# coding: utf-8

"""
    smritea SDK API

    Public SDK API for smritea AI memory system — programmatic memory operations via API key

    The version of the OpenAPI document: 1.0.0
    Contact: support@smritea.ai
    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, StrictFloat, StrictInt, StrictStr
from typing import Any, ClassVar, Dict, List, Optional, Union
from smritea._internal.autogen.smritea_cloud_sdk.models.commondto_persona_domain_config import CommondtoPersonaDomainConfig
from typing import Optional, Set
from typing_extensions import Self

class CommondtoPersonaExtractionConfig(BaseModel):
    """
    CommondtoPersonaExtractionConfig
    """ # noqa: E501
    actor_types: Optional[List[StrictStr]] = Field(default=None, description="ActorTypes filters which actor types to extract personas for (empty = all types). Valid types: user, agent, system")
    domains: Optional[List[CommondtoPersonaDomainConfig]] = Field(default=None, description="Domains specifies which persona domains to extract. Each domain defines a category of traits (preferences, interests, etc.).")
    enabled: Optional[StrictBool] = Field(default=None, description="Enabled controls whether persona extraction is active. When false, no persona traits are extracted from memory content. Unlike Entity/Fact extraction, persona has no multi-pass reflection — it runs a single LLM call.")
    max_tokens: Optional[StrictInt] = Field(default=None, description="MaxTokens is the maximum completion tokens for LLM responses.")
    model: Optional[StrictStr] = Field(default=None, description="Model is the LLM model to use (empty = use provider default).")
    temperature: Optional[Union[StrictFloat, StrictInt]] = Field(default=None, description="Temperature controls LLM randomness for extraction.")
    __properties: ClassVar[List[str]] = ["actor_types", "domains", "enabled", "max_tokens", "model", "temperature"]

    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 CommondtoPersonaExtractionConfig 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,
        )
        # override the default output from pydantic by calling `to_dict()` of each item in domains (list)
        _items = []
        if self.domains:
            for _item_domains in self.domains:
                if _item_domains:
                    _items.append(_item_domains.to_dict())
            _dict['domains'] = _items
        return _dict

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

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

        _obj = cls.model_validate({
            "actor_types": obj.get("actor_types"),
            "domains": [CommondtoPersonaDomainConfig.from_dict(_item) for _item in obj["domains"]] if obj.get("domains") is not None else None,
            "enabled": obj.get("enabled"),
            "max_tokens": obj.get("max_tokens"),
            "model": obj.get("model"),
            "temperature": obj.get("temperature")
        })
        return _obj


