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Pydantic Types

pydantic.types

The types module contains custom types used by pydantic.

StrictBool module-attribute 

StrictBool = Annotated[bool, Strict()]

A boolean that must be either True or False.

PositiveInt module-attribute 

PositiveInt = Annotated[int, Gt(0)]

An integer that must be greater than zero.

from pydantic import BaseModel, PositiveInt, ValidationError

class Model(BaseModel):
    positive_int: PositiveInt

m = Model(positive_int=1)
print(repr(m))
#> Model(positive_int=1)

try:
    Model(positive_int=-1)
except ValidationError as e:
    print(e.errors())
    '''
    [
        {
            'type': 'greater_than',
            'loc': ('positive_int',),
            'msg': 'Input should be greater than 0',
            'input': -1,
            'ctx': {'gt': 0},
            'url': 'https://errors.pydantic.dev/2/v/greater_than',
        }
    ]
    '''

NegativeInt module-attribute 

NegativeInt = Annotated[int, Lt(0)]

An integer that must be less than zero.

from pydantic import BaseModel, NegativeInt, ValidationError

class Model(BaseModel):
    negative_int: NegativeInt

m = Model(negative_int=-1)
print(repr(m))
#> Model(negative_int=-1)

try:
    Model(negative_int=1)
except ValidationError as e:
    print(e.errors())
    '''
    [
        {
            'type': 'less_than',
            'loc': ('negative_int',),
            'msg': 'Input should be less than 0',
            'input': 1,
            'ctx': {'lt': 0},
            'url': 'https://errors.pydantic.dev/2/v/less_than',
        }
    ]
    '''

NonPositiveInt module-attribute 

NonPositiveInt = Annotated[int, Le(0)]

An integer that must be less than or equal to zero.

from pydantic import BaseModel, NonPositiveInt, ValidationError

class Model(BaseModel):
    non_positive_int: NonPositiveInt

m = Model(non_positive_int=0)
print(repr(m))
#> Model(non_positive_int=0)

try:
    Model(non_positive_int=1)
except ValidationError as e:
    print(e.errors())
    '''
    [
        {
            'type': 'less_than_equal',
            'loc': ('non_positive_int',),
            'msg': 'Input should be less than or equal to 0',
            'input': 1,
            'ctx': {'le': 0},
            'url': 'https://errors.pydantic.dev/2/v/less_than_equal',
        }
    ]
    '''

NonNegativeInt module-attribute 

NonNegativeInt = Annotated[int, Ge(0)]

An integer that must be greater than or equal to zero.

from pydantic import BaseModel, NonNegativeInt, ValidationError

class Model(BaseModel):
    non_negative_int: NonNegativeInt

m = Model(non_negative_int=0)
print(repr(m))
#> Model(non_negative_int=0)

try:
    Model(non_negative_int=-1)
except ValidationError as e:
    print(e.errors())
    '''
    [
        {
            'type': 'greater_than_equal',
            'loc': ('non_negative_int',),
            'msg': 'Input should be greater than or equal to 0',
            'input': -1,
            'ctx': {'ge': 0},
            'url': 'https://errors.pydantic.dev/2/v/greater_than_equal',
        }
    ]
    '''

StrictInt module-attribute 

StrictInt = Annotated[int, Strict()]

An integer that must be validated in strict mode.

from pydantic import BaseModel, StrictInt, ValidationError

class StrictIntModel(BaseModel):
    strict_int: StrictInt

try:
    StrictIntModel(strict_int=3.14159)
except ValidationError as e:
    print(e)
    '''
    1 validation error for StrictIntModel
    strict_int
      Input should be a valid integer [type=int_type, input_value=3.14159, input_type=float]
    '''

PositiveFloat module-attribute 

PositiveFloat = Annotated[float, Gt(0)]

A float that must be greater than zero.

from pydantic import BaseModel, PositiveFloat, ValidationError

class Model(BaseModel):
    positive_float: PositiveFloat

m = Model(positive_float=1.0)
print(repr(m))
#> Model(positive_float=1.0)

try:
    Model(positive_float=-1.0)
except ValidationError as e:
    print(e.errors())
    '''
    [
        {
            'type': 'greater_than',
            'loc': ('positive_float',),
            'msg': 'Input should be greater than 0',
            'input': -1.0,
            'ctx': {'gt': 0.0},
            'url': 'https://errors.pydantic.dev/2/v/greater_than',
        }
    ]
    '''

NegativeFloat module-attribute 

NegativeFloat = Annotated[float, Lt(0)]

A float that must be less than zero.

from pydantic import BaseModel, NegativeFloat, ValidationError

class Model(BaseModel):
    negative_float: NegativeFloat

m = Model(negative_float=-1.0)
print(repr(m))
#> Model(negative_float=-1.0)

try:
    Model(negative_float=1.0)
except ValidationError as e:
    print(e.errors())
    '''
    [
        {
            'type': 'less_than',
            'loc': ('negative_float',),
            'msg': 'Input should be less than 0',
            'input': 1.0,
            'ctx': {'lt': 0.0},
            'url': 'https://errors.pydantic.dev/2/v/less_than',
        }
    ]
    '''

NonPositiveFloat module-attribute 

NonPositiveFloat = Annotated[float, Le(0)]

A float that must be less than or equal to zero.

from pydantic import BaseModel, NonPositiveFloat, ValidationError

class Model(BaseModel):
    non_positive_float: NonPositiveFloat

m = Model(non_positive_float=0.0)
print(repr(m))
#> Model(non_positive_float=0.0)

try:
    Model(non_positive_float=1.0)
except ValidationError as e:
    print(e.errors())
    '''
    [
        {
            'type': 'less_than_equal',
            'loc': ('non_positive_float',),
            'msg': 'Input should be less than or equal to 0',
            'input': 1.0,
            'ctx': {'le': 0.0},
            'url': 'https://errors.pydantic.dev/2/v/less_than_equal',
        }
    ]
    '''

NonNegativeFloat module-attribute 

NonNegativeFloat = Annotated[float, Ge(0)]

A float that must be greater than or equal to zero.

from pydantic import BaseModel, NonNegativeFloat, ValidationError

class Model(BaseModel):
    non_negative_float: NonNegativeFloat

m = Model(non_negative_float=0.0)
print(repr(m))
#> Model(non_negative_float=0.0)

try:
    Model(non_negative_float=-1.0)
except ValidationError as e:
    print(e.errors())
    '''
    [
        {
            'type': 'greater_than_equal',
            'loc': ('non_negative_float',),
            'msg': 'Input should be greater than or equal to 0',
            'input': -1.0,
            'ctx': {'ge': 0.0},
            'url': 'https://errors.pydantic.dev/2/v/greater_than_equal',
        }
    ]
    '''

StrictFloat module-attribute 

StrictFloat = Annotated[float, Strict(True)]

A float that must be validated in strict mode.

from pydantic import BaseModel, StrictFloat, ValidationError

class StrictFloatModel(BaseModel):
    strict_float: StrictFloat

try:
    StrictFloatModel(strict_float='1.0')
except ValidationError as e:
    print(e)
    '''
    1 validation error for StrictFloatModel
    strict_float
      Input should be a valid number [type=float_type, input_value='1.0', input_type=str]
    '''

FiniteFloat module-attribute 

FiniteFloat = Annotated[float, AllowInfNan(False)]

A float that must be finite (not -inf, inf, or nan).

from pydantic import BaseModel, FiniteFloat

class Model(BaseModel):
    finite: FiniteFloat

m = Model(finite=1.0)
print(m)
#> finite=1.0

StrictBytes module-attribute 

StrictBytes = Annotated[bytes, Strict()]

A bytes that must be validated in strict mode.

StrictStr module-attribute 

StrictStr = Annotated[str, Strict()]

A string that must be validated in strict mode.

UUID1 module-attribute 

UUID1 = Annotated[UUID, UuidVersion(1)]

A UUID that must be version 1.

import uuid

from pydantic import UUID1, BaseModel

class Model(BaseModel):
    uuid1: UUID1

Model(uuid1=uuid.uuid1())

UUID3 module-attribute 

UUID3 = Annotated[UUID, UuidVersion(3)]

A UUID that must be version 3.

import uuid

from pydantic import UUID3, BaseModel

class Model(BaseModel):
    uuid3: UUID3

Model(uuid3=uuid.uuid3(uuid.NAMESPACE_DNS, 'pydantic.org'))

UUID4 module-attribute 

UUID4 = Annotated[UUID, UuidVersion(4)]

A UUID that must be version 4.

import uuid

from pydantic import UUID4, BaseModel

class Model(BaseModel):
    uuid4: UUID4

Model(uuid4=uuid.uuid4())

UUID5 module-attribute 

UUID5 = Annotated[UUID, UuidVersion(5)]

A UUID that must be version 5.

import uuid

from pydantic import UUID5, BaseModel

class Model(BaseModel):
    uuid5: UUID5

Model(uuid5=uuid.uuid5(uuid.NAMESPACE_DNS, 'pydantic.org'))

FilePath module-attribute 

FilePath = Annotated[Path, PathType('file')]

A path that must point to a file.

from pathlib import Path

from pydantic import BaseModel, FilePath, ValidationError

class Model(BaseModel):
    f: FilePath

path = Path('text.txt')
path.touch()
m = Model(f='text.txt')
print(m.model_dump())
#> {'f': PosixPath('text.txt')}
path.unlink()

path = Path('directory')
path.mkdir(exist_ok=True)
try:
    Model(f='directory')  # directory
except ValidationError as e:
    print(e)
    '''
    1 validation error for Model
    f
      Path does not point to a file [type=path_not_file, input_value='directory', input_type=str]
    '''
path.rmdir()

try:
    Model(f='not-exists-file')
except ValidationError as e:
    print(e)
    '''
    1 validation error for Model
    f
      Path does not point to a file [type=path_not_file, input_value='not-exists-file', input_type=str]
    '''

DirectoryPath module-attribute 

DirectoryPath = Annotated[Path, PathType('dir')]

A path that must point to a directory.

from pathlib import Path

from pydantic import BaseModel, DirectoryPath, ValidationError

class Model(BaseModel):
    f: DirectoryPath

path = Path('directory/')
path.mkdir()
m = Model(f='directory/')
print(m.model_dump())
#> {'f': PosixPath('directory')}
path.rmdir()

path = Path('file.txt')
path.touch()
try:
    Model(f='file.txt')  # file
except ValidationError as e:
    print(e)
    '''
    1 validation error for Model
    f
      Path does not point to a directory [type=path_not_directory, input_value='file.txt', input_type=str]
    '''
path.unlink()

try:
    Model(f='not-exists-directory')
except ValidationError as e:
    print(e)
    '''
    1 validation error for Model
    f
      Path does not point to a directory [type=path_not_directory, input_value='not-exists-directory', input_type=str]
    '''

NewPath module-attribute 

NewPath = Annotated[Path, PathType('new')]

A path for a new file or directory that must not already exist. The parent directory must already exist.

Base64Bytes module-attribute 

Base64Bytes = Annotated[
    bytes, EncodedBytes(encoder=Base64Encoder)
]

A bytes type that is encoded and decoded using the standard (non-URL-safe) base64 encoder.

Note

Under the hood, Base64Bytes use standard library base64.encodebytes and base64.decodebytes functions.

As a result, attempting to decode url-safe base64 data using the Base64Bytes type may fail or produce an incorrect decoding.

from pydantic import Base64Bytes, BaseModel, ValidationError

class Model(BaseModel):
    base64_bytes: Base64Bytes

# Initialize the model with base64 data
m = Model(base64_bytes=b'VGhpcyBpcyB0aGUgd2F5')

# Access decoded value
print(m.base64_bytes)
#> b'This is the way'

# Serialize into the base64 form
print(m.model_dump())
#> {'base64_bytes': b'VGhpcyBpcyB0aGUgd2F5
'}

# Validate base64 data
try:
    print(Model(base64_bytes=b'undecodable').base64_bytes)
except ValidationError as e:
    print(e)
    '''
    1 validation error for Model
    base64_bytes
      Base64 decoding error: 'Incorrect padding' [type=base64_decode, input_value=b'undecodable', input_type=bytes]
    '''

Base64Str module-attribute 

Base64Str = Annotated[
    str, EncodedStr(encoder=Base64Encoder)
]

A str type that is encoded and decoded using the standard (non-URL-safe) base64 encoder.

Note

Under the hood, Base64Bytes use standard library base64.encodebytes and base64.decodebytes functions.

As a result, attempting to decode url-safe base64 data using the Base64Str type may fail or produce an incorrect decoding.

from pydantic import Base64Str, BaseModel, ValidationError

class Model(BaseModel):
    base64_str: Base64Str

# Initialize the model with base64 data
m = Model(base64_str='VGhlc2UgYXJlbid0IHRoZSBkcm9pZHMgeW91J3JlIGxvb2tpbmcgZm9y')

# Access decoded value
print(m.base64_str)
#> These aren't the droids you're looking for

# Serialize into the base64 form
print(m.model_dump())
#> {'base64_str': 'VGhlc2UgYXJlbid0IHRoZSBkcm9pZHMgeW91J3JlIGxvb2tpbmcgZm9y
'}

# Validate base64 data
try:
    print(Model(base64_str='undecodable').base64_str)
except ValidationError as e:
    print(e)
    '''
    1 validation error for Model
    base64_str
      Base64 decoding error: 'Incorrect padding' [type=base64_decode, input_value='undecodable', input_type=str]
    '''

Base64UrlBytes module-attribute 

Base64UrlBytes = Annotated[
    bytes, EncodedBytes(encoder=Base64UrlEncoder)
]

A bytes type that is encoded and decoded using the URL-safe base64 encoder.

Note

Under the hood, Base64UrlBytes use standard library base64.urlsafe_b64encode and base64.urlsafe_b64decode functions.

As a result, the Base64UrlBytes type can be used to faithfully decode "vanilla" base64 data (using '+' and '/').

from pydantic import Base64UrlBytes, BaseModel

class Model(BaseModel):
    base64url_bytes: Base64UrlBytes

# Initialize the model with base64 data
m = Model(base64url_bytes=b'SHc_dHc-TXc==')
print(m)
#> base64url_bytes=b'Hw?tw>Mw'

Base64UrlStr module-attribute 

Base64UrlStr = Annotated[
    str, EncodedStr(encoder=Base64UrlEncoder)
]

A str type that is encoded and decoded using the URL-safe base64 encoder.

Note

Under the hood, Base64UrlStr use standard library base64.urlsafe_b64encode and base64.urlsafe_b64decode functions.

As a result, the Base64UrlStr type can be used to faithfully decode "vanilla" base64 data (using '+' and '/').

from pydantic import Base64UrlStr, BaseModel

class Model(BaseModel):
    base64url_str: Base64UrlStr

# Initialize the model with base64 data
m = Model(base64url_str='SHc_dHc-TXc==')
print(m)
#> base64url_str='Hw?tw>Mw'

JsonValue module-attribute 

JsonValue: TypeAlias = Union[
    List["JsonValue"],
    Dict[str, "JsonValue"],
    str,
    bool,
    int,
    float,
    None,
]

A JsonValue is used to represent a value that can be serialized to JSON.

It may be one of:

  • List['JsonValue']
  • Dict[str, 'JsonValue']
  • str
  • bool
  • int
  • float
  • None

The following example demonstrates how to use JsonValue to validate JSON data, and what kind of errors to expect when input data is not json serializable.

import json

from pydantic import BaseModel, JsonValue, ValidationError

class Model(BaseModel):
    j: JsonValue

valid_json_data = {'j': {'a': {'b': {'c': 1, 'd': [2, None]}}}}
invalid_json_data = {'j': {'a': {'b': ...}}}

print(repr(Model.model_validate(valid_json_data)))
#> Model(j={'a': {'b': {'c': 1, 'd': [2, None]}}})
print(repr(Model.model_validate_json(json.dumps(valid_json_data))))
#> Model(j={'a': {'b': {'c': 1, 'd': [2, None]}}})

try:
    Model.model_validate(invalid_json_data)
except ValidationError as e:
    print(e)
    '''
    1 validation error for Model
    j.dict.a.dict.b
      input was not a valid JSON value [type=invalid-json-value, input_value=Ellipsis, input_type=ellipsis]
    '''

OnErrorOmit module-attribute 

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 being propagated up and the entire iterable being discarded any invalid items are discarded and the valid ones are returned.

Strict dataclass

Bases: PydanticMetadata, BaseMetadata

Usage Documentation

Strict mode with Annotated[..., Strict()]

A field metadata class to indicate that a field should be validated in strict mode. Use this class as an annotation via Annotated, as seen below.

Attributes:

Name Type Description
strict bool

Whether to validate the field in strict mode.
Example 
from typing_extensions import Annotated

from pydantic.types import Strict

StrictBool = Annotated[bool, Strict()]
Source code in pydantic/types.py 
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@_dataclasses.dataclass
class Strict(_fields.PydanticMetadata, BaseMetadata):
    """Usage docs: https://docs.pydantic.dev/2.9/concepts/strict_mode/#strict-mode-with-annotated-strict

    A field metadata class to indicate that a field should be validated in strict mode.
    Use this class as an annotation via [`Annotated`](https://docs.python.org/3/library/typing.html#typing.Annotated), as seen below.

    Attributes:
        strict: Whether to validate the field in strict mode.

    Example:
        ```python
        from typing_extensions import Annotated

        from pydantic.types import Strict

        StrictBool = Annotated[bool, Strict()]
        ```
    """

    strict: bool = True

    def __hash__(self) -> int:
        return hash(self.strict)

AllowInfNan dataclass

Bases: PydanticMetadata

A field metadata class to indicate that a field should allow -inf, inf, and nan.

Use this class as an annotation via Annotated, as seen below.

Attributes:

Name Type Description
allow_inf_nan bool

Whether to allow -inf, inf, and nan. Defaults to True.
Example

```python from typing_extensions import Annotated

from pydantic.types import AllowInfNan

LaxFloat = Annotated[float, AllowInfNan()]

Source code in pydantic/types.py 
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@_dataclasses.dataclass
class AllowInfNan(_fields.PydanticMetadata):
    """A field metadata class to indicate that a field should allow `-inf`, `inf`, and `nan`.

    Use this class as an annotation via [`Annotated`](https://docs.python.org/3/library/typing.html#typing.Annotated), as seen below.

    Attributes:
        allow_inf_nan: Whether to allow `-inf`, `inf`, and `nan`. Defaults to `True`.

    Example:
        ```python
        from typing_extensions import Annotated

        from pydantic.types import AllowInfNan

        LaxFloat = Annotated[float, AllowInfNan()]
    """

    allow_inf_nan: bool = True

    def __hash__(self) -> int:
        return hash(self.allow_inf_nan)

StringConstraints dataclass

Bases: GroupedMetadata

Usage Documentation

String Constraints

A field metadata class to apply constraints to str types. Use this class as an annotation via Annotated, as seen below.

Attributes:

Name Type Description
strip_whitespace bool | None

Whether to remove leading and trailing whitespace.
to_upper bool | None

Whether to convert the string to uppercase.
to_lower bool | None

Whether to convert the string to lowercase.
strict bool | None

Whether to validate the string in strict mode.
min_length int | None

The minimum length of the string.
max_length int | None

The maximum length of the string.
pattern str | Pattern[str] | None

A regex pattern that the string must match.
Example

```python from typing_extensions import Annotated

from pydantic.types import StringConstraints

ConstrainedStr = Annotated[str, StringConstraints(min_length=1, max_length=10)]

Source code in pydantic/types.py 
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@_dataclasses.dataclass(frozen=True)
class StringConstraints(annotated_types.GroupedMetadata):
    """Usage docs: https://docs.pydantic.dev/2.9/concepts/fields/#string-constraints

    A field metadata class to apply constraints to `str` types.
    Use this class as an annotation via [`Annotated`](https://docs.python.org/3/library/typing.html#typing.Annotated), as seen below.

    Attributes:
        strip_whitespace: Whether to remove leading and trailing whitespace.
        to_upper: Whether to convert the string to uppercase.
        to_lower: Whether to convert the string to lowercase.
        strict: Whether to validate the string in strict mode.
        min_length: The minimum length of the string.
        max_length: The maximum length of the string.
        pattern: A regex pattern that the string must match.

    Example:
        ```python
        from typing_extensions import Annotated

        from pydantic.types import StringConstraints

        ConstrainedStr = Annotated[str, StringConstraints(min_length=1, max_length=10)]
    """

    strip_whitespace: bool | None = None
    to_upper: bool | None = None
    to_lower: bool | None = None
    strict: bool | None = None
    min_length: int | None = None
    max_length: int | None = None
    pattern: str | Pattern[str] | None = None

    def __iter__(self) -> Iterator[BaseMetadata]:
        if self.min_length is not None:
            yield MinLen(self.min_length)
        if self.max_length is not None:
            yield MaxLen(self.max_length)
        if self.strict is not None:
            yield Strict(self.strict)
        if (
            self.strip_whitespace is not None
            or self.pattern is not None
            or self.to_lower is not None
            or self.to_upper is not None
        ):
            yield _fields.pydantic_general_metadata(
                strip_whitespace=self.strip_whitespace,
                to_upper=self.to_upper,
                to_lower=self.to_lower,
                pattern=self.pattern,
            )

ImportString

A type that can be used to import a type from a string.

ImportString expects a string and loads the Python object importable at that dotted path. Attributes of modules may be separated from the module by : or ., e.g. if 'math:cos' was provided, the resulting field value would be the functioncos. If a . is used and both an attribute and submodule are present at the same path, the module will be preferred.

On model instantiation, pointers will be evaluated and imported. There is some nuance to this behavior, demonstrated in the examples below.

Good behavior: 

import math

from pydantic import BaseModel, Field, ImportString, ValidationError

class ImportThings(BaseModel):
    obj: ImportString

# A string value will cause an automatic import
my_cos = ImportThings(obj='math.cos')

# You can use the imported function as you would expect
cos_of_0 = my_cos.obj(0)
assert cos_of_0 == 1

# A string whose value cannot be imported will raise an error
try:
    ImportThings(obj='foo.bar')
except ValidationError as e:
    print(e)
    '''
    1 validation error for ImportThings
    obj
      Invalid python path: No module named 'foo.bar' [type=import_error, input_value='foo.bar', input_type=str]
    '''

# Actual python objects can be assigned as well
my_cos = ImportThings(obj=math.cos)
my_cos_2 = ImportThings(obj='math.cos')
my_cos_3 = ImportThings(obj='math:cos')
assert my_cos == my_cos_2 == my_cos_3

# You can set default field value either as Python object:
class ImportThingsDefaultPyObj(BaseModel):
    obj: ImportString = math.cos

# or as a string value (but only if used with `validate_default=True`)
class ImportThingsDefaultString(BaseModel):
    obj: ImportString = Field(default='math.cos', validate_default=True)

my_cos_default1 = ImportThingsDefaultPyObj()
my_cos_default2 = ImportThingsDefaultString()
assert my_cos_default1.obj == my_cos_default2.obj == math.cos

# note: this will not work!
class ImportThingsMissingValidateDefault(BaseModel):
    obj: ImportString = 'math.cos'

my_cos_default3 = ImportThingsMissingValidateDefault()
assert my_cos_default3.obj == 'math.cos'  # just string, not evaluated

Serializing an ImportString type to json is also possible.

```py lint="skip" from pydantic import BaseModel, ImportString

class ImportThings(BaseModel): obj: ImportString

Create an instance

m = ImportThings(obj='math.cos') print(m)

> obj=

print(m.model_dump_json())

>

```

Source code in pydantic/types.py 
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class ImportString:
    """A type that can be used to import a type from a string.

    `ImportString` expects a string and loads the Python object importable at that dotted path.
    Attributes of modules may be separated from the module by `:` or `.`, e.g. if `'math:cos'` was provided,
    the resulting field value would be the function`cos`. If a `.` is used and both an attribute and submodule
    are present at the same path, the module will be preferred.

    On model instantiation, pointers will be evaluated and imported. There is
    some nuance to this behavior, demonstrated in the examples below.

    **Good behavior:**
    ```py
    import math

    from pydantic import BaseModel, Field, ImportString, ValidationError

    class ImportThings(BaseModel):
        obj: ImportString

    # A string value will cause an automatic import
    my_cos = ImportThings(obj='math.cos')

    # You can use the imported function as you would expect
    cos_of_0 = my_cos.obj(0)
    assert cos_of_0 == 1

    # A string whose value cannot be imported will raise an error
    try:
        ImportThings(obj='foo.bar')
    except ValidationError as e:
        print(e)
        '''
        1 validation error for ImportThings
        obj
          Invalid python path: No module named 'foo.bar' [type=import_error, input_value='foo.bar', input_type=str]
        '''

    # Actual python objects can be assigned as well
    my_cos = ImportThings(obj=math.cos)
    my_cos_2 = ImportThings(obj='math.cos')
    my_cos_3 = ImportThings(obj='math:cos')
    assert my_cos == my_cos_2 == my_cos_3

    # You can set default field value either as Python object:
    class ImportThingsDefaultPyObj(BaseModel):
        obj: ImportString = math.cos

    # or as a string value (but only if used with `validate_default=True`)
    class ImportThingsDefaultString(BaseModel):
        obj: ImportString = Field(default='math.cos', validate_default=True)

    my_cos_default1 = ImportThingsDefaultPyObj()
    my_cos_default2 = ImportThingsDefaultString()
    assert my_cos_default1.obj == my_cos_default2.obj == math.cos

    # note: this will not work!
    class ImportThingsMissingValidateDefault(BaseModel):
        obj: ImportString = 'math.cos'

    my_cos_default3 = ImportThingsMissingValidateDefault()
    assert my_cos_default3.obj == 'math.cos'  # just string, not evaluated
    ```

    Serializing an `ImportString` type to json is also possible.

    ```py lint="skip"
    from pydantic import BaseModel, ImportString

    class ImportThings(BaseModel):
        obj: ImportString

    # Create an instance
    m = ImportThings(obj='math.cos')
    print(m)
    #> obj=<built-in function cos>
    print(m.model_dump_json())
    #> {"obj":"math.cos"}
    ```
    """

    @classmethod
    def __class_getitem__(cls, item: AnyType) -> AnyType:
        return Annotated[item, cls()]

    @classmethod
    def __get_pydantic_core_schema__(
        cls, source: type[Any], handler: GetCoreSchemaHandler
    ) -> core_schema.CoreSchema:
        serializer = core_schema.plain_serializer_function_ser_schema(cls._serialize, when_used='json')
        if cls is source:
            # Treat bare usage of ImportString (`schema is None`) as the same as ImportString[Any]
            return core_schema.no_info_plain_validator_function(
                function=_validators.import_string, serialization=serializer
            )
        else:
            return core_schema.no_info_before_validator_function(
                function=_validators.import_string, schema=handler(source), serialization=serializer
            )

    @classmethod
    def __get_pydantic_json_schema__(cls, cs: CoreSchema, handler: GetJsonSchemaHandler) -> JsonSchemaValue:
        return handler(core_schema.str_schema())

    @staticmethod
    def _serialize(v: Any) -> str:
        if isinstance(v, ModuleType):
            return v.__name__
        elif hasattr(v, '__module__') and hasattr(v, '__name__'):
            return f'{v.__module__}.{v.__name__}'
        # Handle special cases for sys.XXX streams
        # if we see more of these, we should consider a more general solution
        elif hasattr(v, 'name'):
            if v.name == '<stdout>':
                return 'sys.stdout'
            elif v.name == '<stdin>':
                return 'sys.stdin'
            elif v.name == '<stderr>':
                return 'sys.stderr'
        else:
            return v

    def __repr__(self) -> str:
        return 'ImportString'

UuidVersion dataclass

A field metadata class to indicate a UUID version.

Use this class as an annotation via Annotated, as seen below.

Attributes:

Name Type Description
uuid_version Literal[1, 3, 4, 5]

The version of the UUID. Must be one of 1, 3, 4, or 5.
Example 
from uuid import UUID

from typing_extensions import Annotated

from pydantic.types import UuidVersion

UUID1 = Annotated[UUID, UuidVersion(1)]
Source code in pydantic/types.py 
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@_dataclasses.dataclass(**_internal_dataclass.slots_true)
class UuidVersion:
    """A field metadata class to indicate a [UUID](https://docs.python.org/3/library/uuid.html) version.

    Use this class as an annotation via [`Annotated`](https://docs.python.org/3/library/typing.html#typing.Annotated), as seen below.

    Attributes:
        uuid_version: The version of the UUID. Must be one of 1, 3, 4, or 5.

    Example:
        ```python
        from uuid import UUID

        from typing_extensions import Annotated

        from pydantic.types import UuidVersion

        UUID1 = Annotated[UUID, UuidVersion(1)]
        ```
    """

    uuid_version: Literal[1, 3, 4, 5]

    def __get_pydantic_json_schema__(
        self, core_schema: core_schema.CoreSchema, handler: GetJsonSchemaHandler
    ) -> JsonSchemaValue:
        field_schema = handler(core_schema)
        field_schema.pop('anyOf', None)  # remove the bytes/str union
        field_schema.update(type='string', format=f'uuid{self.uuid_version}')
        return field_schema

    def __get_pydantic_core_schema__(self, source: Any, handler: GetCoreSchemaHandler) -> core_schema.CoreSchema:
        if isinstance(self, source):
            # used directly as a type
            return core_schema.uuid_schema(version=self.uuid_version)
        else:
            # update existing schema with self.uuid_version
            schema = handler(source)
            _check_annotated_type(schema['type'], 'uuid', self.__class__.__name__)
            schema['version'] = self.uuid_version  # type: ignore
            return schema

    def __hash__(self) -> int:
        return hash(type(self.uuid_version))

Json

A special type wrapper which loads JSON before parsing.

You can use the Json data type to make Pydantic first load a raw JSON string before validating the loaded data into the parametrized type:

from typing import Any, List

from pydantic import BaseModel, Json, ValidationError

class AnyJsonModel(BaseModel):
    json_obj: Json[Any]

class ConstrainedJsonModel(BaseModel):
    json_obj: Json[List[int]]

print(AnyJsonModel(json_obj='{"b": 1}'))
#> json_obj={'b': 1}
print(ConstrainedJsonModel(json_obj='[1, 2, 3]'))
#> json_obj=[1, 2, 3]

try:
    ConstrainedJsonModel(json_obj=12)
except ValidationError as e:
    print(e)
    '''
    1 validation error for ConstrainedJsonModel
    json_obj
      JSON input should be string, bytes or bytearray [type=json_type, input_value=12, input_type=int]
    '''

try:
    ConstrainedJsonModel(json_obj='[a, b]')
except ValidationError as e:
    print(e)
    '''
    1 validation error for ConstrainedJsonModel
    json_obj
      Invalid JSON: expected value at line 1 column 2 [type=json_invalid, input_value='[a, b]', input_type=str]
    '''

try:
    ConstrainedJsonModel(json_obj='["a", "b"]')
except ValidationError as e:
    print(e)
    '''
    2 validation errors for ConstrainedJsonModel
    json_obj.0
      Input should be a valid integer, unable to parse string as an integer [type=int_parsing, input_value='a', input_type=str]
    json_obj.1
      Input should be a valid integer, unable to parse string as an integer [type=int_parsing, input_value='b', input_type=str]
    '''

When you dump the model using model_dump or model_dump_json, the dumped value will be the result of validation, not the original JSON string. However, you can use the argument round_trip=True to get the original JSON string back:

from typing import List

from pydantic import BaseModel, Json

class ConstrainedJsonModel(BaseModel):
    json_obj: Json[List[int]]

print(ConstrainedJsonModel(json_obj='[1, 2, 3]').model_dump_json())
#> {"json_obj":[1,2,3]}
print(
    ConstrainedJsonModel(json_obj='[1, 2, 3]').model_dump_json(round_trip=True)
)
#> {"json_obj":"[1,2,3]"}
Source code in pydantic/types.py 
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class Json:
    """A special type wrapper which loads JSON before parsing.

