# 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 sys
from typing import Any, AsyncIterable, AsyncIterator, Callable, Dict, Optional, TypeVar, Union, cast

from azure.core import AsyncPipelineClient
from azure.core.async_paging import AsyncItemPaged, AsyncList
from azure.core.exceptions import (
    ClientAuthenticationError,
    HttpResponseError,
    ResourceExistsError,
    ResourceNotFoundError,
    ResourceNotModifiedError,
    StreamClosedError,
    StreamConsumedError,
    map_error,
)
from azure.core.pipeline import PipelineResponse
from azure.core.polling import AsyncLROPoller, AsyncNoPolling, AsyncPollingMethod
from azure.core.polling.async_base_polling import AsyncLROBasePolling
from azure.core.rest import AsyncHttpResponse, HttpRequest
from azure.core.tracing.decorator import distributed_trace
from azure.core.tracing.decorator_async import distributed_trace_async
from azure.core.utils import case_insensitive_dict

from ... import models as _models
from ..._serialization import Deserializer, Serializer
from ...operations._spark_job_definition_operations import (
    build_create_or_update_spark_job_definition_request,
    build_debug_spark_job_definition_request,
    build_delete_spark_job_definition_request,
    build_execute_spark_job_definition_request,
    build_get_spark_job_definition_request,
    build_get_spark_job_definitions_by_workspace_request,
    build_rename_spark_job_definition_request,
)
from .._configuration import ArtifactsClientConfiguration

if sys.version_info >= (3, 9):
    from collections.abc import MutableMapping
else:
    from typing import MutableMapping  # type: ignore
T = TypeVar("T")
ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]]


class SparkJobDefinitionOperations:
    """
    .. warning::
        **DO NOT** instantiate this class directly.

        Instead, you should access the following operations through
        :class:`~azure.synapse.artifacts.aio.ArtifactsClient`'s
        :attr:`spark_job_definition` attribute.
    """

    models = _models

    def __init__(self, *args, **kwargs) -> None:
        input_args = list(args)
        self._client: AsyncPipelineClient = input_args.pop(0) if input_args else kwargs.pop("client")
        self._config: ArtifactsClientConfiguration = input_args.pop(0) if input_args else kwargs.pop("config")
        self._serialize: Serializer = input_args.pop(0) if input_args else kwargs.pop("serializer")
        self._deserialize: Deserializer = input_args.pop(0) if input_args else kwargs.pop("deserializer")

    @distributed_trace
    def get_spark_job_definitions_by_workspace(
        self, **kwargs: Any
    ) -> AsyncIterable["_models.SparkJobDefinitionResource"]:
        """Lists spark job definitions.

        :return: An iterator like instance of either SparkJobDefinitionResource or the result of
         cls(response)
        :rtype:
         ~azure.core.async_paging.AsyncItemPaged[~azure.synapse.artifacts.models.SparkJobDefinitionResource]
        :raises ~azure.core.exceptions.HttpResponseError:
        """
        _headers = kwargs.pop("headers", {}) or {}
        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})

        api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2020-12-01"))
        cls: ClsType[_models.SparkJobDefinitionsListResponse] = kwargs.pop("cls", None)

        error_map: MutableMapping = {
            401: ClientAuthenticationError,
            404: ResourceNotFoundError,
            409: ResourceExistsError,
            304: ResourceNotModifiedError,
        }
        error_map.update(kwargs.pop("error_map", {}) or {})

        def prepare_request(next_link=None):
            if not next_link:

                _request = build_get_spark_job_definitions_by_workspace_request(
                    api_version=api_version,
                    headers=_headers,
                    params=_params,
                )
                path_format_arguments = {
                    "endpoint": self._serialize.url(
                        "self._config.endpoint", self._config.endpoint, "str", skip_quote=True
                    ),
                }
                _request.url = self._client.format_url(_request.url, **path_format_arguments)

            else:
                _request = HttpRequest("GET", next_link)
                path_format_arguments = {
                    "endpoint": self._serialize.url(
                        "self._config.endpoint", self._config.endpoint, "str", skip_quote=True
                    ),
                }
                _request.url = self._client.format_url(_request.url, **path_format_arguments)
                _request.method = "GET"
            return _request

        async def extract_data(pipeline_response):
            deserialized = self._deserialize("SparkJobDefinitionsListResponse", pipeline_response)
            list_of_elem = deserialized.value
            if cls:
                list_of_elem = cls(list_of_elem)  # type: ignore
            return deserialized.next_link or None, AsyncList(list_of_elem)

        async def get_next(next_link=None):
            _request = prepare_request(next_link)

