# 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/gw/cgwips3show4vjbttvtj7uocwtsin2z3hk2swl6ehmuz3quabsat.py
# Topologically Sorted Source Nodes: [out, out_1], Original ATen: [aten.convolution, aten.leaky_relu]
# Source node to ATen node mapping:
#   out => convolution
#   out_1 => gt, mul, where
# Graph fragment:
#   %convolution : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_1, %primals_2, %primals_3, [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, 0.01), kwargs = {})
#   %where : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt, %convolution, %mul), kwargs = {})
triton_poi_fused_convolution_leaky_relu_0 = async_compile.triton('triton_poi_fused_convolution_leaky_relu_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=[524288], 
    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_leaky_relu_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_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 524288
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 4096) % 32
    tmp0 = tl.load(in_out_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.01
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(in_out_ptr0 + (x3), tmp7, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/q5/cq5c6b2vnj2yohdbs2yfrzumcstrubm63clhsgiqfksf3dxqgcxs.py
# Topologically Sorted Source Nodes: [out_2, out_3, out_4], Original ATen: [aten.convolution, aten.leaky_relu, aten.add, aten.leaky_relu_backward]
# Source node to ATen node mapping:
#   out_2 => convolution_1
#   out_3 => gt_1, mul_1, where_1
#   out_4 => add
# Graph fragment:
#   %convolution_1 : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%where, %primals_4, %primals_5, [1, 1], [1, 1], [1, 1], False, [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 = (%convolution_1, 0.01), kwargs = {})
#   %where_1 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_1, %convolution_1, %mul_1), kwargs = {})
#   %add : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%where_1, %primals_1), kwargs = {})
#   %gt_10 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%where_1, 0), kwargs = {})
triton_poi_fused_add_convolution_leaky_relu_leaky_relu_backward_1 = async_compile.triton('triton_poi_fused_add_convolution_leaky_relu_leaky_relu_backward_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=[524288], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*i1', 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_add_convolution_leaky_relu_leaky_relu_backward_1', 'mutated_arg_names': [], '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_leaky_relu_leaky_relu_backward_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 524288
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 4096) % 32
    tmp0 = tl.load(in_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr2 + (x3), None)
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.01
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tmp9 = tmp7 + tmp8
    tmp10 = tmp7 > tmp3
    tl.store(out_ptr0 + (x3), tmp9, None)
    tl.store(out_ptr1 + (x3), tmp10, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/vn/cvnmllhhvuga55zco3pxqydujnre2gngaiyxshyh5yffqvzr336w.py
# Topologically Sorted Source Nodes: [out_12, out_13, out_14, out_15], Original ATen: [aten.convolution, aten.leaky_relu, aten.add, aten.leaky_relu_backward]
# Source node to ATen node mapping:
#   out_12 => convolution_5
#   out_13 => gt_5, mul_5, where_5
#   out_14 => add_2
#   out_15 => add_3
# Graph fragment:
#   %convolution_5 : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%where_4, %primals_12, %primals_13, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %gt_5 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_5, 0), kwargs = {})
#   %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_5, 0.01), kwargs = {})
#   %where_5 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_5, %convolution_5, %mul_5), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%where_5, %add_1), kwargs = {})
#   %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_2, %primals_1), kwargs = {})
#   %gt_6 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%where_5, 0), kwargs = {})
triton_poi_fused_add_convolution_leaky_relu_leaky_relu_backward_2 = async_compile.triton('triton_poi_fused_add_convolution_leaky_relu_leaky_relu_backward_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=[524288], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*i1', 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_add_convolution_leaky_relu_leaky_relu_backward_2', 'mutated_arg_names': [], '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_add_convolution_leaky_relu_leaky_relu_backward_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 524288
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 4096) % 32
    tmp0 = tl.load(in_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr2 + (x3), None)
