#!/usr/bin/env python3
# --------------------( LICENSE                            )--------------------
# Copyright (c) 2014-2025 Beartype authors.
# See "LICENSE" for further details.

'''
Beartype **abstract syntax tree (AST) functionality** (i.e., low-level callables
and classes instrumenting well-typed third-party modules with runtime
type-checking dynamically generated by the :func:`beartype.beartype` decorator).

This private submodule is *not* intended for importation by downstream callers.
'''

# ....................{ TODO                               }....................
#FIXME: [PEP 484] Additionally define generator transformers. The idea here is
#that @beartype *CAN* actually automatically type-check generator yields, sends,
#and returns at runtime. How? By automatically injecting appropriate
#die_if_unbearable() calls type-checking the values to be yielded, sent, and
#returned against the appropriate type hints of the current generator factory
#*BEFORE* yielding, sending, and returning those values. Shockingly, typeguard
#already does this -- which is all manner of impressive. See the
#TypeguardTransformer._use_memo() context manager for working code. Wow!
#    https://github.com/agronholm/typeguard/blob/master/src/typeguard/_transformer.py
#
#Note, however, that doing so is extremely complicated in the modern Python era.
#Why? PEP 649. Unquoted forward references in type hints are now resolved
#dynamically at runtime via non-trivial machinery that, ultimately, calls
#__annotate__() dunder methods declared on callables. This means that type hints
#are no longer safely copyable at early AST transformation time, which in turn
#means that AST transformers *CANNOT* safely copy type hints annotating
#callables into parameters passed to die_if_unbearable() calls. Is there no hope
#then? There is hope, actually -- and fairly trivial, too. How? Just reference
#rather than copy nested generator type hints. Since the @beartype decorator
#guarantees nested generator type hints to be resolved by the time these
#die_if_unbearable() calls are actually performed, referencing these type hints
#suffices to preserve sanity: e.g.,
#    from collections.abc import Iterable
#    def muh_generator_func(muh_arg: str) -> Iterable[str]:
#        die_in_unbearable(
#            muh_arg, muh_generator_func.__annotations__['muh_arg])
#        yield muh_arg
#
#Of course, even the convoluted pseudocode above still fails to suffice. Why?
#Because the "__annotations__" dunder dictionary is no longer safely accessible.
#Doing so raises "NameError" exceptions for type hints subscripted by one or
#more unquoted forward references. Instead, we'll need to call some sort of PEP
#649-compliant private beartype utility function (which doesn't even exist,
#currently) to efficiently retrieve specific type hints.
#
#Super-annoying, but also super-feasible. Still, nobody cares at the moment.
#Let's let this one slip onto the back burner for the moment, eh?

#FIXME: [SPEED] Consider generalizing the BeartypeNodeTransformer.__new__()
#class method to internally cache and return "BeartypeNodeTransformer" instances
#depending on the passed "conf" parameter. In general, most codebases
#will only leverage a single @beartype configuration (if any @beartype
#configuration at all); ergo, caching improves everything by enabling us to
#reuse the same "BeartypeNodeTransformer" instance for every hooked module.
#Score @beartype! See the BeartypeConf.__new__() method for relevant logic. \o/
#FIXME: Oh, wait. We probably do *NOT* want to cache -- at least, not without
#defining a comparable reinit() method as we do for "BeartypeDecorMeta". After
#retrieving a cached "BeartypeNodeTransformer" instance, we'll need to
#immediately call BeartypeNodeTransformer.reinit() to reinitialize that
#instance.
#
#This is all feasible, of course -- but let's just roll with the naive
#implementation for now, please.

#FIXME: [PEP 675] *OMG.* See also the third-party "executing" Python package:
#    https://github.com/alexmojaki/executing
#
#IPython itself internally leverages "executing" via "stack_data" (i.e., a
#slightly higher-level third-party Python package that internally leverages
#"executing") to syntax-highlight the currently executing AST node. Indeed,
#"executing" sports an intense test suite (much like ours) effectively
#guaranteeing a one-to-one mapping between stack frames and AST nodes.
#
#So, what's the Big Idea here? The Big Idea here is that @beartype can
#internally (...possibly only optionally, but possibly mandatorily) leverage
#"executing" to begin performing full-blown static type-checking at runtime --
#especially of mission critical type hints like "typing.LiteralString" which can
#*ONLY* be type-checked via static analysis. :o
#
#So, what's the Little Idea here? The Little Idea here is that @beartype can
#generate type-checking wrappers that type-check parameters or returns annotated
#by "typing.LiteralString" by calling an internal private utility function --
#say, "_die_unless_literalstring(func: Callable, arg_name: str) -> None" -- with
#"func" as the current type-checking wrapper and "arg_name" as either the name
#of that parameter or "return". The _die_unless_literalstring() raiser then:
#* Dynamically searches up the call stack for the stack frame encapsulating an
#  external call to the passed "func" callable.
#* Passes that stack frame to the "executing" package.
#* "executing" then returns the AST node corresponding to that stack frame.
#* Introspects that node for the passed parameter whose name is "arg_name".
#* Raises an exception unless the value of that parameter is an AST node
#  corresponding to a string literal.
#
#Of course, that won't necessarily be fast -- but it will be accurate. Since
#security trumps speed, speed is significantly less of a concern insofar as
#"typing.LiteralString" is concerned. Of course, we should also employ
#significant caching... if we even can.
#FIXME: Actually, while demonstrably awesome, even the above fails to suffice to
#to statically type-check "typing.LiteralString". We failed to fully read PEP
#675, which contains a section on inference. In the worst case, nothing less
#than a complete graph of the entire app and all transitive dependencies thereof
#suffices to decide whether a parameter satisfies "typing.LiteralString".
#
#Thankfully, the above idea generalizes from "typing.LiteralString" to other
#fascinating topics as well. Indeed, given sufficient caching, one could begin
#to internally generate and cache a mypy-like graph network whose nodes are
#typed attributes and whose edges are relations between those typed attributes.
