Advice on Porting NBEATS, NBEATSx, and NHITS to R. tuple/float error when creating models object

Description
nixtlaR test
fth
Porting over NBEATS, NBEATSx, and NHITS to R
first, running the python example from here:
https://pypi.org/project/neuralforecast/
import numpy as np
import pandas as pd
# from IPython.display import display, Markdown

# import matplotlib.pyplot as plt
from neuralforecast import NeuralForecast
from neuralforecast.models import NBEATS, NHITS
from neuralforecast.utils import AirPassengersDF

# Split data and declare panel dataset
Y_df = AirPassengersDF
Y_train_df = Y_df[Y_df.ds<='1959-12-31'] # 132 train
Y_test_df = Y_df[Y_df.ds>'1959-12-31'] # 12 test

# Fit and predict with NBEATS and NHITS models
horizon = len(Y_test_df)
models = [NBEATS(input_size=2 * horizon, h=horizon, max_epochs=50),
          NHITS(input_size=2 * horizon, h=horizon, max_epochs=50)]

Global seed set to 1
Global seed set to 1

nf = NeuralForecast(models=models, freq='M')
nf.fit(df=Y_train_df)

Sanity Checking: 0it [00:00, ?it/s]
Sanity Checking:   0%|          | 0/1 [00:00<?, ?it/s]
Sanity Checking DataLoader 0:   0%|          | 0/1 [00:00<?, ?it/s]
Sanity Checking DataLoader 0: 100%|##########| 1/1 [00:00<?, ?it/s]

