updates

Backend/venv/lib/python3.12/site-packages/fastapi/_compat/v2.py

@@ -0,0 +1,479 @@
+import re
+import warnings
+from copy import copy, deepcopy
+from dataclasses import dataclass
+from enum import Enum
+from typing import (
+    Any,
+    Dict,
+    List,
+    Sequence,
+    Set,
+    Tuple,
+    Type,
+    Union,
+    cast,
+)
+
+from fastapi._compat import may_v1, shared
+from fastapi.openapi.constants import REF_TEMPLATE
+from fastapi.types import IncEx, ModelNameMap
+from pydantic import BaseModel, TypeAdapter, create_model
+from pydantic import PydanticSchemaGenerationError as PydanticSchemaGenerationError
+from pydantic import PydanticUndefinedAnnotation as PydanticUndefinedAnnotation
+from pydantic import ValidationError as ValidationError
+from pydantic._internal._schema_generation_shared import (  # type: ignore[attr-defined]
+    GetJsonSchemaHandler as GetJsonSchemaHandler,
+)
+from pydantic._internal._typing_extra import eval_type_lenient
+from pydantic._internal._utils import lenient_issubclass as lenient_issubclass
+from pydantic.fields import FieldInfo as FieldInfo
+from pydantic.json_schema import GenerateJsonSchema as GenerateJsonSchema
+from pydantic.json_schema import JsonSchemaValue as JsonSchemaValue
+from pydantic_core import CoreSchema as CoreSchema
+from pydantic_core import PydanticUndefined, PydanticUndefinedType
+from pydantic_core import Url as Url
+from typing_extensions import Annotated, Literal, get_args, get_origin
+
+try:
+    from pydantic_core.core_schema import (
+        with_info_plain_validator_function as with_info_plain_validator_function,
+    )
+except ImportError:  # pragma: no cover
+    from pydantic_core.core_schema import (
+        general_plain_validator_function as with_info_plain_validator_function,  # noqa: F401
+    )
+
+RequiredParam = PydanticUndefined
+Undefined = PydanticUndefined
+UndefinedType = PydanticUndefinedType
+evaluate_forwardref = eval_type_lenient
+Validator = Any
+
+
+class BaseConfig:
+    pass
+
+
+class ErrorWrapper(Exception):
+    pass
+
+
+@dataclass
+class ModelField:
+    field_info: FieldInfo
+    name: str
+    mode: Literal["validation", "serialization"] = "validation"
+
+    @property
+    def alias(self) -> str:
+        a = self.field_info.alias
+        return a if a is not None else self.name
+
+    @property
+    def required(self) -> bool:
+        return self.field_info.is_required()
+
+    @property
+    def default(self) -> Any:
+        return self.get_default()
+
+    @property
+    def type_(self) -> Any:
+        return self.field_info.annotation
+
+    def __post_init__(self) -> None:
+        with warnings.catch_warnings():
+            # Pydantic >= 2.12.0 warns about field specific metadata that is unused
+            # (e.g. `TypeAdapter(Annotated[int, Field(alias='b')])`). In some cases, we
+            # end up building the type adapter from a model field annotation so we
+            # need to ignore the warning:
+            if shared.PYDANTIC_VERSION_MINOR_TUPLE >= (2, 12):
+                from pydantic.warnings import UnsupportedFieldAttributeWarning
+
+                warnings.simplefilter(
+                    "ignore", category=UnsupportedFieldAttributeWarning
+                )
+            self._type_adapter: TypeAdapter[Any] = TypeAdapter(
+                Annotated[self.field_info.annotation, self.field_info]
+            )
+
+    def get_default(self) -> Any:
+        if self.field_info.is_required():
+            return Undefined
+        return self.field_info.get_default(call_default_factory=True)
+
+    def validate(
+        self,
+        value: Any,
+        values: Dict[str, Any] = {},  # noqa: B006
+        *,
+        loc: Tuple[Union[int, str], ...] = (),
+    ) -> Tuple[Any, Union[List[Dict[str, Any]], None]]:
+        try:
+            return (
+                self._type_adapter.validate_python(value, from_attributes=True),
+                None,
+            )
+        except ValidationError as exc:
+            return None, may_v1._regenerate_error_with_loc(
+                errors=exc.errors(include_url=False), loc_prefix=loc
+            )
+
+    def serialize(
+        self,
+        value: Any,
+        *,
+        mode: Literal["json", "python"] = "json",
+        include: Union[IncEx, None] = None,
+        exclude: Union[IncEx, None] = None,
+        by_alias: bool = True,
+        exclude_unset: bool = False,
+        exclude_defaults: bool = False,
+        exclude_none: bool = False,
+    ) -> Any:
+        # What calls this code passes a value that already called
+        # self._type_adapter.validate_python(value)
+        return self._type_adapter.dump_python(
+            value,
+            mode=mode,
+            include=include,
+            exclude=exclude,
+            by_alias=by_alias,
+            exclude_unset=exclude_unset,
+            exclude_defaults=exclude_defaults,
+            exclude_none=exclude_none,
+        )
+
+    def __hash__(self) -> int:
+        # Each ModelField is unique for our purposes, to allow making a dict from
+        # ModelField to its JSON Schema.
