[AutoPR azure-ai-anomalydetector] Fix topContributorCount to be optional with default of 10

sdk/anomalydetector/azure-ai-anomalydetector/_meta.json

@@ -0,0 +1,15 @@
+{
+  "commit": "324a5095c5b33493b59da70803a2e2e7f35034a9",
+  "repository_url": "https://github.com/Azure/azure-rest-api-specs",
+  "typespec_src": "specification/cognitiveservices/AnomalyDetector",
+  "@azure-tools/typespec-python": {
+    "version": "0.8.4",
+    "resolved": "https://registry.npmjs.org/@azure-tools/typespec-python/-/typespec-python-0.8.4.tgz",
+    "dependencies": {
+      "@autorest/python": {
+        "version": "6.4.11",
+        "resolved": "https://registry.npmjs.org/@autorest/python/-/python-6.4.11.tgz"
+      }
+    }
+  }
+}

sdk/anomalydetector/azure-ai-anomalydetector/azure/ai/anomalydetector/_client.py

@@ -20,23 +20,23 @@
 
 class AnomalyDetectorClient(AnomalyDetectorClientOperationsMixin):  # pylint: disable=client-accepts-api-version-keyword
     """The Anomaly Detector API detects anomalies automatically in time series data.
-    It supports two kinds of mode, one is for stateless using, another is for
-    stateful using. In stateless mode, there are three functionalities. Entire
-    Detect is for detecting the whole series with model trained by the time series,
-    Last Detect is detecting last point with model trained by points before.
-    ChangePoint Detect is for detecting trend changes in time series. In stateful
-    mode, user can store time series, the stored time series will be used for
-    detection anomalies. Under this mode, user can still use the above three
-    functionalities by only giving a time range without preparing time series in
-    client side. Besides the above three functionalities, stateful model also
-    provide group based detection and labeling service. By leveraging labeling
-    service user can provide labels for each detection result, these labels will be
+    It supports both a stateless detection mode and a
+    stateful detection mode. In stateless mode, there are three functionalities. Entire
+    Detect is for detecting the whole series, with the model trained by the time series.
+    Last Detect is for detecting the last point, with the model trained by points before.
+    ChangePoint Detect is for detecting trend changes in the time series. In stateful
+    mode, the user can store time series. The stored time series will be used for
+    detection anomalies. In this mode, the user can still use the preceding three
+    functionalities by only giving a time range without preparing time series on the
+    client side. Besides the preceding three functionalities, the stateful model
+    provides group-based detection and labeling services. By using the labeling
+    service, the user can provide labels for each detection result. These labels will be
     used for retuning or regenerating detection models. Inconsistency detection is
-    a kind of group based detection, this detection will find inconsistency ones in
-    a set of time series. By using anomaly detector service, business customers can
+    a kind of group-based detection that finds inconsistencies in
+    a set of time series. By using the anomaly detector service, business customers can
     discover incidents and establish a logic flow for root cause analysis.
 
-    :param endpoint: Supported Cognitive Services endpoints (protocol and hostname, for example:
+    :param endpoint: Supported Azure Cognitive Services endpoints (protocol and host name, such as
      https://westus2.api.cognitive.microsoft.com). Required.
     :type endpoint: str
     :param credential: Credential needed for the client to connect to Azure. Required.
@@ -49,7 +49,7 @@ class AnomalyDetectorClient(AnomalyDetectorClientOperationsMixin):  # pylint: di
     def __init__(self, endpoint: str, credential: AzureKeyCredential, **kwargs: Any) -> None:
         _endpoint = "{Endpoint}/anomalydetector/{ApiVersion}"
         self._config = AnomalyDetectorClientConfiguration(endpoint=endpoint, credential=credential, **kwargs)
-        self._client = PipelineClient(base_url=_endpoint, config=self._config, **kwargs)
+        self._client: PipelineClient = PipelineClient(base_url=_endpoint, config=self._config, **kwargs)
 
         self._serialize = Serializer()
         self._deserialize = Deserializer()
@@ -91,5 +91,5 @@ def __enter__(self) -> "AnomalyDetectorClient":
         self._client.__enter__()
         return self
 
-    def __exit__(self, *exc_details) -> None:
+    def __exit__(self, *exc_details: Any) -> None:
         self._client.__exit__(*exc_details)

sdk/anomalydetector/azure-ai-anomalydetector/azure/ai/anomalydetector/_configuration.py

@@ -6,7 +6,6 @@
 # Changes may cause incorrect behavior and will be lost if the code is regenerated.
 # --------------------------------------------------------------------------
 
-import sys
 from typing import Any
 
 from azure.core.configuration import Configuration
@@ -15,19 +14,14 @@
 
 from ._version import VERSION
 
-if sys.version_info >= (3, 8):
-    from typing import Literal  # pylint: disable=no-name-in-module, ungrouped-imports
-else:
-    from typing_extensions import Literal  # type: ignore  # pylint: disable=ungrouped-imports
-
 
 class AnomalyDetectorClientConfiguration(Configuration):  # pylint: disable=too-many-instance-attributes
     """Configuration for AnomalyDetectorClient.
 
     Note that all parameters used to create this instance are saved as instance
     attributes.
 
-    :param endpoint: Supported Cognitive Services endpoints (protocol and hostname, for example:
+    :param endpoint: Supported Azure Cognitive Services endpoints (protocol and host name, such as
      https://westus2.api.cognitive.microsoft.com). Required.
     :type endpoint: str
     :param credential: Credential needed for the client to connect to Azure. Required.
@@ -39,7 +33,7 @@ class AnomalyDetectorClientConfiguration(Configuration):  # pylint: disable=too-
 
     def __init__(self, endpoint: str, credential: AzureKeyCredential, **kwargs: Any) -> None:
         super(AnomalyDetectorClientConfiguration, self).__init__(**kwargs)
-        api_version: Literal["v1.1"] = kwargs.pop("api_version", "v1.1")
+        api_version: str = kwargs.pop("api_version", "v1.1")
 
         if endpoint is None:
             raise ValueError("Parameter 'endpoint' must not be None.")