Training: 0it [00:00, ?it/s]
Training:   0%|          | 0/1 [00:00<?, ?it/s]
Epoch 0:   0%|          | 0/1 [00:00<?, ?it/s]
Epoch 0: 100%|##########| 1/1 [00:00<00:00,  3.34it/s]
Epoch 0: 100%|##########| 1/1 [00:00<00:00,  3.34it/s, v_num=13, train_loss_step=46.80]
Epoch 0: 100%|##########| 1/1 [00:00<00:00,  3.33it/s, v_num=13, train_loss_step=46.80, train_loss_epoch=46.80]
Epoch 0:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=46.80, train_loss_epoch=46.80]
Epoch 1:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=46.80, train_loss_epoch=46.80]
Epoch 1: 100%|##########| 1/1 [00:00<00:00,  4.03it/s, v_num=13, train_loss_step=46.80, train_loss_epoch=46.80]
Epoch 1: 100%|##########| 1/1 [00:00<00:00,  4.03it/s, v_num=13, train_loss_step=177.0, train_loss_epoch=46.80]
Epoch 1: 100%|##########| 1/1 [00:00<00:00,  4.03it/s, v_num=13, train_loss_step=177.0, train_loss_epoch=177.0]
Epoch 1:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=177.0, train_loss_epoch=177.0]
Epoch 2:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=177.0, train_loss_epoch=177.0]
Epoch 2: 100%|##########| 1/1 [00:00<00:00,  3.58it/s, v_num=13, train_loss_step=177.0, train_loss_epoch=177.0]
Epoch 2: 100%|##########| 1/1 [00:00<00:00,  3.58it/s, v_num=13, train_loss_step=55.30, train_loss_epoch=177.0]
Epoch 2: 100%|##########| 1/1 [00:00<00:00,  3.58it/s, v_num=13, train_loss_step=55.30, train_loss_epoch=55.30]
Epoch 2:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=55.30, train_loss_epoch=55.30]
Epoch 3:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=55.30, train_loss_epoch=55.30]
Epoch 3: 100%|##########| 1/1 [00:00<00:00,  4.01it/s, v_num=13, train_loss_step=55.30, train_loss_epoch=55.30]
Epoch 3: 100%|##########| 1/1 [00:00<00:00,  4.01it/s, v_num=13, train_loss_step=68.10, train_loss_epoch=55.30]
Epoch 3: 100%|##########| 1/1 [00:00<00:00,  4.01it/s, v_num=13, train_loss_step=68.10, train_loss_epoch=68.10]
Epoch 3:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=68.10, train_loss_epoch=68.10]
Epoch 4:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=68.10, train_loss_epoch=68.10]
Epoch 4: 100%|##########| 1/1 [00:00<00:00,  3.92it/s, v_num=13, train_loss_step=68.10, train_loss_epoch=68.10]
Epoch 4: 100%|##########| 1/1 [00:00<00:00,  3.90it/s, v_num=13, train_loss_step=48.20, train_loss_epoch=68.10]
Epoch 4: 100%|##########| 1/1 [00:00<00:00,  3.90it/s, v_num=13, train_loss_step=48.20, train_loss_epoch=48.20]
Epoch 4:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=48.20, train_loss_epoch=48.20]
Epoch 5:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=48.20, train_loss_epoch=48.20]
Epoch 5: 100%|##########| 1/1 [00:00<00:00,  3.63it/s, v_num=13, train_loss_step=48.20, train_loss_epoch=48.20]
Epoch 5: 100%|##########| 1/1 [00:00<00:00,  3.63it/s, v_num=13, train_loss_step=42.90, train_loss_epoch=48.20]
Epoch 5: 100%|##########| 1/1 [00:00<00:00,  3.63it/s, v_num=13, train_loss_step=42.90, train_loss_epoch=42.90]
Epoch 5:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=42.90, train_loss_epoch=42.90]
Epoch 6:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=42.90, train_loss_epoch=42.90]
Epoch 6: 100%|##########| 1/1 [00:00<00:00,  3.76it/s, v_num=13, train_loss_step=42.90, train_loss_epoch=42.90]
Epoch 6: 100%|##########| 1/1 [00:00<00:00,  3.75it/s, v_num=13, train_loss_step=46.50, train_loss_epoch=42.90]
Epoch 6: 100%|##########| 1/1 [00:00<00:00,  3.74it/s, v_num=13, train_loss_step=46.50, train_loss_epoch=46.50]
Epoch 6:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=46.50, train_loss_epoch=46.50]
Epoch 7:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=46.50, train_loss_epoch=46.50]
Epoch 7: 100%|##########| 1/1 [00:00<00:00,  3.70it/s, v_num=13, train_loss_step=46.50, train_loss_epoch=46.50]
Epoch 7: 100%|##########| 1/1 [00:00<00:00,  3.70it/s, v_num=13, train_loss_step=43.20, train_loss_epoch=46.50]
Epoch 7: 100%|##########| 1/1 [00:00<00:00,  3.70it/s, v_num=13, train_loss_step=43.20, train_loss_epoch=43.20]
Epoch 7:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=43.20, train_loss_epoch=43.20]
Epoch 8:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=43.20, train_loss_epoch=43.20]
Epoch 8: 100%|##########| 1/1 [00:00<00:00,  3.20it/s, v_num=13, train_loss_step=43.20, train_loss_epoch=43.20]
Epoch 8: 100%|##########| 1/1 [00:00<00:00,  3.20it/s, v_num=13, train_loss_step=41.30, train_loss_epoch=43.20]
Epoch 8: 100%|##########| 1/1 [00:00<00:00,  3.20it/s, v_num=13, train_loss_step=41.30, train_loss_epoch=41.30]
Epoch 8:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=41.30, train_loss_epoch=41.30]
Epoch 9:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=41.30, train_loss_epoch=41.30]
Epoch 9: 100%|##########| 1/1 [00:00<00:00,  3.63it/s, v_num=13, train_loss_step=41.30, train_loss_epoch=41.30]
Epoch 9: 100%|##########| 1/1 [00:00<00:00,  3.63it/s, v_num=13, train_loss_step=41.00, train_loss_epoch=41.30]
Epoch 9: 100%|##########| 1/1 [00:00<00:00,  3.63it/s, v_num=13, train_loss_step=41.00, train_loss_epoch=41.00]
Epoch 9:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=41.00, train_loss_epoch=41.00]
Epoch 10:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=41.00, train_loss_epoch=41.00]
Epoch 10: 100%|##########| 1/1 [00:00<00:00,  3.55it/s, v_num=13, train_loss_step=41.00, train_loss_epoch=41.00]
Epoch 10: 100%|##########| 1/1 [00:00<00:00,  3.55it/s, v_num=13, train_loss_step=39.80, train_loss_epoch=41.00]
Epoch 10: 100%|##########| 1/1 [00:00<00:00,  3.55it/s, v_num=13, train_loss_step=39.80, train_loss_epoch=39.80]
Epoch 10:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=39.80, train_loss_epoch=39.80]
Epoch 11:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=39.80, train_loss_epoch=39.80]
Epoch 11: 100%|##########| 1/1 [00:00<00:00,  3.77it/s, v_num=13, train_loss_step=39.80, train_loss_epoch=39.80]
Epoch 11: 100%|##########| 1/1 [00:00<00:00,  3.77it/s, v_num=13, train_loss_step=39.60, train_loss_epoch=39.80]
Epoch 11: 100%|##########| 1/1 [00:00<00:00,  3.77it/s, v_num=13, train_loss_step=39.60, train_loss_epoch=39.60]
Epoch 11:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=39.60, train_loss_epoch=39.60]
Epoch 12:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=39.60, train_loss_epoch=39.60]
Epoch 12: 100%|##########| 1/1 [00:00<00:00,  3.78it/s, v_num=13, train_loss_step=39.60, train_loss_epoch=39.60]
Epoch 12: 100%|##########| 1/1 [00:00<00:00,  3.78it/s, v_num=13, train_loss_step=37.20, train_loss_epoch=39.60]
Epoch 12: 100%|##########| 1/1 [00:00<00:00,  3.78it/s, v_num=13, train_loss_step=37.20, train_loss_epoch=37.20]
Epoch 12:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=37.20, train_loss_epoch=37.20]
Epoch 13:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=37.20, train_loss_epoch=37.20]
Epoch 13: 100%|##########| 1/1 [00:00<00:00,  3.56it/s, v_num=13, train_loss_step=37.20, train_loss_epoch=37.20]
Epoch 13: 100%|##########| 1/1 [00:00<00:00,  3.56it/s, v_num=13, train_loss_step=36.70, train_loss_epoch=37.20]
Epoch 13: 100%|##########| 1/1 [00:00<00:00,  3.56it/s, v_num=13, train_loss_step=36.70, train_loss_epoch=36.70]
Epoch 13:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=36.70, train_loss_epoch=36.70]
Epoch 14:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=36.70, train_loss_epoch=36.70]
Epoch 14: 100%|##########| 1/1 [00:00<00:00,  3.40it/s, v_num=13, train_loss_step=36.70, train_loss_epoch=36.70]
Epoch 14: 100%|##########| 1/1 [00:00<00:00,  3.39it/s, v_num=13, train_loss_step=33.60, train_loss_epoch=36.70]
Epoch 14: 100%|##########| 1/1 [00:00<00:00,  3.39it/s, v_num=13, train_loss_step=33.60, train_loss_epoch=33.60]
Epoch 14:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=33.60, train_loss_epoch=33.60]
Epoch 15:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=33.60, train_loss_epoch=33.60]
Epoch 15: 100%|##########| 1/1 [00:00<00:00,  4.23it/s, v_num=13, train_loss_step=33.60, train_loss_epoch=33.60]
Epoch 15: 100%|##########| 1/1 [00:00<00:00,  4.23it/s, v_num=13, train_loss_step=32.10, train_loss_epoch=33.60]
Epoch 15: 100%|##########| 1/1 [00:00<00:00,  4.23it/s, v_num=13, train_loss_step=32.10, train_loss_epoch=32.10]
Epoch 15:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=32.10, train_loss_epoch=32.10]
Epoch 16:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=32.10, train_loss_epoch=32.10]
Epoch 16: 100%|##########| 1/1 [00:00<00:00,  3.58it/s, v_num=13, train_loss_step=32.10, train_loss_epoch=32.10]
Epoch 16: 100%|##########| 1/1 [00:00<00:00,  3.58it/s, v_num=13, train_loss_step=29.80, train_loss_epoch=32.10]
Epoch 16: 100%|##########| 1/1 [00:00<00:00,  3.58it/s, v_num=13, train_loss_step=29.80, train_loss_epoch=29.80]
Epoch 16:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=29.80, train_loss_epoch=29.80]
Epoch 17:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=29.80, train_loss_epoch=29.80]
Epoch 17: 100%|##########| 1/1 [00:00<00:00,  3.49it/s, v_num=13, train_loss_step=29.80, train_loss_epoch=29.80]
Epoch 17: 100%|##########| 1/1 [00:00<00:00,  3.49it/s, v_num=13, train_loss_step=27.30, train_loss_epoch=29.80]
Epoch 17: 100%|##########| 1/1 [00:00<00:00,  3.49it/s, v_num=13, train_loss_step=27.30, train_loss_epoch=27.30]
Epoch 17:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=27.30, train_loss_epoch=27.30]
Epoch 18:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=27.30, train_loss_epoch=27.30]
Epoch 18: 100%|##########| 1/1 [00:00<00:00,  3.77it/s, v_num=13, train_loss_step=27.30, train_loss_epoch=27.30]
Epoch 18: 100%|##########| 1/1 [00:00<00:00,  3.77it/s, v_num=13, train_loss_step=24.90, train_loss_epoch=27.30]
Epoch 18: 100%|##########| 1/1 [00:00<00:00,  3.77it/s, v_num=13, train_loss_step=24.90, train_loss_epoch=24.90]
Epoch 18:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=24.90, train_loss_epoch=24.90]
Epoch 19:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=24.90, train_loss_epoch=24.90]
Epoch 19: 100%|##########| 1/1 [00:00<00:00,  3.54it/s, v_num=13, train_loss_step=24.90, train_loss_epoch=24.90]
Epoch 19: 100%|##########| 1/1 [00:00<00:00,  3.54it/s, v_num=13, train_loss_step=23.60, train_loss_epoch=24.90]
Epoch 19: 100%|##########| 1/1 [00:00<00:00,  3.54it/s, v_num=13, train_loss_step=23.60, train_loss_epoch=23.60]
Epoch 19:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=23.60, train_loss_epoch=23.60]
Epoch 20:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=23.60, train_loss_epoch=23.60]
Epoch 20: 100%|##########| 1/1 [00:00<00:00,  3.27it/s, v_num=13, train_loss_step=23.60, train_loss_epoch=23.60]
Epoch 20: 100%|##########| 1/1 [00:00<00:00,  3.26it/s, v_num=13, train_loss_step=23.00, train_loss_epoch=23.60]
Epoch 20: 100%|##########| 1/1 [00:00<00:00,  3.26it/s, v_num=13, train_loss_step=23.00, train_loss_epoch=23.00]
Epoch 20:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=23.00, train_loss_epoch=23.00]
Epoch 21:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=23.00, train_loss_epoch=23.00]
Epoch 21: 100%|##########| 1/1 [00:00<00:00,  3.92it/s, v_num=13, train_loss_step=23.00, train_loss_epoch=23.00]
Epoch 21: 100%|##########| 1/1 [00:00<00:00,  3.92it/s, v_num=13, train_loss_step=24.40, train_loss_epoch=23.00]
Epoch 21: 100%|##########| 1/1 [00:00<00:00,  3.91it/s, v_num=13, train_loss_step=24.40, train_loss_epoch=24.40]
Epoch 21:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=24.40, train_loss_epoch=24.40]
Epoch 22:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=24.40, train_loss_epoch=24.40]
Epoch 22: 100%|##########| 1/1 [00:00<00:00,  3.81it/s, v_num=13, train_loss_step=24.40, train_loss_epoch=24.40]
Epoch 22: 100%|##########| 1/1 [00:00<00:00,  3.81it/s, v_num=13, train_loss_step=23.30, train_loss_epoch=24.40]
Epoch 22: 100%|##########| 1/1 [00:00<00:00,  3.81it/s, v_num=13, train_loss_step=23.30, train_loss_epoch=23.30]
Epoch 22:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=23.30, train_loss_epoch=23.30]
Epoch 23:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=23.30, train_loss_epoch=23.30]
Epoch 23: 100%|##########| 1/1 [00:00<00:00,  3.76it/s, v_num=13, train_loss_step=23.30, train_loss_epoch=23.30]
Epoch 23: 100%|##########| 1/1 [00:00<00:00,  3.76it/s, v_num=13, train_loss_step=20.50, train_loss_epoch=23.30]
Epoch 23: 100%|##########| 1/1 [00:00<00:00,  3.76it/s, v_num=13, train_loss_step=20.50, train_loss_epoch=20.50]
Epoch 23:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=20.50, train_loss_epoch=20.50]
Epoch 24:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=20.50, train_loss_epoch=20.50]
Epoch 24: 100%|##########| 1/1 [00:00<00:00,  4.03it/s, v_num=13, train_loss_step=20.50, train_loss_epoch=20.50]
Epoch 24: 100%|##########| 1/1 [00:00<00:00,  4.03it/s, v_num=13, train_loss_step=20.10, train_loss_epoch=20.50]
Epoch 24: 100%|##########| 1/1 [00:00<00:00,  4.03it/s, v_num=13, train_loss_step=20.10, train_loss_epoch=20.10]
Epoch 24:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=20.10, train_loss_epoch=20.10]
Epoch 25:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=20.10, train_loss_epoch=20.10]
