import torch
import torch.nn as nn


class run_latent(nn.Module):

    def __init__(self, in_dim, hidden_dim):
        super(run_latent, self).__init__()
        self.fc_z_mean = nn.Linear(in_dim, hidden_dim)
        self.fc_z_log_sigma = nn.Linear(in_dim, hidden_dim)
        self.fc_gen = nn.Linear(hidden_dim, in_dim)
        self.weights_init()

    def forward(self, x):
        z_mean = self.fc_z_mean(x)
        z_log_sigma_sq = self.fc_z_log_sigma(x)
        z = self.reparameterize(z_mean, z_log_sigma_sq)
        x_recon = self.fc_gen(z)
        x_recon = nn.functional.softmax(x_recon, dim=0)
        return x_recon, z_mean, z_log_sigma_sq

    def reparameterize(self, mu, log_var):
        std = torch.sqrt(torch.clamp(torch.exp(log_var), min=1e-10))
        eps = torch.randn_like(std)
        return mu + eps * std

    def weights_init(self):
        nn.init.xavier_uniform_(self.fc_z_mean.weight)
        nn.init.constant_(self.fc_z_mean.bias, 0)
        nn.init.xavier_uniform_(self.fc_z_log_sigma.weight)
        nn.init.constant_(self.fc_z_log_sigma.bias, 0)
        nn.init.xavier_uniform_(self.fc_gen.weight)
        nn.init.constant_(self.fc_gen.bias, 0)


def get_inputs():
    return [torch.rand([4, 4, 4, 4])]


def get_init_inputs():
    return [[], {'in_dim': 4, 'hidden_dim': 4}]

import torch
from torch import device
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.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_add_clamp_exp_mul_sqrt_0(in_ptr0, in_ptr1, in_ptr2,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask)
    tmp2 = tl.load(in_ptr2 + x0, xmask)
    tmp3 = tl_math.exp(tmp2)
    tmp4 = 1e-10
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = libdevice.sqrt(tmp5)
    tmp7 = tmp1 * tmp6
    tmp8 = tmp0 + tmp7
    tl.store(out_ptr0 + x0, tmp8, xmask)


@triton.jit
def triton_poi_fused__softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 64
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (64 + x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (128 + x0), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (192 + x0), 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 + x2, tmp9, xmask)


@triton.jit
def triton_poi_fused__softmax_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 64
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (64 + x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (128 + x0), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (192 + x0), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + x2, tmp8, 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, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64, 
            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((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_5, reinterpret_tensor(primals_3, (64, 
            4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0
            ), alpha=1, beta=1, out=buf1)
        del primals_4
        del primals_5
        buf2 = torch.ops.aten.randn.default([4, 4, 4, 4], dtype=torch.
            float32, device=device(type='cuda', index=0), pin_memory=False)
        buf3 = buf2
        del buf2
        buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_clamp_exp_mul_sqrt_0[grid(256)](buf0, buf3,
            buf1, buf4, 256, XBLOCK=128, num_warps=4, num_stages=1)
        buf5 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_7, reinterpret_tensor(buf4, (64, 4), (
            4, 1), 0), reinterpret_tensor(primals_6, (4, 4), (1, 4), 0),
            alpha=1, beta=1, out=buf5)
        del primals_7
        buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__softmax_1[grid(256)](buf5, buf6, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf7 = reinterpret_tensor(buf5, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf5
        triton_poi_fused__softmax_2[grid(256)](buf6, buf7, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        del buf6
    return buf7, reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0
        ), reinterpret_tensor(buf1, (4, 4, 4, 4), (64, 16, 4, 1), 0
        ), reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
        ), reinterpret_tensor(buf1, (4, 4, 4, 4), (64, 16, 4, 1), 0
        ), buf3, reinterpret_tensor(buf4, (64, 4), (4, 1), 0), buf7, primals_6


class run_latentNew(nn.Module):

    def __init__(self, in_dim, hidden_dim):
        super(run_latentNew, self).__init__()
        self.fc_z_mean = nn.Linear(in_dim, hidden_dim)
        self.fc_z_log_sigma = nn.Linear(in_dim, hidden_dim)
        self.fc_gen = nn.Linear(hidden_dim, in_dim)
        self.weights_init()

    def reparameterize(self, mu, log_var):
        std = torch.sqrt(torch.clamp(torch.exp(log_var), min=1e-10))
        eps = torch.randn_like(std)
        return mu + eps * std

    def weights_init(self):
        nn.init.xavier_uniform_(self.fc_z_mean.weight)
        nn.init.constant_(self.fc_z_mean.bias, 0)
        nn.init.xavier_uniform_(self.fc_z_log_sigma.weight)
        nn.init.constant_(self.fc_z_log_sigma.bias, 0)
        nn.init.xavier_uniform_(self.fc_gen.weight)
        nn.init.constant_(self.fc_gen.bias, 0)

    def forward(self, input_0):
        primals_1 = self.fc_z_mean.weight
        primals_2 = self.fc_z_mean.bias
        primals_4 = self.fc_z_log_sigma.weight
        primals_5 = self.fc_z_log_sigma.bias
        primals_6 = self.fc_gen.weight
        primals_7 = self.fc_gen.bias
        primals_3 = 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]
