# 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/xe/cxeocopnfqr23dmrrhhvz3ddzisb5tsd2zsarlz55lr3nu2vgh2c.py
# Topologically Sorted Source Nodes: [mul, mean, diff, diff_squared, mul_2, sum_2, add, std], Original ATen: [aten.mul, aten.sum, aten.sub, aten.add, aten.sqrt]
# Source node to ATen node mapping:
#   add => add
#   diff => sub
#   diff_squared => mul_1
#   mean => sum_1
#   mul => mul
#   mul_2 => mul_2
#   std => sqrt
#   sum_2 => sum_2
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%unsqueeze, %unsqueeze_1), kwargs = {})
#   %sum_1 : [num_users=2] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul, [2, 3]), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%unsqueeze_2, %unsqueeze_4), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, %sub), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%unsqueeze_5, %mul_1), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul_2, [2, 3]), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_2, 0.0001), kwargs = {})
#   %sqrt : [num_users=1] = call_function[target=torch.ops.aten.sqrt.default](args = (%add,), kwargs = {})
triton_per_fused_add_mul_sqrt_sub_sum_0 = async_compile.triton('triton_per_fused_add_mul_sqrt_sub_sum_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, 16],
    reduction_hint=ReductionHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32', 5: '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, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_mul_sqrt_sub_sum_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 2, '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_mul_sqrt_sub_sum_0(in_out_ptr0, in_ptr0, in_ptr1, out_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 8
    rnumel = 16
    RBLOCK: tl.constexpr = 16
    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)
    r2 = rindex
    x1 = (xindex // 2)
    x0 = xindex % 2
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (r2 + (16*x1)), xmask, eviction_policy='evict_last', other=0.0)
    tmp1 = tl.load(in_ptr1 + (r2 + (16*x0)), xmask, eviction_policy='evict_last', other=0.0)
    tmp2 = tmp0 * tmp1
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp5 = tl.where(xmask, tmp3, 0)
    tmp6 = tl.sum(tmp5, 1)[:, None]
    tmp7 = tmp1 - tmp6
    tmp8 = tmp7 * tmp7
    tmp9 = tmp0 * tmp8
    tmp10 = tl.broadcast_to(tmp9, [XBLOCK, RBLOCK])
    tmp12 = tl.where(xmask, tmp10, 0)
    tmp13 = tl.sum(tmp12, 1)[:, None]
    tmp14 = 0.0001
    tmp15 = tmp13 + tmp14
    tmp16 = libdevice.sqrt(tmp15)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (x3), tmp16, xmask)
    tl.store(out_ptr0 + (x3), tmp6, xmask)
''', 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), (16, 4, 1))
    assert_size_stride(arg1_1, (2, 4, 4), (16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 2), (2, 1), torch.float32)
        buf1 = empty_strided_cuda((4, 2), (2, 1), torch.float32)
        buf2 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [mul, mean, diff, diff_squared, mul_2, sum_2, add, std], Original ATen: [aten.mul, aten.sum, aten.sub, aten.add, aten.sqrt]
        stream0 = get_raw_stream(0)
        triton_per_fused_add_mul_sqrt_sub_sum_0.run(buf2, arg0_1, arg1_1, buf0, 8, 16, grid=grid(8), stream=stream0)
        del arg0_1
        del arg1_1
    return (buf0, 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), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg1_1 = rand_strided((2, 4, 4), (16, 4, 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.functional
import torch.nn as nn
import torch.nn.init
import torch.onnx


class SpatialMeanAndStd(nn.Module):

    def __init__(self, shape, eps=0.0001, half_size=1.0):
        super(SpatialMeanAndStd, self).__init__()
        p = torch.empty((2, shape[0], shape[1]), dtype=torch.float32)
        p[0, ...] = torch.linspace(-half_size, half_size, shape[1])[None, :]
        p[1, ...] = torch.linspace(-half_size, half_size, shape[0])[:, None]
        self.position_code = nn.Parameter(p)
        self.position_code.requires_grad = False
        self._shape = shape
        self._eps = eps

    def forward(self, x):
        assert x.shape[1] == self._shape[0
            ], f'input shape {x.shape} vs expected {self._shape}'
        assert x.shape[2] == self._shape[1
            ], f'input shape {x.shape} vs expected {self._shape}'
        mean = torch.sum(x[:, None, :, :] * self.position_code[None, ...],
            dim=[2, 3])
        diff = self.position_code[None, ...] - mean[..., None, None]
        diff_squared = diff * diff
        std = torch.sqrt(torch.sum(x[:, None, :, :] * diff_squared, dim=[2,
            3]) + self._eps)
        return mean, std


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


def get_init_inputs():
    return [[], {'shape': [4, 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.nn.functional
import torch.nn as nn
import torch.nn.init
import torch.onnx
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_per_fused_add_mul_sqrt_sub_sum_0(in_out_ptr0, in_ptr0, in_ptr1,
    out_ptr0, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 8
    RBLOCK: tl.constexpr = 16
    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)
    r2 = rindex
    x1 = xindex // 2
    x0 = xindex % 2
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (r2 + 16 * x1), xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp1 = tl.load(in_ptr1 + (r2 + 16 * x0), xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp2 = tmp0 * tmp1
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp5 = tl.where(xmask, tmp3, 0)
    tmp6 = tl.sum(tmp5, 1)[:, None]
    tmp7 = tmp1 - tmp6
    tmp8 = tmp7 * tmp7
    tmp9 = tmp0 * tmp8
    tmp10 = tl.broadcast_to(tmp9, [XBLOCK, RBLOCK])
    tmp12 = tl.where(xmask, tmp10, 0)
    tmp13 = tl.sum(tmp12, 1)[:, None]
    tmp14 = 0.0001
    tmp15 = tmp13 + tmp14
    tmp16 = libdevice.sqrt(tmp15)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x3, tmp16, xmask)
    tl.store(out_ptr0 + x3, tmp6, xmask)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(arg1_1, (2, 4, 4), (16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 2), (2, 1), torch.float32)
        buf1 = empty_strided_cuda((4, 2), (2, 1), torch.float32)
        buf2 = buf1
        del buf1
        get_raw_stream(0)
        triton_per_fused_add_mul_sqrt_sub_sum_0[grid(8)](buf2, arg0_1,
            arg1_1, buf0, 8, 16, XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
    return buf0, buf2


class SpatialMeanAndStdNew(nn.Module):

    def __init__(self, shape, eps=0.0001, half_size=1.0):
        super(SpatialMeanAndStdNew, self).__init__()
        p = torch.empty((2, shape[0], shape[1]), dtype=torch.float32)
        p[0, ...] = torch.linspace(-half_size, half_size, shape[1])[None, :]
        p[1, ...] = torch.linspace(-half_size, half_size, shape[0])[:, None]
        self.position_code = nn.Parameter(p)
        self.position_code.requires_grad = False
        self._shape = shape
        self._eps = eps

    def forward(self, input_0):
        arg1_1 = self.position_code
        arg0_1 = input_0
        output = call([arg0_1, arg1_1])
        return output[0], output[1]
