# AOT ID: ['0_inference']
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/ch/cchwyippaifkg3wzdlar3zblomxznwnqipberzksnr5o2fzygobs.py
# Topologically Sorted Source Nodes: [x_1, contiguous], Original ATen: [aten.avg_pool3d, aten.clone]
# Source node to ATen node mapping:
#   contiguous => clone
#   x_1 => avg_pool3d
# Graph fragment:
#   %avg_pool3d : [num_users=1] = call_function[target=torch.ops.aten.avg_pool3d.default](args = (%permute, [3, 1, 1], [2, 1, 1], [1, 0, 0]), kwargs = {})
#   %clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute_1,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_avg_pool3d_clone_0 = async_compile.triton('triton_poi_fused_avg_pool3d_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=[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_avg_pool3d_clone_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 3, '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_avg_pool3d_clone_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = (xindex // 64)
    x1 = (xindex // 4) % 4
    x0 = xindex % 4
    x5 = xindex % 64
    x4 = xindex
    tmp0 = (-1) + (2*x3)
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tmp2 & tmp4
    tmp6 = x1
    tmp7 = tmp6 >= tmp1
    tmp8 = tmp6 < tmp3
    tmp9 = tmp7 & tmp8
    tmp10 = x0
    tmp11 = tmp10 >= tmp1
    tmp12 = tmp10 < tmp3
    tmp13 = tmp11 & tmp12
    tmp14 = tmp5 & tmp9
    tmp15 = tmp14 & tmp13
    tmp16 = tl.load(in_ptr0 + ((-64) + x5 + (128*x3)), tmp15 & xmask, other=0.0)
    tmp17 = 2*x3
    tmp18 = tmp17 >= tmp1
    tmp19 = tmp17 < tmp3
    tmp20 = tmp18 & tmp19
    tmp21 = tmp20 & tmp9
    tmp22 = tmp21 & tmp13
    tmp23 = tl.load(in_ptr0 + (x5 + (128*x3)), tmp22 & xmask, other=0.0)
    tmp24 = tmp23 + tmp16
    tmp25 = 1 + (2*x3)
    tmp26 = tmp25 >= tmp1
    tmp27 = tmp25 < tmp3
    tmp28 = tmp26 & tmp27
    tmp29 = tmp28 & tmp9
    tmp30 = tmp29 & tmp13
    tmp31 = tl.load(in_ptr0 + (64 + x5 + (128*x3)), tmp30 & xmask, other=0.0)
    tmp32 = tmp31 + tmp24
    tmp33 = (x0*x1) + (((4) * ((4) <= (1 + x0)) + (1 + x0) * ((1 + x0) < (4)))*((4) * ((4) <= (1 + x1)) + (1 + x1) * ((1 + x1) < (4)))) + ((-1)*x0*((4) * ((4) <= (1 + x1)) + (1 + x1) * ((1 + x1) < (4)))) + ((-1)*x1*((4) * ((4) <= (1 + x0)) + (1 + x0) * ((1 + x0) < (4)))) + (x0*x1*((5) * ((5) <= (2 + (2*x3))) + (2 + (2*x3)) * ((2 + (2*x3)) < (5)))) + (((4) * ((4) <= (1 + x0)) + (1 + x0) * ((1 + x0) < (4)))*((4) * ((4) <= (1 + x1)) + (1 + x1) * ((1 + x1) < (4)))*((5) * ((5) <= (2 + (2*x3))) + (2 + (2*x3)) * ((2 + (2*x3)) < (5)))) + ((-1)*x0*((4) * ((4) <= (1 + x1)) + (1 + x1) * ((1 + x1) < (4)))*((5) * ((5) <= (2 + (2*x3))) + (2 + (2*x3)) * ((2 + (2*x3)) < (5)))) + ((-1)*x1*((4) * ((4) <= (1 + x0)) + (1 + x0) * ((1 + x0) < (4)))*((5) * ((5) <= (2 + (2*x3))) + (2 + (2*x3)) * ((2 + (2*x3)) < (5)))) + ((-2)*x0*x1*x3) + ((-2)*x3*((4) * ((4) <= (1 + x0)) + (1 + x0) * ((1 + x0) < (4)))*((4) * ((4) <= (1 + x1)) + (1 + x1) * ((1 + x1) < (4)))) + (2*x0*x3*((4) * ((4) <= (1 + x1)) + (1 + x1) * ((1 + x1) < (4)))) + (2*x1*x3*((4) * ((4) <= (1 + x0)) + (1 + x0) * ((1 + x0) < (4))))
    tmp34 = tmp32 / tmp33
    tl.store(in_out_ptr0 + (x4), tmp34, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((1, 4, 2, 4, 4), (128, 16, 64, 4, 1), torch.float32)
        buf1 = reinterpret_tensor(buf0, (1, 2, 4, 4, 4), (128, 64, 16, 4, 1), 0); del buf0  # reuse
        # Topologically Sorted Source Nodes: [x_1, contiguous], Original ATen: [aten.avg_pool3d, aten.clone]
        stream0 = get_raw_stream(0)
        triton_poi_fused_avg_pool3d_clone_0.run(buf1, arg0_1, 128, grid=grid(128), stream=stream0)
        del arg0_1
    return (reinterpret_tensor(buf1, (2, 4, 4, 4), (64, 16, 4, 1), 0), )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1])
    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
import torch.distributions
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed


class TemporalPooling(nn.Module):

    def __init__(self, frames, kernel_size=3, stride=2, mode='avg'):
        """

        Parameters
        ----------
        frames (int): number of input frames
        kernel_size
        stride
        mode
        """
        super().__init__()
        self.frames = frames
        pad_size = (kernel_size - 1) // stride
        if mode == 'avg':
            self.pool = nn.AvgPool3d(kernel_size=(kernel_size, 1, 1),
                stride=(stride, 1, 1), padding=(pad_size, 0, 0))
        elif mode == 'max':
            self.pool = nn.MaxPool3d(kernel_size=(kernel_size, 1, 1),
                stride=(stride, 1, 1), padding=(pad_size, 0, 0))
        else:
            raise ValueError('only support avg or max')

    def forward(self, x):
        _nt, c, h, w = x.shape
        x = x.view((-1, self.frames) + x.size()[1:]).transpose(1, 2)
        x = self.pool(x)
        x = x.transpose(1, 2).contiguous().view(-1, c, h, w)
        return x


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


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

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
import torch.nn as nn
import torch.distributions
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
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_avg_pool3d_clone_0(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex // 64
    x1 = xindex // 4 % 4
    x0 = xindex % 4
    x5 = xindex % 64
    x4 = xindex
    tmp0 = -1 + 2 * x3
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tmp2 & tmp4
    tmp6 = x1
    tmp7 = tmp6 >= tmp1
    tmp8 = tmp6 < tmp3
    tmp9 = tmp7 & tmp8
    tmp10 = x0
    tmp11 = tmp10 >= tmp1
    tmp12 = tmp10 < tmp3
    tmp13 = tmp11 & tmp12
    tmp14 = tmp5 & tmp9
    tmp15 = tmp14 & tmp13
    tmp16 = tl.load(in_ptr0 + (-64 + x5 + 128 * x3), tmp15 & xmask, other=0.0)
    tmp17 = 2 * x3
    tmp18 = tmp17 >= tmp1
    tmp19 = tmp17 < tmp3
    tmp20 = tmp18 & tmp19
    tmp21 = tmp20 & tmp9
    tmp22 = tmp21 & tmp13
    tmp23 = tl.load(in_ptr0 + (x5 + 128 * x3), tmp22 & xmask, other=0.0)
    tmp24 = tmp23 + tmp16
    tmp25 = 1 + 2 * x3
    tmp26 = tmp25 >= tmp1
    tmp27 = tmp25 < tmp3
    tmp28 = tmp26 & tmp27
    tmp29 = tmp28 & tmp9
    tmp30 = tmp29 & tmp13
    tmp31 = tl.load(in_ptr0 + (64 + x5 + 128 * x3), tmp30 & xmask, other=0.0)
    tmp32 = tmp31 + tmp24
    tmp33 = x0 * x1 + (4 * (4 <= 1 + x0) + (1 + x0) * (1 + x0 < 4)) * (4 *
