[Textanalytics] fix pylint/pyright for next version

sdk/cognitivelanguage/azure-ai-language-questionanswering-authoring/azure/ai/language/questionanswering/authoring/_utils/serialization.py

@@ -1,11 +1,11 @@
+# pylint: disable=line-too-long,useless-suppression,too-many-lines
 # 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) Python Code Generator.
 # Changes may cause incorrect behavior and will be lost if the code is regenerated.
 # --------------------------------------------------------------------------
-# pylint: disable=line-too-long,useless-suppression,too-many-lines,duplicate-code,missing-module-docstring,missing-class-docstring,missing-function-docstring,consider-using-f-string,invalid-name,too-many-locals,too-many-branches
 
 # pyright: reportUnnecessaryTypeIgnoreComment=false
 
@@ -787,7 +787,7 @@ def serialize_data(self, data, data_type, **kwargs):
 
             # If dependencies is empty, try with current data class
             # It has to be a subclass of Enum anyway
-            enum_type = self.dependencies.get(data_type, data.__class__)
+            enum_type = self.dependencies.get(data_type, cast(type, data.__class__))
             if issubclass(enum_type, Enum):
                 return Serializer.serialize_enum(data, enum_obj=enum_type)
 
@@ -821,13 +821,20 @@ def serialize_basic(cls, data, data_type, **kwargs):
         :param str data_type: Type of object in the iterable.
         :rtype: str, int, float, bool
         :return: serialized object
+        :raises TypeError: raise if data_type is not one of str, int, float, bool.
         """
         custom_serializer = cls._get_custom_serializers(data_type, **kwargs)
         if custom_serializer:
             return custom_serializer(data)
         if data_type == "str":
             return cls.serialize_unicode(data)
-        return eval(data_type)(data)  # nosec # pylint: disable=eval-used
+        if data_type == "int":
+            return int(data)
+        if data_type == "float":
+            return float(data)
+        if data_type == "bool":
+            return bool(data)
+        raise TypeError("Unknown basic data type: {}".format(data_type))
 
     @classmethod
     def serialize_unicode(cls, data):
@@ -1757,7 +1764,7 @@ def deserialize_basic(self, attr, data_type):  # pylint: disable=too-many-return
         :param str data_type: deserialization data type.
         :return: Deserialized basic type.
         :rtype: str, int, float or bool
-        :raises TypeError: if string format is not valid.
+        :raises TypeError: if string format is not valid or data_type is not one of str, int, float, bool.
         """
         # If we're here, data is supposed to be a basic type.
         # If it's still an XML node, take the text
@@ -1783,7 +1790,11 @@ def deserialize_basic(self, attr, data_type):  # pylint: disable=too-many-return
 
         if data_type == "str":
             return self.deserialize_unicode(attr)
-        return eval(data_type)(attr)  # nosec # pylint: disable=eval-used
+        if data_type == "int":
+            return int(attr)
+        if data_type == "float":
+            return float(attr)
+        raise TypeError("Unknown basic data type: {}".format(data_type))
 
     @staticmethod
     def deserialize_unicode(data):

sdk/cognitivelanguage/azure-ai-language-questionanswering/azure/ai/language/questionanswering/_utils/serialization.py

@@ -787,7 +787,7 @@ def serialize_data(self, data, data_type, **kwargs):
 
             # If dependencies is empty, try with current data class
             # It has to be a subclass of Enum anyway
-            enum_type = self.dependencies.get(data_type, data.__class__)
+            enum_type = self.dependencies.get(data_type, cast(type, data.__class__))
             if issubclass(enum_type, Enum):
                 return Serializer.serialize_enum(data, enum_obj=enum_type)
 
@@ -821,13 +821,20 @@ def serialize_basic(cls, data, data_type, **kwargs):
         :param str data_type: Type of object in the iterable.
         :rtype: str, int, float, bool
         :return: serialized object
+        :raises TypeError: raise if data_type is not one of str, int, float, bool.
         """
         custom_serializer = cls._get_custom_serializers(data_type, **kwargs)
         if custom_serializer:
             return custom_serializer(data)
         if data_type == "str":
             return cls.serialize_unicode(data)
-        return eval(data_type)(data)  # nosec # pylint: disable=eval-used
+        if data_type == "int":
+            return int(data)
+        if data_type == "float":
+            return float(data)
+        if data_type == "bool":
+            return bool(data)
+        raise TypeError("Unknown basic data type: {}".format(data_type))
 
