# 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/xx/cxx35mtwn5ayt37fld6r3ar4nymqsvja32r4rrr2lkvxuya6njgx.py
# Topologically Sorted Source Nodes: [mean, pow_1, mean_x2, pow_2, var, sub_1, add, sqrt, x_norm, x_norm_1, mul, x_norm_2], Original ATen: [aten.mean, aten.pow, aten.sub, aten.add, aten.sqrt, aten.div, aten.view, aten.mul]
# Source node to ATen node mapping:
#   add => add
#   mean => mean
#   mean_x2 => mean_1
#   mul => mul
#   pow_1 => pow_1
#   pow_2 => pow_2
#   sqrt => sqrt
#   sub_1 => sub_1
#   var => sub
#   x_norm => div
#   x_norm_1 => view_1
#   x_norm_2 => add_1
# Graph fragment:
#   %mean : [num_users=3] = call_function[target=torch.ops.aten.mean.dim](args = (%view, [-1], True), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%view, 2), kwargs = {})
#   %mean_1 : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%pow_1, [-1], True), kwargs = {})
#   %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%mean, 2), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mean_1, %pow_2), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view, %mean), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sub, 1e-05), kwargs = {})
#   %sqrt : [num_users=2] = call_function[target=torch.ops.aten.sqrt.default](args = (%add,), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sub_1, %sqrt), kwargs = {})
#   %view_1 : [num_users=1] = call_function[target=torch.ops.aten.reshape.default](args = (%div, [4, 4, -1]), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_2, %view_1), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %view_3), kwargs = {})
triton_per_fused_add_div_mean_mul_pow_sqrt_sub_view_0 = async_compile.triton('triton_per_fused_add_div_mean_mul_pow_sqrt_sub_view_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=[16, 16],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: 'i32', 7: '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, 6, 7), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_div_mean_mul_pow_sqrt_sub_view_0', 'mutated_arg_names': ['in_out_ptr0', 'in_out_ptr1'], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 2, '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_mul_pow_sqrt_sub_view_0(in_out_ptr0, in_out_ptr1, in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    rnumel = 16
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    x2 = xindex % 4
    tmp0 = tl.load(in_ptr0 + (r1 + (16*x0)), xmask, other=0.0)
    tmp18 = tl.load(in_ptr1 + (x2), xmask, eviction_policy='evict_last')
    tmp22 = tl.load(in_ptr2 + (x2), xmask, eviction_policy='evict_last')
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, 0)
    tmp4 = tl.sum(tmp3, 1)[:, None]
    tmp5 = tmp0 * tmp0
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK, RBLOCK])
    tmp8 = tl.where(xmask, tmp6, 0)
    tmp9 = tl.sum(tmp8, 1)[:, None]
    tmp10 = 16.0
    tmp11 = tmp4 / tmp10
    tmp12 = tmp9 / tmp10
    tmp13 = tmp11 * tmp11
    tmp14 = tmp12 - tmp13
    tmp15 = 1e-05
    tmp16 = tmp14 + tmp15
    tmp17 = libdevice.sqrt(tmp16)
    tmp19 = tmp0 - tmp11
    tmp20 = tmp19 / tmp17
    tmp21 = tmp18 * tmp20
    tmp23 = tmp21 + tmp22
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (x0), tmp11, xmask)
    tl.debug_barrier()
    tl.store(in_out_ptr1 + (x0), tmp17, xmask)
    tl.store(out_ptr0 + (r1 + (16*x0)), tmp23, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, ), (1, ))
    assert_size_stride(primals_3, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        buf2 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        buf1 = reinterpret_tensor(buf0, (4, 4, 1), (4, 1, 1), 0); del buf0  # reuse
        buf3 = reinterpret_tensor(buf2, (4, 4, 1), (4, 1, 1), 0); del buf2  # reuse
        buf4 = empty_strided_cuda((4, 4, 16), (64, 16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mean, pow_1, mean_x2, pow_2, var, sub_1, add, sqrt, x_norm, x_norm_1, mul, x_norm_2], Original ATen: [aten.mean, aten.pow, aten.sub, aten.add, aten.sqrt, aten.div, aten.view, aten.mul]
        stream0 = get_raw_stream(0)
        triton_per_fused_add_div_mean_mul_pow_sqrt_sub_view_0.run(buf1, buf3, primals_1, primals_2, primals_3, buf4, 16, 16, grid=grid(16), stream=stream0)
        del primals_2
        del primals_3
    return (reinterpret_tensor(buf4, (4, 4, 4, 4), (64, 16, 4, 1), 0), primals_1, buf1, buf3, )


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, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3])
    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 torch
from torch import nn
import torch.utils.data
import torch.nn.functional
import torch.autograd


class InstanceNorm(Module):
    """
    ## Instance Normalization Layer

    Instance normalization layer $\\text{IN}$ normalizes the input $X$ as follows:

    When input $X \\in \\mathbb{R}^{B \\times C \\times H \\times W}$ is a batch of image representations,
    where $B$ is the batch size, $C$ is the number of channels, $H$ is the height and $W$ is the width.
    $\\gamma \\in \\mathbb{R}^{C}$ and $\\beta \\in \\mathbb{R}^{C}$. The affine transformation with $gamma$ and
    $beta$ are optional.

