# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream

aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()


# kernel path: runs/run_shard_8/inductor_cache/ow/cowchei7rma2w35xdyipkj4w7w4j7us7kduvuej4vdjmk4ibeenj.py
# Topologically Sorted Source Nodes: [exp, q_v, v, mul, latent], Original ATen: [aten.exp, aten.add, aten.sqrt, aten.mul]
# Source node to ATen node mapping:
#   exp => exp
#   latent => add_1
#   mul => mul
#   q_v => add
#   v => sqrt
# Graph fragment:
#   %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%view_5,), kwargs = {})
#   %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%exp, 0.0001), kwargs = {})
#   %sqrt : [num_users=1] = call_function[target=torch.ops.aten.sqrt.default](args = (%add,), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%normal_functional, %sqrt), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_3, %mul), kwargs = {})
triton_poi_fused_add_exp_mul_sqrt_0 = async_compile.triton('triton_poi_fused_add_exp_mul_sqrt_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_exp_mul_sqrt_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_exp_mul_sqrt_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, 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)
    tmp4 = tl.load(in_ptr1 + (x0), xmask)
    tmp5 = tl.load(in_ptr2 + (x0), xmask)
    tmp1 = tl_math.exp(tmp0)
    tmp2 = 0.0001
    tmp3 = tmp1 + tmp2
    tmp6 = libdevice.sqrt(tmp3)
    tmp7 = tmp5 * tmp6
    tmp8 = tmp4 + tmp7
    tl.store(out_ptr0 + (x0), tmp3, xmask)
    tl.store(out_ptr1 + (x0), tmp8, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

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, (128, 4), (4, 1))
    assert_size_stride(primals_2, (128, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4, 128), (128, 1))
    assert_size_stride(primals_5, (4, ), (1, ))
    assert_size_stride(primals_6, (4, 128), (128, 1))
    assert_size_stride(primals_7, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 128), (128, 1), torch.float32)
        # Topologically Sorted Source Nodes: [q], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 128), (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)
        # Topologically Sorted Source Nodes: [q_m], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_5, buf0, reinterpret_tensor(primals_4, (128, 4), (1, 128), 0), alpha=1, beta=1, out=buf1)
        del primals_5
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_2], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_7, buf0, reinterpret_tensor(primals_6, (128, 4), (1, 128), 0), alpha=1, beta=1, out=buf2)
        del primals_7
        buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [eps], Original ATen: [aten.normal_functional]
        buf5 = torch.ops.aten.normal_functional.default(buf4)
        buf6 = buf5
        del buf5
        buf3 = buf4; del buf4  # reuse
        buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [exp, q_v, v, mul, latent], Original ATen: [aten.exp, aten.add, aten.sqrt, aten.mul]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_exp_mul_sqrt_0.run(buf2, buf1, buf6, buf3, buf7, 256, grid=grid(256), stream=stream0)
    return (reinterpret_tensor(buf1, (4, 4, 4, 4), (64, 16, 4, 1), 0), buf3, buf7, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), buf0, buf2, buf3, buf6, primals_6, primals_4, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((128, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 128), (128, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, 128), (128, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7])
    return print_performance(fn, times=times, repeat=repeat)


if __name__ == "__main__":
    from torch._inductor.wrapper_benchmark import compiled_module_main
    compiled_module_main('None', benchmark_compiled_module)

import torch
from typing import Iterable
from torch.distributions import Normal
from torch import nn as nn


def reparameterize_gaussian(mu, var):
    return Normal(mu, var.sqrt()).rsample()


class Encoder_mse(nn.Module):
    """Encodes data of ``n_input`` dimensions into a latent space of ``n_output``
    dimensions using a fully-connected neural network of ``n_hidden`` layers.

    :param n_input: The dimensionality of the input (data space)
    :param n_output: The dimensionality of the output (latent space)
    :param n_cat_list: A list containing the number of categories
                       for each category of interest. Each category will be
                       included using a one-hot encoding
    :param n_layers: The number of fully-connected hidden layers
    :param n_hidden: The number of nodes per hidden layer
    :dropout_rate: Dropout rate to apply to each of the hidden layers
    """

    def __init__(self, n_input: 'int', n_output: 'int', n_cat_list:
        'Iterable[int]'=None, n_layers: 'int'=1, n_hidden: 'int'=128,
        dropout_rate: 'float'=0.1):
        super().__init__()
        self.encoder = nn.Linear(n_input, n_hidden)
        self.mean_encoder = nn.Linear(n_hidden, n_output)
        self.var_encoder = nn.Linear(n_hidden, n_output)

    def forward(self, x: 'torch.Tensor', *cat_list: int):
        """The forward computation for a single sample.