    You can use the `Json` data type to make Pydantic first load a raw JSON string before
    validating the loaded data into the parametrized type:

    ```py
    from typing import Any, List

    from pydantic import BaseModel, Json, ValidationError

    class AnyJsonModel(BaseModel):
        json_obj: Json[Any]

    class ConstrainedJsonModel(BaseModel):
        json_obj: Json[List[int]]

    print(AnyJsonModel(json_obj='{"b": 1}'))
    #> json_obj={'b': 1}
    print(ConstrainedJsonModel(json_obj='[1, 2, 3]'))
    #> json_obj=[1, 2, 3]

    try:
        ConstrainedJsonModel(json_obj=12)
    except ValidationError as e:
        print(e)
        '''
        1 validation error for ConstrainedJsonModel
        json_obj
          JSON input should be string, bytes or bytearray [type=json_type, input_value=12, input_type=int]
        '''

    try:
        ConstrainedJsonModel(json_obj='[a, b]')
    except ValidationError as e:
        print(e)
        '''
        1 validation error for ConstrainedJsonModel
        json_obj
          Invalid JSON: expected value at line 1 column 2 [type=json_invalid, input_value='[a, b]', input_type=str]
        '''

    try:
        ConstrainedJsonModel(json_obj='["a", "b"]')
    except ValidationError as e:
        print(e)
        '''
        2 validation errors for ConstrainedJsonModel
        json_obj.0
          Input should be a valid integer, unable to parse string as an integer [type=int_parsing, input_value='a', input_type=str]
        json_obj.1
          Input should be a valid integer, unable to parse string as an integer [type=int_parsing, input_value='b', input_type=str]
        '''
    ```

    When you dump the model using `model_dump` or `model_dump_json`, the dumped value will be the result of validation,
    not the original JSON string. However, you can use the argument `round_trip=True` to get the original JSON string back:

    ```py
    from typing import List

    from pydantic import BaseModel, Json

    class ConstrainedJsonModel(BaseModel):
        json_obj: Json[List[int]]

    print(ConstrainedJsonModel(json_obj='[1, 2, 3]').model_dump_json())
    #> {"json_obj":[1,2,3]}
    print(
        ConstrainedJsonModel(json_obj='[1, 2, 3]').model_dump_json(round_trip=True)
    )
    #> {"json_obj":"[1,2,3]"}
    ```
    """

    @classmethod
    def __class_getitem__(cls, item: AnyType) -> AnyType:
        return Annotated[item, cls()]

    @classmethod
    def __get_pydantic_core_schema__(cls, source: Any, handler: GetCoreSchemaHandler) -> core_schema.CoreSchema:
        if cls is source:
            return core_schema.json_schema(None)
        else:
            return core_schema.json_schema(handler(source))

    def __repr__(self) -> str:
        return 'Json'

    def __hash__(self) -> int:
        return hash(type(self))

    def __eq__(self, other: Any) -> bool:
        return type(other) is type(self)

Secret

Bases: _SecretBase[SecretType]

A generic base class used for defining a field with sensitive information that you do not want to be visible in logging or tracebacks.

You may either directly parametrize Secret with a type, or subclass from Secret with a parametrized type. The benefit of subclassing is that you can define a custom _display method, which will be used for repr() and str() methods. The examples below demonstrate both ways of using Secret to create a new secret type.

  1. Directly parametrizing Secret with a type:
from pydantic import BaseModel, Secret

SecretBool = Secret[bool]

class Model(BaseModel):
    secret_bool: SecretBool

m = Model(secret_bool=True)
print(m.model_dump())
#> {'secret_bool': Secret('**********')}

print(m.model_dump_json())
#> {"secret_bool":"**********"}

print(m.secret_bool.get_secret_value())
#> True
  1. Subclassing from parametrized Secret:
from datetime import date

from pydantic import BaseModel, Secret

class SecretDate(Secret[date]):
    def _display(self) -> str:
        return '****/**/**'

class Model(BaseModel):
    secret_date: SecretDate

m = Model(secret_date=date(2022, 1, 1))
print(m.model_dump())
#> {'secret_date': SecretDate('****/**/**')}

print(m.model_dump_json())
#> {"secret_date":"****/**/**"}

print(m.secret_date.get_secret_value())
#> 2022-01-01

The value returned by the _display method will be used for repr() and str().

You can enforce constraints on the underlying type through annotations: For example:

from typing_extensions import Annotated

from pydantic import BaseModel, Field, Secret, ValidationError

SecretPosInt = Secret[Annotated[int, Field(gt=0, strict=True)]]

class Model(BaseModel):
    sensitive_int: SecretPosInt

m = Model(sensitive_int=42)
print(m.model_dump())
#> {'sensitive_int': Secret('**********')}

try:
    m = Model(sensitive_int=-42)  # (1)!
except ValidationError as exc_info:
    print(exc_info.errors(include_url=False, include_input=False))
    '''
    [
        {
            'type': 'greater_than',
            'loc': ('sensitive_int',),
            'msg': 'Input should be greater than 0',
            'ctx': {'gt': 0},
        }
    ]
    '''

try:
    m = Model(sensitive_int='42')  # (2)!
except ValidationError as exc_info:
    print(exc_info.errors(include_url=False, include_input=False))
    '''
    [
        {
            'type': 'int_type',
            'loc': ('sensitive_int',),
            'msg': 'Input should be a valid integer',
        }
    ]
    '''
  1. The input value is not greater than 0, so it raises a validation error.
  2. The input value is not an integer, so it raises a validation error because the SecretPosInt type has strict mode enabled.
Source code in pydantic/types.py 
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class Secret(_SecretBase[SecretType]):
    """A generic base class used for defining a field with sensitive information that you do not want to be visible in logging or tracebacks.

    You may either directly parametrize `Secret` with a type, or subclass from `Secret` with a parametrized type. The benefit of subclassing
    is that you can define a custom `_display` method, which will be used for `repr()` and `str()` methods. The examples below demonstrate both
    ways of using `Secret` to create a new secret type.

    1. Directly parametrizing `Secret` with a type:

    ```py
    from pydantic import BaseModel, Secret

    SecretBool = Secret[bool]

    class Model(BaseModel):
        secret_bool: SecretBool

    m = Model(secret_bool=True)
    print(m.model_dump())
    #> {'secret_bool': Secret('**********')}

    print(m.model_dump_json())
    #> {"secret_bool":"**********"}

    print(m.secret_bool.get_secret_value())
    #> True
    ```

    2. Subclassing from parametrized `Secret`:

    ```py
    from datetime import date

    from pydantic import BaseModel, Secret

    class SecretDate(Secret[date]):
        def _display(self) -> str:
            return '****/**/**'

    class Model(BaseModel):
        secret_date: SecretDate

    m = Model(secret_date=date(2022, 1, 1))
    print(m.model_dump())
    #> {'secret_date': SecretDate('****/**/**')}

    print(m.model_dump_json())
    #> {"secret_date":"****/**/**"}

    print(m.secret_date.get_secret_value())
    #> 2022-01-01
    ```

    The value returned by the `_display` method will be used for `repr()` and `str()`.

    You can enforce constraints on the underlying type through annotations:
    For example:

    ```py
    from typing_extensions import Annotated

    from pydantic import BaseModel, Field, Secret, ValidationError

    SecretPosInt = Secret[Annotated[int, Field(gt=0, strict=True)]]

    class Model(BaseModel):
        sensitive_int: SecretPosInt

    m = Model(sensitive_int=42)
    print(m.model_dump())
    #> {'sensitive_int': Secret('**********')}

    try:
        m = Model(sensitive_int=-42)  # (1)!
    except ValidationError as exc_info:
        print(exc_info.errors(include_url=False, include_input=False))
        '''
        [
            {
                'type': 'greater_than',
                'loc': ('sensitive_int',),
                'msg': 'Input should be greater than 0',
                'ctx': {'gt': 0},
            }
        ]
        '''

    try:
        m = Model(sensitive_int='42')  # (2)!
    except ValidationError as exc_info:
        print(exc_info.errors(include_url=False, include_input=False))
        '''
        [
            {
                'type': 'int_type',
                'loc': ('sensitive_int',),
                'msg': 'Input should be a valid integer',
            }
        ]
        '''
    ```

    1. The input value is not greater than 0, so it raises a validation error.
    2. The input value is not an integer, so it raises a validation error because the `SecretPosInt` type has strict mode enabled.
    """

    def _display(self) -> str | bytes:
        return '**********' if self.get_secret_value() else ''

    @classmethod
    def __get_pydantic_core_schema__(cls, source: type[Any], handler: GetCoreSchemaHandler) -> core_schema.CoreSchema:
        inner_type = None
        # if origin_type is Secret, then cls is a GenericAlias, and we can extract the inner type directly
        origin_type = get_origin(source)
        if origin_type is not None:
            inner_type = get_args(source)[0]
        # otherwise, we need to get the inner type from the base class
        else:
            bases = getattr(cls, '__orig_bases__', getattr(cls, '__bases__', []))
            for base in bases:
                if get_origin(base) is Secret:
                    inner_type = get_args(base)[0]
            if bases == [] or inner_type is None:
                raise TypeError(
                    f"Can't get secret type from {cls.__name__}. "
                    'Please use Secret[<type>], or subclass from Secret[<type>] instead.'
                )

        inner_schema = handler.generate_schema(inner_type)  # type: ignore

        def validate_secret_value(value, handler) -> Secret[SecretType]:
            if isinstance(value, Secret):
                value = value.get_secret_value()
            validated_inner = handler(value)
            return cls(validated_inner)

        def serialize(value: Secret[SecretType], info: core_schema.SerializationInfo) -> str | Secret[SecretType]:
            if info.mode == 'json':
                return str(value)
            else:
                return value

        return core_schema.json_or_python_schema(
            python_schema=core_schema.no_info_wrap_validator_function(
                validate_secret_value,
                inner_schema,
            ),
            json_schema=core_schema.no_info_after_validator_function(lambda x: cls(x), inner_schema),
            serialization=core_schema.plain_serializer_function_ser_schema(
                serialize,
                info_arg=True,
                when_used='always',
            ),
        )

SecretStr

Bases: _SecretField[str]

A string used for storing sensitive information that you do not want to be visible in logging or tracebacks.

When the secret value is nonempty, it is displayed as '**********' instead of the underlying value in calls to repr() and str(). If the value is empty, it is displayed as ''.

from pydantic import BaseModel, SecretStr

class User(BaseModel):
    username: str
    password: SecretStr

user = User(username='scolvin', password='password1')

print(user)
#> username='scolvin' password=SecretStr('**********')
print(user.password.get_secret_value())
#> password1
print((SecretStr('password'), SecretStr('')))
#> (SecretStr('**********'), SecretStr(''))

As seen above, by default, SecretStr (and SecretBytes) will be serialized as ********** when serializing to json.

You can use the field_serializer to dump the secret as plain-text when serializing to json.

from pydantic import BaseModel, SecretBytes, SecretStr, field_serializer

class Model(BaseModel):
    password: SecretStr
    password_bytes: SecretBytes

    @field_serializer('password', 'password_bytes', when_used='json')
    def dump_secret(self, v):
        return v.get_secret_value()

model = Model(password='IAmSensitive', password_bytes=b'IAmSensitiveBytes')
print(model)
#> password=SecretStr('**********') password_bytes=SecretBytes(b'**********')
print(model.password)
#> **********
print(model.model_dump())
'''
{
    'password': SecretStr('**********'),
    'password_bytes': SecretBytes(b'**********'),
}
'''
print(model.model_dump_json())
#> {"password":"IAmSensitive","password_bytes":"IAmSensitiveBytes"}
Source code in pydantic/types.py 
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class SecretStr(_SecretField[str]):
    """A string used for storing sensitive information that you do not want to be visible in logging or tracebacks.

    When the secret value is nonempty, it is displayed as `'**********'` instead of the underlying value in
    calls to `repr()` and `str()`. If the value _is_ empty, it is displayed as `''`.

    ```py
    from pydantic import BaseModel, SecretStr

    class User(BaseModel):
        username: str
        password: SecretStr

    user = User(username='scolvin', password='password1')

    print(user)
    #> username='scolvin' password=SecretStr('**********')
    print(user.password.get_secret_value())
    #> password1
    print((SecretStr('password'), SecretStr('')))
    #> (SecretStr('**********'), SecretStr(''))
    ```

    As seen above, by default, [`SecretStr`][pydantic.types.SecretStr] (and [`SecretBytes`][pydantic.types.SecretBytes])
    will be serialized as `**********` when serializing to json.

    You can use the [`field_serializer`][pydantic.functional_serializers.field_serializer] to dump the
    secret as plain-text when serializing to json.

    ```py
    from pydantic import BaseModel, SecretBytes, SecretStr, field_serializer

    class Model(BaseModel):
        password: SecretStr
        password_bytes: SecretBytes

        @field_serializer('password', 'password_bytes', when_used='json')
        def dump_secret(self, v):
            return v.get_secret_value()

    model = Model(password='IAmSensitive', password_bytes=b'IAmSensitiveBytes')
    print(model)
    #> password=SecretStr('**********') password_bytes=SecretBytes(b'**********')
    print(model.password)
    #> **********
    print(model.model_dump())
    '''
    {
        'password': SecretStr('**********'),
        'password_bytes': SecretBytes(b'**********'),
    }
    '''
    print(model.model_dump_json())
    #> {"password":"IAmSensitive","password_bytes":"IAmSensitiveBytes"}
    ```
    """

    _inner_schema: ClassVar[CoreSchema] = core_schema.str_schema()
    _error_kind: ClassVar[str] = 'string_type'

    def __len__(self) -> int:
        return len(self._secret_value)

    def _display(self) -> str:
        return _secret_display(self._secret_value)

SecretBytes

Bases: _SecretField[bytes]

A bytes used for storing sensitive information that you do not want to be visible in logging or tracebacks.

It displays b'**********' instead of the string value on repr() and str() calls. When the secret value is nonempty, it is displayed as b'**********' instead of the underlying value in calls to repr() and str(). If the value is empty, it is displayed as b''.

from pydantic import BaseModel, SecretBytes

class User(BaseModel):
    username: str
    password: SecretBytes

user = User(username='scolvin', password=b'password1')
#> username='scolvin' password=SecretBytes(b'**********')
print(user.password.get_secret_value())
#> b'password1'
print((SecretBytes(b'password'), SecretBytes(b'')))
#> (SecretBytes(b'**********'), SecretBytes(b''))
Source code in pydantic/types.py 
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class SecretBytes(_SecretField[bytes]):
    """A bytes used for storing sensitive information that you do not want to be visible in logging or tracebacks.

    It displays `b'**********'` instead of the string value on `repr()` and `str()` calls.
    When the secret value is nonempty, it is displayed as `b'**********'` instead of the underlying value in
    calls to `repr()` and `str()`. If the value _is_ empty, it is displayed as `b''`.

    ```py
    from pydantic import BaseModel, SecretBytes

    class User(BaseModel):
        username: str
        password: SecretBytes

    user = User(username='scolvin', password=b'password1')
    #> username='scolvin' password=SecretBytes(b'**********')
    print(user.password.get_secret_value())
    #> b'password1'
    print((SecretBytes(b'password'), SecretBytes(b'')))
    #> (SecretBytes(b'**********'), SecretBytes(b''))
    ```
    """

    _inner_schema: ClassVar[CoreSchema] = core_schema.bytes_schema()
    _error_kind: ClassVar[str] = 'bytes_type'

    def __len__(self) -> int:
        return len(self._secret_value)

    def _display(self) -> bytes:
        return _secret_display(self._secret_value).encode()

PaymentCardNumber

Bases: str

Based on: https://en.wikipedia.org/wiki/Payment_card_number.

