# 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_7/inductor_cache/j5/cj5ctsoyfskkt54irtpcnjzgt6opocbga6u3hypkpfxeaijd4zsp.py
# Topologically Sorted Source Nodes: [exp, mul, weight], Original ATen: [aten.exp, aten.mul, aten.add]
# Source node to ATen node mapping:
#   exp => exp
#   mul => mul
#   weight => add
# Graph fragment:
#   %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%primals_2,), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%exp, %randn), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%primals_1, %mul), kwargs = {})
triton_poi_fused_add_exp_mul_0 = async_compile.triton('triton_poi_fused_add_exp_mul_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=[16], 
    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_exp_mul_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_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    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)
    tmp3 = tl.load(in_ptr2 + (x0), xmask)
    tmp2 = tl_math.exp(tmp1)
    tmp4 = tmp2 * tmp3
    tmp5 = tmp0 + tmp4
    tl.store(out_ptr0 + (x0), tmp5, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/yl/cylfmjzneu3zf4iy7raije3cdgidyiljws7vhoply5hziwhg6bc3.py
# Topologically Sorted Source Nodes: [exp_1, mul_1, bias], Original ATen: [aten.exp, aten.mul, aten.add]
# Source node to ATen node mapping:
#   bias => add_1
#   exp_1 => exp_1
#   mul_1 => mul_1
# Graph fragment:
#   %exp_1 : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%primals_4,), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%exp_1, %randn_1), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%primals_3, %mul_1), kwargs = {})
triton_poi_fused_add_exp_mul_1 = async_compile.triton('triton_poi_fused_add_exp_mul_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=[4], 
    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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_exp_mul_1', '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_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    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)
    tmp3 = tl.load(in_ptr2 + (x0), xmask)
    tmp2 = tl_math.exp(tmp1)
    tmp4 = tmp2 * tmp3
    tmp5 = tmp0 + tmp4
    tl.store(out_ptr0 + (x0), tmp5, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4, ), (1, ))
    assert_size_stride(primals_4, (4, ), (1, ))
    assert_size_stride(primals_5, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [randn_like], Original ATen: [aten.randn_like]
        buf0 = torch.ops.aten.randn.default([4, 4], dtype=torch.float32, device=device(type='cuda', index=0), pin_memory=False)
        buf1 = buf0
        del buf0
        # Topologically Sorted Source Nodes: [randn_like_1], Original ATen: [aten.randn_like]
        buf2 = torch.ops.aten.randn.default([4], dtype=torch.float32, device=device(type='cuda', index=0), pin_memory=False)
        buf3 = buf2
        del buf2
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [exp, mul, weight], Original ATen: [aten.exp, aten.mul, aten.add]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_exp_mul_0.run(primals_1, primals_2, buf1, buf4, 16, grid=grid(16), stream=stream0)
        del primals_1
        buf5 = empty_strided_cuda((4, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [exp_1, mul_1, bias], Original ATen: [aten.exp, aten.mul, aten.add]
        triton_poi_fused_add_exp_mul_1.run(primals_3, primals_4, buf3, buf5, 4, grid=grid(4), stream=stream0)
        del primals_3
        buf6 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [exp_1, mul_1, bias, linear], Original ATen: [aten.exp, aten.mul, aten.add, aten.addmm]
        extern_kernels.addmm(buf5, reinterpret_tensor(primals_5, (64, 4), (4, 1), 0), reinterpret_tensor(buf4, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf6)
        del buf4
        del buf5
    return (reinterpret_tensor(buf6, (4, 4, 4, 4), (64, 16, 4, 1), 0), primals_2, primals_4, buf1, buf3, reinterpret_tensor(primals_5, (64, 4), (4, 1), 0), )


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, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5])
    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)

from torch.nn import Module
import math
import torch
from torch.nn import Parameter
import torch.nn.functional as F


class BayesLinear(Module):
    """
    Applies Bayesian Linear

    Arguments:
        prior_mu (Float): mean of prior normal distribution.
        prior_sigma (Float): sigma of prior normal distribution.

