# 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/kn/cknjlh5mzsg6tap75kwweiwuidyxeolmhbgzpsrthbqvrieuksvv.py
# Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   x_2 => clone
# Graph fragment:
#   %clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute_2,), 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=[1024], 
    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_clone_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, '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, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = (xindex // 4) % 4
    x4 = (xindex // 256)
    x5 = (xindex // 16) % 16
    x3 = (xindex // 64) % 4
    x6 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (4*x5) + (64*x1) + (256*x4)), xmask)
    tmp1 = tl.load(in_ptr1 + (x3 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (x6), tmp2, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (16, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_3, (16, ), (1, ))
    assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(primals_4, primals_2, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 16, 4, 4), (256, 16, 4, 1))
        buf1 = empty_strided_cuda((4, 4, 4, 4, 4, 1), (256, 64, 16, 4, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.clone]
        stream0 = get_raw_stream(0)
        triton_poi_fused_clone_0.run(buf0, primals_3, buf1, 1024, grid=grid(1024), stream=stream0)
        del buf0
        del primals_3
        buf2 = empty_strided_cuda((1, 64, 4), (256, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf1, (1, 64, 16), (0, 16, 1), 0), reinterpret_tensor(primals_1, (1, 16, 4), (64, 4, 1), 0), out=buf2)
        del buf1
    return (reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0), primals_2, primals_4, reinterpret_tensor(primals_1, (1, 4, 16), (64, 1, 4), 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), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((16, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4])
    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._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
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_clone_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4 % 4
    x4 = xindex // 256
    x5 = xindex // 16 % 16
    x3 = xindex // 64 % 4
    x6 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * x5 + 64 * x1 + 256 * x4), xmask)
    tmp1 = tl.load(in_ptr1 + (x3 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + x6, tmp2, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (16, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_3, (16,), (1,))
    assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_4, primals_2, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 16, 4, 4), (256, 16, 4, 1))
        buf1 = empty_strided_cuda((4, 4, 4, 4, 4, 1), (256, 64, 16, 4, 1, 1
            ), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_clone_0[grid(1024)](buf0, primals_3, buf1, 1024,
            XBLOCK=256, num_warps=4, num_stages=1)
        del buf0
        del primals_3
        buf2 = empty_strided_cuda((1, 64, 4), (256, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf1, (1, 64, 16), (0, 16, 1),
            0), reinterpret_tensor(primals_1, (1, 16, 4), (64, 4, 1), 0),
            out=buf2)
        del buf1
    return reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0
        ), primals_2, primals_4, reinterpret_tensor(primals_1, (1, 4, 16),
        (64, 1, 4), 0)


class SpatialGCNew(nn.Module):
    """Sapatial Graph Convolution used in DR-GCB and RAM_r's
            encoder and decoder

    Args:
        in_channels (int): Number of channels in the input sequence data
        out_channels (int): Number of channels produced by the convolution
        kernel_size (int): Size of the graph convolving kernel
        bias (bool, optional): If ``True``, adds a learnable bias to the output.
            Default: ``True``

    Shape:
        - Input[0]: Input graph sequence
            in :math:`(N, M, in_channels, T_{in}, V)` format
        - Input[1]: Input physical graph adjacency matrix
            in :math:`(K, V, V)` format
        - Output[0]: Output physical graph sequence
            in :math:`(N, M, out_channels, T_{out}, V)` format
        - Output[1]: Physical graph adjacency matrix for output data
            in :math:`(K, V, V)` format

        where
            :math:`N` is a batch size,
            :math:`M` is the number of instance in a frame.
            :math:`K` is the spatial kernel size, as :math:`K == kernel_size[1]`,
            :math:`T_{in}/T_{out}` is a length of input/output sequence,
            :math:`V` is the number of graph nodes.
    """

    def __init__(self, in_channels, out_channels, kernel_size, bias=True):
        super().__init__()
        self.kernel_size = kernel_size
        self.conv = nn.Conv2d(in_channels, out_channels * kernel_size,
            kernel_size=(1, 1), bias=bias)

    def forward(self, input_0, input_1):
        primals_2 = self.conv.weight
        primals_3 = self.conv.bias
        primals_4 = input_0
        primals_1 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0], output[1]
