# 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_8/inductor_cache/6n/c6njzhe4gqjobjhhbuh7conwkaxomsvonuql762jyvdwn2lhkfhv.py
# Topologically Sorted Source Nodes: [loss_5], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   loss_5 => cat
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%add_1, %add_3, %add_5],), kwargs = {})
triton_poi_fused_cat_0 = async_compile.triton('triton_poi_fused_cat_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=[4096], 
    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_cat_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 15, '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_cat_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 3072
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = (xindex // 256)
    x5 = xindex % 16
    x1 = (xindex // 4) % 4
    x6 = (xindex // 4) % 64
    x7 = xindex
    tmp0 = x3
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x5), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp6 = 1.0
    tmp7 = tmp6 - tmp5
    tmp8 = tl.load(in_ptr0 + (64 + x5), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp9 = tmp7 + tmp8
    tmp10 = 0.0
    tmp11 = triton_helpers.maximum(tmp9, tmp10)
    tmp12 = 0.5
    tmp13 = tmp11 * tmp12
    tmp14 = tl.load(in_ptr0 + (16 + (4*x1)), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp15 = tl.load(in_ptr0 + (17 + (4*x1)), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp16 = tmp14 - tmp15
    tmp17 = tl.load(in_ptr1 + (x6 + (64*x3)), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp18 = tmp16 * tmp17
    tmp19 = tmp6 - tmp18
    tmp20 = triton_helpers.maximum(tmp19, tmp10)
    tmp21 = tmp20 * tmp12
    tmp22 = tmp13 + tmp21
    tmp23 = tl.full(tmp22.shape, 0.0, tmp22.dtype)
    tmp24 = tl.where(tmp4, tmp22, tmp23)
    tmp25 = tmp0 >= tmp3
    tmp26 = tl.full([1], 8, tl.int64)
    tmp27 = tmp0 < tmp26
    tmp28 = tmp25 & tmp27
    tmp29 = tl.load(in_ptr0 + (x5), tmp28 & xmask, eviction_policy='evict_last', other=0.0)
    tmp30 = tmp6 - tmp29
    tmp31 = tl.load(in_ptr0 + (128 + x5), tmp28 & xmask, eviction_policy='evict_last', other=0.0)
    tmp32 = tmp30 + tmp31
    tmp33 = triton_helpers.maximum(tmp32, tmp10)
    tmp34 = tmp33 * tmp12
    tmp35 = tl.load(in_ptr0 + (16 + (4*x1)), tmp28 & xmask, eviction_policy='evict_last', other=0.0)
    tmp36 = tl.load(in_ptr0 + (17 + (4*x1)), tmp28 & xmask, eviction_policy='evict_last', other=0.0)
    tmp37 = tmp35 - tmp36
    tmp38 = tl.load(in_ptr1 + (x6 + (64*((-4) + x3))), tmp28 & xmask, eviction_policy='evict_last', other=0.0)
    tmp39 = tmp37 * tmp38
    tmp40 = tmp6 - tmp39
    tmp41 = triton_helpers.maximum(tmp40, tmp10)
    tmp42 = tmp41 * tmp12
    tmp43 = tmp34 + tmp42
    tmp44 = tl.full(tmp43.shape, 0.0, tmp43.dtype)
    tmp45 = tl.where(tmp28, tmp43, tmp44)
    tmp46 = tmp0 >= tmp26
    tmp47 = tl.full([1], 12, tl.int64)
    tmp48 = tmp0 < tmp47
    tmp49 = tl.load(in_ptr0 + (x5), tmp46 & xmask, eviction_policy='evict_last', other=0.0)
    tmp50 = tmp6 - tmp49
    tmp51 = tl.load(in_ptr0 + (192 + x5), tmp46 & xmask, eviction_policy='evict_last', other=0.0)
    tmp52 = tmp50 + tmp51
    tmp53 = triton_helpers.maximum(tmp52, tmp10)
    tmp54 = tmp53 * tmp12
    tmp55 = tl.load(in_ptr0 + (16 + (4*x1)), tmp46 & xmask, eviction_policy='evict_last', other=0.0)
    tmp56 = tl.load(in_ptr0 + (17 + (4*x1)), tmp46 & xmask, eviction_policy='evict_last', other=0.0)
    tmp57 = tmp55 - tmp56
    tmp58 = tl.load(in_ptr1 + (x6 + (64*((-8) + x3))), tmp46 & xmask, eviction_policy='evict_last', other=0.0)
    tmp59 = tmp57 * tmp58
    tmp60 = tmp6 - tmp59
    tmp61 = triton_helpers.maximum(tmp60, tmp10)
    tmp62 = tmp61 * tmp12
    tmp63 = tmp54 + tmp62
    tmp64 = tl.full(tmp63.shape, 0.0, tmp63.dtype)
    tmp65 = tl.where(tmp46, tmp63, tmp64)
    tmp66 = tl.where(tmp28, tmp45, tmp65)
    tmp67 = tl.where(tmp4, tmp24, tmp66)
    tl.store(out_ptr0 + (x7), tmp67, 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((12, 4, 4, 4, 4), (256, 64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [loss_5], Original ATen: [aten.cat]
        stream0 = get_raw_stream(0)
        triton_poi_fused_cat_0.run(arg0_1, arg1_1, buf0, 3072, grid=grid(3072), 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


