Skip to content

BaseModel

Pydantic models are simply classes which inherit from BaseModel and define fields as annotated attributes.

pydantic.BaseModel

Usage Documentation

Models

A base class for creating Pydantic models.

Attributes:

Name Type Description
__class_vars__ set[str]

The names of classvars defined on the model.

__private_attributes__ dict[str, ModelPrivateAttr]

Metadata about the private attributes of the model.

__signature__ Signature

The signature for instantiating the model.

__pydantic_complete__ bool

Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__ CoreSchema

The pydantic-core schema used to build the SchemaValidator and SchemaSerializer.

__pydantic_custom_init__ bool

Whether the model has a custom __init__ function.

__pydantic_decorators__ _decorators.DecoratorInfos

Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_generic_metadata__ _generics.PydanticGenericMetadata

Metadata for generic models; contains data used for a similar purpose to args, origin, parameters in typing-module generics. May eventually be replaced by these.

__pydantic_parent_namespace__ dict[str, Any] | None

Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__ None | Literal['model_post_init']

The name of the post-init method for the model, if defined.

__pydantic_root_model__ bool

Whether the model is a RootModel.

__pydantic_serializer__ SchemaSerializer

The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__ SchemaValidator

The pydantic-core SchemaValidator used to validate instances of the model.

__pydantic_extra__ dict[str, Any] | None

An instance attribute with the values of extra fields from validation when model_config['extra'] == 'allow'.

__pydantic_fields_set__ set[str]

An instance attribute with the names of fields explicitly specified during validation.

__pydantic_private__ dict[str, Any] | None

Instance attribute with the values of private attributes set on the model instance.

Source code in pydantic/main.py
  61
  62
  63
  64
  65
  66
  67
  68
  69
  70
  71
  72
  73
  74
  75
  76
  77
  78
  79
  80
  81
  82
  83
  84
  85
  86
  87
  88
  89
  90
  91
  92
  93
  94
  95
  96
  97
  98
  99
 100
 101
 102
 103
 104
 105
 106
 107
 108
 109
 110
 111
 112
 113
 114
 115
 116
 117
 118
 119
 120
 121
 122
 123
 124
 125
 126
 127
 128
 129
 130
 131
 132
 133
 134
 135
 136
 137
 138
 139
 140
 141
 142
 143
 144
 145
 146
 147
 148
 149
 150
 151
 152
 153
 154
 155
 156
 157
 158
 159
 160
 161
 162
 163
 164
 165
 166
 167
 168
 169
 170
 171
 172
 173
 174
 175
 176
 177
 178
 179
 180
 181
 182
 183
 184
 185
 186
 187
 188
 189
 190
 191
 192
 193
 194
 195
 196
 197
 198
 199
 200
 201
 202
 203
 204
 205
 206
 207
 208
 209
 210
 211
 212
 213
 214
 215
 216
 217
 218
 219
 220
 221
 222
 223
 224
 225
 226
 227
 228
 229
 230
 231
 232
 233
 234
 235
 236
 237
 238
 239
 240
 241
 242
 243
 244
 245
 246
 247
 248
 249
 250
 251
 252
 253
 254
 255
 256
 257
 258
 259
 260
 261
 262
 263
 264
 265
 266
 267
 268
 269
 270
 271
 272
 273
 274
 275
 276
 277
 278
 279
 280
 281
 282
 283
 284
 285
 286
 287
 288
 289
 290
 291
 292
 293
 294
 295
 296
 297
 298
 299
 300
 301
 302
 303
 304
 305
 306
 307
 308
 309
 310
 311
 312
 313
 314
 315
 316
 317
 318
 319
 320
 321
 322
 323
 324
 325
 326
 327
 328
 329
 330
 331
 332
 333
 334
 335
 336
 337
 338
 339
 340
 341
 342
 343
 344
 345
 346
 347
 348
 349
 350
 351
 352
 353
 354
 355
 356
 357
 358
 359
 360
 361
 362
 363
 364
 365
 366
 367
 368
 369
 370
 371
 372
 373
 374
 375
 376
 377
 378
 379
 380
 381
 382
 383
 384
 385
 386
 387
 388
 389
 390
 391
 392
 393
 394
 395
 396
 397
 398
 399
 400
 401
 402
 403
 404
 405
 406
 407
 408
 409
 410
 411
 412
 413
 414
 415
 416
 417
 418
 419
 420
 421
 422
 423
 424
 425
 426
 427
 428
 429
 430
 431
 432
 433
 434
 435
 436
 437
 438
 439
 440
 441
 442
 443
 444
 445
 446
 447
 448
 449
 450
 451
 452
 453
 454
 455
 456
 457
 458
 459
 460
 461
 462
 463
 464
 465
 466
 467
 468
 469
 470
 471
 472
 473
 474
 475
 476
 477
 478
 479
 480
 481
 482
 483
 484
 485
 486
 487
 488
 489
 490
 491
 492
 493
 494
 495
 496
 497
 498
 499
 500
 501
 502
 503
 504
 505
 506
 507
 508
 509
 510
 511
 512
 513
 514
 515
 516
 517
 518
 519
 520
 521
 522
 523
 524
 525
 526
 527
 528
 529
 530
 531
 532
 533
 534
 535
 536
 537
 538
 539
 540
 541
 542
 543
 544
 545
 546
 547
 548
 549
 550
 551
 552
 553
 554
 555
 556
 557
 558
 559
 560
 561
 562
 563
 564
 565
 566
 567
 568
 569
 570
 571
 572
 573
 574
 575
 576
 577
 578
 579
 580
 581
 582
 583
 584
 585
 586
 587
 588
 589
 590
 591
 592
 593
 594
 595
 596
 597
 598
 599
 600
 601
 602
 603
 604
 605
 606
 607
 608
 609
 610
 611
 612
 613
 614
 615
 616
 617
 618
 619
 620
 621
 622
 623
 624
 625
 626
 627
 628
 629
 630
 631
 632
 633
 634
 635
 636
 637
 638
 639
 640
 641
 642
 643
 644
 645
 646
 647
 648
 649
 650
 651
 652
 653
 654
 655
 656
 657
 658
 659
 660
 661
 662
 663
 664
 665
 666
 667
 668
 669
 670
 671
 672
 673
 674
 675
 676
 677
 678
 679
 680
 681
 682
 683
 684
 685
 686
 687
 688
 689
 690
 691
 692
 693
 694
 695
 696
 697
 698
 699
 700
 701
 702
 703
 704
 705
 706
 707
 708
 709
 710
 711
 712
 713
 714
 715
 716
 717
 718
 719
 720
 721
 722
 723
 724
 725
 726
 727
 728
 729
 730
 731
 732
 733
 734
 735
 736
 737
 738
 739
 740
 741
 742
 743
 744
 745
 746
 747
 748
 749
 750
 751
 752
 753
 754
 755
 756
 757
 758
 759
 760
 761
 762
 763
 764
 765
 766
 767
 768
 769
 770
 771
 772
 773
 774
 775
 776
 777
 778
 779
 780
 781
 782
 783
 784
 785
 786
 787
 788
 789
 790
 791
 792
 793
 794
 795
 796
 797
 798
 799
 800
 801
 802
 803
 804
 805
 806
 807
 808
 809
 810
 811
 812
 813
 814
 815
 816
 817
 818
 819
 820
 821
 822
 823
 824
 825
 826
 827
 828
 829
 830
 831
 832
 833
 834
 835
 836
 837
 838
 839
 840
 841
 842
 843
 844
 845
 846
 847
 848
 849
 850
 851
 852
 853
 854
 855
 856
 857
 858
 859
 860
 861
 862
 863
 864
 865
 866
 867
 868
 869
 870
 871
 872
 873
 874
 875
 876
 877
 878
 879
 880
 881
 882
 883
 884
 885
 886
 887
 888
 889
 890
 891
 892
 893
 894
 895
 896
 897
 898
 899
 900
 901
 902
 903
 904
 905
 906
 907
 908
 909
 910
 911
 912
 913
 914
 915
 916
 917
 918
 919
 920
 921
 922
 923
 924
 925
 926
 927
 928
 929
 930
 931
 932
 933
 934
 935
 936
 937
 938
 939
 940
 941
 942
 943
 944
 945
 946
 947
 948
 949
 950
 951
 952
 953
 954
 955
 956
 957
 958
 959
 960
 961
 962
 963
 964
 965
 966
 967
 968
 969
 970
 971
 972
 973
 974
 975
 976
 977
 978
 979
 980
 981
 982
 983
 984
 985
 986
 987
 988
 989
 990
 991
 992
 993
 994
 995
 996
 997
 998
 999
1000
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
1079
1080
1081
1082
1083
1084
1085
1086
1087
1088
1089
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
1100
1101
1102
1103
1104
1105
1106
1107
1108
1109
1110
1111
1112
1113
1114
1115
1116
1117
1118
1119
1120
1121
1122
1123
1124
1125
1126
1127
1128
1129
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
1142
1143
1144
1145
1146
1147
1148
1149
1150
1151
1152
1153
1154
1155
1156
1157
1158
1159
1160
1161
1162
1163
1164
1165
1166
1167
1168
1169
1170
1171
1172
1173
1174
1175
1176
1177
1178
1179
1180
1181
1182
1183
1184
1185
1186
1187
1188
1189
1190
1191
1192
1193
1194
1195
1196
1197
1198
1199
1200
1201
1202
1203
1204
1205
1206
1207
1208
1209
1210
1211
1212
1213
1214
1215
1216
1217
1218
1219
1220
1221
1222
1223
1224
1225
1226
1227
1228
1229
1230
1231
1232
1233
1234
1235
class BaseModel(metaclass=_model_construction.ModelMetaclass):
    """usage docs: https://docs.pydantic.dev/2.0/usage/models/

