diff --git a/sdk/cognitivelanguage/azure-ai-textanalytics/CHANGELOG.md b/sdk/cognitivelanguage/azure-ai-textanalytics/CHANGELOG.md new file mode 100644 index 000000000000..628743d283a9 --- /dev/null +++ b/sdk/cognitivelanguage/azure-ai-textanalytics/CHANGELOG.md @@ -0,0 +1,5 @@ +# Release History + +## 1.0.0b1 (1970-01-01) + +- Initial version diff --git a/sdk/cognitivelanguage/azure-ai-textanalytics/LICENSE b/sdk/cognitivelanguage/azure-ai-textanalytics/LICENSE new file mode 100644 index 000000000000..63447fd8bbbf --- /dev/null +++ b/sdk/cognitivelanguage/azure-ai-textanalytics/LICENSE @@ -0,0 +1,21 @@ +Copyright (c) Microsoft Corporation. + +MIT License + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED *AS IS*, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. \ No newline at end of file diff --git a/sdk/cognitivelanguage/azure-ai-textanalytics/MANIFEST.in b/sdk/cognitivelanguage/azure-ai-textanalytics/MANIFEST.in new file mode 100644 index 000000000000..63c8bd493515 --- /dev/null +++ b/sdk/cognitivelanguage/azure-ai-textanalytics/MANIFEST.in @@ -0,0 +1,7 @@ +include *.md +include LICENSE +include azure/ai/textanalytics/py.typed +recursive-include tests *.py +recursive-include samples *.py *.md +include azure/__init__.py +include azure/ai/__init__.py diff --git a/sdk/cognitivelanguage/azure-ai-textanalytics/README.md b/sdk/cognitivelanguage/azure-ai-textanalytics/README.md new file mode 100644 index 000000000000..48ec96c0859e --- /dev/null +++ b/sdk/cognitivelanguage/azure-ai-textanalytics/README.md @@ -0,0 +1,43 @@ +# Azure Ai Textanalytics client library for Python + + +## Getting started + +### Install the package + +```bash +python -m pip install azure-ai-textanalytics +``` + +#### Prequisites + +- Python 3.9 or later is required to use this package. +- You need an [Azure subscription][azure_sub] to use this package. +- An existing Azure Ai Textanalytics instance. + + +## Contributing + +This project welcomes contributions and suggestions. Most contributions require +you to agree to a Contributor License Agreement (CLA) declaring that you have +the right to, and actually do, grant us the rights to use your contribution. +For details, visit https://cla.microsoft.com. + +When you submit a pull request, a CLA-bot will automatically determine whether +you need to provide a CLA and decorate the PR appropriately (e.g., label, +comment). Simply follow the instructions provided by the bot. You will only +need to do this once across all repos using our CLA. + +This project has adopted the +[Microsoft Open Source Code of Conduct][code_of_conduct]. For more information, +see the Code of Conduct FAQ or contact opencode@microsoft.com with any +additional questions or comments. + + +[code_of_conduct]: https://opensource.microsoft.com/codeofconduct/ +[authenticate_with_token]: https://docs.microsoft.com/azure/cognitive-services/authentication?tabs=powershell#authenticate-with-an-authentication-token +[azure_identity_credentials]: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/identity/azure-identity#credentials +[azure_identity_pip]: https://pypi.org/project/azure-identity/ +[default_azure_credential]: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/identity/azure-identity#defaultazurecredential +[pip]: https://pypi.org/project/pip/ +[azure_sub]: https://azure.microsoft.com/free/ diff --git a/sdk/cognitivelanguage/azure-ai-textanalytics/_metadata.json b/sdk/cognitivelanguage/azure-ai-textanalytics/_metadata.json new file mode 100644 index 000000000000..1de99897e08a --- /dev/null +++ b/sdk/cognitivelanguage/azure-ai-textanalytics/_metadata.json @@ -0,0 +1,7 @@ +{ + "apiVersion": "2025-05-15-preview", + "commit": "438119579eac021346cedd524909ab7552ee9a95", + "repository_url": "https://github.com/Azure/azure-rest-api-specs", + "typespec_src": "specification/cognitiveservices/Language.AnalyzeText", + "emitterVersion": "0.48.1" +} \ No newline at end of file diff --git a/sdk/cognitivelanguage/azure-ai-textanalytics/apiview-properties.json b/sdk/cognitivelanguage/azure-ai-textanalytics/apiview-properties.json new file mode 100644 index 000000000000..d27b2debedd5 --- /dev/null +++ b/sdk/cognitivelanguage/azure-ai-textanalytics/apiview-properties.json @@ -0,0 +1,200 @@ +{ + "CrossLanguagePackageId": "Language.Text", + "CrossLanguageDefinitionId": { + "azure.ai.textanalytics.models.AnalyzeTextLROResult": "Language.Text.AnalyzeTextLROResult", + "azure.ai.textanalytics.models.AbstractiveSummarizationLROResult": "Language.Text.AbstractiveSummarizationLROResult", + "azure.ai.textanalytics.models.AnalyzeTextLROTask": "Language.Text.AnalyzeTextLROTask", + "azure.ai.textanalytics.models.AbstractiveSummarizationLROTask": "Language.Text.AbstractiveSummarizationLROTask", + "azure.ai.textanalytics.models.AbstractiveSummarizationResult": "Language.Text.AbstractiveSummarizationResult", + "azure.ai.textanalytics.models.AbstractiveSummarizationTaskParameters": "Language.Text.AbstractiveSummarizationTaskParameters", + "azure.ai.textanalytics.models.AbstractiveSummary": "Language.Text.AbstractiveSummary", + "azure.ai.textanalytics.models.AbstractiveSummaryDocumentResultWithDetectedLanguage": "Language.Text.AbstractiveSummaryDocumentResultWithDetectedLanguage", + "azure.ai.textanalytics.models.BaseMetadata": "Language.Text.BaseMetadata", + "azure.ai.textanalytics.models.AgeMetadata": "Language.Text.AgeMetadata", + "azure.ai.textanalytics.models.BaseEntityOverlapPolicy": "Language.Text.BaseEntityOverlapPolicy", + "azure.ai.textanalytics.models.AllowOverlapEntityPolicyType": "Language.Text.AllowOverlapEntityPolicyType", + "azure.ai.textanalytics.models.AnalyzeTextTask": "Language.Text.AnalyzeTextTask", + "azure.ai.textanalytics.models.AnalyzeTextEntityLinkingInput": "Language.Text.AnalyzeTextEntityLinkingInput", + "azure.ai.textanalytics.models.AnalyzeTextEntityRecognitionInput": "Language.Text.AnalyzeTextEntityRecognitionInput", + "azure.ai.textanalytics.models.AnalyzeTextJobState": "Language.Text.AnalyzeTextJobState", + "azure.ai.textanalytics.models.AnalyzeTextKeyPhraseExtractionInput": "Language.Text.AnalyzeTextKeyPhraseExtractionInput", + "azure.ai.textanalytics.models.AnalyzeTextLanguageDetectionInput": "Language.Text.AnalyzeTextLanguageDetectionInput", + "azure.ai.textanalytics.models.AnalyzeTextPiiEntitiesRecognitionInput": "Language.Text.AnalyzeTextPiiEntitiesRecognitionInput", + "azure.ai.textanalytics.models.AnalyzeTextSentimentAnalysisInput": "Language.Text.AnalyzeTextSentimentAnalysisInput", + "azure.ai.textanalytics.models.AnalyzeTextTaskResult": "Language.Text.AnalyzeTextTaskResult", + "azure.ai.textanalytics.models.AreaMetadata": "Language.Text.AreaMetadata", + "azure.ai.textanalytics.models.BaseRedactionPolicy": "Language.Text.BaseRedactionPolicy", + "azure.ai.textanalytics.models.CharacterMaskPolicyType": "Language.Text.CharacterMaskPolicyType", + "azure.ai.textanalytics.models.ClassificationDocumentResultWithDetectedLanguage": "Language.Text.ClassificationDocumentResultWithDetectedLanguage", + "azure.ai.textanalytics.models.ClassificationResult": "Language.Text.ClassificationResult", + "azure.ai.textanalytics.models.CurrencyMetadata": "Language.Text.CurrencyMetadata", + "azure.ai.textanalytics.models.CustomEntitiesLROTask": "Language.Text.CustomEntitiesLROTask", + "azure.ai.textanalytics.models.CustomEntitiesResult": "Language.Text.CustomEntitiesResult", + "azure.ai.textanalytics.models.CustomEntitiesTaskParameters": "Language.Text.CustomEntitiesTaskParameters", + "azure.ai.textanalytics.models.CustomEntityRecognitionLROResult": "Language.Text.CustomEntityRecognitionLROResult", + "azure.ai.textanalytics.models.CustomLabelClassificationResult": "Language.Text.CustomLabelClassificationResult", + "azure.ai.textanalytics.models.CustomMultiLabelClassificationLROResult": "Language.Text.CustomMultiLabelClassificationLROResult", + "azure.ai.textanalytics.models.CustomMultiLabelClassificationLROTask": "Language.Text.CustomMultiLabelClassificationLROTask", + "azure.ai.textanalytics.models.CustomMultiLabelClassificationTaskParameters": "Language.Text.CustomMultiLabelClassificationTaskParameters", + "azure.ai.textanalytics.models.CustomSingleLabelClassificationLROResult": "Language.Text.CustomSingleLabelClassificationLROResult", + "azure.ai.textanalytics.models.CustomSingleLabelClassificationLROTask": "Language.Text.CustomSingleLabelClassificationLROTask", + "azure.ai.textanalytics.models.CustomSingleLabelClassificationTaskParameters": "Language.Text.CustomSingleLabelClassificationTaskParameters", + "azure.ai.textanalytics.models.DateMetadata": "Language.Text.DateMetadata", + "azure.ai.textanalytics.models.DateTimeMetadata": "Language.Text.DateTimeMetadata", + "azure.ai.textanalytics.models.DateValue": "Language.Text.DateValue", + "azure.ai.textanalytics.models.DetectedLanguage": "Language.Text.DetectedLanguage", + "azure.ai.textanalytics.models.DocumentError": "Language.Text.DocumentError", + "azure.ai.textanalytics.models.DocumentStatistics": "Language.Text.DocumentStatistics", + "azure.ai.textanalytics.models.DocumentWarning": "Language.Text.DocumentWarning", + "azure.ai.textanalytics.models.EntitiesDocumentResultWithDetectedLanguage": "Language.Text.EntitiesDocumentResultWithDetectedLanguage", + "azure.ai.textanalytics.models.EntitiesDocumentResultWithMetadata": "Language.Text.EntitiesDocumentResultWithMetadata", + "azure.ai.textanalytics.models.EntitiesDocumentResultWithMetadataDetectedLanguage": "Language.Text.EntitiesDocumentResultWithMetadataDetectedLanguage", + "azure.ai.textanalytics.models.EntitiesLROTask": "Language.Text.EntitiesLROTask", + "azure.ai.textanalytics.models.EntitiesResult": "Language.Text.EntitiesResult", + "azure.ai.textanalytics.models.EntitiesTaskParameters": "Language.Text.EntitiesTaskParameters", + "azure.ai.textanalytics.models.EntitiesTaskResult": "Language.Text.EntitiesTaskResult", + "azure.ai.textanalytics.models.EntitiesWithMetadataAutoResult": "Language.Text.EntitiesWithMetadataAutoResult", + "azure.ai.textanalytics.models.Entity": "Language.Text.Entity", + "azure.ai.textanalytics.models.EntityInferenceOptions": "Language.Text.EntityInferenceOptions", + "azure.ai.textanalytics.models.EntityLinkingLROResult": "Language.Text.EntityLinkingLROResult", + "azure.ai.textanalytics.models.EntityLinkingLROTask": "Language.Text.EntityLinkingLROTask", + "azure.ai.textanalytics.models.EntityLinkingResult": "Language.Text.EntityLinkingResult", + "azure.ai.textanalytics.models.EntityLinkingResultWithDetectedLanguage": "Language.Text.EntityLinkingResultWithDetectedLanguage", + "azure.ai.textanalytics.models.EntityLinkingTaskParameters": "Language.Text.EntityLinkingTaskParameters", + "azure.ai.textanalytics.models.EntityLinkingTaskResult": "Language.Text.EntityLinkingTaskResult", + "azure.ai.textanalytics.models.EntityMaskPolicyType": "Language.Text.EntityMaskPolicyType", + "azure.ai.textanalytics.models.EntityRecognitionLROResult": "Language.Text.EntityRecognitionLROResult", + "azure.ai.textanalytics.models.EntitySynonym": "Language.Text.EntitySynonym", + "azure.ai.textanalytics.models.EntitySynonyms": "Language.Text.EntitySynonyms", + "azure.ai.textanalytics.models.EntityTag": "Language.Text.EntityTag", + "azure.ai.textanalytics.models.EntityWithMetadata": "Language.Text.EntityWithMetadata", + "azure.ai.textanalytics.models.Error": "Language.Text.Error", + "azure.ai.textanalytics.models.ErrorResponse": "Language.Text.ErrorResponse", + "azure.ai.textanalytics.models.ExtractedSummaryDocumentResultWithDetectedLanguage": "Language.Text.ExtractedSummaryDocumentResultWithDetectedLanguage", + "azure.ai.textanalytics.models.ExtractedSummarySentence": "Language.Text.ExtractedSummarySentence", + "azure.ai.textanalytics.models.ExtractiveSummarizationLROResult": "Language.Text.ExtractiveSummarizationLROResult", + "azure.ai.textanalytics.models.ExtractiveSummarizationLROTask": "Language.Text.ExtractiveSummarizationLROTask", + "azure.ai.textanalytics.models.ExtractiveSummarizationResult": "Language.Text.ExtractiveSummarizationResult", + "azure.ai.textanalytics.models.ExtractiveSummarizationTaskParameters": "Language.Text.ExtractiveSummarizationTaskParameters", + "azure.ai.textanalytics.models.FhirBundle": "Language.Text.FhirBundle", + "azure.ai.textanalytics.models.HealthcareAssertion": "Language.Text.HealthcareAssertion", + "azure.ai.textanalytics.models.HealthcareEntitiesDocumentResultWithDocumentDetectedLanguage": "Language.Text.HealthcareEntitiesDocumentResultWithDocumentDetectedLanguage", + "azure.ai.textanalytics.models.HealthcareEntity": "Language.Text.HealthcareEntity", + "azure.ai.textanalytics.models.HealthcareEntityLink": "Language.Text.HealthcareEntityLink", + "azure.ai.textanalytics.models.HealthcareLROResult": "Language.Text.HealthcareLROResult", + "azure.ai.textanalytics.models.HealthcareLROTask": "Language.Text.HealthcareLROTask", + "azure.ai.textanalytics.models.HealthcareRelation": "Language.Text.HealthcareRelation", + "azure.ai.textanalytics.models.HealthcareRelationEntity": "Language.Text.HealthcareRelationEntity", + "azure.ai.textanalytics.models.HealthcareResult": "Language.Text.HealthcareResult", + "azure.ai.textanalytics.models.HealthcareTaskParameters": "Language.Text.HealthcareTaskParameters", + "azure.ai.textanalytics.models.InformationMetadata": "Language.Text.InformationMetadata", + "azure.ai.textanalytics.models.InnerErrorModel": "Language.Text.InnerErrorModel", + "azure.ai.textanalytics.models.KeyPhraseExtractionLROResult": "Language.Text.KeyPhraseExtractionLROResult", + "azure.ai.textanalytics.models.KeyPhraseLROTask": "Language.Text.KeyPhraseLROTask", + "azure.ai.textanalytics.models.KeyPhraseResult": "Language.Text.KeyPhraseResult", + "azure.ai.textanalytics.models.KeyPhrasesDocumentResultWithDetectedLanguage": "Language.Text.KeyPhrasesDocumentResultWithDetectedLanguage", + "azure.ai.textanalytics.models.KeyPhraseTaskParameters": "Language.Text.KeyPhraseTaskParameters", + "azure.ai.textanalytics.models.KeyPhraseTaskResult": "Language.Text.KeyPhraseTaskResult", + "azure.ai.textanalytics.models.LanguageDetectionAnalysisInput": "Language.Text.LanguageDetectionAnalysisInput", + "azure.ai.textanalytics.models.LanguageDetectionDocumentResult": "Language.Text.LanguageDetectionDocumentResult", + "azure.ai.textanalytics.models.LanguageDetectionResult": "Language.Text.LanguageDetectionResult", + "azure.ai.textanalytics.models.LanguageDetectionTaskParameters": "Language.Text.LanguageDetectionTaskParameters", + "azure.ai.textanalytics.models.LanguageDetectionTaskResult": "Language.Text.LanguageDetectionTaskResult", + "azure.ai.textanalytics.models.LanguageInput": "Language.Text.LanguageInput", + "azure.ai.textanalytics.models.LengthMetadata": "Language.Text.LengthMetadata", + "azure.ai.textanalytics.models.LinkedEntity": "Language.Text.LinkedEntity", + "azure.ai.textanalytics.models.Match": "Language.Text.Match", + "azure.ai.textanalytics.models.MatchLongestEntityPolicyType": "Language.Text.MatchLongestEntityPolicyType", + "azure.ai.textanalytics.models.MultiLanguageAnalysisInput": "Language.Text.MultiLanguageAnalysisInput", + "azure.ai.textanalytics.models.MultiLanguageInput": "Language.Text.MultiLanguageInput", + "azure.ai.textanalytics.models.NoMaskPolicyType": "Language.Text.NoMaskPolicyType", + "azure.ai.textanalytics.models.NumberMetadata": "Language.Text.NumberMetadata", + "azure.ai.textanalytics.models.NumericRangeMetadata": "Language.Text.NumericRangeMetadata", + "azure.ai.textanalytics.models.OrdinalMetadata": "Language.Text.OrdinalMetadata", + "azure.ai.textanalytics.models.PiiEntityRecognitionLROResult": "Language.Text.PiiEntityRecognitionLROResult", + "azure.ai.textanalytics.models.PiiEntityWithTags": "Language.Text.PiiEntityWithTags", + "azure.ai.textanalytics.models.PiiLROTask": "Language.Text.PiiLROTask", + "azure.ai.textanalytics.models.PiiResult": "Language.Text.PiiResult", + "azure.ai.textanalytics.models.PiiResultWithDetectedLanguage": "Language.Text.PiiResultWithDetectedLanguage", + "azure.ai.textanalytics.models.PiiTaskParameters": "Language.Text.PiiTaskParameters", + "azure.ai.textanalytics.models.PiiTaskResult": "Language.Text.PiiTaskResult", + "azure.ai.textanalytics.models.RequestStatistics": "Language.Text.RequestStatistics", + "azure.ai.textanalytics.models.SentenceAssessment": "Language.Text.SentenceAssessment", + "azure.ai.textanalytics.models.SentenceSentiment": "Language.Text.SentenceSentiment", + "azure.ai.textanalytics.models.SentenceTarget": "Language.Text.SentenceTarget", + "azure.ai.textanalytics.models.SentimentAnalysisLROTask": "Language.Text.SentimentAnalysisLROTask", + "azure.ai.textanalytics.models.SentimentAnalysisTaskParameters": "Language.Text.SentimentAnalysisTaskParameters", + "azure.ai.textanalytics.models.SentimentConfidenceScores": "Language.Text.SentimentConfidenceScores", + "azure.ai.textanalytics.models.SentimentDocumentResultWithDetectedLanguage": "Language.Text.SentimentDocumentResultWithDetectedLanguage", + "azure.ai.textanalytics.models.SentimentLROResult": "Language.Text.SentimentLROResult", + "azure.ai.textanalytics.models.SentimentResponse": "Language.Text.SentimentResponse", + "azure.ai.textanalytics.models.SentimentTaskResult": "Language.Text.SentimentTaskResult", + "azure.ai.textanalytics.models.SpeedMetadata": "Language.Text.SpeedMetadata", + "azure.ai.textanalytics.models.SummaryContext": "Language.Text.SummaryContext", + "azure.ai.textanalytics.models.TargetConfidenceScoreLabel": "Language.Text.TargetConfidenceScoreLabel", + "azure.ai.textanalytics.models.TargetRelation": "Language.Text.TargetRelation", + "azure.ai.textanalytics.models.Tasks": "Language.Text.Tasks", + "azure.ai.textanalytics.models.TemperatureMetadata": "Language.Text.TemperatureMetadata", + "azure.ai.textanalytics.models.TemporalSetMetadata": "Language.Text.TemporalSetMetadata", + "azure.ai.textanalytics.models.TemporalSpanMetadata": "Language.Text.TemporalSpanMetadata", + "azure.ai.textanalytics.models.TemporalSpanValues": "Language.Text.TemporalSpanValues", + "azure.ai.textanalytics.models.TimeMetadata": "Language.Text.TimeMetadata", + "azure.ai.textanalytics.models.ValueExclusionPolicy": "Language.Text.ValueExclusionPolicy", + "azure.ai.textanalytics.models.VolumeMetadata": "Language.Text.VolumeMetadata", + "azure.ai.textanalytics.models.WeightMetadata": "Language.Text.WeightMetadata", + "azure.ai.textanalytics.models.AnalyzeTextTaskResultsKind": "Language.Text.AnalyzeTextTaskResultsKind", + "azure.ai.textanalytics.models.ErrorCode": "Language.Text.ErrorCode", + "azure.ai.textanalytics.models.InnerErrorCode": "Language.Text.InnerErrorCode", + "azure.ai.textanalytics.models.WarningCodeValue": "Language.Text.WarningCodeValue", + "azure.ai.textanalytics.models.ScriptKind": "Language.Text.ScriptKind", + "azure.ai.textanalytics.models.ScriptCode": "Language.Text.ScriptCode", + "azure.ai.textanalytics.models.MetadataKind": "Language.Text.MetadataKind", + "azure.ai.textanalytics.models.AgeUnit": "Language.Text.AgeUnit", + "azure.ai.textanalytics.models.AreaUnit": "Language.Text.AreaUnit", + "azure.ai.textanalytics.models.TemporalModifier": "Language.Text.TemporalModifier", + "azure.ai.textanalytics.models.InformationUnit": "Language.Text.InformationUnit", + "azure.ai.textanalytics.models.LengthUnit": "Language.Text.LengthUnit", + "azure.ai.textanalytics.models.NumberKind": "Language.Text.NumberKind", + "azure.ai.textanalytics.models.RangeKind": "Language.Text.RangeKind", + "azure.ai.textanalytics.models.RangeInclusivity": "Language.Text.RangeInclusivity", + "azure.ai.textanalytics.models.RelativeTo": "Language.Text.RelativeTo", + "azure.ai.textanalytics.models.SpeedUnit": "Language.Text.SpeedUnit", + "azure.ai.textanalytics.models.TemperatureUnit": "Language.Text.TemperatureUnit", + "azure.ai.textanalytics.models.VolumeUnit": "Language.Text.VolumeUnit", + "azure.ai.textanalytics.models.WeightUnit": "Language.Text.WeightUnit", + "azure.ai.textanalytics.models.DocumentSentimentValue": "Language.Text.DocumentSentimentValue", + "azure.ai.textanalytics.models.SentenceSentimentValue": "Language.Text.SentenceSentimentValue", + "azure.ai.textanalytics.models.TokenSentimentValue": "Language.Text.TokenSentimentValue", + "azure.ai.textanalytics.models.TargetRelationType": "Language.Text.TargetRelationType", + "azure.ai.textanalytics.models.AnalyzeTextTaskKind": "Language.Text.AnalyzeTextTaskKind", + "azure.ai.textanalytics.models.StringIndexType": "Language.Text.StringIndexType", + "azure.ai.textanalytics.models.EntityCategory": "Language.Text.EntityCategory", + "azure.ai.textanalytics.models.PolicyKind": "Language.Text.policyKind", + "azure.ai.textanalytics.models.PiiDomain": "Language.Text.PiiDomain", + "azure.ai.textanalytics.models.PiiCategory": "Language.Text.PiiCategory", + "azure.ai.textanalytics.models.PiiCategoriesExclude": "Language.Text.PiiCategoriesExclude", + "azure.ai.textanalytics.models.RedactionPolicyKind": "Language.Text.RedactionPolicyKind", + "azure.ai.textanalytics.models.RedactionCharacter": "Language.Text.redactionCharacter", + "azure.ai.textanalytics.models.State": "Language.Text.State", + "azure.ai.textanalytics.models.AnalyzeTextLROResultsKind": "Language.Text.AnalyzeTextLROResultsKind", + "azure.ai.textanalytics.models.HealthcareEntityCategory": "Language.Text.healthcareEntityCategory", + "azure.ai.textanalytics.models.Conditionality": "Language.Text.Conditionality", + "azure.ai.textanalytics.models.Certainty": "Language.Text.Certainty", + "azure.ai.textanalytics.models.Association": "Language.Text.Association", + "azure.ai.textanalytics.models.Temporality": "Language.Text.Temporality", + "azure.ai.textanalytics.models.RelationType": "Language.Text.relationType", + "azure.ai.textanalytics.models.AnalyzeTextLROTaskKind": "Language.Text.AnalyzeTextLROTaskKind", + "azure.ai.textanalytics.models.SummaryLengthBucket": "Language.Text.SummaryLengthBucket", + "azure.ai.textanalytics.models.ExtractiveSummarizationSortingCriteria": "Language.Text.ExtractiveSummarizationSortingCriteria", + "azure.ai.textanalytics.models.FhirVersion": "Language.Text.fhirVersion", + "azure.ai.textanalytics.models.HealthcareDocumentType": "Language.Text.healthcareDocumentType", + "azure.ai.textanalytics.TextClient.analyze_text": "Language.Text.analyzeText", + "azure.ai.textanalytics.aio.TextClient.analyze_text": "Language.Text.analyzeText", + "azure.ai.textanalytics.TextClient.analyze_text_job_status": "Language.Text.analyzeTextJobStatus", + "azure.ai.textanalytics.aio.TextClient.analyze_text_job_status": "Language.Text.analyzeTextJobStatus", + "azure.ai.textanalytics.TextClient.begin_analyze_text_submit_job": "Language.Text.analyzeTextSubmitJob", + "azure.ai.textanalytics.aio.TextClient.begin_analyze_text_submit_job": "Language.Text.analyzeTextSubmitJob", + "azure.ai.textanalytics.TextClient.begin_analyze_text_cancel_job": "Language.Text.analyzeTextCancelJob", + "azure.ai.textanalytics.aio.TextClient.begin_analyze_text_cancel_job": "Language.Text.analyzeTextCancelJob" + } +} \ No newline at end of file diff --git a/sdk/cognitivelanguage/azure-ai-textanalytics/azure/__init__.py b/sdk/cognitivelanguage/azure-ai-textanalytics/azure/__init__.py new file mode 100644 index 000000000000..d55ccad1f573 --- /dev/null +++ b/sdk/cognitivelanguage/azure-ai-textanalytics/azure/__init__.py @@ -0,0 +1 @@ +__path__ = __import__("pkgutil").extend_path(__path__, __name__) # type: ignore diff --git a/sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/__init__.py b/sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/__init__.py new file mode 100644 index 000000000000..d55ccad1f573 --- /dev/null +++ b/sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/__init__.py @@ -0,0 +1 @@ +__path__ = __import__("pkgutil").extend_path(__path__, __name__) # type: ignore diff --git a/sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/textanalytics/__init__.py b/sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/textanalytics/__init__.py new file mode 100644 index 000000000000..7273540b410f --- /dev/null +++ b/sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/textanalytics/__init__.py @@ -0,0 +1,32 @@ +# 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=wrong-import-position + +from typing import TYPE_CHECKING + +if TYPE_CHECKING: + from ._patch import * # pylint: disable=unused-wildcard-import + +from ._client import TextClient # type: ignore +from ._version import VERSION + +__version__ = VERSION + +try: + from ._patch import __all__ as _patch_all + from ._patch import * +except ImportError: + _patch_all = [] +from ._patch import patch_sdk as _patch_sdk + +__all__ = [ + "TextClient", +] +__all__.extend([p for p in _patch_all if p not in __all__]) # pyright: ignore + +_patch_sdk() diff --git a/sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/textanalytics/_client.py b/sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/textanalytics/_client.py new file mode 100644 index 000000000000..29017ae3dfab --- /dev/null +++ b/sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/textanalytics/_client.py @@ -0,0 +1,111 @@ +# pylint: disable=line-too-long,useless-suppression +# 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. +# -------------------------------------------------------------------------- + +from copy import deepcopy +from typing import Any, TYPE_CHECKING, Union +from typing_extensions import Self + +from azure.core import PipelineClient +from azure.core.credentials import AzureKeyCredential +from azure.core.pipeline import policies +from azure.core.rest import HttpRequest, HttpResponse + +from ._configuration import TextClientConfiguration +from ._operations import _TextClientOperationsMixin +from ._utils.serialization import Deserializer, Serializer + +if TYPE_CHECKING: + from azure.core.credentials import TokenCredential + + +class TextClient(_TextClientOperationsMixin): + """The language service API is a suite of natural language processing (NLP) skills 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, language + detection and question answering. Further documentation can be found in https://learn.microsoft.com/azure/cognitive-services/language-service/overview + https://learn.microsoft.com/azure/cognitive-services/language-service/overview>`_.0. + + :param endpoint: Supported Cognitive Services endpoint (e.g., + https://.api.cognitiveservices.azure.com). Required. + :type endpoint: str + :param credential: Credential used to authenticate requests to the service. Is either a key + credential type or a token credential type. Required. + :type credential: ~azure.core.credentials.AzureKeyCredential or + ~azure.core.credentials.TokenCredential + :keyword api_version: The API version to use for this operation. Default value is + "2025-05-15-preview". Note that overriding this default value may result in unsupported + behavior. + :paramtype api_version: str + :keyword int polling_interval: Default waiting time between two polls for LRO operations if no + Retry-After header is present. + """ + + def __init__(self, endpoint: str, credential: Union[AzureKeyCredential, "TokenCredential"], **kwargs: Any) -> None: + _endpoint = "{Endpoint}/language" + self._config = TextClientConfiguration(endpoint=endpoint, credential=credential, **kwargs) + + _policies = kwargs.pop("policies", None) + if _policies is None: + _policies = [ + policies.RequestIdPolicy(**kwargs), + self._config.headers_policy, + self._config.user_agent_policy, + self._config.proxy_policy, + policies.ContentDecodePolicy(**kwargs), + self._config.redirect_policy, + self._config.retry_policy, + self._config.authentication_policy, + self._config.custom_hook_policy, + self._config.logging_policy, + policies.DistributedTracingPolicy(**kwargs), + policies.SensitiveHeaderCleanupPolicy(**kwargs) if self._config.redirect_policy else None, + self._config.http_logging_policy, + ] + self._client: PipelineClient = PipelineClient(base_url=_endpoint, policies=_policies, **kwargs) + + self._serialize = Serializer() + self._deserialize = Deserializer() + self._serialize.client_side_validation = False + + def send_request(self, request: HttpRequest, *, stream: bool = False, **kwargs: Any) -> HttpResponse: + """Runs the network request through the client's chained policies. + + >>> from azure.core.rest import HttpRequest + >>> request = HttpRequest("GET", "https://www.example.org/") + + >>> response = client.send_request(request) + + + For more information on this code flow, see https://aka.ms/azsdk/dpcodegen/python/send_request + + :param request: The network request you want to make. Required. + :type request: ~azure.core.rest.HttpRequest + :keyword bool stream: Whether the response payload will be streamed. Defaults to False. + :return: The response of your network call. Does not do error handling on your response. + :rtype: ~azure.core.rest.HttpResponse + """ + + request_copy = deepcopy(request) + path_format_arguments = { + "Endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str"), + } + + request_copy.url = self._client.format_url(request_copy.url, **path_format_arguments) + return self._client.send_request(request_copy, stream=stream, **kwargs) # type: ignore + + def close(self) -> None: + self._client.close() + + def __enter__(self) -> Self: + self._client.__enter__() + return self + + def __exit__(self, *exc_details: Any) -> None: + self._client.__exit__(*exc_details) diff --git a/sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/textanalytics/_configuration.py b/sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/textanalytics/_configuration.py new file mode 100644 index 000000000000..01208f849008 --- /dev/null +++ b/sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/textanalytics/_configuration.py @@ -0,0 +1,73 @@ +# 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. +# -------------------------------------------------------------------------- + +from typing import Any, TYPE_CHECKING, Union + +from azure.core.credentials import AzureKeyCredential +from azure.core.pipeline import policies + +from ._version import VERSION + +if TYPE_CHECKING: + from azure.core.credentials import TokenCredential + + +class TextClientConfiguration: # pylint: disable=too-many-instance-attributes + """Configuration for TextClient. + + Note that all parameters used to create this instance are saved as instance + attributes. + + :param endpoint: Supported Cognitive Services endpoint (e.g., + https://.api.cognitiveservices.azure.com). Required. + :type endpoint: str + :param credential: Credential used to authenticate requests to the service. Is either a key + credential type or a token credential type. Required. + :type credential: ~azure.core.credentials.AzureKeyCredential or + ~azure.core.credentials.TokenCredential + :keyword api_version: The API version to use for this operation. Default value is + "2025-05-15-preview". Note that overriding this default value may result in unsupported + behavior. + :paramtype api_version: str + """ + + def __init__(self, endpoint: str, credential: Union[AzureKeyCredential, "TokenCredential"], **kwargs: Any) -> None: + api_version: str = kwargs.pop("api_version", "2025-05-15-preview") + + if endpoint is None: + raise ValueError("Parameter 'endpoint' must not be None.") + if credential is None: + raise ValueError("Parameter 'credential' must not be None.") + + self.endpoint = endpoint + self.credential = credential + self.api_version = api_version + self.credential_scopes = kwargs.pop("credential_scopes", ["https://cognitiveservices.azure.com/.default"]) + kwargs.setdefault("sdk_moniker", "ai-textanalytics/{}".format(VERSION)) + self.polling_interval = kwargs.get("polling_interval", 30) + self._configure(**kwargs) + + def _infer_policy(self, **kwargs): + if isinstance(self.credential, AzureKeyCredential): + return policies.AzureKeyCredentialPolicy(self.credential, "Ocp-Apim-Subscription-Key", **kwargs) + if hasattr(self.credential, "get_token"): + return policies.BearerTokenCredentialPolicy(self.credential, *self.credential_scopes, **kwargs) + raise TypeError(f"Unsupported credential: {self.credential}") + + def _configure(self, **kwargs: Any) -> None: + self.user_agent_policy = kwargs.get("user_agent_policy") or policies.UserAgentPolicy(**kwargs) + self.headers_policy = kwargs.get("headers_policy") or policies.HeadersPolicy(**kwargs) + self.proxy_policy = kwargs.get("proxy_policy") or policies.ProxyPolicy(**kwargs) + self.logging_policy = kwargs.get("logging_policy") or policies.NetworkTraceLoggingPolicy(**kwargs) + self.http_logging_policy = kwargs.get("http_logging_policy") or policies.HttpLoggingPolicy(**kwargs) + self.custom_hook_policy = kwargs.get("custom_hook_policy") or policies.CustomHookPolicy(**kwargs) + self.redirect_policy = kwargs.get("redirect_policy") or policies.RedirectPolicy(**kwargs) + self.retry_policy = kwargs.get("retry_policy") or policies.RetryPolicy(**kwargs) + self.authentication_policy = kwargs.get("authentication_policy") + if self.credential and not self.authentication_policy: + self.authentication_policy = self._infer_policy(**kwargs) diff --git a/sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/textanalytics/_operations/__init__.py b/sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/textanalytics/_operations/__init__.py new file mode 100644 index 000000000000..46ed8f84233c --- /dev/null +++ b/sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/textanalytics/_operations/__init__.py @@ -0,0 +1,23 @@ +# 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=wrong-import-position + +from typing import TYPE_CHECKING + +if TYPE_CHECKING: + from ._patch import * # pylint: disable=unused-wildcard-import + +from ._operations import _TextClientOperationsMixin # type: ignore # pylint: disable=unused-import + +from ._patch import __all__ as _patch_all +from ._patch import * +from ._patch import patch_sdk as _patch_sdk + +__all__ = [] +__all__.extend([p for p in _patch_all if p not in __all__]) # pyright: ignore +_patch_sdk() diff --git a/sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/textanalytics/_operations/_operations.py b/sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/textanalytics/_operations/_operations.py new file mode 100644 index 000000000000..85f4b8ec792a --- /dev/null +++ b/sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/textanalytics/_operations/_operations.py @@ -0,0 +1,711 @@ +# 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. +# -------------------------------------------------------------------------- +from collections.abc import MutableMapping +from io import IOBase +import json +from typing import Any, Callable, Dict, IO, Iterator, List, Optional, TypeVar, Union, cast, overload + +from azure.core import PipelineClient +from azure.core.exceptions import ( + ClientAuthenticationError, + HttpResponseError, + ResourceExistsError, + ResourceNotFoundError, + ResourceNotModifiedError, + StreamClosedError, + StreamConsumedError, + map_error, +) +from azure.core.pipeline import PipelineResponse +from azure.core.polling import LROPoller, NoPolling, PollingMethod +from azure.core.polling.base_polling import LROBasePolling +from azure.core.rest import HttpRequest, HttpResponse +from azure.core.tracing.decorator import distributed_trace +from azure.core.utils import case_insensitive_dict + +from .. import models as _models +from .._configuration import TextClientConfiguration +from .._utils.model_base import SdkJSONEncoder, _deserialize, _failsafe_deserialize +from .._utils.serialization import Serializer +from .._utils.utils import ClientMixinABC + +JSON = MutableMapping[str, Any] +_Unset: Any = object() +T = TypeVar("T") +ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]] + +_SERIALIZER = Serializer() +_SERIALIZER.client_side_validation = False + + +def build_text_analyze_text_request(*, show_stats: Optional[bool] = None, **kwargs: Any) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2025-05-15-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/:analyze-text" + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + if show_stats is not None: + _params["showStats"] = _SERIALIZER.query("show_stats", show_stats, "bool") + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="POST", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_text_analyze_text_job_status_request( # pylint: disable=name-too-long + job_id: str, + *, + show_stats: Optional[bool] = None, + top: Optional[int] = None, + skip: Optional[int] = None, + **kwargs: Any +) -> HttpRequest: + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2025-05-15-preview")) + accept = _headers.pop("Accept", "application/json") + + # Construct URL + _url = "/analyze-text/jobs/{jobId}" + path_format_arguments = { + "jobId": _SERIALIZER.url("job_id", job_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + if show_stats is not None: + _params["showStats"] = _SERIALIZER.query("show_stats", show_stats, "bool") + if top is not None: + _params["top"] = _SERIALIZER.query("top", top, "int") + if skip is not None: + _params["skip"] = _SERIALIZER.query("skip", skip, "int") + + # Construct headers + _headers["Accept"] = _SERIALIZER.header("accept", accept, "str") + + return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_text_analyze_text_submit_job_request(**kwargs: Any) -> HttpRequest: # pylint: disable=name-too-long + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2025-05-15-preview")) + # Construct URL + _url = "/analyze-text/jobs" + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + + # Construct headers + if content_type is not None: + _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str") + + return HttpRequest(method="POST", url=_url, params=_params, headers=_headers, **kwargs) + + +def build_text_analyze_text_cancel_job_request( # pylint: disable=name-too-long + job_id: str, **kwargs: Any +) -> HttpRequest: + _params = case_insensitive_dict(kwargs.pop("params", {}) or {}) + + api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2025-05-15-preview")) + # Construct URL + _url = "/analyze-text/jobs/{jobId}:cancel" + path_format_arguments = { + "jobId": _SERIALIZER.url("job_id", job_id, "str"), + } + + _url: str = _url.format(**path_format_arguments) # type: ignore + + # Construct