# coding=utf-8
# --------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for license information.
# Code generated by Microsoft (R) AutoRest Code Generator.
# Changes may cause incorrect behavior and will be lost if the code is regenerated.
# --------------------------------------------------------------------------
import functools
from typing import TYPE_CHECKING
import warnings

from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
from azure.core.paging import ItemPaged
from azure.core.pipeline import PipelineResponse
from azure.core.pipeline.transport import HttpResponse
from azure.core.polling import LROPoller, NoPolling, PollingMethod
from azure.core.rest import HttpRequest
from azure.core.tracing.decorator import distributed_trace
from azure.mgmt.core.exceptions import ARMErrorFormat
from azure.mgmt.core.polling.arm_polling import ARMPolling
from msrest import Serializer

from .. import models as _models
from .._vendor import _convert_request, _format_url_section

if TYPE_CHECKING:
    # pylint: disable=unused-import,ungrouped-imports
    from typing import Any, Callable, Dict, Generic, Iterable, Optional, TypeVar, Union
    T = TypeVar('T')
    ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]]

_SERIALIZER = Serializer()
_SERIALIZER.client_side_validation = False
# fmt: off

def build_list_request(
    subscription_id,  # type: str
    resource_group_name,  # type: str
    workspace_name,  # type: str
    **kwargs  # type: Any
):
    # type: (...) -> HttpRequest
    skip = kwargs.pop('skip', None)  # type: Optional[str]

    api_version = "2022-05-01"
    accept = "application/json"
    # Construct URL
    url = kwargs.pop("template_url", '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes')
    path_format_arguments = {
        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
    }

    url = _format_url_section(url, **path_format_arguments)

    # Construct parameters
    query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
    query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
    if skip is not None:
        query_parameters['$skip'] = _SERIALIZER.query("skip", skip, 'str')

    # Construct headers
    header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
    header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')

    return HttpRequest(
        method="GET",
        url=url,
        params=query_parameters,
        headers=header_parameters,
        **kwargs
    )


def build_get_request(
    subscription_id,  # type: str
    resource_group_name,  # type: str
    workspace_name,  # type: str
    compute_name,  # type: str
    **kwargs  # type: Any
):
    # type: (...) -> HttpRequest
    api_version = "2022-05-01"
    accept = "application/json"
    # Construct URL
    url = kwargs.pop("template_url", '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}')
    path_format_arguments = {
        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
        "computeName": _SERIALIZER.url("compute_name", compute_name, 'str'),
    }

    url = _format_url_section(url, **path_format_arguments)

    # Construct parameters
    query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
    query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')

    # Construct headers
    header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
    header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')

    return HttpRequest(
        method="GET",
        url=url,
        params=query_parameters,
        headers=header_parameters,
        **kwargs
    )


def build_create_or_update_request_initial(
    subscription_id,  # type: str
    resource_group_name,  # type: str
    workspace_name,  # type: str
    compute_name,  # type: str
    **kwargs  # type: Any
):
    # type: (...) -> HttpRequest
    content_type = kwargs.pop('content_type', None)  # type: Optional[str]

    api_version = "2022-05-01"
    accept = "application/json"
    # Construct URL
    url = kwargs.pop("template_url", '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}')
    path_format_arguments = {
        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
        "computeName": _SERIALIZER.url("compute_name", compute_name, 'str'),
    }

    url = _format_url_section(url, **path_format_arguments)

    # Construct parameters
    query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
    query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')

    # Construct headers
    header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
    if content_type is not None:
        header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
    header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')

    return HttpRequest(
        method="PUT",
        url=url,
        params=query_parameters,
        headers=header_parameters,
        **kwargs
    )


def build_update_request_initial(
    subscription_id,  # type: str
    resource_group_name,  # type: str
    workspace_name,  # type: str
    compute_name,  # type: str
    **kwargs  # type: Any
):
    # type: (...) -> HttpRequest
    content_type = kwargs.pop('content_type', None)  # type: Optional[str]

    api_version = "2022-05-01"
    accept = "application/json"
    # Construct URL
    url = kwargs.pop("template_url", '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}')
    path_format_arguments = {
        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
        "computeName": _SERIALIZER.url("compute_name", compute_name, 'str'),
    }

    url = _format_url_section(url, **path_format_arguments)

    # Construct parameters
    query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
    query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')

    # Construct headers
    header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
    if content_type is not None:
        header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str')
    header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')

    return HttpRequest(
        method="PATCH",
        url=url,
        params=query_parameters,
        headers=header_parameters,
        **kwargs
    )


def build_delete_request_initial(
    subscription_id,  # type: str
    resource_group_name,  # type: str
    workspace_name,  # type: str
    compute_name,  # type: str
    **kwargs  # type: Any
):
    # type: (...) -> HttpRequest
    underlying_resource_action = kwargs.pop('underlying_resource_action')  # type: Union[str, "_models.UnderlyingResourceAction"]

    api_version = "2022-05-01"
    accept = "application/json"
    # Construct URL
    url = kwargs.pop("template_url", '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}')
    path_format_arguments = {
        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
        "computeName": _SERIALIZER.url("compute_name", compute_name, 'str'),
    }

    url = _format_url_section(url, **path_format_arguments)

