# 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/r3/cr3febcwm3t44fuoitsx3ou2p6xg4sk4f7unagmmrvffasxf47te.py
# Topologically Sorted Source Nodes: [relu], Original ATen: [aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   relu => relu
# Graph fragment:
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_1,), kwargs = {})
#   %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {})
triton_poi_fused_relu_threshold_backward_0 = async_compile.triton('triton_poi_fused_relu_threshold_backward_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: '*i1', 3: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_threshold_backward_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, '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_relu_threshold_backward_0(in_out_ptr0, 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 % 4
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + (x2), tmp4, xmask)
    tl.store(out_ptr0 + (x2), tmp6, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/rh/crhnrrb4toanchmmuqn56kkwxe5jcbaqmxk7on5tyejsai3msybn.py
# Topologically Sorted Source Nodes: [_logtrans, logsumexp], Original ATen: [aten.add, aten.logsumexp]
# Source node to ATen node mapping:
#   _logtrans => add
#   logsumexp => abs_1, amax, eq, exp, full_default, sub, sum_1, where
# Graph fragment:
#   %add : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%unsqueeze, %unsqueeze_1), kwargs = {})
#   %amax : [num_users=2] = call_function[target=torch.ops.aten.amax.default](args = (%add, [-1], True), kwargs = {})
#   %abs_1 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%amax,), kwargs = {})
#   %eq : [num_users=1] = call_function[target=torch.ops.aten.eq.Scalar](args = (%abs_1, inf), kwargs = {})
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 0.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %where : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%eq, %full_default, %amax), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add, %where), kwargs = {})
#   %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [-1], True), kwargs = {})
triton_poi_fused_add_logsumexp_1 = async_compile.triton('triton_poi_fused_add_logsumexp_1', '''
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=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_logsumexp_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, '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_logsumexp_1(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (4*x0), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (4*x2), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + (1 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (2 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr1 + (3 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = triton_helpers.maximum(tmp2, tmp5)
    tmp9 = tmp7 + tmp8
    tmp10 = triton_helpers.maximum(tmp6, tmp9)
    tmp13 = tmp11 + tmp12
    tmp14 = triton_helpers.maximum(tmp10, tmp13)
    tmp15 = tl_math.abs(tmp14)
    tmp16 = float("inf")
    tmp17 = tmp15 == tmp16
    tmp18 = 0.0
    tmp19 = tl.where(tmp17, tmp18, tmp14)
    tmp20 = tmp2 - tmp19
    tmp21 = tl_math.exp(tmp20)
    tmp22 = tmp5 - tmp19
    tmp23 = tl_math.exp(tmp22)
    tmp24 = tmp21 + tmp23
    tmp25 = tmp9 - tmp19
    tmp26 = tl_math.exp(tmp25)
    tmp27 = tmp24 + tmp26
    tmp28 = tmp13 - tmp19
    tmp29 = tl_math.exp(tmp28)
    tmp30 = tmp27 + tmp29
    tl.store(out_ptr0 + (x2), tmp14, xmask)
    tl.store(out_ptr1 + (x2), tmp30, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/fd/cfdle4rn7ga3bgxxc6qacsnydidihr7a35jf637xjryp2o5irdkx.py
# Topologically Sorted Source Nodes: [_logtrans, logsumexp, sub], Original ATen: [aten.add, aten.logsumexp, aten.sub]
# Source node to ATen node mapping:
#   _logtrans => add
#   logsumexp => abs_1, add_1, eq, full_default, log, where
#   sub => sub_1
# Graph fragment:
#   %add : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%unsqueeze, %unsqueeze_1), kwargs = {})
#   %abs_1 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%amax,), kwargs = {})
#   %eq : [num_users=1] = call_function[target=torch.ops.aten.eq.Scalar](args = (%abs_1, inf), kwargs = {})
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 0.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %where : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%eq, %full_default, %amax), kwargs = {})
#   %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%sum_1,), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%log, %where), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add, %add_1), kwargs = {})
triton_poi_fused_add_logsumexp_sub_2 = async_compile.triton('triton_poi_fused_add_logsumexp_sub_2', '''
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_logsumexp_sub_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, '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_logsumexp_sub_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex % 16
    x4 = xindex
    x5 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (x3), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x4), xmask)
    tmp3 = tl.load(in_ptr2 + (x5), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + (x5), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tl_math.log(tmp3)
    tmp6 = tl_math.abs(tmp5)
    tmp7 = float("inf")
    tmp8 = tmp6 == tmp7
    tmp9 = 0.0
    tmp10 = tl.where(tmp8, tmp9, tmp5)
    tmp11 = tmp4 + tmp10
    tmp12 = tmp2 - tmp11
    tl.store(out_ptr0 + (x4), tmp12, 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 = 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))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf0  # reuse
        buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        # Topologically Sorted Source Nodes: [relu], Original ATen: [aten.relu, aten.threshold_backward]
        stream0 = get_raw_stream(0)
        triton_poi_fused_relu_threshold_backward_0.run(buf1, primals_2, buf6, 256, grid=grid(256), stream=stream0)
        del primals_2
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_5, reinterpret_tensor(buf1, (64, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf2)
        del primals_5
        buf3 = empty_strided_cuda((4, 1, 4, 4, 1), (16, 64, 4, 1, 64), torch.float32)
        buf4 = empty_strided_cuda((4, 1, 4, 4, 1), (16, 64, 4, 1, 64), torch.float32)
        # Topologically Sorted Source Nodes: [_logtrans, logsumexp], Original ATen: [aten.add, aten.logsumexp]
        triton_poi_fused_add_logsumexp_1.run(primals_6, buf2, buf3, buf4, 64, grid=grid(64), stream=stream0)
        buf5 = empty_strided_cuda((4, 1, 4, 4, 4), (64, 64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [_logtrans, logsumexp, sub], Original ATen: [aten.add, aten.logsumexp, aten.sub]
        triton_poi_fused_add_logsumexp_sub_2.run(primals_6, buf2, buf4, buf3, buf5, 256, grid=grid(256), stream=stream0)
        del buf3
        del buf4
    return (buf5, primals_6, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(buf1, (64, 4), (4, 1), 0), buf2, primals_4, buf6, )


