# 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_4/inductor_cache/dk/cdkfylhbmav6z7nn6vy4nwynu3ry6ilbxpjspabpseiuux7csyql.py
# Topologically Sorted Source Nodes: [input_3], Original ATen: [aten.native_group_norm]
# Source node to ATen node mapping:
#   input_3 => add, 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_2, 1e-05), kwargs = {})
#   %rsqrt : [num_users=1] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add,), kwargs = {})
triton_poi_fused_native_group_norm_0 = async_compile.triton('triton_poi_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.pointwise(
    size_hints=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 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': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_native_group_norm_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, '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},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_native_group_norm_0(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (4*x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp1, tmp3)
    tmp5 = tmp2 + tmp4
    tmp7 = triton_helpers.maximum(tmp1, tmp6)
    tmp8 = tmp5 + tmp7
    tmp10 = triton_helpers.maximum(tmp1, tmp9)
    tmp11 = tmp8 + tmp10
    tmp12 = 4.0
    tmp13 = tmp11 / tmp12
    tmp14 = tmp2 - tmp13
    tmp15 = tmp14 * tmp14
    tmp16 = tmp4 - tmp13
    tmp17 = tmp16 * tmp16
    tmp18 = tmp15 + tmp17
    tmp19 = tmp7 - tmp13
    tmp20 = tmp19 * tmp19
    tmp21 = tmp18 + tmp20
    tmp22 = tmp10 - tmp13
    tmp23 = tmp22 * tmp22
    tmp24 = tmp21 + tmp23
    tmp25 = tmp24 / tmp12
    tmp26 = 1e-05
    tmp27 = tmp25 + tmp26
    tmp28 = libdevice.rsqrt(tmp27)
    tl.store(out_ptr0 + (x0), tmp13, xmask)
    tl.store(out_ptr1 + (x0), tmp28, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/kd/ckdswb4lufwiij2wklkypvchblxzpif4ph4uxoqoklvcbijmlski.py
# Topologically Sorted Source Nodes: [input_3], Original ATen: [aten.native_group_norm]
# Source node to ATen node mapping:
#   input_3 => add_1, mul_1
# Graph fragment:
#   %mul_1 : [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_1, %unsqueeze_4), kwargs = {})
triton_poi_fused_native_group_norm_1 = async_compile.triton('triton_poi_fused_native_group_norm_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=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_native_group_norm_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, '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},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_native_group_norm_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x4 = (xindex // 4)
    x1 = (xindex // 4) % 4
    tmp0 = tl.load(in_ptr0 + (x3), xmask)
    tmp3 = tl.load(in_ptr1 + (x4), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr2 + (x4), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr3 + (x1), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr4 + (x1), xmask, eviction_policy='evict_last')
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = tmp2 - tmp3
    tmp6 = tmp4 * tmp5
    tmp8 = tmp6 * tmp7
    tmp10 = tmp8 + tmp9
    tl.store(out_ptr0 + (x3), tmp10, 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, 8, 4, 4), (128, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 3, 3, 3), (108, 27, 9, 3, 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: [x], Original ATen: [aten.max_pool3d_with_indices]
        buf0 = torch.ops.aten.max_pool3d_with_indices.default(primals_1, [2, 2, 2], [2, 2, 2])
        del primals_1
        buf1 = buf0[0]
        del buf0
        # Topologically Sorted Source Nodes: [input_1], Original ATen: [aten.convolution]
        buf3 = extern_kernels.convolution(reinterpret_tensor(buf1, (1, 4, 4, 2, 2), (64, 16, 4, 2, 1), 0), primals_2, 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(buf3, (1, 4, 4, 2, 2), (64, 16, 4, 2, 1))
        buf4 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
        buf5 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
        # Topologically Sorted Source Nodes: [input_3], Original ATen: [aten.native_group_norm]
        stream0 = get_raw_stream(0)
        triton_poi_fused_native_group_norm_0.run(buf3, buf4, buf5, 16, grid=grid(16), stream=stream0)
        buf6 = empty_strided_cuda((4, 4, 2, 2), (16, 4, 2, 1), torch.float32)
        # Topologically Sorted Source Nodes: [input_3], Original ATen: [aten.native_group_norm]
        triton_poi_fused_native_group_norm_1.run(buf3, buf4, buf5, primals_3, primals_4, buf6, 64, grid=grid(64), stream=stream0)
        del primals_4
        # Topologically Sorted Source Nodes: [input_4], Original ATen: [aten.convolution]
        buf7 = extern_kernels.convolution(reinterpret_tensor(buf6, (1, 4, 4, 2, 2), (0, 16, 4, 2, 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(buf7, (1, 4, 4, 2, 2), (64, 16, 4, 2, 1))
        buf8 = buf5; del buf5  # reuse
        buf9 = buf4; del buf4  # reuse
        # Topologically Sorted Source Nodes: [input_6], Original ATen: [aten.native_group_norm]
        triton_poi_fused_native_group_norm_0.run(buf7, buf8, buf9, 16, grid=grid(16), stream=stream0)
        buf10 = empty_strided_cuda((4, 4, 2, 2), (16, 4, 2, 1), torch.float32)
        # Topologically Sorted Source Nodes: [input_6], Original ATen: [aten.native_group_norm]
        triton_poi_fused_native_group_norm_1.run(buf7, buf8, buf9, primals_6, primals_7, buf10, 64, grid=grid(64), stream=stream0)
        del buf8
        del buf9
        del primals_7
    return (buf10, primals_2, primals_3, primals_5, primals_6, reinterpret_tensor(buf1, (1, 4, 4, 2, 2), (64, 16, 4, 2, 1), 0), buf3, reinterpret_tensor(buf6, (1, 4, 4, 2, 2), (64, 16, 4, 2, 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, 8, 4, 4), (128, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4, 3, 3, 3), (108, 27, 9, 3, 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


