# 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/oj/cojl5mb3pzv5jbmfzjkbac5hekbmpvb72kof6ouyyasitrogdd6n.py
# Topologically Sorted Source Nodes: [x], Original ATen: [aten._unsafe_index]
# Source node to ATen node mapping:
#   x => _unsafe_index
# Graph fragment:
#   %_unsafe_index : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%primals_1, [None, None, %unsqueeze, %convert_element_type_1]), kwargs = {})
triton_poi_fused__unsafe_index_0 = async_compile.triton('triton_poi_fused__unsafe_index_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=[1024], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__unsafe_index_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, '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__unsafe_index_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 8) % 8
    x0 = xindex % 8
    x2 = (xindex // 64)
    x4 = xindex
    tmp0 = x1
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 * tmp2
    tmp4 = tmp3.to(tl.int32)
    tmp5 = x0
    tmp6 = tmp5.to(tl.float32)
    tmp7 = tmp6 * tmp2
    tmp8 = tmp7.to(tl.int32)
    tmp9 = tl.load(in_ptr0 + (tmp8 + (4*tmp4) + (16*x2)), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x4), tmp9, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/r2/cr2ne7nbcsyqbhpsyfmxyytqgjmsi2fzvjry5zu2cvhnyfvmthpo.py
# Topologically Sorted Source Nodes: [x_1, instance_norm, x_3], Original ATen: [aten.convolution, aten._native_batch_norm_legit, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   instance_norm => add_4, rsqrt, var_mean
#   x_1 => convolution
#   x_3 => relu
# Graph fragment:
#   %convolution : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%unsqueeze_1, %primals_2, %primals_3, [1, 1, 1], [0, 0, 0], [1, 1, 1], False, [0, 0, 0], 1), kwargs = {})
#   %var_mean : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%unsqueeze_2, [0, 2, 3, 4]), kwargs = {correction: 0, keepdim: True})
#   %add_4 : [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_4,), kwargs = {})
#   %relu : [num_users=1] = call_function[target=torch.ops.aten.relu.default](args = (%squeeze_3,), kwargs = {})
#   %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%squeeze_8, 0), kwargs = {})
triton_per_fused__native_batch_norm_legit_convolution_relu_threshold_backward_1 = async_compile.triton('triton_per_fused__native_batch_norm_legit_convolution_relu_threshold_backward_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.persistent_reduction(
    size_hints=[4, 256],
    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, 9), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__native_batch_norm_legit_convolution_relu_threshold_backward_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': True, '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_convolution_relu_threshold_backward_1(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr2, out_ptr3, out_ptr4, xnumel, rnumel):
    xnumel = 4
    XBLOCK: tl.constexpr = 1
    rnumel = 256
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    xmask = tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    roffset = 0
    rmask = tl.full([RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + (r1 + (256*x0)), None)
    tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last')
