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.distributed
import torch
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__log_softmax_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 320
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex // 16 % 5
    x5 = xindex
    x6 = xindex // 4
    tmp3 = tl.load(in_ptr0 + x5, xmask)
    tmp6 = tl.load(in_ptr0 + 4 * x6, xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (1 + 4 * x6), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (2 + 4 * x6), xmask, eviction_policy='evict_last'
        )
    tmp14 = tl.load(in_ptr0 + (3 + 4 * x6), xmask, eviction_policy='evict_last'
        )
    tmp0 = x2
    tmp1 = tl.full([1], 4, tl.int32)
    tmp2 = tmp0 == tmp1
    tmp4 = float('-inf')
    tmp5 = tl.where(tmp2, tmp4, tmp3)
    tmp7 = tl.where(tmp2, tmp4, tmp6)
    tmp9 = tl.where(tmp2, tmp4, tmp8)
    tmp10 = triton_helpers.maximum(tmp7, tmp9)
    tmp12 = tl.where(tmp2, tmp4, tmp11)
    tmp13 = triton_helpers.maximum(tmp10, tmp12)
    tmp15 = tl.where(tmp2, tmp4, tmp14)
    tmp16 = triton_helpers.maximum(tmp13, tmp15)
    tmp17 = tmp5 - tmp16
    tl.store(out_ptr0 + x5, tmp17, xmask)


@triton.jit
def triton_poi_fused__log_softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 320
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (3 + 4 * x1), 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 + x2, tmp13, 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,), (1,))
    assert_size_stride(primals_3, (4, 5, 4, 4), (80, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((80, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (80, 
            4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0
            ), alpha=1, beta=1, out=buf0)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((4, 5, 4, 4), (80, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__log_softmax_0[grid(320)](buf0, buf1, 320, XBLOCK=
            128, num_warps=4, num_stages=1)
        buf2 = reinterpret_tensor(buf0, (4, 5, 4, 4), (80, 16, 4, 1), 0)
        del buf0
        triton_poi_fused__log_softmax_1[grid(320)](buf1, buf2, 320, XBLOCK=
            256, num_warps=4, num_stages=1)
        del buf1
    return buf2, reinterpret_tensor(primals_3, (80, 4), (4, 1), 0), buf2


def gumbel_softmax(logits, tau=1.0, hard=False, log_mode=True, dim=-1):
    while True:
        gumbels = -torch.empty_like(logits).exponential_().log()
        gumbels = (logits + gumbels) / tau
        if log_mode:
            y_soft = gumbels.log_softmax(dim)
        else:
            y_soft = gumbels.softmax(dim)
        if torch.sum(torch.isnan(y_soft)).item() < 0.01:
            break
    if hard:
        index = y_soft.max(dim, keepdim=True)[1]
        y_hard = torch.zeros_like(logits).scatter_(dim, index, 1.0)
        ret = y_hard - y_soft.detach() + y_soft
    else:
        ret = y_soft
    return ret


class GeneratorNew(nn.Module):

    def __init__(self, vocab_size, dec_hidden_size, pad_idx):
        super(GeneratorNew, self).__init__()
        self.linear = nn.Linear(dec_hidden_size, vocab_size)
        self.softmax = nn.LogSoftmax(dim=-1)
        self.pad_idx = pad_idx

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


Measured latency_ms: 0.035881