            _stream = False
            pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
                _request, stream=_stream, **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)

            return pipeline_response

        return AsyncItemPaged(get_next, extract_data)

    async def _create_or_update_spark_job_definition_initial(  # pylint: disable=name-too-long
        self,
        spark_job_definition_name: str,
        properties: _models.SparkJobDefinition,
        if_match: Optional[str] = None,
        **kwargs: Any
    ) -> AsyncIterator[bytes]:
        error_map: MutableMapping = {
            401: ClientAuthenticationError,
            404: ResourceNotFoundError,
            409: ResourceExistsError,
            304: ResourceNotModifiedError,
        }
        error_map.update(kwargs.pop("error_map", {}) or {})

        _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})

        api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2020-12-01"))
        content_type: str = kwargs.pop("content_type", _headers.pop("Content-Type", "application/json"))
        cls: ClsType[AsyncIterator[bytes]] = kwargs.pop("cls", None)

        _spark_job_definition = _models.SparkJobDefinitionResource(properties=properties)
        _json = self._serialize.body(_spark_job_definition, "SparkJobDefinitionResource")

        _request = build_create_or_update_spark_job_definition_request(
            spark_job_definition_name=spark_job_definition_name,
            if_match=if_match,
            api_version=api_version,
            content_type=content_type,
            json=_json,
            headers=_headers,
            params=_params,
        )
        path_format_arguments = {
            "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True),
        }
        _request.url = self._client.format_url(_request.url, **path_format_arguments)

        _decompress = kwargs.pop("decompress", True)
        _stream = True
        pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
            _request, stream=_stream, **kwargs
        )

        response = pipeline_response.http_response

        if response.status_code not in [200, 202]:
            try:
                await response.read()  # Load the body in memory and close the socket
            except (StreamConsumedError, StreamClosedError):
                pass
            map_error(status_code=response.status_code, response=response, error_map=error_map)
            raise HttpResponseError(response=response)

        deserialized = response.stream_download(self._client._pipeline, decompress=_decompress)

        if cls:
            return cls(pipeline_response, deserialized, {})  # type: ignore

        return deserialized  # type: ignore

    @distributed_trace_async
    async def begin_create_or_update_spark_job_definition(  # pylint: disable=name-too-long
        self,
        spark_job_definition_name: str,
        properties: _models.SparkJobDefinition,
        if_match: Optional[str] = None,
        **kwargs: Any
    ) -> AsyncLROPoller[_models.SparkJobDefinitionResource]:
        """Creates or updates a Spark Job Definition.

        :param spark_job_definition_name: The spark job definition name. Required.
        :type spark_job_definition_name: str
        :param properties: Properties of spark job definition. Required.
        :type properties: ~azure.synapse.artifacts.models.SparkJobDefinition
        :param if_match: ETag of the Spark Job Definition entity.  Should only be specified for update,
         for which it should match existing entity or can be * for unconditional update. Default value
         is None.
        :type if_match: str
        :return: An instance of AsyncLROPoller that returns either SparkJobDefinitionResource or the
         result of cls(response)
        :rtype:
         ~azure.core.polling.AsyncLROPoller[~azure.synapse.artifacts.models.SparkJobDefinitionResource]
        :raises ~azure.core.exceptions.HttpResponseError:
        """
        _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})

