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 libdevice, math as tl_math
import torch.utils.data
import torch.utils
import torch.utils.checkpoint
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_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x4 = xindex // 64
    x3 = xindex // 256
    x5 = xindex % 64
    x6 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 16 * x4), xmask, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr1 + (x5 + 64 * x3), xmask, eviction_policy=
        'evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = libdevice.tanh(tmp2)
    tl.store(out_ptr0 + x6, tmp3, xmask)


@triton.jit
def triton_poi_fused__log_softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 1280
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 5
    x2 = xindex // 20
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + 20 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tl.load(in_ptr0 + (5 + x0 + 20 * x2), xmask, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr0 + (10 + x0 + 20 * x2), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (15 + x0 + 20 * x2), xmask, eviction_policy=
        'evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tl.store(out_ptr0 + x3, tmp8, xmask)


@triton.jit
def triton_poi_fused__log_softmax_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 1280
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 5
    x2 = xindex // 20
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + 20 * x2), xmask, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr0 + (5 + x0 + 20 * x2), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (10 + x0 + 20 * x2), xmask, eviction_policy=
        'evict_last')
    tmp9 = tl.load(in_ptr0 + (15 + x0 + 20 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tl_math.exp(tmp1)
    tmp4 = tl_math.exp(tmp3)
    tmp5 = tmp2 + tmp4
    tmp7 = tl_math.exp(tmp6)
    tmp8 = tmp5 + tmp7
    tmp10 = tl_math.exp(tmp9)
    tmp11 = tmp8 + tmp10
    tmp12 = tl_math.log(tmp11)
    tmp13 = tmp0 - tmp12
    tl.store(out_ptr0 + x3, tmp13, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (5, 4), (4, 1))
    assert_size_stride(primals_4, (5,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4, 4), (256, 64, 16, 4, 1),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_tanh_0[grid(1024)](primals_1, primals_2, buf0,
            1024, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((256, 5), (5, 1), torch.float32)
        extern_kernels.addmm(primals_4, reinterpret_tensor(buf0, (256, 4),
            (4, 1), 0), reinterpret_tensor(primals_3, (4, 5), (1, 4), 0),
            alpha=1, beta=1, out=buf1)
        del primals_3
        del primals_4
        buf2 = empty_strided_cuda((4, 4, 4, 4, 5), (320, 80, 20, 5, 1),
            torch.float32)
        triton_poi_fused__log_softmax_1[grid(1280)](buf1, buf2, 1280,
            XBLOCK=256, num_warps=4, num_stages=1)
        buf3 = reinterpret_tensor(buf1, (4, 4, 4, 4, 5), (320, 80, 20, 5, 1), 0
            )
        del buf1
        triton_poi_fused__log_softmax_2[grid(1280)](buf2, buf3, 1280,
            XBLOCK=256, num_warps=4, num_stages=1)
        del buf2
    return buf3, reinterpret_tensor(buf0, (256, 4), (4, 1), 0), buf3


class JoinerNew(torch.nn.Module):

    def __init__(self, config):
        super(JoinerNew, self).__init__()
        self.tanh = torch.nn.Tanh()
        self.num_outputs = config.num_tokens + 1
        self.blank_index = 0
        self.linear = torch.nn.Linear(config.num_joiner_hidden, self.
            num_outputs)

    def forward_one_step(self, encoder_out, decoder_out):
        combined = encoder_out + decoder_out
        out = self.tanh(combined)
        out = self.linear(out)
        return out

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


Measured latency_ms: 0.036893