    A base class for creating Pydantic models.

    Attributes:
        __class_vars__: The names of classvars defined on the model.
        __private_attributes__: Metadata about the private attributes of the model.
        __signature__: The signature for instantiating the model.

        __pydantic_complete__: Whether model building is completed, or if there are still undefined fields.
        __pydantic_core_schema__: The pydantic-core schema used to build the SchemaValidator and SchemaSerializer.
        __pydantic_custom_init__: Whether the model has a custom `__init__` function.
        __pydantic_decorators__: Metadata containing the decorators defined on the model.
            This replaces `Model.__validators__` and `Model.__root_validators__` from Pydantic V1.
        __pydantic_generic_metadata__: Metadata for generic models; contains data used for a similar purpose to
            __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
        __pydantic_parent_namespace__: Parent namespace of the model, used for automatic rebuilding of models.
        __pydantic_post_init__: The name of the post-init method for the model, if defined.
        __pydantic_root_model__: Whether the model is a `RootModel`.
        __pydantic_serializer__: The pydantic-core SchemaSerializer used to dump instances of the model.
        __pydantic_validator__: The pydantic-core SchemaValidator used to validate instances of the model.

        __pydantic_extra__: An instance attribute with the values of extra fields from validation when
            `model_config['extra'] == 'allow'`.
        __pydantic_fields_set__: An instance attribute with the names of fields explicitly specified during validation.
        __pydantic_private__: Instance attribute with the values of private attributes set on the model instance.
    """

    if typing.TYPE_CHECKING:
        # Here we provide annotations for the attributes of BaseModel.
        # Many of these are populated by the metaclass, which is why this section is in a `TYPE_CHECKING` block.
        # However, for the sake of easy review, we have included type annotations of all class and instance attributes
        # of `BaseModel` here:

        # Class attributes
        model_config: ClassVar[ConfigDict]
        """
        Configuration for the model, should be a dictionary conforming to [`ConfigDict`][pydantic.config.ConfigDict].
        """

        model_fields: ClassVar[dict[str, FieldInfo]]
        """
        Metadata about the fields defined on the model,
        mapping of field names to [`FieldInfo`][pydantic.fields.FieldInfo].

        This replaces `Model.__fields__` from Pydantic V1.
        """

        __class_vars__: ClassVar[set[str]]
        __private_attributes__: ClassVar[dict[str, ModelPrivateAttr]]
        __signature__: ClassVar[Signature]

        __pydantic_complete__: ClassVar[bool]
        __pydantic_core_schema__: ClassVar[CoreSchema]
        __pydantic_custom_init__: ClassVar[bool]
        __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos]
        __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata]
        __pydantic_parent_namespace__: ClassVar[dict[str, Any] | None]
        __pydantic_post_init__: ClassVar[None | Literal['model_post_init']]
        __pydantic_root_model__: ClassVar[bool]
        __pydantic_serializer__: ClassVar[SchemaSerializer]
        __pydantic_validator__: ClassVar[SchemaValidator]

        # Instance attributes
        # Note: we use the non-existent kwarg `init=False` in pydantic.fields.Field below so that @dataclass_transform
        # doesn't think these are valid as keyword arguments to the class initializer.
        __pydantic_extra__: dict[str, Any] | None = _Field(init=False)  # type: ignore
        __pydantic_fields_set__: set[str] = _Field(init=False)  # type: ignore
        __pydantic_private__: dict[str, Any] | None = _Field(init=False)  # type: ignore
    else:
        # `model_fields` and `__pydantic_decorators__` must be set for
        # pydantic._internal._generate_schema.GenerateSchema.model_schema to work for a plain BaseModel annotation
        model_fields = {}
        __pydantic_decorators__ = _decorators.DecoratorInfos()
        # Prevent `BaseModel` from being instantiated directly:
        __pydantic_validator__ = _mock_validator.MockValidator(
            'Pydantic models should inherit from BaseModel, BaseModel cannot be instantiated directly',
            code='base-model-instantiated',
        )

    __slots__ = '__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__'

    model_config = ConfigDict()
    __pydantic_complete__ = False
    __pydantic_root_model__ = False

    def __init__(__pydantic_self__, **data: Any) -> None:  # type: ignore
        """Create a new model by parsing and validating input data from keyword arguments.

        Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
        validated to form a valid model.

        `__init__` uses `__pydantic_self__` instead of the more common `self` for the first arg to
        allow `self` as a field name.
        """
        # `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks
        __tracebackhide__ = True
        __pydantic_self__.__pydantic_validator__.validate_python(data, self_instance=__pydantic_self__)

    # The following line sets a flag that we use to determine when `__init__` gets overridden by the user
    __init__.__pydantic_base_init__ = True  # type: ignore

    @property
    def model_computed_fields(self) -> dict[str, ComputedFieldInfo]:
        """Get the computed fields of this model instance.

        Returns:
            A dictionary of computed field names and their corresponding `ComputedFieldInfo` objects.
        """
        return {k: v.info for k, v in self.__pydantic_decorators__.computed_fields.items()}

    @property
    def model_extra(self) -> dict[str, Any] | None:
        """Get extra fields set during validation.

        Returns:
            A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
        """
        return self.__pydantic_extra__

    @property
    def model_fields_set(self) -> set[str]:
        """Returns the set of fields that have been set on this model instance.

