# 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 Any, Callable, Dict, Generic, Optional, TypeVar
import warnings

from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
from azure.core.pipeline import PipelineResponse
from azure.core.pipeline.transport import AsyncHttpResponse
from azure.core.rest import HttpRequest
from azure.core.tracing.decorator_async import distributed_trace_async
from azure.mgmt.core.exceptions import ARMErrorFormat

from ... import models as _models
from ..._vendor import _convert_request
from ...operations._events_operations import build_batch_post_by_experiment_id_request, build_batch_post_by_experiment_name_request, build_batch_post_request, build_post_by_experiment_id_request, build_post_by_experiment_name_request, build_post_request
T = TypeVar('T')
ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]]

class EventsOperations:
    """EventsOperations async 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) -> None:
        self._client = client
        self._serialize = serializer
        self._deserialize = deserializer
        self._config = config

    @distributed_trace_async
    async def batch_post_by_experiment_name(
        self,
        subscription_id: str,
        resource_group_name: str,
        workspace_name: str,
        experiment_name: str,
        body: Optional["_models.BatchEventCommand"] = None,
        **kwargs: Any
    ) -> "_models.BatchEventCommandResult":
        """batch_post_by_experiment_name.

        :param subscription_id: The Azure Subscription ID.
        :type subscription_id: str
        :param resource_group_name: The Name of the resource group in which the workspace is located.
        :type resource_group_name: str
        :param workspace_name: The name of the workspace.
        :type workspace_name: str
        :param experiment_name:
        :type experiment_name: str
        :param body:
        :type body: ~azure.mgmt.machinelearningservices.models.BatchEventCommand
        :keyword callable cls: A custom type or function that will be passed the direct response
        :return: BatchEventCommandResult, or the result of cls(response)
        :rtype: ~azure.mgmt.machinelearningservices.models.BatchEventCommandResult
        :raises: ~azure.core.exceptions.HttpResponseError
        """
        cls = kwargs.pop('cls', None)  # type: ClsType["_models.BatchEventCommandResult"]
        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]

        if body is not None:
            _json = self._serialize.body(body, 'BatchEventCommand')
        else:
            _json = None

        request = build_batch_post_by_experiment_name_request(
            subscription_id=subscription_id,
            resource_group_name=resource_group_name,
            workspace_name=workspace_name,
            experiment_name=experiment_name,
            content_type=content_type,
            json=_json,
            template_url=self.batch_post_by_experiment_name.metadata['url'],
        )
        request = _convert_request(request)
        request.url = self._client.format_url(request.url)

        pipeline_response = await 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('BatchEventCommandResult', pipeline_response)

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

        return deserialized

    batch_post_by_experiment_name.metadata = {'url': '/history/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/experiments/{experimentName}/batch/events'}  # type: ignore


    @distributed_trace_async
    async def batch_post_by_experiment_id(
        self,
        subscription_id: str,
        resource_group_name: str,
        workspace_name: str,
        experiment_id: str,
        body: Optional["_models.BatchEventCommand"] = None,
        **kwargs: Any
    ) -> "_models.BatchEventCommandResult":
        """batch_post_by_experiment_id.

        :param subscription_id: The Azure Subscription ID.
        :type subscription_id: str
        :param resource_group_name: The Name of the resource group in which the workspace is located.
        :type resource_group_name: str
        :param workspace_name: The name of the workspace.
        :type workspace_name: str
        :param experiment_id:
        :type experiment_id: str
        :param body:
        :type body: ~azure.mgmt.machinelearningservices.models.BatchEventCommand
        :keyword callable cls: A custom type or function that will be passed the direct response
        :return: BatchEventCommandResult, or the result of cls(response)
        :rtype: ~azure.mgmt.machinelearningservices.models.BatchEventCommandResult
        :raises: ~azure.core.exceptions.HttpResponseError
        """
        cls = kwargs.pop('cls', None)  # type: ClsType["_models.BatchEventCommandResult"]
        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]

        if body is not None:
            _json = self._serialize.body(body, 'BatchEventCommand')
        else:
            _json = None

        request = build_batch_post_by_experiment_id_request(
            subscription_id=subscription_id,
            resource_group_name=resource_group_name,
            workspace_name=workspace_name,
            experiment_id=experiment_id,
            content_type=content_type,
            json=_json,
            template_url=self.batch_post_by_experiment_id.metadata['url'],
        )
        request = _convert_request(request)
        request.url = self._client.format_url(request.url)

        pipeline_response = await 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('BatchEventCommandResult', pipeline_response)

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

        return deserialized

    batch_post_by_experiment_id.metadata = {'url': '/history/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/experimentids/{experimentId}/batch/events'}  # type: ignore


    @distributed_trace_async
    async def batch_post(
        self,
        subscription_id: str,
        resource_group_name: str,
        workspace_name: str,
        body: Optional["_models.BatchEventCommand"] = None,
        **kwargs: Any
    ) -> "_models.BatchEventCommandResult":
        """batch_post.