parameters + _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str") + + return HttpRequest(method="POST", url=_url, params=_params, **kwargs) + + +class _TextClientOperationsMixin(ClientMixinABC[PipelineClient[HttpRequest, HttpResponse], TextClientConfiguration]): + + @overload + def analyze_text( + self, + body: _models.AnalyzeTextTask, + *, + show_stats: Optional[bool] = None, + content_type: str = "application/json", + **kwargs: Any + ) -> _models.AnalyzeTextTaskResult: + """Request text analysis over a collection of documents. + + :param body: The input documents to analyze. Required. + :type body: ~azure.ai.textanalytics.models.AnalyzeTextTask + :keyword show_stats: (Optional) if set to true, response will contain request and document + level statistics. Default value is None. + :paramtype show_stats: bool + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: AnalyzeTextTaskResult. The AnalyzeTextTaskResult is compatible with MutableMapping + :rtype: ~azure.ai.textanalytics.models.AnalyzeTextTaskResult + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def analyze_text( + self, body: JSON, *, show_stats: Optional[bool] = None, content_type: str = "application/json", **kwargs: Any + ) -> _models.AnalyzeTextTaskResult: + """Request text analysis over a collection of documents. + + :param body: The input documents to analyze. Required. + :type body: JSON + :keyword show_stats: (Optional) if set to true, response will contain request and document + level statistics. Default value is None. + :paramtype show_stats: bool + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: AnalyzeTextTaskResult. The AnalyzeTextTaskResult is compatible with MutableMapping + :rtype: ~azure.ai.textanalytics.models.AnalyzeTextTaskResult + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def analyze_text( + self, + body: IO[bytes], + *, + show_stats: Optional[bool] = None, + content_type: str = "application/json", + **kwargs: Any + ) -> _models.AnalyzeTextTaskResult: + """Request text analysis over a collection of documents. + + :param body: The input documents to analyze. Required. + :type body: IO[bytes] + :keyword show_stats: (Optional) if set to true, response will contain request and document + level statistics. Default value is None. + :paramtype show_stats: bool + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: AnalyzeTextTaskResult. The AnalyzeTextTaskResult is compatible with MutableMapping + :rtype: ~azure.ai.textanalytics.models.AnalyzeTextTaskResult + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace + def analyze_text( + self, body: Union[_models.AnalyzeTextTask, JSON, IO[bytes]], *, show_stats: Optional[bool] = None, **kwargs: Any + ) -> _models.AnalyzeTextTaskResult: + """Request text analysis over a collection of documents. + + :param body: The input documents to analyze. Is one of the following types: AnalyzeTextTask, + JSON, IO[bytes] Required. + :type body: ~azure.ai.textanalytics.models.AnalyzeTextTask or JSON or IO[bytes] + :keyword show_stats: (Optional) if set to true, response will contain request and document + level statistics. Default value is None. + :paramtype show_stats: bool + :return: AnalyzeTextTaskResult. The AnalyzeTextTaskResult is compatible with MutableMapping + :rtype: ~azure.ai.textanalytics.models.AnalyzeTextTaskResult + :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 = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.AnalyzeTextTaskResult] = kwargs.pop("cls", None) + + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_text_analyze_text_request( + show_stats=show_stats, + content_type=content_type, + api_version=self._config.api_version, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "Endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _failsafe_deserialize(_models.ErrorResponse, response) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.AnalyzeTextTaskResult, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def analyze_text_job_status( + self, + job_id: str, + *, + show_stats: Optional[bool] = None, + top: Optional[int] = None, + skip: Optional[int] = None, + **kwargs: Any + ) -> _models.AnalyzeTextJobState: + """Get analysis status and results. + + Get the status of an analysis job. A job can consist of one or more tasks. After all tasks + succeed, the job transitions to the succeeded state and results are available for each task. + + :param job_id: job ID. Required. + :type job_id: str + :keyword show_stats: (Optional) if set to true, response will contain request and document + level statistics. Default value is None. + :paramtype show_stats: bool + :keyword top: The maximum number of resources to return from the collection. Default value is + None. + :paramtype top: int + :keyword skip: An offset into the collection of the first resource to be returned. Default + value is None. + :paramtype skip: int + :return: AnalyzeTextJobState. The AnalyzeTextJobState is compatible with MutableMapping + :rtype: ~azure.ai.textanalytics.models.AnalyzeTextJobState + :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 = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.AnalyzeTextJobState] = kwargs.pop("cls", None) + + _request = build_text_analyze_text_job_status_request( + job_id=job_id, + show_stats=show_stats, + top=top, + skip=skip, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "Endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _failsafe_deserialize(_models.ErrorResponse, response) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.AnalyzeTextJobState, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + def _analyze_text_submit_job_initial( + self, + body: Union[JSON, IO[bytes]] = _Unset, + *, + analysis_input: _models.MultiLanguageAnalysisInput = _Unset, + tasks: List[_models.AnalyzeTextLROTask] = _Unset, + display_name: Optional[str] = None, + default_language: Optional[str] = None, + cancel_after: Optional[float] = None, + **kwargs: Any + ) -> Iterator[bytes]: + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[Iterator[bytes]] = kwargs.pop("cls", None) + + if body is _Unset: + if analysis_input is _Unset: + raise TypeError("missing required argument: analysis_input") + if tasks is _Unset: + raise TypeError("missing required argument: tasks") + body = { + "analysisInput": analysis_input, + "cancelAfter": cancel_after, + "defaultLanguage": default_language, + "displayName": display_name, + "tasks": tasks, + } + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_text_analyze_text_submit_job_request( + content_type=content_type, + api_version=self._config.api_version, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "Endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = True + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [202]: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _failsafe_deserialize(_models.ErrorResponse, response) + raise HttpResponseError(response=response, model=error) + + response_headers = {} + response_headers["Operation-Location"] = self._deserialize("str", response.headers.get("Operation-Location")) + + deserialized = response.iter_bytes() + + if cls: + return cls(pipeline_response, deserialized, response_headers) # type: ignore + + return deserialized # type: ignore + + @overload + def begin_analyze_text_submit_job( + self, + *, + analysis_input: _models.MultiLanguageAnalysisInput, + tasks: List[_models.AnalyzeTextLROTask], + content_type: str = "application/json", + display_name: Optional[str] = None, + default_language: Optional[str] = None, + cancel_after: Optional[float] = None, + **kwargs: Any + ) -> LROPoller[None]: + """Submit a collection of text documents for analysis. Specify one or more unique tasks to be + executed as a long-running operation. + + :keyword analysis_input: Contains the input to be analyzed. Required. + :paramtype analysis_input: ~azure.ai.textanalytics.models.MultiLanguageAnalysisInput + :keyword tasks: List of tasks to be performed as part of the LRO. Required. + :paramtype tasks: list[~azure.ai.textanalytics.models.AnalyzeTextLROTask] + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword display_name: Name for the task. Default value is None. + :paramtype display_name: str + :keyword default_language: Default language to use for records requesting automatic language + detection. Default value is None. + :paramtype default_language: str + :keyword cancel_after: Optional duration in seconds after which the job will be canceled if not + completed. Default value is None. + :paramtype cancel_after: float + :return: An instance of LROPoller that returns None + :rtype: ~azure.core.polling.LROPoller[None] + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def begin_analyze_text_submit_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 + executed as a long-running operation. + + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: An instance of LROPoller that returns None + :rtype: ~azure.core.polling.LROPoller[None] + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + def begin_analyze_text_submit_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 + executed as a long-running operation. + + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: An instance of LROPoller that returns None + :rtype: ~azure.core.polling.LROPoller[None] + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace + def begin_analyze_text_submit_job( + self, + body: Union[JSON, IO[bytes]] = _Unset, + *, + analysis_input: _models.MultiLanguageAnalysisInput = _Unset, + tasks: List[_models.AnalyzeTextLROTask] = _Unset, + display_name: Optional[str] = None, + default_language: Optional[str] = None, + cancel_after: Optional[float] = None, + **kwargs: Any + ) -> LROPoller[None]: + """Submit a collection of text documents for analysis. Specify one or more unique tasks to be + executed as a long-running operation. + + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword analysis_input: Contains the input to be analyzed. Required. + :paramtype analysis_input: ~azure.ai.textanalytics.models.MultiLanguageAnalysisInput + :keyword tasks: List of tasks to be performed as part of the LRO. Required. + :paramtype tasks: list[~azure.ai.textanalytics.models.AnalyzeTextLROTask] + :keyword display_name: Name for the task. Default value is None. + :paramtype display_name: str + :keyword default_language: Default language to use for records requesting automatic language + detection. Default value is None. + :paramtype default_language: str + :keyword cancel_after: Optional duration in seconds after which the job will be canceled if not + completed. Default value is None. + :paramtype cancel_after: float + :return: An instance of LROPoller that returns None + :rtype: ~azure.core.polling.LROPoller[None] + :raises ~azure.core.exceptions.HttpResponseError: + """ + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[None] = kwargs.pop("cls", None) + polling: Union[bool, PollingMethod] = kwargs.pop("polling", True) + lro_delay = kwargs.pop("polling_interval", self._config.polling_interval) + cont_token: Optional[str] = kwargs.pop("continuation_token", None) + if cont_token is None: + raw_result = self._analyze_text_submit_job_initial( + body=body, + analysis_input=analysis_input, + tasks=tasks, + display_name=display_name, + default_language=default_language, + cancel_after=cancel_after, + content_type=content_type, + cls=lambda x, y, z: x, + headers=_headers, + params=_params, + **kwargs + ) + raw_result.http_response.read() # type: ignore + kwargs.pop("error_map", None) + + def get_long_running_output(pipeline_response): # pylint: disable=inconsistent-return-statements + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + path_format_arguments = { + "Endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str"), + } + + if polling is True: + polling_method: PollingMethod = cast( + PollingMethod, LROBasePolling(lro_delay, path_format_arguments=path_format_arguments, **kwargs) + ) + elif polling is False: + polling_method = cast(PollingMethod, NoPolling()) + else: + polling_method = polling + if cont_token: + return LROPoller[None].from_continuation_token( + polling_method=polling_method, + continuation_token=cont_token, + client=self._client, + deserialization_callback=get_long_running_output, + ) + return LROPoller[None](self._client, raw_result, get_long_running_output, polling_method) # type: ignore + + def _analyze_text_cancel_job_initial(self, job_id: str, **kwargs: Any) -> Iterator[bytes]: + 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 = kwargs.pop("params", {}) or {} + + cls: ClsType[Iterator[bytes]] = kwargs.pop("cls", None) + + _request = build_text_analyze_text_cancel_job_request( + job_id=job_id, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "Endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = True + pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [202]: + try: + response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _failsafe_deserialize(_models.ErrorResponse, response) + raise HttpResponseError(response=response, model=error) + + response_headers = {} + response_headers["Operation-Location"] = self._deserialize("str", response.headers.get("Operation-Location")) + + deserialized = response.iter_bytes() + + if cls: + return cls(pipeline_response, deserialized, response_headers) # type: ignore + + return deserialized # type: ignore + + @distributed_trace + def begin_analyze_text_cancel_job(self, job_id: str, **kwargs: Any) -> LROPoller[None]: + """Cancel a long-running Text Analysis job. + + Cancel a long-running Text Analysis job. + + :param job_id: The job ID to cancel. Required. + :type job_id: str + :return: An instance of LROPoller that returns None + :rtype: ~azure.core.polling.LROPoller[None] + :raises ~azure.core.exceptions.HttpResponseError: + """ + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[None] = kwargs.pop("cls", None) + polling: Union[bool, PollingMethod] = kwargs.pop("polling", True) + lro_delay = kwargs.pop("polling_interval", self._config.polling_interval) + cont_token: Optional[str] = kwargs.pop("continuation_token", None) + if cont_token is None: + raw_result = self._analyze_text_cancel_job_initial( + job_id=job_id, cls=lambda x, y, z: x, headers=_headers, params=_params, **kwargs + ) + raw_result.http_response.read() # type: ignore + kwargs.pop("error_map", None) + + def get_long_running_output(pipeline_response): # pylint: disable=inconsistent-return-statements + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + path_format_arguments = { + "Endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str"), + } + + if polling is True: + polling_method: PollingMethod = cast( + PollingMethod, LROBasePolling(lro_delay, path_format_arguments=path_format_arguments, **kwargs) + ) + elif polling is False: + polling_method = cast(PollingMethod, NoPolling()) + else: + polling_method = polling + if cont_token: + return LROPoller[None].from_continuation_token( + polling_method=polling_method, + continuation_token=cont_token, + client=self._client, + deserialization_callback=get_long_running_output, + ) + return LROPoller[None](self._client, raw_result, get_long_running_output, polling_method) # type: ignore diff --git a/sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/textanalytics/_operations/_patch.py b/sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/textanalytics/_operations/_patch.py new file mode 100644 index 000000000000..8bcb627aa475 --- /dev/null +++ b/sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/textanalytics/_operations/_patch.py @@ -0,0 +1,21 @@ +# 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. +# -------------------------------------------------------------------------- +"""Customize generated code here. + +Follow our quickstart for examples: https://aka.ms/azsdk/python/dpcodegen/python/customize +""" +from typing import List + +__all__: List[str] = [] # Add all objects you want publicly available to users at this package level + + +def patch_sdk(): + """Do not remove from this file. + + `patch_sdk` is a last resort escape hatch that allows you to do customizations + you can't accomplish using the techniques described in + https://aka.ms/azsdk/python/dpcodegen/python/customize + """ diff --git a/sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/textanalytics/_patch.py b/sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/textanalytics/_patch.py new file mode 100644 index 000000000000..8bcb627aa475 --- /dev/null +++ b/sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/textanalytics/_patch.py @@ -0,0 +1,21 @@ +# 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. +# -------------------------------------------------------------------------- +"""Customize generated code here. + +Follow our quickstart for examples: https://aka.ms/azsdk/python/dpcodegen/python/customize +""" +from typing import List + +__all__: List[str] = [] # Add all objects you want publicly available to users at this package level + + +def patch_sdk(): + """Do not remove from this file. + + `patch_sdk` is a last resort escape hatch that allows you to do customizations + you can't accomplish using the techniques described in + https://aka.ms/azsdk/python/dpcodegen/python/customize + """ diff --git a/sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/textanalytics/_utils/__init__.py b/sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/textanalytics/_utils/__init__.py new file mode 100644 index 000000000000..8026245c2abc --- /dev/null +++ b/sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/textanalytics/_utils/__init__.py @@ -0,0 +1,6 @@ +# -------------------------------------------------------------------------- +# 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. +# -------------------------------------------------------------------------- diff --git a/sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/textanalytics/_utils/model_base.py b/sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/textanalytics/_utils/model_base.py new file mode 100644 index 000000000000..c62e7e7784af --- /dev/null +++ b/sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/textanalytics/_utils/model_base.py @@ -0,0 +1,1233 @@ +# pylint: disable=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=protected-access, broad-except + +import copy +import calendar +import decimal +import functools +import sys +import logging +import base64 +import re +import typing +import enum +import email.utils +from datetime import datetime, date, time, timedelta, timezone +from json import JSONEncoder +import xml.etree.ElementTree as ET +from collections.abc import MutableMapping +from typing_extensions import Self +import isodate +from azure.core.exceptions import DeserializationError +from azure.core import CaseInsensitiveEnumMeta +from azure.core.pipeline import PipelineResponse +from azure.core.serialization import _Null +from azure.core.rest import HttpResponse + +_LOGGER = logging.getLogger(__name__) + +__all__ = ["SdkJSONEncoder", "Model", "rest_field", "rest_discriminator"] + +TZ_UTC = timezone.utc +_T = typing.TypeVar("_T") + + +def _timedelta_as_isostr(td: timedelta) -> str: + """Converts a datetime.timedelta object into an ISO 8601 formatted string, e.g. 