# 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/mg/cmgudcchcmpq3zdjvnq2f5sw33xfyigtiica6hu4rjrneeiyjs23.py
# Topologically Sorted Source Nodes: [weights, ge, float_1, mul_1, weights_1, ge_1, float_2, mul_2, weights_2, ge_2, float_3, mul_3, weights_3, ge_3, float_4, mul_4, weights_4, ge_4, float_5, mul_5, weights_5], Original ATen: [aten.mul, aten.ge, aten._to_copy, aten.add]
# Source node to ATen node mapping:
#   float_1 => convert_element_type
#   float_2 => convert_element_type_1
#   float_3 => convert_element_type_2
#   float_4 => convert_element_type_3
#   float_5 => convert_element_type_4
#   ge => ge
#   ge_1 => ge_1
#   ge_2 => ge_2
#   ge_3 => ge_3
#   ge_4 => ge_4
#   mul_1 => mul_1
#   mul_2 => mul_2
#   mul_3 => mul_3
#   mul_4 => mul_4
#   mul_5 => mul_5
#   weights => full_default
#   weights_1 => add
#   weights_2 => add_1
#   weights_3 => add_2
#   weights_4 => add_3
#   weights_5 => add_4
# Graph fragment:
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([4, 4, 4, 4, 4], 3.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %ge : [num_users=1] = call_function[target=torch.ops.aten.ge.Scalar](args = (%arg1_1, 0.5), kwargs = {})
#   %convert_element_type : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%ge, torch.float32), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convert_element_type, 0), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%full_default, %mul_1), kwargs = {})
#   %ge_1 : [num_users=1] = call_function[target=torch.ops.aten.ge.Scalar](args = (%arg1_1, 2), kwargs = {})
#   %convert_element_type_1 : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%ge_1, torch.float32), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convert_element_type_1, 1), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add, %mul_2), kwargs = {})
#   %ge_2 : [num_users=1] = call_function[target=torch.ops.aten.ge.Scalar](args = (%arg1_1, 5), kwargs = {})
#   %convert_element_type_2 : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%ge_2, torch.float32), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convert_element_type_2, 3), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_1, %mul_3), kwargs = {})
#   %ge_3 : [num_users=1] = call_function[target=torch.ops.aten.ge.Scalar](args = (%arg1_1, 10), kwargs = {})
#   %convert_element_type_3 : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%ge_3, torch.float32), kwargs = {})
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convert_element_type_3, 5), kwargs = {})
#   %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_2, %mul_4), kwargs = {})
#   %ge_4 : [num_users=1] = call_function[target=torch.ops.aten.ge.Scalar](args = (%arg1_1, 30), kwargs = {})
#   %convert_element_type_4 : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%ge_4, torch.float32), kwargs = {})
#   %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convert_element_type_4, 20), kwargs = {})
#   %add_4 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_3, %mul_5), kwargs = {})
triton_poi_fused__to_copy_add_ge_mul_0 = async_compile.triton('triton_poi_fused__to_copy_add_ge_mul_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=[1024], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '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': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_add_ge_mul_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, '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},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__to_copy_add_ge_mul_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 256
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp1 = 0.5
    tmp2 = tmp0 >= tmp1
    tmp3 = tmp2.to(tl.float32)
    tmp4 = 0.0
    tmp5 = tmp3 * tmp4
    tmp6 = 3.0
    tmp7 = tmp6 + tmp5
    tmp8 = 2.0
    tmp9 = tmp0 >= tmp8
    tmp10 = tmp9.to(tl.float32)
    tmp11 = 1.0
    tmp12 = tmp10 * tmp11
    tmp13 = tmp7 + tmp12
    tmp14 = 5.0
    tmp15 = tmp0 >= tmp14
    tmp16 = tmp15.to(tl.float32)
    tmp17 = tmp16 * tmp6
    tmp18 = tmp13 + tmp17
    tmp19 = 10.0
    tmp20 = tmp0 >= tmp19
    tmp21 = tmp20.to(tl.float32)
    tmp22 = tmp21 * tmp14
    tmp23 = tmp18 + tmp22
    tmp24 = 30.0
    tmp25 = tmp0 >= tmp24
    tmp26 = tmp25.to(tl.float32)
    tmp27 = 20.0
    tmp28 = tmp26 * tmp27
    tmp29 = tmp23 + tmp28
    tl.store(out_ptr0 + (x2), tmp29, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/3o/c3ooami5fgsmo4pqjdmhybejly2zlj455jeia2huptc7ll24v376.py
# Topologically Sorted Source Nodes: [sub, pow_1, mul_6, mse, sub_1, abs_1, mul_7, mae], Original ATen: [aten.sub, aten.pow, aten.mul, aten.sum, aten.abs]
# Source node to ATen node mapping:
#   abs_1 => abs_1
#   mae => sum_2
#   mse => sum_1
#   mul_6 => mul_6
#   mul_7 => mul_7
#   pow_1 => pow_1
#   sub => sub
#   sub_1 => sub_1
# Graph fragment:
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg0_1, %arg1_1), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub, 2), kwargs = {})
