# 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_1/inductor_cache/xu/cxuz6jfjowuh4p2kufdvngput53c42pcs4vsj5fyr2rnezepy7to.py
# Topologically Sorted Source Nodes: [input_2], Original ATen: [aten.leaky_relu]
# Source node to ATen node mapping:
#   input_2 => gt, mul, where
# Graph fragment:
#   %add_tensor_4 : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default_4, %primals_4), kwargs = {})
#   %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%add_tensor_4, 0), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_tensor_4, 0.2), kwargs = {})
#   %where : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt, %add_tensor_4, %mul), kwargs = {})
triton_poi_fused_leaky_relu_0 = async_compile.triton('triton_poi_fused_leaky_relu_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: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_leaky_relu_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_leaky_relu_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 32
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.2
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(in_out_ptr0 + (x2), tmp7, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/fj/cfjp34vlnurpiazt2zvfffie6nhcxuv3ao3qqi3o4aclizl77w22.py
# Topologically Sorted Source Nodes: [input_6], Original ATen: [aten.leaky_relu]
# Source node to ATen node mapping:
#   input_6 => gt_2, mul_2, where_2
# Graph fragment:
#   %add_tensor_2 : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default_2, %primals_8), kwargs = {})
#   %gt_2 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%add_tensor_2, 0), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_tensor_2, 0.2), kwargs = {})
#   %where_2 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_2, %add_tensor_2, %mul_2), kwargs = {})
triton_poi_fused_leaky_relu_1 = async_compile.triton('triton_poi_fused_leaky_relu_1', '''
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: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_leaky_relu_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_leaky_relu_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 16
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.2
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(in_out_ptr0 + (x2), tmp7, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/6t/c6thf6sub62m63skohfqdmz2npjqndfvodwhjcgnkmehgmx6kylx.py
# Topologically Sorted Source Nodes: [input_12, mul, out], Original ATen: [aten.leaky_relu, aten.mul, aten.sum]
# Source node to ATen node mapping:
#   input_12 => gt_5, mul_5, where_5
#   mul => mul_6
#   out => sum_1
# Graph fragment:
#   %gt_5 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%addmm_5, 0), kwargs = {})
#   %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%addmm_5, 0.2), kwargs = {})
#   %where_5 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt_5, %addmm_5, %mul_5), kwargs = {})
#   %mul_6 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mm, %where_5), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul_6, [1], True), kwargs = {})
triton_per_fused_leaky_relu_mul_sum_2 = async_compile.triton('triton_per_fused_leaky_relu_mul_sum_2', '''
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=[4, 16],
    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=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_leaky_relu_mul_sum_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 1, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_leaky_relu_mul_sum_2(in_ptr0, in_ptr1, out_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    rnumel = 16
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + (16*x0)), xmask, other=0.0)
    tmp1 = tl.load(in_ptr1 + (r1 + (16*x0)), xmask, other=0.0)
    tmp2 = 0.0
    tmp3 = tmp1 > tmp2
    tmp4 = 0.2
    tmp5 = tmp1 * tmp4
    tmp6 = tl.where(tmp3, tmp1, tmp5)
    tmp7 = tmp0 * tmp6
    tmp8 = tl.broadcast_to(tmp7, [XBLOCK, RBLOCK])
    tmp10 = tl.where(xmask, tmp8, 0)
    tmp11 = tl.sum(tmp10, 1)[:, None]
