# 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_7/inductor_cache/xx/cxxhxzeisdha6lseml3xbmn4swjnr2242wdaitskbwmzmjdh5mi6.py
# Topologically Sorted Source Nodes: [y], Original ATen: [aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   y => relu
# Graph fragment:
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_1,), kwargs = {})
#   %le_2 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {})
triton_poi_fused_relu_threshold_backward_0 = async_compile.triton('triton_poi_fused_relu_threshold_backward_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=[262144], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 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_relu_threshold_backward_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, '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_relu_threshold_backward_0(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256000
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 4000
    x1 = (xindex // 4000)
    tmp0 = tl.load(in_out_ptr0 + (x2), None)
    tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + (x2), tmp4, None)
    tl.store(out_ptr0 + (x0 + (4096*x1)), tmp6, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/ga/cgalp2f5yff5rnzv5p47lfgfg67vffoad353wh4dwaqcgcwq4xv3.py
# Topologically Sorted Source Nodes: [y_1], Original ATen: [aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   y_1 => relu_1
# Graph fragment:
#   %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_3,), kwargs = {})
#   %le_1 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_1, 0), kwargs = {})
triton_poi_fused_relu_threshold_backward_1 = async_compile.triton('triton_poi_fused_relu_threshold_backward_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=[131072], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 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_relu_threshold_backward_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, '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_relu_threshold_backward_1(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 128000
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 2000
    x1 = (xindex // 2000)
    tmp0 = tl.load(in_out_ptr0 + (x0 + (2016*x1)), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + (x0 + (2016*x1)), tmp4, xmask)
    tl.store(out_ptr0 + (x0 + (2048*x1)), tmp6, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/ua/cuakpm3ttx5zlma7nnvonftzmojmirfj4xxufltek7inabiouoi4.py
# Topologically Sorted Source Nodes: [y_2], Original ATen: [aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   y_2 => relu_2
# Graph fragment:
#   %relu_2 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_5,), kwargs = {})
#   %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_2, 0), kwargs = {})
triton_poi_fused_relu_threshold_backward_2 = async_compile.triton('triton_poi_fused_relu_threshold_backward_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.pointwise(
    size_hints=[65536], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 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_relu_threshold_backward_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, '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_relu_threshold_backward_2(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64000
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x4 = xindex
    x0 = xindex % 1000
    x2 = xindex % 4000
    x3 = (xindex // 4000)
    tmp0 = tl.load(in_out_ptr0 + (x4), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + (x4), tmp4, xmask)
    tl.store(out_ptr0 + (x2 + (4096*x3)), tmp6, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/xp/cxpqywcqam7evubfwwa5zmt733w2zov6otomgqgpramgjdsnjg5k.py
# Topologically Sorted Source Nodes: [y_3], Original ATen: [aten.sigmoid]
# Source node to ATen node mapping:
#   y_3 => sigmoid
# Graph fragment:
#   %sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%view_7,), kwargs = {})
triton_poi_fused_sigmoid_3 = async_compile.triton('triton_poi_fused_sigmoid_3', '''
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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_sigmoid_3', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, '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_sigmoid_3(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.sigmoid(tmp2)
    tl.store(in_out_ptr0 + (x2), tmp3, 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 = args
    args.clear()
    assert_size_stride(primals_1, (4000, 4), (4, 1))
    assert_size_stride(primals_2, (4000, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (2000, 4000), (4000, 1))
    assert_size_stride(primals_5, (2000, ), (1, ))
    assert_size_stride(primals_6, (1000, 2000), (2000, 1))
    assert_size_stride(primals_7, (1000, ), (1, ))
    assert_size_stride(primals_8, (4, 1000), (1000, 1))
    assert_size_stride(primals_9, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4000), (4000, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4000), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = reinterpret_tensor(buf0, (4, 4, 4, 4000), (64000, 16000, 4000, 1), 0); del buf0  # reuse
        buf10 = empty_strided_cuda((4, 4, 4, 4000), (65536, 16384, 4096, 1), torch.bool)
        # Topologically Sorted Source Nodes: [y], Original ATen: [aten.relu, aten.threshold_backward]
        stream0 = get_raw_stream(0)
        triton_poi_fused_relu_threshold_backward_0.run(buf1, primals_2, buf10, 256000, grid=grid(256000), stream=stream0)
        del primals_2
        buf2 = empty_strided_cuda((64, 2000), (2016, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf1, (64, 4000), (4000, 1), 0), reinterpret_tensor(primals_4, (4000, 2000), (1, 4000), 0), out=buf2)
        buf3 = reinterpret_tensor(buf2, (4, 4, 4, 2000), (32256, 8064, 2016, 1), 0); del buf2  # reuse
        buf9 = empty_strided_cuda((4, 4, 4, 2000), (32768, 8192, 2048, 1), torch.bool)
        # Topologically Sorted Source Nodes: [y_1], Original ATen: [aten.relu, aten.threshold_backward]
        triton_poi_fused_relu_threshold_backward_1.run(buf3, primals_5, buf9, 128000, grid=grid(128000), stream=stream0)
        del primals_5
        buf4 = empty_strided_cuda((64, 1000), (1000, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf3, (64, 2000), (2016, 1), 0), reinterpret_tensor(primals_6, (2000, 1000), (1, 2000), 0), out=buf4)
        buf5 = reinterpret_tensor(buf4, (4, 4, 4, 1000), (16000, 4000, 1000, 1), 0); del buf4  # reuse
        buf8 = empty_strided_cuda((4, 4, 4, 1000), (16384, 4096, 1000, 1), torch.bool)
        # Topologically Sorted Source Nodes: [y_2], Original ATen: [aten.relu, aten.threshold_backward]
        triton_poi_fused_relu_threshold_backward_2.run(buf5, primals_7, buf8, 64000, grid=grid(64000), stream=stream0)
        del primals_7
        buf6 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf5, (64, 1000), (1000, 1), 0), reinterpret_tensor(primals_8, (1000, 4), (1, 1000), 0), out=buf6)
        buf7 = reinterpret_tensor(buf6, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf6  # reuse
        # Topologically Sorted Source Nodes: [y_3], Original ATen: [aten.sigmoid]
        triton_poi_fused_sigmoid_3.run(buf7, primals_9, 256, grid=grid(256), stream=stream0)
        del primals_9
    return (buf7, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(buf1, (64, 4000), (4000, 1), 0), reinterpret_tensor(buf3, (64, 2000), (2016, 1), 0), reinterpret_tensor(buf5, (64, 1000), (1000, 1), 0), buf7, primals_8, buf8, primals_6, buf9, primals_4, buf10, )


