# PLY (Python Lex-Yacc)

David M. Beazley
dave@dabeaz.com

PLY Version: 2.3

- Introduction
- PLY Overview
- Lex
  - Lex Example
  - The tokens list
  - Specification of tokens
  - Token values
  - Discarded tokens
  - Line numbers and positional information
  - Ignored characters
  - Literal characters
  - Error handling
  - Building and using the lexer
  - The @TOKEN decorator
  - Optimized mode
  - Debugging
  - Alternative specification of lexers
  - Maintaining state
  - Duplicating lexers
  - Internal lexer state
  - Conditional lexing and start conditions
  - Miscellaneous Issues
- Parsing basics
- Yacc reference
  - An example
  - Combining Grammar Rule Functions
  - Character Literals
  - Empty Productions
  - Changing the starting symbol
  - Dealing With Ambiguous Grammars
  - The parser.out file
  - Syntax Error Handling
    - Recovery and resynchronization with error rules
    - Panic mode recovery
    - General comments on error handling
  - Line Number and Position Tracking
  - AST Construction
  - Embedded Actions
  - Yacc implementation notes
- Parser and Lexer State Management
- Using Python's Optimized Mode
- Where to go from here?

## 1. Introduction

PLY is a pure-Python implementation of the popular compiler construction tools lex and yacc. The main goal of PLY is to stay fairly faithful to the way in which traditional lex/yacc tools work. This includes supporting LALR(1) parsing as well as providing extensive input validation, error reporting, and diagnostics. Thus, if you've used yacc in another programming language, it should be relatively straightforward to use PLY.

Early versions of PLY were developed to support an Introduction to Compilers Course I taught in 2001 at the University of Chicago. In this course, students built a fully functional compiler for a simple Pascal-like language. Their compiler, implemented entirely in Python, had to include lexical analysis, parsing, type checking, type inference, nested scoping, and code generation for the SPARC processor. Approximately 30 different compiler implementations were completed in this course. Most of PLY's interface and operation has been influenced by common usability problems encountered by students.

Since PLY was primarily developed as an instructional tool, you will find it to be fairly picky about token and grammar rule specification. In part, this added formality is meant to catch common programming mistakes made by novice users. However, advanced users will also find such features to be useful when building complicated grammars for real programming languages. It should also be noted that PLY does not provide much in the way of bells and whistles (e.g., automatic construction of abstract syntax trees, tree traversal, etc.). Nor would I consider it to be a parsing framework. Instead, you will find a bare-bones, yet fully capable lex/yacc implementation written entirely in Python.

The rest of this document assumes that you are somewhat familar with parsing theory, syntax directed translation, and the use of compiler construction tools such as lex and yacc in other programming languages. If you are unfamilar with these topics, you will probably want to consult an introductory text such as "Compilers: Principles, Techniques, and Tools", by Aho, Sethi, and Ullman. O'Reilly's "Lex and Yacc" by John Levine may also be handy. In fact, the O'Reilly book can be used as a reference for PLY as the concepts are virtually identical.

## 2. PLY Overview

PLY consists of two separate modules; `lex.py` and `yacc.py`, both of which are found in a Python package called `ply`. The `lex.py` module is used to break input text into a collection of tokens specified by a collection of regular expression rules. `yacc.py` is used to recognize language syntax that has been specified in the form of a context free grammar. `yacc.py` uses LR parsing and generates its parsing tables using either the LALR(1) (the default) or SLR table generation algorithms.

The two tools are meant to work together. Specifically, `lex.py` provides an external interface in the form of a `token()` function that returns the next valid token on the input stream. `yacc.py` calls this repeatedly to retrieve tokens and invoke grammar rules. The output of `yacc.py` is often an Abstract Syntax Tree (AST). However, this is entirely up to the user. If desired, `yacc.py` can also be used to implement simple one-pass compilers.

Like its Unix counterpart, `yacc.py` provides most of the features you expect including extensive error checking, grammar validation, support for empty productions, error tokens, and ambiguity resolution via precedence rules. In fact, everything that is possible in traditional yacc should be supported in PLY.

The primary difference between `yacc.py` and Unix `yacc` is that `yacc.py` doesn't involve a separate code-generation process. Instead, PLY relies on reflection (introspection) to build its lexers and parsers. Unlike traditional lex/yacc which require a special input file that is converted into a separate source file, the specifications given to PLY are valid Python programs. This means that there are no extra source files nor is there a special compiler construction step (e.g., running yacc to generate Python code for the compiler). Since the generation of the parsing tables is relatively expensive, PLY caches the results and saves them to a file. If no changes are detected in the input source, the tables are read from the cache. Otherwise, they are regenerated.

## 3. Lex

`lex.py` is used to tokenize an input string. For example, suppose you're writing a programming language and a user supplied the following input string:

>     x = 3 + 42 * (s - t)

A tokenizer splits the string into individual tokens

>     'x','=', '3', '+', '42', '*', '(', 's', '-', 't', ')'

Tokens are usually given names to indicate what they are. For example:

>     'ID','EQUALS','NUMBER','PLUS','NUMBER','TIMES',
>     'LPAREN','ID','MINUS','ID','RPAREN'

More specifically, the input is broken into pairs of token types and values. For example:

>     ('ID','x'), ('EQUALS','='), ('NUMBER','3'),
>     ('PLUS','+'), ('NUMBER','42), ('TIMES','*'),
>     ('LPAREN','('), ('ID','s'), ('MINUS','-'),
>     ('ID','t'), ('RPAREN',')'

The identification of tokens is typically done by writing a series of regular expression rules. The next section shows how this is done using `lex.py`.

### 3.1 Lex Example

The following example shows how `lex.py` is used to write a simple tokenizer.

>     # ------------------------------------------------------------
>     # calclex.py
>     #
>     # tokenizer for a simple expression evaluator for
>     # numbers and +,-,*,/
>     # ------------------------------------------------------------
>     import ply.lex as lex

>     # List of token names.   This is always required
>     tokens = (
>        'NUMBER',
>        'PLUS',
>        'MINUS',
>        'TIMES',
>        'DIVIDE',
>        'LPAREN',
>        'RPAREN',
>     )

>     # Regular expression rules for simple tokens
>     t_PLUS    = r'\+'
>     t_MINUS   = r'-'
>     t_TIMES   = r'\*'
>     t_DIVIDE  = r'/'
>     t_LPAREN  = r'\('
>     t_RPAREN  = r'\)'

>     # A regular expression rule with some action code
>     def t_NUMBER(t):
>         r'\d+'
>         try:
>              t.value = int(t.value)
>         except ValueError:
>              print "Line %d: Number %s is too large!" % (t.lineno,t.value)
>     	 t.value = 0
>         return t

>     # Define a rule so we can track line numbers
>     def t_newline(t):
>         r'\n+'
>         t.lexer.lineno += len(t.value)

>     # A string containing ignored characters (spaces and tabs)
>     t_ignore  = ' \t'

>     # Error handling rule
>     def t_error(t):
>         print "Illegal character '%s'" % t.value[0]
>         t.lexer.skip(1)

>     # Build the lexer
>     lex.lex()

To use the lexer, you first need to feed it some input text using its `input()` method. After that, repeated calls to `token()` produce tokens. The following code shows how this works:

>     # Test it out
>     data = '''
>     3 + 4 * 10
>       + -20 *2
>     '''

>     # Give the lexer some input
>     lex.input(data)

>     # Tokenize
>     while 1:
>         tok = lex.token()
>         if not tok: break      # No more input
>         print tok

When executed, the example will produce the following output:

>     $ python example.py
>     LexToken(NUMBER,3,2,1)
>     LexToken(PLUS,'+',2,3)
>     LexToken(NUMBER,4,2,5)
>     LexToken(TIMES,'*',2,7)
>     LexToken(NUMBER,10,2,10)
>     LexToken(PLUS,'+',3,14)
>     LexToken(MINUS,'-',3,16)
>     LexToken(NUMBER,20,3,18)
>     LexToken(TIMES,'*',3,20)
>     LexToken(NUMBER,2,3,21)

The tokens returned by `lex.token()` are instances of `LexToken`. This object has attributes `tok.type`, `tok.value`, `tok.lineno`, and `tok.lexpos`. The following code shows an example of accessing these attributes:

>     # Tokenize
>     while 1:
>         tok = lex.token()
>         if not tok: break      # No more input
>         print tok.type, tok.value, tok.line, tok.lexpos

The `tok.type` and `tok.value` attributes contain the type and value of the token itself. `tok.line` and `tok.lexpos` contain information about the location of the token. `tok.lexpos` is the index of the token relative to the start of the input text.

### 3.2 The tokens list

All lexers must provide a list `tokens` that defines all of the possible token names that can be produced by the lexer. This list is always required and is used to perform a variety of validation checks. The tokens list is also used by the `yacc.py` module to identify terminals.

In the example, the following code specified the token names:

>     tokens = (
>        'NUMBER',
>        'PLUS',
>        'MINUS',
>        'TIMES',
>        'DIVIDE',
>        'LPAREN',
>        'RPAREN',
>     )

### 3.3 Specification of tokens

Each token is specified by writing a regular expression rule. Each of these rules are are defined by making declarations with a special prefix `t_` to indicate that it defines a token. For simple tokens, the regular expression can be specified as strings such as this (note: Python raw strings are used since they are the most convenient way to write regular expression strings):

>     t_PLUS = r'\+'

In this case, the name following the `t_` must exactly match one of the names supplied in `tokens`. If some kind of action needs to be performed, a token rule can be specified as a function. For example, this rule matches numbers and converts the string into a Python integer.

>     def t_NUMBER(t):
>         r'\d+'
>         try:
>              t.value = int(t.value)
>         except ValueError:
>              print "Number %s is too large!" % t.value
>     	 t.value = 0
>         return t

When a function is used, the regular expression rule is specified in the function documentation string. The function always takes a single argument which is an instance of `LexToken`. This object has attributes of `t.type` which is the token type (as a string), `t.value` which is the lexeme (the actual text matched), `t.lineno` which is the current line number, and `t.lexpos` which is the position of the token relative to the beginning of the input text. By default, `t.type` is set to the name following the `t_` prefix. The action function can modify the contents of the `LexToken` object as appropriate. However, when it is done, the resulting token should be returned. If no value is returned by the action function, the token is simply discarded and the next token read.