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((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, ), (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, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6])
    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
import torch.nn as nn
import torch.nn.functional as F
from torch.optim import Adam
from torch.utils.data import BatchSampler
from torch.utils.data import SubsetRandomSampler


class RecurrentNeuralRegressor(nn.Module):

    def __init__(self, sizes, prior, nonlin='relu'):
        super(RecurrentNeuralRegressor, self).__init__()
        self.sizes = sizes
        self.nb_states = self.sizes[-1]
        self.prior = prior
        nlist = dict(relu=F.relu, tanh=F.tanh, softmax=F.log_softmax,
            linear=F.linear)
        self.nonlin = nlist[nonlin]
        self.layer = nn.Linear(self.sizes[0], self.sizes[1])
        self.output = nn.Linear(self.sizes[1], self.sizes[2])
        _mat = 0.95 * torch.eye(self.nb_states) + 0.05 * torch.rand(self.
            nb_states, self.nb_states)
        _mat /= torch.sum(_mat, dim=1, keepdim=True)
        self.logmat = nn.Parameter(torch.log(_mat))
        self.optim = None

    def log_prior(self):
        lp = 0.0
        if self.prior:
            _matrix = torch.exp(self.logmat - torch.logsumexp(self.logmat,
                dim=-1, keepdim=True))
            for k in range(self.nb_states):
                alpha = self.prior['alpha'] * torch.ones(self.nb_states
                    ) + self.prior['kappa'] * torch.as_tensor(torch.arange(
                    self.nb_states) == k, dtype=torch.float32)
                _dirichlet = torch.distributions.dirichlet.Dirichlet(alpha)
                lp += _dirichlet.log_prob(_matrix[k])
        return lp

    def forward(self, xu):
        out = self.output(self.nonlin(self.layer(xu)))
        _logtrans = self.logmat[None, :, :] + out[:, None, :]
        return _logtrans - torch.logsumexp(_logtrans, dim=-1, keepdim=True)

    def elbo(self, zeta, xu):
        logtrans = self.forward(xu)
        return torch.sum(zeta * logtrans) + self.log_prior()

    def fit(self, zeta, xu, nb_iter=100, batch_size=None, lr=0.001):
        self.optim = Adam(self.parameters(), lr=lr)
        batch_size = xu.shape[0] if batch_size is None else batch_size
        batches = list(BatchSampler(SubsetRandomSampler(range(xu.shape[0])),
            batch_size, False))
        for n in range(nb_iter):
            for batch in batches:
                self.optim.zero_grad()
                loss = -self.elbo(zeta[batch], xu[batch])
                loss.backward()
                self.optim.step()


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


def get_init_inputs():
    return [[], {'sizes': [4, 4, 4], 'prior': 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 import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
import torch.nn.functional as F
from torch.optim import Adam
from torch.utils.data import BatchSampler
from torch.utils.data import SubsetRandomSampler
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_relu_threshold_backward_0(in_out_ptr0, 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 % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr0 + x2, tmp6, xmask)


