# 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_4/inductor_cache/45/c45cx2lxqh23a4cwusnudd3symnnclztp6rmlej5jbbq2dwne5tl.py
# Topologically Sorted Source Nodes: [sub, relu, pow_1, mul, sub_1, mul_1, sub_2, relu_1, pow_2, mul_2, L_k, L_k_1, margin_loss], Original ATen: [aten.rsub, aten.relu, aten.pow, aten.mul, aten.sub, aten.add, aten.sum]
# Source node to ATen node mapping:
#   L_k => add
#   L_k_1 => sum_1
#   margin_loss => sum_2
#   mul => mul
#   mul_1 => mul_1
#   mul_2 => mul_2
#   pow_1 => pow_1
#   pow_2 => pow_2
#   relu => relu
#   relu_1 => relu_1
#   sub => sub
#   sub_1 => sub_1
#   sub_2 => sub_2
# Graph fragment:
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (0.9, %arg0_1), kwargs = {})
#   %relu : [num_users=1] = call_function[target=torch.ops.aten.relu.default](args = (%sub,), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%relu, 2), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg1_1, %pow_1), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %arg1_1), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_1, 0.5), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg0_1, 0.1), kwargs = {})
#   %relu_1 : [num_users=1] = call_function[target=torch.ops.aten.relu.default](args = (%sub_2,), kwargs = {})
#   %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%relu_1, 2), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_1, %pow_2), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %mul_2), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%add, [1]), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%sum_1,), kwargs = {})
triton_per_fused_add_mul_pow_relu_rsub_sub_sum_0 = async_compile.triton('triton_per_fused_add_mul_pow_relu_rsub_sub_sum_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.persistent_reduction(
    size_hints=[1, 64],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 4: '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': {3: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 4), equal_to_1=(3,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_mul_pow_relu_rsub_sub_sum_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, 'num_reduction': 1, '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}
)
@triton.jit
def triton_per_fused_add_mul_pow_relu_rsub_sub_sum_0(in_ptr0, in_ptr1, out_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 64
    RBLOCK: tl.constexpr = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex % 16
    r1 = (rindex // 16)
    r2 = rindex
    tmp0 = tl.load(in_ptr0 + (r0 + (64*r1)), None)
    tmp1 = tl.load(in_ptr1 + (r0 + (64*r1)), None)
    tmp18 = tl.load(in_ptr0 + (16 + r0 + (64*r1)), None)
    tmp19 = tl.load(in_ptr1 + (16 + r0 + (64*r1)), None)
    tmp32 = tl.load(in_ptr0 + (32 + r0 + (64*r1)), None)
    tmp33 = tl.load(in_ptr1 + (32 + r0 + (64*r1)), None)
    tmp46 = tl.load(in_ptr0 + (48 + r0 + (64*r1)), None)
    tmp47 = tl.load(in_ptr1 + (48 + r0 + (64*r1)), None)
    tmp2 = 0.9
    tmp3 = tmp2 - tmp1
    tmp4 = tl.full([1, 1], 0, tl.int32)
    tmp5 = triton_helpers.maximum(tmp4, tmp3)
    tmp6 = tmp5 * tmp5
    tmp7 = tmp0 * tmp6
    tmp8 = 1.0
    tmp9 = tmp8 - tmp0
    tmp10 = 0.5
    tmp11 = tmp9 * tmp10
    tmp12 = 0.1
    tmp13 = tmp1 - tmp12
    tmp14 = triton_helpers.maximum(tmp4, tmp13)
    tmp15 = tmp14 * tmp14
    tmp16 = tmp11 * tmp15
    tmp17 = tmp7 + tmp16
    tmp20 = tmp2 - tmp19
    tmp21 = triton_helpers.maximum(tmp4, tmp20)
    tmp22 = tmp21 * tmp21
    tmp23 = tmp18 * tmp22
    tmp24 = tmp8 - tmp18
    tmp25 = tmp24 * tmp10
    tmp26 = tmp19 - tmp12
    tmp27 = triton_helpers.maximum(tmp4, tmp26)
    tmp28 = tmp27 * tmp27
    tmp29 = tmp25 * tmp28
    tmp30 = tmp23 + tmp29
    tmp31 = tmp17 + tmp30
    tmp34 = tmp2 - tmp33
    tmp35 = triton_helpers.maximum(tmp4, tmp34)
    tmp36 = tmp35 * tmp35
    tmp37 = tmp32 * tmp36
    tmp38 = tmp8 - tmp32
    tmp39 = tmp38 * tmp10
    tmp40 = tmp33 - tmp12
    tmp41 = triton_helpers.maximum(tmp4, tmp40)
    tmp42 = tmp41 * tmp41
    tmp43 = tmp39 * tmp42
    tmp44 = tmp37 + tmp43
    tmp45 = tmp31 + tmp44
    tmp48 = tmp2 - tmp47
    tmp49 = triton_helpers.maximum(tmp4, tmp48)
    tmp50 = tmp49 * tmp49
    tmp51 = tmp46 * tmp50
    tmp52 = tmp8 - tmp46
    tmp53 = tmp52 * tmp10
    tmp54 = tmp47 - tmp12
    tmp55 = triton_helpers.maximum(tmp4, tmp54)
    tmp56 = tmp55 * tmp55
    tmp57 = tmp53 * tmp56
    tmp58 = tmp51 + tmp57
    tmp59 = tmp45 + tmp58
    tmp60 = tl.broadcast_to(tmp59, [XBLOCK, RBLOCK])
    tmp62 = tl.sum(tmp60, 1)[:, None]
    tl.store(out_ptr1 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp62, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/xk/cxkwv5coj2xdbodmufpdzzfbs244xblz2r4qke7ld3aivawzenny.py
# Topologically Sorted Source Nodes: [reconstruction_loss, mul_3, caps_loss], Original ATen: [aten.mse_loss, aten.mul, aten.add]
# Source node to ATen node mapping:
#   caps_loss => add_1
#   mul_3 => mul_3
#   reconstruction_loss => pow_3, sub_3, sum_3
# Graph fragment:
#   %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg3_1, %arg2_1), kwargs = {})
#   %pow_3 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub_3, 2), kwargs = {})
#   %sum_3 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%pow_3,), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sum_3, 0.0005), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_2, %mul_3), kwargs = {})
triton_per_fused_add_mse_loss_mul_1 = async_compile.triton('triton_per_fused_add_mse_loss_mul_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.persistent_reduction(
    size_hints=[1, 256],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 4: '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': {3: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 4), equal_to_1=(3,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_mse_loss_mul_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': True, 'num_load': 3, 'num_reduction': 1, '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}
)
@triton.jit
def triton_per_fused_add_mse_loss_mul_1(in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel):
    xnumel = 1
    XBLOCK: tl.constexpr = 1
    rnumel = 256
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    xmask = tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    roffset = 0
    rmask = tl.full([RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + (r0), None)
    tmp1 = tl.load(in_ptr1 + (r0), None)
    tmp7 = tl.load(in_out_ptr0 + (0))
    tmp8 = tl.broadcast_to(tmp7, [1])
    tmp2 = tmp0 - tmp1
    tmp3 = tmp2 * tmp2
    tmp4 = tl.broadcast_to(tmp3, [RBLOCK])
    tmp6 = triton_helpers.promote_to_tensor(tl.sum(tmp4, 0))
    tmp9 = 0.0005
    tmp10 = tmp6 * tmp9
    tmp11 = tmp8 + tmp10
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([1], 0, tl.int32)), tmp11, None)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1, arg2_1, arg3_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg2_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg3_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf1 = empty_strided_cuda((), (), torch.float32)
        # Topologically Sorted Source Nodes: [sub, relu, pow_1, mul, sub_1, mul_1, sub_2, relu_1, pow_2, mul_2, L_k, L_k_1, margin_loss], Original ATen: [aten.rsub, aten.relu, aten.pow, aten.mul, aten.sub, aten.add, aten.sum]
        stream0 = get_raw_stream(0)
        triton_per_fused_add_mul_pow_relu_rsub_sub_sum_0.run(arg1_1, arg0_1, buf1, 1, 64, grid=grid(1), stream=stream0)
        del arg0_1
        del arg1_1
        buf3 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [reconstruction_loss, mul_3, caps_loss], Original ATen: [aten.mse_loss, aten.mul, aten.add]
        triton_per_fused_add_mse_loss_mul_1.run(buf3, arg3_1, arg2_1, 1, 256, grid=grid(1), stream=stream0)
        del arg2_1
        del arg3_1
    return (buf3, )


