# 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_6/inductor_cache/ge/cgev3sqyqdjuthmmnhser34xcrhqixpsu56hzfsiksyixfgxpzls.py
# Topologically Sorted Source Nodes: [mul, mean_inp_times_tar, input_mean, target_mean, input_var, target_var], Original ATen: [aten.mul, aten.mean, aten.var]
# Source node to ATen node mapping:
#   input_mean => mean
#   input_var => var
#   mean_inp_times_tar => mean_2
#   mul => mul
#   target_mean => mean_1
#   target_var => var_1
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view, %view_1), kwargs = {})
#   %mean_2 : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%mul, [-1]), kwargs = {})
#   %mean : [num_users=3] = call_function[target=torch.ops.aten.mean.dim](args = (%view, [-1]), kwargs = {})
#   %mean_1 : [num_users=3] = call_function[target=torch.ops.aten.mean.dim](args = (%view_1, [-1]), kwargs = {})
#   %var : [num_users=1] = call_function[target=torch.ops.aten.var.correction](args = (%view, [-1]), kwargs = {correction: 1})
#   %var_1 : [num_users=1] = call_function[target=torch.ops.aten.var.correction](args = (%view_1, [-1]), kwargs = {correction: 1})
triton_per_fused_mean_mul_var_0 = async_compile.triton('triton_per_fused_mean_mul_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=[4, 64],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: 'i32', 8: '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, 8), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_mean_mul_var_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 9, '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_mean_mul_var_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1, out_ptr2, out_ptr3, out_ptr4, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    rnumel = 64
    RBLOCK: tl.constexpr = 64
    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 + (64*x0)), xmask, other=0.0)
    tmp1 = tl.load(in_ptr1 + (r1 + (64*x0)), xmask, 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 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp9 = tl.where(xmask, tmp7, 0)
    tmp10 = tl.sum(tmp9, 1)[:, None]
    tmp12 = tl.broadcast_to(tmp7, [XBLOCK, RBLOCK])
    tmp14 = tl.where(xmask, tmp12, 0)
    tmp15 = tl.sum(tmp14, 1)[:, None]
    tmp16 = tl.full([XBLOCK, 1], 64, tl.int32)
    tmp17 = tmp16.to(tl.float32)
    tmp18 = tmp15 / tmp17
    tmp19 = tmp7 - tmp18
    tmp20 = tmp19 * tmp19
    tmp21 = tl.broadcast_to(tmp20, [XBLOCK, RBLOCK])
    tmp23 = tl.where(xmask, tmp21, 0)
    tmp24 = tl.sum(tmp23, 1)[:, None]
    tmp25 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp27 = tl.where(xmask, tmp25, 0)
    tmp28 = tl.sum(tmp27, 1)[:, None]
    tmp30 = tl.broadcast_to(tmp25, [XBLOCK, RBLOCK])
    tmp32 = tl.where(xmask, tmp30, 0)
    tmp33 = tl.sum(tmp32, 1)[:, None]
    tmp34 = tmp33 / tmp17
    tmp35 = tmp25 - tmp34
    tmp36 = tmp35 * tmp35
    tmp37 = tl.broadcast_to(tmp36, [XBLOCK, RBLOCK])
    tmp39 = tl.where(xmask, tmp37, 0)
    tmp40 = tl.sum(tmp39, 1)[:, None]
    tl.store(out_ptr0 + (x0), tmp6, xmask)
    tl.store(out_ptr1 + (x0), tmp10, xmask)
    tl.store(out_ptr2 + (x0), tmp24, xmask)
    tl.store(out_ptr3 + (x0), tmp28, xmask)
    tl.store(out_ptr4 + (x0), tmp40, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/s4/cs4cskd3kptvlbliq62ju3c55objjn6yexnbjpgm6wlcf6x2gpve.py
# Topologically Sorted Source Nodes: [mul, mean_inp_times_tar, input_mean, target_mean, mul_1, covariance, mul_2, mul_3, mul_4, input_var, target_var, add, pow_1, pow_2, add_1, mul_5, Q, Q_1], Original ATen: [aten.mul, aten.mean, aten.sub, aten.var, aten.add, aten.pow, aten.div]
# Source node to ATen node mapping:
#   Q => div
#   Q_1 => mean_3
#   add => add
#   add_1 => add_1
#   covariance => sub
#   input_mean => mean
#   input_var => var
#   mean_inp_times_tar => mean_2
#   mul => mul
#   mul_1 => mul_1
#   mul_2 => mul_2
#   mul_3 => mul_3
#   mul_4 => mul_4
#   mul_5 => mul_5
#   pow_1 => pow_1
#   pow_2 => pow_2
#   target_mean => mean_1
#   target_var => var_1
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view, %view_1), kwargs = {})