Internally, `lex.py` uses the `re` module to do its patten matching. When building the master regular expression, rules are added in the following order:

1. All tokens defined by functions are added in the same order as they appear in the lexer file.
2. Tokens defined by strings are added next by sorting them in order of decreasing regular expression length (longer expressions are added first).

Without this ordering, it can be difficult to correctly match certain types of tokens. For example, if you wanted to have separate tokens for "=" and "==", you need to make sure that "==" is checked first. By sorting regular expressions in order of decreasing length, this problem is solved for rules defined as strings. For functions, the order can be explicitly controlled since rules appearing first are checked first.

To handle reserved words, it is usually easier to just match an identifier and do a special name lookup in a function like this:

>     reserved = {
>        'if' : 'IF',
>        'then' : 'THEN',
>        'else' : 'ELSE',
>        'while' : 'WHILE',
>        ...
>     }

>     def t_ID(t):
>         r'[a-zA-Z_][a-zA-Z_0-9]*'
>         t.type = reserved.get(t.value,'ID')    # Check for reserved words
>         return t

This approach greatly reduces the number of regular expression rules and is likely to make things a little faster.

Note: You should avoid writing individual rules for reserved words. For example, if you write rules like this,

>     t_FOR   = r'for'
>     t_PRINT = r'print'

those rules will be triggered for identifiers that include those words as a prefix such as "forget" or "printed". This is probably not what you want.

### 3.4 Token values

When tokens are returned by lex, they have a value that is stored in the `value` attribute. Normally, the value is the text that was matched. However, the value can be assigned to any Python object. For instance, when lexing identifiers, you may want to return both the identifier name and information from some sort of symbol table. To do this, you might write a rule like this:

>     def t_ID(t):
>         ...
>         # Look up symbol table information and return a tuple
>         t.value = (t.value, symbol_lookup(t.value))
>         ...
>         return t

It is important to note that storing data in other attribute names is not recommended. The `yacc.py` module only exposes the contents of the `value` attribute. Thus, accessing other attributes may be unnecessarily awkward.

### 3.5 Discarded tokens

To discard a token, such as a comment, simply define a token rule that returns no value. For example:

>     def t_COMMENT(t):
>         r'\#.*'
>         pass
>         # No return value. Token discarded

Alternatively, you can include the prefix "ignore_" in the token declaration to force a token to be ignored. For example:

>     t_ignore_COMMENT = r'\#.*'

Be advised that if you are ignoring many different kinds of text, you may still want to use functions since these provide more precise control over the order in which regular expressions are matched (i.e., functions are matched in order of specification whereas strings are sorted by regular expression length).

### 3.6 Line numbers and positional information

By default, `lex.py` knows nothing about line numbers. This is because `lex.py` doesn't know anything about what constitutes a "line" of input (e.g., the newline character or even if the input is textual data). To update this information, you need to write a special rule. In the example, the `t_newline()` rule shows how to do this.

>     # Define a rule so we can track line numbers
>     def t_newline(t):
>         r'\n+'
>         t.lexer.lineno += len(t.value)

Within the rule, the `lineno` attribute of the underlying lexer `t.lexer` is updated. After the line number is updated, the token is simply discarded since nothing is returned.

`lex.py` does not perform and kind of automatic column tracking. However, it does record positional information related to each token in the `lexpos` attribute. Using this, it is usually possible to compute column information as a separate step. For instance, just count backwards until you reach a newline.

>     # Compute column.
>     #     input is the input text string
>     #     token is a token instance
>     def find_column(input,token):
>         i = token.lexpos
>         while i > 0:
>             if input[i] == '\n': break
>             i -= 1
>         column = (token.lexpos - i)+1
>         return column

Since column information is often only useful in the context of error handling, calculating the column position can be performed when needed as opposed to doing it for each token.

### 3.7 Ignored characters

The special `t_ignore` rule is reserved by `lex.py` for characters that should be completely ignored in the input stream. Usually this is used to skip over whitespace and other non-essential characters. Although it is possible to define a regular expression rule for whitespace in a manner similar to `t_newline()`, the use of `t_ignore` provides substantially better lexing performance because it is handled as a special case and is checked in a much more efficient manner than the normal regular expression rules.

### 3.8 Literal characters

Literal characters can be specified by defining a variable `literals` in your lexing module. For example:

>     literals = [ '+','-','*','/' ]

or alternatively

>     literals = "+-*/"

A literal character is simply a single character that is returned "as is" when encountered by the lexer. Literals are checked after all of the defined regular expression rules. Thus, if a rule starts with one of the literal characters, it will always take precedence.

When a literal token is returned, both its `type` and `value` attributes are set to the character itself. For example, `'+'`.

### 3.9 Error handling

Finally, the `t_error()` function is used to handle lexing errors that occur when illegal characters are detected. In this case, the `t.value` attribute contains the rest of the input string that has not been tokenized. In the example, the error function was defined as follows:

>     # Error handling rule
>     def t_error(t):
>         print "Illegal character '%s'" % t.value[0]
>         t.lexer.skip(1)

In this case, we simply print the offending character and skip ahead one character by calling `t.lexer.skip(1)`.

### 3.10 Building and using the lexer

To build the lexer, the function `lex.lex()` is used. This function uses Python reflection (or introspection) to read the the regular expression rules out of the calling context and build the lexer. Once the lexer has been built, two functions can be used to control the lexer.

- `lex.input(data)`. Reset the lexer and store a new input string.
- `lex.token()`. Return the next token. Returns a special `LexToken` instance on success or None if the end of the input text has been reached.

If desired, the lexer can also be used as an object. The `lex()` returns a `Lexer` object that can be used for this purpose. For example:

>     lexer = lex.lex()
>     lexer.input(sometext)
>     while 1:
>         tok = lexer.token()
>         if not tok: break
>         print tok

This latter technique should be used if you intend to use multiple lexers in your application. Simply define each lexer in its own module and use the object returned by `lex()` as appropriate.

Note: The global functions `lex.input()` and `lex.token()` are bound to the `input()` and `token()` methods of the last lexer created by the lex module.

### 3.11 The @TOKEN decorator

In some applications, you may want to define build tokens from as a series of more complex regular expression rules. For example:

>     digit            = r'([0-9])'
>     nondigit         = r'([_A-Za-z])'
>     identifier       = r'(' + nondigit + r'(' + digit + r'|' + nondigit + r')*)'

>     def t_ID(t):
>         # want docstring to be identifier above. ?????
>         ...

In this case, we want the regular expression rule for `ID` to be one of the variables above. However, there is no way to directly specify this using a normal documentation string. To solve this problem, you can use the `@TOKEN` decorator. For example:

>     from ply.lex import TOKEN

>     @TOKEN(identifier)
>     def t_ID(t):
>         ...

This will attach `identifier` to the docstring for `t_ID()` allowing `lex.py` to work normally. An alternative approach this problem is to set the docstring directly like this:

>     def t_ID(t):
>         ...

>     t_ID.__doc__ = identifier

NOTE: Use of `@TOKEN` requires Python-2.4 or newer. If you're concerned about backwards compatibility with older versions of Python, use the alternative approach of setting the docstring directly.

### 3.12 Optimized mode

For improved performance, it may be desirable to use Python's optimized mode (e.g., running Python with the `-O` option). However, doing so causes Python to ignore documentation strings. This presents special problems for `lex.py`. To handle this case, you can create your lexer using the `optimize` option as follows:

>     lexer = lex.lex(optimize=1)

Next, run Python in its normal operating mode. When you do this, `lex.py` will write a file called `lextab.py` to the current directory. This file contains all of the regular expression rules and tables used during lexing. On subsequent executions, `lextab.py` will simply be imported to build the lexer. This approach substantially improves the startup time of the lexer and it works in Python's optimized mode.

To change the name of the lexer-generated file, use the `lextab` keyword argument. For example:

>     lexer = lex.lex(optimize=1,lextab="footab")

When running in optimized mode, it is important to note that lex disables most error checking. Thus, this is really only recommended if you're sure everything is working correctly and you're ready to start releasing production code.

### 3.13 Debugging

For the purpose of debugging, you can run `lex()` in a debugging mode as follows:

>     lexer = lex.lex(debug=1)

This will result in a large amount of debugging information to be printed including all of the added rules and the master regular expressions. In addition, `lex.py` comes with a simple main function which will either tokenize input read from standard input or from a file specified on the command line. To use it, simply put this in your lexer:

>     if __name__ == '__main__':
>          lex.runmain()

### 3.14 Alternative specification of lexers

As shown in the example, lexers are specified all within one Python module. If you want to put token rules in a different module from the one in which you invoke `lex()`, use the `module` keyword argument.

