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 libdevice
import collections
from typing import Optional
from typing import Union
from typing import Any
from typing import Callable
from typing import Tuple
import torch.nn
from torch.nn.modules.loss import _Loss
from enum import Enum
import collections.abc
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_mul_sum_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    out_ptr2, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    RBLOCK: tl.constexpr = 16
    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)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 16 * x0), xmask, other=0.0)
    tmp1 = tl.load(in_ptr1 + (r1 + 16 * x0), xmask, other=0.0)
    tmp2 = tmp0 * tmp1
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp5 = tl.where(xmask, tmp3, 0)
    tmp6 = tl.sum(tmp5, 1)[:, None]
    tmp7 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp9 = tl.where(xmask, tmp7, 0)
    tmp10 = tl.sum(tmp9, 1)[:, None]
    tmp11 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp13 = tl.where(xmask, tmp11, 0)
    tmp14 = tl.sum(tmp13, 1)[:, None]
    tl.store(out_ptr0 + x0, tmp6, xmask)
    tl.store(out_ptr1 + x0, tmp10, xmask)
    tl.store(out_ptr2 + x0, tmp14, xmask)


@triton.jit
def triton_poi_fused_index_put_lift_fresh_1(in_ptr0, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 4
    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 * tmp0
    tmp2 = tl.full([1], 1, tl.int32)
    tmp3 = tmp2 / tmp1
    tmp4 = libdevice.isinf(tmp3).to(tl.int1)
    tmp5 = 0.0
    tmp6 = tl.where(tmp4, tmp5, tmp3)
    tl.store(out_ptr0 + x0, tmp6, xmask)


@triton.jit
def triton_poi_fused_mul_reciprocal_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
    x1 = xindex // 4
    x0 = xindex % 4
    x2 = xindex
    tmp3 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + x2, xmask)
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = tmp0 == tmp1
    tmp5 = tmp4 * tmp4
    tmp6 = tl.full([1], 1, tl.int32)
    tmp7 = tmp6 / tmp5
    tmp8 = tl.where(tmp2, tmp3, tmp7)
    tl.store(out_ptr0 + x2, tmp8, xmask)


@triton.jit
def triton_per_fused_index_put_max_3(in_ptr0, in_ptr1, out_ptr2, xnumel,
    rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 4
    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
    tmp2 = tl.load(in_ptr0 + r0, None)
    tmp3 = tl.load(in_ptr1 + r0, None)
    tmp0 = tl.full([1, 1], 0, tl.int32)
    tmp1 = tmp0 == tmp0
    tmp4 = tmp3 * tmp3
    tmp5 = tl.full([1, 1], 1, tl.int32)
    tmp6 = tmp5 / tmp4
    tmp7 = tl.where(tmp1, tmp2, tmp6)
    tmp8 = tl.broadcast_to(tmp7, [XBLOCK, RBLOCK])
    tmp10 = triton_helpers.max2(tmp8, 1)[:, None]
    tmp11 = libdevice.isinf(tmp6).to(tl.int1)
    tmp12 = tl.where(tmp11, tmp10, tmp7)
    tl.store(out_ptr2 + tl.broadcast_to(r0, [XBLOCK, RBLOCK]), tmp12, None)


@triton.jit
def triton_poi_fused_4(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4
    x0 = xindex % 4
    x2 = xindex
    tmp3 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + x2, xmask)
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = tmp0 == tmp1
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tl.store(out_ptr0 + x2, tmp5, xmask)


@triton.jit
def triton_poi_fused_index_put_lift_fresh_5(in_ptr0, out_ptr1, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp3 = tl.load(in_ptr0 + x0, xmask)
    tmp4 = tl.load(in_ptr0 + (4 + x0), xmask)
    tmp0 = tl.full([1], 1, tl.int32)
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = tmp0 == tmp1
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tmp6 = libdevice.isinf(tmp5).to(tl.int1)
    tmp7 = 0.0
    tmp8 = tl.where(tmp6, tmp7, tmp5)
    tl.store(out_ptr1 + (4 + x0), tmp8, xmask)


@triton.jit
def triton_poi_fused_6(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4
    x0 = xindex % 4
    x2 = xindex
    tmp3 = tl.load(in_ptr0 + (4 + x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + x2, xmask)
    tmp0 = x1
    tmp1 = tl.full([1], 1, tl.int32)
    tmp2 = tmp0 == tmp1
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tl.store(out_ptr0 + x2, tmp5, xmask)