    tmp10 = tl.load(in_ptr3 + (x3), None)
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.01
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tmp9 = tmp7 + tmp8
    tmp11 = tmp9 + tmp10
    tmp12 = tmp7 > tmp3
    tl.store(out_ptr0 + (x3), tmp11, None)
    tl.store(out_ptr1 + (x3), tmp12, None)
''', 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, 32, 64, 64), (131072, 4096, 64, 1))
    assert_size_stride(primals_2, (32, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_3, (32, ), (1, ))
    assert_size_stride(primals_4, (32, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_5, (32, ), (1, ))
    assert_size_stride(primals_6, (32, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_7, (32, ), (1, ))
    assert_size_stride(primals_8, (32, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_9, (32, ), (1, ))
    assert_size_stride(primals_10, (32, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_11, (32, ), (1, ))
    assert_size_stride(primals_12, (32, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_13, (32, ), (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_1, primals_2, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 32, 64, 64), (131072, 4096, 64, 1))
        buf1 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [out, out_1], Original ATen: [aten.convolution, aten.leaky_relu]
        stream0 = get_raw_stream(0)
        triton_poi_fused_convolution_leaky_relu_0.run(buf1, primals_3, 524288, grid=grid(524288), stream=stream0)
        del primals_3
        # Topologically Sorted Source Nodes: [out_2], Original ATen: [aten.convolution]
        buf2 = extern_kernels.convolution(buf1, 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, 32, 64, 64), (131072, 4096, 64, 1))
        buf3 = empty_strided_cuda((4, 32, 64, 64), (131072, 4096, 64, 1), torch.float32)
        buf14 = empty_strided_cuda((4, 32, 64, 64), (131072, 4096, 64, 1), torch.bool)
        # Topologically Sorted Source Nodes: [out_2, out_3, out_4], Original ATen: [aten.convolution, aten.leaky_relu, aten.add, aten.leaky_relu_backward]
        triton_poi_fused_add_convolution_leaky_relu_leaky_relu_backward_1.run(buf2, primals_5, primals_1, buf3, buf14, 524288, grid=grid(524288), stream=stream0)
        del primals_5
        # Topologically Sorted Source Nodes: [out_5], Original ATen: [aten.convolution]
        buf4 = extern_kernels.convolution(buf3, 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, 32, 64, 64), (131072, 4096, 64, 1))
        buf5 = buf4; del buf4  # reuse
        # Topologically Sorted Source Nodes: [out_5, out_6], Original ATen: [aten.convolution, aten.leaky_relu]
        triton_poi_fused_convolution_leaky_relu_0.run(buf5, primals_7, 524288, grid=grid(524288), stream=stream0)
        del primals_7
        # Topologically Sorted Source Nodes: [out_7], Original ATen: [aten.convolution]
        buf6 = extern_kernels.convolution(buf5, 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, 32, 64, 64), (131072, 4096, 64, 1))
        buf7 = buf2; del buf2  # reuse
        buf13 = empty_strided_cuda((4, 32, 64, 64), (131072, 4096, 64, 1), torch.bool)
        # Topologically Sorted Source Nodes: [out_7, out_8, out_9], Original ATen: [aten.convolution, aten.leaky_relu, aten.add, aten.leaky_relu_backward]
        triton_poi_fused_add_convolution_leaky_relu_leaky_relu_backward_1.run(buf6, primals_9, buf3, buf7, buf13, 524288, grid=grid(524288), stream=stream0)
        del primals_9
        # Topologically Sorted Source Nodes: [out_10], Original ATen: [aten.convolution]
        buf8 = extern_kernels.convolution(buf7, primals_10, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf8, (4, 32, 64, 64), (131072, 4096, 64, 1))
        buf9 = buf8; del buf8  # reuse
        # Topologically Sorted Source Nodes: [out_10, out_11], Original ATen: [aten.convolution, aten.leaky_relu]
        triton_poi_fused_convolution_leaky_relu_0.run(buf9, primals_11, 524288, grid=grid(524288), stream=stream0)
        del primals_11
        # Topologically Sorted Source Nodes: [out_12], Original ATen: [aten.convolution]
        buf10 = extern_kernels.convolution(buf9, primals_12, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf10, (4, 32, 64, 64), (131072, 4096, 64, 1))
        buf11 = buf6; del buf6  # reuse
        buf12 = empty_strided_cuda((4, 32, 64, 64), (131072, 4096, 64, 1), torch.bool)
        # Topologically Sorted Source Nodes: [out_12, out_13, out_14, out_15], Original ATen: [aten.convolution, aten.leaky_relu, aten.add, aten.leaky_relu_backward]
        triton_poi_fused_add_convolution_leaky_relu_leaky_relu_backward_2.run(buf10, primals_13, buf7, primals_1, buf11, buf12, 524288, grid=grid(524288), stream=stream0)
        del buf10
        del primals_13
    return (buf11, primals_1, primals_2, primals_4, primals_6, primals_8, primals_10, primals_12, buf1, buf3, buf5, buf7, buf9, buf12, buf13, buf14, )