    tmp23 = tl.load(in_ptr1 + (x0), None, eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr2 + (x0), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.broadcast_to(tmp2, [RBLOCK])
    tmp5 = tl.broadcast_to(tmp3, [RBLOCK])
    tmp7 = triton_helpers.promote_to_tensor(tl.sum(tmp5, 0))
    tmp8 = tl.full([1], 256, tl.int32)
    tmp9 = tmp8.to(tl.float32)
    tmp10 = tmp7 / tmp9
    tmp11 = tmp3 - tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tl.broadcast_to(tmp12, [RBLOCK])
    tmp15 = triton_helpers.promote_to_tensor(tl.sum(tmp13, 0))
    tmp16 = tmp2 - tmp10
    tmp17 = 256.0
    tmp18 = tmp15 / tmp17
    tmp19 = 1e-05
    tmp20 = tmp18 + tmp19
    tmp21 = libdevice.rsqrt(tmp20)
    tmp22 = tmp16 * tmp21
    tmp24 = tmp22 * tmp23
    tmp26 = tmp24 + tmp25
    tmp27 = tl.full([1], 0, tl.int32)
    tmp28 = triton_helpers.maximum(tmp27, tmp26)
    tmp29 = 0.0
    tmp30 = tmp28 <= tmp29
    tl.store(in_out_ptr0 + (r1 + (256*x0)), tmp2, None)
    tl.store(out_ptr2 + (r1 + (256*x0)), tmp28, None)
    tl.store(out_ptr3 + (r1 + (256*x0)), tmp30, None)
    tl.store(out_ptr4 + (x0), tmp21, None)
    tl.store(out_ptr0 + (x0), tmp10, None)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 1, 1, 1), (4, 1, 1, 1, 1))
    assert_size_stride(primals_3, (4, ), (1, ))
    assert_size_stride(primals_4, (4, ), (1, ))
    assert_size_stride(primals_5, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 8, 8), (256, 64, 8, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten._unsafe_index]
        stream0 = get_raw_stream(0)
        triton_poi_fused__unsafe_index_0.run(primals_1, buf0, 1024, grid=grid(1024), stream=stream0)
        del primals_1
        # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.convolution]
        buf1 = extern_kernels.convolution(reinterpret_tensor(buf0, (1, 4, 4, 8, 8), (0, 256, 64, 8, 1), 0), primals_2, stride=(1, 1, 1), padding=(0, 0, 0), dilation=(1, 1, 1), transposed=False, output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (1, 4, 4, 8, 8), (1024, 256, 64, 8, 1))
        buf2 = buf1; del buf1  # reuse
        buf3 = empty_strided_cuda((1, 4, 1, 1, 1), (4, 1, 4, 4, 4), torch.float32)
        buf7 = empty_strided_cuda((4, 4, 8, 8), (256, 64, 8, 1), torch.float32)
        buf8 = empty_strided_cuda((4, 4, 8, 8), (256, 64, 8, 1), torch.bool)
        buf6 = empty_strided_cuda((1, 4, 1, 1, 1), (4, 1, 4, 4, 4), torch.float32)
        # Topologically Sorted Source Nodes: [x_1, instance_norm, x_3], Original ATen: [aten.convolution, aten._native_batch_norm_legit, aten.relu, aten.threshold_backward]
        triton_per_fused__native_batch_norm_legit_convolution_relu_threshold_backward_1.run(buf2, primals_3, primals_4, primals_5, buf3, buf7, buf8, buf6, 4, 256, grid=grid(4), stream=stream0)
        del primals_3
        del primals_5
    return (buf7, primals_2, primals_4, reinterpret_tensor(buf0, (1, 4, 4, 8, 8), (1024, 256, 64, 8, 1), 0), buf2, reinterpret_tensor(buf6, (4, ), (1, ), 0), buf8, reinterpret_tensor(buf3, (1, 4, 1, 1, 1), (4, 1, 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((4, 4, 1, 1, 1), (4, 1, 1, 1, 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, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5])
    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


