# coding: utf-8

"""
    Generated by: https://openapi-generator.tech
"""

from __future__ import annotations
import pprint
import re  # noqa: F401
import json

from pydantic import BaseModel, ConfigDict, Field, StrictFloat, StrictInt, StrictStr
from typing import Any, ClassVar, Dict, List, Optional, Union
from regula.facesdk.webclient.gen.models.detection import Detection
from typing import Optional, Set
from typing_extensions import Self
from pydantic import SkipValidation, Field

class DetectResult(BaseModel):
    """
    The detection results.
    """ # noqa: E501
    detections: SkipValidation[List[Detection]] = Field(alias="detections", description="The array of performed checks.")
    detector_type: SkipValidation[Optional[int]] = Field(alias="detectorType", default=None, description="Internal.")
    landmarks_type: SkipValidation[Optional[int]] = Field(alias="landmarksType", default=None, description="Internal.")
    scenario: SkipValidation[Optional[str]] = Field(alias="scenario", default=None, description="The face detection scenario that was set in the `processParam.scenario` field of the request.")
    elapsed_time: SkipValidation[Optional[float]] = Field(alias="elapsedTime", default=None, description="Server processing time, ms. Does not include the time taken to receive the request or deliver the response.")
    __properties: ClassVar[List[str]] = ["detections", "detectorType", "landmarksType", "scenario", "elapsedTime"]

    model_config = ConfigDict(
        populate_by_name=True,
        validate_assignment=True,
        protected_namespaces=(),
        arbitrary_types_allowed=True,
        use_enum_values=True
    )


    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 DetectResult 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 detections (list)
        _items = []
        if self.detections:
            for _item_detections in self.detections:
                if _item_detections:
                    _items.append(_item_detections.to_dict())
            _dict['detections'] = _items
        return _dict

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

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

        _obj = cls.model_validate({
            "detections": [Detection.from_dict(_item) for _item in obj.get("detections", []) if Detection.from_dict(_item) is not None],
            "detectorType": obj.get("detectorType"),
            "landmarksType": obj.get("landmarksType"),
            "scenario": obj.get("scenario"),
            "elapsedTime": obj.get("elapsedTime")
        })
        return _obj


