# Copyright 2022 The Kubeflow Authors
#
# 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.
"""The SDK client for Kubeflow Pipelines API."""

import copy
import datetime
import json
import logging
import os
import re
import tarfile
import tempfile
import time
import warnings
import zipfile
from types import ModuleType
from typing import Any, Callable, List, Mapping, Optional

import kfp_server_api
import yaml
from kfp import compiler
from kfp.client import auth

# Operators on scalar values. Only applies to one of |int_value|,
# |long_value|, |string_value| or |timestamp_value|.
_FILTER_OPERATIONS = {
    'UNKNOWN': 0,
    'EQUALS': 1,
    'NOT_EQUALS': 2,
    'GREATER_THAN': 3,
    'GREATER_THAN_EQUALS': 5,
    'LESS_THAN': 6,
    'LESS_THAN_EQUALS': 7
}

KF_PIPELINES_ENDPOINT_ENV = 'KF_PIPELINES_ENDPOINT'
KF_PIPELINES_UI_ENDPOINT_ENV = 'KF_PIPELINES_UI_ENDPOINT'
KF_PIPELINES_DEFAULT_EXPERIMENT_NAME = 'KF_PIPELINES_DEFAULT_EXPERIMENT_NAME'
KF_PIPELINES_OVERRIDE_EXPERIMENT_NAME = 'KF_PIPELINES_OVERRIDE_EXPERIMENT_NAME'
KF_PIPELINES_IAP_OAUTH2_CLIENT_ID_ENV = 'KF_PIPELINES_IAP_OAUTH2_CLIENT_ID'
KF_PIPELINES_APP_OAUTH2_CLIENT_ID_ENV = 'KF_PIPELINES_APP_OAUTH2_CLIENT_ID'
KF_PIPELINES_APP_OAUTH2_CLIENT_SECRET_ENV = 'KF_PIPELINES_APP_OAUTH2_CLIENT_SECRET'


class JobConfig:

    def __init__(
            self, spec: kfp_server_api.ApiPipelineSpec,
            resource_references: kfp_server_api.ApiResourceReference) -> None:
        self.spec = spec
        self.resource_references = resource_references


class RunPipelineResult:

    def __init__(self, client: 'Client',
                 run_info: kfp_server_api.ApiRun) -> None:
        self._client = client
        self.run_info = run_info
        self.run_id = run_info.id

    def wait_for_run_completion(self, timeout=None):
        timeout = timeout or datetime.timedelta.max
        return self._client.wait_for_run_completion(self.run_id, timeout)

    def __repr__(self):
        return f'RunPipelineResult(run_id={self.run_id})'


class Client:
    """The API Client for KubeFlow Pipelines.

    Args:
        host: The host name to use to talk to Kubeflow Pipelines. If not set,
            the in-cluster service DNS name will be used, which only works if
            the current environment is a pod in the same cluster (such as a
            Jupyter instance spawned by Kubeflow's JupyterHub).
            Set the host based on
            https://www.kubeflow.org/docs/components/pipelines/sdk/connect-api/
        client_id: The client ID used by Identity-Aware Proxy.
        namespace: The namespace where the kubeflow pipeline system is run.
        other_client_id: The client ID used to obtain the auth codes and refresh
            tokens. References:
            https://cloud.google.com/iap/docs/authentication-howto#authenticating_from_a_desktop_app.
        other_client_secret: The client secret used to obtain the auth codes and
            refresh tokens.
        existing_token: Pass in token directly, it's used for cases better get
            token outside of SDK, e.x. GCP Cloud Functions or caller already has
            a token.
        cookies: CookieJar object containing cookies that will be passed to the
            pipelines API.
        proxy: HTTP or HTTPS proxy server.
        ssl_ca_cert: Cert for proxy.
        kube_context: String name of context within kubeconfig to use, defaults
            to the current-context set within kubeconfig.
        credentials: A TokenCredentialsBase object which provides the logic to
            populate the requests with credentials to authenticate against the
            API server.
        ui_host: Base url to use to open the Kubeflow Pipelines UI. This is used
            when running the client from a notebook to generate and print links.
        verify_ssl: Whether to verify the servers TLS certificate or not.
    """

    # in-cluster DNS name of the pipeline service
    IN_CLUSTER_DNS_NAME = 'ml-pipeline.{}.svc.cluster.local:8888'
    KUBE_PROXY_PATH = 'api/v1/namespaces/{}/services/ml-pipeline:http/proxy/'

    # Auto populated path in pods
    # https://kubernetes.io/docs/tasks/access-application-cluster/access-cluster/#accessing-the-api-from-a-pod
    # https://kubernetes.io/docs/reference/access-authn-authz/service-accounts-admin/#serviceaccount-admission-controller
    NAMESPACE_PATH = '/var/run/secrets/kubernetes.io/serviceaccount/namespace'

    LOCAL_KFP_CONTEXT = os.path.expanduser('~/.config/kfp/context.json')

    # TODO: Wrap the configurations for different authentication methods.
    def __init__(
        self,
        host: Optional[str] = None,
        client_id: Optional[str] = None,
        namespace: str = 'kubeflow',
        other_client_id: Optional[str] = None,
        other_client_secret: Optional[str] = None,
        existing_token: Optional[str] = None,
        cookies: Optional[str] = None,
        proxy: Optional[str] = None,
        ssl_ca_cert: Optional[str] = None,
        kube_context: Optional[str] = None,
        credentials: Optional[str] = None,
        ui_host: Optional[str] = None,
        verify_ssl: Optional[bool] = None,
    ) -> None:
        """Create a new instance of kfp client."""
        warnings.warn(
            'This client only works with Kubeflow Pipeline v2.0.0-alpha.0 '
            'and later versions.',
            category=FutureWarning)

        host = host or os.environ.get(KF_PIPELINES_ENDPOINT_ENV)
        self._uihost = os.environ.get(KF_PIPELINES_UI_ENDPOINT_ENV, ui_host or
                                      host)
        client_id = client_id or os.environ.get(
            KF_PIPELINES_IAP_OAUTH2_CLIENT_ID_ENV)
        other_client_id = other_client_id or os.environ.get(
            KF_PIPELINES_APP_OAUTH2_CLIENT_ID_ENV)
        other_client_secret = other_client_secret or os.environ.get(
            KF_PIPELINES_APP_OAUTH2_CLIENT_SECRET_ENV)
        config = self._load_config(host, client_id, namespace, other_client_id,
                                   other_client_secret, existing_token, proxy,
                                   ssl_ca_cert, kube_context, credentials,
                                   verify_ssl)
        # Save the loaded API client configuration, as a reference if update is
        # needed.
        self._load_context_setting_or_default()