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, 32, 64, 64), (131072, 4096, 64, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((32, 32, 3, 3), (288, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((32, 32, 3, 3), (288, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((32, 32, 3, 3), (288, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((32, 32, 3, 3), (288, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((32, 32, 3, 3), (288, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_12 = rand_strided((32, 32, 3, 3), (288, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_13 = rand_strided((32, ), (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


def conv3x3(in_ch, out_ch, stride=1):
    """3x3 convolution with padding."""
    return nn.Conv2d(in_ch, out_ch, kernel_size=3, stride=stride, padding=1)


class ResidualBlock(nn.Module):
    """Simple residual block with two 3x3 convolutions.

    Args:
        in_ch (int): number of input channels
        out_ch (int): number of output channels
    """

    def __init__(self, in_ch, out_ch):
        super().__init__()
        self.conv1 = conv3x3(in_ch, out_ch)
        self.leaky_relu = nn.LeakyReLU(inplace=True)
        self.conv2 = conv3x3(out_ch, out_ch)

    def forward(self, x):
        identity = x
        out = self.conv1(x)
        out = self.leaky_relu(out)
        out = self.conv2(out)
        out = self.leaky_relu(out)
        out = out + identity
        return out


class Enhancement_Block(nn.Module):

    def __init__(self):
        super().__init__()
        self.RB1 = ResidualBlock(32, 32)
        self.RB2 = ResidualBlock(32, 32)
        self.RB3 = ResidualBlock(32, 32)

    def forward(self, x):
        identity = x
        out = self.RB1(x)
        out = self.RB2(out)
        out = self.RB3(out)
        out = out + identity
        return out


def get_inputs():
    return [torch.rand([4, 32, 64, 64])]


def get_init_inputs():
    return [[], {}]

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
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_convolution_leaky_relu_0(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 4096 % 32
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.01
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(in_out_ptr0 + x3, tmp7, None)


@triton.jit
def triton_poi_fused_add_convolution_leaky_relu_leaky_relu_backward_1(in_ptr0,
    in_ptr1, in_ptr2, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 4096 % 32
    tmp0 = tl.load(in_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr2 + x3, None)
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.01
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tmp9 = tmp7 + tmp8
    tmp10 = tmp7 > tmp3
    tl.store(out_ptr0 + x3, tmp9, None)
    tl.store(out_ptr1 + x3, tmp10, None)


@triton.jit
def triton_poi_fused_add_convolution_leaky_relu_leaky_relu_backward_2(in_ptr0,
    in_ptr1, in_ptr2, in_ptr3, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr
    ):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 4096 % 32
    tmp0 = tl.load(in_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr2 + x3, None)
    tmp10 = tl.load(in_ptr3 + x3, None)
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.01
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tmp9 = tmp7 + tmp8
    tmp11 = tmp9 + tmp10
    tmp12 = tmp7 > tmp3
    tl.store(out_ptr0 + x3, tmp11, None)
    tl.store(out_ptr1 + x3, tmp12, None)


