# 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/jo/cjoo56yo7awtt6oev6cvdfwedqercpy7m6xu45f3pq6md6tems26.py
# Topologically Sorted Source Nodes: [out], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   out => convolution
# Graph fragment:
#   %convolution : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_1, %primals_2, %primals_3, [2, 2], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_0 = async_compile.triton('triton_poi_fused_convolution_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=[65536], 
    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_convolution_0', 'mutated_arg_names': ['in_out_ptr0'], '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_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 50176
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 784) % 16
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x3), tmp2, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/pr/cprnjg45payb2mej3jdaa4qcbs5tr6mjibqm3lgom4km7bghpd24.py
# Topologically Sorted Source Nodes: [pow_1, v_length_sq, v_length, add, truediv, scaling_factor, out_4], Original ATen: [aten.pow, aten.sum, aten.sqrt, aten.add, aten.div, aten.mul]
# Source node to ATen node mapping:
#   add => add
#   out_4 => mul
#   pow_1 => pow_1
#   scaling_factor => div_1
#   truediv => div
#   v_length => sqrt
#   v_length_sq => sum_1
# Graph fragment:
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%view_1, 2), kwargs = {})
#   %sum_1 : [num_users=3] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_1, [2], True), kwargs = {})
#   %sqrt : [num_users=1] = call_function[target=torch.ops.aten.sqrt.default](args = (%sum_1,), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_1, 1), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sum_1, %add), kwargs = {})
#   %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%div, %sqrt), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_1, %div_1), kwargs = {})
triton_poi_fused_add_div_mul_pow_sqrt_sum_1 = async_compile.triton('triton_poi_fused_add_div_mul_pow_sqrt_sum_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=[16384, 4], 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_add_div_mul_pow_sqrt_sum_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, '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_add_div_mul_pow_sqrt_sum_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 12544
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 3136
    y1 = (yindex // 3136)
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + ((784*x2) + (3136*(y0 // 784)) + (12544*y1) + (y0 % 784)), xmask & ymask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + ((3136*(y0 // 784)) + (12544*y1) + (y0 % 784)), ymask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (784 + (3136*(y0 // 784)) + (12544*y1) + (y0 % 784)), ymask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (1568 + (3136*(y0 // 784)) + (12544*y1) + (y0 % 784)), ymask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (2352 + (3136*(y0 // 784)) + (12544*y1) + (y0 % 784)), ymask, eviction_policy='evict_last')
    tmp2 = tmp1 * tmp1
    tmp4 = tmp3 * tmp3
    tmp5 = tmp2 + tmp4
    tmp7 = tmp6 * tmp6
    tmp8 = tmp5 + tmp7
    tmp10 = tmp9 * tmp9
    tmp11 = tmp8 + tmp10
    tmp12 = 1.0
    tmp13 = tmp11 + tmp12
    tmp14 = tmp11 / tmp13
    tmp15 = libdevice.sqrt(tmp11)
    tmp16 = tmp14 / tmp15
    tmp17 = tmp0 * tmp16
    tl.store(out_ptr0 + (x2 + (4*y3)), tmp17, xmask & ymask)
''', 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, (4, 4, 64, 64), (16384, 4096, 64, 1))
    assert_size_stride(primals_2, (16, 4, 9, 9), (324, 81, 9, 1))
    assert_size_stride(primals_3, (16, ), (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_1, primals_2, stride=(2, 2), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 16, 28, 28), (12544, 784, 28, 1))
        buf1 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [out], Original ATen: [aten.convolution]
        stream0 = get_raw_stream(0)
        triton_poi_fused_convolution_0.run(buf1, primals_3, 50176, grid=grid(50176), stream=stream0)
        del primals_3
        buf2 = empty_strided_cuda((4, 3136, 4), (12544, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [pow_1, v_length_sq, v_length, add, truediv, scaling_factor, out_4], Original ATen: [aten.pow, aten.sum, aten.sqrt, aten.add, aten.div, aten.mul]
        triton_poi_fused_add_div_mul_pow_sqrt_sum_1.run(buf1, buf2, 12544, 4, grid=grid(12544, 4), stream=stream0)
    return (buf2, primals_1, primals_2, buf1, )


