# 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/q6/cq6gwmkqzrgsbed25n3t4wvathkclo5jeckxfwnebre5sud4mcnj.py
# Topologically Sorted Source Nodes: [normalized_weights], Original ATen: [aten.div]
# Source node to ATen node mapping:
#   normalized_weights => div
# Graph fragment:
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%view_1, %expand), kwargs = {})
triton_poi_fused_div_0 = async_compile.triton('triton_poi_fused_div_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=[64], 
    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_div_0', '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_div_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 16
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (16 + x0), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (32 + x0), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (48 + x0), xmask, 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 = libdevice.sqrt(tmp11)
    tmp13 = 1e-12
    tmp14 = triton_helpers.maximum(tmp12, tmp13)
    tmp15 = tmp0 / tmp14
    tl.store(out_ptr0 + (x2), tmp15, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/ec/cecvzfrpaaz5jk4lgldwe2qaluhsli4gb5osso4bx6vrtrko5grx.py
# Topologically Sorted Source Nodes: [prod_weights], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   prod_weights => exp
# Graph fragment:
#   %mul_tensor : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_2, 1), kwargs = {})
#   %amax_default : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%mul_tensor, [-1], True), kwargs = {})
#   %sub_tensor : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_tensor, %amax_default), kwargs = {})
#   %mul_tensor_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_tensor, 10.0), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%mul_tensor_1,), kwargs = {})
triton_poi_fused__softmax_1 = async_compile.triton('triton_poi_fused__softmax_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=[1024], 
    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__softmax_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__softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp6 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp20 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp1 = -1.0
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp3 = 1.0
    tmp4 = triton_helpers.minimum(tmp2, tmp3)
    tmp5 = tmp4 * tmp3
    tmp7 = triton_helpers.maximum(tmp6, tmp1)
    tmp8 = triton_helpers.minimum(tmp7, tmp3)
    tmp9 = tmp8 * tmp3
    tmp11 = triton_helpers.maximum(tmp10, tmp1)
    tmp12 = triton_helpers.minimum(tmp11, tmp3)
    tmp13 = tmp12 * tmp3
    tmp14 = triton_helpers.maximum(tmp9, tmp13)
    tmp16 = triton_helpers.maximum(tmp15, tmp1)
    tmp17 = triton_helpers.minimum(tmp16, tmp3)
    tmp18 = tmp17 * tmp3
    tmp19 = triton_helpers.maximum(tmp14, tmp18)
    tmp21 = triton_helpers.maximum(tmp20, tmp1)
    tmp22 = triton_helpers.minimum(tmp21, tmp3)
    tmp23 = tmp22 * tmp3
    tmp24 = triton_helpers.maximum(tmp19, tmp23)
    tmp25 = tmp5 - tmp24
    tmp26 = 10.0
    tmp27 = tmp25 * tmp26
    tmp28 = tl_math.exp(tmp27)
    tl.store(out_ptr0 + (x2), tmp28, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/mi/cmiyjejsvgxujqegsxne3sldy3bdyhjzsmbxl6zmvrlyenqewaiu.py
# Topologically Sorted Source Nodes: [prod_weights, mul_1, scores], Original ATen: [aten._softmax, aten.mul, aten.sum]
# Source node to ATen node mapping:
#   mul_1 => mul_1
#   prod_weights => div_1, sum_2
#   scores => sum_3
# Graph fragment:
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [-1], True), kwargs = {})
#   %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_2), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%div_1, %view_2), kwargs = {})
#   %sum_3 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul_1, [-1]), kwargs = {})
triton_poi_fused__softmax_mul_sum_2 = async_compile.triton('triton_poi_fused__softmax_mul_sum_2', '''
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=[256], 
    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__softmax_mul_sum_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, '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__softmax_mul_sum_2(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (4*x0), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (4*x0), xmask, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr1 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp21 = tl.load(in_ptr1 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp27 = tl.load(in_ptr1 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = tmp0 / tmp6
    tmp9 = -1.0
    tmp10 = triton_helpers.maximum(tmp8, tmp9)
    tmp11 = 1.0
    tmp12 = triton_helpers.minimum(tmp10, tmp11)
    tmp13 = tmp7 * tmp12
    tmp14 = tmp1 / tmp6
    tmp16 = triton_helpers.maximum(tmp15, tmp9)
    tmp17 = triton_helpers.minimum(tmp16, tmp11)
    tmp18 = tmp14 * tmp17
    tmp19 = tmp13 + tmp18
    tmp20 = tmp3 / tmp6
    tmp22 = triton_helpers.maximum(tmp21, tmp9)
    tmp23 = triton_helpers.minimum(tmp22, tmp11)
    tmp24 = tmp20 * tmp23
    tmp25 = tmp19 + tmp24
    tmp26 = tmp5 / tmp6
    tmp28 = triton_helpers.maximum(tmp27, tmp9)
    tmp29 = triton_helpers.minimum(tmp28, tmp11)
    tmp30 = tmp26 * tmp29
    tmp31 = tmp25 + tmp30
    tl.store(out_ptr0 + (x0), tmp31, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 16), (16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [normalized_weights], Original ATen: [aten.div]
        stream0 = get_raw_stream(0)
        triton_poi_fused_div_0.run(primals_2, buf0, 64, grid=grid(64), stream=stream0)
        buf1 = empty_strided_cuda((64, 16), (16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [normalized_weights, prod], Original ATen: [aten.div, aten.mm]
        extern_kernels.mm(reinterpret_tensor(primals_1, (64, 4), (4, 1), 0), buf0, out=buf1)
        del buf0
        buf2 = empty_strided_cuda((64, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [prod_weights], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_1.run(buf1, buf2, 1024, grid=grid(1024), stream=stream0)
        buf3 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [prod_weights, mul_1, scores], Original ATen: [aten._softmax, aten.mul, aten.sum]
        triton_poi_fused__softmax_mul_sum_2.run(buf2, buf1, buf3, 256, grid=grid(256), stream=stream0)
        del buf2
    return (buf3, primals_2, buf1, reinterpret_tensor(primals_1, (4, 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, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2])
    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 numpy as np
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Parameter


