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
from torch import 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__softmax_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 2304
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 144
    x2 = xindex // 576
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + 576 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tl.load(in_ptr0 + (144 + x0 + 576 * x2), xmask, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr0 + (288 + x0 + 576 * x2), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (432 + x0 + 576 * 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
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + x3, tmp9, xmask)


@triton.jit
def triton_poi_fused__softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 2304
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 144
    x2 = xindex // 576
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + 576 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tl.load(in_ptr0 + (144 + x0 + 576 * x2), xmask, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr0 + (288 + x0 + 576 * x2), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (432 + x0 + 576 * x2), xmask, eviction_policy=
        'evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + x3, tmp8, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (36, 4), (4, 1))
    assert_size_stride(primals_2, (36,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 36), (36, 1), torch.float32)
        extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64, 
            4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 36), (1, 4), 
            0), alpha=1, beta=1, out=buf0)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((4, 4, 4, 36), (576, 144, 36, 1), torch.
            float32)
        get_raw_stream(0)
        triton_poi_fused__softmax_0[grid(2304)](buf0, buf1, 2304, XBLOCK=
            128, num_warps=4, num_stages=1)
        buf2 = empty_strided_cuda((4, 4, 4, 36), (576, 144, 36, 1), torch.
            float32)
        triton_poi_fused__softmax_1[grid(2304)](buf1, buf2, 2304, XBLOCK=
            128, num_warps=4, num_stages=1)
        del buf1
        buf3 = empty_strided_cuda((4, 0, 4, 36), (0, 144, 36, 1), torch.float32
            )
    return buf2, reinterpret_tensor(buf0, (0, 4, 4), (0, 0, 0), 576
        ), reinterpret_tensor(buf3, (0, 4, 4), (0, 0, 0), 0
        ), reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), buf2, buf3


class ClassicMixtureDensityModuleNew(nn.Module):

    def __init__(self, dim_input, dim_output, num_components):
        super(ClassicMixtureDensityModuleNew, self).__init__()
        self.dim_input = dim_input
        self.dim_output = dim_output
        self.M = num_components
        self.layer_mapping = nn.Linear(dim_input, (2 * dim_output + 1) *
            num_components)
        self.layer_alpha = nn.Softmax(dim=1)

    def forward(self, input_0):
        primals_1 = self.layer_mapping.weight
        primals_2 = self.layer_mapping.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0], output[1], output[2]


Measured latency_ms: 0.037776