# 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_7/inductor_cache/zb/czbaqw3fomitugonrjoilaqhsayirixogubby3of6px57pdaq4lk.py
# Topologically Sorted Source Nodes: [gt, y_binary, mul, y_new, mul_1, sub_1, loss, loss_1, mul_2, sub_2, mul_3, add, loss_2, mean], Original ATen: [aten.gt, aten._to_copy, aten.mul, aten.sub, aten.rsub, aten.relu, aten.pow, aten.add, aten.mean]
# Source node to ATen node mapping:
#   add => add
#   gt => gt
#   loss => relu
#   loss_1 => pow_1
#   loss_2 => mul_4
#   mean => mean
#   mul => mul
#   mul_1 => mul_1
#   mul_2 => mul_2
#   mul_3 => mul_3
#   sub_1 => sub_1
#   sub_2 => sub_2
#   y_binary => convert_element_type
#   y_new => sub
# Graph fragment:
#   %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%arg0_1, 0), kwargs = {})
#   %convert_element_type : [num_users=3] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%gt, torch.float32), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convert_element_type, 2.0), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul, 1.0), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, %arg1_1), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1.0, %mul_1), kwargs = {})
#   %relu : [num_users=1] = call_function[target=torch.ops.aten.relu.default](args = (%sub_1,), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%relu, 2), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convert_element_type, 1.0), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1.0, %convert_element_type), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_2, 1.0), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_2, %mul_3), kwargs = {})
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%pow_1, %add), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%mul_4, [1]), kwargs = {})
triton_poi_fused__to_copy_add_gt_mean_mul_pow_relu_rsub_sub_0 = async_compile.triton('triton_poi_fused__to_copy_add_gt_mean_mul_pow_relu_rsub_sub_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: '*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__to_copy_add_gt_mean_mul_pow_relu_rsub_sub_0', '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__to_copy_add_gt_mean_mul_pow_relu_rsub_sub_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x1 = (xindex // 16)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (64*x1)), xmask)
    tmp8 = tl.load(in_ptr1 + (x0 + (64*x1)), xmask)
    tmp19 = tl.load(in_ptr0 + (16 + x0 + (64*x1)), xmask)
    tmp24 = tl.load(in_ptr1 + (16 + x0 + (64*x1)), xmask)
    tmp35 = tl.load(in_ptr0 + (32 + x0 + (64*x1)), xmask)
    tmp40 = tl.load(in_ptr1 + (32 + x0 + (64*x1)), xmask)
    tmp51 = tl.load(in_ptr0 + (48 + x0 + (64*x1)), xmask)
    tmp56 = tl.load(in_ptr1 + (48 + x0 + (64*x1)), xmask)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = tmp2.to(tl.float32)
    tmp4 = 2.0
    tmp5 = tmp3 * tmp4
    tmp6 = 1.0
    tmp7 = tmp5 - tmp6
    tmp9 = tmp7 * tmp8
    tmp10 = tmp6 - tmp9
    tmp11 = tl.full([1], 0, tl.int32)
    tmp12 = triton_helpers.maximum(tmp11, tmp10)
    tmp13 = tmp12 * tmp12
    tmp14 = tmp3 * tmp6
    tmp15 = tmp6 - tmp3
    tmp16 = tmp15 * tmp6
    tmp17 = tmp14 + tmp16
    tmp18 = tmp13 * tmp17
    tmp20 = tmp19 > tmp1
    tmp21 = tmp20.to(tl.float32)
    tmp22 = tmp21 * tmp4
    tmp23 = tmp22 - tmp6
    tmp25 = tmp23 * tmp24
    tmp26 = tmp6 - tmp25
    tmp27 = triton_helpers.maximum(tmp11, tmp26)
    tmp28 = tmp27 * tmp27
    tmp29 = tmp21 * tmp6
    tmp30 = tmp6 - tmp21
    tmp31 = tmp30 * tmp6
    tmp32 = tmp29 + tmp31
    tmp33 = tmp28 * tmp32
    tmp34 = tmp18 + tmp33
    tmp36 = tmp35 > tmp1
    tmp37 = tmp36.to(tl.float32)
    tmp38 = tmp37 * tmp4
    tmp39 = tmp38 - tmp6
    tmp41 = tmp39 * tmp40
    tmp42 = tmp6 - tmp41
    tmp43 = triton_helpers.maximum(tmp11, tmp42)
    tmp44 = tmp43 * tmp43
    tmp45 = tmp37 * tmp6
    tmp46 = tmp6 - tmp37
    tmp47 = tmp46 * tmp6
    tmp48 = tmp45 + tmp47
    tmp49 = tmp44 * tmp48
    tmp50 = tmp34 + tmp49
    tmp52 = tmp51 > tmp1
    tmp53 = tmp52.to(tl.float32)
    tmp54 = tmp53 * tmp4
    tmp55 = tmp54 - tmp6
    tmp57 = tmp55 * tmp56
    tmp58 = tmp6 - tmp57
    tmp59 = triton_helpers.maximum(tmp11, tmp58)
    tmp60 = tmp59 * tmp59
    tmp61 = tmp53 * tmp6
    tmp62 = tmp6 - tmp53
    tmp63 = tmp62 * tmp6
    tmp64 = tmp61 + tmp63
    tmp65 = tmp60 * tmp64
    tmp66 = tmp50 + tmp65
    tmp67 = 4.0
    tmp68 = tmp66 / tmp67
    tl.store(out_ptr0 + (x2), tmp68, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_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))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [gt, y_binary, mul, y_new, mul_1, sub_1, loss, loss_1, mul_2, sub_2, mul_3, add, loss_2, mean], Original ATen: [aten.gt, aten._to_copy, aten.mul, aten.sub, aten.rsub, aten.relu, aten.pow, aten.add, aten.mean]
        stream0 = get_raw_stream(0)
        triton_poi_fused__to_copy_add_gt_mean_mul_pow_relu_rsub_sub_0.run(arg0_1, arg1_1, buf0, 64, grid=grid(64), stream=stream0)
        del arg0_1
        del arg1_1
    return (buf0, )


