274 lines
8.4 KiB
Python
274 lines
8.4 KiB
Python
import hashlib
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_list = {"list": ("LIST",)}
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_func = {"func": ("FUNC",)}
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_func_extra = {"func_extra": ("FUNC",)}
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_text = {"code": ("STRING", {"multiline": True, "default": "y=x()"})}
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optional = lambda x: {"optional": {k: v for k, v in x.items()}}
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required = lambda x: {"required": {k: v for k, v in x.items()}}
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both = lambda a, b: {**a, **b}
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many = lambda *dicts: {k: v for d in dicts for k, v in d.items()} # non tested
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class FuncBase:
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"""
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Base class for func nodes
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Allows for a function to be passed in as a callable
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and
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for a string to be passed in as code to be executed
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also
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returns the function as a callable
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"""
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def __init__(self):
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from custom_nodes.EternalKernelLiteGraphNodes.image import common_ksampler, TinyTxtToImg
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self.locals = locals()
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self.globals = globals()
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@classmethod
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def INPUT_TYPES(cls):
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req = required(_text)
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opt = optional(
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both(
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both(
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_func,
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_func_extra
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),
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_list
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)
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)
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use = both(req, opt)
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return use
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RETURN_TYPES = ("FUNC",)
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FUNCTION = "func"
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CATEGORY = "ETK/func"
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def func(self, **kwargs):
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import inspect
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self.globals = globals()
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self.locals = locals()
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self.locals["x1"] = kwargs.get("func", None)
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self.locals["x2"] = kwargs.get("func_extra", None)
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self.locals["x_list"] = kwargs.get("list", None)
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self.IN_FUNC = kwargs.get("func", None)
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self.EXTRA_IN_FUNC = kwargs.get("func_extra", None)
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self.exec_code = kwargs.get("code", None)
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self.ARGS = kwargs
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self.CODE = inspect.getsource(self.func)
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exec(self.exec_code, self.globals, self.locals)
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return (self.func,)
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class FuncRender:
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"""
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as input takes a function and a string of code
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optionally takes a list of arguments
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returns rendered results of types
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float, string, int, list
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"""
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def __init__(self):
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from custom_nodes.EternalKernelLiteGraphNodes.image import common_ksampler, TinyTxtToImg
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self.locals = locals()
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self.globals = globals()
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@classmethod
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def INPUT_TYPES(cls):
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use = both(
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required(
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_text),
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optional(both(_list,_func)
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)
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)
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return use
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RETURN_TYPES = ("FLOAT", "STRING", "INT", "LIST")
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FUNCTION = "func"
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CATEGORY = "ETK/func"
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def func(self, **kwargs):
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y_float = None
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y_string = None
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y_int = None
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func = kwargs.get("func", None)
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code = kwargs.get("code", None)
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globals = self.globals
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locals = self.locals
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locals["y_float"] = y_float
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locals["y_string"] = y_string
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locals["y_int"] = y_int
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locals["y_list"] = None
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locals["x"] = func
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locals["x_list"] = kwargs.get("list", None)
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# code should do something like:
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# func like : lambda x:["what"]*x
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# code like : 'f=[x for x in x(2)]'
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exec(code, globals, locals)
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self.globals = globals
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self.locals = locals
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return (locals["y_float"], locals["y_string"], locals["y_int"],locals["y_list"],)
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class FuncRenderImage:
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def __init__(self):
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from custom_nodes.EternalKernelLiteGraphNodes.image import common_ksampler, TinyTxtToImg
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self.locals = locals()
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self.globals = globals()
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@classmethod
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def INPUT_TYPES(cls):
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use = both(
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required(
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both(_func, _text)),
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optional(_list)
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)
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return use
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "func"
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CATEGORY = "ETK/func"
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OUTPUT_NODE = True
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def func(self, func, code, **kwargs):