For example, you might have a dedicated module that just contains the token rules:

>     # module: tokrules.py
>     # This module just contains the lexing rules

>     # List of token names.   This is always required
>     tokens = (
>        'NUMBER',
>        'PLUS',
>        'MINUS',
>        'TIMES',
>        'DIVIDE',
>        'LPAREN',
>        'RPAREN',
>     )

>     # Regular expression rules for simple tokens
>     t_PLUS    = r'\+'
>     t_MINUS   = r'-'
>     t_TIMES   = r'\*'
>     t_DIVIDE  = r'/'
>     t_LPAREN  = r'\('
>     t_RPAREN  = r'\)'

>     # A regular expression rule with some action code
>     def t_NUMBER(t):
>         r'\d+'
>         try:
>              t.value = int(t.value)
>         except ValueError:
>              print "Line %d: Number %s is too large!" % (t.lineno,t.value)
>     	 t.value = 0
>         return t

>     # Define a rule so we can track line numbers
>     def t_newline(t):
>         r'\n+'
>         t.lexer.lineno += len(t.value)

>     # A string containing ignored characters (spaces and tabs)
>     t_ignore  = ' \t'

>     # Error handling rule
>     def t_error(t):
>         print "Illegal character '%s'" % t.value[0]
>         t.lexer.skip(1)

Now, if you wanted to build a tokenizer from these rules from within a different module, you would do the following (shown for Python interactive mode):

>     >>> import tokrules
>     >>> lexer = lex.lex(module=tokrules)
>     >>> lexer.input("3 + 4")
>     >>> lexer.token()
>     LexToken(NUMBER,3,1,1,0)
>     >>> lexer.token()
>     LexToken(PLUS,'+',1,2)
>     >>> lexer.token()
>     LexToken(NUMBER,4,1,4)
>     >>> lexer.token()
>     None
>     >>>

The `object` option can be used to define lexers as a class instead of a module. For example:

>     import ply.lex as lex

>     class MyLexer:
>         # List of token names.   This is always required
>         tokens = (
>            'NUMBER',
>            'PLUS',
>            'MINUS',
>            'TIMES',
>            'DIVIDE',
>            'LPAREN',
>            'RPAREN',
>         )

>         # Regular expression rules for simple tokens
>         t_PLUS    = r'\+'
>         t_MINUS   = r'-'
>         t_TIMES   = r'\*'
>         t_DIVIDE  = r'/'
>         t_LPAREN  = r'\('
>         t_RPAREN  = r'\)'

>         # A regular expression rule with some action code
>         # Note addition of self parameter since we're in a class
>         def t_NUMBER(self,t):
>             r'\d+'
>             try:
>                  t.value = int(t.value)
>             except ValueError:
>                  print "Line %d: Number %s is too large!" % (t.lineno,t.value)
>                  t.value = 0
>             return t

>         # Define a rule so we can track line numbers
>         def t_newline(self,t):
>             r'\n+'
>             t.lexer.lineno += len(t.value)

>         # A string containing ignored characters (spaces and tabs)
>         t_ignore  = ' \t'

>         # Error handling rule
>         def t_error(self,t):
>             print "Illegal character '%s'" % t.value[0]
>             t.lexer.skip(1)

>         # Build the lexer
>         def build(self,**kwargs):
>             self.lexer = lex.lex(object=self, **kwargs)

>         # Test it output
>         def test(self,data):
>             self.lexer.input(data)
>             while 1:
>                  tok = lexer.token()
>                  if not tok: break
>                  print tok

>     # Build the lexer and try it out
>     m = MyLexer()
>     m.build()           # Build the lexer
>     m.test("3 + 4")     # Test it

For reasons that are subtle, you should NOT invoke `lex.lex()` inside the `__init__()` method of your class. If you do, it may cause bizarre behavior if someone tries to duplicate a lexer object. Keep reading.

### 3.15 Maintaining state

In your lexer, you may want to maintain a variety of state information. This might include mode settings, symbol tables, and other details. There are a few different ways to handle this situation. First, you could just keep some global variables:

>     num_count = 0
>     def t_NUMBER(t):
>         r'\d+'
>         global num_count
>         num_count += 1
>         try:
>              t.value = int(t.value)
>         except ValueError:
>              print "Line %d: Number %s is too large!" % (t.lineno,t.value)
>     	 t.value = 0
>         return t

Alternatively, you can store this information inside the Lexer object created by `lex()`. To this, you can use the `lexer` attribute of tokens passed to the various rules. For example:

>     def t_NUMBER(t):
>         r'\d+'
>         t.lexer.num_count += 1     # Note use of lexer attribute
>         try:
>              t.value = int(t.value)
>         except ValueError:
>              print "Line %d: Number %s is too large!" % (t.lineno,t.value)
>     	 t.value = 0
>         return t

>     lexer = lex.lex()
>     lexer.num_count = 0            # Set the initial count

This latter approach has the advantage of storing information inside the lexer itself---something that may be useful if multiple instances of the same lexer have been created. However, it may also feel kind of "hacky" to the purists. Just to put their mind at some ease, all internal attributes of the lexer (with the exception of `lineno`) have names that are prefixed by `lex` (e.g., `lexdata`,`lexpos`, etc.). Thus, it should be perfectly safe to store attributes in the lexer that don't have names starting with that prefix.

A third approach is to define the lexer as a class as shown in the previous example:

>     class MyLexer:
>         ...
>         def t_NUMBER(self,t):
>             r'\d+'
>             self.num_count += 1
>             try:
>                  t.value = int(t.value)
>             except ValueError:
>                  print "Line %d: Number %s is too large!" % (t.lineno,t.value)
>                  t.value = 0
>             return t

>         def build(self, **kwargs):
>             self.lexer = lex.lex(object=self,**kwargs)

>         def __init__(self):
>             self.num_count = 0

>     # Create a lexer
>     m = MyLexer()
>     lexer = lex.lex(object=m)

The class approach may be the easiest to manage if your application is going to be creating multiple instances of the same lexer and you need to manage a lot of state.

### 3.16 Duplicating lexers

NOTE: I am thinking about deprecating this feature. Post comments on ply-hack@googlegroups.com or send me a private email at dave@dabeaz.com.

If necessary, a lexer object can be quickly duplicated by invoking its `clone()` method. For example:

>     lexer = lex.lex()
>     ...
>     newlexer = lexer.clone()

When a lexer is cloned, the copy is identical to the original lexer, including any input text. However, once created, different text can be fed to the clone which can be used independently. This capability may be useful in situations when you are writing a parser/compiler that involves recursive or reentrant processing. For instance, if you needed to scan ahead in the input for some reason, you could create a clone and use it to look ahead.

The advantage of using `clone()` instead of reinvoking `lex()` is that it is significantly faster. Namely, it is not necessary to re-examine all of the token rules, build a regular expression, and construct internal tables. All of this information can simply be reused in the new lexer.

Special considerations need to be made when cloning a lexer that is defined as a class. Previous sections showed an example of a class `MyLexer`. If you have the following code:

>     m = MyLexer()
>     a = lex.lex(object=m)      # Create a lexer

>     b = a.clone()              # Clone the lexer

Then both `a` and `b` are going to be bound to the same object `m`. If the object `m` contains internal state related to lexing, this sharing may lead to quite a bit of confusion. To fix this, the `clone()` method accepts an optional argument that can be used to supply a new object. This can be used to clone the lexer and bind it to a new instance. For example:

>     m = MyLexer()              # Create a lexer
>     a = lex.lex(object=m)

>     # Create a clone
>     n = MyLexer()              # New instance of MyLexer
>     b = a.clone(n)             # New lexer bound to n

It may make sense to encapsulate all of this inside a method:

>     class MyLexer:
>          ...
>          def clone(self):
>              c = MyLexer()        # Create a new instance of myself
>              # Copy attributes from self to c as appropriate
>              ...
>              # Clone the lexer
>              c.lexer = self.lexer.clone(c)
>              return c

The fact that a new instance of `MyLexer` may be created while cloning a lexer is the reason why you should never invoke `lex.lex()` inside `__init__()`. If you do, the lexer will be rebuilt from scratch and you lose all of the performance benefits of using `clone()` in the first place.

### 3.17 Internal lexer state

A Lexer object `lexer` has a number of internal attributes that may be useful in certain situations.

`lexer.lexpos`

> This attribute is an integer that contains the current position within the input text. If you modify the value, it will change the result of the next call to `token()`. Within token rule functions, this points to the first character after the matched text. If the value is modified within a rule, the next returned token will be matched at the new position.

`lexer.lineno`

> The current value of the line number attribute stored in the lexer. This can be modified as needed to change the line number.

`lexer.lexdata`

> The current input text stored in the lexer. This is the string passed with the `input()` method. It would probably be a bad idea to modify this unless you really know what you're doing.

`lexer.lexmatch`

> This is the raw `Match` object returned by the Python `re.match()` function (used internally by PLY) for the current token. If you have written a regular expression that contains named groups, you can use this to retrieve those values.

### 3.18 Conditional lexing and start conditions

In advanced parsing applications, it may be useful to have different lexing states. For instance, you may want the occurrence of a certain token or syntactic construct to trigger a different kind of lexing. PLY supports a feature that allows the underlying lexer to be put into a series of different states. Each state can have its own tokens, lexing rules, and so forth. The implementation is based largely on the "start condition" feature of GNU flex. Details of this can be found at http://www.gnu.org/software/flex/manual/html_chapter/flex_11.html..

To define a new lexing state, it must first be declared. This is done by including a "states" declaration in your lex file. For example:

>     states = (
>        ('foo','exclusive'),
>        ('bar','inclusive'),
>     )

This declaration declares two states, `'foo'` and `'bar'`. States may be of two types; `'exclusive'` and `'inclusive'`. An exclusive state completely overrides the default behavior of the lexer. That is, lex will only return tokens and apply rules defined specifically for that state. An inclusive state adds additional tokens and rules to the default set of rules. Thus, lex will return both the tokens defined by default in addition to those defined for the inclusive state.

Once a state has been declared, tokens and rules are declared by including the state name in token/rule declaration. For example:

>     t_foo_NUMBER = r'\d+'                      # Token 'NUMBER' in state 'foo'
>     t_bar_ID     = r'[a-zA-Z_][a-zA-Z0-9_]*'   # Token 'ID' in state 'bar'

>     def t_foo_newline(t):
>         r'\n'
>         t.lexer.lineno += 1

A token can be declared in multiple states by including multiple state names in the declaration. For example:

>     t_foo_bar_NUMBER = r'\d+'         # Defines token 'NUMBER' in both state 'foo' and 'bar'

Alternative, a token can be declared in all states using the 'ANY' in the name.

