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
from torch import nn
import torch.nn.functional
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_per_fused__softmax_mul_sum_1(in_ptr0, in_ptr1, out_ptr1,
    out_ptr2, out_ptr3, 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)
    r2 = rindex
    x3 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + (r2 + 16 * x3), xmask, other=0.0)
    tmp1 = tl.load(in_ptr0 + (r2 + 64 * x1), xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp2 = tl.load(in_ptr0 + (16 + r2 + 64 * x1), xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp4 = tl.load(in_ptr0 + (32 + r2 + 64 * x1), xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tl.load(in_ptr0 + (48 + r2 + 64 * x1), xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp9 = tl.load(in_ptr1 + (r2 + 16 * x3), xmask, other=0.0)
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tmp10 = tmp8 * tmp9
    tmp11 = tl.broadcast_to(tmp10, [XBLOCK, RBLOCK])
    tmp13 = tl.where(xmask, tmp11, 0)
    tmp14 = tl.sum(tmp13, 1)[:, None]
    tmp15 = tl.broadcast_to(tmp8, [XBLOCK, RBLOCK])
    tmp17 = tl.where(xmask, tmp15, 0)
    tmp18 = tl.sum(tmp17, 1)[:, None]
    tmp19 = tl.broadcast_to(tmp9, [XBLOCK, RBLOCK])
    tmp21 = tl.where(xmask, tmp19, 0)
    tmp22 = tl.sum(tmp21, 1)[:, None]
    tl.store(out_ptr1 + x3, tmp14, xmask)
    tl.store(out_ptr2 + x3, tmp18, xmask)
    tl.store(out_ptr3 + x3, tmp22, xmask)


@triton.jit
def triton_per_fused_add_div_mean_mul_rsub_2(in_out_ptr0, in_ptr0, in_ptr1,
    in_ptr2, 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)
    tmp3 = tl.load(in_ptr1 + r0, None)
    tmp4 = tl.load(in_ptr2 + r0, None)
    tmp1 = 2.0
    tmp2 = tmp0 * tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = 1e-19
    tmp7 = tmp5 + tmp6
    tmp8 = tmp2 / tmp7
    tmp9 = tl.broadcast_to(tmp8, [XBLOCK, RBLOCK])
    tmp11 = tl.sum(tmp9, 1)[:, None]
    tmp12 = 16.0
    tmp13 = tmp11 / tmp12
    tmp14 = 1.0
    tmp15 = tmp14 - tmp13
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp15, 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=
            128, num_warps=4, num_stages=1)
        del arg0_1
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_per_fused__softmax_mul_sum_1[grid(16)](buf0, arg1_1, buf2,
            buf3, buf4, 16, 16, XBLOCK=8, num_warps=2, num_stages=1)
        del arg1_1
        del buf0
        buf5 = empty_strided_cuda((), (), torch.float32)
        buf6 = buf5
        del buf5
        triton_per_fused_add_div_mean_mul_rsub_2[grid(1)](buf6, buf2, buf3,
            buf4, 1, 16, XBLOCK=1, num_warps=2, num_stages=1)
        del buf2
        del buf3
        del buf4
    return buf6,


class DCNew(nn.Module):

    def __init__(self, nb_classes):
        super(DCNew, self).__init__()
        self.softmax = nn.Softmax(1)
        self.nb_classes = nb_classes

    @staticmethod
    def onehot(gt, shape):
        gt = gt.long()
        y_onehot = torch.zeros(shape)
        y_onehot = y_onehot
        y_onehot.scatter_(1, gt, 1)
        return y_onehot

    def reshape(self, output, target):
        output.shape[0]
        if not all([(i == j) for i, j in zip(output.shape, target.shape)]):
            target = self.onehot(target, output.shape)
        target = target.permute(0, 2, 3, 4, 1)
        output = output.permute(0, 2, 3, 4, 1)
        None
        return output, target

    def dice(self, output, target):
        output = self.softmax(output)
        if not all([(i == j) for i, j in zip(output.shape, target.shape)]):
            target = self.onehot(target, output.shape)
        sum_axis = list(range(2, len(target.shape)))
        s = 1e-19
        intersect = torch.sum(output * target, sum_axis)
        dice = 2 * intersect / (torch.sum(output, sum_axis) + torch.sum(
            target, sum_axis) + s)
        return 1.0 - dice.mean()

    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: 0.044657