# AOT ID: ['0_forward']
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/ci/cci3lxmukjzembg3pbo4xshqctklpzl5woxc7frfmy4akmshvr3g.py
# Topologically Sorted Source Nodes: [mean, var, add, sqrt], Original ATen: [aten.mean, aten.var, aten.add, aten.sqrt]
# Source node to ATen node mapping:
#   add => add
#   mean => mean
#   sqrt => sqrt
#   var => var
# Graph fragment:
#   %mean : [num_users=2] = call_function[target=torch.ops.aten.mean.dim](args = (%view, [-1], True), kwargs = {})
#   %var : [num_users=1] = call_function[target=torch.ops.aten.var.correction](args = (%view, [-1]), kwargs = {correction: 1, keepdim: True})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%var, 1e-05), kwargs = {})
#   %sqrt : [num_users=2] = call_function[target=torch.ops.aten.sqrt.default](args = (%add,), kwargs = {})
triton_per_fused_add_mean_sqrt_var_0 = async_compile.triton('triton_per_fused_add_mean_sqrt_var_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.persistent_reduction(
    size_hints=[8, 32],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 4: '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, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_mean_sqrt_var_0', 'mutated_arg_names': ['in_out_ptr0', 'in_out_ptr1'], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 4, '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}
)
@triton.jit
def triton_per_fused_add_mean_sqrt_var_0(in_out_ptr0, in_out_ptr1, in_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 8
    rnumel = 32
    RBLOCK: tl.constexpr = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + (32*x0)), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, 0)
    tmp4 = tl.sum(tmp3, 1)[:, None]
    tmp6 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp8 = tl.where(xmask, tmp6, 0)
    tmp9 = tl.sum(tmp8, 1)[:, None]
    tmp10 = tl.full([XBLOCK, 1], 32, tl.int32)
    tmp11 = tmp10.to(tl.float32)
    tmp12 = tmp9 / tmp11
    tmp13 = tmp1 - tmp12
    tmp14 = tmp13 * tmp13
    tmp15 = tl.broadcast_to(tmp14, [XBLOCK, RBLOCK])
    tmp17 = tl.where(xmask, tmp15, 0)
    tmp18 = tl.sum(tmp17, 1)[:, None]
    tmp19 = 32.0
    tmp20 = tmp4 / tmp19
    tmp21 = 31.0
    tmp22 = tmp18 / tmp21
    tmp23 = 1e-05
    tmp24 = tmp22 + tmp23
    tmp25 = libdevice.sqrt(tmp24)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (x0), tmp20, xmask)
    tl.debug_barrier()
    tl.store(in_out_ptr1 + (x0), tmp25, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/qk/cqkoe64td3xlqhabgekcnmxe4dl3nwn6prbtohfu53u2h5q227tj.py
# Topologically Sorted Source Nodes: [mul, add_1], Original ATen: [aten.mul, aten.add]
# Source node to ATen node mapping:
#   add_1 => add_1
#   mul => mul
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_1, %primals_2), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %primals_3), kwargs = {})
triton_poi_fused_add_mul_1 = async_compile.triton('triton_poi_fused_add_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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '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, 3, 4, 5, 6), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_mul_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, '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_add_mul_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x4 = xindex
    x1 = (xindex // 16) % 4
    tmp0 = tl.load(in_ptr0 + (x4), xmask)
    tmp1 = tl.load(in_ptr1 + ((x4 // 32)), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + ((x4 // 32)), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + (x1), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = tmp2 / tmp3
    tmp6 = tmp4 * tmp5
    tmp8 = tmp6 + tmp7
    tl.store(out_ptr0 + (x4), tmp8, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (1, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_3, (1, 4, 1, 1), (4, 1, 1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 2, 1), (2, 1, 8), torch.float32)
        buf3 = empty_strided_cuda((4, 2, 1), (2, 1, 8), torch.float32)
        buf1 = reinterpret_tensor(buf0, (4, 2, 1), (2, 1, 1), 0); del buf0  # reuse
        buf5 = reinterpret_tensor(buf3, (4, 2, 1), (2, 1, 1), 0); del buf3  # reuse
        # Topologically Sorted Source Nodes: [mean, var, add, sqrt], Original ATen: [aten.mean, aten.var, aten.add, aten.sqrt]
        stream0 = get_raw_stream(0)
        triton_per_fused_add_mean_sqrt_var_0.run(buf1, buf5, primals_1, 8, 32, grid=grid(8), stream=stream0)
        buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mul, add_1], Original ATen: [aten.mul, aten.add]
        triton_poi_fused_add_mul_1.run(primals_1, buf1, buf5, primals_2, primals_3, buf6, 256, grid=grid(256), stream=stream0)
        del primals_2
        del primals_3
    return (buf6, primals_1, buf1, buf5, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((1, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((1, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3])
    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
import torch.utils.data
from torchvision.transforms import functional as F
from torch import nn
from torch.nn import functional as F


class LocalContextNorm(nn.Module):

    def __init__(self, num_features, channels_per_group=2, window_size=(227,
        227), eps=1e-05):
        super(LocalContextNorm, self).__init__()
        self.weight = nn.Parameter(torch.ones(1, num_features, 1, 1))
        self.bias = nn.Parameter(torch.zeros(1, num_features, 1, 1))
        self.channels_per_group = channels_per_group
        self.eps = eps
        self.window_size = window_size

