# 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/je/cjetqbcnd7bc32n35tugus5ijwdxgd2rgnx55splotkzsvjkv2mu.py
# Topologically Sorted Source Nodes: [input_2, sort, cumulative_sum_zs], Original ATen: [aten.sub, aten.sort, aten.cumsum]
# Source node to ATen node mapping:
#   cumulative_sum_zs => cumsum
#   input_2 => sub
#   sort => sort
# Graph fragment:
#   %sub : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view, %expand), 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_sort_sub_0 = async_compile.triton('triton_per_fused_cumsum_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_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_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/rm/crmbpyujmwscp5mkcahmeli47zzzh5qct73vmu2ldmazrzxmqpsx.py
# Topologically Sorted Source Nodes: [mul, bound, gt, is_gt, mul_1, max_2, zs_sparse, sum_1], Original ATen: [aten.mul, aten.add, aten.gt, aten._to_copy, aten.max, aten.sum]
# Source node to ATen node mapping:
#   bound => add
#   gt => gt
#   is_gt => convert_element_type
#   max_2 => max_2
#   mul => mul
#   mul_1 => mul_1
#   sum_1 => sum_1
#   zs_sparse => mul_2
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%expand_1, %getitem_2), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, 1), kwargs = {})
#   %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Tensor](args = (%add, %cumsum), kwargs = {})
#   %convert_element_type : [num_users=2] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%gt, torch.float32), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convert_element_type, %expand_1), kwargs = {})
#   %max_2 : [num_users=1] = call_function[target=torch.ops.aten.max.dim](args = (%mul_1, 1, True), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convert_element_type, %getitem_2), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul_2, [1], True), kwargs = {})
triton_poi_fused__to_copy_add_gt_max_mul_sum_1 = async_compile.triton('triton_poi_fused__to_copy_add_gt_max_mul_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: '*fp32', 3: '*fp32', 4: '*fp32', 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, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_add_gt_max_mul_sum_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 12, '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__to_copy_add_gt_max_mul_sum_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, 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 + (0))
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK])
    tmp2 = tl.load(in_ptr1 + (4*x0), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr2 + (4*x0), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr0 + (1))
    tmp11 = tl.broadcast_to(tmp10, [XBLOCK])
    tmp12 = tl.load(in_ptr1 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr2 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp20 = tl.load(in_ptr0 + (2))
    tmp21 = tl.broadcast_to(tmp20, [XBLOCK])
    tmp22 = tl.load(in_ptr1 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr2 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp30 = tl.load(in_ptr0 + (3))
    tmp31 = tl.broadcast_to(tmp30, [XBLOCK])
    tmp32 = tl.load(in_ptr1 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp35 = tl.load(in_ptr2 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 * tmp2
    tmp4 = 1.0
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 > tmp6
    tmp8 = tmp7.to(tl.float32)
    tmp9 = tmp8 * tmp1
    tmp13 = tmp11 * tmp12
    tmp14 = tmp13 + tmp4
    tmp16 = tmp14 > tmp15
    tmp17 = tmp16.to(tl.float32)
    tmp18 = tmp17 * tmp11
    tmp19 = triton_helpers.maximum(tmp9, tmp18)
    tmp23 = tmp21 * tmp22
    tmp24 = tmp23 + tmp4
    tmp26 = tmp24 > tmp25
    tmp27 = tmp26.to(tl.float32)
    tmp28 = tmp27 * tmp21
    tmp29 = triton_helpers.maximum(tmp19, tmp28)
    tmp33 = tmp31 * tmp32
    tmp34 = tmp33 + tmp4
    tmp36 = tmp34 > tmp35
    tmp37 = tmp36.to(tl.float32)
    tmp38 = tmp37 * tmp31
    tmp39 = triton_helpers.maximum(tmp29, tmp38)
    tmp40 = tmp8 * tmp2
    tmp41 = tmp17 * tmp12
    tmp42 = tmp40 + tmp41
    tmp43 = tmp27 * tmp22
    tmp44 = tmp42 + tmp43
    tmp45 = tmp37 * tmp32
    tmp46 = tmp44 + tmp45
    tl.store(out_ptr0 + (x0), tmp39, xmask)
    tl.store(out_ptr1 + (x0), tmp46, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/xz/cxze32rtorhwy2jkafxfvxxeyi2jg24j22ihfkfwtfd22dqgy2go.py
# Topologically Sorted Source Nodes: [zeros_like, sub_2, max_3], Original ATen: [aten.zeros_like, aten.sub, aten.maximum]
# Source node to ATen node mapping:
#   max_3 => maximum
#   sub_2 => sub_2
#   zeros_like => full_default
# Graph fragment:
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([64, 4], 0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sub, %expand_2), kwargs = {})
#   %maximum : [num_users=2] = call_function[target=torch.ops.aten.maximum.default](args = (%full_default, %sub_2), kwargs = {})
triton_poi_fused_maximum_sub_zeros_like_2 = async_compile.triton('triton_poi_fused_maximum_sub_zeros_like_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: '*fp32', 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_maximum_sub_zeros_like_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, '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_maximum_sub_zeros_like_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')
    tmp4 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = 1.0
    tmp3 = tmp1 - tmp2
    tmp5 = tmp3 / tmp4
    tmp6 = tmp0 - tmp5
    tmp7 = 0.0
    tmp8 = triton_helpers.maximum(tmp7, tmp6)
    tl.store(out_ptr0 + (x2), tmp8, 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((64, 4), (4, 1), torch.float32)
        buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        buf5 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [input_2, sort, cumulative_sum_zs], Original ATen: [aten.sub, aten.sort, aten.cumsum]
        stream0 = get_raw_stream(0)
        triton_per_fused_cumsum_sort_sub_0.run(arg0_1, buf0, buf1, buf5, 64, 4, grid=grid(64), stream=stream0)
        del arg0_1
        # Topologically Sorted Source Nodes: [range_1], Original ATen: [aten.range]
        buf3 = torch.ops.aten.range.step(1, 4, dtype=torch.float32, layout=torch.strided, device=device(type='cuda', index=0))
        buf4 = buf3
        del buf3
        buf6 = empty_strided_cuda((64, 1), (1, 64), torch.float32)
        buf7 = empty_strided_cuda((64, 1), (1, 64), torch.float32)
        # Topologically Sorted Source Nodes: [mul, bound, gt, is_gt, mul_1, max_2, zs_sparse, sum_1], Original ATen: [aten.mul, aten.add, aten.gt, aten._to_copy, aten.max, aten.sum]
        triton_poi_fused__to_copy_add_gt_max_mul_sum_1.run(buf4, buf1, buf5, buf6, buf7, 64, grid=grid(64), stream=stream0)
        del buf1
        del buf4
        buf8 = buf5; del buf5  # reuse
        # Topologically Sorted Source Nodes: [zeros_like, sub_2, max_3], Original ATen: [aten.zeros_like, aten.sub, aten.maximum]
        triton_poi_fused_maximum_sub_zeros_like_2.run(buf0, buf7, buf6, buf8, 256, grid=grid(256), stream=stream0)
        del buf0
        del buf6
        del buf7
    return (reinterpret_tensor(buf8, (4, 4, 4, 4), (64, 16, 4, 1), 0), buf8, )