#   %mul_6 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_4, %pow_1), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul_6, [1, 3, 4]), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg0_1, %arg1_1), kwargs = {})
#   %abs_1 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%sub_1,), kwargs = {})
#   %mul_7 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_4, %abs_1), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul_7, [1, 3, 4]), kwargs = {})
triton_per_fused_abs_mul_pow_sub_sum_1 = async_compile.triton('triton_per_fused_abs_mul_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=[16, 64],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32', 6: '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': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_abs_mul_pow_sub_sum_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, '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_abs_mul_pow_sub_sum_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    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)
    r2 = rindex % 16
    r3 = (rindex // 16)
    x0 = xindex % 4
    x1 = (xindex // 4)
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (r2 + (16*x0) + (64*r3) + (256*x1)), xmask, other=0.0)
    tmp1 = tl.load(in_ptr1 + (r2 + (16*x0) + (64*r3) + (256*x1)), xmask, other=0.0)
    tmp2 = tl.load(in_ptr2 + (r2 + (16*x0) + (64*r3)), xmask, eviction_policy='evict_last', other=0.0)
    tmp3 = tmp1 - tmp2
    tmp4 = tmp3 * tmp3
    tmp5 = tmp0 * tmp4
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK, RBLOCK])
    tmp8 = tl.where(xmask, tmp6, 0)
    tmp9 = tl.sum(tmp8, 1)[:, None]
    tmp10 = tl_math.abs(tmp3)
    tmp11 = tmp0 * tmp10
    tmp12 = tl.broadcast_to(tmp11, [XBLOCK, RBLOCK])
    tmp14 = tl.where(xmask, tmp12, 0)
    tmp15 = tl.sum(tmp14, 1)[:, None]
    tl.store(out_ptr0 + (x4), tmp9, xmask)
    tl.store(out_ptr1 + (x4), tmp15, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/wo/cwodohmd2aco5lta6fbacjld7ld6laopbjd4d3ykomhhwbvmdu75.py
# Topologically Sorted Source Nodes: [mean, mean_1, add_5, mul_8], Original ATen: [aten.mean, aten.add, aten.mul]
# Source node to ATen node mapping:
#   add_5 => add_5
#   mean => mean
#   mean_1 => mean_1
#   mul_8 => mul_8
# Graph fragment:
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%sum_1,), kwargs = {})
#   %mean_1 : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%sum_2,), kwargs = {})
#   %add_5 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mean, %mean_1), kwargs = {})
#   %mul_8 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_5, 5e-06), kwargs = {})
triton_per_fused_add_mean_mul_2 = async_compile.triton('triton_per_fused_add_mean_mul_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, 16],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 4: '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': {3: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 4), equal_to_1=(3,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_mean_mul_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, '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_mean_mul_2(in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 16
    RBLOCK: tl.constexpr = 16
    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)
    tmp4 = tl.load(in_ptr1 + (r0), None)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.sum(tmp1, 1)[:, None]
    tmp5 = tl.broadcast_to(tmp4, [XBLOCK, RBLOCK])
    tmp7 = tl.sum(tmp5, 1)[:, None]
    tmp8 = 16.0
    tmp9 = tmp3 / tmp8
    tmp10 = tmp7 / tmp8
    tmp11 = tmp9 + tmp10
    tmp12 = 5e-06
    tmp13 = tmp11 * tmp12
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp13, 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, 4), (256, 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, 4, 4, 4, 4), (256, 64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [weights, ge, float_1, mul_1, weights_1, ge_1, float_2, mul_2, weights_2, ge_2, float_3, mul_3, weights_3, ge_3, float_4, mul_4, weights_4, ge_4, float_5, mul_5, weights_5], Original ATen: [aten.mul, aten.ge, aten._to_copy, aten.add]
        stream0 = get_raw_stream(0)
        triton_poi_fused__to_copy_add_ge_mul_0.run(arg1_1, buf0, 1024, grid=grid(1024), stream=stream0)
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [sub, pow_1, mul_6, mse, sub_1, abs_1, mul_7, mae], Original ATen: [aten.sub, aten.pow, aten.mul, aten.sum, aten.abs]
        triton_per_fused_abs_mul_pow_sub_sum_1.run(buf0, arg0_1, arg1_1, buf1, buf3, 16, 64, grid=grid(16), stream=stream0)
        del arg0_1
        del arg1_1
        del buf0
        buf2 = empty_strided_cuda((), (), torch.float32)
        buf5 = buf2; del buf2  # reuse
        # Topologically Sorted Source Nodes: [mean, mean_1, add_5, mul_8], Original ATen: [aten.mean, aten.add, aten.mul]
        triton_per_fused_add_mean_mul_2.run(buf5, buf1, buf3, 1, 16, grid=grid(1), stream=stream0)
        del buf1
        del buf3
    return (buf5, )