Source code in pydantic/types.py 
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@deprecated(
    'The `PaymentCardNumber` class is deprecated, use `pydantic_extra_types` instead. '
    'See https://docs.pydantic.dev/latest/api/pydantic_extra_types_payment/#pydantic_extra_types.payment.PaymentCardNumber.',
    category=PydanticDeprecatedSince20,
)
class PaymentCardNumber(str):
    """Based on: https://en.wikipedia.org/wiki/Payment_card_number."""

    strip_whitespace: ClassVar[bool] = True
    min_length: ClassVar[int] = 12
    max_length: ClassVar[int] = 19
    bin: str
    last4: str
    brand: PaymentCardBrand

    def __init__(self, card_number: str):
        self.validate_digits(card_number)

        card_number = self.validate_luhn_check_digit(card_number)

        self.bin = card_number[:6]
        self.last4 = card_number[-4:]
        self.brand = self.validate_brand(card_number)

    @classmethod
    def __get_pydantic_core_schema__(cls, source: type[Any], handler: GetCoreSchemaHandler) -> core_schema.CoreSchema:
        return core_schema.with_info_after_validator_function(
            cls.validate,
            core_schema.str_schema(
                min_length=cls.min_length, max_length=cls.max_length, strip_whitespace=cls.strip_whitespace
            ),
        )

    @classmethod
    def validate(cls, input_value: str, /, _: core_schema.ValidationInfo) -> PaymentCardNumber:
        """Validate the card number and return a `PaymentCardNumber` instance."""
        return cls(input_value)

    @property
    def masked(self) -> str:
        """Mask all but the last 4 digits of the card number.

        Returns:
            A masked card number string.
        """
        num_masked = len(self) - 10  # len(bin) + len(last4) == 10
        return f'{self.bin}{"*" * num_masked}{self.last4}'

    @classmethod
    def validate_digits(cls, card_number: str) -> None:
        """Validate that the card number is all digits."""
        if not card_number.isdigit():
            raise PydanticCustomError('payment_card_number_digits', 'Card number is not all digits')

    @classmethod
    def validate_luhn_check_digit(cls, card_number: str) -> str:
        """Based on: https://en.wikipedia.org/wiki/Luhn_algorithm."""
        sum_ = int(card_number[-1])
        length = len(card_number)
        parity = length % 2
        for i in range(length - 1):
            digit = int(card_number[i])
            if i % 2 == parity:
                digit *= 2
            if digit > 9:
                digit -= 9
            sum_ += digit
        valid = sum_ % 10 == 0
        if not valid:
            raise PydanticCustomError('payment_card_number_luhn', 'Card number is not luhn valid')
        return card_number

    @staticmethod
    def validate_brand(card_number: str) -> PaymentCardBrand:
        """Validate length based on BIN for major brands:
        https://en.wikipedia.org/wiki/Payment_card_number#Issuer_identification_number_(IIN).
        """
        if card_number[0] == '4':
            brand = PaymentCardBrand.visa
        elif 51 <= int(card_number[:2]) <= 55:
            brand = PaymentCardBrand.mastercard
        elif card_number[:2] in {'34', '37'}:
            brand = PaymentCardBrand.amex
        else:
            brand = PaymentCardBrand.other

        required_length: None | int | str = None
        if brand in PaymentCardBrand.mastercard:
            required_length = 16
            valid = len(card_number) == required_length
        elif brand == PaymentCardBrand.visa:
            required_length = '13, 16 or 19'
            valid = len(card_number) in {13, 16, 19}
        elif brand == PaymentCardBrand.amex:
            required_length = 15
            valid = len(card_number) == required_length
        else:
            valid = True

        if not valid:
            raise PydanticCustomError(
                'payment_card_number_brand',
                'Length for a {brand} card must be {required_length}',
                {'brand': brand, 'required_length': required_length},
            )
        return brand

masked property 

masked: str

Mask all but the last 4 digits of the card number.

Returns:

Type Description
str

A masked card number string.

validate classmethod 

validate(
    input_value: str, /, _: ValidationInfo
) -> PaymentCardNumber

Validate the card number and return a PaymentCardNumber instance.

Source code in pydantic/types.py 
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@classmethod
def validate(cls, input_value: str, /, _: core_schema.ValidationInfo) -> PaymentCardNumber:
    """Validate the card number and return a `PaymentCardNumber` instance."""
    return cls(input_value)

validate_digits classmethod 

validate_digits(card_number: str) -> None

Validate that the card number is all digits.

Source code in pydantic/types.py 
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@classmethod
def validate_digits(cls, card_number: str) -> None:
    """Validate that the card number is all digits."""
    if not card_number.isdigit():
        raise PydanticCustomError('payment_card_number_digits', 'Card number is not all digits')

validate_luhn_check_digit classmethod 

validate_luhn_check_digit(card_number: str) -> str

Based on: https://en.wikipedia.org/wiki/Luhn_algorithm.

Source code in pydantic/types.py 
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@classmethod
def validate_luhn_check_digit(cls, card_number: str) -> str:
    """Based on: https://en.wikipedia.org/wiki/Luhn_algorithm."""
    sum_ = int(card_number[-1])
    length = len(card_number)
    parity = length % 2
    for i in range(length - 1):
        digit = int(card_number[i])
        if i % 2 == parity:
            digit *= 2
        if digit > 9:
            digit -= 9
        sum_ += digit
    valid = sum_ % 10 == 0
    if not valid:
        raise PydanticCustomError('payment_card_number_luhn', 'Card number is not luhn valid')
    return card_number

validate_brand staticmethod 

validate_brand(card_number: str) -> PaymentCardBrand

Validate length based on BIN for major brands: https://en.wikipedia.org/wiki/Payment_card_number#Issuer_identification_number_(IIN).

Source code in pydantic/types.py 
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@staticmethod
def validate_brand(card_number: str) -> PaymentCardBrand:
    """Validate length based on BIN for major brands:
    https://en.wikipedia.org/wiki/Payment_card_number#Issuer_identification_number_(IIN).
    """
    if card_number[0] == '4':
        brand = PaymentCardBrand.visa
    elif 51 <= int(card_number[:2]) <= 55:
        brand = PaymentCardBrand.mastercard
    elif card_number[:2] in {'34', '37'}:
        brand = PaymentCardBrand.amex
    else:
        brand = PaymentCardBrand.other

    required_length: None | int | str = None
    if brand in PaymentCardBrand.mastercard:
        required_length = 16
        valid = len(card_number) == required_length
    elif brand == PaymentCardBrand.visa:
        required_length = '13, 16 or 19'
        valid = len(card_number) in {13, 16, 19}
    elif brand == PaymentCardBrand.amex:
        required_length = 15
        valid = len(card_number) == required_length
    else:
        valid = True

    if not valid:
        raise PydanticCustomError(
            'payment_card_number_brand',
            'Length for a {brand} card must be {required_length}',
            {'brand': brand, 'required_length': required_length},
        )
    return brand

ByteSize

Bases: int

Converts a string representing a number of bytes with units (such as '1KB' or '11.5MiB') into an integer.

You can use the ByteSize data type to (case-insensitively) convert a string representation of a number of bytes into an integer, and also to print out human-readable strings representing a number of bytes.

In conformance with IEC 80000-13 Standard we interpret '1KB' to mean 1000 bytes, and '1KiB' to mean 1024 bytes. In general, including a middle 'i' will cause the unit to be interpreted as a power of 2, rather than a power of 10 (so, for example, '1 MB' is treated as 1_000_000 bytes, whereas '1 MiB' is treated as 1_048_576 bytes).

Info

Note that 1b will be parsed as "1 byte" and not "1 bit".

from pydantic import BaseModel, ByteSize

class MyModel(BaseModel):
    size: ByteSize

print(MyModel(size=52000).size)
#> 52000
print(MyModel(size='3000 KiB').size)
#> 3072000

m = MyModel(size='50 PB')
print(m.size.human_readable())
#> 44.4PiB
print(m.size.human_readable(decimal=True))
#> 50.0PB
print(m.size.human_readable(separator=' '))
#> 44.4 PiB

print(m.size.to('TiB'))
#> 45474.73508864641
Source code in pydantic/types.py 
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class ByteSize(int):
    """Converts a string representing a number of bytes with units (such as `'1KB'` or `'11.5MiB'`) into an integer.

    You can use the `ByteSize` data type to (case-insensitively) convert a string representation of a number of bytes into
    an integer, and also to print out human-readable strings representing a number of bytes.

    In conformance with [IEC 80000-13 Standard](https://en.wikipedia.org/wiki/ISO/IEC_80000) we interpret `'1KB'` to mean 1000 bytes,
    and `'1KiB'` to mean 1024 bytes. In general, including a middle `'i'` will cause the unit to be interpreted as a power of 2,
    rather than a power of 10 (so, for example, `'1 MB'` is treated as `1_000_000` bytes, whereas `'1 MiB'` is treated as `1_048_576` bytes).

    !!! info
        Note that `1b` will be parsed as "1 byte" and not "1 bit".

    ```py
    from pydantic import BaseModel, ByteSize

    class MyModel(BaseModel):
        size: ByteSize

    print(MyModel(size=52000).size)
    #> 52000
    print(MyModel(size='3000 KiB').size)
    #> 3072000

    m = MyModel(size='50 PB')
    print(m.size.human_readable())
    #> 44.4PiB
    print(m.size.human_readable(decimal=True))
    #> 50.0PB
    print(m.size.human_readable(separator=' '))
    #> 44.4 PiB

    print(m.size.to('TiB'))
    #> 45474.73508864641
    ```
    """

    byte_sizes = {
        'b': 1,
        'kb': 10**3,
        'mb': 10**6,
        'gb': 10**9,
        'tb': 10**12,
        'pb': 10**15,
        'eb': 10**18,
        'kib': 2**10,
        'mib': 2**20,
        'gib': 2**30,
        'tib': 2**40,
        'pib': 2**50,
        'eib': 2**60,
        'bit': 1 / 8,
        'kbit': 10**3 / 8,
        'mbit': 10**6 / 8,
        'gbit': 10**9 / 8,
        'tbit': 10**12 / 8,
        'pbit': 10**15 / 8,
        'ebit': 10**18 / 8,
        'kibit': 2**10 / 8,
        'mibit': 2**20 / 8,
        'gibit': 2**30 / 8,
        'tibit': 2**40 / 8,
        'pibit': 2**50 / 8,
        'eibit': 2**60 / 8,
    }
    byte_sizes.update({k.lower()[0]: v for k, v in byte_sizes.items() if 'i' not in k})

    byte_string_pattern = r'^\s*(\d*\.?\d+)\s*(\w+)?'
    byte_string_re = re.compile(byte_string_pattern, re.IGNORECASE)

    @classmethod
    def __get_pydantic_core_schema__(cls, source: type[Any], handler: GetCoreSchemaHandler) -> core_schema.CoreSchema:
        return core_schema.with_info_after_validator_function(
            function=cls._validate,
            schema=core_schema.union_schema(
                [
                    core_schema.str_schema(pattern=cls.byte_string_pattern),
                    core_schema.int_schema(ge=0),
                ],
                custom_error_type='byte_size',
                custom_error_message='could not parse value and unit from byte string',
            ),
            serialization=core_schema.plain_serializer_function_ser_schema(
                int, return_schema=core_schema.int_schema(ge=0)
            ),
        )

    @classmethod
    def _validate(cls, input_value: Any, /, _: core_schema.ValidationInfo) -> ByteSize:
        try:
            return cls(int(input_value))
        except ValueError:
            pass

        str_match = cls.byte_string_re.match(str(input_value))
        if str_match is None:
            raise PydanticCustomError('byte_size', 'could not parse value and unit from byte string')

        scalar, unit = str_match.groups()
        if unit is None:
            unit = 'b'

        try:
            unit_mult = cls.byte_sizes[unit.lower()]
        except KeyError:
            raise PydanticCustomError('byte_size_unit', 'could not interpret byte unit: {unit}', {'unit': unit})

        return cls(int(float(scalar) * unit_mult))

    def human_readable(self, decimal: bool = False, separator: str = '') -> str:
        """Converts a byte size to a human readable string.

        Args:
            decimal: If True, use decimal units (e.g. 1000 bytes per KB). If False, use binary units
                (e.g. 1024 bytes per KiB).
            separator: A string used to split the value and unit. Defaults to an empty string ('').

        Returns:
            A human readable string representation of the byte size.
        """
        if decimal:
            divisor = 1000
            units = 'B', 'KB', 'MB', 'GB', 'TB', 'PB'
            final_unit = 'EB'
        else:
            divisor = 1024
            units = 'B', 'KiB', 'MiB', 'GiB', 'TiB', 'PiB'
            final_unit = 'EiB'

        num = float(self)
        for unit in units:
            if abs(num) < divisor:
                if unit == 'B':
                    return f'{num:0.0f}{separator}{unit}'
                else:
                    return f'{num:0.1f}{separator}{unit}'
            num /= divisor

        return f'{num:0.1f}{separator}{final_unit}'

    def to(self, unit: str) -> float:
        """Converts a byte size to another unit, including both byte and bit units.

        Args:
            unit: The unit to convert to. Must be one of the following: B, KB, MB, GB, TB, PB, EB,
                KiB, MiB, GiB, TiB, PiB, EiB (byte units) and
                bit, kbit, mbit, gbit, tbit, pbit, ebit,
                kibit, mibit, gibit, tibit, pibit, eibit (bit units).

        Returns:
            The byte size in the new unit.
        """
        try:
            unit_div = self.byte_sizes[unit.lower()]
        except KeyError:
            raise PydanticCustomError('byte_size_unit', 'Could not interpret byte unit: {unit}', {'unit': unit})

        return self / unit_div

human_readable 

human_readable(
    decimal: bool = False, separator: str = ""
) -> str

Converts a byte size to a human readable string.

Parameters:

Name Type Description Default
decimal bool

If True, use decimal units (e.g. 1000 bytes per KB). If False, use binary units (e.g. 1024 bytes per KiB).

 False
separator str

A string used to split the value and unit. Defaults to an empty string ('').

 ''

Returns:

Type Description
str

A human readable string representation of the byte size.
Source code in pydantic/types.py 
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def human_readable(self, decimal: bool = False, separator: str = '') -> str:
    """Converts a byte size to a human readable string.

    Args:
        decimal: If True, use decimal units (e.g. 1000 bytes per KB). If False, use binary units
            (e.g. 1024 bytes per KiB).
        separator: A string used to split the value and unit. Defaults to an empty string ('').

    Returns:
        A human readable string representation of the byte size.
    """
    if decimal:
        divisor = 1000
        units = 'B', 'KB', 'MB', 'GB', 'TB', 'PB'
        final_unit = 'EB'
    else:
        divisor = 1024
        units = 'B', 'KiB', 'MiB', 'GiB', 'TiB', 'PiB'
        final_unit = 'EiB'

    num = float(self)
    for unit in units:
        if abs(num) < divisor:
            if unit == 'B':
                return f'{num:0.0f}{separator}{unit}'
            else:
                return f'{num:0.1f}{separator}{unit}'
        num /= divisor

    return f'{num:0.1f}{separator}{final_unit}'

to 

to(unit: str) -> float

Converts a byte size to another unit, including both byte and bit units.

Parameters:

Name Type Description Default
unit str

The unit to convert to. Must be one of the following: B, KB, MB, GB, TB, PB, EB, KiB, MiB, GiB, TiB, PiB, EiB (byte units) and bit, kbit, mbit, gbit, tbit, pbit, ebit, kibit, mibit, gibit, tibit, pibit, eibit (bit units).

 required

Returns:

Type Description
float

The byte size in the new unit.
Source code in pydantic/types.py 
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def to(self, unit: str) -> float:
    """Converts a byte size to another unit, including both byte and bit units.

    Args:
        unit: The unit to convert to. Must be one of the following: B, KB, MB, GB, TB, PB, EB,
            KiB, MiB, GiB, TiB, PiB, EiB (byte units) and
            bit, kbit, mbit, gbit, tbit, pbit, ebit,
            kibit, mibit, gibit, tibit, pibit, eibit (bit units).

    Returns:
        The byte size in the new unit.
    """
    try:
        unit_div = self.byte_sizes[unit.lower()]
    except KeyError:
        raise PydanticCustomError('byte_size_unit', 'Could not interpret byte unit: {unit}', {'unit': unit})

    return self / unit_div

PastDate

A date in the past.

Source code in pydantic/types.py 
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class PastDate:
    """A date in the past."""

    @classmethod
    def __get_pydantic_core_schema__(
        cls, source: type[Any], handler: GetCoreSchemaHandler
    ) -> core_schema.CoreSchema:
        if cls is source:
            # used directly as a type
            return core_schema.date_schema(now_op='past')
        else:
            schema = handler(source)
            _check_annotated_type(schema['type'], 'date', cls.__name__)
            schema['now_op'] = 'past'
            return schema

    def __repr__(self) -> str:
        return 'PastDate'

FutureDate

A date in the future.

