# 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_6/inductor_cache/fl/cfl47w3jenzjato5bn5xjoj5tva4lgt6os2dcyaczcbvy2k2fx2y.py
# Topologically Sorted Source Nodes: [logits], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   logits => cat
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%squeeze, %squeeze_1],), 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=[128], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 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': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, '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, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 16)
    x0 = xindex % 16
    x2 = xindex
    tmp6 = tl.load(in_ptr1 + (0))
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK])
    tmp0 = x1
    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 + (x0 + (16*x1)), tmp4 & xmask, other=0.0)
    tmp8 = tmp5 + tmp7
    tmp9 = tl.full(tmp8.shape, 0.0, tmp8.dtype)
    tmp10 = tl.where(tmp4, tmp8, tmp9)
    tmp11 = tmp0 >= tmp3
    tmp12 = tl.full([1], 8, tl.int64)
    tmp13 = tmp0 < tmp12
    tmp14 = tl.load(in_ptr2 + (x0 + (16*((-4) + x1))), tmp11 & xmask, other=0.0)
    tmp15 = tmp14 + tmp7
    tmp16 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
    tmp17 = tl.where(tmp11, tmp15, tmp16)
    tmp18 = tl.where(tmp4, tmp10, tmp17)
    tl.store(out_ptr0 + (x2), tmp18, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (1, 4, 4), (16, 4, 1))
    assert_size_stride(primals_4, (1, ), (1, ))
    assert_size_stride(primals_5, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [bilinear], Original ATen: [aten._trilinear]
        buf0 = torch.ops.aten._trilinear.default(reinterpret_tensor(primals_2, (64, 4), (4, 1), 0), primals_3, reinterpret_tensor(primals_1, (64, 4), (4, 1), 0), [1, 3], [0], [1, 2], [2, 3])
        buf1 = buf0
        del buf0
        # Topologically Sorted Source Nodes: [bilinear_1], Original ATen: [aten._trilinear]
        buf2 = torch.ops.aten._trilinear.default(reinterpret_tensor(primals_5, (64, 4), (4, 1), 0), primals_3, reinterpret_tensor(primals_1, (64, 4), (4, 1), 0), [1, 3], [0], [1, 2], [2, 3])
        del primals_3
        buf3 = buf2
        del buf2
        buf4 = empty_strided_cuda((8, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [logits], Original ATen: [aten.cat]
        stream0 = get_raw_stream(0)
        triton_poi_fused_cat_0.run(buf1, primals_4, buf3, buf4, 128, grid=grid(128), stream=stream0)
        del buf1
        del buf3
        del primals_4
    return (buf4, reinterpret_tensor(primals_2, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (64, 4), (4, 1), 0), reinterpret_tensor(primals_5, (64, 4), (4, 1), 0), )


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, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((1, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5])
    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 Discriminator(nn.Module):
    """
    The discriminator

    .. math::
      \\begin{equation}
        \\mathcal{D}\\left(\\mathbf{h}_{i}^{(r)}, \\mathbf{s}^{(r)}\\right)=\\sigma\\left(\\mathbf{h}_{i}^{(r) T} \\mathbf{M}^{(r)} \\mathbf{s}^{(r)}\\right)
      \\end{equation}

    where :math:`M^{(r)}` is a trainable scoring matrix.
    """

    def __init__(self, n_h):
        super(Discriminator, self).__init__()
        self.f_k_bilinear = nn.Bilinear(n_h, n_h, 1)
        for m in self.modules():
            self.weights_init(m)

    def weights_init(self, m):
        if isinstance(m, nn.Bilinear):
            torch.nn.init.xavier_uniform_(m.weight.data)
            if m.bias is not None:
                m.bias.data.fill_(0.0)

    def forward(self, c, h_pl, h_mi, s_bias1=None, s_bias2=None):
        c_x = c.expand_as(h_pl)
        sc_1 = torch.squeeze(self.f_k_bilinear(h_pl, c_x), 1)
        sc_2 = torch.squeeze(self.f_k_bilinear(h_mi, c_x), 1)
        if s_bias1 is not None:
            sc_1 += s_bias1
        if s_bias2 is not None:
            sc_2 += s_bias2
        logits = torch.cat((sc_1, sc_2), 0)
        return logits


def get_inputs():
    return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4]), torch.rand(
        [4, 4, 4, 4])]


def get_init_inputs():
    return [[], {'n_h': 4}]

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
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
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_cat_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 16
    x0 = xindex % 16
    x2 = xindex
    tmp6 = tl.load(in_ptr1 + 0)
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK])
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 16 * x1), tmp4 & xmask, other=0.0)
    tmp8 = tmp5 + tmp7
    tmp9 = tl.full(tmp8.shape, 0.0, tmp8.dtype)
    tmp10 = tl.where(tmp4, tmp8, tmp9)
    tmp11 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp14 = tl.load(in_ptr2 + (x0 + 16 * (-4 + x1)), tmp11 & xmask, other=0.0)
    tmp15 = tmp14 + tmp7
    tmp16 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
    tmp17 = tl.where(tmp11, tmp15, tmp16)
    tmp18 = tl.where(tmp4, tmp10, tmp17)
    tl.store(out_ptr0 + x2, tmp18, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (1, 4, 4), (16, 4, 1))
    assert_size_stride(primals_4, (1,), (1,))
    assert_size_stride(primals_5, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = torch.ops.aten._trilinear.default(reinterpret_tensor(
            primals_2, (64, 4), (4, 1), 0), primals_3, reinterpret_tensor(
            primals_1, (64, 4), (4, 1), 0), [1, 3], [0], [1, 2], [2, 3])
        buf1 = buf0
        del buf0
        buf2 = torch.ops.aten._trilinear.default(reinterpret_tensor(
            primals_5, (64, 4), (4, 1), 0), primals_3, reinterpret_tensor(
            primals_1, (64, 4), (4, 1), 0), [1, 3], [0], [1, 2], [2, 3])
        del primals_3
        buf3 = buf2
        del buf2
        buf4 = empty_strided_cuda((8, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(128)](buf1, primals_4, buf3, buf4, 128,
            XBLOCK=128, num_warps=4, num_stages=1)
        del buf1
        del buf3
        del primals_4
    return buf4, reinterpret_tensor(primals_2, (64, 4), (4, 1), 0
        ), reinterpret_tensor(primals_1, (64, 4), (4, 1), 0
        ), reinterpret_tensor(primals_5, (64, 4), (4, 1), 0)


class DiscriminatorNew(nn.Module):
    """
    The discriminator

    .. math::
      \\begin{equation}
        \\mathcal{D}\\left(\\mathbf{h}_{i}^{(r)}, \\mathbf{s}^{(r)}\\right)=\\sigma\\left(\\mathbf{h}_{i}^{(r) T} \\mathbf{M}^{(r)} \\mathbf{s}^{(r)}\\right)
      \\end{equation}

    where :math:`M^{(r)}` is a trainable scoring matrix.
    """

    def __init__(self, n_h):
        super(DiscriminatorNew, self).__init__()
        self.f_k_bilinear = nn.Bilinear(n_h, n_h, 1)
        for m in self.modules():
            self.weights_init(m)

    def weights_init(self, m):
        if isinstance(m, nn.Bilinear):
            torch.nn.init.xavier_uniform_(m.weight.data)
            if m.bias is not None:
                m.bias.data.fill_(0.0)

    def forward(self, input_0, input_1, input_2):
        primals_3 = self.f_k_bilinear.weight
        primals_4 = self.f_k_bilinear.bias
        primals_1 = input_0
        primals_2 = input_1
        primals_5 = input_2
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]