>     t_ANY_NUMBER = r'\d+'         # Defines a token 'NUMBER' in all states

If no state name is supplied, as is normally the case, the token is associated with a special state `'INITIAL'`. For example, these two declarations are identical:

>     t_NUMBER = r'\d+'
>     t_INITIAL_NUMBER = r'\d+'

States are also associated with the special `t_ignore` and `t_error()` declarations. For example, if a state treats these differently, you can declare:

>     t_foo_ignore = " \t\n"       # Ignored characters for state 'foo'

>     def t_bar_error(t):          # Special error handler for state 'bar'
>         pass

By default, lexing operates in the `'INITIAL'` state. This state includes all of the normally defined tokens. For users who aren't using different states, this fact is completely transparent. If, during lexing or parsing, you want to change the lexing state, use the `begin()` method. For example:

>     def t_begin_foo(t):
>         r'start_foo'
>         t.lexer.begin('foo')             # Starts 'foo' state

To get out of a state, you use `begin()` to switch back to the initial state. For example:

>     def t_foo_end(t):
>         r'end_foo'
>         t.lexer.begin('INITIAL')        # Back to the initial state

The management of states can also be done with a stack. For example:

>     def t_begin_foo(t):
>         r'start_foo'
>         t.lexer.push_state('foo')             # Starts 'foo' state

>     def t_foo_end(t):
>         r'end_foo'
>         t.lexer.pop_state()                   # Back to the previous state

The use of a stack would be useful in situations where there are many ways of entering a new lexing state and you merely want to go back to the previous state afterwards.

An example might help clarify. Suppose you were writing a parser and you wanted to grab sections of arbitrary C code enclosed by curly braces. That is, whenever you encounter a starting brace '{', you want to read all of the enclosed code up to the ending brace '}' and return it as a string. Doing this with a normal regular expression rule is nearly (if not actually) impossible. This is because braces can be nested and can be included in comments and strings. Thus, simply matching up to the first matching '}' character isn't good enough. Here is how you might use lexer states to do this:

>     # Declare the state
>     states = (
>       ('ccode','exclusive'),
>     )

>     # Match the first {. Enter ccode state.
>     def t_ccode(t):
>         r'\{'
>         t.lexer.code_start = t.lexer.lexpos        # Record the starting position
>         t.lexer.level = 1                          # Initial brace level
>         t.lexer.begin('ccode')                     # Enter 'ccode' state

>     # Rules for the ccode state
>     def t_ccode_lbrace(t):
>         r'\{'
>         t.lexer.level +=1

>     def t_ccode_rbrace(t):
>         r'\}'
>         t.lexer.level -=1

>         # If closing brace, return the code fragment
>         if t.lexer.level == 0:
>              t.value = t.lexer.lexdata[t.lexer.code_start:t.lexer.lexpos+1]
>              t.type = "CCODE"
>              t.lexer.lineno += t.value.count('\n')
>              t.lexer.begin('INITIAL')
>              return t

>     # C or C++ comment (ignore)
>     def t_ccode_comment(t):
>         r'(/\*(.|\n)*?*/)|(//.*)'
>         pass

>     # C string
>     def t_ccode_string(t):
>        r'\"([^\\\n]|(\\.))*?\"'

>     # C character literal
>     def t_ccode_char(t):
>        r'\'([^\\\n]|(\\.))*?\''

>     # Any sequence of non-whitespace characters (not braces, strings)
>     def t_ccode_nonspace(t):
>        r'[^\s\{\}\'\"]+'

>     # Ignored characters (whitespace)
>     t_ccode_ignore = " \t\n"

>     # For bad characters, we just skip over it
>     def t_ccode_error(t):
>         t.lexer.skip(1)

In this example, the occurrence of the first '{' causes the lexer to record the starting position and enter a new state `'ccode'`. A collection of rules then match various parts of the input that follow (comments, strings, etc.). All of these rules merely discard the token (by not returning a value). However, if the closing right brace is encountered, the rule `t_ccode_rbrace` collects all of the code (using the earlier recorded starting position), stores it, and returns a token 'CCODE' containing all of that text. When returning the token, the lexing state is restored back to its initial state.

### 3.19 Miscellaneous Issues

- The lexer requires input to be supplied as a single input string. Since most machines have more than enough memory, this rarely presents a performance concern. However, it means that the lexer currently can't be used with streaming data such as open files or sockets. This limitation is primarily a side-effect of using the `re` module.

- The lexer should work properly with both Unicode strings given as token and pattern matching rules as well as for input text.

- If you need to supply optional flags to the re.compile() function, use the reflags option to lex. For example:

  >     lex.lex(reflags=re.UNICODE)

- Since the lexer is written entirely in Python, its performance is largely determined by that of the Python `re` module. Although the lexer has been written to be as efficient as possible, it's not blazingly fast when used on very large input files. If performance is concern, you might consider upgrading to the most recent version of Python, creating a hand-written lexer, or offloading the lexer into a C extension module.

  If you are going to create a hand-written lexer and you plan to use it with `yacc.py`, it only needs to conform to the following requirements:

  - It must provide a `token()` method that returns the next token or `None` if no more tokens are available.
  - The `token()` method must return an object `tok` that has `type` and `value` attributes.

  ## 4. Parsing basics

  `yacc.py` is used to parse language syntax. Before showing an example, there are a few important bits of background that must be mentioned. First, syntax is usually specified in terms of a BNF grammar. For example, if you wanted to parse simple arithmetic expressions, you might first write an unambiguous grammar specification like this:

  >     expression : expression + term
  >                | expression - term
  >                | term

  >     term       : term * factor
  >                | term / factor
  >                | factor

  >     factor     : NUMBER
  >                | ( expression )

  In the grammar, symbols such as `NUMBER`, `+`, `-`, `*`, and `/` are known as terminals and correspond to raw input tokens. Identifiers such as `term` and `factor` refer to more complex rules, typically comprised of a collection of tokens. These identifiers are known as non-terminals.

  The semantic behavior of a language is often specified using a technique known as syntax directed translation. In syntax directed translation, attributes are attached to each symbol in a given grammar rule along with an action. Whenever a particular grammar rule is recognized, the action describes what to do. For example, given the expression grammar above, you might write the specification for a simple calculator like this:

  >     Grammar                             Action
  >     --------------------------------    --------------------------------------------
  >     expression0 : expression1 + term    expression0.val = expression1.val + term.val
  >                 | expression1 - term    expression0.val = expression1.val - term.val
  >                 | term                  expression0.val = term.val

  >     term0       : term1 * factor        term0.val = term1.val * factor.val
  >                 | term1 / factor        term0.val = term1.val / factor.val
  >                 | factor                term0.val = factor.val

  >     factor      : NUMBER                factor.val = int(NUMBER.lexval)
  >                 | ( expression )        factor.val = expression.val

  A good way to think about syntax directed translation is to simply think of each symbol in the grammar as some kind of object. The semantics of the language are then expressed as a collection of methods/operations on these objects.

  Yacc uses a parsing technique known as LR-parsing or shift-reduce parsing. LR parsing is a bottom up technique that tries to recognize the right-hand-side of various grammar rules. Whenever a valid right-hand-side is found in the input, the appropriate action code is triggered and the grammar symbols are replaced by the grammar symbol on the left-hand-side.

  LR parsing is commonly implemented by shifting grammar symbols onto a stack and looking at the stack and the next input token for patterns. The details of the algorithm can be found in a compiler text, but the following example illustrates the steps that are performed if you wanted to parse the expression `3 + 5 * (10 - 20)` using the grammar defined above:

  >     Step Symbol Stack           Input Tokens            Action
  >     ---- ---------------------  ---------------------   -------------------------------
  >     1    $                      3 + 5 * ( 10 - 20 )$    Shift 3
  >     2    $ 3                      + 5 * ( 10 - 20 )$    Reduce factor : NUMBER
  >     3    $ factor                 + 5 * ( 10 - 20 )$    Reduce term   : factor
  >     4    $ term                   + 5 * ( 10 - 20 )$    Reduce expr : term
  >     5    $ expr                   + 5 * ( 10 - 20 )$    Shift +
  >     6    $ expr +                   5 * ( 10 - 20 )$    Shift 5
  >     7    $ expr + 5                   * ( 10 - 20 )$    Reduce factor : NUMBER
  >     8    $ expr + factor              * ( 10 - 20 )$    Reduce term   : factor
  >     9    $ expr + term                * ( 10 - 20 )$    Shift *
  >     10   $ expr + term *                ( 10 - 20 )$    Shift (
  >     11   $ expr + term * (                10 - 20 )$    Shift 10
  >     12   $ expr + term * ( 10                - 20 )$    Reduce factor : NUMBER
  >     13   $ expr + term * ( factor            - 20 )$    Reduce term : factor
  >     14   $ expr + term * ( term              - 20 )$    Reduce expr : term
  >     15   $ expr + term * ( expr              - 20 )$    Shift -
  >     16   $ expr + term * ( expr -              20 )$    Shift 20
  >     17   $ expr + term * ( expr - 20              )$    Reduce factor : NUMBER
  >     18   $ expr + term * ( expr - factor          )$    Reduce term : factor
  >     19   $ expr + term * ( expr - term            )$    Reduce expr : expr - term
  >     20   $ expr + term * ( expr                   )$    Shift )
  >     21   $ expr + term * ( expr )                  $    Reduce factor : (expr)
  >     22   $ expr + term * factor                    $    Reduce term : term * factor
  >     23   $ expr + term                             $    Reduce expr : expr + term
  >     24   $ expr                                    $    Reduce expr
  >     25   $                                         $    Success!

  When parsing the expression, an underlying state machine and the current input token determine what to do next. If the next token looks like part of a valid grammar rule (based on other items on the stack), it is generally shifted onto the stack. If the top of the stack contains a valid right-hand-side of a grammar rule, it is usually "reduced" and the symbols replaced with the symbol on the left-hand-side. When this reduction occurs, the appropriate action is triggered (if defined). If the input token can't be shifted and the top of stack doesn't match any grammar rules, a syntax error has occurred and the parser must take some kind of recovery step (or bail out).

  It is important to note that the underlying implementation is built around a large finite-state machine that is encoded in a collection of tables. The construction of these tables is quite complicated and beyond the scope of this discussion. However, subtle details of this process explain why, in the example above, the parser chooses to shift a token onto the stack in step 9 rather than reducing the rule `expr : expr + term`.

  ## 5. Yacc reference

  This section describes how to use write parsers in PLY.