        # If custom namespace provided, overwrite the loaded or default one in
        # context settings for current client instance
        if namespace != 'kubeflow':
            self._context_setting['namespace'] = namespace

        self._existing_config = config
        if cookies is None:
            cookies = self._context_setting.get('client_authentication_cookie')
        api_client = kfp_server_api.ApiClient(
            config,
            cookie=cookies,
            header_name=self._context_setting.get(
                'client_authentication_header_name'),
            header_value=self._context_setting.get(
                'client_authentication_header_value'))
        _add_generated_apis(self, kfp_server_api, api_client)
        self._job_api = kfp_server_api.JobServiceApi(api_client)
        self._run_api = kfp_server_api.RunServiceApi(api_client)
        self._experiment_api = kfp_server_api.ExperimentServiceApi(api_client)
        self._pipelines_api = kfp_server_api.PipelineServiceApi(api_client)
        self._upload_api = kfp_server_api.PipelineUploadServiceApi(api_client)
        self._healthz_api = kfp_server_api.HealthzServiceApi(api_client)
        if not self._context_setting['namespace'] and self.get_kfp_healthz(
        ).multi_user is True:
            try:
                with open(Client.NAMESPACE_PATH, 'r') as f:
                    current_namespace = f.read()
                    self.set_user_namespace(current_namespace)
            except FileNotFoundError:
                logging.info(
                    'Failed to automatically set namespace.', exc_info=False)

    def _load_config(
            self, host: Optional[str], client_id: Optional[str], namespace: str,
            other_client_id: Optional[str], other_client_secret: Optional[str],
            existing_token: Optional[str], proxy: Optional[str],
            ssl_ca_cert: Optional[str], kube_context: Optional[str],
            credentials: Optional[str],
            verify_ssl: Optional[bool]) -> kfp_server_api.Configuration:
        config = kfp_server_api.Configuration()

        if proxy:
            # https://github.com/kubeflow/pipelines/blob/c6ac5e0b1fd991e19e96419f0f508ec0a4217c29/backend/api/python_http_client/kfp_server_api/rest.py#L100
            config.proxy = proxy
        if verify_ssl is not None:
            config.verify_ssl = verify_ssl

        if ssl_ca_cert:
            config.ssl_ca_cert = ssl_ca_cert

        host = host or ''

        # Defaults to 'https' if host does not contain 'http' or 'https' protocol.
        if host and not host.startswith('http'):
            warnings.warn(
                f'The host {host} does not contain the "http" or "https" protocol. Defaults to "https".'
            )
            host = 'https://' + host

        # Preprocess the host endpoint to prevent some common user mistakes.
        if not client_id:
            # always preserving the protocol (http://localhost requires it)
            host = host.rstrip('/')

        if host:
            config.host = host

        token = None

        # "existing_token" is designed to accept token generated outside of SDK.
        #
        # https://cloud.google.com/functions/docs/securing/function-identity
        # https://cloud.google.com/endpoints/docs/grpc/service-account-authentication
        #
        # Here is an example.
        #
        # import requests
        # import kfp
        #
        # def get_access_token():
        #     url = 'http://metadata.google.internal/computeMetadata/v1/instance/service-accounts/default/token'
        #     r = requests.get(url, headers={'Metadata-Flavor': 'Google'})
        #     r.raise_for_status()
        #     access_token = r.json()['access_token']
        #     return access_token
        #
        # client = kfp.Client(host='<KFPHost>', existing_token=get_access_token())
        #
        if existing_token:
            token = existing_token
            self._is_refresh_token = False
        elif client_id:
            token = auth.get_auth_token(client_id, other_client_id,
                                        other_client_secret)
            self._is_refresh_token = True
        elif self._is_inverse_proxy_host(host):
            token = auth.get_gcp_access_token()
            self._is_refresh_token = False
        elif credentials:
            config.api_key['authorization'] = 'placeholder'
            config.api_key_prefix['authorization'] = 'Bearer'
            config.refresh_api_key_hook = credentials.refresh_api_key_hook

        if token:
            config.api_key['authorization'] = token
            config.api_key_prefix['authorization'] = 'Bearer'
            return config

        if host:
            # if host is explicitly set with auth token, it's probably a port
            # forward address.
            return config

        import kubernetes as k8s
        in_cluster = True
        try:
            k8s.config.load_incluster_config()
        except:
            in_cluster = False

        if in_cluster:
            config.host = Client.IN_CLUSTER_DNS_NAME.format(namespace)
            config = self._get_config_with_default_credentials(config)
            return config

        try:
            k8s.config.load_kube_config(
                client_configuration=config, context=kube_context)
        except:
            print('Failed to load kube config.')
            return config

        if config.host:
            config.host = config.host + '/' + Client.KUBE_PROXY_PATH.format(
                namespace)
        return config

    def _is_inverse_proxy_host(self, host):
        if host:
            return re.match(r'\S+.googleusercontent.com/{0,1}$', host)
        if re.match(r'\w+', host):
            warnings.warn(
                f'The received host is {host}, please include the full endpoint'
                ' address (with ".(pipelines/notebooks).googleusercontent.com")'
            )
        return False

    def _is_ipython(self) -> bool:
        """Returns whether we are running in notebook."""
        try:
            import IPython
            ipy = IPython.get_ipython()
            if ipy is None:
                return False
        except ImportError:
            return False

        return True

    def _get_url_prefix(self) -> str:
        if self._uihost:
            # User's own connection.
            if self._uihost.startswith('http://') or self._uihost.startswith(
                    'https://'):
                return self._uihost
            else:
                return 'http://' + self._uihost

        # In-cluster pod. We could use relative URL.
        return '/pipeline'

    def _load_context_setting_or_default(self) -> None:
        if os.path.exists(Client.LOCAL_KFP_CONTEXT):
            with open(Client.LOCAL_KFP_CONTEXT, 'r') as f:
                self._context_setting = json.load(f)
        else:
            self._context_setting = {
                'namespace': '',
            }

    def _refresh_api_client_token(self) -> None:
        """Refreshes the existing token associated with the kfp_api_client."""
        if getattr(self, '_is_refresh_token', None):
            return

        new_token = auth.get_gcp_access_token()
        self._existing_config.api_key['authorization'] = new_token

    def _get_config_with_default_credentials(
            self, config: kfp_server_api.Configuration
    ) -> kfp_server_api.Configuration:
        """Apply default credentials to the configuration object.

        This method accepts a Configuration object and extends it with
        some default credentials interface.
        """
        # XXX: The default credentials are audience-based service account tokens
        # projected by the kubelet (ServiceAccountTokenVolumeCredentials). As we
        # implement more and more credentials, we can have some heuristic and
        # choose from a number of options.
        # See https://github.com/kubeflow/pipelines/pull/5287#issuecomment-805654121

        # TODO: auth.ServiceAccountCredentials does not exist... dead code path?
        credentials = auth.ServiceAccountTokenVolumeCredentials()
        config_copy = copy.deepcopy(config)

        try:
            credentials.refresh_api_key_hook(config_copy)
        except Exception:
            logging.warning('Failed to set up default credentials. Proceeding'
                            ' without credentials...')
            return config

        config.refresh_api_key_hook = credentials.refresh_api_key_hook
        config.api_key_prefix['authorization'] = 'Bearer'
        config.refresh_api_key_hook(config)
        return config

    def set_user_namespace(self, namespace: str) -> None:
        """Set user namespace into local context setting file.