        Returns:
            A set of strings representing the fields that have been set,
                i.e. that were not filled from defaults.
        """
        return self.__pydantic_fields_set__

    @classmethod
    def model_construct(cls: type[Model], _fields_set: set[str] | None = None, **values: Any) -> Model:
        """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.
        Behaves as if `Config.extra = 'allow'` was set since it adds all passed values

        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] = {}
        defaults: dict[str, Any] = {}  # keeping this separate from `fields_values` helps us compute `_fields_set`
        for name, field in cls.model_fields.items():
            if field.alias and field.alias in values:
                fields_values[name] = values.pop(field.alias)
            elif name in values:
                fields_values[name] = values.pop(name)
            elif not field.is_required():
                defaults[name] = field.get_default(call_default_factory=True)
        if _fields_set is None:
            _fields_set = set(fields_values.keys())
        fields_values.update(defaults)

        _extra: dict[str, Any] | None = None
        if cls.model_config.get('extra') == 'allow':
            _extra = {}
            for k, v in values.items():
                _extra[k] = v
        else:
            fields_values.update(values)
        _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)
        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

    def model_copy(self: Model, *, update: dict[str, Any] | None = None, deep: bool = False) -> Model:
        """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

    def model_dump(
        self,
        *,
        mode: Literal['json', 'python'] | str = 'python',
        include: IncEx = None,
        exclude: IncEx = None,
        by_alias: bool = False,
        exclude_unset: bool = False,
        exclude_defaults: bool = False,
        exclude_none: bool = False,
        round_trip: bool = False,
        warnings: bool = True,
    ) -> dict[str, Any]:
        """Usage docs: https://docs.pydantic.dev/dev-v2/usage/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 dictionary will only contain JSON serializable types.
                If mode is 'python', the dictionary may contain any Python objects.
            include: A list of fields to include in the output.
            exclude: A list of fields to exclude from the output.
            by_alias: Whether to use the field's alias in the dictionary key if defined.
            exclude_unset: Whether to exclude fields that are unset or None from the output.
            exclude_defaults: Whether to exclude fields that are set to their default value from the output.
            exclude_none: Whether to exclude fields that have a value of `None` from the output.
            round_trip: Whether to enable serialization and deserialization round-trip support.
            warnings: Whether to log warnings when invalid fields are encountered.

        Returns:
            A dictionary representation of the model.
        """
        return self.__pydantic_serializer__.to_python(
            self,
            mode=mode,
            by_alias=by_alias,
            include=include,
            exclude=exclude,
            exclude_unset=exclude_unset,
            exclude_defaults=exclude_defaults,
            exclude_none=exclude_none,
            round_trip=round_trip,
            warnings=warnings,
        )

    def model_dump_json(
        self,
        *,
        indent: int | None = None,
        include: IncEx = None,
        exclude: IncEx = None,
        by_alias: bool = False,
        exclude_unset: bool = False,
        exclude_defaults: bool = False,
        exclude_none: bool = False,
        round_trip: bool = False,
        warnings: bool = True,
    ) -> str:
        """Usage docs: https://docs.pydantic.dev/dev-v2/usage/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. Can take either a string or set of strings.
            exclude: Field(s) to exclude from the JSON output. Can take either a string or set of strings.
            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 have the default value.
            exclude_none: Whether to exclude fields that have a value of `None`.
            round_trip: Whether to use serialization/deserialization between JSON and class instance.
            warnings: Whether to show any warnings that occurred during serialization.

        Returns:
            A JSON string representation of the model.
        """
        return self.__pydantic_serializer__.to_json(
            self,
            indent=indent,
            include=include,
            exclude=exclude,
            by_alias=by_alias,
            exclude_unset=exclude_unset,
            exclude_defaults=exclude_defaults,
            exclude_none=exclude_none,
            round_trip=round_trip,
            warnings=warnings,
        ).decode()

    @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, ass 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
        )

    @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):  # type: ignore[arg-type]
            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}]'

    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

    @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,
            )

    @classmethod
    def model_validate(
        cls: type[Model],
        obj: Any,
        *,
        strict: bool | None = None,
        from_attributes: bool | None = None,
        context: dict[str, Any] | None = None,
    ) -> Model:
        """Validate a pydantic model instance.

        Args:
            obj: The object to validate.
            strict: Whether to raise an exception on invalid fields.
            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
        )

    @classmethod
    def model_validate_json(
        cls: type[Model],
        json_data: str | bytes | bytearray,
        *,
        strict: bool | None = None,
        context: dict[str, Any] | None = None,
    ) -> Model:
        """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)

    @classmethod
    def __get_pydantic_core_schema__(
        cls, __source: type[BaseModel], __handler: _annotated_handlers.GetCoreSchemaHandler
    ) -> CoreSchema:
        """Hook into generating the model's CoreSchema.

        Args:
            __source: The class we are generating a schema for.
                This will generally be the same as the `cls` argument if this is a classmethod.
            __handler: Call into Pydantic's internal JSON schema generation.
                A callable that calls into Pydantic's internal CoreSchema generation logic.

        Returns:
            A `pydantic-core` `CoreSchema`.
        """
        # Only use the cached value from this _exact_ class; we don't want one from a parent class
        # This is why we check `cls.__dict__` and don't use `cls.__pydantic_core_schema__` or similar.
        if '__pydantic_core_schema__' in cls.__dict__:
            # Due to the way generic classes are built, it's possible that an invalid schema may be temporarily
            # set on generic classes. I think we could resolve this to ensure that we get proper schema caching
            # for generics, but for simplicity for now, we just always rebuild if the class has a generic origin.
            if not cls.__pydantic_generic_metadata__['origin']:
                return cls.__pydantic_core_schema__

        return __handler(__source)

    @classmethod
    def __get_pydantic_json_schema__(
        cls,
        __core_schema: CoreSchema,
        __handler: _annotated_handlers.GetJsonSchemaHandler,
    ) -> JsonSchemaValue:
        """Hook into generating the model's JSON schema.

        Args:
            __core_schema: A `pydantic-core` CoreSchema.
                You can ignore this argument and call the handler with a new CoreSchema,
                wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
                or just call the handler with the original schema.
            __handler: Call into Pydantic's internal JSON schema generation.
                This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
                generation fails.
                Since this gets called by `BaseModel.model_json_schema` you can override the
                `schema_generator` argument to that function to change JSON schema generation globally
                for a type.

        Returns:
            A JSON schema, as a Python object.
        """
        return __handler(__core_schema)

    @classmethod
    def __pydantic_init_subclass__(cls, **kwargs: Any) -> None:
        """This is intended to behave just like `__init_subclass__`, but is called by `ModelMetaclass`
        only after the class is actually fully initialized. In particular, attributes like `model_fields` will
        be present when this is called.

        This is necessary because `__init_subclass__` will always be called by `type.__new__`,
        and it would require a prohibitively large refactor to the `ModelMetaclass` to ensure that
        `type.__new__` was called in such a manner that the class would already be sufficiently initialized.

        This will receive the same `kwargs` that would be passed to the standard `__init_subclass__`, namely,
        any kwargs passed to the class definition that aren't used internally by pydantic.

