Module aoe2netwrapper.models.leaderboard
aoe2netwrapper.models.leaderboard
This module contains the model objects to encapsulate the responses from the endpoint at https://aoe2.net/api/leaderboard
View Source
"""
aoe2netwrapper.models.leaderboard
---------------------------------
This module contains the model objects to encapsulate the responses from the endpoint at
https://aoe2.net/api/leaderboard
"""
from __future__ import annotations
from typing import Any
from pydantic import BaseModel, Field
class LeaderBoardSpot(BaseModel):
"""An object to encapsulate any entry in the leaderboard ranking."""
profile_id: int | None = Field(None, description="The ID attributed to the player by AoE II")
rank: int | None = Field(None, description="The player's rank on the ladder")
rating: int | None = Field(None, description="The player's rating in the ELO system")
steam_id: int | None = Field(None, description="ID of the player on the Steam platform")
icon: Any | None = Field(None, description="The player's icon")
name: str | None = Field(None, description="The player's in-game name")
clan: str | None = Field(None, description="The player's clan / team")
country: str | None = Field(None, description="Country the player connected from")
previous_rating: int | None = Field(None, description="Player's rating at their last match")
highest_rating: int | None = Field(None, description="Highest rating achieved by the player")
streak: int | None = Field(None, description="Current number of consecutive wins")
lowest_streak: int | None = Field(None, description="Lowest streak achieved by this player")
highest_streak: int | None = Field(None, description="Highest streak achieved by this player")
games: int | None = Field(None, description="The total amount of games played by the player")
wins: int | None = Field(None, description="Total amount of wins")
losses: int | None = Field(None, description="Total amount of losses")
drops: int | None = Field(None, description="Number of games the player dropped out of")
last_match: int | None = Field(None, description="Timestamp of the last game played")
last_match_time: int | None = Field(None, description="Timestamp of the last game played")
class LeaderBoardResponse(BaseModel):
"""An object to encapsulate the response from the leaderboard API."""
total: int | None = Field(None, description="Total number of entries in the leaderboard")
leaderboard_id: int | None = Field(None, description="ID of the leaderboard queried, aka game type")
start: int | None = Field(None, description="Starting rank of the first entry in the response")
count: int | None = Field(None, description="Number of entries returned")
leaderboard: list[LeaderBoardSpot] | None = Field(None, description="List of LeaderBoardSport entries")
Classes
LeaderBoardResponse
class LeaderBoardResponse(
/,
**data: 'Any'
)
An object to encapsulate the response from the leaderboard API.
View Source
class LeaderBoardResponse(BaseModel):
"""An object to encapsulate the response from the leaderboard API."""
total: int | None = Field(None, description="Total number of entries in the leaderboard")
leaderboard_id: int | None = Field(None, description="ID of the leaderboard queried, aka game type")
start: int | None = Field(None, description="Starting rank of the first entry in the response")
count: int | None = Field(None, description="Number of entries returned")
leaderboard: list[LeaderBoardSpot] | None = Field(None, description="List of LeaderBoardSport entries")
Ancestors (in MRO)
- pydantic.main.BaseModel
Class variables
model_computed_fields
model_config
model_fields
Static methods
construct
def construct(
_fields_set: 'set[str] | None' = None,
**values: 'Any'
) -> 'Self'
View Source
@classmethod
@typing_extensions.deprecated('The `construct` method is deprecated; use `model_construct` instead.', category=None)
def construct(cls, _fields_set: set[str] | None = None, **values: Any) -> Self: # noqa: D102
warnings.warn(
'The `construct` method is deprecated; use `model_construct` instead.', category=PydanticDeprecatedSince20
)
return cls.model_construct(_fields_set=_fields_set, **values)
from_orm
def from_orm(
obj: 'Any'
) -> 'Self'
View Source
@classmethod
@typing_extensions.deprecated(
'The `from_orm` method is deprecated; set '
"`model_config['from_attributes']=True` and use `model_validate` instead.",
category=None,
)
def from_orm(cls, obj: Any) -> Self: # noqa: D102
warnings.warn(
'The `from_orm` method is deprecated; set '
"`model_config['from_attributes']=True` and use `model_validate` instead.",
category=PydanticDeprecatedSince20,
)
if not cls.model_config.get('from_attributes', None):
raise PydanticUserError(
'You must set the config attribute `from_attributes=True` to use from_orm', code=None
)
return cls.model_validate(obj)
model_construct
def model_construct(
_fields_set: 'set[str] | None' = None,
**values: 'Any'
) -> 'Self'
Creates a new instance of the Model class with validated data.
Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Note
model_construct() generally respects the model_config.extra setting on the provided model.
That is, if model_config.extra == 'allow', then all extra passed values are added to the model instance's __dict__
and __pydantic_extra__ fields. If model_config.extra == 'ignore' (the default), then all extra passed values are ignored.
Because no validation is performed with a call to model_construct(), having model_config.extra == 'forbid' does not result in
an error if extra values are passed, but they will be ignored.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| _fields_set | None | The set of field names accepted for the Model instance. | None |
| values | None | Trusted or pre-validated data dictionary. | None |
Returns:
| Type | Description |
|---|---|
| None | A new instance of the Model class with validated data. |
View Source
@classmethod
def model_construct(cls, _fields_set: set[str] | None = None, **values: Any) -> Self: # noqa: C901
"""Creates a new instance of the `Model` class with validated data.
Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
!!! note
`model_construct()` generally respects the `model_config.extra` setting on the provided model.
That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__`
and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored.
Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in
an error if extra values are passed, but they will be ignored.
Args:
_fields_set: The set of field names accepted for the Model instance.
values: Trusted or pre-validated data dictionary.
Returns:
A new instance of the `Model` class with validated data.
"""
m = cls.__new__(cls)
fields_values: dict[str, Any] = {}
fields_set = set()
for name, field in cls.model_fields.items():
if field.alias is not None and field.alias in values:
fields_values[name] = values.pop(field.alias)
fields_set.add(name)
if (name not in fields_set) and (field.validation_alias is not None):
validation_aliases: list[str | AliasPath] = (
field.validation_alias.choices
if isinstance(field.validation_alias, AliasChoices)
else [field.validation_alias]
)
for alias in validation_aliases:
if isinstance(alias, str) and alias in values:
fields_values[name] = values.pop(alias)
fields_set.add(name)
break
elif isinstance(alias, AliasPath):
value = alias.search_dict_for_path(values)
if value is not PydanticUndefined:
fields_values[name] = value
fields_set.add(name)
break
if name not in fields_set:
if name in values:
fields_values[name] = values.pop(name)
fields_set.add(name)
elif not field.is_required():
fields_values[name] = field.get_default(call_default_factory=True)
if _fields_set is None:
_fields_set = fields_set
_extra: dict[str, Any] | None = (
{k: v for k, v in values.items()} if cls.model_config.get('extra') == 'allow' else None
)
_object_setattr(m, '__dict__', fields_values)
_object_setattr(m, '__pydantic_fields_set__', _fields_set)
if not cls.__pydantic_root_model__:
_object_setattr(m, '__pydantic_extra__', _extra)
if cls.__pydantic_post_init__:
m.model_post_init(None)
# update private attributes with values set
if hasattr(m, '__pydantic_private__') and m.__pydantic_private__ is not None:
for k, v in values.items():
if k in m.__private_attributes__:
m.__pydantic_private__[k] = v
elif not cls.__pydantic_root_model__:
# Note: if there are any private attributes, cls.__pydantic_post_init__ would exist
# Since it doesn't, that means that `__pydantic_private__` should be set to None
_object_setattr(m, '__pydantic_private__', None)
return m
model_json_schema
def model_json_schema(
by_alias: 'bool' = True,
ref_template: 'str' = '#/$defs/{model}',
schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>,
mode: 'JsonSchemaMode' = 'validation'
) -> 'dict[str, Any]'
Generates a JSON schema for a model class.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| by_alias | None | Whether to use attribute aliases or not. | None |
| ref_template | None | The reference template. | None |
| schema_generator | None | To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchema with your desired modifications |
None |
| mode | None | The mode in which to generate the schema. | None |
Returns:
| Type | Description |
|---|---|
| None | The JSON schema for the given model class. |
View Source
@classmethod
def model_json_schema(
cls,
by_alias: bool = True,
ref_template: str = DEFAULT_REF_TEMPLATE,
schema_generator: type[GenerateJsonSchema] = GenerateJsonSchema,
mode: JsonSchemaMode = 'validation',
) -> dict[str, Any]:
"""Generates a JSON schema for a model class.
Args:
by_alias: Whether to use attribute aliases or not.
ref_template: The reference template.
schema_generator: To override the logic used to generate the JSON schema, as a subclass of
`GenerateJsonSchema` with your desired modifications
mode: The mode in which to generate the schema.
Returns:
The JSON schema for the given model class.
