two version of R2R are here HEAD master

1 files changed, 1163 insertions, 0 deletions

diff --git a/.venv/lib/python3.12/site-packages/pydantic/v1/schema.py b/.venv/lib/python3.12/site-packages/pydantic/v1/schema.py
new file mode 100644
index 00000000..a91fe2cd
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/pydantic/v1/schema.py
@@ -0,0 +1,1163 @@
+import re
+import warnings
+from collections import defaultdict
+from dataclasses import is_dataclass
+from datetime import date, datetime, time, timedelta
+from decimal import Decimal
+from enum import Enum
+from ipaddress import IPv4Address, IPv4Interface, IPv4Network, IPv6Address, IPv6Interface, IPv6Network
+from pathlib import Path
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Dict,
+    ForwardRef,
+    FrozenSet,
+    Generic,
+    Iterable,
+    List,
+    Optional,
+    Pattern,
+    Sequence,
+    Set,
+    Tuple,
+    Type,
+    TypeVar,
+    Union,
+    cast,
+)
+from uuid import UUID
+
+from typing_extensions import Annotated, Literal
+
+from pydantic.v1.fields import (
+    MAPPING_LIKE_SHAPES,
+    SHAPE_DEQUE,
+    SHAPE_FROZENSET,
+    SHAPE_GENERIC,
+    SHAPE_ITERABLE,
+    SHAPE_LIST,
+    SHAPE_SEQUENCE,
+    SHAPE_SET,
+    SHAPE_SINGLETON,
+    SHAPE_TUPLE,
+    SHAPE_TUPLE_ELLIPSIS,
+    FieldInfo,
+    ModelField,
+)
+from pydantic.v1.json import pydantic_encoder
+from pydantic.v1.networks import AnyUrl, EmailStr
+from pydantic.v1.types import (
+    ConstrainedDecimal,
+    ConstrainedFloat,
+    ConstrainedFrozenSet,
+    ConstrainedInt,
+    ConstrainedList,
+    ConstrainedSet,
+    ConstrainedStr,
+    SecretBytes,
+    SecretStr,
+    StrictBytes,
+    StrictStr,
+    conbytes,
+    condecimal,
+    confloat,
+    confrozenset,
+    conint,
+    conlist,
+    conset,
+    constr,
+)
+from pydantic.v1.typing import (
+    all_literal_values,
+    get_args,
+    get_origin,
+    get_sub_types,
+    is_callable_type,
+    is_literal_type,
+    is_namedtuple,
+    is_none_type,
+    is_union,
+)
+from pydantic.v1.utils import ROOT_KEY, get_model, lenient_issubclass
+
+if TYPE_CHECKING:
+    from pydantic.v1.dataclasses import Dataclass
+    from pydantic.v1.main import BaseModel
+
+default_prefix = '#/definitions/'
+default_ref_template = '#/definitions/{model}'
+
+TypeModelOrEnum = Union[Type['BaseModel'], Type[Enum]]
+TypeModelSet = Set[TypeModelOrEnum]
+
+
+def _apply_modify_schema(
+    modify_schema: Callable[..., None], field: Optional[ModelField], field_schema: Dict[str, Any]
+) -> None:
+    from inspect import signature
+
+    sig = signature(modify_schema)
+    args = set(sig.parameters.keys())
+    if 'field' in args or 'kwargs' in args:
+        modify_schema(field_schema, field=field)
+    else:
+        modify_schema(field_schema)
+
+
+def schema(
+    models: Sequence[Union[Type['BaseModel'], Type['Dataclass']]],
+    *,
+    by_alias: bool = True,
+    title: Optional[str] = None,
+    description: Optional[str] = None,
+    ref_prefix: Optional[str] = None,
+    ref_template: str = default_ref_template,
+) -> Dict[str, Any]:
+    """
+    Process a list of models and generate a single JSON Schema with all of them defined in the ``definitions``
+    top-level JSON key, including their sub-models.
+
+    :param models: a list of models to include in the generated JSON Schema
+    :param by_alias: generate the schemas using the aliases defined, if any
+    :param title: title for the generated schema that includes the definitions
+    :param description: description for the generated schema
+    :param ref_prefix: the JSON Pointer prefix for schema references with ``$ref``, if None, will be set to the
+      default of ``#/definitions/``. Update it if you want the schemas to reference the definitions somewhere
+      else, e.g. for OpenAPI use ``#/components/schemas/``. The resulting generated schemas will still be at the
+      top-level key ``definitions``, so you can extract them from there. But all the references will have the set
+      prefix.
+    :param ref_template: Use a ``string.format()`` template for ``$ref`` instead of a prefix. This can be useful
+      for references that cannot be represented by ``ref_prefix`` such as a definition stored in another file. For
+      a sibling json file in a ``/schemas`` directory use ``"/schemas/${model}.json#"``.
+    :return: dict with the JSON Schema with a ``definitions`` top-level key including the schema definitions for
+      the models and sub-models passed in ``models``.
+    """
+    clean_models = [get_model(model) for model in models]
+    flat_models = get_flat_models_from_models(clean_models)
+    model_name_map = get_model_name_map(flat_models)
+    definitions = {}
+    output_schema: Dict[str, Any] = {}
+    if title:
+        output_schema['title'] = title
+    if description:
+        output_schema['description'] = description
+    for model in clean_models:
+        m_schema, m_definitions, m_nested_models = model_process_schema(
+            model,
+            by_alias=by_alias,
+            model_name_map=model_name_map,
+            ref_prefix=ref_prefix,
+            ref_template=ref_template,
+        )
+        definitions.update(m_definitions)
+        model_name = model_name_map[model]
+        definitions[model_name] = m_schema
+    if definitions:
+        output_schema['definitions'] = definitions
+    return output_schema
+
+
+def model_schema(
+    model: Union[Type['BaseModel'], Type['Dataclass']],
+    by_alias: bool = True,
+    ref_prefix: Optional[str] = None,
+    ref_template: str = default_ref_template,
+) -> Dict[str, Any]:
+    """
+    Generate a JSON Schema for one model. With all the sub-models defined in the ``definitions`` top-level
+    JSON key.
