Source code for annofabapi.pydantic_models.segmentation_metadata

"""


No description provided (generated by Openapi Generator https://github.com/openapitools/openapi-generator)

The version of the OpenAPI document: 1.0.0
Generated by OpenAPI Generator (https://openapi-generator.tech)

Do not edit the class manually.
"""

from __future__ import annotations

import json
import pprint
import re  # noqa: F401
from typing import Any, ClassVar, Dict, List, Set

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




class SegmentationMetadata(BaseModel):
    """
    塗りつぶしアノテーションのメタデータ
    """

    min_width: StrictInt = Field(description="幅の最小値[ピクセル]")
    min_height: StrictInt = Field(description="高さの最小値[ピクセル]")
    min_warn_rule: StrictStr = Field(
        description="サイズの制約に関する情報 * `none` - 制約なし * `or` - 幅と高さの両方が最小値以上 * `and` - 幅と高さのどちらか一方が最小値以上 "
    )
    tolerance: StrictInt | None = Field(default=None, description="許容誤差[ピクセル]")
    __properties: ClassVar[List[str]] = ["min_width", "min_height", "min_warn_rule", "tolerance"]



    @field_validator("min_warn_rule")
    def min_warn_rule_validate_enum(cls, value):
        """Validates the enum"""
        if value not in set(["none", "or", "and"]):
            raise ValueError("must be one of enum values ('none', 'or', 'and')")
        return value


    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) -> Self | None:
        """Create an instance of SegmentationMetadata 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: Dict[str, Any] | None) -> Self | None:
        """Create an instance of SegmentationMetadata from a dict"""
        if obj is None:
            return None

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

        _obj = cls.model_validate(
            {
                "min_width": obj.get("min_width"),
                "min_height": obj.get("min_height"),
                "min_warn_rule": obj.get("min_warn_rule"),
                "tolerance": obj.get("tolerance"),
            }
        )
        return _obj