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.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
import torch.nn.parallel
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_native_layer_norm_0(in_ptr0, 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 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tmp9 = tmp0 - tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = tmp1 - tmp8
    tmp12 = tmp11 * tmp11
    tmp13 = tmp10 + tmp12
    tmp14 = tmp3 - tmp8
    tmp15 = tmp14 * tmp14
    tmp16 = tmp13 + tmp15
    tmp17 = tmp5 - tmp8
    tmp18 = tmp17 * tmp17
    tmp19 = tmp16 + tmp18
    tmp20 = tmp19 / tmp7
    tmp21 = 1e-05
    tmp22 = tmp20 + tmp21
    tmp23 = libdevice.rsqrt(tmp22)
    tl.store(out_ptr0 + x0, tmp8, xmask)
    tl.store(out_ptr1 + x0, tmp23, xmask)


@triton.jit
def triton_poi_fused_native_layer_norm_softplus_sub_1(in_out_ptr0, in_ptr0,
    in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, 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
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + x0, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
    tmp17 = tl.load(in_ptr5 + 0)
    tmp18 = tl.broadcast_to(tmp17, [XBLOCK])
    tmp2 = tmp0 - tmp1
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 * tmp5
    tmp8 = tmp6 + tmp7
    tmp9 = 1.0
    tmp10 = tmp8 * tmp9
    tmp11 = 20.0
    tmp12 = tmp10 > tmp11
    tmp13 = tl_math.exp(tmp10)
    tmp14 = libdevice.log1p(tmp13)
    tmp15 = tmp14 * tmp9
    tmp16 = tl.where(tmp12, tmp8, tmp15)
    tmp19 = tmp16 - tmp18
    tl.store(in_out_ptr0 + x2, tmp19, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = 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, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4,), (1,))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64, 
            4), (4, 1), 0), 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), (16, 4, 1, 64), torch.float32)
        buf2 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_native_layer_norm_0[grid(64)](buf0, buf1, buf2, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf4 = buf3
        del buf3
        triton_poi_fused_native_layer_norm_softplus_sub_1[grid(256)](buf4,
            buf0, buf1, buf2, primals_4, primals_5, primals_6, 256, XBLOCK=
            128, num_warps=4, num_stages=1)
        del buf1
        del buf2
        del primals_6
    return buf4, primals_4, primals_5, reinterpret_tensor(primals_3, (64, 4
        ), (4, 1), 0), buf0


class Shifted_softplus(nn.Module):
    """
  Performs a Shifter softplus loss, which modifies with a value of log(2)
  """

    def __init__(self):
        super(Shifted_softplus, self).__init__()
        self.act = nn.Softplus()
        self.shift = nn.Parameter(torch.tensor([0.6931]), False)

    def forward(self, X):
        """
    Applies the Activation function

    Parameters
    ----------
    node_feats: torch.Tensor
        The node features.

    Returns
    -------
    node_feats: torch.Tensor
        The updated node features.

    """
        node_feats = self.act(X) - self.shift
        return node_feats


class Atom_Wise_ConvolutionNew(nn.Module):
    """
  Performs self convolution to each node
  """

    def __init__(self, input_feature: 'int', output_feature: 'int', dropout:
        'float'=0.2, UseBN: 'bool'=True):
        """
    Parameters
    ----------
    input_feature: int
        Size of input feature size
    output_feature: int
        Size of output feature size
    dropout: float, defult 0.2
        p value for dropout between 0.0 to 1.0
    UseBN: bool
        Setting it to True will perform Batch Normalisation
    """
        super(Atom_Wise_ConvolutionNew, self).__init__()
        self.conv_weights = nn.Linear(input_feature, output_feature)
        self.batch_norm = nn.LayerNorm(output_feature)
        self.UseBN = UseBN
        self.activation = Shifted_softplus()
        self.dropout = nn.Dropout(p=dropout)

    def forward(self, input_0):
        primals_1 = self.conv_weights.weight
        primals_2 = self.conv_weights.bias
        primals_4 = self.batch_norm.weight
        primals_5 = self.batch_norm.bias
        primals_6 = self.activation.shift
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6])
        return output[0]


Measured latency_ms: 0.015701