'P4DT12H30M05S' + + Function adapted from the Tin Can Python project: https://github.com/RusticiSoftware/TinCanPython + + :param timedelta td: The timedelta to convert + :rtype: str + :return: ISO8601 version of this timedelta + """ + + # Split seconds to larger units + seconds = td.total_seconds() + minutes, seconds = divmod(seconds, 60) + hours, minutes = divmod(minutes, 60) + days, hours = divmod(hours, 24) + + days, hours, minutes = list(map(int, (days, hours, minutes))) + seconds = round(seconds, 6) + + # Build date + date_str = "" + if days: + date_str = "%sD" % days + + if hours or minutes or seconds: + # Build time + time_str = "T" + + # Hours + bigger_exists = date_str or hours + if bigger_exists: + time_str += "{:02}H".format(hours) + + # Minutes + bigger_exists = bigger_exists or minutes + if bigger_exists: + time_str += "{:02}M".format(minutes) + + # Seconds + try: + if seconds.is_integer(): + seconds_string = "{:02}".format(int(seconds)) + else: + # 9 chars long w/ leading 0, 6 digits after decimal + seconds_string = "%09.6f" % seconds + # Remove trailing zeros + seconds_string = seconds_string.rstrip("0") + except AttributeError: # int.is_integer() raises + seconds_string = "{:02}".format(seconds) + + time_str += "{}S".format(seconds_string) + else: + time_str = "" + + return "P" + date_str + time_str + + +def _serialize_bytes(o, format: typing.Optional[str] = None) -> str: + encoded = base64.b64encode(o).decode() + if format == "base64url": + return encoded.strip("=").replace("+", "-").replace("/", "_") + return encoded + + +def _serialize_datetime(o, format: typing.Optional[str] = None): + if hasattr(o, "year") and hasattr(o, "hour"): + if format == "rfc7231": + return email.utils.format_datetime(o, usegmt=True) + if format == "unix-timestamp": + return int(calendar.timegm(o.utctimetuple())) + + # astimezone() fails for naive times in Python 2.7, so make make sure o is aware (tzinfo is set) + if not o.tzinfo: + iso_formatted = o.replace(tzinfo=TZ_UTC).isoformat() + else: + iso_formatted = o.astimezone(TZ_UTC).isoformat() + # Replace the trailing "+00:00" UTC offset with "Z" (RFC 3339: https://www.ietf.org/rfc/rfc3339.txt) + return iso_formatted.replace("+00:00", "Z") + # Next try datetime.date or datetime.time + return o.isoformat() + + +def _is_readonly(p): + try: + return p._visibility == ["read"] + except AttributeError: + return False + + +class SdkJSONEncoder(JSONEncoder): + """A JSON encoder that's capable of serializing datetime objects and bytes.""" + + def __init__(self, *args, exclude_readonly: bool = False, format: typing.Optional[str] = None, **kwargs): + super().__init__(*args, **kwargs) + self.exclude_readonly = exclude_readonly + self.format = format + + def default(self, o): # pylint: disable=too-many-return-statements + if _is_model(o): + if self.exclude_readonly: + readonly_props = [p._rest_name for p in o._attr_to_rest_field.values() if _is_readonly(p)] + return {k: v for k, v in o.items() if k not in readonly_props} + return dict(o.items()) + try: + return super(SdkJSONEncoder, self).default(o) + except TypeError: + if isinstance(o, _Null): + return None + if isinstance(o, decimal.Decimal): + return float(o) + if isinstance(o, (bytes, bytearray)): + return _serialize_bytes(o, self.format) + try: + # First try datetime.datetime + return _serialize_datetime(o, self.format) + except AttributeError: + pass + # Last, try datetime.timedelta + try: + return _timedelta_as_isostr(o) + except AttributeError: + # This will be raised when it hits value.total_seconds in the method above + pass + return super(SdkJSONEncoder, self).default(o) + + +_VALID_DATE = re.compile(r"\d{4}[-]\d{2}[-]\d{2}T\d{2}:\d{2}:\d{2}" + r"\.?\d*Z?[-+]?[\d{2}]?:?[\d{2}]?") +_VALID_RFC7231 = re.compile( + r"(Mon|Tue|Wed|Thu|Fri|Sat|Sun),\s\d{2}\s" + r"(Jan|Feb|Mar|Apr|May|Jun|Jul|Aug|Sep|Oct|Nov|Dec)\s\d{4}\s\d{2}:\d{2}:\d{2}\sGMT" +) + + +def _deserialize_datetime(attr: typing.Union[str, datetime]) -> datetime: + """Deserialize ISO-8601 formatted string into Datetime object. + + :param str attr: response string to be deserialized. + :rtype: ~datetime.datetime + :returns: The datetime object from that input + """ + if isinstance(attr, datetime): + # i'm already deserialized + return attr + attr = attr.upper() + match = _VALID_DATE.match(attr) + if not match: + raise ValueError("Invalid datetime string: " + attr) + + check_decimal = attr.split(".") + if len(check_decimal) > 1: + decimal_str = "" + for digit in check_decimal[1]: + if digit.isdigit(): + decimal_str += digit + else: + break + if len(decimal_str) > 6: + attr = attr.replace(decimal_str, decimal_str[0:6]) + + date_obj = isodate.parse_datetime(attr) + test_utc = date_obj.utctimetuple() + if test_utc.tm_year > 9999 or test_utc.tm_year < 1: + raise OverflowError("Hit max or min date") + return date_obj + + +def _deserialize_datetime_rfc7231(attr: typing.Union[str, datetime]) -> datetime: + """Deserialize RFC7231 formatted string into Datetime object. + + :param str attr: response string to be deserialized. + :rtype: ~datetime.datetime + :returns: The datetime object from that input + """ + if isinstance(attr, datetime): + # i'm already deserialized + return attr + match = _VALID_RFC7231.match(attr) + if not match: + raise ValueError("Invalid datetime string: " + attr) + + return email.utils.parsedate_to_datetime(attr) + + +def _deserialize_datetime_unix_timestamp(attr: typing.Union[float, datetime]) -> datetime: + """Deserialize unix timestamp into Datetime object. + + :param str attr: response string to be deserialized. + :rtype: ~datetime.datetime + :returns: The datetime object from that input + """ + if isinstance(attr, datetime): + # i'm already deserialized + return attr + return datetime.fromtimestamp(attr, TZ_UTC) + + +def _deserialize_date(attr: typing.Union[str, date]) -> date: + """Deserialize ISO-8601 formatted string into Date object. + :param str attr: response string to be deserialized. + :rtype: date + :returns: The date object from that input + """ + # This must NOT use defaultmonth/defaultday. Using None ensure this raises an exception. + if isinstance(attr, date): + return attr + return isodate.parse_date(attr, defaultmonth=None, defaultday=None) # type: ignore + + +def _deserialize_time(attr: typing.Union[str, time]) -> time: + """Deserialize ISO-8601 formatted string into time object. + + :param str attr: response string to be deserialized. + :rtype: datetime.time + :returns: The time object from that input + """ + if isinstance(attr, time): + return attr + return isodate.parse_time(attr) + + +def _deserialize_bytes(attr): + if isinstance(attr, (bytes, bytearray)): + return attr + return bytes(base64.b64decode(attr)) + + +def _deserialize_bytes_base64(attr): + if isinstance(attr, (bytes, bytearray)): + return attr + padding = "=" * (3 - (len(attr) + 3) % 4) # type: ignore + attr = attr + padding # type: ignore + encoded = attr.replace("-", "+").replace("_", "/") + return bytes(base64.b64decode(encoded)) + + +def _deserialize_duration(attr): + if isinstance(attr, timedelta): + return attr + return isodate.parse_duration(attr) + + +def _deserialize_decimal(attr): + if isinstance(attr, decimal.Decimal): + return attr + return decimal.Decimal(str(attr)) + + +def _deserialize_int_as_str(attr): + if isinstance(attr, int): + return attr + return int(attr) + + +_DESERIALIZE_MAPPING = { + datetime: _deserialize_datetime, + date: _deserialize_date, + time: _deserialize_time, + bytes: _deserialize_bytes, + bytearray: _deserialize_bytes, + timedelta: _deserialize_duration, + typing.Any: lambda x: x, + decimal.Decimal: _deserialize_decimal, +} + +_DESERIALIZE_MAPPING_WITHFORMAT = { + "rfc3339": _deserialize_datetime, + "rfc7231": _deserialize_datetime_rfc7231, + "unix-timestamp": _deserialize_datetime_unix_timestamp, + "base64": _deserialize_bytes, + "base64url": _deserialize_bytes_base64, +} + + +def get_deserializer(annotation: typing.Any, rf: typing.Optional["_RestField"] = None): + if annotation is int and rf and rf._format == "str": + return _deserialize_int_as_str + if rf and rf._format: + return _DESERIALIZE_MAPPING_WITHFORMAT.get(rf._format) + return _DESERIALIZE_MAPPING.get(annotation) # pyright: ignore + + +def _get_type_alias_type(module_name: str, alias_name: str): + types = { + k: v + for k, v in sys.modules[module_name].__dict__.items() + if isinstance(v, typing._GenericAlias) # type: ignore + } + if alias_name not in types: + return alias_name + return types[alias_name] + + +def _get_model(module_name: str, model_name: str): + models = {k: v for k, v in sys.modules[module_name].__dict__.items() if isinstance(v, type)} + module_end = module_name.rsplit(".", 1)[0] + models.update({k: v for k, v in sys.modules[module_end].__dict__.items() if isinstance(v, type)}) + if isinstance(model_name, str): + model_name = model_name.split(".")[-1] + if model_name not in models: + return model_name + return models[model_name] + + +_UNSET = object() + + +class _MyMutableMapping(MutableMapping[str, typing.Any]): + def __init__(self, data: typing.Dict[str, typing.Any]) -> None: + self._data = data + + def __contains__(self, key: typing.Any) -> bool: + return key in self._data + + def __getitem__(self, key: str) -> typing.Any: + return self._data.__getitem__(key) + + def __setitem__(self, key: str, value: typing.Any) -> None: + self._data.__setitem__(key, value) + + def __delitem__(self, key: str) -> None: + self._data.__delitem__(key) + + def __iter__(self) -> typing.Iterator[typing.Any]: + return self._data.__iter__() + + def __len__(self) -> int: + return self._data.__len__() + + def __ne__(self, other: typing.Any) -> bool: + return not self.__eq__(other) + + def keys(self) -> typing.KeysView[str]: + """ + :returns: a set-like object providing a view on D's keys + :rtype: ~typing.KeysView + """ + return self._data.keys() + + def values(self) -> typing.ValuesView[typing.Any]: + """ + :returns: an object providing a view on D's values + :rtype: ~typing.ValuesView + """ + return self._data.values() + + def items(self) -> typing.ItemsView[str, typing.Any]: + """ + :returns: set-like object providing a view on D's items + :rtype: ~typing.ItemsView + """ + return self._data.items() + + def get(self, key: str, default: typing.Any = None) -> typing.Any: + """ + Get the value for key if key is in the dictionary, else default. + :param str key: The key to look up. + :param any default: The value to return if key is not in the dictionary. Defaults to None + :returns: D[k] if k in D, else d. + :rtype: any + """ + try: + return self[key] + except KeyError: + return default + + @typing.overload + def pop(self, key: str) -> typing.Any: ... # pylint: disable=arguments-differ + + @typing.overload + def pop(self, key: str, default: _T) -> _T: ... # pylint: disable=signature-differs + + @typing.overload + def pop(self, key: str, default: typing.Any) -> typing.Any: ... # pylint: disable=signature-differs + + def pop(self, key: str, default: typing.Any = _UNSET) -> typing.Any: + """ + Removes specified key and return the corresponding value. + :param str key: The key to pop. + :param any default: The value to return if key is not in the dictionary + :returns: The value corresponding to the key. + :rtype: any + :raises KeyError: If key is not found and default is not given. + """ + if default is _UNSET: + return self._data.pop(key) + return self._data.pop(key, default) + + def popitem(self) -> typing.Tuple[str, typing.Any]: + """ + Removes and returns some (key, value) pair + :returns: The (key, value) pair. + :rtype: tuple + :raises KeyError: if D is empty. + """ + return self._data.popitem() + + def clear(self) -> None: + """ + Remove all items from D. + """ + self._data.clear() + + def update(self, *args: typing.Any, **kwargs: typing.Any) -> None: # pylint: disable=arguments-differ + """ + Updates D from mapping/iterable E and F. + :param any args: Either a mapping object or an iterable of key-value pairs. + """ + self._data.update(*args, **kwargs) + + @typing.overload + def setdefault(self, key: str, default: None = None) -> None: ... + + @typing.overload + def setdefault(self, key: str, default: typing.Any) -> typing.Any: ... # pylint: disable=signature-differs + + def setdefault(self, key: str, default: typing.Any = _UNSET) -> typing.Any: + """ + Same as calling D.get(k, d), and setting D[k]=d if k not found + :param str key: The key to look up. + :param any default: The value to set if key is not in the dictionary + :returns: D[k] if k in D, else d. + :rtype: any + """ + if default is _UNSET: + return self._data.setdefault(key) + return self._data.setdefault(key, default) + + def __eq__(self, other: typing.Any) -> bool: + try: + other_model = self.__class__(other) + except Exception: + return False + return self._data == other_model._data + + def __repr__(self) -> str: + return str(self._data) + + +def _is_model(obj: typing.Any) -> bool: + return getattr(obj, "_is_model", False) + + +def _serialize(o, format: typing.Optional[str] = None): # pylint: disable=too-many-return-statements + if isinstance(o, list): + return [_serialize(x, format) for x in o] + if isinstance(o, dict): + return {k: _serialize(v, format) for k, v in o.items()} + if isinstance(o, set): + return {_serialize(x, format) for x in o} + if isinstance(o, tuple): + return tuple(_serialize(x, format) for x in o) + if isinstance(o, (bytes, bytearray)): + return _serialize_bytes(o, format) + if isinstance(o, decimal.Decimal): + return float(o) + if isinstance(o, enum.Enum): + return o.value + if isinstance(o, int): + if format == "str": + return str(o) + return o + try: + # First try datetime.datetime + return _serialize_datetime(o, format) + except AttributeError: + pass + # Last, try datetime.timedelta + try: + return _timedelta_as_isostr(o) + except AttributeError: + # This will be raised when it hits value.total_seconds in the method above + pass + return o + + +def _get_rest_field( + attr_to_rest_field: typing.Dict[str, "_RestField"], rest_name: str +) -> typing.Optional["_RestField"]: + try: + return next(rf for rf in attr_to_rest_field.values() if rf._rest_name == rest_name) + except StopIteration: + return None + + +def _create_value(rf: typing.Optional["_RestField"], value: typing.Any) -> typing.Any: + if not rf: + return _serialize(value, None) + if rf._is_multipart_file_input: + return value + if rf._is_model: + return _deserialize(rf._type, value) + if isinstance(value, ET.Element): + value = _deserialize(rf._type, value) + return _serialize(value, rf._format) + + +class Model(_MyMutableMapping): + _is_model = True + # label whether current class's _attr_to_rest_field has been calculated + # could not see _attr_to_rest_field directly because subclass inherits it from parent class + _calculated: typing.Set[str] = set() + + def __init__(self, *args: typing.Any, **kwargs: typing.Any) -> None: + class_name = self.__class__.__name__ + if len(args) > 1: + raise TypeError(f"{class_name}.__init__() takes 2 positional arguments but {len(args) + 1} were given") + dict_to_pass = { + rest_field._rest_name: rest_field._default + for rest_field in self._attr_to_rest_field.values() + if rest_field._default is not _UNSET + } + if args: # pylint: disable=too-many-nested-blocks + if isinstance(args[0], ET.Element): + existed_attr_keys = [] + model_meta = getattr(self, "_xml", {}) + + for rf in self._attr_to_rest_field.values(): + prop_meta = getattr(rf, "_xml", {}) + xml_name = prop_meta.get("name", rf._rest_name) + xml_ns = prop_meta.get("ns", model_meta.get("ns", None)) + if xml_ns: + xml_name = "{" + xml_ns + "}" + xml_name + + # attribute + if prop_meta.get("attribute", False) and args[0].get(xml_name) is not None: + existed_attr_keys.append(xml_name) + dict_to_pass[rf._rest_name] = _deserialize(rf._type, args[0].get(xml_name)) + continue + + # unwrapped element is array + if prop_meta.get("unwrapped", False): + # unwrapped array could either use prop items meta/prop meta + if prop_meta.get("itemsName"): + xml_name = prop_meta.get("itemsName") + xml_ns = prop_meta.get("itemNs") + if xml_ns: + xml_name = "{" + xml_ns + "}" + xml_name + items = args[0].findall(xml_name) # pyright: ignore + if len(items) > 0: + existed_attr_keys.append(xml_name) + dict_to_pass[rf._rest_name] = _deserialize(rf._type, items) + continue + + # text element is primitive type + if prop_meta.get("text", False): + if args[0].text is not None: + dict_to_pass[rf._rest_name] = _deserialize(rf._type, args[0].text) + continue + + # wrapped element could be normal property or array, it should only have one element + item = args[0].find(xml_name) + if item is not None: + existed_attr_keys.append(xml_name) + dict_to_pass[rf._rest_name] = _deserialize(rf._type, item) + + # rest thing is additional properties + for e in args[0]: + if e.tag not in existed_attr_keys: + dict_to_pass[e.tag] = _convert_element(e) + else: + dict_to_pass.update( + {k: _create_value(_get_rest_field(self._attr_to_rest_field, k), v) for k, v in args[0].items()} + ) + else: + non_attr_kwargs = [k for k in kwargs if k not in self._attr_to_rest_field] + if non_attr_kwargs: + # actual type errors only throw the first wrong keyword arg they see, so following that. + raise TypeError(f"{class_name}.__init__() got an unexpected keyword argument '{non_attr_kwargs[0]}'") + dict_to_pass.update( + { + self._attr_to_rest_field[k]._rest_name: _create_value(self._attr_to_rest_field[k], v) + for k, v in kwargs.items() + if v is not None + } + ) + super().__init__(dict_to_pass) + + def copy(self) -> "Model": + return Model(self.__dict__) + + def __new__(cls, *args: typing.Any, **kwargs: typing.Any) -> Self: + if f"{cls.__module__}.{cls.__qualname__}" not in cls._calculated: + # we know the last nine classes in mro are going to be 'Model', '_MyMutableMapping', 'MutableMapping', + # 'Mapping', 'Collection', 'Sized', 'Iterable', 'Container' and 'object' + mros = cls.__mro__[:-9][::-1] # ignore parents, and reverse the mro order + attr_to_rest_field: typing.Dict[str, _RestField] = { # map attribute name to rest_field property + k: v for mro_class in mros for k, v in mro_class.__dict__.items() if k[0] != "_" and hasattr(v, "_type") + } + annotations = { + k: v + for mro_class in mros + if hasattr(mro_class, "__annotations__") + for k, v in mro_class.__annotations__.items() + } + for attr, rf in attr_to_rest_field.items(): + rf._module = cls.__module__ + if not rf._type: + rf._type = rf._get_deserialize_callable_from_annotation(annotations.get(attr, None)) + if not rf._rest_name_input: + rf._rest_name_input = attr + cls._attr_to_rest_field: typing.Dict[str, _RestField] = dict(attr_to_rest_field.items()) + cls._calculated.add(f"{cls.__module__}.{cls.__qualname__}") + + return super().__new__(cls) + + def __init_subclass__(cls, discriminator: typing.Optional[str] = None) -> None: + for base in cls.__bases__: + if hasattr(base, "__mapping__"): + base.__mapping__[discriminator or cls.__name__] = cls # type: ignore + + @classmethod + def _get_discriminator(cls, exist_discriminators) -> typing.Optional["_RestField"]: + for v in cls.