Epoch 25: 100%|##########| 1/1 [00:00<00:00,  3.72it/s, v_num=13, train_loss_step=20.10, train_loss_epoch=20.10]
Epoch 25: 100%|##########| 1/1 [00:00<00:00,  3.72it/s, v_num=13, train_loss_step=19.80, train_loss_epoch=20.10]
Epoch 25: 100%|##########| 1/1 [00:00<00:00,  3.72it/s, v_num=13, train_loss_step=19.80, train_loss_epoch=19.80]
Epoch 25:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=19.80, train_loss_epoch=19.80]
Epoch 26:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=19.80, train_loss_epoch=19.80]
Epoch 26: 100%|##########| 1/1 [00:00<00:00,  3.34it/s, v_num=13, train_loss_step=19.80, train_loss_epoch=19.80]
Epoch 26: 100%|##########| 1/1 [00:00<00:00,  3.34it/s, v_num=13, train_loss_step=19.70, train_loss_epoch=19.80]
Epoch 26: 100%|##########| 1/1 [00:00<00:00,  3.34it/s, v_num=13, train_loss_step=19.70, train_loss_epoch=19.70]
Epoch 26:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=19.70, train_loss_epoch=19.70]
Epoch 27:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=19.70, train_loss_epoch=19.70]
Epoch 27: 100%|##########| 1/1 [00:00<00:00,  3.91it/s, v_num=13, train_loss_step=19.70, train_loss_epoch=19.70]
Epoch 27: 100%|##########| 1/1 [00:00<00:00,  3.91it/s, v_num=13, train_loss_step=17.90, train_loss_epoch=19.70]
Epoch 27: 100%|##########| 1/1 [00:00<00:00,  3.91it/s, v_num=13, train_loss_step=17.90, train_loss_epoch=17.90]
Epoch 27:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=17.90, train_loss_epoch=17.90]
Epoch 28:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=17.90, train_loss_epoch=17.90]
Epoch 28: 100%|##########| 1/1 [00:00<00:00,  3.56it/s, v_num=13, train_loss_step=17.90, train_loss_epoch=17.90]
Epoch 28: 100%|##########| 1/1 [00:00<00:00,  3.56it/s, v_num=13, train_loss_step=17.00, train_loss_epoch=17.90]
Epoch 28: 100%|##########| 1/1 [00:00<00:00,  3.56it/s, v_num=13, train_loss_step=17.00, train_loss_epoch=17.00]
Epoch 28:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=17.00, train_loss_epoch=17.00]
Epoch 29:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=17.00, train_loss_epoch=17.00]
Epoch 29: 100%|##########| 1/1 [00:00<00:00,  3.74it/s, v_num=13, train_loss_step=17.00, train_loss_epoch=17.00]
Epoch 29: 100%|##########| 1/1 [00:00<00:00,  3.74it/s, v_num=13, train_loss_step=17.60, train_loss_epoch=17.00]
Epoch 29: 100%|##########| 1/1 [00:00<00:00,  3.73it/s, v_num=13, train_loss_step=17.60, train_loss_epoch=17.60]
Epoch 29:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=17.60, train_loss_epoch=17.60]
Epoch 30:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=17.60, train_loss_epoch=17.60]
Epoch 30: 100%|##########| 1/1 [00:00<00:00,  4.00it/s, v_num=13, train_loss_step=17.60, train_loss_epoch=17.60]
Epoch 30: 100%|##########| 1/1 [00:00<00:00,  4.00it/s, v_num=13, train_loss_step=17.30, train_loss_epoch=17.60]
Epoch 30: 100%|##########| 1/1 [00:00<00:00,  4.00it/s, v_num=13, train_loss_step=17.30, train_loss_epoch=17.30]
Epoch 30:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=17.30, train_loss_epoch=17.30]
Epoch 31:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=17.30, train_loss_epoch=17.30]
Epoch 31: 100%|##########| 1/1 [00:00<00:00,  3.33it/s, v_num=13, train_loss_step=17.30, train_loss_epoch=17.30]
Epoch 31: 100%|##########| 1/1 [00:00<00:00,  3.33it/s, v_num=13, train_loss_step=14.70, train_loss_epoch=17.30]
Epoch 31: 100%|##########| 1/1 [00:00<00:00,  3.33it/s, v_num=13, train_loss_step=14.70, train_loss_epoch=14.70]
Epoch 31:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=14.70, train_loss_epoch=14.70]
Epoch 32:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=14.70, train_loss_epoch=14.70]
Epoch 32: 100%|##########| 1/1 [00:00<00:00,  3.15it/s, v_num=13, train_loss_step=14.70, train_loss_epoch=14.70]
Epoch 32: 100%|##########| 1/1 [00:00<00:00,  3.15it/s, v_num=13, train_loss_step=14.30, train_loss_epoch=14.70]
Epoch 32: 100%|##########| 1/1 [00:00<00:00,  3.15it/s, v_num=13, train_loss_step=14.30, train_loss_epoch=14.30]
Epoch 32:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=14.30, train_loss_epoch=14.30]
Epoch 33:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=14.30, train_loss_epoch=14.30]
Epoch 33: 100%|##########| 1/1 [00:00<00:00,  4.41it/s, v_num=13, train_loss_step=14.30, train_loss_epoch=14.30]
Epoch 33: 100%|##########| 1/1 [00:00<00:00,  4.12it/s, v_num=13, train_loss_step=14.60, train_loss_epoch=14.30]
Epoch 33: 100%|##########| 1/1 [00:00<00:00,  4.12it/s, v_num=13, train_loss_step=14.60, train_loss_epoch=14.60]
Epoch 33:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=14.60, train_loss_epoch=14.60]
Epoch 34:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=14.60, train_loss_epoch=14.60]
Epoch 34: 100%|##########| 1/1 [00:00<00:00,  4.27it/s, v_num=13, train_loss_step=14.60, train_loss_epoch=14.60]
Epoch 34: 100%|##########| 1/1 [00:00<00:00,  4.00it/s, v_num=13, train_loss_step=14.00, train_loss_epoch=14.60]
Epoch 34: 100%|##########| 1/1 [00:00<00:00,  4.00it/s, v_num=13, train_loss_step=14.00, train_loss_epoch=14.00]
Epoch 34:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=14.00, train_loss_epoch=14.00]
Epoch 35:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=14.00, train_loss_epoch=14.00]
Epoch 35: 100%|##########| 1/1 [00:00<00:00,  3.72it/s, v_num=13, train_loss_step=14.00, train_loss_epoch=14.00]
Epoch 35: 100%|##########| 1/1 [00:00<00:00,  3.72it/s, v_num=13, train_loss_step=13.60, train_loss_epoch=14.00]
Epoch 35: 100%|##########| 1/1 [00:00<00:00,  3.72it/s, v_num=13, train_loss_step=13.60, train_loss_epoch=13.60]
Epoch 35:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=13.60, train_loss_epoch=13.60]
Epoch 36:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=13.60, train_loss_epoch=13.60]
Epoch 36: 100%|##########| 1/1 [00:00<00:00,  3.77it/s, v_num=13, train_loss_step=13.60, train_loss_epoch=13.60]
Epoch 36: 100%|##########| 1/1 [00:00<00:00,  3.77it/s, v_num=13, train_loss_step=13.70, train_loss_epoch=13.60]
Epoch 36: 100%|##########| 1/1 [00:00<00:00,  3.77it/s, v_num=13, train_loss_step=13.70, train_loss_epoch=13.70]
Epoch 36:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=13.70, train_loss_epoch=13.70]
Epoch 37:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=13.70, train_loss_epoch=13.70]
Epoch 37: 100%|##########| 1/1 [00:00<00:00,  3.96it/s, v_num=13, train_loss_step=13.70, train_loss_epoch=13.70]
Epoch 37: 100%|##########| 1/1 [00:00<00:00,  3.96it/s, v_num=13, train_loss_step=13.70, train_loss_epoch=13.70]
Epoch 37: 100%|##########| 1/1 [00:00<00:00,  3.96it/s, v_num=13, train_loss_step=13.70, train_loss_epoch=13.70]
Epoch 37:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=13.70, train_loss_epoch=13.70]
Epoch 38:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=13.70, train_loss_epoch=13.70]
Epoch 38: 100%|##########| 1/1 [00:00<00:00,  3.40it/s, v_num=13, train_loss_step=13.70, train_loss_epoch=13.70]
Epoch 38: 100%|##########| 1/1 [00:00<00:00,  3.40it/s, v_num=13, train_loss_step=12.70, train_loss_epoch=13.70]
Epoch 38: 100%|##########| 1/1 [00:00<00:00,  3.40it/s, v_num=13, train_loss_step=12.70, train_loss_epoch=12.70]
Epoch 38:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=12.70, train_loss_epoch=12.70]
Epoch 39:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=12.70, train_loss_epoch=12.70]
Epoch 39: 100%|##########| 1/1 [00:00<00:00,  4.27it/s, v_num=13, train_loss_step=12.70, train_loss_epoch=12.70]
Epoch 39: 100%|##########| 1/1 [00:00<00:00,  4.27it/s, v_num=13, train_loss_step=12.40, train_loss_epoch=12.70]
Epoch 39: 100%|##########| 1/1 [00:00<00:00,  4.27it/s, v_num=13, train_loss_step=12.40, train_loss_epoch=12.40]
Epoch 39:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=12.40, train_loss_epoch=12.40]
Epoch 40:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=12.40, train_loss_epoch=12.40]
Epoch 40: 100%|##########| 1/1 [00:00<00:00,  3.59it/s, v_num=13, train_loss_step=12.40, train_loss_epoch=12.40]
Epoch 40: 100%|##########| 1/1 [00:00<00:00,  3.59it/s, v_num=13, train_loss_step=12.80, train_loss_epoch=12.40]
Epoch 40: 100%|##########| 1/1 [00:00<00:00,  3.59it/s, v_num=13, train_loss_step=12.80, train_loss_epoch=12.80]
Epoch 40:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=12.80, train_loss_epoch=12.80]
Epoch 41:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=12.80, train_loss_epoch=12.80]
Epoch 41: 100%|##########| 1/1 [00:00<00:00,  3.37it/s, v_num=13, train_loss_step=12.80, train_loss_epoch=12.80]
Epoch 41: 100%|##########| 1/1 [00:00<00:00,  3.37it/s, v_num=13, train_loss_step=13.10, train_loss_epoch=12.80]
Epoch 41: 100%|##########| 1/1 [00:00<00:00,  3.37it/s, v_num=13, train_loss_step=13.10, train_loss_epoch=13.10]
Epoch 41:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=13.10, train_loss_epoch=13.10]
Epoch 42:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=13.10, train_loss_epoch=13.10]
Epoch 42: 100%|##########| 1/1 [00:00<00:00,  3.37it/s, v_num=13, train_loss_step=13.10, train_loss_epoch=13.10]
Epoch 42: 100%|##########| 1/1 [00:00<00:00,  3.37it/s, v_num=13, train_loss_step=12.00, train_loss_epoch=13.10]
Epoch 42: 100%|##########| 1/1 [00:00<00:00,  3.37it/s, v_num=13, train_loss_step=12.00, train_loss_epoch=12.00]
Epoch 42:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=12.00, train_loss_epoch=12.00]
Epoch 43:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=12.00, train_loss_epoch=12.00]
Epoch 43: 100%|##########| 1/1 [00:00<00:00,  3.57it/s, v_num=13, train_loss_step=12.00, train_loss_epoch=12.00]
Epoch 43: 100%|##########| 1/1 [00:00<00:00,  3.38it/s, v_num=13, train_loss_step=11.40, train_loss_epoch=12.00]
Epoch 43: 100%|##########| 1/1 [00:00<00:00,  3.38it/s, v_num=13, train_loss_step=11.40, train_loss_epoch=11.40]
Epoch 43:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=11.40, train_loss_epoch=11.40]
Epoch 44:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=11.40, train_loss_epoch=11.40]
Epoch 44: 100%|##########| 1/1 [00:00<00:00,  2.89it/s, v_num=13, train_loss_step=11.40, train_loss_epoch=11.40]
Epoch 44: 100%|##########| 1/1 [00:00<00:00,  2.89it/s, v_num=13, train_loss_step=12.50, train_loss_epoch=11.40]
Epoch 44: 100%|##########| 1/1 [00:00<00:00,  2.89it/s, v_num=13, train_loss_step=12.50, train_loss_epoch=12.50]
Epoch 44:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=12.50, train_loss_epoch=12.50]
Epoch 45:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=12.50, train_loss_epoch=12.50]
Epoch 45: 100%|##########| 1/1 [00:00<00:00,  3.43it/s, v_num=13, train_loss_step=12.50, train_loss_epoch=12.50]
Epoch 45: 100%|##########| 1/1 [00:00<00:00,  3.43it/s, v_num=13, train_loss_step=13.20, train_loss_epoch=12.50]
Epoch 45: 100%|##########| 1/1 [00:00<00:00,  3.43it/s, v_num=13, train_loss_step=13.20, train_loss_epoch=13.20]
Epoch 45:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=13.20, train_loss_epoch=13.20]
Epoch 46:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=13.20, train_loss_epoch=13.20]
Epoch 46: 100%|##########| 1/1 [00:00<00:00,  4.06it/s, v_num=13, train_loss_step=13.20, train_loss_epoch=13.20]
Epoch 46: 100%|##########| 1/1 [00:00<00:00,  4.06it/s, v_num=13, train_loss_step=11.90, train_loss_epoch=13.20]
Epoch 46: 100%|##########| 1/1 [00:00<00:00,  4.06it/s, v_num=13, train_loss_step=11.90, train_loss_epoch=11.90]
Epoch 46:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=11.90, train_loss_epoch=11.90]
Epoch 47:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=11.90, train_loss_epoch=11.90]
Epoch 47: 100%|##########| 1/1 [00:00<00:00,  3.79it/s, v_num=13, train_loss_step=11.90, train_loss_epoch=11.90]
Epoch 47: 100%|##########| 1/1 [00:00<00:00,  3.79it/s, v_num=13, train_loss_step=10.60, train_loss_epoch=11.90]
Epoch 47: 100%|##########| 1/1 [00:00<00:00,  3.79it/s, v_num=13, train_loss_step=10.60, train_loss_epoch=10.60]
Epoch 47:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=10.60, train_loss_epoch=10.60]
Epoch 48:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=10.60, train_loss_epoch=10.60]
Epoch 48: 100%|##########| 1/1 [00:00<00:00,  3.60it/s, v_num=13, train_loss_step=10.60, train_loss_epoch=10.60]
Epoch 48: 100%|##########| 1/1 [00:00<00:00,  3.60it/s, v_num=13, train_loss_step=12.10, train_loss_epoch=10.60]
Epoch 48: 100%|##########| 1/1 [00:00<00:00,  3.60it/s, v_num=13, train_loss_step=12.10, train_loss_epoch=12.10]
Epoch 48:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=12.10, train_loss_epoch=12.10]
Epoch 49:   0%|          | 0/1 [00:00<?, ?it/s, v_num=13, train_loss_step=12.10, train_loss_epoch=12.10]
Epoch 49: 100%|##########| 1/1 [00:00<00:00,  3.88it/s, v_num=13, train_loss_step=12.10, train_loss_epoch=12.10]
Epoch 49: 100%|##########| 1/1 [00:00<00:00,  3.88it/s, v_num=13, train_loss_step=12.00, train_loss_epoch=12.10]
Epoch 49: 100%|##########| 1/1 [00:00<00:00,  3.65it/s, v_num=13, train_loss_step=12.00, train_loss_epoch=12.00]
Epoch 49: 100%|##########| 1/1 [00:00<00:00,  3.65it/s, v_num=13, train_loss_step=12.00, train_loss_epoch=12.00]