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)
    arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg2_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg3_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1, arg1_1, arg2_1, arg3_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.nn.functional as F


class MarginLoss(nn.Module):

    def __init__(self, size_average=False, loss_lambda=0.5):
        """
		Margin loss for digit existence
		Eq. (4): L_k = T_k * max(0, m+ - ||v_k||)^2 + lambda * (1 - T_k) * max(0, ||v_k|| - m-)^2
		
		Args:
			size_average: should the losses be averaged (True) or summed (False) over observations for each minibatch.
			loss_lambda: parameter for down-weighting the loss for missing digits
		"""
        super(MarginLoss, self).__init__()
        self.size_average = size_average
        self.m_plus = 0.9
        self.m_minus = 0.1
        self.loss_lambda = loss_lambda

    def forward(self, inputs, labels):
        L_k = labels * F.relu(self.m_plus - inputs) ** 2 + self.loss_lambda * (
            1 - labels) * F.relu(inputs - self.m_minus) ** 2
        L_k = L_k.sum(dim=1)
        if self.size_average:
            return L_k.mean()
        else:
            return L_k.sum()


class CapsuleLoss(nn.Module):

    def __init__(self, loss_lambda=0.5, recon_loss_scale=0.0005,
        size_average=False):
        """
		Combined margin loss and reconstruction loss. Margin loss see above.
		Sum squared error (SSE) was used as a reconstruction loss.
		
		Args:
			recon_loss_scale: 	param for scaling down the the reconstruction loss
			size_average:		if True, reconstruction loss becomes MSE instead of SSE
		"""
        super(CapsuleLoss, self).__init__()
        self.size_average = size_average
        self.margin_loss = MarginLoss(size_average=size_average,
            loss_lambda=loss_lambda)
        self.reconstruction_loss = nn.MSELoss(size_average=size_average)
        self.recon_loss_scale = recon_loss_scale

    def forward(self, inputs, labels, images, reconstructions):
        margin_loss = self.margin_loss(inputs, labels)
        reconstruction_loss = self.reconstruction_loss(reconstructions, images)
        caps_loss = margin_loss + self.recon_loss_scale * reconstruction_loss
        return caps_loss


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


def get_init_inputs():
    return [[], {}]

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
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.nn.functional as F
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_per_fused_add_mul_pow_relu_rsub_sub_sum_0(in_ptr0, in_ptr1,
    out_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 64
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex % 16
    r1 = rindex // 16
    tmp0 = tl.load(in_ptr0 + (r0 + 64 * r1), None)
    tmp1 = tl.load(in_ptr1 + (r0 + 64 * r1), None)
    tmp18 = tl.load(in_ptr0 + (16 + r0 + 64 * r1), None)
    tmp19 = tl.load(in_ptr1 + (16 + r0 + 64 * r1), None)
    tmp32 = tl.load(in_ptr0 + (32 + r0 + 64 * r1), None)
    tmp33 = tl.load(in_ptr1 + (32 + r0 + 64 * r1), None)
    tmp46 = tl.load(in_ptr0 + (48 + r0 + 64 * r1), None)
    tmp47 = tl.load(in_ptr1 + (48 + r0 + 64 * r1), None)
    tmp2 = 0.9
    tmp3 = tmp2 - tmp1
    tmp4 = tl.full([1, 1], 0, tl.int32)
    tmp5 = triton_helpers.maximum(tmp4, tmp3)
    tmp6 = tmp5 * tmp5
    tmp7 = tmp0 * tmp6
    tmp8 = 1.0
    tmp9 = tmp8 - tmp0
    tmp10 = 0.5
    tmp11 = tmp9 * tmp10
    tmp12 = 0.1
    tmp13 = tmp1 - tmp12
    tmp14 = triton_helpers.maximum(tmp4, tmp13)
    tmp15 = tmp14 * tmp14
    tmp16 = tmp11 * tmp15
    tmp17 = tmp7 + tmp16
    tmp20 = tmp2 - tmp19
    tmp21 = triton_helpers.maximum(tmp4, tmp20)
    tmp22 = tmp21 * tmp21
    tmp23 = tmp18 * tmp22
    tmp24 = tmp8 - tmp18
    tmp25 = tmp24 * tmp10
    tmp26 = tmp19 - tmp12
    tmp27 = triton_helpers.maximum(tmp4, tmp26)
    tmp28 = tmp27 * tmp27
    tmp29 = tmp25 * tmp28
    tmp30 = tmp23 + tmp29
    tmp31 = tmp17 + tmp30
    tmp34 = tmp2 - tmp33
    tmp35 = triton_helpers.maximum(tmp4, tmp34)
    tmp36 = tmp35 * tmp35
    tmp37 = tmp32 * tmp36
    tmp38 = tmp8 - tmp32
    tmp39 = tmp38 * tmp10
    tmp40 = tmp33 - tmp12
    tmp41 = triton_helpers.maximum(tmp4, tmp40)
    tmp42 = tmp41 * tmp41
    tmp43 = tmp39 * tmp42
    tmp44 = tmp37 + tmp43
    tmp45 = tmp31 + tmp44
    tmp48 = tmp2 - tmp47
    tmp49 = triton_helpers.maximum(tmp4, tmp48)
    tmp50 = tmp49 * tmp49
    tmp51 = tmp46 * tmp50
    tmp52 = tmp8 - tmp46
    tmp53 = tmp52 * tmp10
    tmp54 = tmp47 - tmp12
    tmp55 = triton_helpers.maximum(tmp4, tmp54)
    tmp56 = tmp55 * tmp55
    tmp57 = tmp53 * tmp56
    tmp58 = tmp51 + tmp57
    tmp59 = tmp45 + tmp58
    tmp60 = tl.broadcast_to(tmp59, [XBLOCK, RBLOCK])
    tmp62 = tl.sum(tmp60, 1)[:, None]
    tl.store(out_ptr1 + tl.full([XBLOCK, 1], 0, tl.int32), tmp62, None)


@triton.jit
def triton_per_fused_add_mse_loss_mul_1(in_out_ptr0, in_ptr0, in_ptr1,
    xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp1 = tl.load(in_ptr1 + r0, None)
    tmp7 = tl.load(in_out_ptr0 + 0)
    tmp8 = tl.broadcast_to(tmp7, [1])
    tmp2 = tmp0 - tmp1
    tmp3 = tmp2 * tmp2
    tmp4 = tl.broadcast_to(tmp3, [RBLOCK])
    tmp6 = triton_helpers.promote_to_tensor(tl.sum(tmp4, 0))
    tmp9 = 0.0005
    tmp10 = tmp6 * tmp9
    tmp11 = tmp8 + tmp10
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp11, None)


def call(args):
    arg0_1, arg1_1, arg2_1, arg3_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg2_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg3_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf1 = empty_strided_cuda((), (), torch.float32)
        get_raw_stream(0)
        triton_per_fused_add_mul_pow_relu_rsub_sub_sum_0[grid(1)](arg1_1,
            arg0_1, buf1, 1, 64, XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
        buf3 = buf1
        del buf1
        triton_per_fused_add_mse_loss_mul_1[grid(1)](buf3, arg3_1, arg2_1, 
            1, 256, num_warps=2, num_stages=1)
        del arg2_1
        del arg3_1
    return buf3,


class MarginLoss(nn.Module):

    def __init__(self, size_average=False, loss_lambda=0.5):
        """
		Margin loss for digit existence
		Eq. (4): L_k = T_k * max(0, m+ - ||v_k||)^2 + lambda * (1 - T_k) * max(0, ||v_k|| - m-)^2
		
		Args:
			size_average: should the losses be averaged (True) or summed (False) over observations for each minibatch.
			loss_lambda: parameter for down-weighting the loss for missing digits
		"""
        super(MarginLoss, self).__init__()
        self.size_average = size_average
        self.m_plus = 0.9
        self.m_minus = 0.1
        self.loss_lambda = loss_lambda

    def forward(self, inputs, labels):
        L_k = labels * F.relu(self.m_plus - inputs) ** 2 + self.loss_lambda * (
            1 - labels) * F.relu(inputs - self.m_minus) ** 2
        L_k = L_k.sum(dim=1)
        if self.size_average:
            return L_k.mean()
        else:
            return L_k.sum()


class CapsuleLossNew(nn.Module):

    def __init__(self, loss_lambda=0.5, recon_loss_scale=0.0005,
        size_average=False):
        """
		Combined margin loss and reconstruction loss. Margin loss see above.
		Sum squared error (SSE) was used as a reconstruction loss.
		
		Args:
			recon_loss_scale: 	param for scaling down the the reconstruction loss
			size_average:		if True, reconstruction loss becomes MSE instead of SSE
		"""
        super(CapsuleLossNew, self).__init__()
        self.size_average = size_average
        self.margin_loss = MarginLoss(size_average=size_average,
            loss_lambda=loss_lambda)
        self.reconstruction_loss = nn.MSELoss(size_average=size_average)
        self.recon_loss_scale = recon_loss_scale

    def forward(self, input_0, input_1, input_2, input_3):
        arg0_1 = input_0
        arg1_1 = input_1
        arg2_1 = input_2
        arg3_1 = input_3
        output = call([arg0_1, arg1_1, arg2_1, arg3_1])
        return output[0]
