# 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/6z/c6zaho43pxjqk5eqidz3d5ckh3z6vlh34gcbntokha6jjsr4all2.py
# Topologically Sorted Source Nodes: [out, l0], Original ATen: [aten.convolution, aten._prelu_kernel]
# Source node to ATen node mapping:
#   l0 => gt, mul, where
#   out => convolution
# Graph fragment:
#   %convolution : [num_users=4] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [4, 4], [2, 2], [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 = (%view, %convolution), kwargs = {})
#   %where : [num_users=3] = call_function[target=torch.ops.aten.where.self](args = (%gt, %convolution, %mul), kwargs = {})
triton_poi_fused__prelu_kernel_convolution_0 = async_compile.triton('triton_poi_fused__prelu_kernel_convolution_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=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__prelu_kernel_convolution_0', '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__prelu_kernel_convolution_0(in_out_ptr0, in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + (0))
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp7 = tmp6 * tmp2
    tmp8 = tl.where(tmp4, tmp2, tmp7)
    tl.store(in_out_ptr0 + (x2), tmp2, xmask)
    tl.store(out_ptr0 + (x2), tmp8, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/v2/cv2gefdciotml3zwtkzv4ghtu2a4dbeoas7q3ue7dcfa4f2mizfk.py
# Topologically Sorted Source Nodes: [out_1, h0, sub], Original ATen: [aten.convolution, aten._prelu_kernel, aten.sub]
# Source node to ATen node mapping:
#   h0 => gt_1, mul_1, where_1
#   out_1 => convolution_1
#   sub => sub
# Graph fragment:
#   %convolution_1 : [num_users=4] = call_function[target=torch.ops.aten.convolution.default](args = (%where, %primals_5, %primals_6, [4, 4], [2, 2], [1, 1], True, [0, 0], 1), kwargs = {})
#   %gt_1 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_1, 0), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_1, %convolution_1), kwargs = {})
#   %where_1 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt_1, %convolution_1, %mul_1), kwargs = {})
#   %sub : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%where_1, %primals_3), kwargs = {})
triton_poi_fused__prelu_kernel_convolution_sub_1 = async_compile.triton('triton_poi_fused__prelu_kernel_convolution_sub_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: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__prelu_kernel_convolution_sub_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 4, '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__prelu_kernel_convolution_sub_1(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, 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')
    tmp5 = tl.load(in_ptr1 + (0))
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
    tmp9 = tl.load(in_ptr2 + (x3), xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp7 = tmp6 * tmp2
    tmp8 = tl.where(tmp4, tmp2, tmp7)
    tmp10 = tmp8 - tmp9
    tl.store(in_out_ptr0 + (x3), tmp2, xmask)
    tl.store(out_ptr0 + (x3), tmp10, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/p4/cp477hx6wscm5qo2wzce4hwmhoeb74h5hefxsw2hibtz4mcbgjag.py
# Topologically Sorted Source Nodes: [out_2, l1, add], Original ATen: [aten.convolution, aten._prelu_kernel, aten.add]
# Source node to ATen node mapping:
#   add => add
#   l1 => gt_2, mul_2, where_2
#   out_2 => convolution_2
# Graph fragment:
#   %convolution_2 : [num_users=4] = call_function[target=torch.ops.aten.convolution.default](args = (%sub, %primals_8, %primals_9, [4, 4], [2, 2], [1, 1], False, [0, 0], 1), kwargs = {})
#   %gt_2 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_2, 0), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_2, %convolution_2), kwargs = {})
#   %where_2 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt_2, %convolution_2, %mul_2), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%where_2, %where), kwargs = {})
triton_poi_fused__prelu_kernel_add_convolution_2 = async_compile.triton('triton_poi_fused__prelu_kernel_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=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__prelu_kernel_add_convolution_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 4, '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__prelu_kernel_add_convolution_2(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + (0))
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
    tmp9 = tl.load(in_ptr2 + (x2), xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp7 = tmp6 * tmp2
    tmp8 = tl.where(tmp4, tmp2, tmp7)
    tmp10 = tmp8 + tmp9
    tl.store(in_out_ptr0 + (x2), tmp2, xmask)
    tl.store(out_ptr0 + (x2), tmp10, 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 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 8, 8), (256, 64, 8, 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, 4, 8, 8), (256, 64, 8, 1))
    assert_size_stride(primals_6, (4, ), (1, ))
    assert_size_stride(primals_7, (1, ), (1, ))
    assert_size_stride(primals_8, (4, 4, 8, 8), (256, 64, 8, 1))
    assert_size_stride(primals_9, (4, ), (1, ))
    assert_size_stride(primals_10, (1, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [out], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(4, 4), padding=(2, 2), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 1, 1), (4, 1, 1, 1))
        buf1 = buf0; del buf0  # reuse
        buf2 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out, l0], Original ATen: [aten.convolution, aten._prelu_kernel]
        stream0 = get_raw_stream(0)
        triton_poi_fused__prelu_kernel_convolution_0.run(buf1, primals_2, primals_4, buf2, 16, grid=grid(16), stream=stream0)
        del primals_2
        # Topologically Sorted Source Nodes: [out_1], Original ATen: [aten.convolution]
        buf3 = extern_kernels.convolution(buf2, primals_5, stride=(4, 4), padding=(2, 2), dilation=(1, 1), transposed=True, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 4, 4, 4), (64, 16, 4, 1))
        buf4 = buf3; del buf3  # reuse
        buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out_1, h0, sub], Original ATen: [aten.convolution, aten._prelu_kernel, aten.sub]
        triton_poi_fused__prelu_kernel_convolution_sub_1.run(buf4, primals_6, primals_7, primals_3, buf5, 256, grid=grid(256), stream=stream0)
        del primals_6
        # Topologically Sorted Source Nodes: [out_2], Original ATen: [aten.convolution]
        buf6 = extern_kernels.convolution(buf5, primals_8, stride=(4, 4), padding=(2, 2), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 4, 1, 1), (4, 1, 1, 1))
        buf7 = buf6; del buf6  # reuse
        buf8 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out_2, l1, add], Original ATen: [aten.convolution, aten._prelu_kernel, aten.add]
        triton_poi_fused__prelu_kernel_add_convolution_2.run(buf7, primals_9, primals_10, buf2, buf8, 16, grid=grid(16), stream=stream0)
        del primals_9
    return (buf8, primals_1, primals_3, primals_4, primals_5, primals_7, primals_8, primals_10, buf1, buf2, buf4, buf5, buf7, )