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, 64, 64), (16384, 4096, 64, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((16, 4, 9, 9), (324, 81, 9, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((16, ), (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 torch
import torch.nn as nn


def squash(x, dim=2):
    v_length_sq = x.pow(2).sum(dim=dim, keepdim=True)
    v_length = torch.sqrt(v_length_sq)
    scaling_factor = v_length_sq / (1 + v_length_sq) / v_length
    return x * scaling_factor


class PrimaryCaps(nn.Module):
    """
    PrimaryCaps layers.
    """

    def __init__(self, in_channels, out_capsules, out_capsule_dim,
        kernel_size=9, stride=2):
        super(PrimaryCaps, self).__init__()
        self.in_channels = in_channels
        self.out_capsules = out_capsules
        self.out_capsule_dim = out_capsule_dim
        self.capsules = nn.Conv2d(in_channels=in_channels, out_channels=
            out_capsules * out_capsule_dim, kernel_size=kernel_size, stride
            =stride, bias=True)

    def forward(self, x):
        """
        Revise based on adambielski's implementation.
        ref: https://github.com/adambielski/CapsNet-pytorch/blob/master/net.py
        """
        batch_size = x.size(0)
        out = self.capsules(x)
        _, _C, H, W = out.size()
        out = out.view(batch_size, self.out_capsules, self.out_capsule_dim,
            H, W)
        out = out.permute(0, 1, 3, 4, 2).contiguous()
        out = out.view(out.size(0), -1, out.size(4))
        out = squash(out, dim=2)
        return out


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


def get_init_inputs():
    return [[], {'in_channels': 4, 'out_capsules': 4, 'out_capsule_dim': 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.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


@triton.jit
def triton_poi_fused_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 50176
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 784 % 16
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, xmask)


@triton.jit
def triton_poi_fused_add_div_mul_pow_sqrt_sum_1(in_ptr0, out_ptr0, ynumel,
    xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 12544
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 3136
    y1 = yindex // 3136
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (784 * x2 + 3136 * (y0 // 784) + 12544 * y1 + 
        y0 % 784), xmask & ymask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (3136 * (y0 // 784) + 12544 * y1 + y0 % 784),
        ymask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (784 + 3136 * (y0 // 784) + 12544 * y1 + y0 % 
        784), ymask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (1568 + 3136 * (y0 // 784) + 12544 * y1 + y0 %
        784), ymask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (2352 + 3136 * (y0 // 784) + 12544 * y1 + y0 %
        784), ymask, eviction_policy='evict_last')
    tmp2 = tmp1 * tmp1
    tmp4 = tmp3 * tmp3
    tmp5 = tmp2 + tmp4
    tmp7 = tmp6 * tmp6
    tmp8 = tmp5 + tmp7
    tmp10 = tmp9 * tmp9
    tmp11 = tmp8 + tmp10
    tmp12 = 1.0
    tmp13 = tmp11 + tmp12
    tmp14 = tmp11 / tmp13
    tmp15 = libdevice.sqrt(tmp11)
    tmp16 = tmp14 / tmp15
    tmp17 = tmp0 * tmp16
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp17, xmask & ymask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 64, 64), (16384, 4096, 64, 1))
    assert_size_stride(primals_2, (16, 4, 9, 9), (324, 81, 9, 1))
    assert_size_stride(primals_3, (16,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(2, 
            2), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 16, 28, 28), (12544, 784, 28, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(50176)](buf1, primals_3, 50176,
            XBLOCK=512, num_warps=4, num_stages=1)
        del primals_3
        buf2 = empty_strided_cuda((4, 3136, 4), (12544, 4, 1), torch.float32)
        triton_poi_fused_add_div_mul_pow_sqrt_sum_1[grid(12544, 4)](buf1,
            buf2, 12544, 4, XBLOCK=4, YBLOCK=256, num_warps=4, num_stages=1)
    return buf2, primals_1, primals_2, buf1


def squash(x, dim=2):
    v_length_sq = x.pow(2).sum(dim=dim, keepdim=True)
    v_length = torch.sqrt(v_length_sq)
    scaling_factor = v_length_sq / (1 + v_length_sq) / v_length
    return x * scaling_factor


class PrimaryCapsNew(nn.Module):
    """
    PrimaryCaps layers.
    """

    def __init__(self, in_channels, out_capsules, out_capsule_dim,
        kernel_size=9, stride=2):
        super(PrimaryCapsNew, self).__init__()
        self.in_channels = in_channels
        self.out_capsules = out_capsules
        self.out_capsule_dim = out_capsule_dim
        self.capsules = nn.Conv2d(in_channels=in_channels, out_channels=
            out_capsules * out_capsule_dim, kernel_size=kernel_size, stride
            =stride, bias=True)

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