        api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2020-12-01"))
        content_type: str = kwargs.pop("content_type", _headers.pop("Content-Type", "application/json"))
        cls: ClsType[_models.SparkJobDefinitionResource] = kwargs.pop("cls", None)
        polling: Union[bool, AsyncPollingMethod] = kwargs.pop("polling", True)
        lro_delay = kwargs.pop("polling_interval", self._config.polling_interval)
        cont_token: Optional[str] = kwargs.pop("continuation_token", None)
        if cont_token is None:
            raw_result = await self._create_or_update_spark_job_definition_initial(
                spark_job_definition_name=spark_job_definition_name,
                properties=properties,
                if_match=if_match,
                api_version=api_version,
                content_type=content_type,
                cls=lambda x, y, z: x,
                headers=_headers,
                params=_params,
                **kwargs
            )
            await raw_result.http_response.read()  # type: ignore
        kwargs.pop("error_map", None)

        def get_long_running_output(pipeline_response):
            deserialized = self._deserialize("SparkJobDefinitionResource", pipeline_response.http_response)
            if cls:
                return cls(pipeline_response, deserialized, {})  # type: ignore
            return deserialized

        path_format_arguments = {
            "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True),
        }

        if polling is True:
            polling_method: AsyncPollingMethod = cast(
                AsyncPollingMethod,
                AsyncLROBasePolling(lro_delay, path_format_arguments=path_format_arguments, **kwargs),
            )
        elif polling is False:
            polling_method = cast(AsyncPollingMethod, AsyncNoPolling())
        else:
            polling_method = polling
        if cont_token:
            return AsyncLROPoller[_models.SparkJobDefinitionResource].from_continuation_token(
                polling_method=polling_method,
                continuation_token=cont_token,
                client=self._client,
                deserialization_callback=get_long_running_output,
            )
        return AsyncLROPoller[_models.SparkJobDefinitionResource](
            self._client, raw_result, get_long_running_output, polling_method  # type: ignore
        )

    @distributed_trace_async
    async def get_spark_job_definition(
        self, spark_job_definition_name: str, if_none_match: Optional[str] = None, **kwargs: Any
    ) -> Optional[_models.SparkJobDefinitionResource]:
        """Gets a Spark Job Definition.

        :param spark_job_definition_name: The spark job definition name. Required.
        :type spark_job_definition_name: str
        :param if_none_match: ETag of the Spark Job Definition entity. Should only be specified for
         get. If the ETag matches the existing entity tag, or if * was provided, then no content will be
         returned. Default value is None.
        :type if_none_match: str
        :return: SparkJobDefinitionResource or None or the result of cls(response)
        :rtype: ~azure.synapse.artifacts.models.SparkJobDefinitionResource or None
        :raises ~azure.core.exceptions.HttpResponseError:
        """
        error_map: MutableMapping = {
            401: ClientAuthenticationError,
            404: ResourceNotFoundError,
            409: ResourceExistsError,
            304: ResourceNotModifiedError,
        }
        error_map.update(kwargs.pop("error_map", {}) or {})

        _headers = kwargs.pop("headers", {}) or {}
        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})

        api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2020-12-01"))
        cls: ClsType[Optional[_models.SparkJobDefinitionResource]] = kwargs.pop("cls", None)

        _request = build_get_spark_job_definition_request(
            spark_job_definition_name=spark_job_definition_name,
            if_none_match=if_none_match,
            api_version=api_version,
            headers=_headers,
            params=_params,
        )
        path_format_arguments = {
            "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True),
        }
        _request.url = self._client.format_url(_request.url, **path_format_arguments)

        _stream = False
        pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
            _request, stream=_stream, **kwargs
        )

        response = pipeline_response.http_response

        if response.status_code not in [200, 304]:
            map_error(status_code=response.status_code, response=response, error_map=error_map)
            raise HttpResponseError(response=response)

        deserialized = None
        if response.status_code == 200:
            deserialized = self._deserialize("SparkJobDefinitionResource", pipeline_response.http_response)

        if cls:
            return cls(pipeline_response, deserialized, {})  # type: ignore

        return deserialized  # type: ignore

    async def _delete_spark_job_definition_initial(
        self, spark_job_definition_name: str, **kwargs: Any
    ) -> AsyncIterator[bytes]:
        error_map: MutableMapping = {
            401: ClientAuthenticationError,
            404: ResourceNotFoundError,
            409: ResourceExistsError,
            304: ResourceNotModifiedError,
        }
        error_map.update(kwargs.pop("error_map", {}) or {})

