# coding: utf-8

"""
    NinjaPear API

    NinjaPear is a data platform that seeks to serve as the single source of truth for B2B data, be it to power your data-driven applications or your sales-driven workflow.  As a data client of NinjaPear API, you can: 1. Look up the customers, investors, and partners/platforms of any business globally. 2. (FREE) Retrieve the logo of any company. 3. (FREE) Find out the nature of an email address. 4. (FREE) Check your credit balance. 5. Monitor companies for updates (blog posts, X/Twitter posts, website changes) via RSS feeds.

    The version of the OpenAPI document: 1.0.0
    Contact: hello@nubela.co
    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, StrictBool
from typing import Any, ClassVar, Dict, List, Optional
from typing_extensions import Annotated
from typing import Optional, Set
from typing_extensions import Self

class TargetSettings(BaseModel):
    """
    Monitoring settings for a target.
    """ # noqa: E501
    monitor_blog: Optional[StrictBool] = Field(default=True, description="Whether to monitor the company's blog RSS feeds")
    monitor_x: Optional[StrictBool] = Field(default=True, description="Whether to monitor the company's X (Twitter) account")
    monitor_website: Optional[StrictBool] = Field(default=True, description="Whether to monitor the company's website for content changes")
    frequency_days: Optional[Annotated[int, Field(le=30, strict=True, ge=1)]] = Field(default=7, description="How often to poll for updates (in days)")
    __properties: ClassVar[List[str]] = ["monitor_blog", "monitor_x", "monitor_website", "frequency_days"]

    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 TargetSettings 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.
        """
        excluded_fields: Set[str] = set([
        ])

        _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 TargetSettings from a dict"""
        if obj is None:
            return None

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

        _obj = cls.model_validate({
            "monitor_blog": obj.get("monitor_blog") if obj.get("monitor_blog") is not None else True,
            "monitor_x": obj.get("monitor_x") if obj.get("monitor_x") is not None else True,
            "monitor_website": obj.get("monitor_website") if obj.get("monitor_website") is not None else True,
            "frequency_days": obj.get("frequency_days") if obj.get("frequency_days") is not None else 7
        })
        return _obj