+
+    :param model: a Pydantic model (a class that inherits from BaseModel)
+    :param by_alias: generate the schemas using the aliases defined, if any
+    :param ref_prefix: the JSON Pointer prefix for schema references with ``$ref``, if None, will be set to the
+      default of ``#/definitions/``. Update it if you want the schemas to reference the definitions somewhere
+      else, e.g. for OpenAPI use ``#/components/schemas/``. The resulting generated schemas will still be at the
+      top-level key ``definitions``, so you can extract them from there. But all the references will have the set
+      prefix.
+    :param ref_template: Use a ``string.format()`` template for ``$ref`` instead of a prefix. This can be useful for
+      references that cannot be represented by ``ref_prefix`` such as a definition stored in another file. For a
+      sibling json file in a ``/schemas`` directory use ``"/schemas/${model}.json#"``.
+    :return: dict with the JSON Schema for the passed ``model``
+    """
+    model = get_model(model)
+    flat_models = get_flat_models_from_model(model)
+    model_name_map = get_model_name_map(flat_models)
+    model_name = model_name_map[model]
+    m_schema, m_definitions, nested_models = model_process_schema(
+        model, by_alias=by_alias, model_name_map=model_name_map, ref_prefix=ref_prefix, ref_template=ref_template
+    )
+    if model_name in nested_models:
+        # model_name is in Nested models, it has circular references
+        m_definitions[model_name] = m_schema
+        m_schema = get_schema_ref(model_name, ref_prefix, ref_template, False)
+    if m_definitions:
+        m_schema.update({'definitions': m_definitions})
+    return m_schema
+
+
+def get_field_info_schema(field: ModelField, schema_overrides: bool = False) -> Tuple[Dict[str, Any], bool]:
+    # If no title is explicitly set, we don't set title in the schema for enums.
+    # The behaviour is the same as `BaseModel` reference, where the default title
+    # is in the definitions part of the schema.
+    schema_: Dict[str, Any] = {}
+    if field.field_info.title or not lenient_issubclass(field.type_, Enum):
+        schema_['title'] = field.field_info.title or field.alias.title().replace('_', ' ')
+
+    if field.field_info.title:
+        schema_overrides = True
+
+    if field.field_info.description:
+        schema_['description'] = field.field_info.description
+        schema_overrides = True
+
+    if not field.required and field.default is not None and not is_callable_type(field.outer_type_):
+        schema_['default'] = encode_default(field.default)
+        schema_overrides = True
+
+    return schema_, schema_overrides
+
+
+def field_schema(
+    field: ModelField,
+    *,
+    by_alias: bool = True,
+    model_name_map: Dict[TypeModelOrEnum, str],
+    ref_prefix: Optional[str] = None,
+    ref_template: str = default_ref_template,
+    known_models: Optional[TypeModelSet] = None,
+) -> Tuple[Dict[str, Any], Dict[str, Any], Set[str]]:
+    """
+    Process a Pydantic field and return a tuple with a JSON Schema for it as the first item.
+    Also return a dictionary of definitions with models as keys and their schemas as values. If the passed field
+    is a model and has sub-models, and those sub-models don't have overrides (as ``title``, ``default``, etc), they
+    will be included in the definitions and referenced in the schema instead of included recursively.
+
+    :param field: a Pydantic ``ModelField``
+    :param by_alias: use the defined alias (if any) in the returned schema
+    :param model_name_map: used to generate the JSON Schema references to other models included in the definitions
+    :param ref_prefix: the JSON Pointer prefix to use for references to other schemas, if None, the default of
+      #/definitions/ will be used
+    :param ref_template: Use a ``string.format()`` template for ``$ref`` instead of a prefix. This can be useful for
+      references that cannot be represented by ``ref_prefix`` such as a definition stored in another file. For a
+      sibling json file in a ``/schemas`` directory use ``"/schemas/${model}.json#"``.
+    :param known_models: used to solve circular references
+    :return: tuple of the schema for this field and additional definitions
+    """
+    s, schema_overrides = get_field_info_schema(field)
+
+    validation_schema = get_field_schema_validations(field)
+    if validation_schema:
+        s.update(validation_schema)
+        schema_overrides = True
+
+    f_schema, f_definitions, f_nested_models = field_type_schema(
+        field,
+        by_alias=by_alias,
+        model_name_map=model_name_map,
+        schema_overrides=schema_overrides,
+        ref_prefix=ref_prefix,
+        ref_template=ref_template,
+        known_models=known_models or set(),
+    )
+
+    # $ref will only be returned when there are no schema_overrides
+    if '$ref' in f_schema:
+        return f_schema, f_definitions, f_nested_models
+    else:
+        s.update(f_schema)
+        return s, f_definitions, f_nested_models
+
+
+numeric_types = (int, float, Decimal)
+_str_types_attrs: Tuple[Tuple[str, Union[type, Tuple[type, ...]], str], ...] = (
+    ('max_length', numeric_types, 'maxLength'),
+    ('min_length', numeric_types, 'minLength'),
+    ('regex', str, 'pattern'),
+)
+
+_numeric_types_attrs: Tuple[Tuple[str, Union[type, Tuple[type, ...]], str], ...] = (
+    ('gt', numeric_types, 'exclusiveMinimum'),
+    ('lt', numeric_types, 'exclusiveMaximum'),
+    ('ge', numeric_types, 'minimum'),
+    ('le', numeric_types, 'maximum'),
+    ('multiple_of', numeric_types, 'multipleOf'),
+)
+
+
+def get_field_schema_validations(field: ModelField) -> Dict[str, Any]:
+    """
+    Get the JSON Schema validation keywords for a ``field`` with an annotation of
+    a Pydantic ``FieldInfo`` with validation arguments.
