theory Ind_Interface

imports Base Simple_Inductive_Package

begin

(*<*)

ML {*

structure SIP = SimpleInductivePackage

*}

(*>*)

section{* The interface *}

text {*

\label{sec:ind-interface}

In order to add a new inductive predicate to a theory with the help of our package, the user

must \emph{invoke} it. For every package, there are essentially two different ways of invoking

it, which we will refer to as \emph{external} and \emph{internal}. By external

invocation we mean that the package is called from within a theory document. In this case,

the type of the inductive predicate, as well as its introduction rules, are given as strings

by the user. Before the package can actually make the definition, the type and introduction

rules have to be parsed. In contrast, internal invocation means that the package is called

by some other package. For example, the function definition package \cite{Krauss-IJCAR06}

calls the inductive definition package to define the graph of the function. However, it is

not a good idea for the function definition package to pass the introduction rules for the

function graph to the inductive definition package as strings. In this case, it is better

to directly pass the rules to the package as a list of terms, which is more robust than

handling strings that are lacking the additional structure of terms. These two ways of

invoking the package are reflected in its ML programming interface, which consists of two

functions:

@{ML_chunk [display] SIMPLE_INDUCTIVE_PACKAGE}

The function for external invocation of the package is called @{ML_open add_inductive (SIP)},

whereas the one for internal invocation is called @{ML_open add_inductive_i (SIP)}. Both

of these functions take as arguments the names and types of the inductive predicates, the

names and types of their parameters, the actual introduction rules and a \emph{local theory}.

They return a local theory containing the definition, together with a tuple containing

the introduction and induction rules, which are stored in the local theory, too.

In contrast to an ordinary theory, which simply consists of a type signature, as

well as tables for constants, axioms and theorems, a local theory also contains

additional context information, such as locally fixed variables and local assumptions

that may be used by the package. The type @{ML_type local_theory} is identical to the

type of \emph{proof contexts} @{ML_type "Proof.context"}, although not every proof context

constitutes a valid local theory.

Note that @{ML_open add_inductive_i (SIP)} expects the types

of the predicates and parameters to be specified using the datatype @{ML_type typ} of Isabelle's

logical framework, whereas @{ML_open add_inductive (SIP)}

expects them to be given as optional strings. If no string is

given for a particular predicate or parameter, this means that the type should be

inferred by the package. Additional \emph{mixfix syntax} may be associated with

the predicates and parameters as well. Note that @{ML_open add_inductive_i (SIP)} does not

allow mixfix syntax to be associated with parameters, since it can only be used

for parsing. The names of the predicates, parameters and rules are represented by the

type @{ML_type Name.binding}. Strings can be turned into elements of the type

@{ML_type Name.binding} using the function

@{ML [display] "Name.binding : string -> Name.binding"}

Each introduction rule is given as a tuple containing its name, a list of \emph{attributes}

and a logical formula. Note that the type @{ML_type Attrib.binding} used in the list of

introduction rules is just a shorthand for the type @{ML_type "Name.binding * Attrib.src list"}.

The function @{ML_open add_inductive_i (SIP)} expects the formula to be specified using the datatype

@{ML_type term}, whereas @{ML_open add_inductive (SIP)} expects it to be given as a string.

An attribute specifies additional actions and transformations that should be applied to

a theorem, such as storing it in the rule databases used by automatic tactics

like the simplifier. The code of the package, which will be described in the following

section, will mostly treat attributes as a black box and just forward them to other

functions for storing theorems in local theories.