     @classmethod
     def serialize_unicode(cls, data):
@@ -1757,7 +1764,7 @@ def deserialize_basic(self, attr, data_type):  # pylint: disable=too-many-return
         :param str data_type: deserialization data type.
         :return: Deserialized basic type.
         :rtype: str, int, float or bool
-        :raises TypeError: if string format is not valid.
+        :raises TypeError: if string format is not valid or data_type is not one of str, int, float, bool.
         """
         # If we're here, data is supposed to be a basic type.
         # If it's still an XML node, take the text
@@ -1783,7 +1790,11 @@ def deserialize_basic(self, attr, data_type):  # pylint: disable=too-many-return
 
         if data_type == "str":
             return self.deserialize_unicode(attr)
-        return eval(data_type)(attr)  # nosec # pylint: disable=eval-used
+        if data_type == "int":
+            return int(attr)
+        if data_type == "float":
+            return float(attr)
+        raise TypeError("Unknown basic data type: {}".format(data_type))
 
     @staticmethod
     def deserialize_unicode(data):

sdk/cognitivelanguage/azure-ai-textanalytics-authoring/azure/ai/textanalytics/authoring/_utils/serialization.py

@@ -787,7 +787,7 @@ def serialize_data(self, data, data_type, **kwargs):
 
             # If dependencies is empty, try with current data class
             # It has to be a subclass of Enum anyway
-            enum_type = self.dependencies.get(data_type, data.__class__)
+            enum_type = self.dependencies.get(data_type, cast(type, data.__class__))
             if issubclass(enum_type, Enum):
                 return Serializer.serialize_enum(data, enum_obj=enum_type)
 
@@ -821,13 +821,20 @@ def serialize_basic(cls, data, data_type, **kwargs):
         :param str data_type: Type of object in the iterable.
         :rtype: str, int, float, bool
         :return: serialized object
+        :raises TypeError: raise if data_type is not one of str, int, float, bool.
         """
         custom_serializer = cls._get_custom_serializers(data_type, **kwargs)
         if custom_serializer:
             return custom_serializer(data)
         if data_type == "str":
             return cls.serialize_unicode(data)
-        return eval(data_type)(data)  # nosec # pylint: disable=eval-used
+        if data_type == "int":
+            return int(data)
+        if data_type == "float":
+            return float(data)
+        if data_type == "bool":
+            return bool(data)
+        raise TypeError("Unknown basic data type: {}".format(data_type))
 
     @classmethod
     def serialize_unicode(cls, data):
@@ -1757,7 +1764,7 @@ def deserialize_basic(self, attr, data_type):  # pylint: disable=too-many-return
         :param str data_type: deserialization data type.
         :return: Deserialized basic type.
         :rtype: str, int, float or bool
-        :raises TypeError: if string format is not valid.
+        :raises TypeError: if string format is not valid or data_type is not one of str, int, float, bool.
         """
         # If we're here, data is supposed to be a basic type.
         # If it's still an XML node, take the text
@@ -1783,7 +1790,11 @@ def deserialize_basic(self, attr, data_type):  # pylint: disable=too-many-return
 
         if data_type == "str":
             return self.deserialize_unicode(attr)
-        return eval(data_type)(attr)  # nosec # pylint: disable=eval-used
+        if data_type == "int":
+            return int(attr)
+        if data_type == "float":
+            return float(attr)
+        raise TypeError("Unknown basic data type: {}".format(data_type))
 
     @staticmethod
     def deserialize_unicode(data):

sdk/cognitivelanguage/azure-ai-textanalytics-authoring/tsp-location.yaml

@@ -1,4 +1,4 @@
-directory: specification/cognitiveservices/Language.AnalyzeText-authoring
-commit: a525e37a2b1546c47a778eda7106cfd7c04051e1
+directory: specification/cognitiveservices/data-plane/LanguageAnalyzeTextAuthoring
+commit: 483301e475684d809bdc7e81aa786e84cfb4236f
 repo: Azure/azure-rest-api-specs
 additionalDirectories: 

sdk/cognitivelanguage/azure-ai-textanalytics/README.md

@@ -69,7 +69,7 @@ from azure.ai.textanalytics import TextAnalysisClient
 endpoint = os.environ["AZURE_TEXT_ENDPOINT"]
 key = os.environ["AZURE_TEXT_KEY"]
 
