# 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/qq/cqqb7lg6q4ad6ao65faglzxqzvt3ad3sdvodh7k57sce43e7szvx.py
# Topologically Sorted Source Nodes: [max_pool2d], Original ATen: [aten.max_pool2d_with_indices]
# Source node to ATen node mapping:
#   max_pool2d => getitem
# Graph fragment:
#   %getitem : [num_users=1] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets, 0), 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=[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_max_pool2d_with_indices_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 16, '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, 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 = (-1) + x0
    tmp6 = tmp5 >= tmp1
    tmp7 = tmp5 < tmp3
    tmp8 = tmp2 & tmp4
    tmp9 = tmp8 & tmp6
    tmp10 = tmp9 & tmp7
    tmp11 = tl.load(in_ptr0 + ((-5) + x4), tmp10 & xmask, other=0.0)
    tmp12 = x0
    tmp13 = tmp12 >= tmp1
    tmp14 = tmp12 < tmp3
    tmp15 = tmp8 & tmp13
    tmp16 = tmp15 & tmp14
    tmp17 = tl.load(in_ptr0 + ((-4) + x4), tmp16 & xmask, other=0.0)
    tmp18 = triton_helpers.maximum(tmp17, tmp11)
    tmp19 = 1 + x0
    tmp20 = tmp19 >= tmp1
    tmp21 = tmp19 < tmp3
    tmp22 = tmp8 & tmp20
    tmp23 = tmp22 & tmp21
    tmp24 = tl.load(in_ptr0 + ((-3) + x4), tmp23 & xmask, other=0.0)
    tmp25 = triton_helpers.maximum(tmp24, tmp18)
    tmp26 = 2 + x0
    tmp27 = tmp26 >= tmp1
    tmp28 = tmp26 < tmp3
    tmp29 = tmp8 & tmp27
    tmp30 = tmp29 & tmp28
    tmp31 = tl.load(in_ptr0 + ((-2) + x4), tmp30 & xmask, other=0.0)
    tmp32 = triton_helpers.maximum(tmp31, tmp25)
    tmp33 = x1
    tmp34 = tmp33 >= tmp1
    tmp35 = tmp33 < tmp3
    tmp36 = tmp34 & tmp35
    tmp37 = tmp36 & tmp6
    tmp38 = tmp37 & tmp7
    tmp39 = tl.load(in_ptr0 + ((-1) + x4), tmp38 & xmask, other=0.0)
    tmp40 = triton_helpers.maximum(tmp39, tmp32)
    tmp41 = tmp36 & tmp13
    tmp42 = tmp41 & tmp14
    tmp43 = tl.load(in_ptr0 + (x4), tmp42 & xmask, other=0.0)
    tmp44 = triton_helpers.maximum(tmp43, tmp40)
    tmp45 = tmp36 & tmp20
    tmp46 = tmp45 & tmp21
    tmp47 = tl.load(in_ptr0 + (1 + x4), tmp46 & xmask, other=0.0)
    tmp48 = triton_helpers.maximum(tmp47, tmp44)
    tmp49 = tmp36 & tmp27
    tmp50 = tmp49 & tmp28
    tmp51 = tl.load(in_ptr0 + (2 + x4), tmp50 & xmask, other=0.0)
    tmp52 = triton_helpers.maximum(tmp51, tmp48)
    tmp53 = 1 + x1
    tmp54 = tmp53 >= tmp1
    tmp55 = tmp53 < tmp3
    tmp56 = tmp54 & tmp55
    tmp57 = tmp56 & tmp6
    tmp58 = tmp57 & tmp7
    tmp59 = tl.load(in_ptr0 + (3 + x4), tmp58 & xmask, other=0.0)
    tmp60 = triton_helpers.maximum(tmp59, tmp52)
    tmp61 = tmp56 & tmp13
    tmp62 = tmp61 & tmp14
    tmp63 = tl.load(in_ptr0 + (4 + x4), tmp62 & xmask, other=0.0)
    tmp64 = triton_helpers.maximum(tmp63, tmp60)
    tmp65 = tmp56 & tmp20
    tmp66 = tmp65 & tmp21
    tmp67 = tl.load(in_ptr0 + (5 + x4), tmp66 & xmask, other=0.0)
    tmp68 = triton_helpers.maximum(tmp67, tmp64)
    tmp69 = tmp56 & tmp27
    tmp70 = tmp69 & tmp28
    tmp71 = tl.load(in_ptr0 + (6 + x4), tmp70 & xmask, other=0.0)
    tmp72 = triton_helpers.maximum(tmp71, tmp68)
    tmp73 = 2 + x1
    tmp74 = tmp73 >= tmp1
    tmp75 = tmp73 < tmp3
    tmp76 = tmp74 & tmp75
    tmp77 = tmp76 & tmp6
    tmp78 = tmp77 & tmp7
    tmp79 = tl.load(in_ptr0 + (7 + x4), tmp78 & xmask, other=0.0)
    tmp80 = triton_helpers.maximum(tmp79, tmp72)
    tmp81 = tmp76 & tmp13
    tmp82 = tmp81 & tmp14
    tmp83 = tl.load(in_ptr0 + (8 + x4), tmp82 & xmask, other=0.0)
    tmp84 = triton_helpers.maximum(tmp83, tmp80)
    tmp85 = tmp76 & tmp20
    tmp86 = tmp85 & tmp21
    tmp87 = tl.load(in_ptr0 + (9 + x4), tmp86 & xmask, other=0.0)
    tmp88 = triton_helpers.maximum(tmp87, tmp84)
    tmp89 = tmp76 & tmp27
    tmp90 = tmp89 & tmp28
    tmp91 = tl.load(in_ptr0 + (10 + x4), tmp90 & xmask, other=0.0)
    tmp92 = triton_helpers.maximum(tmp91, tmp88)
    tl.store(out_ptr0 + (x4), tmp92, 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: [max_pool2d], Original ATen: [aten.max_pool2d_with_indices]
        stream0 = get_raw_stream(0)
        triton_poi_fused_max_pool2d_with_indices_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 math
import torch
from torch import nn
from torch.nn import functional as F


