"""
RCABench API

RCABench - A comprehensive root cause analysis benchmarking platform for microservices

The version of the OpenAPI document: 1.1.52
Contact: team@rcabench.com
Generated by OpenAPI Generator (https://rcabench.openapi-generator.tech)

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

from __future__ import annotations

import json
import pprint
from typing import Any, ClassVar

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

from rcabench.openapi.models.evaluate_datapack_item import EvaluateDatapackItem


class BatchEvaluateDatapackResp(BaseModel):
    """
    BatchEvaluateDatapackResp
    """  # noqa: E501

    failed_count: StrictInt | None = None
    failed_items: list[StrictStr] | None = None
    success_count: StrictInt | None = None
    success_items: list[EvaluateDatapackItem] | None = None
    additional_properties: dict[str, Any] = {}
    __properties: ClassVar[list[str]] = ["failed_count", "failed_items", "success_count", "success_items"]

    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"""
        return self.model_dump_json(by_alias=True, exclude_unset=True)

    @classmethod
    def from_json(cls, json_str: str) -> Self | None:
        """Create an instance of BatchEvaluateDatapackResp 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,
        )
        # override the default output from pydantic by calling `to_dict()` of each item in success_items (list)
        _items = []
        if self.success_items:
            for _item_success_items in self.success_items:
                if _item_success_items:
                    _items.append(_item_success_items.to_dict())
            _dict["success_items"] = _items
        # 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: dict[str, Any] | None) -> Self | None:
        """Create an instance of BatchEvaluateDatapackResp from a dict"""
        if obj is None:
            return None

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

        _obj = cls.model_validate(
            {
                "failed_count": obj.get("failed_count"),
                "failed_items": obj.get("failed_items"),
                "success_count": obj.get("success_count"),
                "success_items": [EvaluateDatapackItem.from_dict(_item) for _item in obj["success_items"]]
                if obj.get("success_items") is not None
                else None,
            }
        )
        # 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