def conv3d(in_channels, out_channels, kernel_size, bias, padding=1, stride=1):
    return nn.Conv3d(in_channels, out_channels, kernel_size, padding=
        padding, bias=bias, stride=stride)


def create_conv(in_channels, out_channels, kernel_size, order, num_groups,
    padding=1):
    """
    Create a list of modules with together constitute a single conv layer with non-linearity
    and optional batchnorm/groupnorm.

    Args:
        in_channels (int): number of input channels
        out_channels (int): number of output channels
        order (string): order of things, e.g.
            'cr' -> conv + ReLU
            'crg' -> conv + ReLU + groupnorm
            'cl' -> conv + LeakyReLU
            'ce' -> conv + ELU
        num_groups (int): number of groups for the GroupNorm
        padding (int): add zero-padding to the input

    Return:
        list of tuple (name, module)
    """
    assert 'c' in order, 'Conv layer MUST be present'
    assert order[0
        ] not in 'rle', 'Non-linearity cannot be the first operation in the layer'
    modules = []
    for i, char in enumerate(order):
        if char == 'r':
            modules.append(('ReLU', nn.ReLU(inplace=True)))
        elif char == 'l':
            modules.append(('LeakyReLU', nn.LeakyReLU(negative_slope=0.1,
                inplace=True)))
        elif char == 'e':
            modules.append(('ELU', nn.ELU(inplace=True)))
        elif char == 'c':
            bias = not ('g' in order or 'b' in order)
            modules.append(('conv', conv3d(in_channels, out_channels,
                kernel_size, bias, padding=padding)))
        elif char == 'g':
            is_before_conv = i < order.index('c')
            assert not is_before_conv, 'GroupNorm MUST go after the Conv3d'
            if out_channels < num_groups:
                num_groups = out_channels
            modules.append(('groupnorm', nn.GroupNorm(num_groups=num_groups,
                num_channels=out_channels)))
        elif char == 'b':
            is_before_conv = i < order.index('c')
            if is_before_conv:
                modules.append(('batchnorm', nn.BatchNorm3d(in_channels)))
            else:
                modules.append(('batchnorm', nn.BatchNorm3d(out_channels)))
        else:
            raise ValueError(
                f"Unsupported layer type '{char}'. MUST be one of ['b', 'g', 'r', 'l', 'e', 'c']"
                )
    return modules


class SingleConv(nn.Sequential):
    """
    Basic convolutional module consisting of a Conv3d, non-linearity and optional batchnorm/groupnorm. The order
    of operations can be specified via the `order` parameter

