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 queue import *
from math import *
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, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 36
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 9
    x1 = xindex // 9
    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
    tmp7 = tl.full([1], 5, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + x1, tmp9 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp11 = tmp0 >= tmp7
    tl.full([1], 9, tl.int64)
    tmp14 = tl.load(in_ptr2 + (4 * x1 + (-5 + x0)), tmp11 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp15 = tl.where(tmp9, tmp10, tmp14)
    tmp16 = tl.where(tmp4, tmp5, tmp15)
    tl.store(out_ptr0 + x2, tmp16, 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) = 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, 4), (4, 1))
    assert_size_stride(primals_4, (4, 9), (9, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (2, 4), (4, 1))
    assert_size_stride(primals_7, (2,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_1, primals_3, out=buf0)
        del primals_3
        buf1 = empty_strided_cuda((4, 1, 1), (1, 1, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf0, (4, 1, 4), (4, 4, 1), 0
            ), reinterpret_tensor(primals_2, (4, 4, 1), (4, 1, 1), 0), out=buf1
            )
        buf2 = empty_strided_cuda((4, 9), (9, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(36)](primals_1, buf1, primals_2, buf2, 
            36, XBLOCK=64, num_warps=1, num_stages=1)
        del buf1
        buf3 = buf0
        del buf0
        extern_kernels.mm(buf2, reinterpret_tensor(primals_4, (9, 4), (1, 9
            ), 0), out=buf3)
        buf4 = buf3
        del buf3
        triton_poi_fused_tanh_1[grid(16)](buf4, primals_5, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del primals_5
        buf5 = empty_strided_cuda((4, 2), (2, 1), torch.float32)
        extern_kernels.addmm(primals_7, buf4, reinterpret_tensor(primals_6,
            (4, 2), (1, 4), 0), alpha=1, beta=1, out=buf5)
        del primals_7
    return buf5, buf2, buf4, primals_6, primals_4, reinterpret_tensor(primals_2
        , (4, 1, 4), (4, 1, 1), 0), reinterpret_tensor(primals_1, (4, 4), (
        1, 4), 0)


class IRHeadNew(nn.Module):

    def __init__(self, hidden_size, dropout=0.5):
        super(IRHeadNew, self).__init__()
        self.M = nn.Parameter(torch.randn(hidden_size, hidden_size))
        self.hidden_layer = nn.Linear(hidden_size * 2 + 1, hidden_size)
        self.opt_layer = nn.Linear(hidden_size, 2)
        self.hidden_drop = nn.Dropout(p=dropout)

    def forward(self, input_0, input_1):
        primals_1 = self.M
        primals_4 = self.hidden_layer.weight
        primals_5 = self.hidden_layer.bias
        primals_6 = self.opt_layer.weight
        primals_7 = self.opt_layer.bias
        primals_2 = input_0
        primals_3 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0]


Measured latency_ms: 0.056877