# 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/dz/cdz3nkgyrhben4dg5ahsmw55wko3y32durc6eb6vfqmjdr6gb3ir.py
# Topologically Sorted Source Nodes: [avg_pool2d], Original ATen: [aten.avg_pool2d]
# Source node to ATen node mapping:
#   avg_pool2d => avg_pool2d
# Graph fragment:
#   %avg_pool2d : [num_users=1] = call_function[target=torch.ops.aten.avg_pool2d.default](args = (%arg0_1, [3, 3], [1, 1], [1, 1], False, False), kwargs = {})
triton_poi_fused_avg_pool2d_0 = async_compile.triton('triton_poi_fused_avg_pool2d_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_avg_pool2d_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_pool2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 4) % 4
    x0 = xindex % 4
    x4 = xindex
    tmp0 = (-1) + 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 = (-1) + x0
    tmp7 = tmp6 >= tmp1
    tmp8 = tmp6 < tmp3
    tmp9 = tmp7 & tmp8
    tmp10 = tmp5 & tmp9
    tmp11 = tl.load(in_ptr0 + ((-5) + x4), tmp10 & xmask, other=0.0)
    tmp12 = x0
    tmp13 = tmp12 >= tmp1
    tmp14 = tmp12 < tmp3
    tmp15 = tmp13 & tmp14
    tmp16 = tmp5 & tmp15
    tmp17 = tl.load(in_ptr0 + ((-4) + x4), tmp16 & xmask, other=0.0)
    tmp18 = tmp17 + tmp11
    tmp19 = 1 + x0
    tmp20 = tmp19 >= tmp1
    tmp21 = tmp19 < tmp3
    tmp22 = tmp20 & tmp21
    tmp23 = tmp5 & tmp22
    tmp24 = tl.load(in_ptr0 + ((-3) + x4), tmp23 & xmask, other=0.0)
    tmp25 = tmp24 + tmp18
    tmp26 = x1
    tmp27 = tmp26 >= tmp1
    tmp28 = tmp26 < tmp3
    tmp29 = tmp27 & tmp28
    tmp30 = tmp29 & tmp9
    tmp31 = tl.load(in_ptr0 + ((-1) + x4), tmp30 & xmask, other=0.0)
    tmp32 = tmp31 + tmp25
    tmp33 = tmp29 & tmp15
    tmp34 = tl.load(in_ptr0 + (x4), tmp33 & xmask, other=0.0)
    tmp35 = tmp34 + tmp32
    tmp36 = tmp29 & tmp22
    tmp37 = tl.load(in_ptr0 + (1 + x4), tmp36 & xmask, other=0.0)
    tmp38 = tmp37 + tmp35
    tmp39 = 1 + x1
    tmp40 = tmp39 >= tmp1
    tmp41 = tmp39 < tmp3
    tmp42 = tmp40 & tmp41
    tmp43 = tmp42 & tmp9
    tmp44 = tl.load(in_ptr0 + (3 + x4), tmp43 & xmask, other=0.0)
    tmp45 = tmp44 + tmp38
    tmp46 = tmp42 & tmp15
    tmp47 = tl.load(in_ptr0 + (4 + x4), tmp46 & xmask, other=0.0)
    tmp48 = tmp47 + tmp45
    tmp49 = tmp42 & tmp22
    tmp50 = tl.load(in_ptr0 + (5 + x4), tmp49 & xmask, other=0.0)
    tmp51 = tmp50 + tmp48
    tmp52 = (((0) * ((0) >= ((-1) + x0)) + ((-1) + x0) * (((-1) + x0) > (0)))*((0) * ((0) >= ((-1) + x1)) + ((-1) + x1) * (((-1) + x1) > (0)))) + (((4) * ((4) <= (2 + x0)) + (2 + x0) * ((2 + x0) < (4)))*((4) * ((4) <= (2 + x1)) + (2 + x1) * ((2 + x1) < (4)))) + ((-1)*((0) * ((0) >= ((-1) + x0)) + ((-1) + x0) * (((-1) + x0) > (0)))*((4) * ((4) <= (2 + x1)) + (2 + x1) * ((2 + x1) < (4)))) + ((-1)*((0) * ((0) >= ((-1) + x1)) + ((-1) + x1) * (((-1) + x1) > (0)))*((4) * ((4) <= (2 + x0)) + (2 + x0) * ((2 + x0) < (4))))
    tmp53 = tmp51 / tmp52
    tl.store(out_ptr0 + (x4), tmp53, 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, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [avg_pool2d], Original ATen: [aten.avg_pool2d]
        stream0 = get_raw_stream(0)
        triton_poi_fused_avg_pool2d_0.run(arg0_1, buf0, 256, grid=grid(256), stream=stream0)
        del arg0_1
    return (buf0, )