The implementation of the function @{ML_open add_inductive (SIP)} for external invocation

of the package is quite simple. Essentially, it just parses the introduction rules

and then passes them on to @{ML_open add_inductive_i (SIP)}:

@{ML_chunk [display] add_inductive}

For parsing and type checking the introduction rules, we use the function

@{ML_open [display] "Specification.read_specification:

  (Name.binding * string option * mixfix) list ->  (*{variables}*)

  (Attrib.binding * string list) list list ->  (*{rules}*)

  local_theory ->

  (((Name.binding * typ) * mixfix) list *

   (Attrib.binding * term list) list) *

  local_theory"}

During parsing, both predicates and parameters are treated as variables, so

the lists \verb!preds_syn! and \verb!params_syn! are just appended

before being passed to @{ML_open read_specification (Specification)}. Note that the format

for rules supported by @{ML_open read_specification (Specification)} is more general than

what is required for our package. It allows several rules to be associated

with one name, and the list of rules can be partitioned into several

sublists. In order for the list \verb!intro_srcs! of introduction rules

to be acceptable as an input for @{ML_open read_specification (Specification)}, we first

have to turn it into a list of singleton lists. This transformation

has to be reversed later on by applying the function

@{ML [display] "the_single: 'a list -> 'a"}

to the list \verb!specs! containing the parsed introduction rules.

The function @{ML_open read_specification (Specification)} also returns the list \verb!vars!

of predicates and parameters that contains the inferred types as well.

This list has to be chopped into the two lists \verb!preds_syn'! and

\verb!params_syn'! for predicates and parameters, respectively.

All variables occurring in a rule but not in the list of variables passed to

@{ML_open read_specification (Specification)} will be bound by a meta-level universal

quantifier.

Finally, @{ML_open read_specification (Specification)} also returns another local theory,

but we can safely discard it. As an example, let us look at how we can use this

function to parse the introduction rules of the @{text trcl} predicate:

@{ML_response [display]

"Specification.read_specification

  [(Name.binding \"trcl\", NONE, NoSyn),

   (Name.binding \"r\", SOME \"'a \<Rightarrow> 'a \<Rightarrow> bool\", NoSyn)]

  [[((Name.binding \"base\", []), [\"trcl r x x\"])],

   [((Name.binding \"step\", []), [\"trcl r x y \<Longrightarrow> r y z \<Longrightarrow> trcl r x z\"])]]

  @{context}"

"((\<dots>,

  [(\<dots>,

    [Const (\"all\", \<dots>) $ Abs (\"x\", TFree (\"'a\", \<dots>),

       Const (\"Trueprop\", \<dots>) $

         (Free (\"trcl\", \<dots>) $ Free (\"r\", \<dots>) $ Bound 0 $ Bound 0))]),

   (\<dots>,

    [Const (\"all\", \<dots>) $ Abs (\"x\", TFree (\"'a\", \<dots>),

       Const (\"all\", \<dots>) $ Abs (\"y\", TFree (\"'a\", \<dots>),

         Const (\"all\", \<dots>) $ Abs (\"z\", TFree (\"'a\", \<dots>),

           Const (\"==>\", \<dots>) $

             (Const (\"Trueprop\", \<dots>) $

                (Free (\"trcl\", \<dots>) $ Free (\"r\", \<dots>) $ Bound 2 $ Bound 1)) $

             (Const (\"==>\", \<dots>) $ \<dots> $ \<dots>))))])]),

 \<dots>)

: (((Name.binding * typ) * mixfix) list *

   (Attrib.binding * term list) list) * local_theory"}

In the list of variables passed to @{ML_open read_specification (Specification)}, we have

used the mixfix annotation @{ML NoSyn} to indicate that we do not want to associate any

mixfix syntax with the variable. Moreover, we have only specified the type of \texttt{r},

whereas the type of \texttt{trcl} is computed using type inference.

The local variables \texttt{x}, \texttt{y} and \texttt{z} of the introduction rules

are turned into bound variables with the de Bruijn indices,

whereas \texttt{trcl} and \texttt{r} remain free variables.

\paragraph{Parsers for theory syntax}

Although the function @{ML_open add_inductive (SIP)} parses terms and types, it still

cannot be used to invoke the package directly from within a theory document.

In order to do this, we have to write another parser. Before we describe

the process of writing parsers for theory syntax in more detail, we first

show some examples of how we would like to use the inductive definition

package.