-text_client = TextAnalysisClient(endpoint, AzureKeyCredential(key))
+text_client = TextAnalysisClient(endpoint, AzureKeyCredential(key)) # pylint:disable=unused-variable
 ```
 
 <!-- END SNIPPET -->
@@ -126,7 +126,7 @@ from azure.ai.textanalytics import TextAnalysisClient
 endpoint = os.environ["AZURE_TEXT_ENDPOINT"]
 key = os.environ["AZURE_TEXT_KEY"]
 
-text_client = TextAnalysisClient(endpoint, AzureKeyCredential(key))
+text_client = TextAnalysisClient(endpoint, AzureKeyCredential(key)) # pylint:disable=unused-variable
 ```
 
 <!-- END SNIPPET -->
@@ -164,7 +164,7 @@ from azure.identity import DefaultAzureCredential
 endpoint = os.environ["AZURE_TEXT_ENDPOINT"]
 credential = DefaultAzureCredential()
 
-text_client = TextAnalysisClient(endpoint, credential=credential)
+text_client = TextAnalysisClient(endpoint, credential=credential) # pylint:disable=unused-variable
 ```
 
 <!-- END SNIPPET -->
@@ -803,19 +803,12 @@ def sample_analyze_healthcare_entities():
                         print(f"  Offset: {entity.offset}")
                         print(f"  Length: {entity.length}")
                         print(f"  Confidence score: {entity.confidence_score}")
-                        if entity.links:
-                            for link in entity.links:
-                                print(f"    Link ID: {link.id}")
-                                print(f"    Data source: {link.data_source}")
                         print()
 
                     # Relations
                     print("Relations:")
                     for relation in doc.relations or []:
                         print(f"  Relation type: {relation.relation_type}")
-                        for rel_entity in relation.entities or []:
-                            print(f"    Role: {rel_entity.role}")
-                            print(f"    Ref: {rel_entity.ref}")
                         print()
             else:
                 # Other action kinds, if present
@@ -824,8 +817,8 @@ def sample_analyze_healthcare_entities():
                         f"\n[Non-healthcare action] name={op_result.task_name}, "
                         f"status={op_result.status}, kind={op_result.kind}"
                     )
-                except Exception:
-                    print("\n[Non-healthcare action present]")
+                except (AttributeError, TypeError) as e:
+                    print(f"\n[Non-healthcare action present] Error: {e}")
 ```
 
 <!-- END SNIPPET -->
@@ -854,7 +847,6 @@ Note: Healthcare Entities Analysis is only available with API version v3.1 and n
 import os
 
 from azure.identity import DefaultAzureCredential
-from azure.core.credentials import AzureKeyCredential
 from azure.ai.textanalytics import TextAnalysisClient
 from azure.ai.textanalytics.models import (
     MultiLanguageTextInput,
@@ -863,7 +855,6 @@ from azure.ai.textanalytics.models import (
     KeyPhraseLROTask,
     EntityRecognitionOperationResult,
     KeyPhraseExtractionOperationResult,
-    EntityTag,
 )
 
 
@@ -931,12 +922,6 @@ def sample_analyze():
                         print(f"    Type: {entity.type}")
                     if hasattr(entity, "subcategory") and entity.subcategory:
                         print(f"    Subcategory: {entity.subcategory}")
-                    if hasattr(entity, "tags") and entity.tags:
-                        print("    Tags:")
-                        for tag in entity.tags:
-                            if isinstance(tag, EntityTag):
-                                print(f"        TagName: {tag.name}")
-                                print(f"        TagConfidenceScore: {tag.confidence_score}")
                     print(f"    Confidence score: {entity.confidence_score}")
                     print()
             for err in action_result.results.errors:

sdk/cognitivelanguage/azure-ai-textanalytics/assets.json

@@ -2,5 +2,5 @@
   "AssetsRepo": "Azure/azure-sdk-assets",
   "AssetsRepoPrefixPath": "python",
   "TagPrefix": "python/cognitivelanguage/azure-ai-textanalytics",
-  "Tag": "python/cognitivelanguage/azure-ai-textanalytics_c820b61b7e"
+  "Tag": "python/cognitivelanguage/azure-ai-textanalytics_95abc8a0dd"
 }

sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/textanalytics/_operations/_operations.py