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, 8, 8), (256, 64, 8, 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, 4, 8, 8), (256, 64, 8, 1), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((4, 4, 8, 8), (256, 64, 8, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((1, ), (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])
    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
from torchvision.transforms import *


class ConvBlock(torch.nn.Module):

    def __init__(self, input_size, output_size, kernel_size=3, stride=1,
        padding=1, bias=True, activation='prelu', norm=None):
        super(ConvBlock, self).__init__()
        self.conv = torch.nn.Conv2d(input_size, output_size, kernel_size,
            stride, padding, bias=bias)
        self.norm = norm
        if self.norm == 'batch':
            self.bn = torch.nn.BatchNorm2d(output_size)
        elif self.norm == 'instance':
            self.bn = torch.nn.InstanceNorm2d(output_size)
        self.activation = activation
        if self.activation == 'relu':
            self.act = torch.nn.ReLU(True)
        elif self.activation == 'prelu':
            self.act = torch.nn.PReLU()
        elif self.activation == 'lrelu':
            self.act = torch.nn.LeakyReLU(0.2, True)
        elif self.activation == 'tanh':
            self.act = torch.nn.Tanh()
        elif self.activation == 'sigmoid':
            self.act = torch.nn.Sigmoid()

    def forward(self, x):
        if self.norm is not None:
            out = self.bn(self.conv(x))
        else:
            out = self.conv(x)
        if self.activation is not None:
            return self.act(out)
        else:
            return out


class DeconvBlock(torch.nn.Module):

