/cwclm5fv4xv4euwdcy5hcf4sovrzgd4oxluv3hdvlhung236wxjs.py
# Topologically Sorted Source Nodes: [conv2d_1, output_1], Original ATen: [aten.convolution, aten.native_group_norm]
# Source node to ATen node mapping:
#   conv2d_1 => convolution_1
#   output_1 => add_2, add_3, mul_5, rsqrt_1, var_mean_1
# Graph fragment:
#   %convolution_1 : [num_users=4] = call_function[target=torch.ops.aten.convolution.default](args = (%add_1, %primals_7, %primals_8, [1, 1], [4, 4], [1, 1], False, [0, 0], 4), kwargs = {})
#   %var_mean_1 : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%view_4, [2, 3]), kwargs = {correction: 0, keepdim: True})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem_2, 1e-08), kwargs = {})
#   %rsqrt_1 : [num_users=2] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add_2,), kwargs = {})
#   %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_5, %unsqueeze_11), kwargs = {})
#   %add_3 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_5, %unsqueeze_8), kwargs = {})
triton_per_fused_convolution_native_group_norm_1 = async_compile.triton('triton_per_fused_convolution_native_group_norm_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=[4, 512],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: '*fp32', 8: 'i32', 9: '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, 7), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_convolution_native_group_norm_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': True, 'num_load': 5, '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_convolution_native_group_norm_1(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, out_ptr2, out_ptr3, xnumel, rnumel):
    xnumel = 4
    XBLOCK: tl.constexpr = 1
    rnumel = 324
    RBLOCK: tl.constexpr = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    xmask = tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    roffset = 0
    rmask = rindex < rnumel
    r3 = rindex
    x0 = xindex
    r2 = (rindex // 81)
    tmp0 = tl.load(in_out_ptr0 + (r3 + (324*x0)), rmask, other=0.0)
    tmp1 = tl.load(in_ptr0 + (r2), rmask, eviction_policy='evict_last', other=0.0)
    tmp5 = tl.load(in_ptr1 + (0))
    tmp6 = tl.broadcast_to(tmp5, [RBLOCK])
    tmp32 = tl.load(in_ptr2 + (r2), rmask, eviction_policy='evict_last', other=0.0)
    tmp34 = tl.load(in_ptr3 + (r2), rmask, eviction_policy='evict_last', other=0.0)
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp7 = tmp6 * tmp2
    tmp8 = tl.where(tmp4, tmp2, tmp7)
    tmp9 = tl.broadcast_to(tmp8, [RBLOCK])
    tmp11 = tl.where(rmask, tmp9, 0)
    tmp12 = tl.broadcast_to(tmp9, [RBLOCK])
    tmp14 = tl.where(rmask, tmp12, 0)
    tmp15 = triton_helpers.promote_to_tensor(tl.sum(tmp14, 0))
    tmp16 = tl.full([1], 324, tl.int32)
    tmp17 = tmp16.to(tl.float32)
    tmp18 = tmp15 / tmp17
    tmp19 = tmp9 - tmp18
    tmp20 = tmp19 * tmp19
    tmp21 = tl.broadcast_to(tmp20, [RBLOCK])
    tmp23 = tl.where(rmask, tmp21, 0)
    tmp24 = triton_helpers.promote_to_tensor(tl.sum(tmp23, 0))
    tmp25 = tmp8 - tmp18
    tmp26 = 324.0
    tmp27 = tmp24 / tmp26
    tmp28 = 1e-08
    tmp29 = tmp27 + tmp28
    tmp30 = libdevice.rsqrt(tmp29)
    tmp31 = tmp25 * tmp30
    tmp33 = tmp31 * tmp32
    tmp35 = tmp33 + tmp34
    tl.store(in_out_ptr0 + (r3 + (324*x0)), tmp2, rmask)
    tl.store(out_ptr2 + (r3 + (324*x0)), tmp35, rmask)
    tl.store(out_ptr3 + (x0), tmp30, None)
    tl.store(out_ptr0 + (x0), tmp18, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/2y/c2yaievmhemktd5uxyiqpcqdksjgyyhguyp576kgpjfy2jk6tilk.py
