Spaces:
Running
Running
'''API for implementing LynxKite operations.''' | |
from __future__ import annotations | |
import enum | |
import functools | |
import inspect | |
import pydantic | |
import typing | |
from typing_extensions import Annotated | |
CATALOGS = {} | |
EXECUTORS = {} | |
typeof = type # We have some arguments called "type". | |
def type_to_json(t): | |
if isinstance(t, type) and issubclass(t, enum.Enum): | |
return {'enum': list(t.__members__.keys())} | |
if getattr(t, '__metadata__', None): | |
return t.__metadata__[-1] | |
return {'type': str(t)} | |
Type = Annotated[ | |
typing.Any, pydantic.PlainSerializer(type_to_json, return_type=dict) | |
] | |
LongStr = Annotated[ | |
str, {'format': 'textarea'} | |
] | |
PathStr = Annotated[ | |
str, {'format': 'path'} | |
] | |
CollapsedStr = Annotated[ | |
str, {'format': 'collapsed'} | |
] | |
NodeAttribute = Annotated[ | |
str, {'format': 'node attribute'} | |
] | |
EdgeAttribute = Annotated[ | |
str, {'format': 'edge attribute'} | |
] | |
class BaseConfig(pydantic.BaseModel): | |
model_config = pydantic.ConfigDict( | |
arbitrary_types_allowed=True, | |
) | |
class Parameter(BaseConfig): | |
'''Defines a parameter for an operation.''' | |
name: str | |
default: typing.Any | |
type: Type = None | |
def options(name, options, default=None): | |
e = enum.Enum(f'OptionsFor_{name}', options) | |
return Parameter.basic(name, e[default or options[0]], e) | |
def collapsed(name, default, type=None): | |
return Parameter.basic(name, default, CollapsedStr) | |
def basic(name, default=None, type=None): | |
if default is inspect._empty: | |
default = None | |
if type is None or type is inspect._empty: | |
type = typeof(default) if default is not None else None | |
return Parameter(name=name, default=default, type=type) | |
class Input(BaseConfig): | |
name: str | |
type: Type | |
position: str = 'left' | |
class Output(BaseConfig): | |
name: str | |
type: Type | |
position: str = 'right' | |
MULTI_INPUT = Input(name='multi', type='*') | |
def basic_inputs(*names): | |
return {name: Input(name=name, type=None) for name in names} | |
def basic_outputs(*names): | |
return {name: Output(name=name, type=None) for name in names} | |
class Op(BaseConfig): | |
func: typing.Callable = pydantic.Field(exclude=True) | |
name: str | |
params: dict[str, Parameter] | |
inputs: dict[str, Input] | |
outputs: dict[str, Output] | |
type: str = 'basic' # The UI to use for this operation. | |
sub_nodes: list[Op] = None # If set, these nodes can be placed inside the operation's node. | |
def __call__(self, *inputs, **params): | |
# Convert parameters. | |
for p in params: | |
if p in self.params: | |
if self.params[p].type == int: | |
params[p] = int(params[p]) | |
elif self.params[p].type == float: | |
params[p] = float(params[p]) | |
elif isinstance(self.params[p].type, enum.EnumMeta): | |
params[p] = self.params[p].type[params[p]] | |
res = self.func(*inputs, **params) | |
return res | |
def op(env: str, name: str, *, view='basic', sub_nodes=None, outputs=None): | |
'''Decorator for defining an operation.''' | |
def decorator(func): | |
sig = inspect.signature(func) | |
# Positional arguments are inputs. | |
inputs = { | |
name: Input(name=name, type=param.annotation) | |
for name, param in sig.parameters.items() | |
if param.kind != param.KEYWORD_ONLY} | |
params = {} | |
for n, param in sig.parameters.items(): | |
if param.kind == param.KEYWORD_ONLY and not n.startswith('_'): | |
params[n] = Parameter.basic(n, param.default, param.annotation) | |
if outputs: | |
_outputs = {name: Output(name=name, type=None) for name in outputs} | |
else: | |
_outputs = {'output': Output(name='output', type=None)} if view == 'basic' else {} | |
op = Op(func=func, name=name, params=params, inputs=inputs, outputs=_outputs, type=view) | |
if sub_nodes is not None: | |
op.sub_nodes = sub_nodes | |
op.type = 'sub_flow' | |
CATALOGS.setdefault(env, {}) | |
CATALOGS[env][name] = op | |
func.__op__ = op | |
return func | |
return decorator | |
def input_position(**kwargs): | |
'''Decorator for specifying unusual positions for the inputs.''' | |
def decorator(func): | |
op = func.__op__ | |
for k, v in kwargs.items(): | |
op.inputs[k].position = v | |
return func | |
return decorator | |
def output_position(**kwargs): | |
'''Decorator for specifying unusual positions for the outputs.''' | |
def decorator(func): | |
op = func.__op__ | |
for k, v in kwargs.items(): | |
op.outputs[k].position = v | |
return func | |
return decorator | |
def no_op(*args, **kwargs): | |
if args: | |
return args[0] | |
return None | |
def register_passive_op(env: str, name: str, inputs=[], outputs=['output'], params=[]): | |
'''A passive operation has no associated code.''' | |
op = Op( | |
func=no_op, | |
name=name, | |
params={p.name: p for p in params}, | |
inputs=dict( | |
(i, Input(name=i, type=None)) if isinstance(i, str) | |
else (i.name, i) for i in inputs), | |
outputs=dict( | |
(o, Output(name=o, type=None)) if isinstance(o, str) | |
else (o.name, o) for o in outputs)) | |
CATALOGS.setdefault(env, {}) | |
CATALOGS[env][name] = op | |
return op | |
def register_executor(env: str): | |
'''Decorator for registering an executor.''' | |
def decorator(func): | |
EXECUTORS[env] = func | |
return func | |
return decorator | |
def op_registration(env: str): | |
return functools.partial(op, env) | |
def passive_op_registration(env: str): | |
return functools.partial(register_passive_op, env) | |
def register_area(env, name, params=[]): | |
'''A node that represents an area. It can contain other nodes, but does not restrict movement in any way.''' | |
op = Op(func=no_op, name=name, params={p.name: p for p in params}, inputs={}, outputs={}, type='area') | |
CATALOGS[env][name] = op | |