class HingeMarginLoss(nn.Module):
    """
    计算hinge loss 接口
    """

    def __init__(self):
        super(HingeMarginLoss, self).__init__()

    def forward(self, t, tr, delt=None, size_average=False):
        """
        计算hingle loss
        """
        if delt is None:
            loss = torch.clamp(1 - t + tr, min=0)
        else:
            loss = torch.clamp(1 - torch.mul(t - tr, torch.squeeze(delt)),
                min=0)
            loss = torch.unsqueeze(loss, dim=-1)
        return loss


class SSWELoss(nn.Module):
    """
    SSWE 中考虑gram得分和情感得分的loss类
    """

    def __init__(self, alpha=0.5):
        super(SSWELoss, self).__init__()
        self.alpha = alpha
        self.hingeLoss = HingeMarginLoss()

    def forward(self, scores, labels, size_average=False):
        """
        [(true_sy_score,true_sem_score), (corrput_sy_score,corrupt_sem_score),...]
        """
        assert len(scores) >= 2
        true_score = scores[0]
        sem_loss = self.hingeLoss(true_score[1][:, 0], true_score[1][:, 1],
            delt=labels)
        loss = []
        for corpt_i in range(1, len(scores)):
            syn_loss = self.hingeLoss(true_score[0], scores[corpt_i][0])
            cur_loss = syn_loss * self.alpha + sem_loss * (1 - self.alpha)
            loss.append(cur_loss)
        loss = torch.cat(loss, dim=0)
        if size_average:
            loss = torch.mean(loss)
        return loss


