# 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_4/inductor_cache/hj/chjyxthnzrs73ha46hvtenecvp5ah45h7qgnlfwqncgkfrqqsj65.py
# Topologically Sorted Source Nodes: [sigmoid], Original ATen: [aten.sigmoid]
# Source node to ATen node mapping:
#   sigmoid => sigmoid
# Graph fragment:
#   %sigmoid : [num_users=2] = call_function[target=torch.ops.aten.sigmoid.default](args = (%view_1,), kwargs = {})
triton_poi_fused_sigmoid_0 = async_compile.triton('triton_poi_fused_sigmoid_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=[16], 
    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_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_sigmoid_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + (x0), xmask)
    tmp1 = tl.load(in_ptr0 + (0))
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tmp4 = tl.sigmoid(tmp3)
    tl.store(in_out_ptr0 + (x0), tmp4, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/gt/cgtftm3ylp7qfttvxomzpetwvamrrbiunk5krcwc6yk2fwawxwru.py
# Topologically Sorted Source Nodes: [max_1], Original ATen: [aten.max]
# Source node to ATen node mapping:
#   max_1 => max_1
# Graph fragment:
#   %max_1 : [num_users=1] = call_function[target=torch.ops.aten.max.dim](args = (%squeeze, -1), kwargs = {})
triton_poi_fused_max_1 = async_compile.triton('triton_poi_fused_max_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=[4], 
    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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_max_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, '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_max_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (4*x0), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp4 = triton_helpers.maximum(tmp2, tmp3)
    tmp6 = triton_helpers.maximum(tmp4, tmp5)
    tl.store(out_ptr0 + (x0), tmp6, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (1, 4), (4, 1))
    assert_size_stride(primals_3, (1, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 1), (1, 4), 0), out=buf0)
        del primals_2
        buf1 = reinterpret_tensor(buf0, (4, 4, 1), (4, 1, 1), 0); del buf0  # reuse
        # Topologically Sorted Source Nodes: [sigmoid], Original ATen: [aten.sigmoid]
        stream0 = get_raw_stream(0)
        triton_poi_fused_sigmoid_0.run(buf1, primals_3, 16, grid=grid(16), stream=stream0)
        del primals_3
        buf2 = empty_strided_cuda((4, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [max_1], Original ATen: [aten.max]
        triton_poi_fused_max_1.run(buf1, buf2, 4, grid=grid(4), stream=stream0)
    return (reinterpret_tensor(buf2, (4, 1), (1, 1), 0), reinterpret_tensor(buf1, (4, 4), (4, 1), 0), reinterpret_tensor(primals_1, (16, 4), (4, 1), 0), buf1, )


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), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((1, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3])
    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 numpy as np
from torch import nn
import torch as tc
from sklearn.metrics import *
from torch.utils.data import DataLoader
from torch.utils.data import WeightedRandomSampler


class myDataset(torch.utils.data.Dataset):

    def __init__(self, x, y):
        self.x = x
        self.y = y

    def __len__(self):
        return len(self.y)

    def __getitem__(self, idx):
        return self.x[idx], self.y[idx]


class MILLR(tc.nn.Module):

    def __init__(self, input_dim, flight_length, device, aggregation=
        'maxpool', output_resize=False):
        super(MILLR, self).__init__()
        self.fc = nn.Linear(input_dim, 1)
        self.sigmoid = nn.Sigmoid()
        self.flight_length = flight_length
        self.D = input_dim
        self.device = device
        self.task = 'binary'
        self.threshold = 0.5
        self.agg = aggregation
        self.output_resize = output_resize

    def forward(self, x, train=True):
        _N, _, _ = x.size()
        self.pi = self.sigmoid(self.fc(x)).squeeze()
        self.proba_time = self.pi
        if self.agg == 'mean':
            p = tc.mean(self.pi, axis=-1)
        elif self.agg == 'maxpool':
            p = tc.max(self.pi, dim=-1)[0]
        p = p.view(-1, 1)
        return p

