# 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/f7/cf7rz22aqis7umipylpjgcc5pa2x2dj7bkpg2w5kl52634qjvyzl.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=2] = 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=[256], 
    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 = 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)
    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/ex/cexx6sx6tmxabn4jt6yi2hcwlgi6alv2u73bmnxs57jfhli2nx43.py
# Topologically Sorted Source Nodes: [exp_1, mul_1, bias, conv2d], Original ATen: [aten.exp, aten.mul, aten.add, aten.convolution]
# Source node to ATen node mapping:
#   bias => add_1
#   conv2d => convolution
#   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 = {})
#   %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_5, %add, %add_1, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_add_convolution_exp_mul_1 = async_compile.triton('triton_poi_fused_add_convolution_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_convolution_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_convolution_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')


# kernel path: runs/run_shard_7/inductor_cache/tx/ctxzqevc6tkfvzdf6yuxhf2qk7dtxu5v2f2auzkqvdbvvzqjkyfv.py
# Topologically Sorted Source Nodes: [exp_1, mul_1, bias, conv2d], Original ATen: [aten.exp, aten.mul, aten.add, aten.convolution]
# Source node to ATen node mapping:
#   bias => add_1
#   conv2d => convolution
#   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 = {})
#   %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_5, %add, %add_1, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_add_convolution_exp_mul_2 = async_compile.triton('triton_poi_fused_add_convolution_exp_mul_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=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_convolution_exp_mul_2', '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_add_convolution_exp_mul_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    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
    tl.store(in_out_ptr0 + (x2), tmp2, 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, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 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, 4, 4], dtype=torch.float32, device=device(type='cuda', index=0), pin_memory=False)
        buf1 = buf0
        del buf0
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 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, buf2, 256, grid=grid(256), stream=stream0)
        del primals_1
        # Topologically Sorted Source Nodes: [randn_like_1], Original ATen: [aten.randn_like]
        buf3 = torch.ops.aten.randn.default([4], dtype=torch.float32, device=device(type='cuda', index=0), pin_memory=False)
        buf4 = buf3
        del buf3
        buf5 = empty_strided_cuda((4, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [exp_1, mul_1, bias, conv2d], Original ATen: [aten.exp, aten.mul, aten.add, aten.convolution]
        triton_poi_fused_add_convolution_exp_mul_1.run(primals_3, primals_4, buf4, buf5, 4, grid=grid(4), stream=stream0)
        del primals_3
        # Topologically Sorted Source Nodes: [exp_1, mul_1, bias, conv2d], Original ATen: [aten.exp, aten.mul, aten.add, aten.convolution]
        buf6 = extern_kernels.convolution(primals_5, buf2, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 4, 1, 1), (4, 1, 1, 1))
        buf7 = buf6; del buf6  # reuse
        # Topologically Sorted Source Nodes: [exp_1, mul_1, bias, conv2d], Original ATen: [aten.exp, aten.mul, aten.add, aten.convolution]
        triton_poi_fused_add_convolution_exp_mul_2.run(buf7, buf5, 16, grid=grid(16), stream=stream0)
        del buf5
    return (buf7, primals_2, primals_4, primals_5, buf1, buf2, buf4, )


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, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4, 4, 4), (64, 16, 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
from torch.nn.modules.utils import _pair


class _BayesConvNd(Module):
    """
    Applies Bayesian Convolution

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

    .. note:: other arguments are following conv of pytorch 1.2.0.
    https://github.com/pytorch/pytorch/blob/master/torch/nn/modules/conv.py
    """
    __constants__ = ['prior_mu', 'prior_sigma', 'stride', 'padding',
        'dilation', 'groups', 'bias', 'padding_mode', 'output_padding',
        'in_channels', 'out_channels', 'kernel_size']