# Topologically Sorted Source Nodes: [residual], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   residual => convolution_2
# Graph fragment:
#   %convolution_2 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%add_3, %primals_12, %primals_13, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_2 = async_compile.triton('triton_poi_fused_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=[2048], 
    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_2', '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_2(in_out_ptr0, in_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_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')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_2, (4, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (1, ), (1, ))
    assert_size_stride(primals_5, (4, ), (1, ))
    assert_size_stride(primals_6, (4, ), (1, ))
    assert_size_stride(primals_7, (4, 1, 4, 4), (16, 16, 4, 1))
    assert_size_stride(primals_8, (4, ), (1, ))
    assert_size_stride(primals_9, (1, ), (1, ))
    assert_size_stride(primals_10, (4, ), (1, ))
    assert_size_stride(primals_11, (4, ), (1, ))
    assert_size_stride(primals_12, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_13, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 4, 4), (64, 16, 4, 1))
        buf1 = buf0; del buf0  # reuse
        buf2 = empty_strided_cuda((4, 1, 1, 1), (1, 4, 4, 4), torch.float32)
        buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf6 = empty_strided_cuda((4, 1, 1, 1), (1, 4, 4, 4), torch.float32)
        # Topologically Sorted Source Nodes: [conv2d, output], Original ATen: [aten.convolution, aten.native_group_norm]
        stream0 = get_raw_stream(0)
        triton_per_fused_convolution_native_group_norm_0.run(buf1, primals_2, primals_4, primals_5, primals_6, buf2, buf5, buf6, 4, 64, grid=grid(4), stream=stream0)
        del primals_2
        del primals_6
        # Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution]
        buf7 = extern_kernels.convolution(buf5, primals_7, stride=(1, 1), padding=(4, 4), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=4, bias=None)
        assert_size_stride(buf7, (4, 4, 9, 9), (324, 81, 9, 1))
        buf8 = buf7; del buf7  # reuse
        buf9 = empty_strided_cuda((4, 1, 1, 1), (1, 4, 4, 4), torch.float32)
        buf12 = empty_strided_cuda((4, 4, 9, 9), (324, 81, 9, 1), torch.float32)
        buf13 = empty_strided_cuda((4, 1, 1, 1), (1, 4, 4, 4), torch.float32)
        # Topologically Sorted Source Nodes: [conv2d_1, output_1], Original ATen: [aten.convolution, aten.native_group_norm]
        triton_per_fused_convolution_native_group_norm_1.run(buf8, primals_8, primals_9, primals_10, primals_11, buf9, buf12, buf13, 4, 324, grid=grid(4), stream=stream0)
        del primals_11
        del primals_8
        # Topologically Sorted Source Nodes: [residual], Original ATen: [aten.convolution]
        buf14 = extern_kernels.convolution(buf12, primals_12, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf14, (4, 4, 9, 9), (324, 81, 9, 1))
        buf15 = buf14; del buf14  # reuse
        # Topologically Sorted Source Nodes: [residual], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_2.run(buf15, primals_13, 1296, grid=grid(1296), stream=stream0)
        del primals_13
    return (buf15, primals_1, primals_3, primals_4, primals_5, primals_7, primals_9, primals_10, primals_12, buf1, buf5, reinterpret_tensor(buf2, (4, 1), (1, 1), 0), reinterpret_tensor(buf6, (4, 1), (1, 1), 0), buf8, buf12, reinterpret_tensor(buf9, (4, 1), (1, 1), 0), reinterpret_tensor(buf13, (4, 1), (1, 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, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((4, 1, 4, 4), (16, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_12 = rand_strided((4, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_13 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13])
    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