+    """
+    f_schema: Dict[str, Any] = {}
+
+    if lenient_issubclass(field.type_, Enum):
+        # schema is already updated by `enum_process_schema`; just update with field extra
+        if field.field_info.extra:
+            f_schema.update(field.field_info.extra)
+        return f_schema
+
+    if lenient_issubclass(field.type_, (str, bytes)):
+        for attr_name, t, keyword in _str_types_attrs:
+            attr = getattr(field.field_info, attr_name, None)
+            if isinstance(attr, t):
+                f_schema[keyword] = attr
+    if lenient_issubclass(field.type_, numeric_types) and not issubclass(field.type_, bool):
+        for attr_name, t, keyword in _numeric_types_attrs:
+            attr = getattr(field.field_info, attr_name, None)
+            if isinstance(attr, t):
+                f_schema[keyword] = attr
+    if field.field_info is not None and field.field_info.const:
+        f_schema['const'] = field.default
+    if field.field_info.extra:
+        f_schema.update(field.field_info.extra)
+    modify_schema = getattr(field.outer_type_, '__modify_schema__', None)
+    if modify_schema:
+        _apply_modify_schema(modify_schema, field, f_schema)
+    return f_schema
+
+
+def get_model_name_map(unique_models: TypeModelSet) -> Dict[TypeModelOrEnum, str]:
+    """
+    Process a set of models and generate unique names for them to be used as keys in the JSON Schema
+    definitions. By default the names are the same as the class name. But if two models in different Python
+    modules have the same name (e.g. "users.Model" and "items.Model"), the generated names will be
+    based on the Python module path for those conflicting models to prevent name collisions.
+
+    :param unique_models: a Python set of models
+    :return: dict mapping models to names
+    """
+    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: Optional[TypeModelSet] = None) -> TypeModelSet:
+    """
+    Take a single ``model`` and generate a set with itself and all the sub-models in the tree. I.e. if you pass
+    model ``Foo`` (subclass of Pydantic ``BaseModel``) as ``model``, and it has a field of type ``Bar`` (also
+    subclass of ``BaseModel``) and that model ``Bar`` has a field of type ``Baz`` (also subclass of ``BaseModel``),
+    the return value will be ``set([Foo, Bar, Baz])``.
+
+    :param model: a Pydantic ``BaseModel`` subclass
+    :param known_models: used to solve circular references
+    :return: a set with the initial model and all its sub-models
+    """
+    known_models = known_models or set()
+    flat_models: TypeModelSet = set()
+    flat_models.add(model)
+    known_models |= flat_models
+    fields = cast(Sequence[ModelField], model.__fields__.values())
+    flat_models |= get_flat_models_from_fields(fields, known_models=known_models)
+    return flat_models
+
+
+def get_flat_models_from_field(field: ModelField, known_models: TypeModelSet) -> TypeModelSet:
+    """
+    Take a single Pydantic ``ModelField`` (from a model) that could have been declared as a subclass of BaseModel
+    (so, it could be a submodel), and generate a set with its model and all the sub-models in the tree.
+    I.e. if you pass a field that was declared to be of type ``Foo`` (subclass of BaseModel) as ``field``, and that
+    model ``Foo`` has a field of type ``Bar`` (also subclass of ``BaseModel``) and that model ``Bar`` has a field of
+    type ``Baz`` (also subclass of ``BaseModel``), the return value will be ``set([Foo, Bar, Baz])``.
+
+    :param field: a Pydantic ``ModelField``
+    :param known_models: used to solve circular references
+    :return: a set with the model used in the declaration for this field, if any, and all its sub-models
+    """
+    from pydantic.v1.main import BaseModel
+
+    flat_models: TypeModelSet = set()
+
+    field_type = field.type_
+    if lenient_issubclass(getattr(field_type, '__pydantic_model__', None), BaseModel):
+        field_type = field_type.__pydantic_model__
+
+    if field.sub_fields and not lenient_issubclass(field_type, BaseModel):
+        flat_models |= get_flat_models_from_fields(field.sub_fields, known_models=known_models)
+    elif lenient_issubclass(field_type, BaseModel) and field_type not in known_models:
+        flat_models |= get_flat_models_from_model(field_type, known_models=known_models)
+    elif lenient_issubclass(field_type, Enum):
+        flat_models.add(field_type)
+    return flat_models
+
+
+def get_flat_models_from_fields(fields: Sequence[ModelField], known_models: TypeModelSet) -> TypeModelSet:
+    """
+    Take a list of Pydantic  ``ModelField``s (from a model) that could have been declared as subclasses of ``BaseModel``
+    (so, any of them could be a submodel), and generate a set with their models and all the sub-models in the tree.
+    I.e. if you pass a the fields of a model ``Foo`` (subclass of ``BaseModel``) as ``fields``, and on of them has a
+    field of type ``Bar`` (also subclass of ``BaseModel``) and that model ``Bar`` has a field of type ``Baz`` (also
+    subclass of ``BaseModel``), the return value will be ``set([Foo, Bar, Baz])``.
+
+    :param fields: a list of Pydantic ``ModelField``s
+    :param known_models: used to solve circular references
+    :return: a set with any model declared in the fields, and all their sub-models
+    """
+    flat_models: TypeModelSet = set()
+    for field in fields:
+        flat_models |= get_flat_models_from_field(field, known_models=known_models)
+    return flat_models
+
+
+def get_flat_models_from_models(models: Sequence[Type['BaseModel']]) -> TypeModelSet:
+    """
+    Take a list of ``models`` and generate a set with them and all their sub-models in their trees. I.e. if you pass
+    a list of two models, ``Foo`` and ``Bar``, both subclasses of Pydantic ``BaseModel`` as models, and ``Bar`` has
+    a field of type ``Baz`` (also subclass of ``BaseModel``), the return value will be ``set([Foo, Bar, Baz])``.
