With events, we can handle only significant tokens to the parser and deal with attaching comments and whitespace when reconstructing tree from a flat list of events. To properly implement incremental reparsing, we should start with a data structure for text which is more efficient to update than String. While we do have quite a few extremely high-quality implementations of ropes, the ecosystem is critically missing a way to talks about them generically.
Homogeneous trees make reactive testing of the grammar possible in theory because you can always produce a text representation of a tree from them. But in practice reactivity requires that "read grammar, compile parser, run it on input" loop is fast.
Lexer Style generating source code of the parser and then compiling it would be too slow, so some kind of interpreted mode is required.
However, this conflicts with the need to be able to extend lexer with custom code. A possible alternative is to use a different, approximate lexer for interactive testing of the grammar. In my experience this makes such testing almost useless because you get different results in interesting cases and interesting cases are what is important for this feature.
In IDEs, a surprisingly complicated problem is managing a list of open and modified files, synchronizing them with the file system, providing consistent file-system snapshots and making sure that things like in-memory buffers are also possible. For parser generators, all this complexity might be dodged by requiring that all of the grammar needs to be specified in a single file.
So we want to write a parser generator that produces lossless parse trees and which has an awesome IDE support. How do we actually parse a text into a tree? Languages which could be described by regular expressions are called regular. They are exactly the same languages which could be recognized by finite state machines.
These two definition mechanisms have nice properties which explain the usefulness of regular languages in real life:. Regular expressions map closely to our thinking and are easy for humans to understand. Note that there are equivalent in power, but much less "natural" meta-languages for describing regular languages: raw finite state machines or regular grammars.
Finite state machines are easy for computers to execute. FSM is just a program which is guaranteed to use constant amount of memory. Regular languages are rather inexpressive, but they work great for lexers. On the opposite side of expressivity spectrum are Turing machines. For them, we also have a number of meta-languages like Rustwhich work great for humans. Moving the head then corresponds to popping from one stack and pushing to another.
And the context-free languages, which are described by CFGs, are exactly in between languages recognized by finite state machines and languages recognized by Turing machines. You need a push-down automaton, or a state machine with one stack, to recognize a context-free language.
CFGs are powerful enough to describe arbitrary nesting structures and seem to be a good fit for describing programming languages. However, there are a couple of problems with CFGs. The obvious answer. We need to tweak the grammar to get rid of this ambiguity:. I think the necessity of such transformations is a problem! So CFGs turn out to be much less practical and simple than regular expressions.
What options do we have then? The first choice is to parse somethingnot necessary a context-free language. A good way to do it is to write a parser by hand. A hand-written parser is usually called a recursive descent parser, but in reality it includes two crucial techniques in addition to just recursive descent.
In theory, this problem is solved by rewriting the grammar and eliminating the left recursion. If you had a formal grammars class, you probably have done this! In practice, this is a completely non-existent problem, because we have loops:. The next problem with recursive descent is that parsing expressions with precedence requires that weird grammar rewriting.
One way to do that would be to parse it with a loop as a list of atoms separated by operators and then reconstruct a tree separately. If you fuse these two stages together, you get a loop, which could recursively call itself and nest, a Pratt parser.
Understanding it for the first time is hard, but you only need to do it once :. The most important feature of hand-written parsers is a great support for error recovery and partial parses. It boils down to two simple tricks. If you are parsing a homogeneous sequence of things i. Here we just skip over it. If you are parsing a particular thing Tand you expect token foobut see barthen, roughly:. Instantaneous feedback and precise location are, in my personal experience, enough to fix syntax errors.
The error message can be just "Syntax error", and more elaborate messages are often make things worse because mapping from an error message to what is actually wrong is harder than just typing and deleting stuff and checking if it works.