Source code in pydantic/types.py 
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class FutureDate:
    """A date in the future."""

    @classmethod
    def __get_pydantic_core_schema__(
        cls, source: type[Any], handler: GetCoreSchemaHandler
    ) -> core_schema.CoreSchema:
        if cls is source:
            # used directly as a type
            return core_schema.date_schema(now_op='future')
        else:
            schema = handler(source)
            _check_annotated_type(schema['type'], 'date', cls.__name__)
            schema['now_op'] = 'future'
            return schema

    def __repr__(self) -> str:
        return 'FutureDate'

AwareDatetime

A datetime that requires timezone info.

Source code in pydantic/types.py 
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class AwareDatetime:
    """A datetime that requires timezone info."""

    @classmethod
    def __get_pydantic_core_schema__(
        cls, source: type[Any], handler: GetCoreSchemaHandler
    ) -> core_schema.CoreSchema:
        if cls is source:
            # used directly as a type
            return core_schema.datetime_schema(tz_constraint='aware')
        else:
            schema = handler(source)
            _check_annotated_type(schema['type'], 'datetime', cls.__name__)
            schema['tz_constraint'] = 'aware'
            return schema

    def __repr__(self) -> str:
        return 'AwareDatetime'

NaiveDatetime

A datetime that doesn't require timezone info.

Source code in pydantic/types.py 
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class NaiveDatetime:
    """A datetime that doesn't require timezone info."""

    @classmethod
    def __get_pydantic_core_schema__(
        cls, source: type[Any], handler: GetCoreSchemaHandler
    ) -> core_schema.CoreSchema:
        if cls is source:
            # used directly as a type
            return core_schema.datetime_schema(tz_constraint='naive')
        else:
            schema = handler(source)
            _check_annotated_type(schema['type'], 'datetime', cls.__name__)
            schema['tz_constraint'] = 'naive'
            return schema

    def __repr__(self) -> str:
        return 'NaiveDatetime'

PastDatetime

A datetime that must be in the past.

Source code in pydantic/types.py 
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class PastDatetime:
    """A datetime that must be in the past."""

    @classmethod
    def __get_pydantic_core_schema__(
        cls, source: type[Any], handler: GetCoreSchemaHandler
    ) -> core_schema.CoreSchema:
        if cls is source:
            # used directly as a type
            return core_schema.datetime_schema(now_op='past')
        else:
            schema = handler(source)
            _check_annotated_type(schema['type'], 'datetime', cls.__name__)
            schema['now_op'] = 'past'
            return schema

    def __repr__(self) -> str:
        return 'PastDatetime'

FutureDatetime

A datetime that must be in the future.

Source code in pydantic/types.py 
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class FutureDatetime:
    """A datetime that must be in the future."""

    @classmethod
    def __get_pydantic_core_schema__(
        cls, source: type[Any], handler: GetCoreSchemaHandler
    ) -> core_schema.CoreSchema:
        if cls is source:
            # used directly as a type
            return core_schema.datetime_schema(now_op='future')
        else:
            schema = handler(source)
            _check_annotated_type(schema['type'], 'datetime', cls.__name__)
            schema['now_op'] = 'future'
            return schema

    def __repr__(self) -> str:
        return 'FutureDatetime'

EncoderProtocol

Bases: Protocol

Protocol for encoding and decoding data to and from bytes.

Source code in pydantic/types.py 
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class EncoderProtocol(Protocol):
    """Protocol for encoding and decoding data to and from bytes."""

    @classmethod
    def decode(cls, data: bytes) -> bytes:
        """Decode the data using the encoder.

        Args:
            data: The data to decode.

        Returns:
            The decoded data.
        """
        ...

    @classmethod
    def encode(cls, value: bytes) -> bytes:
        """Encode the data using the encoder.

        Args:
            value: The data to encode.

        Returns:
            The encoded data.
        """
        ...

    @classmethod
    def get_json_format(cls) -> str:
        """Get the JSON format for the encoded data.

        Returns:
            The JSON format for the encoded data.
        """
        ...

decode classmethod 

decode(data: bytes) -> bytes

Decode the data using the encoder.

Parameters:

Name Type Description Default
data bytes

The data to decode.

 required

Returns:

Type Description
bytes

The decoded data.
Source code in pydantic/types.py 
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@classmethod
def decode(cls, data: bytes) -> bytes:
    """Decode the data using the encoder.

    Args:
        data: The data to decode.

    Returns:
        The decoded data.
    """
    ...

encode classmethod 

encode(value: bytes) -> bytes

Encode the data using the encoder.

Parameters:

Name Type Description Default
value bytes

The data to encode.

 required

Returns:

Type Description
bytes

The encoded data.
Source code in pydantic/types.py 
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@classmethod
def encode(cls, value: bytes) -> bytes:
    """Encode the data using the encoder.

    Args:
        value: The data to encode.

    Returns:
        The encoded data.
    """
    ...

get_json_format classmethod 

get_json_format() -> str

Get the JSON format for the encoded data.

Returns:

Type Description
str

The JSON format for the encoded data.
Source code in pydantic/types.py 
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@classmethod
def get_json_format(cls) -> str:
    """Get the JSON format for the encoded data.

    Returns:
        The JSON format for the encoded data.
    """
    ...

Base64Encoder

Bases: EncoderProtocol

Standard (non-URL-safe) Base64 encoder.

Source code in pydantic/types.py 
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class Base64Encoder(EncoderProtocol):
    """Standard (non-URL-safe) Base64 encoder."""

    @classmethod
    def decode(cls, data: bytes) -> bytes:
        """Decode the data from base64 encoded bytes to original bytes data.

        Args:
            data: The data to decode.

        Returns:
            The decoded data.
        """
        try:
            return base64.decodebytes(data)
        except ValueError as e:
            raise PydanticCustomError('base64_decode', "Base64 decoding error: '{error}'", {'error': str(e)})

    @classmethod
    def encode(cls, value: bytes) -> bytes:
        """Encode the data from bytes to a base64 encoded bytes.

        Args:
            value: The data to encode.

        Returns:
            The encoded data.
        """
        return base64.encodebytes(value)

    @classmethod
    def get_json_format(cls) -> Literal['base64']:
        """Get the JSON format for the encoded data.

        Returns:
            The JSON format for the encoded data.
        """
        return 'base64'

decode classmethod 

decode(data: bytes) -> bytes

Decode the data from base64 encoded bytes to original bytes data.

Parameters:

Name Type Description Default
data bytes

The data to decode.

 required

Returns:

Type Description
bytes

The decoded data.
Source code in pydantic/types.py 
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@classmethod
def decode(cls, data: bytes) -> bytes:
    """Decode the data from base64 encoded bytes to original bytes data.

    Args:
        data: The data to decode.

    Returns:
        The decoded data.
    """
    try:
        return base64.decodebytes(data)
    except ValueError as e:
        raise PydanticCustomError('base64_decode', "Base64 decoding error: '{error}'", {'error': str(e)})

encode classmethod 

encode(value: bytes) -> bytes

Encode the data from bytes to a base64 encoded bytes.

Parameters:

Name Type Description Default
value bytes

The data to encode.

 required

Returns:

Type Description
bytes

The encoded data.
Source code in pydantic/types.py 
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@classmethod
def encode(cls, value: bytes) -> bytes:
    """Encode the data from bytes to a base64 encoded bytes.

    Args:
        value: The data to encode.

    Returns:
        The encoded data.
    """
    return base64.encodebytes(value)

get_json_format classmethod 

get_json_format() -> Literal['base64']

Get the JSON format for the encoded data.

Returns:

Type Description
Literal['base64']

The JSON format for the encoded data.
Source code in pydantic/types.py 
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@classmethod
def get_json_format(cls) -> Literal['base64']:
    """Get the JSON format for the encoded data.

    Returns:
        The JSON format for the encoded data.
    """
    return 'base64'

Base64UrlEncoder

Bases: EncoderProtocol

URL-safe Base64 encoder.

Source code in pydantic/types.py 
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class Base64UrlEncoder(EncoderProtocol):
    """URL-safe Base64 encoder."""

    @classmethod
    def decode(cls, data: bytes) -> bytes:
        """Decode the data from base64 encoded bytes to original bytes data.

        Args:
            data: The data to decode.

        Returns:
            The decoded data.
        """
        try:
            return base64.urlsafe_b64decode(data)
        except ValueError as e:
            raise PydanticCustomError('base64_decode', "Base64 decoding error: '{error}'", {'error': str(e)})

    @classmethod
    def encode(cls, value: bytes) -> bytes:
        """Encode the data from bytes to a base64 encoded bytes.

        Args:
            value: The data to encode.

        Returns:
            The encoded data.
        """
        return base64.urlsafe_b64encode(value)

    @classmethod
    def get_json_format(cls) -> Literal['base64url']:
        """Get the JSON format for the encoded data.

        Returns:
            The JSON format for the encoded data.
        """
        return 'base64url'

decode classmethod 

decode(data: bytes) -> bytes

Decode the data from base64 encoded bytes to original bytes data.

Parameters:

Name Type Description Default
data bytes

The data to decode.

 required

Returns:

Type Description
bytes

The decoded data.
Source code in pydantic/types.py 
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@classmethod
def decode(cls, data: bytes) -> bytes:
    """Decode the data from base64 encoded bytes to original bytes data.

    Args:
        data: The data to decode.

    Returns:
        The decoded data.
    """
    try:
        return base64.urlsafe_b64decode(data)
    except ValueError as e:
        raise PydanticCustomError('base64_decode', "Base64 decoding error: '{error}'", {'error': str(e)})

encode classmethod 

encode(value: bytes) -> bytes

Encode the data from bytes to a base64 encoded bytes.

Parameters:

Name Type Description Default
value bytes

The data to encode.

 required

Returns:

Type Description
bytes

The encoded data.
Source code in pydantic/types.py 
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@classmethod
def encode(cls, value: bytes) -> bytes:
    """Encode the data from bytes to a base64 encoded bytes.

    Args:
        value: The data to encode.

    Returns:
        The encoded data.
    """
    return base64.urlsafe_b64encode(value)

get_json_format classmethod 

get_json_format() -> Literal['base64url']

Get the JSON format for the encoded data.

Returns:

Type Description
Literal['base64url']

The JSON format for the encoded data.
Source code in pydantic/types.py 
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@classmethod
def get_json_format(cls) -> Literal['base64url']:
    """Get the JSON format for the encoded data.

    Returns:
        The JSON format for the encoded data.
    """
    return 'base64url'

EncodedBytes dataclass

A bytes type that is encoded and decoded using the specified encoder.

EncodedBytes needs an encoder that implements EncoderProtocol to operate.

from typing_extensions import Annotated

from pydantic import BaseModel, EncodedBytes, EncoderProtocol, ValidationError

class MyEncoder(EncoderProtocol):
    @classmethod
    def decode(cls, data: bytes) -> bytes:
        if data == b'**undecodable**':
            raise ValueError('Cannot decode data')
        return data[13:]

    @classmethod
    def encode(cls, value: bytes) -> bytes:
        return b'**encoded**: ' + value

    @classmethod
    def get_json_format(cls) -> str:
        return 'my-encoder'

MyEncodedBytes = Annotated[bytes, EncodedBytes(encoder=MyEncoder)]

class Model(BaseModel):
    my_encoded_bytes: MyEncodedBytes

# Initialize the model with encoded data
m = Model(my_encoded_bytes=b'**encoded**: some bytes')

# Access decoded value
print(m.my_encoded_bytes)
#> b'some bytes'

# Serialize into the encoded form
print(m.model_dump())
#> {'my_encoded_bytes': b'**encoded**: some bytes'}

# Validate encoded data
try:
    Model(my_encoded_bytes=b'**undecodable**')
except ValidationError as e:
    print(e)
    '''
    1 validation error for Model
    my_encoded_bytes
      Value error, Cannot decode data [type=value_error, input_value=b'**undecodable**', input_type=bytes]
    '''
Source code in pydantic/types.py 
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@_dataclasses.dataclass(**_internal_dataclass.slots_true)
class EncodedBytes:
    """A bytes type that is encoded and decoded using the specified encoder.

    `EncodedBytes` needs an encoder that implements `EncoderProtocol` to operate.

    ```py
    from typing_extensions import Annotated

    from pydantic import BaseModel, EncodedBytes, EncoderProtocol, ValidationError

    class MyEncoder(EncoderProtocol):
        @classmethod
        def decode(cls, data: bytes) -> bytes:
            if data == b'**undecodable**':
                raise ValueError('Cannot decode data')
            return data[13:]

        @classmethod
        def encode(cls, value: bytes) -> bytes:
            return b'**encoded**: ' + value

        @classmethod
        def get_json_format(cls) -> str:
            return 'my-encoder'

    MyEncodedBytes = Annotated[bytes, EncodedBytes(encoder=MyEncoder)]

    class Model(BaseModel):
        my_encoded_bytes: MyEncodedBytes

    # Initialize the model with encoded data
    m = Model(my_encoded_bytes=b'**encoded**: some bytes')

    # Access decoded value
    print(m.my_encoded_bytes)
    #> b'some bytes'

    # Serialize into the encoded form
    print(m.model_dump())
    #> {'my_encoded_bytes': b'**encoded**: some bytes'}

    # Validate encoded data
    try:
        Model(my_encoded_bytes=b'**undecodable**')
    except ValidationError as e:
        print(e)
        '''
        1 validation error for Model
        my_encoded_bytes
          Value error, Cannot decode data [type=value_error, input_value=b'**undecodable**', input_type=bytes]
        '''
    ```
    """

    encoder: type[EncoderProtocol]

    def __get_pydantic_json_schema__(
        self, core_schema: core_schema.CoreSchema, handler: GetJsonSchemaHandler
    ) -> JsonSchemaValue:
        field_schema = handler(core_schema)
        field_schema.update(type='string', format=self.encoder.get_json_format())
        return field_schema

    def __get_pydantic_core_schema__(self, source: type[Any], handler: GetCoreSchemaHandler) -> core_schema.CoreSchema:
        return core_schema.with_info_after_validator_function(
            function=self.decode,
            schema=core_schema.bytes_schema(),
            serialization=core_schema.plain_serializer_function_ser_schema(function=self.encode),
        )

    def decode(self, data: bytes, _: core_schema.ValidationInfo) -> bytes:
        """Decode the data using the specified encoder.

        Args:
            data: The data to decode.

        Returns:
            The decoded data.
        """
        return self.encoder.decode(data)

    def encode(self, value: bytes) -> bytes:
        """Encode the data using the specified encoder.

        Args:
            value: The data to encode.

        Returns:
            The encoded data.
        """
        return self.encoder.encode(value)

    def __hash__(self) -> int:
        return hash(self.encoder)

decode 

decode(data: bytes, _: ValidationInfo) -> bytes

Decode the data using the specified encoder.

Parameters:

Name Type Description Default
data bytes

The data to decode.

 required

Returns:

Type Description
bytes

The decoded data.
Source code in pydantic/types.py 
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def decode(self, data: bytes, _: core_schema.ValidationInfo) -> bytes:
    """Decode the data using the specified encoder.

    Args:
        data: The data to decode.

    Returns:
        The decoded data.
    """
    return self.encoder.decode(data)

encode 

encode(value: bytes) -> bytes

Encode the data using the specified encoder.

Parameters:

Name Type Description Default
value bytes

The data to encode.

 required

Returns:

Type Description
bytes

The encoded data.
Source code in pydantic/types.py 
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def encode(self, value: bytes) -> bytes:
    """Encode the data using the specified encoder.

    Args:
        value: The data to encode.

    Returns:
        The encoded data.
    """
    return self.encoder.encode(value)

EncodedStr dataclass

Bases: EncodedBytes

A str type that is encoded and decoded using the specified encoder.