    Args:
        in_channels (int): number of input channels
        out_channels (int): number of output channels
        kernel_size (int): size of the convolving kernel
        order (string): determines the order of layers, e.g.
            'cr' -> conv + ReLU
            'crg' -> conv + ReLU + groupnorm
            'cl' -> conv + LeakyReLU
            'ce' -> conv + ELU
        num_groups (int): number of groups for the GroupNorm
    """

    def __init__(self, in_channels, out_channels, kernel_size=3, order=
        'crg', num_groups=8, padding=1):
        super(SingleConv, self).__init__()
        for name, module in create_conv(in_channels, out_channels,
            kernel_size, order, num_groups, padding=padding):
            self.add_module(name, module)


class DoubleConv(nn.Sequential):
    """
    A module consisting of two consecutive convolution layers (e.g. BatchNorm3d+ReLU+Conv3d).
    We use (Conv3d+ReLU+GroupNorm3d) by default.
    This can be changed however by providing the 'order' argument, e.g. in order
    to change to Conv3d+BatchNorm3d+ELU use order='cbe'.
    Use padded convolutions to make sure that the output (H_out, W_out) is the same
    as (H_in, W_in), so that you don't have to crop in the decoder path.

    Args:
        in_channels (int): number of input channels
        out_channels (int): number of output channels
        encoder (bool): if True we're in the encoder path, otherwise we're in the decoder
        kernel_size (int): size of the convolving kernel
        order (string): determines the order of layers, e.g.
            'cr' -> conv + ReLU
            'crg' -> conv + ReLU + groupnorm
            'cl' -> conv + LeakyReLU
            'ce' -> conv + ELU
        num_groups (int): number of groups for the GroupNorm
    """

    def __init__(self, in_channels, out_channels, encoder, kernel_size=3,
        order='crg', num_groups=8):
        super(DoubleConv, self).__init__()
        if encoder:
            conv1_in_channels = in_channels
            conv1_out_channels = out_channels // 2
            if conv1_out_channels < in_channels:
                conv1_out_channels = in_channels
            conv2_in_channels, conv2_out_channels = (conv1_out_channels,
                out_channels)
        else:
            conv1_in_channels, conv1_out_channels = in_channels, out_channels
            conv2_in_channels, conv2_out_channels = out_channels, out_channels
        self.add_module('SingleConv1', SingleConv(conv1_in_channels,
            conv1_out_channels, kernel_size, order, num_groups))
        self.add_module('SingleConv2', SingleConv(conv2_in_channels,
            conv2_out_channels, kernel_size, order, num_groups))


class Encoder(nn.Module):
    """
    A single module from the encoder path consisting of the optional max
    pooling layer (one may specify the MaxPool kernel_size to be different
    than the standard (2,2,2), e.g. if the volumetric data is anisotropic
    (make sure to use complementary scale_factor in the decoder path) followed by
    a DoubleConv module.
    Args:
        in_channels (int): number of input channels
        out_channels (int): number of output channels
        conv_kernel_size (int): size of the convolving kernel
        apply_pooling (bool): if True use MaxPool3d before DoubleConv
        pool_kernel_size (tuple): the size of the window to take a max over
        pool_type (str): pooling layer: 'max' or 'avg'
        basic_module(nn.Module): either ResNetBlock or DoubleConv
        conv_layer_order (string): determines the order of layers
            in `DoubleConv` module. See `DoubleConv` for more info.
        num_groups (int): number of groups for the GroupNorm
    """