@triton.jit
def triton_poi_fused_add_logsumexp_1(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + 4 * x2, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + (1 + 4 * x2), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (2 + 4 * x2), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp12 = tl.load(in_ptr1 + (3 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = triton_helpers.maximum(tmp2, tmp5)
    tmp9 = tmp7 + tmp8
    tmp10 = triton_helpers.maximum(tmp6, tmp9)
    tmp13 = tmp11 + tmp12
    tmp14 = triton_helpers.maximum(tmp10, tmp13)
    tmp15 = tl_math.abs(tmp14)
    tmp16 = float('inf')
    tmp17 = tmp15 == tmp16
    tmp18 = 0.0
    tmp19 = tl.where(tmp17, tmp18, tmp14)
    tmp20 = tmp2 - tmp19
    tmp21 = tl_math.exp(tmp20)
    tmp22 = tmp5 - tmp19
    tmp23 = tl_math.exp(tmp22)
    tmp24 = tmp21 + tmp23
    tmp25 = tmp9 - tmp19
    tmp26 = tl_math.exp(tmp25)
    tmp27 = tmp24 + tmp26
    tmp28 = tmp13 - tmp19
    tmp29 = tl_math.exp(tmp28)
    tmp30 = tmp27 + tmp29
    tl.store(out_ptr0 + x2, tmp14, xmask)
    tl.store(out_ptr1 + x2, tmp30, xmask)


@triton.jit
def triton_poi_fused_add_logsumexp_sub_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex % 16
    x4 = xindex
    x5 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x3, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + x4, xmask)
    tmp3 = tl.load(in_ptr2 + x5, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + x5, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tl_math.log(tmp3)
    tmp6 = tl_math.abs(tmp5)
    tmp7 = float('inf')
    tmp8 = tmp6 == tmp7
    tmp9 = 0.0
    tmp10 = tl.where(tmp8, tmp9, tmp5)
    tmp11 = tmp4 + tmp10
    tmp12 = tmp2 - tmp11
    tl.store(out_ptr0 + x4, tmp12, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = 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))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf0
        buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_relu_threshold_backward_0[grid(256)](buf1,
            primals_2, buf6, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_2
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_5, reinterpret_tensor(buf1, (64, 4), (
            4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0),
            alpha=1, beta=1, out=buf2)
        del primals_5
        buf3 = empty_strided_cuda((4, 1, 4, 4, 1), (16, 64, 4, 1, 64),
            torch.float32)
        buf4 = empty_strided_cuda((4, 1, 4, 4, 1), (16, 64, 4, 1, 64),
            torch.float32)
        triton_poi_fused_add_logsumexp_1[grid(64)](primals_6, buf2, buf3,
            buf4, 64, XBLOCK=64, num_warps=1, num_stages=1)
        buf5 = empty_strided_cuda((4, 1, 4, 4, 4), (64, 64, 16, 4, 1),
            torch.float32)
        triton_poi_fused_add_logsumexp_sub_2[grid(256)](primals_6, buf2,
            buf4, buf3, buf5, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del buf3
        del buf4
    return buf5, primals_6, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
        ), reinterpret_tensor(buf1, (64, 4), (4, 1), 0), buf2, primals_4, buf6


class RecurrentNeuralRegressorNew(nn.Module):

    def __init__(self, sizes, prior, nonlin='relu'):
        super(RecurrentNeuralRegressorNew, self).__init__()
        self.sizes = sizes
        self.nb_states = self.sizes[-1]
        self.prior = prior
        nlist = dict(relu=F.relu, tanh=F.tanh, softmax=F.log_softmax,
            linear=F.linear)
        self.nonlin = nlist[nonlin]
        self.layer = nn.Linear(self.sizes[0], self.sizes[1])
        self.output = nn.Linear(self.sizes[1], self.sizes[2])
        _mat = 0.95 * torch.eye(self.nb_states) + 0.05 * torch.rand(self.
            nb_states, self.nb_states)
        _mat /= torch.sum(_mat, dim=1, keepdim=True)
        self.logmat = nn.Parameter(torch.log(_mat))
        self.optim = None

    def log_prior(self):
        lp = 0.0
        if self.prior:
            _matrix = torch.exp(self.logmat - torch.logsumexp(self.logmat,
                dim=-1, keepdim=True))
            for k in range(self.nb_states):
                alpha = self.prior['alpha'] * torch.ones(self.nb_states
                    ) + self.prior['kappa'] * torch.as_tensor(torch.arange(
                    self.nb_states) == k, dtype=torch.float32)
                _dirichlet = torch.distributions.dirichlet.Dirichlet(alpha)
                lp += _dirichlet.log_prob(_matrix[k])
        return lp

    def elbo(self, zeta, xu):
        logtrans = self.forward(xu)
        return torch.sum(zeta * logtrans) + self.log_prior()

    def fit(self, zeta, xu, nb_iter=100, batch_size=None, lr=0.001):
        self.optim = Adam(self.parameters(), lr=lr)
        batch_size = xu.shape[0] if batch_size is None else batch_size
        batches = list(BatchSampler(SubsetRandomSampler(range(xu.shape[0])),
            batch_size, False))
        for n in range(nb_iter):
            for batch in batches:
                self.optim.zero_grad()
                loss = -self.elbo(zeta[batch], xu[batch])
                loss.backward()
                self.optim.step()

    def forward(self, input_0):
        primals_1 = self.logmat
        primals_4 = self.layer.weight
        primals_2 = self.layer.bias
        primals_6 = self.output.weight
        primals_5 = self.output.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6])
        return output[0]
