google_protobuf_any.py

# coding: utf-8

"""
    Application Load Balancer API

    ### DEPRECATED! Use v2beta2 instead.  This API offers an interface to provision and manage load balancing servers in your STACKIT project. It also has the possibility of pooling target servers for load balancing purposes.  For each application load balancer provided, two VMs are deployed in your OpenStack project subject to a fee.

    The version of the OpenAPI document: 1beta.0.0
    Generated by OpenAPI Generator (https://openapi-generator.tech)

    Do not edit the class manually.
"""  # noqa: E501 docstring might be too long

from __future__ import annotations

import json
import pprint
from typing import Any, ClassVar, Dict, List, Optional, Set

from pydantic import BaseModel, ConfigDict, Field, StrictStr
from typing_extensions import Self


class GoogleProtobufAny(BaseModel):
    """
    Contains an arbitrary serialized message along with a @type that describes the type of the serialized message.
    """

    type: Optional[StrictStr] = Field(default=None, description="The type of the serialized message.", alias="@type")
    additional_properties: Dict[str, Any] = {}
    __properties: ClassVar[List[str]] = ["@type"]

    model_config = ConfigDict(
        populate_by_name=True,
        validate_assignment=True,
        protected_namespaces=(),
    )

    def to_str(self) -> str:
        """Returns the string representation of the model using alias"""
        return pprint.pformat(self.model_dump(by_alias=True))

    def to_json(self) -> str:
        """Returns the JSON representation of the model using alias"""
        # TODO: pydantic v2: use .model_dump_json(by_alias=True, exclude_unset=True) instead
        return json.dumps(self.to_dict())

    @classmethod
    def from_json(cls, json_str: str) -> Optional[Self]:
        """Create an instance of GoogleProtobufAny from a JSON string"""
        return cls.from_dict(json.loads(json_str))

    def to_dict(self) -> Dict[str, Any]:
        """Return the dictionary representation of the model using alias.

        This has the following differences from calling pydantic's
        `self.model_dump(by_alias=True)`:

        * `None` is only added to the output dict for nullable fields that
          were set at model initialization. Other fields with value `None`
          are ignored.
        * Fields in `self.additional_properties` are added to the output dict.
        """
        excluded_fields: Set[str] = set(
            [
                "additional_properties",
            ]
        )

        _dict = self.model_dump(
            by_alias=True,
            exclude=excluded_fields,
            exclude_none=True,
        )
        # puts key-value pairs in additional_properties in the top level
        if self.additional_properties is not None:
            for _key, _value in self.additional_properties.items():
                _dict[_key] = _value

        return _dict

    @classmethod
    def from_dict(cls, obj: Optional[Dict[str, Any]]) -> Optional[Self]:
        """Create an instance of GoogleProtobufAny from a dict"""
        if obj is None:
            return None

        if not isinstance(obj, dict):
            return cls.model_validate(obj)

        _obj = cls.model_validate({"@type": obj.get("@type")})
        # store additional fields in additional_properties
        for _key in obj.keys():
            if _key not in cls.__properties:
                _obj.additional_properties[_key] = obj.get(_key)

        return _obj