# 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/de/cdevjdi7y6x3s4r3k3szbteg2ev2eakun5rtqomlfjaapyyqnqaf.py
# Topologically Sorted Source Nodes: [cat, x, leaky_relu], Original ATen: [aten.cat, aten._native_batch_norm_legit, aten.leaky_relu, aten.leaky_relu_backward]
# Source node to ATen node mapping:
#   cat => cat
#   leaky_relu => gt, mul_2, where
#   x => add, add_1, mul, mul_1, rsqrt, sub, var_mean
# Graph fragment:
#   %cat : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%primals_1, %neg], 1), kwargs = {})
#   %var_mean : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%view, [0, 2, 3]), kwargs = {correction: 0, keepdim: True})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem, 1e-05), kwargs = {})
#   %rsqrt : [num_users=2] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add,), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view, %getitem_1), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, %rsqrt), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul, %unsqueeze_1), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, %unsqueeze_3), kwargs = {})
#   %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%view_1, 0), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_1, 0.01), kwargs = {})
#   %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt, %view_1, %mul_2), kwargs = {})
#   %gt_1 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%view_8, 0), kwargs = {})
triton_per_fused__native_batch_norm_legit_cat_leaky_relu_leaky_relu_backward_0 = async_compile.triton('triton_per_fused__native_batch_norm_legit_cat_leaky_relu_leaky_relu_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.persistent_reduction(
    size_hints=[32, 16],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*i1', 7: '*fp32', 8: 'i32', 9: '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, 8, 9), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__native_batch_norm_legit_cat_leaky_relu_leaky_relu_backward_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 4, '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__native_batch_norm_legit_cat_leaky_relu_leaky_relu_backward_0(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, out_ptr3, out_ptr4, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 32
    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)
    x0 = xindex % 8
    r2 = rindex
    x1 = (xindex // 8)
    x3 = xindex
    tmp37 = tl.load(in_ptr1 + (x3 % 8), xmask, eviction_policy='evict_last')
    tmp39 = tl.load(in_ptr2 + (x3 % 8), xmask, eviction_policy='evict_last')
    tmp0 = x0
    tmp1 = tl.full([1, 1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1, 1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (r2 + (16*x0) + (64*x1)), tmp4 & xmask, other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1, 1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tl.load(in_ptr0 + (r2 + (16*((-4) + x0)) + (64*x1)), tmp6 & xmask, other=0.0)
    tmp10 = -tmp9
    tmp11 = tl.full(tmp10.shape, 0.0, tmp10.dtype)
    tmp12 = tl.where(tmp6, tmp10, tmp11)
    tmp13 = tl.where(tmp4, tmp5, tmp12)
    tmp14 = tl.broadcast_to(tmp13, [XBLOCK, RBLOCK])
    tmp16 = tl.where(xmask, tmp14, 0)
    tmp17 = tl.broadcast_to(tmp14, [XBLOCK, RBLOCK])
    tmp19 = tl.where(xmask, tmp17, 0)
    tmp20 = tl.sum(tmp19, 1)[:, None]
    tmp21 = tl.full([XBLOCK, 1], 16, tl.int32)
    tmp22 = tmp21.to(tl.float32)
    tmp23 = tmp20 / tmp22
    tmp24 = tmp14 - tmp23
    tmp25 = tmp24 * tmp24
    tmp26 = tl.broadcast_to(tmp25, [XBLOCK, RBLOCK])
    tmp28 = tl.where(xmask, tmp26, 0)
    tmp29 = tl.sum(tmp28, 1)[:, None]
    tmp30 = tmp13 - tmp23
    tmp31 = 16.0
    tmp32 = tmp29 / tmp31
    tmp33 = 1e-05
    tmp34 = tmp32 + tmp33
    tmp35 = libdevice.rsqrt(tmp34)
    tmp36 = tmp30 * tmp35
    tmp38 = tmp36 * tmp37
    tmp40 = tmp38 + tmp39
    tmp41 = 0.0
    tmp42 = tmp40 > tmp41
    tmp43 = 0.01
    tmp44 = tmp40 * tmp43
    tmp45 = tl.where(tmp42, tmp40, tmp44)
    tmp46 = tmp45 > tmp41
    tl.store(out_ptr0 + (r2 + (16*x3)), tmp13, xmask)
    tl.store(in_out_ptr0 + (r2 + (16*x3)), tmp45, xmask)
    tl.store(out_ptr3 + (r2 + (16*x3)), tmp46, xmask)
    tl.store(out_ptr4 + (x3), tmp35, xmask)
    tl.store(out_ptr1 + (x3), 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, (8, ), (1, ))
    assert_size_stride(primals_3, (8, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 8, 4, 4), (128, 16, 4, 1), torch.float32)
        buf1 = empty_strided_cuda((1, 32, 1, 1), (32, 1, 32, 32), torch.float32)
        buf5 = empty_strided_cuda((1, 32, 4, 4), (512, 16, 4, 1), torch.float32)
        buf6 = reinterpret_tensor(buf5, (4, 8, 4, 4), (128, 16, 4, 1), 0); del buf5  # reuse
        buf7 = empty_strided_cuda((4, 8, 4, 4), (128, 16, 4, 1), torch.bool)
        buf4 = empty_strided_cuda((1, 32, 1, 1), (32, 1, 32, 32), torch.float32)
        # Topologically Sorted Source Nodes: [cat, x, leaky_relu], Original ATen: [aten.cat, aten._native_batch_norm_legit, aten.leaky_relu, aten.leaky_relu_backward]
        stream0 = get_raw_stream(0)
        triton_per_fused__native_batch_norm_legit_cat_leaky_relu_leaky_relu_backward_0.run(buf6, primals_1, primals_2, primals_3, buf0, buf1, buf7, buf4, 32, 16, grid=grid(32), stream=stream0)
        del primals_1
        del primals_2
        del primals_3
    return (buf6, buf0, reinterpret_tensor(buf4, (32, ), (1, ), 0), buf7, reinterpret_tensor(buf1, (1, 32, 1, 1), (32, 1, 1, 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, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((8, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((8, ), (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)

