Yup, Python clearly pioneered automatic code formatting. Nobody had done it before them...
That document states Bill Gosper wrote one of the first pretty printers, but doesn’t give a date for it.
I would think it is much older, as writing a simple lisp pretty-printer is easy and reading lisp without autoformatting is “less than ideal”.
In what way are context-free languages "a grammatically bloated mess?" Whitespace delimited languages like Python have context-sensitive grammars. Now that is a mess.
Dismissing the whole field of formal language theory with that statement is so bogus, it "is not even wrong." There are tools and techniques for parsing context-free languages that are impossible with context-sensitive languages. Parser generators, structured editing, metacompilers, composable grammars; those are all impossible with context-sensitive languages. Context-free languages are faster to parse, easier to write compilers/interpreters for, and much easier to write tooling for (editors, linters, etc).
Ambiguous context-free grammars are a very different thing from context-sensitive grammars. You can't point to the former and say "so whitespace sensitivity is ok."
I can point to ambiguous context-free grammars and say whitespace sensitivity is ok, because the tools to fix the one are often the same to fix the other. (Ambiguous context-free grammars do turn out to make context sensitive languages, because it's a blurry line in the grass between them.)
Whitespace sensitivity is one of the easier context sensitivity challenges to embed as a sub-language in an otherwise (mostly) context-free language, because you can represent it entirely as pseudo tokens from a simply modified lexing phase in a traditional CFG parser. Python is an easy and clear proof, as that is exactly what it did (Python is also not purely a CFG after whitespace tokenization because of also how it handles the dangling else, but we've already mentioned those weeds).
Even if you don't find the boundaries between the classes of languages fuzzy (and Parser Combinators and PEG grammars have a lot to say here), context-sensitive languages are a tool in a growing toolbelt, not a "complex" evil to be demonized.
Algorithmic complexity is not "moral superiority." The whole point of formal language theory is to show how the different languages differ in terms of how difficult they are to work with.
> just as they use regular expressions for tokenizing and allow regular language sub-languages
That is because regular languages are a subset of context-free languages. There is nothing special to "allow" there.
> I can point to ambiguous context-free grammars and say whitespace sensitivity is ok, because the tools to fix the one are often the same to fix the other.
No they are not. Precedence rules and extra lookahead will help with ambiguous context-free grammars but not with context-sensitive ones. There is also a whole class of algorithms, such as GLR, specialized to handle ambiguous CFGs efficiently.
> Whitespace sensitivity is one of the easier context sensitivity challenges to embed as a sub-language in an otherwise (mostly) context-free language, because you can represent it entirely as pseudo tokens from a simply modified lexing phase in a traditional CFG parser.
That statement is nonsense. There is no way to "embed" a context-sensitive language in a CFG. What you are saying is that it is "easy" to make a whole context-sensitive parser just to produce a context-free language so you can pass that to another CFG parser. That is not "easy," that is bolting crap onto other crap.
Chomsky's hierarchy was designed for production from categories of languages. That it doubles as a useful rule of thumb for algorithmic complexity in parsing is a fascinating dualism in mathematics. That those same rules of thumb reflect basic automaton abstractions is even more fascinating. This is also where it seems the clearest analogy lies to why I find your arguments to "complexity" so useless. It sounds to me like a strange steampunk form of the "640k is all anyone will ever need" fallacy: "why use Turing machines when Pushdown automatons will do just fine?"
Yes, there are a lot of great tools for working with CFGs, just as we've pushed Regular Expressions far past the boundaries of formal Regular Languages, we push these same tools past the boundaries of formal CFGs. We aren't actually constrained by the limits of only using Deterministic Finite Automata or Pushdown Automata in our programming, we have vast Church-Turing–level state machines at our power completely and easily capable of tracking contexts/states.
The "context" in a whitespace-sensitive language is "how many spaces have you seen recently?". This is not a hard question, this is not some mystical "context sensitivity" that needs an entire "context-sensitive language parser". It's a handful of counters. That is the very definition of easy, that's the built-in basics of your average Church-Turing machine, keep a number on the tape and update it as necessary.
Python's specification for the language defines the language in a CFG BNF just as the majority of brackets languages. The one concession to its whitespace sensitivity is that it expects from its lexer INDENT and DEDENT tokens. Those tokens are added simply by counting whitespace between lines, seeing if there is a < or > relationship. After those tokens are in the stream (along with the rest of the tokens defined in their associated (Mostly) Regular Languages) it is parsed by whatever CFG automaton/algorithm the Python team wants (LR, GLR, LALR, PEG, etc). That's not "bolting crap onto other crap" by most stretches of the imagination, that's using a pretty simple stack of tools that any programmer should be able to (re-)build, and not one at all more complicated than (and in fact built entirely inside) the classic tokenizer/lexer feeding a parser stack.
Honestly, from everything you have posted so far, I really get the impression that you have never worked on programming language parsing, or with large code bases. The difference in algorithmic complexity is not "useless," it is the difference between being able to compile millions of lines of code in a few seconds on modest hardware, and needing a cluster just to get your builds done:
https://community.embarcadero.com/blogs/entry/compiling-a-mi...
If we want to fight anecdote for anecdote, I've certainly seen millions of lines of code of Python analyzed ("compiled") in seconds on modest hardware, rather than a cluster. Differences there are more in the static typing versus dynamic typing, and strongly compiled versus weakly compiled/primarily interpreted there. Maybe a better anecdote is millions of lines of Haskell? Seen that too. Whitespace-sensitive languages scale just fine.
I can understand it if you want to admit that you just don't like whitespace-sensitive languages for personal reasons, but there aren't very good technical reasons and you seem unhappy with my explanations of why that is the case. I'm not sure how much more I can try to explain it, and given you seem close to resorting to personal attacks I'm afraid this is likely where the conversation ends.
The advent of automatic code formatting (and Python pioneered this in many ways with pep8, but even huge C++ projects are realizing the value in clang-format, clazy, etc) put the nail in the coffin of arguments against whitespace blocking. You can have an enforced uniform style throughout a project now, with no ambiguity, and in such contexts using whitespace as a block delimiter also has no ambiguity. It just takes advantage of formatting already being done to avoid redundant glyph use.