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((4000, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4000, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((2000, 4000), (4000, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((2000, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((1000, 2000), (2000, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((1000, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((4, 1000), (1000, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((4, ), (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])
    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 time
import torch
import numpy as np
from torch import nn
from torch import optim
from torch.nn import functional as F


class Base(nn.Module):
    """ This class is the base structure for all of classification/regression DNN models.
    Mainly, it provides the general methods for training, evaluating model and predcting the given data.
    """

    def fit(self, train_loader, valid_loader, out, epochs=100, lr=0.0001):
        """Training the DNN model, similar to the scikit-learn or Keras style.
        In the end, the optimal value of parameters will also be persisted on the hard drive.

        Arguments:
            train_loader (DataLoader): Data loader for training set,
                including m X n target FloatTensor and m X l label FloatTensor
                (m is the No. of sample, n is the No. of features, l is the No. of classes or tasks)
            valid_loader (DataLoader): Data loader for validation set.
                The data structure is as same as loader_train.
            out (str): the file path for the model file (suffix with '.pkg')
                and log file (suffix with '.log').
            epochs(int, optional): The maximum of training epochs (default: 100)
            lr (float, optional): learning rate (default: 1e-4)
        """
        if 'optim' in self.__dict__:
            optimizer = self.optim
        else:
            optimizer = optim.Adam(self.parameters(), lr=lr)
        best_loss = np.inf
        last_save = 0
        if not os.path.exists(out):
            try:
                os.makedirs(out)
            except PermissionError:
                None
        log = open(file=out + '.log', mode='w+')
        for epoch in range(epochs):
            time.time()
            for param_group in optimizer.param_groups:
                param_group['lr'] = lr * (1 - 1 / epochs) ** (epoch * 10)
            for i, (Xb, yb) in enumerate(train_loader):
                Xb, yb = Xb, yb
                optimizer.zero_grad()
                y_ = self(Xb, istrain=True)
                ix = yb == yb
                yb, y_ = yb[ix], y_[ix]
                wb = torch.Tensor(yb.size())
                wb[yb == 3.99] = 0.1
                wb[yb != 3.99] = 1
                loss = self.criterion(y_ * wb, yb * wb)
                loss.backward()
                optimizer.step()
            loss_valid = self.evaluate(valid_loader)
            None
            if loss_valid < best_loss:
                torch.save(self.state_dict(), out + '.pkg')
                None
                best_loss = loss_valid
                last_save = epoch
            else:
                None
                if epoch - last_save > 100:
                    break
        log.close()
        self.load_state_dict(torch.load(out + '.pkg'))

    def evaluate(self, loader):
        """Evaluating the performance of the DNN model.

