# 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_8/inductor_cache/ph/cph3ovan3mirgugvdoidxmuks5vw3qnnvzjt47kmtdgwjddev7wm.py
# Topologically Sorted Source Nodes: [neg], Original ATen: [aten.neg]
# Source node to ATen node mapping:
#   neg => neg
# Graph fragment:
#   %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%arg0_1,), kwargs = {})
triton_poi_fused_neg_0 = async_compile.triton('triton_poi_fused_neg_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.pointwise(
    size_hints=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_neg_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_neg_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp1 = -tmp0
    tl.store(out_ptr0 + (x0), tmp1, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/37/c37dcgrnpmbg5yxwhrg7taa4b6pecn2kuqa6pj66lqcx2rylypvq.py
# Topologically Sorted Source Nodes: [mean_1, mean_min_frm, mean_max_frm, sub, temporal_loss, sum_1], Original ATen: [aten.mean, aten.neg, aten.sub, aten.mul, aten.sum]
# Source node to ATen node mapping:
#   mean_1 => mean_1
#   mean_max_frm => mean
#   mean_min_frm => neg_1
#   sub => sub
#   sum_1 => sum_1
#   temporal_loss => mul
# Graph fragment:
#   %mean_1 : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%getitem_2, [1]), kwargs = {})
#   %neg_1 : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%mean_1,), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%getitem, [1]), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%neg_1, %mean), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, %arg1_1), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul, [-1]), kwargs = {})
triton_poi_fused_mean_mul_neg_sub_sum_1 = async_compile.triton('triton_poi_fused_mean_mul_neg_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.pointwise(
    size_hints=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 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, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mean_mul_neg_sub_sum_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 12, '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_mean_mul_neg_sub_sum_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (4*x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + (4*x0), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr2 + (4*x2), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr1 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr2 + (1 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp18 = tl.load(in_ptr0 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp21 = tl.load(in_ptr1 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp24 = tl.load(in_ptr2 + (2 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp27 = tl.load(in_ptr0 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp30 = tl.load(in_ptr1 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp33 = tl.load(in_ptr2 + (3 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp1 = 1.0
    tmp2 = tmp0 / tmp1
    tmp3 = -tmp2
    tmp5 = tmp4 / tmp1
    tmp6 = tmp3 - tmp5
    tmp8 = tmp6 * tmp7
    tmp10 = tmp9 / tmp1
    tmp11 = -tmp10
    tmp13 = tmp12 / tmp1
    tmp14 = tmp11 - tmp13
    tmp16 = tmp14 * tmp15
    tmp17 = tmp8 + tmp16
    tmp19 = tmp18 / tmp1
    tmp20 = -tmp19
    tmp22 = tmp21 / tmp1
    tmp23 = tmp20 - tmp22
    tmp25 = tmp23 * tmp24
    tmp26 = tmp17 + tmp25
    tmp28 = tmp27 / tmp1
    tmp29 = -tmp28
    tmp31 = tmp30 / tmp1
    tmp32 = tmp29 - tmp31
    tmp34 = tmp32 * tmp33
    tmp35 = tmp26 + tmp34
    tl.store(out_ptr0 + (x2), tmp35, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/4x/c4xmsxuhjjrbkkykwocjnxgbxkuvboocjbamgha3w2gecuywvdn3.py