    def __init__(self, in_channels, out_channels, conv_kernel_size=3,
        apply_pooling=True, pool_kernel_size=(2, 2, 2), pool_type='max',
        basic_module=DoubleConv, conv_layer_order='crg', num_groups=8):
        super(Encoder, self).__init__()
        assert pool_type in ['max', 'avg']
        if apply_pooling:
            if pool_type == 'max':
                self.pooling = nn.MaxPool3d(kernel_size=pool_kernel_size)
            else:
                self.pooling = nn.AvgPool3d(kernel_size=pool_kernel_size)
        else:
            self.pooling = None
        self.basic_module = basic_module(in_channels, out_channels, encoder
            =True, kernel_size=conv_kernel_size, order=conv_layer_order,
            num_groups=num_groups)

    def forward(self, x):
        if self.pooling is not None:
            x = self.pooling(x)
        x = self.basic_module(x)
        return x


def get_inputs():
    return [torch.rand([4, 8, 4, 4])]


def get_init_inputs():
    return [[], {'in_channels': 4, 'out_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 import triton_helpers
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_poi_fused_native_group_norm_0(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp1, tmp3)
    tmp5 = tmp2 + tmp4
    tmp7 = triton_helpers.maximum(tmp1, tmp6)
    tmp8 = tmp5 + tmp7
    tmp10 = triton_helpers.maximum(tmp1, tmp9)
    tmp11 = tmp8 + tmp10
    tmp12 = 4.0
    tmp13 = tmp11 / tmp12
    tmp14 = tmp2 - tmp13
    tmp15 = tmp14 * tmp14
    tmp16 = tmp4 - tmp13
    tmp17 = tmp16 * tmp16
    tmp18 = tmp15 + tmp17
    tmp19 = tmp7 - tmp13
    tmp20 = tmp19 * tmp19
    tmp21 = tmp18 + tmp20
    tmp22 = tmp10 - tmp13
    tmp23 = tmp22 * tmp22
    tmp24 = tmp21 + tmp23
    tmp25 = tmp24 / tmp12
    tmp26 = 1e-05
    tmp27 = tmp25 + tmp26
    tmp28 = libdevice.rsqrt(tmp27)
    tl.store(out_ptr0 + x0, tmp13, xmask)
    tl.store(out_ptr1 + x0, tmp28, xmask)


@triton.jit
def triton_poi_fused_native_group_norm_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
    in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x4 = xindex // 4
    x1 = xindex // 4 % 4
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp3 = tl.load(in_ptr1 + x4, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr2 + x4, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr4 + x1, xmask, eviction_policy='evict_last')
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = tmp2 - tmp3
    tmp6 = tmp4 * tmp5
    tmp8 = tmp6 * tmp7
    tmp10 = tmp8 + tmp9
    tl.store(out_ptr0 + x3, tmp10, 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, 8, 4, 4), (128, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 3, 3, 3), (108, 27, 9, 3, 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 = torch.ops.aten.max_pool3d_with_indices.default(primals_1, [2,
            2, 2], [2, 2, 2])
        del primals_1
        buf1 = buf0[0]
        del buf0
        buf3 = extern_kernels.convolution(reinterpret_tensor(buf1, (1, 4, 4,
            2, 2), (64, 16, 4, 2, 1), 0), primals_2, 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(buf3, (1, 4, 4, 2, 2), (64, 16, 4, 2, 1))
        buf4 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
        buf5 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_native_group_norm_0[grid(16)](buf3, buf4, buf5, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        buf6 = empty_strided_cuda((4, 4, 2, 2), (16, 4, 2, 1), torch.float32)
        triton_poi_fused_native_group_norm_1[grid(64)](buf3, buf4, buf5,
            primals_3, primals_4, buf6, 64, XBLOCK=64, num_warps=1,
            num_stages=1)
        del primals_4
        buf7 = extern_kernels.convolution(reinterpret_tensor(buf6, (1, 4, 4,
            2, 2), (0, 16, 4, 2, 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(buf7, (1, 4, 4, 2, 2), (64, 16, 4, 2, 1))
        buf8 = buf5
        del buf5
        buf9 = buf4
        del buf4
        triton_poi_fused_native_group_norm_0[grid(16)](buf7, buf8, buf9, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        buf10 = empty_strided_cuda((4, 4, 2, 2), (16, 4, 2, 1), torch.float32)
        triton_poi_fused_native_group_norm_1[grid(64)](buf7, buf8, buf9,
            primals_6, primals_7, buf10, 64, XBLOCK=64, num_warps=1,
            num_stages=1)
        del buf8
        del buf9
        del primals_7
    return (buf10, primals_2, primals_3, primals_5, primals_6,
        reinterpret_tensor(buf1, (1, 4, 4, 2, 2), (64, 16, 4, 2, 1), 0),
        buf3, reinterpret_tensor(buf6, (1, 4, 4, 2, 2), (64, 16, 4, 2, 1), 
        0), buf7)


