# 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, StrictStr from typing import Any, ClassVar, Dict, List, Optional, cast as _cast from kubernetes.aio.client.models.v1_label_selector import V1LabelSelector 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 V1TopologySpreadConstraint(BaseModel): """ TopologySpreadConstraint specifies how to spread matching pods among the given topology. 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 label_selector: Optional[V1LabelSelector] = Field(default=None, validation_alias=AliasChoices("labelSelector", "label_selector"), serialization_alias="labelSelector") match_label_keys: Optional[List[StrictStr]] = Field(default=None, validation_alias=AliasChoices("matchLabelKeys", "match_label_keys"), serialization_alias="matchLabelKeys", description="MatchLabelKeys is a set of pod label keys to select the pods over which spreading will be calculated. The keys are used to lookup values from the incoming pod labels, those key-value labels are ANDed with labelSelector to select the group of existing pods over which spreading will be calculated for the incoming pod. The same key is forbidden to exist in both MatchLabelKeys and LabelSelector. MatchLabelKeys cannot be set when LabelSelector isn't set. Keys that don't exist in the incoming pod labels will be ignored. A null or empty list means only match against labelSelector. This is a beta field and requires the MatchLabelKeysInPodTopologySpread feature gate to be enabled (enabled by default).") max_skew: StrictInt = Field(validation_alias=AliasChoices("maxSkew", "max_skew"), serialization_alias="maxSkew", description="MaxSkew describes the degree to which pods may be unevenly distributed. When `whenUnsatisfiable=DoNotSchedule`, it is the maximum permitted difference between the number of matching pods in the target topology and the global minimum. The global minimum is the minimum number of matching pods in an eligible domain or zero if the number of eligible domains is less than MinDomains. For example, in a 3-zone cluster, MaxSkew is set to 1, and pods with the same labelSelector spread as 2/2/1: In this case, the global minimum is 1. | zone1 | zone2 | zone3 | | P P | P P | P | - if MaxSkew is 1, incoming pod can only be scheduled to zone3 to become 2/2/2; scheduling it onto zone1(zone2) would make the ActualSkew(3-1) on zone1(zone2) violate MaxSkew(1). - if MaxSkew is 2, incoming pod can be scheduled onto any zone. When `whenUnsatisfiable=ScheduleAnyway`, it is used to give higher precedence to topologies that satisfy it. It's a required field. Default value is 1 and 0 is not allowed.") min_domains: Optional[StrictInt] = Field(default=None, validation_alias=AliasChoices("minDomains", "min_domains"), serialization_alias="minDomains", description="MinDomains indicates a minimum number of eligible domains. When the number of eligible domains with matching topology keys is less than minDomains, Pod Topology Spread treats \"global minimum\" as 0, and then the calculation of Skew is performed. And when the number of eligible domains with matching topology keys equals or greater than minDomains, this value has no effect on scheduling. As a result, when the number of eligible domains is less than minDomains, scheduler won't schedule more than maxSkew Pods to those domains. If value is nil, the constraint behaves as if MinDomains is equal to 1. Valid values are integers greater than 0. When value is not nil, WhenUnsatisfiable must be DoNotSchedule. For example, in a 3-zone cluster, MaxSkew is set to 2, MinDomains is set to 5 and pods with the same labelSelector spread as 2/2/2: | zone1 | zone2 | zone3 | | P P | P P | P P | The number of domains is less than 5(MinDomains), so \"global minimum\" is treated as 0. In this situation, new pod with the same labelSelector cannot be scheduled, because computed skew will be 3(3 - 0) if new Pod is scheduled to any of the three zones, it will violate MaxSkew.") node_affinity_policy: Optional[StrictStr] = Field(default=None, validation_alias=AliasChoices("nodeAffinityPolicy", "node_affinity_policy"), serialization_alias="nodeAffinityPolicy", description="NodeAffinityPolicy indicates how we will treat Pod's nodeAffinity/nodeSelector when calculating pod topology spread skew. Options are: - Honor: only nodes matching nodeAffinity/nodeSelector are included in the calculations. - Ignore: nodeAffinity/nodeSelector are ignored. All nodes are included in the calculations. If this value is nil, the behavior is equivalent to the Honor policy.") node_taints_policy: Optional[StrictStr] = Field(default=None, validation_alias=AliasChoices("nodeTaintsPolicy", "node_taints_policy"), serialization_alias="nodeTaintsPolicy", description="NodeTaintsPolicy indicates how we will treat node taints when calculating pod topology spread skew. Options are: - Honor: nodes without taints, along with tainted nodes for which the incoming pod has a toleration, are included. - Ignore: node taints are ignored. All nodes are included. If this value is nil, the behavior is equivalent to the Ignore policy.") topology_key: StrictStr = Field(validation_alias=AliasChoices("topologyKey", "topology_key"), serialization_alias="topologyKey", description="TopologyKey is the key of node labels. Nodes that have a label with this key and identical values are considered to be in the same topology. We consider each as a \"bucket\", and try to put balanced number of pods