# Topologically Sorted Source Nodes: [max_1, sub_1, abs_1, mean_3], Original ATen: [aten.max, aten.sub, aten.abs, aten.mean]
# Source node to ATen node mapping:
#   abs_1 => abs_1
#   max_1 => max_1
#   mean_3 => mean_3
#   sub_1 => sub_1
# Graph fragment:
#   %max_1 : [num_users=1] = call_function[target=torch.ops.aten.max.dim](args = (%slice_3, -1), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%getitem_4, %select), kwargs = {})
#   %abs_1 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%sub_1,), kwargs = {})
#   %mean_3 : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%abs_1, [-1]), kwargs = {})
triton_poi_fused_abs_max_mean_sub_2 = async_compile.triton('triton_poi_fused_abs_max_mean_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.pointwise(
    size_hints=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 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': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_abs_max_mean_sub_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 32, '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_abs_max_mean_sub_2(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (16*x0), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (16*x2), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + (1 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (2 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (2 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (3 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr1 + (3 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr0 + (4 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp17 = tl.load(in_ptr1 + (4 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr0 + (5 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp20 = tl.load(in_ptr1 + (5 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp23 = tl.load(in_ptr0 + (6 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp24 = tl.load(in_ptr1 + (6 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp27 = tl.load(in_ptr0 + (7 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp28 = tl.load(in_ptr1 + (7 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp33 = tl.load(in_ptr0 + (8 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp34 = tl.load(in_ptr1 + (8 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp36 = tl.load(in_ptr0 + (9 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp37 = tl.load(in_ptr1 + (9 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp40 = tl.load(in_ptr0 + (10 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp41 = tl.load(in_ptr1 + (10 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp44 = tl.load(in_ptr0 + (11 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp45 = tl.load(in_ptr1 + (11 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp50 = tl.load(in_ptr0 + (12 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp51 = tl.load(in_ptr1 + (12 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp53 = tl.load(in_ptr0 + (13 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp54 = tl.load(in_ptr1 + (13 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp57 = tl.load(in_ptr0 + (14 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp58 = tl.load(in_ptr1 + (14 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp61 = tl.load(in_ptr0 + (15 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp62 = tl.load(in_ptr1 + (15 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tmp5 = tmp3 * tmp4
    tmp6 = triton_helpers.maximum(tmp2, tmp5)
    tmp9 = tmp7 * tmp8
    tmp10 = triton_helpers.maximum(tmp6, tmp9)
    tmp13 = tmp11 * tmp12
    tmp14 = tmp10 - tmp13
    tmp15 = tl_math.abs(tmp14)
    tmp18 = tmp16 * tmp17
    tmp21 = tmp19 * tmp20
    tmp22 = triton_helpers.maximum(tmp18, tmp21)
    tmp25 = tmp23 * tmp24
    tmp26 = triton_helpers.maximum(tmp22, tmp25)
    tmp29 = tmp27 * tmp28
    tmp30 = tmp26 - tmp29
    tmp31 = tl_math.abs(tmp30)
    tmp32 = tmp15 + tmp31
    tmp35 = tmp33 * tmp34
    tmp38 = tmp36 * tmp37
    tmp39 = triton_helpers.maximum(tmp35, tmp38)
    tmp42 = tmp40 * tmp41
    tmp43 = triton_helpers.maximum(tmp39, tmp42)
