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.autograd import Function
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_helper_fn_add0(arg0_0, arg1_0):
    tmp0 = arg0_0 + arg1_0
    return tmp0


@triton.jit
def triton_per_fused_cumsum_max_sort_sub_0(in_ptr0, out_ptr0, out_ptr1,
    out_ptr2, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    RBLOCK: tl.constexpr = 4
    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 + 4 * x0), xmask, other=0.0)
    tmp1 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x0), 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 = r1
    tmp10 = tmp9.to(tl.int16)
    tmp11 = tl.broadcast_to(tmp8, [XBLOCK, RBLOCK])
    tmp12 = tl.broadcast_to(tmp10, [XBLOCK, RBLOCK])
    tmp13, _tmp14 = triton_helpers.sort_with_index(tmp11, tmp12, None, 1,
        stable=False, descending=True)
    tmp15 = tmp13.to(tl.float32)
    tmp16 = tl.broadcast_to(tmp15, [XBLOCK, RBLOCK])
    tmp17, = tl.associative_scan((tmp16,), 1, _triton_helper_fn_add0)
    tl.store(out_ptr0 + (r1 + 4 * x0), tmp8, xmask)
    tl.store(out_ptr1 + (r1 + 4 * x0), tmp13, xmask)
    tl.store(out_ptr2 + (r1 + 4 * x0), tmp17, xmask)


@triton.jit
def triton_poi_fused_gt_mul_sub_sum_1(in_ptr0, in_ptr1, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp15 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp18 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp23 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp26 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp1 = 1.0
    tmp2 = tmp1 * tmp0
    tmp4 = tmp3 - tmp1
    tmp5 = tmp2 > tmp4
    tmp6 = tmp5.to(tl.int64)
    tmp8 = 2.0
    tmp9 = tmp8 * tmp7
    tmp11 = tmp10 - tmp1
    tmp12 = tmp9 > tmp11
    tmp13 = tmp12.to(tl.int64)
    tmp14 = tmp6 + tmp13
    tmp16 = 3.0
    tmp17 = tmp16 * tmp15
    tmp19 = tmp18 - tmp1
    tmp20 = tmp17 > tmp19
    tmp21 = tmp20.to(tl.int64)
    tmp22 = tmp14 + tmp21
    tmp24 = 4.0
    tmp25 = tmp24 * tmp23
    tmp27 = tmp26 - tmp1
    tmp28 = tmp25 > tmp27
    tmp29 = tmp28.to(tl.int64)
    tmp30 = tmp22 + tmp29
    tl.store(out_ptr0 + x0, tmp30, xmask)


@triton.jit
def triton_poi_fused_clamp_div_gather_sub_2(in_ptr0, in_ptr1, in_ptr2,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.full([1], 1, tl.int64)
    tmp3 = tmp1 - tmp2
    tmp4 = tl.full([XBLOCK], 4, tl.int32)
    tmp5 = tmp3 + tmp4
    tmp6 = tmp3 < 0
    tmp7 = tl.where(tmp6, tmp5, tmp3)
    tl.device_assert((0 <= tmp7) & (tmp7 < 4) | ~xmask,
        'index out of bounds: 0 <= tmp7 < 4')
    tmp9 = tl.load(in_ptr2 + (tmp7 + 4 * x1), xmask, eviction_policy=
        'evict_last')
    tmp10 = 1.0
    tmp11 = tmp9 - tmp10
    tmp12 = tmp1.to(tl.float32)
    tmp13 = tmp11 / tmp12
    tmp14 = tmp0 - tmp13
    tmp15 = 0.0
    tmp16 = triton_helpers.maximum(tmp14, tmp15)
    tl.store(out_ptr0 + x2, tmp16, xmask)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_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)
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_per_fused_cumsum_max_sort_sub_0[grid(64)](arg0_1, buf0, buf1,
            buf3, 64, 4, XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
        buf4 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.int64)
        triton_poi_fused_gt_mul_sub_sum_1[grid(64)](buf1, buf3, buf4, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        buf5 = buf1
        del buf1
        triton_poi_fused_clamp_div_gather_sub_2[grid(256)](buf0, buf4, buf3,
            buf5, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del buf0
        del buf3
        del buf4
    return buf5,


def _make_ix_like(X, dim):
    d = X.size(dim)
    rho = torch.arange(1, d + 1, device=X.device, dtype=X.dtype)
    view = [1] * X.dim()
    view[0] = -1
    return rho.view(view).transpose(0, dim)


