# 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_7/inductor_cache/j4/cj47gjxddooj3qeijcb25ywjhwfl5gx75ggkyzuyl3muz5nxshah.py
# Topologically Sorted Source Nodes: [x, x_1], Original ATen: [aten.constant_pad_nd, aten.avg_pool3d]
# Source node to ATen node mapping:
#   x => constant_pad_nd
#   x_1 => avg_pool3d
# Graph fragment:
#   %constant_pad_nd : [num_users=1] = call_function[target=torch.ops.aten.constant_pad_nd.default](args = (%arg0_1, [0, 1, 0, 1], 0.0), kwargs = {})
#   %avg_pool3d : [num_users=3] = call_function[target=torch.ops.aten.avg_pool3d.default](args = (%constant_pad_nd, [1, 3, 3], [1, 2, 2]), kwargs = {})
triton_poi_fused_avg_pool3d_constant_pad_nd_0 = async_compile.triton('triton_poi_fused_avg_pool3d_constant_pad_nd_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=[64], 
    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_avg_pool3d_constant_pad_nd_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 9, '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_avg_pool3d_constant_pad_nd_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 2) % 2
    x0 = xindex % 2
    x3 = (xindex // 2)
    x4 = xindex
    tmp0 = 2*x1
    tmp1 = tl.full([1], 4, tl.int64)
    tmp2 = tmp0 < tmp1
    tmp3 = 2*x0
    tmp4 = tmp3 < tmp1
    tmp5 = tmp2 & tmp4
    tmp6 = tl.load(in_ptr0 + ((2*x0) + (8*x3)), tmp5 & xmask, eviction_policy='evict_last', other=0.0)
    tmp7 = 1 + (2*x0)
    tmp8 = tmp7 < tmp1
    tmp9 = tmp2 & tmp8
    tmp10 = tl.load(in_ptr0 + (1 + (2*x0) + (8*x3)), tmp9 & xmask, eviction_policy='evict_last', other=0.0)
    tmp11 = tmp10 + tmp6
    tmp12 = 2 + (2*x0)
    tmp13 = tmp12 < tmp1
    tmp14 = tmp2 & tmp13
    tmp15 = tl.load(in_ptr0 + (2 + (2*x0) + (8*x3)), tmp14 & xmask, eviction_policy='evict_last', other=0.0)
    tmp16 = tmp15 + tmp11
    tmp17 = 1 + (2*x1)
    tmp18 = tmp17 < tmp1
    tmp19 = tmp18 & tmp4
    tmp20 = tl.load(in_ptr0 + (4 + (2*x0) + (8*x3)), tmp19 & xmask, eviction_policy='evict_last', other=0.0)
    tmp21 = tmp20 + tmp16
    tmp22 = tmp18 & tmp8
    tmp23 = tl.load(in_ptr0 + (5 + (2*x0) + (8*x3)), tmp22 & xmask, eviction_policy='evict_last', other=0.0)
    tmp24 = tmp23 + tmp21
    tmp25 = tmp18 & tmp13
    tmp26 = tl.load(in_ptr0 + (6 + (2*x0) + (8*x3)), tmp25 & xmask, eviction_policy='evict_last', other=0.0)
    tmp27 = tmp26 + tmp24
    tmp28 = 2 + (2*x1)
    tmp29 = tmp28 < tmp1
    tmp30 = tmp29 & tmp4
    tmp31 = tl.load(in_ptr0 + (8 + (2*x0) + (8*x3)), tmp30 & xmask, eviction_policy='evict_last', other=0.0)
    tmp32 = tmp31 + tmp27
    tmp33 = tmp29 & tmp8
    tmp34 = tl.load(in_ptr0 + (9 + (2*x0) + (8*x3)), tmp33 & xmask, eviction_policy='evict_last', other=0.0)
    tmp35 = tmp34 + tmp32
    tmp36 = tmp29 & tmp13
    tmp37 = tl.load(in_ptr0 + (10 + (2*x0) + (8*x3)), tmp36 & xmask, eviction_policy='evict_last', other=0.0)
    tmp38 = tmp37 + tmp35
    tmp39 = 0.1111111111111111
    tmp40 = tmp38 * tmp39
    tl.store(out_ptr0 + (x4), tmp40, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/wu/cwumdhgny2yfc2fqegw3q6scmzh4l35mrpxzpy7u3hofzgr4dlgf.py