        Arguments:
            loader (torch.util.data.DataLoader): data loader for test set,
                including m X n target FloatTensor and l X n label FloatTensor
                (m is the No. of sample, n is the No. of features, l is the No. of classes or tasks)

        Return:
            loss (float): the average loss value based on the calculation of loss function with given test set.
        """
        loss = 0
        for Xb, yb in loader:
            Xb, yb = Xb, yb
            y_ = self.forward(Xb)
            ix = yb == yb
            yb, y_ = yb[ix], y_[ix]
            wb = torch.Tensor(yb.size())
            wb[yb == 3.99] = 0.1
            wb[yb != 3.99] = 1
            loss += self.criterion(y_ * wb, yb * wb).item()
        loss = loss / len(loader)
        return loss

    def predict(self, loader):
        """Predicting the probability of each sample in the given dataset.

        Arguments:
            loader (torch.util.data.DataLoader): data loader for test set,
                only including m X n target FloatTensor
                (m is the No. of sample, n is the No. of features)

        Return:
            score (ndarray): probability of each sample in the given dataset,
                it is a m X l FloatTensor (m is the No. of sample, l is the No. of classes or tasks.)
        """
        score = []
        for Xb, yb in loader:
            Xb = Xb
            y_ = self.forward(Xb)
            score.append(y_.detach().cpu())
        score = torch.cat(score, dim=0).numpy()
        return score


class MTFullyConnected(Base):
    """Multi-task DNN classification/regression model. It contains four fully connected layers
    between which are dropout layer for robustness.

    Arguments:
        n_dim (int): the No. of columns (features) for input tensor
        n_task (int): the No. of columns (tasks) for output tensor.
        is_reg (bool, optional): Regression model (True) or Classification model (False)
    """

    def __init__(self, n_dim, n_task, is_reg=False):
        super(MTFullyConnected, self).__init__()
        self.n_task = n_task
        self.dropout = nn.Dropout(0.25)
        self.fc0 = nn.Linear(n_dim, 4000)
        self.fc1 = nn.Linear(4000, 2000)
        self.fc2 = nn.Linear(2000, 1000)
        self.output = nn.Linear(1000, n_task)
        self.is_reg = is_reg
        if is_reg:
            self.criterion = nn.MSELoss()
        else:
            self.criterion = nn.BCELoss()
            self.activation = nn.Sigmoid()
        self

    def forward(self, X, istrain=False):
        """Invoke the class directly as a function

        Arguments:
            X (FloatTensor): m X n FloatTensor, m is the No. of samples, n is the No. of features.
            istrain (bool, optional): is it invoked during training process (True)
                or just for prediction (False)

        Return:
            y (FloatTensor): m X l FloatTensor, m is the No. of samples, n is the No. of tasks
        """
        y = F.relu(self.fc0(X))
        if istrain:
            y = self.dropout(y)
        y = F.relu(self.fc1(y))
        if istrain:
            y = self.dropout(y)
        y = F.relu(self.fc2(y))
        if istrain:
            y = self.dropout(y)
        if self.is_reg:
            y = self.output(y)
        else:
            y = self.activation(self.output(y))
        return y


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


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

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
from torch._inductor.runtime import triton_helpers
import time
import numpy as np
from torch import nn
from torch import optim
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_relu_threshold_backward_0(in_out_ptr0, in_ptr0,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 4000
    x1 = xindex // 4000
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + x2, tmp4, None)
    tl.store(out_ptr0 + (x0 + 4096 * x1), tmp6, None)


@triton.jit
def triton_poi_fused_relu_threshold_backward_1(in_out_ptr0, in_ptr0,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 128000
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 2000
    x1 = xindex // 2000
    tmp0 = tl.load(in_out_ptr0 + (x0 + 2016 * x1), xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + (x0 + 2016 * x1), tmp4, xmask)
    tl.store(out_ptr0 + (x0 + 2048 * x1), tmp6, xmask)


