# 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_6/inductor_cache/et/cetbvewldbe3nltlmooux3gvif26azg2nu4k4k7ld7hdklxsohux.py
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.native_group_norm]
# Source node to ATen node mapping:
#   x => add, add_1, mul_2, rsqrt, var_mean
# Graph fragment:
#   %var_mean : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%view, [2, 3]), kwargs = {correction: 0, keepdim: True})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem, 0.001), kwargs = {})
#   %rsqrt : [num_users=2] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add,), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_1, %unsqueeze_7), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_2, %unsqueeze_4), kwargs = {})
triton_per_fused_native_group_norm_0 = async_compile.triton('triton_per_fused_native_group_norm_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=[4, 64],
    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, 7), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_native_group_norm_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, '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_group_norm_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr2, out_ptr3, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    rnumel = 64
    RBLOCK: tl.constexpr = 64
    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
    r3 = (rindex // 16)
    tmp0 = tl.load(in_ptr0 + (r1 + (64*x0)), xmask, other=0.0)
    tmp29 = tl.load(in_ptr1 + (r3), None, eviction_policy='evict_last')
    tmp31 = tl.load(in_ptr2 + (r3), None, eviction_policy='evict_last')
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 0.01
    tmp4 = tmp0 * tmp3
    tmp5 = tl.where(tmp2, tmp0, tmp4)
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK, RBLOCK])
    tmp8 = tl.where(xmask, tmp6, 0)
    tmp9 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK])
    tmp11 = tl.where(xmask, tmp9, 0)
    tmp12 = tl.sum(tmp11, 1)[:, None]
    tmp13 = tl.full([XBLOCK, 1], 64, tl.int32)
    tmp14 = tmp13.to(tl.float32)
    tmp15 = tmp12 / tmp14
    tmp16 = tmp6 - tmp15
    tmp17 = tmp16 * tmp16
    tmp18 = tl.broadcast_to(tmp17, [XBLOCK, RBLOCK])
    tmp20 = tl.where(xmask, tmp18, 0)
    tmp21 = tl.sum(tmp20, 1)[:, None]
    tmp22 = tmp5 - tmp15
    tmp23 = 64.0
    tmp24 = tmp21 / tmp23
    tmp25 = 0.001
    tmp26 = tmp24 + tmp25
    tmp27 = libdevice.rsqrt(tmp26)
    tmp28 = tmp22 * tmp27
    tmp30 = tmp28 * tmp29
    tmp32 = tmp30 + tmp31
    tl.store(out_ptr2 + (r1 + (64*x0)), tmp32, xmask)
    tl.store(out_ptr3 + (x0), tmp27, xmask)
    tl.store(out_ptr0 + (x0), tmp15, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 3, 3, 3), (108, 27, 9, 3, 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, 3, 3, 3), (108, 27, 9, 3, 1))
    assert_size_stride(primals_6, (4, ), (1, ))
    assert_size_stride(primals_7, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [conv3d], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(reinterpret_tensor(primals_2, (1, 4, 4, 4, 4), (256, 64, 16, 4, 1), 0), primals_1, stride=(1, 1, 1), padding=(1, 1, 1), dilation=(1, 1, 1), transposed=False, output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (1, 4, 4, 4, 4), (256, 64, 16, 4, 1))
        buf1 = empty_strided_cuda((4, 1, 1, 1), (1, 4, 4, 4), torch.float32)
        buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf4 = empty_strided_cuda((4, 1, 1, 1), (1, 4, 4, 4), torch.float32)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.native_group_norm]
        stream0 = get_raw_stream(0)
        triton_per_fused_native_group_norm_0.run(buf0, primals_3, primals_4, buf1, buf5, buf4, 4, 64, grid=grid(4), stream=stream0)
        del primals_4
        # Topologically Sorted Source Nodes: [conv3d_1], Original ATen: [aten.convolution]
        buf6 = extern_kernels.convolution(reinterpret_tensor(buf5, (1, 4, 4, 4, 4), (0, 64, 16, 4, 1), 0), primals_5, stride=(1, 1, 1), padding=(1, 1, 1), dilation=(1, 1, 1), transposed=False, output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (1, 4, 4, 4, 4), (256, 64, 16, 4, 1))
        buf7 = empty_strided_cuda((4, 1, 1, 1), (1, 4, 4, 4), torch.float32)
        buf10 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf11 = empty_strided_cuda((4, 1, 1, 1), (1, 4, 4, 4), torch.float32)
        # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.native_group_norm]
        triton_per_fused_native_group_norm_0.run(buf6, primals_6, primals_7, buf7, buf10, buf11, 4, 64, grid=grid(4), stream=stream0)
        del primals_7
    return (buf10, primals_1, primals_3, primals_5, primals_6, reinterpret_tensor(primals_2, (1, 4, 4, 4, 4), (256, 64, 16, 4, 1), 0), buf0, reinterpret_tensor(buf1, (4, 1), (1, 1), 0), reinterpret_tensor(buf4, (4, 1), (1, 1), 0), reinterpret_tensor(buf5, (1, 4, 4, 4, 4), (256, 64, 16, 4, 1), 0), buf6, reinterpret_tensor(buf7, (4, 1), (1, 1), 0), reinterpret_tensor(buf11, (4, 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, 3, 3, 3), (108, 27, 9, 3, 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, 3, 3, 3), (108, 27, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7])
    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
from torch import nn
from torch.nn import functional as F