"""
return model_json_schema(
cls, by_alias=by_alias, ref_template=ref_template, schema_generator=schema_generator, mode=mode
)
model_parametrized_name
def model_parametrized_name(
params: 'tuple[type[Any], ...]'
) -> 'str'
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| params | None | Tuple of types of the class. Given a generic classModel with 2 type variables and a concrete model Model[str, int],the value (str, int) would be passed to params. |
None |
Returns:
| Type | Description |
|---|---|
| None | String representing the new class where params are passed to cls as type variables. |
Raises:
| Type | Description |
|---|---|
| TypeError | Raised when trying to generate concrete names for non-generic models. |
View Source
@classmethod
def model_parametrized_name(cls, params: tuple[type[Any], ...]) -> str:
"""Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Args:
params: Tuple of types of the class. Given a generic class
`Model` with 2 type variables and a concrete model `Model[str, int]`,
the value `(str, int)` would be passed to `params`.
Returns:
String representing the new class where `params` are passed to `cls` as type variables.
Raises:
TypeError: Raised when trying to generate concrete names for non-generic models.
"""
if not issubclass(cls, typing.Generic):
raise TypeError('Concrete names should only be generated for generic models.')
# Any strings received should represent forward references, so we handle them specially below.
# If we eventually move toward wrapping them in a ForwardRef in __class_getitem__ in the future,
# we may be able to remove this special case.
param_names = [param if isinstance(param, str) else _repr.display_as_type(param) for param in params]
params_component = ', '.join(param_names)
return f'{cls.__name__}[{params_component}]'
model_rebuild
def model_rebuild(
*,
force: 'bool' = False,
raise_errors: 'bool' = True,
_parent_namespace_depth: 'int' = 2,
_types_namespace: 'dict[str, Any] | None' = None
) -> 'bool | None'
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| force | None | Whether to force the rebuilding of the model schema, defaults to False. |
None |
| raise_errors | None | Whether to raise errors, defaults to True. |
None |
| _parent_namespace_depth | None | The depth level of the parent namespace, defaults to 2. | None |
| _types_namespace | None | The types namespace, defaults to None. |
None |
Returns:
| Type | Description |
|---|---|
| None | Returns None if the schema is already "complete" and rebuilding was not required.If rebuilding was required, returns True if rebuilding was successful, otherwise False. |
View Source
@classmethod
def model_rebuild(
cls,
*,
force: bool = False,
raise_errors: bool = True,
_parent_namespace_depth: int = 2,
_types_namespace: dict[str, Any] | None = None,
) -> bool | None:
"""Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
the initial attempt to build the schema, and automatic rebuilding fails.
Args:
force: Whether to force the rebuilding of the model schema, defaults to `False`.
raise_errors: Whether to raise errors, defaults to `True`.
_parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
_types_namespace: The types namespace, defaults to `None`.
Returns:
Returns `None` if the schema is already "complete" and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
"""
if not force and cls.__pydantic_complete__:
return None
else:
if '__pydantic_core_schema__' in cls.__dict__:
delattr(cls, '__pydantic_core_schema__') # delete cached value to ensure full rebuild happens
if _types_namespace is not None:
types_namespace: dict[str, Any] | None = _types_namespace.copy()
else:
if _parent_namespace_depth > 0:
frame_parent_ns = _typing_extra.parent_frame_namespace(parent_depth=_parent_namespace_depth) or {}
cls_parent_ns = (
_model_construction.unpack_lenient_weakvaluedict(cls.__pydantic_parent_namespace__) or {}
)
types_namespace = {**cls_parent_ns, **frame_parent_ns}
cls.__pydantic_parent_namespace__ = _model_construction.build_lenient_weakvaluedict(types_namespace)
else:
types_namespace = _model_construction.unpack_lenient_weakvaluedict(
cls.__pydantic_parent_namespace__
)
types_namespace = _typing_extra.get_cls_types_namespace(cls, types_namespace)
# manually override defer_build so complete_model_class doesn't skip building the model again
config = {**cls.model_config, 'defer_build': False}
return _model_construction.complete_model_class(
cls,
cls.__name__,
_config.ConfigWrapper(config, check=False),
raise_errors=raise_errors,
types_namespace=types_namespace,
)
model_validate
def model_validate(
obj: 'Any',
*,
strict: 'bool | None' = None,
from_attributes: 'bool | None' = None,
context: 'Any | None' = None
) -> 'Self'
Validate a pydantic model instance.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| obj | None | The object to validate. | None |
| strict | None | Whether to enforce types strictly. | None |
| from_attributes | None | Whether to extract data from object attributes. | None |
| context | None | Additional context to pass to the validator. | None |
Returns:
| Type | Description |
|---|---|
| None | The validated model instance. |
Raises:
| Type | Description |
|---|---|
| ValidationError | If the object could not be validated. |
View Source
@classmethod
def model_validate(
cls,
obj: Any,
*,
strict: bool | None = None,
from_attributes: bool | None = None,
context: Any | None = None,
) -> Self:
"""Validate a pydantic model instance.
Args:
obj: The object to validate.
strict: Whether to enforce types strictly.
from_attributes: Whether to extract data from object attributes.
context: Additional context to pass to the validator.
Raises:
ValidationError: If the object could not be validated.
Returns:
The validated model instance.
"""
# `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks
__tracebackhide__ = True
return cls.__pydantic_validator__.validate_python(
obj, strict=strict, from_attributes=from_attributes, context=context
)
model_validate_json
def model_validate_json(
json_data: 'str | bytes | bytearray',
*,
strict: 'bool | None' = None,
context: 'Any | None' = None
) -> 'Self'
Usage docs: https://docs.pydantic.dev/2.8/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| json_data | None | The JSON data to validate. | None |
| strict | None | Whether to enforce types strictly. | None |
| context | None | Extra variables to pass to the validator. | None |
Returns:
| Type | Description |
|---|---|
| None | The validated Pydantic model. |
Raises:
| Type | Description |
|---|---|
| ValueError | If json_data is not a JSON string. |
View Source
@classmethod
def model_validate_json(
cls,
json_data: str | bytes | bytearray,
*,
strict: bool | None = None,
context: Any | None = None,
) -> Self:
"""Usage docs: https://docs.pydantic.dev/2.8/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
Args:
json_data: The JSON data to validate.
strict: Whether to enforce types strictly.
context: Extra variables to pass to the validator.
Returns:
The validated Pydantic model.
Raises:
ValueError: If `json_data` is not a JSON string.
"""
# `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks
__tracebackhide__ = True
return cls.__pydantic_validator__.validate_json(json_data, strict=strict, context=context)
model_validate_strings
def model_validate_strings(
obj: 'Any',
*,
strict: 'bool | None' = None,
context: 'Any | None' = None
) -> 'Self'
Validate the given object with string data against the Pydantic model.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| obj | None | The object containing string data to validate. | None |
| strict | None | Whether to enforce types strictly. | None |
| context | None | Extra variables to pass to the validator. | None |
Returns:
| Type | Description |
|---|---|
| None | The validated Pydantic model. |
View Source
@classmethod
def model_validate_strings(
cls,
obj: Any,
*,
strict: bool | None = None,
context: Any | None = None,
) -> Self:
"""Validate the given object with string data against the Pydantic model.
Args:
obj: The object containing string data to validate.
strict: Whether to enforce types strictly.
context: Extra variables to pass to the validator.
Returns:
The validated Pydantic model.