+        return id(self)
+
+
+def get_annotation_from_field_info(
+    annotation: Any, field_info: FieldInfo, field_name: str
+) -> Any:
+    return annotation
+
+
+def _model_rebuild(model: Type[BaseModel]) -> None:
+    model.model_rebuild()
+
+
+def _model_dump(
+    model: BaseModel, mode: Literal["json", "python"] = "json", **kwargs: Any
+) -> Any:
+    return model.model_dump(mode=mode, **kwargs)
+
+
+def _get_model_config(model: BaseModel) -> Any:
+    return model.model_config
+
+
+def get_schema_from_model_field(
+    *,
+    field: ModelField,
+    model_name_map: ModelNameMap,
+    field_mapping: Dict[
+        Tuple[ModelField, Literal["validation", "serialization"]], JsonSchemaValue
+    ],
+    separate_input_output_schemas: bool = True,
+) -> Dict[str, Any]:
+    override_mode: Union[Literal["validation"], None] = (
+        None if separate_input_output_schemas else "validation"
+    )
+    # This expects that GenerateJsonSchema was already used to generate the definitions
+    json_schema = field_mapping[(field, override_mode or field.mode)]
+    if "$ref" not in json_schema:
+        # TODO remove when deprecating Pydantic v1
+        # Ref: https://github.com/pydantic/pydantic/blob/d61792cc42c80b13b23e3ffa74bc37ec7c77f7d1/pydantic/schema.py#L207
+        json_schema["title"] = field.field_info.title or field.alias.title().replace(
+            "_", " "
+        )
+    return json_schema
+
+
+def get_definitions(
+    *,
+    fields: Sequence[ModelField],
+    model_name_map: ModelNameMap,
+    separate_input_output_schemas: bool = True,
+) -> Tuple[
+    Dict[Tuple[ModelField, Literal["validation", "serialization"]], JsonSchemaValue],
+    Dict[str, Dict[str, Any]],
+]:
+    schema_generator = GenerateJsonSchema(ref_template=REF_TEMPLATE)
+    override_mode: Union[Literal["validation"], None] = (
+        None if separate_input_output_schemas else "validation"
+    )
+    validation_fields = [field for field in fields if field.mode == "validation"]
+    serialization_fields = [field for field in fields if field.mode == "serialization"]
+    flat_validation_models = get_flat_models_from_fields(
+        validation_fields, known_models=set()
+    )
+    flat_serialization_models = get_flat_models_from_fields(
+        serialization_fields, known_models=set()
+    )
+    flat_validation_model_fields = [
+        ModelField(
+            field_info=FieldInfo(annotation=model),
+            name=model.__name__,
+            mode="validation",
+        )
+        for model in flat_validation_models
+    ]
+    flat_serialization_model_fields = [
+        ModelField(
+            field_info=FieldInfo(annotation=model),
+            name=model.__name__,
+            mode="serialization",
+        )
+        for model in flat_serialization_models
+    ]
+    flat_model_fields = flat_validation_model_fields + flat_serialization_model_fields
+    input_types = {f.type_ for f in fields}
+    unique_flat_model_fields = {
+        f for f in flat_model_fields if f.type_ not in input_types
+    }
+
+    inputs = [
+        (field, override_mode or field.mode, field._type_adapter.core_schema)
+        for field in list(fields) + list(unique_flat_model_fields)
+    ]
+    field_mapping, definitions = schema_generator.generate_definitions(inputs=inputs)
+    for item_def in cast(Dict[str, Dict[str, Any]], definitions).values():
+        if "description" in item_def:
+            item_description = cast(str, item_def["description"]).split("\f")[0]
+            item_def["description"] = item_description
+    new_mapping, new_definitions = _remap_definitions_and_field_mappings(
+        model_name_map=model_name_map,
+        definitions=definitions,  # type: ignore[arg-type]
+        field_mapping=field_mapping,
+    )
+    return new_mapping, new_definitions
+
+
+def _replace_refs(
+    *,
+    schema: Dict[str, Any],
+    old_name_to_new_name_map: Dict[str, str],
+) -> Dict[str, Any]:
+    new_schema = deepcopy(schema)