    .. note:: other arguments are following linear of pytorch 1.2.0.
    https://github.com/pytorch/pytorch/blob/master/torch/nn/modules/linear.py
    
    """
    __constants__ = ['prior_mu', 'prior_sigma', 'bias', 'in_features',
        'out_features']

    def __init__(self, prior_mu, prior_sigma, in_features, out_features,
        bias=True):
        super(BayesLinear, self).__init__()
        self.in_features = in_features
        self.out_features = out_features
        self.prior_mu = prior_mu
        self.prior_sigma = prior_sigma
        self.prior_log_sigma = math.log(prior_sigma)
        self.weight_mu = Parameter(torch.Tensor(out_features, in_features))
        self.weight_log_sigma = Parameter(torch.Tensor(out_features,
            in_features))
        self.register_buffer('weight_eps', None)
        if bias is None or bias is False:
            self.bias = False
        else:
            self.bias = True
        if self.bias:
            self.bias_mu = Parameter(torch.Tensor(out_features))
            self.bias_log_sigma = Parameter(torch.Tensor(out_features))
            self.register_buffer('bias_eps', None)
        else:
            self.register_parameter('bias_mu', None)
            self.register_parameter('bias_log_sigma', None)
            self.register_buffer('bias_eps', None)
        self.reset_parameters()

    def reset_parameters(self):
        stdv = 1.0 / math.sqrt(self.weight_mu.size(1))
        self.weight_mu.data.uniform_(-stdv, stdv)
        self.weight_log_sigma.data.fill_(self.prior_log_sigma)
        if self.bias:
            self.bias_mu.data.uniform_(-stdv, stdv)
            self.bias_log_sigma.data.fill_(self.prior_log_sigma)

    def freeze(self):
        self.weight_eps = torch.randn_like(self.weight_log_sigma)
        if self.bias:
            self.bias_eps = torch.randn_like(self.bias_log_sigma)

    def unfreeze(self):
        self.weight_eps = None
        if self.bias:
            self.bias_eps = None

    def forward(self, input):
        """
        Overriden.
        """
        if self.weight_eps is None:
            weight = self.weight_mu + torch.exp(self.weight_log_sigma
                ) * torch.randn_like(self.weight_log_sigma)
        else:
            weight = self.weight_mu + torch.exp(self.weight_log_sigma
                ) * self.weight_eps
        if self.bias:
            if self.bias_eps is None:
                bias = self.bias_mu + torch.exp(self.bias_log_sigma
                    ) * torch.randn_like(self.bias_log_sigma)
            else:
                bias = self.bias_mu + torch.exp(self.bias_log_sigma
                    ) * self.bias_eps
        else:
            bias = None
        return F.linear(input, weight, bias)

    def extra_repr(self):
        """
        Overriden.
        """
        return (
            'prior_mu={}, prior_sigma={}, in_features={}, out_features={}, bias={}'
            .format(self.prior_mu, self.prior_sigma, self.in_features, self
            .out_features, self.bias is not None))


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


def get_init_inputs():
    return [[], {'prior_mu': 4, 'prior_sigma': 4, 'in_features': 4,
        'out_features': 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.triton_helpers import math as tl_math
from torch.nn import Module
import math
from torch.nn import Parameter
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_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    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)
    tmp3 = tl.load(in_ptr2 + x0, xmask)
    tmp2 = tl_math.exp(tmp1)
    tmp4 = tmp2 * tmp3
    tmp5 = tmp0 + tmp4
    tl.store(out_ptr0 + x0, tmp5, xmask)