@triton.jit
def triton_poi_fused_relu_threshold_backward_2(in_out_ptr0, in_ptr0,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64000
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x4 = xindex
    x0 = xindex % 1000
    x2 = xindex % 4000
    x3 = xindex // 4000
    tmp0 = tl.load(in_out_ptr0 + x4, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + x4, tmp4, xmask)
    tl.store(out_ptr0 + (x2 + 4096 * x3), tmp6, xmask)


@triton.jit
def triton_poi_fused_sigmoid_3(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.sigmoid(tmp2)
    tl.store(in_out_ptr0 + x2, tmp3, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9) = args
    args.clear()
    assert_size_stride(primals_1, (4000, 4), (4, 1))
    assert_size_stride(primals_2, (4000,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (2000, 4000), (4000, 1))
    assert_size_stride(primals_5, (2000,), (1,))
    assert_size_stride(primals_6, (1000, 2000), (2000, 1))
    assert_size_stride(primals_7, (1000,), (1,))
    assert_size_stride(primals_8, (4, 1000), (1000, 1))
    assert_size_stride(primals_9, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4000), (4000, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 4000), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = reinterpret_tensor(buf0, (4, 4, 4, 4000), (64000, 16000, 
            4000, 1), 0)
        del buf0
        buf10 = empty_strided_cuda((4, 4, 4, 4000), (65536, 16384, 4096, 1),
            torch.bool)
        get_raw_stream(0)
        triton_poi_fused_relu_threshold_backward_0[grid(256000)](buf1,
            primals_2, buf10, 256000, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_2
        buf2 = empty_strided_cuda((64, 2000), (2016, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf1, (64, 4000), (4000, 1), 0
            ), reinterpret_tensor(primals_4, (4000, 2000), (1, 4000), 0),
            out=buf2)
        buf3 = reinterpret_tensor(buf2, (4, 4, 4, 2000), (32256, 8064, 2016,
            1), 0)
        del buf2
        buf9 = empty_strided_cuda((4, 4, 4, 2000), (32768, 8192, 2048, 1),
            torch.bool)
        triton_poi_fused_relu_threshold_backward_1[grid(128000)](buf3,
            primals_5, buf9, 128000, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_5
        buf4 = empty_strided_cuda((64, 1000), (1000, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf3, (64, 2000), (2016, 1), 0
            ), reinterpret_tensor(primals_6, (2000, 1000), (1, 2000), 0),
            out=buf4)
        buf5 = reinterpret_tensor(buf4, (4, 4, 4, 1000), (16000, 4000, 1000,
            1), 0)
        del buf4
        buf8 = empty_strided_cuda((4, 4, 4, 1000), (16384, 4096, 1000, 1),
            torch.bool)
        triton_poi_fused_relu_threshold_backward_2[grid(64000)](buf5,
            primals_7, buf8, 64000, XBLOCK=512, num_warps=4, num_stages=1)
        del primals_7
        buf6 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf5, (64, 1000), (1000, 1), 0
            ), reinterpret_tensor(primals_8, (1000, 4), (1, 1000), 0), out=buf6
            )
        buf7 = reinterpret_tensor(buf6, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf6
        triton_poi_fused_sigmoid_3[grid(256)](buf7, primals_9, 256, XBLOCK=
            128, num_warps=4, num_stages=1)
        del primals_9
    return buf7, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
        ), reinterpret_tensor(buf1, (64, 4000), (4000, 1), 0
        ), reinterpret_tensor(buf3, (64, 2000), (2016, 1), 0
        ), reinterpret_tensor(buf5, (64, 1000), (1000, 1), 0
        ), buf7, primals_8, buf8, primals_6, buf9, primals_4, buf10


class Base(nn.Module):
    """ This class is the base structure for all of classification/regression DNN models.
    Mainly, it provides the general methods for training, evaluating model and predcting the given data.
    """

    def fit(self, train_loader, valid_loader, out, epochs=100, lr=0.0001):
        """Training the DNN model, similar to the scikit-learn or Keras style.
        In the end, the optimal value of parameters will also be persisted on the hard drive.

