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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())
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