    def get_feature_importance(self, columns, n_top=5):
        coeffs = self.fc.weight.flatten().detach().numpy()
        sorted_feat_idx = np.argsort(coeffs)[::-1]
        sorted_columns = columns[sorted_feat_idx[:n_top]]
        top_values = coeffs[sorted_feat_idx[:n_top]]
        return sorted_columns, top_values

    def cross_time_steps_loss(self, Pi):
        diff = (Pi[:, :-1] - Pi[:, 1:]) ** 2
        return tc.mean(tc.mean(diff, axis=-1))

    def train_LR(self, X_train, y_train, X_val, y_val, batch_size,
        print_every_epochs=5, l2=0.001, learning_rate=0.001,
        use_stratified_batch_size=True, verbose=1, num_epochs=100,
        optimizer='adam', momentum=0.99):
        self.train()
        if 'cuda' in self.device:
            self
        else:
            self.cpu()
        self.batch_size = batch_size
        criterion = nn.BCELoss()
        if optimizer == 'adam':
            optimizer = tc.optim.Adam(self.parameters(), lr=learning_rate,
                weight_decay=l2)
        else:
            optimizer = tc.optim.SGD(self.parameters(), momentum=momentum,
                lr=learning_rate, weight_decay=l2)
        hist = np.zeros(num_epochs)
        val_hist = np.zeros(num_epochs)
        b_acc = np.zeros(num_epochs)
        val_b_acc = np.zeros(num_epochs)
        f1 = np.zeros(num_epochs)
        val_f1 = np.zeros(num_epochs)
        if not tc.is_tensor(X_train):
            X_train = tc.Tensor(X_train)
        if not tc.is_tensor(y_train):
            y_train = tc.Tensor(y_train.flatten())
        if X_val is not None:
            if not tc.is_tensor(X_val):
                X_val = tc.Tensor(X_val)
            if not tc.is_tensor(y_val):
                y_val = tc.Tensor(y_val)
            data_val = myDataset(X_val, y_val)
        data_train = myDataset(X_train, y_train)
        if use_stratified_batch_size is False:
            None
            dataloader_train = DataLoader(data_train, batch_size=self.
                batch_size, shuffle=True)
        else:
            None
            weights = []
            for label in tc.unique(y_train):
                count = len(tc.where(y_train == label)[0])
                weights.append(1 / count)
            weights = tc.tensor(weights)
            samples_weights = weights[y_train.type(tc.LongTensor)]
            sampler = WeightedRandomSampler(samples_weights, len(
                samples_weights), replacement=True)
            dataloader_train = DataLoader(data_train, batch_size=self.
                batch_size, sampler=sampler)
        if X_val is not None:
            dataloader_val = DataLoader(data_val, batch_size=self.
                batch_size, shuffle=False)
        try:
            for epoch in tqdm(range(num_epochs)):
                batch_acc = []
                batch_val_acc = []
                batch_f1 = []
                batch_val_f1 = []
                for iteration, (batch_x, batch_y) in enumerate(dataloader_train
                    ):
                    batch_x, batch_y = batch_x, batch_y
                    if epoch == 0 and iteration == 0:
                        for c in tc.unique(y_train):
                            None
                    outputs = self.forward(batch_x)
                    g_loss = self.cross_time_steps_loss(self.pi)
                    loss = criterion(outputs.flatten(), batch_y.view(-1).
                        flatten()) + g_loss
                    hist[epoch] = loss.item()
                    if 'cuda' in self.device:
                        temp_outpouts = (outputs.cpu().detach().numpy() >
                            self.threshold).astype(int)
                        y_batch = batch_y.view(-1).cpu().detach().numpy()
                        b_acc[epoch] = balanced_accuracy_score(y_batch,
                            temp_outpouts)
                    else:
                        temp_outpouts = (outputs.detach().numpy() > self.
                            threshold).astype(int)
                        y_batch = batch_y.view(-1).detach().numpy()
                        b_acc[epoch] = balanced_accuracy_score(y_batch,
                            temp_outpouts)
                    batch_acc.append(b_acc[epoch])
                    batch_f1.append(f1_score(y_batch, temp_outpouts,
                        average='binary'))
                    optimizer.zero_grad()
                    loss.backward()
                    optimizer.step()
                    if X_val is not None:
                        with tc.no_grad():
                            mini_loss = []
                            for batch_X_val, batch_y_val in dataloader_val:
                                batch_X_val, batch_y_val = (batch_X_val,
                                    batch_y_val)
                                self.valYhat = self.forward(batch_X_val)
                                g_loss_val = self.cross_time_steps_loss(self.pi
                                    )
                                val_loss = criterion(self.valYhat,
                                    batch_y_val.flatten()) + g_loss_val
                                mini_loss.append(val_loss.item())
                                if self.task == 'binary':
                                    if 'cuda' in self.device:
                                        temp_out_y = (self.valYhat.cpu().detach
                                            ().numpy() > self.threshold).astype(int
                                            )
                                        y_val_batch = batch_y_val.view(-1).cpu(
                                            ).detach().numpy()
                                        val_b_acc[epoch] = balanced_accuracy_score(
                                            y_val_batch, temp_out_y)
                                    else:
                                        temp_out_y = (self.valYhat.detach().
                                            numpy() > self.threshold).astype(int)
                                        y_val_batch = batch_y_val.view(-1).detach(
                                            ).numpy()
                                        val_b_acc[epoch] = balanced_accuracy_score(
                                            y_val_batch, temp_out_y)
                                    batch_val_acc.append(val_b_acc[epoch])
                                    batch_val_f1.append(f1_score(
                                        y_val_batch, temp_out_y, average=
                                        'binary'))
                            val_hist[epoch] = np.mean(mini_loss)
                    if verbose == 1:
                        if self.task == 'binary':
                            if epoch % 10 == 0:
                                None
                                None
                                None
                                if X_val is not None:
                                    None
                                    None
                                    None
                        elif epoch % print_every_epochs == 0:
                            None
                if self.task == 'binary':
                    b_acc[epoch] = np.mean(batch_acc)
                    val_b_acc[epoch] = np.mean(batch_val_acc)
                    f1[epoch] = np.mean(batch_f1)
                    val_f1[epoch] = np.mean(batch_val_f1)
        except KeyboardInterrupt:
            self.cpu()
            self.device = 'cpu'
            self.eval()
            self.x_train = X_train
            self.x_test = X_val
            self.hist = hist
            self.val_hist = val_hist
        except:
            raise
        self.cpu()
        self.device = 'cpu'
        self.eval()
        self.x_train = X_train
        self.x_test = X_val
        self.hist = hist
        self.val_hist = val_hist