into each bucket. We define a domain as a particular instance of a topology. Also, we define an eligible domain as a domain whose nodes meet the requirements of nodeAffinityPolicy and nodeTaintsPolicy. e.g. If TopologyKey is \"kubernetes.io/hostname\", each Node is a domain of that topology. And, if TopologyKey is \"topology.kubernetes.io/zone\", each zone is a domain of that topology. It's a required field.") when_unsatisfiable: StrictStr = Field(validation_alias=AliasChoices("whenUnsatisfiable", "when_unsatisfiable"), serialization_alias="whenUnsatisfiable", description="WhenUnsatisfiable indicates how to deal with a pod if it doesn't satisfy the spread constraint. - DoNotSchedule (default) tells the scheduler not to schedule it. - ScheduleAnyway tells the scheduler to schedule the pod in any location, but giving higher precedence to topologies that would help reduce the skew. A constraint is considered \"Unsatisfiable\" for an incoming pod if and only if every possible node assignment for that pod would violate \"MaxSkew\" on some topology. For example, in a 3-zone cluster, MaxSkew is set to 1, and pods with the same labelSelector spread as 3/1/1: | zone1 | zone2 | zone3 | | P P P | P | P | If WhenUnsatisfiable is set to DoNotSchedule, incoming pod can only be scheduled to zone2(zone3) to become 3/2/1(3/1/2) as ActualSkew(2-1) on zone2(zone3) satisfies MaxSkew(1). In other words, the cluster can still be imbalanced, but scheduler won't make it *more* imbalanced. It's a required field.") openapi_types: ClassVar[Dict[str, str]] = { "label_selector": "V1LabelSelector", "match_label_keys": "List[str]", "max_skew": "int", "min_domains": "int", "node_affinity_policy": "str", "node_taints_policy": "str", "topology_key": "str", "when_unsatisfiable": "str" } attribute_map: ClassVar[Dict[str, str]] = { "label_selector": "labelSelector", "match_label_keys": "matchLabelKeys", "max_skew": "maxSkew", "min_domains": "minDomains", "node_affinity_policy": "nodeAffinityPolicy", "node_taints_policy": "nodeTaintsPolicy", "topology_key": "topologyKey", "when_unsatisfiable": "whenUnsatisfiable" } __properties: ClassVar[List[str]] = ["labelSelector", "matchLabelKeys", "maxSkew", "minDomains", "nodeAffinityPolicy", "nodeTaintsPolicy", "topologyKey", "whenUnsatisfiable"] @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 "labelSelector" not in obj and "label_selector" in obj: obj["labelSelector"] = obj["label_selector"] obj.pop("label_selector", None) if "matchLabelKeys" not in obj and "match_label_keys" in obj: obj["matchLabelKeys"] = obj["match_label_keys"] obj.pop("match_label_keys", None) if "maxSkew" not in obj and "max_skew" in obj: obj["maxSkew"] = obj["max_skew"] obj.pop("max_skew", None) if "minDomains" not in obj and "min_domains" in obj: obj["minDomains"] = obj["min_domains"] obj.pop("min_domains", None) if "nodeAffinityPolicy" not in obj and "node_affinity_policy" in obj: obj["nodeAffinityPolicy"] = obj["node_affinity_policy"] obj.pop("node_affinity_policy", None) if "nodeTaintsPolicy" not in obj and "node_taints_policy" in obj: obj["nodeTaintsPolicy"] = obj["node_taints_policy"] obj.pop("node_taints_policy", None) if "topologyKey" not in obj and "topology_key" in obj: obj["topologyKey"] = obj["topology_key"] obj.pop("topology_key", None) if "whenUnsatisfiable" not in obj and "when_unsatisfiable" in obj: obj["whenUnsatisfiable"] = obj["when_unsatisfiable"] obj.pop("when_unsatisfiable", 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, V1TopologySpreadConstraint): 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, V1TopologySpreadConstraint): 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 V1TopologySpreadConstraint 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 { ("labelSelector" if serialize else "label_selector"): _to_legacy_value(getattr(self, "label_selector", None), serialize), ("matchLabelKeys" if serialize else "match_label_keys"): _to_legacy_value(getattr(self, "match_label_keys", None), serialize), ("maxSkew" if serialize else "max_skew"): _to_legacy_value(getattr(self, "max_skew", None), serialize), ("minDomains" if serialize else "min_domains"): _to_legacy_value(getattr(self, "min_domains", None), serialize), ("nodeAffinityPolicy" if serialize else "node_affinity_policy"): _to_legacy_value(getattr(self, "node_affinity_policy", None), serialize), ("nodeTaintsPolicy" if serialize else "node_taints_policy"): _to_legacy_value(getattr(self, "node_taints_policy", None), serialize), ("topologyKey" if serialize else "topology_key"): _to_legacy_value(getattr(self, "topology_key", None), serialize), ("whenUnsatisfiable" if serialize else "when_unsatisfiable"): _to_legacy_value(getattr(self, "when_unsatisfiable", 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 label_selector if self.label_selector: _dict['labelSelector'] = _to_openapi_value(self.label_selector) 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 V1TopologySpreadConstraint 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({ "labelSelector": V1LabelSelector.from_dict(obj["labelSelector"]) if obj.get("labelSelector") is not None else None, "matchLabelKeys": obj.get("matchLabelKeys"), "maxSkew": obj.get("maxSkew"), "minDomains": obj.get("minDomains"), "nodeAffinityPolicy": obj.get("nodeAffinityPolicy"), "nodeTaintsPolicy": obj.get("nodeTaintsPolicy"), "topologyKey": obj.get("topologyKey"), "whenUnsatisfiable": obj.get("whenUnsatisfiable") }) return _obj