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, 32, 64, 64), (131072, 4096, 64, 1))
    assert_size_stride(primals_2, (32, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_3, (32,), (1,))
    assert_size_stride(primals_4, (32, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_5, (32,), (1,))
    assert_size_stride(primals_6, (32, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_7, (32,), (1,))
    assert_size_stride(primals_8, (32, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_9, (32,), (1,))
    assert_size_stride(primals_10, (32, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_11, (32,), (1,))
    assert_size_stride(primals_12, (32, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_13, (32,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(1, 
            1), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 32, 64, 64), (131072, 4096, 64, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_leaky_relu_0[grid(524288)](buf1,
            primals_3, 524288, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_3
        buf2 = extern_kernels.convolution(buf1, 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, 32, 64, 64), (131072, 4096, 64, 1))
        buf3 = empty_strided_cuda((4, 32, 64, 64), (131072, 4096, 64, 1),
            torch.float32)
        buf14 = empty_strided_cuda((4, 32, 64, 64), (131072, 4096, 64, 1),
            torch.bool)
        triton_poi_fused_add_convolution_leaky_relu_leaky_relu_backward_1[grid
            (524288)](buf2, primals_5, primals_1, buf3, buf14, 524288,
            XBLOCK=512, num_warps=8, num_stages=1)
        del primals_5
        buf4 = extern_kernels.convolution(buf3, 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, 32, 64, 64), (131072, 4096, 64, 1))
        buf5 = buf4
        del buf4
        triton_poi_fused_convolution_leaky_relu_0[grid(524288)](buf5,
            primals_7, 524288, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_7
        buf6 = extern_kernels.convolution(buf5, 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, 32, 64, 64), (131072, 4096, 64, 1))
        buf7 = buf2
        del buf2
        buf13 = empty_strided_cuda((4, 32, 64, 64), (131072, 4096, 64, 1),
            torch.bool)
        triton_poi_fused_add_convolution_leaky_relu_leaky_relu_backward_1[grid
            (524288)](buf6, primals_9, buf3, buf7, buf13, 524288, XBLOCK=
            512, num_warps=8, num_stages=1)
        del primals_9
        buf8 = extern_kernels.convolution(buf7, primals_10, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf8, (4, 32, 64, 64), (131072, 4096, 64, 1))
        buf9 = buf8
        del buf8
        triton_poi_fused_convolution_leaky_relu_0[grid(524288)](buf9,
            primals_11, 524288, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_11
        buf10 = extern_kernels.convolution(buf9, primals_12, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf10, (4, 32, 64, 64), (131072, 4096, 64, 1))
        buf11 = buf6
        del buf6
        buf12 = empty_strided_cuda((4, 32, 64, 64), (131072, 4096, 64, 1),
            torch.bool)
        triton_poi_fused_add_convolution_leaky_relu_leaky_relu_backward_2[grid
            (524288)](buf10, primals_13, buf7, primals_1, buf11, buf12, 
            524288, XBLOCK=1024, num_warps=4, num_stages=1)
        del buf10
        del primals_13
    return (buf11, primals_1, primals_2, primals_4, primals_6, primals_8,
        primals_10, primals_12, buf1, buf3, buf5, buf7, buf9, buf12, buf13,
        buf14)


def conv3x3(in_ch, out_ch, stride=1):
    """3x3 convolution with padding."""
    return nn.Conv2d(in_ch, out_ch, kernel_size=3, stride=stride, padding=1)


class ResidualBlock(nn.Module):
    """Simple residual block with two 3x3 convolutions.

    Args:
        in_ch (int): number of input channels
        out_ch (int): number of output channels
    """

    def __init__(self, in_ch, out_ch):
        super().__init__()
        self.conv1 = conv3x3(in_ch, out_ch)
        self.leaky_relu = nn.LeakyReLU(inplace=True)
        self.conv2 = conv3x3(out_ch, out_ch)

    def forward(self, x):
        identity = x
        out = self.conv1(x)
        out = self.leaky_relu(out)
        out = self.conv2(out)
        out = self.leaky_relu(out)
        out = out + identity
        return out


class Enhancement_BlockNew(nn.Module):

    def __init__(self):
        super().__init__()
        self.RB1 = ResidualBlock(32, 32)
        self.RB2 = ResidualBlock(32, 32)
        self.RB3 = ResidualBlock(32, 32)

    def forward(self, input_0):
        primals_2 = self.RB1.conv1.weight
        primals_3 = self.RB1.conv1.bias
        primals_4 = self.RB1.conv2.weight
        primals_5 = self.RB1.conv2.bias
        primals_6 = self.RB2.conv1.weight
        primals_7 = self.RB2.conv1.bias
        primals_8 = self.RB2.conv2.weight
        primals_9 = self.RB2.conv2.bias
        primals_10 = self.RB3.conv1.weight
        primals_11 = self.RB3.conv1.bias
        primals_12 = self.RB3.conv2.weight
        primals_13 = self.RB3.conv2.bias
        primals_1 = 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]