    tl.store(out_ptr0 + (x0), tmp11, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15 = args
    args.clear()
    assert_size_stride(primals_1, (4, 1), (1, 1))
    assert_size_stride(primals_2, (4, 1), (1, 1))
    assert_size_stride(primals_3, (32, 1), (1, 1))
    assert_size_stride(primals_4, (32, ), (1, ))
    assert_size_stride(primals_5, (32, 32), (32, 1))
    assert_size_stride(primals_6, (32, ), (1, ))
    assert_size_stride(primals_7, (16, 32), (32, 1))
    assert_size_stride(primals_8, (16, ), (1, ))
    assert_size_stride(primals_9, (32, 1), (1, 1))
    assert_size_stride(primals_10, (32, ), (1, ))
    assert_size_stride(primals_11, (32, 32), (32, 1))
    assert_size_stride(primals_12, (32, ), (1, ))
    assert_size_stride(primals_13, (16, 32), (32, 1))
    assert_size_stride(primals_14, (16, ), (1, ))
    assert_size_stride(primals_15, (16, 16), (16, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 32), (32, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(primals_1, reinterpret_tensor(primals_3, (1, 32), (1, 1), 0), out=buf0)
        del primals_3
        buf1 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [input_2], Original ATen: [aten.leaky_relu]
        stream0 = get_raw_stream(0)
        triton_poi_fused_leaky_relu_0.run(buf1, primals_4, 128, grid=grid(128), stream=stream0)
        del primals_4
        buf2 = empty_strided_cuda((4, 32), (32, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(buf1, reinterpret_tensor(primals_5, (32, 32), (1, 32), 0), out=buf2)
        buf3 = buf2; del buf2  # reuse
        # Topologically Sorted Source Nodes: [input_4], Original ATen: [aten.leaky_relu]
        triton_poi_fused_leaky_relu_0.run(buf3, primals_6, 128, grid=grid(128), stream=stream0)
        del primals_6
        buf4 = empty_strided_cuda((4, 16), (16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(buf3, reinterpret_tensor(primals_7, (32, 16), (1, 32), 0), out=buf4)
        buf5 = buf4; del buf4  # reuse
        # Topologically Sorted Source Nodes: [input_6], Original ATen: [aten.leaky_relu]
        triton_poi_fused_leaky_relu_1.run(buf5, primals_8, 64, grid=grid(64), stream=stream0)
        del primals_8
        buf6 = empty_strided_cuda((4, 32), (32, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(primals_2, reinterpret_tensor(primals_9, (1, 32), (1, 1), 0), out=buf6)
        del primals_9
        buf7 = buf6; del buf6  # reuse
        # Topologically Sorted Source Nodes: [input_8], Original ATen: [aten.leaky_relu]
        triton_poi_fused_leaky_relu_0.run(buf7, primals_10, 128, grid=grid(128), stream=stream0)
        del primals_10
        buf8 = empty_strided_cuda((4, 32), (32, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(buf7, reinterpret_tensor(primals_11, (32, 32), (1, 32), 0), out=buf8)
        buf9 = buf8; del buf8  # reuse
        # Topologically Sorted Source Nodes: [input_10], Original ATen: [aten.leaky_relu]
        triton_poi_fused_leaky_relu_0.run(buf9, primals_12, 128, grid=grid(128), stream=stream0)
        del primals_12
        buf10 = empty_strided_cuda((4, 16), (16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [input_11], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_14, buf9, reinterpret_tensor(primals_13, (32, 16), (1, 32), 0), alpha=1, beta=1, out=buf10)
        del primals_14
        buf11 = empty_strided_cuda((4, 16), (16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mm], Original ATen: [aten.mm]
        extern_kernels.mm(buf5, primals_15, out=buf11)
        buf12 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [input_12, mul, out], Original ATen: [aten.leaky_relu, aten.mul, aten.sum]
        triton_per_fused_leaky_relu_mul_sum_2.run(buf11, buf10, buf12, 4, 16, grid=grid(4), stream=stream0)
    return (buf12, primals_2, primals_1, buf1, buf3, buf5, buf7, buf9, buf10, buf11, reinterpret_tensor(primals_15, (16, 16), (1, 16), 0), primals_13, primals_11, primals_7, primals_5, )