#   %mean_2 : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%mul, [-1]), kwargs = {})
#   %mean : [num_users=3] = call_function[target=torch.ops.aten.mean.dim](args = (%view, [-1]), kwargs = {})
#   %mean_1 : [num_users=3] = call_function[target=torch.ops.aten.mean.dim](args = (%view_1, [-1]), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mean, %mean_1), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mean_2, %mul_1), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, 4.0), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_2, %mean), kwargs = {})
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_3, %mean_1), kwargs = {})
#   %var : [num_users=1] = call_function[target=torch.ops.aten.var.correction](args = (%view, [-1]), kwargs = {correction: 1})
#   %var_1 : [num_users=1] = call_function[target=torch.ops.aten.var.correction](args = (%view_1, [-1]), kwargs = {correction: 1})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%var, %var_1), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%mean, 2), kwargs = {})
#   %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%mean_1, 2), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%pow_1, %pow_2), kwargs = {})
#   %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add, %add_1), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%mul_4, %mul_5), kwargs = {})
#   %mean_3 : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%div,), kwargs = {})
triton_per_fused_add_div_mean_mul_pow_sub_var_1 = async_compile.triton('triton_per_fused_add_div_mean_mul_pow_sub_var_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.persistent_reduction(
    size_hints=[1, 4],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: 'i32', 7: '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': {6: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=(6,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_div_mean_mul_pow_sub_var_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 1, '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_div_mean_mul_pow_sub_var_1(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 4
    RBLOCK: tl.constexpr = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + (r0), None)
    tmp3 = tl.load(in_ptr1 + (r0), None)
    tmp5 = tl.load(in_ptr2 + (r0), None)
    tmp13 = tl.load(in_ptr3 + (r0), None)
    tmp16 = tl.load(in_ptr4 + (r0), None)
    tmp1 = 64.0
    tmp2 = tmp0 / tmp1
    tmp4 = tmp3 / tmp1
    tmp6 = tmp5 / tmp1
    tmp7 = tmp4 * tmp6
    tmp8 = tmp2 - tmp7
    tmp9 = 4.0
    tmp10 = tmp8 * tmp9
    tmp11 = tmp10 * tmp4
    tmp12 = tmp11 * tmp6
    tmp14 = 63.0
    tmp15 = tmp13 / tmp14
    tmp17 = tmp16 / tmp14
    tmp18 = tmp15 + tmp17
    tmp19 = tmp4 * tmp4
    tmp20 = tmp6 * tmp6
    tmp21 = tmp19 + tmp20
    tmp22 = tmp18 * tmp21
    tmp23 = tmp12 / tmp22
    tmp24 = tl.broadcast_to(tmp23, [XBLOCK, RBLOCK])
    tmp26 = tl.sum(tmp24, 1)[:, None]
    tmp27 = tmp26 / tmp9
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp27, None)
''', 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, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, ), (1, ), torch.float32)
        buf1 = empty_strided_cuda((4, ), (1, ), torch.float32)
        buf4 = empty_strided_cuda((4, ), (1, ), torch.float32)
        buf2 = empty_strided_cuda((4, ), (1, ), torch.float32)
        buf7 = empty_strided_cuda((4, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [mul, mean_inp_times_tar, input_mean, target_mean, input_var, target_var], Original ATen: [aten.mul, aten.mean, aten.var]
        stream0 = get_raw_stream(0)
        triton_per_fused_mean_mul_var_0.run(arg0_1, arg1_1, buf0, buf1, buf4, buf2, buf7, 4, 64, grid=grid(4), stream=stream0)
        del arg0_1
        del arg1_1
        buf9 = empty_strided_cuda((), (), torch.float32)
        buf10 = buf9; del buf9  # reuse
        # Topologically Sorted Source Nodes: [mul, mean_inp_times_tar, input_mean, target_mean, mul_1, covariance, mul_2, mul_3, mul_4, input_var, target_var, add, pow_1, pow_2, add_1, mul_5, Q, Q_1], Original ATen: [aten.mul, aten.mean, aten.sub, aten.var, aten.add, aten.pow, aten.div]
        triton_per_fused_add_div_mean_mul_pow_sub_var_1.run(buf10, buf0, buf1, buf2, buf4, buf7, 1, 4, grid=grid(1), stream=stream0)
        del buf0
        del buf1
        del buf2
        del buf4
        del buf7
    return (buf10, )