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globals = self.globals
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locals = self.locals
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locals["x"] = func
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locals["x_list"] = kwargs.get("list", None)
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# code should do something like:
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# func like : lambda x:["what"]*x
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# code like : 'f=[x for x in x(2)]'
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exec(code, globals, locals)
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self.globals = globals
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self.locals = locals
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return (locals["y"],)
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class ExecWidget:
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import torch
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"""runs eval on the given text"""
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@classmethod
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def INPUT_TYPES(cls):
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return {"optional":
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{
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"code_str_in": ("STRING", {"multiline": True,
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"default": ""
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}),
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"image1_in": ("IMAGE",),
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"float1_in": ("FLOAT", {"multiline": False}),
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"string1_in": ("STRING", {"multiline": False}),
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"int1_in": ("INT", {"multiline": False}),
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},
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"required": {
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"text_to_eval": ("STRING",
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{"multiline": True,
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"default": "int_out=int_out\n"
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"float_out=float_out\n"
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"string_out=string_out\n"
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"image_out=image_out\n"
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}),
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}
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}
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CATEGORY = "text"
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RETURN_TYPES = ("STRING", "IMAGE", "FLOAT", "INT", "TUPLE", "CODE")
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FUNCTION = "pre_exec_handler"
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INTERNAL_STATE_DISPLAY_CODE = True
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OUTPUT_NODE = True
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def pre_exec_handler(self, **kwargs):
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image1_in = kwargs.get("image1_in", None)
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float1_in = kwargs.get("float1_in", 0.0)
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string1_in = kwargs.get("string1_in", "")
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int1_in = kwargs.get("int1_in", 0)
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tuple1_in = kwargs.get("tuple1_in", None)
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name = kwargs.get("name", "exec_func")
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text_to_eval = kwargs.get("text_to_eval", "")
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code_str_in = kwargs.get("code_str_in", "")
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if code_str_in != "":
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text_to_eval = code_str_in
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else:
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code_str_in = None
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ret = self.exec_handler(text_to_eval, image1_in, float1_in, string1_in, int1_in, tuple1_in, name)
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ret_formatted = {"ui": {"text": [ret[-1]]}, "result": ret}
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return ret_formatted
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def exec_handler(self, text_to_eval, image_obj: torch.Tensor = None, float_obj: float = 0.0, string_obj: str = "",
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int_obj: int = 0, tuple_obj: tuple = None, name: str = "exec_func"):
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"""
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>>> ExecWidget().exec_handler("2 + 3")
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'5'
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"""
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new_locals = {"float_out": float_obj,
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"image_out": image_obj,
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"string_out": string_obj,
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"int_out": int_obj,
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"tuple_out": tuple_obj,
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}
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try:
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if isinstance(text_to_eval, list):
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text_to_eval = text_to_eval[0]
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# evaluate for code tag
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if "<code>" in text_to_eval and "</code>" in text_to_eval:
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start = text_to_eval.find("<code>")
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end = text_to_eval.find("</code>")
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text_to_eval = text_to_eval[start + 6:end]
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# also evaluate for ```python tag
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if "```python" in text_to_eval and "```" in text_to_eval:
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start = text_to_eval.find("```python")
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end = text_to_eval.find("```", start + 8)
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text_to_eval = text_to_eval[start + 9:end]
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if "```" in text_to_eval:
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start = text_to_eval.find("```")
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end = text_to_eval.find("```", start + 2)
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text_to_eval = text_to_eval[start + 3:end]
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exec(text_to_eval, globals(), new_locals)
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source_code = text_to_eval
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except Exception as e:
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string_obj = str(e)
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source_code = text_to_eval
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string_obj = new_locals.get("string_out", string_obj)
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image_obj = new_locals.get("image_out", image_obj)
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float_obj = new_locals.get("float_out", float_obj)
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int_obj = new_locals.get("int_out", int_obj)
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tuple_obj = new_locals.get("tuple_out", tuple_obj)
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return (string_obj, image_obj, float_obj, int_obj, tuple_obj, text_to_eval)
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@classmethod
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def IS_CHANGED_NOPE(s, text_to_eval, image1_in: torch.Tensor = None, float1_in: float = 0.0, string1_in: str = "",
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int1_in: int = 0, name: str = "exec_func"):
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m = hashlib.sha256()
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m.update(name.encode("utf-8"))
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return m.digest().hex()
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if __name__ == "__main__":
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test = {}
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pass
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