def normalize(x, dim, p=2, eps=1e-12):
    if torch.onnx.is_in_onnx_export():
        return OnnxLpNormalization.apply(x, dim, p, eps)
    else:
        return F.normalize(x, dim=dim)


class OnnxLpNormalization(torch.autograd.Function):

    @staticmethod
    def forward(ctx, x, axis=0, p=2, eps=1e-12):
        denom = x.norm(2, axis, True).clamp_min(eps).expand_as(x)
        return x / denom

    @staticmethod
    def symbolic(g, x, axis=0, p=2, eps=1e-12):
        return g.op('LpNormalization', x, axis_i=int(axis), p_i=int(p))


class AngleMultipleLinear(nn.Module):
    """Based on SoftTriplet loss: https://arxiv.org/pdf/1909.05235.pdf
    """

    def __init__(self, in_features, num_classes, num_centers, scale=10.0,
        reg_weight=0.2, reg_threshold=0.2):
        super(AngleMultipleLinear, self).__init__()
        self.in_features = in_features
        assert in_features > 0
        self.num_classes = num_classes
        assert num_classes >= 2
        self.num_centers = num_centers
        assert num_centers >= 1
        self.scale = scale
        assert scale > 0.0
        weight_shape = [in_features, num_classes, num_centers
            ] if num_centers > 1 else [in_features, num_classes]
        self.weight = Parameter(torch.Tensor(*weight_shape))
        self.weight.data.normal_().renorm_(2, 1, 1e-05).mul_(100000.0)
        self.enable_regularization = (reg_weight is not None and reg_weight >
            0.0)
        if self.enable_regularization:
            self.reg_weight = reg_weight
            if num_centers == 1:
                self.reg_threshold = reg_threshold
                assert self.reg_threshold >= 0.0
                reg_valid_mask = np.triu(np.ones((num_classes, num_classes),
                    dtype=np.float32), k=1)
            else:
                self.reg_weight /= num_classes
                if num_centers > 2:
                    self.reg_weight /= (num_centers - 1) * (num_centers - 2)
                reg_valid_mask = np.tile(np.triu(np.ones((1, num_centers,
                    num_centers), dtype=np.float32), k=1), (num_classes, 1, 1))
            self.register_buffer('reg_mask', torch.from_numpy(reg_valid_mask))
        else:
            self.reg_weight = None
            self.reg_mask = None