@@ -280,7 +280,10 @@ def analyze_text(
                 except (StreamConsumedError, StreamClosedError):
                     pass
             map_error(status_code=response.status_code, response=response, error_map=error_map)
-            error = _failsafe_deserialize(_models.ErrorResponse, response)
+            error = _failsafe_deserialize(
+                _models.ErrorResponse,
+                response,
+            )
             raise HttpResponseError(response=response, model=error)
 
         if _stream:
@@ -365,7 +368,10 @@ def get_job_status(
                 except (StreamConsumedError, StreamClosedError):
                     pass
             map_error(status_code=response.status_code, response=response, error_map=error_map)
-            error = _failsafe_deserialize(_models.ErrorResponse, response)
+            error = _failsafe_deserialize(
+                _models.ErrorResponse,
+                response,
+            )
             raise HttpResponseError(response=response, model=error)
 
         if _stream:
@@ -448,7 +454,10 @@ def _analyze_text_job_initial(
             except (StreamConsumedError, StreamClosedError):
                 pass
             map_error(status_code=response.status_code, response=response, error_map=error_map)
-            error = _failsafe_deserialize(_models.ErrorResponse, response)
+            error = _failsafe_deserialize(
+                _models.ErrorResponse,
+                response,
+            )
             raise HttpResponseError(response=response, model=error)
 
         response_headers = {}
@@ -462,7 +471,7 @@ def _analyze_text_job_initial(
         return deserialized  # type: ignore
 
     @overload
-    def _begin_analyze_text_job(
+    def begin_analyze_text_job(
         self,
         *,
         text_input: _models.MultiLanguageTextInput,
@@ -497,7 +506,7 @@ def _begin_analyze_text_job(
         """
 
     @overload
-    def _begin_analyze_text_job(
+    def begin_analyze_text_job(
         self, body: JSON, *, content_type: str = "application/json", **kwargs: Any
     ) -> LROPoller[None]:
         """Submit a collection of text documents for analysis. Specify one or more unique tasks to be
@@ -514,7 +523,7 @@ def _begin_analyze_text_job(
         """
 
     @overload
-    def _begin_analyze_text_job(
+    def begin_analyze_text_job(
         self, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any
     ) -> LROPoller[None]:
         """Submit a collection of text documents for analysis. Specify one or more unique tasks to be
@@ -531,7 +540,7 @@ def _begin_analyze_text_job(
         """
 
     @distributed_trace
-    def _begin_analyze_text_job(
+    def begin_analyze_text_job(
         self,
         body: Union[JSON, IO[bytes]] = _Unset,
         *,
@@ -651,7 +660,10 @@ def _cancel_job_initial(self, job_id: str, **kwargs: Any) -> Iterator[bytes]:
             except (StreamConsumedError, StreamClosedError):
                 pass
             map_error(status_code=response.status_code, response=response, error_map=error_map)
-            error = _failsafe_deserialize(_models.ErrorResponse, response)
+            error = _failsafe_deserialize(
+                _models.ErrorResponse,
+                response,
+            )
             raise HttpResponseError(response=response, model=error)
 
         response_headers = {}

sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/textanalytics/_patch.py

@@ -141,7 +141,7 @@ def __init__(
             kwargs["api_version"] = api_version
         super().__init__(endpoint=endpoint, credential=credential, **kwargs)
 
-    @overload
+    @overload # type: ignore[override]
     def begin_analyze_text_job(
         self,
         *,
@@ -178,7 +178,7 @@ def begin_analyze_text_job(
         """
 
     @overload
-    def begin_analyze_text_job(
+    def begin_analyze_text_job( # type: ignore[override]
         self, body: JSON, *, content_type: str = "application/json", **kwargs: Any
     ) -> AnalyzeTextLROPoller[ItemPaged["TextActions"]]:
         """Submit a collection of text documents for analysis. Specify one or more unique tasks to be
@@ -196,7 +196,7 @@ def begin_analyze_text_job(
         """
 
     @overload
-    def begin_analyze_text_job(
+    def begin_analyze_text_job( # type: ignore[override]
         self, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any
     ) -> AnalyzeTextLROPoller[ItemPaged["TextActions"]]:
         """Submit a collection of text documents for analysis. Specify one or more unique tasks to be