# 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_1/inductor_cache/jg/cjgtviiizh5ssfmytvsw5ugabco6mn5iifxdeperduxtp54znwav.py
# Topologically Sorted Source Nodes: [out_1, lr], Original ATen: [aten._native_batch_norm_legit, aten._prelu_kernel]
# Source node to ATen node mapping:
#   lr => gt, mul_1, where
#   out_1 => add, rsqrt, var_mean
# Graph fragment:
#   %var_mean : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%view, [0, 2, 3]), kwargs = {correction: 0, keepdim: True})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem, 1e-05), kwargs = {})
#   %rsqrt : [num_users=2] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add,), kwargs = {})
#   %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%view_1, 0), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_2, %view_1), kwargs = {})
#   %where : [num_users=3] = call_function[target=torch.ops.aten.where.self](args = (%gt, %view_1, %mul_1), kwargs = {})
triton_per_fused__native_batch_norm_legit__prelu_kernel_0 = async_compile.triton('triton_per_fused__native_batch_norm_legit__prelu_kernel_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.persistent_reduction(
    size_hints=[16, 128],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32', 6: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__native_batch_norm_legit__prelu_kernel_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 4, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused__native_batch_norm_legit__prelu_kernel_0(in_out_ptr0, in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    rnumel = 81
    RBLOCK: tl.constexpr = 128
    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 = rindex < rnumel
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + (81*x0)), rmask & xmask, other=0.0)
    tmp26 = tl.load(in_ptr1 + (0))
    tmp27 = tl.broadcast_to(tmp26, [XBLOCK, RBLOCK])
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(rmask & xmask, tmp1, 0)
    tmp4 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp6 = tl.where(rmask & xmask, tmp4, 0)
    tmp7 = tl.sum(tmp6, 1)[:, None]
    tmp8 = tl.full([XBLOCK, 1], 81, tl.int32)
    tmp9 = tmp8.to(tl.float32)
    tmp10 = tmp7 / tmp9
    tmp11 = tmp1 - tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tl.broadcast_to(tmp12, [XBLOCK, RBLOCK])
    tmp15 = tl.where(rmask & xmask, tmp13, 0)
    tmp16 = tl.sum(tmp15, 1)[:, None]
    tmp17 = 81.0
    tmp18 = tmp16 / tmp17
    tmp19 = 1e-05
    tmp20 = tmp18 + tmp19
    tmp21 = libdevice.rsqrt(tmp20)
    tmp22 = tmp0 - tmp10
    tmp23 = tmp22 * tmp21
    tmp24 = 0.0
    tmp25 = tmp23 > tmp24
    tmp28 = tmp27 * tmp23
    tmp29 = tl.where(tmp25, tmp23, tmp28)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (x0), tmp21, xmask)
    tl.store(out_ptr1 + (r1 + (81*x0)), tmp29, rmask & xmask)
    tl.store(out_ptr0 + (x0), tmp10, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/y7/cy7wf5syp7242qc5v6om2jztrblllvhfhxdiictfx2fe3f2mpoxl.py
# Topologically Sorted Source Nodes: [hr, add, mul, residue], Original ATen: [aten.leaky_relu, aten.add, aten.mul]
# Source node to ATen node mapping:
#   add => add_1
#   hr => gt_1, mul_2, where_1
#   mul => mul_3
#   residue => add_2
# Graph fragment:
#   %gt_1 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_1, 0), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_1, 0.01), kwargs = {})
#   %where_1 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt_1, %convolution_1, %mul_2), kwargs = {})
#   %add_1 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem_3, 1), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%where_1, %add_1), kwargs = {})
#   %add_2 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem_2, %mul_3), kwargs = {})
triton_poi_fused_add_leaky_relu_mul_1 = async_compile.triton('triton_poi_fused_add_leaky_relu_mul_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: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_leaky_relu_mul_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_leaky_relu_mul_1(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 64
