# coding: utf-8

"""
    IBM Watson Machine Learning REST API

    ## Authorization  ### IBM Watson Machine Learning Credentials  To start working with API one needs to generate an `access token` using the `username` and `password` available on the Service Credentials tab of the IBM Watson Machine Learning service instance or also available in the VCAP environment variable.  Example of the Service Credentials:  ```json {     \"url\": \"https://ibm-watson-ml.mybluemix.net\",     \"access_key\": \"ERY9vcBfE4sE+F4g8hcotF9L+j81WXWeZv\",     \"username\": \"c1ef4b80-2ee2-458e-ab92-e9ca97ec657d\",     \"password\": \"030528d4-5a3e-4d4c-9258-5d553513be6f\" } ```  Example of obtaining `access token` from Token Endpoint using HTTP Basic Auth (for details please refer to Token section below):  ` curl --basic --user username:password https://ibm-watson-ml.mybluemix.net/v2/identity/token `   ### Apache Spark Service Credentials  The IBM Watson Machine Learning co-operates with the Apache Spark as a Service to deploy pipeline models. For API methods requiring Apache Spark Service instance a custom header: `X-Spark-Service-Instance` with Service Credentials must be specified. The header value is a **base64 encoded** string with the Service Credentials JSON data.  [Example of API method requiring Apache Spark Service](https://console.ng.bluemix.net/docs/services/PredictiveModeling/index-gentopic1.html#pm_service_api_spark_batch) 

    OpenAPI spec version: 2.0.0
    
    Generated by: https://github.com/swagger-api/swagger-codegen.git

    Licensed under the Apache License, Version 2.0 (the "License");
    you may not use this file except in compliance with the License.
    You may obtain a copy of the License at

        http://www.apache.org/licenses/LICENSE-2.0

    Unless required by applicable law or agreed to in writing, software
    distributed under the License is distributed on an "AS IS" BASIS,
    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
    See the License for the specific language governing permissions and
    limitations under the License.
"""

#  (C) Copyright IBM Corp. 2021.
#
#  Licensed under the Apache License, Version 2.0 (the "License");
#  you may not use this file except in compliance with the License.
#  You may obtain a copy of the License at
#
#       http://www.apache.org/licenses/LICENSE-2.0
#
#  Unless required by applicable law or agreed to in writing, software
#  distributed under the License is distributed on an "AS IS" BASIS,
#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#  See the License for the specific language governing permissions and
#  limitations under the License.

from pprint import pformat
from six import iteritems
import re


class ModelVersionOutputEntity(object):
    """
    NOTE: This class is auto generated by the swagger code generator program.
    Do not edit the class manually.
    """
    def __init__(self, model=None, content_href=None, training_data_ref=None, evaluation=None, current_metrics=None,
                 content_status=None, content_location=None,hyper_parameters=None):
        """
        ModelVersionOutputEntity - 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 = {
            'model': 'ModelVersionOutputEntityModel',
            'content_href': 'str',
            'training_data_ref': 'ModelTrainingDataRef',
            'evaluation': 'EvaluationDefinition',
            'current_metrics': 'ModelMetrics',
            'content_status': 'ContentStatus',
            'content_location': 'ContentLocation',
            'hyper_parameters' : 'list[HyperParameters]'
        }

        self.attribute_map = {
            'model': 'model',
            'content_href': 'contentHref',
            'training_data_ref': 'trainingDataRef',
            'evaluation': 'evaluation',
            'current_metrics': 'currentMetrics',
            'content_status': 'contentStatus',
            'content_location': 'contentLocation',
            'hyper_parameters': 'hyper_parameters'
        }

        self._model = model
        self._content_href = content_href
        self._training_data_ref = training_data_ref
        self._evaluation = evaluation
        self._current_metrics = current_metrics
        self._content_status = content_status
        self._content_location = content_location
        self._hyper_parameters = hyper_parameters

    @property
    def model(self):
        """
        Gets the model of this ModelVersionOutputEntity.


        :return: The model of this ModelVersionOutputEntity.
        :rtype: ModelVersionOutputEntityModel
        """
        return self._model

    @model.setter
    def model(self, model):
        """
        Sets the model of this ModelVersionOutputEntity.


