# 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_1/inductor_cache/wl/cwl5ist2kzqaxx2f4xhoq3vo7lkczwnfdactx3vhtrjoslgmnfkd.py
# Topologically Sorted Source Nodes: [sub, pow_1, h_tv, sub_1, pow_2, w_tv, add, truediv], Original ATen: [aten.sub, aten.pow, aten.mean, aten.add, aten.div]
# Source node to ATen node mapping:
#   add => add
#   h_tv => mean
#   pow_1 => pow_1
#   pow_2 => pow_2
#   sub => sub
#   sub_1 => sub_1
#   truediv => div
#   w_tv => mean_1
# 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 = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%pow_1,), kwargs = {})
#   %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 = {})
#   %mean_1 : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%pow_2,), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mean, %mean_1), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%add, 2), kwargs = {})
triton_per_fused_add_div_mean_pow_sub_0 = async_compile.triton('triton_per_fused_add_div_mean_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.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=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), '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_add_div_mean_pow_sub_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': True, 'num_load': 4, 'num_reduction': 2, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_pow_sub_0(in_out_ptr0, in_ptr0, 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)
    r1 = (rindex // 4) % 4
    r4 = rindex
    r0 = rindex % 4
    tmp0 = 1 + r1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tmp2 & tmp4
    tmp6 = tl.load(in_ptr0 + (tl.broadcast_to(4 + r4, [RBLOCK])), tmp5, other=0.0)
    tmp7 = (-1) + r1
    tmp8 = tmp7 >= tmp1
    tmp9 = tmp7 < tmp3
    tmp10 = tmp8 & tmp9
    tmp11 = tl.load(in_ptr0 + (tl.broadcast_to((-4) + r4, [RBLOCK])), tmp10, other=0.0)
    tmp12 = tmp6 - tmp11
    tmp13 = tmp12 * tmp12
    tmp14 = tl.broadcast_to(tmp13, [RBLOCK])
    tmp16 = triton_helpers.promote_to_tensor(tl.sum(tmp14, 0))
    tmp17 = 1 + r0
    tmp18 = tmp17 >= tmp1
    tmp19 = tmp17 < tmp3
    tmp20 = tmp18 & tmp19
    tmp21 = tl.load(in_ptr0 + (tl.broadcast_to(1 + r4, [RBLOCK])), tmp20, other=0.0)
    tmp22 = (-1) + r0
    tmp23 = tmp22 >= tmp1
    tmp24 = tmp22 < tmp3
    tmp25 = tmp23 & tmp24
    tmp26 = tl.load(in_ptr0 + (tl.broadcast_to((-1) + r4, [RBLOCK])), tmp25, other=0.0)
    tmp27 = tmp21 - tmp26
    tmp28 = tmp27 * tmp27
    tmp29 = tl.broadcast_to(tmp28, [RBLOCK])
    tmp31 = triton_helpers.promote_to_tensor(tl.sum(tmp29, 0))
    tmp32 = 256.0
    tmp33 = tmp16 / tmp32
    tmp34 = tmp31 / tmp32
    tmp35 = tmp33 + tmp34
    tmp36 = 0.5
    tmp37 = tmp35 * tmp36
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([1], 0, tl.int32)), tmp37, None)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((), (), torch.float32)
        buf2 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [sub, pow_1, h_tv, sub_1, pow_2, w_tv, add, truediv], Original ATen: [aten.sub, aten.pow, aten.mean, aten.add, aten.div]
        stream0 = get_raw_stream(0)
        triton_per_fused_add_div_mean_pow_sub_0.run(buf2, arg0_1, 1, 256, grid=grid(1), stream=stream0)
        del arg0_1
    return (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, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_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 Smooth_loss(nn.Module):

    def __init__(self, Smooth_weight=1):
        super(Smooth_loss, self).__init__()
        self.Smooth_weight = Smooth_weight

    def forward(self, x):
        _b, _c, h, w = x.size()
        x_h = F.pad(x, (0, 0, 1, 1))
        h_tv = torch.mean(torch.pow(x_h[:, :, 2:, :] - x_h[:, :, :h, :], 2))
        x_w = F.pad(x, (1, 1, 0, 0))
        w_tv = torch.mean(torch.pow(x_w[:, :, :, 2:] - x_w[:, :, :, :w], 2))
        self.loss = (h_tv + w_tv) / 2
        return self.loss


def get_inputs():
    return [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
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_add_div_mean_pow_sub_0(in_out_ptr0, in_ptr0, 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)
    r1 = rindex // 4 % 4
    r4 = rindex
    r0 = rindex % 4
    tmp0 = 1 + r1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tmp2 & tmp4
    tmp6 = tl.load(in_ptr0 + tl.broadcast_to(4 + r4, [RBLOCK]), tmp5, other=0.0
        )
    tmp7 = -1 + r1
    tmp8 = tmp7 >= tmp1
    tmp9 = tmp7 < tmp3
    tmp10 = tmp8 & tmp9
    tmp11 = tl.load(in_ptr0 + tl.broadcast_to(-4 + r4, [RBLOCK]), tmp10,
        other=0.0)
    tmp12 = tmp6 - tmp11
    tmp13 = tmp12 * tmp12
    tmp14 = tl.broadcast_to(tmp13, [RBLOCK])
    tmp16 = triton_helpers.promote_to_tensor(tl.sum(tmp14, 0))
    tmp17 = 1 + r0
    tmp18 = tmp17 >= tmp1
    tmp19 = tmp17 < tmp3
    tmp20 = tmp18 & tmp19
    tmp21 = tl.load(in_ptr0 + tl.broadcast_to(1 + r4, [RBLOCK]), tmp20,
        other=0.0)
    tmp22 = -1 + r0
    tmp23 = tmp22 >= tmp1
    tmp24 = tmp22 < tmp3
    tmp25 = tmp23 & tmp24
    tmp26 = tl.load(in_ptr0 + tl.broadcast_to(-1 + r4, [RBLOCK]), tmp25,
        other=0.0)
    tmp27 = tmp21 - tmp26
    tmp28 = tmp27 * tmp27
    tmp29 = tl.broadcast_to(tmp28, [RBLOCK])
    tmp31 = triton_helpers.promote_to_tensor(tl.sum(tmp29, 0))
    tmp32 = 256.0
    tmp33 = tmp16 / tmp32
    tmp34 = tmp31 / tmp32
    tmp35 = tmp33 + tmp34
    tmp36 = 0.5
    tmp37 = tmp35 * tmp36
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp37, None)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((), (), torch.float32)
        buf2 = buf0
        del buf0
        get_raw_stream(0)
        triton_per_fused_add_div_mean_pow_sub_0[grid(1)](buf2, arg0_1, 1, 
            256, num_warps=2, num_stages=1)
        del arg0_1
    return buf2,


class Smooth_lossNew(nn.Module):

    def __init__(self, Smooth_weight=1):
        super(Smooth_lossNew, self).__init__()
        self.Smooth_weight = Smooth_weight

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