class DepthConv2d(nn.Module):

    def __init__(self, input_channel, hidden_channel, kernel, padding,
        dilation=1):
        super(DepthConv2d, self).__init__()
        self.conv2d = nn.Conv2d(input_channel, hidden_channel, 1)
        self.padding = padding
        self.dconv2d = nn.Conv2d(hidden_channel, hidden_channel, kernel,
            dilation=dilation, groups=hidden_channel, padding=self.padding)
        self.res_out = nn.Conv2d(hidden_channel, input_channel, 1)
        self.nonlinearity1 = nn.PReLU()
        self.nonlinearity2 = nn.PReLU()
        self.reg1 = nn.GroupNorm(1, hidden_channel, eps=1e-08)
        self.reg2 = nn.GroupNorm(1, hidden_channel, eps=1e-08)

    def forward(self, input):
        output = self.reg1(self.nonlinearity1(self.conv2d(input)))
        output = self.reg2(self.nonlinearity2(self.dconv2d(output)))
        residual = self.res_out(output)
        return residual


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


def get_init_inputs():
    return [[], {'input_channel': 4, 'hidden_channel': 4, 'kernel': 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 import triton_helpers
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
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_per_fused_convolution_native_group_norm_0(in_out_ptr0, in_ptr0,
    in_ptr1, in_ptr2, in_ptr3, out_ptr0, out_ptr2, out_ptr3, xnumel, rnumel,
    XBLOCK: tl.constexpr):
    xnumel = 4
    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)
    r3 = rindex
    x0 = xindex
    r2 = rindex // 16
    tmp0 = tl.load(in_out_ptr0 + (r3 + 64 * x0), xmask, other=0.0)
    tmp1 = tl.load(in_ptr0 + r2, None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + 0)
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK, RBLOCK])
    tmp32 = tl.load(in_ptr2 + r2, None, eviction_policy='evict_last')
    tmp34 = tl.load(in_ptr3 + r2, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp7 = tmp6 * tmp2
    tmp8 = tl.where(tmp4, tmp2, tmp7)
    tmp9 = tl.broadcast_to(tmp8, [XBLOCK, RBLOCK])
    tl.where(xmask, tmp9, 0)
    tmp12 = tl.broadcast_to(tmp9, [XBLOCK, RBLOCK])
    tmp14 = tl.where(xmask, tmp12, 0)
    tmp15 = tl.sum(tmp14, 1)[:, None]
    tmp16 = tl.full([XBLOCK, 1], 64, tl.int32)
    tmp17 = tmp16.to(tl.float32)
    tmp18 = tmp15 / tmp17
    tmp19 = tmp9 - tmp18
    tmp20 = tmp19 * tmp19
    tmp21 = tl.broadcast_to(tmp20, [XBLOCK, RBLOCK])
    tmp23 = tl.where(xmask, tmp21, 0)
    tmp24 = tl.sum(tmp23, 1)[:, None]
    tmp25 = tmp8 - tmp18
    tmp26 = 64.0
    tmp27 = tmp24 / tmp26
    tmp28 = 1e-08
    tmp29 = tmp27 + tmp28
    tmp30 = libdevice.rsqrt(tmp29)
    tmp31 = tmp25 * tmp30
    tmp33 = tmp31 * tmp32
    tmp35 = tmp33 + tmp34
    tl.store(in_out_ptr0 + (r3 + 64 * x0), tmp2, xmask)
    tl.store(out_ptr2 + (r3 + 64 * x0), tmp35, xmask)
    tl.store(out_ptr3 + x0, tmp30, xmask)
    tl.store(out_ptr0 + x0, tmp18, xmask)


@triton.jit
def triton_per_fused_convolution_native_group_norm_1(in_out_ptr0, in_ptr0,
    in_ptr1, in_ptr2, in_ptr3, out_ptr0, out_ptr2, out_ptr3, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    rnumel = 324
    RBLOCK: tl.constexpr = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    rmask = rindex < rnumel
    r3 = rindex
    x0 = xindex
    r2 = rindex // 81
    tmp0 = tl.load(in_out_ptr0 + (r3 + 324 * x0), rmask, other=0.0)
    tmp1 = tl.load(in_ptr0 + r2, rmask, eviction_policy='evict_last', other=0.0
        )
    tmp5 = tl.load(in_ptr1 + 0)
    tmp6 = tl.broadcast_to(tmp5, [RBLOCK])
    tmp32 = tl.load(in_ptr2 + r2, rmask, eviction_policy='evict_last',
        other=0.0)
    tmp34 = tl.load(in_ptr3 + r2, rmask, eviction_policy='evict_last',
        other=0.0)
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp7 = tmp6 * tmp2
    tmp8 = tl.where(tmp4, tmp2, tmp7)
    tmp9 = tl.broadcast_to(tmp8, [RBLOCK])
    tl.where(rmask, tmp9, 0)
    tmp12 = tl.broadcast_to(tmp9, [RBLOCK])
    tmp14 = tl.where(rmask, tmp12, 0)
    tmp15 = triton_helpers.promote_to_tensor(tl.sum(tmp14, 0))
    tmp16 = tl.full([1], 324, tl.int32)
    tmp17 = tmp16.to(tl.float32)
    tmp18 = tmp15 / tmp17
    tmp19 = tmp9 - tmp18
    tmp20 = tmp19 * tmp19
    tmp21 = tl.broadcast_to(tmp20, [RBLOCK])
    tmp23 = tl.where(rmask, tmp21, 0)
    tmp24 = triton_helpers.promote_to_tensor(tl.sum(tmp23, 0))
    tmp25 = tmp8 - tmp18
    tmp26 = 324.0
    tmp27 = tmp24 / tmp26
    tmp28 = 1e-08
    tmp29 = tmp27 + tmp28
    tmp30 = libdevice.rsqrt(tmp29)
    tmp31 = tmp25 * tmp30
    tmp33 = tmp31 * tmp32
    tmp35 = tmp33 + tmp34
    tl.store(in_out_ptr0 + (r3 + 324 * x0), tmp2, rmask)
    tl.store(out_ptr2 + (r3 + 324 * x0), tmp35, rmask)
    tl.store(out_ptr3 + x0, tmp30, None)
    tl.store(out_ptr0 + x0, tmp18, None)