Sanity Checking: 0it [00:00, ?it/s]
Sanity Checking:   0%|          | 0/1 [00:00<?, ?it/s]
Sanity Checking DataLoader 0:   0%|          | 0/1 [00:00<?, ?it/s]
Sanity Checking DataLoader 0: 100%|##########| 1/1 [00:00<?, ?it/s]

Training: 0it [00:00, ?it/s]
Training:   0%|          | 0/1 [00:00<?, ?it/s]
Epoch 0:   0%|          | 0/1 [00:00<?, ?it/s]
Epoch 0: 100%|##########| 1/1 [00:00<00:00,  3.81it/s]
Epoch 0: 100%|##########| 1/1 [00:00<00:00,  3.81it/s, v_num=14, train_loss_step=47.00]
Epoch 0: 100%|##########| 1/1 [00:00<00:00,  3.81it/s, v_num=14, train_loss_step=47.00, train_loss_epoch=47.00]
Epoch 0:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=47.00, train_loss_epoch=47.00]
Epoch 1:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=47.00, train_loss_epoch=47.00]
Epoch 1: 100%|##########| 1/1 [00:00<00:00,  3.83it/s, v_num=14, train_loss_step=47.00, train_loss_epoch=47.00]
Epoch 1: 100%|##########| 1/1 [00:00<00:00,  3.81it/s, v_num=14, train_loss_step=82.90, train_loss_epoch=47.00]
Epoch 1: 100%|##########| 1/1 [00:00<00:00,  3.81it/s, v_num=14, train_loss_step=82.90, train_loss_epoch=82.90]
Epoch 1:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=82.90, train_loss_epoch=82.90]
Epoch 2:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=82.90, train_loss_epoch=82.90]
Epoch 2: 100%|##########| 1/1 [00:00<00:00,  3.30it/s, v_num=14, train_loss_step=82.90, train_loss_epoch=82.90]
Epoch 2: 100%|##########| 1/1 [00:00<00:00,  3.30it/s, v_num=14, train_loss_step=52.40, train_loss_epoch=82.90]
Epoch 2: 100%|##########| 1/1 [00:00<00:00,  3.30it/s, v_num=14, train_loss_step=52.40, train_loss_epoch=52.40]
Epoch 2:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=52.40, train_loss_epoch=52.40]
Epoch 3:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=52.40, train_loss_epoch=52.40]
Epoch 3: 100%|##########| 1/1 [00:00<00:00,  3.98it/s, v_num=14, train_loss_step=52.40, train_loss_epoch=52.40]
Epoch 3: 100%|##########| 1/1 [00:00<00:00,  3.98it/s, v_num=14, train_loss_step=47.60, train_loss_epoch=52.40]
Epoch 3: 100%|##########| 1/1 [00:00<00:00,  3.98it/s, v_num=14, train_loss_step=47.60, train_loss_epoch=47.60]
Epoch 3:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=47.60, train_loss_epoch=47.60]
Epoch 4:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=47.60, train_loss_epoch=47.60]
Epoch 4: 100%|##########| 1/1 [00:00<00:00,  3.79it/s, v_num=14, train_loss_step=47.60, train_loss_epoch=47.60]
Epoch 4: 100%|##########| 1/1 [00:00<00:00,  3.58it/s, v_num=14, train_loss_step=42.70, train_loss_epoch=47.60]
Epoch 4: 100%|##########| 1/1 [00:00<00:00,  3.58it/s, v_num=14, train_loss_step=42.70, train_loss_epoch=42.70]
Epoch 4:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=42.70, train_loss_epoch=42.70]
Epoch 5:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=42.70, train_loss_epoch=42.70]
Epoch 5: 100%|##########| 1/1 [00:00<00:00,  3.56it/s, v_num=14, train_loss_step=42.70, train_loss_epoch=42.70]
Epoch 5: 100%|##########| 1/1 [00:00<00:00,  3.56it/s, v_num=14, train_loss_step=39.80, train_loss_epoch=42.70]
Epoch 5: 100%|##########| 1/1 [00:00<00:00,  3.56it/s, v_num=14, train_loss_step=39.80, train_loss_epoch=39.80]
Epoch 5:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=39.80, train_loss_epoch=39.80]
Epoch 6:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=39.80, train_loss_epoch=39.80]
Epoch 6: 100%|##########| 1/1 [00:00<00:00,  3.76it/s, v_num=14, train_loss_step=39.80, train_loss_epoch=39.80]
Epoch 6: 100%|##########| 1/1 [00:00<00:00,  3.76it/s, v_num=14, train_loss_step=41.70, train_loss_epoch=39.80]
Epoch 6: 100%|##########| 1/1 [00:00<00:00,  3.76it/s, v_num=14, train_loss_step=41.70, train_loss_epoch=41.70]
Epoch 6:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=41.70, train_loss_epoch=41.70]
Epoch 7:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=41.70, train_loss_epoch=41.70]
Epoch 7: 100%|##########| 1/1 [00:00<00:00,  3.34it/s, v_num=14, train_loss_step=41.70, train_loss_epoch=41.70]
Epoch 7: 100%|##########| 1/1 [00:00<00:00,  3.34it/s, v_num=14, train_loss_step=40.80, train_loss_epoch=41.70]
Epoch 7: 100%|##########| 1/1 [00:00<00:00,  3.34it/s, v_num=14, train_loss_step=40.80, train_loss_epoch=40.80]
Epoch 7:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=40.80, train_loss_epoch=40.80]
Epoch 8:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=40.80, train_loss_epoch=40.80]
Epoch 8: 100%|##########| 1/1 [00:00<00:00,  3.56it/s, v_num=14, train_loss_step=40.80, train_loss_epoch=40.80]
Epoch 8: 100%|##########| 1/1 [00:00<00:00,  3.56it/s, v_num=14, train_loss_step=36.90, train_loss_epoch=40.80]
Epoch 8: 100%|##########| 1/1 [00:00<00:00,  3.56it/s, v_num=14, train_loss_step=36.90, train_loss_epoch=36.90]
Epoch 8:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=36.90, train_loss_epoch=36.90]
Epoch 9:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=36.90, train_loss_epoch=36.90]
Epoch 9: 100%|##########| 1/1 [00:00<00:00,  3.68it/s, v_num=14, train_loss_step=36.90, train_loss_epoch=36.90]
Epoch 9: 100%|##########| 1/1 [00:00<00:00,  3.68it/s, v_num=14, train_loss_step=36.50, train_loss_epoch=36.90]
Epoch 9: 100%|##########| 1/1 [00:00<00:00,  3.68it/s, v_num=14, train_loss_step=36.50, train_loss_epoch=36.50]
Epoch 9:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=36.50, train_loss_epoch=36.50]
Epoch 10:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=36.50, train_loss_epoch=36.50]
Epoch 10: 100%|##########| 1/1 [00:00<00:00,  3.77it/s, v_num=14, train_loss_step=36.50, train_loss_epoch=36.50]
Epoch 10: 100%|##########| 1/1 [00:00<00:00,  3.77it/s, v_num=14, train_loss_step=34.00, train_loss_epoch=36.50]
Epoch 10: 100%|##########| 1/1 [00:00<00:00,  3.77it/s, v_num=14, train_loss_step=34.00, train_loss_epoch=34.00]
Epoch 10:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=34.00, train_loss_epoch=34.00]
Epoch 11:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=34.00, train_loss_epoch=34.00]
Epoch 11: 100%|##########| 1/1 [00:00<00:00,  3.51it/s, v_num=14, train_loss_step=34.00, train_loss_epoch=34.00]
Epoch 11: 100%|##########| 1/1 [00:00<00:00,  3.51it/s, v_num=14, train_loss_step=32.10, train_loss_epoch=34.00]
Epoch 11: 100%|##########| 1/1 [00:00<00:00,  3.51it/s, v_num=14, train_loss_step=32.10, train_loss_epoch=32.10]
Epoch 11:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=32.10, train_loss_epoch=32.10]
Epoch 12:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=32.10, train_loss_epoch=32.10]
Epoch 12: 100%|##########| 1/1 [00:00<00:00,  3.56it/s, v_num=14, train_loss_step=32.10, train_loss_epoch=32.10]
Epoch 12: 100%|##########| 1/1 [00:00<00:00,  3.56it/s, v_num=14, train_loss_step=29.70, train_loss_epoch=32.10]
Epoch 12: 100%|##########| 1/1 [00:00<00:00,  3.56it/s, v_num=14, train_loss_step=29.70, train_loss_epoch=29.70]
Epoch 12:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=29.70, train_loss_epoch=29.70]
Epoch 13:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=29.70, train_loss_epoch=29.70]
Epoch 13: 100%|##########| 1/1 [00:00<00:00,  3.80it/s, v_num=14, train_loss_step=29.70, train_loss_epoch=29.70]
Epoch 13: 100%|##########| 1/1 [00:00<00:00,  3.80it/s, v_num=14, train_loss_step=29.10, train_loss_epoch=29.70]
Epoch 13: 100%|##########| 1/1 [00:00<00:00,  3.80it/s, v_num=14, train_loss_step=29.10, train_loss_epoch=29.10]
Epoch 13:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=29.10, train_loss_epoch=29.10]
Epoch 14:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=29.10, train_loss_epoch=29.10]
Epoch 14: 100%|##########| 1/1 [00:00<00:00,  3.24it/s, v_num=14, train_loss_step=29.10, train_loss_epoch=29.10]
Epoch 14: 100%|##########| 1/1 [00:00<00:00,  3.24it/s, v_num=14, train_loss_step=30.00, train_loss_epoch=29.10]
Epoch 14: 100%|##########| 1/1 [00:00<00:00,  3.24it/s, v_num=14, train_loss_step=30.00, train_loss_epoch=30.00]
Epoch 14:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=30.00, train_loss_epoch=30.00]
Epoch 15:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=30.00, train_loss_epoch=30.00]
Epoch 15: 100%|##########| 1/1 [00:00<00:00,  4.16it/s, v_num=14, train_loss_step=30.00, train_loss_epoch=30.00]
Epoch 15: 100%|##########| 1/1 [00:00<00:00,  4.16it/s, v_num=14, train_loss_step=29.20, train_loss_epoch=30.00]
Epoch 15: 100%|##########| 1/1 [00:00<00:00,  3.91it/s, v_num=14, train_loss_step=29.20, train_loss_epoch=29.20]
Epoch 15:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=29.20, train_loss_epoch=29.20]
Epoch 16:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=29.20, train_loss_epoch=29.20]
Epoch 16: 100%|##########| 1/1 [00:00<00:00,  3.97it/s, v_num=14, train_loss_step=29.20, train_loss_epoch=29.20]
Epoch 16: 100%|##########| 1/1 [00:00<00:00,  3.97it/s, v_num=14, train_loss_step=25.20, train_loss_epoch=29.20]
Epoch 16: 100%|##########| 1/1 [00:00<00:00,  3.97it/s, v_num=14, train_loss_step=25.20, train_loss_epoch=25.20]
Epoch 16:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=25.20, train_loss_epoch=25.20]
Epoch 17:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=25.20, train_loss_epoch=25.20]
Epoch 17: 100%|##########| 1/1 [00:00<00:00,  3.60it/s, v_num=14, train_loss_step=25.20, train_loss_epoch=25.20]
Epoch 17: 100%|##########| 1/1 [00:00<00:00,  3.60it/s, v_num=14, train_loss_step=28.50, train_loss_epoch=25.20]
Epoch 17: 100%|##########| 1/1 [00:00<00:00,  3.58it/s, v_num=14, train_loss_step=28.50, train_loss_epoch=28.50]
Epoch 17:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=28.50, train_loss_epoch=28.50]
Epoch 18:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=28.50, train_loss_epoch=28.50]
Epoch 18: 100%|##########| 1/1 [00:00<00:00,  4.04it/s, v_num=14, train_loss_step=28.50, train_loss_epoch=28.50]
Epoch 18: 100%|##########| 1/1 [00:00<00:00,  4.04it/s, v_num=14, train_loss_step=23.60, train_loss_epoch=28.50]
Epoch 18: 100%|##########| 1/1 [00:00<00:00,  4.04it/s, v_num=14, train_loss_step=23.60, train_loss_epoch=23.60]
Epoch 18:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=23.60, train_loss_epoch=23.60]
Epoch 19:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=23.60, train_loss_epoch=23.60]
Epoch 19: 100%|##########| 1/1 [00:00<00:00,  3.22it/s, v_num=14, train_loss_step=23.60, train_loss_epoch=23.60]
Epoch 19: 100%|##########| 1/1 [00:00<00:00,  3.21it/s, v_num=14, train_loss_step=24.90, train_loss_epoch=23.60]
Epoch 19: 100%|##########| 1/1 [00:00<00:00,  3.20it/s, v_num=14, train_loss_step=24.90, train_loss_epoch=24.90]
Epoch 19:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=24.90, train_loss_epoch=24.90]
Epoch 20:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=24.90, train_loss_epoch=24.90]
Epoch 20: 100%|##########| 1/1 [00:00<00:00,  3.37it/s, v_num=14, train_loss_step=24.90, train_loss_epoch=24.90]
Epoch 20: 100%|##########| 1/1 [00:00<00:00,  3.37it/s, v_num=14, train_loss_step=21.70, train_loss_epoch=24.90]
Epoch 20: 100%|##########| 1/1 [00:00<00:00,  3.37it/s, v_num=14, train_loss_step=21.70, train_loss_epoch=21.70]
Epoch 20:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=21.70, train_loss_epoch=21.70]
Epoch 21:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=21.70, train_loss_epoch=21.70]
Epoch 21: 100%|##########| 1/1 [00:00<00:00,  3.54it/s, v_num=14, train_loss_step=21.70, train_loss_epoch=21.70]
Epoch 21: 100%|##########| 1/1 [00:00<00:00,  3.54it/s, v_num=14, train_loss_step=23.70, train_loss_epoch=21.70]
Epoch 21: 100%|##########| 1/1 [00:00<00:00,  3.54it/s, v_num=14, train_loss_step=23.70, train_loss_epoch=23.70]
Epoch 21:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=23.70, train_loss_epoch=23.70]
Epoch 22:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=23.70, train_loss_epoch=23.70]
Epoch 22: 100%|##########| 1/1 [00:00<00:00,  3.64it/s, v_num=14, train_loss_step=23.70, train_loss_epoch=23.70]
Epoch 22: 100%|##########| 1/1 [00:00<00:00,  3.64it/s, v_num=14, train_loss_step=21.50, train_loss_epoch=23.70]
Epoch 22: 100%|##########| 1/1 [00:00<00:00,  3.64it/s, v_num=14, train_loss_step=21.50, train_loss_epoch=21.50]
Epoch 22:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=21.50, train_loss_epoch=21.50]
Epoch 23:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=21.50, train_loss_epoch=21.50]
Epoch 23: 100%|##########| 1/1 [00:00<00:00,  3.51it/s, v_num=14, train_loss_step=21.50, train_loss_epoch=21.50]
Epoch 23: 100%|##########| 1/1 [00:00<00:00,  3.51it/s, v_num=14, train_loss_step=21.90, train_loss_epoch=21.50]
Epoch 23: 100%|##########| 1/1 [00:00<00:00,  3.51it/s, v_num=14, train_loss_step=21.90, train_loss_epoch=21.90]
Epoch 23:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=21.90, train_loss_epoch=21.90]
Epoch 24:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=21.90, train_loss_epoch=21.90]
Epoch 24: 100%|##########| 1/1 [00:00<00:00,  3.29it/s, v_num=14, train_loss_step=21.90, train_loss_epoch=21.90]
Epoch 24: 100%|##########| 1/1 [00:00<00:00,  3.29it/s, v_num=14, train_loss_step=21.00, train_loss_epoch=21.90]
Epoch 24: 100%|##########| 1/1 [00:00<00:00,  3.29it/s, v_num=14, train_loss_step=21.00, train_loss_epoch=21.00]