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)
    fn = lambda: call([arg0_1, arg1_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 HingeLoss(nn.Module):
    """Hinge loss function module for multi-label classification"""

    def __init__(self, margin=1.0, power=2, cost_weighted=False):
        """
        Args:
            margin (float, optional): margin for the hinge loss. Default 1.0
            power (int, optional): exponent for the hinge loss. Default to 2 for squared-hinge
            cost_weighted (bool, optional): whether to use label value as weight. Default False
        """
        super(HingeLoss, self).__init__()
        self.margin = margin
        self.power = power
        self.cost_weighted = cost_weighted

    def forward(self, z, y, C_pos=1.0, C_neg=1.0):
        """Compute the hinge loss

        Args:
            z (torch.tensor): predicted matrix of size: (batch_size * output_size)
            y (torch.tensor): 0/1 ground truth of size: (batch_size * output_size)
            C_pos (float, optional): positive penalty for the hinge loss. Default 1.0
            C_neg (float, optional): negative penalty for the hinge loss. Default 1.0

        Returns:
            loss (torch.tensor): the tensor of average loss
        """
        y_binary = (y > 0).float()
        y_new = 2.0 * y_binary - 1.0
        loss = F.relu(self.margin - y_new * z)
        loss = loss ** self.power
        if self.cost_weighted:
            loss = loss * (C_pos * y + C_neg * (1.0 - y_binary))
        else:
            loss = loss * (C_pos * y_binary + C_neg * (1.0 - y_binary))
        return loss.mean(1)


