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 libdevice
import math
import torch.nn as nn
import torch.nn.functional as F
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_add_tanh_0(in_out_ptr0, in_ptr0, in_ptr1, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x2, xmask)
    tmp3 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp5 = libdevice.tanh(tmp4)
    tl.store(in_out_ptr0 + x2, tmp5, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = 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))
    assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_5, (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),
            primals_1, out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_4, (64, 4), (4, 1), 0),
            primals_3, out=buf1)
        del primals_3
        buf2 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf0
        get_raw_stream(0)
        triton_poi_fused_add_tanh_0[grid(256)](buf2, buf1, primals_5, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        del buf1
        del primals_5
    return buf2, buf2, reinterpret_tensor(primals_4, (4, 64), (1, 4), 0
        ), reinterpret_tensor(primals_2, (4, 64), (1, 4), 0)


class VanillaRNNCellNew(nn.Module):

    def __init__(self, input_size, hidden_size, nonlinearity='tanh', ct=False):
        super(VanillaRNNCellNew, self).__init__()
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.nonlinearity = nonlinearity
        self.ct = ct
        self.weight_ih = nn.Parameter(torch.zeros((input_size, hidden_size)))
        self.weight_hh = nn.Parameter(torch.zeros((hidden_size, hidden_size)))
        self.bias = nn.Parameter(torch.zeros(hidden_size))
        self.reset_parameters()
        if self.nonlinearity == 'tanh':
            self.act = F.tanh
        elif self.nonlinearity == 'relu':
            self.act = F.relu
        else:
            raise RuntimeError('Unknown nonlinearity: {}'.format(self.
                nonlinearity))

    def reset_parameters(self):
        stdv = 1.0 / math.sqrt(self.hidden_size)
        for weight in self.parameters():
            nn.init.uniform_(weight, -stdv, stdv)

    def init_hidden(self, batch_s):
        return torch.zeros(batch_s, self.hidden_size)

    def forward(self, input_0, input_1):
        primals_1 = self.weight_ih
        primals_3 = self.weight_hh
        primals_5 = self.bias
        primals_2 = input_0
        primals_4 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]


Measured latency_ms: 0.035194