    def __init__(self, input_size, output_size, kernel_size=4, stride=2,
        padding=1, bias=True, activation='prelu', norm=None):
        super(DeconvBlock, self).__init__()
        self.deconv = torch.nn.ConvTranspose2d(input_size, output_size,
            kernel_size, stride, padding, bias=bias)
        self.norm = norm
        if self.norm == 'batch':
            self.bn = torch.nn.BatchNorm2d(output_size)
        elif self.norm == 'instance':
            self.bn = torch.nn.InstanceNorm2d(output_size)
        self.activation = activation
        if self.activation == 'relu':
            self.act = torch.nn.ReLU(True)
        elif self.activation == 'prelu':
            self.act = torch.nn.PReLU()
        elif self.activation == 'lrelu':
            self.act = torch.nn.LeakyReLU(0.2, True)
        elif self.activation == 'tanh':
            self.act = torch.nn.Tanh()
        elif self.activation == 'sigmoid':
            self.act = torch.nn.Sigmoid()

    def forward(self, x):
        if self.norm is not None:
            out = self.bn(self.deconv(x))
        else:
            out = self.deconv(x)
        if self.activation is not None:
            return self.act(out)
        else:
            return out


class DownBlock(torch.nn.Module):

    def __init__(self, num_filter, kernel_size=8, stride=4, padding=2, bias
        =True, activation='prelu', norm=None):
        super(DownBlock, self).__init__()
        self.down_conv1 = ConvBlock(num_filter, num_filter, kernel_size,
            stride, padding, activation, norm=None)
        self.down_conv2 = DeconvBlock(num_filter, num_filter, kernel_size,
            stride, padding, activation, norm=None)
        self.down_conv3 = ConvBlock(num_filter, num_filter, kernel_size,
            stride, padding, activation, norm=None)

    def forward(self, x):
        l0 = self.down_conv1(x)
        h0 = self.down_conv2(l0)
        l1 = self.down_conv3(h0 - x)
        return l1 + l0


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


def get_init_inputs():
    return [[], {'num_filter': 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 torchvision.transforms import *
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__prelu_kernel_convolution_0(in_out_ptr0, in_ptr0,
    in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + 0)
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp7 = tmp6 * tmp2
    tmp8 = tl.where(tmp4, tmp2, tmp7)
    tl.store(in_out_ptr0 + x2, tmp2, xmask)
    tl.store(out_ptr0 + x2, tmp8, xmask)


@triton.jit
def triton_poi_fused__prelu_kernel_convolution_sub_1(in_out_ptr0, in_ptr0,
    in_ptr1, in_ptr2, 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')
    tmp5 = tl.load(in_ptr1 + 0)
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
    tmp9 = tl.load(in_ptr2 + x3, xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp7 = tmp6 * tmp2
    tmp8 = tl.where(tmp4, tmp2, tmp7)
    tmp10 = tmp8 - tmp9
    tl.store(in_out_ptr0 + x3, tmp2, xmask)
    tl.store(out_ptr0 + x3, tmp10, xmask)


@triton.jit
def triton_poi_fused__prelu_kernel_add_convolution_2(in_out_ptr0, in_ptr0,
    in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + 0)
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
    tmp9 = tl.load(in_ptr2 + x2, xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp7 = tmp6 * tmp2
    tmp8 = tl.where(tmp4, tmp2, tmp7)
    tmp10 = tmp8 + tmp9
    tl.store(in_out_ptr0 + x2, tmp2, xmask)
    tl.store(out_ptr0 + x2, tmp10, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 8, 8), (256, 64, 8, 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, 4, 8, 8), (256, 64, 8, 1))
    assert_size_stride(primals_6, (4,), (1,))
    assert_size_stride(primals_7, (1,), (1,))
    assert_size_stride(primals_8, (4, 4, 8, 8), (256, 64, 8, 1))
    assert_size_stride(primals_9, (4,), (1,))
    assert_size_stride(primals_10, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(4, 
            4), padding=(2, 2), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 1, 1), (4, 1, 1, 1))
        buf1 = buf0
        del buf0
        buf2 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__prelu_kernel_convolution_0[grid(16)](buf1,
            primals_2, primals_4, buf2, 16, XBLOCK=16, num_warps=1,
            num_stages=1)
        del primals_2
        buf3 = extern_kernels.convolution(buf2, primals_5, stride=(4, 4),
            padding=(2, 2), dilation=(1, 1), transposed=True,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 4, 4, 4), (64, 16, 4, 1))
        buf4 = buf3
        del buf3
        buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__prelu_kernel_convolution_sub_1[grid(256)](buf4,
            primals_6, primals_7, primals_3, buf5, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        del primals_6
        buf6 = extern_kernels.convolution(buf5, primals_8, stride=(4, 4),
            padding=(2, 2), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 4, 1, 1), (4, 1, 1, 1))
        buf7 = buf6
        del buf6
        buf8 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.float32)
        triton_poi_fused__prelu_kernel_add_convolution_2[grid(16)](buf7,
            primals_9, primals_10, buf2, buf8, 16, XBLOCK=16, num_warps=1,
            num_stages=1)
        del primals_9
    return (buf8, primals_1, primals_3, primals_4, primals_5, primals_7,
        primals_8, primals_10, buf1, buf2, buf4, buf5, buf7)