@triton.jit
def triton_poi_fused_add_exp_mul_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    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)
    tmp3 = tl.load(in_ptr2 + x0, xmask)
    tmp2 = tl_math.exp(tmp1)
    tmp4 = tmp2 * tmp3
    tmp5 = tmp0 + tmp4
    tl.store(out_ptr0 + x0, tmp5, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4,), (1,))
    assert_size_stride(primals_5, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = torch.ops.aten.randn.default([4, 4], dtype=torch.float32,
            device=device(type='cuda', index=0), pin_memory=False)
        buf1 = buf0
        del buf0
        buf2 = torch.ops.aten.randn.default([4], dtype=torch.float32,
            device=device(type='cuda', index=0), pin_memory=False)
        buf3 = buf2
        del buf2
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_exp_mul_0[grid(16)](primals_1, primals_2, buf1,
            buf4, 16, XBLOCK=16, num_warps=1, num_stages=1)
        del primals_1
        buf5 = empty_strided_cuda((4,), (1,), torch.float32)
        triton_poi_fused_add_exp_mul_1[grid(4)](primals_3, primals_4, buf3,
            buf5, 4, XBLOCK=4, num_warps=1, num_stages=1)
        del primals_3
        buf6 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(buf5, reinterpret_tensor(primals_5, (64, 4), (
            4, 1), 0), reinterpret_tensor(buf4, (4, 4), (1, 4), 0), alpha=1,
            beta=1, out=buf6)
        del buf4
        del buf5
    return reinterpret_tensor(buf6, (4, 4, 4, 4), (64, 16, 4, 1), 0
        ), primals_2, primals_4, buf1, buf3, reinterpret_tensor(primals_5,
        (64, 4), (4, 1), 0)


class BayesLinearNew(Module):
    """
    Applies Bayesian Linear

    Arguments:
        prior_mu (Float): mean of prior normal distribution.
        prior_sigma (Float): sigma of prior normal distribution.

    .. note:: other arguments are following linear of pytorch 1.2.0.
    https://github.com/pytorch/pytorch/blob/master/torch/nn/modules/linear.py
    
    """
    __constants__ = ['prior_mu', 'prior_sigma', 'bias', 'in_features',
        'out_features']

    def __init__(self, prior_mu, prior_sigma, in_features, out_features,
        bias=True):
        super(BayesLinearNew, self).__init__()
        self.in_features = in_features
        self.out_features = out_features
        self.prior_mu = prior_mu
        self.prior_sigma = prior_sigma
        self.prior_log_sigma = math.log(prior_sigma)
        self.weight_mu = Parameter(torch.Tensor(out_features, in_features))
        self.weight_log_sigma = Parameter(torch.Tensor(out_features,
            in_features))
        self.register_buffer('weight_eps', None)
        if bias is None or bias is False:
            self.bias = False
        else:
            self.bias = True
        if self.bias:
            self.bias_mu = Parameter(torch.Tensor(out_features))
            self.bias_log_sigma = Parameter(torch.Tensor(out_features))
            self.register_buffer('bias_eps', None)
        else:
            self.register_parameter('bias_mu', None)
            self.register_parameter('bias_log_sigma', None)
            self.register_buffer('bias_eps', None)
        self.reset_parameters()

    def reset_parameters(self):
        stdv = 1.0 / math.sqrt(self.weight_mu.size(1))
        self.weight_mu.data.uniform_(-stdv, stdv)
        self.weight_log_sigma.data.fill_(self.prior_log_sigma)
        if self.bias:
            self.bias_mu.data.uniform_(-stdv, stdv)
            self.bias_log_sigma.data.fill_(self.prior_log_sigma)

    def freeze(self):
        self.weight_eps = torch.randn_like(self.weight_log_sigma)
        if self.bias:
            self.bias_eps = torch.randn_like(self.bias_log_sigma)

    def unfreeze(self):
        self.weight_eps = None
        if self.bias:
            self.bias_eps = None

    def extra_repr(self):
        """
        Overriden.
        """
        return (
            'prior_mu={}, prior_sigma={}, in_features={}, out_features={}, bias={}'
            .format(self.prior_mu, self.prior_sigma, self.in_features, self
            .out_features, self.bias is not None))

    def forward(self, input_0):
        primals_1 = self.weight_mu
        primals_2 = self.weight_log_sigma
        primals_3 = self.bias_mu
        primals_4 = self.bias_log_sigma
        primals_5 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]