    x1 = (xindex // 64)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (64 + x0 + (128*x1)), xmask)
    tmp3 = tl.load(in_ptr0 + (x0 + (128*x1)), xmask)
    tmp4 = tl.load(in_ptr1 + (x2), xmask)
    tmp1 = 1.0
    tmp2 = tmp0 + tmp1
    tmp5 = 0.0
    tmp6 = tmp4 > tmp5
    tmp7 = 0.01
    tmp8 = tmp4 * tmp7
    tmp9 = tl.where(tmp6, tmp4, tmp8)
    tmp10 = tmp9 * tmp2
    tmp11 = tmp3 + tmp10
    tl.store(out_ptr0 + (x2), tmp2, xmask)
    tl.store(out_ptr1 + (x2), tmp11, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/dk/cdkgpa2ze742pnjuwwkdqd7raqd65bqcsvj7miczbwuml43fuqmv.py
# Topologically Sorted Source Nodes: [out_4, l_residue, add_2, mul_1, add_3], Original ATen: [aten._native_batch_norm_legit, aten._prelu_kernel, aten.add, aten.mul]
# Source node to ATen node mapping:
#   add_2 => add_4
#   add_3 => add_5
#   l_residue => gt_2, mul_5, where_2
#   mul_1 => mul_6
#   out_4 => add_3, rsqrt_1, var_mean_1
# Graph fragment:
#   %var_mean_1 : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%view_3, [0, 2, 3]), kwargs = {correction: 0, keepdim: True})
#   %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem_4, 1e-05), kwargs = {})
#   %rsqrt_1 : [num_users=2] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add_3,), kwargs = {})
#   %gt_2 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%view_4, 0), kwargs = {})
#   %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_5, %view_4), kwargs = {})
#   %where_2 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt_2, %view_4, %mul_5), kwargs = {})
#   %add_4 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem_7, 1), kwargs = {})
#   %mul_6 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%where_2, %add_4), kwargs = {})
#   %add_5 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem_6, %mul_6), kwargs = {})
triton_per_fused__native_batch_norm_legit__prelu_kernel_add_mul_2 = async_compile.triton('triton_per_fused__native_batch_norm_legit__prelu_kernel_add_mul_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.persistent_reduction(
    size_hints=[16, 128],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: 'i32', 8: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), '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__native_batch_norm_legit__prelu_kernel_add_mul_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 4, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused__native_batch_norm_legit__prelu_kernel_add_mul_2(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, out_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    rnumel = 81
    RBLOCK: tl.constexpr = 128
    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 = rindex < rnumel
    r1 = rindex
    x0 = xindex
    x2 = xindex % 4
    x3 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (r1 + (81*x0)), rmask & xmask, other=0.0)
    tmp22 = tl.load(in_ptr1 + (324 + r1 + (81*x2) + (648*x3)), rmask & xmask, other=0.0)
    tmp25 = tl.load(in_ptr1 + (r1 + (81*x2) + (648*x3)), rmask & xmask, other=0.0)
    tmp30 = tl.load(in_ptr2 + (0))
    tmp31 = tl.broadcast_to(tmp30, [XBLOCK, RBLOCK])
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(rmask & xmask, tmp1, 0)
    tmp4 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp6 = tl.where(rmask & xmask, tmp4, 0)
    tmp7 = tl.sum(tmp6, 1)[:, None]
    tmp8 = tl.full([XBLOCK, 1], 81, tl.int32)
    tmp9 = tmp8.to(tl.float32)
    tmp10 = tmp7 / tmp9
    tmp11 = tmp1 - tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tl.broadcast_to(tmp12, [XBLOCK, RBLOCK])
    tmp15 = tl.where(rmask & xmask, tmp13, 0)
    tmp16 = tl.sum(tmp15, 1)[:, None]
    tmp17 = 81.0
    tmp18 = tmp16 / tmp17
    tmp19 = 1e-05
    tmp20 = tmp18 + tmp19
    tmp21 = libdevice.rsqrt(tmp20)
    tmp23 = 1.0
    tmp24 = tmp22 + tmp23
    tmp26 = tmp0 - tmp10
    tmp27 = tmp26 * tmp21
    tmp28 = 0.0
    tmp29 = tmp27 > tmp28
    tmp32 = tmp31 * tmp27
    tmp33 = tl.where(tmp29, tmp27, tmp32)