    def fit(self, **kw):
        self.train_LR(**kw)


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


def get_init_inputs():
    return [[], {'input_dim': 4, 'flight_length': 4, 'device': 0}]

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 numpy as np
from torch import nn
import torch as tc
from sklearn.metrics import *
from torch.utils.data import DataLoader
from torch.utils.data import WeightedRandomSampler
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_sigmoid_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr0 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tmp4 = tl.sigmoid(tmp3)
    tl.store(in_out_ptr0 + x0, tmp4, xmask)


@triton.jit
def triton_poi_fused_max_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp4 = triton_helpers.maximum(tmp2, tmp3)
    tmp6 = triton_helpers.maximum(tmp4, tmp5)
    tl.store(out_ptr0 + x0, tmp6, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (1, 4), (4, 1))
    assert_size_stride(primals_3, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 1), (1, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 1), (1, 4), 0), out=buf0)
        del primals_2
        buf1 = reinterpret_tensor(buf0, (4, 4, 1), (4, 1, 1), 0)
        del buf0
        get_raw_stream(0)
        triton_poi_fused_sigmoid_0[grid(16)](buf1, primals_3, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del primals_3
        buf2 = empty_strided_cuda((4,), (1,), torch.float32)
        triton_poi_fused_max_1[grid(4)](buf1, buf2, 4, XBLOCK=4, num_warps=
            1, num_stages=1)
    return reinterpret_tensor(buf2, (4, 1), (1, 1), 0), reinterpret_tensor(buf1
        , (4, 4), (4, 1), 0), reinterpret_tensor(primals_1, (16, 4), (4, 1), 0
        ), buf1