class ConvBlock(torch.nn.Module):

    def __init__(self, input_size, output_size, kernel_size=3, stride=1,
        padding=1, bias=True, activation='prelu', norm=None):
        super(ConvBlock, self).__init__()
        self.conv = torch.nn.Conv2d(input_size, output_size, kernel_size,
            stride, padding, bias=bias)
        self.norm = norm
        if self.norm == 'batch':
            self.bn = torch.nn.BatchNorm2d(output_size)
        elif self.norm == 'instance':
            self.bn = torch.nn.InstanceNorm2d(output_size)
        self.activation = activation
        if self.activation == 'relu':
            self.act = torch.nn.ReLU(True)
        elif self.activation == 'prelu':
            self.act = torch.nn.PReLU()
        elif self.activation == 'lrelu':
            self.act = torch.nn.LeakyReLU(0.2, True)
        elif self.activation == 'tanh':
            self.act = torch.nn.Tanh()
        elif self.activation == 'sigmoid':
            self.act = torch.nn.Sigmoid()

    def forward(self, x):
        if self.norm is not None:
            out = self.bn(self.conv(x))
        else:
            out = self.conv(x)
        if self.activation is not None:
            return self.act(out)
        else:
            return out


class DeconvBlock(torch.nn.Module):

    def __init__(self, input_size, output_size, kernel_size=4, stride=2,
        padding=1, bias=True, activation='prelu', norm=None):
        super(DeconvBlock, self).__init__()
        self.deconv = torch.nn.ConvTranspose2d(input_size, output_size,
            kernel_size, stride, padding, bias=bias)
        self.norm = norm
        if self.norm == 'batch':
            self.bn = torch.nn.BatchNorm2d(output_size)
        elif self.norm == 'instance':
            self.bn = torch.nn.InstanceNorm2d(output_size)
        self.activation = activation
        if self.activation == 'relu':
            self.act = torch.nn.ReLU(True)
        elif self.activation == 'prelu':
            self.act = torch.nn.PReLU()
        elif self.activation == 'lrelu':
            self.act = torch.nn.LeakyReLU(0.2, True)
        elif self.activation == 'tanh':
            self.act = torch.nn.Tanh()
        elif self.activation == 'sigmoid':
            self.act = torch.nn.Sigmoid()

    def forward(self, x):
        if self.norm is not None:
            out = self.bn(self.deconv(x))
        else:
            out = self.deconv(x)
        if self.activation is not None:
            return self.act(out)
        else:
            return out


class DownBlockNew(torch.nn.Module):

    def __init__(self, num_filter, kernel_size=8, stride=4, padding=2, bias
        =True, activation='prelu', norm=None):
        super(DownBlockNew, self).__init__()
        self.down_conv1 = ConvBlock(num_filter, num_filter, kernel_size,
            stride, padding, activation, norm=None)
        self.down_conv2 = DeconvBlock(num_filter, num_filter, kernel_size,
            stride, padding, activation, norm=None)
        self.down_conv3 = ConvBlock(num_filter, num_filter, kernel_size,
            stride, padding, activation, norm=None)

    def forward(self, input_0):
        primals_1 = self.down_conv1.conv.weight
        primals_2 = self.down_conv1.conv.bias
        primals_4 = self.down_conv1.act.weight
        primals_5 = self.down_conv2.deconv.weight
        primals_6 = self.down_conv2.deconv.bias
        primals_7 = self.down_conv2.act.weight
        primals_8 = self.down_conv3.conv.weight
        primals_9 = self.down_conv3.conv.bias
        primals_10 = self.down_conv3.act.weight
        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])
        return output[0]