+    """
+    flat_models: TypeModelSet = set()
+    for model in models:
+        flat_models |= get_flat_models_from_model(model)
+    return flat_models
+
+
+def get_long_model_name(model: TypeModelOrEnum) -> str:
+    return f'{model.__module__}__{model.__qualname__}'.replace('.', '__')
+
+
+def field_type_schema(
+    field: ModelField,
+    *,
+    by_alias: bool,
+    model_name_map: Dict[TypeModelOrEnum, str],
+    ref_template: str,
+    schema_overrides: bool = False,
+    ref_prefix: Optional[str] = None,
+    known_models: TypeModelSet,
+) -> Tuple[Dict[str, Any], Dict[str, Any], Set[str]]:
+    """
+    Used by ``field_schema()``, you probably should be using that function.
+
+    Take a single ``field`` and generate the schema for its type only, not including additional
+    information as title, etc. Also return additional schema definitions, from sub-models.
+    """
+    from pydantic.v1.main import BaseModel  # noqa: F811
+
+    definitions = {}
+    nested_models: Set[str] = set()
+    f_schema: Dict[str, Any]
+    if field.shape in {
+        SHAPE_LIST,
+        SHAPE_TUPLE_ELLIPSIS,
+        SHAPE_SEQUENCE,
+        SHAPE_SET,
+        SHAPE_FROZENSET,
+        SHAPE_ITERABLE,
+        SHAPE_DEQUE,
+    }:
+        items_schema, f_definitions, f_nested_models = field_singleton_schema(
+            field,
+            by_alias=by_alias,
+            model_name_map=model_name_map,
+            ref_prefix=ref_prefix,
+            ref_template=ref_template,
+            known_models=known_models,
+        )
+        definitions.update(f_definitions)
+        nested_models.update(f_nested_models)
+        f_schema = {'type': 'array', 'items': items_schema}
+        if field.shape in {SHAPE_SET, SHAPE_FROZENSET}:
+            f_schema['uniqueItems'] = True
+
+    elif field.shape in MAPPING_LIKE_SHAPES:
+        f_schema = {'type': 'object'}
+        key_field = cast(ModelField, field.key_field)
+        regex = getattr(key_field.type_, 'regex', None)
+        items_schema, f_definitions, f_nested_models = field_singleton_schema(
+            field,
+            by_alias=by_alias,
+            model_name_map=model_name_map,
+            ref_prefix=ref_prefix,
+            ref_template=ref_template,
+            known_models=known_models,
+        )
+        definitions.update(f_definitions)
+        nested_models.update(f_nested_models)
+        if regex:
+            # Dict keys have a regex pattern
+            # items_schema might be a schema or empty dict, add it either way
+            f_schema['patternProperties'] = {ConstrainedStr._get_pattern(regex): items_schema}
+        if items_schema:
+            # The dict values are not simply Any, so they need a schema
+            f_schema['additionalProperties'] = items_schema
+    elif field.shape == SHAPE_TUPLE or (field.shape == SHAPE_GENERIC and not issubclass(field.type_, BaseModel)):
+        sub_schema = []
+        sub_fields = cast(List[ModelField], field.sub_fields)
+        for sf in sub_fields:
+            sf_schema, sf_definitions, sf_nested_models = field_type_schema(
+                sf,
+                by_alias=by_alias,
+                model_name_map=model_name_map,
+                ref_prefix=ref_prefix,
+                ref_template=ref_template,
+                known_models=known_models,
+            )
+            definitions.update(sf_definitions)
+            nested_models.update(sf_nested_models)
+            sub_schema.append(sf_schema)
+
+        sub_fields_len = len(sub_fields)
+        if field.shape == SHAPE_GENERIC:
+            all_of_schemas = sub_schema[0] if sub_fields_len == 1 else {'type': 'array', 'items': sub_schema}
+            f_schema = {'allOf': [all_of_schemas]}
+        else:
+            f_schema = {
+                'type': 'array',
+                'minItems': sub_fields_len,
+                'maxItems': sub_fields_len,
+            }
+            if sub_fields_len >= 1:
+                f_schema['items'] = sub_schema
+    else:
+        assert field.shape in {SHAPE_SINGLETON, SHAPE_GENERIC}, field.shape
+        f_schema, f_definitions, f_nested_models = field_singleton_schema(
+            field,
+            by_alias=by_alias,
+            model_name_map=model_name_map,
+            schema_overrides=schema_overrides,
+            ref_prefix=ref_prefix,
+            ref_template=ref_template,
+            known_models=known_models,
+        )
+        definitions.update(f_definitions)
+        nested_models.update(f_nested_models)
+
+    # check field type to avoid repeated calls to the same __modify_schema__ method
+    if field.type_ != field.outer_type_:
+        if field.shape == SHAPE_GENERIC:
+            field_type = field.type_
+        else:
+            field_type = field.outer_type_
+        modify_schema = getattr(field_type, '__modify_schema__', None)
+        if modify_schema:
+            _apply_modify_schema(modify_schema, field, f_schema)
+    return f_schema, definitions, nested_models
+
+
+def model_process_schema(
+    model: TypeModelOrEnum,
+    *,
+    by_alias: bool = True,
+    model_name_map: Dict[TypeModelOrEnum, str],
+    ref_prefix: Optional[str] = None,
+    ref_template: str = default_ref_template,
+    known_models: Optional[TypeModelSet] = None,
+    field: Optional[ModelField] = None,
+) -> Tuple[Dict[str, Any], Dict[str, Any], Set[str]]:
+    """
+    Used by ``model_schema()``, you probably should be using that function.
+
+    Take a single ``model`` and generate its schema. Also return additional schema definitions, from sub-models. The
+    sub-models of the returned schema will be referenced, but their definitions will not be included in the schema. All
+    the definitions are returned as the second value.