    # Construct parameters
    query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
    query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')
    query_parameters['underlyingResourceAction'] = _SERIALIZER.query("underlying_resource_action", underlying_resource_action, 'str')

    # Construct headers
    header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
    header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')

    return HttpRequest(
        method="DELETE",
        url=url,
        params=query_parameters,
        headers=header_parameters,
        **kwargs
    )


def build_list_nodes_request(
    subscription_id,  # type: str
    resource_group_name,  # type: str
    workspace_name,  # type: str
    compute_name,  # type: str
    **kwargs  # type: Any
):
    # type: (...) -> HttpRequest
    api_version = "2022-05-01"
    accept = "application/json"
    # Construct URL
    url = kwargs.pop("template_url", '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}/listNodes')
    path_format_arguments = {
        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
        "computeName": _SERIALIZER.url("compute_name", compute_name, 'str'),
    }

    url = _format_url_section(url, **path_format_arguments)

    # Construct parameters
    query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
    query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')

    # Construct headers
    header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
    header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')

    return HttpRequest(
        method="POST",
        url=url,
        params=query_parameters,
        headers=header_parameters,
        **kwargs
    )


def build_list_keys_request(
    subscription_id,  # type: str
    resource_group_name,  # type: str
    workspace_name,  # type: str
    compute_name,  # type: str
    **kwargs  # type: Any
):
    # type: (...) -> HttpRequest
    api_version = "2022-05-01"
    accept = "application/json"
    # Construct URL
    url = kwargs.pop("template_url", '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}/listKeys')
    path_format_arguments = {
        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
        "computeName": _SERIALIZER.url("compute_name", compute_name, 'str'),
    }

    url = _format_url_section(url, **path_format_arguments)

    # Construct parameters
    query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
    query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')

    # Construct headers
    header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
    header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')

    return HttpRequest(
        method="POST",
        url=url,
        params=query_parameters,
        headers=header_parameters,
        **kwargs
    )


def build_start_request_initial(
    subscription_id,  # type: str
    resource_group_name,  # type: str
    workspace_name,  # type: str
    compute_name,  # type: str
    **kwargs  # type: Any
):
    # type: (...) -> HttpRequest
    api_version = "2022-05-01"
    accept = "application/json"
    # Construct URL
    url = kwargs.pop("template_url", '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}/start')
    path_format_arguments = {
        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
        "computeName": _SERIALIZER.url("compute_name", compute_name, 'str'),
    }

    url = _format_url_section(url, **path_format_arguments)

    # Construct parameters
    query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
    query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')

    # Construct headers
    header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
    header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')

    return HttpRequest(
        method="POST",
        url=url,
        params=query_parameters,
        headers=header_parameters,
        **kwargs
    )


def build_stop_request_initial(
    subscription_id,  # type: str
    resource_group_name,  # type: str
    workspace_name,  # type: str
    compute_name,  # type: str
    **kwargs  # type: Any
):
    # type: (...) -> HttpRequest
    api_version = "2022-05-01"
    accept = "application/json"
    # Construct URL
    url = kwargs.pop("template_url", '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}/stop')
    path_format_arguments = {
        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
        "computeName": _SERIALIZER.url("compute_name", compute_name, 'str'),
    }

    url = _format_url_section(url, **path_format_arguments)

    # Construct parameters
    query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
    query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')

    # Construct headers
    header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
    header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')

    return HttpRequest(
        method="POST",
        url=url,
        params=query_parameters,
        headers=header_parameters,
        **kwargs
    )


def build_restart_request_initial(
    subscription_id,  # type: str
    resource_group_name,  # type: str
    workspace_name,  # type: str
    compute_name,  # type: str
    **kwargs  # type: Any
):
    # type: (...) -> HttpRequest
    api_version = "2022-05-01"
    accept = "application/json"
    # Construct URL
    url = kwargs.pop("template_url", '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}/restart')
    path_format_arguments = {
        "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str', min_length=1),
        "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str', max_length=90, min_length=1),
        "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'),
        "computeName": _SERIALIZER.url("compute_name", compute_name, 'str'),
    }

    url = _format_url_section(url, **path_format_arguments)

    # Construct parameters
    query_parameters = kwargs.pop("params", {})  # type: Dict[str, Any]
    query_parameters['api-version'] = _SERIALIZER.query("api_version", api_version, 'str')

    # Construct headers
    header_parameters = kwargs.pop("headers", {})  # type: Dict[str, Any]
    header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')

    return HttpRequest(
        method="POST",
        url=url,
        params=query_parameters,
        headers=header_parameters,
        **kwargs
    )

# fmt: on
class ComputeOperations(object):
    """ComputeOperations operations.

    You should not instantiate this class directly. Instead, you should create a Client instance that
    instantiates it for you and attaches it as an attribute.

    :ivar models: Alias to model classes used in this operation group.
    :type models: ~azure.mgmt.machinelearningservices.models
    :param client: Client for service requests.
    :param config: Configuration of service client.
    :param serializer: An object model serializer.
    :param deserializer: An object model deserializer.
    """

    models = _models

    def __init__(self, client, config, serializer, deserializer):
        self._client = client
        self._serialize = serializer
        self._deserialize = deserializer
        self._config = config

    @distributed_trace
    def list(
        self,
        resource_group_name,  # type: str
        workspace_name,  # type: str
        skip=None,  # type: Optional[str]
        **kwargs  # type: Any
    ):
        # type: (...) -> Iterable["_models.PaginatedComputeResourcesList"]
        """Gets computes in specified workspace.