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.parallel
import torch.optim
import torch.utils.data


class ReLUConvBN(nn.Module):
    """
    Parameters
    ---
    C_in: int
        the number of input channels
    C_out: int
        the number of output channels
    stride: int
        stride of the convolution
    padding: int
        zero-padding added to both sides of the input
    dilation: int
        spacing between kernel elements
    bn_affine: bool
        If set to ``True``, ``torch.nn.BatchNorm2d`` will have learnable affine parameters. Default: True
    bn_momentun: float
        the value used for the running_mean and running_var computation. Default: 0.1
    bn_track_running_stats: bool
        When set to ``True``, ``torch.nn.BatchNorm2d`` tracks the running mean and variance. Default: True
    """

    def __init__(self, C_in, C_out, kernel_size, stride, padding, dilation,
        bn_affine=True, bn_momentum=0.1, bn_track_running_stats=True):
        super(ReLUConvBN, self).__init__()
        self.op = nn.Sequential(nn.ReLU(inplace=False), nn.Conv2d(C_in,
            C_out, kernel_size, stride=stride, padding=padding, dilation=
            dilation, bias=False), nn.BatchNorm2d(C_out, affine=bn_affine,
            momentum=bn_momentum, track_running_stats=bn_track_running_stats))

    def forward(self, x):
        """
        Parameters
        ---
        x: torch.Tensor
            input tensor
        """
        return self.op(x)


class Pooling(nn.Module):
    """
    Parameters
    ---
    C_in: int
        the number of input channels
    C_out: int
        the number of output channels
    stride: int
        stride of the convolution
    bn_affine: bool
        If set to ``True``, ``torch.nn.BatchNorm2d`` will have learnable affine parameters. Default: True
    bn_momentun: float
        the value used for the running_mean and running_var computation. Default: 0.1
    bn_track_running_stats: bool
        When set to ``True``, ``torch.nn.BatchNorm2d`` tracks the running mean and variance. Default: True
    """

    def __init__(self, C_in, C_out, stride, bn_affine=True, bn_momentum=0.1,
        bn_track_running_stats=True):
        super(Pooling, self).__init__()
        if C_in == C_out:
            self.preprocess = None
        else:
            self.preprocess = ReLUConvBN(C_in, C_out, 1, 1, 0, 0, bn_affine,
                bn_momentum, bn_track_running_stats)
        self.op = nn.AvgPool2d(3, stride=stride, padding=1,
            count_include_pad=False)

    def forward(self, x):
        """
        Parameters
        ---
        x: torch.Tensor
            input tensor
        """
        if self.preprocess:
            x = self.preprocess(x)
        return self.op(x)


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


def get_init_inputs():
    return [[], {'C_in': 4, 'C_out': 4, 'stride': 1}]