+    """
+    from inspect import getdoc, signature
+
+    known_models = known_models or set()
+    if lenient_issubclass(model, Enum):
+        model = cast(Type[Enum], model)
+        s = enum_process_schema(model, field=field)
+        return s, {}, set()
+    model = cast(Type['BaseModel'], model)
+    s = {'title': model.__config__.title or model.__name__}
+    doc = getdoc(model)
+    if doc:
+        s['description'] = doc
+    known_models.add(model)
+    m_schema, m_definitions, nested_models = model_type_schema(
+        model,
+        by_alias=by_alias,
+        model_name_map=model_name_map,
+        ref_prefix=ref_prefix,
+        ref_template=ref_template,
+        known_models=known_models,
+    )
+    s.update(m_schema)
+    schema_extra = model.__config__.schema_extra
+    if callable(schema_extra):
+        if len(signature(schema_extra).parameters) == 1:
+            schema_extra(s)
+        else:
+            schema_extra(s, model)
+    else:
+        s.update(schema_extra)
+    return s, m_definitions, nested_models
+
+
+def model_type_schema(
+    model: Type['BaseModel'],
+    *,
+    by_alias: bool,
+    model_name_map: Dict[TypeModelOrEnum, str],
+    ref_template: str,
+    ref_prefix: Optional[str] = None,
+    known_models: TypeModelSet,
+) -> Tuple[Dict[str, Any], Dict[str, Any], Set[str]]:
+    """
+    You probably should be using ``model_schema()``, this function is indirectly used by that function.
+
+    Take a single ``model`` and generate the schema for its type only, not including additional
+    information as title, etc. Also return additional schema definitions, from sub-models.
+    """
+    properties = {}
+    required = []
+    definitions: Dict[str, Any] = {}
+    nested_models: Set[str] = set()
+    for k, f in model.__fields__.items():
+        try:
+            f_schema, f_definitions, f_nested_models = field_schema(
+                f,
+                by_alias=by_alias,
+                model_name_map=model_name_map,
+                ref_prefix=ref_prefix,
+                ref_template=ref_template,
+                known_models=known_models,
+            )
+        except SkipField as skip:
+            warnings.warn(skip.message, UserWarning)
+            continue
+        definitions.update(f_definitions)
+        nested_models.update(f_nested_models)
+        if by_alias:
+            properties[f.alias] = f_schema
+            if f.required:
+                required.append(f.alias)
+        else:
+            properties[k] = f_schema
+            if f.required:
+                required.append(k)
+    if ROOT_KEY in properties:
+        out_schema = properties[ROOT_KEY]
+        out_schema['title'] = model.__config__.title or model.__name__
+    else:
+        out_schema = {'type': 'object', 'properties': properties}
+        if required:
+            out_schema['required'] = required
+    if model.__config__.extra == 'forbid':
+        out_schema['additionalProperties'] = False
+    return out_schema, definitions, nested_models
+
+
+def enum_process_schema(enum: Type[Enum], *, field: Optional[ModelField] = None) -> Dict[str, Any]:
+    """
+    Take a single `enum` and generate its schema.
+
+    This is similar to the `model_process_schema` function, but applies to ``Enum`` objects.
+    """
+    import inspect
+
+    schema_: Dict[str, Any] = {
+        'title': enum.__name__,
+        # Python assigns all enums a default docstring value of 'An enumeration', so
+        # all enums will have a description field even if not explicitly provided.
+        'description': inspect.cleandoc(enum.__doc__ or 'An enumeration.'),
+        # Add enum values and the enum field type to the schema.
+        'enum': [item.value for item in cast(Iterable[Enum], enum)],
+    }
+
+    add_field_type_to_schema(enum, schema_)
+
+    modify_schema = getattr(enum, '__modify_schema__', None)
+    if modify_schema:
+        _apply_modify_schema(modify_schema, field, schema_)
+
+    return schema_
+
+
+def field_singleton_sub_fields_schema(
+    field: ModelField,
+    *,
+    by_alias: bool,
+    model_name_map: Dict[TypeModelOrEnum, str],
+    ref_template: str,
+    schema_overrides: bool = False,
+    ref_prefix: Optional[str] = None,
+    known_models: TypeModelSet,
+) -> Tuple[Dict[str, Any], Dict[str, Any], Set[str]]:
+    """
+    This function is indirectly used by ``field_schema()``, you probably should be using that function.
+
+    Take a list of Pydantic ``ModelField`` from the declaration of a type with parameters, and generate their
+    schema. I.e., fields used as "type parameters", like ``str`` and ``int`` in ``Tuple[str, int]``.
+    """
+    sub_fields = cast(List[ModelField], field.sub_fields)
+    definitions = {}
+    nested_models: Set[str] = set()
+    if len(sub_fields) == 1:
+        return field_type_schema(
+            sub_fields[0],
+            by_alias=by_alias,
+            model_name_map=model_name_map,
+            schema_overrides=schema_overrides,
+            ref_prefix=ref_prefix,
+            ref_template=ref_template,
+            known_models=known_models,
+        )
+    else:
+        s: Dict[str, Any] = {}
+        # https://github.com/OAI/OpenAPI-Specification/blob/master/versions/3.0.2.md#discriminator-object
+        field_has_discriminator: bool = field.discriminator_key is not None
+        if field_has_discriminator:
+            assert field.sub_fields_mapping is not None
+
+            discriminator_models_refs: Dict[str, Union[str, Dict[str, Any]]] = {}
+
+            for discriminator_value, sub_field in field.sub_fields_mapping.items():
+                if isinstance(discriminator_value, Enum):
+                    discriminator_value = str(discriminator_value.value)
+                # sub_field is either a `BaseModel` or directly an `Annotated` `Union` of many
+                if is_union(get_origin(sub_field.type_)):
+                    sub_models = get_sub_types(sub_field.type_)
+                    discriminator_models_refs[discriminator_value] = {
+                        model_name_map[sub_model]: get_schema_ref(
+                            model_name_map[sub_model], ref_prefix, ref_template, False
+                        )
+                        for sub_model in sub_models
+                    }
+                else:
+                    sub_field_type = sub_field.type_
+                    if hasattr(sub_field_type, '__pydantic_model__'):
+                        sub_field_type = sub_field_type.__pydantic_model__
+
+                    discriminator_model_name = model_name_map[sub_field_type]
+                    discriminator_model_ref = get_schema_ref(discriminator_model_name, ref_prefix, ref_template, False)
+                    discriminator_models_refs[discriminator_value] = discriminator_model_ref['$ref']
+
+            s['discriminator'] = {
+                'propertyName': field.discriminator_alias if by_alias else field.discriminator_key,
+                'mapping': discriminator_models_refs,
+            }
+
+        sub_field_schemas = []
+        for sf in sub_fields:
+            sub_schema, sub_definitions, sub_nested_models = field_type_schema(
+                sf,
+                by_alias=by_alias,
+                model_name_map=model_name_map,
+                schema_overrides=schema_overrides,
+                ref_prefix=ref_prefix,
+                ref_template=ref_template,
+                known_models=known_models,
+            )
+            definitions.update(sub_definitions)
+            if schema_overrides and 'allOf' in sub_schema:
+                # if the sub_field is a referenced schema we only need the referenced
+                # object. Otherwise we will end up with several allOf inside anyOf/oneOf.