This is what Grammar Kit and fall do. Another choice is to stay within CFG class but avoid dealing with ambiguity by producing all possible parse trees for a given input. Yet another choice is to give up full generality and restrict the parser generator to a subset of unambiguous grammars, for which we actually could verify the absence of ambiguity. The very important advantage of these parsers is that you get a strong guarantee that the grammar works and does not have nasty surprises.
Recursive descent parsers, which are more or less LL 1recover from errors splendidly, and LR 1 has strictly more information than an LL 1 one. It seems to me that the two extremes are the most promising: hand written parser gives you utmost control over everything, which is important when you need to parse some language, not designed by you, which is hostile to the usual parsing techniques.
On the other hand, classical LR-style parsers give you a proof that the grammar is unambiguous, Lexer Style is very useful if you are creating your own language.
Ultimately, I think that being able to produce lossless parse trees supporting partial parses is more important than any particular parsing technique, so perhaps supporting both approaches with a single API is the right choice? The most basic requirement is that you parse it. Parsing is considered a solved problem. The specific reason is that I care way Lexer Style much about the Rust programming language and. I think today it is the best language for writing compiler-like stuff yes, better than OCaml!
UX Although this text is written in Emacs, I strongly believe that a semantic-based, reliable, and fast support from tooling is a great boon to learnability and productivity. Errors and warnings inline, with fixes if available. Extract rule refactoring, pairs well with extend selection. Code formatting. Code completion: although for parser generators dumb word-based completion tends to work OK. API Parse Tree Traditionally, parser generators work by allowing the user to specify custom code for each rule, which is then copy-pasted into the generated parser.
Type of a node: is it a function definition, a parameter, a comment? Region of the source text covered by the node. Incremental Reparsing Another important feature for modern parser generator is support for incremental reparsing, which is obviously useful for IDEs. One thing that greatly helps here is the split between parser and lexer phases. Lexer Traditional lex-style lexers struggle with special cases like ml-style properly nested comments or Rust raw literals which are even not context-free.
Parser A nice trick to make parser more general and fast is not to construct parse tree directly, but emit a stream of events like "start internal node", "eat token", "finish internal node". Miscellaneous concerns To properly implement incremental reparsing, we should start with a data structure for text which is more efficient to update than String. Parsing Techniques So we want to write a parser generator that produces lossless parse trees and which has an awesome IDE support.
Abandoning CFG The first choice is to parse somethingnot necessary a context-free language. Embracing ambiguity Another choice is to stay within CFG class but avoid dealing with ambiguity by producing all possible parse trees for a given input. Abandoning generality Yet another choice is to give up full generality and restrict the parser generator to a subset of unambiguous grammars, for which we actually could verify the absence of ambiguity.
The beginning is marked with an import of Token class and definition of GrammarStruct interface for specifying how single-token-matching regexp container should look like. Next comes the Lexer class with few properties which names speak for themselves. Now, let's move on to the methods then. First internal method getRegex is used to generate a single regexp from passed expr which is generated from joined GrammarStruct matchers and ensure that the lastIndex is properly set when the regexp needed to be regenerated when adding new GrammarStruct.
The loadDefinition and loadGrammar function are responsible for loading GrammarStruct s i, Lexer Style. Data is just a string, appended to the longer in-lexer one. But there's nothing magical about this one as well. It just matches the next token in data using regexp, processes it and adds new Token based on the generated data to the list i. It additionally checks for any newlines and whitespaces matchers for them are predefined by default in Lexer and handles them properly to calculate locations line and column number of each token.
Basically what it does is it matches all tokens possible in supplied data until no token can be found and returns the whole list of them at once. It sorts and arranges the tokens array in a clean, functional way. First, the array is filtered against tokens that are empty - have no value. Next, they're sorted by their respected locations. Lastly, tokens are mapped to arrange them to start from the line and column number 1, which involves checks for newlines and whitespaces. This method has its usage later on in most of Token class methods.
It does the dirty work of emptying Lexer 's data for its reuse grammar definitions remains loaded. And that's it for the Lexer class! It isn't that much complicated - if even complicated at all! But that's how everything should be - why make a big problem from something so easy to solve? Of course, some improvements probably can be made, but the basic idea remains the same.
