# 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/xo/cxo7navcvfdngvouei3426orwczlourihu7qpxajq6oy5kvzifnp.py
# Topologically Sorted Source Nodes: [sub, mul_1, max_1, mul, pos, sub_1, loss, setitem, loss_1], Original ATen: [aten.rsub, aten.mul, aten.max, aten.sum, aten.sub, aten.add, aten.lift_fresh, aten.index_put, aten.mean]
# Source node to ATen node mapping:
#   loss => add
#   loss_1 => mean
#   max_1 => max_1
#   mul => mul
#   mul_1 => mul_1
#   pos => sum_1
#   setitem => full_default, index_put
#   sub => sub
#   sub_1 => sub_1
# Graph fragment:
#   %sub : [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, %arg0_1), kwargs = {})
#   %max_1 : [num_users=1] = call_function[target=torch.ops.aten.max.dim](args = (%mul_1, 2), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg0_1, %arg1_1), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul, [2]), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%getitem, %sum_1), kwargs = {})
#   %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%sub_1, 1), kwargs = {})
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 0.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cpu, pin_memory: False})
#   %index_put : [num_users=1] = call_function[target=torch.ops.aten.index_put_.default](args = (%add, [%lt], %full_default), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%index_put,), kwargs = {})
triton_per_fused_add_index_put_lift_fresh_max_mean_mul_rsub_sub_sum_0 = async_compile.triton('triton_per_fused_add_index_put_lift_fresh_max_mean_mul_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_index_put_lift_fresh_max_mean_mul_rsub_sub_sum_0', 'mutated_arg_names': ['in_out_ptr1'], '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_index_put_lift_fresh_max_mean_mul_rsub_sub_sum_0(in_out_ptr1, in_ptr0, in_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 % 4
    r1 = (rindex // 4)
    r2 = rindex
    tmp0 = tl.load(in_ptr0 + (r0 + (16*r1)), None)
    tmp3 = tl.load(in_ptr1 + (r0 + (16*r1)), None)
    tmp5 = tl.load(in_ptr0 + (4 + r0 + (16*r1)), None)
    tmp7 = tl.load(in_ptr1 + (4 + r0 + (16*r1)), None)
    tmp10 = tl.load(in_ptr0 + (8 + r0 + (16*r1)), None)
    tmp12 = tl.load(in_ptr1 + (8 + r0 + (16*r1)), None)
    tmp15 = tl.load(in_ptr0 + (12 + r0 + (16*r1)), None)
    tmp17 = tl.load(in_ptr1 + (12 + r0 + (16*r1)), None)
    tmp1 = 1.0
    tmp2 = tmp1 - tmp0
    tmp4 = tmp2 * tmp3
    tmp6 = tmp1 - tmp5
    tmp8 = tmp6 * tmp7
    tmp9 = triton_helpers.maximum(tmp4, tmp8)
    tmp11 = tmp1 - tmp10
    tmp13 = tmp11 * tmp12
    tmp14 = triton_helpers.maximum(tmp9, tmp13)
    tmp16 = tmp1 - tmp15
    tmp18 = tmp16 * tmp17
    tmp19 = triton_helpers.maximum(tmp14, tmp18)
    tmp20 = tmp3 * tmp0
    tmp21 = tmp7 * tmp5
    tmp22 = tmp20 + tmp21
    tmp23 = tmp12 * tmp10
    tmp24 = tmp22 + tmp23
    tmp25 = tmp17 * tmp15
    tmp26 = tmp24 + tmp25
    tmp27 = tmp19 - tmp26
    tmp28 = tmp27 + tmp1
    tmp29 = 0.0
    tmp30 = tmp28 < tmp29
    tmp31 = tl.where(tmp30, tmp29, tmp28)
    tmp32 = tl.broadcast_to(tmp31, [XBLOCK, RBLOCK])
    tmp34 = tl.sum(tmp32, 1)[:, None]
    tmp35 = 64.0
    tmp36 = tmp34 / tmp35
    tl.debug_barrier()
    tl.store(in_out_ptr1 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp36, None)
''', 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)
        buf2 = empty_strided_cuda((), (), torch.float32)
        buf3 = buf2; del buf2  # reuse
        # Topologically Sorted Source Nodes: [sub, mul_1, max_1, mul, pos, sub_1, loss, setitem, loss_1], Original ATen: [aten.rsub, aten.mul, aten.max, aten.sum, aten.sub, aten.add, aten.lift_fresh, aten.index_put, aten.mean]
        stream0 = get_raw_stream(0)
        triton_per_fused_add_index_put_lift_fresh_max_mean_mul_rsub_sub_sum_0.run(buf3, arg1_1, arg0_1, 1, 64, grid=grid(1), stream=stream0)
        del arg0_1
        del arg1_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)
    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 my_Hingeloss(nn.Module):