        _headers = kwargs.pop("headers", {}) or {}
        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})

        api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2020-12-01"))
        cls: ClsType[AsyncIterator[bytes]] = kwargs.pop("cls", None)

        _request = build_delete_spark_job_definition_request(
            spark_job_definition_name=spark_job_definition_name,
            api_version=api_version,
            headers=_headers,
            params=_params,
        )
        path_format_arguments = {
            "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True),
        }
        _request.url = self._client.format_url(_request.url, **path_format_arguments)

        _decompress = kwargs.pop("decompress", True)
        _stream = True
        pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
            _request, stream=_stream, **kwargs
        )

        response = pipeline_response.http_response

        if response.status_code not in [200, 202, 204]:
            try:
                await response.read()  # Load the body in memory and close the socket
            except (StreamConsumedError, StreamClosedError):
                pass
            map_error(status_code=response.status_code, response=response, error_map=error_map)
            raise HttpResponseError(response=response)

        deserialized = response.stream_download(self._client._pipeline, decompress=_decompress)

        if cls:
            return cls(pipeline_response, deserialized, {})  # type: ignore

        return deserialized  # type: ignore

    @distributed_trace_async
    async def begin_delete_spark_job_definition(
        self, spark_job_definition_name: str, **kwargs: Any
    ) -> AsyncLROPoller[None]:
        """Deletes a Spark Job Definition.

        :param spark_job_definition_name: The spark job definition name. Required.
        :type spark_job_definition_name: str
        :return: An instance of AsyncLROPoller that returns either None or the result of cls(response)
        :rtype: ~azure.core.polling.AsyncLROPoller[None]
        :raises ~azure.core.exceptions.HttpResponseError:
        """
        _headers = kwargs.pop("headers", {}) or {}
        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})

        api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2020-12-01"))
        cls: ClsType[None] = kwargs.pop("cls", None)
        polling: Union[bool, AsyncPollingMethod] = kwargs.pop("polling", True)
        lro_delay = kwargs.pop("polling_interval", self._config.polling_interval)
        cont_token: Optional[str] = kwargs.pop("continuation_token", None)
        if cont_token is None:
            raw_result = await self._delete_spark_job_definition_initial(
                spark_job_definition_name=spark_job_definition_name,
                api_version=api_version,
                cls=lambda x, y, z: x,
                headers=_headers,
                params=_params,
                **kwargs
            )
            await raw_result.http_response.read()  # type: ignore
        kwargs.pop("error_map", None)

        def get_long_running_output(pipeline_response):  # pylint: disable=inconsistent-return-statements
            if cls:
                return cls(pipeline_response, None, {})  # type: ignore

        path_format_arguments = {
            "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True),
        }

        if polling is True:
            polling_method: AsyncPollingMethod = cast(
                AsyncPollingMethod,
                AsyncLROBasePolling(lro_delay, path_format_arguments=path_format_arguments, **kwargs),
            )
        elif polling is False:
            polling_method = cast(AsyncPollingMethod, AsyncNoPolling())
        else:
            polling_method = polling
        if cont_token:
            return AsyncLROPoller[None].from_continuation_token(
                polling_method=polling_method,
                continuation_token=cont_token,
                client=self._client,
                deserialization_callback=get_long_running_output,
            )
        return AsyncLROPoller[None](self._client, raw_result, get_long_running_output, polling_method)  # type: ignore

    async def _execute_spark_job_definition_initial(
        self, spark_job_definition_name: str, **kwargs: Any
    ) -> AsyncIterator[bytes]:
        error_map: MutableMapping = {
            401: ClientAuthenticationError,
            404: ResourceNotFoundError,
            409: ResourceExistsError,
            304: ResourceNotModifiedError,
        }
        error_map.update(kwargs.pop("error_map", {}) or {})

        _headers = kwargs.pop("headers", {}) or {}
        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})

        api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2020-12-01"))
        cls: ClsType[AsyncIterator[bytes]] = kwargs.pop("cls", None)