+                # See https://github.com/pydantic/pydantic/issues/1209
+                sub_schema = sub_schema['allOf'][0]
+
+            if sub_schema.keys() == {'discriminator', 'oneOf'}:
+                # we don't want discriminator information inside oneOf choices, this is dealt with elsewhere
+                sub_schema.pop('discriminator')
+            sub_field_schemas.append(sub_schema)
+            nested_models.update(sub_nested_models)
+        s['oneOf' if field_has_discriminator else 'anyOf'] = sub_field_schemas
+        return s, definitions, nested_models
+
+
+# Order is important, e.g. subclasses of str must go before str
+# this is used only for standard library types, custom types should use __modify_schema__ instead
+field_class_to_schema: Tuple[Tuple[Any, Dict[str, Any]], ...] = (
+    (Path, {'type': 'string', 'format': 'path'}),
+    (datetime, {'type': 'string', 'format': 'date-time'}),
+    (date, {'type': 'string', 'format': 'date'}),
+    (time, {'type': 'string', 'format': 'time'}),
+    (timedelta, {'type': 'number', 'format': 'time-delta'}),
+    (IPv4Network, {'type': 'string', 'format': 'ipv4network'}),
+    (IPv6Network, {'type': 'string', 'format': 'ipv6network'}),
+    (IPv4Interface, {'type': 'string', 'format': 'ipv4interface'}),
+    (IPv6Interface, {'type': 'string', 'format': 'ipv6interface'}),
+    (IPv4Address, {'type': 'string', 'format': 'ipv4'}),
+    (IPv6Address, {'type': 'string', 'format': 'ipv6'}),
+    (Pattern, {'type': 'string', 'format': 'regex'}),
+    (str, {'type': 'string'}),
+    (bytes, {'type': 'string', 'format': 'binary'}),
+    (bool, {'type': 'boolean'}),
+    (int, {'type': 'integer'}),
+    (float, {'type': 'number'}),
+    (Decimal, {'type': 'number'}),
+    (UUID, {'type': 'string', 'format': 'uuid'}),
+    (dict, {'type': 'object'}),
+    (list, {'type': 'array', 'items': {}}),
+    (tuple, {'type': 'array', 'items': {}}),
+    (set, {'type': 'array', 'items': {}, 'uniqueItems': True}),
+    (frozenset, {'type': 'array', 'items': {}, 'uniqueItems': True}),
+)
+
+json_scheme = {'type': 'string', 'format': 'json-string'}
+
+
+def add_field_type_to_schema(field_type: Any, schema_: Dict[str, Any]) -> None:
+    """
+    Update the given `schema` with the type-specific metadata for the given `field_type`.
+
+    This function looks through `field_class_to_schema` for a class that matches the given `field_type`,
+    and then modifies the given `schema` with the information from that type.
+    """
+    for type_, t_schema in field_class_to_schema:
+        # Fallback for `typing.Pattern` and `re.Pattern` as they are not a valid class
+        if lenient_issubclass(field_type, type_) or field_type is type_ is Pattern:
+            schema_.update(t_schema)
+            break
+
+
+def get_schema_ref(name: str, ref_prefix: Optional[str], ref_template: str, schema_overrides: bool) -> Dict[str, Any]:
+    if ref_prefix:
+        schema_ref = {'$ref': ref_prefix + name}
+    else:
+        schema_ref = {'$ref': ref_template.format(model=name)}
+    return {'allOf': [schema_ref]} if schema_overrides else schema_ref
+
+
+def field_singleton_schema(  # noqa: C901 (ignore complexity)
+    field: ModelField,
+    *,
+    by_alias: bool,
+    model_name_map: Dict[TypeModelOrEnum, str],
+    ref_template: str,
+    schema_overrides: bool = False,
+    ref_prefix: Optional[str] = None,
+    known_models: TypeModelSet,
+) -> Tuple[Dict[str, Any], Dict[str, Any], Set[str]]:
+    """
+    This function is indirectly used by ``field_schema()``, you should probably be using that function.
+
+    Take a single Pydantic ``ModelField``, and return its schema and any additional definitions from sub-models.
+    """
+    from pydantic.v1.main import BaseModel
+
+    definitions: Dict[str, Any] = {}
+    nested_models: Set[str] = set()
+    field_type = field.type_
+
+    # Recurse into this field if it contains sub_fields and is NOT a
+    # BaseModel OR that BaseModel is a const
+    if field.sub_fields and (
+        (field.field_info and field.field_info.const) or not lenient_issubclass(field_type, BaseModel)
+    ):
+        return field_singleton_sub_fields_schema(
+            field,
+            by_alias=by_alias,
+            model_name_map=model_name_map,
+            schema_overrides=schema_overrides,
+            ref_prefix=ref_prefix,
+            ref_template=ref_template,
+            known_models=known_models,
+        )
+    if field_type is Any or field_type is object or field_type.__class__ == TypeVar or get_origin(field_type) is type:
+        return {}, definitions, nested_models  # no restrictions
+    if is_none_type(field_type):
+        return {'type': 'null'}, definitions, nested_models
+    if is_callable_type(field_type):
+        raise SkipField(f'Callable {field.name} was excluded from schema since JSON schema has no equivalent type.')
