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NexQuant/rdagent/core/utils.py
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from __future__ import annotations
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import importlib
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import json
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import multiprocessing as mp
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import pickle
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from collections.abc import Callable
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from typing import Any, ClassVar, NoReturn, cast
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from fuzzywuzzy import fuzz # type: ignore[import-untyped]
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class RDAgentException(Exception): # noqa: N818
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pass
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class SingletonBaseClass:
"""
Because we try to support defining Singleton with `class A(SingletonBaseClass)`
instead of `A(metaclass=SingletonMeta)` this class becomes necessary.
"""
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_instance_dict: ClassVar[dict] = {}
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def __new__(cls, *args: Any, **kwargs: Any) -> Any:
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# Since it's hard to align the difference call using args and kwargs, we strictly ask to use kwargs in Singleton
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if args:
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# TODO: this restriction can be solved.
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exception_message = "Please only use kwargs in Singleton to avoid misunderstanding."
raise RDAgentException(exception_message)
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class_name = [(-1, f"{cls.__module__}.{cls.__name__}")]
args_l = [(i, args[i]) for i in args]
kwargs_l = sorted(kwargs.items())
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all_args = class_name + args_l + kwargs_l
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kwargs_hash = hash(tuple(all_args))
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if kwargs_hash not in cls._instance_dict:
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cls._instance_dict[kwargs_hash] = super().__new__(cls) # Corrected call
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return cls._instance_dict[kwargs_hash]
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def __reduce__(self) -> NoReturn:
"""
NOTE:
When loading an object from a pickle, the __new__ method does not receive the `kwargs`
it was initialized with. This makes it difficult to retrieve the correct singleton object.
Therefore, we have made it unpickable.
"""
msg = f"Instances of {self.__class__.__name__} cannot be pickled"
raise pickle.PicklingError(msg)
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def parse_json(response: str) -> Any:
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try:
return json.loads(response)
except json.decoder.JSONDecodeError:
pass
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error_message = f"Failed to parse response: {response}, please report it or help us to fix it."
raise ValueError(error_message)
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def similarity(text1: str, text2: str) -> int:
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text1 = text1 if isinstance(text1, str) else ""
text2 = text2 if isinstance(text2, str) else ""
# Maybe we can use other similarity algorithm such as tfidf
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return cast(int, fuzz.ratio(text1, text2)) # mypy does not reguard it as int
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def import_class(class_path: str) -> Any:
"""
Parameters
----------
class_path : str
class path like"scripts.factor_implementation.baselines.naive.one_shot.OneshotFactorGen"
Returns
-------
class of `class_path`
"""
module_path, class_name = class_path.rsplit(".", 1)
module = importlib.import_module(module_path)
return getattr(module, class_name)
def multiprocessing_wrapper(func_calls: list[tuple[Callable, tuple]], n: int) -> list:
"""It will use multiprocessing to call the functions in func_calls with the given parameters.
The results equals to `return [f(*args) for f, args in func_calls]`
It will not call multiprocessing if `n=1`
Parameters
----------
func_calls : List[Tuple[Callable, Tuple]]
the list of functions and their parameters
n : int
the number of subprocesses
Returns
-------
list
"""
if n == 1:
return [f(*args) for f, args in func_calls]
with mp.Pool(processes=n) as pool:
results = [pool.apply_async(f, args) for f, args in func_calls]
return [result.get() for result in results]