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_cat_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 3072
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex // 256
    x5 = xindex % 16
    x1 = xindex // 4 % 4
    x6 = xindex // 4 % 64
    x7 = xindex
    tmp0 = x3
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + x5, tmp4 & xmask, eviction_policy='evict_last',
        other=0.0)
    tmp6 = 1.0
    tmp7 = tmp6 - tmp5
    tmp8 = tl.load(in_ptr0 + (64 + x5), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp9 = tmp7 + tmp8
    tmp10 = 0.0
    tmp11 = triton_helpers.maximum(tmp9, tmp10)
    tmp12 = 0.5
    tmp13 = tmp11 * tmp12
    tmp14 = tl.load(in_ptr0 + (16 + 4 * x1), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp15 = tl.load(in_ptr0 + (17 + 4 * x1), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp16 = tmp14 - tmp15
    tmp17 = tl.load(in_ptr1 + (x6 + 64 * x3), tmp4 & xmask, eviction_policy
        ='evict_last', other=0.0)
    tmp18 = tmp16 * tmp17
    tmp19 = tmp6 - tmp18
    tmp20 = triton_helpers.maximum(tmp19, tmp10)
    tmp21 = tmp20 * tmp12
    tmp22 = tmp13 + tmp21
    tmp23 = tl.full(tmp22.shape, 0.0, tmp22.dtype)
    tmp24 = tl.where(tmp4, tmp22, tmp23)
    tmp25 = tmp0 >= tmp3
    tmp26 = tl.full([1], 8, tl.int64)
    tmp27 = tmp0 < tmp26
    tmp28 = tmp25 & tmp27
    tmp29 = tl.load(in_ptr0 + x5, tmp28 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp30 = tmp6 - tmp29
    tmp31 = tl.load(in_ptr0 + (128 + x5), tmp28 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp32 = tmp30 + tmp31
    tmp33 = triton_helpers.maximum(tmp32, tmp10)
    tmp34 = tmp33 * tmp12
    tmp35 = tl.load(in_ptr0 + (16 + 4 * x1), tmp28 & xmask, eviction_policy
        ='evict_last', other=0.0)
    tmp36 = tl.load(in_ptr0 + (17 + 4 * x1), tmp28 & xmask, eviction_policy
        ='evict_last', other=0.0)
    tmp37 = tmp35 - tmp36
    tmp38 = tl.load(in_ptr1 + (x6 + 64 * (-4 + x3)), tmp28 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp39 = tmp37 * tmp38
    tmp40 = tmp6 - tmp39
    tmp41 = triton_helpers.maximum(tmp40, tmp10)
    tmp42 = tmp41 * tmp12
    tmp43 = tmp34 + tmp42
    tmp44 = tl.full(tmp43.shape, 0.0, tmp43.dtype)
    tmp45 = tl.where(tmp28, tmp43, tmp44)
    tmp46 = tmp0 >= tmp26
    tl.full([1], 12, tl.int64)
    tmp49 = tl.load(in_ptr0 + x5, tmp46 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp50 = tmp6 - tmp49
    tmp51 = tl.load(in_ptr0 + (192 + x5), tmp46 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp52 = tmp50 + tmp51
    tmp53 = triton_helpers.maximum(tmp52, tmp10)
    tmp54 = tmp53 * tmp12
    tmp55 = tl.load(in_ptr0 + (16 + 4 * x1), tmp46 & xmask, eviction_policy
        ='evict_last', other=0.0)
    tmp56 = tl.load(in_ptr0 + (17 + 4 * x1), tmp46 & xmask, eviction_policy
        ='evict_last', other=0.0)
    tmp57 = tmp55 - tmp56
    tmp58 = tl.load(in_ptr1 + (x6 + 64 * (-8 + x3)), tmp46 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp59 = tmp57 * tmp58
    tmp60 = tmp6 - tmp59
    tmp61 = triton_helpers.maximum(tmp60, tmp10)
    tmp62 = tmp61 * tmp12
    tmp63 = tmp54 + tmp62
    tmp64 = tl.full(tmp63.shape, 0.0, tmp63.dtype)
    tmp65 = tl.where(tmp46, tmp63, tmp64)
    tmp66 = tl.where(tmp28, tmp45, tmp65)
    tmp67 = tl.where(tmp4, tmp24, tmp66)
    tl.store(out_ptr0 + x7, tmp67, 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((12, 4, 4, 4, 4), (256, 64, 16, 4, 1),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(3072)](arg0_1, arg1_1, buf0, 3072,
            XBLOCK=256, num_warps=4, num_stages=1)
        del arg0_1
        del arg1_1
    return buf0,


class HingeMarginLoss(nn.Module):
    """
    计算hinge loss 接口
    """

    def __init__(self):
        super(HingeMarginLoss, self).__init__()

    def forward(self, t, tr, delt=None, size_average=False):
        """
        计算hingle loss
        """
        if delt is None:
            loss = torch.clamp(1 - t + tr, min=0)
        else:
            loss = torch.clamp(1 - torch.mul(t - tr, torch.squeeze(delt)),
                min=0)
            loss = torch.unsqueeze(loss, dim=-1)
        return loss


class SSWELossNew(nn.Module):
    """
    SSWE 中考虑gram得分和情感得分的loss类
    """

    def __init__(self, alpha=0.5):
        super(SSWELossNew, self).__init__()
        self.alpha = alpha
        self.hingeLoss = HingeMarginLoss()

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