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.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 = 8
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 2
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 2 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 2 * x1), xmask, eviction_policy='evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp4 = tmp0 - tmp3
    tmp5 = tmp1 - tmp3
    tmp6 = tl_math.exp(tmp5)
    tmp7 = tmp2 - tmp3
    tmp8 = tl_math.exp(tmp7)
    tmp9 = tmp6 + tmp8
    tmp10 = tl_math.log(tmp9)
    tmp11 = tmp4 - tmp10
    tl.store(out_ptr0 + x2, tmp11, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (2, 16), (16, 1))
    assert_size_stride(primals_3, (2,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 2), (2, 1), torch.float32)
        extern_kernels.addmm(primals_3, reinterpret_tensor(primals_1, (4, 
            16), (16, 1), 0), reinterpret_tensor(primals_2, (16, 2), (1, 16
            ), 0), alpha=1, beta=1, out=buf0)
        del primals_2
        del primals_3
        buf1 = empty_strided_cuda((4, 2), (2, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__log_softmax_0[grid(8)](buf0, buf1, 8, XBLOCK=8,
            num_warps=1, num_stages=1)
        del buf0
    return buf1, reinterpret_tensor(primals_1, (4, 16), (16, 1), 0), buf1


class GeneratorNew(nn.Module):

    def __init__(self, embed_size, max_size, nlayers=0, activation_type='tanh'
        ):
        super(GeneratorNew, self).__init__()
        hidden = max_size * embed_size
        if activation_type == 'tanh':
            activation = nn.Tanh()
        if nlayers > 0:
            hidden_layers = [nn.Linear(hidden, embed_size), activation]
            for n in range(1, nlayers):
                hidden_layers.append(nn.Linear(embed_size, embed_size))
                hidden_layers.append(activation)
            self.hidden_layers = nn.ModuleList(hidden_layers)
            self.proj = nn.Linear(embed_size, 2)
        else:
            self.hidden_layers = None
            self.proj = nn.Linear(hidden, 2)

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


Measured latency_ms: 0.022896