        _request = build_execute_spark_job_definition_request(
            spark_job_definition_name=spark_job_definition_name,
            api_version=api_version,
            headers=_headers,
            params=_params,
        )
        path_format_arguments = {
            "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True),
        }
        _request.url = self._client.format_url(_request.url, **path_format_arguments)

        _decompress = kwargs.pop("decompress", True)
        _stream = True
        pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
            _request, stream=_stream, **kwargs
        )

        response = pipeline_response.http_response

        if response.status_code not in [200, 202]:
            try:
                await response.read()  # Load the body in memory and close the socket
            except (StreamConsumedError, StreamClosedError):
                pass
            map_error(status_code=response.status_code, response=response, error_map=error_map)
            raise HttpResponseError(response=response)

        deserialized = response.stream_download(self._client._pipeline, decompress=_decompress)

        if cls:
            return cls(pipeline_response, deserialized, {})  # type: ignore

        return deserialized  # type: ignore

    @distributed_trace_async
    async def begin_execute_spark_job_definition(
        self, spark_job_definition_name: str, **kwargs: Any
    ) -> AsyncLROPoller[_models.SparkBatchJob]:
        """Executes the spark job definition.

        :param spark_job_definition_name: The spark job definition name. Required.
        :type spark_job_definition_name: str
        :return: An instance of AsyncLROPoller that returns either SparkBatchJob or the result of
         cls(response)
        :rtype: ~azure.core.polling.AsyncLROPoller[~azure.synapse.artifacts.models.SparkBatchJob]
        :raises ~azure.core.exceptions.HttpResponseError:
        """
        _headers = kwargs.pop("headers", {}) or {}
        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})

        api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2020-12-01"))
        cls: ClsType[_models.SparkBatchJob] = kwargs.pop("cls", None)
        polling: Union[bool, AsyncPollingMethod] = kwargs.pop("polling", True)
        lro_delay = kwargs.pop("polling_interval", self._config.polling_interval)
        cont_token: Optional[str] = kwargs.pop("continuation_token", None)
        if cont_token is None:
            raw_result = await self._execute_spark_job_definition_initial(
                spark_job_definition_name=spark_job_definition_name,
                api_version=api_version,
                cls=lambda x, y, z: x,
                headers=_headers,
                params=_params,
                **kwargs
            )
            await raw_result.http_response.read()  # type: ignore
        kwargs.pop("error_map", None)

        def get_long_running_output(pipeline_response):
            deserialized = self._deserialize("SparkBatchJob", pipeline_response.http_response)
            if cls:
                return cls(pipeline_response, deserialized, {})  # type: ignore
            return deserialized

        path_format_arguments = {
            "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True),
        }

        if polling is True:
            polling_method: AsyncPollingMethod = cast(
                AsyncPollingMethod,
                AsyncLROBasePolling(
                    lro_delay,
                    lro_options={"final-state-via": "location"},
                    path_format_arguments=path_format_arguments,
                    **kwargs
                ),
            )
        elif polling is False:
            polling_method = cast(AsyncPollingMethod, AsyncNoPolling())
        else:
            polling_method = polling
        if cont_token:
            return AsyncLROPoller[_models.SparkBatchJob].from_continuation_token(
                polling_method=polling_method,
                continuation_token=cont_token,
                client=self._client,
                deserialization_callback=get_long_running_output,
            )
        return AsyncLROPoller[_models.SparkBatchJob](
            self._client, raw_result, get_long_running_output, polling_method  # type: ignore
        )

    async def _rename_spark_job_definition_initial(
        self, spark_job_definition_name: str, new_name: Optional[str] = None, **kwargs: Any
    ) -> AsyncIterator[bytes]:
        error_map: MutableMapping = {
            401: ClientAuthenticationError,
            404: ResourceNotFoundError,
            409: ResourceExistsError,
            304: ResourceNotModifiedError,
        }
        error_map.update(kwargs.pop("error_map", {}) or {})

        _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})

        api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2020-12-01"))
        content_type: str = kwargs.pop("content_type", _headers.pop("Content-Type", "application/json"))
        cls: ClsType[AsyncIterator[bytes]] = kwargs.pop("cls", None)