@triton.jit
def triton_per_fused_index_put_max_7(in_ptr0, in_ptr1, out_ptr2, xnumel,
    rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 4
    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
    tmp2 = tl.load(in_ptr0 + (4 + r0), None)
    tmp9 = tl.load(in_ptr1 + r0, None)
    tmp10 = tl.load(in_ptr1 + (4 + r0), None)
    tmp0 = tl.full([1, 1], 1, tl.int32)
    tmp1 = tmp0 == tmp0
    tmp3 = tl.where(tmp1, tmp2, tmp2)
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
    tmp6 = triton_helpers.max2(tmp4, 1)[:, None]
    tmp7 = tl.full([1, 1], 0, tl.int32)
    tmp8 = tmp0 == tmp7
    tmp11 = tl.where(tmp8, tmp9, tmp10)
    tmp12 = libdevice.isinf(tmp11).to(tl.int1)
    tmp13 = tl.where(tmp12, tmp6, tmp3)
    tl.store(out_ptr2 + tl.broadcast_to(4 + r0, [XBLOCK, RBLOCK]), tmp13, None)


@triton.jit
def triton_poi_fused_index_put_lift_fresh_8(in_ptr0, out_ptr1, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp3 = tl.load(in_ptr0 + (4 + x0), xmask)
    tmp4 = tl.load(in_ptr0 + (8 + x0), xmask)
    tmp0 = tl.full([1], 2, tl.int32)
    tmp1 = tl.full([1], 1, tl.int32)
    tmp2 = tmp0 == tmp1
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tmp6 = libdevice.isinf(tmp5).to(tl.int1)
    tmp7 = 0.0
    tmp8 = tl.where(tmp6, tmp7, tmp5)
    tl.store(out_ptr1 + (8 + x0), tmp8, xmask)


@triton.jit
def triton_poi_fused_9(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4
    x0 = xindex % 4
    x2 = xindex
    tmp3 = tl.load(in_ptr0 + (8 + x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + x2, xmask)
    tmp0 = x1
    tmp1 = tl.full([1], 2, tl.int32)
    tmp2 = tmp0 == tmp1
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tl.store(out_ptr0 + x2, tmp5, xmask)


@triton.jit
def triton_per_fused_index_put_max_10(in_ptr0, in_ptr1, out_ptr2, xnumel,
    rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 4
    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
    tmp2 = tl.load(in_ptr0 + (8 + r0), None)
    tmp9 = tl.load(in_ptr1 + (4 + r0), None)
    tmp10 = tl.load(in_ptr1 + (8 + r0), None)
    tmp0 = tl.full([1, 1], 2, tl.int32)
    tmp1 = tmp0 == tmp0
    tmp3 = tl.where(tmp1, tmp2, tmp2)
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
    tmp6 = triton_helpers.max2(tmp4, 1)[:, None]
    tmp7 = tl.full([1, 1], 1, tl.int32)
    tmp8 = tmp0 == tmp7
    tmp11 = tl.where(tmp8, tmp9, tmp10)
    tmp12 = libdevice.isinf(tmp11).to(tl.int1)
    tmp13 = tl.where(tmp12, tmp6, tmp3)
    tl.store(out_ptr2 + tl.broadcast_to(8 + r0, [XBLOCK, RBLOCK]), tmp13, None)


@triton.jit
def triton_poi_fused_index_put_lift_fresh_11(in_ptr0, out_ptr1, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp3 = tl.load(in_ptr0 + (8 + x0), xmask)
    tmp4 = tl.load(in_ptr0 + (12 + x0), xmask)
    tmp0 = tl.full([1], 3, tl.int32)
    tmp1 = tl.full([1], 2, tl.int32)
    tmp2 = tmp0 == tmp1
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tmp6 = libdevice.isinf(tmp5).to(tl.int1)
    tmp7 = 0.0
    tmp8 = tl.where(tmp6, tmp7, tmp5)
    tl.store(out_ptr1 + (12 + x0), tmp8, xmask)


@triton.jit
def triton_poi_fused_12(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4
    x0 = xindex % 4
    x2 = xindex
    tmp3 = tl.load(in_ptr0 + (12 + x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + x2, xmask)
    tmp0 = x1
    tmp1 = tl.full([1], 3, tl.int32)
    tmp2 = tmp0 == tmp1
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tl.store(out_ptr0 + x2, tmp5, xmask)