def conv3d(in_channels, out_channels, kernel_size, bias, padding=1, stride=1):
    return nn.Conv3d(in_channels, out_channels, kernel_size, padding=
        padding, bias=bias, stride=stride)


def create_conv(in_channels, out_channels, kernel_size, order, num_groups,
    padding=1):
    """
    Create a list of modules with together constitute a single conv layer with non-linearity
    and optional batchnorm/groupnorm.

    Args:
        in_channels (int): number of input channels
        out_channels (int): number of output channels
        order (string): order of things, e.g.
            'cr' -> conv + ReLU
            'crg' -> conv + ReLU + groupnorm
            'cl' -> conv + LeakyReLU
            'ce' -> conv + ELU
        num_groups (int): number of groups for the GroupNorm
        padding (int): add zero-padding to the input

    Return:
        list of tuple (name, module)
    """
    assert 'c' in order, 'Conv layer MUST be present'
    assert order[0
        ] not in 'rle', 'Non-linearity cannot be the first operation in the layer'
    modules = []
    for i, char in enumerate(order):
        if char == 'r':
            modules.append(('ReLU', nn.ReLU(inplace=True)))
        elif char == 'l':
            modules.append(('LeakyReLU', nn.LeakyReLU(negative_slope=0.1,
                inplace=True)))
        elif char == 'e':
            modules.append(('ELU', nn.ELU(inplace=True)))
        elif char == 'c':
            bias = not ('g' in order or 'b' in order)
            modules.append(('conv', conv3d(in_channels, out_channels,
                kernel_size, bias, padding=padding)))
        elif char == 'g':
            is_before_conv = i < order.index('c')
            assert not is_before_conv, 'GroupNorm MUST go after the Conv3d'
            if out_channels < num_groups:
                num_groups = out_channels
            modules.append(('groupnorm', nn.GroupNorm(num_groups=num_groups,
                num_channels=out_channels)))
        elif char == 'b':
            is_before_conv = i < order.index('c')
            if is_before_conv:
                modules.append(('batchnorm', nn.BatchNorm3d(in_channels)))
            else:
                modules.append(('batchnorm', nn.BatchNorm3d(out_channels)))
        else:
            raise ValueError(
                f"Unsupported layer type '{char}'. MUST be one of ['b', 'g', 'r', 'l', 'e', 'c']"
                )
    return modules


class SingleConv(nn.Sequential):
    """
    Basic convolutional module consisting of a Conv3d, non-linearity and optional batchnorm/groupnorm. The order
    of operations can be specified via the `order` parameter

    Args:
        in_channels (int): number of input channels
        out_channels (int): number of output channels
        kernel_size (int): size of the convolving kernel
        order (string): determines the order of layers, e.g.
            'cr' -> conv + ReLU
            'crg' -> conv + ReLU + groupnorm
            'cl' -> conv + LeakyReLU
            'ce' -> conv + ELU
        num_groups (int): number of groups for the GroupNorm
    """

    def __init__(self, in_channels, out_channels, kernel_size=3, order=
        'crg', num_groups=8, padding=1):
        super(SingleConv, self).__init__()
        for name, module in create_conv(in_channels, out_channels,
            kernel_size, order, num_groups, padding=padding):
            self.add_module(name, module)


class DoubleConv(nn.Sequential):
    """
    A module consisting of two consecutive convolution layers (e.g. BatchNorm3d+ReLU+Conv3d).
    We use (Conv3d+ReLU+GroupNorm3d) by default.
    This can be changed however by providing the 'order' argument, e.g. in order
    to change to Conv3d+BatchNorm3d+ELU use order='cbe'.
    Use padded convolutions to make sure that the output (H_out, W_out) is the same
    as (H_in, W_in), so that you don't have to crop in the decoder path.

