# 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_1/inductor_cache/6q/c6qlsta5ip5tfujcql5z3azfliauyzyc7np2ixqgfyfolowbdlu2.py
# Topologically Sorted Source Nodes: [x_i_1, cat], Original ATen: [aten._native_batch_norm_legit, aten.cat, aten.native_batch_norm_backward]
# Source node to ATen node mapping:
#   cat => cat
#   x_i_1 => add, rsqrt, var_mean
# Graph fragment:
#   %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 = {})
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%view_1, %slice_6], 1), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view, %unsqueeze_6), kwargs = {})
triton_per_fused__native_batch_norm_legit_cat_native_batch_norm_backward_0 = async_compile.triton('triton_per_fused__native_batch_norm_legit_cat_native_batch_norm_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=[8, 16],
    reduction_hint=ReductionHint.DEFAULT,
    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=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 7), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__native_batch_norm_legit_cat_native_batch_norm_backward_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 4, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_native_batch_norm_backward_0(in_ptr0, in_ptr1, in_ptr2, out_ptr2, out_ptr3, out_ptr4, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 8
    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 % 2
    x3 = (xindex // 2)
    tmp0 = tl.load(in_ptr0 + (r1 + (16*(x0 % 2)) + (64*(x0 // 2))), xmask, other=0.0)
    tmp17 = tl.load(in_ptr0 + (r1 + (16*x2) + (64*x3)), xmask, other=0.0)
    tmp25 = tl.load(in_ptr1 + (x2), xmask, eviction_policy='evict_last')
    tmp27 = 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.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp6 = tl.where(xmask, tmp4, 0)
    tmp7 = tl.sum(tmp6, 1)[:, None]
    tmp8 = tl.full([XBLOCK, 1], 16, 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.where(xmask, tmp13, 0)
    tmp16 = tl.sum(tmp15, 1)[:, None]
    tmp18 = tmp17 - tmp10
    tmp19 = 16.0
    tmp20 = tmp16 / tmp19
    tmp21 = 1e-05
    tmp22 = tmp20 + tmp21
    tmp23 = libdevice.rsqrt(tmp22)
    tmp24 = tmp18 * tmp23
    tmp26 = tmp24 * tmp25
    tmp28 = tmp26 + tmp27
    tmp29 = tmp0 - tmp10
    tl.store(out_ptr2 + (r1 + (16*x2) + (64*x3)), tmp28, xmask)
    tl.store(out_ptr3 + (r1 + (16*x0)), tmp29, xmask)
    tl.store(out_ptr4 + (x0), tmp23, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/iy/ciyhzyc3ywy4netbdeyt4yf6iyphrxljlsrfqr2gxtpdjcgbo36k.py
# Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   cat => cat
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%view_1, %slice_6], 1), kwargs = {})
triton_poi_fused_cat_1 = async_compile.triton('triton_poi_fused_cat_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=[128], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_cat_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 32
    x1 = (xindex // 32)
    tmp0 = tl.load(in_ptr0 + (32 + x0 + (64*x1)), xmask)
    tl.store(out_ptr0 + (x0 + (64*x1)), tmp0, 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, (2, ), (1, ))
    assert_size_stride(primals_3, (2, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf4 = reinterpret_tensor(buf6, (4, 2, 4, 4), (64, 16, 4, 1), 0)  # alias
        buf7 = empty_strided_cuda((1, 8, 4, 4), (128, 16, 4, 1), torch.float32)
        buf3 = empty_strided_cuda((1, 8, 1, 1), (8, 1, 8, 8), torch.float32)
        # Topologically Sorted Source Nodes: [x_i_1, cat], Original ATen: [aten._native_batch_norm_legit, aten.cat, aten.native_batch_norm_backward]
        stream0 = get_raw_stream(0)
        triton_per_fused__native_batch_norm_legit_cat_native_batch_norm_backward_0.run(primals_1, primals_2, primals_3, buf4, buf7, buf3, 8, 16, grid=grid(8), stream=stream0)
        del primals_2
        del primals_3
        buf5 = reinterpret_tensor(buf6, (4, 2, 4, 4), (64, 16, 4, 1), 32)  # alias
        # Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat]
        triton_poi_fused_cat_1.run(primals_1, buf5, 128, grid=grid(128), stream=stream0)
        del primals_1
    return (buf6, reinterpret_tensor(buf3, (8, ), (1, ), 0), buf7, )