@triton.jit
def triton_per_fused_index_put_max_13(in_ptr0, in_ptr1, out_ptr2, xnumel,
    rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 4
    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
    tmp2 = tl.load(in_ptr0 + (12 + r0), None)
    tmp9 = tl.load(in_ptr1 + (8 + r0), None)
    tmp10 = tl.load(in_ptr1 + (12 + r0), None)
    tmp0 = tl.full([1, 1], 3, tl.int32)
    tmp1 = tmp0 == tmp0
    tmp3 = tl.where(tmp1, tmp2, tmp2)
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
    tmp6 = triton_helpers.max2(tmp4, 1)[:, None]
    tmp7 = tl.full([1, 1], 2, tl.int32)
    tmp8 = tmp0 == tmp7
    tmp11 = tl.where(tmp8, tmp9, tmp10)
    tmp12 = libdevice.isinf(tmp11).to(tl.int1)
    tmp13 = tl.where(tmp12, tmp6, tmp3)
    tl.store(out_ptr2 + tl.broadcast_to(12 + r0, [XBLOCK, RBLOCK]), tmp13, None
        )


@triton.jit
def triton_per_fused_add_div_mean_mul_rsub_sum_14(in_out_ptr0, in_ptr0,
    in_ptr1, in_ptr2, in_ptr3, xnumel, rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 4
    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 + 4 * r0, None, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + 12)
    tmp5 = tl.broadcast_to(tmp4, [XBLOCK, RBLOCK])
    tmp6 = tl.load(in_ptr1 + 4 * r0, None, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr1 + 13)
    tmp11 = tl.broadcast_to(tmp10, [XBLOCK, RBLOCK])
    tmp12 = tl.load(in_ptr1 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr0 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp17 = tl.load(in_ptr1 + 14)
    tmp18 = tl.broadcast_to(tmp17, [XBLOCK, RBLOCK])
    tmp19 = tl.load(in_ptr1 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp23 = tl.load(in_ptr0 + (3 + 4 * r0), None, eviction_policy='evict_last')
    tmp24 = tl.load(in_ptr1 + 15)
    tmp25 = tl.broadcast_to(tmp24, [XBLOCK, RBLOCK])
    tmp26 = tl.load(in_ptr1 + (3 + 4 * r0), None, eviction_policy='evict_last')
    tmp30 = tl.load(in_ptr2 + 4 * r0, None, eviction_policy='evict_last')
    tmp31 = tl.load(in_ptr3 + 4 * r0, None, eviction_policy='evict_last')
    tmp34 = tl.load(in_ptr2 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp35 = tl.load(in_ptr3 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp39 = tl.load(in_ptr2 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp40 = tl.load(in_ptr3 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp44 = tl.load(in_ptr2 + (3 + 4 * r0), None, eviction_policy='evict_last')
    tmp45 = tl.load(in_ptr3 + (3 + 4 * r0), None, eviction_policy='evict_last')
    tmp1 = r0
    tmp2 = tl.full([1, 1], 3, tl.int32)
    tmp3 = tmp1 == tmp2
    tmp7 = tl.where(tmp3, tmp5, tmp6)
    tmp8 = tmp0 * tmp7
    tmp13 = tl.where(tmp3, tmp11, tmp12)
    tmp14 = tmp9 * tmp13
    tmp15 = tmp8 + tmp14
    tmp20 = tl.where(tmp3, tmp18, tmp19)
    tmp21 = tmp16 * tmp20
    tmp22 = tmp15 + tmp21
    tmp27 = tl.where(tmp3, tmp25, tmp26)
    tmp28 = tmp23 * tmp27
    tmp29 = tmp22 + tmp28
    tmp32 = tmp30 + tmp31
    tmp33 = tmp32 * tmp7
    tmp36 = tmp34 + tmp35
    tmp37 = tmp36 * tmp13
    tmp38 = tmp33 + tmp37
    tmp41 = tmp39 + tmp40
    tmp42 = tmp41 * tmp20
    tmp43 = tmp38 + tmp42
    tmp46 = tmp44 + tmp45
    tmp47 = tmp46 * tmp27
    tmp48 = tmp43 + tmp47
    tmp49 = 2.0
    tmp50 = tmp29 * tmp49
    tmp51 = 1e-05
    tmp52 = tmp50 + tmp51
    tmp53 = tmp48 + tmp51
    tmp54 = tmp52 / tmp53
    tmp55 = 1.0
    tmp56 = tmp55 - tmp54
    tmp57 = tl.broadcast_to(tmp56, [XBLOCK, RBLOCK])
    tmp59 = tl.sum(tmp57, 1)[:, None]
    tmp60 = 4.0
    tmp61 = tmp59 / tmp60
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp61, None)


