# 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/su/csul6ivujixdnmbzkuhqbyt6uzov5dosckt6dbjdbwgpskz6gi73.py
# Topologically Sorted Source Nodes: [conv2d, out_c1], Original ATen: [aten.convolution, aten.leaky_relu, aten.leaky_relu_backward]
# Source node to ATen node mapping:
#   conv2d => convolution
#   out_c1 => gt, mul, where
# Graph fragment:
#   %convolution : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution, 0), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution, 0.05), kwargs = {})
#   %where : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt, %convolution, %mul), kwargs = {})
#   %gt_5 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%where, 0), kwargs = {})
triton_poi_fused_convolution_leaky_relu_leaky_relu_backward_0 = async_compile.triton('triton_poi_fused_convolution_leaky_relu_leaky_relu_backward_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: '*i1', 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_convolution_leaky_relu_leaky_relu_backward_0', '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_leaky_relu_leaky_relu_backward_0(in_out_ptr0, 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
    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
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.05
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tmp8 = tmp7 > tmp3
    tl.store(in_out_ptr0 + (x3), tmp7, xmask)
    tl.store(out_ptr0 + (x3), tmp8, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/27/c27vmhvziyjnpixog35mvm6ej6g27gmaqtsayp6lupu4h2w33xpl.py
# Topologically Sorted Source Nodes: [out], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   out => cat
# Graph fragment:
#   %cat : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%getitem_12, %getitem_14, %getitem_16, %convolution_3], 1), kwargs = {})
triton_poi_fused_cat_1 = async_compile.triton('triton_poi_fused_cat_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: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 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_poi_fused_cat_1', '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_cat_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 16) % 4
    x0 = xindex % 16
    x2 = (xindex // 64)
    x3 = xindex
    tmp20 = tl.load(in_ptr4 + (0))
    tmp21 = tl.broadcast_to(tmp20, [XBLOCK])
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + (16*x1) + (64*x2)), tmp4 & xmask, other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 2, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + (x0 + (16*((-1) + x1)) + (64*x2)), tmp9 & xmask, other=0.0)
    tmp11 = tmp0 >= tmp7
    tmp12 = tl.full([1], 3, tl.int64)
    tmp13 = tmp0 < tmp12
    tmp14 = tmp11 & tmp13
    tmp15 = tl.load(in_ptr2 + (x0 + (16*((-2) + x1)) + (64*x2)), tmp14 & xmask, other=0.0)
    tmp16 = tmp0 >= tmp12
    tmp17 = tl.full([1], 4, tl.int64)
    tmp18 = tmp0 < tmp17
    tmp19 = tl.load(in_ptr3 + (x0 + (16*x2)), tmp16 & xmask, eviction_policy='evict_last', other=0.0)
    tmp22 = tmp19 + tmp21
    tmp23 = tl.full(tmp22.shape, 0.0, tmp22.dtype)
    tmp24 = tl.where(tmp16, tmp22, tmp23)
    tmp25 = tl.where(tmp14, tmp15, tmp24)
    tmp26 = tl.where(tmp9, tmp10, tmp25)
    tmp27 = tl.where(tmp4, tmp5, tmp26)
    tl.store(out_ptr0 + (x3), tmp27, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/yl/cyl57twtgf3lzd5sst7snomgtzysir6mpvrzx6jm7k4lxpcq6sru.py
# Topologically Sorted Source Nodes: [conv2d_4, out_fused], Original ATen: [aten.convolution, aten.add]
# Source node to ATen node mapping:
#   conv2d_4 => convolution_4
#   out_fused => add
# Graph fragment:
#   %convolution_4 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%cat, %primals_10, %primals_11, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convolution_4, %primals_3), 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_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + (x3), xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + 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, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 3, 3), (36, 9, 3, 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, (4, 3, 3, 3), (27, 9, 3, 1))
    assert_size_stride(primals_5, (4, ), (1, ))
    assert_size_stride(primals_6, (4, 3, 3, 3), (27, 9, 3, 1))
    assert_size_stride(primals_7, (4, ), (1, ))
    assert_size_stride(primals_8, (1, 3, 3, 3), (27, 9, 3, 1))
    assert_size_stride(primals_9, (1, ), (1, ))
    assert_size_stride(primals_10, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_11, (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=(1, 1), 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
        buf12 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        # Topologically Sorted Source Nodes: [conv2d, out_c1], Original ATen: [aten.convolution, aten.leaky_relu, aten.leaky_relu_backward]
        stream0 = get_raw_stream(0)
        triton_poi_fused_convolution_leaky_relu_leaky_relu_backward_0.run(buf1, primals_2, buf12, 256, grid=grid(256), stream=stream0)
        del primals_2
        # Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution]
        buf2 = extern_kernels.convolution(reinterpret_tensor(buf1, (4, 3, 4, 4), (64, 16, 4, 1), 16), primals_4, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 4, 4, 4), (64, 16, 4, 1))
        buf3 = buf2; del buf2  # reuse
        buf11 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        # Topologically Sorted Source Nodes: [conv2d_1, out_c2], Original ATen: [aten.convolution, aten.leaky_relu, aten.leaky_relu_backward]
        triton_poi_fused_convolution_leaky_relu_leaky_relu_backward_0.run(buf3, primals_5, buf11, 256, grid=grid(256), stream=stream0)
        del primals_5
        # Topologically Sorted Source Nodes: [conv2d_2], Original ATen: [aten.convolution]
        buf4 = extern_kernels.convolution(reinterpret_tensor(buf3, (4, 3, 4, 4), (64, 16, 4, 1), 16), primals_6, 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
        buf10 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        # Topologically Sorted Source Nodes: [conv2d_2, out_c3], Original ATen: [aten.convolution, aten.leaky_relu, aten.leaky_relu_backward]
        triton_poi_fused_convolution_leaky_relu_leaky_relu_backward_0.run(buf5, primals_7, buf10, 256, grid=grid(256), stream=stream0)
        del primals_7
        # Topologically Sorted Source Nodes: [out_c4], Original ATen: [aten.convolution]
        buf6 = extern_kernels.convolution(reinterpret_tensor(buf5, (4, 3, 4, 4), (64, 16, 4, 1), 16), primals_8, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 1, 4, 4), (16, 16, 4, 1))
        buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out], Original ATen: [aten.cat]
        triton_poi_fused_cat_1.run(buf1, buf3, buf5, buf6, primals_9, buf7, 256, grid=grid(256), stream=stream0)
        del buf6
        del primals_9
        # Topologically Sorted Source Nodes: [conv2d_4], Original ATen: [aten.convolution]
        buf8 = extern_kernels.convolution(buf7, primals_10, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf8, (4, 4, 4, 4), (64, 16, 4, 1))
        buf9 = buf8; del buf8  # reuse
        # Topologically Sorted Source Nodes: [conv2d_4, out_fused], Original ATen: [aten.convolution, aten.add]
        triton_poi_fused_add_convolution_2.run(buf9, primals_11, primals_3, 256, grid=grid(256), stream=stream0)
        del primals_11
    return (buf9, primals_1, primals_3, primals_4, primals_6, primals_8, primals_10, reinterpret_tensor(buf1, (4, 3, 4, 4), (64, 16, 4, 1), 16), reinterpret_tensor(buf3, (4, 3, 4, 4), (64, 16, 4, 1), 16), reinterpret_tensor(buf5, (4, 3, 4, 4), (64, 16, 4, 1), 16), buf7, buf10, buf11, buf12, )