\noindent

The definition of the transitive closure should look as follows:

*}

simple_inductive

  trcl for r :: "'a \<Rightarrow> 'a \<Rightarrow> bool"

where

  base: "trcl r x x"

| step: "trcl r x y \<Longrightarrow> r y z \<Longrightarrow> trcl r x z"

(*<*)

thm trcl_def

thm trcl.induct

thm base

thm step

thm trcl.intros

lemma trcl_strong_induct:

  assumes trcl: "trcl r x y"

  and I1: "\<And>x. P x x"

  and I2: "\<And>x y z. P x y \<Longrightarrow> trcl r x y \<Longrightarrow> r y z \<Longrightarrow> P x z"

  shows "P x y" 

proof -

  from trcl

  have "P x y \<and> trcl r x y"

  proof induct

    case (base x)

    from I1 and trcl.base show ?case ..

  next

    case (step x y z)

    then have "P x y" and "trcl r x y" by simp_all

    from `P x y` `trcl r x y` `r y z` have "P x z"

      by (rule I2)

    moreover from `trcl r x y` `r y z` have "trcl r x z"

      by (rule trcl.step)

    ultimately show ?case ..

  qed

  then show ?thesis ..

qed

(*>*)

text {*

\noindent

Even and odd numbers can be defined by

*}

simple_inductive

  even and odd

where

  even0: "even 0"

| evenS: "odd n \<Longrightarrow> even (Suc n)"

| oddS: "even n \<Longrightarrow> odd (Suc n)"

(*<*)

thm even_def odd_def

thm even.induct odd.induct

thm even0

thm evenS

thm oddS

thm even_odd.intros

(*>*)

text {*

\noindent

The accessible part of a relation can be introduced as follows:

*}

simple_inductive

  accpart for r :: "'a \<Rightarrow> 'a \<Rightarrow> bool"

where

  accpartI: "(\<And>y. r y x \<Longrightarrow> accpart r y) \<Longrightarrow> accpart r x"

(*<*)

thm accpart_def

thm accpart.induct

thm accpartI

(*>*)

text {*

\noindent

Moreover, it should also be possible to define the accessible part

inside a locale fixing the relation @{text r}:

*}

locale rel =

  fixes r :: "'a \<Rightarrow> 'a \<Rightarrow> bool"

simple_inductive (in rel) accpart'

where

  accpartI': "\<And>x. (\<And>y. r y x \<Longrightarrow> accpart' y) \<Longrightarrow> accpart' x"

(*<*)

context rel

begin

thm accpartI'

thm accpart'.induct

end

thm rel.accpartI'

thm rel.accpart'.induct

ML {*

val (result, lthy) = SimpleInductivePackage.add_inductive

  [(Name.binding "trcl'", NONE, NoSyn)] [(Name.binding "r", SOME "'a \<Rightarrow> 'a \<Rightarrow> bool", NoSyn)]

  [((Name.binding "base", []), "\<And>x. trcl' r x x"), ((Name.binding "step", []), "\<And>x y z. trcl' r x y \<Longrightarrow> r y z \<Longrightarrow> trcl' r x z")]

  (TheoryTarget.init NONE @{theory})

*}

(*>*)

text {*

\noindent

In this context, it is important to note that Isabelle distinguishes

between \emph{outer} and \emph{inner} syntax. Theory commands such as

\isa{\isacommand{simple{\isacharunderscore}inductive} $\ldots$ \isacommand{for} $\ldots$ \isacommand{where} $\ldots$}

belong to the outer syntax, whereas items in quotation marks, in particular

terms such as @{text [source] "trcl r x x"} and types such as

@{text [source] "'a \<Rightarrow> 'a \<Rightarrow> bool"} belong to the inner syntax.

Separating the two layers of outer and inner syntax greatly simplifies

matters, because the parser for terms and types does not have to know

anything about the possible syntax of theory commands, and the parser

for theory commands need not be concerned about the syntactic structure

of terms and types.

\medskip

\noindent

The syntax of the \isa{\isacommand{simple{\isacharunderscore}inductive}} command

can be described by the following railroad diagram:

\begin{rail}

  'simple\_inductive' target? fixes ('for' fixes)? \\

  ('where' (thmdecl? prop + '|'))?

