lean4-htt/tests/lean/run/946.lean
Joachim Breitner d975e4302e
feat: fine-grained equational lemmas for non-recursive functions (#4154)
This is part of #3983.

Fine-grained equational lemmas are useful even for non-recursive
functions, so this adds them.

The new option `eqns.nonrecursive` can be set to `false` to have the old
behavior.

### Breaking channge

This is a breaking change: Previously, `rw [Option.map]` would rewrite
`Option.map f o` to `match o with … `. Now this rewrite will fail
because the equational lemmas require constructors here (like they do
for, say, `List.map`).

Remedies:

 * Split on `o` before rewriting.
* Use `rw [Option.map.eq_def]`, which rewrites any (saturated)
application of `Option.map`
* Use `set_option eqns.nonrecursive false` when *defining* the function
in question.

### Interaction with simp

The `simp` tactic so far had a special provision for non-recursive
functions so that `simp [f]` will try to use the equational lemmas, but
will also unfold `f` else, so less breakage here (but maybe performance
improvements with functions with many cases when applied to a
constructor, as the simplifier will no longer unfold to a large
`match`-statement and then collapse it right away).

For projection functions and functions marked `[reducible]`, `simp [f]`
won’t use the equational theorems, and will only use its internal
unfolding machinery.

### Implementation notes

It uses the same `mkEqnTypes` function as for recursive functions, so we
are close to a consistency here. There is still the wrinkle that for
recursive functions we don't split matches without an interesting
recursive call inside. Unifying that is future work.
2024-08-22 13:26:58 +00:00

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inductive DataType
| TInt
| TFloat
| TString
open DataType
inductive DataEntry
| EInt (i : Int)
| EFloat (f : Float)
| EString (s : String)
| NULL
def NULL := DataEntry.NULL
instance : Coe Int DataEntry where
coe := DataEntry.EInt
instance : Coe Float DataEntry where
coe := DataEntry.EFloat
instance : OfNat DataEntry n where
ofNat := DataEntry.EInt n
instance : OfScientific DataEntry where
ofScientific m s e := DataEntry.EFloat (OfScientific.ofScientific m s e)
instance : Coe String DataEntry where
coe := DataEntry.EString
namespace DataEntry
@[simp] def isOf (e : DataEntry) (t : DataType) : Prop :=
match e, t with
| EInt _, TInt => True
| EFloat _, TFloat => True
| EString _, TString => True
| NULL, _ => True
| _, _ => False
-- Needed since the introduction of the fine-grained lemmas
@[simp] theorem isOf_lit (n : Nat) : isOf (no_index (OfNat.ofNat n)) TInt = True := rfl
end DataEntry
abbrev Header := List (DataType × String)
def Header.colTypes (h : Header) : List DataType :=
h.map fun x => x.1
def Header.colNames (h : Header) : List String :=
h.map fun x => x.2
abbrev Row := List DataEntry
@[simp] def rowOfTypes : Row → List DataType → Prop
| [], [] => True
| eh :: et, th :: tt => eh.isOf th ∧ rowOfTypes et tt
| _, _ => False
@[simp] def rowsOfTypes : List Row → List DataType → Prop
| row :: rows, types => rowOfTypes row types ∧ rowsOfTypes rows types
| [], _ => True
structure DataFrame where
header : Header
rows : List Row
consistent : rowsOfTypes rows header.colTypes := by simp
namespace DataFrame
def empty (header : Header := []) : DataFrame :=
⟨header, [], by simp⟩
theorem consistentConcatOfConsistentRow
{df : DataFrame} (row : List DataEntry)
(hc : rowOfTypes row df.header.colTypes) :
rowsOfTypes (df.rows.concat row) (Header.colTypes df.header) :=
match df with
| ⟨_, rows, hr⟩ => by
induction rows with
| nil => simp at hc; simp [hc]
| cons _ _ hi => exact ⟨hr.1, hi hr.2 hc⟩
def addRow (df : DataFrame) (row : List DataEntry)
(h : rowOfTypes row df.header.colTypes := by simp) : DataFrame :=
⟨df.header, df.rows.concat row, consistentConcatOfConsistentRow row h⟩
end DataFrame
def h : Header := [(TInt, "id"), (TString, "name")]
def r : List Row := [[1, "alex"]]
-- this no longer works
def df1 : DataFrame := DataFrame.mk h r
-- and this ofc breaks now
def df2 : DataFrame := df1.addRow [2, "juddy"]
-- this doesn't work anymore either
def df3 : DataFrame := DataFrame.empty h |>.addRow [3, "john"]