        _request = _models.ArtifactRenameRequest(new_name=new_name)
        _json = self._serialize.body(_request, "ArtifactRenameRequest")

        _request = build_rename_spark_job_definition_request(
            spark_job_definition_name=spark_job_definition_name,
            api_version=api_version,
            content_type=content_type,
            json=_json,
            headers=_headers,
            params=_params,
        )
        path_format_arguments = {
            "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True),
        }
        _request.url = self._client.format_url(_request.url, **path_format_arguments)

        _decompress = kwargs.pop("decompress", True)
        _stream = True
        pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
            _request, stream=_stream, **kwargs
        )

        response = pipeline_response.http_response

        if response.status_code not in [200, 202]:
            try:
                await response.read()  # Load the body in memory and close the socket
            except (StreamConsumedError, StreamClosedError):
                pass
            map_error(status_code=response.status_code, response=response, error_map=error_map)
            raise HttpResponseError(response=response)

        deserialized = response.stream_download(self._client._pipeline, decompress=_decompress)

        if cls:
            return cls(pipeline_response, deserialized, {})  # type: ignore

        return deserialized  # type: ignore

    @distributed_trace_async
    async def begin_rename_spark_job_definition(
        self, spark_job_definition_name: str, new_name: Optional[str] = None, **kwargs: Any
    ) -> AsyncLROPoller[None]:
        """Renames a sparkJobDefinition.

        :param spark_job_definition_name: The spark job definition name. Required.
        :type spark_job_definition_name: str
        :param new_name: New name of the artifact. Default value is None.
        :type new_name: str
        :return: An instance of AsyncLROPoller that returns either None or the result of cls(response)
        :rtype: ~azure.core.polling.AsyncLROPoller[None]
        :raises ~azure.core.exceptions.HttpResponseError:
        """
        _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})

        api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2020-12-01"))
        content_type: str = kwargs.pop("content_type", _headers.pop("Content-Type", "application/json"))
        cls: ClsType[None] = kwargs.pop("cls", None)
        polling: Union[bool, AsyncPollingMethod] = kwargs.pop("polling", True)
        lro_delay = kwargs.pop("polling_interval", self._config.polling_interval)
        cont_token: Optional[str] = kwargs.pop("continuation_token", None)
        if cont_token is None:
            raw_result = await self._rename_spark_job_definition_initial(
                spark_job_definition_name=spark_job_definition_name,
                new_name=new_name,
                api_version=api_version,
                content_type=content_type,
                cls=lambda x, y, z: x,
                headers=_headers,
                params=_params,
                **kwargs
            )
            await raw_result.http_response.read()  # type: ignore
        kwargs.pop("error_map", None)

        def get_long_running_output(pipeline_response):  # pylint: disable=inconsistent-return-statements
            if cls:
                return cls(pipeline_response, None, {})  # type: ignore

        path_format_arguments = {
            "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True),
        }

        if polling is True:
            polling_method: AsyncPollingMethod = cast(
                AsyncPollingMethod,
                AsyncLROBasePolling(lro_delay, path_format_arguments=path_format_arguments, **kwargs),
            )
        elif polling is False:
            polling_method = cast(AsyncPollingMethod, AsyncNoPolling())
        else:
            polling_method = polling
        if cont_token:
            return AsyncLROPoller[None].from_continuation_token(
                polling_method=polling_method,
                continuation_token=cont_token,
                client=self._client,
                deserialization_callback=get_long_running_output,
            )
        return AsyncLROPoller[None](self._client, raw_result, get_long_running_output, polling_method)  # type: ignore

    async def _debug_spark_job_definition_initial(
        self, properties: _models.SparkJobDefinition, **kwargs: Any
    ) -> AsyncIterator[bytes]:
        error_map: MutableMapping = {
            401: ClientAuthenticationError,
            404: ResourceNotFoundError,
            409: ResourceExistsError,
            304: ResourceNotModifiedError,
        }
        error_map.update(kwargs.pop("error_map", {}) or {})

