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
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_poi_fused_clamp_ge_le_logical_and_0(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = -1.0
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp3 = 1.0
    tmp4 = triton_helpers.minimum(tmp2, tmp3)
    tmp5 = tmp0 >= tmp1
    tmp6 = tmp0 <= tmp3
    tmp7 = tmp5 & tmp6
    tl.store(out_ptr0 + x0, tmp4, xmask)
    tl.store(out_ptr1 + x0, tmp7, xmask)


@triton.jit
def triton_poi_fused__log_softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 16
    x2 = xindex // 64
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), 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
    tl.store(out_ptr0 + x3, tmp8, xmask)


@triton.jit
def triton_poi_fused__log_softmax_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 16
    x2 = xindex // 64
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp9 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tl_math.exp(tmp1)
    tmp4 = tl_math.exp(tmp3)
    tmp5 = tmp2 + tmp4
    tmp7 = tl_math.exp(tmp6)
    tmp8 = tmp5 + tmp7
    tmp10 = tl_math.exp(tmp9)
    tmp11 = tmp8 + tmp10
    tmp12 = tl_math.log(tmp11)
    tmp13 = tmp0 - tmp12
    tl.store(out_ptr0 + x3, tmp13, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (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((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf0)
        del primals_2
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_clamp_ge_le_logical_and_0[grid(256)](buf0, buf1,
            buf5, 256, XBLOCK=128, num_warps=4, num_stages=1)
        buf2 = buf0
        del buf0
        extern_kernels.mm(reinterpret_tensor(primals_1, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_3, (4, 4), (1, 4), 0), out=buf2)
        del primals_3
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__log_softmax_1[grid(256)](buf2, buf3, 256, XBLOCK=
            256, num_warps=4, num_stages=1)
        buf4 = reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf2
        triton_poi_fused__log_softmax_2[grid(256)](buf3, buf4, 256, XBLOCK=
            128, num_warps=4, num_stages=1)
        del buf3
    return buf4, buf1, reinterpret_tensor(primals_1, (64, 4), (4, 1), 0
        ), buf4, buf5


def create_representation(inputdim, layers, activation):
    """Helper function to generate the representation function for DSM.

  Deep Survival Machines learns a representation (\\ Phi(X) \\) for the input
  data. This representation is parameterized using a Non Linear Multilayer
  Perceptron (`torch.nn.Module`). This is a helper function designed to
  instantiate the representation for Deep Survival Machines.

  .. warning::
    Not designed to be used directly.

  Parameters
  ----------
  inputdim: int
      Dimensionality of the input features.
  layers: list
      A list consisting of the number of neurons in each hidden layer.
  activation: str
      Choice of activation function: One of 'ReLU6', 'ReLU' or 'SeLU'.

  Returns
  ----------
  an MLP with torch.nn.Module with the specfied structure.

  """
    if activation == 'ReLU6':
        act = nn.ReLU6()
    elif activation == 'ReLU':
        act = nn.ReLU()
    elif activation == 'SeLU':
        act = nn.SELU()
    modules = []
    prevdim = inputdim
    for hidden in layers:
        modules.append(nn.Linear(prevdim, hidden, bias=False))
        modules.append(act)
        prevdim = hidden
    return nn.Sequential(*modules)


class DeepCoxMixturesTorchNew(nn.Module):
    """PyTorch model definition of the Deep Cox Mixture Survival Model.

  The Cox Mixture involves the assumption that the survival function
  of the individual to be a mixture of K Cox Models. Conditioned on each
  subgroup Z=k; the PH assumptions are assumed to hold and the baseline
  hazard rates is determined non-parametrically using an spline-interpolated
  Breslow's estimator.
  """

    def _init_dcm_layers(self, lastdim):
        self.gate = torch.nn.Linear(lastdim, self.k, bias=False)
        self.expert = torch.nn.Linear(lastdim, self.k, bias=False)

    def __init__(self, inputdim, k, gamma=1, use_activation=False, layers=
        None, optimizer='Adam'):
        super(DeepCoxMixturesTorchNew, self).__init__()
        if not isinstance(k, int):
            raise ValueError(f'k must be int, but supplied k is {type(k)}')
        self.k = k
        self.optimizer = optimizer
        if layers is None:
            layers = []
        self.layers = layers
        if len(layers) == 0:
            lastdim = inputdim
        else:
            lastdim = layers[-1]
        self._init_dcm_layers(lastdim)
        self.embedding = create_representation(inputdim, layers, 'ReLU6')
        self.gamma = gamma
        self.use_activation = use_activation

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


Measured latency_ms: 0.054892