EncodedStr needs an encoder that implements EncoderProtocol to operate.

from typing_extensions import Annotated

from pydantic import BaseModel, EncodedStr, EncoderProtocol, ValidationError

class MyEncoder(EncoderProtocol):
    @classmethod
    def decode(cls, data: bytes) -> bytes:
        if data == b'**undecodable**':
            raise ValueError('Cannot decode data')
        return data[13:]

    @classmethod
    def encode(cls, value: bytes) -> bytes:
        return b'**encoded**: ' + value

    @classmethod
    def get_json_format(cls) -> str:
        return 'my-encoder'

MyEncodedStr = Annotated[str, EncodedStr(encoder=MyEncoder)]

class Model(BaseModel):
    my_encoded_str: MyEncodedStr

# Initialize the model with encoded data
m = Model(my_encoded_str='**encoded**: some str')

# Access decoded value
print(m.my_encoded_str)
#> some str

# Serialize into the encoded form
print(m.model_dump())
#> {'my_encoded_str': '**encoded**: some str'}

# Validate encoded data
try:
    Model(my_encoded_str='**undecodable**')
except ValidationError as e:
    print(e)
    '''
    1 validation error for Model
    my_encoded_str
      Value error, Cannot decode data [type=value_error, input_value='**undecodable**', input_type=str]
    '''
Source code in pydantic/types.py 
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@_dataclasses.dataclass(**_internal_dataclass.slots_true)
class EncodedStr(EncodedBytes):
    """A str type that is encoded and decoded using the specified encoder.

    `EncodedStr` needs an encoder that implements `EncoderProtocol` to operate.

    ```py
    from typing_extensions import Annotated

    from pydantic import BaseModel, EncodedStr, EncoderProtocol, ValidationError

    class MyEncoder(EncoderProtocol):
        @classmethod
        def decode(cls, data: bytes) -> bytes:
            if data == b'**undecodable**':
                raise ValueError('Cannot decode data')
            return data[13:]

        @classmethod
        def encode(cls, value: bytes) -> bytes:
            return b'**encoded**: ' + value

        @classmethod
        def get_json_format(cls) -> str:
            return 'my-encoder'

    MyEncodedStr = Annotated[str, EncodedStr(encoder=MyEncoder)]

    class Model(BaseModel):
        my_encoded_str: MyEncodedStr

    # Initialize the model with encoded data
    m = Model(my_encoded_str='**encoded**: some str')

    # Access decoded value
    print(m.my_encoded_str)
    #> some str

    # Serialize into the encoded form
    print(m.model_dump())
    #> {'my_encoded_str': '**encoded**: some str'}

    # Validate encoded data
    try:
        Model(my_encoded_str='**undecodable**')
    except ValidationError as e:
        print(e)
        '''
        1 validation error for Model
        my_encoded_str
          Value error, Cannot decode data [type=value_error, input_value='**undecodable**', input_type=str]
        '''
    ```
    """

    def __get_pydantic_core_schema__(self, source: type[Any], handler: GetCoreSchemaHandler) -> core_schema.CoreSchema:
        return core_schema.with_info_after_validator_function(
            function=self.decode_str,
            schema=super(EncodedStr, self).__get_pydantic_core_schema__(source=source, handler=handler),  # noqa: UP008
            serialization=core_schema.plain_serializer_function_ser_schema(function=self.encode_str),
        )

    def decode_str(self, data: bytes, _: core_schema.ValidationInfo) -> str:
        """Decode the data using the specified encoder.

        Args:
            data: The data to decode.

        Returns:
            The decoded data.
        """
        return data.decode()

    def encode_str(self, value: str) -> str:
        """Encode the data using the specified encoder.

        Args:
            value: The data to encode.

        Returns:
            The encoded data.
        """
        return super(EncodedStr, self).encode(value=value.encode()).decode()  # noqa: UP008

    def __hash__(self) -> int:
        return hash(self.encoder)

decode_str 

decode_str(data: bytes, _: ValidationInfo) -> str

Decode the data using the specified encoder.

Parameters:

Name Type Description Default
data bytes

The data to decode.

 required

Returns:

Type Description
str

The decoded data.
Source code in pydantic/types.py 
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def decode_str(self, data: bytes, _: core_schema.ValidationInfo) -> str:
    """Decode the data using the specified encoder.

    Args:
        data: The data to decode.

    Returns:
        The decoded data.
    """
    return data.decode()

encode_str 

encode_str(value: str) -> str

Encode the data using the specified encoder.

Parameters:

Name Type Description Default
value str

The data to encode.

 required

Returns:

Type Description
str

The encoded data.
Source code in pydantic/types.py 
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def encode_str(self, value: str) -> str:
    """Encode the data using the specified encoder.

    Args:
        value: The data to encode.

    Returns:
        The encoded data.
    """
    return super(EncodedStr, self).encode(value=value.encode()).decode()  # noqa: UP008

GetPydanticSchema dataclass

Usage Documentation

Using GetPydanticSchema to reduce boilerplate

A convenience class for creating an annotation that provides pydantic custom type hooks.

This class is intended to eliminate the need to create a custom "marker" which defines the __get_pydantic_core_schema__ and __get_pydantic_json_schema__ custom hook methods.

For example, to have a field treated by type checkers as int, but by pydantic as Any, you can do: 

from typing import Any

from typing_extensions import Annotated

from pydantic import BaseModel, GetPydanticSchema

HandleAsAny = GetPydanticSchema(lambda _s, h: h(Any))

class Model(BaseModel):
    x: Annotated[int, HandleAsAny]  # pydantic sees `x: Any`

print(repr(Model(x='abc').x))
#> 'abc'

Source code in pydantic/types.py 
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@_dataclasses.dataclass(**_internal_dataclass.slots_true)
class GetPydanticSchema:
    """Usage docs: https://docs.pydantic.dev/2.9/concepts/types/#using-getpydanticschema-to-reduce-boilerplate

    A convenience class for creating an annotation that provides pydantic custom type hooks.

    This class is intended to eliminate the need to create a custom "marker" which defines the
     `__get_pydantic_core_schema__` and `__get_pydantic_json_schema__` custom hook methods.

    For example, to have a field treated by type checkers as `int`, but by pydantic as `Any`, you can do:
    ```python
    from typing import Any

    from typing_extensions import Annotated

    from pydantic import BaseModel, GetPydanticSchema

    HandleAsAny = GetPydanticSchema(lambda _s, h: h(Any))

    class Model(BaseModel):
        x: Annotated[int, HandleAsAny]  # pydantic sees `x: Any`

    print(repr(Model(x='abc').x))
    #> 'abc'
    ```
    """

    get_pydantic_core_schema: Callable[[Any, GetCoreSchemaHandler], CoreSchema] | None = None
    get_pydantic_json_schema: Callable[[Any, GetJsonSchemaHandler], JsonSchemaValue] | None = None

    # Note: we may want to consider adding a convenience staticmethod `def for_type(type_: Any) -> GetPydanticSchema:`
    #   which returns `GetPydanticSchema(lambda _s, h: h(type_))`

    if not TYPE_CHECKING:
        # We put `__getattr__` in a non-TYPE_CHECKING block because otherwise, mypy allows arbitrary attribute access

        def __getattr__(self, item: str) -> Any:
            """Use this rather than defining `__get_pydantic_core_schema__` etc. to reduce the number of nested calls."""
            if item == '__get_pydantic_core_schema__' and self.get_pydantic_core_schema:
                return self.get_pydantic_core_schema
            elif item == '__get_pydantic_json_schema__' and self.get_pydantic_json_schema:
                return self.get_pydantic_json_schema
            else:
                return object.__getattribute__(self, item)

    __hash__ = object.__hash__

Tag dataclass

Provides a way to specify the expected tag to use for a case of a (callable) discriminated union.

Also provides a way to label a union case in error messages.

When using a callable Discriminator, attach a Tag to each case in the Union to specify the tag that should be used to identify that case. For example, in the below example, the Tag is used to specify that if get_discriminator_value returns 'apple', the input should be validated as an ApplePie, and if it returns 'pumpkin', the input should be validated as a PumpkinPie.

The primary role of the Tag here is to map the return value from the callable Discriminator function to the appropriate member of the Union in question.

from typing import Any, Union

from typing_extensions import Annotated, Literal

from pydantic import BaseModel, Discriminator, Tag

class Pie(BaseModel):
    time_to_cook: int
    num_ingredients: int

class ApplePie(Pie):
    fruit: Literal['apple'] = 'apple'

class PumpkinPie(Pie):
    filling: Literal['pumpkin'] = 'pumpkin'

def get_discriminator_value(v: Any) -> str:
    if isinstance(v, dict):
        return v.get('fruit', v.get('filling'))
    return getattr(v, 'fruit', getattr(v, 'filling', None))

class ThanksgivingDinner(BaseModel):
    dessert: Annotated[
        Union[
            Annotated[ApplePie, Tag('apple')],
            Annotated[PumpkinPie, Tag('pumpkin')],
        ],
        Discriminator(get_discriminator_value),
    ]

apple_variation = ThanksgivingDinner.model_validate(
    {'dessert': {'fruit': 'apple', 'time_to_cook': 60, 'num_ingredients': 8}}
)
print(repr(apple_variation))
'''
ThanksgivingDinner(dessert=ApplePie(time_to_cook=60, num_ingredients=8, fruit='apple'))
'''

pumpkin_variation = ThanksgivingDinner.model_validate(
    {
        'dessert': {
            'filling': 'pumpkin',
            'time_to_cook': 40,
            'num_ingredients': 6,
        }
    }
)
print(repr(pumpkin_variation))
'''
ThanksgivingDinner(dessert=PumpkinPie(time_to_cook=40, num_ingredients=6, filling='pumpkin'))
'''

Note

You must specify a Tag for every case in a Tag that is associated with a callable Discriminator. Failing to do so will result in a PydanticUserError with code callable-discriminator-no-tag.

See the Discriminated Unions concepts docs for more details on how to use Tags.

Source code in pydantic/types.py 
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@_dataclasses.dataclass(**_internal_dataclass.slots_true, frozen=True)
class Tag:
    """Provides a way to specify the expected tag to use for a case of a (callable) discriminated union.

    Also provides a way to label a union case in error messages.

    When using a callable `Discriminator`, attach a `Tag` to each case in the `Union` to specify the tag that
    should be used to identify that case. For example, in the below example, the `Tag` is used to specify that
    if `get_discriminator_value` returns `'apple'`, the input should be validated as an `ApplePie`, and if it
    returns `'pumpkin'`, the input should be validated as a `PumpkinPie`.

    The primary role of the `Tag` here is to map the return value from the callable `Discriminator` function to
    the appropriate member of the `Union` in question.

    ```py
    from typing import Any, Union

    from typing_extensions import Annotated, Literal

    from pydantic import BaseModel, Discriminator, Tag

    class Pie(BaseModel):
        time_to_cook: int
        num_ingredients: int

    class ApplePie(Pie):
        fruit: Literal['apple'] = 'apple'

    class PumpkinPie(Pie):
        filling: Literal['pumpkin'] = 'pumpkin'

    def get_discriminator_value(v: Any) -> str:
        if isinstance(v, dict):
            return v.get('fruit', v.get('filling'))
        return getattr(v, 'fruit', getattr(v, 'filling', None))

    class ThanksgivingDinner(BaseModel):
        dessert: Annotated[
            Union[
                Annotated[ApplePie, Tag('apple')],
                Annotated[PumpkinPie, Tag('pumpkin')],
            ],
            Discriminator(get_discriminator_value),
        ]

    apple_variation = ThanksgivingDinner.model_validate(
        {'dessert': {'fruit': 'apple', 'time_to_cook': 60, 'num_ingredients': 8}}
    )
    print(repr(apple_variation))
    '''
    ThanksgivingDinner(dessert=ApplePie(time_to_cook=60, num_ingredients=8, fruit='apple'))
    '''

    pumpkin_variation = ThanksgivingDinner.model_validate(
        {
            'dessert': {
                'filling': 'pumpkin',
                'time_to_cook': 40,
                'num_ingredients': 6,
            }
        }
    )
    print(repr(pumpkin_variation))
    '''
    ThanksgivingDinner(dessert=PumpkinPie(time_to_cook=40, num_ingredients=6, filling='pumpkin'))
    '''
    ```

    !!! note
        You must specify a `Tag` for every case in a `Tag` that is associated with a
        callable `Discriminator`. Failing to do so will result in a `PydanticUserError` with code
        [`callable-discriminator-no-tag`](../errors/usage_errors.md#callable-discriminator-no-tag).

    See the [Discriminated Unions] concepts docs for more details on how to use `Tag`s.

    [Discriminated Unions]: ../concepts/unions.md#discriminated-unions
    """

    tag: str

    def __get_pydantic_core_schema__(self, source_type: Any, handler: GetCoreSchemaHandler) -> CoreSchema:
        schema = handler(source_type)
        metadata = schema.setdefault('metadata', {})
        assert isinstance(metadata, dict)
        metadata[_core_utils.TAGGED_UNION_TAG_KEY] = self.tag
        return schema

Discriminator dataclass

Usage Documentation

Discriminated Unions with callable Discriminator

Provides a way to use a custom callable as the way to extract the value of a union discriminator.

This allows you to get validation behavior like you'd get from Field(discriminator=<field_name>), but without needing to have a single shared field across all the union choices. This also makes it possible to handle unions of models and primitive types with discriminated-union-style validation errors. Finally, this allows you to use a custom callable as the way to identify which member of a union a value belongs to, while still seeing all the performance benefits of a discriminated union.

Consider this example, which is much more performant with the use of Discriminator and thus a TaggedUnion than it would be as a normal Union.

from typing import Any, Union

from typing_extensions import Annotated, Literal

from pydantic import BaseModel, Discriminator, Tag

class Pie(BaseModel):
    time_to_cook: int
    num_ingredients: int

class ApplePie(Pie):
    fruit: Literal['apple'] = 'apple'

class PumpkinPie(Pie):
    filling: Literal['pumpkin'] = 'pumpkin'

def get_discriminator_value(v: Any) -> str:
    if isinstance(v, dict):
        return v.get('fruit', v.get('filling'))
    return getattr(v, 'fruit', getattr(v, 'filling', None))

class ThanksgivingDinner(BaseModel):
    dessert: Annotated[
        Union[
            Annotated[ApplePie, Tag('apple')],
            Annotated[PumpkinPie, Tag('pumpkin')],
        ],
        Discriminator(get_discriminator_value),
    ]

apple_variation = ThanksgivingDinner.model_validate(
    {'dessert': {'fruit': 'apple', 'time_to_cook': 60, 'num_ingredients': 8}}
)
print(repr(apple_variation))
'''
ThanksgivingDinner(dessert=ApplePie(time_to_cook=60, num_ingredients=8, fruit='apple'))
'''

pumpkin_variation = ThanksgivingDinner.model_validate(
    {
        'dessert': {
            'filling': 'pumpkin',
            'time_to_cook': 40,
            'num_ingredients': 6,
        }
    }
)
print(repr(pumpkin_variation))
'''
ThanksgivingDinner(dessert=PumpkinPie(time_to_cook=40, num_ingredients=6, filling='pumpkin'))
'''

See the Discriminated Unions concepts docs for more details on how to use Discriminators.

Source code in pydantic/types.py 
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@_dataclasses.dataclass(**_internal_dataclass.slots_true, frozen=True)
class Discriminator:
    """Usage docs: https://docs.pydantic.dev/2.9/concepts/unions/#discriminated-unions-with-callable-discriminator

    Provides a way to use a custom callable as the way to extract the value of a union discriminator.

    This allows you to get validation behavior like you'd get from `Field(discriminator=<field_name>)`,
    but without needing to have a single shared field across all the union choices. This also makes it
    possible to handle unions of models and primitive types with discriminated-union-style validation errors.
    Finally, this allows you to use a custom callable as the way to identify which member of a union a value
    belongs to, while still seeing all the performance benefits of a discriminated union.

    Consider this example, which is much more performant with the use of `Discriminator` and thus a `TaggedUnion`
    than it would be as a normal `Union`.