        This function should only be used when Kubeflow Pipelines is in the
        multi-user mode.

        Args:
            namespace: kubernetes namespace the user has access to.

        Returns:
            None
        """
        self._context_setting['namespace'] = namespace
        if not os.path.exists(os.path.dirname(Client.LOCAL_KFP_CONTEXT)):
            os.makedirs(os.path.dirname(Client.LOCAL_KFP_CONTEXT))
        with open(Client.LOCAL_KFP_CONTEXT, 'w') as f:
            json.dump(self._context_setting, f)

    def get_kfp_healthz(self) -> kfp_server_api.ApiGetHealthzResponse:
        """Gets healthz info of KFP deployment.

        Returns:
            json formatted response from the healtz endpoint.
        """
        count = 0
        response = None
        max_attempts = 5
        while not response:
            count += 1
            if count > max_attempts:
                raise TimeoutError(
                    f'Failed getting healthz endpoint after {max_attempts} attempts.'
                )

            try:
                response = self._healthz_api.get_healthz()
                return response
            # ApiException, including network errors, is the only type that may
            # recover after retry.
            except kfp_server_api.ApiException:
                # logging.exception also logs detailed info about the ApiException
                logging.exception(
                    f'Failed to get healthz info attempt {count} of 5.')
                time.sleep(5)

    def get_user_namespace(self) -> str:
        """Get user namespace in context config.

        Returns:
            kubernetes namespace from the local context file or empty if it
            wasn't set.
        """
        return self._context_setting['namespace']

    def create_experiment(
            self,
            name: str,
            description: str = None,
            namespace: str = None) -> kfp_server_api.ApiExperiment:
        """Create a new experiment.

        Args:
            name: The name of the experiment.
            description: Description of the experiment.
            namespace: The Kubernetes namespace where the experiment should be
                created.
                For single user deployment, leave it as None;
                For multi user, input a namespace where the user is authorized.

        Returns:
            kfp_server_api.ApiExperiment: ApiExperiment object.
        """
        namespace = namespace or self.get_user_namespace()
        experiment = None
        try:
            experiment = self.get_experiment(
                experiment_name=name, namespace=namespace)
        except ValueError as error:
            # Ignore error if the experiment does not exist.
            if not str(error).startswith('No experiment is found with name'):
                raise error

        if not experiment:
            logging.info(f'Creating experiment {name}.')

            resource_references = []
            if namespace:
                key = kfp_server_api.ApiResourceKey(
                    id=namespace, type=kfp_server_api.ApiResourceType.NAMESPACE)
                reference = kfp_server_api.ApiResourceReference(
                    key=key, relationship=kfp_server_api.ApiRelationship.OWNER)
                resource_references.append(reference)

            experiment = kfp_server_api.ApiExperiment(
                name=name,
                description=description,
                resource_references=resource_references)
            experiment = self._experiment_api.create_experiment(body=experiment)

        if self._is_ipython():
            import IPython
            html = \
                f'<a href="{self._get_url_prefix()}/#/experiments/details/{experiment.id}" target="_blank" >Experiment details</a>.'
            IPython.display.display(IPython.display.HTML(html))
        return experiment

    def get_pipeline_id(self, name) -> Optional[str]:
        """Find the id of a pipeline by name.

        Args:
            name: Pipeline name.

        Returns:
            The pipeline id if a pipeline with the name exists.
        """
        pipeline_filter = json.dumps({
            'predicates': [{
                'op': _FILTER_OPERATIONS['EQUALS'],
                'key': 'name',
                'stringValue': name,
            }]
        })
        result = self._pipelines_api.list_pipelines(filter=pipeline_filter)
        if result.pipelines is None:
            return None
        if len(result.pipelines) == 1:
            return result.pipelines[0].id
        elif len(result.pipelines) > 1:
            raise ValueError(
                f'Multiple pipelines with the name: {name} found, the name needs to be unique'
            )
        return None

    def list_experiments(
        self,
        page_token: str = '',
        page_size: int = 10,
        sort_by: str = '',
        namespace: Optional[str] = None,
        filter: Optional[str] = None
    ) -> kfp_server_api.ApiListExperimentsResponse:
        """Lists experiments.

        Args:
            page_token: Token for starting of the page.
            page_size: Size of the page.
            sort_by: Can be '[field_name]', '[field_name] desc'. For example,
                'name desc'.
            namespace: Kubernetes namespace where the experiment was created.
                For single user deployment, leave it as None;
                For multi user, input a namespace where the user is authorized.
            filter: A url-encoded, JSON-serialized Filter protocol buffer
                (see [filter.proto](https://github.com/kubeflow/pipelines/blob/master/backend/api/filter.proto)).

                An example filter string would be:

                    # For the list of filter operations please see:
                    # https://github.com/kubeflow/pipelines/blob/master/sdk/python/kfp/client/client.py#L36
                    json.dumps({
                        "predicates": [{
                            "op": _FILTER_OPERATIONS["EQUALS"],
                            "key": "name",
                            "stringValue": "my-name",
                        }]
                    })

        Returns:
            kfp_server_api.ApiListExperimentsResponse: ApiListExperimentsResponse object.
        """
        namespace = namespace or self.get_user_namespace()
        response = self._experiment_api.list_experiment(
            page_token=page_token,
            page_size=page_size,
            sort_by=sort_by,
            resource_reference_key_type=kfp_server_api.ApiResourceType
            .NAMESPACE,
            resource_reference_key_id=namespace,
            filter=filter)
        return response

    def get_experiment(self,
                       experiment_id=None,
                       experiment_name=None,
                       namespace=None) -> kfp_server_api.ApiExperiment:
        """Gets details of an experiment.

        Either experiment_id or experiment_name is required.

        Args:
            experiment_id (Optional): Id of the experiment.
            experiment_name (Optional): Name of the experiment.
            namespace: Kubernetes namespace where the experiment was created.
                For single user deployment, leave it as None;
                For multi user, input the namespace where the user is authorized.

        Returns:
            kfp_server_api.ApiExperiment: ApiExperiment object.

        Raises:
            kfp_server_api.ApiException: If experiment is not found or None of
              the arguments is provided
        """
        namespace = namespace or self.get_user_namespace()
        if experiment_id is None and experiment_name is None:
            raise ValueError(
                'Either experiment_id or experiment_name is required')
        if experiment_id is not None:
            return self._experiment_api.get_experiment(id=experiment_id)
        experiment_filter = json.dumps({
            'predicates': [{
                'op': _FILTER_OPERATIONS['EQUALS'],
                'key': 'name',
                'stringValue': experiment_name,
            }]
        })
        if namespace:
            result = self._experiment_api.list_experiment(
                filter=experiment_filter,
                resource_reference_key_type=kfp_server_api.ApiResourceType
                .NAMESPACE,
                resource_reference_key_id=namespace)
        else:
            result = self._experiment_api.list_experiment(
                filter=experiment_filter)
        if not result.experiments:
            raise ValueError(
                f'No experiment is found with name {experiment_name}.')
        if len(result.experiments) > 1:
            raise ValueError(
                f'Multiple experiments is found with name {experiment_name}.')
        return result.experiments[0]

    def archive_experiment(self, experiment_id: str) -> dict:
        """Archives an experiment.

