(** At the close of the section, this produces exactly the same
     result:  the functions that "need" to be parametrized by
     [V] or [default] are given extra parameters.  We can test
     this claim, as follows: *)

Goal SectionExample1.empty = SectionExample2.empty.Goal SectionExample1.lookup = SectionExample2.lookup.Definition key := nat.Inductive tree : Type :=
 | E : tree
 | T: tree -> key -> V -> tree -> tree.Inductive tree : Type :=
 | E : tree
 | T: tree -> key -> V -> tree -> tree.Inductive tree : Type :=
 | E : tree
 | T: tree -> key -> V -> tree -> tree.Definition empty_tree : tree := E.Fixpoint elements' (s: tree) (base: list (key*V)) : list (key * V) :=
 match s with
 | E => base
 | T a k v b => elements' a ((k,v) :: elements' b base)
 end.Definition elements (s: tree) : list (key * V) := elements' s nil.(* : forall (A : Type) (x : id) (v : A),
       t_empty v x = v *)

(** So, if we like those properties that [total_map] is proved to have,
   and we can prove that searchtrees behave like maps, then we don't
   have to reprove each individual property about searchtrees.

   More generally:  a job worth doing is worth doing well.
   It does no good to prove the "correctness" of a program, if you
   prove that it satisfies a wrong or useless specification. *)

(* ################################################################# *)
(** * Efficiency of Search Trees *)

(** We use binary search trees because they are efficient.  That is,
     if there are [N] elements in a (reasonably well balanced) BST,
     each insertion or lookup takes about logN time.

     What could go wrong?

     1.  The search tree might not be balanced.  In that case, each
           insertion or lookup will take as much as linear time.

	   - SOLUTION: use an algorithm, such as "red-black trees",
	   that ensures the trees stay balanced.  We'll do that in 
           Chapter [RedBlack].

     2.  Our keys are natural numbers, and Coq's [nat] type takes
           linear time _per comparison_.  That is, computing (j <? k)
           takes time proportional to the _value_ of [k-j].

	   - SOLUTION: represent keys by a data type that has a more
           efficient comparison operator.  We just use [nat] in this
           chapter because it's something you're already familiar with.

     3.  There's no notion of "run time" in Coq.  That is, we can't
          say what it means that a Coq function "takes N steps to
          evaluate."  Therefore, we can't prove that binary search
          trees are efficient.

	  - SOLUTION 1: Don't prove (in Coq) that they're efficient;
          just prove that they are correct.  Prove things about their
          efficiency the old-fashioned way, on pencil and paper.

	  - SOLUTION 2: Prove in Coq some facts about the height of
          the trees, which have direct bearing on their efficiency.
          We'll explore that in later chapters.

      4.  Our functions in Coq aren't real implementations; they are
          just pretend models of real implementations.  What if there
          are bugs in the correspondence between the Coq function and
          the real implementation?

          - SOLUTION: Use Coq's [extraction] feature to derive the
	    real implementation (in Ocaml or Haskell) automatically
	    from the Coq function.  Or, use Coq's [vm_compute] or
	    [native_compute] feature to compile and run the programs
	    efficiently inside Coq.  We'll explore [extraction] in a
	    later chapter. *)

(* ################################################################# *)
(** * Proof of Correctness *)

(** We claim that a [tree] "corresponds" to a [total_map].
     So we must exhibit an "abstraction relation"
     [Abs: tree -> total_map V -> Prop].

    The idea is that [Abs t m] says that tree [t]
    is a representation of map [m]; or that map [m]
    is an abstraction of tree [t].  How should we
    define this abstraction relation?

    The empty tree is easy:   [Abs E (fun x => default)].

    Now, what about this tree?: *)

Definition example_tree (v2 v4 v5 : V) :=
   T (T E 2 v2 E) 4 v4 (T E 5 v5 E).(** **** Exercise: 2 stars (example_map)  *)
(* Fill in the definition of example_map with a total_map that 
  you think example_tree should correspond to.  Use
  [t_update] and [(t_empty default)]. *)

Definition example_map (v2 v4 v5: V) : total_map V
  (* REPLACE THIS LINE WITH ":= _your_definition_ ." *).(** [combine pivot a b] uses the map [a] on any input less than
   [pivot], and uses map [b] on any input [ >= pivot]. *)

Inductive Abs:  tree -> total_map V -> Prop :=
| Abs_E: Abs E (t_empty default)
| Abs_T: forall a b l k v r,
      Abs l a ->
      Abs r b ->
      Abs (T l k v r)  (t_update (combine k a b) k v).(** [combine pivot a b] uses the map [a] on any input less than
   [pivot], and uses map [b] on any input [ >= pivot]. *)

Inductive Abs:  tree -> total_map V -> Prop :=
| Abs_E: Abs E (t_empty default)
| Abs_T: forall a b l k v r,
      Abs l a ->
      Abs r b ->
      Abs (T l k v r)  (t_update (combine k a b) k v).(** [combine pivot a b] uses the map [a] on any input less than
   [pivot], and uses map [b] on any input [ >= pivot]. *)

Inductive Abs:  tree -> total_map V -> Prop :=
| Abs_E: Abs E (t_empty default)
| Abs_T: forall a b l k v r,
      Abs l a ->
      Abs r b ->
      Abs (T l k v r)  (t_update (combine k a b) k v).

Lemma check_too_clever: forall v2 v4 v5: V, True.