# 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/mq/cmqtp2s3ajrawllvv7yktyagiepgbh3v37gm6lnmb47oig2pbb24.py
# Topologically Sorted Source Nodes: [batch_centers], Original ATen: [aten.index]
# Source node to ATen node mapping:
#   batch_centers => index
# Graph fragment:
#   %index : [num_users=2] = call_function[target=torch.ops.aten.index.Tensor](args = (%primals_2, [%primals_3]), kwargs = {})
triton_poi_fused_index_0 = async_compile.triton('triton_poi_fused_index_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=[8], 
    filename=__file__,
    triton_meta={'signature': {0: '*i64', 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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_index_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_index_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 8
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 2)
    x0 = xindex % 2
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp1 = tl.full([XBLOCK], 10, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tl.device_assert(((0 <= tmp4) & (tmp4 < 10)) | ~(xmask), "index out of bounds: 0 <= tmp4 < 10")
    tmp6 = tl.load(in_ptr1 + (x0 + (2*tmp4)), xmask)
    tl.store(out_ptr0 + (x2), tmp6, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/ud/cudvavrusbez73v43vpuylyu7myz3irk7rwah7mqzk54xluze6xl.py
# Topologically Sorted Source Nodes: [sub, dist, clamp, sum_1, truediv, loss], Original ATen: [aten.sub, aten.pow, aten.clamp, aten.sum, aten.div]
# Source node to ATen node mapping:
#   clamp => clamp_max, clamp_min
#   dist => pow_1
#   loss => div_1
#   sub => sub
#   sum_1 => sum_1
#   truediv => div
# Graph fragment:
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%primals_1, %index), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub, 2), kwargs = {})
#   %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%pow_1, 1e-12), kwargs = {})
#   %clamp_max : [num_users=1] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min, 1000000000000.0), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%clamp_max,), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sum_1, 4), kwargs = {})
#   %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%div, 2), kwargs = {})
triton_per_fused_clamp_div_pow_sub_sum_1 = async_compile.triton('triton_per_fused_clamp_div_pow_sub_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.persistent_reduction(
    size_hints=[1, 128],
    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_clamp_div_pow_sub_sum_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, '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_clamp_div_pow_sub_sum_1(in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 128
    RBLOCK: tl.constexpr = 128
    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)
    r2 = rindex
    r0 = rindex % 8
    tmp0 = tl.load(in_ptr0 + (r2), None)
    tmp1 = tl.load(in_ptr1 + (r0), None, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp3 = tmp2 * tmp2
    tmp4 = 1e-12
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = 1000000000000.0
    tmp7 = triton_helpers.minimum(tmp5, tmp6)
    tmp8 = tl.broadcast_to(tmp7, [XBLOCK, RBLOCK])
    tmp10 = tl.sum(tmp8, 1)[:, None]
    tmp11 = 0.25
    tmp12 = tmp10 * tmp11
    tmp13 = 0.5
    tmp14 = tmp12 * tmp13
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp14, None)
''', 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, 4, 2), (32, 8, 2, 1))
    assert_size_stride(primals_2, (10, 2), (2, 1))
    assert_size_stride(primals_3, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 2), (2, 1), torch.float32)
        # Topologically Sorted Source Nodes: [batch_centers], Original ATen: [aten.index]
        stream0 = get_raw_stream(0)
        triton_poi_fused_index_0.run(primals_3, primals_2, buf0, 8, grid=grid(8), stream=stream0)
        del primals_2
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf2 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [sub, dist, clamp, sum_1, truediv, loss], Original ATen: [aten.sub, aten.pow, aten.clamp, aten.sum, aten.div]
        triton_per_fused_clamp_div_pow_sub_sum_1.run(buf2, primals_1, buf0, 1, 128, grid=grid(1), stream=stream0)
    return (buf2, primals_1, primals_3, buf0, )


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, 2), (32, 8, 2, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((10, 2), (2, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.int64)
    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
from torch import nn


class CenterLoss(nn.Module):
    """Center loss.

    Reference:
    Wen et al. A Discriminative Feature Learning Approach for Deep Face Recognition. ECCV 2016.

    Args:
        num_classes (int): number of classes.
        feat_dim (int): feature dimension.
    """

    def __init__(self, num_classes=10, feat_dim=2):
        super(CenterLoss, self).__init__()
        self.num_classes = num_classes
        self.feat_dim = feat_dim
        if torch.cuda.is_available():
            self.centers = nn.Parameter(torch.randn(self.num_classes, self.
                feat_dim))
        else:
            self.centers = nn.Parameter(torch.randn(self.num_classes, self.
                feat_dim))

    def forward_old(self, x, labels):
        """
        Args:
            x: feature matrix with shape (batch_size, feat_dim).
            labels: ground truth labels with shape (batch_size).
        """
        batch_size = x.size(0)
        distmat = torch.pow(x, 2).sum(dim=1, keepdim=True).expand(batch_size,
            self.num_classes) + torch.pow(self.centers, 2).sum(dim=1,
            keepdim=True).expand(self.num_classes, batch_size).t()
        distmat.addmm_(x, self.centers.t(), beta=1, alpha=-2)
        classes = torch.arange(self.num_classes).long()
        if torch.cuda.is_available():
            classes = classes
        labels = labels.unsqueeze(1).expand(batch_size, self.num_classes)
        mask = labels.eq(classes.expand(batch_size, self.num_classes))
        dist = distmat * mask.float()
        loss = dist.clamp(min=1e-12, max=1000000000000.0).sum(
            ) / batch_size / self.feat_dim
        return loss

    def forward(self, x, labels):
        """
        Args:
            x: feature matrix with shape (batch_size, feat_dim).
            labels: ground truth labels with shape (batch_size).
        """
        batch_size = x.size(0)
        batch_centers = self.centers[labels]
        dist = torch.pow(x - batch_centers, 2)
        loss = dist.clamp(min=1e-12, max=1000000000000.0).sum(
            ) / batch_size / self.feat_dim
        return loss


def get_inputs():
    return [torch.rand([4, 4, 4, 2]), torch.ones([4], dtype=torch.int64)]


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
from torch import 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_index_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 8
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 2
    x0 = xindex % 2
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp1 = tl.full([XBLOCK], 10, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tl.device_assert((0 <= tmp4) & (tmp4 < 10) | ~xmask,
        'index out of bounds: 0 <= tmp4 < 10')
    tmp6 = tl.load(in_ptr1 + (x0 + 2 * tmp4), xmask)
    tl.store(out_ptr0 + x2, tmp6, xmask)


@triton.jit
def triton_per_fused_clamp_div_pow_sub_sum_1(in_out_ptr0, in_ptr0, in_ptr1,
    xnumel, rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 128
    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)
    r2 = rindex
    r0 = rindex % 8
    tmp0 = tl.load(in_ptr0 + r2, None)
    tmp1 = tl.load(in_ptr1 + r0, None, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp3 = tmp2 * tmp2
    tmp4 = 1e-12
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = 1000000000000.0
    tmp7 = triton_helpers.minimum(tmp5, tmp6)
    tmp8 = tl.broadcast_to(tmp7, [XBLOCK, RBLOCK])
    tmp10 = tl.sum(tmp8, 1)[:, None]
    tmp11 = 0.25
    tmp12 = tmp10 * tmp11
    tmp13 = 0.5
    tmp14 = tmp12 * tmp13
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp14, None)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 2), (32, 8, 2, 1))
    assert_size_stride(primals_2, (10, 2), (2, 1))
    assert_size_stride(primals_3, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 2), (2, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_index_0[grid(8)](primals_3, primals_2, buf0, 8,
            XBLOCK=8, num_warps=1, num_stages=1)
        del primals_2
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf2 = buf1
        del buf1
        triton_per_fused_clamp_div_pow_sub_sum_1[grid(1)](buf2, primals_1,
            buf0, 1, 128, XBLOCK=1, num_warps=2, num_stages=1)
    return buf2, primals_1, primals_3, buf0


class CenterLossNew(nn.Module):
    """Center loss.

    Reference:
    Wen et al. A Discriminative Feature Learning Approach for Deep Face Recognition. ECCV 2016.

    Args:
        num_classes (int): number of classes.
        feat_dim (int): feature dimension.
    """

    def __init__(self, num_classes=10, feat_dim=2):
        super(CenterLossNew, self).__init__()
        self.num_classes = num_classes
        self.feat_dim = feat_dim
        if torch.cuda.is_available():
            self.centers = nn.Parameter(torch.randn(self.num_classes, self.
                feat_dim))
        else:
            self.centers = nn.Parameter(torch.randn(self.num_classes, self.
                feat_dim))

    def forward_old(self, x, labels):
        """
        Args:
            x: feature matrix with shape (batch_size, feat_dim).
            labels: ground truth labels with shape (batch_size).
        """
        batch_size = x.size(0)
        distmat = torch.pow(x, 2).sum(dim=1, keepdim=True).expand(batch_size,
            self.num_classes) + torch.pow(self.centers, 2).sum(dim=1,
            keepdim=True).expand(self.num_classes, batch_size).t()
        distmat.addmm_(x, self.centers.t(), beta=1, alpha=-2)
        classes = torch.arange(self.num_classes).long()
        if torch.cuda.is_available():
            classes = classes
        labels = labels.unsqueeze(1).expand(batch_size, self.num_classes)
        mask = labels.eq(classes.expand(batch_size, self.num_classes))
        dist = distmat * mask.float()
        loss = dist.clamp(min=1e-12, max=1000000000000.0).sum(
            ) / batch_size / self.feat_dim
        return loss

    def forward(self, input_0, input_1):
        primals_2 = self.centers
        primals_1 = input_0
        primals_3 = input_1
        output = call([primals_1, primals_2, primals_3])
        return output[0]
