# AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream

aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()


# kernel path: runs/run_shard_4/inductor_cache/nk/cnkmgeqruamtgccvbn4zkgty33cqveg7s4ow6q4qrojcnzzpb3wy.py
# Topologically Sorted Source Nodes: [max_1, X, sort, cumsum], Original ATen: [aten.max, aten.sub, aten.sort, aten.cumsum]
# Source node to ATen node mapping:
#   X => sub
#   cumsum => cumsum
#   max_1 => max_1
#   sort => sort
# Graph fragment:
#   %max_1 : [num_users=1] = call_function[target=torch.ops.aten.max.dim](args = (%arg0_1, -1, True), kwargs = {})
#   %sub : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg0_1, %getitem), kwargs = {})
#   %sort : [num_users=1] = call_function[target=torch.ops.aten.sort.default](args = (%sub, -1, True), kwargs = {})
#   %cumsum : [num_users=1] = call_function[target=torch.ops.aten.cumsum.default](args = (%getitem_2, -1), kwargs = {})
triton_per_fused_cumsum_max_sort_sub_0 = async_compile.triton('triton_per_fused_cumsum_max_sort_sub_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton.jit
def _triton_helper_fn_add0(arg0_0, arg1_0):
    tmp0 = arg0_0 + arg1_0
    return tmp0

@triton_heuristics.persistent_reduction(
    size_hints=[64, 4],
    reduction_hint=ReductionHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_cumsum_max_sort_sub_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@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
    rnumel = 4
    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, :]
    roffset = 0
    rmask = 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)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/uc/cuc2f2cgz6j7wqohouz3uu6ir7263uhsfcodyhv3lka5vxtu4ewc.py
# Topologically Sorted Source Nodes: [mul, topk_cumsum, support, sum_1], Original ATen: [aten.mul, aten.sub, aten.gt, aten.sum]
# Source node to ATen node mapping:
#   mul => mul_1
#   sum_1 => sum_1
#   support => gt
#   topk_cumsum => sub_1
# Graph fragment:
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%permute, %getitem_2), kwargs = {})
#   %sub_1 : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%cumsum, 1), kwargs = {})
#   %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Tensor](args = (%mul_1, %sub_1), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%gt, [-1]), kwargs = {})
triton_poi_fused_gt_mul_sub_sum_1 = async_compile.triton('triton_poi_fused_gt_mul_sub_sum_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i64', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_gt_mul_sub_sum_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@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)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/v4/cv4frpxvm4rhejbwxhericho6gutfmihwrgomlmqytuxrptdu7cn.py
# Topologically Sorted Source Nodes: [topk_cumsum, sub_2, tau, tau_1, sub_3, output], Original ATen: [aten.sub, aten.gather, aten.div, aten.clamp]
# Source node to ATen node mapping:
#   output => clamp_min
#   sub_2 => sub_2
#   sub_3 => sub_3
#   tau => gather
#   tau_1 => div
#   topk_cumsum => sub_1
# Graph fragment:
#   %sub_1 : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%cumsum, 1), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%unsqueeze, 1), kwargs = {})
#   %gather : [num_users=1] = call_function[target=torch.ops.aten.gather.default](args = (%sub_1, -1, %sub_2), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%gather, %unsqueeze), kwargs = {})
#   %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sub, %div), kwargs = {})
#   %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%sub_3, 0), kwargs = {})
triton_poi_fused_clamp_div_gather_sub_2 = async_compile.triton('triton_poi_fused_clamp_div_gather_sub_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*i64', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clamp_div_gather_sub_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@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)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

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)
        # Topologically Sorted Source Nodes: [max_1, X, sort, cumsum], Original ATen: [aten.max, aten.sub, aten.sort, aten.cumsum]
        stream0 = get_raw_stream(0)
        triton_per_fused_cumsum_max_sort_sub_0.run(arg0_1, buf0, buf1, buf3, 64, 4, grid=grid(64), stream=stream0)
        del arg0_1
        buf4 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.int64)
        # Topologically Sorted Source Nodes: [mul, topk_cumsum, support, sum_1], Original ATen: [aten.mul, aten.sub, aten.gt, aten.sum]
        triton_poi_fused_gt_mul_sub_sum_1.run(buf1, buf3, buf4, 64, grid=grid(64), stream=stream0)
        buf5 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [topk_cumsum, sub_2, tau, tau_1, sub_3, output], Original ATen: [aten.sub, aten.gather, aten.div, aten.clamp]
        triton_poi_fused_clamp_div_gather_sub_2.run(buf0, buf4, buf3, buf5, 256, grid=grid(256), stream=stream0)
        del buf0
        del buf3
        del buf4
    return (buf5, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1])
    return print_performance(fn, times=times, repeat=repeat)


if __name__ == "__main__":
    from torch._inductor.wrapper_benchmark import compiled_module_main
    compiled_module_main('None', benchmark_compiled_module)

from torch.autograd import Function
import torch
import torch.nn as nn


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 Sparsemax(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(Sparsemax, self).__init__()

    def forward(self, X):
        return sparsemax(X, dim=self.dim, k=self.k)


def get_inputs():
    return [torch.rand([4, 4, 4, 4])]


def get_init_inputs():
    return [[], {}]

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
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_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=32, 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=256, 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]