    def forward(self, x):
        N, C, H, W = x.size()
        G = C // self.channels_per_group
        assert C % self.channels_per_group == 0
        if self.window_size[0] < H and self.window_size[1] < W:
            torch.device(torch.cuda.current_device() if torch.cuda.
                is_available() else 'cpu')
            x_squared = x * x
            integral_img = x.cumsum(dim=2).cumsum(dim=3)
            integral_img_sq = x_squared.cumsum(dim=2).cumsum(dim=3)
            d = 1, self.window_size[0], self.window_size[1]
            integral_img = torch.unsqueeze(integral_img, dim=1)
            integral_img_sq = torch.unsqueeze(integral_img_sq, dim=1)
            kernel = torch.tensor([[[[[1.0, -1.0], [-1.0, 1.0]]]]])
            c_kernel = torch.ones((1, 1, self.channels_per_group, 1, 1))
            with torch.no_grad():
                sums = F.conv3d(integral_img, kernel, stride=[1, 1, 1],
                    dilation=d)
                sums = F.conv3d(sums, c_kernel, stride=[self.
                    channels_per_group, 1, 1])
                squares = F.conv3d(integral_img_sq, kernel, stride=[1, 1, 1
                    ], dilation=d)
                squares = F.conv3d(squares, c_kernel, stride=[self.
                    channels_per_group, 1, 1])
            n = self.window_size[0] * self.window_size[1
                ] * self.channels_per_group
            means = torch.squeeze(sums / n, dim=1)
            var = torch.squeeze(1.0 / n * (squares - sums * sums / n), dim=1)
            _, _, h, w = means.size()
            pad2d = int(math.floor((W - w) / 2)), int(math.ceil((W - w) / 2)
                ), int(math.floor((H - h) / 2)), int(math.ceil((H - h) / 2))
            padded_means = F.pad(means, pad2d, 'replicate')
            padded_vars = F.pad(var, pad2d, 'replicate') + self.eps
            for i in range(G):
                x[:, i * self.channels_per_group:i * self.
                    channels_per_group + self.channels_per_group, :, :] = (x
                    [:, i * self.channels_per_group:i * self.
                    channels_per_group + self.channels_per_group, :, :] -
                    torch.unsqueeze(padded_means[:, i, :, :], dim=1)
                    ) / torch.unsqueeze(padded_vars[:, i, :, :], dim=1).sqrt()
            del integral_img
            del integral_img_sq
        else:
            x = x.view(N, G, -1)
            mean = x.mean(-1, keepdim=True)
            var = x.var(-1, keepdim=True)
            x = (x - mean) / (var + self.eps).sqrt()
            x = x.view(N, C, H, W)
        return x * self.weight + self.bias


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


def get_init_inputs():
    return [[], {'num_features': 4}]

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.triton_helpers import libdevice
import torch.utils.data
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_per_fused_add_mean_sqrt_var_0(in_out_ptr0, in_out_ptr1, in_ptr0,
    xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 8
    RBLOCK: tl.constexpr = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 32 * x0), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, 0)
    tmp4 = tl.sum(tmp3, 1)[:, None]
    tmp6 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp8 = tl.where(xmask, tmp6, 0)
    tmp9 = tl.sum(tmp8, 1)[:, None]
    tmp10 = tl.full([XBLOCK, 1], 32, tl.int32)
    tmp11 = tmp10.to(tl.float32)
    tmp12 = tmp9 / tmp11
    tmp13 = tmp1 - tmp12
    tmp14 = tmp13 * tmp13
    tmp15 = tl.broadcast_to(tmp14, [XBLOCK, RBLOCK])
    tmp17 = tl.where(xmask, tmp15, 0)
    tmp18 = tl.sum(tmp17, 1)[:, None]
    tmp19 = 32.0
    tmp20 = tmp4 / tmp19
    tmp21 = 31.0
    tmp22 = tmp18 / tmp21
    tmp23 = 1e-05
    tmp24 = tmp22 + tmp23
    tmp25 = libdevice.sqrt(tmp24)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp20, xmask)
    tl.debug_barrier()
    tl.store(in_out_ptr1 + x0, tmp25, xmask)


@triton.jit
def triton_poi_fused_add_mul_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x4 = xindex
    x1 = xindex // 16 % 4
    tmp0 = tl.load(in_ptr0 + x4, xmask)
    tmp1 = tl.load(in_ptr1 + x4 // 32, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x4 // 32, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = tmp2 / tmp3
    tmp6 = tmp4 * tmp5
    tmp8 = tmp6 + tmp7
    tl.store(out_ptr0 + x4, tmp8, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (1, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_3, (1, 4, 1, 1), (4, 1, 1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 2, 1), (2, 1, 8), torch.float32)
        buf3 = empty_strided_cuda((4, 2, 1), (2, 1, 8), torch.float32)
        buf1 = reinterpret_tensor(buf0, (4, 2, 1), (2, 1, 1), 0)
        del buf0
        buf5 = reinterpret_tensor(buf3, (4, 2, 1), (2, 1, 1), 0)
        del buf3
        get_raw_stream(0)
        triton_per_fused_add_mean_sqrt_var_0[grid(8)](buf1, buf5, primals_1,
            8, 32, XBLOCK=8, num_warps=2, num_stages=1)
        buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_add_mul_1[grid(256)](primals_1, buf1, buf5,
            primals_2, primals_3, buf6, 256, XBLOCK=256, num_warps=4,
            num_stages=1)
        del primals_2
        del primals_3
    return buf6, primals_1, buf1, buf5


class LocalContextNormNew(nn.Module):

    def __init__(self, num_features, channels_per_group=2, window_size=(227,
        227), eps=1e-05):
        super(LocalContextNormNew, self).__init__()
        self.weight = nn.Parameter(torch.ones(1, num_features, 1, 1))
        self.bias = nn.Parameter(torch.zeros(1, num_features, 1, 1))
        self.channels_per_group = channels_per_group
        self.eps = eps
        self.window_size = window_size

    def forward(self, input_0):
        primals_2 = self.weight
        primals_3 = self.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]