        :param resource_group_name: The name of the resource group. The name is case insensitive.
        :type resource_group_name: str
        :param workspace_name: Name of Azure Machine Learning workspace.
        :type workspace_name: str
        :param skip: Continuation token for pagination.
        :type skip: str
        :keyword callable cls: A custom type or function that will be passed the direct response
        :return: An iterator like instance of either PaginatedComputeResourcesList or the result of
         cls(response)
        :rtype:
         ~azure.core.paging.ItemPaged[~azure.mgmt.machinelearningservices.models.PaginatedComputeResourcesList]
        :raises: ~azure.core.exceptions.HttpResponseError
        """
        cls = kwargs.pop('cls', None)  # type: ClsType["_models.PaginatedComputeResourcesList"]
        error_map = {
            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
        }
        error_map.update(kwargs.pop('error_map', {}))
        def prepare_request(next_link=None):
            if not next_link:
                
                request = build_list_request(
                    subscription_id=self._config.subscription_id,
                    resource_group_name=resource_group_name,
                    workspace_name=workspace_name,
                    skip=skip,
                    template_url=self.list.metadata['url'],
                )
                request = _convert_request(request)
                request.url = self._client.format_url(request.url)

            else:
                
                request = build_list_request(
                    subscription_id=self._config.subscription_id,
                    resource_group_name=resource_group_name,
                    workspace_name=workspace_name,
                    skip=skip,
                    template_url=next_link,
                )
                request = _convert_request(request)
                request.url = self._client.format_url(request.url)
                request.method = "GET"
            return request

        def extract_data(pipeline_response):
            deserialized = self._deserialize("PaginatedComputeResourcesList", pipeline_response)
            list_of_elem = deserialized.value
            if cls:
                list_of_elem = cls(list_of_elem)
            return deserialized.next_link or None, iter(list_of_elem)

        def get_next(next_link=None):
            request = prepare_request(next_link)

            pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
            response = pipeline_response.http_response

            if response.status_code not in [200]:
                map_error(status_code=response.status_code, response=response, error_map=error_map)
                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)

            return pipeline_response


        return ItemPaged(
            get_next, extract_data
        )
    list.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes'}  # type: ignore

    @distributed_trace
    def get(
        self,
        resource_group_name,  # type: str
        workspace_name,  # type: str
        compute_name,  # type: str
        **kwargs  # type: Any
    ):
        # type: (...) -> "_models.ComputeResource"
        """Gets compute definition by its name. Any secrets (storage keys, service credentials, etc) are
        not returned - use 'keys' nested resource to get them.

        :param resource_group_name: The name of the resource group. The name is case insensitive.
        :type resource_group_name: str
        :param workspace_name: Name of Azure Machine Learning workspace.
        :type workspace_name: str
        :param compute_name: Name of the Azure Machine Learning compute.
        :type compute_name: str
        :keyword callable cls: A custom type or function that will be passed the direct response
        :return: ComputeResource, or the result of cls(response)
        :rtype: ~azure.mgmt.machinelearningservices.models.ComputeResource
        :raises: ~azure.core.exceptions.HttpResponseError
        """
        cls = kwargs.pop('cls', None)  # type: ClsType["_models.ComputeResource"]
        error_map = {
            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
        }
        error_map.update(kwargs.pop('error_map', {}))

        
        request = build_get_request(
            subscription_id=self._config.subscription_id,
            resource_group_name=resource_group_name,
            workspace_name=workspace_name,
            compute_name=compute_name,
            template_url=self.get.metadata['url'],
        )
        request = _convert_request(request)
        request.url = self._client.format_url(request.url)

        pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
        response = pipeline_response.http_response

        if response.status_code not in [200]:
            map_error(status_code=response.status_code, response=response, error_map=error_map)
            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)

        deserialized = self._deserialize('ComputeResource', pipeline_response)

        if cls:
            return cls(pipeline_response, deserialized, {})

        return deserialized

    get.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}'}  # type: ignore


    def _create_or_update_initial(
        self,
        resource_group_name,  # type: str
        workspace_name,  # type: str
        compute_name,  # type: str
        parameters,  # type: "_models.ComputeResource"
        **kwargs  # type: Any
    ):
        # type: (...) -> "_models.ComputeResource"
        cls = kwargs.pop('cls', None)  # type: ClsType["_models.ComputeResource"]
        error_map = {
            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
        }
        error_map.update(kwargs.pop('error_map', {}))

        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]

        _json = self._serialize.body(parameters, 'ComputeResource')

        request = build_create_or_update_request_initial(
            subscription_id=self._config.subscription_id,
            resource_group_name=resource_group_name,
            workspace_name=workspace_name,
            compute_name=compute_name,
            content_type=content_type,
            json=_json,
            template_url=self._create_or_update_initial.metadata['url'],
        )
        request = _convert_request(request)
        request.url = self._client.format_url(request.url)

        pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
        response = pipeline_response.http_response

        if response.status_code not in [200, 201]:
            map_error(status_code=response.status_code, response=response, error_map=error_map)
            raise HttpResponseError(response=response, error_format=ARMErrorFormat)

        response_headers = {}
        if response.status_code == 200:
            deserialized = self._deserialize('ComputeResource', pipeline_response)

        if response.status_code == 201:
            response_headers['Azure-AsyncOperation']=self._deserialize('str', response.headers.get('Azure-AsyncOperation'))
            
            deserialized = self._deserialize('ComputeResource', pipeline_response)

        if cls:
            return cls(pipeline_response, deserialized, response_headers)

        return deserialized

    _create_or_update_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}'}  # type: ignore