def call(args):
    arg0_1, arg1_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))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf29 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_per_fused_mul_sum_0[grid(16)](arg1_1, arg0_1, buf0, buf1,
            buf29, 16, 16, XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
        buf2 = empty_strided_cuda((4,), (1,), torch.float32)
        triton_poi_fused_index_put_lift_fresh_1[grid(4)](buf1, buf2, 4,
            XBLOCK=4, num_warps=1, num_stages=1)
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_mul_reciprocal_2[grid(16)](buf2, buf1, buf4, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        triton_per_fused_index_put_max_3[grid(1)](buf2, buf1, buf4, 1, 4,
            XBLOCK=1, num_warps=2, num_stages=1)
        del buf2
        buf7 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_4[grid(16)](buf4, buf7, 16, XBLOCK=16, num_warps=1,
            num_stages=1)
        triton_poi_fused_index_put_lift_fresh_5[grid(4)](buf4, buf7, 4,
            XBLOCK=4, num_warps=1, num_stages=1)
        buf11 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_6[grid(16)](buf7, buf11, 16, XBLOCK=16, num_warps=
            1, num_stages=1)
        triton_per_fused_index_put_max_7[grid(1)](buf7, buf4, buf11, 1, 4,
            XBLOCK=1, num_warps=2, num_stages=1)
        buf14 = buf7
        del buf7
        triton_poi_fused_6[grid(16)](buf11, buf14, 16, XBLOCK=16, num_warps
            =1, num_stages=1)
        triton_poi_fused_index_put_lift_fresh_8[grid(4)](buf11, buf14, 4,
            XBLOCK=4, num_warps=1, num_stages=1)
        buf18 = buf4
        del buf4
        triton_poi_fused_9[grid(16)](buf14, buf18, 16, XBLOCK=16, num_warps
            =1, num_stages=1)
        triton_per_fused_index_put_max_10[grid(1)](buf14, buf11, buf18, 1, 
            4, XBLOCK=1, num_warps=2, num_stages=1)
        buf21 = buf14
        del buf14
        triton_poi_fused_9[grid(16)](buf18, buf21, 16, XBLOCK=16, num_warps
            =1, num_stages=1)
        triton_poi_fused_index_put_lift_fresh_11[grid(4)](buf18, buf21, 4,
            XBLOCK=4, num_warps=1, num_stages=1)
        buf25 = buf11
        del buf11
        triton_poi_fused_12[grid(16)](buf21, buf25, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        triton_per_fused_index_put_max_13[grid(1)](buf21, buf18, buf25, 1, 
            4, XBLOCK=1, num_warps=2, num_stages=1)
        del buf18
        del buf21
        buf31 = empty_strided_cuda((), (), torch.float32)
        buf32 = buf31
        del buf31
        triton_per_fused_add_div_mean_mul_rsub_sum_14[grid(1)](buf32, buf0,
            buf25, buf1, buf29, 1, 4, XBLOCK=1, num_warps=2, num_stages=1)
        del buf0
        del buf1
        del buf25
        del buf29
    return buf32,


def issequenceiterable(obj: 'Any') ->bool:
    """
    Determine if the object is an iterable sequence and is not a string.
    """
    if torch.is_tensor(obj):
        return int(obj.dim()) > 0
    return isinstance(obj, collections.abc.Iterable) and not isinstance(obj,
        str)


def ensure_tuple(vals: 'Any') ->Tuple[Any, ...]:
    """
    Returns a tuple of `vals`.
    """
    if not issequenceiterable(vals):
        vals = vals,
    return tuple(vals)


def ensure_tuple_size(tup: 'Any', dim: 'int', pad_val: 'Any'=0) ->Tuple[Any,
    ...]:
    """
    Returns a copy of `tup` with `dim` values by either shortened or padded with `pad_val` as necessary.
    """
    tup = ensure_tuple(tup) + (pad_val,) * dim
    return tuple(tup[:dim])


def one_hot(labels: 'torch.Tensor', num_classes: 'int', dtype:
    'torch.dtype'=torch.float, dim: 'int'=1) ->torch.Tensor:
    """
    For a tensor `labels` of dimensions B1[spatial_dims], return a tensor of dimensions `BN[spatial_dims]`
    for `num_classes` N number of classes.