        Args:
            **kwargs: Any keyword arguments passed to the class definition that aren't used internally
                by pydantic.
        """
        pass

    def __class_getitem__(
        cls, typevar_values: type[Any] | tuple[type[Any], ...]
    ) -> type[BaseModel] | _forward_ref.PydanticRecursiveRef:
        cached = _generics.get_cached_generic_type_early(cls, typevar_values)
        if cached is not None:
            return cached

        if cls is BaseModel:
            raise TypeError('Type parameters should be placed on typing.Generic, not BaseModel')
        if not hasattr(cls, '__parameters__'):
            raise TypeError(f'{cls} cannot be parametrized because it does not inherit from typing.Generic')
        if not cls.__pydantic_generic_metadata__['parameters'] and typing.Generic not in cls.__bases__:
            raise TypeError(f'{cls} is not a generic class')

        if not isinstance(typevar_values, tuple):
            typevar_values = (typevar_values,)
        _generics.check_parameters_count(cls, typevar_values)

        # Build map from generic typevars to passed params
        typevars_map: dict[_typing_extra.TypeVarType, type[Any]] = dict(
            zip(cls.__pydantic_generic_metadata__['parameters'], typevar_values)
        )

        if _utils.all_identical(typevars_map.keys(), typevars_map.values()) and typevars_map:
            submodel = cls  # if arguments are equal to parameters it's the same object
            _generics.set_cached_generic_type(cls, typevar_values, submodel)
        else:
            parent_args = cls.__pydantic_generic_metadata__['args']
            if not parent_args:
                args = typevar_values
            else:
                args = tuple(_generics.replace_types(arg, typevars_map) for arg in parent_args)

            origin = cls.__pydantic_generic_metadata__['origin'] or cls
            model_name = origin.model_parametrized_name(args)
            params = tuple(
                {param: None for param in _generics.iter_contained_typevars(typevars_map.values())}
            )  # use dict as ordered set

            with _generics.generic_recursion_self_type(origin, args) as maybe_self_type:
                if maybe_self_type is not None:
                    return maybe_self_type

                cached = _generics.get_cached_generic_type_late(cls, typevar_values, origin, args)
                if cached is not None:
                    return cached

                # Attempt to rebuild the origin in case new types have been defined
                try:
                    # depth 3 gets you above this __class_getitem__ call
                    origin.model_rebuild(_parent_namespace_depth=3)
                except PydanticUndefinedAnnotation:
                    # It's okay if it fails, it just means there are still undefined types
                    # that could be evaluated later.
                    # TODO: Make sure validation fails if there are still undefined types, perhaps using MockValidator
                    pass

                submodel = _generics.create_generic_submodel(model_name, origin, args, params)

                # Update cache
                _generics.set_cached_generic_type(cls, typevar_values, submodel, origin, args)

        return submodel

    def __copy__(self: Model) -> Model:
        """Returns a shallow copy of the model."""
        cls = type(self)
        m = cls.__new__(cls)
        _object_setattr(m, '__dict__', copy(self.__dict__))
        _object_setattr(m, '__pydantic_extra__', copy(self.__pydantic_extra__))
        _object_setattr(m, '__pydantic_fields_set__', copy(self.__pydantic_fields_set__))

        if self.__pydantic_private__ is None:
            _object_setattr(m, '__pydantic_private__', None)
        else:
            _object_setattr(
                m,
                '__pydantic_private__',
                {k: v for k, v in self.__pydantic_private__.items() if v is not PydanticUndefined},
            )

        return m

    def __deepcopy__(self: Model, memo: dict[int, Any] | None = None) -> Model:
        """Returns a deep copy of the model."""
        cls = type(self)
        m = cls.__new__(cls)
        _object_setattr(m, '__dict__', deepcopy(self.__dict__, memo=memo))
        _object_setattr(m, '__pydantic_extra__', deepcopy(self.__pydantic_extra__, memo=memo))
        # This next line doesn't need a deepcopy because __pydantic_fields_set__ is a set[str],
        # and attempting a deepcopy would be marginally slower.
        _object_setattr(m, '__pydantic_fields_set__', copy(self.__pydantic_fields_set__))

        if self.__pydantic_private__ is None:
            _object_setattr(m, '__pydantic_private__', None)
        else:
            _object_setattr(
                m,
                '__pydantic_private__',
                deepcopy({k: v for k, v in self.__pydantic_private__.items() if v is not PydanticUndefined}, memo=memo),
            )

        return m

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

        def __getattr__(self, item: str) -> Any:
            private_attributes = object.__getattribute__(self, '__private_attributes__')
            if item in private_attributes:
                attribute = private_attributes[item]
                if hasattr(attribute, '__get__'):
                    return attribute.__get__(self, type(self))  # type: ignore

                try:
                    # Note: self.__pydantic_private__ cannot be None if self.__private_attributes__ has items
                    return self.__pydantic_private__[item]  # type: ignore
                except KeyError as exc:
                    raise AttributeError(f'{type(self).__name__!r} object has no attribute {item!r}') from exc
            else:
                pydantic_extra = object.__getattribute__(self, '__pydantic_extra__')
                if pydantic_extra is not None:
                    try:
                        return pydantic_extra[item]
                    except KeyError as exc:
                        raise AttributeError(f'{type(self).__name__!r} object has no attribute {item!r}') from exc
                else:
                    if hasattr(self.__class__, item):
                        return super().__getattribute__(item)  # Raises AttributeError if appropriate
                    else:
                        # this is the current error
                        raise AttributeError(f'{type(self).__name__!r} object has no attribute {item!r}')

    def __setattr__(self, name: str, value: Any) -> None:
        if name in self.__class_vars__:
            raise AttributeError(
                f'{name!r} is a ClassVar of `{self.__class__.__name__}` and cannot be set on an instance. '
                f'If you want to set a value on the class, use `{self.__class__.__name__}.{name} = value`.'
            )
        elif not _fields.is_valid_field_name(name):
            if self.__pydantic_private__ is None or name not in self.__private_attributes__:
                _object_setattr(self, name, value)
            else:
                attribute = self.__private_attributes__[name]
                if hasattr(attribute, '__set__'):
                    attribute.__set__(self, value)  # type: ignore
                else:
                    self.__pydantic_private__[name] = value
            return
        elif self.model_config.get('frozen', None):
            error: pydantic_core.InitErrorDetails = {
                'type': 'frozen_instance',
                'loc': (name,),
                'input': value,
            }
            raise pydantic_core.ValidationError.from_exception_data(self.__class__.__name__, [error])

        attr = getattr(self.__class__, name, None)
        if isinstance(attr, property):
            attr.__set__(self, value)
        elif self.model_config.get('validate_assignment', None):
            self.__pydantic_validator__.validate_assignment(self, name, value)
        elif self.model_config.get('extra') != 'allow' and name not in self.model_fields:
            # TODO - matching error
            raise ValueError(f'"{self.__class__.__name__}" object has no field "{name}"')
        elif self.model_config.get('extra') == 'allow' and name not in self.model_fields:
            # SAFETY: __pydantic_extra__ is not None when extra = 'allow'
            self.__pydantic_extra__[name] = value  # type: ignore
        else:
            self.__dict__[name] = value
            self.__pydantic_fields_set__.add(name)

    def __delattr__(self, item: str) -> Any:
        if item in self.__private_attributes__:
            attribute = self.__private_attributes__[item]
            if hasattr(attribute, '__delete__'):
                attribute.__delete__(self)  # type: ignore
                return

            try:
                # Note: self.__pydantic_private__ cannot be None if self.__private_attributes__ has items
                del self.__pydantic_private__[item]  # type: ignore
            except KeyError as exc:
                raise AttributeError(f'{type(self).__name__!r} object has no attribute {item!r}') from exc
        elif item in self.model_fields:
            object.__delattr__(self, item)
        elif self.__pydantic_extra__ is not None and item in self.__pydantic_extra__:
            del self.__pydantic_extra__[item]
        else:
            try:
                object.__delattr__(self, item)
            except AttributeError:
                raise AttributeError(f'{type(self).__name__!r} object has no attribute {item!r}')