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, 3, 3), (36, 9, 3, 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((4, 3, 3, 3), (27, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, 3, 3, 3), (27, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((1, 3, 3, 3), (27, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((4, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_11 = 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])
    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.model_zoo


def activation(act_type, inplace=True, neg_slope=0.05, n_prelu=1):
    act_type = act_type.lower()
    if act_type == 'relu':
        layer = nn.ReLU(inplace)
    elif act_type == 'lrelu':
        layer = nn.LeakyReLU(neg_slope, inplace)
    elif act_type == 'prelu':
        layer = nn.PReLU(num_parameters=n_prelu, init=neg_slope)
    else:
        raise NotImplementedError('activation layer [{:s}] is not found'.
            format(act_type))
    return layer


def conv_layer(in_channels, out_channels, kernel_size, stride=1, dilation=1,
    groups=1):
    padding = int((kernel_size - 1) / 2) * dilation
    return nn.Conv2d(in_channels, out_channels, kernel_size, stride,
        padding=padding, bias=True, dilation=dilation, groups=groups)


class IMDModule_speed(nn.Module):

    def __init__(self, in_channels, distillation_rate=0.25):
        super(IMDModule_speed, self).__init__()
        self.distilled_channels = int(in_channels * distillation_rate)
        self.remaining_channels = int(in_channels - self.distilled_channels)
        self.c1 = conv_layer(in_channels, in_channels, 3)
        self.c2 = conv_layer(self.remaining_channels, in_channels, 3)
        self.c3 = conv_layer(self.remaining_channels, in_channels, 3)
        self.c4 = conv_layer(self.remaining_channels, self.
            distilled_channels, 3)
        self.act = activation('lrelu', neg_slope=0.05)
        self.c5 = conv_layer(self.distilled_channels * 4, in_channels, 1)

    def forward(self, x):
        out_c1 = self.act(self.c1(x))
        distilled_c1, remaining_c1 = torch.split(out_c1, (self.
            distilled_channels, self.remaining_channels), dim=1)
        out_c2 = self.act(self.c2(remaining_c1))
        distilled_c2, remaining_c2 = torch.split(out_c2, (self.
            distilled_channels, self.remaining_channels), dim=1)
        out_c3 = self.act(self.c3(remaining_c2))
        distilled_c3, remaining_c3 = torch.split(out_c3, (self.
            distilled_channels, self.remaining_channels), dim=1)
        out_c4 = self.c4(remaining_c3)
        out = torch.cat([distilled_c1, distilled_c2, distilled_c3, out_c4],
            dim=1)
        out_fused = self.c5(out) + x
        return out_fused


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


def get_init_inputs():
    return [[], {'in_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
import torch.nn as nn
import torch.utils.model_zoo
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_convolution_leaky_relu_leaky_relu_backward_0(in_out_ptr0,
    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
    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
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.05
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tmp8 = tmp7 > tmp3
    tl.store(in_out_ptr0 + x3, tmp7, xmask)
    tl.store(out_ptr0 + x3, tmp8, xmask)


@triton.jit
def triton_poi_fused_cat_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 16 % 4
    x0 = xindex % 16
    x2 = xindex // 64
    x3 = xindex
    tmp20 = tl.load(in_ptr4 + 0)
    tmp21 = tl.broadcast_to(tmp20, [XBLOCK])
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 16 * x1 + 64 * x2), tmp4 & xmask, other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 2, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + (x0 + 16 * (-1 + x1) + 64 * x2), tmp9 & xmask,
        other=0.0)
    tmp11 = tmp0 >= tmp7
    tmp12 = tl.full([1], 3, tl.int64)
