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# coding: utf-8 # Copyright (c) 2016, 2026, Oracle and/or its affiliates. All rights reserved. # This software is dual-licensed to you under the Universal Permissive License (UPL) 1.0 as shown at https://oss.oracle.com/licenses/upl or Apache License 2.0 as shown at http://www.apache.org/licenses/LICENSE-2.0. You may choose either license. # NOTE: This class is auto generated by OracleSDKGenerator. DO NOT EDIT. API Version: 20231130 from oci.util import formatted_flat_dict, NONE_SENTINEL, value_allowed_none_or_none_sentinel # noqa: F401 from oci.decorators import init_model_state_from_kwargs @init_model_state_from_kwargs class ModelMetrics(object): """ Model metrics during the creation of a new model. """ #: A constant which can be used with the model_metrics_type property of a ModelMetrics. #: This constant has a value of "TEXT_GENERATION_MODEL_METRICS" MODEL_METRICS_TYPE_TEXT_GENERATION_MODEL_METRICS = "TEXT_GENERATION_MODEL_METRICS" #: A constant which can be used with the model_metrics_type property of a ModelMetrics. #: This constant has a value of "CHAT_MODEL_METRICS" MODEL_METRICS_TYPE_CHAT_MODEL_METRICS = "CHAT_MODEL_METRICS" def __init__(self, **kwargs): """ Initializes a new ModelMetrics object with values from keyword arguments. This class has the following subclasses and if you are using this class as input to a service operations then you should favor using a subclass over the base class: * :class:`~oci.generative_ai.models.TextGenerationModelMetrics` * :class:`~oci.generative_ai.models.ChatModelMetrics` The following keyword arguments are supported (corresponding to the getters/setters of this class): :param model_metrics_type: The value to assign to the model_metrics_type property of this ModelMetrics. Allowed values for this property are: "TEXT_GENERATION_MODEL_METRICS", "CHAT_MODEL_METRICS", 'UNKNOWN_ENUM_VALUE'. Any unrecognized values returned by a service will be mapped to 'UNKNOWN_ENUM_VALUE'. :type model_metrics_type: str """ self.swagger_types = { 'model_metrics_type': 'str' } self.attribute_map = { 'model_metrics_type': 'modelMetricsType' } self._model_metrics_type = None @staticmethod def get_subtype(object_dictionary): """ Given the hash representation of a subtype of this class, use the info in the hash to return the class of the subtype. """ type = object_dictionary['modelMetricsType'] if type == 'TEXT_GENERATION_MODEL_METRICS': return 'TextGenerationModelMetrics' if type == 'CHAT_MODEL_METRICS': return 'ChatModelMetrics' else: return 'ModelMetrics' @property def model_metrics_type(self): """ **[Required]** Gets the model_metrics_type of this ModelMetrics. The type of the model metrics. Each type of model can expect a different set of model metrics. Allowed values for this property are: "TEXT_GENERATION_MODEL_METRICS", "CHAT_MODEL_METRICS", 'UNKNOWN_ENUM_VALUE'. Any unrecognized values returned by a service will be mapped to 'UNKNOWN_ENUM_VALUE'. :return: The model_metrics_type of this ModelMetrics. :rtype: str """ return self._model_metrics_type @model_metrics_type.setter def model_metrics_type(self, model_metrics_type): """ Sets the model_metrics_type of this ModelMetrics. The type of the model metrics. Each type of model can expect a different set of model metrics. :param model_metrics_type: The model_metrics_type of this ModelMetrics. :type: str """ allowed_values = ["TEXT_GENERATION_MODEL_METRICS", "CHAT_MODEL_METRICS"] if not value_allowed_none_or_none_sentinel(model_metrics_type, allowed_values): model_metrics_type = 'UNKNOWN_ENUM_VALUE' self._model_metrics_type = model_metrics_type def __repr__(self): return formatted_flat_dict(self) def __eq__(self, other): if other is None: return False return self.__dict__ == other.__dict__ def __ne__(self, other): return not self == other
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