Epoch 24:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=21.00, train_loss_epoch=21.00]
Epoch 25:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=21.00, train_loss_epoch=21.00]
Epoch 25: 100%|##########| 1/1 [00:00<00:00,  3.77it/s, v_num=14, train_loss_step=21.00, train_loss_epoch=21.00]
Epoch 25: 100%|##########| 1/1 [00:00<00:00,  3.77it/s, v_num=14, train_loss_step=20.40, train_loss_epoch=21.00]
Epoch 25: 100%|##########| 1/1 [00:00<00:00,  3.77it/s, v_num=14, train_loss_step=20.40, train_loss_epoch=20.40]
Epoch 25:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=20.40, train_loss_epoch=20.40]
Epoch 26:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=20.40, train_loss_epoch=20.40]
Epoch 26: 100%|##########| 1/1 [00:00<00:00,  3.92it/s, v_num=14, train_loss_step=20.40, train_loss_epoch=20.40]
Epoch 26: 100%|##########| 1/1 [00:00<00:00,  3.92it/s, v_num=14, train_loss_step=21.10, train_loss_epoch=20.40]
Epoch 26: 100%|##########| 1/1 [00:00<00:00,  3.92it/s, v_num=14, train_loss_step=21.10, train_loss_epoch=21.10]
Epoch 26:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=21.10, train_loss_epoch=21.10]
Epoch 27:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=21.10, train_loss_epoch=21.10]
Epoch 27: 100%|##########| 1/1 [00:00<00:00,  3.53it/s, v_num=14, train_loss_step=21.10, train_loss_epoch=21.10]
Epoch 27: 100%|##########| 1/1 [00:00<00:00,  3.53it/s, v_num=14, train_loss_step=19.30, train_loss_epoch=21.10]
Epoch 27: 100%|##########| 1/1 [00:00<00:00,  3.53it/s, v_num=14, train_loss_step=19.30, train_loss_epoch=19.30]
Epoch 27:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=19.30, train_loss_epoch=19.30]
Epoch 28:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=19.30, train_loss_epoch=19.30]
Epoch 28: 100%|##########| 1/1 [00:00<00:00,  3.64it/s, v_num=14, train_loss_step=19.30, train_loss_epoch=19.30]
Epoch 28: 100%|##########| 1/1 [00:00<00:00,  3.64it/s, v_num=14, train_loss_step=19.90, train_loss_epoch=19.30]
Epoch 28: 100%|##########| 1/1 [00:00<00:00,  3.64it/s, v_num=14, train_loss_step=19.90, train_loss_epoch=19.90]
Epoch 28:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=19.90, train_loss_epoch=19.90]
Epoch 29:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=19.90, train_loss_epoch=19.90]
Epoch 29: 100%|##########| 1/1 [00:00<00:00,  3.74it/s, v_num=14, train_loss_step=19.90, train_loss_epoch=19.90]
Epoch 29: 100%|##########| 1/1 [00:00<00:00,  3.74it/s, v_num=14, train_loss_step=19.00, train_loss_epoch=19.90]
Epoch 29: 100%|##########| 1/1 [00:00<00:00,  3.74it/s, v_num=14, train_loss_step=19.00, train_loss_epoch=19.00]
Epoch 29:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=19.00, train_loss_epoch=19.00]
Epoch 30:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=19.00, train_loss_epoch=19.00]
Epoch 30: 100%|##########| 1/1 [00:00<00:00,  3.36it/s, v_num=14, train_loss_step=19.00, train_loss_epoch=19.00]
Epoch 30: 100%|##########| 1/1 [00:00<00:00,  3.36it/s, v_num=14, train_loss_step=19.20, train_loss_epoch=19.00]
Epoch 30: 100%|##########| 1/1 [00:00<00:00,  3.35it/s, v_num=14, train_loss_step=19.20, train_loss_epoch=19.20]
Epoch 30:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=19.20, train_loss_epoch=19.20]
Epoch 31:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=19.20, train_loss_epoch=19.20]
Epoch 31: 100%|##########| 1/1 [00:00<00:00,  3.41it/s, v_num=14, train_loss_step=19.20, train_loss_epoch=19.20]
Epoch 31: 100%|##########| 1/1 [00:00<00:00,  3.41it/s, v_num=14, train_loss_step=17.20, train_loss_epoch=19.20]
Epoch 31: 100%|##########| 1/1 [00:00<00:00,  3.41it/s, v_num=14, train_loss_step=17.20, train_loss_epoch=17.20]
Epoch 31:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=17.20, train_loss_epoch=17.20]
Epoch 32:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=17.20, train_loss_epoch=17.20]
Epoch 32: 100%|##########| 1/1 [00:00<00:00,  4.27it/s, v_num=14, train_loss_step=17.20, train_loss_epoch=17.20]
Epoch 32: 100%|##########| 1/1 [00:00<00:00,  4.27it/s, v_num=14, train_loss_step=17.50, train_loss_epoch=17.20]
Epoch 32: 100%|##########| 1/1 [00:00<00:00,  4.27it/s, v_num=14, train_loss_step=17.50, train_loss_epoch=17.50]
Epoch 32:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=17.50, train_loss_epoch=17.50]
Epoch 33:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=17.50, train_loss_epoch=17.50]
Epoch 33: 100%|##########| 1/1 [00:00<00:00,  3.77it/s, v_num=14, train_loss_step=17.50, train_loss_epoch=17.50]
Epoch 33: 100%|##########| 1/1 [00:00<00:00,  3.77it/s, v_num=14, train_loss_step=16.70, train_loss_epoch=17.50]
Epoch 33: 100%|##########| 1/1 [00:00<00:00,  3.77it/s, v_num=14, train_loss_step=16.70, train_loss_epoch=16.70]
Epoch 33:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=16.70, train_loss_epoch=16.70]
Epoch 34:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=16.70, train_loss_epoch=16.70]
Epoch 34: 100%|##########| 1/1 [00:00<00:00,  3.68it/s, v_num=14, train_loss_step=16.70, train_loss_epoch=16.70]
Epoch 34: 100%|##########| 1/1 [00:00<00:00,  3.68it/s, v_num=14, train_loss_step=16.50, train_loss_epoch=16.70]
Epoch 34: 100%|##########| 1/1 [00:00<00:00,  3.68it/s, v_num=14, train_loss_step=16.50, train_loss_epoch=16.50]
Epoch 34:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=16.50, train_loss_epoch=16.50]
Epoch 35:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=16.50, train_loss_epoch=16.50]
Epoch 35: 100%|##########| 1/1 [00:00<00:00,  3.72it/s, v_num=14, train_loss_step=16.50, train_loss_epoch=16.50]
Epoch 35: 100%|##########| 1/1 [00:00<00:00,  3.72it/s, v_num=14, train_loss_step=16.40, train_loss_epoch=16.50]
Epoch 35: 100%|##########| 1/1 [00:00<00:00,  3.72it/s, v_num=14, train_loss_step=16.40, train_loss_epoch=16.40]
Epoch 35:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=16.40, train_loss_epoch=16.40]
Epoch 36:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=16.40, train_loss_epoch=16.40]
Epoch 36: 100%|##########| 1/1 [00:00<00:00,  3.41it/s, v_num=14, train_loss_step=16.40, train_loss_epoch=16.40]
Epoch 36: 100%|##########| 1/1 [00:00<00:00,  3.41it/s, v_num=14, train_loss_step=15.70, train_loss_epoch=16.40]
Epoch 36: 100%|##########| 1/1 [00:00<00:00,  3.41it/s, v_num=14, train_loss_step=15.70, train_loss_epoch=15.70]
Epoch 36:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=15.70, train_loss_epoch=15.70]
Epoch 37:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=15.70, train_loss_epoch=15.70]
Epoch 37: 100%|##########| 1/1 [00:00<00:00,  3.99it/s, v_num=14, train_loss_step=15.70, train_loss_epoch=15.70]
Epoch 37: 100%|##########| 1/1 [00:00<00:00,  3.99it/s, v_num=14, train_loss_step=14.80, train_loss_epoch=15.70]
Epoch 37: 100%|##########| 1/1 [00:00<00:00,  3.99it/s, v_num=14, train_loss_step=14.80, train_loss_epoch=14.80]
Epoch 37:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=14.80, train_loss_epoch=14.80]
Epoch 38:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=14.80, train_loss_epoch=14.80]
Epoch 38: 100%|##########| 1/1 [00:00<00:00,  4.31it/s, v_num=14, train_loss_step=14.80, train_loss_epoch=14.80]
Epoch 38: 100%|##########| 1/1 [00:00<00:00,  4.31it/s, v_num=14, train_loss_step=15.10, train_loss_epoch=14.80]
Epoch 38: 100%|##########| 1/1 [00:00<00:00,  4.31it/s, v_num=14, train_loss_step=15.10, train_loss_epoch=15.10]
Epoch 38:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=15.10, train_loss_epoch=15.10]
Epoch 39:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=15.10, train_loss_epoch=15.10]
Epoch 39: 100%|##########| 1/1 [00:00<00:00,  3.72it/s, v_num=14, train_loss_step=15.10, train_loss_epoch=15.10]
Epoch 39: 100%|##########| 1/1 [00:00<00:00,  3.51it/s, v_num=14, train_loss_step=14.90, train_loss_epoch=15.10]
Epoch 39: 100%|##########| 1/1 [00:00<00:00,  3.51it/s, v_num=14, train_loss_step=14.90, train_loss_epoch=14.90]
Epoch 39:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=14.90, train_loss_epoch=14.90]
Epoch 40:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=14.90, train_loss_epoch=14.90]
Epoch 40: 100%|##########| 1/1 [00:00<00:00,  3.20it/s, v_num=14, train_loss_step=14.90, train_loss_epoch=14.90]
Epoch 40: 100%|##########| 1/1 [00:00<00:00,  3.20it/s, v_num=14, train_loss_step=14.40, train_loss_epoch=14.90]
Epoch 40: 100%|##########| 1/1 [00:00<00:00,  3.19it/s, v_num=14, train_loss_step=14.40, train_loss_epoch=14.40]
Epoch 40:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=14.40, train_loss_epoch=14.40]
Epoch 41:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=14.40, train_loss_epoch=14.40]
Epoch 41: 100%|##########| 1/1 [00:00<00:00,  3.03it/s, v_num=14, train_loss_step=14.40, train_loss_epoch=14.40]
Epoch 41: 100%|##########| 1/1 [00:00<00:00,  3.03it/s, v_num=14, train_loss_step=14.30, train_loss_epoch=14.40]
Epoch 41: 100%|##########| 1/1 [00:00<00:00,  3.03it/s, v_num=14, train_loss_step=14.30, train_loss_epoch=14.30]
Epoch 41:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=14.30, train_loss_epoch=14.30]
Epoch 42:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=14.30, train_loss_epoch=14.30]
Epoch 42: 100%|##########| 1/1 [00:00<00:00,  3.37it/s, v_num=14, train_loss_step=14.30, train_loss_epoch=14.30]
Epoch 42: 100%|##########| 1/1 [00:00<00:00,  3.37it/s, v_num=14, train_loss_step=14.30, train_loss_epoch=14.30]
Epoch 42: 100%|##########| 1/1 [00:00<00:00,  3.37it/s, v_num=14, train_loss_step=14.30, train_loss_epoch=14.30]
Epoch 42:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=14.30, train_loss_epoch=14.30]
Epoch 43:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=14.30, train_loss_epoch=14.30]
Epoch 43: 100%|##########| 1/1 [00:00<00:00,  4.21it/s, v_num=14, train_loss_step=14.30, train_loss_epoch=14.30]
Epoch 43: 100%|##########| 1/1 [00:00<00:00,  4.21it/s, v_num=14, train_loss_step=13.40, train_loss_epoch=14.30]
Epoch 43: 100%|##########| 1/1 [00:00<00:00,  4.21it/s, v_num=14, train_loss_step=13.40, train_loss_epoch=13.40]
Epoch 43:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=13.40, train_loss_epoch=13.40]
Epoch 44:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=13.40, train_loss_epoch=13.40]
Epoch 44: 100%|##########| 1/1 [00:00<00:00,  3.77it/s, v_num=14, train_loss_step=13.40, train_loss_epoch=13.40]
Epoch 44: 100%|##########| 1/1 [00:00<00:00,  3.77it/s, v_num=14, train_loss_step=14.50, train_loss_epoch=13.40]
Epoch 44: 100%|##########| 1/1 [00:00<00:00,  3.77it/s, v_num=14, train_loss_step=14.50, train_loss_epoch=14.50]
Epoch 44:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=14.50, train_loss_epoch=14.50]
Epoch 45:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=14.50, train_loss_epoch=14.50]
Epoch 45: 100%|##########| 1/1 [00:00<00:00,  3.73it/s, v_num=14, train_loss_step=14.50, train_loss_epoch=14.50]
Epoch 45: 100%|##########| 1/1 [00:00<00:00,  3.73it/s, v_num=14, train_loss_step=14.40, train_loss_epoch=14.50]
Epoch 45: 100%|##########| 1/1 [00:00<00:00,  3.73it/s, v_num=14, train_loss_step=14.40, train_loss_epoch=14.40]
Epoch 45:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=14.40, train_loss_epoch=14.40]
Epoch 46:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=14.40, train_loss_epoch=14.40]
Epoch 46: 100%|##########| 1/1 [00:00<00:00,  3.41it/s, v_num=14, train_loss_step=14.40, train_loss_epoch=14.40]
Epoch 46: 100%|##########| 1/1 [00:00<00:00,  3.41it/s, v_num=14, train_loss_step=12.90, train_loss_epoch=14.40]
Epoch 46: 100%|##########| 1/1 [00:00<00:00,  3.41it/s, v_num=14, train_loss_step=12.90, train_loss_epoch=12.90]
Epoch 46:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=12.90, train_loss_epoch=12.90]
Epoch 47:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=12.90, train_loss_epoch=12.90]
Epoch 47: 100%|##########| 1/1 [00:00<00:00,  3.54it/s, v_num=14, train_loss_step=12.90, train_loss_epoch=12.90]
Epoch 47: 100%|##########| 1/1 [00:00<00:00,  3.54it/s, v_num=14, train_loss_step=13.20, train_loss_epoch=12.90]
Epoch 47: 100%|##########| 1/1 [00:00<00:00,  3.54it/s, v_num=14, train_loss_step=13.20, train_loss_epoch=13.20]
Epoch 47:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=13.20, train_loss_epoch=13.20]
Epoch 48:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=13.20, train_loss_epoch=13.20]
Epoch 48: 100%|##########| 1/1 [00:00<00:00,  3.31it/s, v_num=14, train_loss_step=13.20, train_loss_epoch=13.20]
Epoch 48: 100%|##########| 1/1 [00:00<00:00,  3.30it/s, v_num=14, train_loss_step=14.10, train_loss_epoch=13.20]
Epoch 48: 100%|##########| 1/1 [00:00<00:00,  3.30it/s, v_num=14, train_loss_step=14.10, train_loss_epoch=14.10]
Epoch 48:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=14.10, train_loss_epoch=14.10]
Epoch 49:   0%|          | 0/1 [00:00<?, ?it/s, v_num=14, train_loss_step=14.10, train_loss_epoch=14.10]
Epoch 49: 100%|##########| 1/1 [00:00<00:00,  3.42it/s, v_num=14, train_loss_step=14.10, train_loss_epoch=14.10]
Epoch 49: 100%|##########| 1/1 [00:00<00:00,  3.41it/s, v_num=14, train_loss_step=12.90, train_loss_epoch=14.10]
Epoch 49: 100%|##########| 1/1 [00:00<00:00,  3.38it/s, v_num=14, train_loss_step=12.90, train_loss_epoch=12.90]
Epoch 49: 100%|##########| 1/1 [00:00<00:00,  3.36it/s, v_num=14, train_loss_step=12.90, train_loss_epoch=12.90]