    ```py
    from typing import Any, Union

    from typing_extensions import Annotated, Literal

    from pydantic import BaseModel, Discriminator, Tag

    class Pie(BaseModel):
        time_to_cook: int
        num_ingredients: int

    class ApplePie(Pie):
        fruit: Literal['apple'] = 'apple'

    class PumpkinPie(Pie):
        filling: Literal['pumpkin'] = 'pumpkin'

    def get_discriminator_value(v: Any) -> str:
        if isinstance(v, dict):
            return v.get('fruit', v.get('filling'))
        return getattr(v, 'fruit', getattr(v, 'filling', None))

    class ThanksgivingDinner(BaseModel):
        dessert: Annotated[
            Union[
                Annotated[ApplePie, Tag('apple')],
                Annotated[PumpkinPie, Tag('pumpkin')],
            ],
            Discriminator(get_discriminator_value),
        ]

    apple_variation = ThanksgivingDinner.model_validate(
        {'dessert': {'fruit': 'apple', 'time_to_cook': 60, 'num_ingredients': 8}}
    )
    print(repr(apple_variation))
    '''
    ThanksgivingDinner(dessert=ApplePie(time_to_cook=60, num_ingredients=8, fruit='apple'))
    '''

    pumpkin_variation = ThanksgivingDinner.model_validate(
        {
            'dessert': {
                'filling': 'pumpkin',
                'time_to_cook': 40,
                'num_ingredients': 6,
            }
        }
    )
    print(repr(pumpkin_variation))
    '''
    ThanksgivingDinner(dessert=PumpkinPie(time_to_cook=40, num_ingredients=6, filling='pumpkin'))
    '''
    ```

    See the [Discriminated Unions] concepts docs for more details on how to use `Discriminator`s.

    [Discriminated Unions]: ../concepts/unions.md#discriminated-unions
    """

    discriminator: str | Callable[[Any], Hashable]
    """The callable or field name for discriminating the type in a tagged union.

    A `Callable` discriminator must extract the value of the discriminator from the input.
    A `str` discriminator must be the name of a field to discriminate against.
    """
    custom_error_type: str | None = None
    """Type to use in [custom errors](../errors/errors.md#custom-errors) replacing the standard discriminated union
    validation errors.
    """
    custom_error_message: str | None = None
    """Message to use in custom errors."""
    custom_error_context: dict[str, int | str | float] | None = None
    """Context to use in custom errors."""

    def __get_pydantic_core_schema__(self, source_type: Any, handler: GetCoreSchemaHandler) -> CoreSchema:
        origin = _typing_extra.get_origin(source_type)
        if not origin or not _typing_extra.origin_is_union(origin):
            raise TypeError(f'{type(self).__name__} must be used with a Union type, not {source_type}')

        if isinstance(self.discriminator, str):
            from pydantic import Field

            return handler(Annotated[source_type, Field(discriminator=self.discriminator)])
        else:
            original_schema = handler(source_type)
            return self._convert_schema(original_schema)

    def _convert_schema(self, original_schema: core_schema.CoreSchema) -> core_schema.TaggedUnionSchema:
        if original_schema['type'] != 'union':
            # This likely indicates that the schema was a single-item union that was simplified.
            # In this case, we do the same thing we do in
            # `pydantic._internal._discriminated_union._ApplyInferredDiscriminator._apply_to_root`, namely,
            # package the generated schema back into a single-item union.
            original_schema = core_schema.union_schema([original_schema])

        tagged_union_choices = {}
        for choice in original_schema['choices']:
            tag = None
            if isinstance(choice, tuple):
                choice, tag = choice
            metadata = choice.get('metadata')
            if metadata is not None:
                metadata_tag = metadata.get(_core_utils.TAGGED_UNION_TAG_KEY)
                if metadata_tag is not None:
                    tag = metadata_tag
            if tag is None:
                raise PydanticUserError(
                    f'`Tag` not provided for choice {choice} used with `Discriminator`',
                    code='callable-discriminator-no-tag',
                )
            tagged_union_choices[tag] = choice

        # Have to do these verbose checks to ensure falsy values ('' and {}) don't get ignored
        custom_error_type = self.custom_error_type
        if custom_error_type is None:
            custom_error_type = original_schema.get('custom_error_type')

        custom_error_message = self.custom_error_message
        if custom_error_message is None:
            custom_error_message = original_schema.get('custom_error_message')

        custom_error_context = self.custom_error_context
        if custom_error_context is None:
            custom_error_context = original_schema.get('custom_error_context')

        custom_error_type = original_schema.get('custom_error_type') if custom_error_type is None else custom_error_type
        return core_schema.tagged_union_schema(
            tagged_union_choices,
            self.discriminator,
            custom_error_type=custom_error_type,
            custom_error_message=custom_error_message,
            custom_error_context=custom_error_context,
            strict=original_schema.get('strict'),
            ref=original_schema.get('ref'),
            metadata=original_schema.get('metadata'),
            serialization=original_schema.get('serialization'),
        )

discriminator instance-attribute 

discriminator: str | Callable[[Any], Hashable]

The callable or field name for discriminating the type in a tagged union.

A Callable discriminator must extract the value of the discriminator from the input. A str discriminator must be the name of a field to discriminate against.

custom_error_type class-attribute instance-attribute 

custom_error_type: str | None = None

Type to use in custom errors replacing the standard discriminated union validation errors.

custom_error_message class-attribute instance-attribute 

custom_error_message: str | None = None

Message to use in custom errors.

custom_error_context class-attribute instance-attribute 

custom_error_context: (
    dict[str, int | str | float] | None
) = None

Context to use in custom errors.

FailFast dataclass

Bases: PydanticMetadata, BaseMetadata

A FailFast annotation can be used to specify that validation should stop at the first error.

This can be useful when you want to validate a large amount of data and you only need to know if it's valid or not.

You might want to enable this setting if you want to validate your data faster (basically, if you use this, validation will be more performant with the caveat that you get less information).

from typing import List

from typing_extensions import Annotated

from pydantic import BaseModel, FailFast, ValidationError

class Model(BaseModel):
    x: Annotated[List[int], FailFast()]

# This will raise a single error for the first invalid value and stop validation
try:
    obj = Model(x=[1, 2, 'a', 4, 5, 'b', 7, 8, 9, 'c'])
except ValidationError as e:
    print(e)
    '''
    1 validation error for Model
    x.2
      Input should be a valid integer, unable to parse string as an integer [type=int_parsing, input_value='a', input_type=str]
    '''
Source code in pydantic/types.py 
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@_dataclasses.dataclass
class FailFast(_fields.PydanticMetadata, BaseMetadata):
    """A `FailFast` annotation can be used to specify that validation should stop at the first error.

    This can be useful when you want to validate a large amount of data and you only need to know if it's valid or not.

    You might want to enable this setting if you want to validate your data faster (basically, if you use this,
    validation will be more performant with the caveat that you get less information).

    ```py
    from typing import List

    from typing_extensions import Annotated

    from pydantic import BaseModel, FailFast, ValidationError

    class Model(BaseModel):
        x: Annotated[List[int], FailFast()]

    # This will raise a single error for the first invalid value and stop validation
    try:
        obj = Model(x=[1, 2, 'a', 4, 5, 'b', 7, 8, 9, 'c'])
    except ValidationError as e:
        print(e)
        '''
        1 validation error for Model
        x.2
          Input should be a valid integer, unable to parse string as an integer [type=int_parsing, input_value='a', input_type=str]
        '''
    ```
    """

    fail_fast: bool = True

conint 

conint(
    *,
    strict: bool | None = None,
    gt: int | None = None,
    ge: int | None = None,
    lt: int | None = None,
    le: int | None = None,
    multiple_of: int | None = None
) -> type[int]

Discouraged

This function is discouraged in favor of using Annotated with Field instead.

This function will be deprecated in Pydantic 3.0.

The reason is that conint returns a type, which doesn't play well with static analysis tools.

from pydantic import BaseModel, conint

class Foo(BaseModel):
    bar: conint(strict=True, gt=0)

from typing_extensions import Annotated

from pydantic import BaseModel, Field

class Foo(BaseModel):
    bar: Annotated[int, Field(strict=True, gt=0)]

A wrapper around int that allows for additional constraints.

Parameters:

Name Type Description Default
strict bool | None

Whether to validate the integer in strict mode. Defaults to None.

 None
gt int | None

The value must be greater than this.

 None
ge int | None

The value must be greater than or equal to this.

 None
lt int | None

The value must be less than this.

 None
le int | None

The value must be less than or equal to this.

 None
multiple_of int | None

The value must be a multiple of this.

 None

Returns:

Type Description
type[int]

The wrapped integer type.

 
from pydantic import BaseModel, ValidationError, conint

class ConstrainedExample(BaseModel):
    constrained_int: conint(gt=1)

m = ConstrainedExample(constrained_int=2)
print(repr(m))
#> ConstrainedExample(constrained_int=2)

try:
    ConstrainedExample(constrained_int=0)
except ValidationError as e:
    print(e.errors())
    '''
    [
        {
            'type': 'greater_than',
            'loc': ('constrained_int',),
            'msg': 'Input should be greater than 1',
            'input': 0,
            'ctx': {'gt': 1},
            'url': 'https://errors.pydantic.dev/2/v/greater_than',
        }
    ]
    '''
Source code in pydantic/types.py 
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def conint(
    *,
    strict: bool | None = None,
    gt: int | None = None,
    ge: int | None = None,
    lt: int | None = None,
    le: int | None = None,
    multiple_of: int | None = None,
) -> type[int]:
    """
    !!! warning "Discouraged"
        This function is **discouraged** in favor of using
        [`Annotated`](https://docs.python.org/3/library/typing.html#typing.Annotated) with
        [`Field`][pydantic.fields.Field] instead.

        This function will be **deprecated** in Pydantic 3.0.

        The reason is that `conint` returns a type, which doesn't play well with static analysis tools.

        === ":x: Don't do this"
            ```py
            from pydantic import BaseModel, conint

            class Foo(BaseModel):
                bar: conint(strict=True, gt=0)
            ```

        === ":white_check_mark: Do this"
            ```py
            from typing_extensions import Annotated

            from pydantic import BaseModel, Field

            class Foo(BaseModel):
                bar: Annotated[int, Field(strict=True, gt=0)]
            ```

    A wrapper around `int` that allows for additional constraints.

    Args:
        strict: Whether to validate the integer in strict mode. Defaults to `None`.
        gt: The value must be greater than this.
        ge: The value must be greater than or equal to this.
        lt: The value must be less than this.
        le: The value must be less than or equal to this.
        multiple_of: The value must be a multiple of this.

    Returns:
        The wrapped integer type.

    ```py
    from pydantic import BaseModel, ValidationError, conint

    class ConstrainedExample(BaseModel):
        constrained_int: conint(gt=1)

    m = ConstrainedExample(constrained_int=2)
    print(repr(m))
    #> ConstrainedExample(constrained_int=2)

    try:
        ConstrainedExample(constrained_int=0)
    except ValidationError as e:
        print(e.errors())
        '''
        [
            {
                'type': 'greater_than',
                'loc': ('constrained_int',),
                'msg': 'Input should be greater than 1',
                'input': 0,
                'ctx': {'gt': 1},
                'url': 'https://errors.pydantic.dev/2/v/greater_than',
            }
        ]
        '''
    ```

    """  # noqa: D212
    return Annotated[  # pyright: ignore[reportReturnType]
        int,
        Strict(strict) if strict is not None else None,
        annotated_types.Interval(gt=gt, ge=ge, lt=lt, le=le),
        annotated_types.MultipleOf(multiple_of) if multiple_of is not None else None,
    ]

confloat 

confloat(
    *,
    strict: bool | None = None,
    gt: float | None = None,
    ge: float | None = None,
    lt: float | None = None,
    le: float | None = None,
    multiple_of: float | None = None,
    allow_inf_nan: bool | None = None
) -> type[float]

Discouraged

This function is discouraged in favor of using Annotated with Field instead.

This function will be deprecated in Pydantic 3.0.

The reason is that confloat returns a type, which doesn't play well with static analysis tools.

from pydantic import BaseModel, confloat

class Foo(BaseModel):
    bar: confloat(strict=True, gt=0)

from typing_extensions import Annotated

from pydantic import BaseModel, Field

class Foo(BaseModel):
    bar: Annotated[float, Field(strict=True, gt=0)]

A wrapper around float that allows for additional constraints.

Parameters:

Name Type Description Default
strict bool | None

Whether to validate the float in strict mode.

 None
gt float | None

The value must be greater than this.

 None
ge float | None

The value must be greater than or equal to this.

 None
lt float | None

The value must be less than this.

 None
le float | None

The value must be less than or equal to this.

 None
multiple_of float | None

The value must be a multiple of this.

 None
allow_inf_nan bool | None

Whether to allow -inf, inf, and nan.

 None

Returns:

Type Description
type[float]

The wrapped float type.

 
from pydantic import BaseModel, ValidationError, confloat

class ConstrainedExample(BaseModel):
    constrained_float: confloat(gt=1.0)

m = ConstrainedExample(constrained_float=1.1)
print(repr(m))
#> ConstrainedExample(constrained_float=1.1)

try:
    ConstrainedExample(constrained_float=0.9)
except ValidationError as e:
    print(e.errors())
    '''
    [
        {
            'type': 'greater_than',
            'loc': ('constrained_float',),
            'msg': 'Input should be greater than 1',
            'input': 0.9,
            'ctx': {'gt': 1.0},
            'url': 'https://errors.pydantic.dev/2/v/greater_than',
        }
    ]
    '''
Source code in pydantic/types.py 
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def confloat(
    *,
    strict: bool | None = None,
    gt: float | None = None,
    ge: float | None = None,
    lt: float | None = None,
    le: float | None = None,
    multiple_of: float | None = None,
    allow_inf_nan: bool | None = None,
) -> type[float]:
    """
    !!! warning "Discouraged"
        This function is **discouraged** in favor of using
        [`Annotated`](https://docs.python.org/3/library/typing.html#typing.Annotated) with
        [`Field`][pydantic.fields.Field] instead.

        This function will be **deprecated** in Pydantic 3.0.

        The reason is that `confloat` returns a type, which doesn't play well with static analysis tools.

        === ":x: Don't do this"
            ```py
            from pydantic import BaseModel, confloat

            class Foo(BaseModel):
                bar: confloat(strict=True, gt=0)
            ```

        === ":white_check_mark: Do this"
            ```py
            from typing_extensions import Annotated

            from pydantic import BaseModel, Field

            class Foo(BaseModel):
                bar: Annotated[float, Field(strict=True, gt=0)]
            ```

    A wrapper around `float` that allows for additional constraints.

    Args:
        strict: Whether to validate the float in strict mode.
        gt: The value must be greater than this.
        ge: The value must be greater than or equal to this.
        lt: The value must be less than this.
        le: The value must be less than or equal to this.
        multiple_of: The value must be a multiple of this.
        allow_inf_nan: Whether to allow `-inf`, `inf`, and `nan`.

    Returns:
        The wrapped float type.

    ```py
    from pydantic import BaseModel, ValidationError, confloat

    class ConstrainedExample(BaseModel):
        constrained_float: confloat(gt=1.0)

    m = ConstrainedExample(constrained_float=1.1)
    print(repr(m))
    #> ConstrainedExample(constrained_float=1.1)

    try:
        ConstrainedExample(constrained_float=0.9)
    except ValidationError as e:
        print(e.errors())
        '''
        [
            {
                'type': 'greater_than',
                'loc': ('constrained_float',),
                'msg': 'Input should be greater than 1',
                'input': 0.9,
                'ctx': {'gt': 1.0},
                'url': 'https://errors.pydantic.dev/2/v/greater_than',
            }
        ]
        '''
    ```
    """  # noqa: D212
    return Annotated[  # pyright: ignore[reportReturnType]
        float,
        Strict(strict) if strict is not None else None,
        annotated_types.Interval(gt=gt, ge=ge, lt=lt, le=le),
        annotated_types.MultipleOf(multiple_of) if multiple_of is not None else None,
        AllowInfNan(allow_inf_nan) if allow_inf_nan is not None else None,
    ]

conbytes 

conbytes(
    *,
    min_length: int | None = None,
    max_length: int | None = None,
    strict: bool | None = None
) -> type[bytes]

A wrapper around bytes that allows for additional constraints.

Parameters:

Name Type Description Default
min_length int | None

The minimum length of the bytes.

 None
max_length int | None

The maximum length of the bytes.