        Args:
            experiment_id: id of the experiment.

        Returns:
            dict: Empty dictionary.
        """
        return self._experiment_api.archive_experiment(id=experiment_id)

    def unarchive_experiment(self, experiment_id: str) -> dict:
        """Unarchives an experiment.

        Args:
            experiment_id: id of the experiment.

        Returns:
            dict: Empty dictionary.
        """
        return self._experiment_api.unarchive_experiment(id=experiment_id)

    def delete_experiment(self, experiment_id: str) -> dict:
        """Delete experiment.

        Args:
            experiment_id: id of the experiment.

        Returns:
            dict: Empty dictionary.

        Raises:
            kfp_server_api.ApiException: If experiment is not found.
        """
        return self._experiment_api.delete_experiment(id=experiment_id)

    def _extract_pipeline_yaml(self, package_file: str) -> str:

        def _choose_pipeline_file(file_list: List[str]) -> str:
            pipeline_files = [
                file for file in file_list if file.endswith('.yaml')
            ]
            if not pipeline_files:
                raise ValueError(
                    'Invalid package. Missing pipeline yaml file in the package.'
                )

            if 'pipeline.yaml' in pipeline_files:
                return 'pipeline.yaml'
            elif len(pipeline_files) == 1:
                return pipeline_files[0]
            else:
                raise ValueError(
                    'Invalid package. There is no pipeline.json file or there '
                    'are multiple yaml files.')

        if package_file.endswith('.tar.gz') or package_file.endswith('.tgz'):
            with tarfile.open(package_file, 'r:gz') as tar:
                file_names = [member.name for member in tar if member.isfile()]
                pipeline_file = _choose_pipeline_file(file_names)
                with tar.extractfile(
                        tar.getmember(pipeline_file)) as f:  # type: ignore
                    return yaml.safe_load(f)
        elif package_file.endswith('.zip'):
            with zipfile.ZipFile(package_file, 'r') as zip:
                pipeline_file = _choose_pipeline_file(zip.namelist())
                with zip.open(pipeline_file) as f:
                    return yaml.safe_load(f)
        elif package_file.endswith('.yaml') or package_file.endswith('.yml'):
            with open(package_file, 'r') as f:
                return yaml.safe_load(f)
        else:
            raise ValueError(
                f'The package_file {package_file} should end with one of the '
                'following formats: [.tar.gz, .tgz, .zip, .yaml, .yml].')

    def _override_caching_options(self, workflow: str,
                                  enable_caching: bool) -> None:
        raise NotImplementedError('enable_caching is not supported yet.')

    def list_pipelines(
        self,
        page_token: str = '',
        page_size: int = 10,
        sort_by: str = '',
        filter: Optional[str] = None
    ) -> kfp_server_api.ApiListPipelinesResponse:
        """Lists pipelines.

        Args:
            page_token: Token for starting of the page.
            page_size: Size of the page.
            sort_by: one of 'field_name', 'field_name desc'. For example,
                'name desc'.
            filter: A url-encoded, JSON-serialized Filter protocol buffer
                (see [filter.proto](https://github.com/kubeflow/pipelines/blob/master/backend/api/filter.proto)).

                An example filter string would be:

                    # For the list of filter operations please see:
                    # https://github.com/kubeflow/pipelines/blob/master/sdk/python/kfp/_client.py#L40
                    json.dumps({
                        "predicates": [{
                            "op": _FILTER_OPERATIONS["EQUALS"],
                            "key": "name",
                            "stringValue": "my-name",
                        }]
                    })

        Returns:
            kfp_server_api.ApiListPipelinesResponse: ApiListPipelinesResponse object.
        """
        return self._pipelines_api.list_pipelines(
            page_token=page_token,
            page_size=page_size,
            sort_by=sort_by,
            filter=filter)

    # TODO: provide default namespace, similar to kubectl default namespaces.
    def run_pipeline(
        self,
        experiment_id: str,
        job_name: str,
        pipeline_package_path: Optional[str] = None,
        params: Optional[Mapping[str, Any]] = None,
        pipeline_id: Optional[str] = None,
        version_id: Optional[str] = None,
        pipeline_root: Optional[str] = None,
        enable_caching: Optional[bool] = None,
        service_account: Optional[str] = None,
    ) -> kfp_server_api.ApiRun:
        """Runs a specified pipeline.

        Args:
            experiment_id: The id of an experiment.
            job_name: Name of the job.
            pipeline_package_path: Local path of the pipeline package (the
                filename should end with one of the following .tar.gz, .tgz,
                .zip, .json).
            params: A dictionary with key (string) as param name and value
                (string) as as param value.
            pipeline_id: The id of a pipeline.
            version_id: The id of a pipeline version.
                If both pipeline_id and version_id are specified, version_id
                will take precendence.
                If only pipeline_id is specified, the default version of this
                pipeline is used to create the run.
            pipeline_root: The root path of the pipeline outputs.
            enable_caching (Optional): . Whether or not to enable caching for the
                run. If not set, defaults to the compile time settings, which
                are True for all tasks by default, while users may specify
                different caching options for individual tasks. If set, the
                setting applies to all tasks in the pipeline (overrides the
                compile time settings).
            service_account (Optional): Specifies which Kubernetes service
                account this run uses.

        Returns:
            kfp_server_api.ApiRun: ApiRun object.
        """
        if params is None:
            params = {}

        job_config = self._create_job_config(
            experiment_id=experiment_id,
            params=params,
            pipeline_package_path=pipeline_package_path,
            pipeline_id=pipeline_id,
            version_id=version_id,
            enable_caching=enable_caching,
            pipeline_root=pipeline_root,
        )
        run_body = kfp_server_api.ApiRun(
            pipeline_spec=job_config.spec,
            resource_references=job_config.resource_references,
            name=job_name,
            service_account=service_account)

        response = self._run_api.create_run(body=run_body)

        if self._is_ipython():
            import IPython
            html = (
                f'<a href="{self._get_url_prefix()}/#/runs/details/{response.run.id}" target="_blank" >Run details</a>.'
            )
            IPython.display.display(IPython.display.HTML(html))
        return response.run

    def archive_run(self, run_id: str) -> dict:
        """Archives a run.

        Args:
            run_id: id of the run.

        Returns:
            dict: Empty dictionary.
        """
        return self._run_api.archive_run(id=run_id)

    def unarchive_run(self, run_id: str) -> dict:
        """Restores an archived run.