    def __getstate__(self) -> dict[Any, Any]:
        private = self.__pydantic_private__
        if private:
            private = {k: v for k, v in private.items() if v is not PydanticUndefined}
        return {
            '__dict__': self.__dict__,
            '__pydantic_extra__': self.__pydantic_extra__,
            '__pydantic_fields_set__': self.__pydantic_fields_set__,
            '__pydantic_private__': private,
        }

    def __setstate__(self, state: dict[Any, Any]) -> None:
        _object_setattr(self, '__pydantic_fields_set__', state['__pydantic_fields_set__'])
        _object_setattr(self, '__pydantic_extra__', state['__pydantic_extra__'])
        _object_setattr(self, '__pydantic_private__', state['__pydantic_private__'])
        _object_setattr(self, '__dict__', state['__dict__'])

    def __eq__(self, other: Any) -> bool:
        if isinstance(other, BaseModel):
            # When comparing instances of generic types for equality, as long as all field values are equal,
            # only require their generic origin types to be equal, rather than exact type equality.
            # This prevents headaches like MyGeneric(x=1) != MyGeneric[Any](x=1).
            self_type = self.__pydantic_generic_metadata__['origin'] or self.__class__
            other_type = other.__pydantic_generic_metadata__['origin'] or other.__class__

            return (
                self_type == other_type
                and self.__dict__ == other.__dict__
                and self.__pydantic_private__ == other.__pydantic_private__
                and self.__pydantic_extra__ == other.__pydantic_extra__
            )
        else:
            return NotImplemented  # delegate to the other item in the comparison

    if typing.TYPE_CHECKING:
        # We put `__init_subclass__` in a TYPE_CHECKING block because, even though we want the type-checking benefits
        # described in the signature of `__init_subclass__` below, we don't want to modify the default behavior of
        # subclass initialization.

        def __init_subclass__(cls, **kwargs: Unpack[ConfigDict]):
            """This signature is included purely to help type-checkers check arguments to class declaration, which
            provides a way to conveniently set model_config key/value pairs.

            ```py
            from pydantic import BaseModel

            class MyModel(BaseModel, extra='allow'):
                ...
            ```

            However, this may be deceiving, since the _actual_ calls to `__init_subclass__` will not receive any
            of the config arguments, and will only receive any keyword arguments passed during class initialization
            that are _not_ expected keys in ConfigDict. (This is due to the way `ModelMetaclass.__new__` works.)

            Args:
                **kwargs: Keyword arguments passed to the class definition, which set model_config

            Note:
                You may want to override `__pydantic_init_subclass__` instead, which behaves similarly but is called
                *after* the class is fully initialized.
            """

    def __iter__(self) -> TupleGenerator:
        """So `dict(model)` works."""
        yield from self.__dict__.items()
        extra = self.__pydantic_extra__
        if extra:
            yield from extra.items()

    def __repr__(self) -> str:
        return f'{self.__repr_name__()}({self.__repr_str__(", ")})'

    def __repr_args__(self) -> _repr.ReprArgs:
        for k, v in self.__dict__.items():
            field = self.model_fields.get(k)
            if field and field.repr:
                yield k, v
        pydantic_extra = self.__pydantic_extra__
        if pydantic_extra is not None:
            yield from ((k, v) for k, v in pydantic_extra.items())
        yield from ((k, getattr(self, k)) for k, v in self.model_computed_fields.items() if v.repr)

    # take logic from `_repr.Representation` without the side effects of inheritance, see #5740
    __repr_name__ = _repr.Representation.__repr_name__
    __repr_str__ = _repr.Representation.__repr_str__
    __pretty__ = _repr.Representation.__pretty__
    __rich_repr__ = _repr.Representation.__rich_repr__

    def __str__(self) -> str:
        return self.__repr_str__(' ')

    # ##### Deprecated methods from v1 #####
    @property
    @typing_extensions.deprecated(
        'The `__fields__` attribute is deprecated, use `model_fields` instead.', category=PydanticDeprecatedSince20
    )
    def __fields__(self) -> dict[str, FieldInfo]:
        warnings.warn('The `__fields__` attribute is deprecated, use `model_fields` instead.', DeprecationWarning)
        return self.model_fields

    @property
    @typing_extensions.deprecated(
        'The `__fields_set__` attribute is deprecated, use `model_fields_set` instead.',
        category=PydanticDeprecatedSince20,
    )
    def __fields_set__(self) -> set[str]:
        warnings.warn(
            'The `__fields_set__` attribute is deprecated, use `model_fields_set` instead.', DeprecationWarning
        )
        return self.__pydantic_fields_set__

    @typing_extensions.deprecated(
        'The `dict` method is deprecated; use `model_dump` instead.', category=PydanticDeprecatedSince20
    )
    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,
    ) -> typing.Dict[str, Any]:  # noqa UP006
        warnings.warn('The `dict` method is deprecated; use `model_dump` instead.', DeprecationWarning)
        return self.model_dump(
            include=include,
            exclude=exclude,
            by_alias=by_alias,
            exclude_unset=exclude_unset,
            exclude_defaults=exclude_defaults,
            exclude_none=exclude_none,
        )

    @typing_extensions.deprecated(
        'The `json` method is deprecated; use `model_dump_json` instead.', category=PydanticDeprecatedSince20
    )
    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: typing.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.', DeprecationWarning)
        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,
        )

    @classmethod
    @typing_extensions.deprecated(
        'The `parse_obj` method is deprecated; use `model_validate` instead.', category=PydanticDeprecatedSince20
    )
    def parse_obj(cls: type[Model], obj: Any) -> Model:  # noqa: D102
        warnings.warn('The `parse_obj` method is deprecated; use `model_validate` instead.', DeprecationWarning)
        return cls.model_validate(obj)

    @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=PydanticDeprecatedSince20,
    )
    def parse_raw(  # noqa: D102
        cls: type[Model],
        b: str | bytes,
        *,
        content_type: str | None = None,
        encoding: str = 'utf8',
        proto: _deprecated_parse.Protocol | None = None,
        allow_pickle: bool = False,
    ) -> Model:  # 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.',
            DeprecationWarning,
        )
        try:
            obj = _deprecated_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)

    @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=PydanticDeprecatedSince20,
    )
    def parse_file(  # noqa: D102
        cls: type[Model],
        path: str | Path,
        *,
        content_type: str | None = None,
        encoding: str = 'utf8',
        proto: _deprecated_parse.Protocol | None = None,
        allow_pickle: bool = False,
    ) -> Model:
        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.',
            DeprecationWarning,
        )
        obj = _deprecated_parse.load_file(
            path,
            proto=proto,
            content_type=content_type,
            encoding=encoding,
            allow_pickle=allow_pickle,
        )
        return cls.parse_obj(obj)

    @classmethod
    @typing_extensions.deprecated(
        "The `from_orm` method is deprecated; set "
        "`model_config['from_attributes']=True` and use `model_validate` instead.",
        category=PydanticDeprecatedSince20,
    )
    def from_orm(cls: type[Model], obj: Any) -> Model:  # noqa: D102
        warnings.warn(
            'The `from_orm` method is deprecated; set `model_config["from_attributes"]=True` '
            'and use `model_validate` instead.',
            DeprecationWarning,
        )
        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)

    @classmethod
    @typing_extensions.deprecated(
        'The `construct` method is deprecated; use `model_construct` instead.', category=PydanticDeprecatedSince20
    )
    def construct(cls: type[Model], _fields_set: set[str] | None = None, **values: Any) -> Model:  # noqa: D102
        warnings.warn('The `construct` method is deprecated; use `model_construct` instead.', DeprecationWarning)
        return cls.model_construct(_fields_set=_fields_set, **values)

