import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import numpy as np
from torch import nn
from torch.utils.data import DataLoader
import torch.nn.functional as F
from torchvision import transforms
from sklearn.preprocessing import StandardScaler
from sklearn import metrics
from torch.utils.data import Dataset
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_per_fused__softmax_0(in_ptr0, out_ptr0, out_ptr1, xnumel, rnumel,
    XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 4
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = triton_helpers.max2(tmp1, 1)[:, None]
    tmp4 = tmp0 - tmp3
    tmp5 = tl_math.exp(tmp4)
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK, RBLOCK])
    tmp8 = tl.sum(tmp6, 1)[:, None]
    tl.store(out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp3, None)
    tl.store(out_ptr1 + tl.full([XBLOCK, 1], 0, tl.int32), tmp8, None)


@triton.jit
def triton_poi_fused__softmax_mul_sum_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex // 16
    x3 = xindex % 16
    x0 = xindex % 4
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x3 + 64 * x2), xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr2 + 0)
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK])
    tmp6 = tl.load(in_ptr3 + 0)
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK])
    tmp10 = tl.load(in_ptr0 + (16 + x3 + 64 * x2), xmask)
    tmp13 = tl.load(in_ptr0 + (32 + x3 + 64 * x2), xmask)
    tmp16 = tl.load(in_ptr0 + (48 + x3 + 64 * x2), xmask)
    tmp4 = tmp1 - tmp3
    tmp5 = tl_math.exp(tmp4)
    tmp8 = tmp5 / tmp7
    tmp9 = tmp0 * tmp8
    tmp11 = tmp10 * tmp8
    tmp12 = tmp9 + tmp11
    tmp14 = tmp13 * tmp8
    tmp15 = tmp12 + tmp14
    tmp17 = tmp16 * tmp8
    tmp18 = tmp15 + tmp17
    tl.store(out_ptr0 + x4, tmp18, xmask)


def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((1,), (1,), torch.float32)
        buf1 = empty_strided_cuda((1,), (1,), torch.float32)
        get_raw_stream(0)
        triton_per_fused__softmax_0[grid(1)](primals_2, buf0, buf1, 1, 4,
            XBLOCK=1, num_warps=2, num_stages=1)
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused__softmax_mul_sum_1[grid(64)](primals_1, primals_2,
            buf0, buf1, buf2, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del buf0
        del buf1
    return buf2, primals_1, primals_2


def compute_auc(labels, scores, pos_label=1):
    fpr, tpr, _thresholds = metrics.roc_curve(labels, scores, pos_label=
        pos_label)
    return metrics.auc(fpr, tpr)


class Subset(Dataset):

    def __init__(self, data, labels, normalize=False):
        self.ims = data
        self.labels = labels
        self.normalize = normalize
        if normalize:
            self.T = transforms.Normalize(0.5, 0.5)
        else:
            self.T = lambda x: x

    def __getitem__(self, idx):
        ret = {'ims': self.T(self.ims[idx]), 'labels': self.labels[idx]}
        return ret

    def __len__(self):
        return self.labels.shape[0]


class AlphaClassifierNew(nn.Module):

    def __init__(self):
        super(AlphaClassifierNew, self).__init__()
        self.alpha_params = nn.Parameter(torch.Tensor(np.ones(4)))
        self.scaler = StandardScaler()

    def get_alpha(self):
        return F.softmax(self.alpha_params, dim=0)

    def predict_prob(self, x):
        return torch.sigmoid(self(x))

    def predict(self, x):
        return torch.round(self.predict_prob(x))

    def binary_acc(self, x, y):
        y_true = np.array(y)
        y_pred = self.predict(x).detach().numpy()
        nhits = (y_pred == y_true).sum()
        return nhits / y_true.shape[0]

    def auc(self, x, y):
        scores = self.predict_prob(x)
        return compute_auc(y, scores.detach().numpy())

    def scaler_fit(self, x):
        self.scaler.fit(x)

    def scaler_transform(self, x):
        return self.scaler.transform(x)

    def save_weights(self, f):
        np.save(f, self.alpha_params.detach().numpy())

    def fit(self, x, y, tst_x=None, tst_y=None, nepochs=200, batch_size=256,
        lr=0.001, workers=1, balanced=True, verb=True, scale=False,
        early_stopping=False, patience=10):
        if scale:
            self.scaler_fit(x)
            x = self.scaler_transform(x)
            tst_x = tst_x if tst_x is None else self.scaler_transform(tst_x)
        if balanced:
            n1 = int(sum(y))
            n0 = len(y) - n1
            if n0 < n1:
                p = int(np.floor(n1 / n0))
                X = np.concatenate((x[y == 0].repeat(p, 0), x[y == 1]), 0)
                Y = np.concatenate((y[y == 0].repeat(p, 0), y[y == 1]), 0)
            else:
                p = int(np.floor(n0 / n1))
                X = np.concatenate((x[y == 1].repeat(p, 0), x[y == 0]), 0)
                Y = np.concatenate((y[y == 1].repeat(p, 0), y[y == 0]), 0)
        else:
            X = x
            Y = y
        loader = DataLoader(Subset(torch.tensor(X).float(), torch.Tensor(Y)
            ), batch_size=batch_size, shuffle=True, num_workers=workers)
        criterion = nn.BCEWithLogitsLoss()
        opt = torch.optim.Adam(self.parameters(), lr=lr)
        best_auc = self.auc(x, y)
        pat = 0
        for epoch in range(nepochs):
            if verb:
                criterion(self(torch.Tensor(x)), torch.Tensor(y)).detach(
                    ).numpy().round(3)
                self.auc(x, y).round(3)
                self.binary_acc(x, y).round(3)
                np.NAN if tst_x is None else self.auc(tst_x, tst_y).round(3)
                None
            if early_stopping:
                cur_auc = self.auc(x, y)
                if cur_auc < best_auc:
                    if pat < patience:
                        pat += 1
                    else:
                        if verb:
                            None
                        return
                else:
                    best_auc = cur_auc
                    pat = 0
            for batch in loader:
                _x, _y = batch['ims'], batch['labels']
                opt.zero_grad()
                pred = self(_x)
                loss = criterion(pred, _y)
                loss.backward()
                opt.step()

    def forward(self, input_0):
        primals_2 = self.alpha_params
        primals_1 = input_0
        output = call([primals_1, primals_2])
        return output[0]


Measured latency_ms: 0.030238