# coding: utf-8

"""
    Kubernetes

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

    The version of the OpenAPI document: release-1.37
    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 collections.abc import Mapping as _Mapping
from pydantic import AliasChoices, BaseModel, ConfigDict, Field, StrictInt
from typing import Any, ClassVar, Dict, List, Optional, cast as _cast
from kubernetes.client.models.v2_cross_version_object_reference import V2CrossVersionObjectReference
from kubernetes.client.models.v2_horizontal_pod_autoscaler_behavior import V2HorizontalPodAutoscalerBehavior
from kubernetes.client.models.v2_metric_spec import V2MetricSpec
from typing import Optional, Set
from typing_extensions import Self
from pydantic_core import to_jsonable_python


_OPENAPI_GENERATOR_TO_DICT = "_openapi_generator_to_dict"


def _get_openapi_to_dict(value: Any) -> Any:
    # Reciprocal function references preserve inherited generated methods
    # without reserving model member names. Checking both directions also
    # rejects overrides whose decorators copy function attributes.
    to_dict = getattr(value, "to_dict", None)
    type_to_dict = getattr(type(value), "to_dict", None)
    if (
        not callable(to_dict)
        or getattr(to_dict, "__func__", None) is not type_to_dict
    ):
        return None

    openapi_to_dict = getattr(
        type_to_dict, _OPENAPI_GENERATOR_TO_DICT, None
    )
    if (
        not callable(openapi_to_dict)
        or getattr(
            openapi_to_dict,
            _OPENAPI_GENERATOR_TO_DICT,
            None,
        ) is not type_to_dict
    ):
        return None
    return openapi_to_dict


def _to_legacy_item(value: Any, serialize: bool) -> Any:
    to_dict = getattr(value, "to_dict", None)
    if _get_openapi_to_dict(value) is not None and callable(to_dict):
        return to_dict(serialize=serialize)
    if callable(to_dict):
        return to_dict()
    return value


def _to_legacy_value(value: Any, serialize: bool) -> Any:
    # python-legacy converted only immediate list elements or dictionary values:
    # https://github.com/OpenAPITools/openapi-generator/blob/c84b949df1a9ec04ba75989cb49b45c940c2f694/modules/openapi-generator/src/main/resources/python-legacy/model.mustache#L203-L231
    if isinstance(value, list):
        return [_to_legacy_item(item, serialize) for item in value]
    if isinstance(value, dict):
        return {
            key: _to_legacy_item(item, serialize)
            for key, item in value.items()
        }
    return _to_legacy_item(value, serialize)


def _to_openapi_value(value: Any) -> Any:
    if isinstance(value, list):
        return [_to_openapi_value(item) for item in value]
    if isinstance(value, dict):
        return {key: _to_openapi_value(item) for key, item in value.items()}

    to_openapi_dict = _get_openapi_to_dict(value)
    if to_openapi_dict is not None:
        return to_openapi_dict(value)

    to_dict = getattr(value, "to_dict", None)
    if callable(to_dict):
        return to_dict()
    return value


class V2HorizontalPodAutoscalerSpec(BaseModel):
    """
    HorizontalPodAutoscalerSpec describes the desired functionality of the HorizontalPodAutoscaler.

    Attributes:
      openapi_types (dict): The key is attribute name
                            and the value is attribute type.
      attribute_map (dict): The key is attribute name
                            and the value is json key in definition.
    """ # noqa: E501
    behavior: Optional[V2HorizontalPodAutoscalerBehavior] = None
    max_replicas: StrictInt = Field(validation_alias=AliasChoices("maxReplicas", "max_replicas"), serialization_alias="maxReplicas", description="maxReplicas is the upper limit for the number of replicas to which the autoscaler can scale up. It cannot be less that minReplicas.")
    metrics: Optional[List[V2MetricSpec]] = Field(default=None, description="metrics contains the specifications for which to use to calculate the desired replica count (the maximum replica count across all metrics will be used).  The desired replica count is calculated multiplying the ratio between the target value and the current value by the current number of pods.  Ergo, metrics used must decrease as the pod count is increased, and vice-versa.  See the individual metric source types for more information about how each type of metric must respond. If not set, the default metric will be set to 80% average CPU utilization.")
    min_replicas: Optional[StrictInt] = Field(default=None, validation_alias=AliasChoices("minReplicas", "min_replicas"), serialization_alias="minReplicas", description="minReplicas is the lower limit for the number of replicas to which the autoscaler can scale down.  It defaults to 1 pod.  minReplicas is allowed to be 0 if the alpha feature gate HPAScaleToZero is enabled and at least one Object or External metric is configured.  Scaling is active as long as at least one metric value is available.")
    scale_target_ref: V2CrossVersionObjectReference = Field(validation_alias=AliasChoices("scaleTargetRef", "scale_target_ref"), serialization_alias="scaleTargetRef")
    openapi_types: ClassVar[Dict[str, str]] = {
        "behavior": "V2HorizontalPodAutoscalerBehavior",
        "max_replicas": "int",
        "metrics": "List[V2MetricSpec]",
        "min_replicas": "int",
        "scale_target_ref": "V2CrossVersionObjectReference"
    }

    attribute_map: ClassVar[Dict[str, str]] = {
        "behavior": "behavior",
        "max_replicas": "maxReplicas",
        "metrics": "metrics",
        "min_replicas": "minReplicas",
        "scale_target_ref": "scaleTargetRef"
    }
    __properties: ClassVar[List[str]] = ["behavior", "maxReplicas", "metrics", "minReplicas", "scaleTargetRef"]