+    for key, value in new_schema.items():
+        if key == "$ref":
+            value = schema["$ref"]
+            if isinstance(value, str):
+                ref_name = schema["$ref"].split("/")[-1]
+                if ref_name in old_name_to_new_name_map:
+                    new_name = old_name_to_new_name_map[ref_name]
+                    new_schema["$ref"] = REF_TEMPLATE.format(model=new_name)
+            continue
+        if isinstance(value, dict):
+            new_schema[key] = _replace_refs(
+                schema=value,
+                old_name_to_new_name_map=old_name_to_new_name_map,
+            )
+        elif isinstance(value, list):
+            new_value = []
+            for item in value:
+                if isinstance(item, dict):
+                    new_item = _replace_refs(
+                        schema=item,
+                        old_name_to_new_name_map=old_name_to_new_name_map,
+                    )
+                    new_value.append(new_item)
+
+                else:
+                    new_value.append(item)
+            new_schema[key] = new_value
+    return new_schema
+
+
+def _remap_definitions_and_field_mappings(
+    *,
+    model_name_map: ModelNameMap,
+    definitions: Dict[str, Any],
+    field_mapping: Dict[
+        Tuple[ModelField, Literal["validation", "serialization"]], JsonSchemaValue
+    ],
+) -> Tuple[
+    Dict[Tuple[ModelField, Literal["validation", "serialization"]], JsonSchemaValue],
+    Dict[str, Any],
+]:
+    old_name_to_new_name_map = {}
+    for field_key, schema in field_mapping.items():
+        model = field_key[0].type_
+        if model not in model_name_map:
+            continue
+        new_name = model_name_map[model]
+        old_name = schema["$ref"].split("/")[-1]
+        if old_name in {f"{new_name}-Input", f"{new_name}-Output"}:
+            continue
+        old_name_to_new_name_map[old_name] = new_name
+
+    new_field_mapping: Dict[
+        Tuple[ModelField, Literal["validation", "serialization"]], JsonSchemaValue
+    ] = {}
+    for field_key, schema in field_mapping.items():
+        new_schema = _replace_refs(
+            schema=schema,
+            old_name_to_new_name_map=old_name_to_new_name_map,
+        )
+        new_field_mapping[field_key] = new_schema
+
+    new_definitions = {}
+    for key, value in definitions.items():
+        if key in old_name_to_new_name_map:
+            new_key = old_name_to_new_name_map[key]
+        else:
+            new_key = key
+        new_value = _replace_refs(
+            schema=value,
+            old_name_to_new_name_map=old_name_to_new_name_map,
+        )
+        new_definitions[new_key] = new_value
+    return new_field_mapping, new_definitions
+
+
+def is_scalar_field(field: ModelField) -> bool:
+    from fastapi import params
+
+    return shared.field_annotation_is_scalar(
+        field.field_info.annotation
+    ) and not isinstance(field.field_info, params.Body)
+
+
+def is_sequence_field(field: ModelField) -> bool:
+    return shared.field_annotation_is_sequence(field.field_info.annotation)
+
+
+def is_scalar_sequence_field(field: ModelField) -> bool:
+    return shared.field_annotation_is_scalar_sequence(field.field_info.annotation)
+
+
+def is_bytes_field(field: ModelField) -> bool:
+    return shared.is_bytes_or_nonable_bytes_annotation(field.type_)
+
+
+def is_bytes_sequence_field(field: ModelField) -> bool:
+    return shared.is_bytes_sequence_annotation(field.type_)
+
+
+def copy_field_info(*, field_info: FieldInfo, annotation: Any) -> FieldInfo:
+    cls = type(field_info)
+    merged_field_info = cls.from_annotation(annotation)
+    new_field_info = copy(field_info)
+    new_field_info.metadata = merged_field_info.metadata
+    new_field_info.annotation = merged_field_info.annotation
+    return new_field_info
+
+
+def serialize_sequence_value(*, field: ModelField, value: Any) -> Sequence[Any]:
+    origin_type = get_origin(field.field_info.annotation) or field.field_info.annotation