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
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
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_pool2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4 % 4
    x0 = xindex % 4
    x4 = xindex
    tmp0 = -1 + 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 = -1 + x0
    tmp7 = tmp6 >= tmp1
    tmp8 = tmp6 < tmp3
    tmp9 = tmp7 & tmp8
    tmp10 = tmp5 & tmp9
    tmp11 = tl.load(in_ptr0 + (-5 + x4), tmp10 & xmask, other=0.0)
    tmp12 = x0
    tmp13 = tmp12 >= tmp1
    tmp14 = tmp12 < tmp3
    tmp15 = tmp13 & tmp14
    tmp16 = tmp5 & tmp15
    tmp17 = tl.load(in_ptr0 + (-4 + x4), tmp16 & xmask, other=0.0)
    tmp18 = tmp17 + tmp11
    tmp19 = 1 + x0
    tmp20 = tmp19 >= tmp1
    tmp21 = tmp19 < tmp3
    tmp22 = tmp20 & tmp21
    tmp23 = tmp5 & tmp22
    tmp24 = tl.load(in_ptr0 + (-3 + x4), tmp23 & xmask, other=0.0)
    tmp25 = tmp24 + tmp18
    tmp26 = x1
    tmp27 = tmp26 >= tmp1
    tmp28 = tmp26 < tmp3
    tmp29 = tmp27 & tmp28
    tmp30 = tmp29 & tmp9
    tmp31 = tl.load(in_ptr0 + (-1 + x4), tmp30 & xmask, other=0.0)
    tmp32 = tmp31 + tmp25
    tmp33 = tmp29 & tmp15
    tmp34 = tl.load(in_ptr0 + x4, tmp33 & xmask, other=0.0)
    tmp35 = tmp34 + tmp32
    tmp36 = tmp29 & tmp22
    tmp37 = tl.load(in_ptr0 + (1 + x4), tmp36 & xmask, other=0.0)
    tmp38 = tmp37 + tmp35
    tmp39 = 1 + x1
    tmp40 = tmp39 >= tmp1
    tmp41 = tmp39 < tmp3
    tmp42 = tmp40 & tmp41
    tmp43 = tmp42 & tmp9
    tmp44 = tl.load(in_ptr0 + (3 + x4), tmp43 & xmask, other=0.0)
    tmp45 = tmp44 + tmp38
    tmp46 = tmp42 & tmp15
    tmp47 = tl.load(in_ptr0 + (4 + x4), tmp46 & xmask, other=0.0)
    tmp48 = tmp47 + tmp45
    tmp49 = tmp42 & tmp22
    tmp50 = tl.load(in_ptr0 + (5 + x4), tmp49 & xmask, other=0.0)
    tmp51 = tmp50 + tmp48
    tmp52 = (0 * (0 >= -1 + x0) + (-1 + x0) * (-1 + x0 > 0)) * (0 * (0 >= -
        1 + x1) + (-1 + x1) * (-1 + x1 > 0)) + (4 * (4 <= 2 + x0) + (2 + x0
        ) * (2 + x0 < 4)) * (4 * (4 <= 2 + x1) + (2 + x1) * (2 + x1 < 4)
        ) + -1 * (0 * (0 >= -1 + x0) + (-1 + x0) * (-1 + x0 > 0)) * (4 * (4 <=
        2 + x1) + (2 + x1) * (2 + x1 < 4)) + -1 * (0 * (0 >= -1 + x1) + (-1 +
        x1) * (-1 + x1 > 0)) * (4 * (4 <= 2 + x0) + (2 + x0) * (2 + x0 < 4))
    tmp53 = tmp51 / tmp52
    tl.store(out_ptr0 + x4, tmp53, 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, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_avg_pool2d_0[grid(256)](arg0_1, buf0, 256, XBLOCK=
            256, num_warps=4, num_stages=1)
        del arg0_1
    return buf0,


class ReLUConvBN(nn.Module):
    """
    Parameters
    ---
    C_in: int
        the number of input channels
    C_out: int
        the number of output channels
    stride: int
        stride of the convolution
    padding: int
        zero-padding added to both sides of the input
    dilation: int
        spacing between kernel elements
    bn_affine: bool
        If set to ``True``, ``torch.nn.BatchNorm2d`` will have learnable affine parameters. Default: True
    bn_momentun: float
        the value used for the running_mean and running_var computation. Default: 0.1
    bn_track_running_stats: bool
        When set to ``True``, ``torch.nn.BatchNorm2d`` tracks the running mean and variance. Default: True
    """

    def __init__(self, C_in, C_out, kernel_size, stride, padding, dilation,
        bn_affine=True, bn_momentum=0.1, bn_track_running_stats=True):
        super(ReLUConvBN, self).__init__()
        self.op = nn.Sequential(nn.ReLU(inplace=False), nn.Conv2d(C_in,
            C_out, kernel_size, stride=stride, padding=padding, dilation=
            dilation, bias=False), nn.BatchNorm2d(C_out, affine=bn_affine,
            momentum=bn_momentum, track_running_stats=bn_track_running_stats))

    def forward(self, x):
        """
        Parameters
        ---
        x: torch.Tensor
            input tensor
        """
        return self.op(x)


class PoolingNew(nn.Module):
    """
    Parameters
    ---
    C_in: int
        the number of input channels
    C_out: int
        the number of output channels
    stride: int
        stride of the convolution
    bn_affine: bool
        If set to ``True``, ``torch.nn.BatchNorm2d`` will have learnable affine parameters. Default: True
    bn_momentun: float
        the value used for the running_mean and running_var computation. Default: 0.1
    bn_track_running_stats: bool
        When set to ``True``, ``torch.nn.BatchNorm2d`` tracks the running mean and variance. Default: True
    """

    def __init__(self, C_in, C_out, stride, bn_affine=True, bn_momentum=0.1,
        bn_track_running_stats=True):
        super(PoolingNew, self).__init__()
        if C_in == C_out:
            self.preprocess = None
        else:
            self.preprocess = ReLUConvBN(C_in, C_out, 1, 1, 0, 0, bn_affine,
                bn_momentum, bn_track_running_stats)
        self.op = nn.AvgPool2d(3, stride=stride, padding=1,
            count_include_pad=False)

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