+    f_schema: Dict[str, Any] = {}
+    if field.field_info is not None and field.field_info.const:
+        f_schema['const'] = field.default
+
+    if is_literal_type(field_type):
+        values = tuple(x.value if isinstance(x, Enum) else x for x in all_literal_values(field_type))
+
+        if len({v.__class__ for v in values}) > 1:
+            return field_schema(
+                multitypes_literal_field_for_schema(values, field),
+                by_alias=by_alias,
+                model_name_map=model_name_map,
+                ref_prefix=ref_prefix,
+                ref_template=ref_template,
+                known_models=known_models,
+            )
+
+        # All values have the same type
+        field_type = values[0].__class__
+        f_schema['enum'] = list(values)
+        add_field_type_to_schema(field_type, f_schema)
+    elif lenient_issubclass(field_type, Enum):
+        enum_name = model_name_map[field_type]
+        f_schema, schema_overrides = get_field_info_schema(field, schema_overrides)
+        f_schema.update(get_schema_ref(enum_name, ref_prefix, ref_template, schema_overrides))
+        definitions[enum_name] = enum_process_schema(field_type, field=field)
+    elif is_namedtuple(field_type):
+        sub_schema, *_ = model_process_schema(
+            field_type.__pydantic_model__,
+            by_alias=by_alias,
+            model_name_map=model_name_map,
+            ref_prefix=ref_prefix,
+            ref_template=ref_template,
+            known_models=known_models,
+            field=field,
+        )
+        items_schemas = list(sub_schema['properties'].values())
+        f_schema.update(
+            {
+                'type': 'array',
+                'items': items_schemas,
+                'minItems': len(items_schemas),
+                'maxItems': len(items_schemas),
+            }
+        )
+    elif not hasattr(field_type, '__pydantic_model__'):
+        add_field_type_to_schema(field_type, f_schema)
+
+        modify_schema = getattr(field_type, '__modify_schema__', None)
+        if modify_schema:
+            _apply_modify_schema(modify_schema, field, f_schema)
+
+    if f_schema:
+        return f_schema, definitions, nested_models
+
+    # Handle dataclass-based models
+    if lenient_issubclass(getattr(field_type, '__pydantic_model__', None), BaseModel):
+        field_type = field_type.__pydantic_model__
+
+    if issubclass(field_type, BaseModel):
+        model_name = model_name_map[field_type]
+        if field_type not in known_models:
+            sub_schema, sub_definitions, sub_nested_models = model_process_schema(
+                field_type,
+                by_alias=by_alias,
+                model_name_map=model_name_map,
+                ref_prefix=ref_prefix,
+                ref_template=ref_template,
+                known_models=known_models,
+                field=field,
+            )
+            definitions.update(sub_definitions)
+            definitions[model_name] = sub_schema
+            nested_models.update(sub_nested_models)
+        else:
+            nested_models.add(model_name)
+        schema_ref = get_schema_ref(model_name, ref_prefix, ref_template, schema_overrides)
+        return schema_ref, definitions, nested_models
+
+    # For generics with no args
+    args = get_args(field_type)
+    if args is not None and not args and Generic in field_type.__bases__:
+        return f_schema, definitions, nested_models
+
+    raise ValueError(f'Value not declarable with JSON Schema, field: {field}')
+
+
+def multitypes_literal_field_for_schema(values: Tuple[Any, ...], field: ModelField) -> ModelField:
+    """
+    To support `Literal` with values of different types, we split it into multiple `Literal` with same type
+    e.g. `Literal['qwe', 'asd', 1, 2]` becomes `Union[Literal['qwe', 'asd'], Literal[1, 2]]`
+    """
+    literal_distinct_types = defaultdict(list)
+    for v in values:
+        literal_distinct_types[v.__class__].append(v)
+    distinct_literals = (Literal[tuple(same_type_values)] for same_type_values in literal_distinct_types.values())
+
+    return ModelField(
+        name=field.name,
+        type_=Union[tuple(distinct_literals)],  # type: ignore
+        class_validators=field.class_validators,
+        model_config=field.model_config,
+        default=field.default,
+        required=field.required,
+        alias=field.alias,
+        field_info=field.field_info,
+    )
+
+
+def encode_default(dft: Any) -> Any:
+    from pydantic.v1.main import BaseModel
+
+    if isinstance(dft, BaseModel) or is_dataclass(dft):
+        dft = cast('dict[str, Any]', pydantic_encoder(dft))
+
+    if isinstance(dft, dict):
+        return {encode_default(k): encode_default(v) for k, v in dft.items()}
+    elif isinstance(dft, Enum):
+        return dft.value
+    elif isinstance(dft, (int, float, str)):
+        return dft
+    elif isinstance(dft, (list, tuple)):
+        t = dft.__class__
+        seq_args = (encode_default(v) for v in dft)
+        return t(*seq_args) if is_namedtuple(t) else t(seq_args)
+    elif dft is None:
+        return None
+    else:
+        return pydantic_encoder(dft)
+
+
+_map_types_constraint: Dict[Any, Callable[..., type]] = {int: conint, float: confloat, Decimal: condecimal}
+
+
+def get_annotation_from_field_info(
+    annotation: Any, field_info: FieldInfo, field_name: str, validate_assignment: bool = False
+) -> Type[Any]:
+    """
+    Get an annotation with validation implemented for numbers and strings based on the field_info.