"""
# `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks
__tracebackhide__ = True
return cls.__pydantic_validator__.validate_strings(obj, strict=strict, context=context)
parse_file
def parse_file(
path: 'str | Path',
*,
content_type: 'str | None' = None,
encoding: 'str' = 'utf8',
proto: 'DeprecatedParseProtocol | None' = None,
allow_pickle: 'bool' = False
) -> 'Self'
View Source
@classmethod
@typing_extensions.deprecated(
'The `parse_file` method is deprecated; load the data from file, then if your data is JSON '
'use `model_validate_json`, otherwise `model_validate` instead.',
category=None,
)
def parse_file( # noqa: D102
cls,
path: str | Path,
*,
content_type: str | None = None,
encoding: str = 'utf8',
proto: DeprecatedParseProtocol | None = None,
allow_pickle: bool = False,
) -> Self:
warnings.warn(
'The `parse_file` method is deprecated; load the data from file, then if your data is JSON '
'use `model_validate_json`, otherwise `model_validate` instead.',
category=PydanticDeprecatedSince20,
)
from .deprecated import parse
obj = parse.load_file(
path,
proto=proto,
content_type=content_type,
encoding=encoding,
allow_pickle=allow_pickle,
)
return cls.parse_obj(obj)
parse_obj
def parse_obj(
obj: 'Any'
) -> 'Self'
View Source
@classmethod
@typing_extensions.deprecated('The `parse_obj` method is deprecated; use `model_validate` instead.', category=None)
def parse_obj(cls, obj: Any) -> Self: # noqa: D102
warnings.warn(
'The `parse_obj` method is deprecated; use `model_validate` instead.', category=PydanticDeprecatedSince20
)
return cls.model_validate(obj)
parse_raw
def parse_raw(
b: 'str | bytes',
*,
content_type: 'str | None' = None,
encoding: 'str' = 'utf8',
proto: 'DeprecatedParseProtocol | None' = None,
allow_pickle: 'bool' = False
) -> 'Self'
View Source
@classmethod
@typing_extensions.deprecated(
'The `parse_raw` method is deprecated; if your data is JSON use `model_validate_json`, '
'otherwise load the data then use `model_validate` instead.',
category=None,
)
def parse_raw( # noqa: D102
cls,
b: str | bytes,
*,
content_type: str | None = None,
encoding: str = 'utf8',
proto: DeprecatedParseProtocol | None = None,
allow_pickle: bool = False,
) -> Self: # pragma: no cover
warnings.warn(
'The `parse_raw` method is deprecated; if your data is JSON use `model_validate_json`, '
'otherwise load the data then use `model_validate` instead.',
category=PydanticDeprecatedSince20,
)
from .deprecated import parse
try:
obj = parse.load_str_bytes(
b,
proto=proto,
content_type=content_type,
encoding=encoding,
allow_pickle=allow_pickle,
)
except (ValueError, TypeError) as exc:
import json
# try to match V1
if isinstance(exc, UnicodeDecodeError):
type_str = 'value_error.unicodedecode'
elif isinstance(exc, json.JSONDecodeError):
type_str = 'value_error.jsondecode'
elif isinstance(exc, ValueError):
type_str = 'value_error'
else:
type_str = 'type_error'
# ctx is missing here, but since we've added `input` to the error, we're not pretending it's the same
error: pydantic_core.InitErrorDetails = {
# The type: ignore on the next line is to ignore the requirement of LiteralString
'type': pydantic_core.PydanticCustomError(type_str, str(exc)), # type: ignore
'loc': ('__root__',),
'input': b,
}
raise pydantic_core.ValidationError.from_exception_data(cls.__name__, [error])
return cls.model_validate(obj)
schema
def schema(
by_alias: 'bool' = True,
ref_template: 'str' = '#/$defs/{model}'
) -> 'Dict[str, Any]'
View Source
@classmethod
@typing_extensions.deprecated('The `schema` method is deprecated; use `model_json_schema` instead.', category=None)
def schema( # noqa: D102
cls, by_alias: bool = True, ref_template: str = DEFAULT_REF_TEMPLATE
) -> Dict[str, Any]: # noqa UP006
warnings.warn(
'The `schema` method is deprecated; use `model_json_schema` instead.', category=PydanticDeprecatedSince20
)
return cls.model_json_schema(by_alias=by_alias, ref_template=ref_template)
schema_json
def schema_json(
*,
by_alias: 'bool' = True,
ref_template: 'str' = '#/$defs/{model}',
**dumps_kwargs: 'Any'
) -> 'str'
View Source
@classmethod
@typing_extensions.deprecated(
'The `schema_json` method is deprecated; use `model_json_schema` and json.dumps instead.',
category=None,
)
def schema_json( # noqa: D102
cls, *, by_alias: bool = True, ref_template: str = DEFAULT_REF_TEMPLATE, **dumps_kwargs: Any
) -> str: # pragma: no cover
warnings.warn(
'The `schema_json` method is deprecated; use `model_json_schema` and json.dumps instead.',
category=PydanticDeprecatedSince20,
)
import json
from .deprecated.json import pydantic_encoder
return json.dumps(
cls.model_json_schema(by_alias=by_alias, ref_template=ref_template),
default=pydantic_encoder,
**dumps_kwargs,
)
update_forward_refs
def update_forward_refs(
**localns: 'Any'
) -> 'None'
View Source
@classmethod
@typing_extensions.deprecated(
'The `update_forward_refs` method is deprecated; use `model_rebuild` instead.',
category=None,
)
def update_forward_refs(cls, **localns: Any) -> None: # noqa: D102
warnings.warn(
'The `update_forward_refs` method is deprecated; use `model_rebuild` instead.',
category=PydanticDeprecatedSince20,
)
if localns: # pragma: no cover
raise TypeError('`localns` arguments are not longer accepted.')
cls.model_rebuild(force=True)
validate
def validate(
value: 'Any'
) -> 'Self'
View Source
@classmethod
@typing_extensions.deprecated('The `validate` method is deprecated; use `model_validate` instead.', category=None)
def validate(cls, value: Any) -> Self: # noqa: D102
warnings.warn(
'The `validate` method is deprecated; use `model_validate` instead.', category=PydanticDeprecatedSince20
)
return cls.model_validate(value)
Instance variables
model_extra
Get extra fields set during validation.
model_fields_set
Returns the set of fields that have been explicitly set on this model instance.
Methods
copy
def copy(
self,
*,
include: 'AbstractSetIntStr | MappingIntStrAny | None' = None,
exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None,
update: 'Dict[str, Any] | None' = None,
deep: 'bool' = False
) -> 'Self'
Returns a copy of the model.
Deprecated
This method is now deprecated; use model_copy instead.
If you need include or exclude, use:
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| include | None | Optional set or mapping specifying which fields to include in the copied model. | None |
| exclude | None | Optional set or mapping specifying which fields to exclude in the copied model. | None |
| update | None | Optional dictionary of field-value pairs to override field values in the copied model. | None |
| deep | None | If True, the values of fields that are Pydantic models will be deep-copied. | None |
Returns:
| Type | Description |
|---|---|
| None | A copy of the model with included, excluded and updated fields as specified. |
View Source
@typing_extensions.deprecated(
'The `copy` method is deprecated; use `model_copy` instead. '
'See the docstring of `BaseModel.copy` for details about how to handle `include` and `exclude`.',
category=None,
)
def copy(
self,
*,
include: AbstractSetIntStr | MappingIntStrAny | None = None,
exclude: AbstractSetIntStr | MappingIntStrAny | None = None,
update: Dict[str, Any] | None = None, # noqa UP006
deep: bool = False,
) -> Self: # pragma: no cover
"""Returns a copy of the model.
!!! warning "Deprecated"
This method is now deprecated; use `model_copy` instead.
If you need `include` or `exclude`, use:
```py
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
"""
warnings.warn(
'The `copy` method is deprecated; use `model_copy` instead. '
'See the docstring of `BaseModel.copy` for details about how to handle `include` and `exclude`.',
category=PydanticDeprecatedSince20,
)
from .deprecated import copy_internals
values = dict(
copy_internals._iter(
self, to_dict=False, by_alias=False, include=include, exclude=exclude, exclude_unset=False
),
**(update or {}),
)
if self.__pydantic_private__ is None:
private = None
else:
private = {k: v for k, v in self.__pydantic_private__.items() if v is not PydanticUndefined}
if self.__pydantic_extra__ is None:
extra: dict[str, Any] | None = None
else:
extra = self.__pydantic_extra__.copy()
for k in list(self.__pydantic_extra__):
if k not in values: # k was in the exclude
extra.pop(k)
for k in list(values):
if k in self.__pydantic_extra__: # k must have come from extra
extra[k] = values.pop(k)
# new `__pydantic_fields_set__` can have unset optional fields with a set value in `update` kwarg
if update:
fields_set = self.__pydantic_fields_set__ | update.keys()
else:
fields_set = set(self.__pydantic_fields_set__)
# removing excluded fields from `__pydantic_fields_set__`
if exclude:
fields_set -= set(exclude)
return copy_internals._copy_and_set_values(self, values, fields_set, extra, private, deep=deep)
dict
def dict(
self,
*,
include: 'IncEx' = None,
exclude: 'IncEx' = None,
by_alias: 'bool' = False,
exclude_unset: 'bool' = False,
exclude_defaults: 'bool' = False,
exclude_none: 'bool' = False
) -> 'Dict[str, Any]'
View Source
@typing_extensions.deprecated('The `dict` method is deprecated; use `model_dump` instead.', category=None)
def dict( # noqa: D102
self,
*,
include: IncEx = None,
exclude: IncEx = None,
by_alias: bool = False,
exclude_unset: bool = False,
exclude_defaults: bool = False,
exclude_none: bool = False,
) -> Dict[str, Any]: # noqa UP006
warnings.warn('The `dict` method is deprecated; use `model_dump` instead.', category=PydanticDeprecatedSince20)
return self.model_dump(
include=include,
exclude=exclude,
by_alias=by_alias,
exclude_unset=exclude_unset,
exclude_defaults=exclude_defaults,
exclude_none=exclude_none,
)
json
def json(
self,
*,
include: 'IncEx' = None,
exclude: 'IncEx' = None,
by_alias: 'bool' = False,
exclude_unset: 'bool' = False,
exclude_defaults: 'bool' = False,
exclude_none: 'bool' = False,
encoder: 'Callable[[Any], Any] | None' = PydanticUndefined,
models_as_dict: 'bool' = PydanticUndefined,
**dumps_kwargs: 'Any'
) -> 'str'
View Source
@typing_extensions.deprecated('The `json` method is deprecated; use `model_dump_json` instead.', category=None)
def json( # noqa: D102
self,
*,
include: IncEx = None,
exclude: IncEx = None,
by_alias: bool = False,
exclude_unset: bool = False,
exclude_defaults: bool = False,
exclude_none: bool = False,
encoder: Callable[[Any], Any] | None = PydanticUndefined, # type: ignore[assignment]
models_as_dict: bool = PydanticUndefined, # type: ignore[assignment]
**dumps_kwargs: Any,
) -> str:
warnings.warn(
'The `json` method is deprecated; use `model_dump_json` instead.', category=PydanticDeprecatedSince20
)
if encoder is not PydanticUndefined:
raise TypeError('The `encoder` argument is no longer supported; use field serializers instead.')