        Args:
            run_id: id of the run.

        Returns:
            dict: Empty dictionary.
        """
        return self._run_api.unarchive_run(id=run_id)

    def delete_run(self, run_id: str) -> dict:
        """Deletes a run.

        Args:
            run_id: id of the run.

        Returns:
            dict: Empty dictionary.
        """
        return self._run_api.delete_run(id=run_id)

    def terminate_run(self, run_id: str) -> dict:
        """Terminates a run.

        Args:
            run_id: id of the run.

        Returns:
            dict: Empty dictionary.
        """
        return self._run_api.terminate_run(run_id=run_id)

    def create_recurring_run(
        self,
        experiment_id: str,
        job_name: str,
        description: Optional[str] = None,
        start_time: Optional[str] = None,
        end_time: Optional[str] = None,
        interval_second: Optional[int] = None,
        cron_expression: Optional[str] = None,
        max_concurrency: Optional[int] = 1,
        no_catchup: Optional[bool] = None,
        params: Optional[dict] = None,
        pipeline_package_path: Optional[str] = None,
        pipeline_id: Optional[str] = None,
        version_id: Optional[str] = None,
        enabled: bool = True,
        pipeline_root: Optional[str] = None,
        enable_caching: Optional[bool] = None,
        service_account: Optional[str] = None,
    ) -> kfp_server_api.ApiJob:
        """Creates a recurring run.

        Args:
            experiment_id: The string id of an experiment.
            job_name: Name of the job.
            description (Optional): Job description.
            start_time: The RFC3339 time string of the time when to start the
                job.
            end_time: The RFC3339 time string of the time when to end the job.
            interval_second: Integer indicating the seconds between two
                recurring runs in for a periodic schedule.
            cron_expression: A cron expression representing a set of times,
                using 6 space-separated fields, e.g. "0 0 9 ? * 2-6". See:
                https://pkg.go.dev/github.com/robfig/cron#hdr-CRON_Expression_Format
            max_concurrency: Integer indicating how many jobs can be run in
                parallel.
            no_catchup: Whether the recurring run should catch up if behind
                schedule. For example, if the recurring run is paused for a
                while and re-enabled afterwards. If no_catchup=False, the
                scheduler will catch up on (backfill) each missed interval.
                Otherwise, it only schedules the latest interval if more than
                one interval is ready to be scheduled. Usually, if your pipeline
                handles backfill internally, you should turn catchup off to
                avoid duplicate backfill. (default: {False})
            pipeline_package_path: Local path of the pipeline package (the
                filename should end with one of the following .tar.gz, .tgz,
                .zip, .json).
            params: A dictionary with key as param name and value as param value.
            pipeline_id: The id of a pipeline.
            version_id: The id of a pipeline version.
                If both pipeline_id and version_id are specified, version_id
                will take precedence.
                If only pipeline_id is specified, the default version of this
                pipeline is used to create the run.
            enabled: A bool indicating whether the recurring run is enabled or
                disabled.
            pipeline_root: The root path of the pipeline outputs.
            enable_caching (Optional): Whether or not to enable caching for the
                run. If not set, defaults to the compile time settings, which
                are True for all tasks by default, while users may specify
                different caching options for individual tasks. If set, the
                setting applies to all tasks in the pipeline (overrides the
                compile time settings).
            service_account (Optional): Specifies which Kubernetes service
                account this recurring run uses.
        Returns:
            kfp_server_api.ApiJob: ApiJob object.

        Raises:
            ValueError: If required parameters are not supplied.
        """

        job_config = self._create_job_config(
            experiment_id=experiment_id,
            params=params,
            pipeline_package_path=pipeline_package_path,
            pipeline_id=pipeline_id,
            version_id=version_id,
            enable_caching=enable_caching,
            pipeline_root=pipeline_root,
        )

        if all([interval_second, cron_expression
               ]) or not any([interval_second, cron_expression]):
            raise ValueError(
                'Either interval_second or cron_expression is required')
        if interval_second is not None:
            trigger = kfp_server_api.ApiTrigger(
                periodic_schedule=kfp_server_api.ApiPeriodicSchedule(
                    start_time=start_time,
                    end_time=end_time,
                    interval_second=interval_second))
        if cron_expression is not None:
            trigger = kfp_server_api.ApiTrigger(
                cron_schedule=kfp_server_api.ApiCronSchedule(
                    start_time=start_time,
                    end_time=end_time,
                    cron=cron_expression))

        job_body = kfp_server_api.ApiJob(
            enabled=enabled,
            pipeline_spec=job_config.spec,
            resource_references=job_config.resource_references,
            name=job_name,
            description=description,
            no_catchup=no_catchup,
            trigger=trigger,
            max_concurrency=max_concurrency,
            service_account=service_account)
        return self._job_api.create_job(body=job_body)

    def _create_job_config(
        self,
        experiment_id: str,
        params: Optional[Mapping[str, Any]],
        pipeline_package_path: Optional[str],
        pipeline_id: Optional[str],
        version_id: Optional[str],
        enable_caching: Optional[bool],
        pipeline_root: Optional[str],
    ) -> JobConfig:
        """Creates a JobConfig with spec and resource_references.

        Args:
            experiment_id: The id of an experiment.
            pipeline_package_path: Local path of the pipeline package (the
                filename should end with one of the following .tar.gz, .tgz,
                .zip, .yaml, .yml).
            params: A dictionary with key as param name and value as param value.
            pipeline_id: The id of a pipeline.
            version_id: The id of a pipeline version.
                If both pipeline_id and version_id are specified, version_id
                will take precedence. If only pipeline_id is specified, the
                default version of this pipeline is used to create the run.
            enable_caching (Optional): Whether or not to enable caching for the
                run. If not set, defaults to the compile time settings, which
                are True for all tasks by default, while users may specify
                different caching options for individual tasks. If set, the
                setting applies to all tasks in the pipeline (overrides the
                compile time settings).
            pipeline_root: The root path of the pipeline outputs.

