theory Ind_Interfaceimports "../Base" Simple_Inductive_Packagebeginsection{* The Interface \label{sec:ind-interface} *}text {* 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}*}text {* The function for external invocation of the package is called @{ML add_inductive in SimpleInductivePackage}, whereas the one for internal invocation is called @{ML add_inductive_i in SimpleInductivePackage}. 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 add_inductive_i in SimpleInductivePackage} expects the types of the predicates and parameters to be specified using the datatype @{ML_type typ} of Isabelle's logical framework, whereas @{ML add_inductive in SimpleInductivePackage} 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 add_inductive_i in SimpleInductivePackage} 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 Binding.binding}. Strings can be turned into elements of the type @{ML_type Binding.binding} using the function @{ML [display] "Binding.name : string -> Binding.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 "Binding.binding * Attrib.src list"}. The function @{ML add_inductive_i in SimpleInductivePackage} expects the formula to be specified using the datatype @{ML_type term}, whereas @{ML add_inductive in SimpleInductivePackage} 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 add_inductive in SimpleInductivePackage} for external invocation of the package is quite simple. Essentially, it just parses the introduction rules and then passes them on to @{ML add_inductive_i in SimpleInductivePackage}: @{ML_chunk [display] add_inductive} For parsing and type checking the introduction rules, we use the function @{ML [display] "Specification.read_specification: (Binding.binding * string option * mixfix) list -> (*{variables}*) (Attrib.binding * string list) list list -> (*{rules}*) local_theory -> (((Binding.binding * typ) * mixfix) list * (Attrib.binding * term list) list) * local_theory"}*}text {* 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 read_specification in Specification}. Note that the format for rules supported by @{ML read_specification in 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 read_specification in 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 read_specification in 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 read_specification in Specification} will be bound by a meta-level universal quantifier.*}text {* Finally, @{ML read_specification in 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 [(Binding.name \"trcl\", NONE, NoSyn), (Binding.name \"r\", SOME \"'a \<Rightarrow> 'a \<Rightarrow> bool\", NoSyn)] [[((Binding.name \"base\", []), [\"trcl r x x\"])], [((Binding.name \"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>): (((Binding.binding * typ) * mixfix) list * (Attrib.binding * term list) list) * local_theory"} In the list of variables passed to @{ML read_specification in 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.*}text {* \paragraph{Parsers for theory syntax} Although the function @{ML add_inductive in SimpleInductivePackage} 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. 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_defthm trcl.inductthm basethm stepthm trcl.introslemma 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 {* Even and odd numbers can be defined by *}simple_inductive even and oddwhere even0: "even 0"| evenS: "odd n \<Longrightarrow> even (Suc n)"| oddS: "even n \<Longrightarrow> odd (Suc n)"(*<*)thm even_def odd_defthm even.induct odd.inductthm even0thm evenSthm oddSthm even_odd.intros(*>*)text {* 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_defthm accpart.inductthm accpartI(*>*)text {* 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 relbeginthm accpartI'thm accpart'.inductendthm rel.accpartI'thm rel.accpart'.inductML{*val (result, lthy) = SimpleInductivePackage.add_inductive [(Binding.name "trcl'", NONE, NoSyn)] [(Binding.name "r", SOME "'a \<Rightarrow> 'a \<Rightarrow> bool", NoSyn)] [((Binding.name "base", []), "\<And>x. trcl' r x x"), ((Binding.name "step", []), "\<And>x y z. trcl' r x y \<Longrightarrow> r y z \<Longrightarrow> trcl' r x z")] (TheoryTarget.init NONE @{theory})*}(*>*)text {* 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{table} @{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 "optional: ('a -> 'b * 'a) -> 'b -> 'a -> 'b * 'a" in Scan} \\ @{ML "repeat: ('a -> 'b * 'a) -> 'a -> 'b list * 'a" in Scan} \\ @{ML "repeat1: ('a -> 'b * 'a) -> 'a -> 'b list * 'a" in Scan} \\ @{ML ">> : ('a -> 'b * 'c) * ('b -> 'd) -> 'a -> 'd * 'c"} \\ @{ML "!! : ('a * string