def get_inputs():
    return [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
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__to_copy_add_gt_mean_mul_pow_relu_rsub_sub_0(in_ptr0,
    in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x1 = xindex // 16
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 64 * x1), xmask)
    tmp8 = tl.load(in_ptr1 + (x0 + 64 * x1), xmask)
    tmp19 = tl.load(in_ptr0 + (16 + x0 + 64 * x1), xmask)
    tmp24 = tl.load(in_ptr1 + (16 + x0 + 64 * x1), xmask)
    tmp35 = tl.load(in_ptr0 + (32 + x0 + 64 * x1), xmask)
    tmp40 = tl.load(in_ptr1 + (32 + x0 + 64 * x1), xmask)
    tmp51 = tl.load(in_ptr0 + (48 + x0 + 64 * x1), xmask)
    tmp56 = tl.load(in_ptr1 + (48 + x0 + 64 * x1), xmask)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = tmp2.to(tl.float32)
    tmp4 = 2.0
    tmp5 = tmp3 * tmp4
    tmp6 = 1.0
    tmp7 = tmp5 - tmp6
    tmp9 = tmp7 * tmp8
    tmp10 = tmp6 - tmp9
    tmp11 = tl.full([1], 0, tl.int32)
    tmp12 = triton_helpers.maximum(tmp11, tmp10)
    tmp13 = tmp12 * tmp12
    tmp14 = tmp3 * tmp6
    tmp15 = tmp6 - tmp3
    tmp16 = tmp15 * tmp6
    tmp17 = tmp14 + tmp16
    tmp18 = tmp13 * tmp17
    tmp20 = tmp19 > tmp1
    tmp21 = tmp20.to(tl.float32)
    tmp22 = tmp21 * tmp4
    tmp23 = tmp22 - tmp6
    tmp25 = tmp23 * tmp24
    tmp26 = tmp6 - tmp25
    tmp27 = triton_helpers.maximum(tmp11, tmp26)
    tmp28 = tmp27 * tmp27
    tmp29 = tmp21 * tmp6
    tmp30 = tmp6 - tmp21
    tmp31 = tmp30 * tmp6
    tmp32 = tmp29 + tmp31
    tmp33 = tmp28 * tmp32
    tmp34 = tmp18 + tmp33
    tmp36 = tmp35 > tmp1
    tmp37 = tmp36.to(tl.float32)
    tmp38 = tmp37 * tmp4
    tmp39 = tmp38 - tmp6
    tmp41 = tmp39 * tmp40
    tmp42 = tmp6 - tmp41
    tmp43 = triton_helpers.maximum(tmp11, tmp42)
    tmp44 = tmp43 * tmp43
    tmp45 = tmp37 * tmp6
    tmp46 = tmp6 - tmp37
    tmp47 = tmp46 * tmp6
    tmp48 = tmp45 + tmp47
    tmp49 = tmp44 * tmp48
    tmp50 = tmp34 + tmp49
    tmp52 = tmp51 > tmp1
    tmp53 = tmp52.to(tl.float32)
    tmp54 = tmp53 * tmp4
    tmp55 = tmp54 - tmp6
    tmp57 = tmp55 * tmp56
    tmp58 = tmp6 - tmp57
    tmp59 = triton_helpers.maximum(tmp11, tmp58)
    tmp60 = tmp59 * tmp59
    tmp61 = tmp53 * tmp6
    tmp62 = tmp6 - tmp53
    tmp63 = tmp62 * tmp6
    tmp64 = tmp61 + tmp63
    tmp65 = tmp60 * tmp64
    tmp66 = tmp50 + tmp65
    tmp67 = 4.0
    tmp68 = tmp66 / tmp67
    tl.store(out_ptr0 + x2, tmp68, xmask)


def call(args):
    arg0_1, arg1_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))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__to_copy_add_gt_mean_mul_pow_relu_rsub_sub_0[grid(64)
            ](arg0_1, arg1_1, buf0, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del arg0_1
        del arg1_1
    return buf0,


class HingeLossNew(nn.Module):
    """Hinge loss function module for multi-label classification"""

    def __init__(self, margin=1.0, power=2, cost_weighted=False):
        """
        Args:
            margin (float, optional): margin for the hinge loss. Default 1.0
            power (int, optional): exponent for the hinge loss. Default to 2 for squared-hinge
            cost_weighted (bool, optional): whether to use label value as weight. Default False
        """
        super(HingeLossNew, self).__init__()
        self.margin = margin
        self.power = power
        self.cost_weighted = cost_weighted

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