Convert config

Author: Val Brodsky

libs/labelbox/src/labelbox/data/annotation_types/data/tiled_image.py

@@ -12,7 +12,7 @@
 from PIL import Image
 from pyproj import Transformer
 from pygeotile.point import Point as PygeoPoint
-from pydantic import BaseModel, model_validator, field_validator
+from pydantic import BaseModel, model_validator, field_validator, ConfigDict
 
 from labelbox.data.annotation_types import Rectangle, Point, Line, Polygon
 from .base_data import BaseData
@@ -361,8 +361,7 @@ class EPSGTransformer(BaseModel):
     in different formats.
     """
 
-    class Config:
-        arbitrary_types_allowed = True
+    model_config = ConfigDict(arbitrary_types_allowed=True)
 
     transformer: Any
 

libs/labelbox/src/labelbox/data/annotation_types/data/video.py

@@ -12,7 +12,7 @@
 from .base_data import BaseData
 from ..types import TypedArray
 
-from pydantic import model_validator, field_validator
+from pydantic import model_validator, ConfigDict, Extra
 
 logger = logging.getLogger(__name__)
 
@@ -168,6 +168,4 @@ def __repr__(self) -> str:
                 f"frames={'...' if self.frames is not None else None}," \
                 f"url={self.url})"
 
-    class Config:
-        # Required for discriminating between data types
-        extra = 'forbid'
+    model_config = ConfigDict(extra=Extra.forbid,)

libs/labelbox/src/labelbox/data/annotation_types/label.py

@@ -59,9 +59,10 @@ def validate_data(cls, label):
         if isinstance(label.get("data"), Dict):
             label["data"]["class_name"] = "GenericDataRowData"
         else:
-            warnings.warn(
-                f"Using {type(label['data']).__name__} class for label.data is deprecated. "
-                "Use a dict or an instance of GenericDataRowData instead.")
+            if not isinstance(label.get("data"), GenericDataRowData):
+                warnings.warn(
+                    f"Using {type(label['data']).__name__} class for label.data is deprecated. "
+                    "Use a dict or an instance of GenericDataRowData instead.")
         return label
 
     def object_annotations(self) -> List[ObjectAnnotation]:

libs/labelbox/src/labelbox/data/annotation_types/metrics/base.py

@@ -1,7 +1,7 @@
 from abc import ABC
 from typing import Annotated, Dict, Optional, Any, Union
 
-from pydantic import BaseModel, field_validator, Field
+from pydantic import BaseModel, field_validator, Field, ValidationError
 
 ConfidenceValue = Annotated[float, Field(ge=0, le=1)]
 
@@ -25,12 +25,11 @@ def validate_value(cls, value):
         if isinstance(value, Dict):
             if not (MIN_CONFIDENCE_SCORES <= len(value) <=
                     MAX_CONFIDENCE_SCORES):
-                raise pydantic_compat.ValidationError([
-                    pydantic_compat.ErrorWrapper(ValueError(
+                raise ValidationError([
+                    ValueError(
                         "Number of confidence scores must be greater"
                         f" than or equal to {MIN_CONFIDENCE_SCORES} and"
                         f" less than or equal to {MAX_CONFIDENCE_SCORES}. Found {len(value)}"
                     ),
-                                                 loc='value')
                 ], cls)
         return value

libs/labelbox/src/labelbox/data/annotation_types/types.py

@@ -5,10 +5,9 @@
 from packaging import version
 import numpy as np
 
-from pydantic import ValidationInfo
-from labelbox import pydantic_compat
+from pydantic import ValidationInfo, Field
 
-Cuid = Annotated[str, pydantic_compat.Field(min_length=25, max_length=25)]
+Cuid = Annotated[str, Field(min_length=25, max_length=25)]
 
 DType = TypeVar('DType')
 DShape = TypeVar('DShape')

libs/labelbox/src/labelbox/data/serialization/labelbox_v1/feature.py

@@ -1,6 +1,6 @@
 from typing import Optional
 
-from pydantic import BaseModel, model_validator
+from pydantic import BaseModel, model_validator, ConfigDict
 
 from labelbox.utils import camel_case
 from ...annotation_types.types import Cuid
@@ -27,6 +27,5 @@ def dict(self, *args, **kwargs):
             res.pop('keyframe')
         return res
 
-    class Config:
-        populate_by_name = True
-        alias_generator = camel_case
+    model_config = ConfigDict(allow_population_by_field_name=True,
+                              alias_generator=camel_case)

libs/labelbox/src/labelbox/data/serialization/labelbox_v1/label.py

@@ -1,8 +1,9 @@
+from pyexpat import model
 from labelbox.data.annotation_types.data.tiled_image import TiledImageData
 from labelbox.utils import camel_case
 from typing import List, Optional, Union, Dict, Any
 
