# coding: utf-8

"""
Code generated by OpenAPI Generator (https://openapi-generator.tech), manual changes will be lost - read more on https://github.com/algolia/api-clients-automation. DO NOT EDIT.
"""
from __future__ import annotations

from json import loads
from typing import Annotated, Any, Dict, Optional, Self, Union

from pydantic import BaseModel, Field

from algoliasearch.abtesting.models.effect import Effect


class MinimumDetectableEffect(BaseModel):
    """
    Configuration for the smallest difference between test variants you want to detect.
    """

    size: Optional[
        Union[
            Annotated[float, Field(le=1, strict=True, ge=0)],
            Annotated[int, Field(le=1, strict=True, ge=0)],
        ]
    ] = Field(
        default=None,
        description="Smallest difference in an observable metric between variants. For example, to detect a 10% difference between variants, set this value to 0.1. ",
    )
    effect: Optional[Effect] = None

    model_config = {"populate_by_name": True, "validate_assignment": True}

    def to_json(self) -> str:
        return self.model_dump_json(by_alias=True, exclude_unset=True)

    @classmethod
    def from_json(cls, json_str: str) -> Self:
        """Create an instance of MinimumDetectableEffect from a JSON string"""
        return cls.from_dict(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.
        """
        _dict = self.model_dump(
            by_alias=True,
            exclude={},
            exclude_none=True,
        )
        return _dict

    @classmethod
    def from_dict(cls, obj: Dict) -> Self:
        """Create an instance of MinimumDetectableEffect from a dict"""
        if obj is None:
            return None

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

        _obj = cls.model_validate(
            {"size": obj.get("size"), "effect": obj.get("effect")}
        )
        return _obj