    def __init__(self, prior_mu, prior_sigma, in_channels, out_channels,
        kernel_size, stride, padding, dilation, transposed, output_padding,
        groups, bias, padding_mode):
        super(_BayesConvNd, self).__init__()
        if in_channels % groups != 0:
            raise ValueError('in_channels must be divisible by groups')
        if out_channels % groups != 0:
            raise ValueError('out_channels must be divisible by groups')
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.kernel_size = kernel_size
        self.stride = stride
        self.padding = padding
        self.dilation = dilation
        self.transposed = transposed
        self.output_padding = output_padding
        self.groups = groups
        self.padding_mode = padding_mode
        self.prior_mu = prior_mu
        self.prior_sigma = prior_sigma
        self.prior_log_sigma = math.log(prior_sigma)
        if transposed:
            self.weight_mu = Parameter(torch.Tensor(in_channels, 
                out_channels // groups, *kernel_size))
            self.weight_log_sigma = Parameter(torch.Tensor(in_channels, 
                out_channels // groups, *kernel_size))
            self.register_buffer('weight_eps', None)
        else:
            self.weight_mu = Parameter(torch.Tensor(out_channels, 
                in_channels // groups, *kernel_size))
            self.weight_log_sigma = Parameter(torch.Tensor(out_channels, 
                in_channels // groups, *kernel_size))
            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_channels))
            self.bias_log_sigma = Parameter(torch.Tensor(out_channels))
            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):
        n = self.in_channels
        n *= self.kernel_size[0] ** 2
        stdv = 1.0 / math.sqrt(n)
        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):
        s = (
            '{prior_mu}, {prior_sigma}, {in_channels}, {out_channels}, kernel_size={kernel_size}, stride={stride}'
            )
        if self.padding != (0,) * len(self.padding):
            s += ', padding={padding}'
        if self.dilation != (1,) * len(self.dilation):
            s += ', dilation={dilation}'
        if self.output_padding != (0,) * len(self.output_padding):
            s += ', output_padding={output_padding}'
        if self.groups != 1:
            s += ', groups={groups}'
        if self.bias is False:
            s += ', bias=False'
        return s.format(**self.__dict__)

    def __setstate__(self, state):
        super(_BayesConvNd, self).__setstate__(state)
        if not hasattr(self, 'padding_mode'):
            self.padding_mode = 'zeros'


class BayesConv2d(_BayesConvNd):
    """
    Applies Bayesian Convolution for 2D inputs

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

    .. note:: other arguments are following conv of pytorch 1.2.0.
    https://github.com/pytorch/pytorch/blob/master/torch/nn/modules/conv.py
    
    """

    def __init__(self, prior_mu, prior_sigma, in_channels, out_channels,
        kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True,
        padding_mode='zeros'):
        kernel_size = _pair(kernel_size)
        stride = _pair(stride)
        padding = _pair(padding)
        dilation = _pair(dilation)
        super(BayesConv2d, self).__init__(prior_mu, prior_sigma,
            in_channels, out_channels, kernel_size, stride, padding,
            dilation, False, _pair(0), groups, bias, padding_mode)

    def conv2d_forward(self, input, weight):
        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
        if self.padding_mode == 'circular':
            expanded_padding = (self.padding[1] + 1) // 2, self.padding[1
                ] // 2, (self.padding[0] + 1) // 2, self.padding[0] // 2
            return F.conv2d(F.pad(input, expanded_padding, mode='circular'),
                weight, bias, self.stride, _pair(0), self.dilation, self.groups
                )
        return F.conv2d(input, weight, bias, self.stride, self.padding,
            self.dilation, self.groups)

    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
        return self.conv2d_forward(input, weight)


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


def get_init_inputs():
    return [[], {'prior_mu': 4, 'prior_sigma': 4, 'in_channels': 4,
        'out_channels': 4, 'kernel_size': 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
import torch.nn.functional as F
from torch.nn.modules.utils import _pair
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_poi_fused_add_exp_mul_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)
    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_convolution_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)


@triton.jit
def triton_poi_fused_add_convolution_exp_mul_2(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 16
    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
    tl.store(in_out_ptr0 + x2, tmp2, 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, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 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, 4, 4], dtype=torch.
            float32, device=device(type='cuda', index=0), pin_memory=False)
        buf1 = buf0
        del buf0
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_exp_mul_0[grid(256)](primals_1, primals_2,
            buf1, buf2, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_1
        buf3 = torch.ops.aten.randn.default([4], dtype=torch.float32,
            device=device(type='cuda', index=0), pin_memory=False)
        buf4 = buf3
        del buf3
        buf5 = empty_strided_cuda((4,), (1,), torch.float32)
        triton_poi_fused_add_convolution_exp_mul_1[grid(4)](primals_3,
            primals_4, buf4, buf5, 4, XBLOCK=4, num_warps=1, num_stages=1)
        del primals_3
        buf6 = extern_kernels.convolution(primals_5, buf2, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 4, 1, 1), (4, 1, 1, 1))
        buf7 = buf6
        del buf6
        triton_poi_fused_add_convolution_exp_mul_2[grid(16)](buf7, buf5, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        del buf5
    return buf7, primals_2, primals_4, primals_5, buf1, buf2, buf4