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, ConfigDict, Field
 
 from ...annotation_types.annotation import (ClassificationAnnotation,
                                             ObjectAnnotation)
@@ -100,8 +101,7 @@ def from_common(
 
         return result
 
-    class Config:
-        populate_by_name = True
+    model_config = ConfigDict(allow_population_by_field_name=True)
 
 
 class Review(BaseModel):
@@ -111,8 +111,7 @@ class Review(BaseModel):
     created_by: str
     label_id: Optional[str] = None
 
-    class Config:
-        alias_generator = camel_case
+    model_config = ConfigDict(alias_generator=camel_case)
 
 
 Extra = lambda name: Field(None, alias=name, extra_field=True)
@@ -245,5 +244,4 @@ def _is_url(self) -> bool:
             ("http://", "https://", "gs://",
              "s3://")) or "tileLayerUrl" in self.row_data
 
-    class Config:
-        populate_by_name = True
+    model_config = ConfigDict(allow_population_by_field_name=True)

libs/labelbox/src/labelbox/data/serialization/ndjson/classification.py

@@ -1,6 +1,6 @@
 from typing import Any, Dict, List, Union, Optional
 
-from pydantic import BaseModel, model_validator, Field
+from pydantic import BaseModel, ConfigDict, model_validator, Field
 from labelbox.data.mixins import ConfidenceMixin, CustomMetric, CustomMetricsMixin
 from labelbox.data.serialization.ndjson.base import DataRow, NDAnnotation
 
@@ -39,9 +39,8 @@ def dict(self, *args, **kwargs):
             ]
         return res
 
-    class Config:
-        populate_by_name = True
-        alias_generator = camel_case
+    model_config = ConfigDict(allow_population_by_field_name=True,
+                              alias_generator=camel_case)
 
 
 class FrameLocation(BaseModel):

libs/labelbox/src/labelbox/data/serialization/ndjson/metric.py

@@ -1,5 +1,7 @@
 from typing import Optional, Union, Type
 
+from pydantic import ConfigDict
+
 from labelbox.data.annotation_types.data import ImageData, TextData
 from labelbox.data.serialization.ndjson.base import DataRow, NDJsonBase
 from labelbox.data.annotation_types.metrics.scalar import (
@@ -15,8 +17,7 @@ class BaseNDMetric(NDJsonBase):
     feature_name: Optional[str] = None
     subclass_name: Optional[str] = None
 
-    class Config:
-        use_enum_values = True
+    model_config = ConfigDict(use_enum_values=True)
 
     def dict(self, *args, **kwargs):
         res = super().dict(*args, **kwargs)

libs/labelbox/src/labelbox/schema/bulk_import_request.py

@@ -8,7 +8,7 @@
 from google.api_core import retry
 from labelbox import parser
 import requests
-from pydantic import BaseModel, model_validator, StringConstraints, Field as PydanticField, field_validator
+from pydantic import BaseModel, model_validator, StringConstraints, Field as PydanticField, field_validator, ValidationError, Extra, ConfigDict
 from typing_extensions import Literal
 from typing import (Any, List, Optional, BinaryIO, Dict, Iterable, Tuple, Union,
                     Type, Set, TYPE_CHECKING, Annotated)
@@ -428,8 +428,7 @@ def _validate_ndjson(lines: Iterable[Dict[str, Any]],
                     f'{uuid} already used in this import job, '
                     'must be unique for the project.')
             uids.add(uuid)
-        except (pydantic_compat.ValidationError, ValueError, TypeError,
-                KeyError) as e:
+        except (ValidationError, ValueError, TypeError, KeyError) as e:
             raise lb_exceptions.MALValidationError(
                 f"Invalid NDJson on line {idx}") from e
 
@@ -528,7 +527,7 @@ class VideoSupported(BaseModel):
 
 
 #Base class for a special kind of union.
-# Compatible with pydantic_compat. Improves error messages over a traditional union
+# Improves error messages over a traditional union
 class SpecialUnion:
 
     def __new__(cls, **kwargs):
@@ -561,7 +560,7 @@ def build(cls: Any, data: Union[dict, BaseModel]) -> "NDBase":
                 data  (Union[dict, BaseModel]) : The data for constructing one of the objects in the union
             raises:
                 KeyError: data does not contain the determinant fields for any of the types supported by this SpecialUnion
-                pydantic_compat.ValidationError: Error while trying to construct a specific object in the union
+                ValidationError: Error while trying to construct a specific object in the union
 
         """
         if isinstance(data, BaseModel):
@@ -659,9 +658,7 @@ def validate_instance(self, valid_feature_schemas_by_id,
         self.validate_feature_schemas(valid_feature_schemas_by_id,
                                       valid_feature_schemas_by_name)
 
-    class Config:
-        #Users shouldn't to add extra data to the payload
-        extra = 'forbid'
+        model_config = ConfigDict(extra=Extra.forbid,)
 
         @staticmethod
         def determinants(parent_cls) -> List[str]: