tidymodels see .notes error but only with step_xxx() functions in a certain order

I have what I think is weird behavior where if I do a step_dummy() BEFORE a step_normalize() (the recommended order from use_glmnet(), the model fitting errs out with the "Warning all models failed. See the .notes column" error. But, if I instead do step_normalize() and then step_dummy(), it works just fine. I have a reproducible example below ... sorry it's sort of long because I couldn't reproduce it with different data.

library(tidyverse)         # for graphing and data cleaning
library(tidymodels)        # for modeling
#> Registered S3 method overwritten by 'tune':
#>   method                   from   
#>   required_pkgs.model_spec parsnip
library(naniar)            # for analyzing missing values
library(vip)               # for variable importance plots
#> 
#> Attaching package: 'vip'
#> The following object is masked from 'package:utils':
#> 
#>     vi

hotels <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-02-11/hotels.csv')
#> Rows: 119390 Columns: 32
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> chr  (13): hotel, arrival_date_month, meal, country, market_segment, distrib...
#> dbl  (18): is_canceled, lead_time, arrival_date_year, arrival_date_week_numb...
#> date  (1): reservation_status_date
#> 
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

hotels_mod <- hotels %>% 
  mutate(is_canceled = as.factor(is_canceled)) %>% 
  mutate(across(where(is.character), as.factor)) %>% 
  select(-arrival_date_year,
         -reservation_status,
         -reservation_status_date) %>% 
  add_n_miss() %>% 
  filter(n_miss_all == 0) %>% 
  select(-n_miss_all)

set.seed(494)

# Randomly assigns 50% of the data to training.
hotels_split <- initial_split(hotels_mod, 
                              prop = .5, 
                              strata = is_canceled)

hotels_training <- training(hotels_split)
hotels_testing <- testing(hotels_split)

hotel_recipe <- recipe(is_canceled ~ .,
                       data = hotels_training) %>% 
  step_mutate_at(children, babies, previous_cancellations,
                 fn = ~ as.numeric(. > 0)) %>%
  step_mutate_at(agent, company,
                 fn = ~ as.numeric(. == "NULL")) %>%
  step_mutate(country, 
              country_grp = fct_lump_n(country, n = 5)) %>% 
  step_rm(country) %>% 
  ## If I put this step_dummy() AFTER step_normalize(), it runs fine
  step_dummy(all_nominal(),
             -all_outcomes()) %>% 
  step_normalize(all_predictors(),
                 -all_nominal(),
                 -all_outcomes()) 

hotel_lasso_mod <- 
  # Define a lasso model 
  logistic_reg(mixture = 1) %>% 
  # Set the engine to "glmnet" 
  set_engine("glmnet") %>% 
  # The parameters we will tune.
  set_args(penalty = tune()) %>% 
  # Use "regression"
  set_mode("classification")

hotel_lasso_wf <- 
  # Set up the workflow
  workflow() %>% 
  # Add the recipe
  add_recipe(hotel_recipe) %>% 
  # Add the modeling
  add_model(hotel_lasso_mod)

set.seed(494) # for reproducibility

#5-fold cv
hotel_cv <- vfold_cv(hotels_training, v = 5)

# potential penalty parameters
penalty_grid <- grid_regular(penalty(),
                             levels = 10)

hotel_lasso_tune <- 
  hotel_lasso_wf %>% 
  tune_grid(
    resamples = hotel_cv,
    grid = penalty_grid
  )
#> x Fold1: preprocessor 1/1, model 1/1: Error in lognet(xd, is.sparse, ix, jx, y, w...
#> x Fold2: preprocessor 1/1, model 1/1: Error in lognet(xd, is.sparse, ix, jx, y, w...
#> x Fold3: preprocessor 1/1, model 1/1: Error in lognet(xd, is.sparse, ix, jx, y, w...
#> x Fold4: preprocessor 1/1, model 1/1: Error in lognet(xd, is.sparse, ix, jx, y, w...
#> x Fold5: preprocessor 1/1, model 1/1: Error in lognet(xd, is.sparse, ix, jx, y, w...
#> Warning: All models failed. See the `.notes` column.

Created on 2021-09-09 by the reprex package (v2.0.0)

I suspect that one of the dummy variables generates a column with all zero (or something similar). The variance calculations fail and the data can't be standardized.

Try putting a step_zv(all_predictors()) before the normalized step.

Also, we have things like all_numeric_predictors() now, so you can simplify selections that use statements like: