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 math as tl_math
import numpy as np
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_arange_cos_mul_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4
    x0 = xindex % 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp1 = 1 + x0
    tmp2 = tmp1.to(tl.float32)
    tmp3 = 3.141592653589793
    tmp4 = tmp2 * tmp3
    tmp5 = tmp0 * tmp4
    tmp6 = tl_math.cos(tmp5)
    tl.store(out_ptr0 + x2, tmp6, 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,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4, 4), (256, 64, 16, 4, 1),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused_arange_cos_mul_0[grid(1024)](primals_1, buf0, 1024,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((256, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_3, reinterpret_tensor(buf0, (256, 4),
            (4, 1), 0), reinterpret_tensor(primals_2, (4, 4), (1, 4), 0),
            alpha=1, beta=1, out=buf1)
        del primals_2
        del primals_3
    return reinterpret_tensor(buf1, (4, 4, 4, 4, 4), (256, 64, 16, 4, 1), 0
        ), reinterpret_tensor(buf0, (256, 4), (4, 1), 0)


def cosine_basis_functions(x, n_basis_functions=64):
    """Cosine basis functions used to embed quantile thresholds.

    Args:
        x (torch.Tensor): Input.
        n_basis_functions (int): Number of cosine basis functions.

    Returns:
        ndarray: Embedding with shape of (x.shape + (n_basis_functions,)).
    """
    i_pi = torch.arange(1, n_basis_functions + 1, dtype=torch.float, device
        =x.device) * np.pi
    embedding = torch.cos(x[..., None] * i_pi)
    assert embedding.shape == x.shape + (n_basis_functions,)
    return embedding


class CosineBasisLinearNew(nn.Module):
    """Linear layer following cosine basis functions.

    Args:
        n_basis_functions (int): Number of cosine basis functions.
        out_size (int): Output size.
    """

    def __init__(self, n_basis_functions, out_size):
        super().__init__()
        self.linear = nn.Linear(n_basis_functions, out_size)
        self.n_basis_functions = n_basis_functions
        self.out_size = out_size

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


Measured latency_ms: 0.0244