    @typing_extensions.deprecated(
        'The copy method is deprecated; use `model_copy` instead.', category=PydanticDeprecatedSince20
    )
    def copy(
        self: Model,
        *,
        include: AbstractSetIntStr | MappingIntStrAny | None = None,
        exclude: AbstractSetIntStr | MappingIntStrAny | None = None,
        update: typing.Dict[str, Any] | None = None,  # noqa UP006
        deep: bool = False,
    ) -> Model:  # 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`.',
            DeprecationWarning,
        )

        values = dict(
            _deprecated_copy_internals._iter(  # type: ignore
                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 _deprecated_copy_internals._copy_and_set_values(self, values, fields_set, extra, private, deep=deep)

    @classmethod
    @typing_extensions.deprecated(
        'The `schema` method is deprecated; use `model_json_schema` instead.', category=PydanticDeprecatedSince20
    )
    def schema(  # noqa: D102
        cls, by_alias: bool = True, ref_template: str = DEFAULT_REF_TEMPLATE
    ) -> typing.Dict[str, Any]:  # noqa UP006
        warnings.warn('The `schema` method is deprecated; use `model_json_schema` instead.', DeprecationWarning)
        return cls.model_json_schema(by_alias=by_alias, ref_template=ref_template)

    @classmethod
    @typing_extensions.deprecated(
        'The `schema_json` method is deprecated; use `model_json_schema` and json.dumps instead.',
        category=PydanticDeprecatedSince20,
    )
    def schema_json(  # noqa: D102
        cls, *, by_alias: bool = True, ref_template: str = DEFAULT_REF_TEMPLATE, **dumps_kwargs: Any
    ) -> str:  # pragma: no cover
        import json

        warnings.warn(
            'The `schema_json` method is deprecated; use `model_json_schema` and json.dumps instead.',
            DeprecationWarning,
        )
        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,
        )

    @classmethod
    @typing_extensions.deprecated(
        'The `validate` method is deprecated; use `model_validate` instead.', category=PydanticDeprecatedSince20
    )
    def validate(cls: type[Model], value: Any) -> Model:  # noqa: D102
        warnings.warn('The `validate` method is deprecated; use `model_validate` instead.', DeprecationWarning)
        return cls.model_validate(value)

    @classmethod
    @typing_extensions.deprecated(
        'The `update_forward_refs` method is deprecated; use `model_rebuild` instead.',
        category=PydanticDeprecatedSince20,
    )
    def update_forward_refs(cls, **localns: Any) -> None:  # noqa: D102
        warnings.warn(
            'The `update_forward_refs` method is deprecated; use `model_rebuild` instead.', DeprecationWarning
        )
        if localns:  # pragma: no cover
            raise TypeError('`localns` arguments are not longer accepted.')
        cls.model_rebuild(force=True)

    @typing_extensions.deprecated(
        'The private method `_iter` will be removed and should no longer be used.', category=PydanticDeprecatedSince20
    )
    def _iter(self, *args: Any, **kwargs: Any) -> Any:
        warnings.warn('The private method `_iter` will be removed and should no longer be used.', DeprecationWarning)
        return _deprecated_copy_internals._iter(self, *args, **kwargs)  # type: ignore

    @typing_extensions.deprecated(
        'The private method `_copy_and_set_values` will be removed and should no longer be used.',
        category=PydanticDeprecatedSince20,
    )
    def _copy_and_set_values(self, *args: Any, **kwargs: Any) -> Any:
        warnings.warn(
            'The private method  `_copy_and_set_values` will be removed and should no longer be used.',
            DeprecationWarning,
        )
        return _deprecated_copy_internals._copy_and_set_values(self, *args, **kwargs)  # type: ignore

    @classmethod
    @typing_extensions.deprecated(
        'The private method `_get_value` will be removed and should no longer be used.',
        category=PydanticDeprecatedSince20,
    )
    def _get_value(cls, *args: Any, **kwargs: Any) -> Any:
        warnings.warn(
            'The private method  `_get_value` will be removed and should no longer be used.', DeprecationWarning
        )
        return _deprecated_copy_internals._get_value(cls, *args, **kwargs)  # type: ignore

    @typing_extensions.deprecated(
        'The private method `_calculate_keys` will be removed and should no longer be used.',
        category=PydanticDeprecatedSince20,
    )
    def _calculate_keys(self, *args: Any, **kwargs: Any) -> Any:
        warnings.warn(
            'The private method `_calculate_keys` will be removed and should no longer be used.', DeprecationWarning
        )
        return _deprecated_copy_internals._calculate_keys(self, *args, **kwargs)  # type: ignore

__init__

__init__(__pydantic_self__, **data)

Raises ValidationError if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Source code in pydantic/main.py
148
149
150
151
152
153
154
155
156
157
158
159
def __init__(__pydantic_self__, **data: Any) -> None:  # type: ignore
    """Create a new model by parsing and validating input data from keyword arguments.

    Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
    validated to form a valid model.

    `__init__` uses `__pydantic_self__` instead of the more common `self` for the first arg to
    allow `self` as a field name.
    """
    # `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks
    __tracebackhide__ = True
    __pydantic_self__.__pydantic_validator__.validate_python(data, self_instance=__pydantic_self__)

model_config instance-attribute class-attribute

model_config: ConfigDict = ConfigDict()

Configuration for the model, should be a dictionary conforming to ConfigDict.

model_computed_fields property

model_computed_fields: dict[str, ComputedFieldInfo]

Get the computed fields of this model instance.

Returns:

Type Description
dict[str, ComputedFieldInfo]

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_extra property

model_extra: dict[str, Any] | None

Get extra fields set during validation.

Returns:

Type Description
dict[str, Any] | None

A dictionary of extra fields, or None if config.extra is not set to "allow".

model_fields_set property

model_fields_set: set[str]

Returns the set of fields that have been set on this model instance.

Returns:

Type Description
set[str]

A set of strings representing the fields that have been set, i.e. that were not filled from defaults.

model_construct classmethod

model_construct(_fields_set=None, **values)

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. Behaves as if Config.extra = 'allow' was set since it adds all passed values

Parameters:

Name Type Description Default
_fields_set set[str] | None

The set of field names accepted for the Model instance.

None
values Any

Trusted or pre-validated data dictionary.

{}

Returns:

Type Description
Model

A new instance of the Model class with validated data.

Source code in pydantic/main.py
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
@classmethod
def model_construct(cls: type[Model], _fields_set: set[str] | None = None, **values: Any) -> Model:
    """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.
    Behaves as if `Config.extra = 'allow'` was set since it adds all passed values

    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] = {}
    defaults: dict[str, Any] = {}  # keeping this separate from `fields_values` helps us compute `_fields_set`
    for name, field in cls.model_fields.items():
        if field.alias and field.alias in values:
            fields_values[name] = values.pop(field.alias)
        elif name in values:
            fields_values[name] = values.pop(name)
        elif not field.is_required():
            defaults[name] = field.get_default(call_default_factory=True)
    if _fields_set is None:
        _fields_set = set(fields_values.keys())
    fields_values.update(defaults)

    _extra: dict[str, Any] | None = None
    if cls.model_config.get('extra') == 'allow':
        _extra = {}
        for k, v in values.items():
            _extra[k] = v
    else:
        fields_values.update(values)
    _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)
    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_copy

model_copy(*, update=None, deep=False)

Returns a copy of the model.

Parameters:

Name Type Description Default
update dict[str, Any] | 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 bool

Set to True to make a deep copy of the model.

False

Returns:

Type Description
Model

New model instance.

Source code in pydantic/main.py
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
def model_copy(self: Model, *, update: dict[str, Any] | None = None, deep: bool = False) -> Model:
    """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

model_dump(
    *,
    mode="python",
    include=None,
    exclude=None,
    by_alias=False,
    exclude_unset=False,
    exclude_defaults=False,
    exclude_none=False,
    round_trip=False,
    warnings=True
)

Usage Documentation

model.model_dump(...)