Y_hat_df = nf.predict().reset_index()

Predicting: 0it [00:00, ?it/s]
Predicting:   0%|          | 0/1 [00:00<?, ?it/s]
Predicting DataLoader 0:   0%|          | 0/1 [00:00<?, ?it/s]
Predicting DataLoader 0: 100%|##########| 1/1 [00:00<00:00, 167.11it/s]
Predicting DataLoader 0: 100%|##########| 1/1 [00:00<00:00, 167.11it/s]

Predicting: 0it [00:00, ?it/s]
Predicting:   0%|          | 0/1 [00:00<?, ?it/s]
Predicting DataLoader 0:   0%|          | 0/1 [00:00<?, ?it/s]
Predicting DataLoader 0: 100%|##########| 1/1 [00:00<00:00, 83.56it/s]
Predicting DataLoader 0: 100%|##########| 1/1 [00:00<00:00, 77.14it/s]

# Plot predictions
# fig, ax = plt.subplots(1, 1, figsize = (20, 7))
Y_hat_df = Y_test_df.merge(Y_hat_df, how='left', on=['unique_id', 'ds'])
plot_df = pd.concat([Y_train_df, Y_hat_df]).set_index('ds')

# plot_df[['y', 'NBEATS', 'NHITS']].plot(ax=ax, linewidth=2)
#
# ax.set_title('AirPassengers Forecast', fontsize=22)
# ax.set_ylabel('Monthly Passengers', fontsize=20)
# ax.set_xlabel('Timestamp [t]', fontsize=20)
# ax.legend(prop={'size': 15})
# ax.grid()

porting over to R with reticulate
library(reticulate)

ideas for bridging and porting to R from python, these would be handled
in the library so that it is easy to wrap with NBEATS, or NHITS:
# forecasting tool
NeuralForecast <- py_run_string("from neuralforecast import NeuralForecast")

# models we want
# NBEATS <- py_run_string("from neuralforecast.models import NBEATS")
# NHITS  <- py_run_string("from neuralforecast.models import NHITS")

nf <- reticulate::import("neuralforecast") # importing entire module during testing instead of subs for models

# test dataset
AirPassengersDF <- py_run_string("from neuralforecast.utils import AirPassengersDF")

data split in R:
library(dplyr)

Attaching package: 'dplyr'

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

# Split data and declare panel dataset
Y_df = AirPassengersDF$AirPassengersDF
Y_train_df = Y_df |>
              dplyr::filter(ds<='1959-12-31') # 132 train

Y_test_df = Y_df |>
             dplyr::filter(ds>'1959-12-31') # 12 test

The Issue:
# Fit and predict with NBEATS and NHITS models
horizon <- length(Y_test_df$ds)

# running into issues here ??? same setup as python ???? ----
# models <- c(nf$models$NBEATS(input_size=(2 * horizon), h=horizon, max_steps=50),
#              nf$models$NHITS(input_size=(2 * horizon), h=horizon, max_steps=50))

print("Global seed set to 1
Error in py_call_impl(callable, call_args$unnamed, call_args$named) :
  TypeError: empty(): argument 'size' must be tuple of ints, but found element of type float at pos 2")

[1] "Global seed set to 1\nError in py_call_impl(callable, call_args$unnamed, call_args$named) : \n  TypeError: empty(): argument 'size' must be tuple of ints, but found element of type float at pos 2"

although, could train models in python and reference like so, but would
like advice on creating models object without a tuple error.
# using the py envs models until I have a fix.
neural_setup <- NeuralForecast$NeuralForecast(models=py$models, freq='M')

neural_fit <- neural_setup$fit(df=Y_train_df)

neural_cast <- neural_setup$predict(neural_fit)

Warning in py_to_r.pandas.core.frame.DataFrame(result): index contains
duplicated values: row names not set

neural_cast

                    ds   NBEATS    NHITS
1  1960-01-30 16:00:00 415.5529 416.9964
2  1960-02-28 16:00:00 393.3172 393.3534
3  1960-03-30 16:00:00 474.9370 467.4177
4  1960-04-29 17:00:00 460.1109 469.5287
5  1960-05-30 17:00:00 466.7129 471.2119
6  1960-06-29 17:00:00 554.7439 552.1646
7  1960-07-30 17:00:00 630.1102 631.9827
8  1960-08-30 17:00:00 637.1500 639.9821
9  1960-09-29 16:00:00 536.7354 550.8594
10 1960-10-30 16:00:00 463.9492 460.3989
11 1960-11-29 16:00:00 403.9283 405.4719
12 1960-12-30 16:00:00 457.2660 458.0701

Use case
I'm looking to leverage the current python model APIs within R via reticulate. Using reticulate should make it easy but ran into an issue with tuples . Looking for advice to create the proper model objects without running a python code chunk.  The issue seems to be with the format of input_size but wasn't able to force the creation of the model objects. "argument 'size' must be tuple of ints, but found element of type float at pos 2"

Hey @frankiethull,
Thanks a lot for this effort. Once you have it working, please let us know. We will gladly include a tutorial in the documentation.
Regarding your bug, I would respect the types in the instantiation of the models.
For instance, you declare input_size=(2 * horizon) as a tuple with an integer rather than an integer input_size=2 * horizon.

Hi @kdgutier - thanks for the quick reply. Hopefully I can get something working then!
I have tried things like:
nf$models$NBEATS(input_size=2 * horizon, h=horizon, max_steps=50)
nf$models$NBEATS(input_size=2*horizon, h=horizon, max_steps=50)
nf$models$NBEATS(input_size=(2*horizon), h=horizon, max_steps=50)
nf$models$NBEATS(input_size=24, h=horizon, max_steps=50)
or going one step further into submodules:
nf$models$nbeats$NBEATS(input_size=2 * horizon, h=horizon, max_steps=50)
But all return the same error which I am surprised. My initial PoC with with statsforecast's AutoARIMA and I did not have the type errors.

CAn you try using as.integer(2 * horizon)?

That worked!!!

tuple / float issue on R side fixed using as.integer()

@frankiethull, If you have the example running, adding it to a tutorial in the documentation would be super cool.
I can gladly help with that. Let me know.