    @distributed_trace
    def begin_create_or_update(
        self,
        resource_group_name,  # type: str
        workspace_name,  # type: str
        compute_name,  # type: str
        parameters,  # type: "_models.ComputeResource"
        **kwargs  # type: Any
    ):
        # type: (...) -> LROPoller["_models.ComputeResource"]
        """Creates or updates compute. This call will overwrite a compute if it exists. This is a
        nonrecoverable operation. If your intent is to create a new compute, do a GET first to verify
        that it does not exist yet.

        :param resource_group_name: The name of the resource group. The name is case insensitive.
        :type resource_group_name: str
        :param workspace_name: Name of Azure Machine Learning workspace.
        :type workspace_name: str
        :param compute_name: Name of the Azure Machine Learning compute.
        :type compute_name: str
        :param parameters: Payload with Machine Learning compute definition.
        :type parameters: ~azure.mgmt.machinelearningservices.models.ComputeResource
        :keyword callable cls: A custom type or function that will be passed the direct response
        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
         operation to not poll, or pass in your own initialized polling object for a personal polling
         strategy.
        :paramtype polling: bool or ~azure.core.polling.PollingMethod
        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
         Retry-After header is present.
        :return: An instance of LROPoller that returns either ComputeResource or the result of
         cls(response)
        :rtype:
         ~azure.core.polling.LROPoller[~azure.mgmt.machinelearningservices.models.ComputeResource]
        :raises: ~azure.core.exceptions.HttpResponseError
        """
        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
        polling = kwargs.pop('polling', True)  # type: Union[bool, azure.core.polling.PollingMethod]
        cls = kwargs.pop('cls', None)  # type: ClsType["_models.ComputeResource"]
        lro_delay = kwargs.pop(
            'polling_interval',
            self._config.polling_interval
        )
        cont_token = kwargs.pop('continuation_token', None)  # type: Optional[str]
        if cont_token is None:
            raw_result = self._create_or_update_initial(
                resource_group_name=resource_group_name,
                workspace_name=workspace_name,
                compute_name=compute_name,
                parameters=parameters,
                content_type=content_type,
                cls=lambda x,y,z: x,
                **kwargs
            )
        kwargs.pop('error_map', None)

        def get_long_running_output(pipeline_response):
            response = pipeline_response.http_response
            deserialized = self._deserialize('ComputeResource', pipeline_response)
            if cls:
                return cls(pipeline_response, deserialized, {})
            return deserialized


        if polling is True: polling_method = ARMPolling(lro_delay, **kwargs)
        elif polling is False: polling_method = NoPolling()
        else: polling_method = polling
        if cont_token:
            return LROPoller.from_continuation_token(
                polling_method=polling_method,
                continuation_token=cont_token,
                client=self._client,
                deserialization_callback=get_long_running_output
            )
        else:
            return LROPoller(self._client, raw_result, get_long_running_output, polling_method)

    begin_create_or_update.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}'}  # type: ignore

    def _update_initial(
        self,
        resource_group_name,  # type: str
        workspace_name,  # type: str
        compute_name,  # type: str
        parameters,  # type: "_models.ClusterUpdateParameters"
        **kwargs  # type: Any
    ):
        # type: (...) -> "_models.ComputeResource"
        cls = kwargs.pop('cls', None)  # type: ClsType["_models.ComputeResource"]
        error_map = {
            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
        }
        error_map.update(kwargs.pop('error_map', {}))

        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]

        _json = self._serialize.body(parameters, 'ClusterUpdateParameters')

        request = build_update_request_initial(
            subscription_id=self._config.subscription_id,
            resource_group_name=resource_group_name,
            workspace_name=workspace_name,
            compute_name=compute_name,
            content_type=content_type,
            json=_json,
            template_url=self._update_initial.metadata['url'],
        )
        request = _convert_request(request)
        request.url = self._client.format_url(request.url)

        pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
        response = pipeline_response.http_response

        if response.status_code not in [200]:
            map_error(status_code=response.status_code, response=response, error_map=error_map)
            raise HttpResponseError(response=response, error_format=ARMErrorFormat)

        deserialized = self._deserialize('ComputeResource', pipeline_response)

        if cls:
            return cls(pipeline_response, deserialized, {})

        return deserialized

    _update_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}'}  # type: ignore


    @distributed_trace
    def begin_update(
        self,
        resource_group_name,  # type: str
        workspace_name,  # type: str
        compute_name,  # type: str
        parameters,  # type: "_models.ClusterUpdateParameters"
        **kwargs  # type: Any
    ):
        # type: (...) -> LROPoller["_models.ComputeResource"]
        """Updates properties of a compute. This call will overwrite a compute if it exists. This is a
        nonrecoverable operation.