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, 4, 4, 4), (64, 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 as nn


class Q_Index(nn.Module):
    """
    Quality measurement between perturbated (image with applied noise) and denoised target image.
    This module works only for images with a single color channel (grayscale)
    """

    def __init__(self):
        super().__init__()

    def forward(self, input, target):
        batch_size = input.shape[0]
        input = input.view(batch_size, -1)
        target = target.view(batch_size, -1)
        input_mean = input.mean(dim=-1)
        target_mean = target.mean(dim=-1)
        input_var = input.var(dim=-1)
        target_var = target.var(dim=-1)
        mean_inp_times_tar = torch.mean(input * target, dim=-1)
        covariance = mean_inp_times_tar - input_mean * target_mean
        Q = 4.0 * covariance * input_mean * target_mean / ((input_var +
            target_var) * (input_mean ** 2 + target_mean ** 2))
        Q = Q.mean()
        return Q


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


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

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
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_per_fused_mean_mul_var_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    out_ptr2, out_ptr3, out_ptr4, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    RBLOCK: tl.constexpr = 64
    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 + 64 * x0), xmask, other=0.0)
    tmp1 = tl.load(in_ptr1 + (r1 + 64 * x0), xmask, 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 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp9 = tl.where(xmask, tmp7, 0)
    tmp10 = tl.sum(tmp9, 1)[:, None]
    tmp12 = tl.broadcast_to(tmp7, [XBLOCK, RBLOCK])
    tmp14 = tl.where(xmask, tmp12, 0)
    tmp15 = tl.sum(tmp14, 1)[:, None]
    tmp16 = tl.full([XBLOCK, 1], 64, tl.int32)
    tmp17 = tmp16.to(tl.float32)
    tmp18 = tmp15 / tmp17
    tmp19 = tmp7 - tmp18
    tmp20 = tmp19 * tmp19
    tmp21 = tl.broadcast_to(tmp20, [XBLOCK, RBLOCK])
    tmp23 = tl.where(xmask, tmp21, 0)
    tmp24 = tl.sum(tmp23, 1)[:, None]
    tmp25 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp27 = tl.where(xmask, tmp25, 0)
    tmp28 = tl.sum(tmp27, 1)[:, None]
    tmp30 = tl.broadcast_to(tmp25, [XBLOCK, RBLOCK])
    tmp32 = tl.where(xmask, tmp30, 0)
    tmp33 = tl.sum(tmp32, 1)[:, None]
    tmp34 = tmp33 / tmp17
    tmp35 = tmp25 - tmp34
    tmp36 = tmp35 * tmp35
    tmp37 = tl.broadcast_to(tmp36, [XBLOCK, RBLOCK])
    tmp39 = tl.where(xmask, tmp37, 0)
    tmp40 = tl.sum(tmp39, 1)[:, None]
    tl.store(out_ptr0 + x0, tmp6, xmask)
    tl.store(out_ptr1 + x0, tmp10, xmask)
    tl.store(out_ptr2 + x0, tmp24, xmask)
    tl.store(out_ptr3 + x0, tmp28, xmask)
    tl.store(out_ptr4 + x0, tmp40, xmask)


@triton.jit
def triton_per_fused_add_div_mean_mul_pow_sub_var_1(in_out_ptr0, in_ptr0,
    in_ptr1, in_ptr2, in_ptr3, in_ptr4, xnumel, rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 4
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp3 = tl.load(in_ptr1 + r0, None)
    tmp5 = tl.load(in_ptr2 + r0, None)
    tmp13 = tl.load(in_ptr3 + r0, None)
    tmp16 = tl.load(in_ptr4 + r0, None)
    tmp1 = 64.0
    tmp2 = tmp0 / tmp1
    tmp4 = tmp3 / tmp1
    tmp6 = tmp5 / tmp1
    tmp7 = tmp4 * tmp6
    tmp8 = tmp2 - tmp7
    tmp9 = 4.0
    tmp10 = tmp8 * tmp9
    tmp11 = tmp10 * tmp4
    tmp12 = tmp11 * tmp6
    tmp14 = 63.0
    tmp15 = tmp13 / tmp14
    tmp17 = tmp16 / tmp14
    tmp18 = tmp15 + tmp17
    tmp19 = tmp4 * tmp4
    tmp20 = tmp6 * tmp6
    tmp21 = tmp19 + tmp20
    tmp22 = tmp18 * tmp21
    tmp23 = tmp12 / tmp22
    tmp24 = tl.broadcast_to(tmp23, [XBLOCK, RBLOCK])
    tmp26 = tl.sum(tmp24, 1)[:, None]
    tmp27 = tmp26 / tmp9
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp27, None)


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, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4,), (1,), torch.float32)
        buf1 = empty_strided_cuda((4,), (1,), torch.float32)
        buf4 = empty_strided_cuda((4,), (1,), torch.float32)
        buf2 = empty_strided_cuda((4,), (1,), torch.float32)
        buf7 = empty_strided_cuda((4,), (1,), torch.float32)
        get_raw_stream(0)
        triton_per_fused_mean_mul_var_0[grid(4)](arg0_1, arg1_1, buf0, buf1,
            buf4, buf2, buf7, 4, 64, XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
        buf9 = empty_strided_cuda((), (), torch.float32)
        buf10 = buf9
        del buf9
        triton_per_fused_add_div_mean_mul_pow_sub_var_1[grid(1)](buf10,
            buf0, buf1, buf2, buf4, buf7, 1, 4, XBLOCK=1, num_warps=2,
            num_stages=1)
        del buf0
        del buf1
        del buf2
        del buf4
        del buf7
    return buf10,


class Q_IndexNew(nn.Module):
    """
    Quality measurement between perturbated (image with applied noise) and denoised target image.
    This module works only for images with a single color channel (grayscale)
    """

    def __init__(self):
        super().__init__()

    def forward(self, input_0, input_1):
        arg0_1 = input_0
        arg1_1 = input_1
        output = call([arg0_1, arg1_1])
        return output[0]