@kdgutier, I was thinking about creating a library for this (basically a few helper fcns). But until then, here's a quick tutorial of setup + use case based on the air passengers dataset (cleaned up code from above).
https://github.com/frankiethull/nixtla-r-tutorial
Let me know what you think and I am more than happy to work on more of these with you all. In addition, let me know what your thoughts are on the R extension library and if that would be something you would like to collaborate on. 90% of my time is spent in R so I know a lot of us R programmers would like to use the current and future nixtla algos. Appreciate your help today

@kdgutier - I kept the original setup tutorial (nixtla_r_tutorial.md) but see the new readme doc here:
https://github.com/frankiethull/nixtla-r-tutorial
I created helper functions for using neuralforecast via R. Have yet to pkg but think this works pretty well for a lite and easy implementation for my uses.
Let me know how you'd like to proceed on adding a tutorial on your end and I can test more on the R side.


Update damir-tomicic.yml

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Data insertion failing without 'id' PK

if id PK is not specified explicitly in the database, data insertion is failing and sqb is returning Unable to insert new row.
Database structure that causing insertion problem

When I debug the code marked as 1 and as 2 code lines the following picture is returning undefined.

Converted database structure that not causing insertion problem

if id is inserted into the database structure then like the above picture then marked as 1 and as 2 code lines are not returning undefined (returning id) and insertion completed successfully.

It seems your table has no primary key. So SQB can not fetch row after insert. If you add a primary key to your table it will work as expected.


Making osmoutils compile on chains that don't use our SDK fork

What is the purpose of the change
Osmoutils wouldn't compile on chains that didn't use osmosis' sdk fork because ClawbackVestingAccount is not available.
This ensures that that struct is not needed at compile time.
Doing this via a global variable might be a bit controversial, but I think it's the cleanest abstraction to avoid having to maintain several other packages and/or have implementors override this with replace/fork.
We could use this pattern in other reusable packages and it might even be usable for supporting different versions of the sdk or ibc.
Brief Changelog
(for example:)

ClawbackVestingAccount is no longer part of osmoutils and is now injected in app.go

Testing and Verifying
all tests pass
Documentation and Release Note

Does this pull request introduce a new feature or user-facing behavior changes? (/ no)
Is a relevant changelog entry added to the Unreleased section in CHANGELOG.md? (/ no)
How is the feature or change documented? (not applicable )

Hrmm, I really don't like this being done from the osmosis app. (Otherwise the reflection is good w/ me)
I mainly just don't want complexity pushed to app.go.
Happy to merge for now though! Theres more high leverage places to do next, for unwinding the globals complexity

Another option we may want to evaluate in future, is compile time build flags

Added the changelog entry.
I experimented with reflection first here, but you can't conditionally import something just based on the name, and you'd need the struct to exist to get it to compile.
Compile time build flags would work if it works like rust, but I think we'd run into the same issue as with reflection


CI: Markdownlint Addition

Change Summary
This PR adds markdownlint to our pre-commit and CI workflow process. This will assist contributors and maintainers when working on our documentation and prevent minor syntax issues. Current workflow will try and fix all basic errors present, if this is not possible users will get an error and rule that is broken . This PR also includes any fixes triggered by this linter.  Also adds Flake8 summary on pre-commit workflow.
Proposed changes
Add markdownlint to pre-commit workflow. Any markdown rule overrides have been set to adhere to current documentation practices. For example, enabling inline HTML in documents. A full list of rule edits can be viewed at .github/.markdownlint.yaml.
This PR also adds .github/.markdownlintignore. This will disable the linter from running on the directories listed below. If any others should be included, please notify contributor.
ansible_collections/arista/avd/molecule/
ansible_collections/arista/avd/examples/single-dc-l3ls/documentation/
ansible_collections/arista/avd/tests/unit/filters/toc_files/

How to test
Checkout this PR and run pre-commit to view example output with no issues.
> pre-commit run --all-files markdownlint
markdownlint.............................................................Passed

ansible-avd on markdown-lint                                                           py mk-venv
>

Example when issue is present.
> pre-commit run --all-files markdownlint
markdownlint.............................................................Failed
- hook id: markdownlint
- exit code: 1

ansible_collections/arista/avd/README.md:5 MD025/single-title/single-h1 Multiple top-level headings in the same document [Context: "# About"]

ansible-avd on markdown-lint !1                                                        py mk-venv
>

Checklist
Repository Checklist

[x] My code has been rebased from devel before I start
[x] I have read the CONTRIBUTING document.
[x] My change requires a change to the documentation and documentation have been updated accordingly.
[x] I have updated molecule CI testing accordingly. (check the box if not applicable)

I had to modify this a bit, for example not checking the input-variables files that are a work in progress but getting errors on pre commit. Also Corrected minor issue on release notes for broken links as well as on fabric-variables.md in eos_designs.

Hmm I think my PR went a bit weird there with the merge vs a rebase. Apologies for the series of commits.


add manual detect method. fixes #23

I could not figure out how to get the test to pass. It produces this:
TestingLibraryElementError: Unable to find an element by: [data-testid="techdocs-native-shadowroot"]

So I've left the test as skipped.

how to get the test to pass

Last time I checked mermaid wasn't able to render inside the test dom, so I fear that this isn't easily testable.

Released as v0.6.2, thanks for adding this 🤩


Fix linting issues

done https://github.com/C-Lodder/lightning/commit/393dfae0630152d1627fc0ee25bedde958304501


raw sql to support name placeholders

Problem
I would like to write raw sql that refers to column names that are only known in runtime:
const { column, value } = await getColumnAndValue()
db.table.raw('$column = $value', { column, value })

with objection/knex, that is possible with ?? = ?. I don't see how it's possible with pqb.
Suggestions
Perhaps a special syntax like $$column?

So what if column happens to be "; DROP DATABASE users; -- ?

Some background: I'm porting https://github.com/IlyaSemenov/objection-graphql-resolver to orchid-orm. That's why I need some edge cases which are not normally needed in userland...runtime column names (coming from graphql inner data) in particular.

That's awesome that you've chosen Orchid as a replacement for the Objection and building an open-source project for it!
We could chat about things in Discord if you're willing.

Changed raw method:

renamed to sql - seems to be a better name
changed the way it works:
instead of  raw('$$column = $value', { column: 'column', value: 'value' })

not this is  sql({ raw: '$$column = $value', values: { column: 'column', value: 'value' } })

it's more to write, but raw here is needed to remind the devs that this is a raw SQL, interpolation here is dangerous
and now it supports template literal, yay!  sql`column = ${123}`

it will collect and pass values in a safe way.
Passing a column name with $$ is still possible, though looks awkward:  sql({ column: 'column' })`$$column = ${123}`

raw method is preserved so it's not a breaking change, added a deprecated flag to it.

@romeerez
db.user.sql({ column: 'column' })`$$column = ${123}`

this gives TS error in<EMAIL_ADDRESS><EMAIL_ADDRESS>
Has it been changed since then?

Yes, it's { values: { column: 'column' } } (note the values)


is fftset faster than nnpack?

Hi!
I've had a look at the NNPACK API and I suspect that it so-greatly differs from fftset in both use-cases and features that I don't think they are comparable. NNPACK is an "acceleration package for neural network computations" (something I know close-to-nothing about) while fftset is designed for performing strictly one-dimension convolutions. fftset does not support multi-dimensional data sets (which NNPACK appears to do) and is based on a mixed-radix and multi-pass implementation. It does not support more interesting (/prime) length DFTs. fftset targets the C90 abstract machine (with extensions when they can be detected) and also supports building on Windows with Microsoft toolchains which NNPACK does not support.
If the use of NNPACK was restricted to the use-case of evaluating single dimension convolutions (which I think it can do - but probably was not designed for), there would be some work in creating a profiling harness to figure out which library is faster. This is not something I'm willing to do as part of this project at the moment.
Thanks,
Nick

thank you very much! hopping support multi-dimensional data in the future


Question about invalidate in RCTRootContentView

Description
Why do you write this code, what problem is it to solve, can you tell me, thank you very much
`- (void)invalidate
{
if (self.userInteractionEnabled) { // this line
self.userInteractionEnabled = NO; // this line
[(RCTRootView *)self.superview contentViewInvalidated]; //  and this line
[_bridge enqueueJSCall:@"AppRegistry"
                method:@"unmountApplicationComponentAtRootTag"
                  args:@[ self.reactTag ]
            completion:NULL];

}
}`
Version
0.71.0
Output of npx react-native info
none
Steps to reproduce
none
Snack, code example, screenshot, or link to a repository
none

Also a duplicate of https://github.com/facebook/react-native-website/issues/3443


Invalid side: Dedicated Server.

The mod could use a check to disable itself if it is found on a server. :)
crash-2024-09-12_17.31.14-fml.txt

@kreezxil  - thanks so much for pointing this out!
Pushing fixes for this in the next update!


Enery integrated on HA

Hi Alex,
how i can add sonoff consumption on integrated energy panel in HA? I can't find Sonoff pow in the drop down menu. There is a way to create a Sonoff sensor compatible with integrated energy panel?
Thank you in advance

have the same question for Dual R2

Duplicate of #562


WIP: This will break. Hopefully. So don't merge it.

Experimenting to make sure I'm wrong and that we are testing changes in the PR.
/hold work-in-progress

So...prow is just ignoring this repo now? Fun.

/ok-to-test

Yeah, didn't really expect that to do anything either.

Verified my hypothesis, fixed in #258


[Question] size mismatch for mm_projector

Question
the error:
'''
image features: tensor([[[ 0.4863, -0.5620,  0.2654,  ...,  0.3267,  0.1172,  0.2654],
         [ 0.2347, -0.2360,  0.3865,  ...,  0.6465,  0.9463,  0.1743],
         [ 1.7607,  1.1641, -0.4541,  ...,  2.9082, -0.4727,  1.9062],
         ...,
         [ 1.6406,  1.4521,  0.0803,  ...,  2.3066, -0.6611,  1.5059],
         [ 1.6182,  1.1221, -0.3940,  ...,  2.7715, -0.3125,  1.8516],
         [ 0.2793,  0.3804,  0.5850,  ...,  0.5508,  0.4658,  0.1287]]],
       device='cuda:0', dtype=torch.float16) torch.Size([1, 576, 1024])
mm_projector: Sequential(
  (0): Linear4bit(in_features=1024, out_features=5120, bias=True)
  (1): GELU(approximate='none')
  (2): Linear4bit(in_features=5120, out_features=5120, bias=True)
)
Traceback (most recent call last):
  File "/home/xmj/projects/LLaVA/llava/eval/test.py", line 111, in <module>
    output_ids = model.generate(
  File "/home/xmj/anaconda3/envs/llava/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
    return func(*args, **kwargs)
  File "/home/xmj/anaconda3/envs/llava/lib/python3.10/site-packages/transformers/generation/utils.py", line 1538, in generate
    return self.greedy_search(
  File "/home/xmj/anaconda3/envs/llava/lib/python3.10/site-packages/transformers/generation/utils.py", line 2362, in greedy_search
    outputs = self(
  File "/home/xmj/anaconda3/envs/llava/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
    return forward_call(*args, **kwargs)
  File "/home/xmj/anaconda3/envs/llava/lib/python3.10/site-packages/accelerate/hooks.py", line 165, in new_forward
    output = old_forward(*args, **kwargs)
  File "/home/xmj/projects/LLaVA/llava/model/language_model/llava_llama.py", line 79, in forward
    ) = self.prepare_inputs_labels_for_multimodal(
  File "/home/xmj/projects/LLaVA/llava/model/llava_arch.py", line 124, in prepare_inputs_labels_for_multimodal
    image_features = self.encode_images(images)
  File "/home/xmj/projects/LLaVA/llava/model/llava_arch.py", line 98, in encode_images
    image_features = self.get_model().mm_projector(image_features)
  File "/home/xmj/anaconda3/envs/llava/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
    return forward_call(*args, **kwargs)
  File "/home/xmj/anaconda3/envs/llava/lib/python3.10/site-packages/accelerate/hooks.py", line 165, in new_forward
    output = old_forward(*args, **kwargs)
  File "/home/xmj/anaconda3/envs/llava/lib/python3.10/site-packages/torch/nn/modules/container.py", line 217, in forward
    input = module(input)
  File "/home/xmj/anaconda3/envs/llava/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
    return forward_call(*args, **kwargs)
  File "/home/xmj/anaconda3/envs/llava/lib/python3.10/site-packages/accelerate/hooks.py", line 165, in new_forward
    output = old_forward(*args, **kwargs)
  File "/home/xmj/anaconda3/envs/llava/lib/python3.10/site-packages/bitsandbytes/nn/modules.py", line 248, in forward
    out = bnb.matmul_4bit(x, self.weight.t(), bias=bias, quant_state=self.weight.quant_state)
  File "/home/xmj/anaconda3/envs/llava/lib/python3.10/site-packages/bitsandbytes/autograd/_functions.py", line 579, in matmul_4bit
    return MatMul4Bit.apply(A, B, out, bias, quant_state)
  File "/home/xmj/anaconda3/envs/llava/lib/python3.10/site-packages/torch/autograd/function.py", line 506, in apply
    return super().apply(*args, **kwargs)  # type: ignore[misc]
  File "/home/xmj/anaconda3/envs/llava/lib/python3.10/site-packages/bitsandbytes/autograd/_functions.py", line 516, in forward
    output = torch.nn.functional.linear(A, F.dequantize_4bit(B, state).to(A.dtype).t(), bias)
RuntimeError: mat1 and mat2 shapes cannot be multiplied (576x1024 and 1x2621440)