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, 1), (1, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 1), (1, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((32, 1), (1, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((32, 32), (32, 1), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((16, 32), (32, 1), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((32, 1), (1, 1), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((32, 32), (32, 1), device='cuda:0', dtype=torch.float32)
    primals_12 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_13 = rand_strided((16, 32), (32, 1), device='cuda:0', dtype=torch.float32)
    primals_14 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_15 = rand_strided((16, 16), (16, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15])
    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.utils.data
import torch.nn as nn


def add_layer(seq, ix, n_inputs, n_outputs, nonlin, normalization):
    seq.add_module('L' + str(ix), nn.Linear(n_inputs, n_outputs))
    if ix > 0 and normalization:
        if normalization == 'LN':
            seq.main.add_module('A' + str(ix), nn.LayerNorm(n_outputs))
        else:
            raise ValueError('Unknown normalization: {}'.format(normalization))
    if nonlin == 'LeakyReLU':
        seq.add_module('N' + str(ix), nn.LeakyReLU(0.2, inplace=True))
    elif nonlin == 'ReLU':
        seq.add_module('N' + str(ix), nn.ReLU(inplace=True))
    elif nonlin == 'Sigmoid':
        seq.add_module('N' + str(ix), nn.Sigmoid())


class D_phiVpsi(nn.Module):

    def __init__(self, insizes=[1, 1], layerSizes=[[32, 32, 16]] * 2,
        nonlin='LeakyReLU', normalization=None):
        super(D_phiVpsi, self).__init__()
        self.phi_x, self.psi_y = nn.Sequential(), nn.Sequential()
        for seq, insize, layerSize in [(self.phi_x, insizes[0], layerSizes[
            0]), (self.psi_y, insizes[1], layerSizes[1])]:
            for ix, n_inputs, n_outputs in zip(range(len(layerSize)), [
                insize] + layerSize[:-1], layerSize):
                add_layer(seq, ix, n_inputs, n_outputs, nonlin, normalization)
        self.phiD, self.psiD = layerSizes[0][-1], layerSizes[1][-1]
        self.W = nn.Parameter(torch.randn(self.phiD, self.psiD))

    def forward(self, x, y):
        x = x.view(x.size(0), -1)
        y = y.view(x.size(0), 1)
        phi_x = self.phi_x(x)
        psi_y = self.psi_y(y)
        out = (torch.mm(phi_x, self.W) * psi_y).sum(1, keepdim=True)
        return out


def get_inputs():
    return [torch.rand([4, 1]), torch.rand([4, 1])]


def get_init_inputs():
    return [[], {}]

import torch
from torch._inductor.select_algorithm import extern_kernels
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.utils.data
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_leaky_relu_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 32
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.2
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(in_out_ptr0 + x2, tmp7, xmask)


@triton.jit
def triton_poi_fused_leaky_relu_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 16
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.2
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(in_out_ptr0 + x2, tmp7, xmask)


@triton.jit
def triton_per_fused_leaky_relu_mul_sum_2(in_ptr0, in_ptr1, out_ptr0,
    xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 16 * x0), xmask, other=0.0)
    tmp1 = tl.load(in_ptr1 + (r1 + 16 * x0), xmask, other=0.0)
    tmp2 = 0.0
    tmp3 = tmp1 > tmp2
    tmp4 = 0.2
    tmp5 = tmp1 * tmp4
    tmp6 = tl.where(tmp3, tmp1, tmp5)
    tmp7 = tmp0 * tmp6
    tmp8 = tl.broadcast_to(tmp7, [XBLOCK, RBLOCK])
    tmp10 = tl.where(xmask, tmp8, 0)
    tmp11 = tl.sum(tmp10, 1)[:, None]
    tl.store(out_ptr0 + x0, tmp11, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
        primals_13, primals_14, primals_15) = args
    args.clear()
    assert_size_stride(primals_1, (4, 1), (1, 1))
    assert_size_stride(primals_2, (4, 1), (1, 1))
    assert_size_stride(primals_3, (32, 1), (1, 1))
    assert_size_stride(primals_4, (32,), (1,))
    assert_size_stride(primals_5, (32, 32), (32, 1))
    assert_size_stride(primals_6, (32,), (1,))
    assert_size_stride(primals_7, (16, 32), (32, 1))
    assert_size_stride(primals_8, (16,), (1,))
    assert_size_stride(primals_9, (32, 1), (1, 1))
    assert_size_stride(primals_10, (32,), (1,))
    assert_size_stride(primals_11, (32, 32), (32, 1))
    assert_size_stride(primals_12, (32,), (1,))
    assert_size_stride(primals_13, (16, 32), (32, 1))
    assert_size_stride(primals_14, (16,), (1,))
    assert_size_stride(primals_15, (16, 16), (16, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 32), (32, 1), torch.float32)
        extern_kernels.mm(primals_1, reinterpret_tensor(primals_3, (1, 32),
            (1, 1), 0), out=buf0)
        del primals_3
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_leaky_relu_0[grid(128)](buf1, primals_4, 128,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_4
        buf2 = empty_strided_cuda((4, 32), (32, 1), torch.float32)
        extern_kernels.mm(buf1, reinterpret_tensor(primals_5, (32, 32), (1,
            32), 0), out=buf2)
        buf3 = buf2
        del buf2
        triton_poi_fused_leaky_relu_0[grid(128)](buf3, primals_6, 128,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_6
        buf4 = empty_strided_cuda((4, 16), (16, 1), torch.float32)
        extern_kernels.mm(buf3, reinterpret_tensor(primals_7, (32, 16), (1,
            32), 0), out=buf4)
        buf5 = buf4
        del buf4
        triton_poi_fused_leaky_relu_1[grid(64)](buf5, primals_8, 64, XBLOCK
            =64, num_warps=1, num_stages=1)
        del primals_8
        buf6 = empty_strided_cuda((4, 32), (32, 1), torch.float32)
        extern_kernels.mm(primals_2, reinterpret_tensor(primals_9, (1, 32),
            (1, 1), 0), out=buf6)
        del primals_9
        buf7 = buf6
        del buf6
        triton_poi_fused_leaky_relu_0[grid(128)](buf7, primals_10, 128,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_10
        buf8 = empty_strided_cuda((4, 32), (32, 1), torch.float32)
        extern_kernels.mm(buf7, reinterpret_tensor(primals_11, (32, 32), (1,
            32), 0), out=buf8)
        buf9 = buf8
        del buf8
        triton_poi_fused_leaky_relu_0[grid(128)](buf9, primals_12, 128,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_12
        buf10 = empty_strided_cuda((4, 16), (16, 1), torch.float32)
        extern_kernels.addmm(primals_14, buf9, reinterpret_tensor(
            primals_13, (32, 16), (1, 32), 0), alpha=1, beta=1, out=buf10)
        del primals_14
        buf11 = empty_strided_cuda((4, 16), (16, 1), torch.float32)
        extern_kernels.mm(buf5, primals_15, out=buf11)
        buf12 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        triton_per_fused_leaky_relu_mul_sum_2[grid(4)](buf11, buf10, buf12,
            4, 16, XBLOCK=1, num_warps=2, num_stages=1)
    return (buf12, primals_2, primals_1, buf1, buf3, buf5, buf7, buf9,
        buf10, buf11, reinterpret_tensor(primals_15, (16, 16), (1, 16), 0),
        primals_13, primals_11, primals_7, primals_5)