import torch
import torch.nn as nn
import torch.nn.functional as F


class CReLU_IN(nn.Module):

    def __init__(self, channels):
        super(CReLU_IN, self).__init__()
        self.bn = nn.InstanceNorm2d(channels * 2, eps=1e-05, momentum=0.1,
            affine=True)

    def forward(self, x):
        cat = torch.cat((x, -x), 1)
        x = self.bn(cat)
        return F.leaky_relu(x, 0.01, inplace=True)


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
import torch.nn as nn
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__native_batch_norm_legit_cat_leaky_relu_leaky_relu_backward_0(
    in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, out_ptr3,
    out_ptr4, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 32
    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)
    x0 = xindex % 8
    r2 = rindex
    x1 = xindex // 8
    x3 = xindex
    tmp37 = tl.load(in_ptr1 + x3 % 8, xmask, eviction_policy='evict_last')
    tmp39 = tl.load(in_ptr2 + x3 % 8, xmask, eviction_policy='evict_last')
    tmp0 = x0
    tl.full([1, 1], 0, tl.int64)
    tmp3 = tl.full([1, 1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (r2 + 16 * x0 + 64 * x1), tmp4 & xmask, other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1, 1], 8, tl.int64)
    tmp9 = tl.load(in_ptr0 + (r2 + 16 * (-4 + x0) + 64 * x1), tmp6 & xmask,
        other=0.0)
    tmp10 = -tmp9
    tmp11 = tl.full(tmp10.shape, 0.0, tmp10.dtype)
    tmp12 = tl.where(tmp6, tmp10, tmp11)
    tmp13 = tl.where(tmp4, tmp5, tmp12)
    tmp14 = tl.broadcast_to(tmp13, [XBLOCK, RBLOCK])
    tl.where(xmask, tmp14, 0)
    tmp17 = tl.broadcast_to(tmp14, [XBLOCK, RBLOCK])
    tmp19 = tl.where(xmask, tmp17, 0)
    tmp20 = tl.sum(tmp19, 1)[:, None]
    tmp21 = tl.full([XBLOCK, 1], 16, tl.int32)
    tmp22 = tmp21.to(tl.float32)
    tmp23 = tmp20 / tmp22
    tmp24 = tmp14 - tmp23
    tmp25 = tmp24 * tmp24
    tmp26 = tl.broadcast_to(tmp25, [XBLOCK, RBLOCK])
    tmp28 = tl.where(xmask, tmp26, 0)
    tmp29 = tl.sum(tmp28, 1)[:, None]
    tmp30 = tmp13 - tmp23
    tmp31 = 16.0
    tmp32 = tmp29 / tmp31
    tmp33 = 1e-05
    tmp34 = tmp32 + tmp33
    tmp35 = libdevice.rsqrt(tmp34)
    tmp36 = tmp30 * tmp35
    tmp38 = tmp36 * tmp37
    tmp40 = tmp38 + tmp39
    tmp41 = 0.0
    tmp42 = tmp40 > tmp41
    tmp43 = 0.01
    tmp44 = tmp40 * tmp43
    tmp45 = tl.where(tmp42, tmp40, tmp44)
    tmp46 = tmp45 > tmp41
    tl.store(out_ptr0 + (r2 + 16 * x3), tmp13, xmask)
    tl.store(in_out_ptr0 + (r2 + 16 * x3), tmp45, xmask)
    tl.store(out_ptr3 + (r2 + 16 * x3), tmp46, xmask)
    tl.store(out_ptr4 + x3, tmp35, xmask)
    tl.store(out_ptr1 + x3, 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, (8,), (1,))
    assert_size_stride(primals_3, (8,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 8, 4, 4), (128, 16, 4, 1), torch.float32)
        buf1 = empty_strided_cuda((1, 32, 1, 1), (32, 1, 32, 32), torch.float32
            )
        buf5 = empty_strided_cuda((1, 32, 4, 4), (512, 16, 4, 1), torch.float32
            )
        buf6 = reinterpret_tensor(buf5, (4, 8, 4, 4), (128, 16, 4, 1), 0)
        del buf5
        buf7 = empty_strided_cuda((4, 8, 4, 4), (128, 16, 4, 1), torch.bool)
        buf4 = empty_strided_cuda((1, 32, 1, 1), (32, 1, 32, 32), torch.float32
            )
        get_raw_stream(0)
        triton_per_fused__native_batch_norm_legit_cat_leaky_relu_leaky_relu_backward_0[
            grid(32)](buf6, primals_1, primals_2, primals_3, buf0, buf1,
            buf7, buf4, 32, 16, XBLOCK=32, num_warps=4, num_stages=1)
        del primals_1
        del primals_2
        del primals_3
    return buf6, buf0, reinterpret_tensor(buf4, (32,), (1,), 0
        ), buf7, reinterpret_tensor(buf1, (1, 32, 1, 1), (32, 1, 1, 1), 0)


class CReLU_INNew(nn.Module):

    def __init__(self, channels):
        super(CReLU_INNew, self).__init__()
        self.bn = nn.InstanceNorm2d(channels * 2, eps=1e-05, momentum=0.1,
            affine=True)

    def forward(self, input_0):
        primals_2 = self.bn.weight
        primals_3 = self.bn.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]
