# 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_4/inductor_cache/7q/c7qo7xkoynuj5iw5frdzk4ubwdvllwhmpivznfp5teeegjzck2py.py
# Topologically Sorted Source Nodes: [x2_1, sub, sub_1, max_margin, mean], Original ATen: [aten.cat, aten.sub, aten.rsub, aten.relu, aten.mean]
# Source node to ATen node mapping:
#   max_margin => relu
#   mean => mean
#   sub => sub
#   sub_1 => sub_1
#   x2_1 => cat
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%view_2, %view_3],), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view_1, %cat), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %sub), kwargs = {})
#   %relu : [num_users=1] = call_function[target=torch.ops.aten.relu.default](args = (%sub_1,), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%relu,), kwargs = {})
triton_per_fused_cat_mean_relu_rsub_sub_0 = async_compile.triton('triton_per_fused_cat_mean_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.persistent_reduction(
    size_hints=[1, 32],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 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': {2: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 3), equal_to_1=(2,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_cat_mean_relu_rsub_sub_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 3, '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_cat_mean_relu_rsub_sub_0(in_out_ptr0, in_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 32
    RBLOCK: tl.constexpr = 32
    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
    tmp0 = tl.load(in_ptr0 + (5*((r0 % 16) // 4)), None, eviction_policy='evict_last')
    tmp1 = r0
    tmp2 = tl.full([1, 1], 0, tl.int64)
    tmp3 = tmp1 >= tmp2
    tmp4 = tl.full([1, 1], 16, tl.int64)
    tmp5 = tmp1 < tmp4
    tmp6 = tl.load(in_ptr0 + (tl.broadcast_to(r0, [XBLOCK, RBLOCK])), tmp5, eviction_policy='evict_last', other=0.0)
    tmp7 = tmp1 >= tmp4
    tmp8 = tl.full([1, 1], 32, tl.int64)
    tmp9 = tmp1 < tmp8
    tmp10 = tl.load(in_ptr0 + (tl.broadcast_to((4*(((-16) + r0) % 4)) + ((((-16) + r0) // 4) % 4), [XBLOCK, RBLOCK])), tmp7, eviction_policy='evict_last', other=0.0)
    tmp11 = tl.where(tmp5, tmp6, tmp10)
    tmp12 = tmp0 - tmp11
    tmp13 = 1.0
    tmp14 = tmp13 - tmp12
    tmp15 = tl.full([1, 1], 0, tl.int32)
    tmp16 = triton_helpers.maximum(tmp15, tmp14)
    tmp17 = tl.broadcast_to(tmp16, [XBLOCK, RBLOCK])
    tmp19 = tl.sum(tmp17, 1)[:, None]
    tmp20 = 32.0
    tmp21 = tmp19 / tmp20
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp21, None)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((), (), torch.float32)
        buf1 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [x2_1, sub, sub_1, max_margin, mean], Original ATen: [aten.cat, aten.sub, aten.rsub, aten.relu, aten.mean]
        stream0 = get_raw_stream(0)
        triton_per_fused_cat_mean_relu_rsub_sub_0.run(buf1, arg0_1, 1, 32, grid=grid(1), stream=stream0)
        del arg0_1
    return (buf1, )


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, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_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.functional as F
import torch.nn as nn
import torch as th
import torch.optim
import torch.utils.data


class MaxMarginRankingLoss(nn.Module):

    def __init__(self, margin=1):
        super(MaxMarginRankingLoss, self).__init__()
        self.loss = th.nn.MarginRankingLoss(margin)
        self.margin = margin

    def forward(self, x):
        n = x.size()[0]
        x1 = th.diag(x)
        x1 = x1.unsqueeze(1)
        x1 = x1.expand(n, n)
        x1 = x1.contiguous().view(-1, 1)
        x1 = th.cat((x1, x1), 0)
        x2 = x.view(-1, 1)
        x3 = x.transpose(0, 1).contiguous().view(-1, 1)
        x2 = th.cat((x2, x3), 0)
        max_margin = F.relu(self.margin - (x1 - x2))
        return max_margin.mean()


def get_inputs():
    return [torch.rand([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
import torch as th
import torch.optim
import torch.utils.data
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_cat_mean_relu_rsub_sub_0(in_out_ptr0, in_ptr0, xnumel,
    rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 32
    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
    tmp0 = tl.load(in_ptr0 + 5 * (r0 % 16 // 4), None, eviction_policy=
        'evict_last')
    tmp1 = r0
    tl.full([1, 1], 0, tl.int64)
    tmp4 = tl.full([1, 1], 16, tl.int64)
    tmp5 = tmp1 < tmp4
    tmp6 = tl.load(in_ptr0 + tl.broadcast_to(r0, [XBLOCK, RBLOCK]), tmp5,
        eviction_policy='evict_last', other=0.0)
    tmp7 = tmp1 >= tmp4
    tl.full([1, 1], 32, tl.int64)
    tmp10 = tl.load(in_ptr0 + tl.broadcast_to(4 * ((-16 + r0) % 4) + (-16 +
        r0) // 4 % 4, [XBLOCK, RBLOCK]), tmp7, eviction_policy='evict_last',
        other=0.0)
    tmp11 = tl.where(tmp5, tmp6, tmp10)
    tmp12 = tmp0 - tmp11
    tmp13 = 1.0
    tmp14 = tmp13 - tmp12
    tmp15 = tl.full([1, 1], 0, tl.int32)
    tmp16 = triton_helpers.maximum(tmp15, tmp14)
    tmp17 = tl.broadcast_to(tmp16, [XBLOCK, RBLOCK])
    tmp19 = tl.sum(tmp17, 1)[:, None]
    tmp20 = 32.0
    tmp21 = tmp19 / tmp20
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp21, None)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((), (), torch.float32)
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_per_fused_cat_mean_relu_rsub_sub_0[grid(1)](buf1, arg0_1, 1,
            32, XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
    return buf1,


class MaxMarginRankingLossNew(nn.Module):

    def __init__(self, margin=1):
        super(MaxMarginRankingLossNew, self).__init__()
        self.loss = th.nn.MarginRankingLoss(margin)
        self.margin = margin

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