        Returns:
            JobConfig: A JobConfig object with attributes .spec and .resource_reference.
        """

        params = params or {}
        pipeline_yaml_string = None
        if pipeline_package_path:
            pipeline_obj = self._extract_pipeline_yaml(pipeline_package_path)

            # Caching option set at submission time overrides the compile time
            # settings.
            if enable_caching is not None:
                self._override_caching_options(pipeline_obj, enable_caching)

            pipeline_yaml_string = yaml.dump(pipeline_obj, sort_keys=True)

        runtime_config = kfp_server_api.PipelineSpecRuntimeConfig(
            pipeline_root=pipeline_root,
            parameters=params,
        )
        resource_references = []
        key = kfp_server_api.ApiResourceKey(
            id=experiment_id, type=kfp_server_api.ApiResourceType.EXPERIMENT)
        reference = kfp_server_api.ApiResourceReference(
            key=key, relationship=kfp_server_api.ApiRelationship.OWNER)
        resource_references.append(reference)

        if version_id:
            key = kfp_server_api.ApiResourceKey(
                id=version_id,
                type=kfp_server_api.ApiResourceType.PIPELINE_VERSION)
            reference = kfp_server_api.ApiResourceReference(
                key=key, relationship=kfp_server_api.ApiRelationship.CREATOR)
            resource_references.append(reference)

        spec = kfp_server_api.ApiPipelineSpec(
            pipeline_id=pipeline_id,
            pipeline_manifest=pipeline_yaml_string,
            runtime_config=runtime_config,
        )
        return JobConfig(spec=spec, resource_references=resource_references)

    def create_run_from_pipeline_func(
        self,
        pipeline_func: Callable,
        arguments: Optional[Mapping[str, Any]] = None,
        run_name: Optional[str] = None,
        experiment_name: Optional[str] = None,
        namespace: Optional[str] = None,
        pipeline_root: Optional[str] = None,
        enable_caching: Optional[bool] = None,
        service_account: Optional[str] = None,
    ) -> RunPipelineResult:
        """Runs pipeline on KFP-enabled Kubernetes cluster.

        This command compiles the pipeline function, creates or gets an
        experiment and submits the pipeline for execution.

        Args:
            pipeline_func: A function that describes a pipeline by calling
                components and composing them into execution graph.
            arguments: Arguments to the pipeline function provided as a dict.
            run_name (Optional): Name of the run to be shown in the UI.
            experiment_name (Optional): Name of the experiment to add the run to.
            namespace: Kubernetes namespace where the pipeline runs are created.
                For single user deployment, leave it as None;
                For multi user, input a namespace where the user is authorized
            pipeline_root: The root path of the pipeline outputs.
            enable_caching (Optional): Whether or not to enable caching for the
                run. If not set, defaults to the compile time settings, which
                are True for all tasks by default, while users may specify
                different caching options for individual tasks. If set, the
                setting applies to all tasks in the pipeline (overrides the
                compile time settings).
            service_account (Optional): Specifies which Kubernetes service
                account this run uses.

        Returns:
            RunPipelineResult: RunPipelineResult object containing information about the pipeline run.
        """
        #TODO: Check arguments against the pipeline function
        pipeline_name = pipeline_func.__name__
        run_name = run_name or pipeline_name + ' ' + datetime.datetime.now(
        ).strftime('%Y-%m-%d %H-%M-%S')

        with tempfile.TemporaryDirectory() as tmpdir:
            pipeline_package_path = os.path.join(tmpdir, 'pipeline.yaml')
            compiler.Compiler().compile(
                pipeline_func=pipeline_func,
                package_path=pipeline_package_path,
            )

            return self.create_run_from_pipeline_package(
                pipeline_file=pipeline_package_path,
                arguments=arguments,
                run_name=run_name,
                experiment_name=experiment_name,
                namespace=namespace,
                pipeline_root=pipeline_root,
                enable_caching=enable_caching,
                service_account=service_account,
            )

    def create_run_from_pipeline_package(
        self,
        pipeline_file: str,
        arguments: Optional[Mapping[str, str]] = None,
        run_name: Optional[str] = None,
        experiment_name: Optional[str] = None,
        namespace: Optional[str] = None,
        pipeline_root: Optional[str] = None,
        enable_caching: Optional[bool] = None,
        service_account: Optional[str] = None,
    ) -> RunPipelineResult:
        """Runs pipeline on KFP-enabled Kubernetes cluster.

        This command takes a local pipeline package, creates or gets an
        experiment and submits the pipeline for execution.

        Args:
            pipeline_file: A compiled pipeline package file.
            arguments: Arguments to the pipeline function provided as a dict.
            run_name (Optional):  Name of the run to be shown in the UI.
            experiment_name (Optional): Name of the experiment to add the run to.
            namespace (Optional): Kubernetes namespace where the pipeline runs are created.
              For single user deployment, leave it as None;
              For multi user, input a namespace where the user is authorized
            pipeline_root (Optional): The root path of the pipeline outputs.
            enable_caching (Optional): Whether or not to enable caching for the
                run. If not set, defaults to the compile time settings, which
                are True for all tasks by default, while users may specify
                different caching options for individual tasks. If set, the
                setting applies to all tasks in the pipeline (overrides the
                compile time settings).
            service_account (Optional): Specifies which Kubernetes service
                account this run uses.

        Returns:
            RunPipelineResult: RunPipelineResult object containing information about the pipeline run.
        """

        #TODO: Check arguments against the pipeline function
        pipeline_name = os.path.basename(pipeline_file)
        experiment_name = experiment_name or os.environ.get(
            KF_PIPELINES_DEFAULT_EXPERIMENT_NAME, None)
        overridden_experiment_name = os.environ.get(
            KF_PIPELINES_OVERRIDE_EXPERIMENT_NAME, experiment_name)
        if overridden_experiment_name != experiment_name:
            warnings.warn(
                f'Changing experiment name from "{experiment_name}" to "{overridden_experiment_name}".'
            )
        experiment_name = overridden_experiment_name or 'Default'
        run_name = run_name or (
            pipeline_name + ' ' +
            datetime.datetime.now().strftime('%Y-%m-%d %H-%M-%S'))
        experiment = self.create_experiment(
            name=experiment_name, namespace=namespace)
        run_info = self.run_pipeline(
            experiment_id=experiment.id,
            job_name=run_name,
            pipeline_package_path=pipeline_file,
            params=arguments,
            pipeline_root=pipeline_root,
            enable_caching=enable_caching,
            service_account=service_account,
        )
        return RunPipelineResult(self, run_info)

    def delete_job(self, job_id: str) -> dict:
        """Deletes a job.

        Args:
            job_id: id of the job.

        Returns:
            dict: Empty dictionary.

        Raises:
            kfp_server_api.ApiException: If the job is not found.
        """
        return self._job_api.delete_job(id=job_id)

    def disable_job(self, job_id: str) -> dict:
        """Disables a job.

        Args:
            job_id: id of the job.

        Returns:
            dict: Empty dictionary.

        Raises:
            kfp_server_api.ApiException: If the job is not found.
        """
        return self._job_api.disable_job(id=job_id)

    def enable_job(self, job_id: str) -> dict:
        """Enables a job.

        Args:
            job_id: id of the job.

        Returns:
            dict: Empty dictionary.

        Raises:
            kfp_server_api.ApiException: If the job is not found.
        """
        return self._job_api.enable_job(id=job_id)

    def list_runs(
            self,
            page_token: str = '',
            page_size: int = 10,
            sort_by: str = '',
            experiment_id: Optional[str] = None,
            namespace: Optional[str] = None,
            filter: Optional[str] = None) -> kfp_server_api.ApiListRunsResponse:
        """List runs, optionally can be filtered by experiment or namespace.