  ;

\end{rail}

\paragraph{Functional parsers}

For parsing terms and types, Isabelle uses a rather general and sophisticated

algorithm due to Earley, which is driven by \emph{priority grammars}.

In contrast, parsers for theory syntax are built up using a set of combinators.

Functional parsing using combinators is a well-established technique, which

has been described by many authors, including Paulson \cite{paulson-ML-91}

and Wadler \cite{Wadler-AFP95}. 

The central idea is that a parser is a function of type @{ML_type "'a list -> 'b * 'a list"},

where @{ML_type "'a"} is a type of \emph{tokens}, and @{ML_type "'b"} is a type for

encoding items that the parser has recognized. When a parser is applied to a

list of tokens whose prefix it can recognize, it returns an encoding of the

prefix as an element of type @{ML_type "'b"}, together with the suffix of the list

containing the remaining tokens. Otherwise, the parser raises an exception

indicating a syntax error. The library for writing functional parsers in

Isabelle can roughly be split up into two parts. The first part consists of a

collection of generic parser combinators that are contained in the structure

@{ML_struct Scan} defined in the file @{ML_file "Pure/General/scan.ML"} in the Isabelle

sources. While these combinators do not make any assumptions about the concrete

structure of the tokens used, the second part of the library consists of combinators

for dealing with specific token types.

The following is an excerpt from the signature of @{ML_struct Scan}:

\begin{mytable}

@{ML "|| : ('a -> 'b) * ('a -> 'b) -> 'a -> 'b"} \\

@{ML "-- : ('a -> 'b * 'c) * ('c -> 'd * 'e) -> 'a -> ('b * 'd) * 'e"} \\

@{ML "|-- : ('a -> 'b * 'c) * ('c -> 'd * 'e) -> 'a -> 'd * 'e"} \\

@{ML "--| : ('a -> 'b * 'c) * ('c -> 'd * 'e) -> 'a -> 'b * 'e"} \\

@{ML_open "optional: ('a -> 'b * 'a) -> 'b -> 'a -> 'b * 'a" (Scan)} \\

@{ML_open "repeat: ('a -> 'b * 'a) -> 'a -> 'b list * 'a" (Scan)} \\

@{ML_open "repeat1: ('a -> 'b * 'a) -> 'a -> 'b list * 'a" (Scan)} \\

@{ML ">> : ('a -> 'b * 'c) * ('b -> 'd) -> 'a -> 'd * 'c"} \\

@{ML "!! : ('a * string option -> string) -> ('a -> 'b) -> 'a -> 'b"}

\end{mytable}

Interestingly, the functions shown above are so generic that they do not

even rely on the input and output of the parser being a list of tokens.

If \texttt{p} succeeds, i.e.\ does not raise an exception, the parser

@{ML_open "p || q" for p q} returns the result of \texttt{p}, otherwise it returns

the result of \texttt{q}. The parser @{ML_open "p -- q" for p q} first parses an

item of type @{ML_type "'b"} using \texttt{p}, then passes the remaining tokens

of type @{ML_type "'c"} to \texttt{q}, which parses an item of type @{ML_type "'d"}

and returns the remaining tokens of type @{ML_type "'e"}, which are finally

returned together with a pair of type @{ML_type "'b * 'd"} containing the two

parsed items. The parsers @{ML_open "p |-- q" for p q} and @{ML_open "p --| q" for p q}

work in a similar way as the previous one, with the difference that they

discard the item parsed by the first and the second parser, respectively.

If \texttt{p} succeeds, the parser @{ML_open "optional p x" for p x (Scan)} returns the result

of \texttt{p}, otherwise it returns the default value \texttt{x}. The parser

@{ML_open "repeat p" for p (Scan)} applies \texttt{p} as often as it can, returning a possibly

empty list of parsed items. The parser @{ML_open "repeat1 p" for p (Scan)} is similar,

but requires \texttt{p} to succeed at least once. The parser

@{ML_open "p >> f" for p f} uses \texttt{p} to parse an item of type @{ML_type "'b"}, to which

it applies the function \texttt{f} yielding a value of type @{ML_type "'d"}, which

is returned together with the remaining tokens of type @{ML_type "'c"}.

