# 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/pk/cpkb2mcyb2qesvmdu5p4ftvqi4wcrcxj2ewwby63ggg7slwrrxvk.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,), 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=[262144], 
    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_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, xnumel, XBLOCK : tl.constexpr):
    xnumel = 262144
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 4096
    x1 = (xindex // 4096) % 4
    x2 = (xindex // 16384) % 4
    x3 = (xindex // 65536)
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (4096*x2) + (16384*x1) + (65536*x3)), None)
    tl.store(out_ptr0 + (x4), tmp0, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/e7/ce7f7focc67g3xs64gvyppnvzfkawnwxjxgg6rypx2yodlcvwk4z.py
# Topologically Sorted Source Nodes: [x_6], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   x_6 => clone_1
# Graph fragment:
#   %clone_1 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute_1,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_1 = async_compile.triton('triton_poi_fused_clone_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=[262144], 
    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_clone_1', '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_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 238144
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 3721
    x1 = (xindex // 3721) % 4
    x2 = (xindex // 14884) % 4
    x3 = (xindex // 59536)
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (3721*x2) + (14884*x1) + (59536*x3)), xmask)
    tl.store(out_ptr0 + (x4), tmp0, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (16, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_2, (4, 16, 64, 64), (65536, 4096, 64, 1))
    assert_size_stride(primals_3, (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_2, primals_1, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=4, bias=None)
        assert_size_stride(buf0, (4, 16, 64, 64), (65536, 4096, 64, 1))
        buf1 = empty_strided_cuda((4, 4, 4, 64, 64), (65536, 16384, 4096, 64, 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, buf1, 262144, grid=grid(262144), stream=stream0)
        del buf0
        # Topologically Sorted Source Nodes: [x_4], Original ATen: [aten.convolution]
        buf2 = extern_kernels.convolution(reinterpret_tensor(buf1, (16, 4, 64, 64), (16384, 4096, 64, 1), 0), primals_3, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (16, 4, 61, 61), (14884, 3721, 61, 1))
        buf3 = empty_strided_cuda((4, 4, 4, 61, 61), (59536, 14884, 3721, 61, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_6], Original ATen: [aten.clone]
        triton_poi_fused_clone_1.run(buf2, buf3, 238144, grid=grid(238144), stream=stream0)
        del buf2
    return (reinterpret_tensor(buf3, (4, 16, 61, 61), (59536, 3721, 61, 1), 0), primals_1, primals_2, primals_3, reinterpret_tensor(buf1, (16, 4, 64, 64), (16384, 4096, 64, 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((16, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 16, 64, 64), (65536, 4096, 64, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = 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])
    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 math
import torch
from torch import nn


class Caps_Conv(nn.Module):

    def __init__(self, in_C, in_D, out_C, out_D, kernel_size, stride=1,
        padding=0, dilation=1, bias=False):
        super(Caps_Conv, self).__init__()
        self.in_C = in_C
        self.in_D = in_D
        self.out_C = out_C
        self.out_D = out_D
        self.conv_D = nn.Conv2d(in_C * in_D, in_C * out_D, 1, groups=in_C,
            bias=False)
        self.conv_C = nn.Conv2d(in_C, out_C, kernel_size, stride=stride,
            padding=padding, dilation=dilation, bias=bias)
        m = self.conv_D.kernel_size[0] * self.conv_D.kernel_size[1
            ] * self.conv_D.out_channels
        self.conv_D.weight.data.normal_(0, math.sqrt(2.0 / m))
        n = self.conv_C.kernel_size[0] * self.conv_C.kernel_size[1
            ] * self.conv_C.out_channels
        self.conv_C.weight.data.normal_(0, math.sqrt(2.0 / n))
        if bias:
            self.conv_C.bias.data.zero_()