    tmp34 = tmp33 * tmp24
    tmp35 = tmp25 + tmp34
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (x0), tmp21, xmask)
    tl.store(out_ptr1 + (r1 + (81*x0)), tmp24, rmask & xmask)
    tl.store(out_ptr2 + (r1 + (81*x0)), tmp35, rmask & xmask)
    tl.store(out_ptr0 + (x0), 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 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (1, ), (1, ))
    assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_5, (8, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_6, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_7, (1, ), (1, ))
    assert_size_stride(primals_8, (8, 4, 1, 1), (4, 1, 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_2, primals_1, stride=(1, 1), padding=(4, 4), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 9, 9), (324, 81, 9, 1))
        buf1 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 1, 1), torch.float32)
        buf2 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.float32)
        buf4 = reinterpret_tensor(buf2, (1, 16, 1, 1), (16, 1, 1, 1), 0); del buf2  # reuse
        buf5 = empty_strided_cuda((4, 4, 9, 9), (324, 81, 9, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out_1, lr], Original ATen: [aten._native_batch_norm_legit, aten._prelu_kernel]
        stream0 = get_raw_stream(0)
        triton_per_fused__native_batch_norm_legit__prelu_kernel_0.run(buf4, buf0, primals_3, buf1, buf5, 16, 81, grid=grid(16), stream=stream0)
        # Topologically Sorted Source Nodes: [out_2], Original ATen: [aten.convolution]
        buf6 = extern_kernels.convolution(buf5, primals_4, stride=(1, 1), padding=(4, 4), dilation=(1, 1), transposed=True, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 4, 4, 4), (64, 16, 4, 1))
        # Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution]
        buf7 = extern_kernels.convolution(primals_2, primals_5, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf7, (4, 8, 4, 4), (128, 16, 4, 1))
        buf8 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf9 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [hr, add, mul, residue], Original ATen: [aten.leaky_relu, aten.add, aten.mul]
        triton_poi_fused_add_leaky_relu_mul_1.run(buf7, buf6, buf8, buf9, 256, grid=grid(256), stream=stream0)
        del buf7
        # Topologically Sorted Source Nodes: [out_3], Original ATen: [aten.convolution]
        buf10 = extern_kernels.convolution(buf9, primals_6, stride=(1, 1), padding=(4, 4), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf10, (4, 4, 9, 9), (324, 81, 9, 1))
        # Topologically Sorted Source Nodes: [conv2d_3], Original ATen: [aten.convolution]
        buf15 = extern_kernels.convolution(buf5, primals_8, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf15, (4, 8, 9, 9), (648, 81, 9, 1))
        buf11 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 1, 1), torch.float32)
        buf12 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.float32)
        buf14 = reinterpret_tensor(buf12, (1, 16, 1, 1), (16, 1, 1, 1), 0); del buf12  # reuse
        buf16 = empty_strided_cuda((4, 4, 9, 9), (324, 81, 9, 1), torch.float32)
        buf17 = empty_strided_cuda((4, 4, 9, 9), (324, 81, 9, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out_4, l_residue, add_2, mul_1, add_3], Original ATen: [aten._native_batch_norm_legit, aten._prelu_kernel, aten.add, aten.mul]
        triton_per_fused__native_batch_norm_legit__prelu_kernel_add_mul_2.run(buf14, buf10, buf15, primals_7, buf11, buf16, buf17, 16, 81, grid=grid(16), stream=stream0)
        del buf15
    return (buf17, primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, buf0, buf1, buf4, buf5, buf6, buf8, buf9, buf10, buf11, buf14, buf16, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((8, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((8, 4, 1, 1), (4, 1, 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])
    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 torch import nn


