# AOT ID: ['0_inference']
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/6w/c6wgqvz4kdrnqiec7t2vuvmkn46mmwv4k4fko7qcq7iqq7l5jzmk.py
# Topologically Sorted Source Nodes: [local_response_norm], Original ATen: [aten.constant_pad_nd, aten.avg_pool3d, aten.mul, aten.add, aten.pow, aten.div]
# Source node to ATen node mapping:
#   local_response_norm => add, avg_pool3d, constant_pad_nd, div, mul_1, pow_1
# Graph fragment:
#   %constant_pad_nd : [num_users=1] = call_function[target=torch.ops.aten.constant_pad_nd.default](args = (%view, [0, 0, 0, 0, 2, 2], 0.0), kwargs = {})
#   %avg_pool3d : [num_users=1] = call_function[target=torch.ops.aten.avg_pool3d.default](args = (%constant_pad_nd, [5, 1, 1], [1, 1, 1]), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%squeeze, 9.999999747378752e-05), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, 1.0), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%add, 0.75), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%arg0_1, %pow_1), kwargs = {})
triton_poi_fused_add_avg_pool3d_constant_pad_nd_div_mul_pow_0 = async_compile.triton('triton_poi_fused_add_avg_pool3d_constant_pad_nd_div_mul_pow_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: '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_add_avg_pool3d_constant_pad_nd_div_mul_pow_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 6, '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_avg_pool3d_constant_pad_nd_div_mul_pow_0(in_out_ptr0, in_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
    x3 = xindex
    tmp48 = tl.load(in_ptr0 + (x3), xmask)
    tmp0 = (-2) + x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tmp2 & tmp4
    tmp6 = tl.load(in_ptr0 + ((-32) + x3), tmp5 & xmask, other=0.0)
    tmp7 = tmp6 * tmp6
    tmp8 = tl.full(tmp7.shape, 0.0, tmp7.dtype)
    tmp9 = tl.where(tmp5, tmp7, tmp8)
    tmp10 = (-1) + x1
    tmp11 = tmp10 >= tmp1
    tmp12 = tmp10 < tmp3
    tmp13 = tmp11 & tmp12
    tmp14 = tl.load(in_ptr0 + ((-16) + x3), tmp13 & xmask, other=0.0)
    tmp15 = tmp14 * tmp14
    tmp16 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
    tmp17 = tl.where(tmp13, tmp15, tmp16)
    tmp18 = tmp17 + tmp9
    tmp19 = x1
    tmp20 = tmp19 >= tmp1
    tmp21 = tmp19 < tmp3
    tmp22 = tmp20 & tmp21
    tmp23 = tl.load(in_ptr0 + (x3), tmp22 & xmask, other=0.0)
    tmp24 = tmp23 * tmp23
    tmp25 = tl.full(tmp24.shape, 0.0, tmp24.dtype)
    tmp26 = tl.where(tmp22, tmp24, tmp25)
    tmp27 = tmp26 + tmp18
    tmp28 = 1 + x1
    tmp29 = tmp28 >= tmp1
    tmp30 = tmp28 < tmp3
    tmp31 = tmp29 & tmp30
    tmp32 = tl.load(in_ptr0 + (16 + x3), tmp31 & xmask, other=0.0)
    tmp33 = tmp32 * tmp32
    tmp34 = tl.full(tmp33.shape, 0.0, tmp33.dtype)
    tmp35 = tl.where(tmp31, tmp33, tmp34)
    tmp36 = tmp35 + tmp27
    tmp37 = 2 + x1
    tmp38 = tmp37 >= tmp1
    tmp39 = tmp37 < tmp3
    tmp40 = tmp38 & tmp39
    tmp41 = tl.load(in_ptr0 + (32 + x3), tmp40 & xmask, other=0.0)
    tmp42 = tmp41 * tmp41
    tmp43 = tl.full(tmp42.shape, 0.0, tmp42.dtype)
    tmp44 = tl.where(tmp40, tmp42, tmp43)
    tmp45 = tmp44 + tmp36
    tmp46 = 0.2
    tmp47 = tmp45 * tmp46
    tmp49 = 9.999999747378752e-05
    tmp50 = tmp47 * tmp49
    tmp51 = 1.0
    tmp52 = tmp50 + tmp51
    tmp53 = 0.75
    tmp54 = libdevice.pow(tmp52, tmp53)
    tmp55 = tmp48 / tmp54
    tl.store(in_out_ptr0 + (x3), tmp55, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1, 4, 4, 4), (64, 64, 16, 4, 1), torch.float32)
        buf1 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf0  # reuse
        # Topologically Sorted Source Nodes: [local_response_norm], Original ATen: [aten.constant_pad_nd, aten.avg_pool3d, aten.mul, aten.add, aten.pow, aten.div]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_avg_pool3d_constant_pad_nd_div_mul_pow_0.run(buf1, arg0_1, 256, grid=grid(256), stream=stream0)
        del arg0_1
    return (buf1, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1])
    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.nn.functional as F