    def forward(self, x):
        x = self.conv_D(x)
        x = x.view(x.shape[0], self.in_C, self.out_D, x.shape[2], x.shape[3])
        x = torch.transpose(x, 1, 2).contiguous()
        x = x.view(-1, self.in_C, x.shape[3], x.shape[4])
        x = self.conv_C(x)
        x = x.view(-1, self.out_D, self.out_C, x.shape[2], x.shape[3])
        x = torch.transpose(x, 1, 2).contiguous()
        x = x.view(-1, self.out_C * self.out_D, x.shape[3], x.shape[4])
        return x


def get_inputs():
    return [torch.rand([4, 16, 64, 64])]


def get_init_inputs():
    return [[], {'in_C': 4, 'in_D': 4, 'out_C': 4, 'out_D': 4,
        'kernel_size': 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
import math
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_clone_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 4096
    x1 = xindex // 4096 % 4
    x2 = xindex // 16384 % 4
    x3 = xindex // 65536
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 4096 * x2 + 16384 * x1 + 65536 * x3), None)
    tl.store(out_ptr0 + x4, tmp0, None)


@triton.jit
def triton_poi_fused_clone_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 238144
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 3721
    x1 = xindex // 3721 % 4
    x2 = xindex // 14884 % 4
    x3 = xindex // 59536
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 3721 * x2 + 14884 * x1 + 59536 * x3), xmask)
    tl.store(out_ptr0 + x4, tmp0, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (16, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_2, (4, 16, 64, 64), (65536, 4096, 64, 1))
    assert_size_stride(primals_3, (4, 4, 4, 4), (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), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=4, bias=None)
        assert_size_stride(buf0, (4, 16, 64, 64), (65536, 4096, 64, 1))
        buf1 = empty_strided_cuda((4, 4, 4, 64, 64), (65536, 16384, 4096, 
            64, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_clone_0[grid(262144)](buf0, buf1, 262144, XBLOCK=
            512, num_warps=8, num_stages=1)
        del buf0
        buf2 = extern_kernels.convolution(reinterpret_tensor(buf1, (16, 4, 
            64, 64), (16384, 4096, 64, 1), 0), primals_3, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (16, 4, 61, 61), (14884, 3721, 61, 1))
        buf3 = empty_strided_cuda((4, 4, 4, 61, 61), (59536, 14884, 3721, 
            61, 1), torch.float32)
        triton_poi_fused_clone_1[grid(238144)](buf2, buf3, 238144, XBLOCK=
            512, num_warps=8, num_stages=1)
        del buf2
    return reinterpret_tensor(buf3, (4, 16, 61, 61), (59536, 3721, 61, 1), 0
        ), primals_1, primals_2, primals_3, reinterpret_tensor(buf1, (16, 4,
        64, 64), (16384, 4096, 64, 1), 0)


class Caps_ConvNew(nn.Module):

    def __init__(self, in_C, in_D, out_C, out_D, kernel_size, stride=1,
        padding=0, dilation=1, bias=False):
        super(Caps_ConvNew, self).__init__()
        self.in_C = in_C
        self.in_D = in_D
        self.out_C = out_C
        self.out_D = out_D
        self.conv_D = nn.Conv2d(in_C * in_D, in_C * out_D, 1, groups=in_C,
            bias=False)
        self.conv_C = nn.Conv2d(in_C, out_C, kernel_size, stride=stride,
            padding=padding, dilation=dilation, bias=bias)
        m = self.conv_D.kernel_size[0] * self.conv_D.kernel_size[1
            ] * self.conv_D.out_channels
        self.conv_D.weight.data.normal_(0, math.sqrt(2.0 / m))
        n = self.conv_C.kernel_size[0] * self.conv_C.kernel_size[1
            ] * self.conv_C.out_channels
        self.conv_C.weight.data.normal_(0, math.sqrt(2.0 / n))
        if bias:
            self.conv_C.bias.data.zero_()

    def forward(self, input_0):
        primals_1 = self.conv_D.weight
        primals_3 = self.conv_C.weight
        primals_2 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]