    Args:
        in_channels (int): number of input channels
        out_channels (int): number of output channels
        encoder (bool): if True we're in the encoder path, otherwise we're in the decoder
        kernel_size (int): size of the convolving kernel
        order (string): determines the order of layers, e.g.
            'cr' -> conv + ReLU
            'crg' -> conv + ReLU + groupnorm
            'cl' -> conv + LeakyReLU
            'ce' -> conv + ELU
        num_groups (int): number of groups for the GroupNorm
    """

    def __init__(self, in_channels, out_channels, encoder, kernel_size=3,
        order='crg', num_groups=8):
        super(DoubleConv, self).__init__()
        if encoder:
            conv1_in_channels = in_channels
            conv1_out_channels = out_channels // 2
            if conv1_out_channels < in_channels:
                conv1_out_channels = in_channels
            conv2_in_channels, conv2_out_channels = (conv1_out_channels,
                out_channels)
        else:
            conv1_in_channels, conv1_out_channels = in_channels, out_channels
            conv2_in_channels, conv2_out_channels = out_channels, out_channels
        self.add_module('SingleConv1', SingleConv(conv1_in_channels,
            conv1_out_channels, kernel_size, order, num_groups))
        self.add_module('SingleConv2', SingleConv(conv2_in_channels,
            conv2_out_channels, kernel_size, order, num_groups))


class EncoderNew(nn.Module):
    """
    A single module from the encoder path consisting of the optional max
    pooling layer (one may specify the MaxPool kernel_size to be different
    than the standard (2,2,2), e.g. if the volumetric data is anisotropic
    (make sure to use complementary scale_factor in the decoder path) followed by
    a DoubleConv module.
    Args:
        in_channels (int): number of input channels
        out_channels (int): number of output channels
        conv_kernel_size (int): size of the convolving kernel
        apply_pooling (bool): if True use MaxPool3d before DoubleConv
        pool_kernel_size (tuple): the size of the window to take a max over
        pool_type (str): pooling layer: 'max' or 'avg'
        basic_module(nn.Module): either ResNetBlock or DoubleConv
        conv_layer_order (string): determines the order of layers
            in `DoubleConv` module. See `DoubleConv` for more info.
        num_groups (int): number of groups for the GroupNorm
    """

    def __init__(self, in_channels, out_channels, conv_kernel_size=3,
        apply_pooling=True, pool_kernel_size=(2, 2, 2), pool_type='max',
        basic_module=DoubleConv, conv_layer_order='crg', num_groups=8):
        super(EncoderNew, self).__init__()
        assert pool_type in ['max', 'avg']
        if apply_pooling:
            if pool_type == 'max':
                self.pooling = nn.MaxPool3d(kernel_size=pool_kernel_size)
            else:
                self.pooling = nn.AvgPool3d(kernel_size=pool_kernel_size)
        else:
            self.pooling = None
        self.basic_module = basic_module(in_channels, out_channels, encoder
            =True, kernel_size=conv_kernel_size, order=conv_layer_order,
            num_groups=num_groups)

    def forward(self, input_0):
        primals_2 = self.basic_module.SingleConv1.conv.weight
        primals_3 = self.basic_module.SingleConv1.groupnorm.weight
        primals_4 = self.basic_module.SingleConv1.groupnorm.bias
        primals_5 = self.basic_module.SingleConv2.conv.weight
        primals_6 = self.basic_module.SingleConv2.groupnorm.weight
        primals_7 = self.basic_module.SingleConv2.groupnorm.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0]