class MaxPool2dDynamicSamePadding(nn.MaxPool2d):
    """2D MaxPooling like TensorFlow's 'SAME' mode, with a dynamic image size.
       The padding is operated in forward function by calculating dynamically.
    """

    def __init__(self, kernel_size, stride, padding=0, dilation=1,
        return_indices=False, ceil_mode=False):
        super().__init__(kernel_size, stride, padding, dilation,
            return_indices, ceil_mode)
        self.stride = [self.stride] * 2 if isinstance(self.stride, int
            ) else self.stride
        self.kernel_size = [self.kernel_size] * 2 if isinstance(self.
            kernel_size, int) else self.kernel_size
        self.dilation = [self.dilation] * 2 if isinstance(self.dilation, int
            ) else self.dilation

    def forward(self, x):
        ih, iw = x.size()[-2:]
        kh, kw = self.kernel_size
        sh, sw = self.stride
        oh, ow = math.ceil(ih / sh), math.ceil(iw / sw)
        pad_h = max((oh - 1) * self.stride[0] + (kh - 1) * self.dilation[0] +
            1 - ih, 0)
        pad_w = max((ow - 1) * self.stride[1] + (kw - 1) * self.dilation[1] +
            1 - iw, 0)
        if pad_h > 0 or pad_w > 0:
            x = F.pad(x, [pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h -
                pad_h // 2])
        return F.max_pool2d(x, self.kernel_size, self.stride, self.padding,
            self.dilation, self.ceil_mode, self.return_indices)