        Args:
            page_token: Token for starting of the page.
            page_size: Size of the page.
            sort_by: One of 'field_name', 'field_name desc'. For example,
                'name desc'.
            experiment_id: Experiment id to filter upon
            namespace: Kubernetes namespace to filter upon.
                For single user deployment, leave it as None;
                For multi user, input a namespace where the user is authorized.
            filter: A url-encoded, JSON-serialized Filter protocol buffer
                (see [filter.proto](https://github.com/kubeflow/pipelines/blob/master/backend/api/filter.proto)).

                An example filter string would be:

                    # For the list of filter operations please see:
                    # https://github.com/kubeflow/pipelines/blob/master/sdk/python/kfp/_client.py#L40
                    json.dumps({
                        "predicates": [{
                            "op": _FILTER_OPERATIONS["EQUALS"],
                            "key": "name",
                            "stringValue": "my-name",
                        }]
                    })

          Returns:
              kfp_server_api.ApiListRunsResponse: ApiListRunsResponse object.
        """
        namespace = namespace or self.get_user_namespace()
        if experiment_id is not None:
            response = self._run_api.list_runs(
                page_token=page_token,
                page_size=page_size,
                sort_by=sort_by,
                resource_reference_key_type=kfp_server_api.ApiResourceType
                .EXPERIMENT,
                resource_reference_key_id=experiment_id,
                filter=filter)
        elif namespace:
            response = self._run_api.list_runs(
                page_token=page_token,
                page_size=page_size,
                sort_by=sort_by,
                resource_reference_key_type=kfp_server_api.ApiResourceType
                .NAMESPACE,
                resource_reference_key_id=namespace,
                filter=filter)
        else:
            response = self._run_api.list_runs(
                page_token=page_token,
                page_size=page_size,
                sort_by=sort_by,
                filter=filter)
        return response

    def list_recurring_runs(
            self,
            page_token: str = '',
            page_size: int = 10,
            sort_by: str = '',
            experiment_id: Optional[str] = None,
            filter: Optional[str] = None) -> kfp_server_api.ApiListJobsResponse:
        """Lists recurring runs.

        Args:
            page_token: Token for starting of the page.
            page_size: Size of the page.
            sort_by: One of 'field_name', 'field_name desc'. For example,
                'name desc'.
            experiment_id: Experiment id to filter upon.
            filter: A url-encoded, JSON-serialized Filter protocol buffer
                (see [filter.proto](https://github.com/kubeflow/pipelines/blob/master/backend/api/filter.proto)).

                An example filter string would be:

                    # For the list of filter operations please see:
                    # https://github.com/kubeflow/pipelines/blob/master/sdk/python/kfp/_client.py#L40
                    json.dumps({
                        "predicates": [{
                            "op": _FILTER_OPERATIONS["EQUALS"],
                            "key": "name",
                            "stringValue": "my-name",
                        }]
                    })

        Returns:
            kfp_server_api.ApiListJobsResponse: ApiListJobsResponse object.
        """
        if experiment_id is not None:
            response = self._job_api.list_jobs(
                page_token=page_token,
                page_size=page_size,
                sort_by=sort_by,
                resource_reference_key_type=kfp_server_api.ApiResourceType
                .EXPERIMENT,
                resource_reference_key_id=experiment_id,
                filter=filter)
        else:
            response = self._job_api.list_jobs(
                page_token=page_token,
                page_size=page_size,
                sort_by=sort_by,
                filter=filter)
        return response

    def get_recurring_run(self, job_id: str) -> kfp_server_api.ApiJob:
        """Gets recurring_run details.

        Args:
            job_id: id of the recurring_run.

        Returns:
            kfp_server_api.ApiJob: ApiJob object.

        Raises:
            kfp_server_api.ApiException: If recurring_run is not found.
        """
        return self._job_api.get_job(id=job_id)

    def get_run(self, run_id: str) -> kfp_server_api.ApiRun:
        """Gets run details.

        Args:
            run_id: id of the run.

        Returns:
            kfp_server_api.ApiRun: ApiRun object.

        Raises:
            kfp_server_api.ApiException: If run is not found.
        """
        return self._run_api.get_run(run_id=run_id)

    def wait_for_run_completion(self, run_id: str,
                                timeout: int) -> kfp_server_api.ApiRun:
        """Waits for a run to complete.

        Args:
            run_id: Run id, returned from run_pipeline.
            timeout: Timeout in seconds.

        Returns:
            kfp_server_api.ApiRun: ApiRun object.

        Raises:
            TimeoutError: if the pipeline run failed to finish before the
                specified timeout.
        """
        status = 'Running:'
        start_time = datetime.datetime.now()
        if isinstance(timeout, datetime.timedelta):
            timeout = timeout.total_seconds()
        is_valid_token = False
        while (status is None or status.lower()
               not in ['succeeded', 'failed', 'skipped', 'error']):
            try:
                get_run_response = self._run_api.get_run(run_id=run_id)
                is_valid_token = True
            except kfp_server_api.ApiException as api_ex:
                # if the token is valid but receiving 401 Unauthorized error
                # then refresh the token
                if is_valid_token and api_ex.status == 401:
                    logging.info('Access token has expired !!! Refreshing ...')
                    self._refresh_api_client_token()
                    continue
                else:
                    raise api_ex
            status = get_run_response.run.status
            elapsed_time = (datetime.datetime.now() -
                            start_time).total_seconds()
            logging.info('Waiting for the job to complete...')
            if elapsed_time > timeout:
                raise TimeoutError('Run timeout')
            time.sleep(5)
        return get_run_response

    def _get_workflow_json(self, run_id: str) -> dict:
        """Gets the workflow json.

        Args:
            run_id: run id, returned from run_pipeline.

        Returns:
            Workflow JSON.
        """
        get_run_response = self._run_api.get_run(run_id=run_id)
        workflow = get_run_response.pipeline_runtime.workflow_manifest
        workflow_json = json.loads(workflow)
        return workflow_json

    def upload_pipeline(
        self,
        pipeline_package_path: str = None,
        pipeline_name: str = None,
        description: str = None,
    ) -> kfp_server_api.ApiPipeline:
        """Uploads the pipeline to the Kubeflow Pipelines cluster.

        Args:
            pipeline_package_path: Local path to the pipeline package.
            pipeline_name (Optional): Name of the pipeline to be shown in the UI.
            description (Optional): Description of the pipeline to be shown in
                the UI.

        Returns:
            kfp_server_api.ApiPipeline: ApiPipeline object.
        """
        if pipeline_name is None:
            pipeline_name = os.path.splitext(
                os.path.basename('something/file.txt'))[0]

        validate_pipeline_resource_name(pipeline_name)
        response = self._upload_api.upload_pipeline(
            pipeline_package_path, name=pipeline_name, description=description)
        if self._is_ipython():
            import IPython
            html = f'<a href={self._get_url_prefix()}/#/pipelines/details/{response.id}>Pipeline details</a>.'
            IPython.display.display(IPython.display.HTML(html))
        return response

    def upload_pipeline_version(
        self,
        pipeline_package_path: str,
        pipeline_version_name: str,
        pipeline_id: Optional[str] = None,
        pipeline_name: Optional[str] = None,
        description: Optional[str] = None,
    ) -> kfp_server_api.ApiPipelineVersion:
        """Uploads a new version of the pipeline to the KFP cluster.