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, 4), (256, 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
from torch import nn


class weightedLoss(nn.Module):

    def __init__(self):
        super().__init__()
        self.thresholds = [0.5, 2, 5, 10, 30]
        self.weights = [1, 1, 2, 5, 10, 30]

    def forward(self, pred, label):
        weights = torch.ones_like(pred) * 3
        for i, threshold in enumerate(self.thresholds):
            weights = weights + (self.weights[i + 1] - self.weights[i]) * (
                label >= threshold).float()
        mse = torch.sum(weights * (pred - label) ** 2, (1, 3, 4))
        mae = torch.sum(weights * torch.abs(pred - label), (1, 3, 4))
        return (torch.mean(mse) + torch.mean(mae)) * 5e-06


def get_inputs():
    return [torch.rand([4, 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.triton_helpers import math as tl_math
from torch import 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_poi_fused__to_copy_add_ge_mul_0(in_ptr0, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 256
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp1 = 0.5
    tmp2 = tmp0 >= tmp1
    tmp3 = tmp2.to(tl.float32)
    tmp4 = 0.0
    tmp5 = tmp3 * tmp4
    tmp6 = 3.0
    tmp7 = tmp6 + tmp5
    tmp8 = 2.0
    tmp9 = tmp0 >= tmp8
    tmp10 = tmp9.to(tl.float32)
    tmp11 = 1.0
    tmp12 = tmp10 * tmp11
    tmp13 = tmp7 + tmp12
    tmp14 = 5.0
    tmp15 = tmp0 >= tmp14
    tmp16 = tmp15.to(tl.float32)
    tmp17 = tmp16 * tmp6
    tmp18 = tmp13 + tmp17
    tmp19 = 10.0
    tmp20 = tmp0 >= tmp19
    tmp21 = tmp20.to(tl.float32)
    tmp22 = tmp21 * tmp14
    tmp23 = tmp18 + tmp22
    tmp24 = 30.0
    tmp25 = tmp0 >= tmp24
    tmp26 = tmp25.to(tl.float32)
    tmp27 = 20.0
    tmp28 = tmp26 * tmp27
    tmp29 = tmp23 + tmp28
    tl.store(out_ptr0 + x2, tmp29, xmask)


@triton.jit
def triton_per_fused_abs_mul_pow_sub_sum_1(in_ptr0, in_ptr1, in_ptr2,
    out_ptr0, out_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    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)
    r2 = rindex % 16
    r3 = rindex // 16
    x0 = xindex % 4
    x1 = xindex // 4
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (r2 + 16 * x0 + 64 * r3 + 256 * x1), xmask,
        other=0.0)
    tmp1 = tl.load(in_ptr1 + (r2 + 16 * x0 + 64 * r3 + 256 * x1), xmask,
        other=0.0)
    tmp2 = tl.load(in_ptr2 + (r2 + 16 * x0 + 64 * r3), xmask,
        eviction_policy='evict_last', other=0.0)
    tmp3 = tmp1 - tmp2
    tmp4 = tmp3 * tmp3
    tmp5 = tmp0 * tmp4
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK, RBLOCK])
    tmp8 = tl.where(xmask, tmp6, 0)
    tmp9 = tl.sum(tmp8, 1)[:, None]
    tmp10 = tl_math.abs(tmp3)
    tmp11 = tmp0 * tmp10
    tmp12 = tl.broadcast_to(tmp11, [XBLOCK, RBLOCK])
    tmp14 = tl.where(xmask, tmp12, 0)
    tmp15 = tl.sum(tmp14, 1)[:, None]
    tl.store(out_ptr0 + x4, tmp9, xmask)
    tl.store(out_ptr1 + x4, tmp15, xmask)


@triton.jit
def triton_per_fused_add_mean_mul_2(in_out_ptr0, in_ptr0, in_ptr1, xnumel,
    rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 16
    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)
    tmp4 = tl.load(in_ptr1 + r0, None)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.sum(tmp1, 1)[:, None]
    tmp5 = tl.broadcast_to(tmp4, [XBLOCK, RBLOCK])
    tmp7 = tl.sum(tmp5, 1)[:, None]
    tmp8 = 16.0
    tmp9 = tmp3 / tmp8
    tmp10 = tmp7 / tmp8
    tmp11 = tmp9 + tmp10
    tmp12 = 5e-06
    tmp13 = tmp11 * tmp12
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp13, None)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4, 4), (256, 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, 4, 4, 4, 4), (256, 64, 16, 4, 1),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused__to_copy_add_ge_mul_0[grid(1024)](arg1_1, buf0, 
            1024, XBLOCK=256, num_warps=4, num_stages=1)
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_per_fused_abs_mul_pow_sub_sum_1[grid(16)](buf0, arg0_1,
            arg1_1, buf1, buf3, 16, 64, XBLOCK=8, num_warps=4, num_stages=1)
        del arg0_1
        del arg1_1
        del buf0
        buf2 = empty_strided_cuda((), (), torch.float32)
        buf5 = buf2
        del buf2
        triton_per_fused_add_mean_mul_2[grid(1)](buf5, buf1, buf3, 1, 16,
            XBLOCK=1, num_warps=2, num_stages=1)
        del buf1
        del buf3
    return buf5,


class weightedLossNew(nn.Module):

    def __init__(self):
        super().__init__()
        self.thresholds = [0.5, 2, 5, 10, 30]
        self.weights = [1, 1, 2, 5, 10, 30]

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