| from __future__ import annotations | |
| import json | |
| import pathlib | |
| import copy | |
| from typing import Any | |
| from transformers.configuration_utils import PretrainedConfig | |
| class ModelConfig(PretrainedConfig): | |
| def __init__(self: ModelConfig, config_file: pathlib.Path | str | None = None, **kwargs): | |
| """ | |
| """ | |
| super().__init__(**kwargs) | |
| if config_file is None: | |
| self.attention_probs_dropout_prob: float = 0.1 | |
| self.hidden_dropout_prob = 0.1 | |
| self.hidden_size = 768 | |
| self.intermediate_size = 2560 | |
| self.max_sequence_length = 512 | |
| self.position_bucket_size = 32 | |
| self.num_attention_heads = 12 | |
| self.num_layers = 12 | |
| self.vocab_size = 8192 | |
| self.layer_norm_eps = 1e-5 | |
| else: | |
| if config_file == "str": | |
| config_file = pathlib.Path(config_file) | |
| config: dict[str, Any] = json.load(config_file.open("r")) | |
| for key, value in config.items(): | |
| setattr(self, key, value) | |
| def __repr__(self) -> str: | |
| return str(self.to_json_string()) | |
| def to_dict(self) -> dict[str, Any]: | |
| """Serializes this instance to a Python dictionary.""" | |
| output: dict[str, Any] = copy.deepcopy(self.__dict__) | |
| return output | |
| def to_json_string(self) -> str: | |
| """Serializes this instance to a JSON string.""" | |
| return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n" | |
| def to_json_file(self, json_file_path: pathlib.Path | str) -> None: | |
| """Save this instance to a json file.""" | |
| if isinstance(json_file_path, str): | |
| json_file_path: pathlib.Path = pathlib.Path(json_file_path) | |
| with json_file_path.open("w", encoding='utf-8') as writer: | |
| writer.write(self.to_json_string()) | |