class FGFunction(nn.Module):
    """Module used for F and G
	
	Archi :
	conv -> BN -> ReLu -> conv -> BN -> ReLu
	"""

    def __init__(self, channels):
        super(FGFunction, self).__init__()
        self.gn1 = nn.GroupNorm(1, channels, eps=0.001)
        self.conv1 = nn.Conv3d(channels, channels, 3, padding=1, bias=False)
        self.gn2 = nn.GroupNorm(1, channels, eps=0.001)
        self.conv2 = nn.Conv3d(channels, channels, 3, padding=1, bias=False)

    def forward(self, x):
        x = self.gn1(F.leaky_relu(self.conv1(x), inplace=True))
        x = self.gn2(F.leaky_relu(self.conv2(x), inplace=True))
        return x


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


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

import torch
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 libdevice
from torch import 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_group_norm_0(in_ptr0, in_ptr1, in_ptr2,
    out_ptr0, out_ptr2, out_ptr3, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    RBLOCK: tl.constexpr = 64
    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
    r3 = rindex // 16
    tmp0 = tl.load(in_ptr0 + (r1 + 64 * x0), xmask, other=0.0)
    tmp29 = tl.load(in_ptr1 + r3, None, eviction_policy='evict_last')
    tmp31 = tl.load(in_ptr2 + r3, None, eviction_policy='evict_last')
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 0.01
    tmp4 = tmp0 * tmp3
    tmp5 = tl.where(tmp2, tmp0, tmp4)
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK, RBLOCK])
    tl.where(xmask, tmp6, 0)
    tmp9 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK])
    tmp11 = tl.where(xmask, tmp9, 0)
    tmp12 = tl.sum(tmp11, 1)[:, None]
    tmp13 = tl.full([XBLOCK, 1], 64, tl.int32)
    tmp14 = tmp13.to(tl.float32)
    tmp15 = tmp12 / tmp14
    tmp16 = tmp6 - tmp15
    tmp17 = tmp16 * tmp16
    tmp18 = tl.broadcast_to(tmp17, [XBLOCK, RBLOCK])
    tmp20 = tl.where(xmask, tmp18, 0)
    tmp21 = tl.sum(tmp20, 1)[:, None]
    tmp22 = tmp5 - tmp15
    tmp23 = 64.0
    tmp24 = tmp21 / tmp23
    tmp25 = 0.001
    tmp26 = tmp24 + tmp25
    tmp27 = libdevice.rsqrt(tmp26)
    tmp28 = tmp22 * tmp27
    tmp30 = tmp28 * tmp29
    tmp32 = tmp30 + tmp31
    tl.store(out_ptr2 + (r1 + 64 * x0), tmp32, xmask)
    tl.store(out_ptr3 + x0, tmp27, xmask)
    tl.store(out_ptr0 + x0, tmp15, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 3, 3, 3), (108, 27, 9, 3, 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, 3, 3, 3), (108, 27, 9, 3, 1))
    assert_size_stride(primals_6, (4,), (1,))
    assert_size_stride(primals_7, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(reinterpret_tensor(primals_2, (1,