if models_as_dict is not PydanticUndefined:
raise TypeError('The `models_as_dict` argument is no longer supported; use a model serializer instead.')
if dumps_kwargs:
raise TypeError('`dumps_kwargs` keyword arguments are no longer supported.')
return self.model_dump_json(
include=include,
exclude=exclude,
by_alias=by_alias,
exclude_unset=exclude_unset,
exclude_defaults=exclude_defaults,
exclude_none=exclude_none,
)
model_copy
def model_copy(
self,
*,
update: 'dict[str, Any] | None' = None,
deep: 'bool' = False
) -> 'Self'
Usage docs: https://docs.pydantic.dev/2.8/concepts/serialization/#model_copy
Returns a copy of the model.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| update | None | Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data. |
None |
| deep | None | Set to True to make a deep copy of the model. |
None |
Returns:
| Type | Description |
|---|---|
| None | New model instance. |
View Source
def model_copy(self, *, update: dict[str, Any] | None = None, deep: bool = False) -> Self:
"""Usage docs: https://docs.pydantic.dev/2.8/concepts/serialization/#model_copy
Returns a copy of the model.
Args:
update: Values to change/add in the new model. Note: the data is not validated
before creating the new model. You should trust this data.
deep: Set to `True` to make a deep copy of the model.
Returns:
New model instance.
"""
copied = self.__deepcopy__() if deep else self.__copy__()
if update:
if self.model_config.get('extra') == 'allow':
for k, v in update.items():
if k in self.model_fields:
copied.__dict__[k] = v
else:
if copied.__pydantic_extra__ is None:
copied.__pydantic_extra__ = {}
copied.__pydantic_extra__[k] = v
else:
copied.__dict__.update(update)
copied.__pydantic_fields_set__.update(update.keys())
return copied
model_dump
def model_dump(
self,
*,
mode: "Literal['json', 'python'] | str" = 'python',
include: 'IncEx' = None,
exclude: 'IncEx' = None,
context: 'Any | None' = None,
by_alias: 'bool' = False,
exclude_unset: 'bool' = False,
exclude_defaults: 'bool' = False,
exclude_none: 'bool' = False,
round_trip: 'bool' = False,
warnings: "bool | Literal['none', 'warn', 'error']" = True,
serialize_as_any: 'bool' = False
) -> 'dict[str, Any]'
Usage docs: https://docs.pydantic.dev/2.8/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| mode | None | The mode in which to_python should run.If mode is 'json', the output will only contain JSON serializable types. If mode is 'python', the output may contain non-JSON-serializable Python objects. |
None |
| include | None | A set of fields to include in the output. | None |
| exclude | None | A set of fields to exclude from the output. | None |
| context | None | Additional context to pass to the serializer. | None |
| by_alias | None | Whether to use the field's alias in the dictionary key if defined. | None |
| exclude_unset | None | Whether to exclude fields that have not been explicitly set. | None |
| exclude_defaults | None | Whether to exclude fields that are set to their default value. | None |
| exclude_none | None | Whether to exclude fields that have a value of None. |
None |
| round_trip | None | If True, dumped values should be valid as input for non-idempotent types such as Json[T]. | None |
| warnings | None | How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors, "error" raises a [ PydanticSerializationError][pydantic_core.PydanticSerializationError]. |
None |
| serialize_as_any | None | Whether to serialize fields with duck-typing serialization behavior. | None |
Returns:
| Type | Description |
|---|---|
| None | A dictionary representation of the model. |
View Source
def model_dump(
self,
*,
mode: Literal['json', 'python'] | str = 'python',
include: IncEx = None,
exclude: IncEx = None,
context: Any | None = None,
by_alias: bool = False,
exclude_unset: bool = False,
exclude_defaults: bool = False,
exclude_none: bool = False,
round_trip: bool = False,
warnings: bool | Literal['none', 'warn', 'error'] = True,
serialize_as_any: bool = False,
) -> dict[str, Any]:
"""Usage docs: https://docs.pydantic.dev/2.8/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
Args:
mode: The mode in which `to_python` should run.
If mode is 'json', the output will only contain JSON serializable types.
If mode is 'python', the output may contain non-JSON-serializable Python objects.
include: A set of fields to include in the output.
exclude: A set of fields to exclude from the output.
context: Additional context to pass to the serializer.
by_alias: Whether to use the field's alias in the dictionary key if defined.
exclude_unset: Whether to exclude fields that have not been explicitly set.
exclude_defaults: Whether to exclude fields that are set to their default value.
exclude_none: Whether to exclude fields that have a value of `None`.
round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors,
"error" raises a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError].
serialize_as_any: Whether to serialize fields with duck-typing serialization behavior.
Returns:
A dictionary representation of the model.
"""
return self.__pydantic_serializer__.to_python(
self,
mode=mode,
by_alias=by_alias,
include=include,
exclude=exclude,
context=context,
exclude_unset=exclude_unset,
exclude_defaults=exclude_defaults,
exclude_none=exclude_none,
round_trip=round_trip,
warnings=warnings,
serialize_as_any=serialize_as_any,
)
model_dump_json
def model_dump_json(
self,
*,
indent: 'int | None' = None,
include: 'IncEx' = None,
exclude: 'IncEx' = None,
context: 'Any | None' = None,
by_alias: 'bool' = False,
exclude_unset: 'bool' = False,
exclude_defaults: 'bool' = False,
exclude_none: 'bool' = False,
round_trip: 'bool' = False,
warnings: "bool | Literal['none', 'warn', 'error']" = True,
serialize_as_any: 'bool' = False
) -> 'str'
Usage docs: https://docs.pydantic.dev/2.8/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic's to_json method.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| indent | None | Indentation to use in the JSON output. If None is passed, the output will be compact. | None |
| include | None | Field(s) to include in the JSON output. | None |
| exclude | None | Field(s) to exclude from the JSON output. | None |
| context | None | Additional context to pass to the serializer. | None |
| by_alias | None | Whether to serialize using field aliases. | None |
| exclude_unset | None | Whether to exclude fields that have not been explicitly set. | None |
| exclude_defaults | None | Whether to exclude fields that are set to their default value. | None |
| exclude_none | None | Whether to exclude fields that have a value of None. |
None |
| round_trip | None | If True, dumped values should be valid as input for non-idempotent types such as Json[T]. | None |
| warnings | None | How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors, "error" raises a [ PydanticSerializationError][pydantic_core.PydanticSerializationError]. |
None |
| serialize_as_any | None | Whether to serialize fields with duck-typing serialization behavior. | None |
Returns:
| Type | Description |
|---|---|
| None | A JSON string representation of the model. |
View Source
def model_dump_json(
self,
*,
indent: int | None = None,
include: IncEx = None,
exclude: IncEx = None,
context: Any | None = None,
by_alias: bool = False,
exclude_unset: bool = False,
exclude_defaults: bool = False,
exclude_none: bool = False,
round_trip: bool = False,
warnings: bool | Literal['none', 'warn', 'error'] = True,
serialize_as_any: bool = False,
) -> str:
"""Usage docs: https://docs.pydantic.dev/2.8/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic's `to_json` method.
Args:
indent: Indentation to use in the JSON output. If None is passed, the output will be compact.
include: Field(s) to include in the JSON output.
exclude: Field(s) to exclude from the JSON output.
context: Additional context to pass to the serializer.
by_alias: Whether to serialize using field aliases.
exclude_unset: Whether to exclude fields that have not been explicitly set.
exclude_defaults: Whether to exclude fields that are set to their default value.
exclude_none: Whether to exclude fields that have a value of `None`.
round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors,
"error" raises a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError].
serialize_as_any: Whether to serialize fields with duck-typing serialization behavior.
Returns:
A JSON string representation of the model.
"""
return self.__pydantic_serializer__.to_json(
self,
indent=indent,
include=include,
exclude=exclude,
context=context,
by_alias=by_alias,
exclude_unset=exclude_unset,
exclude_defaults=exclude_defaults,
exclude_none=exclude_none,
round_trip=round_trip,
warnings=warnings,
serialize_as_any=serialize_as_any,
).decode()
model_post_init
def model_post_init(
self,
_BaseModel__context: 'Any'
) -> 'None'
Override this method to perform additional initialization after __init__ and model_construct.