+    assert issubclass(origin_type, shared.sequence_types)  # type: ignore[arg-type]
+    return shared.sequence_annotation_to_type[origin_type](value)  # type: ignore[no-any-return]
+
+
+def get_missing_field_error(loc: Tuple[str, ...]) -> Dict[str, Any]:
+    error = ValidationError.from_exception_data(
+        "Field required", [{"type": "missing", "loc": loc, "input": {}}]
+    ).errors(include_url=False)[0]
+    error["input"] = None
+    return error  # type: ignore[return-value]
+
+
+def create_body_model(
+    *, fields: Sequence[ModelField], model_name: str
+) -> Type[BaseModel]:
+    field_params = {f.name: (f.field_info.annotation, f.field_info) for f in fields}
+    BodyModel: Type[BaseModel] = create_model(model_name, **field_params)  # type: ignore[call-overload]
+    return BodyModel
+
+
+def get_model_fields(model: Type[BaseModel]) -> List[ModelField]:
+    return [
+        ModelField(field_info=field_info, name=name)
+        for name, field_info in model.model_fields.items()
+    ]
+
+
+# Duplicate of several schema functions from Pydantic v1 to make them compatible with
+# Pydantic v2 and allow mixing the models
+
+TypeModelOrEnum = Union[Type["BaseModel"], Type[Enum]]
+TypeModelSet = Set[TypeModelOrEnum]
+
+
+def normalize_name(name: str) -> str:
+    return re.sub(r"[^a-zA-Z0-9.\-_]", "_", name)
+
+
+def get_model_name_map(unique_models: TypeModelSet) -> Dict[TypeModelOrEnum, str]:
+    name_model_map = {}
+    conflicting_names: Set[str] = set()
+    for model in unique_models:
+        model_name = normalize_name(model.__name__)
+        if model_name in conflicting_names:
+            model_name = get_long_model_name(model)
+            name_model_map[model_name] = model
+        elif model_name in name_model_map:
+            conflicting_names.add(model_name)
+            conflicting_model = name_model_map.pop(model_name)
+            name_model_map[get_long_model_name(conflicting_model)] = conflicting_model
+            name_model_map[get_long_model_name(model)] = model
+        else:
+            name_model_map[model_name] = model
+    return {v: k for k, v in name_model_map.items()}
+
+
+def get_flat_models_from_model(
+    model: Type["BaseModel"], known_models: Union[TypeModelSet, None] = None
+) -> TypeModelSet:
+    known_models = known_models or set()
+    fields = get_model_fields(model)
+    get_flat_models_from_fields(fields, known_models=known_models)
+    return known_models
+
+
+def get_flat_models_from_annotation(
+    annotation: Any, known_models: TypeModelSet
+) -> TypeModelSet:
+    origin = get_origin(annotation)
+    if origin is not None:
+        for arg in get_args(annotation):
+            if lenient_issubclass(arg, (BaseModel, Enum)) and arg not in known_models:
+                known_models.add(arg)
+                if lenient_issubclass(arg, BaseModel):
+                    get_flat_models_from_model(arg, known_models=known_models)
+            else:
+                get_flat_models_from_annotation(arg, known_models=known_models)
+    return known_models
+
+
+def get_flat_models_from_field(
+    field: ModelField, known_models: TypeModelSet
+) -> TypeModelSet:
+    field_type = field.type_
+    if lenient_issubclass(field_type, BaseModel):
+        if field_type in known_models:
+            return known_models
+        known_models.add(field_type)
+        get_flat_models_from_model(field_type, known_models=known_models)
+    elif lenient_issubclass(field_type, Enum):
+        known_models.add(field_type)
+    else:
+        get_flat_models_from_annotation(field_type, known_models=known_models)
+    return known_models
+
+
+def get_flat_models_from_fields(
+    fields: Sequence[ModelField], known_models: TypeModelSet
+) -> TypeModelSet:
+    for field in fields:
+        get_flat_models_from_field(field, known_models=known_models)
+    return known_models
+
+
+def get_long_model_name(model: TypeModelOrEnum) -> str:
+    return f"{model.__module__}__{model.__qualname__}".replace(".", "__")