__dict__.values(): + if isinstance(v, _RestField) and v._is_discriminator and v._rest_name not in exist_discriminators: + return v + return None + + @classmethod + def _deserialize(cls, data, exist_discriminators): + if not hasattr(cls, "__mapping__"): + return cls(data) + discriminator = cls._get_discriminator(exist_discriminators) + if discriminator is None: + return cls(data) + exist_discriminators.append(discriminator._rest_name) + if isinstance(data, ET.Element): + model_meta = getattr(cls, "_xml", {}) + prop_meta = getattr(discriminator, "_xml", {}) + xml_name = prop_meta.get("name", discriminator._rest_name) + xml_ns = prop_meta.get("ns", model_meta.get("ns", None)) + if xml_ns: + xml_name = "{" + xml_ns + "}" + xml_name + + if data.get(xml_name) is not None: + discriminator_value = data.get(xml_name) + else: + discriminator_value = data.find(xml_name).text # pyright: ignore + else: + discriminator_value = data.get(discriminator._rest_name) + mapped_cls = cls.__mapping__.get(discriminator_value, cls) # pyright: ignore # pylint: disable=no-member + return mapped_cls._deserialize(data, exist_discriminators) + + def as_dict(self, *, exclude_readonly: bool = False) -> typing.Dict[str, typing.Any]: + """Return a dict that can be turned into json using json.dump. + + :keyword bool exclude_readonly: Whether to remove the readonly properties. + :returns: A dict JSON compatible object + :rtype: dict + """ + + result = {} + readonly_props = [] + if exclude_readonly: + readonly_props = [p._rest_name for p in self._attr_to_rest_field.values() if _is_readonly(p)] + for k, v in self.items(): + if exclude_readonly and k in readonly_props: # pyright: ignore + continue + is_multipart_file_input = False + try: + is_multipart_file_input = next( + rf for rf in self._attr_to_rest_field.values() if rf._rest_name == k + )._is_multipart_file_input + except StopIteration: + pass + result[k] = v if is_multipart_file_input else Model._as_dict_value(v, exclude_readonly=exclude_readonly) + return result + + @staticmethod + def _as_dict_value(v: typing.Any, exclude_readonly: bool = False) -> typing.Any: + if v is None or isinstance(v, _Null): + return None + if isinstance(v, (list, tuple, set)): + return type(v)(Model._as_dict_value(x, exclude_readonly=exclude_readonly) for x in v) + if isinstance(v, dict): + return {dk: Model._as_dict_value(dv, exclude_readonly=exclude_readonly) for dk, dv in v.items()} + return v.as_dict(exclude_readonly=exclude_readonly) if hasattr(v, "as_dict") else v + + +def _deserialize_model(model_deserializer: typing.Optional[typing.Callable], obj): + if _is_model(obj): + return obj + return _deserialize(model_deserializer, obj) + + +def _deserialize_with_optional(if_obj_deserializer: typing.Optional[typing.Callable], obj): + if obj is None: + return obj + return _deserialize_with_callable(if_obj_deserializer, obj) + + +def _deserialize_with_union(deserializers, obj): + for deserializer in deserializers: + try: + return _deserialize(deserializer, obj) + except DeserializationError: + pass + raise DeserializationError() + + +def _deserialize_dict( + value_deserializer: typing.Optional[typing.Callable], + module: typing.Optional[str], + obj: typing.Dict[typing.Any, typing.Any], +): + if obj is None: + return obj + if isinstance(obj, ET.Element): + obj = {child.tag: child for child in obj} + return {k: _deserialize(value_deserializer, v, module) for k, v in obj.items()} + + +def _deserialize_multiple_sequence( + entry_deserializers: typing.List[typing.Optional[typing.Callable]], + module: typing.Optional[str], + obj, +): + if obj is None: + return obj + return type(obj)(_deserialize(deserializer, entry, module) for entry, deserializer in zip(obj, entry_deserializers)) + + +def _deserialize_sequence( + deserializer: typing.Optional[typing.Callable], + module: typing.Optional[str], + obj, +): + if obj is None: + return obj + if isinstance(obj, ET.Element): + obj = list(obj) + return type(obj)(_deserialize(deserializer, entry, module) for entry in obj) + + +def _sorted_annotations(types: typing.List[typing.Any]) -> typing.List[typing.Any]: + return sorted( + types, + key=lambda x: hasattr(x, "__name__") and x.__name__.lower() in ("str", "float", "int", "bool"), + ) + + +def _get_deserialize_callable_from_annotation( # pylint: disable=too-many-return-statements, too-many-branches + annotation: typing.Any, + module: typing.Optional[str], + rf: typing.Optional["_RestField"] = None, +) -> typing.Optional[typing.Callable[[typing.Any], typing.Any]]: + if not annotation: + return None + + # is it a type alias? + if isinstance(annotation, str): + if module is not None: + annotation = _get_type_alias_type(module, annotation) + + # is it a forward ref / in quotes? + if isinstance(annotation, (str, typing.ForwardRef)): + try: + model_name = annotation.__forward_arg__ # type: ignore + except AttributeError: + model_name = annotation + if module is not None: + annotation = _get_model(module, model_name) # type: ignore + + try: + if module and _is_model(annotation): + if rf: + rf._is_model = True + + return functools.partial(_deserialize_model, annotation) # pyright: ignore + except Exception: + pass + + # is it a literal? + try: + if annotation.__origin__ is typing.Literal: # pyright: ignore + return None + except AttributeError: + pass + + # is it optional? + try: + if any(a for a in annotation.__args__ if a == type(None)): # pyright: ignore + if len(annotation.__args__) <= 2: # pyright: ignore + if_obj_deserializer = _get_deserialize_callable_from_annotation( + next(a for a in annotation.__args__ if a != type(None)), module, rf # pyright: ignore + ) + + return functools.partial(_deserialize_with_optional, if_obj_deserializer) + # the type is Optional[Union[...]], we need to remove the None type from the Union + annotation_copy = copy.copy(annotation) + annotation_copy.__args__ = [a for a in annotation_copy.__args__ if a != type(None)] # pyright: ignore + return _get_deserialize_callable_from_annotation(annotation_copy, module, rf) + except AttributeError: + pass + + # is it union? + if getattr(annotation, "__origin__", None) is typing.Union: + # initial ordering is we make `string` the last deserialization option, because it is often them most generic + deserializers = [ + _get_deserialize_callable_from_annotation(arg, module, rf) + for arg in _sorted_annotations(annotation.__args__) # pyright: ignore + ] + + return functools.partial(_deserialize_with_union, deserializers) + + try: + if annotation._name == "Dict": # pyright: ignore + value_deserializer = _get_deserialize_callable_from_annotation( + annotation.__args__[1], module, rf # pyright: ignore + ) + + return functools.partial( + _deserialize_dict, + value_deserializer, + module, + ) + except (AttributeError, IndexError): + pass + try: + if annotation._name in ["List", "Set", "Tuple", "Sequence"]: # pyright: ignore + if len(annotation.__args__) > 1: # pyright: ignore + entry_deserializers = [ + _get_deserialize_callable_from_annotation(dt, module, rf) + for dt in annotation.__args__ # pyright: ignore + ] + return functools.partial(_deserialize_multiple_sequence, entry_deserializers, module) + deserializer = _get_deserialize_callable_from_annotation( + annotation.__args__[0], module, rf # pyright: ignore + ) + + return functools.partial(_deserialize_sequence, deserializer, module) + except (TypeError, IndexError, AttributeError, SyntaxError): + pass + + def _deserialize_default( + deserializer, + obj, + ): + if obj is None: + return obj + try: + return _deserialize_with_callable(deserializer, obj) + except Exception: + pass + return obj + + if get_deserializer(annotation, rf): + return functools.partial(_deserialize_default, get_deserializer(annotation, rf)) + + return functools.partial(_deserialize_default, annotation) + + +def _deserialize_with_callable( + deserializer: typing.Optional[typing.Callable[[typing.Any], typing.Any]], + value: typing.Any, +): # pylint: disable=too-many-return-statements + try: + if value is None or isinstance(value, _Null): + return None + if isinstance(value, ET.Element): + if deserializer is str: + return value.text or "" + if deserializer is int: + return int(value.text) if value.text else None + if deserializer is float: + return float(value.text) if value.text else None + if deserializer is bool: + return value.text == "true" if value.text else None + if deserializer is None: + return value + if deserializer in [int, float, bool]: + return deserializer(value) + if isinstance(deserializer, CaseInsensitiveEnumMeta): + try: + return deserializer(value) + except ValueError: + # for unknown value, return raw value + return value + if isinstance(deserializer, type) and issubclass(deserializer, Model): + return deserializer._deserialize(value, []) + return typing.cast(typing.Callable[[typing.Any], typing.Any], deserializer)(value) + except Exception as e: + raise DeserializationError() from e + + +def _deserialize( + deserializer: typing.Any, + value: typing.Any, + module: typing.Optional[str] = None, + rf: typing.Optional["_RestField"] = None, + format: typing.Optional[str] = None, +) -> typing.Any: + if isinstance(value, PipelineResponse): + value = value.http_response.json() + if rf is None and format: + rf = _RestField(format=format) + if not isinstance(deserializer, functools.partial): + deserializer = _get_deserialize_callable_from_annotation(deserializer, module, rf) + return _deserialize_with_callable(deserializer, value) + + +def _failsafe_deserialize( + deserializer: typing.Any, + response: HttpResponse, + module: typing.Optional[str] = None, + rf: typing.Optional["_RestField"] = None, + format: typing.Optional[str] = None, +) -> typing.Any: + try: + return _deserialize(deserializer, response.json(), module, rf, format) + except DeserializationError: + _LOGGER.warning( + "Ran into a deserialization error. Ignoring since this is failsafe deserialization", exc_info=True + ) + return None + + +def _failsafe_deserialize_xml( + deserializer: typing.Any, + response: HttpResponse, +) -> typing.Any: + try: + return _deserialize_xml(deserializer, response.text()) + except DeserializationError: + _LOGGER.warning( + "Ran into a deserialization error. Ignoring since this is failsafe deserialization", exc_info=True + ) + return None + + +class _RestField: + def __init__( + self, + *, + name: typing.Optional[str] = None, + type: typing.Optional[typing.Callable] = None, # pylint: disable=redefined-builtin + is_discriminator: bool = False, + visibility: typing.Optional[typing.List[str]] = None, + default: typing.Any = _UNSET, + format: typing.Optional[str] = None, + is_multipart_file_input: bool = False, + xml: typing.Optional[typing.Dict[str, typing.Any]] = None, + ): + self._type = type + self._rest_name_input = name + self._module: typing.Optional[str] = None + self._is_discriminator = is_discriminator + self._visibility = visibility + self._is_model = False + self._default = default + self._format = format + self._is_multipart_file_input = is_multipart_file_input + self._xml = xml if xml is not None else {} + + @property + def _class_type(self) -> typing.Any: + return getattr(self._type, "args", [None])[0] + + @property + def _rest_name(self) -> str: + if self._rest_name_input is None: + raise ValueError("Rest name was never set") + return self._rest_name_input + + def __get__(self, obj: Model, type=None): # pylint: disable=redefined-builtin + # by this point, type and rest_name will have a value bc we default + # them in __new__ of the Model class + item = obj.get(self._rest_name) + if item is None: + return item + if self._is_model: + return item + return _deserialize(self._type, _serialize(item, self._format), rf=self) + + def __set__(self, obj: Model, value) -> None: + if value is None: + # we want to wipe out entries if users set attr to None + try: + obj.__delitem__(self._rest_name) + except KeyError: + pass + return + if self._is_model: + if not _is_model(value): + value = _deserialize(self._type, value) + obj.__setitem__(self._rest_name, value) + return + obj.__setitem__(self._rest_name, _serialize(value, self._format)) + + def _get_deserialize_callable_from_annotation( + self, annotation: typing.Any + ) -> typing.Optional[typing.Callable[[typing.Any], typing.Any]]: + return _get_deserialize_callable_from_annotation(annotation, self._module, self) + + +def rest_field( + *, + name: typing.Optional[str] = None, + type: typing.Optional[typing.Callable] = None, # pylint: disable=redefined-builtin + visibility: typing.Optional[typing.List[str]] = None, + default: typing.Any = _UNSET, + format: typing.Optional[str] = None, + is_multipart_file_input: bool = False, + xml: typing.Optional[typing.Dict[str, typing.Any]] = None, +) -> typing.Any: + return _RestField( + name=name, + type=type, + visibility=visibility, + default=default, + format=format, + is_multipart_file_input=is_multipart_file_input, + xml=xml, + ) + + +def rest_discriminator( + *, + name: typing.Optional[str] = None, + type: typing.Optional[typing.Callable] = None, # pylint: disable=redefined-builtin + visibility: typing.Optional[typing.List[str]] = None, + xml: typing.Optional[typing.Dict[str, typing.Any]] = None, +) -> typing.Any: + return _RestField(name=name, type=type, is_discriminator=True, visibility=visibility, xml=xml) + + +def serialize_xml(model: Model, exclude_readonly: bool = False) -> str: + """Serialize a model to XML. + + :param Model model: The model to serialize. + :param bool exclude_readonly: Whether to exclude readonly properties. + :returns: The XML representation of the model. + :rtype: str + """ + return ET.tostring(_get_element(model, exclude_readonly), encoding="unicode") # type: ignore + + +def _get_element( + o: typing.Any, + exclude_readonly: bool = False, + parent_meta: typing.Optional[typing.Dict[str, typing.Any]] = None, + wrapped_element: typing.Optional[ET.Element] = None, +) -> typing.Union[ET.Element, typing.List[ET.Element]]: + if _is_model(o): + model_meta = getattr(o, "_xml", {}) + + # if prop is a model, then use the prop element directly, else generate a wrapper of model + if wrapped_element is None: + wrapped_element = _create_xml_element( + model_meta.get("name", o.__class__.__name__), + model_meta.get("prefix"), + model_meta.get("ns"), + ) + + readonly_props = [] + if exclude_readonly: + readonly_props = [p._rest_name for p in o._attr_to_rest_field.values() if _is_readonly(p)] + + for k, v in o.items(): + # do not serialize readonly properties + if exclude_readonly and k in readonly_props: + continue + + prop_rest_field = _get_rest_field(o._attr_to_rest_field, k) + if prop_rest_field: + prop_meta = getattr(prop_rest_field, "_xml").copy() + # use the wire name as xml name if no specific name is set + if prop_meta.get("name") is None: + prop_meta["name"] = k + else: + # additional properties will not have rest field, use the wire name as xml name + prop_meta = {"name": k} + + # if no ns for prop, use model's + if prop_meta.get("ns") is None and model_meta.get("ns"): + prop_meta["ns"] = model_meta.get("ns") + prop_meta["prefix"] = model_meta.get("prefix") + + if prop_meta.get("unwrapped", False): + # unwrapped could only set on array + wrapped_element.extend(_get_element(v, exclude_readonly, prop_meta)) + elif prop_meta.get("text", False): + # text could only set on primitive type + wrapped_element.text = _get_primitive_type_value(v) + elif prop_meta.get("attribute", False): + xml_name = prop_meta.get("name", k) + if prop_meta.get("ns"): + ET.register_namespace(prop_meta.get("prefix"), prop_meta.get("ns")) # pyright: ignore + xml_name = "{" + prop_meta.get("ns") + "}" + xml_name # pyright: ignore + # attribute should be primitive type + wrapped_element.set(xml_name, _get_primitive_type_value(v)) + else: + # other wrapped prop element + wrapped_element.append(_get_wrapped_element(v, exclude_readonly, prop_meta)) + return wrapped_element + if isinstance(o, list): + return [_get_element(x, exclude_readonly, parent_meta) for x in o] # type: ignore + if isinstance(o, dict): + result = [] + for k, v in o.items(): + result.append( + _get_wrapped_element( + v, + exclude_readonly, + { + "name": k, + "ns": parent_meta.get("ns") if parent_meta else None, + "prefix": parent_meta.get("prefix") if parent_meta else None, + }, + ) + ) + return result + + # primitive case need to create element based on parent_meta + if parent_meta: + return _get_wrapped_element( + o, + exclude_readonly, + { + "name": parent_meta.get("itemsName", parent_meta.get("name")), + "prefix": parent_meta.get("itemsPrefix", parent_meta.get("prefix")), + "ns": parent_meta.get("itemsNs", parent_meta.get("ns")), + }, + ) + + raise ValueError("Could not serialize value into xml: " + o) + + +def _get_wrapped_element( + v: typing.Any, + exclude_readonly: bool, + meta: typing.Optional[typing.Dict[str, typing.Any]], +) -> ET.Element: + wrapped_element = _create_xml_element( + meta.get("name") if meta else None, meta.get("prefix") if meta else None, meta.get("ns") if meta else None + ) + if isinstance(v, (dict, list)): + wrapped_element.extend(_get_element(v, exclude_readonly, meta)) + elif _is_model(v): + _get_element(v, exclude_readonly, meta, wrapped_element) + else: + wrapped_element.text = _get_primitive_type_value(v) + return wrapped_element + + +def _get_primitive_type_value(v) -> str: + if v is True: + return "true" + if v is False: + return "false" + if isinstance(v, _Null): + return "" + return str(v) + + +def _create_xml_element(tag, prefix=None, ns=None): + if prefix and ns: + ET.register_namespace(prefix, ns) + if ns: + return ET.Element("{" + ns + "}" + tag) + return ET.Element(tag) + + +def _deserialize_xml( + deserializer: typing.Any, + value: str, +) -> typing.Any: + element = ET.fromstring(value) # nosec + return _deserialize(deserializer, element) + + +def _convert_element(e: ET.Element): + # dict case + if len(e.attrib) > 0 or len({child.tag for child in e}) > 1: + dict_result: typing.Dict[str, typing.Any] = {} + for child in e: + if dict_result.get(child.tag) is not None: + if isinstance(dict_result[child.tag], list): + dict_result[child.tag].append(_convert_element(child)) + else: + dict_result[child.tag] = [dict_result[child.tag], _convert_element(child)] + else: + dict_result[child.tag] = _convert_element(child) + dict_result.update(e.attrib) + return dict_result + # array case + if len(e) > 0: + array_result: typing.List[typing.Any] = [] + for child in e: + array_result.append(_convert_element(child)) + return array_result + # primitive case + return e.text diff --git a/sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/textanalytics/_utils/serialization.py b/sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/textanalytics/_utils/serialization.py new file mode 100644 index 000000000000..eb86ea23c965 --- /dev/null +++ b/sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/textanalytics/_utils/serialization.py @@ -0,0 +1,2032 @@ +# 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. +# -------------------------------------------------------------------------- + +# pyright: reportUnnecessaryTypeIgnoreComment=false + +from base64 import b64decode, b64encode +import calendar +import datetime +import decimal +import email +from enum import Enum +import json +import logging +import re +import sys +import codecs +from typing import ( + Dict, + Any, + cast, + Optional, + Union, + AnyStr, + IO, + Mapping, + Callable, + MutableMapping, + List, +) + +try: + from urllib import quote # type: ignore +except ImportError: + from urllib.parse import quote +import xml.etree.ElementTree as ET + +import isodate # type: ignore +from typing_extensions import Self + +from azure.core.exceptions import DeserializationError, SerializationError +from azure.core.serialization import NULL as CoreNull + +_BOM = codecs.BOM_UTF8.decode(encoding="utf-8") + +JSON = MutableMapping[str, Any] + + +class RawDeserializer: + + # Accept "text" because we're open minded people... + JSON_REGEXP = re.compile(r"^(application|text)/([a-z+.]