# 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/yx/cyxwcwgbozey4rpepwv2gcbun3js4cdbc2hqrvjd5khr6fuk545n.py
# Topologically Sorted Source Nodes: [contiguous], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   contiguous => clone
# Graph fragment:
#   %clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_0 = async_compile.triton('triton_poi_fused_clone_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=[4096, 2], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_clone_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 4096
    xnumel = 2
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x6 = xindex
    y0 = yindex % 4
    y1 = (yindex // 4) % 2
    y2 = (yindex // 8) % 4
    y3 = (yindex // 32) % 2
    y4 = (yindex // 64) % 4
    y5 = (yindex // 256)
    y7 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + (4*y2) + (16*y4) + (64*x6) + (128*y1) + (256*y3) + (512*y5)), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x6 + (2*y7)), tmp0, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (32, 4, 1, 1, 1), (4, 1, 1, 1, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4, 4), (256, 64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [out], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(primals_2, primals_1, 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(buf0, (4, 32, 4, 4, 4), (2048, 64, 16, 4, 1))
        buf1 = empty_strided_cuda((16, 4, 2, 4, 2, 4, 2), (512, 128, 64, 16, 8, 2, 1), torch.float32)
        # Topologically Sorted Source Nodes: [contiguous], Original ATen: [aten.clone]
        stream0 = get_raw_stream(0)
        triton_poi_fused_clone_0.run(buf0, buf1, 4096, 2, grid=grid(4096, 2), stream=stream0)
        del buf0
    return (reinterpret_tensor(buf1, (4, 4, 8, 8, 8), (2048, 512, 64, 8, 1), 0), primals_1, primals_2, )


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((32, 4, 1, 1, 1), (4, 1, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4, 4, 4, 4), (256, 64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2])
    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 torchvision import models as models
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torch.onnx


class shuffle(nn.Module):

    def __init__(self, ratio):
        super(shuffle, self).__init__()
        self.ratio = ratio

    def forward(self, x):
        batch_size, in_channels, d, h, w = x.shape
        out_channels = in_channels // (self.ratio * self.ratio * self.ratio)
        out = x.view(batch_size * out_channels, self.ratio, self.ratio,
            self.ratio, d, h, w)
        out = out.permute(0, 4, 1, 5, 2, 6, 3)
        return out.contiguous().view(batch_size, out_channels, d * self.
            ratio, h * self.ratio, w * self.ratio)


class UpsamplingPixelShuffle(nn.Module):

    def __init__(self, input_channels, output_channels, ratio=2):
        super(UpsamplingPixelShuffle, self).__init__()
        self.input_channels = input_channels
        self.output_channels = output_channels
        self.conv = nn.Conv3d(in_channels=input_channels, out_channels=
            output_channels * int(ratio ** 3), kernel_size=1, padding=0,
            bias=False)
        self.relu = nn.LeakyReLU(0.02, inplace=True)
        self.shuffle = shuffle(ratio=ratio)

    def forward(self, x):
        out = self.conv(x)
        out = self.shuffle(out)
        return out


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


def get_init_inputs():
    return [[], {'input_channels': 4, 'output_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 import nn
from torchvision import models as models
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torch.onnx
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_clone_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    xnumel = 2
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x6 = xindex
    y0 = yindex % 4
    y1 = yindex // 4 % 2
    y2 = yindex // 8 % 4
    y3 = yindex // 32 % 2
    y4 = yindex // 64 % 4
    y5 = yindex // 256
    y7 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * y2 + 16 * y4 + 64 * x6 + 128 * y1 + 
        256 * y3 + 512 * y5), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x6 + 2 * y7), tmp0, xmask)


def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (32, 4, 1, 1, 1), (4, 1, 1, 1, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4, 4), (256, 64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_2, primals_1, 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(buf0, (4, 32, 4, 4, 4), (2048, 64, 16, 4, 1))
        buf1 = empty_strided_cuda((16, 4, 2, 4, 2, 4, 2), (512, 128, 64, 16,
            8, 2, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_clone_0[grid(4096, 2)](buf0, buf1, 4096, 2, XBLOCK
            =2, YBLOCK=512, num_warps=4, num_stages=1)
        del buf0
    return reinterpret_tensor(buf1, (4, 4, 8, 8, 8), (2048, 512, 64, 8, 1), 0
        ), primals_1, primals_2


class shuffle(nn.Module):

    def __init__(self, ratio):
        super(shuffle, self).__init__()
        self.ratio = ratio

    def forward(self, x):
        batch_size, in_channels, d, h, w = x.shape
        out_channels = in_channels // (self.ratio * self.ratio * self.ratio)
        out = x.view(batch_size * out_channels, self.ratio, self.ratio,
            self.ratio, d, h, w)
        out = out.permute(0, 4, 1, 5, 2, 6, 3)
        return out.contiguous().view(batch_size, out_channels, d * self.
            ratio, h * self.ratio, w * self.ratio)


class UpsamplingPixelShuffleNew(nn.Module):

    def __init__(self, input_channels, output_channels, ratio=2):
        super(UpsamplingPixelShuffleNew, self).__init__()
        self.input_channels = input_channels
        self.output_channels = output_channels
        self.conv = nn.Conv3d(in_channels=input_channels, out_channels=
            output_channels * int(ratio ** 3), kernel_size=1, padding=0,
            bias=False)
        self.relu = nn.LeakyReLU(0.02, inplace=True)
        self.shuffle = shuffle(ratio=ratio)

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