        Arguments:
            train_loader (DataLoader): Data loader for training set,
                including m X n target FloatTensor and m X l label FloatTensor
                (m is the No. of sample, n is the No. of features, l is the No. of classes or tasks)
            valid_loader (DataLoader): Data loader for validation set.
                The data structure is as same as loader_train.
            out (str): the file path for the model file (suffix with '.pkg')
                and log file (suffix with '.log').
            epochs(int, optional): The maximum of training epochs (default: 100)
            lr (float, optional): learning rate (default: 1e-4)
        """
        if 'optim' in self.__dict__:
            optimizer = self.optim
        else:
            optimizer = optim.Adam(self.parameters(), lr=lr)
        best_loss = np.inf
        last_save = 0
        if not os.path.exists(out):
            try:
                os.makedirs(out)
            except PermissionError:
                None
        log = open(file=out + '.log', mode='w+')
        for epoch in range(epochs):
            time.time()
            for param_group in optimizer.param_groups:
                param_group['lr'] = lr * (1 - 1 / epochs) ** (epoch * 10)
            for i, (Xb, yb) in enumerate(train_loader):
                Xb, yb = Xb, yb
                optimizer.zero_grad()
                y_ = self(Xb, istrain=True)
                ix = yb == yb
                yb, y_ = yb[ix], y_[ix]
                wb = torch.Tensor(yb.size())
                wb[yb == 3.99] = 0.1
                wb[yb != 3.99] = 1
                loss = self.criterion(y_ * wb, yb * wb)
                loss.backward()
                optimizer.step()
            loss_valid = self.evaluate(valid_loader)
            None
            if loss_valid < best_loss:
                torch.save(self.state_dict(), out + '.pkg')
                None
                best_loss = loss_valid
                last_save = epoch
            else:
                None
                if epoch - last_save > 100:
                    break
        log.close()
        self.load_state_dict(torch.load(out + '.pkg'))

    def evaluate(self, loader):
        """Evaluating the performance of the DNN model.

        Arguments:
            loader (torch.util.data.DataLoader): data loader for test set,
                including m X n target FloatTensor and l X n label FloatTensor
                (m is the No. of sample, n is the No. of features, l is the No. of classes or tasks)

        Return:
            loss (float): the average loss value based on the calculation of loss function with given test set.
        """
        loss = 0
        for Xb, yb in loader:
            Xb, yb = Xb, yb
            y_ = self.forward(Xb)
            ix = yb == yb
            yb, y_ = yb[ix], y_[ix]
            wb = torch.Tensor(yb.size())
            wb[yb == 3.99] = 0.1
            wb[yb != 3.99] = 1
            loss += self.criterion(y_ * wb, yb * wb).item()
        loss = loss / len(loader)
        return loss

    def predict(self, loader):
        """Predicting the probability of each sample in the given dataset.

        Arguments:
            loader (torch.util.data.DataLoader): data loader for test set,
                only including m X n target FloatTensor
                (m is the No. of sample, n is the No. of features)

        Return:
            score (ndarray): probability of each sample in the given dataset,
                it is a m X l FloatTensor (m is the No. of sample, l is the No. of classes or tasks.)
        """
        score = []
        for Xb, yb in loader:
            Xb = Xb
            y_ = self.forward(Xb)
            score.append(y_.detach().cpu())
        score = torch.cat(score, dim=0).numpy()
        return score


class MTFullyConnectedNew(Base):
    """Multi-task DNN classification/regression model. It contains four fully connected layers
    between which are dropout layer for robustness.

    Arguments:
        n_dim (int): the No. of columns (features) for input tensor
        n_task (int): the No. of columns (tasks) for output tensor.
        is_reg (bool, optional): Regression model (True) or Classification model (False)
    """

    def __init__(self, n_dim, n_task, is_reg=False):
        super(MTFullyConnectedNew, self).__init__()
        self.n_task = n_task
        self.dropout = nn.Dropout(0.25)
        self.fc0 = nn.Linear(n_dim, 4000)
        self.fc1 = nn.Linear(4000, 2000)
        self.fc2 = nn.Linear(2000, 1000)
        self.output = nn.Linear(1000, n_task)
        self.is_reg = is_reg
        if is_reg:
            self.criterion = nn.MSELoss()
        else:
            self.criterion = nn.BCELoss()
            self.activation = nn.Sigmoid()
        self

    def forward(self, input_0):
        primals_1 = self.fc0.weight
        primals_2 = self.fc0.bias
        primals_4 = self.fc1.weight
        primals_5 = self.fc1.bias
        primals_6 = self.fc2.weight
        primals_7 = self.fc2.bias
        primals_8 = self.output.weight
        primals_9 = self.output.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9])
        return output[0]