    tmp46 = tmp44 * tmp45
    tmp47 = tmp43 - tmp46
    tmp48 = tl_math.abs(tmp47)
    tmp49 = tmp32 + tmp48
    tmp52 = tmp50 * tmp51
    tmp55 = tmp53 * tmp54
    tmp56 = triton_helpers.maximum(tmp52, tmp55)
    tmp59 = tmp57 * tmp58
    tmp60 = triton_helpers.maximum(tmp56, tmp59)
    tmp63 = tmp61 * tmp62
    tmp64 = tmp60 - tmp63
    tmp65 = tl_math.abs(tmp64)
    tmp66 = tmp49 + tmp65
    tmp67 = 4.0
    tmp68 = tmp66 / tmp67
    tl.store(out_ptr0 + (x2), tmp68, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/6r/c6rwifsjow2nseyok6d3ofas2zra57cknfftf2bb4ooaloeb5zbm.py
# Topologically Sorted Source Nodes: [temporal_loss_1, mean_4, categorcial_loss, add], Original ATen: [aten.mean, aten.mul, aten.add]
# Source node to ATen node mapping:
#   add => add
#   categorcial_loss => mul_2
#   mean_4 => mean_4
#   temporal_loss_1 => mean_2
# Graph fragment:
#   %mean_2 : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%sum_1, [-1]), kwargs = {})
#   %mean_4 : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%mean_3, [-1]), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mean_4, -1.0), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mean_2, %mul_2), kwargs = {})
triton_poi_fused_add_mean_mul_3 = async_compile.triton('triton_poi_fused_add_mean_mul_3', '''
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=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 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': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_mean_mul_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, '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_mean_mul_3(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (4*x2), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr1 + (4*x1), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr1 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr1 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr1 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tmp11 = tmp9 + tmp10
    tmp13 = tmp11 + tmp12
    tmp15 = tmp13 + tmp14
    tmp16 = tmp15 / tmp7
    tmp17 = -1.0
    tmp18 = tmp16 * tmp17
    tmp19 = tmp8 + tmp18
    tl.store(out_ptr0 + (x2), tmp19, 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, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [topk], Original ATen: [aten.topk]
        buf0 = torch.ops.aten.topk.default(arg0_1, 1, 1)
        buf1 = buf0[0]
        del buf0
        buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [neg], Original ATen: [aten.neg]
        stream0 = get_raw_stream(0)
        triton_poi_fused_neg_0.run(arg0_1, buf3, 64, grid=grid(64), stream=stream0)
        # Topologically Sorted Source Nodes: [neg, topk_1], Original ATen: [aten.neg, aten.topk]
        buf4 = torch.ops.aten.topk.default(buf3, 1, 1)
        buf5 = buf4[0]
        del buf4
        buf7 = buf3; del buf3  # reuse
        # Topologically Sorted Source Nodes: [mean_1, mean_min_frm, mean_max_frm, sub, temporal_loss, sum_1], Original ATen: [aten.mean, aten.neg, aten.sub, aten.mul, aten.sum]
        triton_poi_fused_mean_mul_neg_sub_sum_1.run(buf5, buf1, arg1_1, buf7, 64, grid=grid(64), stream=stream0)
        buf8 = reinterpret_tensor(buf5, (4, 1, 4), (4, 16, 1), 0); del buf5  # reuse
        # Topologically Sorted Source Nodes: [max_1, sub_1, abs_1, mean_3], Original ATen: [aten.max, aten.sub, aten.abs, aten.mean]
        triton_poi_fused_abs_max_mean_sub_2.run(arg0_1, arg1_1, buf8, 16, grid=grid(16), stream=stream0)
        del arg0_1
        del arg1_1
        buf9 = reinterpret_tensor(buf1, (4, 4), (4, 1), 0); del buf1  # reuse
        # Topologically Sorted Source Nodes: [temporal_loss_1, mean_4, categorcial_loss, add], Original ATen: [aten.mean, aten.mul, aten.add]
        triton_poi_fused_add_mean_mul_3.run(buf7, buf8, buf9, 16, grid=grid(16), stream=stream0)
        del buf7
        del buf8
    return (buf9, )