class LocalResponseNormLayer(nn.Module):

    def forward(self, tensor, size=5, alpha=9.999999747378752e-05, beta=
        0.75, k=1.0):
        return F.local_response_norm(tensor, size=size, alpha=alpha, beta=
            beta, k=k)


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


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

import torch
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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_add_avg_pool3d_constant_pad_nd_div_mul_pow_0(in_out_ptr0,
    in_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
    x3 = xindex
    tmp48 = tl.load(in_ptr0 + x3, xmask)
    tmp0 = -2 + x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tmp2 & tmp4
    tmp6 = tl.load(in_ptr0 + (-32 + x3), tmp5 & xmask, other=0.0)
    tmp7 = tmp6 * tmp6
    tmp8 = tl.full(tmp7.shape, 0.0, tmp7.dtype)
    tmp9 = tl.where(tmp5, tmp7, tmp8)
    tmp10 = -1 + x1
    tmp11 = tmp10 >= tmp1
    tmp12 = tmp10 < tmp3
    tmp13 = tmp11 & tmp12
    tmp14 = tl.load(in_ptr0 + (-16 + x3), tmp13 & xmask, other=0.0)
    tmp15 = tmp14 * tmp14
    tmp16 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
    tmp17 = tl.where(tmp13, tmp15, tmp16)
    tmp18 = tmp17 + tmp9
    tmp19 = x1
    tmp20 = tmp19 >= tmp1
    tmp21 = tmp19 < tmp3
    tmp22 = tmp20 & tmp21
    tmp23 = tl.load(in_ptr0 + x3, tmp22 & xmask, other=0.0)
    tmp24 = tmp23 * tmp23
    tmp25 = tl.full(tmp24.shape, 0.0, tmp24.dtype)
    tmp26 = tl.where(tmp22, tmp24, tmp25)
    tmp27 = tmp26 + tmp18
    tmp28 = 1 + x1
    tmp29 = tmp28 >= tmp1
    tmp30 = tmp28 < tmp3
    tmp31 = tmp29 & tmp30
    tmp32 = tl.load(in_ptr0 + (16 + x3), tmp31 & xmask, other=0.0)
    tmp33 = tmp32 * tmp32
    tmp34 = tl.full(tmp33.shape, 0.0, tmp33.dtype)
    tmp35 = tl.where(tmp31, tmp33, tmp34)
    tmp36 = tmp35 + tmp27
    tmp37 = 2 + x1
    tmp38 = tmp37 >= tmp1
    tmp39 = tmp37 < tmp3
    tmp40 = tmp38 & tmp39
    tmp41 = tl.load(in_ptr0 + (32 + x3), tmp40 & xmask, other=0.0)
    tmp42 = tmp41 * tmp41
    tmp43 = tl.full(tmp42.shape, 0.0, tmp42.dtype)
    tmp44 = tl.where(tmp40, tmp42, tmp43)
    tmp45 = tmp44 + tmp36
    tmp46 = 0.2
    tmp47 = tmp45 * tmp46
    tmp49 = 9.999999747378752e-05
    tmp50 = tmp47 * tmp49
    tmp51 = 1.0
    tmp52 = tmp50 + tmp51
    tmp53 = 0.75
    tmp54 = libdevice.pow(tmp52, tmp53)
    tmp55 = tmp48 / tmp54
    tl.store(in_out_ptr0 + x3, tmp55, xmask)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1, 4, 4, 4), (64, 64, 16, 4, 1),
            torch.float32)
        buf1 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf0
        get_raw_stream(0)
        triton_poi_fused_add_avg_pool3d_constant_pad_nd_div_mul_pow_0[grid(256)
            ](buf1, arg0_1, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del arg0_1
    return buf1,


class LocalResponseNormLayerNew(nn.Module):

    def forward(self, input_0):
        arg0_1 = input_0
        output = call([arg0_1])
        return output[0]
