# AOT ID: ['0_inference']
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_6/inductor_cache/ct/ccta4p4sdavpmwiwpezkphgwype4gbkqq5xszhr2dkhhm2onyavq.py
# Topologically Sorted Source Nodes: [mean_1, sub, std_1, add, mean_3, add_3], Original ATen: [aten.mean, aten.sub, aten.std, aten.add, aten.div]
# Source node to ATen node mapping:
#   add => add
#   add_3 => add_3
#   mean_1 => mean
#   mean_3 => div
#   std_1 => sqrt, var
#   sub => sub
# Graph fragment:
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%getitem,), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%getitem, %mean), kwargs = {})
#   %var : [num_users=1] = call_function[target=torch.ops.aten.var.correction](args = (%getitem,), kwargs = {correction: 1.0})
#   %sqrt : [num_users=1] = call_function[target=torch.ops.aten.sqrt.default](args = (%var,), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sqrt, 1e-05), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sub, %add), kwargs = {})
#   %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%div, %randn), kwargs = {})
triton_per_fused_add_div_mean_std_sub_0 = async_compile.triton('triton_per_fused_add_div_mean_std_sub_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.persistent_reduction(
    size_hints=[1, 128],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 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': {2: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 3), equal_to_1=(2,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_div_mean_std_sub_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 4, '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}
)
@triton.jit
def triton_per_fused_add_div_mean_std_sub_0(in_out_ptr0, in_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 128
    RBLOCK: tl.constexpr = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex % 2
    r1 = (rindex // 2)
    r2 = rindex
    tmp0 = tl.load(in_ptr0 + (r0 + (4*r1)), None)
    tmp25 = tl.load(in_out_ptr0 + (r2), None)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.sum(tmp1, 1)[:, None]
    tmp5 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp7 = tl.sum(tmp5, 1)[:, None]
    tmp8 = tl.full([XBLOCK, 1], 128, tl.int32)
    tmp9 = tmp8.to(tl.float32)
    tmp10 = tmp7 / tmp9
    tmp11 = tmp1 - tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tl.broadcast_to(tmp12, [XBLOCK, RBLOCK])
    tmp15 = tl.sum(tmp13, 1)[:, None]
    tmp16 = 128.0
    tmp17 = tmp3 / tmp16
    tmp18 = tmp0 - tmp17
    tmp19 = 127.0
    tmp20 = tmp15 / tmp19
    tmp21 = libdevice.sqrt(tmp20)
    tmp22 = 1e-05
    tmp23 = tmp21 + tmp22
    tmp24 = tmp18 / tmp23
    tmp26 = tmp24 + tmp25
    tl.store(in_out_ptr0 + (tl.broadcast_to(r2, [XBLOCK, RBLOCK])), tmp26, None)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [noise], Original ATen: [aten.randn_like]
        buf4 = torch.ops.aten.randn.default([4, 4, 4, 2], dtype=torch.float32, device=device(type='cuda', index=0), pin_memory=False)
        buf5 = buf4
        del buf4
        buf6 = buf5; del buf5  # reuse
        # Topologically Sorted Source Nodes: [mean_1, sub, std_1, add, mean_3, add_3], Original ATen: [aten.mean, aten.sub, aten.std, aten.add, aten.div]
        stream0 = get_raw_stream(0)
        triton_per_fused_add_div_mean_std_sub_0.run(buf6, arg0_1, 1, 128, grid=grid(1), stream=stream0)
        del arg0_1
    return (buf6, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1])
    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.utils.checkpoint


def norm(x, axis=None, eps=1e-05):
    if axis is not None:
        return (x - x.mean(axis, True)) / (x.std(axis, keepdim=True) + eps)
    else:
        return (x - x.mean()) / (x.std() + eps)


class NormalSamples(nn.Module):
    """The [reparameterization trick](https://arxiv.org/abs/1312.6114v10) for sampling values from Gaussian distributions with learned mean & stddev.

    The input vector must be twice as big as the output. And, normalize it, since we don't impose a loss-based regularization on mean & stddev here like VAEs do."""

    def __init__(self):
        super().__init__()

    def forward(self, mean_std):
        mean, std = mean_std.split(int(mean_std.shape[-1]) // 2, -1)
        mean, std = norm(mean), norm(std) + 1
        noise = torch.randn_like(mean)
        return mean + noise


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


def get_init_inputs():
    return [[], {}]

import torch
from torch import device
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
import torch.nn as nn
import torch.utils.checkpoint
assert_size_stride = torch._C._dynamo.guards.assert_size_stride


@triton.jit
def triton_per_fused_add_div_mean_std_sub_0(in_out_ptr0, in_ptr0, xnumel,
    rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 128
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex % 2
    r1 = rindex // 2
    r2 = rindex
    tmp0 = tl.load(in_ptr0 + (r0 + 4 * r1), None)
    tmp25 = tl.load(in_out_ptr0 + r2, None)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.sum(tmp1, 1)[:, None]
    tmp5 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp7 = tl.sum(tmp5, 1)[:, None]
    tmp8 = tl.full([XBLOCK, 1], 128, tl.int32)
    tmp9 = tmp8.to(tl.float32)
    tmp10 = tmp7 / tmp9
    tmp11 = tmp1 - tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tl.broadcast_to(tmp12, [XBLOCK, RBLOCK])
    tmp15 = tl.sum(tmp13, 1)[:, None]
    tmp16 = 128.0
    tmp17 = tmp3 / tmp16
    tmp18 = tmp0 - tmp17
    tmp19 = 127.0
    tmp20 = tmp15 / tmp19
    tmp21 = libdevice.sqrt(tmp20)
    tmp22 = 1e-05
    tmp23 = tmp21 + tmp22
    tmp24 = tmp18 / tmp23
    tmp26 = tmp24 + tmp25
    tl.store(in_out_ptr0 + tl.broadcast_to(r2, [XBLOCK, RBLOCK]), tmp26, None)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf4 = torch.ops.aten.randn.default([4, 4, 4, 2], dtype=torch.
            float32, device=device(type='cuda', index=0), pin_memory=False)
        buf5 = buf4
        del buf4
        buf6 = buf5
        del buf5
        get_raw_stream(0)
        triton_per_fused_add_div_mean_std_sub_0[grid(1)](buf6, arg0_1, 1, 
            128, XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
    return buf6,


def norm(x, axis=None, eps=1e-05):
    if axis is not None:
        return (x - x.mean(axis, True)) / (x.std(axis, keepdim=True) + eps)
    else:
        return (x - x.mean()) / (x.std() + eps)


class NormalSamplesNew(nn.Module):
    """The [reparameterization trick](https://arxiv.org/abs/1312.6114v10) for sampling values from Gaussian distributions with learned mean & stddev.

    The input vector must be twice as big as the output. And, normalize it, since we don't impose a loss-based regularization on mean & stddev here like VAEs do."""

    def __init__(self):
        super().__init__()

    def forward(self, input_0):
        arg0_1 = input_0
        output = call([arg0_1])
        return output[0]
