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__log_softmax_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 1920
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 30
    x2 = xindex // 120
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + 120 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tl.load(in_ptr0 + (30 + x0 + 120 * x2), xmask, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr0 + (60 + x0 + 120 * x2), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (90 + x0 + 120 * 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_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 1920
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 30
    x2 = xindex // 120
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + 120 * x2), xmask, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr0 + (30 + x0 + 120 * x2), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (60 + x0 + 120 * x2), xmask, eviction_policy=
        'evict_last')
    tmp9 = tl.load(in_ptr0 + (90 + x0 + 120 * 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, primals_5, primals_6,
        primals_7) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (30, 4), (4, 1))
    assert_size_stride(primals_3, (30,), (1,))
    assert_size_stride(primals_4, (120, 4), (4, 1))
    assert_size_stride(primals_5, (120,), (1,))
    assert_size_stride(primals_6, (120, 4), (4, 1))
    assert_size_stride(primals_7, (120,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 30), (30, 1), torch.float32)
        extern_kernels.addmm(primals_3, reinterpret_tensor(primals_1, (64, 
            4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 30), (1, 4), 
            0), alpha=1, beta=1, out=buf0)
        del primals_2
        del primals_3
        buf1 = empty_strided_cuda((4, 4, 4, 30), (480, 120, 30, 1), torch.
            float32)
        get_raw_stream(0)
        triton_poi_fused__log_softmax_0[grid(1920)](buf0, buf1, 1920,
            XBLOCK=128, num_warps=4, num_stages=1)
        buf2 = reinterpret_tensor(buf0, (4, 4, 4, 30), (480, 120, 30, 1), 0)
        del buf0
        triton_poi_fused__log_softmax_1[grid(1920)](buf1, buf2, 1920,
            XBLOCK=256, num_warps=4, num_stages=1)
        del buf1
        buf3 = empty_strided_cuda((64, 120), (120, 1), torch.float32)
        extern_kernels.addmm(primals_5, reinterpret_tensor(primals_1, (64, 
            4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 120), (1, 4),
            0), alpha=1, beta=1, out=buf3)
        del primals_4
        del primals_5
        buf4 = empty_strided_cuda((64, 120), (120, 1), torch.float32)
        extern_kernels.addmm(primals_7, reinterpret_tensor(primals_1, (64, 
            4), (4, 1), 0), reinterpret_tensor(primals_6, (4, 120), (1, 4),
            0), alpha=1, beta=1, out=buf4)
        del primals_6
        del primals_7
    return buf2, reinterpret_tensor(buf3, (4, 16, 30, 4), (1920, 120, 4, 1), 0
        ), reinterpret_tensor(buf4, (4, 16, 30, 4), (1920, 120, 4, 1), 0
        ), reinterpret_tensor(primals_1, (64, 4), (4, 1), 0), buf2


class MDNLayerNew(nn.Module):
    """ Mixture Density Network layer

    The input maps to the parameters of a Mixture of Gaussians (MoG) probability
    distribution, where each Gaussian has out_dim dimensions and diagonal covariance.
    If dim_wise is True, features for each dimension are modeld by independent 1-D GMMs
    instead of modeling jointly. This would workaround training difficulty
    especially for high dimensional data.

    Implementation references:
    1. Mixture Density Networks by Mike Dusenberry https://mikedusenberry.com/mixture-density-networks
    2. PRML book https://www.microsoft.com/en-us/research/people/cmbishop/prml-book/
    3. sagelywizard/pytorch-mdn https://github.com/sagelywizard/pytorch-mdn
    4. sksq96/pytorch-mdn https://github.com/sksq96/pytorch-mdn

    Attributes:
        in_dim (int): the number of dimensions in the input
        out_dim (int): the number of dimensions in the output
        num_gaussians (int): the number of mixture component
        dim_wise (bool): whether to model data for each dimension seperately
    """

    def __init__(self, in_dim, out_dim, num_gaussians=30, dim_wise=False):
        super(MDNLayerNew, self).__init__()
        self.in_dim = in_dim
        self.out_dim = out_dim
        self.num_gaussians = num_gaussians
        self.dim_wise = dim_wise
        odim_log_pi = out_dim * num_gaussians if dim_wise else num_gaussians
        self.log_pi = nn.Linear(in_dim, odim_log_pi)
        self.log_sigma = nn.Linear(in_dim, out_dim * num_gaussians)
        self.mu = nn.Linear(in_dim, out_dim * num_gaussians)

    def forward(self, input_0):
        primals_2 = self.log_pi.weight
        primals_3 = self.log_pi.bias
        primals_4 = self.log_sigma.weight
        primals_5 = self.log_sigma.bias
        primals_6 = self.mu.weight
        primals_7 = self.mu.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0], output[1], output[2]


Measured latency_ms: 0.053732