Finally, @{ML "!!"} is used for transforming exceptions produced by parsers.

If \texttt{p} raises an exception indicating that it cannot parse a given input,

then an enclosing parser such as

@{ML_open [display] "q -- p || r" for p q r}

will try the alternative parser \texttt{r}. By writing

@{ML_open [display] "q -- !! err p || r" for err p q r}

instead, one can achieve that a failure of \texttt{p} causes the whole parser to abort.

The @{ML "!!"} operator is similar to the \emph{cut} operator in Prolog, which prevents

the interpreter from backtracking. The \texttt{err} function supplied as an argument

to @{ML "!!"} can be used to produce an error message depending on the current

state of the parser, as well as the optional error message returned by \texttt{p}.

So far, we have only looked at combinators that construct more complex parsers

from simpler parsers. In order for these combinators to be useful, we also need

some basic parsers. As an example, we consider the following two parsers

defined in @{ML_struct Scan}:

\begin{mytable}

@{ML_open "one: ('a -> bool) -> 'a list -> 'a * 'a list" (Scan)} \\

@{ML_open "$$ : string -> string list -> string * string list"}

\end{mytable}

The parser @{ML_open "one pred" for pred (Scan)} parses exactly one token that

satisfies the predicate \texttt{pred}, whereas @{ML_open "$$ s" for s} only

accepts a token that equals the string \texttt{s}. Note that we can easily

express @{ML_open "$$ s" for s} using @{ML_open "one" (Scan)}:

@{ML_open [display] "one (fn s' => s' = s)" for s (Scan)}

As an example, let us look at how we can use @{ML "$$"} and @{ML "--"} to parse

the prefix ``\texttt{hello}'' of the character list ``\texttt{hello world}'':

@{ML_response [display]

"($$ \"h\" -- $$ \"e\" -- $$ \"l\" -- $$ \"l\" -- $$ \"o\")

[\"h\", \"e\", \"l\", \"l\", \"o\", \" \", \"w\", \"o\", \"r\", \"l\", \"d\"]"

"(((((\"h\", \"e\"), \"l\"), \"l\"), \"o\"), [\" \", \"w\", \"o\", \"r\", \"l\", \"d\"])

: ((((string * string) * string) * string) * string) * string list"}

Most of the time, however, we will have to deal with tokens that are not just strings.

The parsers for the theory syntax, as well as the parsers for the argument syntax

of proof methods and attributes use the token type @{ML_type OuterParse.token},

The parser functions for the theory syntax are contained in the structure

@{ML_struct OuterParse} defined in the file @{ML_file "Pure/Isar/outer_parse.ML"}.

In our parser, we will use the following functions:

\begin{mytable}

@{ML_open "$$$ : string -> token list -> string * token list" (OuterParse)} \\

@{ML_open "enum1: string -> (token list -> 'a * token list) -> token list ->

  'a list * token list" (OuterParse)} \\

@{ML_open "prop: token list -> string * token list" (OuterParse)} \\

@{ML_open "opt_target: token list -> string option * token list" (OuterParse)} \\

@{ML_open "fixes: token list ->

  (Name.binding * string option * mixfix) list * token list" (OuterParse)} \\

@{ML_open "for_fixes: token list ->

  (Name.binding * string option * mixfix) list * token list" (OuterParse)} \\

@{ML_open "!!! : (token list -> 'a) -> token list -> 'a" (OuterParse)}

\end{mytable}

The parsers @{ML_open "$$$" (OuterParse)} and @{ML_open "!!!" (OuterParse)} are

defined using the parsers @{ML_open "one" (Scan)} and @{ML "!!"} from

@{ML_struct Scan}.

The parser @{ML_open "enum1 s p" for s p (OuterParse)} parses a non-emtpy list of items

recognized by the parser \texttt{p}, where the items are separated by \texttt{s}.