class ConvBlock(torch.nn.Module):

    def __init__(self, input_size, output_size, kernel_size, stride,
        padding, bias=True):
        super(ConvBlock, self).__init__()
        self.conv = torch.nn.Conv2d(input_size, output_size, kernel_size,
            stride, padding, bias=bias)
        self.act = torch.nn.PReLU()
        self.bn = nn.InstanceNorm2d(output_size)

    def forward(self, x):
        out = self.conv(x)
        out = self.bn(out)
        return self.act(out)


class DeconvBlock(torch.nn.Module):

    def __init__(self, input_size, output_size, kernel_size, stride,
        padding, bias=True):
        super(DeconvBlock, self).__init__()
        self.deconv = torch.nn.ConvTranspose2d(input_size, output_size,
            kernel_size, stride, padding, bias=bias)
        self.act = torch.nn.LeakyReLU()

    def forward(self, x):
        out = self.deconv(x)
        return self.act(out)


class DownBlock(torch.nn.Module):

    def __init__(self, input_size, output_size, kernel_size, stride, padding):
        super(DownBlock, self).__init__()
        self.conv1 = ConvBlock(input_size, output_size, kernel_size, stride,
            padding, bias=False)
        self.conv2 = DeconvBlock(output_size, output_size, kernel_size,
            stride, padding, bias=False)
        self.conv3 = ConvBlock(output_size, output_size, kernel_size,
            stride, padding, bias=False)
        self.local_weight1 = nn.Conv2d(input_size, 2 * output_size,
            kernel_size=1, stride=1, padding=0, bias=False)
        self.local_weight2 = nn.Conv2d(output_size, 2 * output_size,
            kernel_size=1, stride=1, padding=0, bias=False)

    def forward(self, x):
        lr = self.conv1(x)
        hr = self.conv2(lr)
        mean, var = self.local_weight1(x).chunk(2, dim=1)
        residue = mean + hr * (1 + var)
        l_residue = self.conv3(residue)
        mean, var = self.local_weight2(lr).chunk(2, dim=1)
        return mean + l_residue * (1 + var)


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


def get_init_inputs():
    return [[], {'input_size': 4, 'output_size': 4, 'kernel_size': 4,
        'stride': 1, '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.triton_helpers import libdevice
from torch import 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__native_batch_norm_legit__prelu_kernel_0(in_out_ptr0,
    in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 16
    rnumel = 81
    RBLOCK: tl.constexpr = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    rmask = rindex < rnumel
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 81 * x0), rmask & xmask, other=0.0)
    tmp26 = tl.load(in_ptr1 + 0)
    tmp27 = tl.broadcast_to(tmp26, [XBLOCK, RBLOCK])
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tl.where(rmask & xmask, tmp1, 0)
    tmp4 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp6 = tl.where(rmask & xmask, tmp4, 0)
    tmp7 = tl.sum(tmp6, 1)[:, None]
    tmp8 = tl.full([XBLOCK, 1], 81, tl.int32)
    tmp9 = tmp8.to(tl.float32)
    tmp10 = tmp7 / tmp9
    tmp11 = tmp1 - tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tl.broadcast_to(tmp12, [XBLOCK, RBLOCK])
    tmp15 = tl.where(rmask & xmask, tmp13, 0)
    tmp16 = tl.sum(tmp15, 1)[:, None]
    tmp17 = 81.0
    tmp18 = tmp16 / tmp17
    tmp19 = 1e-05
    tmp20 = tmp18 + tmp19
    tmp21 = libdevice.rsqrt(tmp20)
    tmp22 = tmp0 - tmp10
    tmp23 = tmp22 * tmp21
    tmp24 = 0.0
    tmp25 = tmp23 > tmp24
    tmp28 = tmp27 * tmp23
    tmp29 = tl.where(tmp25, tmp23, tmp28)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp21, xmask)
    tl.store(out_ptr1 + (r1 + 81 * x0), tmp29, rmask & xmask)
    tl.store(out_ptr0 + x0, tmp10, xmask)