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

Name Type Description Default
mode Literal['json', 'python'] | str

The mode in which to_python should run. If mode is 'json', the dictionary will only contain JSON serializable types. If mode is 'python', the dictionary may contain any Python objects.

'python'
include IncEx

A list of fields to include in the output.

None
exclude IncEx

A list of fields to exclude from the output.

None
by_alias bool

Whether to use the field's alias in the dictionary key if defined.

False
exclude_unset bool

Whether to exclude fields that are unset or None from the output.

False
exclude_defaults bool

Whether to exclude fields that are set to their default value from the output.

False
exclude_none bool

Whether to exclude fields that have a value of None from the output.

False
round_trip bool

Whether to enable serialization and deserialization round-trip support.

False
warnings bool

Whether to log warnings when invalid fields are encountered.

True

Returns:

Type Description
dict[str, Any]

A dictionary representation of the model.

Source code in pydantic/main.py
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
def model_dump(
    self,
    *,
    mode: Literal['json', 'python'] | str = 'python',
    include: IncEx = None,
    exclude: IncEx = None,
    by_alias: bool = False,
    exclude_unset: bool = False,
    exclude_defaults: bool = False,
    exclude_none: bool = False,
    round_trip: bool = False,
    warnings: bool = True,
) -> dict[str, Any]:
    """Usage docs: https://docs.pydantic.dev/dev-v2/usage/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 dictionary will only contain JSON serializable types.
            If mode is 'python', the dictionary may contain any Python objects.
        include: A list of fields to include in the output.
        exclude: A list of fields to exclude from the output.
        by_alias: Whether to use the field's alias in the dictionary key if defined.
        exclude_unset: Whether to exclude fields that are unset or None from the output.
        exclude_defaults: Whether to exclude fields that are set to their default value from the output.
        exclude_none: Whether to exclude fields that have a value of `None` from the output.
        round_trip: Whether to enable serialization and deserialization round-trip support.
        warnings: Whether to log warnings when invalid fields are encountered.

    Returns:
        A dictionary representation of the model.
    """
    return self.__pydantic_serializer__.to_python(
        self,
        mode=mode,
        by_alias=by_alias,
        include=include,
        exclude=exclude,
        exclude_unset=exclude_unset,
        exclude_defaults=exclude_defaults,
        exclude_none=exclude_none,
        round_trip=round_trip,
        warnings=warnings,
    )

model_dump_json

model_dump_json(
    *,
    indent=None,
    include=None,
    exclude=None,
    by_alias=False,
    exclude_unset=False,
    exclude_defaults=False,
    exclude_none=False,
    round_trip=False,
    warnings=True
)

Usage Documentation

model.model_dump_json(...)

Generates a JSON representation of the model using Pydantic's to_json method.

Parameters:

Name Type Description Default
indent int | None

Indentation to use in the JSON output. If None is passed, the output will be compact.

None
include IncEx

Field(s) to include in the JSON output. Can take either a string or set of strings.

None
exclude IncEx

Field(s) to exclude from the JSON output. Can take either a string or set of strings.

None
by_alias bool

Whether to serialize using field aliases.

False
exclude_unset bool

Whether to exclude fields that have not been explicitly set.

False
exclude_defaults bool

Whether to exclude fields that have the default value.

False
exclude_none bool

Whether to exclude fields that have a value of None.

False
round_trip bool

Whether to use serialization/deserialization between JSON and class instance.

False
warnings bool

Whether to show any warnings that occurred during serialization.

True

Returns:

Type Description
str

A JSON string representation of the model.

Source code in pydantic/main.py
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
def model_dump_json(
    self,
    *,
    indent: int | None = None,
    include: IncEx = None,
    exclude: IncEx = None,
    by_alias: bool = False,
    exclude_unset: bool = False,
    exclude_defaults: bool = False,
    exclude_none: bool = False,
    round_trip: bool = False,
    warnings: bool = True,
) -> str:
    """Usage docs: https://docs.pydantic.dev/dev-v2/usage/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. Can take either a string or set of strings.
        exclude: Field(s) to exclude from the JSON output. Can take either a string or set of strings.
        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 have the default value.
        exclude_none: Whether to exclude fields that have a value of `None`.
        round_trip: Whether to use serialization/deserialization between JSON and class instance.
        warnings: Whether to show any warnings that occurred during serialization.

    Returns:
        A JSON string representation of the model.
    """
    return self.__pydantic_serializer__.to_json(
        self,
        indent=indent,
        include=include,
        exclude=exclude,
        by_alias=by_alias,
        exclude_unset=exclude_unset,
        exclude_defaults=exclude_defaults,
        exclude_none=exclude_none,
        round_trip=round_trip,
        warnings=warnings,
    ).decode()

model_json_schema classmethod

model_json_schema(
    by_alias=True,
    ref_template=DEFAULT_REF_TEMPLATE,
    schema_generator=GenerateJsonSchema,
    mode="validation",
)

Generates a JSON schema for a model class.

Parameters:

Name Type Description Default
by_alias bool

Whether to use attribute aliases or not.

True
ref_template str

The reference template.

DEFAULT_REF_TEMPLATE
schema_generator type[GenerateJsonSchema]

To override the logic used to generate the JSON schema, ass a subclass of GenerateJsonSchema with your desired modifications

GenerateJsonSchema
mode JsonSchemaMode

The mode in which to generate the schema.

'validation'

Returns:

Type Description
dict[str, Any]

The JSON schema for the given model class.

Source code in pydantic/main.py
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
@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, ass 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 classmethod

model_parametrized_name(params)

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 tuple[type[Any], ...]

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.

required

Returns:

Type Description
str

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.

Source code in pydantic/main.py
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
@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):  # type: ignore[arg-type]
        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_post_init

model_post_init(__context)

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.

Source code in pydantic/main.py
409
410
411
412
413
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

model_rebuild classmethod

model_rebuild(
    *,
    force=False,
    raise_errors=True,
    _parent_namespace_depth=2,
    _types_namespace=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 bool

Whether to force the rebuilding of the model schema, defaults to False.

False
raise_errors bool

Whether to raise errors, defaults to True.

True
_parent_namespace_depth int

The depth level of the parent namespace, defaults to 2.

2
_types_namespace dict[str, Any] | None

The types namespace, defaults to None.

None

Returns:

Type Description
bool | None

Returns None if the schema is already "complete" and rebuilding was not required.

bool | None

If rebuilding was required, returns True if rebuilding was successful, otherwise False.

Source code in pydantic/main.py
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
@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 classmethod

model_validate(
    obj, *, strict=None, from_attributes=None, context=None
)

Validate a pydantic model instance.

Parameters:

Name Type Description Default
obj Any

The object to validate.

required
strict bool | None

Whether to raise an exception on invalid fields.

None
from_attributes bool | None

Whether to extract data from object attributes.

None
context dict[str, Any] | None

Additional context to pass to the validator.

None

Raises:

Type Description
ValidationError

If the object could not be validated.

Returns:

Type Description
Model

The validated model instance.

Source code in pydantic/main.py
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
@classmethod
def model_validate(
    cls: type[Model],
    obj: Any,
    *,
    strict: bool | None = None,
    from_attributes: bool | None = None,
    context: dict[str, Any] | None = None,
) -> Model:
    """Validate a pydantic model instance.

    Args:
        obj: The object to validate.
        strict: Whether to raise an exception on invalid fields.
        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 classmethod

model_validate_json(
    json_data, *, strict=None, context=None
)

Validate the given JSON data against the Pydantic model.

