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 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_sin_0(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
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tl_math.sin(tmp0)
    tl.store(out_ptr0 + x0, tmp1, 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, 4), (64, 16, 4, 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, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_sin_0[grid(256)](buf0, buf1, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
    return buf1, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), buf0


class Sine(nn.Module):
    """
    A wrapper for PyTorch sine function.
    """

    def __init__(self, w0=1.0):
        super().__init__()
        self.w0 = w0

    @staticmethod
    def forward(x):
        return torch.sin(x)


class SirenNew(nn.Module):
    """
    An implementation of the Sine activation function known as Siren.
    """

    def __init__(self, dim_in, dim_out, w0=30.0, c=6.0, is_first=False):
        """

        :param dim_in: input dimension.
        :param dim_out: output dimension.
        :param w0: initial weight.
        :param c: parameter to distribute the weights uniformly so that after sine activation the input is
        arcsine-distributed.
        :param is_first: boolean to check if it's the first layer.
        """
        super().__init__()
        self.dim_in = dim_in
        self.is_first = is_first
        weight = torch.zeros(dim_out, dim_in)
        bias = torch.zeros(dim_out)
        self.initialization(weight, bias, c=c, w0=w0)
        self.weight = nn.Parameter(weight)
        self.bias = nn.Parameter(bias)
        self.activation = Sine(w0)

    def initialization(self, weight, bias, c, w0):
        dim = self.dim_in
        w_std = 1 / dim if self.is_first else math.sqrt(c / dim) / w0
        weight.uniform_(-w_std, w_std)
        if bias is not None:
            bias.uniform_(-w_std, w_std)

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


Measured latency_ms: 0.023345