    $$\\text{IN}(X) = \\gamma
    \\frac{X - \\underset{H, W}{\\mathbb{E}}[X]}{\\sqrt{\\underset{H, W}{Var}[X] + \\epsilon}}
    + \\beta$$
    """

    def __init__(self, channels: 'int', *, eps: float=1e-05, affine: bool=True
        ):
        """
        * `channels` is the number of features in the input
        * `eps` is $\\epsilon$, used in $\\sqrt{Var[X] + \\epsilon}$ for numerical stability
        * `affine` is whether to scale and shift the normalized value
        """
        super().__init__()
        self.channels = channels
        self.eps = eps
        self.affine = affine
        if self.affine:
            self.scale = nn.Parameter(torch.ones(channels))
            self.shift = nn.Parameter(torch.zeros(channels))

    def forward(self, x: 'torch.Tensor'):
        """
        `x` is a tensor of shape `[batch_size, channels, *]`.
        `*` denotes any number of (possibly 0) dimensions.
         For example, in an image (2D) convolution this will be
        `[batch_size, channels, height, width]`
        """
        x_shape = x.shape
        batch_size = x_shape[0]
        assert self.channels == x.shape[1]
        x = x.view(batch_size, self.channels, -1)
        mean = x.mean(dim=[-1], keepdim=True)
        mean_x2 = (x ** 2).mean(dim=[-1], keepdim=True)
        var = mean_x2 - mean ** 2
        x_norm = (x - mean) / torch.sqrt(var + self.eps)
        x_norm = x_norm.view(batch_size, self.channels, -1)
        if self.affine:
            x_norm = self.scale.view(1, -1, 1) * x_norm + self.shift.view(1,
                -1, 1)
        return x_norm.view(x_shape)


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


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

import torch
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
from torch.nn import Module
from torch import nn
import torch.utils.data
import torch.nn.functional
import torch.autograd
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_per_fused_add_div_mean_mul_pow_sqrt_sub_view_0(in_out_ptr0,
    in_out_ptr1, in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, rnumel,
    XBLOCK: tl.constexpr):
    xnumel = 16
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    x2 = xindex % 4
    tmp0 = tl.load(in_ptr0 + (r1 + 16 * x0), xmask, other=0.0)
    tmp18 = tl.load(in_ptr1 + x2, xmask, eviction_policy='evict_last')
    tmp22 = tl.load(in_ptr2 + x2, xmask, eviction_policy='evict_last')
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, 0)
    tmp4 = tl.sum(tmp3, 1)[:, None]
    tmp5 = tmp0 * tmp0
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK, RBLOCK])
    tmp8 = tl.where(xmask, tmp6, 0)
    tmp9 = tl.sum(tmp8, 1)[:, None]
    tmp10 = 16.0
    tmp11 = tmp4 / tmp10
    tmp12 = tmp9 / tmp10
    tmp13 = tmp11 * tmp11
    tmp14 = tmp12 - tmp13
    tmp15 = 1e-05
    tmp16 = tmp14 + tmp15
    tmp17 = libdevice.sqrt(tmp16)
    tmp19 = tmp0 - tmp11
    tmp20 = tmp19 / tmp17
    tmp21 = tmp18 * tmp20
    tmp23 = tmp21 + tmp22
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp11, xmask)
    tl.debug_barrier()
    tl.store(in_out_ptr1 + x0, tmp17, xmask)
    tl.store(out_ptr0 + (r1 + 16 * x0), tmp23, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        buf2 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        buf1 = reinterpret_tensor(buf0, (4, 4, 1), (4, 1, 1), 0)
        del buf0
        buf3 = reinterpret_tensor(buf2, (4, 4, 1), (4, 1, 1), 0)
        del buf2
        buf4 = empty_strided_cuda((4, 4, 16), (64, 16, 1), torch.float32)
        get_raw_stream(0)
        triton_per_fused_add_div_mean_mul_pow_sqrt_sub_view_0[grid(16)](buf1,
            buf3, primals_1, primals_2, primals_3, buf4, 16, 16, XBLOCK=8,
            num_warps=2, num_stages=1)
        del primals_2
        del primals_3
    return reinterpret_tensor(buf4, (4, 4, 4, 4), (64, 16, 4, 1), 0
        ), primals_1, buf1, buf3


class InstanceNormNew(Module):
    """
    ## Instance Normalization Layer

    Instance normalization layer $\\text{IN}$ normalizes the input $X$ as follows:

    When input $X \\in \\mathbb{R}^{B \\times C \\times H \\times W}$ is a batch of image representations,
    where $B$ is the batch size, $C$ is the number of channels, $H$ is the height and $W$ is the width.
    $\\gamma \\in \\mathbb{R}^{C}$ and $\\beta \\in \\mathbb{R}^{C}$. The affine transformation with $gamma$ and
    $beta$ are optional.

    $$\\text{IN}(X) = \\gamma
    \\frac{X - \\underset{H, W}{\\mathbb{E}}[X]}{\\sqrt{\\underset{H, W}{Var}[X] + \\epsilon}}
    + \\beta$$
    """

    def __init__(self, channels: 'int', *, eps: float=1e-05, affine: bool=True
        ):
        """
        * `channels` is the number of features in the input
        * `eps` is $\\epsilon$, used in $\\sqrt{Var[X] + \\epsilon}$ for numerical stability
        * `affine` is whether to scale and shift the normalized value
        """
        super().__init__()
        self.channels = channels
        self.eps = eps
        self.affine = affine
        if self.affine:
            self.scale = nn.Parameter(torch.ones(channels))
            self.shift = nn.Parameter(torch.zeros(channels))

    def forward(self, input_0):
        primals_2 = self.scale
        primals_3 = self.shift
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]