+\+)?json$") + + # Name used in context + CONTEXT_NAME = "deserialized_data" + + @classmethod + def deserialize_from_text(cls, data: Optional[Union[AnyStr, IO]], content_type: Optional[str] = None) -> Any: + """Decode data according to content-type. + + Accept a stream of data as well, but will be load at once in memory for now. + + If no content-type, will return the string version (not bytes, not stream) + + :param data: Input, could be bytes or stream (will be decoded with UTF8) or text + :type data: str or bytes or IO + :param str content_type: The content type. + :return: The deserialized data. + :rtype: object + """ + if hasattr(data, "read"): + # Assume a stream + data = cast(IO, data).read() + + if isinstance(data, bytes): + data_as_str = data.decode(encoding="utf-8-sig") + else: + # Explain to mypy the correct type. + data_as_str = cast(str, data) + + # Remove Byte Order Mark if present in string + data_as_str = data_as_str.lstrip(_BOM) + + if content_type is None: + return data + + if cls.JSON_REGEXP.match(content_type): + try: + return json.loads(data_as_str) + except ValueError as err: + raise DeserializationError("JSON is invalid: {}".format(err), err) from err + elif "xml" in (content_type or []): + try: + + try: + if isinstance(data, unicode): # type: ignore + # If I'm Python 2.7 and unicode XML will scream if I try a "fromstring" on unicode string + data_as_str = data_as_str.encode(encoding="utf-8") # type: ignore + except NameError: + pass + + return ET.fromstring(data_as_str) # nosec + except ET.ParseError as err: + # It might be because the server has an issue, and returned JSON with + # content-type XML.... + # So let's try a JSON load, and if it's still broken + # let's flow the initial exception + def _json_attemp(data): + try: + return True, json.loads(data) + except ValueError: + return False, None # Don't care about this one + + success, json_result = _json_attemp(data) + if success: + return json_result + # If i'm here, it's not JSON, it's not XML, let's scream + # and raise the last context in this block (the XML exception) + # The function hack is because Py2.7 messes up with exception + # context otherwise. + _LOGGER.critical("Wasn't XML not JSON, failing") + raise DeserializationError("XML is invalid") from err + elif content_type.startswith("text/"): + return data_as_str + raise DeserializationError("Cannot deserialize content-type: {}".format(content_type)) + + @classmethod + def deserialize_from_http_generics(cls, body_bytes: Optional[Union[AnyStr, IO]], headers: Mapping) -> Any: + """Deserialize from HTTP response. + + Use bytes and headers to NOT use any requests/aiohttp or whatever + specific implementation. + Headers will tested for "content-type" + + :param bytes body_bytes: The body of the response. + :param dict headers: The headers of the response. + :returns: The deserialized data. + :rtype: object + """ + # Try to use content-type from headers if available + content_type = None + if "content-type" in headers: + content_type = headers["content-type"].split(";")[0].strip().lower() + # Ouch, this server did not declare what it sent... + # Let's guess it's JSON... + # Also, since Autorest was considering that an empty body was a valid JSON, + # need that test as well.... + else: + content_type = "application/json" + + if body_bytes: + return cls.deserialize_from_text(body_bytes, content_type) + return None + + +_LOGGER = logging.getLogger(__name__) + +try: + _long_type = long # type: ignore +except NameError: + _long_type = int + +TZ_UTC = datetime.timezone.utc + +_FLATTEN = re.compile(r"(? None: + self.additional_properties: Optional[Dict[str, Any]] = {} + for k in kwargs: # pylint: disable=consider-using-dict-items + if k not in self._attribute_map: + _LOGGER.warning("%s is not a known attribute of class %s and will be ignored", k, self.__class__) + elif k in self._validation and self._validation[k].get("readonly", False): + _LOGGER.warning("Readonly attribute %s will be ignored in class %s", k, self.__class__) + else: + setattr(self, k, kwargs[k]) + + def __eq__(self, other: Any) -> bool: + """Compare objects by comparing all attributes. + + :param object other: The object to compare + :returns: True if objects are equal + :rtype: bool + """ + if isinstance(other, self.__class__): + return self.__dict__ == other.__dict__ + return False + + def __ne__(self, other: Any) -> bool: + """Compare objects by comparing all attributes. + + :param object other: The object to compare + :returns: True if objects are not equal + :rtype: bool + """ + return not self.__eq__(other) + + def __str__(self) -> str: + return str(self.__dict__) + + @classmethod + def enable_additional_properties_sending(cls) -> None: + cls._attribute_map["additional_properties"] = {"key": "", "type": "{object}"} + + @classmethod + def is_xml_model(cls) -> bool: + try: + cls._xml_map # type: ignore + except AttributeError: + return False + return True + + @classmethod + def _create_xml_node(cls): + """Create XML node. + + :returns: The XML node + :rtype: xml.etree.ElementTree.Element + """ + try: + xml_map = cls._xml_map # type: ignore + except AttributeError: + xml_map = {} + + return _create_xml_node(xml_map.get("name", cls.__name__), xml_map.get("prefix", None), xml_map.get("ns", None)) + + def serialize(self, keep_readonly: bool = False, **kwargs: Any) -> JSON: + """Return the JSON that would be sent to server from this model. + + This is an alias to `as_dict(full_restapi_key_transformer, keep_readonly=False)`. + + If you want XML serialization, you can pass the kwargs is_xml=True. + + :param bool keep_readonly: If you want to serialize the readonly attributes + :returns: A dict JSON compatible object + :rtype: dict + """ + serializer = Serializer(self._infer_class_models()) + return serializer._serialize( # type: ignore # pylint: disable=protected-access + self, keep_readonly=keep_readonly, **kwargs + ) + + def as_dict( + self, + keep_readonly: bool = True, + key_transformer: Callable[[str, Dict[str, Any], Any], Any] = attribute_transformer, + **kwargs: Any + ) -> JSON: + """Return a dict that can be serialized using json.dump. + + Advanced usage might optionally use a callback as parameter: + + .. code::python + + def my_key_transformer(key, attr_desc, value): + return key + + Key is the attribute name used in Python. Attr_desc + is a dict of metadata. Currently contains 'type' with the + msrest type and 'key' with the RestAPI encoded key. + Value is the current value in this object. + + The string returned will be used to serialize the key. + If the return type is a list, this is considered hierarchical + result dict. + + See the three examples in this file: + + - attribute_transformer + - full_restapi_key_transformer + - last_restapi_key_transformer + + If you want XML serialization, you can pass the kwargs is_xml=True. + + :param bool keep_readonly: If you want to serialize the readonly attributes + :param function key_transformer: A key transformer function. + :returns: A dict JSON compatible object + :rtype: dict + """ + serializer = Serializer(self._infer_class_models()) + return serializer._serialize( # type: ignore # pylint: disable=protected-access + self, key_transformer=key_transformer, keep_readonly=keep_readonly, **kwargs + ) + + @classmethod + def _infer_class_models(cls): + try: + str_models = cls.__module__.rsplit(".", 1)[0] + models = sys.modules[str_models] + client_models = {k: v for k, v in models.__dict__.items() if isinstance(v, type)} + if cls.__name__ not in client_models: + raise ValueError("Not Autorest generated code") + except Exception: # pylint: disable=broad-exception-caught + # Assume it's not Autorest generated (tests?). Add ourselves as dependencies. + client_models = {cls.__name__: cls} + return client_models + + @classmethod + def deserialize(cls, data: Any, content_type: Optional[str] = None) -> Self: + """Parse a str using the RestAPI syntax and return a model. + + :param str data: A str using RestAPI structure. JSON by default. + :param str content_type: JSON by default, set application/xml if XML. + :returns: An instance of this model + :raises DeserializationError: if something went wrong + :rtype: Self + """ + deserializer = Deserializer(cls._infer_class_models()) + return deserializer(cls.__name__, data, content_type=content_type) # type: ignore + + @classmethod + def from_dict( + cls, + data: Any, + key_extractors: Optional[Callable[[str, Dict[str, Any], Any], Any]] = None, + content_type: Optional[str] = None, + ) -> Self: + """Parse a dict using given key extractor return a model. + + By default consider key + extractors (rest_key_case_insensitive_extractor, attribute_key_case_insensitive_extractor + and last_rest_key_case_insensitive_extractor) + + :param dict data: A dict using RestAPI structure + :param function key_extractors: A key extractor function. + :param str content_type: JSON by default, set application/xml if XML. + :returns: An instance of this model + :raises DeserializationError: if something went wrong + :rtype: Self + """ + deserializer = Deserializer(cls._infer_class_models()) + deserializer.key_extractors = ( # type: ignore + [ # type: ignore + attribute_key_case_insensitive_extractor, + rest_key_case_insensitive_extractor, + last_rest_key_case_insensitive_extractor, + ] + if key_extractors is None + else key_extractors + ) + return deserializer(cls.__name__, data, content_type=content_type) # type: ignore + + @classmethod + def _flatten_subtype(cls, key, objects): + if "_subtype_map" not in cls.__dict__: + return {} + result = dict(cls._subtype_map[key]) + for valuetype in cls._subtype_map[key].values(): + result.update(objects[valuetype]._flatten_subtype(key, objects)) # pylint: disable=protected-access + return result + + @classmethod + def _classify(cls, response, objects): + """Check the class _subtype_map for any child classes. + We want to ignore any inherited _subtype_maps. + + :param dict response: The initial data + :param dict objects: The class objects + :returns: The class to be used + :rtype: class + """ + for subtype_key in cls.__dict__.get("_subtype_map", {}).keys(): + subtype_value = None + + if not isinstance(response, ET.Element): + rest_api_response_key = cls._get_rest_key_parts(subtype_key)[-1] + subtype_value = response.get(rest_api_response_key, None) or response.get(subtype_key, None) + else: + subtype_value = xml_key_extractor(subtype_key, cls._attribute_map[subtype_key], response) + if subtype_value: + # Try to match base class. Can be class name only + # (bug to fix in Autorest to support x-ms-discriminator-name) + if cls.__name__ == subtype_value: + return cls + flatten_mapping_type = cls._flatten_subtype(subtype_key, objects) + try: + return objects[flatten_mapping_type[subtype_value]] # type: ignore + except KeyError: + _LOGGER.warning( + "Subtype value %s has no mapping, use base class %s.", + subtype_value, + cls.__name__, + ) + break + else: + _LOGGER.warning("Discriminator %s is absent or null, use base class %s.", subtype_key, cls.__name__) + break + return cls + + @classmethod + def _get_rest_key_parts(cls, attr_key): + """Get the RestAPI key of this attr, split it and decode part + :param str attr_key: Attribute key must be in attribute_map. + :returns: A list of RestAPI part + :rtype: list + """ + rest_split_key = _FLATTEN.split(cls._attribute_map[attr_key]["key"]) + return [_decode_attribute_map_key(key_part) for key_part in rest_split_key] + + +def _decode_attribute_map_key(key): + """This decode a key in an _attribute_map to the actual key we want to look at + inside the received data. + + :param str key: A key string from the generated code + :returns: The decoded key + :rtype: str + """ + return key.replace("\\.", ".") + + +class Serializer: # pylint: disable=too-many-public-methods + """Request object model serializer.""" + + basic_types = {str: "str", int: "int", bool: "bool", float: "float"} + + _xml_basic_types_serializers = {"bool": lambda x: str(x).lower()} + days = {0: "Mon", 1: "Tue", 2: "Wed", 3: "Thu", 4: "Fri", 5: "Sat", 6: "Sun"} + months = { + 1: "Jan", + 2: "Feb", + 3: "Mar", + 4: "Apr", + 5: "May", + 6: "Jun", + 7: "Jul", + 8: "Aug", + 9: "Sep", + 10: "Oct", + 11: "Nov", + 12: "Dec", + } + validation = { + "min_length": lambda x, y: len(x) < y, + "max_length": lambda x, y: len(x) > y, + "minimum": lambda x, y: x < y, + "maximum": lambda x, y: x > y, + "minimum_ex": lambda x, y: x <= y, + "maximum_ex": lambda x, y: x >= y, + "min_items": lambda x, y: len(x) < y, + "max_items": lambda x, y: len(x) > y, + "pattern": lambda x, y: not re.match(y, x, re.UNICODE), + "unique": lambda x, y: len(x) != len(set(x)), + "multiple": lambda x, y: x % y != 0, + } + + def __init__(self, classes: Optional[Mapping[str, type]] = None) -> None: + self.serialize_type = { + "iso-8601": Serializer.serialize_iso, + "rfc-1123": Serializer.serialize_rfc, + "unix-time": Serializer.serialize_unix, + "duration": Serializer.serialize_duration, + "date": Serializer.serialize_date, + "time": Serializer.serialize_time, + "decimal": Serializer.serialize_decimal, + "long": Serializer.serialize_long, + "bytearray": Serializer.serialize_bytearray, + "base64": Serializer.serialize_base64, + "object": self.serialize_object, + "[]": self.serialize_iter, + "{}": self.serialize_dict, + } + self.dependencies: Dict[str, type] = dict(classes) if classes else {} + self.key_transformer = full_restapi_key_transformer + self.client_side_validation = True + + def _serialize( # pylint: disable=too-many-nested-blocks, too-many-branches, too-many-statements, too-many-locals + self, target_obj, data_type=None, **kwargs + ): + """Serialize data into a string according to type. + + :param object target_obj: The data to be serialized. + :param str data_type: The type to be serialized from. + :rtype: str, dict + :raises SerializationError: if serialization fails. + :returns: The serialized data. + """ + key_transformer = kwargs.get("key_transformer", self.key_transformer) + keep_readonly = kwargs.get("keep_readonly", False) + if target_obj is None: + return None + + attr_name = None + class_name = target_obj.__class__.__name__ + + if data_type: + return self.serialize_data(target_obj, data_type, **kwargs) + + if not hasattr(target_obj, "_attribute_map"): + data_type = type(target_obj).__name__ + if data_type in self.basic_types.values(): + return self.serialize_data(target_obj, data_type, **kwargs) + + # Force "is_xml" kwargs if we detect a XML model + try: + is_xml_model_serialization = kwargs["is_xml"] + except KeyError: + is_xml_model_serialization = kwargs.setdefault("is_xml", target_obj.is_xml_model()) + + serialized = {} + if is_xml_model_serialization: + serialized = target_obj._create_xml_node() # pylint: disable=protected-access + try: + attributes = target_obj._attribute_map # pylint: disable=protected-access + for attr, attr_desc in attributes.items(): + attr_name = attr + if not keep_readonly and target_obj._validation.get( # pylint: disable=protected-access + attr_name, {} + ).get("readonly", False): + continue + + if attr_name == "additional_properties" and attr_desc["key"] == "": + if target_obj.additional_properties is not None: + serialized.update(target_obj.additional_properties) + continue + try: + + orig_attr = getattr(target_obj, attr) + if is_xml_model_serialization: + pass # Don't provide "transformer" for XML for now. Keep "orig_attr" + else: # JSON + keys, orig_attr = key_transformer(attr, attr_desc.copy(), orig_attr) + keys = keys if isinstance(keys, list) else [keys] + + kwargs["serialization_ctxt"] = attr_desc + new_attr = self.serialize_data(orig_attr, attr_desc["type"], **kwargs) + + if is_xml_model_serialization: + xml_desc = attr_desc.get("xml", {}) + xml_name = xml_desc.get("name", attr_desc["key"]) + xml_prefix = xml_desc.get("prefix", None) + xml_ns = xml_desc.get("ns", None) + if xml_desc.get("attr", False): + if xml_ns: + ET.register_namespace(xml_prefix, xml_ns) + xml_name = "{{{}}}{}".format(xml_ns, xml_name) + serialized.set(xml_name, new_attr) # type: ignore + continue + if xml_desc.get("text", False): + serialized.text = new_attr # type: ignore + continue + if isinstance(new_attr, list): + serialized.extend(new_attr) # type: ignore + elif isinstance(new_attr, ET.Element): + # If the down XML has no XML/Name, + # we MUST replace the tag with the local tag. But keeping the namespaces. + if "name" not in getattr(orig_attr, "_xml_map", {}): + splitted_tag = new_attr.tag.split("}") + if len(splitted_tag) == 2: # Namespace + new_attr.tag = "}".join([splitted_tag[0], xml_name]) + else: + new_attr.tag = xml_name + serialized.append(new_attr) # type: ignore + else: # That's a basic type + # Integrate namespace if necessary + local_node = _create_xml_node(xml_name, xml_prefix, xml_ns) + local_node.text = str(new_attr) + serialized.append(local_node) # type: ignore + else: # JSON + for k in reversed(keys): # type: ignore + new_attr = {k: new_attr} + + _new_attr = new_attr + _serialized = serialized + for k in keys: # type: ignore + if k not in _serialized: + _serialized.update(_new_attr) # type: ignore + _new_attr = _new_attr[k] # type: ignore + _serialized = _serialized[k] + except ValueError as err: + if isinstance(err, SerializationError): + raise + + except (AttributeError, KeyError, TypeError) as err: + msg = "Attribute {} in object {} cannot be serialized.