def add_layer(seq, ix, n_inputs, n_outputs, nonlin, normalization):
    seq.add_module('L' + str(ix), nn.Linear(n_inputs, n_outputs))
    if ix > 0 and normalization:
        if normalization == 'LN':
            seq.main.add_module('A' + str(ix), nn.LayerNorm(n_outputs))
        else:
            raise ValueError('Unknown normalization: {}'.format(normalization))
    if nonlin == 'LeakyReLU':
        seq.add_module('N' + str(ix), nn.LeakyReLU(0.2, inplace=True))
    elif nonlin == 'ReLU':
        seq.add_module('N' + str(ix), nn.ReLU(inplace=True))
    elif nonlin == 'Sigmoid':
        seq.add_module('N' + str(ix), nn.Sigmoid())


class D_phiVpsiNew(nn.Module):

    def __init__(self, insizes=[1, 1], layerSizes=[[32, 32, 16]] * 2,
        nonlin='LeakyReLU', normalization=None):
        super(D_phiVpsiNew, self).__init__()
        self.phi_x, self.psi_y = nn.Sequential(), nn.Sequential()
        for seq, insize, layerSize in [(self.phi_x, insizes[0], layerSizes[
            0]), (self.psi_y, insizes[1], layerSizes[1])]:
            for ix, n_inputs, n_outputs in zip(range(len(layerSize)), [
                insize] + layerSize[:-1], layerSize):
                add_layer(seq, ix, n_inputs, n_outputs, nonlin, normalization)
        self.phiD, self.psiD = layerSizes[0][-1], layerSizes[1][-1]
        self.W = nn.Parameter(torch.randn(self.phiD, self.psiD))

    def forward(self, input_0, input_1):
        primals_15 = self.W
        primals_3 = self.phi_x.L0.weight
        primals_4 = self.phi_x.L0.bias
        primals_5 = self.phi_x.L1.weight
        primals_6 = self.phi_x.L1.bias
        primals_7 = self.phi_x.L2.weight
        primals_8 = self.phi_x.L2.bias
        primals_9 = self.psi_y.L0.weight
        primals_10 = self.psi_y.L0.bias
        primals_11 = self.psi_y.L1.weight
        primals_12 = self.psi_y.L1.bias
        primals_13 = self.psi_y.L2.weight
        primals_14 = self.psi_y.L2.bias
        primals_1 = input_0
        primals_2 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11, primals_12, primals_13, primals_14,
            primals_15])
        return output[0]