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((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 FrmScrLoss(nn.Module):

    def __init__(self, propotion):
        super().__init__()
        self.s = propotion

    def forward(self, frm_scrs, label):
        _n, t, _c = frm_scrs.size()
        max_frm_values, _ = torch.topk(frm_scrs, max(int(t // self.s), 1), 1)
        mean_max_frm = max_frm_values.mean(1)
        min_frm_values, _ = torch.topk(-frm_scrs, max(int(t // self.s), 1), 1)
        mean_min_frm = -min_frm_values.mean(1)
        temporal_loss = (mean_min_frm - mean_max_frm) * label
        temporal_loss = temporal_loss.sum(-1).mean(-1)
        frm_scrs = frm_scrs * label[:, None, :]
        frm_act_scrs = frm_scrs[..., :-1]
        frm_bck_scr = frm_scrs[..., -1]
        frm_act_scr = frm_act_scrs.max(-1)[0]
        categorcial_loss = -1.0 * torch.abs(frm_act_scr - frm_bck_scr).mean(-1
            ).mean(-1)
        return temporal_loss + categorcial_loss


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


def get_init_inputs():
    return [[], {'propotion': 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 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
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_neg_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = -tmp0
    tl.store(out_ptr0 + x0, tmp1, xmask)


@triton.jit
def triton_poi_fused_mean_mul_neg_sub_sum_1(in_ptr0, in_ptr1, in_ptr2,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr2 + 4 * x2, xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp15 = tl.load(in_ptr2 + (1 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp18 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp21 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp24 = tl.load(in_ptr2 + (2 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp27 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp30 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp33 = tl.load(in_ptr2 + (3 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp1 = 1.0
    tmp2 = tmp0 / tmp1
    tmp3 = -tmp2
    tmp5 = tmp4 / tmp1
    tmp6 = tmp3 - tmp5
    tmp8 = tmp6 * tmp7
    tmp10 = tmp9 / tmp1
    tmp11 = -tmp10
    tmp13 = tmp12 / tmp1
    tmp14 = tmp11 - tmp13
    tmp16 = tmp14 * tmp15
    tmp17 = tmp8 + tmp16
    tmp19 = tmp18 / tmp1
    tmp20 = -tmp19
    tmp22 = tmp21 / tmp1
    tmp23 = tmp20 - tmp22
    tmp25 = tmp23 * tmp24
    tmp26 = tmp17 + tmp25
    tmp28 = tmp27 / tmp1
    tmp29 = -tmp28
    tmp31 = tmp30 / tmp1
    tmp32 = tmp29 - tmp31
    tmp34 = tmp32 * tmp33
    tmp35 = tmp26 + tmp34
    tl.store(out_ptr0 + x2, tmp35, xmask)


@triton.jit
def triton_poi_fused_abs_max_mean_sub_2(in_ptr0, in_ptr1, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + 16 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + 16 * x2, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + 16 * x0), xmask, eviction_policy='evict_last'
        )
    tmp4 = tl.load(in_ptr1 + (1 + 16 * x2), xmask, eviction_policy='evict_last'
        )
    tmp7 = tl.load(in_ptr0 + (2 + 16 * x0), xmask, eviction_policy='evict_last'
        )
    tmp8 = tl.load(in_ptr1 + (2 + 16 * x2), xmask, eviction_policy='evict_last'
        )
    tmp11 = tl.load(in_ptr0 + (3 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp12 = tl.load(in_ptr1 + (3 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp16 = tl.load(in_ptr0 + (4 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp17 = tl.load(in_ptr1 + (4 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp19 = tl.load(in_ptr0 + (5 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp20 = tl.load(in_ptr1 + (5 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp23 = tl.load(in_ptr0 + (6 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp24 = tl.load(in_ptr1 + (6 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp27 = tl.load(in_ptr0 + (7 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp28 = tl.load(in_ptr1 + (7 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp33 = tl.load(in_ptr0 + (8 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp34 = tl.load(in_ptr1 + (8 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp36 = tl.load(in_ptr0 + (9 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp37 = tl.load(in_ptr1 + (9 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp40 = tl.load(in_ptr0 + (10 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp41 = tl.load(in_ptr1 + (10 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp44 = tl.load(in_ptr0 + (11 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp45 = tl.load(in_ptr1 + (11 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp50 = tl.load(in_ptr0 + (12 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp51 = tl.load(in_ptr1 + (12 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp53 = tl.load(in_ptr0 + (13 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp54 = tl.load(in_ptr1 + (13 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp57 = tl.load(in_ptr0 + (14 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp58 = tl.load(in_ptr1 + (14 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp61 = tl.load(in_ptr0 + (15 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp62 = tl.load(in_ptr1 + (15 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tmp0 * tmp1
    tmp5 = tmp3 * tmp4
    tmp6 = triton_helpers.maximum(tmp2, tmp5)
    tmp9 = tmp7 * tmp8
    tmp10 = triton_helpers.maximum(tmp6, tmp9)
    tmp13 = tmp11 * tmp12
    tmp14 = tmp10 - tmp13
    tmp15 = tl_math.abs(tmp14)
    tmp18 = tmp16 * tmp17
    tmp21 = tmp19 * tmp20
    tmp22 = triton_helpers.maximum(tmp18, tmp21)
    tmp25 = tmp23 * tmp24
    tmp26 = triton_helpers.maximum(tmp22, tmp25)
    tmp29 = tmp27 * tmp28
    tmp30 = tmp26 - tmp29
    tmp31 = tl_math.abs(tmp30)
    tmp32 = tmp15 + tmp31
    tmp35 = tmp33 * tmp34
    tmp38 = tmp36 * tmp37
    tmp39 = triton_helpers.maximum(tmp35, tmp38)
    tmp42 = tmp40 * tmp41
    tmp43 = triton_helpers.maximum(tmp39, tmp42)
    tmp46 = tmp44 * tmp45
    tmp47 = tmp43 - tmp46
    tmp48 = tl_math.abs(tmp47)
    tmp49 = tmp32 + tmp48
    tmp52 = tmp50 * tmp51
    tmp55 = tmp53 * tmp54
    tmp56 = triton_helpers.maximum(tmp52, tmp55)
    tmp59 = tmp57 * tmp58
    tmp60 = triton_helpers.maximum(tmp56, tmp59)
    tmp63 = tmp61 * tmp62
    tmp64 = tmp60 - tmp63
    tmp65 = tl_math.abs(tmp64)
    tmp66 = tmp49 + tmp65
    tmp67 = 4.0
    tmp68 = tmp66 / tmp67
    tl.store(out_ptr0 + x2, tmp68, xmask)