def _roll_last(X, dim):
    if dim == -1:
        return X
    elif dim < 0:
        dim = X.dim() - dim
    perm = [i for i in range(X.dim()) if i != dim] + [dim]
    return X.permute(perm)


def _sparsemax_threshold_and_support(X, dim=-1, k=None):
    """Core computation for sparsemax: optimal threshold and support size.
    Parameters
    ----------
    X : torch.Tensor
        The input tensor to compute thresholds over.
    dim : int
        The dimension along which to apply sparsemax.
    k : int or None
        number of largest elements to partial-sort over. For optimal
        performance, should be slightly bigger than the expected number of
        nonzeros in the solution. If the solution is more than k-sparse,
        this function is recursively called with a 2*k schedule.
        If `None`, full sorting is performed from the beginning.
    Returns
    -------
    tau : torch.Tensor like `X`, with all but the `dim` dimension intact
        the threshold value for each vector
    support_size : torch LongTensor, shape like `tau`
        the number of nonzeros in each vector.
    """
    if k is None or k >= X.shape[dim]:
        topk, _ = torch.sort(X, dim=dim, descending=True)
    else:
        topk, _ = torch.topk(X, k=k, dim=dim)
    topk_cumsum = topk.cumsum(dim) - 1
    rhos = _make_ix_like(topk, dim)
    support = rhos * topk > topk_cumsum
    support_size = support.sum(dim=dim).unsqueeze(dim)
    tau = topk_cumsum.gather(dim, support_size - 1)
    tau /= support_size
    if k is not None and k < X.shape[dim]:
        unsolved = (support_size == k).squeeze(dim)
        if torch.any(unsolved):
            in_ = _roll_last(X, dim)[unsolved]
            tau_, ss_ = _sparsemax_threshold_and_support(in_, dim=-1, k=2 * k)
            _roll_last(tau, dim)[unsolved] = tau_
            _roll_last(support_size, dim)[unsolved] = ss_
    return tau, support_size


def sparsemax(X, dim=-1, k=None):
    """sparsemax: normalizing sparse transform (a la softmax).
    Solves the projection:
        min_p ||x - p||_2   s.t.    p >= 0, sum(p) == 1.
    Parameters
    ----------
    X : torch.Tensor
        The input tensor.
    dim : int
        The dimension along which to apply sparsemax.
    k : int or None
        number of largest elements to partial-sort over. For optimal
        performance, should be slightly bigger than the expected number of
        nonzeros in the solution. If the solution is more than k-sparse,
        this function is recursively called with a 2*k schedule.
        If `None`, full sorting is performed from the beginning.
    Returns
    -------
    P : torch tensor, same shape as X
        The projection result, such that P.sum(dim=dim) == 1 elementwise.
    """
    return SparsemaxFunction.apply(X, dim, k)


class SparsemaxFunction(Function):

    @classmethod
    def forward(cls, ctx, X, dim=-1, k=None):
        ctx.dim = dim
        max_val, _ = X.max(dim=dim, keepdim=True)
        X = X - max_val
        tau, supp_size = _sparsemax_threshold_and_support(X, dim=dim, k=k)
        output = torch.clamp(X - tau, min=0)
        ctx.save_for_backward(supp_size, output)
        return output

    @classmethod
    def backward(cls, ctx, grad_output):
        supp_size, output = ctx.saved_tensors
        dim = ctx.dim
        grad_input = grad_output.clone()
        grad_input[output == 0] = 0
        v_hat = grad_input.sum(dim=dim) / supp_size.squeeze()
        v_hat = v_hat.unsqueeze(dim)
        grad_input = torch.where(output != 0, grad_input - v_hat, grad_input)
        return grad_input, None, None


class SparsemaxNew(nn.Module):

    def __init__(self, dim=-1, k=None):
        """sparsemax: normalizing sparse transform (a la softmax).
        Solves the projection:
            min_p ||x - p||_2   s.t.    p >= 0, sum(p) == 1.
        Parameters
        ----------
        dim : int
            The dimension along which to apply sparsemax.
        k : int or None
            number of largest elements to partial-sort over. For optimal
            performance, should be slightly bigger than the expected number of
            nonzeros in the solution. If the solution is more than k-sparse,
            this function is recursively called with a 2*k schedule.
            If `None`, full sorting is performed from the beginning.
        """
        self.dim = dim
        self.k = k
        super(SparsemaxNew, self).__init__()

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


Measured latency_ms: 0.04298