This is useful if you want to do some validation that requires the entire model to be initialized.
View Source
def model_post_init(self, __context: Any) -> None:
"""Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
"""
pass
LeaderBoardSpot
class LeaderBoardSpot(
/,
**data: 'Any'
)
An object to encapsulate any entry in the leaderboard ranking.
View Source
class LeaderBoardSpot(BaseModel):
"""An object to encapsulate any entry in the leaderboard ranking."""
profile_id: int | None = Field(None, description="The ID attributed to the player by AoE II")
rank: int | None = Field(None, description="The player's rank on the ladder")
rating: int | None = Field(None, description="The player's rating in the ELO system")
steam_id: int | None = Field(None, description="ID of the player on the Steam platform")
icon: Any | None = Field(None, description="The player's icon")
name: str | None = Field(None, description="The player's in-game name")
clan: str | None = Field(None, description="The player's clan / team")
country: str | None = Field(None, description="Country the player connected from")
previous_rating: int | None = Field(None, description="Player's rating at their last match")
highest_rating: int | None = Field(None, description="Highest rating achieved by the player")
streak: int | None = Field(None, description="Current number of consecutive wins")
lowest_streak: int | None = Field(None, description="Lowest streak achieved by this player")
highest_streak: int | None = Field(None, description="Highest streak achieved by this player")
games: int | None = Field(None, description="The total amount of games played by the player")
wins: int | None = Field(None, description="Total amount of wins")
losses: int | None = Field(None, description="Total amount of losses")
drops: int | None = Field(None, description="Number of games the player dropped out of")
last_match: int | None = Field(None, description="Timestamp of the last game played")
last_match_time: int | None = Field(None, description="Timestamp of the last game played")
Ancestors (in MRO)
- pydantic.main.BaseModel
Class variables
model_computed_fields
model_config
model_fields
Static methods
construct
def construct(
_fields_set: 'set[str] | None' = None,
**values: 'Any'
) -> 'Self'
View Source
@classmethod
@typing_extensions.deprecated('The `construct` method is deprecated; use `model_construct` instead.', category=None)
def construct(cls, _fields_set: set[str] | None = None, **values: Any) -> Self: # noqa: D102
warnings.warn(
'The `construct` method is deprecated; use `model_construct` instead.', category=PydanticDeprecatedSince20
)
return cls.model_construct(_fields_set=_fields_set, **values)
from_orm
def from_orm(
obj: 'Any'
) -> 'Self'
View Source
@classmethod
@typing_extensions.deprecated(
'The `from_orm` method is deprecated; set '
"`model_config['from_attributes']=True` and use `model_validate` instead.",
category=None,
)
def from_orm(cls, obj: Any) -> Self: # noqa: D102
warnings.warn(
'The `from_orm` method is deprecated; set '
"`model_config['from_attributes']=True` and use `model_validate` instead.",
category=PydanticDeprecatedSince20,
)
if not cls.model_config.get('from_attributes', None):
raise PydanticUserError(
'You must set the config attribute `from_attributes=True` to use from_orm', code=None
)
return cls.model_validate(obj)
model_construct
def model_construct(
_fields_set: 'set[str] | None' = None,
**values: 'Any'
) -> 'Self'
Creates a new instance of the Model class with validated data.
Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Note
model_construct() generally respects the model_config.extra setting on the provided model.
That is, if model_config.extra == 'allow', then all extra passed values are added to the model instance's __dict__
and __pydantic_extra__ fields. If model_config.extra == 'ignore' (the default), then all extra passed values are ignored.
Because no validation is performed with a call to model_construct(), having model_config.extra == 'forbid' does not result in
an error if extra values are passed, but they will be ignored.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| _fields_set | None | The set of field names accepted for the Model instance. | None |
| values | None | Trusted or pre-validated data dictionary. | None |
Returns:
| Type | Description |
|---|---|
| None | A new instance of the Model class with validated data. |
View Source
@classmethod
def model_construct(cls, _fields_set: set[str] | None = None, **values: Any) -> Self: # noqa: C901
"""Creates a new instance of the `Model` class with validated data.
Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
!!! note
`model_construct()` generally respects the `model_config.extra` setting on the provided model.
That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__`
and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored.
Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in
an error if extra values are passed, but they will be ignored.
Args:
_fields_set: The set of field names accepted for the Model instance.
values: Trusted or pre-validated data dictionary.
Returns:
A new instance of the `Model` class with validated data.
"""
m = cls.__new__(cls)
fields_values: dict[str, Any] = {}
fields_set = set()
for name, field in cls.model_fields.items():
if field.alias is not None and field.alias in values:
fields_values[name] = values.pop(field.alias)
fields_set.add(name)
if (name not in fields_set) and (field.validation_alias is not None):
validation_aliases: list[str | AliasPath] = (
field.validation_alias.choices
if isinstance(field.validation_alias, AliasChoices)
else [field.validation_alias]
)
for alias in validation_aliases:
if isinstance(alias, str) and alias in values:
fields_values[name] = values.pop(alias)
fields_set.add(name)
break
elif isinstance(alias, AliasPath):
value = alias.search_dict_for_path(values)
if value is not PydanticUndefined:
fields_values[name] = value
fields_set.add(name)
break
if name not in fields_set:
if name in values:
fields_values[name] = values.pop(name)
fields_set.add(name)
elif not field.is_required():
fields_values[name] = field.get_default(call_default_factory=True)
if _fields_set is None:
_fields_set = fields_set
_extra: dict[str, Any] | None = (
{k: v for k, v in values.items()} if cls.model_config.get('extra') == 'allow' else None
)
_object_setattr(m, '__dict__', fields_values)
_object_setattr(m, '__pydantic_fields_set__', _fields_set)
if not cls.__pydantic_root_model__:
_object_setattr(m, '__pydantic_extra__', _extra)
if cls.__pydantic_post_init__:
m.model_post_init(None)
# update private attributes with values set
if hasattr(m, '__pydantic_private__') and m.__pydantic_private__ is not None:
for k, v in values.items():
if k in m.__private_attributes__:
m.__pydantic_private__[k] = v
elif not cls.__pydantic_root_model__:
# Note: if there are any private attributes, cls.__pydantic_post_init__ would exist
# Since it doesn't, that means that `__pydantic_private__` should be set to None
_object_setattr(m, '__pydantic_private__', None)
return m
model_json_schema
def model_json_schema(
by_alias: 'bool' = True,
ref_template: 'str' = '#/$defs/{model}',
schema_generator: 'type[GenerateJsonSchema]' = <class 'pydantic.json_schema.GenerateJsonSchema'>,
mode: 'JsonSchemaMode' = 'validation'
) -> 'dict[str, Any]'
Generates a JSON schema for a model class.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| by_alias | None | Whether to use attribute aliases or not. | None |
| ref_template | None | The reference template. | None |
| schema_generator | None | To override the logic used to generate the JSON schema, as a subclass ofGenerateJsonSchema with your desired modifications |
None |
| mode | None | The mode in which to generate the schema. | None |
Returns:
| Type | Description |
|---|---|
| None | The JSON schema for the given model class. |
View Source
@classmethod
def model_json_schema(
cls,
by_alias: bool = True,
ref_template: str = DEFAULT_REF_TEMPLATE,
schema_generator: type[GenerateJsonSchema] = GenerateJsonSchema,
mode: JsonSchemaMode = 'validation',
) -> dict[str, Any]:
"""Generates a JSON schema for a model class.
Args:
by_alias: Whether to use attribute aliases or not.
ref_template: The reference template.
schema_generator: To override the logic used to generate the JSON schema, as a subclass of
`GenerateJsonSchema` with your desired modifications
mode: The mode in which to generate the schema.
Returns:
The JSON schema for the given model class.
"""
return model_json_schema(
cls, by_alias=by_alias, ref_template=ref_template, schema_generator=schema_generator, mode=mode
)
model_parametrized_name
def model_parametrized_name(
params: 'tuple[type[Any], ...]'
) -> 'str'
Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| params | None | Tuple of types of the class. Given a generic classModel with 2 type variables and a concrete model Model[str, int],the value (str, int) would be passed to params. |
None |
Returns:
| Type | Description |
|---|---|
| None | String representing the new class where params are passed to cls as type variables. |
Raises:
| Type | Description |
|---|---|
| TypeError | Raised when trying to generate concrete names for non-generic models. |
View Source
@classmethod
def model_parametrized_name(cls, params: tuple[type[Any], ...]) -> str:
"""Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.
Args:
params: Tuple of types of the class. Given a generic class
`Model` with 2 type variables and a concrete model `Model[str, int]`,
the value `(str, int)` would be passed to `params`.
Returns:
String representing the new class where `params` are passed to `cls` as type variables.
Raises:
TypeError: Raised when trying to generate concrete names for non-generic models.
