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 torch.nn as nn
from torch.autograd import Variable
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, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 8
    x1 = xindex // 8
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (4 * x1 + x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp9 = tl.load(in_ptr1 + (4 * x1 + (-4 + x0)), tmp6 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + x2, tmp10, xmask)


@triton.jit
def triton_poi_fused_tanh_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 16
    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 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = libdevice.tanh(tmp2)
    tl.store(in_out_ptr0 + x2, tmp3, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4, 8), (8, 1))
    assert_size_stride(primals_4, (4,), (1,))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    assert_size_stride(primals_6, (4,), (1,))
    assert_size_stride(primals_7, (4, 8), (8, 1))
    assert_size_stride(primals_8, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(32)](primals_1, primals_2, buf0, 32,
            XBLOCK=32, num_warps=1, num_stages=1)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_4, buf0, reinterpret_tensor(primals_3,
            (8, 4), (1, 8), 0), alpha=1, beta=1, out=buf1)
        del primals_3
        del primals_4
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf1, reinterpret_tensor(primals_5, (4, 4), (1, 4
            ), 0), out=buf2)
        buf3 = buf2
        del buf2
        triton_poi_fused_tanh_1[grid(16)](buf3, primals_6, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del primals_6
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf0, reinterpret_tensor(primals_7, (8, 4), (1, 8
            ), 0), out=buf4)
        del primals_7
        buf5 = buf4
        del buf4
        triton_poi_fused_tanh_1[grid(16)](buf5, primals_8, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del primals_8
    return buf5, buf3, buf0, buf1, buf3, buf5, primals_5


class FNew(nn.Module):

    def __init__(self, input_size, hidden_size, output_size, learning_rate=
        0.001):
        super().__init__()
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.output_size = output_size
        self.i2h = nn.Linear(input_size + hidden_size, hidden_size)
        self.h2h = nn.Linear(hidden_size, hidden_size)
        self.i2o = nn.Linear(input_size + hidden_size, output_size)
        self.tanh = nn.Tanh()
        self.criterion = nn.MSELoss()
        self.optimizer = torch.optim.SGD(self.parameters(), lr=learning_rate)

    def init_hidden(self, batch_size=1):
        return Variable(torch.zeros(batch_size, self.hidden_size)).float()

    def train_sample(self, sample, target):
        self.zero_grad()
        hidden = self.init_hidden()
        for input in sample:
            output, hidden = self.forward(input.unsqueeze(0), hidden)
        target = target.unsqueeze(0)
        loss = self.criterion(output, target)
        loss.backward()
        self.optimizer.step()
        return loss.item()

    def train_batch(self, batch_samples, batch_targets):
        self.zero_grad()
        batch_size = batch_samples.shape[0]
        sample_size = batch_samples.shape[1]
        hidden = self.init_hidden(batch_size)
        outputs = []
        for i in range(sample_size):
            input = batch_samples[:, i]
            output, hidden = self.forward(input, hidden)
            outputs.append(output)
        outputs = torch.stack(outputs, 1)
        loss = self.criterion(outputs, batch_targets)
        loss.backward()
        self.optimizer.step()
        return loss.item()

    def run_sample(self, sample):
        with torch.no_grad():
            hidden = self.init_hidden()
            for input in sample:
                output, hidden = self.forward(input.unsqueeze(0), hidden)
            return output, hidden

    def predict(self, warmup_sample, prediction_count):
        with torch.no_grad():
            output, hidden = self.run_sample(warmup_sample)
            predictions = []
            for _ in range(prediction_count):
                output, hidden = self.forward(output, hidden)
                predictions.append(output)
            return predictions

    def forward(self, input_0, input_1):
        primals_3 = self.i2h.weight
        primals_4 = self.i2h.bias
        primals_1 = self.h2h.weight
        primals_6 = self.h2h.bias
        primals_7 = self.i2o.weight
        primals_8 = self.i2o.bias
        primals_2 = input_0
        primals_5 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8])
        return output[0], output[1]


Measured latency_ms: 0.060322