ai-content-maker/.venv/Lib/site-packages/torch/fx/experimental/unification/variable.py

86 lines
2.0 KiB
Python

from contextlib import contextmanager
from .utils import hashable
from .dispatch import dispatch
_global_logic_variables = set() # type: ignore[var-annotated]
_glv = _global_logic_variables
class Var:
""" Logic Variable """
_id = 1
def __new__(cls, *token):
if len(token) == 0:
token = f"_{Var._id}" # type: ignore[assignment]
Var._id += 1
elif len(token) == 1:
token = token[0]
obj = object.__new__(cls)
obj.token = token # type: ignore[attr-defined]
return obj
def __str__(self):
return "~" + str(self.token) # type: ignore[attr-defined]
__repr__ = __str__
def __eq__(self, other):
return type(self) == type(other) and self.token == other.token # type: ignore[attr-defined]
def __hash__(self):
return hash((type(self), self.token)) # type: ignore[attr-defined]
def var():
return lambda *args: Var(*args)
def vars():
return lambda n: [var() for i in range(n)]
@dispatch(Var)
def isvar(v):
return True
isvar
@dispatch(object) # type: ignore[no-redef]
def isvar(o):
return not not _glv and hashable(o) and o in _glv
@contextmanager
def variables(*variables):
"""
Context manager for logic variables
Example:
>>> # xdoctest: +SKIP("undefined vars")
>>> from __future__ import with_statement
>>> with variables(1):
... print(isvar(1))
True
>>> print(isvar(1))
False
>>> # Normal approach
>>> from unification import unify
>>> x = var('x')
>>> unify(x, 1)
{~x: 1}
>>> # Context Manager approach
>>> with variables('x'):
... print(unify('x', 1))
{'x': 1}
"""
old_global_logic_variables = _global_logic_variables.copy()
_global_logic_variables.update(set(variables))
try:
yield
finally:
_global_logic_variables.clear()
_global_logic_variables.update(old_global_logic_variables)