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 math as tl_math
import torch.nn as 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__softmax_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 16
    x2 = xindex // 64
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + x3, tmp9, xmask)


@triton.jit
def triton_poi_fused__softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 16
    x2 = xindex // 64
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + x3, tmp8, xmask)


@triton.jit
def triton_per_fused_add_mul_pow_sum_2(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    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 + 64 * x0), xmask, other=0.0)
    tmp1 = tl.load(in_ptr1 + (r1 + 64 * 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 = tmp0 * tmp0
    tmp8 = tmp1 * tmp1
    tmp9 = tmp7 + tmp8
    tmp10 = tl.broadcast_to(tmp9, [XBLOCK, RBLOCK])
    tmp12 = tl.where(xmask, tmp10, 0)
    tmp13 = tl.sum(tmp12, 1)[:, None]
    tl.store(out_ptr0 + x0, tmp6, xmask)
    tl.store(out_ptr1 + x0, tmp13, xmask)


@triton.jit
def triton_per_fused_add_mul_pow_sum_3(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    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 + (48 + r1 + 64 * x0), xmask, other=0.0)
    tmp1 = tl.load(in_ptr1 + (48 + r1 + 64 * 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 = tmp0 * tmp0
    tmp8 = tmp1 * tmp1
    tmp9 = tmp7 + tmp8
    tmp10 = tl.broadcast_to(tmp9, [XBLOCK, RBLOCK])
    tmp12 = tl.where(xmask, tmp10, 0)
    tmp13 = tl.sum(tmp12, 1)[:, None]
    tl.store(out_ptr0 + x0, tmp6, xmask)
    tl.store(out_ptr1 + x0, tmp13, xmask)


@triton.jit
def triton_per_fused_add_mul_pow_sum_4(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    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 + (16 + r1 + 64 * x0), xmask, other=0.0)
    tmp1 = tl.load(in_ptr1 + (16 + r1 + 64 * 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 = tmp0 * tmp0
    tmp8 = tmp1 * tmp1
    tmp9 = tmp7 + tmp8
    tmp10 = tl.broadcast_to(tmp9, [XBLOCK, RBLOCK])
    tmp12 = tl.where(xmask, tmp10, 0)
    tmp13 = tl.sum(tmp12, 1)[:, None]
    tl.store(out_ptr0 + x0, tmp6, xmask)
    tl.store(out_ptr1 + x0, tmp13, xmask)


@triton.jit
def triton_per_fused_add_mul_pow_sum_5(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    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 + (32 + r1 + 64 * x0), xmask, other=0.0)
    tmp1 = tl.load(in_ptr1 + (32 + r1 + 64 * 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 = tmp0 * tmp0
    tmp8 = tmp1 * tmp1
    tmp9 = tmp7 + tmp8
    tmp10 = tl.broadcast_to(tmp9, [XBLOCK, RBLOCK])
    tmp12 = tl.where(xmask, tmp10, 0)
    tmp13 = tl.sum(tmp12, 1)[:, None]
    tl.store(out_ptr0 + x0, tmp6, xmask)
    tl.store(out_ptr1 + x0, tmp13, xmask)