    tmp13 = tmp0 < tmp12
    tmp14 = tmp11 & tmp13
    tmp15 = tl.load(in_ptr2 + (x0 + 16 * (-2 + x1) + 64 * x2), tmp14 &
        xmask, other=0.0)
    tmp16 = tmp0 >= tmp12
    tl.full([1], 4, tl.int64)
    tmp19 = tl.load(in_ptr3 + (x0 + 16 * x2), tmp16 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp22 = tmp19 + tmp21
    tmp23 = tl.full(tmp22.shape, 0.0, tmp22.dtype)
    tmp24 = tl.where(tmp16, tmp22, tmp23)
    tmp25 = tl.where(tmp14, tmp15, tmp24)
    tmp26 = tl.where(tmp9, tmp10, tmp25)
    tmp27 = tl.where(tmp4, tmp5, tmp26)
    tl.store(out_ptr0 + x3, tmp27, 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_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + x3, xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tl.store(in_out_ptr0 + x3, tmp4, 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) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 3, 3), (36, 9, 3, 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, (4, 3, 3, 3), (27, 9, 3, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 3, 3, 3), (27, 9, 3, 1))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (1, 3, 3, 3), (27, 9, 3, 1))
    assert_size_stride(primals_9, (1,), (1,))
    assert_size_stride(primals_10, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_11, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 
            1), padding=(1, 1), 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
        buf12 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_convolution_leaky_relu_leaky_relu_backward_0[grid(256)
            ](buf1, primals_2, buf12, 256, XBLOCK=128, num_warps=4,
            num_stages=1)
        del primals_2
        buf2 = extern_kernels.convolution(reinterpret_tensor(buf1, (4, 3, 4,
            4), (64, 16, 4, 1), 16), primals_4, stride=(1, 1), padding=(1, 
            1), dilation=(1, 1), transposed=False, output_padding=(0, 0),
            groups=1, bias=None)
        assert_size_stride(buf2, (4, 4, 4, 4), (64, 16, 4, 1))
        buf3 = buf2
        del buf2
        buf11 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        triton_poi_fused_convolution_leaky_relu_leaky_relu_backward_0[grid(256)
            ](buf3, primals_5, buf11, 256, XBLOCK=128, num_warps=4,
            num_stages=1)
        del primals_5
        buf4 = extern_kernels.convolution(reinterpret_tensor(buf3, (4, 3, 4,
            4), (64, 16, 4, 1), 16), primals_6, 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
        buf10 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        triton_poi_fused_convolution_leaky_relu_leaky_relu_backward_0[grid(256)
            ](buf5, primals_7, buf10, 256, XBLOCK=128, num_warps=4,
            num_stages=1)
        del primals_7
        buf6 = extern_kernels.convolution(reinterpret_tensor(buf5, (4, 3, 4,
            4), (64, 16, 4, 1), 16), primals_8, stride=(1, 1), padding=(1, 
            1), dilation=(1, 1), transposed=False, output_padding=(0, 0),
            groups=1, bias=None)
        assert_size_stride(buf6, (4, 1, 4, 4), (16, 16, 4, 1))
        buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_cat_1[grid(256)](buf1, buf3, buf5, buf6, primals_9,
            buf7, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del buf6
        del primals_9
        buf8 = extern_kernels.convolution(buf7, primals_10, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf8, (4, 4, 4, 4), (64, 16, 4, 1))
        buf9 = buf8
        del buf8
        triton_poi_fused_add_convolution_2[grid(256)](buf9, primals_11,
            primals_3, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_11
    return (buf9, primals_1, primals_3, primals_4, primals_6, primals_8,
        primals_10, reinterpret_tensor(buf1, (4, 3, 4, 4), (64, 16, 4, 1), 
        16), reinterpret_tensor(buf3, (4, 3, 4, 4), (64, 16, 4, 1), 16),
        reinterpret_tensor(buf5, (4, 3, 4, 4), (64, 16, 4, 1), 16), buf7,
        buf10, buf11, buf12)