        :param resource_group_name: The name of the resource group. The name is case insensitive.
        :type resource_group_name: str
        :param workspace_name: Name of Azure Machine Learning workspace.
        :type workspace_name: str
        :param compute_name: Name of the Azure Machine Learning compute.
        :type compute_name: str
        :param parameters: Additional parameters for cluster update.
        :type parameters: ~azure.mgmt.machinelearningservices.models.ClusterUpdateParameters
        :keyword callable cls: A custom type or function that will be passed the direct response
        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
         operation to not poll, or pass in your own initialized polling object for a personal polling
         strategy.
        :paramtype polling: bool or ~azure.core.polling.PollingMethod
        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
         Retry-After header is present.
        :return: An instance of LROPoller that returns either ComputeResource or the result of
         cls(response)
        :rtype:
         ~azure.core.polling.LROPoller[~azure.mgmt.machinelearningservices.models.ComputeResource]
        :raises: ~azure.core.exceptions.HttpResponseError
        """
        content_type = kwargs.pop('content_type', "application/json")  # type: Optional[str]
        polling = kwargs.pop('polling', True)  # type: Union[bool, azure.core.polling.PollingMethod]
        cls = kwargs.pop('cls', None)  # type: ClsType["_models.ComputeResource"]
        lro_delay = kwargs.pop(
            'polling_interval',
            self._config.polling_interval
        )
        cont_token = kwargs.pop('continuation_token', None)  # type: Optional[str]
        if cont_token is None:
            raw_result = self._update_initial(
                resource_group_name=resource_group_name,
                workspace_name=workspace_name,
                compute_name=compute_name,
                parameters=parameters,
                content_type=content_type,
                cls=lambda x,y,z: x,
                **kwargs
            )
        kwargs.pop('error_map', None)

        def get_long_running_output(pipeline_response):
            response = pipeline_response.http_response
            deserialized = self._deserialize('ComputeResource', pipeline_response)
            if cls:
                return cls(pipeline_response, deserialized, {})
            return deserialized


        if polling is True: polling_method = ARMPolling(lro_delay, **kwargs)
        elif polling is False: polling_method = NoPolling()
        else: polling_method = polling
        if cont_token:
            return LROPoller.from_continuation_token(
                polling_method=polling_method,
                continuation_token=cont_token,
                client=self._client,
                deserialization_callback=get_long_running_output
            )
        else:
            return LROPoller(self._client, raw_result, get_long_running_output, polling_method)

    begin_update.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}'}  # type: ignore

    def _delete_initial(
        self,
        resource_group_name,  # type: str
        workspace_name,  # type: str
        compute_name,  # type: str
        underlying_resource_action,  # type: Union[str, "_models.UnderlyingResourceAction"]
        **kwargs  # type: Any
    ):
        # type: (...) -> None
        cls = kwargs.pop('cls', None)  # type: ClsType[None]
        error_map = {
            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
        }
        error_map.update(kwargs.pop('error_map', {}))

        
        request = build_delete_request_initial(
            subscription_id=self._config.subscription_id,
            resource_group_name=resource_group_name,
            workspace_name=workspace_name,
            compute_name=compute_name,
            underlying_resource_action=underlying_resource_action,
            template_url=self._delete_initial.metadata['url'],
        )
        request = _convert_request(request)
        request.url = self._client.format_url(request.url)

        pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
        response = pipeline_response.http_response

        if response.status_code not in [200, 202, 204]:
            map_error(status_code=response.status_code, response=response, error_map=error_map)
            raise HttpResponseError(response=response, error_format=ARMErrorFormat)

        response_headers = {}
        if response.status_code == 202:
            response_headers['Azure-AsyncOperation']=self._deserialize('str', response.headers.get('Azure-AsyncOperation'))
            response_headers['Location']=self._deserialize('str', response.headers.get('Location'))
            

        if cls:
            return cls(pipeline_response, None, response_headers)

    _delete_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}'}  # type: ignore


    @distributed_trace
    def begin_delete(
        self,
        resource_group_name,  # type: str
        workspace_name,  # type: str
        compute_name,  # type: str
        underlying_resource_action,  # type: Union[str, "_models.UnderlyingResourceAction"]
        **kwargs  # type: Any
    ):
        # type: (...) -> LROPoller[None]
        """Deletes specified Machine Learning compute.

        :param resource_group_name: The name of the resource group. The name is case insensitive.
        :type resource_group_name: str
        :param workspace_name: Name of Azure Machine Learning workspace.
        :type workspace_name: str
        :param compute_name: Name of the Azure Machine Learning compute.
        :type compute_name: str
        :param underlying_resource_action: Delete the underlying compute if 'Delete', or detach the
         underlying compute from workspace if 'Detach'.
        :type underlying_resource_action: str or
         ~azure.mgmt.machinelearningservices.models.UnderlyingResourceAction
        :keyword callable cls: A custom type or function that will be passed the direct response
        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
         operation to not poll, or pass in your own initialized polling object for a personal polling
         strategy.
        :paramtype polling: bool or ~azure.core.polling.PollingMethod
        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
         Retry-After header is present.
        :return: An instance of LROPoller that returns either None or the result of cls(response)
        :rtype: ~azure.core.polling.LROPoller[None]
        :raises: ~azure.core.exceptions.HttpResponseError
        """
        polling = kwargs.pop('polling', True)  # type: Union[bool, azure.core.polling.PollingMethod]
        cls = kwargs.pop('cls', None)  # type: ClsType[None]
        lro_delay = kwargs.pop(
            'polling_interval',
            self._config.polling_interval
        )
        cont_token = kwargs.pop('continuation_token', None)  # type: Optional[str]
        if cont_token is None:
            raw_result = self._delete_initial(
                resource_group_name=resource_group_name,
                workspace_name=workspace_name,
                compute_name=compute_name,
                underlying_resource_action=underlying_resource_action,
                cls=lambda x,y,z: x,
                **kwargs
            )
        kwargs.pop('error_map', None)

