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
import torch.nn as nn
import torch.utils.data
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 = 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 + (16 * x0 + 64 * (r1 // 16) + r1 % 16), xmask,
        other=0.0)
    tmp1 = tl.load(in_ptr1 + (16 * x0 + 64 * (r1 // 16) + r1 % 16), 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 = tl.broadcast_to(tmp7, [XBLOCK, RBLOCK])
    tmp10 = tl.where(xmask, tmp8, 0)
    tmp11 = tl.sum(tmp10, 1)[:, None]
    tmp12 = tmp1 * tmp1
    tmp13 = tl.broadcast_to(tmp12, [XBLOCK, RBLOCK])
    tmp15 = tl.where(xmask, tmp13, 0)
    tmp16 = tl.sum(tmp15, 1)[:, None]
    tl.store(out_ptr0 + x0, tmp6, xmask)
    tl.store(out_ptr1 + x0, tmp11, xmask)
    tl.store(out_ptr2 + x0, tmp16, xmask)


@triton.jit
def triton_per_fused_add_clamp_div_mul_rsub_sum_1(in_ptr0, in_ptr1, in_ptr2,
    out_ptr0, 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)
    tmp1 = tl.load(in_ptr1 + r0, None)
    tmp2 = tl.load(in_ptr2 + r0, None)
    tmp3 = tmp1 + tmp2
    tmp4 = 1e-07
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = tmp0 / tmp5
    tmp7 = 2.0
    tmp8 = tmp6 * tmp7
    tmp9 = 1.0
    tmp10 = tmp9 - tmp8
    tmp11 = tl.broadcast_to(tmp10, [XBLOCK, RBLOCK])
    tmp13 = tl.sum(tmp11, 1)[:, None]
    tl.store(out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp13, 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,), (1,), torch.float32)
        buf1 = empty_strided_cuda((4,), (1,), torch.float32)
        buf2 = empty_strided_cuda((4,), (1,), torch.float32)
        get_raw_stream(0)
        triton_per_fused_mul_sum_0[grid(4)](arg0_1, arg1_1, buf0, buf1,
            buf2, 4, 64, XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
        buf3 = empty_strided_cuda((), (), torch.float32)
        triton_per_fused_add_clamp_div_mul_rsub_sum_1[grid(1)](buf0, buf1,
            buf2, buf3, 1, 4, XBLOCK=1, num_warps=2, num_stages=1)
        del buf0
        del buf1
        del buf2
    return buf3,


def flatten_samples(input_):
    """
    Flattens a tensor or a variable such that the channel axis is first and the sample axis
    is second. The shapes are transformed as follows:
        (N, C, H, W) --> (C, N * H * W)
        (N, C, D, H, W) --> (C, N * D * H * W)
        (N, C) --> (C, N)
    The input must be atleast 2d.
    """
    num_channels = input_.size(1)
    permute_axes = list(range(input_.dim()))
    permute_axes[0], permute_axes[1] = permute_axes[1], permute_axes[0]
    permuted = input_.permute(*permute_axes).contiguous()
    flattened = permuted.view(num_channels, -1)
    return flattened


def dice_score(input_, target, invert=False, channelwise=True, eps=1e-07):
    if channelwise:
        input_ = flatten_samples(input_)
        target = flatten_samples(target)
        numerator = (input_ * target).sum(-1)
        denominator = (input_ * input_).sum(-1) + (target * target).sum(-1)
        channelwise_score = 2 * (numerator / denominator.clamp(min=eps))
        if invert:
            channelwise_score = 1.0 - channelwise_score
        score = channelwise_score.sum()
    else:
        numerator = (input_ * target).sum()
        denominator = (input_ * input_).sum() + (target * target).sum()
        score = 2.0 * (numerator / denominator.clamp(min=eps))
        if invert:
            score = 1.0 - score
    return score


class DiceLossNew(nn.Module):

    def __init__(self, channelwise=True, eps=1e-07):
        super().__init__()
        self.channelwise = channelwise
        self.eps = eps
        self.init_kwargs = {'channelwise': channelwise, 'eps': self.eps}

    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: 0.032754