@triton.jit
def triton_poi_fused_convolution_2(in_out_ptr0, in_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_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)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
        primals_13) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (1,), (1,))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4,), (1,))
    assert_size_stride(primals_7, (4, 1, 4, 4), (16, 16, 4, 1))
    assert_size_stride(primals_8, (4,), (1,))
    assert_size_stride(primals_9, (1,), (1,))
    assert_size_stride(primals_10, (4,), (1,))
    assert_size_stride(primals_11, (4,), (1,))
    assert_size_stride(primals_12, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_13, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 4, 4), (64, 16, 4, 1))
        buf1 = buf0
        del buf0
        buf2 = empty_strided_cuda((4, 1, 1, 1), (1, 4, 4, 4), torch.float32)
        buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf6 = empty_strided_cuda((4, 1, 1, 1), (1, 4, 4, 4), torch.float32)
        get_raw_stream(0)
        triton_per_fused_convolution_native_group_norm_0[grid(4)](buf1,
            primals_2, primals_4, primals_5, primals_6, buf2, buf5, buf6, 4,
            64, XBLOCK=1, num_warps=2, num_stages=1)
        del primals_2
        del primals_6
        buf7 = extern_kernels.convolution(buf5, primals_7, stride=(1, 1),
            padding=(4, 4), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=4, bias=None)
        assert_size_stride(buf7, (4, 4, 9, 9), (324, 81, 9, 1))
        buf8 = buf7
        del buf7
        buf9 = empty_strided_cuda((4, 1, 1, 1), (1, 4, 4, 4), torch.float32)
        buf12 = empty_strided_cuda((4, 4, 9, 9), (324, 81, 9, 1), torch.float32
            )
        buf13 = empty_strided_cuda((4, 1, 1, 1), (1, 4, 4, 4), torch.float32)
        triton_per_fused_convolution_native_group_norm_1[grid(4)](buf8,
            primals_8, primals_9, primals_10, primals_11, buf9, buf12,
            buf13, 4, 324, num_warps=4, num_stages=1)
        del primals_11
        del primals_8
        buf14 = extern_kernels.convolution(buf12, primals_12, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf14, (4, 4, 9, 9), (324, 81, 9, 1))
        buf15 = buf14
        del buf14
        triton_poi_fused_convolution_2[grid(1296)](buf15, primals_13, 1296,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_13
    return (buf15, primals_1, primals_3, primals_4, primals_5, primals_7,
        primals_9, primals_10, primals_12, buf1, buf5, reinterpret_tensor(
        buf2, (4, 1), (1, 1), 0), reinterpret_tensor(buf6, (4, 1), (1, 1), 
        0), buf8, buf12, reinterpret_tensor(buf9, (4, 1), (1, 1), 0),
        reinterpret_tensor(buf13, (4, 1), (1, 1), 0))


class DepthConv2dNew(nn.Module):

    def __init__(self, input_channel, hidden_channel, kernel, padding,
        dilation=1):
        super(DepthConv2dNew, self).__init__()
        self.conv2d = nn.Conv2d(input_channel, hidden_channel, 1)
        self.padding = padding
        self.dconv2d = nn.Conv2d(hidden_channel, hidden_channel, kernel,
            dilation=dilation, groups=hidden_channel, padding=self.padding)
        self.res_out = nn.Conv2d(hidden_channel, input_channel, 1)
        self.nonlinearity1 = nn.PReLU()
        self.nonlinearity2 = nn.PReLU()
        self.reg1 = nn.GroupNorm(1, hidden_channel, eps=1e-08)
        self.reg2 = nn.GroupNorm(1, hidden_channel, eps=1e-08)

    def forward(self, input_0):
        primals_1 = self.conv2d.weight
        primals_2 = self.conv2d.bias
        primals_7 = self.dconv2d.weight
        primals_5 = self.dconv2d.bias
        primals_12 = self.res_out.weight
        primals_6 = self.res_out.bias
        primals_4 = self.nonlinearity1.weight
        primals_9 = self.nonlinearity2.weight
        primals_8 = self.reg1.weight
        primals_10 = self.reg1.bias
        primals_11 = self.reg2.weight
        primals_13 = self.reg2.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11, primals_12, primals_13])
        return output[0]