class myDataset(torch.utils.data.Dataset):

    def __init__(self, x, y):
        self.x = x
        self.y = y

    def __len__(self):
        return len(self.y)

    def __getitem__(self, idx):
        return self.x[idx], self.y[idx]


class MILLRNew(tc.nn.Module):

    def __init__(self, input_dim, flight_length, device, aggregation=
        'maxpool', output_resize=False):
        super(MILLRNew, self).__init__()
        self.fc = nn.Linear(input_dim, 1)
        self.sigmoid = nn.Sigmoid()
        self.flight_length = flight_length
        self.D = input_dim
        self.device = device
        self.task = 'binary'
        self.threshold = 0.5
        self.agg = aggregation
        self.output_resize = output_resize

    def get_feature_importance(self, columns, n_top=5):
        coeffs = self.fc.weight.flatten().detach().numpy()
        sorted_feat_idx = np.argsort(coeffs)[::-1]
        sorted_columns = columns[sorted_feat_idx[:n_top]]
        top_values = coeffs[sorted_feat_idx[:n_top]]
        return sorted_columns, top_values

    def cross_time_steps_loss(self, Pi):
        diff = (Pi[:, :-1] - Pi[:, 1:]) ** 2
        return tc.mean(tc.mean(diff, axis=-1))

    def train_LR(self, X_train, y_train, X_val, y_val, batch_size,
        print_every_epochs=5, l2=0.001, learning_rate=0.001,
        use_stratified_batch_size=True, verbose=1, num_epochs=100,
        optimizer='adam', momentum=0.99):
        self.train()
        if 'cuda' in self.device:
            self
        else:
            self.cpu()
        self.batch_size = batch_size
        criterion = nn.BCELoss()
        if optimizer == 'adam':
            optimizer = tc.optim.Adam(self.parameters(), lr=learning_rate,
                weight_decay=l2)
        else:
            optimizer = tc.optim.SGD(self.parameters(), momentum=momentum,
                lr=learning_rate, weight_decay=l2)
        hist = np.zeros(num_epochs)
        val_hist = np.zeros(num_epochs)
        b_acc = np.zeros(num_epochs)
        val_b_acc = np.zeros(num_epochs)
        f1 = np.zeros(num_epochs)
        val_f1 = np.zeros(num_epochs)
        if not tc.is_tensor(X_train):
            X_train = tc.Tensor(X_train)
        if not tc.is_tensor(y_train):
            y_train = tc.Tensor(y_train.flatten())
        if X_val is not None:
            if not tc.is_tensor(X_val):
                X_val = tc.Tensor(X_val)
            if not tc.is_tensor(y_val):
                y_val = tc.Tensor(y_val)
            data_val = myDataset(X_val, y_val)
        data_train = myDataset(X_train, y_train)
        if use_stratified_batch_size is False:
            None
            dataloader_train = DataLoader(data_train, batch_size=self.
                batch_size, shuffle=True)
        else:
            None
            weights = []
            for label in tc.unique(y_train):
                count = len(tc.where(y_train == label)[0])
                weights.append(1 / count)
            weights = tc.tensor(weights)
            samples_weights = weights[y_train.type(tc.LongTensor)]
            sampler = WeightedRandomSampler(samples_weights, len(
                samples_weights), replacement=True)
            dataloader_train = DataLoader(data_train, batch_size=self.
                batch_size, sampler=sampler)
        if X_val is not None:
            dataloader_val = DataLoader(data_val, batch_size=self.
                batch_size, shuffle=False)
        try:
            for epoch in tqdm(range(num_epochs)):
                batch_acc = []
                batch_val_acc = []
                batch_f1 = []
                batch_val_f1 = []
                for iteration, (batch_x, batch_y) in enumerate(dataloader_train
                    ):
                    batch_x, batch_y = batch_x, batch_y
                    if epoch == 0 and iteration == 0:
                        for c in tc.unique(y_train):
                            None
                    outputs = self.forward(batch_x)
                    g_loss = self.cross_time_steps_loss(self.pi)
                    loss = criterion(outputs.flatten(), batch_y.view(-1).
                        flatten()) + g_loss
                    hist[epoch] = loss.item()
                    if 'cuda' in self.device:
                        temp_outpouts = (outputs.cpu().detach().numpy() >
                            self.threshold).astype(int)