option -> string) -> ('a -> 'b) -> 'a -> 'b"} \end{table} 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 "p || q" for p q} returns the result of \texttt{p}, otherwise it returns the result of \texttt{q}. The parser @{ML "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 "p |-- q" for p q} and @{ML "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 "optional p x" for p x in Scan} returns the result of \texttt{p}, otherwise it returns the default value \texttt{x}. The parser @{ML "repeat p" for p in Scan} applies \texttt{p} as often as it can, returning a possibly empty list of parsed items. The parser @{ML "repeat1 p" for p in Scan} is similar, but requires \texttt{p} to succeed at least once. The parser @{ML "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 [display] "q -- p || r" for p q r} will try the alternative parser \texttt{r}. By writing @{ML [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{table} @{ML "one: ('a -> bool) -> 'a list -> 'a * 'a list" in Scan} \\ @{ML "$$ : string -> string list -> string * string list"} \end{table} The parser @{ML "one pred" for pred in Scan} parses exactly one token that satisfies the predicate \texttt{pred}, whereas @{ML "$$ s" for s} only accepts a token that equals the string \texttt{s}. Note that we can easily express @{ML "$$ s" for s} using @{ML "one" in Scan}: @{ML [display] "one (fn s' => s' = s)" for s in Scan} As an example, let us look at how we can use @{ML "$$"} and @{ML "--"} to parse the prefix ``\texttt{hello}'' of the character list ``\texttt{hello world}'': @{ML_response [display] "($$ \"h\" -- $$ \"e\" -- $$ \"l\" -- $$ \"l\" -- $$ \"o\") [\"h\", \"e\", \"l\", \"l\", \"o\", \" \", \"w\", \"o\", \"r\", \"l\", \"d\"]" "(((((\"h\", \"e\"), \"l\"), \"l\"), \"o\"), [\" \", \"w\", \"o\", \"r\", \"l\", \"d\"]) : ((((string * string) * string) * string) * string) * string list"} Most of the time, however, we will have to deal with tokens that are not just strings. The parsers for the theory syntax, as well as the parsers for the argument syntax of proof methods and attributes use the token type @{ML_type OuterParse.token}, which is identical to @{ML_type OuterLex.token}. The parser functions for the theory syntax are contained in the structure @{ML_struct OuterParse} defined in the file @{ML_file "Pure/Isar/outer_parse.ML"}. In our parser, we will use the following functions: \begin{table} @{ML "$$$ : string -> token list -> string * token list" in OuterParse} \\ @{ML "enum1: string -> (token list -> 'a * token list) -> token list -> 'a list * token list" in OuterParse} \\ @{ML "prop: token list -> string * token list" in OuterParse} \\ @{ML "opt_target: token list -> string option * token list" in OuterParse} \\ @{ML "fixes: token list -> (Binding.binding * string option * mixfix) list * token list" in OuterParse} \\ @{ML "for_fixes: token list -> (Binding.binding * string option * mixfix) list * token list" in OuterParse} \\ @{ML "!!! : (token list -> 'a) -> token list -> 'a" in OuterParse} \end{table} The parsers @{ML "$$$" in OuterParse} and @{ML "!!!" in OuterParse} are defined using the parsers @{ML "one" in Scan} and @{ML "!!"} from @{ML_struct Scan}. The parser @{ML "enum1 s p" for s p in 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 prop in OuterParse}. Essentially, a proposition is just a string or an identifier, but using the specific parser function @{ML prop in 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 opt_target in OuterParse}. The lists of names of the predicates and parameters, together with optional types and syntax, are parsed using the functions @{ML "fixes" in OuterParse} and @{ML for_fixes in 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{table} @{ML "opt_thm_name: string -> token list -> Attrib.binding * token list" in SpecParse} \end{table} 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 "!!!" in 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 "!!!" in 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 add_inductive in 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. (FIXME : needs to be adjusted to new parser type) {\it The whole parser for our command has type @{ML_text [display] "OuterLex.token list -> (Toplevel.transition -> Toplevel.transition) * OuterLex.token list"} which is abbreviated by @{ML_text OuterSyntax.parser_fn}. The new command can be added to the system via the function @{ML_text [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 thy_decl in 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 thy_goal in 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(*>*)