def apply_known_metadata(annotation: Any, schema: CoreSchema) -> CoreSchema | None:  # noqa: C901
    """Apply `annotation` to `schema` if it is an annotation we know about (Gt, Le, etc.).
    Otherwise return `None`.

    This does not handle all known annotations. If / when it does, it can always
    return a CoreSchema and return the unmodified schema if the annotation should be ignored.

    Assumes that GroupedMetadata has already been expanded via `expand_grouped_metadata`.

    Args:
        annotation: The annotation.
        schema: The schema.

    Returns:
        An updated schema with annotation if it is an annotation we know about, `None` otherwise.

    Raises:
        PydanticCustomError: If `Predicate` fails.
    """
    import annotated_types as at

    from ._validators import NUMERIC_VALIDATOR_LOOKUP, forbid_inf_nan_check

    schema = schema.copy()
    schema_update, other_metadata = collect_known_metadata([annotation])
    schema_type = schema['type']

    chain_schema_constraints: set[str] = {
        'pattern',
        'strip_whitespace',
        'to_lower',
        'to_upper',
        'coerce_numbers_to_str',
    }
    chain_schema_steps: list[CoreSchema] = []

    for constraint, value in schema_update.items():
        if constraint not in CONSTRAINTS_TO_ALLOWED_SCHEMAS:
            raise ValueError(f'Unknown constraint {constraint}')
        allowed_schemas = CONSTRAINTS_TO_ALLOWED_SCHEMAS[constraint]

        # if it becomes necessary to handle more than one constraint
        # in this recursive case with function-after or function-wrap, we should refactor
        # this is a bit challenging because we sometimes want to apply constraints to the inner schema,
        # whereas other times we want to wrap the existing schema with a new one that enforces a new constraint.
        if schema_type in {'function-before', 'function-wrap', 'function-after'} and constraint == 'strict':
            schema['schema'] = apply_known_metadata(annotation, schema['schema'])  # type: ignore  # schema is function schema
            return schema

        # if we're allowed to apply constraint directly to the schema, like le to int, do that
        if schema_type in allowed_schemas:
            if constraint == 'union_mode' and schema_type == 'union':
                schema['mode'] = value  # type: ignore  # schema is UnionSchema
            else:
                schema[constraint] = value
            continue

        #  else, apply a function after validator to the schema to enforce the corresponding constraint
        if constraint in chain_schema_constraints:

            def _apply_constraint_with_incompatibility_info(
                value: Any, handler: cs.ValidatorFunctionWrapHandler
            ) -> Any:
                try:
                    x = handler(value)
                except ValidationError as ve:
                    # if the error is about the type, it's likely that the constraint is incompatible the type of the field
                    # for example, the following invalid schema wouldn't be caught during schema build, but rather at this point
                    # with a cryptic 'string_type' error coming from the string validator,
                    # that we'd rather express as a constraint incompatibility error (TypeError)
                    # Annotated[list[int], Field(pattern='abc')]
                    if 'type' in ve.errors()[0]['type']:
                        raise TypeError(
                            f"Unable to apply constraint '{constraint}' to supplied value {value} for schema of type '{schema_type}'"  # noqa: B023
                        )
                    raise ve
                return x

            chain_schema_steps.append(
                cs.no_info_wrap_validator_function(
                    _apply_constraint_with_incompatibility_info, cs.str_schema(**{constraint: value})
                )
            )
        elif constraint in NUMERIC_VALIDATOR_LOOKUP:
            if constraint in LENGTH_CONSTRAINTS:
                inner_schema = schema
                while inner_schema['type'] in {'function-before', 'function-wrap', 'function-after'}:
                    inner_schema = inner_schema['schema']  # type: ignore
                inner_schema_type = inner_schema['type']
                if inner_schema_type == 'list' or (
                    inner_schema_type == 'json-or-python' and inner_schema['json_schema']['type'] == 'list'  # type: ignore
                ):
                    js_constraint_key = 'minItems' if constraint == 'min_length' else 'maxItems'
                else:
                    js_constraint_key = 'minLength' if constraint == 'min_length' else 'maxLength'
            else:
                js_constraint_key = constraint

            schema = cs.no_info_after_validator_function(
                partial(NUMERIC_VALIDATOR_LOOKUP[constraint], **{constraint: value}), schema
            )
            metadata = schema.get('metadata', {})
            if (existing_json_schema_updates := metadata.get('pydantic_js_updates')) is not None:
                metadata['pydantic_js_updates'] = {
                    **existing_json_schema_updates,
                    **{js_constraint_key: as_jsonable_value(value)},
                }
            else:
                metadata['pydantic_js_updates'] = {js_constraint_key: as_jsonable_value(value)}
            schema['metadata'] = metadata
        elif constraint == 'allow_inf_nan' and value is False:
            schema = cs.no_info_after_validator_function(
                forbid_inf_nan_check,
                schema,
            )
        else:
            # It's rare that we'd get here, but it's possible if we add a new constraint and forget to handle it
            # Most constraint errors are caught at runtime during attempted application
            raise RuntimeError(f"Unable to apply constraint '{constraint}' to schema of type '{schema_type}'")

    for annotation in other_metadata:
        if (annotation_type := type(annotation)) in (at_to_constraint_map := _get_at_to_constraint_map()):
            constraint = at_to_constraint_map[annotation_type]
            validator = NUMERIC_VALIDATOR_LOOKUP.get(constraint)
            if validator is None:
                raise ValueError(f'Unknown constraint {constraint}')
            schema = cs.no_info_after_validator_function(
                partial(validator, {constraint: getattr(annotation, constraint)}), schema
            )
            continue
        elif isinstance(annotation, (at.Predicate, at.Not)):
            predicate_name = f'{annotation.func.__qualname__}' if hasattr(annotation.func, '__qualname__') else ''

            def val_func(v: Any) -> Any:
                predicate_satisfied = annotation.func(v)  # noqa: B023

                # annotation.func may also raise an exception, let it pass through
                if isinstance(annotation, at.Predicate):  # noqa: B023
                    if not predicate_satisfied:
                        raise PydanticCustomError(
                            'predicate_failed',
                            f'Predicate {predicate_name} failed',  # type: ignore  # noqa: B023
                        )
                else:
                    if predicate_satisfied:
                        raise PydanticCustomError(
                            'not_operation_failed',
                            f'Not of {predicate_name} failed',  # type: ignore  # noqa: B023
                        )

                return v

            schema = cs.no_info_after_validator_function(val_func, schema)
        else:
            # ignore any other unknown metadata
            return None

    if chain_schema_steps:
        chain_schema_steps = [schema] + chain_schema_steps
        return cs.chain_schema(chain_schema_steps)

    return schema
Apply `annotation` to `schema` if it is an annotation we know about (Gt, Le, etc.).
    Otherwise return `None`.

    This does not handle all known annotations. If / when it does, it can always
    return a CoreSchema and return the unmodified schema if the annotation should be ignored.

    Assumes that GroupedMetadata has already been expanded via `expand_grouped_metadata`.

    Args:
        annotation: The annotation.
        schema: The schema.

    Returns:
        An updated schema with annotation if it is an annotation we know about, `None` otherwise.

    Raises:
        PydanticCustomError: If `Predicate` fails.
    
pydantic/_internal/_known_annotated_metadata.py