# 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/f7/cf7hx372qefhlftxcqa7olm5jzjr5hkpax45u64xcjyn54f7o5wq.py
# Topologically Sorted Source Nodes: [input_1], Original ATen: [aten.max_pool2d_with_indices]
# Source node to ATen node mapping:
#   input_1 => _low_memory_max_pool2d_with_offsets
# Graph fragment:
#   %_low_memory_max_pool2d_with_offsets : [num_users=1] = call_function[target=torch.ops.prims._low_memory_max_pool2d_with_offsets.default](args = (%arg0_1, [2, 2], [1, 1], [0, 0], [1, 1], False), kwargs = {})
triton_poi_fused_max_pool2d_with_indices_0 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_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=[16, 16], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: '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_max_pool2d_with_indices_0', '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_max_pool2d_with_indices_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 16
    xnumel = 9
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex % 3
    x3 = (xindex // 3)
    y4 = yindex
    x5 = xindex
    y0 = yindex % 4
    y1 = (yindex // 4)
    tmp0 = tl.load(in_ptr0 + (x2 + (4*x3) + (16*y4)), xmask & ymask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + x2 + (4*x3) + (16*y4)), xmask & ymask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (4 + x2 + (4*x3) + (16*y4)), xmask & ymask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (5 + x2 + (4*x3) + (16*y4)), xmask & ymask, eviction_policy='evict_last')
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tl.store(out_ptr0 + (y0 + (4*x5) + (36*y1)), tmp6, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/wo/cwo4qykn5dymhtuee5vudoe47fsa2oaxditdnm2h6ih37by6wwag.py
# Topologically Sorted Source Nodes: [input_1, input_2], Original ATen: [aten.max_pool2d_with_indices, aten.convolution]
# Source node to ATen node mapping:
#   input_1 => _low_memory_max_pool2d_with_offsets
#   input_2 => convolution
# Graph fragment:
#   %_low_memory_max_pool2d_with_offsets : [num_users=1] = call_function[target=torch.ops.prims._low_memory_max_pool2d_with_offsets.default](args = (%arg0_1, [2, 2], [1, 1], [0, 0], [1, 1], False), kwargs = {})
#   %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%getitem, %arg1_1, None, [2, 2], [1, 1], [1, 1], False, [0, 0], 4), kwargs = {})
triton_poi_fused_convolution_max_pool2d_with_indices_1 = async_compile.triton('triton_poi_fused_convolution_max_pool2d_with_indices_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=[16, 4], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: '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_max_pool2d_with_indices_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_max_pool2d_with_indices_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 16
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 4
    y1 = (yindex // 4)
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + (4*x2) + (16*y1)), xmask & ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + (4*y3)), tmp0, xmask & ymask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 1, 3, 3), (9, 9, 3, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 3, 3), (36, 1, 12, 4), torch.float32)
        # Topologically Sorted Source Nodes: [input_1], Original ATen: [aten.max_pool2d_with_indices]
        stream0 = get_raw_stream(0)
        triton_poi_fused_max_pool2d_with_indices_0.run(arg0_1, buf0, 16, 9, grid=grid(16, 9), stream=stream0)
        del arg0_1
        # Topologically Sorted Source Nodes: [input_1, input_2], Original ATen: [aten.max_pool2d_with_indices, aten.convolution]
        buf1 = extern_kernels.convolution(buf0, arg1_1, stride=(2, 2), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=4, bias=None)
        assert_size_stride(buf1, (4, 4, 2, 2), (16, 1, 8, 4))
        del arg1_1
        del buf0
        buf2 = empty_strided_cuda((4, 4, 2, 2), (16, 4, 2, 1), torch.float32)
        # Topologically Sorted Source Nodes: [input_1, input_2], Original ATen: [aten.max_pool2d_with_indices, aten.convolution]
        triton_poi_fused_convolution_max_pool2d_with_indices_1.run(buf1, buf2, 16, 4, grid=grid(16, 4), stream=stream0)
        del buf1
    return (buf2, )