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

    def forward(self, output, target):
        pos = torch.sum(output * target, 2)
        neg = torch.max((1 - target) * output, 2)
        loss = neg[0] - pos + 1
        loss[loss < 0] = 0
        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_per_fused_add_index_put_lift_fresh_max_mean_mul_rsub_sub_sum_0(
    in_out_ptr1, in_ptr0, in_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 % 4
    r1 = rindex // 4
    tmp0 = tl.load(in_ptr0 + (r0 + 16 * r1), None)
    tmp3 = tl.load(in_ptr1 + (r0 + 16 * r1), None)
    tmp5 = tl.load(in_ptr0 + (4 + r0 + 16 * r1), None)
    tmp7 = tl.load(in_ptr1 + (4 + r0 + 16 * r1), None)
    tmp10 = tl.load(in_ptr0 + (8 + r0 + 16 * r1), None)
    tmp12 = tl.load(in_ptr1 + (8 + r0 + 16 * r1), None)
    tmp15 = tl.load(in_ptr0 + (12 + r0 + 16 * r1), None)
    tmp17 = tl.load(in_ptr1 + (12 + r0 + 16 * r1), None)
    tmp1 = 1.0
    tmp2 = tmp1 - tmp0
    tmp4 = tmp2 * tmp3
    tmp6 = tmp1 - tmp5
    tmp8 = tmp6 * tmp7
    tmp9 = triton_helpers.maximum(tmp4, tmp8)
    tmp11 = tmp1 - tmp10
    tmp13 = tmp11 * tmp12
    tmp14 = triton_helpers.maximum(tmp9, tmp13)
    tmp16 = tmp1 - tmp15
    tmp18 = tmp16 * tmp17
    tmp19 = triton_helpers.maximum(tmp14, tmp18)
    tmp20 = tmp3 * tmp0
    tmp21 = tmp7 * tmp5
    tmp22 = tmp20 + tmp21
    tmp23 = tmp12 * tmp10
    tmp24 = tmp22 + tmp23
    tmp25 = tmp17 * tmp15
    tmp26 = tmp24 + tmp25
    tmp27 = tmp19 - tmp26
    tmp28 = tmp27 + tmp1
    tmp29 = 0.0
    tmp30 = tmp28 < tmp29
    tmp31 = tl.where(tmp30, tmp29, tmp28)
    tmp32 = tl.broadcast_to(tmp31, [XBLOCK, RBLOCK])
    tmp34 = tl.sum(tmp32, 1)[:, None]
    tmp35 = 64.0
    tmp36 = tmp34 / tmp35
    tl.debug_barrier()
    tl.store(in_out_ptr1 + tl.full([XBLOCK, 1], 0, tl.int32), tmp36, None)


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)
        buf2 = empty_strided_cuda((), (), torch.float32)
        buf3 = buf2
        del buf2
        get_raw_stream(0)
        triton_per_fused_add_index_put_lift_fresh_max_mean_mul_rsub_sub_sum_0[
            grid(1)](buf3, arg1_1, arg0_1, 1, 64, XBLOCK=1, num_warps=2,
            num_stages=1)
        del arg0_1
        del arg1_1
    return buf3,


class my_HingelossNew(nn.Module):

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

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