# 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/jb/cjbprg2adil5ynwgs27eksae22pr7nqq74cbmg7xyyfifovo2s4i.py
# Topologically Sorted Source Nodes: [sub, pow_1, h_tv], Original ATen: [aten.sub, aten.pow, aten.sum]
# Source node to ATen node mapping:
#   h_tv => sum_1
#   pow_1 => pow_1
#   sub => sub
# Graph fragment:
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%slice_3, %slice_7), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub, 2), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%pow_1,), kwargs = {})
triton_per_fused_pow_sub_sum_0 = async_compile.triton('triton_per_fused_pow_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=[1, 256],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: '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': {2: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 3), equal_to_1=(2,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_pow_sub_sum_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, '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_pow_sub_sum_0(in_ptr0, out_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 192
    RBLOCK: tl.constexpr = 256
    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 = rindex < rnumel
    r0 = rindex % 12
    r1 = (rindex // 12)
    tmp0 = tl.load(in_ptr0 + (4 + r0 + (16*r1)), rmask, other=0.0)
    tmp1 = tl.load(in_ptr0 + (r0 + (16*r1)), rmask, other=0.0)
    tmp2 = tmp0 - tmp1
    tmp3 = tmp2 * tmp2
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
    tmp6 = tl.where(rmask, tmp4, 0)
    tmp7 = tl.sum(tmp6, 1)[:, None]
    tl.store(out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp7, None)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/z3/cz3b4qhx5z7q2mtytwrrr7736hknw6lpeqtgoccvjmlfybga7vh2.py
# Topologically Sorted Source Nodes: [sub_1, pow_2, w_tv], Original ATen: [aten.sub, aten.pow, aten.sum]
# Source node to ATen node mapping:
#   pow_2 => pow_2
#   sub_1 => sub_1
#   w_tv => sum_2
# Graph fragment:
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%slice_12, %slice_16), kwargs = {})
#   %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub_1, 2), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%pow_2,), kwargs = {})
triton_per_fused_pow_sub_sum_1 = async_compile.triton('triton_per_fused_pow_sub_sum_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, 256],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: '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': {2: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 3), equal_to_1=(2,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_pow_sub_sum_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, '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_pow_sub_sum_1(in_ptr0, out_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 192
    RBLOCK: tl.constexpr = 256
    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 = rindex < rnumel
    r0 = rindex % 3
    r1 = (rindex // 3)
    tmp0 = tl.load(in_ptr0 + (1 + r0 + (4*r1)), rmask, other=0.0)
    tmp1 = tl.load(in_ptr0 + (r0 + (4*r1)), rmask, other=0.0)
    tmp2 = tmp0 - tmp1
    tmp3 = tmp2 * tmp2
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
    tmp6 = tl.where(rmask, tmp4, 0)
    tmp7 = tl.sum(tmp6, 1)[:, None]
    tl.store(out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp7, None)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/ad/cadcluppyfzj6ua3ag6vqkfwx3prkvvszqssvuzsu2tqrhus4oie.py
# Topologically Sorted Source Nodes: [sub_2, pow_3, mean, mul, sub_3, relu, sub_4, abs_1, sub_5, pow_4, mul_1, mean_1, mul_2, add_1, truediv, truediv_1, tvloss, mul_3, loss], Original ATen: [aten.sub, aten.pow, aten.mean, aten.mul, aten.relu, aten.rsub, aten.abs, aten.add, aten.div]
# Source node to ATen node mapping:
#   abs_1 => abs_1
#   add_1 => add_1
#   loss => add_2
#   mean => mean
#   mean_1 => mean_1
#   mul => mul
#   mul_1 => mul_1
#   mul_2 => mul_2
#   mul_3 => mul_3
#   pow_3 => pow_3
#   pow_4 => pow_4
#   relu => relu
#   sub_2 => sub_2
#   sub_3 => sub_3
#   sub_4 => sub_4
#   sub_5 => sub_5
#   truediv => div
#   truediv_1 => div_1
#   tvloss => add
# Graph fragment:
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg1_1, %arg2_1), kwargs = {})