                        y_batch = batch_y.view(-1).cpu().detach().numpy()
                        b_acc[epoch] = balanced_accuracy_score(y_batch,
                            temp_outpouts)
                    else:
                        temp_outpouts = (outputs.detach().numpy() > self.
                            threshold).astype(int)
                        y_batch = batch_y.view(-1).detach().numpy()
                        b_acc[epoch] = balanced_accuracy_score(y_batch,
                            temp_outpouts)
                    batch_acc.append(b_acc[epoch])
                    batch_f1.append(f1_score(y_batch, temp_outpouts,
                        average='binary'))
                    optimizer.zero_grad()
                    loss.backward()
                    optimizer.step()
                    if X_val is not None:
                        with tc.no_grad():
                            mini_loss = []
                            for batch_X_val, batch_y_val in dataloader_val:
                                batch_X_val, batch_y_val = (batch_X_val,
                                    batch_y_val)
                                self.valYhat = self.forward(batch_X_val)
                                g_loss_val = self.cross_time_steps_loss(self.pi
                                    )
                                val_loss = criterion(self.valYhat,
                                    batch_y_val.flatten()) + g_loss_val
                                mini_loss.append(val_loss.item())
                                if self.task == 'binary':
                                    if 'cuda' in self.device:
                                        temp_out_y = (self.valYhat.cpu().detach
                                            ().numpy() > self.threshold).astype(int
                                            )
                                        y_val_batch = batch_y_val.view(-1).cpu(
                                            ).detach().numpy()
                                        val_b_acc[epoch] = balanced_accuracy_score(
                                            y_val_batch, temp_out_y)
                                    else:
                                        temp_out_y = (self.valYhat.detach().
                                            numpy() > self.threshold).astype(int)
                                        y_val_batch = batch_y_val.view(-1).detach(
                                            ).numpy()
                                        val_b_acc[epoch] = balanced_accuracy_score(
                                            y_val_batch, temp_out_y)
                                    batch_val_acc.append(val_b_acc[epoch])
                                    batch_val_f1.append(f1_score(
                                        y_val_batch, temp_out_y, average=
                                        'binary'))
                            val_hist[epoch] = np.mean(mini_loss)
                    if verbose == 1:
                        if self.task == 'binary':
                            if epoch % 10 == 0:
                                None
                                None
                                None
                                if X_val is not None:
                                    None
                                    None
                                    None
                        elif epoch % print_every_epochs == 0:
                            None
                if self.task == 'binary':
                    b_acc[epoch] = np.mean(batch_acc)
                    val_b_acc[epoch] = np.mean(batch_val_acc)
                    f1[epoch] = np.mean(batch_f1)
                    val_f1[epoch] = np.mean(batch_val_f1)
        except KeyboardInterrupt:
            self.cpu()
            self.device = 'cpu'
            self.eval()
            self.x_train = X_train
            self.x_test = X_val
            self.hist = hist
            self.val_hist = val_hist
        except:
            raise
        self.cpu()
        self.device = 'cpu'
        self.eval()
        self.x_train = X_train
        self.x_test = X_val
        self.hist = hist
        self.val_hist = val_hist

    def fit(self, **kw):
        self.train_LR(**kw)

    def forward(self, input_0):
        primals_2 = self.fc.weight
        primals_3 = self.fc.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]