# Topologically Sorted Source Nodes: [mul, truediv, setitem, mul_1, truediv_1, setitem_1], Original ATen: [aten.mul, aten.div, aten.copy]
# Source node to ATen node mapping:
#   mul => mul
#   mul_1 => mul_1
#   setitem => copy
#   setitem_1 => copy_1
#   truediv => div
#   truediv_1 => div_1
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%select, 9), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%mul, 6), kwargs = {})
#   %copy : [num_users=1] = call_function[target=torch.ops.aten.copy.default](args = (%select_1, %div), kwargs = {})
#   %select_scatter_default : [num_users=4] = call_function[target=torch.ops.aten.select_scatter.default](args = (%avg_pool3d, %copy, 3, -1), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%select_4, 9), kwargs = {})
#   %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%mul_1, 6), kwargs = {})
#   %copy_1 : [num_users=1] = call_function[target=torch.ops.aten.copy.default](args = (%select_6, %div_1), kwargs = {})
#   %select_scatter_default_1 : [num_users=1] = call_function[target=torch.ops.aten.select_scatter.default](args = (%select_scatter_default, %copy_1, 2, -1), kwargs = {})
triton_poi_fused_copy_div_mul_1 = async_compile.triton('triton_poi_fused_copy_div_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=[64], 
    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_copy_div_mul_1', '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_copy_div_mul_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 2) % 2
    x0 = xindex % 2
    x2 = (xindex // 4)
    x4 = (xindex // 2)
    x5 = xindex
    tmp5 = tl.load(in_ptr0 + (3 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr0 + (2 + x0 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr0 + (1 + (2*x4)), xmask, eviction_policy='evict_last')
    tmp17 = tl.load(in_ptr0 + (x5), xmask)
    tmp0 = x1
    tmp1 = tl.full([1], 1, tl.int32)
    tmp2 = tmp0 == tmp1
    tmp3 = x0
    tmp4 = tmp3 == tmp1
    tmp6 = 9.0
    tmp7 = tmp5 * tmp6
    tmp8 = 0.16666666666666666
    tmp9 = tmp7 * tmp8
    tmp11 = tl.where(tmp4, tmp9, tmp10)
    tmp12 = tmp11 * tmp6
    tmp13 = tmp12 * tmp8
    tmp15 = tmp14 * tmp6
    tmp16 = tmp15 * tmp8
    tmp18 = tl.where(tmp4, tmp16, tmp17)
    tmp19 = tl.where(tmp2, tmp13, tmp18)
    tl.store(out_ptr0 + (x5), tmp19, 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, 4, 2, 2), (16, 4, 2, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x, x_1], Original ATen: [aten.constant_pad_nd, aten.avg_pool3d]
        stream0 = get_raw_stream(0)
        triton_poi_fused_avg_pool3d_constant_pad_nd_0.run(arg0_1, buf0, 64, grid=grid(64), stream=stream0)
        del arg0_1
        buf1 = empty_strided_cuda((4, 4, 2, 2), (16, 4, 2, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mul, truediv, setitem, mul_1, truediv_1, setitem_1], Original ATen: [aten.mul, aten.div, aten.copy]
        triton_poi_fused_copy_div_mul_1.run(buf0, buf1, 64, grid=grid(64), stream=stream0)
        del buf0
    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
from torch import Tensor
from torch import nn


