import torch
from torch._inductor.select_algorithm import extern_kernels
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._inductor.runtime.triton_helpers import math as tl_math
from torch import 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_poi_fused__softmax_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 16
    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_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), 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 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + x2, tmp9, xmask)


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


@triton.jit
def triton_per_fused__softmax_cumsum_1(in_ptr0, out_ptr0, out_ptr1, xnumel,
    rnumel, XBLOCK: tl.constexpr):
    xnumel = 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, :]
    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 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tmp9 = tmp8.to(tl.float32)
    tmp10 = tl.broadcast_to(tmp9, [XBLOCK, RBLOCK])
    tmp11, = tl.associative_scan((tmp10,), 1, _triton_helper_fn_add0)
    tl.store(out_ptr0 + (r1 + 4 * x0), tmp8, xmask)
    tl.store(out_ptr1 + (r1 + 5 * x0), tmp11, xmask)


@triton.jit
def triton_poi_fused_log_mul_neg_sum_2(in_ptr0, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 4
    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_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp1 = tl_math.log(tmp0)
    tmp2 = tmp0 * tmp1
    tmp4 = tl_math.log(tmp3)
    tmp5 = tmp3 * tmp4
    tmp6 = tmp2 + tmp5
    tmp8 = tl_math.log(tmp7)
    tmp9 = tmp7 * tmp8
    tmp10 = tmp6 + tmp9
    tmp12 = tl_math.log(tmp11)
    tmp13 = tmp11 * tmp12
    tmp14 = tmp10 + tmp13
    tmp15 = -tmp14
    tl.store(out_ptr0 + x0, tmp15, xmask)


@triton.jit
def triton_poi_fused_zeros_3(out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = 0.0
    tl.store(out_ptr0 + 5 * x0, tmp0, xmask)


@triton.jit
def triton_poi_fused_add_div_4(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 5 * x1), xmask)
    tmp1 = tl.load(in_ptr0 + (1 + x0 + 5 * x1), xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = 0.5
    tmp4 = tmp2 * tmp3
    tl.store(out_ptr0 + x2, tmp4, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_2, primals_3, reinterpret_tensor(
            primals_1, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf0)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__softmax_0[grid(16)](buf0, buf1, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf2 = buf0
        del buf0
        buf6 = empty_strided_cuda((4, 5), (5, 1), torch.float32)
        buf4 = reinterpret_tensor(buf6, (4, 4), (5, 1), 1)
        triton_per_fused__softmax_cumsum_1[grid(4)](buf1, buf2, buf4, 4, 4,
            XBLOCK=1, num_warps=2, num_stages=1)
        buf3 = empty_strided_cuda((4,), (1,), torch.float32)
        triton_poi_fused_log_mul_neg_sum_2[grid(4)](buf2, buf3, 4, XBLOCK=4,
            num_warps=1, num_stages=1)
        buf5 = reinterpret_tensor(buf6, (4, 1), (5, 1), 0)
        triton_poi_fused_zeros_3[grid(4)](buf5, 4, XBLOCK=4, num_warps=1,
            num_stages=1)
        buf7 = buf1
        del buf1
        triton_poi_fused_add_div_4[grid(16)](buf6, buf7, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
    return buf6, buf7, buf3, primals_3, buf2


class FractionProposalModelNew(nn.Module):

    def __init__(self, in_dim, out_dim):
        super(FractionProposalModelNew, self).__init__()
        self.in_dim = in_dim
        self.out_dim = out_dim
        self.layer = nn.Linear(self.in_dim, self.out_dim)
        nn.init.xavier_uniform_(self.layer.weight)
        self.layer.bias.data.zero_()

    def forward(self, input_0):
        primals_1 = self.layer.weight
        primals_2 = self.layer.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0], output[1], output[2]


Measured latency_ms: 0.073523