#   %pow_3 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub_2, 2), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%pow_3,), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg3_1, 0.5), kwargs = {})
#   %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg4_1, %arg0_1), kwargs = {})
#   %relu : [num_users=1] = call_function[target=torch.ops.aten.relu.default](args = (%sub_3,), kwargs = {})
#   %sub_4 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (0.3, %relu), kwargs = {})
#   %abs_1 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%sub_4,), kwargs = {})
#   %sub_5 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg0_1, %arg4_1), kwargs = {})
#   %pow_4 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub_5, 2), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%abs_1, %pow_4), kwargs = {})
#   %mean_1 : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%mul_1,), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul, %mean_1), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mean, %mul_2), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sum_1, 48), kwargs = {})
#   %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sum_2, 48), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%div, %div_1), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add, 0.05), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_1, %mul_3), kwargs = {})
triton_per_fused_abs_add_div_mean_mul_pow_relu_rsub_sub_2 = async_compile.triton('triton_per_fused_abs_add_div_mean_mul_pow_relu_rsub_sub_2', '''
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, 256],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: '*fp32', 8: 'i32', 9: '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': {8: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7, 9), equal_to_1=(8,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_abs_add_div_mean_mul_pow_relu_rsub_sub_2', 'mutated_arg_names': [], 'no_x_dim': True, 'num_load': 7, '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_abs_add_div_mean_mul_pow_relu_rsub_sub_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, out_ptr2, xnumel, rnumel):
    xnumel = 1
    XBLOCK: tl.constexpr = 1
    rnumel = 256
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    xmask = tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    roffset = 0
    rmask = tl.full([RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + (r0), None)
    tmp1 = tl.load(in_ptr1 + (r0), None)
    tmp7 = tl.load(in_ptr2 + (r0), None)
    tmp8 = tl.load(in_ptr3 + (r0), None)
    tmp23 = tl.load(in_ptr4 + (r0), None)
    tmp29 = tl.load(in_ptr5 + (0))
    tmp30 = tl.broadcast_to(tmp29, [RBLOCK])
    tmp33 = tl.load(in_ptr6 + (0))
    tmp34 = tl.broadcast_to(tmp33, [RBLOCK])
    tmp2 = tmp0 - tmp1
    tmp3 = tmp2 * tmp2
    tmp4 = tl.broadcast_to(tmp3, [RBLOCK])
    tmp6 = triton_helpers.promote_to_tensor(tl.sum(tmp4, 0))
    tmp9 = tmp7 - tmp8
    tmp10 = tl.full([1], 0, tl.int32)
    tmp11 = triton_helpers.maximum(tmp10, tmp9)
    tmp12 = 0.3
    tmp13 = tmp12 - tmp11
    tmp14 = tl_math.abs(tmp13)
    tmp15 = tmp8 - tmp7
    tmp16 = tmp15 * tmp15
    tmp17 = tmp14 * tmp16
    tmp18 = tl.broadcast_to(tmp17, [RBLOCK])
    tmp20 = triton_helpers.promote_to_tensor(tl.sum(tmp18, 0))
    tmp21 = 256.0
    tmp22 = tmp6 / tmp21
    tmp24 = 0.5
    tmp25 = tmp23 * tmp24
    tmp26 = tmp20 / tmp21
    tmp27 = tmp25 * tmp26
    tmp28 = tmp22 + tmp27
    tmp31 = 0.020833333333333332
    tmp32 = tmp30 * tmp31
    tmp35 = tmp34 * tmp31
    tmp36 = tmp32 + tmp35
    tmp37 = 0.05
    tmp38 = tmp36 * tmp37
    tmp39 = tmp28 + tmp38
    tl.store(out_ptr2 + (tl.broadcast_to(r0, [RBLOCK])), tmp39, None)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1, arg2_1, arg3_1, arg4_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))
    assert_size_stride(arg2_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg3_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg4_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf2 = empty_strided_cuda((), (), torch.float32)
        # Topologically Sorted Source Nodes: [sub, pow_1, h_tv], Original ATen: [aten.sub, aten.pow, aten.sum]
        stream0 = get_raw_stream(0)
        triton_per_fused_pow_sub_sum_0.run(arg0_1, buf2, 1, 192, grid=grid(1), stream=stream0)
        buf3 = empty_strided_cuda((), (), torch.float32)
        # Topologically Sorted Source Nodes: [sub_1, pow_2, w_tv], Original ATen: [aten.sub, aten.pow, aten.sum]
        triton_per_fused_pow_sub_sum_1.run(arg0_1, buf3, 1, 192, grid=grid(1), stream=stream0)
        buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [sub_2, pow_3, mean, mul, sub_3, relu, sub_4, abs_1, sub_5, pow_4, mul_1, mean_1, mul_2, add_1, truediv, truediv_1, tvloss, mul_3, loss], Original ATen: [aten.sub, aten.pow, aten.mean, aten.mul, aten.relu, aten.rsub, aten.abs, aten.add, aten.div]
        triton_per_fused_abs_add_div_mean_mul_pow_relu_rsub_sub_2.run(arg1_1, arg2_1, arg4_1, arg0_1, arg3_1, buf2, buf3, buf4, 1, 256, grid=grid(1), stream=stream0)
        del arg0_1
        del arg1_1
        del arg2_1
        del arg3_1
        del arg4_1
        del buf2
        del buf3
    return (buf4, )