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)
    arg1_1 = rand_strided((4, 1, 3, 3), (9, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1, arg1_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


class BlurPool2d(nn.Sequential):
    """Blur Pooling Layer (MaxPool2d replacement)
    See: https://richzhang.github.io/antialiased-cnns/
    Paper: https://arxiv.org/abs/1904.11486
    """
    __constants__ = ['in_features']
    _blur_kernel = torch.tensor([[1 / 16, 2 / 16, 1 / 16], [2 / 16, 4 / 16,
        2 / 16], [1 / 16, 2 / 16, 1 / 16]])

    def __init__(self, in_features):
        """
        Args:
            in_features (int): The number of channels in the input
        """
        super().__init__()
        self.in_features = in_features
        self.add_module('maxpool', nn.MaxPool2d(2, stride=1))
        blurpool = nn.Conv2d(in_features, in_features, kernel_size=3,
            padding=1, stride=2, bias=False, groups=in_features)
        blurpool.weight = torch.nn.Parameter(self._blur_kernel.repeat(
            in_features, 1, 1, 1), requires_grad=False)
        self.add_module('blurpool', blurpool)

    def forward(self, x):
        return super(BlurPool2d, self).forward(x)

    def extra_repr(self):
        return 'in_features={}'.format(self.in_features)


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


def get_init_inputs():
    return [[], {'in_features': 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 import triton_helpers
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_max_pool2d_with_indices_0(in_ptr0, out_ptr0, ynumel,
    xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 9
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex % 3
    x3 = xindex // 3
    y4 = yindex
    x5 = xindex
    y0 = yindex % 4
    y1 = yindex // 4
    tmp0 = tl.load(in_ptr0 + (x2 + 4 * x3 + 16 * y4), xmask & ymask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + x2 + 4 * x3 + 16 * y4), xmask & ymask,
        eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (4 + x2 + 4 * x3 + 16 * y4), xmask & ymask,
        eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (5 + x2 + 4 * x3 + 16 * y4), xmask & ymask,
        eviction_policy='evict_last')
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tl.store(out_ptr0 + (y0 + 4 * x5 + 36 * y1), tmp6, xmask & ymask)


@triton.jit
def triton_poi_fused_convolution_max_pool2d_with_indices_1(in_ptr0,
    out_ptr0, ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 16 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 1, 3, 3), (9, 9, 3, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 3, 3), (36, 1, 12, 4), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_max_pool2d_with_indices_0[grid(16, 9)](arg0_1,
            buf0, 16, 9, XBLOCK=16, YBLOCK=16, num_warps=4, num_stages=1)
        del arg0_1
        buf1 = extern_kernels.convolution(buf0, arg1_1, stride=(2, 2),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=4, bias=None)
        assert_size_stride(buf1, (4, 4, 2, 2), (16, 1, 8, 4))
        del arg1_1
        del buf0
        buf2 = empty_strided_cuda((4, 4, 2, 2), (16, 4, 2, 1), torch.float32)
        triton_poi_fused_convolution_max_pool2d_with_indices_1[grid(16, 4)](
            buf1, buf2, 16, 4, XBLOCK=4, YBLOCK=16, num_warps=1, num_stages=1)
        del buf1
    return buf2,


class BlurPool2dNew(nn.Sequential):
    """Blur Pooling Layer (MaxPool2d replacement)
    See: https://richzhang.github.io/antialiased-cnns/
    Paper: https://arxiv.org/abs/1904.11486
    """
    __constants__ = ['in_features']
    _blur_kernel = torch.tensor([[1 / 16, 2 / 16, 1 / 16], [2 / 16, 4 / 16,
        2 / 16], [1 / 16, 2 / 16, 1 / 16]])

    def __init__(self, in_features):
        """
        Args:
            in_features (int): The number of channels in the input
        """
        super().__init__()
        self.in_features = in_features
        self.add_module('maxpool', nn.MaxPool2d(2, stride=1))
        blurpool = nn.Conv2d(in_features, in_features, kernel_size=3,
            padding=1, stride=2, bias=False, groups=in_features)
        blurpool.weight = torch.nn.Parameter(self._blur_kernel.repeat(
            in_features, 1, 1, 1), requires_grad=False)
        self.add_module('blurpool', blurpool)

    def extra_repr(self):
        return 'in_features={}'.format(self.in_features)

    def forward(self, input_0):
        arg1_1 = self.blurpool.weight
        arg0_1 = input_0
        output = call([arg0_1, arg1_1])
        return output[0]
