import math
import torch
import torch.jit
import torch.nn.functional
from torch import nn
import torch.nn


class BesselBasis(nn.Module):
    r_max: 'float'
    prefactor: 'float'

    def __init__(self, r_max, num_basis=8, trainable=True):
        """Radial Bessel Basis, as proposed in DimeNet: https://arxiv.org/abs/2003.03123


        Parameters
        ----------
        r_max : float
            Cutoff radius

        num_basis : int
            Number of Bessel Basis functions

        trainable : bool
            Train the :math:`n \\pi` part or not.
        """
        super(BesselBasis, self).__init__()
        self.trainable = trainable
        self.num_basis = num_basis
        self.r_max = float(r_max)
        self.prefactor = 2.0 / self.r_max
        bessel_weights = torch.linspace(start=1.0, end=num_basis, steps=
            num_basis) * math.pi
        if self.trainable:
            self.bessel_weights = nn.Parameter(bessel_weights)
        else:
            self.register_buffer('bessel_weights', bessel_weights)

    def forward(self, x):
        """
        Evaluate Bessel Basis for input x.

        Parameters
        ----------
        x : torch.Tensor
            Input
        """
        numerator = torch.sin(self.bessel_weights * x.unsqueeze(-1) / self.
            r_max)
        return self.prefactor * (numerator / x.unsqueeze(-1))


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


def get_init_inputs():
    return [[], {'r_max': 4}]

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.triton_helpers import math as tl_math
import math
import torch.jit
import torch.nn.functional
from torch import nn
import torch.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_poi_fused_div_mul_sin_0(in_ptr0, in_ptr1, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 8
    x1 = xindex // 8
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tmp3 = 0.25
    tmp4 = tmp2 * tmp3
    tmp5 = tl_math.sin(tmp4)
    tmp6 = tmp5 / tmp1
    tmp7 = 0.5
    tmp8 = tmp6 * tmp7
    tl.store(out_ptr0 + x2, tmp8, None)


def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (8,), (1,))
    assert_size_stride(primals_2, (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, 8), (512, 128, 32, 8, 1),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused_div_mul_sin_0[grid(2048)](primals_1, primals_2,
            buf0, 2048, XBLOCK=128, num_warps=4, num_stages=1)
    return buf0, primals_1, primals_2


class BesselBasisNew(nn.Module):
    r_max: 'float'
    prefactor: 'float'

    def __init__(self, r_max, num_basis=8, trainable=True):
        """Radial Bessel Basis, as proposed in DimeNet: https://arxiv.org/abs/2003.03123


        Parameters
        ----------
        r_max : float
            Cutoff radius

        num_basis : int
            Number of Bessel Basis functions

        trainable : bool
            Train the :math:`n \\pi` part or not.
        """
        super(BesselBasisNew, self).__init__()
        self.trainable = trainable
        self.num_basis = num_basis
        self.r_max = float(r_max)
        self.prefactor = 2.0 / self.r_max
        bessel_weights = torch.linspace(start=1.0, end=num_basis, steps=
            num_basis) * math.pi
        if self.trainable:
            self.bessel_weights = nn.Parameter(bessel_weights)
        else:
            self.register_buffer('bessel_weights', bessel_weights)

    def forward(self, input_0):
        primals_1 = self.bessel_weights
        primals_2 = input_0
        output = call([primals_1, primals_2])
        return output[0]
