Add types.OnErrorOmit (pydantic#8222)

pydantic/__init__.py

@@ -205,6 +205,7 @@
     'ValidatorFunctionWrapHandler',
     'FieldSerializationInfo',
     'SerializerFunctionWrapHandler',
+    'OnErrorOmit',
 )
 
 # A mapping of {<member name>: (package, <module name>)} defining dynamic imports
@@ -328,6 +329,7 @@
     'Tag': (__package__, '.types'),
     'Discriminator': (__package__, '.types'),
     'JsonValue': (__package__, '.types'),
+    'OnErrorOmit': (__package__, '.types'),
     # type_adapter
     'TypeAdapter': (__package__, '.type_adapter'),
     # warnings

pydantic/types.py

@@ -104,9 +104,13 @@
     'Tag',
     'Discriminator',
     'JsonValue',
+    'OnErrorOmit',
 )
 
 
+T = TypeVar('T')
+
+
 @_dataclasses.dataclass
 class Strict(_fields.PydanticMetadata, BaseMetadata):
     """Usage docs: https://docs.pydantic.dev/2.6/concepts/strict_mode/#strict-mode-with-annotated-strict
@@ -2800,3 +2804,21 @@ class Model(BaseModel):
             _AllowAnyJson,
         ],
     )
+
+
+class _OnErrorOmit:
+    @classmethod
+    def __get_pydantic_core_schema__(cls, source_type: Any, handler: GetCoreSchemaHandler) -> CoreSchema:
+        # there is no actual default value here but we use with_default_schema since it already has the on_error
+        # behavior implemented and it would be no more efficient to implement it on every other validator
+        # or as a standalone validator
+        return core_schema.with_default_schema(schema=handler(source_type), on_error='omit')
+
+
+OnErrorOmit = Annotated[T, _OnErrorOmit]
+"""
+When used as an item in a list, the key type in a dict, optional values of a TypedDict, etc.
+this annotation omits the item from the iteration if there is any error validating it.
+That is, instead of a [`ValidationError`][pydantic_core.ValidationError] being propagated up and the entire iterable being discarded
+any invalid items are discarded and the valid ones are returned.
+"""

tests/test_types.py

@@ -40,14 +40,15 @@
 import pytest
 from dirty_equals import HasRepr, IsFloatNan, IsOneOf, IsStr
 from pydantic_core import CoreSchema, PydanticCustomError, SchemaError, core_schema
-from typing_extensions import Annotated, Literal, TypedDict, get_args
+from typing_extensions import Annotated, Literal, NotRequired, TypedDict, get_args
 
 from pydantic import (
     UUID1,
     UUID3,
     UUID4,
     UUID5,
     AfterValidator,
+    AllowInfNan,
     AwareDatetime,
     Base64Bytes,
     Base64Str,
@@ -64,6 +65,8 @@
     FutureDate,
     FutureDatetime,
     GetCoreSchemaHandler,
+    GetPydanticSchema,
+    ImportString,
     InstanceOf,
     Json,
     JsonValue,
@@ -76,20 +79,24 @@
     NonNegativeInt,
     NonPositiveFloat,
     NonPositiveInt,
+    OnErrorOmit,
     PastDate,
     PastDatetime,
     PositiveFloat,
     PositiveInt,
     PydanticInvalidForJsonSchema,
+    PydanticSchemaGenerationError,
     SecretBytes,
     SecretStr,
     SerializeAsAny,
     SkipValidation,
+    Strict,
     StrictBool,
     StrictBytes,
     StrictFloat,
     StrictInt,
     StrictStr,
+    StringConstraints,
     Tag,
     TypeAdapter,
     ValidationError,
@@ -107,9 +114,6 @@
     validate_call,
 )
 from pydantic.dataclasses import dataclass as pydantic_dataclass
-from pydantic.errors import PydanticSchemaGenerationError
-from pydantic.functional_validators import AfterValidator
-from pydantic.types import AllowInfNan, GetPydanticSchema, ImportString, Strict, StringConstraints
 
 try:
     import email_validator
@@ -6129,3 +6133,38 @@ class MyModel(BaseModel):
 
     round_trip_value = json.loads(MyModel(val=True).model_dump_json())['val']
     assert round_trip_value is True, round_trip_value
+
+
+def test_on_error_omit() -> None:
+    OmittableInt = OnErrorOmit[int]
+
+    class MyTypedDict(TypedDict):
+        a: NotRequired[OmittableInt]
+        b: NotRequired[OmittableInt]
+
+    class Model(BaseModel):
+        a_list: List[OmittableInt]
+        a_dict: Dict[OmittableInt, OmittableInt]
+        a_typed_dict: MyTypedDict
+
+    actual = Model(
+        a_list=[1, 2, 'a', 3],
+        a_dict={1: 1, 2: 2, 'a': 'a', 'b': 0, 3: 'c', 4: 4},
+        a_typed_dict=MyTypedDict(a=1, b='xyz'),  # type: ignore
+    )
+
+    expected = Model(a_list=[1, 2, 3], a_dict={1: 1, 2: 2, 4: 4}, a_typed_dict=MyTypedDict(a=1))
+
+    assert actual == expected
+
+
+def test_on_error_omit_top_level() -> None:
+    ta = TypeAdapter(OnErrorOmit[int])
+
+    assert ta.validate_python(1) == 1
+    assert ta.validate_python('1') == 1
+
+    # we might want to just raise the OmitError or convert it to a ValidationError
+    # if it hits the top level, but this documents the current behavior at least
+    with pytest.raises(SchemaError, match='Uncaught Omit error'):
+        ta.validate_python('a')