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, 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_per_fused_binary_cross_entropy_with_logits_0(in_ptr0, in_ptr1,
    out_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)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp3 = tl.load(in_ptr1 + r0, None)
    tmp1 = 1.0
    tmp2 = tmp1 - tmp0
    tmp4 = tmp2 * tmp3
    tmp5 = 0.0
    tmp6 = triton_helpers.minimum(tmp5, tmp3)
    tmp7 = tl_math.abs(tmp3)
    tmp8 = -tmp7
    tmp9 = tl_math.exp(tmp8)
    tmp10 = libdevice.log1p(tmp9)
    tmp11 = tmp6 - tmp10
    tmp12 = tmp4 - tmp11
    tmp13 = tl.broadcast_to(tmp12, [RBLOCK])
    tmp15 = triton_helpers.promote_to_tensor(tl.sum(tmp13, 0))
    tl.store(out_ptr0 + tl.full([1], 0, tl.int32), tmp15, None)


@triton.jit
def triton_per_fused_mul_sum_1(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    out_ptr2, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    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)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 64 * x0), xmask, other=0.0)
    tmp2 = tl.load(in_ptr1 + (r1 + 64 * x0), xmask, other=0.0)
    tmp1 = tl.sigmoid(tmp0)
    tmp3 = tmp1 * tmp2
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
    tmp6 = tl.where(xmask, tmp4, 0)
    tmp7 = tl.sum(tmp6, 1)[:, None]
    tmp8 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp10 = tl.where(xmask, tmp8, 0)
    tmp11 = tl.sum(tmp10, 1)[:, None]
    tmp12 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp14 = tl.where(xmask, tmp12, 0)
    tmp15 = tl.sum(tmp14, 1)[:, None]
    tl.store(out_ptr0 + x0, tmp7, xmask)
    tl.store(out_ptr1 + x0, tmp11, xmask)
    tl.store(out_ptr2 + x0, tmp15, xmask)


@triton.jit
def triton_per_fused_add_binary_cross_entropy_with_logits_div_mul_sub_sum_2(
    in_out_ptr0, in_ptr0, in_ptr1, in_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
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp5 = tl.load(in_ptr1 + r0, None)
    tmp6 = tl.load(in_ptr2 + r0, None)
    tmp13 = tl.load(in_out_ptr0 + 0)
    tmp14 = tl.broadcast_to(tmp13, [XBLOCK, 1])
    tmp1 = 2.0
    tmp2 = tmp0 * tmp1
    tmp3 = 1e-05
    tmp4 = tmp2 + tmp3
    tmp7 = tmp5 + tmp6
    tmp8 = tmp7 + tmp3
    tmp9 = tmp4 / tmp8
    tmp10 = tl.broadcast_to(tmp9, [XBLOCK, RBLOCK])
    tmp12 = tl.sum(tmp10, 1)[:, None]
    tmp15 = 256.0
    tmp16 = tmp14 / tmp15
    tmp17 = 0.5
    tmp18 = tmp16 * tmp17
    tmp19 = 0.25
    tmp20 = tmp12 * tmp19
    tmp21 = tmp18 - tmp20
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp21, 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((), (), torch.float32)
        get_raw_stream(0)
        triton_per_fused_binary_cross_entropy_with_logits_0[grid(1)](arg0_1,
            arg1_1, buf0, 1, 256, num_warps=2, num_stages=1)
        buf1 = empty_strided_cuda((4,), (1,), torch.float32)
        buf2 = empty_strided_cuda((4,), (1,), torch.float32)
        buf3 = empty_strided_cuda((4,), (1,), torch.float32)
        triton_per_fused_mul_sum_1[grid(4)](arg1_1, arg0_1, buf1, buf2,
            buf3, 4, 64, XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
        buf5 = buf0
        del buf0
        triton_per_fused_add_binary_cross_entropy_with_logits_div_mul_sub_sum_2[
            grid(1)](buf5, buf1, buf2, buf3, 1, 4, XBLOCK=1, num_warps=2,
            num_stages=1)
        del buf1
        del buf2
        del buf3
    return buf5,


class DiceCE_LossNew(nn.Module):
    """
    Taken from https://www.kaggle.com/bigironsphere/loss-function-library-keras-pytorch
    """

    def __init__(self, weight=None, size_average=True):
        super(DiceCE_LossNew, self).__init__()

    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: 0.01553