    Example:

        For every value v = labels[b,1,h,w], the value in the result at [b,v,h,w] will be 1 and all others 0.
        Note that this will include the background label, thus a binary mask should be treated as having 2 classes.
    """
    assert labels.dim() > 0, 'labels should have dim of 1 or more.'
    if labels.ndim < dim + 1:
        shape = ensure_tuple_size(labels.shape, dim + 1, 1)
        labels = labels.reshape(*shape)
    sh = list(labels.shape)
    assert sh[dim
        ] == 1, 'labels should have a channel with length equals to one.'
    sh[dim] = num_classes
    o = torch.zeros(size=sh, dtype=dtype, device=labels.device)
    labels = o.scatter_(dim=dim, index=labels.long(), value=1)
    return labels


class LossReduction(Enum):
    """
    See also:
        - :py:class:`monai.losses.dice.DiceLoss`
        - :py:class:`monai.losses.dice.GeneralizedDiceLoss`
        - :py:class:`monai.losses.focal_loss.FocalLoss`
        - :py:class:`monai.losses.tversky.TverskyLoss`
    """
    NONE = 'none'
    MEAN = 'mean'
    SUM = 'sum'


class Weight(Enum):
    """
    See also: :py:class:`monai.losses.dice.GeneralizedDiceLoss`
    """
    SQUARE = 'square'
    SIMPLE = 'simple'
    UNIFORM = 'uniform'


class GeneralizedDiceLossNew(_Loss):
    """
    Compute the generalised Dice loss defined in:

        Sudre, C. et. al. (2017) Generalised Dice overlap as a deep learning
        loss function for highly unbalanced segmentations. DLMIA 2017.

    Adapted from:
        https://github.com/NifTK/NiftyNet/blob/v0.6.0/niftynet/layer/loss_segmentation.py#L279
    """

    def __init__(self, include_background: 'bool'=True, to_onehot_y: 'bool'
        =False, sigmoid: 'bool'=False, softmax: 'bool'=False, other_act:
        'Optional[Callable]'=None, w_type: 'Union[Weight, str]'=Weight.
        SQUARE, reduction: 'Union[LossReduction, str]'=LossReduction.MEAN,
        smooth_nr: 'float'=1e-05, smooth_dr: 'float'=1e-05, batch: 'bool'=False
        ) ->None:
        """
        Args:
            include_background: If False channel index 0 (background category) is excluded from the calculation.
            to_onehot_y: whether to convert `y` into the one-hot format. Defaults to False.
            sigmoid: If True, apply a sigmoid function to the prediction.
            softmax: If True, apply a softmax function to the prediction.
            other_act: if don't want to use `sigmoid` or `softmax`, use other callable function to execute
                other activation layers, Defaults to ``None``. for example:
                `other_act = torch.tanh`.
            squared_pred: use squared versions of targets and predictions in the denominator or not.
            w_type: {``"square"``, ``"simple"``, ``"uniform"``}
                Type of function to transform ground truth volume to a weight factor. Defaults to ``"square"``.
            reduction: {``"none"``, ``"mean"``, ``"sum"``}
                Specifies the reduction to apply to the output. Defaults to ``"mean"``.

                - ``"none"``: no reduction will be applied.
                - ``"mean"``: the sum of the output will be divided by the number of elements in the output.
                - ``"sum"``: the output will be summed.
            smooth_nr: a small constant added to the numerator to avoid zero.
            smooth_dr: a small constant added to the denominator to avoid nan.
            batch: whether to sum the intersection and union areas over the batch dimension before the dividing.
                Defaults to False, intersection over union is computed from each item in the batch.

        Raises:
            TypeError: When ``other_act`` is not an ``Optional[Callable]``.
            ValueError: When more than 1 of [``sigmoid=True``, ``softmax=True``, ``other_act is not None``].
                Incompatible values.

        """
        super().__init__(reduction=LossReduction(reduction).value)
        if other_act is not None and not callable(other_act):
            raise TypeError(
                f'other_act must be None or callable but is {type(other_act).__name__}.'
                )
        if int(sigmoid) + int(softmax) + int(other_act is not None) > 1:
            raise ValueError(
                'Incompatible values: more than 1 of [sigmoid=True, softmax=True, other_act is not None].'
                )
        self.include_background = include_background
        self.to_onehot_y = to_onehot_y
        self.sigmoid = sigmoid
        self.softmax = softmax
        self.other_act = other_act
        w_type = Weight(w_type)
        self.w_func: 'Callable' = torch.ones_like
        if w_type == Weight.SIMPLE:
            self.w_func = torch.reciprocal
        elif w_type == Weight.SQUARE:
            self.w_func = lambda x: torch.reciprocal(x * x)
        self.smooth_nr = float(smooth_nr)
        self.smooth_dr = float(smooth_dr)
        self.batch = batch

    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: 0.013816