        :param model: The model of this ModelVersionOutputEntity.
        :type: ModelVersionOutputEntityModel
        """

        self._model = model

    @property
    def hyper_parameters(self):
        """
        Gets the hyper_parameters of this MlAssetsCreateModelInput.


        :return: The hyper_parameters of this MlAssetsCreateModelInput.
        :rtype: HyperParameters
        """
        return self._hyper_parameters

    @hyper_parameters.setter
    def hyper_parameters(self, hyper_parameters):
        """
        Sets the framework of this MlAssetsCreateModelInput.


        :param framework: The framework of this MlAssetsCreateModelInput.
        :type: HyperParameters
        """
        self._hyper_parameters = hyper_parameters

    @property
    def content_href(self):
        """
        Gets the content_href of this ModelVersionOutputEntity.
        Href to the binary content of the model version. Make GET on it to download this file

        :return: The content_href of this ModelVersionOutputEntity.
        :rtype: str
        """
        return self._content_href

    @content_href.setter
    def content_href(self, content_href):
        """
        Sets the content_href of this ModelVersionOutputEntity.
        Href to the binary content of the model version. Make GET on it to download this file

        :param content_href: The content_href of this ModelVersionOutputEntity.
        :type: str
        """

        self._content_href = content_href

    @property
    def training_data_ref(self):
        """
        Gets the training_data_ref of this ModelVersionOutputEntity.


        :return: The training_data_ref of this ModelVersionOutputEntity.
        :rtype: ModelTrainingDataRef
        """
        return self._training_data_ref

    @training_data_ref.setter
    def training_data_ref(self, training_data_ref):
        """
        Sets the training_data_ref of this ModelVersionOutputEntity.


        :param training_data_ref: The training_data_ref of this ModelVersionOutputEntity.
        :type: ModelTrainingDataRef
        """

        self._training_data_ref = training_data_ref

    @property
    def evaluation(self):
        """
        Gets the evaluation of this ModelVersionOutputEntity.
        Definition of the evaluation method used for pipeline models

        :return: The evaluation of this ModelVersionOutputEntity.
        :rtype: EvaluationDefinition
        """
        return self._evaluation

    @evaluation.setter
    def evaluation(self, evaluation):
        """
        Sets the evaluation of this ModelVersionOutputEntity.
        Definition of the evaluation method used for pipeline models

        :param evaluation: The evaluation of this ModelVersionOutputEntity.
        :type: EvaluationDefinition
        """

        self._evaluation = evaluation

    @property
    def current_metrics(self):
        """
        Gets the current_metrics of this ModelVersionOutputEntity.


        :return: The current_metrics of this ModelVersionOutputEntity.
        :rtype: ModelMetrics
        """
        return self._current_metrics

    @current_metrics.setter
    def current_metrics(self, current_metrics):
        """
        Sets the current_metrics of this ModelVersionOutputEntity.


        :param current_metrics: The current_metrics of this ModelVersionOutputEntity.
        :type: ModelMetrics
        """

        self._current_metrics = current_metrics

    @property
    def content_status(self):
        """
        Gets the content_status of this ModelVersionOutputEntity.


        :return: The content_status of this ModelVersionOutputEntity.
        :rtype: ContentStatus
        """
        return self._content_status

    @content_status.setter
    def content_status(self, content_status):
        """
        Sets the content_status of this ModelVersionOutputEntity.


        :param content_status: The content_status of this ModelVersionOutputEntity.
        :type: ContentStatus
        """
        self._content_status = content_status

    @property
    def content_location(self):
        """
        Gets the content_status of this ModelVersionOutputEntity.


        :return: The content_status of this ModelVersionOutputEntity.
        :rtype: ContentLocation
        """
        return self._content_location

    @content_location.setter
    def content_location(self, content_location):
        """
        Sets the content_location of this ModelVersionOutputEntity.


        :param content_location: The content_status of this ModelVersionOutputEntity.
        :type: ContentLocation
        """
        self._content_status = content_location

    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
