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 numpy as np
from torch import nn
from torch.nn.parameter import Parameter
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_add_0(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
    tl.store(in_out_ptr0 + x2, tmp2, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (64, 4), (4, 1), 0),
            primals_2, out=buf0)
        del primals_2
        buf1 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf0
        get_raw_stream(0)
        triton_poi_fused_add_0[grid(256)](buf1, primals_3, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        del primals_3
    return buf1, reinterpret_tensor(primals_1, (4, 64), (1, 4), 0)


class BackgroundRelationModelNew(nn.Module):

    def __init__(self, n_bg, n_ml):
        """
        n_bg: number of background tags
        n_ml: number of ml tags
        """
        super().__init__()
        self.config = {'n_bg': n_bg, 'n_ml': n_ml}
        self.W = Parameter(torch.randn(n_bg, n_ml))
        self.b = Parameter(torch.randn(n_ml) / 2)
        nn.init.xavier_uniform_(self.W)
        if torch.cuda.is_available():
            self.device = torch.device('cuda')
        else:
            self.device = torch.device('cpu')
        self

    def fit(self, alpha_bg, alpha_ml, lr=0.001, epochs=10):
        """
        alpha_bg: the alpha matrix (np array) with columns corresponding to the background tags
        alpha_ml: the alpha matrix (np array) with columns corresponding to the ML tags
        lr: learning rate
        epochs: number of epochs to train
        """
        self.train()
        optimizer = torch.optim.Adam(self.parameters(), lr=lr)
        loss_fn = nn.MSELoss()
        ys = torch.tensor(alpha_ml)
        for epoch in range(epochs):
            optimizer.zero_grad()
            y_hats = self.forward(alpha_bg)
            loss = loss_fn(y_hats, ys)
            loss.backward()
            optimizer.step()
            None

    def auto_fit(self, alpha_bg, alpha_ml, val_alpha_bg, val_alpha_ml, lr=
        0.001, reg=0.01, patience=10):
        """
        alpha_bg: the alpha matrix (np array) with columns corresponding to the background tags
        alpha_ml: the alpha matrix (np array) with columns corresponding to the ML tags
        val_*: same data but from the validation set
        lr: learning rate
        reg: weight decay coefficient (similar to L2 penalty)
        patience: number of epochs to continue evaluating even after loss not decreasing
        """
        self.train()
        optimizer = torch.optim.Adam(self.parameters(), lr=lr, weight_decay=reg
            )
        loss_fn = nn.MSELoss()
        epoch = 0
        frustration = 0
        best_loss = np.inf
        ys = torch.tensor(alpha_ml)
        val_ys = torch.tensor(val_alpha_ml)
        while True:
            optimizer.zero_grad()
            y_hats = self.forward(alpha_bg)
            loss = loss_fn(y_hats, ys)
            loss.backward()
            optimizer.step()
            val_y_hats = self.forward(val_alpha_bg)
            val_loss = loss_fn(val_y_hats, val_ys)
            None
            if val_loss.item() < best_loss:
                best_loss = val_loss.item()
                frustration = 0
            else:
                frustration += 1
            if frustration > patience:
                break
            epoch += 1

    def predict(self, alpha_bg):
        self.eval()
        return self.forward(alpha_bg).cpu().detach().numpy()

    def save(self, model_path):
        model_state = {'state_dict': self.state_dict(), 'config': self.config}
        torch.save(model_state, model_path)

    @classmethod
    def load(cls, model_path):
        model_state = torch.load(str(model_path), map_location=lambda
            storage, loc: storage)
        args = model_state['config']
        model = cls(**args)
        model.load_state_dict(model_state['state_dict'])
        if torch.cuda.is_available():
            model.device = torch.device('cuda')
        else:
            model.device = torch.device('cpu')
        model
        return model

    def forward(self, input_0):
        primals_2 = self.W
        primals_3 = self.b
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]


Measured latency_ms: 0.021767