How to train a LSTM on a sequence of dates?
You could hand over the date split into three inputs: One would then be the year, the other the month, and the last the day. While normalizing your inputs definitely makes sense, however I would not entirely agree with your "LSTM requires".
Day and month are already limited to a range of values which can be scaled

day (1 - 31)
month (1 - 12)

For year you need to make an educated assumption based on your application. So that year can then also be transferred to a scaled value. Judging from your data, it might be that year is constant at 2012 and it is not needed to begin with.

year (2012 - 2013(?))

Note: Ask yourself whether you give the neural network enough system information to be able to predict the next date - meaning, is there already enough of a pattern in your data? Otherwise you might end up training a random predictor.
if i wanted to train an lstm to predict the next date in a sequence of dates, how would i do that since lstm require a scaled value?
example of data:




date
next date




2012-05-12
2012-05-13


2012-05-13
2012-05-19


2012-05-19
2012-05-20


2012-05-20
2012-05-22


2012-05-22
2012-05-26


2012-05-26
2012-05-27


2012-05-27
2012-05-30


2012-05-30
2012-06-12


2012-06-12
2012-05-19


2012-06-19
2012-06-25