@triton.jit
def triton_poi_fused_add_leaky_relu_mul_1(in_ptr0, in_ptr1, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 64
    x1 = xindex // 64
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (64 + x0 + 128 * x1), xmask)
    tmp3 = tl.load(in_ptr0 + (x0 + 128 * x1), xmask)
    tmp4 = tl.load(in_ptr1 + x2, xmask)
    tmp1 = 1.0
    tmp2 = tmp0 + tmp1
    tmp5 = 0.0
    tmp6 = tmp4 > tmp5
    tmp7 = 0.01
    tmp8 = tmp4 * tmp7
    tmp9 = tl.where(tmp6, tmp4, tmp8)
    tmp10 = tmp9 * tmp2
    tmp11 = tmp3 + tmp10
    tl.store(out_ptr0 + x2, tmp2, xmask)
    tl.store(out_ptr1 + x2, tmp11, xmask)


@triton.jit
def triton_per_fused__native_batch_norm_legit__prelu_kernel_add_mul_2(
    in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, out_ptr2,
    xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    rnumel = 81
    RBLOCK: tl.constexpr = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    rmask = rindex < rnumel
    r1 = rindex
    x0 = xindex
    x2 = xindex % 4
    x3 = xindex // 4
    tmp0 = tl.load(in_ptr0 + (r1 + 81 * x0), rmask & xmask, other=0.0)
    tmp22 = tl.load(in_ptr1 + (324 + r1 + 81 * x2 + 648 * x3), rmask &
        xmask, other=0.0)
    tmp25 = tl.load(in_ptr1 + (r1 + 81 * x2 + 648 * x3), rmask & xmask,
        other=0.0)
    tmp30 = tl.load(in_ptr2 + 0)
    tmp31 = tl.broadcast_to(tmp30, [XBLOCK, RBLOCK])
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tl.where(rmask & xmask, tmp1, 0)
    tmp4 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp6 = tl.where(rmask & xmask, tmp4, 0)
    tmp7 = tl.sum(tmp6, 1)[:, None]
    tmp8 = tl.full([XBLOCK, 1], 81, tl.int32)
    tmp9 = tmp8.to(tl.float32)
    tmp10 = tmp7 / tmp9
    tmp11 = tmp1 - tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tl.broadcast_to(tmp12, [XBLOCK, RBLOCK])
    tmp15 = tl.where(rmask & xmask, tmp13, 0)
    tmp16 = tl.sum(tmp15, 1)[:, None]
    tmp17 = 81.0
    tmp18 = tmp16 / tmp17
    tmp19 = 1e-05
    tmp20 = tmp18 + tmp19
    tmp21 = libdevice.rsqrt(tmp20)
    tmp23 = 1.0
    tmp24 = tmp22 + tmp23
    tmp26 = tmp0 - tmp10
    tmp27 = tmp26 * tmp21
    tmp28 = 0.0
    tmp29 = tmp27 > tmp28
    tmp32 = tmp31 * tmp27
    tmp33 = tl.where(tmp29, tmp27, tmp32)
    tmp34 = tmp33 * tmp24
    tmp35 = tmp25 + tmp34
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp21, xmask)
    tl.store(out_ptr1 + (r1 + 81 * x0), tmp24, rmask & xmask)
    tl.store(out_ptr2 + (r1 + 81 * x0), tmp35, rmask & xmask)
    tl.store(out_ptr0 + x0, tmp10, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (1,), (1,))
    assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_5, (8, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_6, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_7, (1,), (1,))
    assert_size_stride(primals_8, (8, 4, 1, 1), (4, 1, 1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_2, primals_1, stride=(1, 
            1), padding=(4, 4), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 9, 9), (324, 81, 9, 1))
        buf1 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 1, 1), torch.float32)
        buf2 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.float32
            )
        buf4 = reinterpret_tensor(buf2, (1, 16, 1, 1), (16, 1, 1, 1), 0)
        del buf2
        buf5 = empty_strided_cuda((4, 4, 9, 9), (324, 81, 9, 1), torch.float32)
        get_raw_stream(0)
        triton_per_fused__native_batch_norm_legit__prelu_kernel_0[grid(16)](
            buf4, buf0, primals_3, buf1, buf5, 16, 81, XBLOCK=1, num_warps=
            2, num_stages=1)
        buf6 = extern_kernels.convolution(buf5, primals_4, stride=(1, 1),
            padding=(4, 4), dilation=(1, 1), transposed=True,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 4, 4, 4), (64, 16, 4, 1))
        buf7 = extern_kernels.convolution(primals_2, primals_5, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf7, (4, 8, 4, 4), (128, 16, 4, 1))
        buf8 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf9 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_add_leaky_relu_mul_1[grid(256)](buf7, buf6, buf8,
            buf9, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del buf7
        buf10 = extern_kernels.convolution(buf9, primals_6, stride=(1, 1),
            padding=(4, 4), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf10, (4, 4, 9, 9), (324, 81, 9, 1))
        buf15 = extern_kernels.convolution(buf5, primals_8, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf15, (4, 8, 9, 9), (648, 81, 9, 1))
        buf11 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 1, 1), torch.float32)
        buf12 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.
            float32)
        buf14 = reinterpret_tensor(buf12, (1, 16, 1, 1), (16, 1, 1, 1), 0)
        del buf12
        buf16 = empty_strided_cuda((4, 4, 9, 9), (324, 81, 9, 1), torch.float32
            )
        buf17 = empty_strided_cuda((4, 4, 9, 9), (324, 81, 9, 1), torch.float32
            )
        triton_per_fused__native_batch_norm_legit__prelu_kernel_add_mul_2[grid
            (16)](buf14, buf10, buf15, primals_7, buf11, buf16, buf17, 16, 
            81, XBLOCK=1, num_warps=2, num_stages=1)
        del buf15
    return (buf17, primals_1, primals_2, primals_3, primals_4, primals_5,
        primals_6, primals_7, primals_8, buf0, buf1, buf4, buf5, buf6, buf8,
        buf9, buf10, buf11, buf14, buf16)


class ConvBlock(torch.nn.Module):

    def __init__(self, input_size, output_size, kernel_size, stride,
        padding, bias=True):
        super(ConvBlock, self).__init__()
        self.conv = torch.nn.Conv2d(input_size, output_size, kernel_size,
            stride, padding, bias=bias)
        self.act = torch.nn.PReLU()
        self.bn = nn.InstanceNorm2d(output_size)

    def forward(self, x):
        out = self.conv(x)
        out = self.bn(out)
        return self.act(out)


class DeconvBlock(torch.nn.Module):

    def __init__(self, input_size, output_size, kernel_size, stride,
        padding, bias=True):
        super(DeconvBlock, self).__init__()
        self.deconv = torch.nn.ConvTranspose2d(input_size, output_size,
            kernel_size, stride, padding, bias=bias)
        self.act = torch.nn.LeakyReLU()

    def forward(self, x):
        out = self.deconv(x)
        return self.act(out)


class DownBlockNew(torch.nn.Module):

    def __init__(self, input_size, output_size, kernel_size, stride, padding):
        super(DownBlockNew, self).__init__()
        self.conv1 = ConvBlock(input_size, output_size, kernel_size, stride,
            padding, bias=False)
        self.conv2 = DeconvBlock(output_size, output_size, kernel_size,
            stride, padding, bias=False)
        self.conv3 = ConvBlock(output_size, output_size, kernel_size,
            stride, padding, bias=False)
        self.local_weight1 = nn.Conv2d(input_size, 2 * output_size,
            kernel_size=1, stride=1, padding=0, bias=False)
        self.local_weight2 = nn.Conv2d(output_size, 2 * output_size,
            kernel_size=1, stride=1, padding=0, bias=False)

    def forward(self, input_0):
        primals_1 = self.conv1.conv.weight
        primals_3 = self.conv1.act.weight
        primals_2 = self.conv2.deconv.weight
        primals_4 = self.conv3.conv.weight
        primals_7 = self.conv3.act.weight
        primals_5 = self.local_weight1.weight
        primals_8 = self.local_weight2.weight
        primals_6 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8])
        return output[0]