  ### 5.1 An example

  Suppose you wanted to make a grammar for simple arithmetic expressions as previously described. Here is how you would do it with `yacc.py`:

  >     # Yacc example

  >     import ply.yacc as yacc

  >     # Get the token map from the lexer.  This is required.
  >     from calclex import tokens

  >     def p_expression_plus(p):
  >         'expression : expression PLUS term'
  >         p[0] = p[1] + p[3]

  >     def p_expression_minus(p):
  >         'expression : expression MINUS term'
  >         p[0] = p[1] - p[3]

  >     def p_expression_term(p):
  >         'expression : term'
  >         p[0] = p[1]

  >     def p_term_times(p):
  >         'term : term TIMES factor'
  >         p[0] = p[1] * p[3]

  >     def p_term_div(p):
  >         'term : term DIVIDE factor'
  >         p[0] = p[1] / p[3]

  >     def p_term_factor(p):
  >         'term : factor'
  >         p[0] = p[1]

  >     def p_factor_num(p):
  >         'factor : NUMBER'
  >         p[0] = p[1]

  >     def p_factor_expr(p):
  >         'factor : LPAREN expression RPAREN'
  >         p[0] = p[2]

  >     # Error rule for syntax errors
  >     def p_error(p):
  >         print "Syntax error in input!"

  >     # Build the parser
  >     yacc.yacc()

  >     # Use this if you want to build the parser using SLR instead of LALR
  >     # yacc.yacc(method="SLR")

  >     while 1:
  >        try:
  >            s = raw_input('calc > ')
  >        except EOFError:
  >            break
  >        if not s: continue
  >        result = yacc.parse(s)
  >        print result

  In this example, each grammar rule is defined by a Python function where the docstring to that function contains the appropriate context-free grammar specification. Each function accepts a single argument `p` that is a sequence containing the values of each grammar symbol in the corresponding rule. The values of `p[i]` are mapped to grammar symbols as shown here:

  >     def p_expression_plus(p):
  >         'expression : expression PLUS term'
  >         #   ^            ^        ^    ^
  >         #  p[0]         p[1]     p[2] p[3]

  >         p[0] = p[1] + p[3]

  For tokens, the "value" of the corresponding `p[i]` is the same as the `p.value` attribute assigned in the lexer module. For non-terminals, the value is determined by whatever is placed in `p[0]` when rules are reduced. This value can be anything at all. However, it probably most common for the value to be a simple Python type, a tuple, or an instance. In this example, we are relying on the fact that the `NUMBER` token stores an integer value in its value field. All of the other rules simply perform various types of integer operations and store the result.

  Note: The use of negative indices have a special meaning in yacc---specially `p[-1]` does not have the same value as `p[3]` in this example. Please see the section on "Embedded Actions" for further details.

  The first rule defined in the yacc specification determines the starting grammar symbol (in this case, a rule for `expression` appears first). Whenever the starting rule is reduced by the parser and no more input is available, parsing stops and the final value is returned (this value will be whatever the top-most rule placed in `p[0]`). Note: an alternative starting symbol can be specified using the `start` keyword argument to `yacc()`.

  The `p_error(p)` rule is defined to catch syntax errors. See the error handling section below for more detail.

  To build the parser, call the `yacc.yacc()` function. This function looks at the module and attempts to construct all of the LR parsing tables for the grammar you have specified. The first time `yacc.yacc()` is invoked, you will get a message such as this:

  >     $ python calcparse.py
  >     yacc: Generating LALR parsing table...
  >     calc >

  Since table construction is relatively expensive (especially for large grammars), the resulting parsing table is written to the current directory in a file called `parsetab.py`. In addition, a debugging file called `parser.out` is created. On subsequent executions, `yacc` will reload the table from `parsetab.py` unless it has detected a change in the underlying grammar (in which case the tables and `parsetab.py` file are regenerated). Note: The names of parser output files can be changed if necessary. See the notes that follow later.

  If any errors are detected in your grammar specification, `yacc.py` will produce diagnostic messages and possibly raise an exception. Some of the errors that can be detected include:

  - Duplicated function names (if more than one rule function have the same name in the grammar file).
  - Shift/reduce and reduce/reduce conflicts generated by ambiguous grammars.
  - Badly specified grammar rules.
  - Infinite recursion (rules that can never terminate).
  - Unused rules and tokens
  - Undefined rules and tokens
  The next few sections now discuss a few finer points of grammar construction.

  ### 5.2 Combining Grammar Rule Functions

  When grammar rules are similar, they can be combined into a single function. For example, consider the two rules in our earlier example:

  >     def p_expression_plus(p):
  >         'expression : expression PLUS term'
  >         p[0] = p[1] + p[3]

  >     def p_expression_minus(t):
  >         'expression : expression MINUS term'
  >         p[0] = p[1] - p[3]

  Instead of writing two functions, you might write a single function like this:

  >     def p_expression(p):
  >         '''expression : expression PLUS term
  >                       | expression MINUS term'''
  >         if p[2] == '+':
  >             p[0] = p[1] + p[3]
  >         elif p[2] == '-':
  >             p[0] = p[1] - p[3]

  In general, the doc string for any given function can contain multiple grammar rules. So, it would have also been legal (although possibly confusing) to write this:

  >     def p_binary_operators(p):
  >         '''expression : expression PLUS term
  >                       | expression MINUS term
  >            term       : term TIMES factor
  >                       | term DIVIDE factor'''
  >         if p[2] == '+':
  >             p[0] = p[1] + p[3]
  >         elif p[2] == '-':
  >             p[0] = p[1] - p[3]
  >         elif p[2] == '*':
  >             p[0] = p[1] * p[3]
  >         elif p[2] == '/':
  >             p[0] = p[1] / p[3]

  When combining grammar rules into a single function, it is usually a good idea for all of the rules to have a similar structure (e.g., the same number of terms). Otherwise, the corresponding action code may be more complicated than necessary. However, it is possible to handle simple cases using len(). For example:

  >     def p_expressions(p):
  >         '''expression : expression MINUS expression
  >                       | MINUS expression'''
  >         if (len(p) == 4):
  >             p[0] = p[1] - p[3]
  >         elif (len(p) == 3):
  >             p[0] = -p[2]

  ### 5.3 Character Literals

  If desired, a grammar may contain tokens defined as single character literals. For example:

  >     def p_binary_operators(p):
  >         '''expression : expression '+' term
  >                       | expression '-' term
  >            term       : term '*' factor
  >                       | term '/' factor'''
  >         if p[2] == '+':
  >             p[0] = p[1] + p[3]
  >         elif p[2] == '-':
  >             p[0] = p[1] - p[3]
  >         elif p[2] == '*':
  >             p[0] = p[1] * p[3]
  >         elif p[2] == '/':
  >             p[0] = p[1] / p[3]

  A character literal must be enclosed in quotes such as `'+'`. In addition, if literals are used, they must be declared in the corresponding `lex` file through the use of a special `literals` declaration.

  >     # Literals.  Should be placed in module given to lex()
  >     literals = ['+','-','*','/' ]

  Character literals are limited to a single character. Thus, it is not legal to specify literals such as `'<='` or `'=='`. For this, use the normal lexing rules (e.g., define a rule such as `t_EQ = r'=='`).

  ### 5.4 Empty Productions

  `yacc.py` can handle empty productions by defining a rule like this:

  >     def p_empty(p):
  >         'empty :'
  >         pass

  Now to use the empty production, simply use 'empty' as a symbol. For example:

  >     def p_optitem(p):
  >         'optitem : item'
  >         '        | empty'
  >         ...

  Note: You can write empty rules anywhere by simply specifying an empty right hand side. However, I personally find that writing an "empty" rule and using "empty" to denote an empty production is easier to read.

  ### 5.5 Changing the starting symbol

  Normally, the first rule found in a yacc specification defines the starting grammar rule (top level rule). To change this, simply supply a `start` specifier in your file. For example:

  >     start = 'foo'

  >     def p_bar(p):
  >         'bar : A B'

  >     # This is the starting rule due to the start specifier above
  >     def p_foo(p):
  >         'foo : bar X'
  >     ...

  The use of a `start` specifier may be useful during debugging since you can use it to have yacc build a subset of a larger grammar. For this purpose, it is also possible to specify a starting symbol as an argument to `yacc()`. For example:

  >     yacc.yacc(start='foo')

  ### 5.6 Dealing With Ambiguous Grammars

  The expression grammar given in the earlier example has been written in a special format to eliminate ambiguity. However, in many situations, it is extremely difficult or awkward to write grammars in this format. A much more natural way to express the grammar is in a more compact form like this:

  >     expression : expression PLUS expression
  >                | expression MINUS expression
  >                | expression TIMES expression
  >                | expression DIVIDE expression
  >                | LPAREN expression RPAREN
  >                | NUMBER

  Unfortunately, this grammar specification is ambiguous. For example, if you are parsing the string "3 * 4 + 5", there is no way to tell how the operators are supposed to be grouped. For example, does the expression mean "(3 * 4) + 5" or is it "3 * (4+5)"?

  When an ambiguous grammar is given to `yacc.py` it will print messages about "shift/reduce conflicts" or a "reduce/reduce conflicts". A shift/reduce conflict is caused when the parser generator can't decide whether or not to reduce a rule or shift a symbol on the parsing stack. For example, consider the string "3 * 4 + 5" and the internal parsing stack:

  >     Step Symbol Stack           Input Tokens            Action
  >     ---- ---------------------  ---------------------   -------------------------------
  >     1    $                                3 * 4 + 5$    Shift 3
  >     2    $ 3                                * 4 + 5$    Reduce : expression : NUMBER
  >     3    $ expr                             * 4 + 5$    Shift *
  >     4    $ expr *                             4 + 5$    Shift 4
  >     5    $ expr * 4                             + 5$    Reduce: expression : NUMBER
  >     6    $ expr * expr                          + 5$    SHIFT/REDUCE CONFLICT ????

  In this case, when the parser reaches step 6, it has two options. One is to reduce the rule `expr : expr * expr` on the stack. The other option is to shift the token `+` on the stack. Both options are perfectly legal from the rules of the context-free-grammar.

  By default, all shift/reduce conflicts are resolved in favor of shifting. Therefore, in the above example, the parser will always shift the `+` instead of reducing. Although this strategy works in many cases (including the ambiguous if-then-else), it is not enough for arithmetic expressions. In fact, in the above example, the decision to shift `+` is completely wrong---we should have reduced `expr * expr` since multiplication has higher mathematical precedence than addition.