    @classmethod
    def __preprocess_input_names(
        cls,
        obj: Any,
        remove_hidden_storage_names: bool = True,
    ) -> Any:
        if not isinstance(obj, _Mapping):
            return obj
        obj = dict(obj)
        if "maxReplicas" not in obj and "max_replicas" in obj:
            obj["maxReplicas"] = obj["max_replicas"]
        obj.pop("max_replicas", None)
        if "minReplicas" not in obj and "min_replicas" in obj:
            obj["minReplicas"] = obj["min_replicas"]
        obj.pop("min_replicas", None)
        if "scaleTargetRef" not in obj and "scale_target_ref" in obj:
            obj["scaleTargetRef"] = obj["scale_target_ref"]
        obj.pop("scale_target_ref", None)
        return obj

    model_config = ConfigDict(
        validate_by_name=True,
        validate_by_alias=True,
        validate_assignment=True,
        extra="forbid",
        protected_namespaces=(),
    )


    def to_str(self) -> str:
        """Returns the string representation of the model"""
        return pprint.pformat(self.to_dict())

    def __repr__(self) -> str:
        """For print and pprint"""
        return self.to_str()

    def __eq__(self, other: object) -> bool:
        """Returns true if both objects are equal"""
        if not isinstance(other, V2HorizontalPodAutoscalerSpec):
            return False

        return self.to_dict() == other.to_dict()

    def __ne__(self, other: object) -> bool:
        """Returns true if both objects are not equal"""
        if not isinstance(other, V2HorizontalPodAutoscalerSpec):
            return True

        return not (self == other)


    def to_json(self) -> str:
        """Returns the JSON representation of the model using alias"""
        to_openapi_dict = _get_openapi_to_dict(self)
        if to_openapi_dict is not None:
            return json.dumps(to_jsonable_python(to_openapi_dict(self)))
        return json.dumps(to_jsonable_python(self.to_dict()))

    @classmethod
    def from_json(cls, json_str: str) -> Optional[Self]:
        """Create an instance of V2HorizontalPodAutoscalerSpec from a JSON string"""
        return cls.from_dict(json.loads(json_str))

    def to_dict(self, serialize: bool = False) -> Dict[str, Any]:
        """Return all declared model fields using public or wire names."""
        return {
            ("behavior" if serialize else "behavior"): _to_legacy_value(getattr(self, "behavior", None), serialize),
            ("maxReplicas" if serialize else "max_replicas"): _to_legacy_value(getattr(self, "max_replicas", None), serialize),
            ("metrics" if serialize else "metrics"): _to_legacy_value(getattr(self, "metrics", None), serialize),
            ("minReplicas" if serialize else "min_replicas"): _to_legacy_value(getattr(self, "min_replicas", None), serialize),
            ("scaleTargetRef" if serialize else "scale_target_ref"): _to_legacy_value(getattr(self, "scale_target_ref", None), serialize),
        }

    def __openapi_generator_modern_projection(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 behavior
        if self.behavior:
            _dict['behavior'] = _to_openapi_value(self.behavior)
        # override the default output from pydantic by calling `to_dict()` of each item in metrics (list)
        _items = []
        if self.metrics:
            for _item_metrics in self.metrics:
                _items.append(_to_openapi_value(_item_metrics) if _item_metrics is not None else None)
            _dict['metrics'] = _items
        # override the default output from pydantic by calling `to_dict()` of scale_target_ref
        if self.scale_target_ref:
            _dict['scaleTargetRef'] = _to_openapi_value(self.scale_target_ref)
        return _dict

    setattr(
        to_dict,
        _OPENAPI_GENERATOR_TO_DICT,
        __openapi_generator_modern_projection,
    )
    setattr(
        __openapi_generator_modern_projection,
        _OPENAPI_GENERATOR_TO_DICT,
        to_dict,
    )
    del __openapi_generator_modern_projection

    @classmethod
    def from_dict(cls, obj: Optional[Dict[str, Any]]) -> Optional[Self]:
        """Create an instance of V2HorizontalPodAutoscalerSpec from a dict"""
        if obj is None:
            return None

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

        obj = _cast(
            Dict[str, Any],
            cls.__preprocess_input_names(
                obj,
                remove_hidden_storage_names=True,
            ),
        )

        _obj = cls.model_validate({
            "behavior": V2HorizontalPodAutoscalerBehavior.from_dict(obj["behavior"]) if obj.get("behavior") is not None else None,
            "maxReplicas": obj.get("maxReplicas"),
            "metrics": [V2MetricSpec.from_dict(_item) for _item in obj["metrics"]] if obj.get("metrics") is not None else None,
            "minReplicas": obj.get("minReplicas"),
            "scaleTargetRef": V2CrossVersionObjectReference.from_dict(obj["scaleTargetRef"]) if obj.get("scaleTargetRef") is not None else None
        })
        return _obj