        _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})

        api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2020-12-01"))
        content_type: str = kwargs.pop("content_type", _headers.pop("Content-Type", "application/json"))
        cls: ClsType[AsyncIterator[bytes]] = kwargs.pop("cls", None)

        _spark_job_definition_azure_resource = _models.SparkJobDefinitionResource(properties=properties)
        _json = self._serialize.body(_spark_job_definition_azure_resource, "SparkJobDefinitionResource")

        _request = build_debug_spark_job_definition_request(
            api_version=api_version,
            content_type=content_type,
            json=_json,
            headers=_headers,
            params=_params,
        )
        path_format_arguments = {
            "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True),
        }
        _request.url = self._client.format_url(_request.url, **path_format_arguments)

        _decompress = kwargs.pop("decompress", True)
        _stream = True
        pipeline_response: PipelineResponse = await self._client._pipeline.run(  # pylint: disable=protected-access
            _request, stream=_stream, **kwargs
        )

        response = pipeline_response.http_response

        if response.status_code not in [200, 202]:
            try:
                await response.read()  # Load the body in memory and close the socket
            except (StreamConsumedError, StreamClosedError):
                pass
            map_error(status_code=response.status_code, response=response, error_map=error_map)
            raise HttpResponseError(response=response)

        deserialized = response.stream_download(self._client._pipeline, decompress=_decompress)

        if cls:
            return cls(pipeline_response, deserialized, {})  # type: ignore

        return deserialized  # type: ignore

    @distributed_trace_async
    async def begin_debug_spark_job_definition(
        self, properties: _models.SparkJobDefinition, **kwargs: Any
    ) -> AsyncLROPoller[_models.SparkBatchJob]:
        """Debug the spark job definition.

        :param properties: Properties of spark job definition. Required.
        :type properties: ~azure.synapse.artifacts.models.SparkJobDefinition
        :return: An instance of AsyncLROPoller that returns either SparkBatchJob or the result of
         cls(response)
        :rtype: ~azure.core.polling.AsyncLROPoller[~azure.synapse.artifacts.models.SparkBatchJob]
        :raises ~azure.core.exceptions.HttpResponseError:
        """
        _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
        _params = case_insensitive_dict(kwargs.pop("params", {}) or {})

        api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2020-12-01"))
        content_type: str = kwargs.pop("content_type", _headers.pop("Content-Type", "application/json"))
        cls: ClsType[_models.SparkBatchJob] = kwargs.pop("cls", None)
        polling: Union[bool, AsyncPollingMethod] = kwargs.pop("polling", True)
        lro_delay = kwargs.pop("polling_interval", self._config.polling_interval)
        cont_token: Optional[str] = kwargs.pop("continuation_token", None)
        if cont_token is None:
            raw_result = await self._debug_spark_job_definition_initial(
                properties=properties,
                api_version=api_version,
                content_type=content_type,
                cls=lambda x, y, z: x,
                headers=_headers,
                params=_params,
                **kwargs
            )
            await raw_result.http_response.read()  # type: ignore
        kwargs.pop("error_map", None)

        def get_long_running_output(pipeline_response):
            deserialized = self._deserialize("SparkBatchJob", pipeline_response.http_response)
            if cls:
                return cls(pipeline_response, deserialized, {})  # type: ignore
            return deserialized

        path_format_arguments = {
            "endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True),
        }

        if polling is True:
            polling_method: AsyncPollingMethod = cast(
                AsyncPollingMethod,
                AsyncLROBasePolling(
                    lro_delay,
                    lro_options={"final-state-via": "location"},
                    path_format_arguments=path_format_arguments,
                    **kwargs
                ),
            )
        elif polling is False:
            polling_method = cast(AsyncPollingMethod, AsyncNoPolling())
        else:
            polling_method = polling
        if cont_token:
            return AsyncLROPoller[_models.SparkBatchJob].from_continuation_token(
                polling_method=polling_method,
                continuation_token=cont_token,
                client=self._client,
                deserialization_callback=get_long_running_output,
            )
        return AsyncLROPoller[_models.SparkBatchJob](
            self._client, raw_result, get_long_running_output, polling_method  # type: ignore
        )