+    :param annotation: an annotation from a field specification, as ``str``, ``ConstrainedStr``
+    :param field_info: an instance of FieldInfo, possibly with declarations for validations and JSON Schema
+    :param field_name: name of the field for use in error messages
+    :param validate_assignment: default False, flag for BaseModel Config value of validate_assignment
+    :return: the same ``annotation`` if unmodified or a new annotation with validation in place
+    """
+    constraints = field_info.get_constraints()
+    used_constraints: Set[str] = set()
+    if constraints:
+        annotation, used_constraints = get_annotation_with_constraints(annotation, field_info)
+    if validate_assignment:
+        used_constraints.add('allow_mutation')
+
+    unused_constraints = constraints - used_constraints
+    if unused_constraints:
+        raise ValueError(
+            f'On field "{field_name}" the following field constraints are set but not enforced: '
+            f'{", ".join(unused_constraints)}. '
+            f'\nFor more details see https://docs.pydantic.dev/usage/schema/#unenforced-field-constraints'
+        )
+
+    return annotation
+
+
+def get_annotation_with_constraints(annotation: Any, field_info: FieldInfo) -> Tuple[Type[Any], Set[str]]:  # noqa: C901
+    """
+    Get an annotation with used constraints implemented for numbers and strings based on the field_info.
+
+    :param annotation: an annotation from a field specification, as ``str``, ``ConstrainedStr``
+    :param field_info: an instance of FieldInfo, possibly with declarations for validations and JSON Schema
+    :return: the same ``annotation`` if unmodified or a new annotation along with the used constraints.
+    """
+    used_constraints: Set[str] = set()
+
+    def go(type_: Any) -> Type[Any]:
+        if (
+            is_literal_type(type_)
+            or isinstance(type_, ForwardRef)
+            or lenient_issubclass(type_, (ConstrainedList, ConstrainedSet, ConstrainedFrozenSet))
+        ):
+            return type_
+        origin = get_origin(type_)
+        if origin is not None:
+            args: Tuple[Any, ...] = get_args(type_)
+            if any(isinstance(a, ForwardRef) for a in args):
+                # forward refs cause infinite recursion below
+                return type_
+
+            if origin is Annotated:
+                return go(args[0])
+            if is_union(origin):
+                return Union[tuple(go(a) for a in args)]  # type: ignore
+
+            if issubclass(origin, List) and (
+                field_info.min_items is not None
+                or field_info.max_items is not None
+                or field_info.unique_items is not None
+            ):
+                used_constraints.update({'min_items', 'max_items', 'unique_items'})
+                return conlist(
+                    go(args[0]),
+                    min_items=field_info.min_items,
+                    max_items=field_info.max_items,
+                    unique_items=field_info.unique_items,
+                )
+
+            if issubclass(origin, Set) and (field_info.min_items is not None or field_info.max_items is not None):
+                used_constraints.update({'min_items', 'max_items'})
+                return conset(go(args[0]), min_items=field_info.min_items, max_items=field_info.max_items)
+
+            if issubclass(origin, FrozenSet) and (field_info.min_items is not None or field_info.max_items is not None):
+                used_constraints.update({'min_items', 'max_items'})
+                return confrozenset(go(args[0]), min_items=field_info.min_items, max_items=field_info.max_items)
+
+            for t in (Tuple, List, Set, FrozenSet, Sequence):
+                if issubclass(origin, t):  # type: ignore
+                    return t[tuple(go(a) for a in args)]  # type: ignore
+
+            if issubclass(origin, Dict):
+                return Dict[args[0], go(args[1])]  # type: ignore
+
+        attrs: Optional[Tuple[str, ...]] = None
+        constraint_func: Optional[Callable[..., type]] = None
+        if isinstance(type_, type):
+            if issubclass(type_, (SecretStr, SecretBytes)):
+                attrs = ('max_length', 'min_length')
+
+                def constraint_func(**kw: Any) -> Type[Any]:  # noqa: F811
+                    return type(type_.__name__, (type_,), kw)
+
+            elif issubclass(type_, str) and not issubclass(type_, (EmailStr, AnyUrl)):
+                attrs = ('max_length', 'min_length', 'regex')
+                if issubclass(type_, StrictStr):
+
+                    def constraint_func(**kw: Any) -> Type[Any]:
+                        return type(type_.__name__, (type_,), kw)
+
+                else:
+                    constraint_func = constr
+            elif issubclass(type_, bytes):
+                attrs = ('max_length', 'min_length', 'regex')
+                if issubclass(type_, StrictBytes):
+
+                    def constraint_func(**kw: Any) -> Type[Any]:
+                        return type(type_.__name__, (type_,), kw)
+
+                else:
+                    constraint_func = conbytes
+            elif issubclass(type_, numeric_types) and not issubclass(
+                type_,
+                (
+                    ConstrainedInt,
+                    ConstrainedFloat,
+                    ConstrainedDecimal,
+                    ConstrainedList,
+                    ConstrainedSet,
+                    ConstrainedFrozenSet,
+                    bool,
+                ),
+            ):
+                # Is numeric type
+                attrs = ('gt', 'lt', 'ge', 'le', 'multiple_of')
+                if issubclass(type_, float):
+                    attrs += ('allow_inf_nan',)
+                if issubclass(type_, Decimal):
+                    attrs += ('max_digits', 'decimal_places')
+                numeric_type = next(t for t in numeric_types if issubclass(type_, t))  # pragma: no branch
+                constraint_func = _map_types_constraint[numeric_type]
+
+        if attrs:
+            used_constraints.update(set(attrs))
+            kwargs = {
+                attr_name: attr
+                for attr_name, attr in ((attr_name, getattr(field_info, attr_name)) for attr_name in attrs)
+                if attr is not None
+            }
+            if kwargs:
+                constraint_func = cast(Callable[..., type], constraint_func)
+                return constraint_func(**kwargs)
+        return type_
+
+    return go(annotation), used_constraints
+
+
+def normalize_name(name: str) -> str:
+    """
+    Normalizes the given name. This can be applied to either a model *or* enum.
+    """
+    return re.sub(r'[^a-zA-Z0-9.\-_]', '_', name)
+
+
+class SkipField(Exception):
+    """
+    Utility exception used to exclude fields from schema.
+    """
+
+    def __init__(self, message: str) -> None:
+        self.message = message