def activation(act_type, inplace=True, neg_slope=0.05, n_prelu=1):
    act_type = act_type.lower()
    if act_type == 'relu':
        layer = nn.ReLU(inplace)
    elif act_type == 'lrelu':
        layer = nn.LeakyReLU(neg_slope, inplace)
    elif act_type == 'prelu':
        layer = nn.PReLU(num_parameters=n_prelu, init=neg_slope)
    else:
        raise NotImplementedError('activation layer [{:s}] is not found'.
            format(act_type))
    return layer


def conv_layer(in_channels, out_channels, kernel_size, stride=1, dilation=1,
    groups=1):
    padding = int((kernel_size - 1) / 2) * dilation
    return nn.Conv2d(in_channels, out_channels, kernel_size, stride,
        padding=padding, bias=True, dilation=dilation, groups=groups)


class IMDModule_speedNew(nn.Module):

    def __init__(self, in_channels, distillation_rate=0.25):
        super(IMDModule_speedNew, self).__init__()
        self.distilled_channels = int(in_channels * distillation_rate)
        self.remaining_channels = int(in_channels - self.distilled_channels)
        self.c1 = conv_layer(in_channels, in_channels, 3)
        self.c2 = conv_layer(self.remaining_channels, in_channels, 3)
        self.c3 = conv_layer(self.remaining_channels, in_channels, 3)
        self.c4 = conv_layer(self.remaining_channels, self.
            distilled_channels, 3)
        self.act = activation('lrelu', neg_slope=0.05)
        self.c5 = conv_layer(self.distilled_channels * 4, in_channels, 1)

    def forward(self, input_0):
        primals_1 = self.c1.weight
        primals_2 = self.c1.bias
        primals_4 = self.c2.weight
        primals_5 = self.c2.bias
        primals_6 = self.c3.weight
        primals_7 = self.c3.bias
        primals_8 = self.c4.weight
        primals_9 = self.c4.bias
        primals_10 = self.c5.weight
        primals_11 = self.c5.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])
        return output[0]