  To resolve ambiguity, especially in expression grammars, `yacc.py` allows individual tokens to be assigned a precedence level and associativity. This is done by adding a variable `precedence` to the grammar file like this:

  >     precedence = (
  >         ('left', 'PLUS', 'MINUS'),
  >         ('left', 'TIMES', 'DIVIDE'),
  >     )

  This declaration specifies that `PLUS`/`MINUS` have the same precedence level and are left-associative and that `TIMES`/`DIVIDE` have the same precedence and are left-associative. Within the `precedence` declaration, tokens are ordered from lowest to highest precedence. Thus, this declaration specifies that `TIMES`/`DIVIDE` have higher precedence than `PLUS`/`MINUS` (since they appear later in the precedence specification).

  The precedence specification works by associating a numerical precedence level value and associativity direction to the listed tokens. For example, in the above example you get:

  >     PLUS      : level = 1,  assoc = 'left'
  >     MINUS     : level = 1,  assoc = 'left'
  >     TIMES     : level = 2,  assoc = 'left'
  >     DIVIDE    : level = 2,  assoc = 'left'

  These values are then used to attach a numerical precedence value and associativity direction to each grammar rule. This is always determined by looking at the precedence of the right-most terminal symbol. For example:

  >     expression : expression PLUS expression                 # level = 1, left
  >                | expression MINUS expression                # level = 1, left
  >                | expression TIMES expression                # level = 2, left
  >                | expression DIVIDE expression               # level = 2, left
  >                | LPAREN expression RPAREN                   # level = None (not specified)
  >                | NUMBER                                     # level = None (not specified)

  When shift/reduce conflicts are encountered, the parser generator resolves the conflict by looking at the precedence rules and associativity specifiers.

  1. If the current token has higher precedence, it is shifted.
  2. If the grammar rule on the stack has higher precedence, the rule is reduced.
  3. If the current token and the grammar rule have the same precedence, the rule is reduced for left associativity, whereas the token is shifted for right associativity.
  4. If nothing is known about the precedence, shift/reduce conflicts are resolved in favor of shifting (the default).
  For example, if "expression PLUS expression" has been parsed and the next token is "TIMES", the action is going to be a shift because "TIMES" has a higher precedence level than "PLUS". On the other hand, if "expression TIMES expression" has been parsed and the next token is "PLUS", the action is going to be reduce because "PLUS" has a lower precedence than "TIMES."

  When shift/reduce conflicts are resolved using the first three techniques (with the help of precedence rules), `yacc.py` will report no errors or conflicts in the grammar.

  One problem with the precedence specifier technique is that it is sometimes necessary to change the precedence of an operator in certain contents. For example, consider a unary-minus operator in "3 + 4 * -5". Normally, unary minus has a very high precedence--being evaluated before the multiply. However, in our precedence specifier, MINUS has a lower precedence than TIMES. To deal with this, precedence rules can be given for fictitious tokens like this:

  >     precedence = (
  >         ('left', 'PLUS', 'MINUS'),
  >         ('left', 'TIMES', 'DIVIDE'),
  >         ('right', 'UMINUS'),            # Unary minus operator
  >     )

  Now, in the grammar file, we can write our unary minus rule like this:

  >     def p_expr_uminus(p):
  >         'expression : MINUS expression %prec UMINUS'
  >         p[0] = -p[2]

  In this case, `%prec UMINUS` overrides the default rule precedence--setting it to that of UMINUS in the precedence specifier.

  At first, the use of UMINUS in this example may appear very confusing. UMINUS is not an input token or a grammer rule. Instead, you should think of it as the name of a special marker in the precedence table. When you use the `%prec` qualifier, you're simply telling yacc that you want the precedence of the expression to be the same as for this special marker instead of the usual precedence.

  It is also possible to specify non-associativity in the `precedence` table. This would be used when you don't want operations to chain together. For example, suppose you wanted to support comparison operators like `<` and `>` but you didn't want to allow combinations like `a < b < c`. To do this, simply specify a rule like this:

  >     precedence = (
  >         ('nonassoc', 'LESSTHAN', 'GREATERTHAN'),  # Nonassociative operators
  >         ('left', 'PLUS', 'MINUS'),
  >         ('left', 'TIMES', 'DIVIDE'),
  >         ('right', 'UMINUS'),            # Unary minus operator
  >     )

  If you do this, the occurrence of input text such as `a < b < c` will result in a syntax error. However, simple expressions such as `a < b` will still be fine.

  Reduce/reduce conflicts are caused when there are multiple grammar rules that can be applied to a given set of symbols. This kind of conflict is almost always bad and is always resolved by picking the rule that appears first in the grammar file. Reduce/reduce conflicts are almost always caused when different sets of grammar rules somehow generate the same set of symbols. For example:

  >     assignment :  ID EQUALS NUMBER
  >                |  ID EQUALS expression

  >     expression : expression PLUS expression
  >                | expression MINUS expression
  >                | expression TIMES expression
  >                | expression DIVIDE expression
  >                | LPAREN expression RPAREN
  >                | NUMBER

  In this case, a reduce/reduce conflict exists between these two rules:

  >     assignment  : ID EQUALS NUMBER
  >     expression  : NUMBER

  For example, if you wrote "a = 5", the parser can't figure out if this is supposed to be reduced as `assignment : ID EQUALS NUMBER` or whether it's supposed to reduce the 5 as an expression and then reduce the rule `assignment : ID EQUALS expression`.

  It should be noted that reduce/reduce conflicts are notoriously difficult to spot simply looking at the input grammer. To locate these, it is usually easier to look at the `parser.out` debugging file with an appropriately high level of caffeination.

  ### 5.7 The parser.out file

  Tracking down shift/reduce and reduce/reduce conflicts is one of the finer pleasures of using an LR parsing algorithm. To assist in debugging, `yacc.py` creates a debugging file called 'parser.out' when it generates the parsing table. The contents of this file look like the following:

  >     Unused terminals:

  >     Grammar

  >     Rule 1     expression -> expression PLUS expression
  >     Rule 2     expression -> expression MINUS expression
  >     Rule 3     expression -> expression TIMES expression
  >     Rule 4     expression -> expression DIVIDE expression
  >     Rule 5     expression -> NUMBER
  >     Rule 6     expression -> LPAREN expression RPAREN

  >     Terminals, with rules where they appear

  >     TIMES                : 3
  >     error                :
  >     MINUS                : 2
  >     RPAREN               : 6
  >     LPAREN               : 6
  >     DIVIDE               : 4
  >     PLUS                 : 1
  >     NUMBER               : 5

  >     Nonterminals, with rules where they appear

  >     expression           : 1 1 2 2 3 3 4 4 6 0

  >     Parsing method: LALR

  >     state 0

  >         S' -> . expression
  >         expression -> . expression PLUS expression
  >         expression -> . expression MINUS expression
  >         expression -> . expression TIMES expression
  >         expression -> . expression DIVIDE expression
  >         expression -> . NUMBER
  >         expression -> . LPAREN expression RPAREN

  >         NUMBER          shift and go to state 3
  >         LPAREN          shift and go to state 2

  >     state 1

  >         S' -> expression .
  >         expression -> expression . PLUS expression
  >         expression -> expression . MINUS expression
  >         expression -> expression . TIMES expression
  >         expression -> expression . DIVIDE expression

  >         PLUS            shift and go to state 6
  >         MINUS           shift and go to state 5
  >         TIMES           shift and go to state 4
  >         DIVIDE          shift and go to state 7

  >     state 2

  >         expression -> LPAREN . expression RPAREN
  >         expression -> . expression PLUS expression
  >         expression -> . expression MINUS expression
  >         expression -> . expression TIMES expression
  >         expression -> . expression DIVIDE expression
  >         expression -> . NUMBER
  >         expression -> . LPAREN expression RPAREN

  >         NUMBER          shift and go to state 3
  >         LPAREN          shift and go to state 2

  >     state 3

  >         expression -> NUMBER .

  >         $               reduce using rule 5
  >         PLUS            reduce using rule 5
  >         MINUS           reduce using rule 5
  >         TIMES           reduce using rule 5
  >         DIVIDE          reduce using rule 5
  >         RPAREN          reduce using rule 5

  >     state 4

  >         expression -> expression TIMES . expression
  >         expression -> . expression PLUS expression
  >         expression -> . expression MINUS expression
  >         expression -> . expression TIMES expression
  >         expression -> . expression DIVIDE expression
  >         expression -> . NUMBER
  >         expression -> . LPAREN expression RPAREN

  >         NUMBER          shift and go to state 3
  >         LPAREN          shift and go to state 2

  >     state 5

  >         expression -> expression MINUS . expression
  >         expression -> . expression PLUS expression
  >         expression -> . expression MINUS expression
  >         expression -> . expression TIMES expression
  >         expression -> . expression DIVIDE expression
  >         expression -> . NUMBER
  >         expression -> . LPAREN expression RPAREN

  >         NUMBER          shift and go to state 3
  >         LPAREN          shift and go to state 2

  >     state 6

  >         expression -> expression PLUS . expression
  >         expression -> . expression PLUS expression
  >         expression -> . expression MINUS expression
  >         expression -> . expression TIMES expression
  >         expression -> . expression DIVIDE expression
  >         expression -> . NUMBER
  >         expression -> . LPAREN expression RPAREN

  >         NUMBER          shift and go to state 3
  >         LPAREN          shift and go to state 2

  >     state 7

  >         expression -> expression DIVIDE . expression
  >         expression -> . expression PLUS expression
  >         expression -> . expression MINUS expression
  >         expression -> . expression TIMES expression
  >         expression -> . expression DIVIDE expression
  >         expression -> . NUMBER
  >         expression -> . LPAREN expression RPAREN

  >         NUMBER          shift and go to state 3
  >         LPAREN          shift and go to state 2

  >     state 8

  >         expression -> LPAREN expression . RPAREN
  >         expression -> expression . PLUS expression
  >         expression -> expression . MINUS expression
  >         expression -> expression . TIMES expression
  >         expression -> expression . DIVIDE expression