A proposition can be parsed using the function @{ML_open prop (OuterParse)}.

Essentially, a proposition is just a string or an identifier, but using the

specific parser function @{ML_open prop (OuterParse)} leads to more instructive

error messages, since the parser will complain that a proposition was expected

when something else than a string or identifier is found.

An optional locale target specification of the form \isa{(\isacommand{in}\ $\ldots$)}

can be parsed using @{ML_open opt_target (OuterParse)}.

The lists of names of the predicates and parameters, together with optional

types and syntax, are parsed using the functions @{ML_open "fixes" (OuterParse)}

and @{ML_open for_fixes (OuterParse)}, respectively.

In addition, the following function from @{ML_struct SpecParse} for parsing

an optional theorem name and attribute, followed by a delimiter, will be useful:

\begin{mytable}

@{ML_open "opt_thm_name:

  string -> token list -> Attrib.binding * token list" (SpecParse)}

\end{mytable}

We now have all the necessary tools to write the parser for our

\isa{\isacommand{simple{\isacharunderscore}inductive}} command:

@{ML_chunk [display] syntax}

The definition of the parser \verb!ind_decl! closely follows the railroad

diagram shown above. In order to make the code more readable, the structures

@{ML_struct OuterParse} and @{ML_struct OuterKeyword} are abbreviated by

\texttt{P} and \texttt{K}, respectively. Note how the parser combinator

@{ML_open "!!!" (OuterParse)} is used: once the keyword \texttt{where}

has been parsed, a non-empty list of introduction rules must follow.

Had we not used the combinator @{ML_open "!!!" (OuterParse)}, a

\texttt{where} not followed by a list of rules would have caused the parser

to respond with the somewhat misleading error message

\begin{verbatim}

  Outer syntax error: end of input expected, but keyword where was found

\end{verbatim}

rather than with the more instructive message

\begin{verbatim}

  Outer syntax error: proposition expected, but terminator was found

\end{verbatim}

Once all arguments of the command have been parsed, we apply the function

@{ML_open add_inductive (SimpleInductivePackage)}, which yields a local theory

transformer of type @{ML_type "local_theory -> local_theory"}. Commands in

Isabelle/Isar are realized by transition transformers of type

@{ML_type [display] "Toplevel.transition -> Toplevel.transition"}

We can turn a local theory transformer into a transition transformer by using

the function

@{ML [display] "Toplevel.local_theory : string option ->

  (local_theory -> local_theory) ->

  Toplevel.transition -> Toplevel.transition"}

which, apart from the local theory transformer, takes an optional name of a locale

to be used as a basis for the local theory. The whole parser for our command has type

@{ML_type [display] "OuterLex.token list ->

  (Toplevel.transition -> Toplevel.transition) * OuterLex.token list"}

which is abbreviated by @{ML_type OuterSyntax.parser_fn}. The new command can be added

to the system via the function

@{ML [display] "OuterSyntax.command :

  string -> string -> OuterKeyword.T -> OuterSyntax.parser_fn -> unit"}

which imperatively updates the parser table behind the scenes. In addition to the parser, this

function takes two strings representing the name of the command and a short description,

as well as an element of type @{ML_type OuterKeyword.T} describing which \emph{kind} of

command we intend to add. Since we want to add a command for declaring new concepts,

we choose the kind @{ML "OuterKeyword.thy_decl"}. Other kinds include

@{ML "OuterKeyword.thy_goal"}, which is similar to @{ML_open thy_decl (OuterKeyword)},

but requires the user to prove a goal before making the declaration, or

@{ML "OuterKeyword.diag"}, which corresponds to a purely diagnostic command that does

not change the context. For example, the @{ML_open thy_goal (OuterKeyword)} kind is used

by the \isa{\isacommand{function}} command \cite{Krauss-IJCAR06}, which requires the user

to prove that a given set of equations is non-overlapping and covers all cases. The kind

of the command should be chosen with care, since selecting the wrong one can cause strange

behaviour of the user interface, such as failure of the undo mechanism.

*}

(*<*)

end

(*>*)