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)

import torch
import torch.nn as nn


class Sparsemax(nn.Module):
    """Sparsemax function."""

    def __init__(self, dim=None):
        """Initialize sparsemax activation
        
        Args:
            dim (int, optional): The dimension over which to apply the sparsemax function.
        """
        super(Sparsemax, self).__init__()
        self.dim = -1 if dim is None else dim

    def forward(self, input):
        """Forward function.
        Args:
            input (torch.Tensor): Input tensor. First dimension should be the batch size
        Returns:
            torch.Tensor: [batch_size x number_of_logits] Output tensor
        """
        original_size = input.size()
        input = input.view(-1, input.size(self.dim))
        dim = 1
        number_of_logits = input.size(dim)
        input = input - torch.max(input, dim=dim, keepdim=True)[0].expand_as(
            input)
        zs = torch.sort(input=input, dim=dim, descending=True)[0]
        range = torch.range(start=1, end=number_of_logits, device=input.device
            ).view(1, -1)
        range = range.expand_as(zs)
        bound = 1 + range * zs
        cumulative_sum_zs = torch.cumsum(zs, dim)
        is_gt = torch.gt(bound, cumulative_sum_zs).type(input.type())
        k = torch.max(is_gt * range, dim, keepdim=True)[0]
        zs_sparse = is_gt * zs
        taus = (torch.sum(zs_sparse, dim, keepdim=True) - 1) / k
        taus = taus.expand_as(input)
        self.output = torch.max(torch.zeros_like(input), input - taus)
        output = self.output.view(original_size)
        return output

    def backward(self, grad_output):
        """Backward function."""
        dim = 1
        nonzeros = torch.ne(self.output, 0)
        sum = torch.sum(grad_output * nonzeros, dim=dim) / torch.sum(nonzeros,
            dim=dim)
        self.grad_input = nonzeros * (grad_output - sum.expand_as(grad_output))
        return self.grad_input