        :param subscription_id: The Azure Subscription ID.
        :type subscription_id: str
        :param resource_group_name: The Name of the resource group in which the workspace is located.
        :type resource_group_name: str
        :param workspace_name: The name of the workspace.
        :type workspace_name: str
        :param body:
        :type body: ~azure.mgmt.machinelearningservices.models.BatchEventCommand
        :keyword callable cls: A custom type or function that will be passed the direct response
        :return: BatchEventCommandResult, or the result of cls(response)
        :rtype: ~azure.mgmt.machinelearningservices.models.BatchEventCommandResult
        :raises: ~azure.core.exceptions.HttpResponseError
        """
        cls = kwargs.pop('cls', None)  # type: ClsType["_models.BatchEventCommandResult"]
        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]

        if body is not None:
            _json = self._serialize.body(body, 'BatchEventCommand')
        else:
            _json = None

        request = build_batch_post_request(
            subscription_id=subscription_id,
            resource_group_name=resource_group_name,
            workspace_name=workspace_name,
            content_type=content_type,
            json=_json,
            template_url=self.batch_post.metadata['url'],
        )
        request = _convert_request(request)
        request.url = self._client.format_url(request.url)

        pipeline_response = await 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('BatchEventCommandResult', pipeline_response)

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

        return deserialized

    batch_post.metadata = {'url': '/history/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/batch/events'}  # type: ignore


    @distributed_trace_async
    async def post_by_experiment_name(
        self,
        subscription_id: str,
        resource_group_name: str,
        workspace_name: str,
        run_id: str,
        experiment_name: str,
        body: Optional["_models.BaseEvent"] = None,
        **kwargs: Any
    ) -> None:
        """post_by_experiment_name.

        :param subscription_id: The Azure Subscription ID.
        :type subscription_id: str
        :param resource_group_name: The Name of the resource group in which the workspace is located.
        :type resource_group_name: str
        :param workspace_name: The name of the workspace.
        :type workspace_name: str
        :param run_id:
        :type run_id: str
        :param experiment_name:
        :type experiment_name: str
        :param body:
        :type body: ~azure.mgmt.machinelearningservices.models.BaseEvent
        :keyword callable cls: A custom type or function that will be passed the direct response
        :return: None, or the result of cls(response)
        :rtype: None
        :raises: ~azure.core.exceptions.HttpResponseError
        """
        cls = kwargs.pop('cls', None)  # type: ClsType[None]
        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]

        if body is not None:
            _json = self._serialize.body(body, 'BaseEvent')
        else:
            _json = None

        request = build_post_by_experiment_name_request(
            subscription_id=subscription_id,
            resource_group_name=resource_group_name,
            workspace_name=workspace_name,
            run_id=run_id,
            experiment_name=experiment_name,
            content_type=content_type,
            json=_json,
            template_url=self.post_by_experiment_name.metadata['url'],
        )
        request = _convert_request(request)
        request.url = self._client.format_url(request.url)

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

        if response.status_code not in []:
            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)

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

    post_by_experiment_name.metadata = {'url': '/history/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/experiments/{experimentName}/runs/{runId}/events'}  # type: ignore


    @distributed_trace_async
    async def post_by_experiment_id(
        self,
        subscription_id: str,
        resource_group_name: str,
        workspace_name: str,
        run_id: str,
        experiment_id: str,
        body: Optional["_models.BaseEvent"] = None,
        **kwargs: Any
    ) -> None:
        """post_by_experiment_id.

        :param subscription_id: The Azure Subscription ID.
        :type subscription_id: str
        :param resource_group_name: The Name of the resource group in which the workspace is located.
        :type resource_group_name: str
        :param workspace_name: The name of the workspace.
        :type workspace_name: str
        :param run_id:
        :type run_id: str
        :param experiment_id:
        :type experiment_id: str
        :param body:
        :type body: ~azure.mgmt.machinelearningservices.models.BaseEvent
        :keyword callable cls: A custom type or function that will be passed the direct response
        :return: None, or the result of cls(response)
        :rtype: None
        :raises: ~azure.core.exceptions.HttpResponseError
        """
        cls = kwargs.pop('cls', None)  # type: ClsType[None]
        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]

        if body is not None:
            _json = self._serialize.body(body, 'BaseEvent')
        else:
            _json = None

        request = build_post_by_experiment_id_request(
            subscription_id=subscription_id,
            resource_group_name=resource_group_name,
            workspace_name=workspace_name,
            run_id=run_id,
            experiment_id=experiment_id,
            content_type=content_type,
            json=_json,
            template_url=self.post_by_experiment_id.metadata['url'],
        )
        request = _convert_request(request)
        request.url = self._client.format_url(request.url)

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

        if response.status_code not in []:
            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)

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

    post_by_experiment_id.metadata = {'url': '/history/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/experimentids/{experimentId}/runs/{runId}/events'}  # type: ignore


    @distributed_trace_async
    async def post(
        self,
        subscription_id: str,
        resource_group_name: str,
        workspace_name: str,
        run_id: str,
        body: Optional["_models.BaseEvent"] = None,
        **kwargs: Any
    ) -> None:
        """post.

        :param subscription_id: The Azure Subscription ID.
        :type subscription_id: str
        :param resource_group_name: The Name of the resource group in which the workspace is located.
        :type resource_group_name: str
        :param workspace_name: The name of the workspace.
        :type workspace_name: str
        :param run_id:
        :type run_id: str
        :param body:
        :type body: ~azure.mgmt.machinelearningservices.models.BaseEvent
        :keyword callable cls: A custom type or function that will be passed the direct response
        :return: None, or the result of cls(response)
        :rtype: None
        :raises: ~azure.core.exceptions.HttpResponseError
        """
        cls = kwargs.pop('cls', None)  # type: ClsType[None]
        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]

        if body is not None:
            _json = self._serialize.body(body, 'BaseEvent')
        else:
            _json = None

        request = build_post_request(
            subscription_id=subscription_id,
            resource_group_name=resource_group_name,
            workspace_name=workspace_name,
            run_id=run_id,
            content_type=content_type,
            json=_json,
            template_url=self.post.metadata['url'],
        )
        request = _convert_request(request)
        request.url = self._client.format_url(request.url)

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

        if response.status_code not in []:
            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)

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

    post.metadata = {'url': '/history/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/runs/{runId}/events'}  # type: ignore