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


def get_init_inputs():
    return [[], {'kernel_size': 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
from torch._inductor.runtime import triton_helpers
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_max_pool2d_with_indices_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 = -1 + x0
    tmp6 = tmp5 >= tmp1
    tmp7 = tmp5 < tmp3
    tmp8 = tmp2 & tmp4
    tmp9 = tmp8 & tmp6
    tmp10 = tmp9 & tmp7
    tmp11 = tl.load(in_ptr0 + (-5 + x4), tmp10 & xmask, other=0.0)
    tmp12 = x0
    tmp13 = tmp12 >= tmp1
    tmp14 = tmp12 < tmp3
    tmp15 = tmp8 & tmp13
    tmp16 = tmp15 & tmp14
    tmp17 = tl.load(in_ptr0 + (-4 + x4), tmp16 & xmask, other=0.0)
    tmp18 = triton_helpers.maximum(tmp17, tmp11)
    tmp19 = 1 + x0
    tmp20 = tmp19 >= tmp1
    tmp21 = tmp19 < tmp3
    tmp22 = tmp8 & tmp20
    tmp23 = tmp22 & tmp21
    tmp24 = tl.load(in_ptr0 + (-3 + x4), tmp23 & xmask, other=0.0)
    tmp25 = triton_helpers.maximum(tmp24, tmp18)
    tmp26 = 2 + x0
    tmp27 = tmp26 >= tmp1
    tmp28 = tmp26 < tmp3
    tmp29 = tmp8 & tmp27
    tmp30 = tmp29 & tmp28
    tmp31 = tl.load(in_ptr0 + (-2 + x4), tmp30 & xmask, other=0.0)
    tmp32 = triton_helpers.maximum(tmp31, tmp25)
    tmp33 = x1
    tmp34 = tmp33 >= tmp1
    tmp35 = tmp33 < tmp3
    tmp36 = tmp34 & tmp35
    tmp37 = tmp36 & tmp6
    tmp38 = tmp37 & tmp7
    tmp39 = tl.load(in_ptr0 + (-1 + x4), tmp38 & xmask, other=0.0)
    tmp40 = triton_helpers.maximum(tmp39, tmp32)
    tmp41 = tmp36 & tmp13
    tmp42 = tmp41 & tmp14
    tmp43 = tl.load(in_ptr0 + x4, tmp42 & xmask, other=0.0)
    tmp44 = triton_helpers.maximum(tmp43, tmp40)
    tmp45 = tmp36 & tmp20
    tmp46 = tmp45 & tmp21
    tmp47 = tl.load(in_ptr0 + (1 + x4), tmp46 & xmask, other=0.0)
    tmp48 = triton_helpers.maximum(tmp47, tmp44)
    tmp49 = tmp36 & tmp27
    tmp50 = tmp49 & tmp28
    tmp51 = tl.load(in_ptr0 + (2 + x4), tmp50 & xmask, other=0.0)
    tmp52 = triton_helpers.maximum(tmp51, tmp48)
    tmp53 = 1 + x1
    tmp54 = tmp53 >= tmp1
    tmp55 = tmp53 < tmp3
    tmp56 = tmp54 & tmp55
    tmp57 = tmp56 & tmp6
    tmp58 = tmp57 & tmp7
    tmp59 = tl.load(in_ptr0 + (3 + x4), tmp58 & xmask, other=0.0)
    tmp60 = triton_helpers.maximum(tmp59, tmp52)
    tmp61 = tmp56 & tmp13
    tmp62 = tmp61 & tmp14
    tmp63 = tl.load(in_ptr0 + (4 + x4), tmp62 & xmask, other=0.0)
    tmp64 = triton_helpers.maximum(tmp63, tmp60)
    tmp65 = tmp56 & tmp20
    tmp66 = tmp65 & tmp21
    tmp67 = tl.load(in_ptr0 + (5 + x4), tmp66 & xmask, other=0.0)
    tmp68 = triton_helpers.maximum(tmp67, tmp64)
    tmp69 = tmp56 & tmp27
    tmp70 = tmp69 & tmp28
    tmp71 = tl.load(in_ptr0 + (6 + x4), tmp70 & xmask, other=0.0)
    tmp72 = triton_helpers.maximum(tmp71, tmp68)
    tmp73 = 2 + x1
    tmp74 = tmp73 >= tmp1
    tmp75 = tmp73 < tmp3
    tmp76 = tmp74 & tmp75
    tmp77 = tmp76 & tmp6
    tmp78 = tmp77 & tmp7
    tmp79 = tl.load(in_ptr0 + (7 + x4), tmp78 & xmask, other=0.0)
    tmp80 = triton_helpers.maximum(tmp79, tmp72)
    tmp81 = tmp76 & tmp13
    tmp82 = tmp81 & tmp14
    tmp83 = tl.load(in_ptr0 + (8 + x4), tmp82 & xmask, other=0.0)
    tmp84 = triton_helpers.maximum(tmp83, tmp80)
    tmp85 = tmp76 & tmp20
    tmp86 = tmp85 & tmp21
    tmp87 = tl.load(in_ptr0 + (9 + x4), tmp86 & xmask, other=0.0)
    tmp88 = triton_helpers.maximum(tmp87, tmp84)
    tmp89 = tmp76 & tmp27
    tmp90 = tmp89 & tmp28
    tmp91 = tl.load(in_ptr0 + (10 + x4), tmp90 & xmask, other=0.0)
    tmp92 = triton_helpers.maximum(tmp91, tmp88)
    tl.store(out_ptr0 + x4, tmp92, 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_max_pool2d_with_indices_0[grid(256)](arg0_1, buf0,
            256, XBLOCK=256, num_warps=4, num_stages=1)
        del arg0_1
    return buf0,


class MaxPool2dDynamicSamePaddingNew(nn.MaxPool2d):
    """2D MaxPooling like TensorFlow's 'SAME' mode, with a dynamic image size.
       The padding is operated in forward function by calculating dynamically.
    """

    def __init__(self, kernel_size, stride, padding=0, dilation=1,
        return_indices=False, ceil_mode=False):
        super().__init__(kernel_size, stride, padding, dilation,
            return_indices, ceil_mode)
        self.stride = [self.stride] * 2 if isinstance(self.stride, int
            ) else self.stride
        self.kernel_size = [self.kernel_size] * 2 if isinstance(self.
            kernel_size, int) else self.kernel_size
        self.dilation = [self.dilation] * 2 if isinstance(self.dilation, int
            ) else self.dilation

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