@triton.jit
def triton_poi_fused_add_mean_mul_3(in_ptr0, in_ptr1, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + 4 * x2, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 4 * x2), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + 4 * x2), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + 4 * x2), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr1 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr1 + (1 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp12 = tl.load(in_ptr1 + (2 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp14 = tl.load(in_ptr1 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tmp11 = tmp9 + tmp10
    tmp13 = tmp11 + tmp12
    tmp15 = tmp13 + tmp14
    tmp16 = tmp15 / tmp7
    tmp17 = -1.0
    tmp18 = tmp16 * tmp17
    tmp19 = tmp8 + tmp18
    tl.store(out_ptr0 + x2, tmp19, 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, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = torch.ops.aten.topk.default(arg0_1, 1, 1)
        buf1 = buf0[0]
        del buf0
        buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_neg_0[grid(64)](arg0_1, buf3, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf4 = torch.ops.aten.topk.default(buf3, 1, 1)
        buf5 = buf4[0]
        del buf4
        buf7 = buf3
        del buf3
        triton_poi_fused_mean_mul_neg_sub_sum_1[grid(64)](buf5, buf1,
            arg1_1, buf7, 64, XBLOCK=64, num_warps=1, num_stages=1)
        buf8 = reinterpret_tensor(buf5, (4, 1, 4), (4, 16, 1), 0)
        del buf5
        triton_poi_fused_abs_max_mean_sub_2[grid(16)](arg0_1, arg1_1, buf8,
            16, XBLOCK=16, num_warps=1, num_stages=1)
        del arg0_1
        del arg1_1
        buf9 = reinterpret_tensor(buf1, (4, 4), (4, 1), 0)
        del buf1
        triton_poi_fused_add_mean_mul_3[grid(16)](buf7, buf8, buf9, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        del buf7
        del buf8
    return buf9,


class FrmScrLossNew(nn.Module):

    def __init__(self, propotion):
        super().__init__()
        self.s = propotion

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