        def get_long_running_output(pipeline_response):
            if cls:
                return cls(pipeline_response, None, {})


        if polling is True: polling_method = ARMPolling(lro_delay, **kwargs)
        elif polling is False: polling_method = NoPolling()
        else: polling_method = polling
        if cont_token:
            return LROPoller.from_continuation_token(
                polling_method=polling_method,
                continuation_token=cont_token,
                client=self._client,
                deserialization_callback=get_long_running_output
            )
        else:
            return LROPoller(self._client, raw_result, get_long_running_output, polling_method)

    begin_delete.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}'}  # type: ignore

    @distributed_trace
    def list_nodes(
        self,
        resource_group_name,  # type: str
        workspace_name,  # type: str
        compute_name,  # type: str
        **kwargs  # type: Any
    ):
        # type: (...) -> Iterable["_models.AmlComputeNodesInformation"]
        """Get the details (e.g IP address, port etc) of all the compute nodes in the compute.

        :param resource_group_name: The name of the resource group. The name is case insensitive.
        :type resource_group_name: str
        :param workspace_name: Name of Azure Machine Learning workspace.
        :type workspace_name: str
        :param compute_name: Name of the Azure Machine Learning compute.
        :type compute_name: str
        :keyword callable cls: A custom type or function that will be passed the direct response
        :return: An iterator like instance of either AmlComputeNodesInformation or the result of
         cls(response)
        :rtype:
         ~azure.core.paging.ItemPaged[~azure.mgmt.machinelearningservices.models.AmlComputeNodesInformation]
        :raises: ~azure.core.exceptions.HttpResponseError
        """
        cls = kwargs.pop('cls', None)  # type: ClsType["_models.AmlComputeNodesInformation"]
        error_map = {
            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
        }
        error_map.update(kwargs.pop('error_map', {}))
        def prepare_request(next_link=None):
            if not next_link:
                
                request = build_list_nodes_request(
                    subscription_id=self._config.subscription_id,
                    resource_group_name=resource_group_name,
                    workspace_name=workspace_name,
                    compute_name=compute_name,
                    template_url=self.list_nodes.metadata['url'],
                )
                request = _convert_request(request)
                request.url = self._client.format_url(request.url)

            else:
                
                request = build_list_nodes_request(
                    subscription_id=self._config.subscription_id,
                    resource_group_name=resource_group_name,
                    workspace_name=workspace_name,
                    compute_name=compute_name,
                    template_url=next_link,
                )
                request = _convert_request(request)
                request.url = self._client.format_url(request.url)
                request.method = "GET"
            return request

        def extract_data(pipeline_response):
            deserialized = self._deserialize("AmlComputeNodesInformation", pipeline_response)
            list_of_elem = deserialized.nodes
            if cls:
                list_of_elem = cls(list_of_elem)
            return deserialized.next_link or None, iter(list_of_elem)

        def get_next(next_link=None):
            request = prepare_request(next_link)

            pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
            response = pipeline_response.http_response

            if response.status_code not in [200]:
                map_error(status_code=response.status_code, response=response, error_map=error_map)
                error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
                raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)

            return pipeline_response


        return ItemPaged(
            get_next, extract_data
        )
    list_nodes.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}/listNodes'}  # type: ignore

    @distributed_trace
    def list_keys(
        self,
        resource_group_name,  # type: str
        workspace_name,  # type: str
        compute_name,  # type: str
        **kwargs  # type: Any
    ):
        # type: (...) -> "_models.ComputeSecrets"
        """Gets secrets related to Machine Learning compute (storage keys, service credentials, etc).

        :param resource_group_name: The name of the resource group. The name is case insensitive.
        :type resource_group_name: str
        :param workspace_name: Name of Azure Machine Learning workspace.
        :type workspace_name: str
        :param compute_name: Name of the Azure Machine Learning compute.
        :type compute_name: str
        :keyword callable cls: A custom type or function that will be passed the direct response
        :return: ComputeSecrets, or the result of cls(response)
        :rtype: ~azure.mgmt.machinelearningservices.models.ComputeSecrets
        :raises: ~azure.core.exceptions.HttpResponseError
        """
        cls = kwargs.pop('cls', None)  # type: ClsType["_models.ComputeSecrets"]
        error_map = {
            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
        }
        error_map.update(kwargs.pop('error_map', {}))

        
        request = build_list_keys_request(
            subscription_id=self._config.subscription_id,
            resource_group_name=resource_group_name,
            workspace_name=workspace_name,
            compute_name=compute_name,
            template_url=self.list_keys.metadata['url'],
        )
        request = _convert_request(request)
        request.url = self._client.format_url(request.url)

        pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
        response = pipeline_response.http_response

        if response.status_code not in [200]:
            map_error(status_code=response.status_code, response=response, error_map=error_map)
            error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response)
            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)

        deserialized = self._deserialize('ComputeSecrets', pipeline_response)

        if cls:
            return cls(pipeline_response, deserialized, {})

        return deserialized

    list_keys.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}/listKeys'}  # type: ignore


    def _start_initial(
        self,
        resource_group_name,  # type: str
        workspace_name,  # type: str
        compute_name,  # type: str
        **kwargs  # type: Any
    ):
        # type: (...) -> None
        cls = kwargs.pop('cls', None)  # type: ClsType[None]
        error_map = {
            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
        }
        error_map.update(kwargs.pop('error_map', {}))

        
        request = build_start_request_initial(
            subscription_id=self._config.subscription_id,
            resource_group_name=resource_group_name,
            workspace_name=workspace_name,
            compute_name=compute_name,
            template_url=self._start_initial.metadata['url'],
        )
        request = _convert_request(request)
        request.url = self._client.format_url(request.url)

        pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
        response = pipeline_response.http_response

        if response.status_code not in [202]:
            map_error(status_code=response.status_code, response=response, error_map=error_map)
            raise HttpResponseError(response=response, error_format=ARMErrorFormat)

        if cls:
            return cls(pipeline_response, None, {})

    _start_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}/start'}  # type: ignore


    @distributed_trace
    def begin_start(
        self,
        resource_group_name,  # type: str
        workspace_name,  # type: str
        compute_name,  # type: str
        **kwargs  # type: Any
    ):
        # type: (...) -> LROPoller[None]
        """Posts a start action to a compute instance.

        :param resource_group_name: The name of the resource group. The name is case insensitive.
        :type resource_group_name: str
        :param workspace_name: Name of Azure Machine Learning workspace.
        :type workspace_name: str
        :param compute_name: Name of the Azure Machine Learning compute.
        :type compute_name: str
        :keyword callable cls: A custom type or function that will be passed the direct response
        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
         operation to not poll, or pass in your own initialized polling object for a personal polling
         strategy.
        :paramtype polling: bool or ~azure.core.polling.PollingMethod
        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
         Retry-After header is present.
        :return: An instance of LROPoller that returns either None or the result of cls(response)
        :rtype: ~azure.core.polling.LROPoller[None]
        :raises: ~azure.core.exceptions.HttpResponseError
        """
        polling = kwargs.pop('polling', True)  # type: Union[bool, azure.core.polling.PollingMethod]
        cls = kwargs.pop('cls', None)  # type: ClsType[None]
        lro_delay = kwargs.pop(
            'polling_interval',
            self._config.polling_interval
        )
        cont_token = kwargs.pop('continuation_token', None)  # type: Optional[str]
        if cont_token is None:
            raw_result = self._start_initial(
                resource_group_name=resource_group_name,
                workspace_name=workspace_name,
                compute_name=compute_name,
                cls=lambda x,y,z: x,
                **kwargs
            )
        kwargs.pop('error_map', None)

        def get_long_running_output(pipeline_response):
            if cls:
                return cls(pipeline_response, None, {})


        if polling is True: polling_method = ARMPolling(lro_delay, **kwargs)
        elif polling is False: polling_method = NoPolling()
        else: polling_method = polling
        if cont_token:
            return LROPoller.from_continuation_token(
                polling_method=polling_method,
                continuation_token=cont_token,
                client=self._client,
                deserialization_callback=get_long_running_output
            )
        else:
            return LROPoller(self._client, raw_result, get_long_running_output, polling_method)

    begin_start.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}/start'}  # type: ignore

    def _stop_initial(
        self,
        resource_group_name,  # type: str
        workspace_name,  # type: str
        compute_name,  # type: str
        **kwargs  # type: Any
    ):
        # type: (...) -> None
        cls = kwargs.pop('cls', None)  # type: ClsType[None]
        error_map = {
            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
        }
        error_map.update(kwargs.pop('error_map', {}))

        
        request = build_stop_request_initial(
            subscription_id=self._config.subscription_id,
            resource_group_name=resource_group_name,
            workspace_name=workspace_name,
            compute_name=compute_name,
            template_url=self._stop_initial.metadata['url'],
        )
        request = _convert_request(request)
        request.url = self._client.format_url(request.url)

        pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
        response = pipeline_response.http_response

        if response.status_code not in [202]:
            map_error(status_code=response.status_code, response=response, error_map=error_map)
            raise HttpResponseError(response=response, error_format=ARMErrorFormat)

        if cls:
            return cls(pipeline_response, None, {})

    _stop_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}/stop'}  # type: ignore


    @distributed_trace
    def begin_stop(
        self,
        resource_group_name,  # type: str
        workspace_name,  # type: str
        compute_name,  # type: str
        **kwargs  # type: Any
    ):
        # type: (...) -> LROPoller[None]
        """Posts a stop action to a compute instance.