         #. Encodes the data into latent space using the encoder network
         #. Generates a mean \\\\( q_m \\\\) and variance \\\\( q_v \\\\) (clamped to \\\\( [-5, 5] \\\\))
         #. Samples a new value from an i.i.d. multivariate normal \\\\( \\\\sim N(q_m, \\\\mathbf{I}q_v) \\\\)

        :param x: tensor with shape (n_input,)
        :param cat_list: list of category membership(s) for this sample
        :return: tensors of shape ``(n_latent,)`` for mean and var, and sample
        :rtype: 3-tuple of :py:class:`torch.Tensor`
        """
        q = self.encoder(x)
        q_m = self.mean_encoder(q)
        q_v = torch.exp(self.var_encoder(q)) + 0.0001
        latent = reparameterize_gaussian(q_m, q_v)
        return q_m, q_v, latent


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


def get_init_inputs():
    return [[], {'n_input': 4, 'n_output': 4}]

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.triton_helpers import libdevice, math as tl_math
from typing import Iterable
from torch.distributions import Normal
from torch import 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_exp_mul_sqrt_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0,
    out_ptr1, 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)
    tmp4 = tl.load(in_ptr1 + x0, xmask)
    tmp5 = tl.load(in_ptr2 + x0, xmask)
    tmp1 = tl_math.exp(tmp0)
    tmp2 = 0.0001
    tmp3 = tmp1 + tmp2
    tmp6 = libdevice.sqrt(tmp3)
    tmp7 = tmp5 * tmp6
    tmp8 = tmp4 + tmp7
    tl.store(out_ptr0 + x0, tmp3, xmask)
    tl.store(out_ptr1 + x0, 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, (128, 4), (4, 1))
    assert_size_stride(primals_2, (128,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4, 128), (128, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 128), (128, 1))
    assert_size_stride(primals_7, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 128), (128, 1), torch.float32)
        extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64, 
            4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 128), (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, buf0, reinterpret_tensor(primals_4,
            (128, 4), (1, 128), 0), alpha=1, beta=1, out=buf1)
        del primals_5
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_7, buf0, reinterpret_tensor(primals_6,
            (128, 4), (1, 128), 0), alpha=1, beta=1, out=buf2)
        del primals_7
        buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf5 = torch.ops.aten.normal_functional.default(buf4)
        buf6 = buf5
        del buf5
        buf3 = buf4
        del buf4
        buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_exp_mul_sqrt_0[grid(256)](buf2, buf1, buf6,
            buf3, buf7, 256, XBLOCK=256, num_warps=4, num_stages=1)
    return reinterpret_tensor(buf1, (4, 4, 4, 4), (64, 16, 4, 1), 0
        ), buf3, buf7, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
        ), buf0, buf2, buf3, buf6, primals_6, primals_4


def reparameterize_gaussian(mu, var):
    return Normal(mu, var.sqrt()).rsample()


class Encoder_mseNew(nn.Module):
    """Encodes data of ``n_input`` dimensions into a latent space of ``n_output``
    dimensions using a fully-connected neural network of ``n_hidden`` layers.

    :param n_input: The dimensionality of the input (data space)
    :param n_output: The dimensionality of the output (latent space)
    :param n_cat_list: A list containing the number of categories
                       for each category of interest. Each category will be
                       included using a one-hot encoding
    :param n_layers: The number of fully-connected hidden layers
    :param n_hidden: The number of nodes per hidden layer
    :dropout_rate: Dropout rate to apply to each of the hidden layers
    """

    def __init__(self, n_input: 'int', n_output: 'int', n_cat_list:
        'Iterable[int]'=None, n_layers: 'int'=1, n_hidden: 'int'=128,
        dropout_rate: 'float'=0.1):
        super().__init__()
        self.encoder = nn.Linear(n_input, n_hidden)
        self.mean_encoder = nn.Linear(n_hidden, n_output)
        self.var_encoder = nn.Linear(n_hidden, n_output)

    def forward(self, input_0):
        primals_1 = self.encoder.weight
        primals_2 = self.encoder.bias
        primals_4 = self.mean_encoder.weight
        primals_5 = self.mean_encoder.bias
        primals_6 = self.var_encoder.weight
        primals_7 = self.var_encoder.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]
