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import json
from typing import Any, Dict, Optional
class BaseConsciousnessTuringMachineConfig:
def __init__(
self,
ctm_name: Optional[str] = None,
max_iter_num: int = 3,
output_threshold: float = 0.5,
groups_of_processors: Dict[
str, Any
] = {}, # Better to avoid mutable default arguments
supervisor: str = "gpt4_supervisor",
**kwargs: Any,
) -> None:
self.ctm_name: Optional[str] = ctm_name
self.max_iter_num: int = max_iter_num
self.output_threshold: float = output_threshold
self.groups_of_processors: Dict[str, Any] = groups_of_processors
self.supervisor: str = supervisor
# Handle additional, possibly unknown configuration parameters
for key, value in kwargs.items():
setattr(self, key, value)
def to_json_string(self) -> str:
"""Serializes this instance to a JSON string."""
return json.dumps(self.__dict__, indent=2) + "\n"
@classmethod
def from_json_file(
cls, json_file: str
) -> "BaseConsciousnessTuringMachineConfig":
"""Creates an instance from a JSON file."""
with open(json_file, "r", encoding="utf-8") as reader:
text = reader.read()
return cls(**json.loads(text))
@classmethod
def from_ctm(cls, ctm_name: str) -> "BaseConsciousnessTuringMachineConfig":
"""
Simulate fetching a model configuration from a ctm model repository.
This example assumes the configuration is already downloaded and saved locally.
"""
# This path would be generated dynamically based on `model_name_or_path`
# For simplicity, we're directly using it as a path to a local file
config_file = f"../ctm_conf/{ctm_name}_config.json"
return cls.from_json_file(config_file)
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