import torch


class PotCoSirenModule(torch.nn.Module):

    def __init__(self, in_features, out_features, weight_multiplier=1.0):
        super(PotCoSirenModule, self).__init__()
        self.linear = torch.nn.Linear(in_features, out_features // 2)
        torch.nn.init.uniform_(self.linear.weight, a=-weight_multiplier, b=
            weight_multiplier)
        self.linear.weight.data = 2 ** self.linear.weight.data

    def forward(self, x):
        x = self.linear(x)
        return torch.cat([torch.sin(x), torch.cos(x)], dim=-1)


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


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

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
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_cat_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 % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 2, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (2 * x1 + x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tl_math.sin(tmp5)
    tmp7 = tl.full(tmp6.shape, 0.0, tmp6.dtype)
    tmp8 = tl.where(tmp4, tmp6, tmp7)
    tmp9 = tmp0 >= tmp3
    tl.full([1], 4, tl.int64)
    tmp12 = tl.load(in_ptr0 + (2 * x1 + (-2 + x0)), tmp9 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp13 = tl_math.cos(tmp12)
    tmp14 = tl.full(tmp13.shape, 0.0, tmp13.dtype)
    tmp15 = tl.where(tmp9, tmp13, tmp14)
    tmp16 = tl.where(tmp4, tmp8, tmp15)
    tl.store(out_ptr0 + x2, tmp16, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (2, 4), (4, 1))
    assert_size_stride(primals_2, (2,), (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, 2), (2, 1), torch.float32)
        extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64, 
            4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 2), (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_cat_0[grid(256)](buf0, buf1, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
    return buf1, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), buf0


class PotCoSirenModuleNew(torch.nn.Module):

    def __init__(self, in_features, out_features, weight_multiplier=1.0):
        super(PotCoSirenModuleNew, self).__init__()
        self.linear = torch.nn.Linear(in_features, out_features // 2)
        torch.nn.init.uniform_(self.linear.weight, a=-weight_multiplier, b=
            weight_multiplier)
        self.linear.weight.data = 2 ** self.linear.weight.data

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