  >         RPAREN          shift and go to state 13
  >         PLUS            shift and go to state 6
  >         MINUS           shift and go to state 5
  >         TIMES           shift and go to state 4
  >         DIVIDE          shift and go to state 7

  >     state 9

  >         expression -> expression TIMES expression .
  >         expression -> expression . PLUS expression
  >         expression -> expression . MINUS expression
  >         expression -> expression . TIMES expression
  >         expression -> expression . DIVIDE expression

  >         $               reduce using rule 3
  >         PLUS            reduce using rule 3
  >         MINUS           reduce using rule 3
  >         TIMES           reduce using rule 3
  >         DIVIDE          reduce using rule 3
  >         RPAREN          reduce using rule 3

  >       ! PLUS            [ shift and go to state 6 ]
  >       ! MINUS           [ shift and go to state 5 ]
  >       ! TIMES           [ shift and go to state 4 ]
  >       ! DIVIDE          [ shift and go to state 7 ]

  >     state 10

  >         expression -> expression MINUS expression .
  >         expression -> expression . PLUS expression
  >         expression -> expression . MINUS expression
  >         expression -> expression . TIMES expression
  >         expression -> expression . DIVIDE expression

  >         $               reduce using rule 2
  >         PLUS            reduce using rule 2
  >         MINUS           reduce using rule 2
  >         RPAREN          reduce using rule 2
  >         TIMES           shift and go to state 4
  >         DIVIDE          shift and go to state 7

  >       ! TIMES           [ reduce using rule 2 ]
  >       ! DIVIDE          [ reduce using rule 2 ]
  >       ! PLUS            [ shift and go to state 6 ]
  >       ! MINUS           [ shift and go to state 5 ]

  >     state 11

  >         expression -> expression PLUS expression .
  >         expression -> expression . PLUS expression
  >         expression -> expression . MINUS expression
  >         expression -> expression . TIMES expression
  >         expression -> expression . DIVIDE expression

  >         $               reduce using rule 1
  >         PLUS            reduce using rule 1
  >         MINUS           reduce using rule 1
  >         RPAREN          reduce using rule 1
  >         TIMES           shift and go to state 4
  >         DIVIDE          shift and go to state 7

  >       ! TIMES           [ reduce using rule 1 ]
  >       ! DIVIDE          [ reduce using rule 1 ]
  >       ! PLUS            [ shift and go to state 6 ]
  >       ! MINUS           [ shift and go to state 5 ]

  >     state 12

  >         expression -> expression DIVIDE expression .
  >         expression -> expression . PLUS expression
  >         expression -> expression . MINUS expression
  >         expression -> expression . TIMES expression
  >         expression -> expression . DIVIDE expression

  >         $               reduce using rule 4
  >         PLUS            reduce using rule 4
  >         MINUS           reduce using rule 4
  >         TIMES           reduce using rule 4
  >         DIVIDE          reduce using rule 4
  >         RPAREN          reduce using rule 4

  >       ! PLUS            [ shift and go to state 6 ]
  >       ! MINUS           [ shift and go to state 5 ]
  >       ! TIMES           [ shift and go to state 4 ]
  >       ! DIVIDE          [ shift and go to state 7 ]

  >     state 13

  >         expression -> LPAREN expression RPAREN .

  >         $               reduce using rule 6
  >         PLUS            reduce using rule 6
  >         MINUS           reduce using rule 6
  >         TIMES           reduce using rule 6
  >         DIVIDE          reduce using rule 6
  >         RPAREN          reduce using rule 6

  In the file, each state of the grammar is described. Within each state the "." indicates the current location of the parse within any applicable grammar rules. In addition, the actions for each valid input token are listed. When a shift/reduce or reduce/reduce conflict arises, rules not selected are prefixed with an !. For example:

  >       ! TIMES           [ reduce using rule 2 ]
  >       ! DIVIDE          [ reduce using rule 2 ]
  >       ! PLUS            [ shift and go to state 6 ]
  >       ! MINUS           [ shift and go to state 5 ]

  By looking at these rules (and with a little practice), you can usually track down the source of most parsing conflicts. It should also be stressed that not all shift-reduce conflicts are bad. However, the only way to be sure that they are resolved correctly is to look at `parser.out`.

  ### 5.8 Syntax Error Handling

  When a syntax error occurs during parsing, the error is immediately detected (i.e., the parser does not read any more tokens beyond the source of the error). Error recovery in LR parsers is a delicate topic that involves ancient rituals and black-magic. The recovery mechanism provided by `yacc.py` is comparable to Unix yacc so you may want consult a book like O'Reilly's "Lex and Yacc" for some of the finer details.

  When a syntax error occurs, `yacc.py` performs the following steps:

  1. On the first occurrence of an error, the user-defined `p_error()` function is called with the offending token as an argument. Afterwards, the parser enters an "error-recovery" mode in which it will not make future calls to `p_error()` until it has successfully shifted at least 3 tokens onto the parsing stack.

  2. If no recovery action is taken in `p_error()`, the offending lookahead token is replaced with a special `error` token.

  3. If the offending lookahead token is already set to `error`, the top item of the parsing stack is deleted.

  4. If the entire parsing stack is unwound, the parser enters a restart state and attempts to start parsing from its initial state.

  5. If a grammar rule accepts `error` as a token, it will be shifted onto the parsing stack.

  6. If the top item of the parsing stack is `error`, lookahead tokens will be discarded until the parser can successfully shift a new symbol or reduce a rule involving `error`.

  #### 5.8.1 Recovery and resynchronization with error rules

  The most well-behaved approach for handling syntax errors is to write grammar rules that include the `error` token. For example, suppose your language had a grammar rule for a print statement like this:

  >     def p_statement_print(p):
  >          'statement : PRINT expr SEMI'
  >          ...

  To account for the possibility of a bad expression, you might write an additional grammar rule like this:

  >     def p_statement_print_error(p):
  >          'statement : PRINT error SEMI'
  >          print "Syntax error in print statement. Bad expression"

  In this case, the `error` token will match any sequence of tokens that might appear up to the first semicolon that is encountered. Once the semicolon is reached, the rule will be invoked and the `error` token will go away.

  This type of recovery is sometimes known as parser resynchronization. The `error` token acts as a wildcard for any bad input text and the token immediately following `error` acts as a synchronization token.

  It is important to note that the `error` token usually does not appear as the last token on the right in an error rule. For example:

  >     def p_statement_print_error(p):
  >         'statement : PRINT error'
  >         print "Syntax error in print statement. Bad expression"

  This is because the first bad token encountered will cause the rule to be reduced--which may make it difficult to recover if more bad tokens immediately follow.

  #### 5.8.2 Panic mode recovery

  An alternative error recovery scheme is to enter a panic mode recovery in which tokens are discarded to a point where the parser might be able to recover in some sensible manner.

  Panic mode recovery is implemented entirely in the `p_error()` function. For example, this function starts discarding tokens until it reaches a closing '}'. Then, it restarts the parser in its initial state.

  >     def p_error(p):
  >         print "Whoa. You are seriously hosed."
  >         # Read ahead looking for a closing '}'
  >         while 1:
  >             tok = yacc.token()             # Get the next token
  >             if not tok or tok.type == 'RBRACE': break
  >         yacc.restart()

  This function simply discards the bad token and tells the parser that the error was ok.

  >     def p_error(p):
  >         print "Syntax error at token", p.type
  >         # Just discard the token and tell the parser it's okay.
  >         yacc.errok()

  Within the `p_error()` function, three functions are available to control the behavior of the parser:

  - `yacc.errok()`. This resets the parser state so it doesn't think it's in error-recovery mode. This will prevent an `error` token from being generated and will reset the internal error counters so that the next syntax error will call `p_error()` again.

  - `yacc.token()`. This returns the next token on the input stream.

  - `yacc.restart()`. This discards the entire parsing stack and resets the parser to its initial state.
  Note: these functions are only available when invoking `p_error()` and are not available at any other time.

  To supply the next lookahead token to the parser, `p_error()` can return a token. This might be useful if trying to synchronize on special characters. For example:

  >     def p_error(p):
  >         # Read ahead looking for a terminating ";"
  >         while 1:
  >             tok = yacc.token()             # Get the next token
  >             if not tok or tok.type == 'SEMI': break
  >         yacc.errok()

  >         # Return SEMI to the parser as the next lookahead token
  >         return tok

  #### 5.8.3 General comments on error handling

  For normal types of languages, error recovery with error rules and resynchronization characters is probably the most reliable technique. This is because you can instrument the grammar to catch errors at selected places where it is relatively easy to recover and continue parsing. Panic mode recovery is really only useful in certain specialized applications where you might want to discard huge portions of the input text to find a valid restart point.

  ### 5.9 Line Number and Position Tracking

  Position tracking is often a tricky problem when writing compilers. By default, PLY tracks the line number and position of all tokens. This information is available using the following functions:
  - `p.lineno(num)`. Return the line number for symbol num
  - `p.lexpos(num)`. Return the lexing position for symbol num
  For example:

  >     def p_expression(p):
  >         'expression : expression PLUS expression'
  >         line   = p.lineno(2)        # line number of the PLUS token
  >         index  = p.lexpos(2)        # Position of the PLUS token

  As an optional feature, `yacc.py` can automatically track line numbers and positions for all of the grammar symbols as well. However, this extra tracking requires extra processing and can significantly slow down parsing. Therefore, it must be enabled by passing the `tracking=True` option to `yacc.parse()`. For example:

  >     yacc.parse(data,tracking=True)

  Once enabled, the `lineno()` and `lexpos()` methods work for all grammar symbols. In addition, two additional methods can be used:
  - `p.linespan(num)`. Return a tuple (startline,endline) with the starting and ending line number for symbol num.
  - `p.lexspan(num)`. Return a tuple (start,end) with the starting and ending positions for symbol num.
  For example:

  >     def p_expression(p):
  >         'expression : expression PLUS expression'
  >         p.lineno(1)        # Line number of the left expression
  >         p.lineno(2)        # line number of the PLUS operator
  >         p.lineno(3)        # line number of the right expression
  >         ...
  >         start,end = p.linespan(3)    # Start,end lines of the right expression
  >         starti,endi = p.lexspan(3)   # Start,end positions of right expression

  Note: The `lexspan()` function only returns the range of values up to the start of the last grammar symbol.