\n{}".format(attr_name, class_name, str(target_obj)) + raise SerializationError(msg) from err + return serialized + + def body(self, data, data_type, **kwargs): + """Serialize data intended for a request body. + + :param object data: The data to be serialized. + :param str data_type: The type to be serialized from. + :rtype: dict + :raises SerializationError: if serialization fails. + :raises ValueError: if data is None + :returns: The serialized request body + """ + + # Just in case this is a dict + internal_data_type_str = data_type.strip("[]{}") + internal_data_type = self.dependencies.get(internal_data_type_str, None) + try: + is_xml_model_serialization = kwargs["is_xml"] + except KeyError: + if internal_data_type and issubclass(internal_data_type, Model): + is_xml_model_serialization = kwargs.setdefault("is_xml", internal_data_type.is_xml_model()) + else: + is_xml_model_serialization = False + if internal_data_type and not isinstance(internal_data_type, Enum): + try: + deserializer = Deserializer(self.dependencies) + # Since it's on serialization, it's almost sure that format is not JSON REST + # We're not able to deal with additional properties for now. + deserializer.additional_properties_detection = False + if is_xml_model_serialization: + deserializer.key_extractors = [ # type: ignore + attribute_key_case_insensitive_extractor, + ] + else: + deserializer.key_extractors = [ + rest_key_case_insensitive_extractor, + attribute_key_case_insensitive_extractor, + last_rest_key_case_insensitive_extractor, + ] + data = deserializer._deserialize(data_type, data) # pylint: disable=protected-access + except DeserializationError as err: + raise SerializationError("Unable to build a model: " + str(err)) from err + + return self._serialize(data, data_type, **kwargs) + + def url(self, name, data, data_type, **kwargs): + """Serialize data intended for a URL path. + + :param str name: The name of the URL path parameter. + :param object data: The data to be serialized. + :param str data_type: The type to be serialized from. + :rtype: str + :returns: The serialized URL path + :raises TypeError: if serialization fails. + :raises ValueError: if data is None + """ + try: + output = self.serialize_data(data, data_type, **kwargs) + if data_type == "bool": + output = json.dumps(output) + + if kwargs.get("skip_quote") is True: + output = str(output) + output = output.replace("{", quote("{")).replace("}", quote("}")) + else: + output = quote(str(output), safe="") + except SerializationError as exc: + raise TypeError("{} must be type {}.".format(name, data_type)) from exc + return output + + def query(self, name, data, data_type, **kwargs): + """Serialize data intended for a URL query. + + :param str name: The name of the query parameter. + :param object data: The data to be serialized. + :param str data_type: The type to be serialized from. + :rtype: str, list + :raises TypeError: if serialization fails. + :raises ValueError: if data is None + :returns: The serialized query parameter + """ + try: + # Treat the list aside, since we don't want to encode the div separator + if data_type.startswith("["): + internal_data_type = data_type[1:-1] + do_quote = not kwargs.get("skip_quote", False) + return self.serialize_iter(data, internal_data_type, do_quote=do_quote, **kwargs) + + # Not a list, regular serialization + output = self.serialize_data(data, data_type, **kwargs) + if data_type == "bool": + output = json.dumps(output) + if kwargs.get("skip_quote") is True: + output = str(output) + else: + output = quote(str(output), safe="") + except SerializationError as exc: + raise TypeError("{} must be type {}.".format(name, data_type)) from exc + return str(output) + + def header(self, name, data, data_type, **kwargs): + """Serialize data intended for a request header. + + :param str name: The name of the header. + :param object data: The data to be serialized. + :param str data_type: The type to be serialized from. + :rtype: str + :raises TypeError: if serialization fails. + :raises ValueError: if data is None + :returns: The serialized header + """ + try: + if data_type in ["[str]"]: + data = ["" if d is None else d for d in data] + + output = self.serialize_data(data, data_type, **kwargs) + if data_type == "bool": + output = json.dumps(output) + except SerializationError as exc: + raise TypeError("{} must be type {}.".format(name, data_type)) from exc + return str(output) + + def serialize_data(self, data, data_type, **kwargs): + """Serialize generic data according to supplied data type. + + :param object data: The data to be serialized. + :param str data_type: The type to be serialized from. + :raises AttributeError: if required data is None. + :raises ValueError: if data is None + :raises SerializationError: if serialization fails. + :returns: The serialized data. + :rtype: str, int, float, bool, dict, list + """ + if data is None: + raise ValueError("No value for given attribute") + + try: + if data is CoreNull: + return None + if data_type in self.basic_types.values(): + return self.serialize_basic(data, data_type, **kwargs) + + if data_type in self.serialize_type: + return self.serialize_type[data_type](data, **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__) + if issubclass(enum_type, Enum): + return Serializer.serialize_enum(data, enum_obj=enum_type) + + iter_type = data_type[0] + data_type[-1] + if iter_type in self.serialize_type: + return self.serialize_type[iter_type](data, data_type[1:-1], **kwargs) + + except (ValueError, TypeError) as err: + msg = "Unable to serialize value: {!r} as type: {!r}." + raise SerializationError(msg.format(data, data_type)) from err + return self._serialize(data, **kwargs) + + @classmethod + def _get_custom_serializers(cls, data_type, **kwargs): # pylint: disable=inconsistent-return-statements + custom_serializer = kwargs.get("basic_types_serializers", {}).get(data_type) + if custom_serializer: + return custom_serializer + if kwargs.get("is_xml", False): + return cls._xml_basic_types_serializers.get(data_type) + + @classmethod + def serialize_basic(cls, data, data_type, **kwargs): + """Serialize basic builting data type. + Serializes objects to str, int, float or bool. + + Possible kwargs: + - basic_types_serializers dict[str, callable] : If set, use the callable as serializer + - is_xml bool : If set, use xml_basic_types_serializers + + :param obj data: Object to be serialized. + :param str data_type: Type of object in the iterable. + :rtype: str, int, float, bool + :return: serialized object + """ + 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 + + @classmethod + def serialize_unicode(cls, data): + """Special handling for serializing unicode strings in Py2. + Encode to UTF-8 if unicode, otherwise handle as a str. + + :param str data: Object to be serialized. + :rtype: str + :return: serialized object + """ + try: # If I received an enum, return its value + return data.value + except AttributeError: + pass + + try: + if isinstance(data, unicode): # type: ignore + # Don't change it, JSON and XML ElementTree are totally able + # to serialize correctly u'' strings + return data + except NameError: + return str(data) + return str(data) + + def serialize_iter(self, data, iter_type, div=None, **kwargs): + """Serialize iterable. + + Supported kwargs: + - serialization_ctxt dict : The current entry of _attribute_map, or same format. + serialization_ctxt['type'] should be same as data_type. + - is_xml bool : If set, serialize as XML + + :param list data: Object to be serialized. + :param str iter_type: Type of object in the iterable. + :param str div: If set, this str will be used to combine the elements + in the iterable into a combined string. Default is 'None'. + Defaults to False. + :rtype: list, str + :return: serialized iterable + """ + if isinstance(data, str): + raise SerializationError("Refuse str type as a valid iter type.") + + serialization_ctxt = kwargs.get("serialization_ctxt", {}) + is_xml = kwargs.get("is_xml", False) + + serialized = [] + for d in data: + try: + serialized.append(self.serialize_data(d, iter_type, **kwargs)) + except ValueError as err: + if isinstance(err, SerializationError): + raise + serialized.append(None) + + if kwargs.get("do_quote", False): + serialized = ["" if s is None else quote(str(s), safe="") for s in serialized] + + if div: + serialized = ["" if s is None else str(s) for s in serialized] + serialized = div.join(serialized) + + if "xml" in serialization_ctxt or is_xml: + # XML serialization is more complicated + xml_desc = serialization_ctxt.get("xml", {}) + xml_name = xml_desc.get("name") + if not xml_name: + xml_name = serialization_ctxt["key"] + + # Create a wrap node if necessary (use the fact that Element and list have "append") + is_wrapped = xml_desc.get("wrapped", False) + node_name = xml_desc.get("itemsName", xml_name) + if is_wrapped: + final_result = _create_xml_node(xml_name, xml_desc.get("prefix", None), xml_desc.get("ns", None)) + else: + final_result = [] + # All list elements to "local_node" + for el in serialized: + if isinstance(el, ET.Element): + el_node = el + else: + el_node = _create_xml_node(node_name, xml_desc.get("prefix", None), xml_desc.get("ns", None)) + if el is not None: # Otherwise it writes "None" :-p + el_node.text = str(el) + final_result.append(el_node) + return final_result + return serialized + + def serialize_dict(self, attr, dict_type, **kwargs): + """Serialize a dictionary of objects. + + :param dict attr: Object to be serialized. + :param str dict_type: Type of object in the dictionary. + :rtype: dict + :return: serialized dictionary + """ + serialization_ctxt = kwargs.get("serialization_ctxt", {}) + serialized = {} + for key, value in attr.items(): + try: + serialized[self.serialize_unicode(key)] = self.serialize_data(value, dict_type, **kwargs) + except ValueError as err: + if isinstance(err, SerializationError): + raise + serialized[self.serialize_unicode(key)] = None + + if "xml" in serialization_ctxt: + # XML serialization is more complicated + xml_desc = serialization_ctxt["xml"] + xml_name = xml_desc["name"] + + final_result = _create_xml_node(xml_name, xml_desc.get("prefix", None), xml_desc.get("ns", None)) + for key, value in serialized.items(): + ET.SubElement(final_result, key).text = value + return final_result + + return serialized + + def serialize_object(self, attr, **kwargs): # pylint: disable=too-many-return-statements + """Serialize a generic object. + This will be handled as a dictionary. If object passed in is not + a basic type (str, int, float, dict, list) it will simply be + cast to str. + + :param dict attr: Object to be serialized. + :rtype: dict or str + :return: serialized object + """ + if attr is None: + return None + if isinstance(attr, ET.Element): + return attr + obj_type = type(attr) + if obj_type in self.basic_types: + return self.serialize_basic(attr, self.basic_types[obj_type], **kwargs) + if obj_type is _long_type: + return self.serialize_long(attr) + if obj_type is str: + return self.serialize_unicode(attr) + if obj_type is datetime.datetime: + return self.serialize_iso(attr) + if obj_type is datetime.date: + return self.serialize_date(attr) + if obj_type is datetime.time: + return self.serialize_time(attr) + if obj_type is datetime.timedelta: + return self.serialize_duration(attr) + if obj_type is decimal.Decimal: + return self.serialize_decimal(attr) + + # If it's a model or I know this dependency, serialize as a Model + if obj_type in self.dependencies.values() or isinstance(attr, Model): + return self._serialize(attr) + + if obj_type == dict: + serialized = {} + for key, value in attr.items(): + try: + serialized[self.serialize_unicode(key)] = self.serialize_object(value, **kwargs) + except ValueError: + serialized[self.serialize_unicode(key)] = None + return serialized + + if obj_type == list: + serialized = [] + for obj in attr: + try: + serialized.append(self.serialize_object(obj, **kwargs)) + except ValueError: + pass + return serialized + return str(attr) + + @staticmethod + def serialize_enum(attr, enum_obj=None): + try: + result = attr.value + except AttributeError: + result = attr + try: + enum_obj(result) # type: ignore + return result + except ValueError as exc: + for enum_value in enum_obj: # type: ignore + if enum_value.value.lower() == str(attr).lower(): + return enum_value.value + error = "{!r} is not valid value for enum {!r}" + raise SerializationError(error.format(attr, enum_obj)) from exc + + @staticmethod + def serialize_bytearray(attr, **kwargs): # pylint: disable=unused-argument + """Serialize bytearray into base-64 string. + + :param str attr: Object to be serialized. + :rtype: str + :return: serialized base64 + """ + return b64encode(attr).decode() + + @staticmethod + def serialize_base64(attr, **kwargs): # pylint: disable=unused-argument + """Serialize str into base-64 string. + + :param str attr: Object to be serialized. + :rtype: str + :return: serialized base64 + """ + encoded = b64encode(attr).decode("ascii") + return encoded.strip("=").replace("+", "-").replace("/", "_") + + @staticmethod + def serialize_decimal(attr, **kwargs): # pylint: disable=unused-argument + """Serialize Decimal object to float. + + :param decimal attr: Object to be serialized. + :rtype: float + :return: serialized decimal + """ + return float(attr) + + @staticmethod + def serialize_long(attr, **kwargs): # pylint: disable=unused-argument + """Serialize long (Py2) or int (Py3). + + :param int attr: Object to be serialized. + :rtype: int/long + :return: serialized long + """ + return _long_type(attr) + + @staticmethod + def serialize_date(attr, **kwargs): # pylint: disable=unused-argument + """Serialize Date object into ISO-8601 formatted string. + + :param Date attr: Object to be serialized. + :rtype: str + :return: serialized date + """ + if isinstance(attr, str): + attr = isodate.parse_date(attr) + t = "{:04}-{:02}-{:02}".format(attr.year, attr.month, attr.day) + return t + + @staticmethod + def serialize_time(attr, **kwargs): # pylint: disable=unused-argument + """Serialize Time object into ISO-8601 formatted string. + + :param datetime.time attr: Object to be serialized. + :rtype: str + :return: serialized time + """ + if isinstance(attr, str): + attr = isodate.parse_time(attr) + t = "{:02}:{:02}:{:02}".format(attr.hour, attr.minute, attr.second) + if attr.microsecond: + t += ".{:02}".format(attr.microsecond) + return t + + @staticmethod + def serialize_duration(attr, **kwargs): # pylint: disable=unused-argument + """Serialize TimeDelta object into ISO-8601 formatted string. + + :param TimeDelta attr: Object to be serialized. + :rtype: str + :return: serialized duration + """ + if isinstance(attr, str): + attr = isodate.parse_duration(attr) + return isodate.duration_isoformat(attr) + + @staticmethod + def serialize_rfc(attr, **kwargs): # pylint: disable=unused-argument + """Serialize Datetime object into RFC-1123 formatted string. + + :param Datetime attr: Object to be serialized. + :rtype: str + :raises TypeError: if format invalid. + :return: serialized rfc + """ + try: + if not attr.tzinfo: + _LOGGER.warning("Datetime with no tzinfo will be considered UTC.") + utc = attr.utctimetuple() + except AttributeError as exc: + raise TypeError("RFC1123 object must be valid Datetime object.") from exc + + return "{}, {:02} {} {:04} {:02}:{:02}:{:02} GMT".format( + Serializer.days[utc.tm_wday], + utc.tm_mday, + Serializer.months[utc.tm_mon], + utc.tm_year, + utc.tm_hour, + utc.tm_min, + utc.tm_sec, + ) + + @staticmethod + def serialize_iso(attr, **kwargs): # pylint: disable=unused-argument + """Serialize Datetime object into ISO-8601 formatted string. + + :param Datetime attr: Object to be serialized. + :rtype: str + :raises SerializationError: if format invalid. + :return: serialized iso + """ + if isinstance(attr, str): + attr = isodate.parse_datetime(attr) + try: + if not attr.tzinfo: + _LOGGER.warning("Datetime with no tzinfo will be considered UTC.") + utc = attr.utctimetuple() + if utc.tm_year > 9999 or utc.tm_year < 1: + raise OverflowError("Hit max or min date") + + microseconds = str(attr.microsecond).rjust(6, "0").rstrip("0").ljust(3, "0") + if microseconds: + microseconds = "." + microseconds + date = "{:04}-{:02}-{:02}T{:02}:{:02}:{:02}".format( + utc.tm_year, utc.tm_mon, utc.tm_mday, utc.tm_hour, utc.tm_min, utc.tm_sec + ) + return date + microseconds + "Z" + except (ValueError, OverflowError) as err: + msg = "Unable to serialize datetime object." + raise SerializationError(msg) from err + except AttributeError as err: + msg = "ISO-8601 object must be valid Datetime object." + raise TypeError(msg) from err + + @staticmethod + def serialize_unix(attr, **kwargs): # pylint: disable=unused-argument + """Serialize Datetime object into IntTime format. + This is represented as seconds. + + :param Datetime attr: Object to be serialized. + :rtype: int + :raises SerializationError: if format invalid + :return: serialied unix + """ + if isinstance(attr, int): + return attr + try: + if not attr.tzinfo: + _LOGGER.warning("Datetime with no tzinfo will be considered UTC.") + return int(calendar.timegm(attr.utctimetuple())) + except AttributeError as exc: + raise TypeError("Unix time object must be valid Datetime object.") from exc + + +def rest_key_extractor(attr, attr_desc, data): # pylint: disable=unused-argument + key = attr_desc["key"] + working_data = data + + while "." in key: + # Need the cast, as for some reasons "split" is typed as list[str | Any] + dict_keys = cast(List[str], _FLATTEN.split(key)) + if len(dict_keys) == 1: + key = _decode_attribute_map_key(dict_keys[0]) + break + working_key = _decode_attribute_map_key(dict_keys[0]) + working_data = working_data.get(working_key, data) + if working_data is None: + # If at any point while following flatten JSON path see None, it means + # that all properties under are None as well + return None + key = ".".join(dict_keys[1:]) + + return working_data.get(key) + + +def rest_key_case_insensitive_extractor( # pylint: disable=unused-argument, inconsistent-return-statements + attr, attr_desc, data +): + key = attr_desc["key"] + working_data = data + + while "." in key: + dict_keys = _FLATTEN.split(key) + if len(dict_keys) == 1: + key = _decode_attribute_map_key(dict_keys[0]) + break + working_key = _decode_attribute_map_key(dict_keys[0]) + working_data = attribute_key_case_insensitive_extractor(working_key, None, working_data) + if working_data is None: + # If at any point while following flatten JSON path see None, it means + # that all properties under are None as well + return None + key = ".".join(dict_keys[1:]) + + if working_data: + return attribute_key_case_insensitive_extractor(key, None, working_data) + + +def last_rest_key_extractor(attr, attr_desc, data): # pylint: disable=unused-argument + """Extract the attribute in "data" based on the last part of the JSON path key. + + :param str attr: The attribute to extract + :param dict attr_desc: The attribute description + :param dict data: The data to extract from + :rtype: object + :returns: The extracted attribute + """ + key = attr_desc["key"] + dict_keys = _FLATTEN.split(key) + return attribute_key_extractor(dict_keys[-1], None, data) + + +def last_rest_key_case_insensitive_extractor(attr, attr_desc, data): # pylint: disable=unused-argument + """Extract the attribute in "data" based on the last part of the JSON path key. + + This is the case insensitive version of "last_rest_key_extractor" + :param str attr: The attribute to extract + :param dict attr_desc: The attribute description + :param dict data: The data to extract from + :rtype: object + :returns: The extracted attribute + """ + key = attr_desc["key"] + dict_keys = _FLATTEN.split(key) + return attribute_key_case_insensitive_extractor(dict_keys[-1], None, data) + + +def attribute_key_extractor(attr, _, data): + return data.get(attr) + + +def attribute_key_case_insensitive_extractor(attr, _, data): + found_key = None + lower_attr = attr.lower() + for key in data: + if lower_attr == key.lower(): + found_key = key + break + + return data.get(found_key) + + +def _extract_name_from_internal_type(internal_type): + """Given an internal type XML description, extract correct XML name with namespace. + + :param dict internal_type: An model type + :rtype: tuple + :returns: A tuple XML name + namespace dict + """ + internal_type_xml_map = getattr(internal_type, "_xml_map", {}) + xml_name = internal_type_xml_map.get("name", internal_type.