class Upsample(nn.Module):

    def __init__(self, in_channels, out_channels, scale_factor=2):
        super().__init__()
        self.trilinear = nn.Upsample(scale_factor=scale_factor)
        self.conv1 = nn.Conv3d(in_channels, out_channels, kernel_size=1)
        self.bn1 = nn.InstanceNorm3d(out_channels, affine=True)
        self.relu = nn.ReLU(inplace=True)

    def forward(self, x):
        x = self.trilinear(x)
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        return x


def get_inputs():
    return [torch.rand([4, 4, 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
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_poi_fused__unsafe_index_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 8 % 8
    x0 = xindex % 8
    x2 = xindex // 64
    x4 = xindex
    tmp0 = x1
    tmp1 = tmp0.to(tl.float32)
    tmp2 = 0.5
    tmp3 = tmp1 * tmp2
    tmp4 = tmp3.to(tl.int32)
    tmp5 = x0
    tmp6 = tmp5.to(tl.float32)
    tmp7 = tmp6 * tmp2
    tmp8 = tmp7.to(tl.int32)
    tmp9 = tl.load(in_ptr0 + (tmp8 + 4 * tmp4 + 16 * x2), xmask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + x4, tmp9, xmask)


@triton.jit
def triton_per_fused__native_batch_norm_legit_convolution_relu_threshold_backward_1(
    in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr2, out_ptr3,
    out_ptr4, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + (r1 + 256 * x0), None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp23 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr2 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.broadcast_to(tmp2, [RBLOCK])
    tmp5 = tl.broadcast_to(tmp3, [RBLOCK])
    tmp7 = triton_helpers.promote_to_tensor(tl.sum(tmp5, 0))
    tmp8 = tl.full([1], 256, tl.int32)
    tmp9 = tmp8.to(tl.float32)
    tmp10 = tmp7 / tmp9
    tmp11 = tmp3 - tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tl.broadcast_to(tmp12, [RBLOCK])
    tmp15 = triton_helpers.promote_to_tensor(tl.sum(tmp13, 0))
    tmp16 = tmp2 - tmp10
    tmp17 = 256.0
    tmp18 = tmp15 / tmp17
    tmp19 = 1e-05
    tmp20 = tmp18 + tmp19
    tmp21 = libdevice.rsqrt(tmp20)
    tmp22 = tmp16 * tmp21
    tmp24 = tmp22 * tmp23
    tmp26 = tmp24 + tmp25
    tmp27 = tl.full([1], 0, tl.int32)
    tmp28 = triton_helpers.maximum(tmp27, tmp26)
    tmp29 = 0.0
    tmp30 = tmp28 <= tmp29
    tl.store(in_out_ptr0 + (r1 + 256 * x0), tmp2, None)
    tl.store(out_ptr2 + (r1 + 256 * x0), tmp28, None)
    tl.store(out_ptr3 + (r1 + 256 * x0), tmp30, None)
    tl.store(out_ptr4 + x0, tmp21, None)
    tl.store(out_ptr0 + x0, tmp10, None)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 1, 1, 1), (4, 1, 1, 1, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4,), (1,))
    assert_size_stride(primals_5, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 8, 8), (256, 64, 8, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__unsafe_index_0[grid(1024)](primals_1, buf0, 1024,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_1
        buf1 = extern_kernels.convolution(reinterpret_tensor(buf0, (1, 4, 4,
            8, 8), (0, 256, 64, 8, 1), 0), primals_2, stride=(1, 1, 1),
            padding=(0, 0, 0), dilation=(1, 1, 1), transposed=False,
            output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (1, 4, 4, 8, 8), (1024, 256, 64, 8, 1))
        buf2 = buf1
        del buf1
        buf3 = empty_strided_cuda((1, 4, 1, 1, 1), (4, 1, 4, 4, 4), torch.
            float32)
        buf7 = empty_strided_cuda((4, 4, 8, 8), (256, 64, 8, 1), torch.float32)
        buf8 = empty_strided_cuda((4, 4, 8, 8), (256, 64, 8, 1), torch.bool)
        buf6 = empty_strided_cuda((1, 4, 1, 1, 1), (4, 1, 4, 4, 4), torch.
            float32)
        triton_per_fused__native_batch_norm_legit_convolution_relu_threshold_backward_1[
            grid(4)](buf2, primals_3, primals_4, primals_5, buf3, buf7,
            buf8, buf6, 4, 256, num_warps=2, num_stages=1)
        del primals_3
        del primals_5
    return buf7, primals_2, primals_4, reinterpret_tensor(buf0, (1, 4, 4, 8,
        8), (1024, 256, 64, 8, 1), 0), buf2, reinterpret_tensor(buf6, (4,),
        (1,), 0), buf8, reinterpret_tensor(buf3, (1, 4, 1, 1, 1), (4, 1, 1,
        1, 1), 0)


class UpsampleNew(nn.Module):

    def __init__(self, in_channels, out_channels, scale_factor=2):
        super().__init__()
        self.trilinear = nn.Upsample(scale_factor=scale_factor)
        self.conv1 = nn.Conv3d(in_channels, out_channels, kernel_size=1)
        self.bn1 = nn.InstanceNorm3d(out_channels, affine=True)
        self.relu = nn.ReLU(inplace=True)

    def forward(self, input_0):
        primals_2 = self.conv1.weight
        primals_3 = self.conv1.bias
        primals_4 = self.bn1.weight
        primals_5 = self.bn1.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]