class _BayesConvNd(Module):
    """
    Applies Bayesian Convolution

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

    .. note:: other arguments are following conv of pytorch 1.2.0.
    https://github.com/pytorch/pytorch/blob/master/torch/nn/modules/conv.py
    """
    __constants__ = ['prior_mu', 'prior_sigma', 'stride', 'padding',
        'dilation', 'groups', 'bias', 'padding_mode', 'output_padding',
        'in_channels', 'out_channels', 'kernel_size']

    def __init__(self, prior_mu, prior_sigma, in_channels, out_channels,
        kernel_size, stride, padding, dilation, transposed, output_padding,
        groups, bias, padding_mode):
        super(_BayesConvNd, self).__init__()
        if in_channels % groups != 0:
            raise ValueError('in_channels must be divisible by groups')
        if out_channels % groups != 0:
            raise ValueError('out_channels must be divisible by groups')
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.kernel_size = kernel_size
        self.stride = stride
        self.padding = padding
        self.dilation = dilation
        self.transposed = transposed
        self.output_padding = output_padding
        self.groups = groups
        self.padding_mode = padding_mode
        self.prior_mu = prior_mu
        self.prior_sigma = prior_sigma
        self.prior_log_sigma = math.log(prior_sigma)
        if transposed:
            self.weight_mu = Parameter(torch.Tensor(in_channels, 
                out_channels // groups, *kernel_size))
            self.weight_log_sigma = Parameter(torch.Tensor(in_channels, 
                out_channels // groups, *kernel_size))
            self.register_buffer('weight_eps', None)
        else:
            self.weight_mu = Parameter(torch.Tensor(out_channels, 
                in_channels // groups, *kernel_size))
            self.weight_log_sigma = Parameter(torch.Tensor(out_channels, 
                in_channels // groups, *kernel_size))
            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_channels))
            self.bias_log_sigma = Parameter(torch.Tensor(out_channels))
            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):
        n = self.in_channels
        n *= self.kernel_size[0] ** 2
        stdv = 1.0 / math.sqrt(n)
        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):
        s = (
            '{prior_mu}, {prior_sigma}, {in_channels}, {out_channels}, kernel_size={kernel_size}, stride={stride}'
            )
        if self.padding != (0,) * len(self.padding):
            s += ', padding={padding}'
        if self.dilation != (1,) * len(self.dilation):
            s += ', dilation={dilation}'
        if self.output_padding != (0,) * len(self.output_padding):
            s += ', output_padding={output_padding}'
        if self.groups != 1:
            s += ', groups={groups}'
        if self.bias is False:
            s += ', bias=False'
        return s.format(**self.__dict__)

    def __setstate__(self, state):
        super(_BayesConvNd, self).__setstate__(state)
        if not hasattr(self, 'padding_mode'):
            self.padding_mode = 'zeros'


class BayesConv2dNew(_BayesConvNd):
    """
    Applies Bayesian Convolution for 2D inputs

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

    .. note:: other arguments are following conv of pytorch 1.2.0.
    https://github.com/pytorch/pytorch/blob/master/torch/nn/modules/conv.py
    
    """

    def __init__(self, prior_mu, prior_sigma, in_channels, out_channels,
        kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True,
        padding_mode='zeros'):
        kernel_size = _pair(kernel_size)
        stride = _pair(stride)
        padding = _pair(padding)
        dilation = _pair(dilation)
        super(BayesConv2dNew, self).__init__(prior_mu, prior_sigma,
            in_channels, out_channels, kernel_size, stride, padding,
            dilation, False, _pair(0), groups, bias, padding_mode)

    def conv2d_forward(self, input, weight):
        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
        if self.padding_mode == 'circular':
            expanded_padding = (self.padding[1] + 1) // 2, self.padding[1
                ] // 2, (self.padding[0] + 1) // 2, self.padding[0] // 2
            return F.conv2d(F.pad(input, expanded_padding, mode='circular'),
                weight, bias, self.stride, _pair(0), self.dilation, self.groups
                )
        return F.conv2d(input, weight, bias, self.stride, self.padding,
            self.dilation, self.groups)

    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]