            4, 4, 4, 4), (256, 64, 16, 4, 1), 0), primals_1, stride=(1, 1, 
            1), padding=(1, 1, 1), dilation=(1, 1, 1), transposed=False,
            output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (1, 4, 4, 4, 4), (256, 64, 16, 4, 1))
        buf1 = empty_strided_cuda((4, 1, 1, 1), (1, 4, 4, 4), torch.float32)
        buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf4 = empty_strided_cuda((4, 1, 1, 1), (1, 4, 4, 4), torch.float32)
        get_raw_stream(0)
        triton_per_fused_native_group_norm_0[grid(4)](buf0, primals_3,
            primals_4, buf1, buf5, buf4, 4, 64, XBLOCK=1, num_warps=2,
            num_stages=1)
        del primals_4
        buf6 = extern_kernels.convolution(reinterpret_tensor(buf5, (1, 4, 4,
            4, 4), (0, 64, 16, 4, 1), 0), primals_5, stride=(1, 1, 1),
            padding=(1, 1, 1), dilation=(1, 1, 1), transposed=False,
            output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (1, 4, 4, 4, 4), (256, 64, 16, 4, 1))
        buf7 = empty_strided_cuda((4, 1, 1, 1), (1, 4, 4, 4), torch.float32)
        buf10 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf11 = empty_strided_cuda((4, 1, 1, 1), (1, 4, 4, 4), torch.float32)
        triton_per_fused_native_group_norm_0[grid(4)](buf6, primals_6,
            primals_7, buf7, buf10, buf11, 4, 64, XBLOCK=1, num_warps=2,
            num_stages=1)
        del primals_7
    return (buf10, primals_1, primals_3, primals_5, primals_6,
        reinterpret_tensor(primals_2, (1, 4, 4, 4, 4), (256, 64, 16, 4, 1),
        0), buf0, reinterpret_tensor(buf1, (4, 1), (1, 1), 0),
        reinterpret_tensor(buf4, (4, 1), (1, 1), 0), reinterpret_tensor(
        buf5, (1, 4, 4, 4, 4), (256, 64, 16, 4, 1), 0), buf6,
        reinterpret_tensor(buf7, (4, 1), (1, 1), 0), reinterpret_tensor(
        buf11, (4, 1), (1, 1), 0))


class FGFunctionNew(nn.Module):
    """Module used for F and G
	
	Archi :
	conv -> BN -> ReLu -> conv -> BN -> ReLu
	"""

    def __init__(self, channels):
        super(FGFunctionNew, self).__init__()
        self.gn1 = nn.GroupNorm(1, channels, eps=0.001)
        self.conv1 = nn.Conv3d(channels, channels, 3, padding=1, bias=False)
        self.gn2 = nn.GroupNorm(1, channels, eps=0.001)
        self.conv2 = nn.Conv3d(channels, channels, 3, padding=1, bias=False)

    def forward(self, input_0):
        primals_3 = self.gn1.weight
        primals_4 = self.gn1.bias
        primals_1 = self.conv1.weight
        primals_6 = self.gn2.weight
        primals_7 = self.gn2.bias
        primals_5 = self.conv2.weight
        primals_2 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0]