        (4 <= 1 + x1) + (1 + x1) * (1 + x1 < 4)) + -1 * x0 * (4 * (4 <= 1 +
        x1) + (1 + x1) * (1 + x1 < 4)) + -1 * x1 * (4 * (4 <= 1 + x0) + (1 +
        x0) * (1 + x0 < 4)) + x0 * x1 * (5 * (5 <= 2 + 2 * x3) + (2 + 2 *
        x3) * (2 + 2 * x3 < 5)) + (4 * (4 <= 1 + x0) + (1 + x0) * (1 + x0 < 4)
        ) * (4 * (4 <= 1 + x1) + (1 + x1) * (1 + x1 < 4)) * (5 * (5 <= 2 + 
        2 * x3) + (2 + 2 * x3) * (2 + 2 * x3 < 5)) + -1 * x0 * (4 * (4 <= 1 +
        x1) + (1 + x1) * (1 + x1 < 4)) * (5 * (5 <= 2 + 2 * x3) + (2 + 2 *
        x3) * (2 + 2 * x3 < 5)) + -1 * x1 * (4 * (4 <= 1 + x0) + (1 + x0) *
        (1 + x0 < 4)) * (5 * (5 <= 2 + 2 * x3) + (2 + 2 * x3) * (2 + 2 * x3 <
        5)) + -2 * x0 * x1 * x3 + -2 * x3 * (4 * (4 <= 1 + x0) + (1 + x0) *
        (1 + x0 < 4)) * (4 * (4 <= 1 + x1) + (1 + x1) * (1 + x1 < 4)
        ) + 2 * x0 * x3 * (4 * (4 <= 1 + x1) + (1 + x1) * (1 + x1 < 4)
        ) + 2 * x1 * x3 * (4 * (4 <= 1 + x0) + (1 + x0) * (1 + x0 < 4))
    tmp34 = tmp32 / tmp33
    tl.store(in_out_ptr0 + x4, tmp34, xmask)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((1, 4, 2, 4, 4), (128, 16, 64, 4, 1),
            torch.float32)
        buf1 = reinterpret_tensor(buf0, (1, 2, 4, 4, 4), (128, 64, 16, 4, 1), 0
            )
        del buf0
        get_raw_stream(0)
        triton_poi_fused_avg_pool3d_clone_0[grid(128)](buf1, arg0_1, 128,
            XBLOCK=128, num_warps=4, num_stages=1)
        del arg0_1
    return reinterpret_tensor(buf1, (2, 4, 4, 4), (64, 16, 4, 1), 0),


class TemporalPoolingNew(nn.Module):

    def __init__(self, frames, kernel_size=3, stride=2, mode='avg'):
        """

        Parameters
        ----------
        frames (int): number of input frames
        kernel_size
        stride
        mode
        """
        super().__init__()
        self.frames = frames
        pad_size = (kernel_size - 1) // stride
        if mode == 'avg':
            self.pool = nn.AvgPool3d(kernel_size=(kernel_size, 1, 1),
                stride=(stride, 1, 1), padding=(pad_size, 0, 0))
        elif mode == 'max':
            self.pool = nn.MaxPool3d(kernel_size=(kernel_size, 1, 1),
                stride=(stride, 1, 1), padding=(pad_size, 0, 0))
        else:
            raise ValueError('only support avg or max')

    def forward(self, input_0):
        arg0_1 = input_0
        output = call([arg0_1])
        return output[0]