 None
strict bool | None

Whether to validate the bytes in strict mode.

 None

Returns:

Type Description
type[bytes]

The wrapped bytes type.
Source code in pydantic/types.py 
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def conbytes(
    *,
    min_length: int | None = None,
    max_length: int | None = None,
    strict: bool | None = None,
) -> type[bytes]:
    """A wrapper around `bytes` that allows for additional constraints.

    Args:
        min_length: The minimum length of the bytes.
        max_length: The maximum length of the bytes.
        strict: Whether to validate the bytes in strict mode.

    Returns:
        The wrapped bytes type.
    """
    return Annotated[  # pyright: ignore[reportReturnType]
        bytes,
        Strict(strict) if strict is not None else None,
        annotated_types.Len(min_length or 0, max_length),
    ]

constr 

constr(
    *,
    strip_whitespace: bool | None = None,
    to_upper: bool | None = None,
    to_lower: bool | None = None,
    strict: bool | None = None,
    min_length: int | None = None,
    max_length: int | None = None,
    pattern: str | Pattern[str] | None = None
) -> type[str]

Discouraged

This function is discouraged in favor of using Annotated with StringConstraints instead.

This function will be deprecated in Pydantic 3.0.

The reason is that constr returns a type, which doesn't play well with static analysis tools.

from pydantic import BaseModel, constr

class Foo(BaseModel):
    bar: constr(strip_whitespace=True, to_upper=True, pattern=r'^[A-Z]+$')

from typing_extensions import Annotated

from pydantic import BaseModel, StringConstraints

class Foo(BaseModel):
    bar: Annotated[
        str,
        StringConstraints(
            strip_whitespace=True, to_upper=True, pattern=r'^[A-Z]+$'
        ),
    ]

A wrapper around str that allows for additional constraints.

from pydantic import BaseModel, constr

class Foo(BaseModel):
    bar: constr(strip_whitespace=True, to_upper=True)

foo = Foo(bar='  hello  ')
print(foo)
#> bar='HELLO'

Parameters:

Name Type Description Default
strip_whitespace bool | None

Whether to remove leading and trailing whitespace.

 None
to_upper bool | None

Whether to turn all characters to uppercase.

 None
to_lower bool | None

Whether to turn all characters to lowercase.

 None
strict bool | None

Whether to validate the string in strict mode.

 None
min_length int | None

The minimum length of the string.

 None
max_length int | None

The maximum length of the string.

 None
pattern str | Pattern[str] | None

A regex pattern to validate the string against.

 None

Returns:

Type Description
type[str]

The wrapped string type.
Source code in pydantic/types.py 
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def constr(
    *,
    strip_whitespace: bool | None = None,
    to_upper: bool | None = None,
    to_lower: bool | None = None,
    strict: bool | None = None,
    min_length: int | None = None,
    max_length: int | None = None,
    pattern: str | Pattern[str] | None = None,
) -> type[str]:
    """
    !!! warning "Discouraged"
        This function is **discouraged** in favor of using
        [`Annotated`](https://docs.python.org/3/library/typing.html#typing.Annotated) with
        [`StringConstraints`][pydantic.types.StringConstraints] instead.

        This function will be **deprecated** in Pydantic 3.0.

        The reason is that `constr` returns a type, which doesn't play well with static analysis tools.

        === ":x: Don't do this"
            ```py
            from pydantic import BaseModel, constr

            class Foo(BaseModel):
                bar: constr(strip_whitespace=True, to_upper=True, pattern=r'^[A-Z]+$')
            ```

        === ":white_check_mark: Do this"
            ```py
            from typing_extensions import Annotated

            from pydantic import BaseModel, StringConstraints

            class Foo(BaseModel):
                bar: Annotated[
                    str,
                    StringConstraints(
                        strip_whitespace=True, to_upper=True, pattern=r'^[A-Z]+$'
                    ),
                ]
            ```

    A wrapper around `str` that allows for additional constraints.

    ```py
    from pydantic import BaseModel, constr

    class Foo(BaseModel):
        bar: constr(strip_whitespace=True, to_upper=True)

    foo = Foo(bar='  hello  ')
    print(foo)
    #> bar='HELLO'
    ```

    Args:
        strip_whitespace: Whether to remove leading and trailing whitespace.
        to_upper: Whether to turn all characters to uppercase.
        to_lower: Whether to turn all characters to lowercase.
        strict: Whether to validate the string in strict mode.
        min_length: The minimum length of the string.
        max_length: The maximum length of the string.
        pattern: A regex pattern to validate the string against.

    Returns:
        The wrapped string type.
    """  # noqa: D212
    return Annotated[  # pyright: ignore[reportReturnType]
        str,
        StringConstraints(
            strip_whitespace=strip_whitespace,
            to_upper=to_upper,
            to_lower=to_lower,
            strict=strict,
            min_length=min_length,
            max_length=max_length,
            pattern=pattern,
        ),
    ]

conset 

conset(
    item_type: type[HashableItemType],
    *,
    min_length: int | None = None,
    max_length: int | None = None
) -> type[set[HashableItemType]]

A wrapper around typing.Set that allows for additional constraints.

Parameters:

Name Type Description Default
item_type type[HashableItemType]

The type of the items in the set.

 required
min_length int | None

The minimum length of the set.

 None
max_length int | None

The maximum length of the set.

 None

Returns:

Type Description
type[set[HashableItemType]]

The wrapped set type.
Source code in pydantic/types.py 
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def conset(
    item_type: type[HashableItemType], *, min_length: int | None = None, max_length: int | None = None
) -> type[set[HashableItemType]]:
    """A wrapper around `typing.Set` that allows for additional constraints.

    Args:
        item_type: The type of the items in the set.
        min_length: The minimum length of the set.
        max_length: The maximum length of the set.

    Returns:
        The wrapped set type.
    """
    return Annotated[Set[item_type], annotated_types.Len(min_length or 0, max_length)]  # pyright: ignore[reportReturnType]

confrozenset 

confrozenset(
    item_type: type[HashableItemType],
    *,
    min_length: int | None = None,
    max_length: int | None = None
) -> type[frozenset[HashableItemType]]

A wrapper around typing.FrozenSet that allows for additional constraints.

Parameters:

Name Type Description Default
item_type type[HashableItemType]

The type of the items in the frozenset.

 required
min_length int | None

The minimum length of the frozenset.

 None
max_length int | None

The maximum length of the frozenset.

 None

Returns:

Type Description
type[frozenset[HashableItemType]]

The wrapped frozenset type.
Source code in pydantic/types.py 
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def confrozenset(
    item_type: type[HashableItemType], *, min_length: int | None = None, max_length: int | None = None
) -> type[frozenset[HashableItemType]]:
    """A wrapper around `typing.FrozenSet` that allows for additional constraints.

    Args:
        item_type: The type of the items in the frozenset.
        min_length: The minimum length of the frozenset.
        max_length: The maximum length of the frozenset.

    Returns:
        The wrapped frozenset type.
    """
    return Annotated[FrozenSet[item_type], annotated_types.Len(min_length or 0, max_length)]  # pyright: ignore[reportReturnType]

conlist 

conlist(
    item_type: type[AnyItemType],
    *,
    min_length: int | None = None,
    max_length: int | None = None,
    unique_items: bool | None = None
) -> type[list[AnyItemType]]

A wrapper around typing.List that adds validation.

Parameters:

Name Type Description Default
item_type type[AnyItemType]

The type of the items in the list.

 required
min_length int | None

The minimum length of the list. Defaults to None.

 None
max_length int | None

The maximum length of the list. Defaults to None.

 None
unique_items bool | None

Whether the items in the list must be unique. Defaults to None.

Warning

The unique_items parameter is deprecated, use Set instead. See this issue for more details.

 None

Returns:

Type Description
type[list[AnyItemType]]

The wrapped list type.
Source code in pydantic/types.py 
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def conlist(
    item_type: type[AnyItemType],
    *,
    min_length: int | None = None,
    max_length: int | None = None,
    unique_items: bool | None = None,
) -> type[list[AnyItemType]]:
    """A wrapper around typing.List that adds validation.

    Args:
        item_type: The type of the items in the list.
        min_length: The minimum length of the list. Defaults to None.
        max_length: The maximum length of the list. Defaults to None.
        unique_items: Whether the items in the list must be unique. Defaults to None.
            !!! warning Deprecated
                The `unique_items` parameter is deprecated, use `Set` instead.
                See [this issue](https://github.com/pydantic/pydantic-core/issues/296) for more details.

    Returns:
        The wrapped list type.
    """
    if unique_items is not None:
        raise PydanticUserError(
            (
                '`unique_items` is removed, use `Set` instead'
                '(this feature is discussed in https://github.com/pydantic/pydantic-core/issues/296)'
            ),
            code='removed-kwargs',
        )
    return Annotated[List[item_type], annotated_types.Len(min_length or 0, max_length)]  # pyright: ignore[reportReturnType]

condecimal 

condecimal(
    *,
    strict: bool | None = None,
    gt: int | Decimal | None = None,
    ge: int | Decimal | None = None,
    lt: int | Decimal | None = None,
    le: int | Decimal | None = None,
    multiple_of: int | Decimal | None = None,
    max_digits: int | None = None,
    decimal_places: int | None = None,
    allow_inf_nan: bool | None = None
) -> type[Decimal]

Discouraged

This function is discouraged in favor of using Annotated with Field instead.

This function will be deprecated in Pydantic 3.0.

The reason is that condecimal returns a type, which doesn't play well with static analysis tools.

from pydantic import BaseModel, condecimal

class Foo(BaseModel):
    bar: condecimal(strict=True, allow_inf_nan=True)

from decimal import Decimal

from typing_extensions import Annotated

from pydantic import BaseModel, Field

class Foo(BaseModel):
    bar: Annotated[Decimal, Field(strict=True, allow_inf_nan=True)]

A wrapper around Decimal that adds validation.

Parameters:

Name Type Description Default
strict bool | None

Whether to validate the value in strict mode. Defaults to None.

 None
gt int | Decimal | None

The value must be greater than this. Defaults to None.

 None
ge int | Decimal | None

The value must be greater than or equal to this. Defaults to None.

 None
lt int | Decimal | None

The value must be less than this. Defaults to None.

 None
le int | Decimal | None

The value must be less than or equal to this. Defaults to None.

 None
multiple_of int | Decimal | None

The value must be a multiple of this. Defaults to None.

 None
max_digits int | None

The maximum number of digits. Defaults to None.

 None
decimal_places int | None

The number of decimal places. Defaults to None.

 None
allow_inf_nan bool | None

Whether to allow infinity and NaN. Defaults to None.

 None 
from decimal import Decimal

from pydantic import BaseModel, ValidationError, condecimal

class ConstrainedExample(BaseModel):
    constrained_decimal: condecimal(gt=Decimal('1.0'))

m = ConstrainedExample(constrained_decimal=Decimal('1.1'))
print(repr(m))
#> ConstrainedExample(constrained_decimal=Decimal('1.1'))

try:
    ConstrainedExample(constrained_decimal=Decimal('0.9'))
except ValidationError as e:
    print(e.errors())
    '''
    [
        {
            'type': 'greater_than',
            'loc': ('constrained_decimal',),
            'msg': 'Input should be greater than 1.0',
            'input': Decimal('0.9'),
            'ctx': {'gt': Decimal('1.0')},
            'url': 'https://errors.pydantic.dev/2/v/greater_than',
        }
    ]
    '''
Source code in pydantic/types.py 
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def condecimal(
    *,
    strict: bool | None = None,
    gt: int | Decimal | None = None,
    ge: int | Decimal | None = None,
    lt: int | Decimal | None = None,
    le: int | Decimal | None = None,
    multiple_of: int | Decimal | None = None,
    max_digits: int | None = None,
    decimal_places: int | None = None,
    allow_inf_nan: bool | None = None,
) -> type[Decimal]:
    """
    !!! warning "Discouraged"
        This function is **discouraged** in favor of using
        [`Annotated`](https://docs.python.org/3/library/typing.html#typing.Annotated) with
        [`Field`][pydantic.fields.Field] instead.

        This function will be **deprecated** in Pydantic 3.0.

        The reason is that `condecimal` returns a type, which doesn't play well with static analysis tools.

        === ":x: Don't do this"
            ```py
            from pydantic import BaseModel, condecimal

            class Foo(BaseModel):
                bar: condecimal(strict=True, allow_inf_nan=True)
            ```

        === ":white_check_mark: Do this"
            ```py
            from decimal import Decimal

            from typing_extensions import Annotated

            from pydantic import BaseModel, Field

            class Foo(BaseModel):
                bar: Annotated[Decimal, Field(strict=True, allow_inf_nan=True)]
            ```

    A wrapper around Decimal that adds validation.

    Args:
        strict: Whether to validate the value in strict mode. Defaults to `None`.
        gt: The value must be greater than this. Defaults to `None`.
        ge: The value must be greater than or equal to this. Defaults to `None`.
        lt: The value must be less than this. Defaults to `None`.
        le: The value must be less than or equal to this. Defaults to `None`.
        multiple_of: The value must be a multiple of this. Defaults to `None`.
        max_digits: The maximum number of digits. Defaults to `None`.
        decimal_places: The number of decimal places. Defaults to `None`.
        allow_inf_nan: Whether to allow infinity and NaN. Defaults to `None`.

    ```py
    from decimal import Decimal

    from pydantic import BaseModel, ValidationError, condecimal

    class ConstrainedExample(BaseModel):
        constrained_decimal: condecimal(gt=Decimal('1.0'))

    m = ConstrainedExample(constrained_decimal=Decimal('1.1'))
    print(repr(m))
    #> ConstrainedExample(constrained_decimal=Decimal('1.1'))

    try:
        ConstrainedExample(constrained_decimal=Decimal('0.9'))
    except ValidationError as e:
        print(e.errors())
        '''
        [
            {
                'type': 'greater_than',
                'loc': ('constrained_decimal',),
                'msg': 'Input should be greater than 1.0',
                'input': Decimal('0.9'),
                'ctx': {'gt': Decimal('1.0')},
                'url': 'https://errors.pydantic.dev/2/v/greater_than',
            }
        ]
        '''
    ```
    """  # noqa: D212
    return Annotated[  # pyright: ignore[reportReturnType]
        Decimal,
        Strict(strict) if strict is not None else None,
        annotated_types.Interval(gt=gt, ge=ge, lt=lt, le=le),
        annotated_types.MultipleOf(multiple_of) if multiple_of is not None else None,
        _fields.pydantic_general_metadata(max_digits=max_digits, decimal_places=decimal_places),
        AllowInfNan(allow_inf_nan) if allow_inf_nan is not None else None,
    ]

condate 

condate(
    *,
    strict: bool | None = None,
    gt: date | None = None,
    ge: date | None = None,
    lt: date | None = None,
    le: date | None = None
) -> type[date]

A wrapper for date that adds constraints.

Parameters:

Name Type Description Default
strict bool | None

Whether to validate the date value in strict mode. Defaults to None.

 None
gt date | None

The value must be greater than this. Defaults to None.

 None
ge date | None

The value must be greater than or equal to this. Defaults to None.

 None
lt date | None

The value must be less than this. Defaults to None.

 None
le date | None

The value must be less than or equal to this. Defaults to None.

 None

Returns:

Type Description
type[date]

A date type with the specified constraints.
Source code in pydantic/types.py 
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def condate(
    *,
    strict: bool | None = None,
    gt: date | None = None,
    ge: date | None = None,
    lt: date | None = None,
    le: date | None = None,
) -> type[date]:
    """A wrapper for date that adds constraints.

    Args:
        strict: Whether to validate the date value in strict mode. Defaults to `None`.
        gt: The value must be greater than this. Defaults to `None`.
        ge: The value must be greater than or equal to this. Defaults to `None`.
        lt: The value must be less than this. Defaults to `None`.
        le: The value must be less than or equal to this. Defaults to `None`.

    Returns:
        A date type with the specified constraints.
    """
    return Annotated[  # pyright: ignore[reportReturnType]
        date,
        Strict(strict) if strict is not None else None,
        annotated_types.Interval(gt=gt, ge=ge, lt=lt, le=le),
    ]