    def forward(self, normalized_x):
        normalized_x = normalized_x.view(-1, self.in_features)
        normalized_weights = normalize(self.weight.view(self.in_features, -
            1), dim=0)
        prod = normalized_x.mm(normalized_weights)
        if not torch.onnx.is_in_onnx_export():
            prod = prod.clamp(-1.0, 1.0)
        if self.num_centers > 1:
            prod = prod.view(-1, self.num_classes, self.num_centers)
            prod_weights = F.softmax(self.scale * prod, dim=-1)
            scores = torch.sum(prod_weights * prod, dim=-1)
        else:
            scores = prod
        return scores

    def loss(self, name):
        out_losses = dict()
        if self.enable_regularization:
            normalized_weights = F.normalize(self.weight, dim=0)
            if self.num_centers == 1:
                all_pairwise_scores = normalized_weights.permute(1, 0).matmul(
                    normalized_weights)
                valid_pairwise_scores = all_pairwise_scores[self.reg_mask > 0.0
                    ]
                losses = valid_pairwise_scores[valid_pairwise_scores > self
                    .reg_threshold] - self.reg_threshold
                out_losses['loss/cpush' + name
                    ] = self.reg_weight * losses.mean() if losses.numel(
                    ) > 0 else losses.sum()
            else:
                all_pairwise_scores = normalized_weights.permute(1, 2, 0
                    ).matmul(normalized_weights.permute(1, 0, 2))
                valid_pairwise_scores = all_pairwise_scores[self.reg_mask > 0.0
                    ]
                losses = 1.0 - valid_pairwise_scores
                out_losses['loss/st_reg' + name
                    ] = self.reg_weight * losses.sum()
        return out_losses


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


def get_init_inputs():
    return [[], {'in_features': 4, 'num_classes': 4, 'num_centers': 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 import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Parameter
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_div_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 16
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (16 + x0), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (32 + x0), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (48 + x0), xmask, 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 = libdevice.sqrt(tmp11)
    tmp13 = 1e-12
    tmp14 = triton_helpers.maximum(tmp12, tmp13)
    tmp15 = tmp0 / tmp14
    tl.store(out_ptr0 + x2, tmp15, xmask)


@triton.jit
def triton_poi_fused__softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp6 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp15 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp20 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp1 = -1.0
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp3 = 1.0
    tmp4 = triton_helpers.minimum(tmp2, tmp3)
    tmp5 = tmp4 * tmp3
    tmp7 = triton_helpers.maximum(tmp6, tmp1)
    tmp8 = triton_helpers.minimum(tmp7, tmp3)
    tmp9 = tmp8 * tmp3
    tmp11 = triton_helpers.maximum(tmp10, tmp1)
    tmp12 = triton_helpers.minimum(tmp11, tmp3)
    tmp13 = tmp12 * tmp3
    tmp14 = triton_helpers.maximum(tmp9, tmp13)
    tmp16 = triton_helpers.maximum(tmp15, tmp1)
    tmp17 = triton_helpers.minimum(tmp16, tmp3)
    tmp18 = tmp17 * tmp3
    tmp19 = triton_helpers.maximum(tmp14, tmp18)
    tmp21 = triton_helpers.maximum(tmp20, tmp1)
    tmp22 = triton_helpers.minimum(tmp21, tmp3)
    tmp23 = tmp22 * tmp3
    tmp24 = triton_helpers.maximum(tmp19, tmp23)
    tmp25 = tmp5 - tmp24
    tmp26 = 10.0
    tmp27 = tmp25 * tmp26
    tmp28 = tl_math.exp(tmp27)
    tl.store(out_ptr0 + x2, tmp28, xmask)


@triton.jit
def triton_poi_fused__softmax_mul_sum_2(in_ptr0, in_ptr1, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp21 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp27 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = tmp0 / tmp6
    tmp9 = -1.0
    tmp10 = triton_helpers.maximum(tmp8, tmp9)
    tmp11 = 1.0
    tmp12 = triton_helpers.minimum(tmp10, tmp11)
    tmp13 = tmp7 * tmp12
    tmp14 = tmp1 / tmp6
    tmp16 = triton_helpers.maximum(tmp15, tmp9)
    tmp17 = triton_helpers.minimum(tmp16, tmp11)
    tmp18 = tmp14 * tmp17
    tmp19 = tmp13 + tmp18
    tmp20 = tmp3 / tmp6
    tmp22 = triton_helpers.maximum(tmp21, tmp9)
    tmp23 = triton_helpers.minimum(tmp22, tmp11)
    tmp24 = tmp20 * tmp23
    tmp25 = tmp19 + tmp24
    tmp26 = tmp5 / tmp6
    tmp28 = triton_helpers.maximum(tmp27, tmp9)
    tmp29 = triton_helpers.minimum(tmp28, tmp11)
    tmp30 = tmp26 * tmp29
    tmp31 = tmp25 + tmp30
    tl.store(out_ptr0 + x0, tmp31, xmask)


def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 16), (16, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_div_0[grid(64)](primals_2, buf0, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf1 = empty_strided_cuda((64, 16), (16, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (64, 4), (4, 1), 0),
            buf0, out=buf1)
        del buf0
        buf2 = empty_strided_cuda((64, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused__softmax_1[grid(1024)](buf1, buf2, 1024, XBLOCK=
            128, num_warps=4, num_stages=1)
        buf3 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        triton_poi_fused__softmax_mul_sum_2[grid(256)](buf2, buf1, buf3, 
            256, XBLOCK=256, num_warps=4, num_stages=1)
        del buf2
    return buf3, primals_2, buf1, reinterpret_tensor(primals_1, (4, 64), (1,
        4), 0)


def normalize(x, dim, p=2, eps=1e-12):
    if torch.onnx.is_in_onnx_export():
        return OnnxLpNormalization.apply(x, dim, p, eps)
    else:
        return F.normalize(x, dim=dim)


class OnnxLpNormalization(torch.autograd.Function):

    @staticmethod
    def forward(ctx, x, axis=0, p=2, eps=1e-12):
        denom = x.norm(2, axis, True).clamp_min(eps).expand_as(x)
        return x / denom

    @staticmethod
    def symbolic(g, x, axis=0, p=2, eps=1e-12):
        return g.op('LpNormalization', x, axis_i=int(axis), p_i=int(p))


class AngleMultipleLinearNew(nn.Module):
    """Based on SoftTriplet loss: https://arxiv.org/pdf/1909.05235.pdf
    """

    def __init__(self, in_features, num_classes, num_centers, scale=10.0,
        reg_weight=0.2, reg_threshold=0.2):
        super(AngleMultipleLinearNew, self).__init__()
        self.in_features = in_features
        assert in_features > 0
        self.num_classes = num_classes
        assert num_classes >= 2
        self.num_centers = num_centers
        assert num_centers >= 1
        self.scale = scale
        assert scale > 0.0
        weight_shape = [in_features, num_classes, num_centers
            ] if num_centers > 1 else [in_features, num_classes]
        self.weight = Parameter(torch.Tensor(*weight_shape))
        self.weight.data.normal_().renorm_(2, 1, 1e-05).mul_(100000.0)
        self.enable_regularization = (reg_weight is not None and reg_weight >
            0.0)
        if self.enable_regularization:
            self.reg_weight = reg_weight
            if num_centers == 1:
                self.reg_threshold = reg_threshold
                assert self.reg_threshold >= 0.0
                reg_valid_mask = np.triu(np.ones((num_classes, num_classes),
                    dtype=np.float32), k=1)
            else:
                self.reg_weight /= num_classes
                if num_centers > 2:
                    self.reg_weight /= (num_centers - 1) * (num_centers - 2)
                reg_valid_mask = np.tile(np.triu(np.ones((1, num_centers,
                    num_centers), dtype=np.float32), k=1), (num_classes, 1, 1))
            self.register_buffer('reg_mask', torch.from_numpy(reg_valid_mask))
        else:
            self.reg_weight = None
            self.reg_mask = None

    def loss(self, name):
        out_losses = dict()
        if self.enable_regularization:
            normalized_weights = F.normalize(self.weight, dim=0)
            if self.num_centers == 1:
                all_pairwise_scores = normalized_weights.permute(1, 0).matmul(
                    normalized_weights)
                valid_pairwise_scores = all_pairwise_scores[self.reg_mask > 0.0
                    ]
                losses = valid_pairwise_scores[valid_pairwise_scores > self
                    .reg_threshold] - self.reg_threshold
                out_losses['loss/cpush' + name
                    ] = self.reg_weight * losses.mean() if losses.numel(
                    ) > 0 else losses.sum()
            else:
                all_pairwise_scores = normalized_weights.permute(1, 2, 0
                    ).matmul(normalized_weights.permute(1, 0, 2))
                valid_pairwise_scores = all_pairwise_scores[self.reg_mask > 0.0
                    ]
                losses = 1.0 - valid_pairwise_scores
                out_losses['loss/st_reg' + name
                    ] = self.reg_weight * losses.sum()
        return out_losses

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