# 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 datetime
import sys
from typing import Any, Callable, Dict, Optional, TypeVar

from azure.core.exceptions import (
    ClientAuthenticationError,
    HttpResponseError,
    ResourceExistsError,
    ResourceNotFoundError,
    ResourceNotModifiedError,
    map_error,
)
from azure.core.pipeline import PipelineResponse
from azure.core.rest import AsyncHttpResponse, HttpRequest
from azure.core.tracing.decorator_async import distributed_trace_async
from azure.core.utils import case_insensitive_dict
from azure.mgmt.core.exceptions import ARMErrorFormat

from ... import models as _models
from ...operations._predictive_metric_operations import build_get_request

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

        Instead, you should access the following operations through
        :class:`~azure.mgmt.monitor.aio.MonitorManagementClient`'s
        :attr:`predictive_metric` attribute.
    """

    models = _models

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

    @distributed_trace_async
    async def get(
        self,
        resource_group_name: str,
        autoscale_setting_name: str,
        timespan: str,
        interval: datetime.timedelta,
        metric_namespace: str,
        metric_name: str,
        aggregation: str,
        **kwargs: Any
    ) -> _models.PredictiveResponse:
        """get predictive autoscale metric future data.

        :param resource_group_name: The name of the resource group. The name is case insensitive.
         Required.
        :type resource_group_name: str
        :param autoscale_setting_name: The autoscale setting name. Required.
        :type autoscale_setting_name: str
        :param timespan: The timespan of the query. It is a string with the following format
         'startDateTime_ISO/endDateTime_ISO'. Required.
        :type timespan: str
        :param interval: The interval (i.e. timegrain) of the query. Required.
        :type interval: ~datetime.timedelta
        :param metric_namespace: Metric namespace to query metric definitions for. Required.
        :type metric_namespace: str
        :param metric_name: The names of the metrics (comma separated) to retrieve. Special case: If a
         metricname itself has a comma in it then use %2 to indicate it. Eg: 'Metric,Name1' should be
         **'Metric%2Name1'**. Required.
        :type metric_name: str
        :param aggregation: The list of aggregation types (comma separated) to retrieve. Required.
        :type aggregation: str
        :return: PredictiveResponse or the result of cls(response)
        :rtype: ~azure.mgmt.monitor.models.PredictiveResponse
        :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", "2022-10-01"))
        cls: ClsType[_models.PredictiveResponse] = kwargs.pop("cls", None)

        _request = build_get_request(
            resource_group_name=resource_group_name,
            autoscale_setting_name=autoscale_setting_name,
            subscription_id=self._config.subscription_id,
            timespan=timespan,
            interval=interval,
            metric_namespace=metric_namespace,
            metric_name=metric_name,
            aggregation=aggregation,
            api_version=api_version,
            headers=_headers,
            params=_params,
        )
        _request.url = self._client.format_url(_request.url)

        _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)
            error = self._deserialize.failsafe_deserialize(_models.AutoscaleErrorResponse, pipeline_response)
            raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)

        deserialized = self._deserialize("PredictiveResponse", pipeline_response.http_response)

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

        return deserialized  # type: ignore
