from semantha_sdk.api.semantha_endpoint import SemanthaAPIEndpoint
from semantha_sdk.model.smart_cluster_response_container import SmartClusterResponseContainer, SmartClusterResponseContainerSchema
from semantha_sdk.model.smart_cluster_semi_supervised_request import SmartClusterSemiSupervisedRequest, SmartClusterSemiSupervisedRequestSchema
from semantha_sdk.rest.rest_client import RestClient


class ClustersEndpoint(SemanthaAPIEndpoint):
    """ author semantha, this is a generated class do not change manually! """

    @property
    def _endpoint(self) -> str:
        return self._parent_endpoint + "/clusters"

    def get(self
        , documentids: str = None
        , name: str = None
        , createdafter: int = None
        , createdbefore: int = None
        , updatedafter: int = None
        , updatedbefore: int = None
        , tags: str = None
        , documentclassids: str = None
        , withoutdocumentclass: bool = None
        , mincharacters: int = None
        , metadata: str = None
        , comment: str = None
        , minclustersize: str = None
        , clusteringstructure: str = None
        , reduceoutliers: bool = None
        , range: str = None
        , neighbors: int = None
        ) -> SmartClusterResponseContainer:
        """
        Clusters reference documents based on their semantic content and provides labels for each cluster 
        Args:
            documentids (str): List of document Ids for target. The limit here is 65000 IDs.
        The IDs are passed as a JSON array.
            name (str): The document name in your library (in contrast to the file name being used during upload).
            createdafter (int): Use this parameter to filter the returned reference documents by their date of creation using a UNIX timestamp. The createdafter filter only works when also using the parameters offset and limit.
            createdbefore (int): Use this parameter to filter the returned reference documents by their date of creation using a UNIX timestamp. The createdbefore filter only works when also using the parameters offset and limit.
            updatedafter (int): 
            updatedbefore (int): 
            tags (str): List of tags to filter the reference library. You can combine the tags using a comma (OR) and using a plus sign (AND).
            documentclassids (str): List of documentclass IDs for the target. The limit here is 1000 IDs.
        The IDs are passed as a JSON array.
        This does not apply on the GET referencedocuments call. Here the ids are separated with a comma.
            withoutdocumentclass (bool): Use this parameter to filter the returned reference documents to include only documents that are not linked to a documentclass. The parameter is of type boolean and is set to false by default.
            mincharacters (int): 
            metadata (str): Filter by metadata
            comment (str): Use this parameter to add a comment to your reference document.
            minclustersize (str): Determines the size of the cluster, values are LOW, MEDIUM, HIGH
            clusteringstructure (str): Determines how clusters are created, values are LOCAL, BALANCED, GLOBAL
            reduceoutliers (bool): Boolean to try to reduce outlier on clustering
            range (str): Use for topic over time clustering, values are HOURS, DAYS, MONTHS, YEARS
            neighbors (int): 
            
        """
        q_params = {}
        if documentids is not None:
            q_params["documentids"] = documentids
        if name is not None:
            q_params["name"] = name
        if createdafter is not None:
            q_params["createdafter"] = createdafter
        if createdbefore is not None:
            q_params["createdbefore"] = createdbefore
        if updatedafter is not None:
            q_params["updatedafter"] = updatedafter
        if updatedbefore is not None:
            q_params["updatedbefore"] = updatedbefore
        if tags is not None:
            q_params["tags"] = tags
        if documentclassids is not None:
            q_params["documentclassids"] = documentclassids
        if withoutdocumentclass is not None:
            q_params["withoutdocumentclass"] = withoutdocumentclass
        if mincharacters is not None:
            q_params["mincharacters"] = mincharacters
        if metadata is not None:
            q_params["metadata"] = metadata
        if comment is not None:
            q_params["comment"] = comment
        if minclustersize is not None:
            q_params["minclustersize"] = minclustersize
        if clusteringstructure is not None:
            q_params["clusteringstructure"] = clusteringstructure
        if reduceoutliers is not None:
            q_params["reduceoutliers"] = reduceoutliers
        if range is not None:
            q_params["range"] = range
        if neighbors is not None:
            q_params["neighbors"] = neighbors
        return self._session.get(self._endpoint, q_params=q_params).execute().to(SmartClusterResponseContainerSchema)

    def put(self, smartclustersemisupervisedrequest: SmartClusterSemiSupervisedRequest) -> SmartClusterResponseContainer:
        """
        
        """
        return self._session.put(
            url=self._endpoint,
            json=SmartClusterSemiSupervisedRequestSchema().dump(smartclustersemisupervisedrequest)
        ).execute().to(SmartClusterResponseContainerSchema)


    def __init__(self, session: RestClient, parent_endpoint: str) -> None:
        super().__init__(session, parent_endpoint)