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((2, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((2, ), (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 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_native_batch_norm_backward_0(
    in_ptr0, in_ptr1, in_ptr2, out_ptr2, out_ptr3, out_ptr4, xnumel, rnumel,
    XBLOCK: tl.constexpr):
    xnumel = 8
    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 % 2
    x3 = xindex // 2
    tmp0 = tl.load(in_ptr0 + (r1 + 16 * (x0 % 2) + 64 * (x0 // 2)), xmask,
        other=0.0)
    tmp17 = tl.load(in_ptr0 + (r1 + 16 * x2 + 64 * x3), xmask, other=0.0)
    tmp25 = tl.load(in_ptr1 + x2, xmask, eviction_policy='evict_last')
    tmp27 = tl.load(in_ptr2 + x2, xmask, eviction_policy='evict_last')
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tl.where(xmask, tmp1, 0)
    tmp4 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp6 = tl.where(xmask, tmp4, 0)
    tmp7 = tl.sum(tmp6, 1)[:, None]
    tmp8 = tl.full([XBLOCK, 1], 16, 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.where(xmask, tmp13, 0)
    tmp16 = tl.sum(tmp15, 1)[:, None]
    tmp18 = tmp17 - tmp10
    tmp19 = 16.0
    tmp20 = tmp16 / tmp19
    tmp21 = 1e-05
    tmp22 = tmp20 + tmp21
    tmp23 = libdevice.rsqrt(tmp22)
    tmp24 = tmp18 * tmp23
    tmp26 = tmp24 * tmp25
    tmp28 = tmp26 + tmp27
    tmp29 = tmp0 - tmp10
    tl.store(out_ptr2 + (r1 + 16 * x2 + 64 * x3), tmp28, xmask)
    tl.store(out_ptr3 + (r1 + 16 * x0), tmp29, xmask)
    tl.store(out_ptr4 + x0, tmp23, xmask)


@triton.jit
def triton_poi_fused_cat_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 32
    x1 = xindex // 32
    tmp0 = tl.load(in_ptr0 + (32 + x0 + 64 * x1), xmask)
    tl.store(out_ptr0 + (x0 + 64 * x1), tmp0, 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, (2,), (1,))
    assert_size_stride(primals_3, (2,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf4 = reinterpret_tensor(buf6, (4, 2, 4, 4), (64, 16, 4, 1), 0)
        buf7 = empty_strided_cuda((1, 8, 4, 4), (128, 16, 4, 1), torch.float32)
        buf3 = empty_strided_cuda((1, 8, 1, 1), (8, 1, 8, 8), torch.float32)
        get_raw_stream(0)
        triton_per_fused__native_batch_norm_legit_cat_native_batch_norm_backward_0[
            grid(8)](primals_1, primals_2, primals_3, buf4, buf7, buf3, 8, 
            16, XBLOCK=1, num_warps=2, num_stages=1)
        del primals_2
        del primals_3
        buf5 = reinterpret_tensor(buf6, (4, 2, 4, 4), (64, 16, 4, 1), 32)
        triton_poi_fused_cat_1[grid(128)](primals_1, buf5, 128, XBLOCK=128,
            num_warps=4, num_stages=1)
        del primals_1
    return buf6, reinterpret_tensor(buf3, (8,), (1,), 0), buf7


class HINetNew(nn.Module):

    def __init__(self, in_ch):
        super(HINetNew, self).__init__()
        self.instance_norm = nn.InstanceNorm2d(in_ch - in_ch // 2, affine=True)

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