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)
    arg2_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg3_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg4_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_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
import torch.nn.functional as F


class fixed_loss(nn.Module):

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

    def forward(self, out_image, gt_image, est_noise, gt_noise, if_asym):
        h_x = est_noise.size()[2]
        w_x = est_noise.size()[3]
        count_h = self._tensor_size(est_noise[:, :, 1:, :])
        count_w = self._tensor_size(est_noise[:, :, :, 1:])
        h_tv = torch.pow(est_noise[:, :, 1:, :] - est_noise[:, :, :h_x - 1,
            :], 2).sum()
        w_tv = torch.pow(est_noise[:, :, :, 1:] - est_noise[:, :, :, :w_x -
            1], 2).sum()
        tvloss = h_tv / count_h + w_tv / count_w
        loss = torch.mean(torch.pow(out_image - gt_image, 2)
            ) + if_asym * 0.5 * torch.mean(torch.mul(torch.abs(0.3 - F.relu
            (gt_noise - est_noise)), torch.pow(est_noise - gt_noise, 2))
            ) + 0.05 * tvloss
        return loss

    def _tensor_size(self, t):
        return t.size()[1] * t.size()[2] * t.size()[3]


def get_inputs():
    return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4]), torch.rand(
        [4, 4, 4, 4]), 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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
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_pow_sub_sum_0(in_ptr0, out_ptr0, xnumel, rnumel,
    XBLOCK: tl.constexpr):
    rnumel = 192
    RBLOCK: tl.constexpr = 256
    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, :]
    rmask = rindex < rnumel
    r0 = rindex % 12
    r1 = rindex // 12
    tmp0 = tl.load(in_ptr0 + (4 + r0 + 16 * r1), rmask, other=0.0)
    tmp1 = tl.load(in_ptr0 + (r0 + 16 * r1), rmask, other=0.0)
    tmp2 = tmp0 - tmp1
    tmp3 = tmp2 * tmp2
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
    tmp6 = tl.where(rmask, tmp4, 0)
    tmp7 = tl.sum(tmp6, 1)[:, None]
    tl.store(out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp7, None)


@triton.jit
def triton_per_fused_pow_sub_sum_1(in_ptr0, out_ptr0, xnumel, rnumel,
    XBLOCK: tl.constexpr):
    rnumel = 192
    RBLOCK: tl.constexpr = 256
    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, :]
    rmask = rindex < rnumel
    r0 = rindex % 3
    r1 = rindex // 3
    tmp0 = tl.load(in_ptr0 + (1 + r0 + 4 * r1), rmask, other=0.0)
    tmp1 = tl.load(in_ptr0 + (r0 + 4 * r1), rmask, other=0.0)
    tmp2 = tmp0 - tmp1
    tmp3 = tmp2 * tmp2
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
    tmp6 = tl.where(rmask, tmp4, 0)
    tmp7 = tl.sum(tmp6, 1)[:, None]
    tl.store(out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp7, None)