        Args:
            pipeline_package_path: Local path to the pipeline package.
            pipeline_version_name:  Name of the pipeline version to be shown in
                the UI.
            pipeline_id (Optional): Id of the pipeline.
            pipeline_name (Optional): Name of the pipeline.
            description (Optional): Description of the pipeline version to be
                shown in the UI.

        Returns:
            kfp_server_api.ApiPipelineVersion: ApiPipelineVersion object.

        Raises:
            ValueError: when none or both of pipeline_id or pipeline_name are
                specified.
            kfp_server_api.ApiException: If pipeline id is not found.
        """

        if all([pipeline_id, pipeline_name
               ]) or not any([pipeline_id, pipeline_name]):
            raise ValueError('Either pipeline_id or pipeline_name is required')

        if pipeline_name:
            pipeline_id = self.get_pipeline_id(pipeline_name)
        kwargs = dict(
            name=pipeline_version_name,
            pipelineid=pipeline_id,
        )

        if description:
            kwargs['description'] = description

        response = self._upload_api.upload_pipeline_version(
            pipeline_package_path, **kwargs)

        if self._is_ipython():
            import IPython
            html = f'<a href={self._get_url_prefix()}/#/pipelines/details/{response.id}>Pipeline details</a>.'
            IPython.display.display(IPython.display.HTML(html))
        return response

    def get_pipeline(self, pipeline_id: str) -> kfp_server_api.ApiPipeline:
        """Gets pipeline details.

        Args:
            pipeline_id: id of the pipeline.

        Returns:
            kfp_server_api.ApiPipeline: ApiPipeline object.

        Raises:
            kfp_server_api.ApiException: If pipeline is not found.
        """
        return self._pipelines_api.get_pipeline(id=pipeline_id)

    def delete_pipeline(self, pipeline_id: str) -> dict:
        """Deletes a pipeline.

        Args:
            pipeline_id: id of the pipeline.

        Returns:
            dict: Empty dictionary.

        Raises:
            kfp_server_api.ApiException: If pipeline is not found.
        """
        return self._pipelines_api.delete_pipeline(id=pipeline_id)

    def list_pipeline_versions(
        self,
        pipeline_id: str,
        page_token: str = '',
        page_size: int = 10,
        sort_by: str = '',
        filter: Optional[str] = None
    ) -> kfp_server_api.ApiListPipelineVersionsResponse:
        """Lists pipeline versions.

        Args:
            pipeline_id: Id of the pipeline to list versions
            page_token: Token for starting of the page.
            page_size: Size of the page.
            sort_by: One of 'field_name', 'field_name desc'. For example,
                'name desc'.
            filter: A url-encoded, JSON-serialized Filter protocol buffer
                (see [filter.proto](https://github.com/kubeflow/pipelines/blob/master/backend/api/filter.proto)).

                An example filter string would be:

                    # For the list of filter operations please see:
                    # https://github.com/kubeflow/pipelines/blob/master/sdk/python/kfp/_client.py#L40
                    json.dumps({
                        "predicates": [{
                            "op": _FILTER_OPERATIONS["EQUALS"],
                            "key": "name",
                            "stringValue": "my-name",
                        }]
                    })

        Returns:
            kfp_server_api.ApiListPipelineVersionsResponse: ApiListPipelineVersionsResponse object.

        Raises:
            kfp_server_api.ApiException: If pipeline is not found.
        """

        return self._pipelines_api.list_pipeline_versions(
            page_token=page_token,
            page_size=page_size,
            sort_by=sort_by,
            resource_key_type=kfp_server_api.ApiResourceType.PIPELINE,
            resource_key_id=pipeline_id,
            filter=filter)

    def get_pipeline_version(
            self, version_id: str) -> kfp_server_api.ApiPipelineVersion:
        """Gets a pipeline version.

        Args:
            version_id: id of the pipeline version.

        Returns:
            kfp_server_api.ApiPipelineVersion: ApiPipelineVersion object.

        Raises:
            kfp_server_api.ApiException: If pipeline version is not found.
        """
        return self._pipelines_api.get_pipeline_version(version_id=version_id)

    def delete_pipeline_version(self, version_id: str) -> dict:
        """Deletes a pipeline version.

        Args:
            version_id: id of the pipeline version.

        Returns:
            dict: Empty dictionary.

        Raises:
            kfp_server_api.ApiException: If pipeline is not found.
        """
        return self._pipelines_api.delete_pipeline_version(
            version_id=version_id)


def _add_generated_apis(target_struct: Any, api_module: ModuleType,
                        api_client: kfp_server_api.ApiClient):
    """Initializes a hierarchical API object based on the generated API module.

    PipelineServiceApi.create_pipeline becomes
    target_struct.pipelines.create_pipeline
    """
    Struct = type('Struct', (), {})

    def camel_case_to_snake_case(name: str) -> str:
        return re.sub('([a-z0-9])([A-Z])', r'\1_\2', name).lower()

    for api_name in dir(api_module):
        if not api_name.endswith('ServiceApi'):
            continue

        short_api_name = camel_case_to_snake_case(
            api_name[0:-len('ServiceApi')]) + 's'
        api_struct = Struct()
        setattr(target_struct, short_api_name, api_struct)
        service_api = getattr(api_module.api, api_name)
        initialized_service_api = service_api(api_client)
        for member_name in dir(initialized_service_api):
            if member_name.startswith('_') or member_name.endswith(
                    '_with_http_info'):
                continue

            bound_member = getattr(initialized_service_api, member_name)
            setattr(api_struct, member_name, bound_member)
    models_struct = Struct()
    for member_name in dir(api_module.models):
        if not member_name[0].islower():
            setattr(models_struct, member_name,
                    getattr(api_module.models, member_name))
    target_struct.api_models = models_struct


# TODO: merge with maybe_rename_for_k8s or not?
def _sanitize_k8s_name(name: str) -> str:
    """Cleans and converts the name for k8s requirements.

    Args:
        name: The original name.

    Returns:
        The sanitized name.
    """
    return re.sub('-+', '-', re.sub('[^-_0-9A-Za-z]+', '-',
                                    name)).lstrip('-').rstrip('-')


def validate_pipeline_resource_name(name: str) -> None:
    REGEX = r'[a-z0-9]([-a-z0-9]*[a-z0-9])?(\\.[a-z0-9]([-a-z0-9]*[a-z0-9])?)*'

    if re.fullmatch(REGEX, name) is None:
        raise ValueError(
            f'Invalid pipeline name: "{name}". Pipeline name must conform to the regex: "{REGEX}".'
        )