"""
if not issubclass(cls, typing.Generic):
raise TypeError('Concrete names should only be generated for generic models.')
# Any strings received should represent forward references, so we handle them specially below.
# If we eventually move toward wrapping them in a ForwardRef in __class_getitem__ in the future,
# we may be able to remove this special case.
param_names = [param if isinstance(param, str) else _repr.display_as_type(param) for param in params]
params_component = ', '.join(param_names)
return f'{cls.__name__}[{params_component}]'
model_rebuild
def model_rebuild(
*,
force: 'bool' = False,
raise_errors: 'bool' = True,
_parent_namespace_depth: 'int' = 2,
_types_namespace: 'dict[str, Any] | None' = None
) -> 'bool | None'
Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| force | None | Whether to force the rebuilding of the model schema, defaults to False. |
None |
| raise_errors | None | Whether to raise errors, defaults to True. |
None |
| _parent_namespace_depth | None | The depth level of the parent namespace, defaults to 2. | None |
| _types_namespace | None | The types namespace, defaults to None. |
None |
Returns:
| Type | Description |
|---|---|
| None | Returns None if the schema is already "complete" and rebuilding was not required.If rebuilding was required, returns True if rebuilding was successful, otherwise False. |
View Source
@classmethod
def model_rebuild(
cls,
*,
force: bool = False,
raise_errors: bool = True,
_parent_namespace_depth: int = 2,
_types_namespace: dict[str, Any] | None = None,
) -> bool | None:
"""Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
the initial attempt to build the schema, and automatic rebuilding fails.
Args:
force: Whether to force the rebuilding of the model schema, defaults to `False`.
raise_errors: Whether to raise errors, defaults to `True`.
_parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
_types_namespace: The types namespace, defaults to `None`.
Returns:
Returns `None` if the schema is already "complete" and rebuilding was not required.
If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
"""
if not force and cls.__pydantic_complete__:
return None
else:
if '__pydantic_core_schema__' in cls.__dict__:
delattr(cls, '__pydantic_core_schema__') # delete cached value to ensure full rebuild happens
if _types_namespace is not None:
types_namespace: dict[str, Any] | None = _types_namespace.copy()
else:
if _parent_namespace_depth > 0:
frame_parent_ns = _typing_extra.parent_frame_namespace(parent_depth=_parent_namespace_depth) or {}
cls_parent_ns = (
_model_construction.unpack_lenient_weakvaluedict(cls.__pydantic_parent_namespace__) or {}
)
types_namespace = {**cls_parent_ns, **frame_parent_ns}
cls.__pydantic_parent_namespace__ = _model_construction.build_lenient_weakvaluedict(types_namespace)
else:
types_namespace = _model_construction.unpack_lenient_weakvaluedict(
cls.__pydantic_parent_namespace__
)
types_namespace = _typing_extra.get_cls_types_namespace(cls, types_namespace)
# manually override defer_build so complete_model_class doesn't skip building the model again
config = {**cls.model_config, 'defer_build': False}
return _model_construction.complete_model_class(
cls,
cls.__name__,
_config.ConfigWrapper(config, check=False),
raise_errors=raise_errors,
types_namespace=types_namespace,
)
model_validate
def model_validate(
obj: 'Any',
*,
strict: 'bool | None' = None,
from_attributes: 'bool | None' = None,
context: 'Any | None' = None
) -> 'Self'
Validate a pydantic model instance.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| obj | None | The object to validate. | None |
| strict | None | Whether to enforce types strictly. | None |
| from_attributes | None | Whether to extract data from object attributes. | None |
| context | None | Additional context to pass to the validator. | None |
Returns:
| Type | Description |
|---|---|
| None | The validated model instance. |
Raises:
| Type | Description |
|---|---|
| ValidationError | If the object could not be validated. |
View Source
@classmethod
def model_validate(
cls,
obj: Any,
*,
strict: bool | None = None,
from_attributes: bool | None = None,
context: Any | None = None,
) -> Self:
"""Validate a pydantic model instance.
Args:
obj: The object to validate.
strict: Whether to enforce types strictly.
from_attributes: Whether to extract data from object attributes.
context: Additional context to pass to the validator.
Raises:
ValidationError: If the object could not be validated.
Returns:
The validated model instance.
"""
# `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks
__tracebackhide__ = True
return cls.__pydantic_validator__.validate_python(
obj, strict=strict, from_attributes=from_attributes, context=context
)
model_validate_json
def model_validate_json(
json_data: 'str | bytes | bytearray',
*,
strict: 'bool | None' = None,
context: 'Any | None' = None
) -> 'Self'
Usage docs: https://docs.pydantic.dev/2.8/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| json_data | None | The JSON data to validate. | None |
| strict | None | Whether to enforce types strictly. | None |
| context | None | Extra variables to pass to the validator. | None |
Returns:
| Type | Description |
|---|---|
| None | The validated Pydantic model. |
Raises:
| Type | Description |
|---|---|
| ValueError | If json_data is not a JSON string. |
View Source
@classmethod
def model_validate_json(
cls,
json_data: str | bytes | bytearray,
*,
strict: bool | None = None,
context: Any | None = None,
) -> Self:
"""Usage docs: https://docs.pydantic.dev/2.8/concepts/json/#json-parsing
Validate the given JSON data against the Pydantic model.
Args:
json_data: The JSON data to validate.
strict: Whether to enforce types strictly.
context: Extra variables to pass to the validator.
Returns:
The validated Pydantic model.
Raises:
ValueError: If `json_data` is not a JSON string.
"""
# `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks
__tracebackhide__ = True
return cls.__pydantic_validator__.validate_json(json_data, strict=strict, context=context)
model_validate_strings
def model_validate_strings(
obj: 'Any',
*,
strict: 'bool | None' = None,
context: 'Any | None' = None
) -> 'Self'
Validate the given object with string data against the Pydantic model.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| obj | None | The object containing string data to validate. | None |
| strict | None | Whether to enforce types strictly. | None |
| context | None | Extra variables to pass to the validator. | None |
Returns:
| Type | Description |
|---|---|
| None | The validated Pydantic model. |
View Source
@classmethod
def model_validate_strings(
cls,
obj: Any,
*,
strict: bool | None = None,
context: Any | None = None,
) -> Self:
"""Validate the given object with string data against the Pydantic model.
Args:
obj: The object containing string data to validate.
strict: Whether to enforce types strictly.
context: Extra variables to pass to the validator.
Returns:
The validated Pydantic model.
