# coding: utf-8

"""
    BGP Monitors API

    **Note:** Private monitor data is not available for ThousandEyes for Government instance.  Retrieve information about BGP monitors available to your ThousandEyes account. ThousandEyes ingests BGP routing data from dozens of global BGP collectors and automatically integrates that visibility as a configurable layer under service, network, and path visualization layers.  When you specify a service URL in a test, layered BGP tests automatically track reachability and path changes for any relevant prefix. When you use an IP address as the target for a test, the ThousandEyes platform monitors the relevant internet-routed prefix. You can also create specific BGP monitoring for a prefix, and can alert on hijacks and leaks.  For more information about monitors, see [Inside-Out BGP Visibility](https://docs.thousandeyes.com/product-documentation/internet-and-wan-monitoring/tests/bgp-tests/inside-out-bgp-visibility). 

    Generated by OpenAPI Generator (https://openapi-generator.tech)

    Do not edit the class manually.
"""  # noqa: E501


from __future__ import annotations
import pprint
import re  # noqa: F401
import json

from pydantic import BaseModel, ConfigDict, Field, StrictStr
from typing import Any, ClassVar, Dict, List, Optional
from thousandeyes_sdk.bgp_monitors.models.monitor_type import MonitorType
from typing import Optional, Set
from typing_extensions import Self

class Monitor(BaseModel):
    """
    Monitor
    """ # noqa: E501
    country_id: Optional[StrictStr] = Field(default=None, description="Country ID", alias="countryId")
    monitor_id: Optional[StrictStr] = Field(default=None, description="BGP monitor ID", alias="monitorId")
    ip_address: Optional[StrictStr] = Field(default=None, description="IP address of the BGP monitor", alias="ipAddress")
    network: Optional[StrictStr] = Field(default=None, description="Name of the autonomous system in which the monitor is found")
    monitor_type: Optional[MonitorType] = Field(default=None, alias="monitorType")
    monitor_name: Optional[StrictStr] = Field(default=None, description="Display name of the BGP monitor", alias="monitorName")
    __properties: ClassVar[List[str]] = ["countryId", "monitorId", "ipAddress", "network", "monitorType", "monitorName"]

    model_config = ConfigDict(
        populate_by_name=True,
        validate_assignment=True,
        protected_namespaces=(),
        extra="allow",
    )


    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 self.model_dump_json(by_alias=True, exclude_unset=True, exclude_none=True)

    @classmethod
    def from_json(cls, json_str: str) -> Optional[Self]:
        """Create an instance of Monitor 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.
        * OpenAPI `readOnly` fields are excluded.
        * OpenAPI `readOnly` fields are excluded.
        """
        excluded_fields: Set[str] = set([
            "country_id",
            "monitor_id",
        ])

        _dict = self.model_dump(
            by_alias=True,
            exclude=excluded_fields,
            exclude_none=True,
        )
        return _dict

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

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

        _obj = cls.model_validate({
            "countryId": obj.get("countryId"),
            "monitorId": obj.get("monitorId"),
            "ipAddress": obj.get("ipAddress"),
            "network": obj.get("network"),
            "monitorType": obj.get("monitorType"),
            "monitorName": obj.get("monitorName")
        })
        return _obj