In this file, the even more simple Token class is declared. It basically looks like this:. At the very beginning, we have an import of the Lexer class for types definition purposes and declaration of the TokenData interface, which defines all values needed to create a new token. Token class is nothing more than a simple collector for Lexer Style data with some helper functions. Lexer is required to be passed as so-called context for later interaction between its methods and Token API.
This is one of many token-editing methods, which can be optionally used for the basic edition of generated tokens. Its second parameter, with a default value of trueindicates if Lexer should call update method after all other tasks.
After the movement is indicated, the token is moved in the array by Lexer 's update method. So, Token class mainly features some methods for editing its data. It may not always be needed but it's a good functionality to have. In grammar.
We provide id as an identifier for the type of matched token and match as a regexp in form of string for later concatenation. One thing to note here. Because our complete regexp is being generated in a linear way, the right order of GrammarStruct matchers must be kept. After all the code above is put together you can find the full source code at core package of AIM multi-repo its time to use this creation! It all comes down to as much as the code below:. Now, I may end this story here, but there's one more catch.
You see, the lexer is used only to process linear text into an array of tokens. The one aspect that's especially well-related to this problem is the implementation of string s in our grammar. How can you match with a single regexp all of the possibilities i.
The simple answer is you don't. Maybe the solution is obvious to some of you but it really required some deep thinking from me most likely I wasn't open-minded enough. You have to work with string char by char at least this is what I came up with.
lexer definition: Noun (plural lexers) 1. (computing) A computer program that performs lexical derbattmogegefilykornorolsoftcat.xyzinfo lex + -er, ultimately from lexical. Recall: It was the lexer's job to identify valid graph ids based on the 1st sub-grammar. By the time the data hits the parser, we know we have a valid graph id, and as long as it plugs in to the structure of the grammar in the right place, we are prepared to accept any valid graph id. A lexer is an object governing the styling of the various elements of the source code. In other words it is the “syntax highlighting” that discover elements in the text and applies styles. The Lexer classes simply encapsulates various style elements for example. The STC handles text and uses a lexer to add syntax highlighting to this text. There are a lot of internal lexers for the most common derbattmogegefilykornorolsoftcat.xyzinfoally, as soon as you scroll a page or modify it, scintilla calls a style routine to style that text. The lexer than applies that style to the text. Apr 30, · The lexer, also known as a Tokenizer or Scanner, is the first step of the compilation process. The job of the lexer is to take the programmer's input and split it up into individual tokens -- things like keywords, variable names, punctuation, and operators -- . Register the on style needed event for the window (this is windows specific) static const int WINDOW_ID = ; BEGIN_MESSAGE_MAP(CDocumentWindow, CDocumentWindowsBaseClass) ON_NOTIFY(SCN_STYLENEEDED, WINDOW_ID, OnStyleNeeded) END_MESSAGE_MAP() (WINDOW_ID is used in the CreateWindow call when you create the window). Combining the creativity of top architects and the reliability of experienced local construction teams, a Lexar home is simply built better for your best life. A lexer may be written as a script in the Lua language instead of in C++. This is a little simpler and allows lexers to be developed without using a C++ compiler. A script lexer is attached by setting the file lexer to be a name that starts with "script_". Styles and other properties can . C-style language lexer for a syntax highlighter. Ask Question Asked 6 years, 7 months ago. Active 5 years, 7 months ago. Viewed 2k times 1 \$\begingroup\$ I'm working on implementing a syntax highlighter for a simple text editor I've been working on. To do this, I need a simple lexer for various languages (I don't need a full one - I'm only. The %x definition makes C_COMMENT an exclusive state, which means the lexer will only match rules that are "tagged" once it enters the state. as opposed to the C style block comments in which opening /* is terminated by the nearest closing */ and the .
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