[text analtyics] Regenerate with v3.0

sdk/textanalytics/azure-ai-textanalytics/azure/ai/textanalytics/_generated/__init__.py

@@ -8,3 +8,9 @@
 
 from ._text_analytics_client import TextAnalyticsClient
 __all__ = ['TextAnalyticsClient']
+
+try:
+    from ._patch import patch_sdk
+    patch_sdk()
+except ImportError:
+    pass

sdk/textanalytics/azure-ai-textanalytics/azure/ai/textanalytics/_generated/_configuration.py

@@ -6,11 +6,17 @@
 # Changes may cause incorrect behavior and will be lost if the code is regenerated.
 # --------------------------------------------------------------------------
 
-from typing import Any
+from typing import TYPE_CHECKING
 
 from azure.core.configuration import Configuration
 from azure.core.pipeline import policies
 
+if TYPE_CHECKING:
+    # pylint: disable=unused-import,ungrouped-imports
+    from typing import Any
+
+    from azure.core.credentials import TokenCredential
+
 VERSION = "unknown"
 
 class TextAnalyticsClientConfiguration(Configuration):
@@ -20,7 +26,7 @@ class TextAnalyticsClientConfiguration(Configuration):
     attributes.
 
     :param credential: Credential needed for the client to connect to Azure.
-    :type credential: azure.core.credentials.TokenCredential
+    :type credential: ~azure.core.credentials.TokenCredential
     :param endpoint: Supported Cognitive Services endpoints (protocol and hostname, for example: https://westus.api.cognitive.microsoft.com).
     :type endpoint: str
     """
@@ -41,6 +47,7 @@ def __init__(
         self.credential = credential
         self.endpoint = endpoint
         self.credential_scopes = ['https://cognitiveservices.azure.com/.default']
+        self.credential_scopes.extend(kwargs.pop('credential_scopes', []))
         kwargs.setdefault('sdk_moniker', 'ai-textanalytics/{}'.format(VERSION))
         self._configure(**kwargs)
 

sdk/textanalytics/azure-ai-textanalytics/azure/ai/textanalytics/_generated/_text_analytics_client.py

@@ -6,11 +6,15 @@
 # Changes may cause incorrect behavior and will be lost if the code is regenerated.
 # --------------------------------------------------------------------------
 
-from typing import Any
+from typing import TYPE_CHECKING
 
 from azure.core import PipelineClient
 from msrest import Deserializer, Serializer
 
+if TYPE_CHECKING:
+    # pylint: disable=unused-import,ungrouped-imports
+    from typing import Any
+
 from ._configuration import TextAnalyticsClientConfiguration
 from .operations import TextAnalyticsClientOperationsMixin
 from . import models
@@ -20,9 +24,10 @@ class TextAnalyticsClient(TextAnalyticsClientOperationsMixin):
     """The Text Analytics API is a suite of text analytics web services built with best-in-class Microsoft machine learning algorithms. The API can be used to analyze unstructured text for tasks such as sentiment analysis, key phrase extraction and language detection. No training data is needed to use this API; just bring your text data. This API uses advanced natural language processing techniques to deliver best in class predictions. Further documentation can be found in https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/overview.
 
     :param credential: Credential needed for the client to connect to Azure.
-    :type credential: azure.core.credentials.TokenCredential
+    :type credential: ~azure.core.credentials.TokenCredential
     :param endpoint: Supported Cognitive Services endpoints (protocol and hostname, for example: https://westus.api.cognitive.microsoft.com).
     :type endpoint: str
+    :keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present.
     """
 
     def __init__(
@@ -32,7 +37,7 @@ def __init__(
         **kwargs  # type: Any
     ):
         # type: (...) -> None
-        base_url = '{Endpoint}/text/analytics/v3.0-preview.1'
+        base_url = '{Endpoint}/text/analytics/v3.0'
         self._config = TextAnalyticsClientConfiguration(credential, endpoint, **kwargs)
         self._client = PipelineClient(base_url=base_url, config=self._config, **kwargs)
 

sdk/textanalytics/azure-ai-textanalytics/azure/ai/textanalytics/_generated/aio/_configuration_async.py

@@ -6,11 +6,15 @@
 # Changes may cause incorrect behavior and will be lost if the code is regenerated.
 # --------------------------------------------------------------------------
 
-from typing import Any
+from typing import Any, TYPE_CHECKING
 
 from azure.core.configuration import Configuration
 from azure.core.pipeline import policies
 
+if TYPE_CHECKING:
+    # pylint: disable=unused-import,ungrouped-imports
+    from azure.core.credentials import TokenCredential
+
 VERSION = "unknown"
 
 class TextAnalyticsClientConfiguration(Configuration):
@@ -20,7 +24,7 @@ class TextAnalyticsClientConfiguration(Configuration):
     attributes.
 
     :param credential: Credential needed for the client to connect to Azure.
-    :type credential: azure.core.credentials.AsyncTokenCredential
+    :type credential: ~azure.core.credentials_async.AsyncTokenCredential
     :param endpoint: Supported Cognitive Services endpoints (protocol and hostname, for example: https://westus.api.cognitive.microsoft.com).
     :type endpoint: str
     """
@@ -40,6 +44,7 @@ def __init__(
         self.credential = credential
         self.endpoint = endpoint
         self.credential_scopes = ['https://cognitiveservices.azure.com/.default']
+        self.credential_scopes.extend(kwargs.pop('credential_scopes', []))
         kwargs.setdefault('sdk_moniker', 'ai-textanalytics/{}'.format(VERSION))
         self._configure(**kwargs)
 

sdk/textanalytics/azure-ai-textanalytics/azure/ai/textanalytics/_generated/aio/_text_analytics_client_async.py

@@ -20,9 +20,10 @@ class TextAnalyticsClient(TextAnalyticsClientOperationsMixin):
     """The Text Analytics API is a suite of text analytics web services built with best-in-class Microsoft machine learning algorithms. The API can be used to analyze unstructured text for tasks such as sentiment analysis, key phrase extraction and language detection. No training data is needed to use this API; just bring your text data. This API uses advanced natural language processing techniques to deliver best in class predictions. Further documentation can be found in https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/overview.
 
     :param credential: Credential needed for the client to connect to Azure.
-    :type credential: azure.core.credentials.AsyncTokenCredential
+    :type credential: ~azure.core.credentials_async.AsyncTokenCredential
     :param endpoint: Supported Cognitive Services endpoints (protocol and hostname, for example: https://westus.api.cognitive.microsoft.com).
     :type endpoint: str
+    :keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present.
     """
 
     def __init__(
@@ -31,7 +32,7 @@ def __init__(
         endpoint: str,
         **kwargs: Any
     ) -> None:
-        base_url = '{Endpoint}/text/analytics/v3.0-preview.1'
+        base_url = '{Endpoint}/text/analytics/v3.0'
         self._config = TextAnalyticsClientConfiguration(credential, endpoint, **kwargs)
         self._client = AsyncPipelineClient(base_url=base_url, config=self._config, **kwargs)
 

sdk/textanalytics/azure-ai-textanalytics/azure/ai/textanalytics/_generated/aio/operations_async/_text_analytics_client_operations_async.py

@@ -21,7 +21,7 @@ class TextAnalyticsClientOperationsMixin:
 
     async def entities_recognition_general(
         self,
-        documents: List["MultiLanguageInput"],
+        documents: List["models.MultiLanguageInput"],
         model_version: Optional[str] = None,
         show_stats: Optional[bool] = None,
         **kwargs
@@ -44,13 +44,14 @@ async def entities_recognition_general(
         :raises: ~azure.core.exceptions.HttpResponseError
         """
         cls = kwargs.pop('cls', None)  # type: ClsType["models.EntitiesResult"]
-        error_map = kwargs.pop('error_map', {404: ResourceNotFoundError, 409: ResourceExistsError})
+        error_map = {404: ResourceNotFoundError, 409: ResourceExistsError}
+        error_map.update(kwargs.pop('error_map', {}))
 
         _input = models.MultiLanguageBatchInput(documents=documents)
         content_type = kwargs.pop("content_type", "application/json")
 
         # Construct URL
-        url = self.entities_recognition_general.metadata['url']
+        url = self.entities_recognition_general.metadata['url']  # type: ignore
         path_format_arguments = {
             'Endpoint': self._serialize.url("self._config.endpoint", self._config.endpoint, 'str', skip_quote=True),
         }
@@ -88,82 +89,11 @@ async def entities_recognition_general(
           return cls(pipeline_response, deserialized, {})
 
         return deserialized
-    entities_recognition_general.metadata = {'url': '/entities/recognition/general'}
-
-    async def entities_recognition_pii(
-        self,
-        documents: List["MultiLanguageInput"],
-        model_version: Optional[str] = None,
-        show_stats: Optional[bool] = None,
-        **kwargs
-    ) -> "models.EntitiesResult":
-        """The API returns a list of entities with personal information (\"SSN\", \"Bank Account\" etc) in the document. For the list of supported entity types, check :code:`<a href="https://aka.ms/tanerpii">Supported Entity Types in Text Analytics API</a>`. See the :code:`<a href="https://aka.ms/talangs">Supported languages in Text Analytics API</a>` for the list of enabled languages.
-
-        Entities containing personal information.
-
-        :param documents: The set of documents to process as part of this batch.
-        :type documents: list[~azure.ai.textanalytics.models.MultiLanguageInput]
-        :param model_version: (Optional) This value indicates which model will be used for scoring. If
-         a model-version is not specified, the API should default to the latest, non-preview version.
-        :type model_version: str
-        :param show_stats: (Optional) if set to true, response will contain input and document level
-         statistics.
-        :type show_stats: bool
-        :keyword callable cls: A custom type or function that will be passed the direct response
-        :return: EntitiesResult or the result of cls(response)
-        :rtype: ~azure.ai.textanalytics.models.EntitiesResult
-        :raises: ~azure.core.exceptions.HttpResponseError
-        """
-        cls = kwargs.pop('cls', None)  # type: ClsType["models.EntitiesResult"]
-        error_map = kwargs.pop('error_map', {404: ResourceNotFoundError, 409: ResourceExistsError})
-
-        _input = models.MultiLanguageBatchInput(documents=documents)
-        content_type = kwargs.pop("content_type", "application/json")
-
-        # Construct URL
-        url = self.entities_recognition_pii.metadata['url']
-        path_format_arguments = {
-            'Endpoint': self._serialize.url("self._config.endpoint", self._config.endpoint, 'str', skip_quote=True),
-        }
-        url = self._client.format_url(url, **path_format_arguments)
-
-        # Construct parameters
-        query_parameters = {}  # type: Dict[str, Any]
-        if model_version is not None:
-            query_parameters['model-version'] = self._serialize.query("model_version", model_version, 'str')
-        if show_stats is not None:
-            query_parameters['showStats'] = self._serialize.query("show_stats", show_stats, 'bool')
-
-        # Construct headers
-        header_parameters = {}  # type: Dict[str, Any]
-        header_parameters['Content-Type'] = self._serialize.header("content_type", content_type, 'str')
-        header_parameters['Accept'] = 'application/json'
-
-        # Construct and send request
-        body_content_kwargs = {}  # type: Dict[str, Any]
-        body_content = self._serialize.body(_input, 'MultiLanguageBatchInput')
-        body_content_kwargs['content'] = body_content
-        request = self._client.post(url, query_parameters, header_parameters, **body_content_kwargs)
-
-        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(models.TextAnalyticsError, response)
-            raise HttpResponseError(response=response, model=error)
-
-        deserialized = self._deserialize('EntitiesResult', pipeline_response)
-
-        if cls:
-          return cls(pipeline_response, deserialized, {})
-
-        return deserialized
-    entities_recognition_pii.metadata = {'url': '/entities/recognition/pii'}
+    entities_recognition_general.metadata = {'url': '/entities/recognition/general'}  # type: ignore
 
     async def entities_linking(
         self,
-        documents: List["MultiLanguageInput"],
+        documents: List["models.MultiLanguageInput"],
         model_version: Optional[str] = None,
         show_stats: Optional[bool] = None,
         **kwargs
@@ -186,13 +116,14 @@ async def entities_linking(
         :raises: ~azure.core.exceptions.HttpResponseError
         """
         cls = kwargs.pop('cls', None)  # type: ClsType["models.EntityLinkingResult"]
-        error_map = kwargs.pop('error_map', {404: ResourceNotFoundError, 409: ResourceExistsError})
+        error_map = {404: ResourceNotFoundError, 409: ResourceExistsError}
+        error_map.update(kwargs.pop('error_map', {}))
 
         _input = models.MultiLanguageBatchInput(documents=documents)
         content_type = kwargs.pop("content_type", "application/json")
 
         # Construct URL
-        url = self.entities_linking.metadata['url']
+        url = self.entities_linking.metadata['url']  # type: ignore
         path_format_arguments = {
             'Endpoint': self._serialize.url("self._config.endpoint", self._config.endpoint, 'str', skip_quote=True),
         }
@@ -230,11 +161,11 @@ async def entities_linking(
           return cls(pipeline_response, deserialized, {})
 
         return deserialized
-    entities_linking.metadata = {'url': '/entities/linking'}
+    entities_linking.metadata = {'url': '/entities/linking'}  # type: ignore
 
     async def key_phrases(
         self,
-        documents: List["MultiLanguageInput"],
+        documents: List["models.MultiLanguageInput"],
         model_version: Optional[str] = None,
         show_stats: Optional[bool] = None,
         **kwargs
@@ -257,13 +188,14 @@ async def key_phrases(
         :raises: ~azure.core.exceptions.HttpResponseError
         """
         cls = kwargs.pop('cls', None)  # type: ClsType["models.KeyPhraseResult"]
-        error_map = kwargs.pop('error_map', {404: ResourceNotFoundError, 409: ResourceExistsError})
+        error_map = {404: ResourceNotFoundError, 409: ResourceExistsError}
+        error_map.update(kwargs.pop('error_map', {}))
 
         _input = models.MultiLanguageBatchInput(documents=documents)
         content_type = kwargs.pop("content_type", "application/json")
 