the origin code is:
`
class LlavaMetaForCausalLM(ABC):
def encode_images(self, images):
    image_features = self.get_model().get_vision_tower()(images)
    print('image features:', image_features, image_features.shape)
    print('mm_projector:', self.get_model().mm_projector)
    image_features = self.get_model().mm_projector(image_features)
    print('image features2:', image_features, image_features.shape)
    return image_features

`
why the image features can't multiply with the mm_projector weights?

Hello! I faced this error too. This thread helped me out

this is because code wrong.

@mengjiexu
Can you share how you fixed it? It would be beneficial to the community if we can incorporate the fix. Thanks.

I used myself code to load the mm_projector.bin again after the load_model func, so it is wrong, I remove the code then the model is normal.

@mengjiexu Could you share the code you used to reload mm_projector?


Sometimes the message not received well and sometimes received fine from second time attempt

I work with signalR to receive notifications so I prepare my logic for receiving connect with in signal R
class SignalRService {
    private var connection: HubConnection
    var notificationDelgate: HubConnectionDelegate?
    @available(iOS 13.0, *)
    public init(url: URL,notificationDelgate: HubConnectionDelegate?) {
        connection = HubConnectionBuilder(url: url)
            .withLogging(minLogLevel: .debug)
            .withHttpConnectionOptions() { httpConnectionOptions in
                httpConnectionOptions.requestTimeout = 60
                httpConnectionOptions.accessTokenProvider = { return AuthManager.shared.token }
            }
            .withHubConnectionOptions(configureHubConnectionOptions: {options in
                options.keepAliveInterval = 15 })
            .withHubConnectionDelegate(delegate: notificationDelgate!)
//            .withPermittedTransportTypes(.all)
            .withAutoReconnect()
            .build()
   connection.on(method: "NOTIFICATIONS_BOX", callback: { (notificationsBox: ModelArgumentNotification?) in
            do {
                if let notificationsBox {
                    self.handleMessage(notificationsBox)
                }
            }
        })
        startConnection()
    }

    public func startConnection() {
        connection.start()
    }

    public func closeConnection() {
        connection.stop()
    }

    private func handleMessage(_ notificationsBox: ModelArgumentNotification) {
        NotificationCenter.default.post(Notification(name: .customValueUpdated, object: nil, userInfo: ["contentNotification": notificationsBox]))
    }
}

And this is init for delegate signalR
self.notificationHubConnectionDelegate = NotifcationHubConnectionDelegate(controller: self)
            if #available(iOS 13.0, *) {
                signalR = SignalRService.init(url: URL(string: SignalRURLS.notificationHub.rawValue)!, notificationDelgate: self.notificationHubConnectionDelegate)
        }

In first time notification receive fine without refresh the view controller but when i send again message the notification not receive

I must send the new message to trigger the changes again from signalR

Please provide logs. Also, please provide more context on how the signalR variable is being used.

Assuming this is no longer an issue.


feat: Setup the Gitpod ready-to-code system

Changes proposed

Setup the Gitpod ready-to-code system.

With Gitpod.io, you can easily build the website and install the necessary dependencies in one single click.

Note to reviewers
None yet.

@Panquesito7 Thank you,
but it's showing me this

Looks great, can we update this URL

That URL is generated automatically and cannot be changed. Once the PR is merged, it should show up as the original repository link.

Okay, thank you


[WICKET-6885] Add scope = col to Columnheaders

For accessibility purposes it is necessary to include a scope=col attribute in th column header elements, see
https://www.w3schools.com/tags/att_th_scope.asp

Done https://issues.apache.org/jira/browse/WICKET-6885

Thank you, @renoth !


Cow: Watch accessfixtures.com

Cow requests the watch of the watch_keyword accessfixtures\.com. See the MS search here and the Stack Exchange search in text, in URLs, and in code.

accessfixtures\.com has been seen in 1 true positive, 0 false positives, and 0 NAAs.

Approved by Spevacus in Charcoal HQ


Issue with Popup Components Rendering Behind Map Before Triggered by Marker Click

Hi,
I'm encountering an issue with the Popup component in my Svelte app. I'm not sure if this is due to an error on my part or if it's a bug.
Problem:
When the page initially loads, all components placed inside the Popup slots are rendered "behind" the map. When I click on a marker to open its corresponding popup, it removes the component from behind the map (and the DOM).
Explanation:
Here is my map code:
{#each $properties as property (property.id)}
  <Marker
    latlng={new LatLng(property.lat, property.lng)}
    bind:instance={markerInstances[property.id]}
  >
    <Popup>
      <PropertyCard {property} />
    </Popup>
  </Marker>
{/each}

When the page loads, the HTML structure appears like this:

...

...

...

Observations:

Upon initial load, there are div elements corresponding to each PropertyCard component within the popup.
When I click on a marker and its popup opens, the corresponding div element is removed from the DOM.
This results in a height increase for the map to accommodate all the undesired div elements.

Temporary Workaround:
I could fix the increased map height issue by using overflow: hidden or some other way. However, I just wanted to  ask if this is the intended behavior.
Is it normal for the popup component to render inside the DOM before a marker click triggers them, and should they be removed from the DOM once the popup is activated?
Thanks for your assistance!

Hi,
I'm encountering an issue with the Popup component in my Svelte app. I'm not sure if this is due to an error on my part or if it's a bug.
Problem:
When the page initially loads, all components placed inside the Popup slots are rendered "behind" the map. When I click on a marker to open its corresponding popup, it removes the component from behind the map (and the DOM).
Explanation:
Here is my map code:
{#each $properties as property (property.id)}
  <Marker
    latlng={new LatLng(property.lat, property.lng)}
    bind:instance={markerInstances[property.id]}
  >
    <Popup>
      <PropertyCard {property} />
    </Popup>
  </Marker>
{/each}

When the page loads, the HTML structure appears like this:

...

...

...

Observations:

Upon initial load, there are div elements corresponding to each PropertyCard component within the popup.
When I click on a marker and its popup opens, the corresponding div element is removed from the DOM.
This results in a height increase for the map to accommodate all the undesired div elements.

Temporary Workaround:
I could fix the increased map height issue by using overflow: hidden or some other way. However, I just wanted to ask if this is the intended behavior.
Is it normal for the popup component to render inside the DOM before a marker click triggers them, and should they be removed from the DOM once the popup is activated?
Thanks for your assistance!

Sorry, a little busy this week, will check whether this issue is a problem this weekend. :)

Hi,
I'm encountering an issue with the Popup component in my Svelte app. I'm not sure if this is due to an error on my part or if it's a bug.
Problem:
When the page initially loads, all components placed inside the Popup slots are rendered "behind" the map. When I click on a marker to open its corresponding popup, it removes the component from behind the map (and the DOM).
Explanation:
Here is my map code:
{#each $properties as property (property.id)}
  <Marker
    latlng={new LatLng(property.lat, property.lng)}
    bind:instance={markerInstances[property.id]}
  >
    <Popup>
      <PropertyCard {property} />
    </Popup>
  </Marker>
{/each}

When the page loads, the HTML structure appears like this:

...

...

...

Observations:

Upon initial load, there are div elements corresponding to each PropertyCard component within the popup.
When I click on a marker and its popup opens, the corresponding div element is removed from the DOM.
This results in a height increase for the map to accommodate all the undesired div elements.

Temporary Workaround:
I could fix the increased map height issue by using overflow: hidden or some other way. However, I just wanted to ask if this is the intended behavior.
Is it normal for the popup component to render inside the DOM before a marker click triggers them, and should they be removed from the DOM once the popup is activated?
Thanks for your assistance!

Hi luka, may I know which version you're using now? Maybe this feature is fixed by #cc63719.
Update your Sveaflet version, and hope it helpful for you.

Wow, I just learned that running npm update doesn't update your dependencies to the latest available version so i was stuck on version 0.0.4 even though i thought i was getting the latest one after running update.
Yes, it is fixed now after updating to 0.0.10.
Sorry for the false issue and thank you for allowing me to learn something new :sweat_smile:


Oracle Dialect: Unsupported JDBC data type "-8"

One of our customers is getting the following error when creating a new virtual schema on Oracle. Other virtual schemas have already successfully been created to that Database, therefore the connection can't be the cause. It seems like an issue with an unknown datatype.

The error message tells me that it's an old version of VS. We fixed this bug already.
But I can see that we don't support ROWID column type in Oracle in the latest release. It's just skipped during columns mapping.
I'm going to add support for ROWID column type.

A colleague had another error with "-3" instead of "-8".
-3 is the VARBINARY type, could you also include that one?

VARBINARY is officially not supported. VARBINARY columns can only be skipped, it happens automatically in the latest release.

Hi Anastasiia, I have attached a csv file which contains the filtered data types which we do not support. I understand some of them will not be possible. Some data types which we support may be on that list, apologies for that.
incompatible_data_types_found.txt
Here is the complete Excel spreadsheet the prospect sent to me which contains more info.
data_types.xlsx
If you need anything or any help, let me know.

I checked data types from the file. All user defined data types are not supported.
Standard data types from the list:
LONG - supported
CLOB - supported
LONG RAW - won't be supported
NVARCHAR2 - supported
RAW - won't be supported
ROWID - will be supported in the next release
TIMESTAMP - supported
I don't know why supported types are in the list. We need CREATE TABLE statement to investigate the issue.
Additional information about data types and mapping in Oracle can be found at the end of this file:
https://github.com/exasol/virtual-schemas/blob/master/doc/dialects/oracle.md


Port over tests for RewardActor

Tests for the actors are in the specs-actors repo. They needed to be ported (1:1) to our actor implementations to ensure that our VM implementations match.
specs-actors repo: https://github.com/filecoin-project/specs-actors/tree/master/actors/builtin

Tests have been ported over


Unpair offline device

I added a device to the instance which I use no longer. Therefor I put it away. Now I want to unpair it from the HomeKit instance. After clicking the "unpair" button I get the error "Cannot unpair from device IP-XX:XX:XX:XX:XX:XX because of error undefined: connect EHOSTUNREACH <IP_ADDRESS>:60581".

How can I force unpair the device?

Just delete the objects of this devcie in adminand restart the adapter ... but just these objects :-)

I reopen, because I might want to add a delete button or such