import torch
import torch.nn as nn
import torch.utils.data.distributed
from torch.backends import cudnn as cudnn


class BCEWithLogitsLoss2d(nn.Module):
    """Computationally stable version of 2D BCE loss.
    """

    def __init__(self):
        super(BCEWithLogitsLoss2d, self).__init__()
        self.bce_loss = nn.BCEWithLogitsLoss(None, reduction='mean')

    def forward(self, logits, targets):
        logits_flat = logits.view(-1)
        targets_flat = targets.view(-1)
        return self.bce_loss(logits_flat, targets_flat)


class SoftDiceLoss(nn.Module):
    """SoftJaccard loss for binary problems.
    """

    def forward(self, logits, labels):
        num = labels.size(0)
        m1 = torch.sigmoid(logits.view(num, -1))
        m2 = labels.view(num, -1)
        intersection = (m1 * m2).sum(1)
        score = (intersection + 1e-15) / (m1.sum(1) + m2.sum(1) + 1e-15)
        dice = score.sum(0) / num
        return 1 - dice


class BCEDiceLoss(torch.nn.Module):

    def __init__(self):
        super(BCEDiceLoss, self).__init__()
        self.dice = SoftDiceLoss()
        self.bce = BCEWithLogitsLoss2d()

    def forward(self, logits, targets):
        targets.size(0)
        loss = self.bce(logits, targets)
        loss += self.dice(logits, targets)
        return loss


def get_inputs():
    return [torch.rand([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 import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
import torch.utils.data.distributed
from torch.backends import cudnn as cudnn
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_sigmoid_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_rsub_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)
    tmp3 = tl.load(in_ptr1 + r0, None)
    tmp4 = tl.load(in_ptr2 + r0, None)
    tmp11 = tl.load(in_out_ptr0 + 0)
    tmp12 = tl.broadcast_to(tmp11, [XBLOCK, 1])
    tmp1 = 1e-15
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp5 + tmp1
    tmp7 = tmp2 / tmp6
    tmp8 = tl.broadcast_to(tmp7, [XBLOCK, RBLOCK])
    tmp10 = tl.sum(tmp8, 1)[:, None]
    tmp13 = 256.0
    tmp14 = tmp12 / tmp13
    tmp15 = 0.25
    tmp16 = tmp10 * tmp15
    tmp17 = 1.0
    tmp18 = tmp17 - tmp16
    tmp19 = tmp14 + tmp18
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp19, 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_sigmoid_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_rsub_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 BCEWithLogitsLoss2d(nn.Module):
    """Computationally stable version of 2D BCE loss.
    """

    def __init__(self):
        super(BCEWithLogitsLoss2d, self).__init__()
        self.bce_loss = nn.BCEWithLogitsLoss(None, reduction='mean')

    def forward(self, logits, targets):
        logits_flat = logits.view(-1)
        targets_flat = targets.view(-1)
        return self.bce_loss(logits_flat, targets_flat)


class SoftDiceLoss(nn.Module):
    """SoftJaccard loss for binary problems.
    """

    def forward(self, logits, labels):
        num = labels.size(0)
        m1 = torch.sigmoid(logits.view(num, -1))
        m2 = labels.view(num, -1)
        intersection = (m1 * m2).sum(1)
        score = (intersection + 1e-15) / (m1.sum(1) + m2.sum(1) + 1e-15)
        dice = score.sum(0) / num
        return 1 - dice


class BCEDiceLossNew(torch.nn.Module):

    def __init__(self):
        super(BCEDiceLossNew, self).__init__()
        self.dice = SoftDiceLoss()
        self.bce = BCEWithLogitsLoss2d()

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