         # Construct URL
-        url = self.key_phrases.metadata['url']
+        url = self.key_phrases.metadata['url']  # type: ignore
         path_format_arguments = {
             'Endpoint': self._serialize.url("self._config.endpoint", self._config.endpoint, 'str', skip_quote=True),
         }
@@ -301,11 +233,11 @@ async def key_phrases(
           return cls(pipeline_response, deserialized, {})
 
         return deserialized
-    key_phrases.metadata = {'url': '/keyPhrases'}
+    key_phrases.metadata = {'url': '/keyPhrases'}  # type: ignore
 
     async def languages(
         self,
-        documents: List["LanguageInput"],
+        documents: List["models.LanguageInput"],
         model_version: Optional[str] = None,
         show_stats: Optional[bool] = None,
         **kwargs
@@ -328,13 +260,14 @@ async def languages(
         :raises: ~azure.core.exceptions.HttpResponseError
         """
         cls = kwargs.pop('cls', None)  # type: ClsType["models.LanguageResult"]
-        error_map = kwargs.pop('error_map', {404: ResourceNotFoundError, 409: ResourceExistsError})
+        error_map = {404: ResourceNotFoundError, 409: ResourceExistsError}
+        error_map.update(kwargs.pop('error_map', {}))
 
         _input = models.LanguageBatchInput(documents=documents)
         content_type = kwargs.pop("content_type", "application/json")
 
         # Construct URL
-        url = self.languages.metadata['url']
+        url = self.languages.metadata['url']  # type: ignore
         path_format_arguments = {
             'Endpoint': self._serialize.url("self._config.endpoint", self._config.endpoint, 'str', skip_quote=True),
         }
@@ -372,11 +305,11 @@ async def languages(
           return cls(pipeline_response, deserialized, {})
 
         return deserialized
-    languages.metadata = {'url': '/languages'}
+    languages.metadata = {'url': '/languages'}  # type: ignore
 
     async def sentiment(
         self,
-        documents: List["MultiLanguageInput"],
+        documents: List["models.MultiLanguageInput"],
         model_version: Optional[str] = None,
         show_stats: Optional[bool] = None,
         **kwargs
@@ -399,13 +332,14 @@ async def sentiment(
         :raises: ~azure.core.exceptions.HttpResponseError
         """
         cls = kwargs.pop('cls', None)  # type: ClsType["models.SentimentResponse"]
-        error_map = kwargs.pop('error_map', {404: ResourceNotFoundError, 409: ResourceExistsError})
+        error_map = {404: ResourceNotFoundError, 409: ResourceExistsError}
+        error_map.update(kwargs.pop('error_map', {}))
 
         _input = models.MultiLanguageBatchInput(documents=documents)
         content_type = kwargs.pop("content_type", "application/json")
 
         # Construct URL
-        url = self.sentiment.metadata['url']
+        url = self.sentiment.metadata['url']  # type: ignore
         path_format_arguments = {
             'Endpoint': self._serialize.url("self._config.endpoint", self._config.endpoint, 'str', skip_quote=True),
         }
@@ -443,4 +377,4 @@ async def sentiment(
           return cls(pipeline_response, deserialized, {})
 
         return deserialized
-    sentiment.metadata = {'url': '/sentiment'}
+    sentiment.metadata = {'url': '/sentiment'}  # type: ignore

sdk/textanalytics/azure-ai-textanalytics/azure/ai/textanalytics/_generated/models/__init__.py

@@ -32,6 +32,7 @@
     from ._models_py3 import SentimentConfidenceScorePerLabel
     from ._models_py3 import SentimentResponse
     from ._models_py3 import TextAnalyticsError
+    from ._models_py3 import TextAnalyticsWarning
 except (SyntaxError, ImportError):
     from ._models import DetectedLanguage  # type: ignore
     from ._models import DocumentEntities  # type: ignore
@@ -58,12 +59,14 @@
     from ._models import SentimentConfidenceScorePerLabel  # type: ignore
     from ._models import SentimentResponse  # type: ignore
     from ._models import TextAnalyticsError  # type: ignore
+    from ._models import TextAnalyticsWarning  # type: ignore
 
 from ._text_analytics_client_enums import (
     DocumentSentimentValue,
     ErrorCodeValue,
     InnerErrorCodeValue,
     SentenceSentimentValue,
+    WarningCodeValue,
 )
 
 __all__ = [
@@ -92,8 +95,10 @@
     'SentimentConfidenceScorePerLabel',
     'SentimentResponse',
     'TextAnalyticsError',
+    'TextAnalyticsWarning',
     'DocumentSentimentValue',
     'ErrorCodeValue',
     'InnerErrorCodeValue',
     'SentenceSentimentValue',
+    'WarningCodeValue',
 ]