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 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_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_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-06
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = tmp0 / tmp5
    tmp7 = -2.0
    tmp8 = tmp6 * tmp7
    tmp9 = tl.broadcast_to(tmp8, [XBLOCK, RBLOCK])
    tmp11 = tl.sum(tmp9, 1)[:, None]
    tl.store(out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp11, 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_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 assert_(condition, message='', exception_type=AssertionError):
    """Like assert, but with arbitrary exception types."""
    if not condition:
        raise exception_type(message)


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.
    """
    assert_(input_.dim() >= 2,
        'Tensor or variable must be atleast 2D. Got one of dim {}.'.format(
        input_.dim()), ShapeError)
    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


class ShapeError(ValueError):
    pass


class SorensenDiceLossNew(nn.Module):
    """
    Computes a loss scalar, which when minimized maximizes the Sorensen-Dice similarity
    between the input and the target. For both inputs and targets it must be the case that
    `input_or_target.size(1) = num_channels`.
    """

    def __init__(self, weight=None, channelwise=True, eps=1e-06):
        """
        Parameters
        ----------
        weight : torch.FloatTensor or torch.cuda.FloatTensor
            Class weights. Applies only if `channelwise = True`.
        channelwise : bool
            Whether to apply the loss channelwise and sum the results (True)
            or to apply it on all channels jointly (False).
        """
        super(SorensenDiceLossNew, self).__init__()
        self.register_buffer('weight', weight)
        self.channelwise = channelwise
        self.eps = 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: 0.015094