"""
# `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks
__tracebackhide__ = True
return cls.__pydantic_validator__.validate_strings(obj, strict=strict, context=context)
parse_file
def parse_file(
path: 'str | Path',
*,
content_type: 'str | None' = None,
encoding: 'str' = 'utf8',
proto: 'DeprecatedParseProtocol | None' = None,
allow_pickle: 'bool' = False
) -> 'Self'
View Source
@classmethod
@typing_extensions.deprecated(
'The `parse_file` method is deprecated; load the data from file, then if your data is JSON '
'use `model_validate_json`, otherwise `model_validate` instead.',
category=None,
)
def parse_file( # noqa: D102
cls,
path: str | Path,
*,
content_type: str | None = None,
encoding: str = 'utf8',
proto: DeprecatedParseProtocol | None = None,
allow_pickle: bool = False,
) -> Self:
warnings.warn(
'The `parse_file` method is deprecated; load the data from file, then if your data is JSON '
'use `model_validate_json`, otherwise `model_validate` instead.',
category=PydanticDeprecatedSince20,
)
from .deprecated import parse
obj = parse.load_file(
path,
proto=proto,
content_type=content_type,
encoding=encoding,
allow_pickle=allow_pickle,
)
return cls.parse_obj(obj)
parse_obj
def parse_obj(
obj: 'Any'
) -> 'Self'
View Source
@classmethod
@typing_extensions.deprecated('The `parse_obj` method is deprecated; use `model_validate` instead.', category=None)
def parse_obj(cls, obj: Any) -> Self: # noqa: D102
warnings.warn(
'The `parse_obj` method is deprecated; use `model_validate` instead.', category=PydanticDeprecatedSince20
)
return cls.model_validate(obj)
parse_raw
def parse_raw(
b: 'str | bytes',
*,
content_type: 'str | None' = None,
encoding: 'str' = 'utf8',
proto: 'DeprecatedParseProtocol | None' = None,
allow_pickle: 'bool' = False
) -> 'Self'
View Source
@classmethod
@typing_extensions.deprecated(
'The `parse_raw` method is deprecated; if your data is JSON use `model_validate_json`, '
'otherwise load the data then use `model_validate` instead.',
category=None,
)
def parse_raw( # noqa: D102
cls,
b: str | bytes,
*,
content_type: str | None = None,
encoding: str = 'utf8',
proto: DeprecatedParseProtocol | None = None,
allow_pickle: bool = False,
) -> Self: # pragma: no cover
warnings.warn(
'The `parse_raw` method is deprecated; if your data is JSON use `model_validate_json`, '
'otherwise load the data then use `model_validate` instead.',
category=PydanticDeprecatedSince20,
)
from .deprecated import parse
try:
obj = parse.load_str_bytes(
b,
proto=proto,
content_type=content_type,
encoding=encoding,
allow_pickle=allow_pickle,
)
except (ValueError, TypeError) as exc:
import json
# try to match V1
if isinstance(exc, UnicodeDecodeError):
type_str = 'value_error.unicodedecode'
elif isinstance(exc, json.JSONDecodeError):
type_str = 'value_error.jsondecode'
elif isinstance(exc, ValueError):
type_str = 'value_error'
else:
type_str = 'type_error'
# ctx is missing here, but since we've added `input` to the error, we're not pretending it's the same
error: pydantic_core.InitErrorDetails = {
# The type: ignore on the next line is to ignore the requirement of LiteralString
'type': pydantic_core.PydanticCustomError(type_str, str(exc)), # type: ignore
'loc': ('__root__',),
'input': b,
}
raise pydantic_core.ValidationError.from_exception_data(cls.__name__, [error])
return cls.model_validate(obj)
schema
def schema(
by_alias: 'bool' = True,
ref_template: 'str' = '#/$defs/{model}'
) -> 'Dict[str, Any]'
View Source
@classmethod
@typing_extensions.deprecated('The `schema` method is deprecated; use `model_json_schema` instead.', category=None)
def schema( # noqa: D102
cls, by_alias: bool = True, ref_template: str = DEFAULT_REF_TEMPLATE
) -> Dict[str, Any]: # noqa UP006
warnings.warn(
'The `schema` method is deprecated; use `model_json_schema` instead.', category=PydanticDeprecatedSince20
)
return cls.model_json_schema(by_alias=by_alias, ref_template=ref_template)
schema_json
def schema_json(
*,
by_alias: 'bool' = True,
ref_template: 'str' = '#/$defs/{model}',
**dumps_kwargs: 'Any'
) -> 'str'
View Source
@classmethod
@typing_extensions.deprecated(
'The `schema_json` method is deprecated; use `model_json_schema` and json.dumps instead.',
category=None,
)
def schema_json( # noqa: D102
cls, *, by_alias: bool = True, ref_template: str = DEFAULT_REF_TEMPLATE, **dumps_kwargs: Any
) -> str: # pragma: no cover
warnings.warn(
'The `schema_json` method is deprecated; use `model_json_schema` and json.dumps instead.',
category=PydanticDeprecatedSince20,
)
import json
from .deprecated.json import pydantic_encoder
return json.dumps(
cls.model_json_schema(by_alias=by_alias, ref_template=ref_template),
default=pydantic_encoder,
**dumps_kwargs,
)
update_forward_refs
def update_forward_refs(
**localns: 'Any'
) -> 'None'
View Source
@classmethod
@typing_extensions.deprecated(
'The `update_forward_refs` method is deprecated; use `model_rebuild` instead.',
category=None,
)
def update_forward_refs(cls, **localns: Any) -> None: # noqa: D102
warnings.warn(
'The `update_forward_refs` method is deprecated; use `model_rebuild` instead.',
category=PydanticDeprecatedSince20,
)
if localns: # pragma: no cover
raise TypeError('`localns` arguments are not longer accepted.')
cls.model_rebuild(force=True)
validate
def validate(
value: 'Any'
) -> 'Self'
View Source
@classmethod
@typing_extensions.deprecated('The `validate` method is deprecated; use `model_validate` instead.', category=None)
def validate(cls, value: Any) -> Self: # noqa: D102
warnings.warn(
'The `validate` method is deprecated; use `model_validate` instead.', category=PydanticDeprecatedSince20
)
return cls.model_validate(value)
Instance variables
model_extra
Get extra fields set during validation.
model_fields_set
Returns the set of fields that have been explicitly set on this model instance.
Methods
copy
def copy(
self,
*,
include: 'AbstractSetIntStr | MappingIntStrAny | None' = None,
exclude: 'AbstractSetIntStr | MappingIntStrAny | None' = None,
update: 'Dict[str, Any] | None' = None,
deep: 'bool' = False
) -> 'Self'
Returns a copy of the model.
Deprecated
This method is now deprecated; use model_copy instead.
If you need include or exclude, use:
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| include | None | Optional set or mapping specifying which fields to include in the copied model. | None |
| exclude | None | Optional set or mapping specifying which fields to exclude in the copied model. | None |
| update | None | Optional dictionary of field-value pairs to override field values in the copied model. | None |
| deep | None | If True, the values of fields that are Pydantic models will be deep-copied. | None |
Returns:
| Type | Description |
|---|---|
| None | A copy of the model with included, excluded and updated fields as specified. |
View Source
@typing_extensions.deprecated(
'The `copy` method is deprecated; use `model_copy` instead. '
'See the docstring of `BaseModel.copy` for details about how to handle `include` and `exclude`.',
category=None,
)
def copy(
self,
*,
include: AbstractSetIntStr | MappingIntStrAny | None = None,
exclude: AbstractSetIntStr | MappingIntStrAny | None = None,
update: Dict[str, Any] | None = None, # noqa UP006
deep: bool = False,
) -> Self: # pragma: no cover
"""Returns a copy of the model.
!!! warning "Deprecated"
This method is now deprecated; use `model_copy` instead.
If you need `include` or `exclude`, use:
```py
data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = {**data, **(update or {})}
copied = self.model_validate(data)
```
Args:
include: Optional set or mapping specifying which fields to include in the copied model.
exclude: Optional set or mapping specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values in the copied model.
deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:
A copy of the model with included, excluded and updated fields as specified.
"""
warnings.warn(
'The `copy` method is deprecated; use `model_copy` instead. '
'See the docstring of `BaseModel.copy` for details about how to handle `include` and `exclude`.',
category=PydanticDeprecatedSince20,
)
from .deprecated import copy_internals
values = dict(
copy_internals._iter(
self, to_dict=False, by_alias=False, include=include, exclude=exclude, exclude_unset=False
),
**(update or {}),
)
if self.__pydantic_private__ is None:
private = None
else:
private = {k: v for k, v in self.__pydantic_private__.items() if v is not PydanticUndefined}
if self.__pydantic_extra__ is None:
extra: dict[str, Any] | None = None
else:
extra = self.__pydantic_extra__.copy()
for k in list(self.__pydantic_extra__):
if k not in values: # k was in the exclude
extra.pop(k)
for k in list(values):
if k in self.__pydantic_extra__: # k must have come from extra
extra[k] = values.pop(k)
# new `__pydantic_fields_set__` can have unset optional fields with a set value in `update` kwarg
if update:
fields_set = self.__pydantic_fields_set__ | update.keys()
else:
fields_set = set(self.__pydantic_fields_set__)
# removing excluded fields from `__pydantic_fields_set__`
if exclude:
fields_set -= set(exclude)
return copy_internals._copy_and_set_values(self, values, fields_set, extra, private, deep=deep)
dict
def dict(
self,
*,
include: 'IncEx' = None,
exclude: 'IncEx' = None,
by_alias: 'bool' = False,
exclude_unset: 'bool' = False,
exclude_defaults: 'bool' = False,
exclude_none: 'bool' = False
) -> 'Dict[str, Any]'
View Source
@typing_extensions.deprecated('The `dict` method is deprecated; use `model_dump` instead.', category=None)
def dict( # noqa: D102
self,
*,
include: IncEx = None,
exclude: IncEx = None,
by_alias: bool = False,
exclude_unset: bool = False,
exclude_defaults: bool = False,
exclude_none: bool = False,
) -> Dict[str, Any]: # noqa UP006
warnings.warn('The `dict` method is deprecated; use `model_dump` instead.', category=PydanticDeprecatedSince20)
return self.model_dump(
include=include,
exclude=exclude,
by_alias=by_alias,
exclude_unset=exclude_unset,
exclude_defaults=exclude_defaults,
exclude_none=exclude_none,
)
json
def json(
self,
*,
include: 'IncEx' = None,
exclude: 'IncEx' = None,
by_alias: 'bool' = False,
exclude_unset: 'bool' = False,
exclude_defaults: 'bool' = False,
exclude_none: 'bool' = False,
encoder: 'Callable[[Any], Any] | None' = PydanticUndefined,
models_as_dict: 'bool' = PydanticUndefined,
**dumps_kwargs: 'Any'
) -> 'str'
View Source
@typing_extensions.deprecated('The `json` method is deprecated; use `model_dump_json` instead.', category=None)
def json( # noqa: D102
self,
*,
include: IncEx = None,
exclude: IncEx = None,
by_alias: bool = False,
exclude_unset: bool = False,
exclude_defaults: bool = False,
exclude_none: bool = False,
encoder: Callable[[Any], Any] | None = PydanticUndefined, # type: ignore[assignment]
models_as_dict: bool = PydanticUndefined, # type: ignore[assignment]
**dumps_kwargs: Any,
) -> str:
warnings.warn(
'The `json` method is deprecated; use `model_dump_json` instead.', category=PydanticDeprecatedSince20
)
if encoder is not PydanticUndefined:
raise TypeError('The `encoder` argument is no longer supported; use field serializers instead.')
