import torch
import torch as tr
import torch.nn as nn


class quadexp(nn.Module):

    def __init__(self, sigma=2.0):
        super(quadexp, self).__init__()
        self.sigma = sigma

    def forward(self, x):
        return tr.exp(-x ** 2 / self.sigma ** 2)


class OneHiddenLayer(nn.Module):

    def __init__(self, d_int, H, d_out, non_linearity=quadexp(), bias=False):
        super(OneHiddenLayer, self).__init__()
        self.linear1 = tr.nn.Linear(d_int, H, bias=bias)
        self.linear2 = tr.nn.Linear(H, d_out, bias=bias)
        self.non_linearity = non_linearity
        self.d_int = d_int
        self.d_out = d_out

    def weights_init(self, center, std):
        self.linear1.weights_init(center, std)
        self.linear2.weights_init(center, std)

    def forward(self, x):
        h1_relu = self.linear1(x).clamp(min=0)
        h2_relu = self.linear2(h1_relu)
        h2_relu = self.non_linearity(h2_relu)
        return h2_relu


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


def get_init_inputs():
    return [[], {'d_int': 4, 'H': 4, 'd_out': 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 import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch as tr
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_clamp_ge_0(in_ptr0, out_ptr0, out_ptr1, 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 = 0.0
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp3 = tmp0 >= tmp1
    tl.store(out_ptr0 + x0, tmp2, xmask)
    tl.store(out_ptr1 + x0, tmp3, xmask)


@triton.jit
def triton_poi_fused_div_exp_neg_pow_1(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 = tmp0 * tmp0
    tmp2 = -tmp1
    tmp3 = 0.25
    tmp4 = tmp2 * tmp3
    tmp5 = tl_math.exp(tmp4)
    tl.store(out_ptr0 + x0, tmp5, 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, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_2, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_clamp_ge_0[grid(256)](buf0, buf1, buf4, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        buf2 = buf0
        del buf0
        extern_kernels.mm(reinterpret_tensor(buf1, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_3, (4, 4), (1, 4), 0), out=buf2)
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_div_exp_neg_pow_1[grid(256)](buf2, buf3, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
    return buf3, reinterpret_tensor(primals_2, (64, 4), (4, 1), 0
        ), reinterpret_tensor(buf1, (64, 4), (4, 1), 0
        ), buf2, buf3, primals_3, buf4


class quadexp(nn.Module):

    def __init__(self, sigma=2.0):
        super(quadexp, self).__init__()
        self.sigma = sigma

    def forward(self, x):
        return tr.exp(-x ** 2 / self.sigma ** 2)


class OneHiddenLayerNew(nn.Module):

    def __init__(self, d_int, H, d_out, non_linearity=quadexp(), bias=False):
        super(OneHiddenLayerNew, self).__init__()
        self.linear1 = tr.nn.Linear(d_int, H, bias=bias)
        self.linear2 = tr.nn.Linear(H, d_out, bias=bias)
        self.non_linearity = non_linearity
        self.d_int = d_int
        self.d_out = d_out

    def weights_init(self, center, std):
        self.linear1.weights_init(center, std)
        self.linear2.weights_init(center, std)

    def forward(self, input_0):
        primals_1 = self.linear1.weight
        primals_3 = self.linear2.weight
        primals_2 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]
