# coding: utf-8

"""
Jaqpot API

A modern RESTful API for model management and prediction services, built using Spring Boot and Kotlin. Supports seamless integration with machine learning workflows.

The version of the OpenAPI document: 1.0.0
Contact: upci.ntua@gmail.com
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
from typing import Any, ClassVar, Dict, List, Optional
from jaqpotpy.api.openapi.models.scores import Scores
from typing import Optional, Set
from typing_extensions import Self


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

    train: Optional[List[Scores]] = None
    test: Optional[List[Scores]] = None
    cross_validation: Optional[List[Scores]] = Field(
        default=None, alias="crossValidation"
    )
    __properties: ClassVar[List[str]] = ["train", "test", "crossValidation"]

    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 ModelScores 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 train (list)
        _items = []
        if self.train:
            for _item_train in self.train:
                if _item_train:
                    _items.append(_item_train.to_dict())
            _dict["train"] = _items
        # override the default output from pydantic by calling `to_dict()` of each item in test (list)
        _items = []
        if self.test:
            for _item_test in self.test:
                if _item_test:
                    _items.append(_item_test.to_dict())
            _dict["test"] = _items
        # override the default output from pydantic by calling `to_dict()` of each item in cross_validation (list)
        _items = []
        if self.cross_validation:
            for _item_cross_validation in self.cross_validation:
                if _item_cross_validation:
                    _items.append(_item_cross_validation.to_dict())
            _dict["crossValidation"] = _items
        return _dict

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

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

        _obj = cls.model_validate(
            {
                "train": [Scores.from_dict(_item) for _item in obj["train"]]
                if obj.get("train") is not None
                else None,
                "test": [Scores.from_dict(_item) for _item in obj["test"]]
                if obj.get("test") is not None
                else None,
                "crossValidation": [
                    Scores.from_dict(_item) for _item in obj["crossValidation"]
                ]
                if obj.get("crossValidation") is not None
                else None,
            }
        )
        return _obj