class tfAvgPool3D(nn.Module):

    def __init__(self) ->None:
        super().__init__()
        self.avgf = nn.AvgPool3d((1, 3, 3), stride=(1, 2, 2))

    def forward(self, x: 'Tensor') ->Tensor:
        if x.shape[-1] != x.shape[-2]:
            raise RuntimeError('only same shape for h and w ' +
                'are supported by avg with tf_like')
        if x.shape[-1] != x.shape[-2]:
            raise RuntimeError('only same shape for h and w ' +
                'are supported by avg with tf_like')
        f1 = x.shape[-1] % 2 != 0
        if f1:
            padding_pad = 0, 0, 0, 0
        else:
            padding_pad = 0, 1, 0, 1
        x = torch.nn.functional.pad(x, padding_pad)
        if f1:
            x = torch.nn.functional.avg_pool3d(x, (1, 3, 3), stride=(1, 2, 
                2), count_include_pad=False, padding=(0, 1, 1))
        else:
            x = self.avgf(x)
            x[..., -1] = x[..., -1] * 9 / 6
            x[..., -1, :] = x[..., -1, :] * 9 / 6
        return x


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 import 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_avg_pool3d_constant_pad_nd_0(in_ptr0, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 2 % 2
    x0 = xindex % 2
    x3 = xindex // 2
    x4 = xindex
    tmp0 = 2 * x1
    tmp1 = tl.full([1], 4, tl.int64)
    tmp2 = tmp0 < tmp1
    tmp3 = 2 * x0
    tmp4 = tmp3 < tmp1
    tmp5 = tmp2 & tmp4
    tmp6 = tl.load(in_ptr0 + (2 * x0 + 8 * x3), tmp5 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp7 = 1 + 2 * x0
    tmp8 = tmp7 < tmp1
    tmp9 = tmp2 & tmp8
    tmp10 = tl.load(in_ptr0 + (1 + 2 * x0 + 8 * x3), tmp9 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp11 = tmp10 + tmp6
    tmp12 = 2 + 2 * x0
    tmp13 = tmp12 < tmp1
    tmp14 = tmp2 & tmp13
    tmp15 = tl.load(in_ptr0 + (2 + 2 * x0 + 8 * x3), tmp14 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp16 = tmp15 + tmp11
    tmp17 = 1 + 2 * x1
    tmp18 = tmp17 < tmp1
    tmp19 = tmp18 & tmp4
    tmp20 = tl.load(in_ptr0 + (4 + 2 * x0 + 8 * x3), tmp19 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp21 = tmp20 + tmp16
    tmp22 = tmp18 & tmp8
    tmp23 = tl.load(in_ptr0 + (5 + 2 * x0 + 8 * x3), tmp22 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp24 = tmp23 + tmp21
    tmp25 = tmp18 & tmp13
    tmp26 = tl.load(in_ptr0 + (6 + 2 * x0 + 8 * x3), tmp25 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp27 = tmp26 + tmp24
    tmp28 = 2 + 2 * x1
    tmp29 = tmp28 < tmp1
    tmp30 = tmp29 & tmp4
    tmp31 = tl.load(in_ptr0 + (8 + 2 * x0 + 8 * x3), tmp30 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp32 = tmp31 + tmp27
    tmp33 = tmp29 & tmp8
    tmp34 = tl.load(in_ptr0 + (9 + 2 * x0 + 8 * x3), tmp33 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp35 = tmp34 + tmp32
    tmp36 = tmp29 & tmp13
    tmp37 = tl.load(in_ptr0 + (10 + 2 * x0 + 8 * x3), tmp36 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp38 = tmp37 + tmp35
    tmp39 = 0.1111111111111111
    tmp40 = tmp38 * tmp39
    tl.store(out_ptr0 + x4, tmp40, xmask)


@triton.jit
def triton_poi_fused_copy_div_mul_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 2 % 2
    x0 = xindex % 2
    x2 = xindex // 4
    x4 = xindex // 2
    x5 = xindex
    tmp5 = tl.load(in_ptr0 + (3 + 4 * x2), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr0 + (2 + x0 + 4 * x2), xmask, eviction_policy=
        'evict_last')
    tmp14 = tl.load(in_ptr0 + (1 + 2 * x4), xmask, eviction_policy='evict_last'
        )
    tmp17 = tl.load(in_ptr0 + x5, xmask)
    tmp0 = x1
    tmp1 = tl.full([1], 1, tl.int32)
    tmp2 = tmp0 == tmp1
    tmp3 = x0
    tmp4 = tmp3 == tmp1
    tmp6 = 9.0
    tmp7 = tmp5 * tmp6
    tmp8 = 0.16666666666666666
    tmp9 = tmp7 * tmp8
    tmp11 = tl.where(tmp4, tmp9, tmp10)
    tmp12 = tmp11 * tmp6
    tmp13 = tmp12 * tmp8
    tmp15 = tmp14 * tmp6
    tmp16 = tmp15 * tmp8
    tmp18 = tl.where(tmp4, tmp16, tmp17)
    tmp19 = tl.where(tmp2, tmp13, tmp18)
    tl.store(out_ptr0 + x5, tmp19, 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, 4, 2, 2), (16, 4, 2, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_avg_pool3d_constant_pad_nd_0[grid(64)](arg0_1,
            buf0, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del arg0_1
        buf1 = empty_strided_cuda((4, 4, 2, 2), (16, 4, 2, 1), torch.float32)
        triton_poi_fused_copy_div_mul_1[grid(64)](buf0, buf1, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del buf0
    return buf1,


class tfAvgPool3DNew(nn.Module):

    def __init__(self) ->None:
        super().__init__()
        self.avgf = nn.AvgPool3d((1, 3, 3), stride=(1, 2, 2))

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