chore: add annotations for attrs and eval correct annotation type

eval will be here once I find a different way to parse types into
python

Signed-off-by: Aaron <29749331+aarnphm@users.noreply.github.com>

src/openllm/_configuration.py

@@ -61,8 +61,6 @@ if t.TYPE_CHECKING:
     import transformers
     from transformers.generation.beam_constraints import Constraint
 
-    from ._types import Attribute
-
     P = t.ParamSpec("P")
 
     F = t.Callable[P, t.Any]
@@ -513,12 +511,10 @@ class LLMConfig:
 
                 annotation = anns.get(key, None)
                 if annotation is not None:
-                    try:
-                        annotation = orjson.loads(annotation)
-                    except orjson.JSONDecodeError as err:
-                        raise ValueError(f"Failed to load from environment variables: {err}")
+                    # NOTE: eval is dangerous, but we don't provide any specific imports here.
+                    annotation = eval(annotation, {}, {})
 
-                attribute: attr.Attribute[t.Any] = t.cast(Attribute, attr.Attribute).from_counting_attr(
+                attribute: attr.Attribute[t.Any] = attr.Attribute.from_counting_attr(
                     key, dantic.Field(default, env=env_key, alias=key), type=annotation
                 )
 

src/openllm/_types.py

@@ -20,9 +20,6 @@ from __future__ import annotations
 
 import typing as t
 
-from attr import Attribute as _Attribute
-from attr._make import _CountingAttr
-
 if not t.TYPE_CHECKING:
     raise RuntimeError(f"{__name__} should not be imported during runtime")
 
@@ -34,10 +31,3 @@ LLMModel = transformers.PreTrainedModel | transformers.TFPreTrainedModel | trans
 LLMTokenizer = (
     transformers.PreTrainedTokenizer | transformers.PreTrainedTokenizerFast | transformers.PreTrainedTokenizerBase
 )
-
-
-# NOTE: We are using attr.Attribute as a internal API for attrs
-class Attribute(_Attribute):
-    @classmethod
-    def from_counting_attr(cls, name: str, ca: _CountingAttr, type: type[t.Any] = None):
-        ...