Updating get token to work with autogenerated client

Author: singankit

sdk/evaluation/azure-ai-evaluation/azure/ai/evaluation/_azure/_clients.py

@@ -201,4 +201,4 @@ def _generate_path(self, *paths: str) -> str:
         return url
 
     def _get_headers(self) -> Dict[str, str]:
-        return {"Authorization": f"Bearer {self.get_token()}", "Content-Type": "application/json"}
+        return {"Authorization": f"Bearer {self.get_token().token}", "Content-Type": "application/json"}

sdk/evaluation/azure-ai-evaluation/azure/ai/evaluation/_azure/_token_manager.py

@@ -5,7 +5,7 @@
 import logging
 import time
 import inspect
-from typing import cast, Optional, Union
+from typing import cast, Optional, Union, Any
 
 from azure.core.credentials import TokenCredential, AccessToken
 from azure.identity import AzureCliCredential, DefaultAzureCredential, ManagedIdentityCredential
@@ -71,7 +71,7 @@ def get_aad_credential(self) -> Union[DefaultAzureCredential, ManagedIdentityCre
             # Fall back to using the parent implementation
             return super().get_aad_credential()
 
-    def get_token(self) -> str:
+    def get_token(self, *scopes: str, claims: str | None = None, tenant_id: str | None = None, enable_cae: bool = False, **kwargs: Any) -> AccessToken:
         """Get the API token. If the token is not available or has expired, refresh the token.
 
         :return: API token
@@ -82,7 +82,7 @@ def get_token(self) -> str:
             access_token = credential.get_token(self.token_scope)
             self._update_token(access_token)
 
-        return cast(str, self.token)  # check for none is hidden in the _token_needs_update method
+        return self.token  # check for none is hidden in the _token_needs_update method
 
     async def get_token_async(self) -> str:
         """Get the API token asynchronously. If the token is not available or has expired, refresh it.
@@ -112,7 +112,7 @@ def _token_needs_update(self) -> bool:
         )
 
     def _update_token(self, access_token: AccessToken) -> None:
-        self.token = cast(str, access_token.token)
+        self.token = access_token
         self.token_expiry_time = access_token.expires_on
         self.last_refresh_time = time.time()
         self.logger.info("Refreshed Azure management token.")

sdk/evaluation/azure-ai-evaluation/azure/ai/evaluation/_evaluate/_eval_run.py

@@ -295,7 +295,7 @@ def get_metrics_url(self):
         return f"https://{self._url_base}" "/mlflow/v2.0" f"{self._get_scope()}" f"/api/2.0/mlflow/runs/log-metric"
 
     def _get_token(self) -> str:
-        return self._management_client.get_token()
+        return self._management_client.get_token().token
 
     def request_with_retry(
         self, url: str, method: str, json_dict: Dict[str, Any], headers: Optional[Dict[str, str]] = None

sdk/evaluation/azure-ai-evaluation/azure/ai/evaluation/_evaluate/_utils.py

@@ -140,17 +140,11 @@ def _log_metrics_and_instance_results_onedp(
     from azure.ai.evaluation._common import EvaluationServiceOneDPClient, EvaluationUpload
 
     credentials = AzureMLTokenManager(
-        TokenScope.CONGNITIVE_SERVICES.value, LOGGER, credential=kwargs.get("credential")
+        TokenScope.COGNITIVE_SERVICES.value, LOGGER, credential=kwargs.get("credential")
     )
     client = EvaluationServiceOneDPClient(
         endpoint=project_url,
-        credentials=credentials
-    )
-
-    upload_run_response = client.start_evaluation_run(
-        evaluation=EvaluationUpload(
-            display_name=evaluation_name,
-        )
+        credential=credentials
     )
 
     # Massaging before artifacts are put on disk
@@ -187,6 +181,12 @@ def _log_metrics_and_instance_results_onedp(
             metrics=metrics
         )
 
+        upload_run_response = client.start_evaluation_run(
+            evaluation=EvaluationUpload(
+                display_name=evaluation_name,
+            )
+        )
+
         update_run_response = client.update_evaluation_run(
             name=upload_run_response.id,
             evaluation=EvaluationUpload(