@triton.jit
def triton_per_fused_add_div_rsub_sum_6(in_out_ptr0, in_ptr0, in_ptr1,
    in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, 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)
    tmp10 = tl.load(in_ptr2 + r0, None)
    tmp12 = tl.load(in_ptr3 + r0, None)
    tmp19 = tl.load(in_ptr4 + r0, None)
    tmp21 = tl.load(in_ptr5 + r0, None)
    tmp28 = tl.load(in_ptr6 + r0, None)
    tmp30 = tl.load(in_ptr7 + r0, None)
    tmp1 = 1.0
    tmp2 = tmp0 + tmp1
    tmp4 = tmp3 + tmp1
    tmp5 = tmp2 / tmp4
    tmp6 = tmp1 - tmp5
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK])
    tmp9 = tl.sum(tmp7, 1)[:, None]
    tmp11 = tmp10 + tmp1
    tmp13 = tmp12 + tmp1
    tmp14 = tmp11 / tmp13
    tmp15 = tmp1 - tmp14
    tmp16 = tl.broadcast_to(tmp15, [XBLOCK, RBLOCK])
    tmp18 = tl.sum(tmp16, 1)[:, None]
    tmp20 = tmp19 + tmp1
    tmp22 = tmp21 + tmp1
    tmp23 = tmp20 / tmp22
    tmp24 = tmp1 - tmp23
    tmp25 = tl.broadcast_to(tmp24, [XBLOCK, RBLOCK])
    tmp27 = tl.sum(tmp25, 1)[:, None]
    tmp29 = tmp28 + tmp1
    tmp31 = tmp30 + tmp1
    tmp32 = tmp29 / tmp31
    tmp33 = tmp1 - tmp32
    tmp34 = tl.broadcast_to(tmp33, [XBLOCK, RBLOCK])
    tmp36 = tl.sum(tmp34, 1)[:, None]
    tmp37 = 0.0
    tmp38 = tmp9 + tmp37
    tmp39 = tmp38 + tmp18
    tmp40 = tmp39 + tmp27
    tmp41 = tmp40 + tmp36
    tmp42 = 0.25
    tmp43 = tmp41 * tmp42
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp43, 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, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__softmax_0[grid(256)](arg0_1, buf0, 256, XBLOCK=
            256, num_warps=4, num_stages=1)
        del arg0_1
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__softmax_1[grid(256)](buf0, buf1, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        del buf0
        buf2 = empty_strided_cuda((4,), (1,), torch.float32)
        buf3 = empty_strided_cuda((4,), (1,), torch.float32)
        triton_per_fused_add_mul_pow_sum_2[grid(4)](buf1, arg1_1, buf2,
            buf3, 4, 16, XBLOCK=1, num_warps=2, num_stages=1)
        buf11 = empty_strided_cuda((4,), (1,), torch.float32)
        buf12 = empty_strided_cuda((4,), (1,), torch.float32)
        triton_per_fused_add_mul_pow_sum_3[grid(4)](buf1, arg1_1, buf11,
            buf12, 4, 16, XBLOCK=1, num_warps=2, num_stages=1)
        buf5 = empty_strided_cuda((4,), (1,), torch.float32)
        buf6 = empty_strided_cuda((4,), (1,), torch.float32)
        triton_per_fused_add_mul_pow_sum_4[grid(4)](buf1, arg1_1, buf5,
            buf6, 4, 16, XBLOCK=1, num_warps=2, num_stages=1)
        buf8 = empty_strided_cuda((4,), (1,), torch.float32)
        buf9 = empty_strided_cuda((4,), (1,), torch.float32)
        triton_per_fused_add_mul_pow_sum_5[grid(4)](buf1, arg1_1, buf8,
            buf9, 4, 16, XBLOCK=1, num_warps=2, num_stages=1)
        del arg1_1
        del buf1
        buf10 = empty_strided_cuda((), (), torch.float32)
        buf14 = buf10
        del buf10
        triton_per_fused_add_div_rsub_sum_6[grid(1)](buf14, buf2, buf3,
            buf5, buf6, buf8, buf9, buf11, buf12, 1, 4, XBLOCK=1, num_warps
            =2, num_stages=1)
        del buf11
        del buf12
        del buf2
        del buf3
        del buf5
        del buf6
        del buf8
        del buf9
    return buf14,


class BinaryDiceLoss(nn.Module):
    """Dice loss of binary class
    Args:
        smooth: A float number to smooth loss, and avoid NaN error, default: 1
        p: Denominator value: \\sum{x^p} + \\sum{y^p}, default: 2
        predict: A tensor of shape [N, *]
        target: A tensor of shape same with predict
        reduction: Reduction method to apply, return mean over batch if 'mean',
            return sum if 'sum', return a tensor of shape [N,] if 'none'
    Returns:
        Loss tensor according to arg reduction
    Raise:
        Exception if unexpected reduction
    """

    def __init__(self, smooth=1, p=2, reduction='sum'):
        super(BinaryDiceLoss, self).__init__()
        self.smooth = smooth
        self.p = p
        self.reduction = reduction

    def forward(self, predict, target):
        assert predict.shape[0] == target.shape[0
            ], "predict & target batch size don't match"
        predict = predict.contiguous().view(predict.shape[0], -1)
        target = target.contiguous().view(target.shape[0], -1)
        num = torch.sum(torch.mul(predict, target), dim=1) + self.smooth
        den = torch.sum(predict.pow(self.p) + target.pow(self.p), dim=1
            ) + self.smooth
        loss = 1 - num / den
        if self.reduction == 'mean':
            return loss.mean()
        elif self.reduction == 'sum':
            return loss.sum()
        elif self.reduction == 'none':
            return loss
        else:
            raise Exception('Unexpected reduction {}'.format(self.reduction))


class DiceLossNew(nn.Module):
    """Dice loss, need one hot encode input
    Args:
        weight: An array of shape [num_classes,]
        ignore_index: class index to ignore
        predict: A tensor of shape [N, C, *]
        target: A tensor of same shape with predict
        other args pass to BinaryDiceLoss
    Return:
        same as BinaryDiceLoss
    """

    def __init__(self, weight=None, ignore_index=None, **kwargs):
        super(DiceLossNew, self).__init__()
        self.kwargs = kwargs
        self.weight = weight
        self.ignore_index = ignore_index

    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: 0.104845