if models_as_dict is not PydanticUndefined:
raise TypeError('The `models_as_dict` argument is no longer supported; use a model serializer instead.')
if dumps_kwargs:
raise TypeError('`dumps_kwargs` keyword arguments are no longer supported.')
return self.model_dump_json(
include=include,
exclude=exclude,
by_alias=by_alias,
exclude_unset=exclude_unset,
exclude_defaults=exclude_defaults,
exclude_none=exclude_none,
)
model_copy
def model_copy(
self,
*,
update: 'dict[str, Any] | None' = None,
deep: 'bool' = False
) -> 'Self'
Usage docs: https://docs.pydantic.dev/2.8/concepts/serialization/#model_copy
Returns a copy of the model.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| update | None | Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data. |
None |
| deep | None | Set to True to make a deep copy of the model. |
None |
Returns:
| Type | Description |
|---|---|
| None | New model instance. |
View Source
def model_copy(self, *, update: dict[str, Any] | None = None, deep: bool = False) -> Self:
"""Usage docs: https://docs.pydantic.dev/2.8/concepts/serialization/#model_copy
Returns a copy of the model.
Args:
update: Values to change/add in the new model. Note: the data is not validated
before creating the new model. You should trust this data.
deep: Set to `True` to make a deep copy of the model.
Returns:
New model instance.
"""
copied = self.__deepcopy__() if deep else self.__copy__()
if update:
if self.model_config.get('extra') == 'allow':
for k, v in update.items():
if k in self.model_fields:
copied.__dict__[k] = v
else:
if copied.__pydantic_extra__ is None:
copied.__pydantic_extra__ = {}
copied.__pydantic_extra__[k] = v
else:
copied.__dict__.update(update)
copied.__pydantic_fields_set__.update(update.keys())
return copied
model_dump
def model_dump(
self,
*,
mode: "Literal['json', 'python'] | str" = 'python',
include: 'IncEx' = None,
exclude: 'IncEx' = None,
context: 'Any | None' = None,
by_alias: 'bool' = False,
exclude_unset: 'bool' = False,
exclude_defaults: 'bool' = False,
exclude_none: 'bool' = False,
round_trip: 'bool' = False,
warnings: "bool | Literal['none', 'warn', 'error']" = True,
serialize_as_any: 'bool' = False
) -> 'dict[str, Any]'
Usage docs: https://docs.pydantic.dev/2.8/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| mode | None | The mode in which to_python should run.If mode is 'json', the output will only contain JSON serializable types. If mode is 'python', the output may contain non-JSON-serializable Python objects. |
None |
| include | None | A set of fields to include in the output. | None |
| exclude | None | A set of fields to exclude from the output. | None |
| context | None | Additional context to pass to the serializer. | None |
| by_alias | None | Whether to use the field's alias in the dictionary key if defined. | None |
| exclude_unset | None | Whether to exclude fields that have not been explicitly set. | None |
| exclude_defaults | None | Whether to exclude fields that are set to their default value. | None |
| exclude_none | None | Whether to exclude fields that have a value of None. |
None |
| round_trip | None | If True, dumped values should be valid as input for non-idempotent types such as Json[T]. | None |
| warnings | None | How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors, "error" raises a [ PydanticSerializationError][pydantic_core.PydanticSerializationError]. |
None |
| serialize_as_any | None | Whether to serialize fields with duck-typing serialization behavior. | None |
Returns:
| Type | Description |
|---|---|
| None | A dictionary representation of the model. |
View Source
def model_dump(
self,
*,
mode: Literal['json', 'python'] | str = 'python',
include: IncEx = None,
exclude: IncEx = None,
context: Any | None = None,
by_alias: bool = False,
exclude_unset: bool = False,
exclude_defaults: bool = False,
exclude_none: bool = False,
round_trip: bool = False,
warnings: bool | Literal['none', 'warn', 'error'] = True,
serialize_as_any: bool = False,
) -> dict[str, Any]:
"""Usage docs: https://docs.pydantic.dev/2.8/concepts/serialization/#modelmodel_dump
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
Args:
mode: The mode in which `to_python` should run.
If mode is 'json', the output will only contain JSON serializable types.
If mode is 'python', the output may contain non-JSON-serializable Python objects.
include: A set of fields to include in the output.
exclude: A set of fields to exclude from the output.
context: Additional context to pass to the serializer.
by_alias: Whether to use the field's alias in the dictionary key if defined.
exclude_unset: Whether to exclude fields that have not been explicitly set.
exclude_defaults: Whether to exclude fields that are set to their default value.
exclude_none: Whether to exclude fields that have a value of `None`.
round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors,
"error" raises a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError].
serialize_as_any: Whether to serialize fields with duck-typing serialization behavior.
Returns:
A dictionary representation of the model.
"""
return self.__pydantic_serializer__.to_python(
self,
mode=mode,
by_alias=by_alias,
include=include,
exclude=exclude,
context=context,
exclude_unset=exclude_unset,
exclude_defaults=exclude_defaults,
exclude_none=exclude_none,
round_trip=round_trip,
warnings=warnings,
serialize_as_any=serialize_as_any,
)
model_dump_json
def model_dump_json(
self,
*,
indent: 'int | None' = None,
include: 'IncEx' = None,
exclude: 'IncEx' = None,
context: 'Any | None' = None,
by_alias: 'bool' = False,
exclude_unset: 'bool' = False,
exclude_defaults: 'bool' = False,
exclude_none: 'bool' = False,
round_trip: 'bool' = False,
warnings: "bool | Literal['none', 'warn', 'error']" = True,
serialize_as_any: 'bool' = False
) -> 'str'
Usage docs: https://docs.pydantic.dev/2.8/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic's to_json method.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| indent | None | Indentation to use in the JSON output. If None is passed, the output will be compact. | None |
| include | None | Field(s) to include in the JSON output. | None |
| exclude | None | Field(s) to exclude from the JSON output. | None |
| context | None | Additional context to pass to the serializer. | None |
| by_alias | None | Whether to serialize using field aliases. | None |
| exclude_unset | None | Whether to exclude fields that have not been explicitly set. | None |
| exclude_defaults | None | Whether to exclude fields that are set to their default value. | None |
| exclude_none | None | Whether to exclude fields that have a value of None. |
None |
| round_trip | None | If True, dumped values should be valid as input for non-idempotent types such as Json[T]. | None |
| warnings | None | How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors, "error" raises a [ PydanticSerializationError][pydantic_core.PydanticSerializationError]. |
None |
| serialize_as_any | None | Whether to serialize fields with duck-typing serialization behavior. | None |
Returns:
| Type | Description |
|---|---|
| None | A JSON string representation of the model. |
View Source
def model_dump_json(
self,
*,
indent: int | None = None,
include: IncEx = None,
exclude: IncEx = None,
context: Any | None = None,
by_alias: bool = False,
exclude_unset: bool = False,
exclude_defaults: bool = False,
exclude_none: bool = False,
round_trip: bool = False,
warnings: bool | Literal['none', 'warn', 'error'] = True,
serialize_as_any: bool = False,
) -> str:
"""Usage docs: https://docs.pydantic.dev/2.8/concepts/serialization/#modelmodel_dump_json
Generates a JSON representation of the model using Pydantic's `to_json` method.
Args:
indent: Indentation to use in the JSON output. If None is passed, the output will be compact.
include: Field(s) to include in the JSON output.
exclude: Field(s) to exclude from the JSON output.
context: Additional context to pass to the serializer.
by_alias: Whether to serialize using field aliases.
exclude_unset: Whether to exclude fields that have not been explicitly set.
exclude_defaults: Whether to exclude fields that are set to their default value.
exclude_none: Whether to exclude fields that have a value of `None`.
round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors,
"error" raises a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError].
serialize_as_any: Whether to serialize fields with duck-typing serialization behavior.
Returns:
A JSON string representation of the model.
"""
return self.__pydantic_serializer__.to_json(
self,
indent=indent,
include=include,
exclude=exclude,
context=context,
by_alias=by_alias,
exclude_unset=exclude_unset,
exclude_defaults=exclude_defaults,
exclude_none=exclude_none,
round_trip=round_trip,
warnings=warnings,
serialize_as_any=serialize_as_any,
).decode()
model_post_init
def model_post_init(
self,
_BaseModel__context: 'Any'
) -> 'None'
Override this method to perform additional initialization after __init__ and model_construct.
This is useful if you want to do some validation that requires the entire model to be initialized.
View Source
def model_post_init(self, __context: Any) -> None:
"""Override this method to perform additional initialization after `__init__` and `model_construct`.
This is useful if you want to do some validation that requires the entire model to be initialized.
"""
pass