# 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_4/inductor_cache/pr/cprn5wlfbozhbgejfw6zx6ipjpwn5cixgwfbxawqa5mm6agsadkr.py
# Topologically Sorted Source Nodes: [Ex_E, pow_1], Original ATen: [aten.sub, aten.pow]
# Source node to ATen node mapping:
#   Ex_E => sub
#   pow_1 => pow_1
# Graph fragment:
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%convolution, %convolution_1), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub, 2), kwargs = {})
triton_poi_fused_pow_sub_0 = async_compile.triton('triton_poi_fused_pow_sub_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=[16384], 
    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_pow_sub_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, '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_pow_sub_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 14400
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + (x0), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask)
    tmp2 = tmp0 - tmp1
    tmp3 = tmp2 * tmp2
    tl.store(in_out_ptr0 + (x0), tmp3, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1, arg2_1 = args
    args.clear()
    assert_size_stride(arg0_1, (1, 1, 5, 5), (25, 25, 5, 1))
    assert_size_stride(arg1_1, (4, 1, 64, 64), (4096, 4096, 64, 1))
    assert_size_stride(arg2_1, (1, 1, 5, 5), (25, 25, 5, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(arg1_1, arg0_1, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 1, 60, 60), (3600, 3600, 60, 1))
        del arg0_1
        # Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution]
        buf1 = extern_kernels.convolution(arg1_1, arg2_1, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 1, 60, 60), (3600, 3600, 60, 1))
        del arg1_1
        buf2 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [Ex_E, pow_1], Original ATen: [aten.sub, aten.pow]
        stream0 = get_raw_stream(0)
        triton_poi_fused_pow_sub_0.run(buf2, buf1, 14400, grid=grid(14400), stream=stream0)
        del buf1
        # Topologically Sorted Source Nodes: [Ex_E, pow_1, conv2d_2], Original ATen: [aten.sub, aten.pow, aten.convolution]
        buf3 = extern_kernels.convolution(buf2, arg2_1, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 1, 56, 56), (3136, 3136, 56, 1))
        del arg2_1
        del buf2
    return (buf3, )


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((1, 1, 5, 5), (25, 25, 5, 1), device='cuda:0', dtype=torch.float32)
    arg1_1 = rand_strided((4, 1, 64, 64), (4096, 4096, 64, 1), device='cuda:0', dtype=torch.float32)
    arg2_1 = rand_strided((1, 1, 5, 5), (25, 25, 5, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1, arg1_1, arg2_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 numpy as np
import torch.nn as nn
import torch.nn.functional as F


class VarianceLayer(nn.Module):

    def __init__(self, patch_size=5, channels=1):
        self.patch_size = patch_size
        super(VarianceLayer, self).__init__()
        mean_mask = np.ones((channels, channels, patch_size, patch_size)) / (
            patch_size * patch_size)
        self.mean_mask = nn.Parameter(data=torch.FloatTensor(mean_mask),
            requires_grad=False)
        mask = np.zeros((channels, channels, patch_size, patch_size))
        mask[:, :, patch_size // 2, patch_size // 2] = 1.0
        self.ones_mask = nn.Parameter(data=torch.FloatTensor(mask),
            requires_grad=False)

    def forward(self, x):
        Ex_E = F.conv2d(x, self.ones_mask) - F.conv2d(x, self.mean_mask)
        return F.conv2d(Ex_E ** 2, self.mean_mask)


def get_inputs():
    return [torch.rand([4, 1, 64, 64])]


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

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
import numpy as np
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride


@triton.jit
def triton_poi_fused_pow_sub_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 14400
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask)
    tmp2 = tmp0 - tmp1
    tmp3 = tmp2 * tmp2
    tl.store(in_out_ptr0 + x0, tmp3, xmask)


def call(args):
    arg0_1, arg1_1, arg2_1 = args
    args.clear()
    assert_size_stride(arg0_1, (1, 1, 5, 5), (25, 25, 5, 1))
    assert_size_stride(arg1_1, (4, 1, 64, 64), (4096, 4096, 64, 1))
    assert_size_stride(arg2_1, (1, 1, 5, 5), (25, 25, 5, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(arg1_1, arg0_1, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 1, 60, 60), (3600, 3600, 60, 1))
        del arg0_1
        buf1 = extern_kernels.convolution(arg1_1, arg2_1, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 1, 60, 60), (3600, 3600, 60, 1))
        del arg1_1
        buf2 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_pow_sub_0[grid(14400)](buf2, buf1, 14400, XBLOCK=
            128, num_warps=4, num_stages=1)
        del buf1
        buf3 = extern_kernels.convolution(buf2, arg2_1, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 1, 56, 56), (3136, 3136, 56, 1))
        del arg2_1
        del buf2
    return buf3,


class VarianceLayerNew(nn.Module):

    def __init__(self, patch_size=5, channels=1):
        self.patch_size = patch_size
        super(VarianceLayerNew, self).__init__()
        mean_mask = np.ones((channels, channels, patch_size, patch_size)) / (
            patch_size * patch_size)
        self.mean_mask = nn.Parameter(data=torch.FloatTensor(mean_mask),
            requires_grad=False)
        mask = np.zeros((channels, channels, patch_size, patch_size))
        mask[:, :, patch_size // 2, patch_size // 2] = 1.0
        self.ones_mask = nn.Parameter(data=torch.FloatTensor(mask),
            requires_grad=False)

    def forward(self, input_0):
        arg0_1 = self.mean_mask
        arg2_1 = self.ones_mask
        arg1_1 = input_0
        output = call([arg0_1, arg1_1, arg2_1])
        return output[0]
