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]


Measured latency_ms: 0.042443