        :param resource_group_name: The name of the resource group. The name is case insensitive.
        :type resource_group_name: str
        :param workspace_name: Name of Azure Machine Learning workspace.
        :type workspace_name: str
        :param compute_name: Name of the Azure Machine Learning compute.
        :type compute_name: str
        :keyword callable cls: A custom type or function that will be passed the direct response
        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
         operation to not poll, or pass in your own initialized polling object for a personal polling
         strategy.
        :paramtype polling: bool or ~azure.core.polling.PollingMethod
        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
         Retry-After header is present.
        :return: An instance of LROPoller that returns either None or the result of cls(response)
        :rtype: ~azure.core.polling.LROPoller[None]
        :raises: ~azure.core.exceptions.HttpResponseError
        """
        polling = kwargs.pop('polling', True)  # type: Union[bool, azure.core.polling.PollingMethod]
        cls = kwargs.pop('cls', None)  # type: ClsType[None]
        lro_delay = kwargs.pop(
            'polling_interval',
            self._config.polling_interval
        )
        cont_token = kwargs.pop('continuation_token', None)  # type: Optional[str]
        if cont_token is None:
            raw_result = self._stop_initial(
                resource_group_name=resource_group_name,
                workspace_name=workspace_name,
                compute_name=compute_name,
                cls=lambda x,y,z: x,
                **kwargs
            )
        kwargs.pop('error_map', None)

        def get_long_running_output(pipeline_response):
            if cls:
                return cls(pipeline_response, None, {})


        if polling is True: polling_method = ARMPolling(lro_delay, **kwargs)
        elif polling is False: polling_method = NoPolling()
        else: polling_method = polling
        if cont_token:
            return LROPoller.from_continuation_token(
                polling_method=polling_method,
                continuation_token=cont_token,
                client=self._client,
                deserialization_callback=get_long_running_output
            )
        else:
            return LROPoller(self._client, raw_result, get_long_running_output, polling_method)

    begin_stop.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}/stop'}  # type: ignore

    def _restart_initial(
        self,
        resource_group_name,  # type: str
        workspace_name,  # type: str
        compute_name,  # type: str
        **kwargs  # type: Any
    ):
        # type: (...) -> None
        cls = kwargs.pop('cls', None)  # type: ClsType[None]
        error_map = {
            401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
        }
        error_map.update(kwargs.pop('error_map', {}))

        
        request = build_restart_request_initial(
            subscription_id=self._config.subscription_id,
            resource_group_name=resource_group_name,
            workspace_name=workspace_name,
            compute_name=compute_name,
            template_url=self._restart_initial.metadata['url'],
        )
        request = _convert_request(request)
        request.url = self._client.format_url(request.url)

        pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)
        response = pipeline_response.http_response

        if response.status_code not in [202]:
            map_error(status_code=response.status_code, response=response, error_map=error_map)
            raise HttpResponseError(response=response, error_format=ARMErrorFormat)

        if cls:
            return cls(pipeline_response, None, {})

    _restart_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}/restart'}  # type: ignore


    @distributed_trace
    def begin_restart(
        self,
        resource_group_name,  # type: str
        workspace_name,  # type: str
        compute_name,  # type: str
        **kwargs  # type: Any
    ):
        # type: (...) -> LROPoller[None]
        """Posts a restart action to a compute instance.

        :param resource_group_name: The name of the resource group. The name is case insensitive.
        :type resource_group_name: str
        :param workspace_name: Name of Azure Machine Learning workspace.
        :type workspace_name: str
        :param compute_name: Name of the Azure Machine Learning compute.
        :type compute_name: str
        :keyword callable cls: A custom type or function that will be passed the direct response
        :keyword str continuation_token: A continuation token to restart a poller from a saved state.
        :keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
         operation to not poll, or pass in your own initialized polling object for a personal polling
         strategy.
        :paramtype polling: bool or ~azure.core.polling.PollingMethod
        :keyword int polling_interval: Default waiting time between two polls for LRO operations if no
         Retry-After header is present.
        :return: An instance of LROPoller that returns either None or the result of cls(response)
        :rtype: ~azure.core.polling.LROPoller[None]
        :raises: ~azure.core.exceptions.HttpResponseError
        """
        polling = kwargs.pop('polling', True)  # type: Union[bool, azure.core.polling.PollingMethod]
        cls = kwargs.pop('cls', None)  # type: ClsType[None]
        lro_delay = kwargs.pop(
            'polling_interval',
            self._config.polling_interval
        )
        cont_token = kwargs.pop('continuation_token', None)  # type: Optional[str]
        if cont_token is None:
            raw_result = self._restart_initial(
                resource_group_name=resource_group_name,
                workspace_name=workspace_name,
                compute_name=compute_name,
                cls=lambda x,y,z: x,
                **kwargs
            )
        kwargs.pop('error_map', None)

        def get_long_running_output(pipeline_response):
            if cls:
                return cls(pipeline_response, None, {})


        if polling is True: polling_method = ARMPolling(lro_delay, **kwargs)
        elif polling is False: polling_method = NoPolling()
        else: polling_method = polling
        if cont_token:
            return LROPoller.from_continuation_token(
                polling_method=polling_method,
                continuation_token=cont_token,
                client=self._client,
                deserialization_callback=get_long_running_output
            )
        else:
            return LROPoller(self._client, raw_result, get_long_running_output, polling_method)

    begin_restart.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/computes/{computeName}/restart'}  # type: ignore