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


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

import torch
from torch import device
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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@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_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__to_copy_add_gt_max_mul_sum_1(in_ptr0, in_ptr1,
    in_ptr2, out_ptr0, out_ptr1, 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 + 0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK])
    tmp2 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr2 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr0 + 1)
    tmp11 = tl.broadcast_to(tmp10, [XBLOCK])
    tmp12 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp15 = tl.load(in_ptr2 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp20 = tl.load(in_ptr0 + 2)
    tmp21 = tl.broadcast_to(tmp20, [XBLOCK])
    tmp22 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp25 = tl.load(in_ptr2 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp30 = tl.load(in_ptr0 + 3)
    tmp31 = tl.broadcast_to(tmp30, [XBLOCK])
    tmp32 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp35 = tl.load(in_ptr2 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp3 = tmp1 * tmp2
    tmp4 = 1.0
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 > tmp6
    tmp8 = tmp7.to(tl.float32)
    tmp9 = tmp8 * tmp1
    tmp13 = tmp11 * tmp12
    tmp14 = tmp13 + tmp4
    tmp16 = tmp14 > tmp15
    tmp17 = tmp16.to(tl.float32)
    tmp18 = tmp17 * tmp11
    tmp19 = triton_helpers.maximum(tmp9, tmp18)
    tmp23 = tmp21 * tmp22
    tmp24 = tmp23 + tmp4
    tmp26 = tmp24 > tmp25
    tmp27 = tmp26.to(tl.float32)
    tmp28 = tmp27 * tmp21
    tmp29 = triton_helpers.maximum(tmp19, tmp28)
    tmp33 = tmp31 * tmp32
    tmp34 = tmp33 + tmp4
    tmp36 = tmp34 > tmp35
    tmp37 = tmp36.to(tl.float32)
    tmp38 = tmp37 * tmp31
    tmp39 = triton_helpers.maximum(tmp29, tmp38)
    tmp40 = tmp8 * tmp2
    tmp41 = tmp17 * tmp12
    tmp42 = tmp40 + tmp41
    tmp43 = tmp27 * tmp22
    tmp44 = tmp42 + tmp43
    tmp45 = tmp37 * tmp32
    tmp46 = tmp44 + tmp45
    tl.store(out_ptr0 + x0, tmp39, xmask)
    tl.store(out_ptr1 + x0, tmp46, xmask)


@triton.jit
def triton_poi_fused_maximum_sub_zeros_like_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')
    tmp4 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp2 = 1.0
    tmp3 = tmp1 - tmp2
    tmp5 = tmp3 / tmp4
    tmp6 = tmp0 - tmp5
    tmp7 = 0.0
    tmp8 = triton_helpers.maximum(tmp7, tmp6)
    tl.store(out_ptr0 + x2, tmp8, 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((64, 4), (4, 1), torch.float32)
        buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        buf5 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_per_fused_cumsum_sort_sub_0[grid(64)](arg0_1, buf0, buf1,
            buf5, 64, 4, XBLOCK=8, num_warps=2, num_stages=1)
        del arg0_1
        buf3 = torch.ops.aten.range.step(1, 4, dtype=torch.float32, layout=
            torch.strided, device=device(type='cuda', index=0))
        buf4 = buf3
        del buf3
        buf6 = empty_strided_cuda((64, 1), (1, 64), torch.float32)
        buf7 = empty_strided_cuda((64, 1), (1, 64), torch.float32)
        triton_poi_fused__to_copy_add_gt_max_mul_sum_1[grid(64)](buf4, buf1,
            buf5, buf6, buf7, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del buf1
        del buf4
        buf8 = buf5
        del buf5
        triton_poi_fused_maximum_sub_zeros_like_2[grid(256)](buf0, buf7,
            buf6, buf8, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del buf0
        del buf6
        del buf7
    return reinterpret_tensor(buf8, (4, 4, 4, 4), (64, 16, 4, 1), 0), buf8


class SparsemaxNew(nn.Module):
    """Sparsemax function."""

    def __init__(self, dim=None):
        """Initialize sparsemax activation
        
        Args:
            dim (int, optional): The dimension over which to apply the sparsemax function.
        """
        super(SparsemaxNew, self).__init__()
        self.dim = -1 if dim is None else dim

    def backward(self, grad_output):
        """Backward function."""
        dim = 1
        nonzeros = torch.ne(self.output, 0)
        sum = torch.sum(grad_output * nonzeros, dim=dim) / torch.sum(nonzeros,
            dim=dim)
        self.grad_input = nonzeros * (grad_output - sum.expand_as(grad_output))
        return self.grad_input

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