text: # coding: utf-8
  
  """
      Kubernetes
  
      No description provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen)
  
      OpenAPI spec version: v1.5.3
      
      Generated by: https://github.com/swagger-api/swagger-codegen.git
  """
  
  
  from pprint import pformat
  from six import iteritems
  import re
  
  
  class V1beta1HorizontalPodAutoscalerSpec(object):
      """
      NOTE: This class is auto generated by the swagger code generator program.
      Do not edit the class manually.
      """
      def __init__(self, cpu_utilization=None, max_replicas=None, min_replicas=None, scale_ref=None):
          """
          V1beta1HorizontalPodAutoscalerSpec - a model defined in Swagger
  
          :param dict swaggerTypes: The key is attribute name
                                    and the value is attribute type.
          :param dict attributeMap: The key is attribute name
                                    and the value is json key in definition.
          """
          self.swagger_types = {
              'cpu_utilization': 'V1beta1CPUTargetUtilization',
              'max_replicas': 'int',
              'min_replicas': 'int',
              'scale_ref': 'V1beta1SubresourceReference'
          }
  
          self.attribute_map = {
              'cpu_utilization': 'cpuUtilization',
              'max_replicas': 'maxReplicas',
              'min_replicas': 'minReplicas',
              'scale_ref': 'scaleRef'
          }
  
          self._cpu_utilization = cpu_utilization
          self._max_replicas = max_replicas
          self._min_replicas = min_replicas
          self._scale_ref = scale_ref
  
      @property
      def cpu_utilization(self):
          """
          Gets the cpu_utilization of this V1beta1HorizontalPodAutoscalerSpec.
          target average CPU utilization (represented as a percentage of requested CPU) over all the pods; if not specified it defaults to the target CPU utilization at 80% of the requested resources.
  
          :return: The cpu_utilization of this V1beta1HorizontalPodAutoscalerSpec.
          :rtype: V1beta1CPUTargetUtilization
          """
          return self._cpu_utilization
  
      @cpu_utilization.setter
      def cpu_utilization(self, cpu_utilization):
          """
          Sets the cpu_utilization of this V1beta1HorizontalPodAutoscalerSpec.
          target average CPU utilization (represented as a percentage of requested CPU) over all the pods; if not specified it defaults to the target CPU utilization at 80% of the requested resources.
  
          :param cpu_utilization: The cpu_utilization of this V1beta1HorizontalPodAutoscalerSpec.
          :type: V1beta1CPUTargetUtilization
          """
  
          self._cpu_utilization = cpu_utilization
  
      @property
      def max_replicas(self):
          """
          Gets the max_replicas of this V1beta1HorizontalPodAutoscalerSpec.
          upper limit for the number of pods that can be set by the autoscaler; cannot be smaller than MinReplicas.
  
          :return: The max_replicas of this V1beta1HorizontalPodAutoscalerSpec.
          :rtype: int
          """
          return self._max_replicas
  
      @max_replicas.setter
      def max_replicas(self, max_replicas):
          """
          Sets the max_replicas of this V1beta1HorizontalPodAutoscalerSpec.
          upper limit for the number of pods that can be set by the autoscaler; cannot be smaller than MinReplicas.
  
          :param max_replicas: The max_replicas of this V1beta1HorizontalPodAutoscalerSpec.
          :type: int
          """
          if max_replicas is None:
              raise ValueError("Invalid value for `max_replicas`, must not be `None`")
  
          self._max_replicas = max_replicas
  
      @property
      def min_replicas(self):
          """
          Gets the min_replicas of this V1beta1HorizontalPodAutoscalerSpec.
          lower limit for the number of pods that can be set by the autoscaler, default 1.
  
          :return: The min_replicas of this V1beta1HorizontalPodAutoscalerSpec.
          :rtype: int
          """
          return self._min_replicas
  
      @min_replicas.setter
      def min_replicas(self, min_replicas):
          """
          Sets the min_replicas of this V1beta1HorizontalPodAutoscalerSpec.
          lower limit for the number of pods that can be set by the autoscaler, default 1.
  
          :param min_replicas: The min_replicas of this V1beta1HorizontalPodAutoscalerSpec.
          :type: int
          """
  
          self._min_replicas = min_replicas
  
      @property
      def scale_ref(self):
          """
          Gets the scale_ref of this V1beta1HorizontalPodAutoscalerSpec.
          reference to Scale subresource; horizontal pod autoscaler will learn the current resource consumption from its status, and will set the desired number of pods by modifying its spec.
  
          :return: The scale_ref of this V1beta1HorizontalPodAutoscalerSpec.
          :rtype: V1beta1SubresourceReference
          """
          return self._scale_ref
  
      @scale_ref.setter
      def scale_ref(self, scale_ref):
          """
          Sets the scale_ref of this V1beta1HorizontalPodAutoscalerSpec.
          reference to Scale subresource; horizontal pod autoscaler will learn the current resource consumption from its status, and will set the desired number of pods by modifying its spec.
  
          :param scale_ref: The scale_ref of this V1beta1HorizontalPodAutoscalerSpec.
          :type: V1beta1SubresourceReference
          """
          if scale_ref is None:
              raise ValueError("Invalid value for `scale_ref`, must not be `None`")
  
          self._scale_ref = scale_ref
  
      def to_dict(self):
          """
          Returns the model properties as a dict
          """
          result = {}
  
          for attr, _ in iteritems(self.swagger_types):
              value = getattr(self, attr)
              if isinstance(value, list):
                  result[attr] = list(map(
                      lambda x: x.to_dict() if hasattr(x, "to_dict") else x,
                      value
                  ))
              elif hasattr(value, "to_dict"):
                  result[attr] = value.to_dict()
              elif isinstance(value, dict):
                  result[attr] = dict(map(
                      lambda item: (item[0], item[1].to_dict())
                      if hasattr(item[1], "to_dict") else item,
                      value.items()
                  ))
              else:
                  result[attr] = value
  
          return result
  
      def to_str(self):
          """
          Returns the string representation of the model
          """
          return pformat(self.to_dict())
  
      def __repr__(self):
          """
          For `print` and `pprint`
          """
          return self.to_str()
  
      def __eq__(self, other):
          """
          Returns true if both objects are equal
          """
          return self.__dict__ == other.__dict__
  
      def __ne__(self, other):
          """
          Returns true if both objects are not equal
          """
          return not self == other
  