  Although it may be convenient for PLY to track position information on all grammar symbols, this is often unnecessary. For example, if you are merely using line number information in an error message, you can often just key off of a specific token in the grammar rule. For example:

  >     def p_bad_func(p):
  >         'funccall : fname LPAREN error RPAREN'
  >         # Line number reported from LPAREN token
  >         print "Bad function call at line", p.lineno(2)

  Similarly, you may get better parsing performance if you only propagate line number information where it's needed. For example:

  >     def p_fname(p):
  >         'fname : ID'
  >         p[0] = (p[1],p.lineno(1))

  Finally, it should be noted that PLY does not store position information after a rule has been processed. If it is important for you to retain this information in an abstract syntax tree, you must make your own copy.

  ### 5.10 AST Construction

  `yacc.py` provides no special functions for constructing an abstract syntax tree. However, such construction is easy enough to do on your own. Simply create a data structure for abstract syntax tree nodes and assign nodes to `p[0]` in each rule. For example:

  >     class Expr: pass

  >     class BinOp(Expr):
  >         def __init__(self,left,op,right):
  >             self.type = "binop"
  >             self.left = left
  >             self.right = right
  >             self.op = op

  >     class Number(Expr):
  >         def __init__(self,value):
  >             self.type = "number"
  >             self.value = value

  >     def p_expression_binop(p):
  >         '''expression : expression PLUS expression
  >                       | expression MINUS expression
  >                       | expression TIMES expression
  >                       | expression DIVIDE expression'''

  >         p[0] = BinOp(p[1],p[2],p[3])

  >     def p_expression_group(p):
  >         'expression : LPAREN expression RPAREN'
  >         p[0] = p[2]

  >     def p_expression_number(p):
  >         'expression : NUMBER'
  >         p[0] = Number(p[1])

  To simplify tree traversal, it may make sense to pick a very generic tree structure for your parse tree nodes. For example:

  >     class Node:
  >         def __init__(self,type,children=None,leaf=None):
  >              self.type = type
  >              if children:
  >                   self.children = children
  >              else:
  >                   self.children = [ ]
  >              self.leaf = leaf

  >     def p_expression_binop(p):
  >         '''expression : expression PLUS expression
  >                       | expression MINUS expression
  >                       | expression TIMES expression
  >                       | expression DIVIDE expression'''

  >         p[0] = Node("binop", [p[1],p[3]], p[2])

  ### 5.11 Embedded Actions

  The parsing technique used by yacc only allows actions to be executed at the end of a rule. For example, suppose you have a rule like this:

  >     def p_foo(p):
  >         "foo : A B C D"
  >         print "Parsed a foo", p[1],p[2],p[3],p[4]

  In this case, the supplied action code only executes after all of the symbols `A`, `B`, `C`, and `D` have been parsed. Sometimes, however, it is useful to execute small code fragments during intermediate stages of parsing. For example, suppose you wanted to perform some action immediately after `A` has been parsed. To do this, you can write a empty rule like this:

  >     def p_foo(p):
  >         "foo : A seen_A B C D"
  >         print "Parsed a foo", p[1],p[3],p[4],p[5]
  >         print "seen_A returned", p[2]

  >     def p_seen_A(p):
  >         "seen_A :"
  >         print "Saw an A = ", p[-1]   # Access grammar symbol to left
  >         p[0] = some_value            # Assign value to seen_A

  In this example, the empty `seen_A` rule executes immediately after `A` is shifted onto the parsing stack. Within this rule, `p[-1]` refers to the symbol on the stack that appears immediately to the left of the `seen_A` symbol. In this case, it would be the value of `A` in the `foo` rule immediately above. Like other rules, a value can be returned from an embedded action by simply assigning it to `p[0]`

  The use of embedded actions can sometimes introduce extra shift/reduce conflicts. For example, this grammar has no conflicts:

  >     def p_foo(p):
  >         """foo : abcd
  >                | abcx"""

  >     def p_abcd(p):
  >         "abcd : A B C D"

  >     def p_abcx(p):
  >         "abcx : A B C X"

  However, if you insert an embedded action into one of the rules like this,

  >     def p_foo(p):
  >         """foo : abcd
  >                | abcx"""

  >     def p_abcd(p):
  >         "abcd : A B C D"

  >     def p_abcx(p):
  >         "abcx : A B seen_AB C X"

  >     def p_seen_AB(p):
  >         "seen_AB :"

  an extra shift-reduce conflict will be introduced. This conflict is caused by the fact that the same symbol `C` appears next in both the `abcd` and `abcx` rules. The parser can either shift the symbol (`abcd` rule) or reduce the empty rule `seen_AB` (`abcx` rule).

  A common use of embedded rules is to control other aspects of parsing such as scoping of local variables. For example, if you were parsing C code, you might write code like this:

  >     def p_statements_block(p):
  >         "statements: LBRACE new_scope statements RBRACE"""
  >         # Action code
  >         ...
  >         pop_scope()        # Return to previous scope

  >     def p_new_scope(p):
  >         "new_scope :"
  >         # Create a new scope for local variables
  >         s = new_scope()
  >         push_scope(s)
  >         ...

  In this case, the embedded action `new_scope` executes immediately after a `LBRACE` (`{`) symbol is parsed. This might adjust internal symbol tables and other aspects of the parser. Upon completion of the rule `statements_block`, code might undo the operations performed in the embedded action (e.g., `pop_scope()`).

  ### 5.12 Yacc implementation notes

  - The default parsing method is LALR. To use SLR instead, run yacc() as follows:

    >     yacc.yacc(method="SLR")

    Note: LALR table generation takes approximately twice as long as SLR table generation. There is no difference in actual parsing performance---the same code is used in both cases. LALR is preferred when working with more complicated grammars since it is more powerful.

  - By default, `yacc.py` relies on `lex.py` for tokenizing. However, an alternative tokenizer can be supplied as follows:

    >     yacc.parse(lexer=x)

    in this case, `x` must be a Lexer object that minimally has a `x.token()` method for retrieving the next token. If an input string is given to `yacc.parse()`, the lexer must also have an `x.input()` method.

  - By default, the yacc generates tables in debugging mode (which produces the parser.out file and other output). To disable this, use

    >     yacc.yacc(debug=0)

  - To change the name of the `parsetab.py` file, use:

    >     yacc.yacc(tabmodule="foo")

  - To change the directory in which the `parsetab.py` file (and other output files) are written, use:

    >     yacc.yacc(tabmodule="foo",outputdir="somedirectory")

  - To prevent yacc from generating any kind of parser table file, use:

    >     yacc.yacc(write_tables=0)

    Note: If you disable table generation, yacc() will regenerate the parsing tables each time it runs (which may take awhile depending on how large your grammar is).

  - To print copious amounts of debugging during parsing, use:

    >     yacc.parse(debug=1)

  - To redirect the debugging output to a filename of your choosing, use:

    >     yacc.parse(debug=1, debugfile="debugging.out")

  - The `yacc.yacc()` function really returns a parser object. If you want to support multiple parsers in the same application, do this:

    >     p = yacc.yacc()
    >     ...
    >     p.parse()

    Note: The function `yacc.parse()` is bound to the last parser that was generated.

  - Since the generation of the LALR tables is relatively expensive, previously generated tables are cached and reused if possible. The decision to regenerate the tables is determined by taking an MD5 checksum of all grammar rules and precedence rules. Only in the event of a mismatch are the tables regenerated.

    It should be noted that table generation is reasonably efficient, even for grammars that involve around a 100 rules and several hundred states. For more complex languages such as C, table generation may take 30-60 seconds on a slow machine. Please be patient.

  - Since LR parsing is driven by tables, the performance of the parser is largely independent of the size of the grammar. The biggest bottlenecks will be the lexer and the complexity of the code in your grammar rules.

  ## 6. Parser and Lexer State Management

  In advanced parsing applications, you may want to have multiple parsers and lexers. Furthermore, the parser may want to control the behavior of the lexer in some way.

  To do this, it is important to note that both the lexer and parser are actually implemented as objects. These objects are returned by the `lex()` and `yacc()` functions respectively. For example:

  >     lexer  = lex.lex()       # Return lexer object
  >     parser = yacc.yacc()     # Return parser object

  To attach the lexer and parser together, make sure you use the `lexer` argumemnt to parse. For example:

  >     parser.parse(text,lexer=lexer)

  Within lexer and parser rules, these objects are also available. In the lexer, the "lexer" attribute of a token refers to the lexer object in use. For example:

  >     def t_NUMBER(t):
  >        r'\d+'
  >        ...
  >        print t.lexer           # Show lexer object

  In the parser, the "lexer" and "parser" attributes refer to the lexer and parser objects respectively.

  >     def p_expr_plus(p):
  >        'expr : expr PLUS expr'
  >        ...
  >        print p.parser          # Show parser object
  >        print p.lexer           # Show lexer object

  If necessary, arbitrary attributes can be attached to the lexer or parser object. For example, if you wanted to have different parsing modes, you could attach a mode attribute to the parser object and look at it later.

  ## 7. Using Python's Optimized Mode

  Because PLY uses information from doc-strings, parsing and lexing information must be gathered while running the Python interpreter in normal mode (i.e., not with the -O or -OO options). However, if you specify optimized mode like this:

  >     lex.lex(optimize=1)
  >     yacc.yacc(optimize=1)

  then PLY can later be used when Python runs in optimized mode. To make this work, make sure you first run Python in normal mode. Once the lexing and parsing tables have been generated the first time, run Python in optimized mode. PLY will use the tables without the need for doc strings.

  Beware: running PLY in optimized mode disables a lot of error checking. You should only do this when your project has stabilized and you don't need to do any debugging.

  ## 8. Where to go from here?

  The `examples` directory of the PLY distribution contains several simple examples. Please consult a compilers textbook for the theory and underlying implementation details or LR parsing.