Parameters:

Name Type Description Default
json_data str | bytes | bytearray

The JSON data to validate.

required
strict bool | None

Whether to enforce types strictly.

None
context dict[str, Any] | None

Extra variables to pass to the validator.

None

Returns:

Type Description
Model

The validated Pydantic model.

Raises:

Type Description
ValueError

If json_data is not a JSON string.

Source code in pydantic/main.py
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
@classmethod
def model_validate_json(
    cls: type[Model],
    json_data: str | bytes | bytearray,
    *,
    strict: bool | None = None,
    context: dict[str, Any] | None = None,
) -> Model:
    """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)

copy

copy(
    *, include=None, exclude=None, update=None, deep=False
)

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 AbstractSetIntStr | MappingIntStrAny | None

Optional set or mapping specifying which fields to include in the copied model.

None
exclude AbstractSetIntStr | MappingIntStrAny | None

Optional set or mapping specifying which fields to exclude in the copied model.

None
update typing.Dict[str, Any] | None

Optional dictionary of field-value pairs to override field values in the copied model.

None
deep bool

If True, the values of fields that are Pydantic models will be deep copied.

False

Returns:

Type Description
Model

A copy of the model with included, excluded and updated fields as specified.

Source code in pydantic/main.py
1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
1079
1080
1081
1082
1083
1084
1085
1086
1087
1088
1089
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
1100
1101
1102
1103
1104
1105
1106
1107
1108
1109
1110
1111
1112
1113
1114
1115
1116
1117
1118
1119
1120
1121
1122
1123
1124
1125
1126
1127
1128
1129
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
1142
1143
@typing_extensions.deprecated(
    'The copy method is deprecated; use `model_copy` instead.', category=PydanticDeprecatedSince20
)
def copy(
    self: Model,
    *,
    include: AbstractSetIntStr | MappingIntStrAny | None = None,
    exclude: AbstractSetIntStr | MappingIntStrAny | None = None,
    update: typing.Dict[str, Any] | None = None,  # noqa UP006
    deep: bool = False,
) -> Model:  # 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`.',
        DeprecationWarning,
    )

    values = dict(
        _deprecated_copy_internals._iter(  # type: ignore
            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 _deprecated_copy_internals._copy_and_set_values(self, values, fields_set, extra, private, deep=deep)

pydantic.create_model

pydantic.create_model(
    __model_name,
    *,
    __config__=None,
    __base__=None,
    __module__=__name__,
    __validators__=None,
    __cls_kwargs__=None,
    __slots__=None,
    **field_definitions
)

Dynamically creates and returns a new Pydantic model, in other words, create_model dynamically creates a subclass of BaseModel.

Parameters:

Name Type Description Default
__model_name str

The name of the newly created model.

required
__config__ ConfigDict | None

The configuration of the new model.

None
__base__ type[Model] | tuple[type[Model], ...] | None

The base class for the new model.

None
__module__ str

The name of the module that the model belongs to.

__name__
__validators__ dict[str, AnyClassMethod] | None

A dictionary of methods that validate fields.

None
__cls_kwargs__ dict[str, Any] | None

A dictionary of keyword arguments for class creation.

None
__slots__ tuple[str, ...] | None

Deprecated. Should not be passed to create_model.

None
**field_definitions Any

Attributes of the new model. They should be passed in the format: <name>=(<type>, <default value>) or <name>=<default value>. For more complex cases, they can be passed in the format: <name>=<Field> or <name>=(<type>, <FieldInfo>).

{}

Returns:

Type Description
type[Model]

The new model.

Raises:

Type Description
PydanticUserError

If __base__ and __config__ are both passed.

Source code in pydantic/main.py
1266
1267
1268
1269
1270
1271
1272
1273
1274
1275
1276
1277
1278
1279
1280
1281
1282
1283
1284
1285
1286
1287
1288
1289
1290
1291
1292
1293
1294
1295
1296
1297
1298
1299
1300
1301
1302
1303
1304
1305
1306
1307
1308
1309
1310
1311
1312
1313
1314
1315
1316
1317
1318
1319
1320
1321
1322
1323
1324
1325
1326
1327
1328
1329
1330
1331
1332
1333
1334
1335
1336
1337
1338
1339
1340
1341
1342
1343
1344
1345
1346
1347
1348
1349
def create_model(
    __model_name: str,
    *,
    __config__: ConfigDict | None = None,
    __base__: type[Model] | tuple[type[Model], ...] | None = None,
    __module__: str = __name__,
    __validators__: dict[str, AnyClassMethod] | None = None,
    __cls_kwargs__: dict[str, Any] | None = None,
    __slots__: tuple[str, ...] | None = None,
    **field_definitions: Any,
) -> type[Model]:
    """Dynamically creates and returns a new Pydantic model, in other words, `create_model` dynamically creates a
    subclass of [`BaseModel`][pydantic.BaseModel].

    Args:
        __model_name: The name of the newly created model.
        __config__: The configuration of the new model.
        __base__: The base class for the new model.
        __module__: The name of the module that the model belongs to.
        __validators__: A dictionary of methods that validate
            fields.
        __cls_kwargs__: A dictionary of keyword arguments for class creation.
        __slots__: Deprecated. Should not be passed to `create_model`.
        **field_definitions: Attributes of the new model. They should be passed in the format:
            `<name>=(<type>, <default value>)` or `<name>=<default value>`. For more complex cases, they can be
            passed in the format: `<name>=<Field>` or `<name>=(<type>, <FieldInfo>)`.

    Returns:
        The new [model][pydantic.BaseModel].

    Raises:
        PydanticUserError: If `__base__` and `__config__` are both passed.
    """
    if __slots__ is not None:
        # __slots__ will be ignored from here on
        warnings.warn('__slots__ should not be passed to create_model', RuntimeWarning)

    if __base__ is not None:
        if __config__ is not None:
            raise PydanticUserError(
                'to avoid confusion `__config__` and `__base__` cannot be used together',
                code='create-model-config-base',
            )
        if not isinstance(__base__, tuple):
            __base__ = (__base__,)
    else:
        __base__ = (typing.cast(typing.Type['Model'], BaseModel),)

    __cls_kwargs__ = __cls_kwargs__ or {}

    fields = {}
    annotations = {}

    for f_name, f_def in field_definitions.items():
        if not _fields.is_valid_field_name(f_name):
            warnings.warn(f'fields may not start with an underscore, ignoring "{f_name}"', RuntimeWarning)
        if isinstance(f_def, tuple):
            f_def = typing.cast('tuple[str, Any]', f_def)
            try:
                f_annotation, f_value = f_def
            except ValueError as e:
                raise PydanticUserError(
                    'Field definitions should be a `(<type>, <default>)`.',
                    code='create-model-field-definitions',
                ) from e
        else:
            f_annotation, f_value = None, f_def

        if f_annotation:
            annotations[f_name] = f_annotation
        fields[f_name] = f_value

    namespace: dict[str, Any] = {'__annotations__': annotations, '__module__': __module__}
    if __validators__:
        namespace.update(__validators__)
    namespace.update(fields)
    if __config__:
        namespace['model_config'] = _config.ConfigWrapper(__config__).config_dict
    resolved_bases = types.resolve_bases(__base__)
    meta, ns, kwds = types.prepare_class(__model_name, resolved_bases, kwds=__cls_kwargs__)
    if resolved_bases is not __base__:
        ns['__orig_bases__'] = __base__
    namespace.update(ns)
    return meta(__model_name, resolved_bases, namespace, __pydantic_reset_parent_namespace__=False, **kwds)