@triton.jit
def triton_per_fused_abs_add_div_mean_mul_pow_relu_rsub_sub_2(in_ptr0,
    in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, out_ptr2, xnumel,
    rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp1 = tl.load(in_ptr1 + r0, None)
    tmp7 = tl.load(in_ptr2 + r0, None)
    tmp8 = tl.load(in_ptr3 + r0, None)
    tmp23 = tl.load(in_ptr4 + r0, None)
    tmp29 = tl.load(in_ptr5 + 0)
    tmp30 = tl.broadcast_to(tmp29, [RBLOCK])
    tmp33 = tl.load(in_ptr6 + 0)
    tmp34 = tl.broadcast_to(tmp33, [RBLOCK])
    tmp2 = tmp0 - tmp1
    tmp3 = tmp2 * tmp2
    tmp4 = tl.broadcast_to(tmp3, [RBLOCK])
    tmp6 = triton_helpers.promote_to_tensor(tl.sum(tmp4, 0))
    tmp9 = tmp7 - tmp8
    tmp10 = tl.full([1], 0, tl.int32)
    tmp11 = triton_helpers.maximum(tmp10, tmp9)
    tmp12 = 0.3
    tmp13 = tmp12 - tmp11
    tmp14 = tl_math.abs(tmp13)
    tmp15 = tmp8 - tmp7
    tmp16 = tmp15 * tmp15
    tmp17 = tmp14 * tmp16
    tmp18 = tl.broadcast_to(tmp17, [RBLOCK])
    tmp20 = triton_helpers.promote_to_tensor(tl.sum(tmp18, 0))
    tmp21 = 256.0
    tmp22 = tmp6 / tmp21
    tmp24 = 0.5
    tmp25 = tmp23 * tmp24
    tmp26 = tmp20 / tmp21
    tmp27 = tmp25 * tmp26
    tmp28 = tmp22 + tmp27
    tmp31 = 0.020833333333333332
    tmp32 = tmp30 * tmp31
    tmp35 = tmp34 * tmp31
    tmp36 = tmp32 + tmp35
    tmp37 = 0.05
    tmp38 = tmp36 * tmp37
    tmp39 = tmp28 + tmp38
    tl.store(out_ptr2 + tl.broadcast_to(r0, [RBLOCK]), tmp39, None)


def call(args):
    arg0_1, arg1_1, arg2_1, arg3_1, arg4_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))
    assert_size_stride(arg2_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg3_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg4_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf2 = empty_strided_cuda((), (), torch.float32)
        get_raw_stream(0)
        triton_per_fused_pow_sub_sum_0[grid(1)](arg0_1, buf2, 1, 192,
            XBLOCK=1, num_warps=2, num_stages=1)
        buf3 = empty_strided_cuda((), (), torch.float32)
        triton_per_fused_pow_sub_sum_1[grid(1)](arg0_1, buf3, 1, 192,
            XBLOCK=1, num_warps=2, num_stages=1)
        buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_per_fused_abs_add_div_mean_mul_pow_relu_rsub_sub_2[grid(1)](
            arg1_1, arg2_1, arg4_1, arg0_1, arg3_1, buf2, buf3, buf4, 1, 
            256, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
        del arg2_1
        del arg3_1
        del arg4_1
        del buf2
        del buf3
    return buf4,


class fixed_lossNew(nn.Module):

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

    def _tensor_size(self, t):
        return t.size()[1] * t.size()[2] * t.size()[3]

    def forward(self, input_0, input_1, input_2, input_3, input_4):
        arg0_1 = input_0
        arg1_1 = input_1
        arg2_1 = input_2
        arg3_1 = input_3
        arg4_1 = input_4
        output = call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1])
        return output[0]
