Why am I getting this error when using glmnet in R - r

Question

When trying to predict I am getting this error
error in evaluating the argument 'x' in selecting a method for function 'as.matrix': Cholmod error 'X and/or Y have wrong dimensions' at file ../MatrixOps/cholmod_sdmult.c, line 90
Here is my code so far
library(data.table)
library(caret)
library(Metrics)
library(glmnet)
library(plotmo)
library(lubridate)
#Reading in the necessary data needed for this project
train <- fread("project/volume/data/processed/start_train.csv")
test<-fread("project/volume/data/processed/start_test.csv")
example_sub<-fread("project/volume/data/processed/example_submission.csv")
card_tab <- fread("project/volume/data/processed/card_tab.csv")
#Merging the card_tab dataset with both my train and test datasets to add more variables to each
train = merge(train, card_tab, by = "id")
test = merge(test, card_tab, by = "id")
train$power = as.numeric(train$power)
train$toughness = as.numeric(train$toughness)
test$power = as.numeric(test$power)
test$toughness = as.numeric(test$toughness)
train$powerovertough = train$power/train$toughness
test$powerovertough = test$power/test$toughness
train$current_date<-as_date(train$current_date)
train<-train[order(-current_date)]
test$current_date<-as_date(test$current_date)
test<-test[order(-current_date)]
#Handling NA values in both train and test. The NA values will be set to 0
train[is.na(train)] <- 0
test[is.na(test)] <- 0
# Specifying which columns of our model we will be dropping and applying it to our train and test datasets
drops<- c('id','future_date','current_date','card_name','power','loyalty','cmc','type','colors','mana_cost','subtypes', 'text','set','set_name')
train<-train[, !drops, with = FALSE]
test<-test[, !drops, with = FALSE]
#Saving the response variable in the train dataset
train_y<-train$future_price
test$future_price<-0
#Using dummies to train model
dummies <- dummyVars(future_price ~ ., data = train)
train<-predict(dummies, newdata = train)
test<-predict(dummies, newdata = test)
train<-data.table(train)
test<-data.table(test)
#Cross validating the model to fin the best lamda value
train<-as.matrix(train)
test<-as.matrix(test)
gl_model<-cv.glmnet(train, train_y, alpha = 1,family="gaussian")
bestlam<-gl_model$lambda.min
# Fitting the full model
gl_model<-glmnet(train, train_y, alpha = 1,family="gaussian")
plot_glmnet(gl_model)
saveRDS(gl_model,"./project/volume/models/gl_model.model")
test<-as.matrix(test)
#use the full model
pred<-predict(gl_model,s=bestlam, newx = test)
I am trying to predict future_price for my test set. The error is saying my dimensions are wrong but I don't know what could be causing them to be different. I have tried observing the data sets as it runs through the code and they seem to have the same variables.
Here is the dput
> dput(head(train))
structure(c(0.25, 0.1, 0.1, 0.1, 0.25, 0.25, 1, 0, 0, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 3, 0, 2, 0, 0, 0, 0.333333333333333,
0, 1, 0), .Dim = c(6L, 20L), .Dimnames = list(NULL, c("current_price",
"typesArtifact", "typesArtifact Creature", "typesCreature", "typesEnchantment",
"typesEnchantment Artifact", "typesEnchantment Creature", "typesInstant",
"typesLand", "typesPlaneswalker", "typesSorcery", "supertypes",
"supertypesBasic", "supertypesLegendary", "rarityCommon", "rarityMythic",
"rarityRare", "rarityUncommon", "toughness", "powerovertough"
)))
> dput(head(test))
structure(c(0.15, 0.16, 2, 0.39, 0.16, 0.19, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0,
1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,
1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0), .Dim = c(6L, 18L), .Dimnames = list(
NULL, c("current_price", "typesArtifact", "typesArtifact Creature",
"typesCreature", "typesEnchantment", "typesInstant", "typesLand",
"typesPlaneswalker", "typesSorcery", "supertypes", "supertypesBasic",
"supertypesLegendary", "rarityCommon", "rarityMythic", "rarityRare",
"rarityUncommon", "toughness", "powerovertough")))