__name__) + xml_ns = internal_type_xml_map.get("ns", None) + if xml_ns: + xml_name = "{{{}}}{}".format(xml_ns, xml_name) + return xml_name + + +def xml_key_extractor(attr, attr_desc, data): # pylint: disable=unused-argument,too-many-return-statements + if isinstance(data, dict): + return None + + # Test if this model is XML ready first + if not isinstance(data, ET.Element): + return None + + xml_desc = attr_desc.get("xml", {}) + xml_name = xml_desc.get("name", attr_desc["key"]) + + # Look for a children + is_iter_type = attr_desc["type"].startswith("[") + is_wrapped = xml_desc.get("wrapped", False) + internal_type = attr_desc.get("internalType", None) + internal_type_xml_map = getattr(internal_type, "_xml_map", {}) + + # Integrate namespace if necessary + xml_ns = xml_desc.get("ns", internal_type_xml_map.get("ns", None)) + if xml_ns: + xml_name = "{{{}}}{}".format(xml_ns, xml_name) + + # If it's an attribute, that's simple + if xml_desc.get("attr", False): + return data.get(xml_name) + + # If it's x-ms-text, that's simple too + if xml_desc.get("text", False): + return data.text + + # Scenario where I take the local name: + # - Wrapped node + # - Internal type is an enum (considered basic types) + # - Internal type has no XML/Name node + if is_wrapped or (internal_type and (issubclass(internal_type, Enum) or "name" not in internal_type_xml_map)): + children = data.findall(xml_name) + # If internal type has a local name and it's not a list, I use that name + elif not is_iter_type and internal_type and "name" in internal_type_xml_map: + xml_name = _extract_name_from_internal_type(internal_type) + children = data.findall(xml_name) + # That's an array + else: + if internal_type: # Complex type, ignore itemsName and use the complex type name + items_name = _extract_name_from_internal_type(internal_type) + else: + items_name = xml_desc.get("itemsName", xml_name) + children = data.findall(items_name) + + if len(children) == 0: + if is_iter_type: + if is_wrapped: + return None # is_wrapped no node, we want None + return [] # not wrapped, assume empty list + return None # Assume it's not there, maybe an optional node. + + # If is_iter_type and not wrapped, return all found children + if is_iter_type: + if not is_wrapped: + return children + # Iter and wrapped, should have found one node only (the wrap one) + if len(children) != 1: + raise DeserializationError( + "Tried to deserialize an array not wrapped, and found several nodes '{}'. Maybe you should declare this array as wrapped?".format( + xml_name + ) + ) + return list(children[0]) # Might be empty list and that's ok. + + # Here it's not a itertype, we should have found one element only or empty + if len(children) > 1: + raise DeserializationError("Find several XML '{}' where it was not expected".format(xml_name)) + return children[0] + + +class Deserializer: + """Response object model deserializer. + + :param dict classes: Class type dictionary for deserializing complex types. + :ivar list key_extractors: Ordered list of extractors to be used by this deserializer. + """ + + basic_types = {str: "str", int: "int", bool: "bool", float: "float"} + + valid_date = re.compile(r"\d{4}[-]\d{2}[-]\d{2}T\d{2}:\d{2}:\d{2}\.?\d*Z?[-+]?[\d{2}]?:?[\d{2}]?") + + def __init__(self, classes: Optional[Mapping[str, type]] = None) -> None: + self.deserialize_type = { + "iso-8601": Deserializer.deserialize_iso, + "rfc-1123": Deserializer.deserialize_rfc, + "unix-time": Deserializer.deserialize_unix, + "duration": Deserializer.deserialize_duration, + "date": Deserializer.deserialize_date, + "time": Deserializer.deserialize_time, + "decimal": Deserializer.deserialize_decimal, + "long": Deserializer.deserialize_long, + "bytearray": Deserializer.deserialize_bytearray, + "base64": Deserializer.deserialize_base64, + "object": self.deserialize_object, + "[]": self.deserialize_iter, + "{}": self.deserialize_dict, + } + self.deserialize_expected_types = { + "duration": (isodate.Duration, datetime.timedelta), + "iso-8601": (datetime.datetime), + } + self.dependencies: Dict[str, type] = dict(classes) if classes else {} + self.key_extractors = [rest_key_extractor, xml_key_extractor] + # Additional properties only works if the "rest_key_extractor" is used to + # extract the keys. Making it to work whatever the key extractor is too much + # complicated, with no real scenario for now. + # So adding a flag to disable additional properties detection. This flag should be + # used if your expect the deserialization to NOT come from a JSON REST syntax. + # Otherwise, result are unexpected + self.additional_properties_detection = True + + def __call__(self, target_obj, response_data, content_type=None): + """Call the deserializer to process a REST response. + + :param str target_obj: Target data type to deserialize to. + :param requests.Response response_data: REST response object. + :param str content_type: Swagger "produces" if available. + :raises DeserializationError: if deserialization fails. + :return: Deserialized object. + :rtype: object + """ + data = self._unpack_content(response_data, content_type) + return self._deserialize(target_obj, data) + + def _deserialize(self, target_obj, data): # pylint: disable=inconsistent-return-statements + """Call the deserializer on a model. + + Data needs to be already deserialized as JSON or XML ElementTree + + :param str target_obj: Target data type to deserialize to. + :param object data: Object to deserialize. + :raises DeserializationError: if deserialization fails. + :return: Deserialized object. + :rtype: object + """ + # This is already a model, go recursive just in case + if hasattr(data, "_attribute_map"): + constants = [name for name, config in getattr(data, "_validation", {}).items() if config.get("constant")] + try: + for attr, mapconfig in data._attribute_map.items(): # pylint: disable=protected-access + if attr in constants: + continue + value = getattr(data, attr) + if value is None: + continue + local_type = mapconfig["type"] + internal_data_type = local_type.strip("[]{}") + if internal_data_type not in self.dependencies or isinstance(internal_data_type, Enum): + continue + setattr(data, attr, self._deserialize(local_type, value)) + return data + except AttributeError: + return + + response, class_name = self._classify_target(target_obj, data) + + if isinstance(response, str): + return self.deserialize_data(data, response) + if isinstance(response, type) and issubclass(response, Enum): + return self.deserialize_enum(data, response) + + if data is None or data is CoreNull: + return data + try: + attributes = response._attribute_map # type: ignore # pylint: disable=protected-access + d_attrs = {} + for attr, attr_desc in attributes.items(): + # Check empty string. If it's not empty, someone has a real "additionalProperties"... + if attr == "additional_properties" and attr_desc["key"] == "": + continue + raw_value = None + # Enhance attr_desc with some dynamic data + attr_desc = attr_desc.copy() # Do a copy, do not change the real one + internal_data_type = attr_desc["type"].strip("[]{}") + if internal_data_type in self.dependencies: + attr_desc["internalType"] = self.dependencies[internal_data_type] + + for key_extractor in self.key_extractors: + found_value = key_extractor(attr, attr_desc, data) + if found_value is not None: + if raw_value is not None and raw_value != found_value: + msg = ( + "Ignoring extracted value '%s' from %s for key '%s'" + " (duplicate extraction, follow extractors order)" + ) + _LOGGER.warning(msg, found_value, key_extractor, attr) + continue + raw_value = found_value + + value = self.deserialize_data(raw_value, attr_desc["type"]) + d_attrs[attr] = value + except (AttributeError, TypeError, KeyError) as err: + msg = "Unable to deserialize to object: " + class_name # type: ignore + raise DeserializationError(msg) from err + additional_properties = self._build_additional_properties(attributes, data) + return self._instantiate_model(response, d_attrs, additional_properties) + + def _build_additional_properties(self, attribute_map, data): + if not self.additional_properties_detection: + return None + if "additional_properties" in attribute_map and attribute_map.get("additional_properties", {}).get("key") != "": + # Check empty string. If it's not empty, someone has a real "additionalProperties" + return None + if isinstance(data, ET.Element): + data = {el.tag: el.text for el in data} + + known_keys = { + _decode_attribute_map_key(_FLATTEN.split(desc["key"])[0]) + for desc in attribute_map.values() + if desc["key"] != "" + } + present_keys = set(data.keys()) + missing_keys = present_keys - known_keys + return {key: data[key] for key in missing_keys} + + def _classify_target(self, target, data): + """Check to see whether the deserialization target object can + be classified into a subclass. + Once classification has been determined, initialize object. + + :param str target: The target object type to deserialize to. + :param str/dict data: The response data to deserialize. + :return: The classified target object and its class name. + :rtype: tuple + """ + if target is None: + return None, None + + if isinstance(target, str): + try: + target = self.dependencies[target] + except KeyError: + return target, target + + try: + target = target._classify(data, self.dependencies) # type: ignore # pylint: disable=protected-access + except AttributeError: + pass # Target is not a Model, no classify + return target, target.__class__.__name__ # type: ignore + + def failsafe_deserialize(self, target_obj, data, content_type=None): + """Ignores any errors encountered in deserialization, + and falls back to not deserializing the object. Recommended + for use in error deserialization, as we want to return the + HttpResponseError to users, and not have them deal with + a deserialization error. + + :param str target_obj: The target object type to deserialize to. + :param str/dict data: The response data to deserialize. + :param str content_type: Swagger "produces" if available. + :return: Deserialized object. + :rtype: object + """ + try: + return self(target_obj, data, content_type=content_type) + except: # pylint: disable=bare-except + _LOGGER.debug( + "Ran into a deserialization error. Ignoring since this is failsafe deserialization", exc_info=True + ) + return None + + @staticmethod + def _unpack_content(raw_data, content_type=None): + """Extract the correct structure for deserialization. + + If raw_data is a PipelineResponse, try to extract the result of RawDeserializer. + if we can't, raise. Your Pipeline should have a RawDeserializer. + + If not a pipeline response and raw_data is bytes or string, use content-type + to decode it. If no content-type, try JSON. + + If raw_data is something else, bypass all logic and return it directly. + + :param obj raw_data: Data to be processed. + :param str content_type: How to parse if raw_data is a string/bytes. + :raises JSONDecodeError: If JSON is requested and parsing is impossible. + :raises UnicodeDecodeError: If bytes is not UTF8 + :rtype: object + :return: Unpacked content. + """ + # Assume this is enough to detect a Pipeline Response without importing it + context = getattr(raw_data, "context", {}) + if context: + if RawDeserializer.CONTEXT_NAME in context: + return context[RawDeserializer.CONTEXT_NAME] + raise ValueError("This pipeline didn't have the RawDeserializer policy; can't deserialize") + + # Assume this is enough to recognize universal_http.ClientResponse without importing it + if hasattr(raw_data, "body"): + return RawDeserializer.deserialize_from_http_generics(raw_data.text(), raw_data.headers) + + # Assume this enough to recognize requests.Response without importing it. + if hasattr(raw_data, "_content_consumed"): + return RawDeserializer.deserialize_from_http_generics(raw_data.text, raw_data.headers) + + if isinstance(raw_data, (str, bytes)) or hasattr(raw_data, "read"): + return RawDeserializer.deserialize_from_text(raw_data, content_type) # type: ignore + return raw_data + + def _instantiate_model(self, response, attrs, additional_properties=None): + """Instantiate a response model passing in deserialized args. + + :param Response response: The response model class. + :param dict attrs: The deserialized response attributes. + :param dict additional_properties: Additional properties to be set. + :rtype: Response + :return: The instantiated response model. + """ + if callable(response): + subtype = getattr(response, "_subtype_map", {}) + try: + readonly = [ + k + for k, v in response._validation.items() # pylint: disable=protected-access # type: ignore + if v.get("readonly") + ] + const = [ + k + for k, v in response._validation.items() # pylint: disable=protected-access # type: ignore + if v.get("constant") + ] + kwargs = {k: v for k, v in attrs.items() if k not in subtype and k not in readonly + const} + response_obj = response(**kwargs) + for attr in readonly: + setattr(response_obj, attr, attrs.get(attr)) + if additional_properties: + response_obj.additional_properties = additional_properties # type: ignore + return response_obj + except TypeError as err: + msg = "Unable to deserialize {} into model {}. ".format(kwargs, response) # type: ignore + raise DeserializationError(msg + str(err)) from err + else: + try: + for attr, value in attrs.items(): + setattr(response, attr, value) + return response + except Exception as exp: + msg = "Unable to populate response model. " + msg += "Type: {}, Error: {}".format(type(response), exp) + raise DeserializationError(msg) from exp + + def deserialize_data(self, data, data_type): # pylint: disable=too-many-return-statements + """Process data for deserialization according to data type. + + :param str data: The response string to be deserialized. + :param str data_type: The type to deserialize to. + :raises DeserializationError: if deserialization fails. + :return: Deserialized object. + :rtype: object + """ + if data is None: + return data + + try: + if not data_type: + return data + if data_type in self.basic_types.values(): + return self.deserialize_basic(data, data_type) + if data_type in self.deserialize_type: + if isinstance(data, self.deserialize_expected_types.get(data_type, tuple())): + return data + + is_a_text_parsing_type = lambda x: x not in [ # pylint: disable=unnecessary-lambda-assignment + "object", + "[]", + r"{}", + ] + if isinstance(data, ET.Element) and is_a_text_parsing_type(data_type) and not data.text: + return None + data_val = self.deserialize_type[data_type](data) + return data_val + + iter_type = data_type[0] + data_type[-1] + if iter_type in self.deserialize_type: + return self.deserialize_type[iter_type](data, data_type[1:-1]) + + obj_type = self.dependencies[data_type] + if issubclass(obj_type, Enum): + if isinstance(data, ET.Element): + data = data.text + return self.deserialize_enum(data, obj_type) + + except (ValueError, TypeError, AttributeError) as err: + msg = "Unable to deserialize response data." + msg += " Data: {}, {}".format(data, data_type) + raise DeserializationError(msg) from err + return self._deserialize(obj_type, data) + + def deserialize_iter(self, attr, iter_type): + """Deserialize an iterable. + + :param list attr: Iterable to be deserialized. + :param str iter_type: The type of object in the iterable. + :return: Deserialized iterable. + :rtype: list + """ + if attr is None: + return None + if isinstance(attr, ET.Element): # If I receive an element here, get the children + attr = list(attr) + if not isinstance(attr, (list, set)): + raise DeserializationError("Cannot deserialize as [{}] an object of type {}".format(iter_type, type(attr))) + return [self.deserialize_data(a, iter_type) for a in attr] + + def deserialize_dict(self, attr, dict_type): + """Deserialize a dictionary. + + :param dict/list attr: Dictionary to be deserialized. Also accepts + a list of key, value pairs. + :param str dict_type: The object type of the items in the dictionary. + :return: Deserialized dictionary. + :rtype: dict + """ + if isinstance(attr, list): + return {x["key"]: self.deserialize_data(x["value"], dict_type) for x in attr} + + if isinstance(attr, ET.Element): + # Transform value into {"Key": "value"} + attr = {el.tag: el.text for el in attr} + return {k: self.deserialize_data(v, dict_type) for k, v in attr.items()} + + def deserialize_object(self, attr, **kwargs): # pylint: disable=too-many-return-statements + """Deserialize a generic object. + This will be handled as a dictionary. + + :param dict attr: Dictionary to be deserialized. + :return: Deserialized object. + :rtype: dict + :raises TypeError: if non-builtin datatype encountered. + """ + if attr is None: + return None + if isinstance(attr, ET.Element): + # Do no recurse on XML, just return the tree as-is + return attr + if isinstance(attr, str): + return self.deserialize_basic(attr, "str") + obj_type = type(attr) + if obj_type in self.basic_types: + return self.deserialize_basic(attr, self.basic_types[obj_type]) + if obj_type is _long_type: + return self.deserialize_long(attr) + + if obj_type == dict: + deserialized = {} + for key, value in attr.items(): + try: + deserialized[key] = self.deserialize_object(value, **kwargs) + except ValueError: + deserialized[key] = None + return deserialized + + if obj_type == list: + deserialized = [] + for obj in attr: + try: + deserialized.append(self.deserialize_object(obj, **kwargs)) + except ValueError: + pass + return deserialized + + error = "Cannot deserialize generic object with type: " + raise TypeError(error + str(obj_type)) + + def deserialize_basic(self, attr, data_type): # pylint: disable=too-many-return-statements + """Deserialize basic builtin data type from string. + Will attempt to convert to str, int, float and bool. + This function will also accept '1', '0', 'true' and 'false' as + valid bool values. + + :param str attr: response string to be deserialized. + :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. + """ + # If we're here, data is supposed to be a basic type. + # If it's still an XML node, take the text + if isinstance(attr, ET.Element): + attr = attr.text + if not attr: + if data_type == "str": + # None or '', node is empty string. + return "" + # None or '', node with a strong type is None. + # Don't try to model "empty bool" or "empty int" + return None + + if data_type == "bool": + if attr in [True, False, 1, 0]: + return bool(attr) + if isinstance(attr, str): + if attr.lower() in ["true", "1"]: + return True + if attr.lower() in ["false", "0"]: + return False + raise TypeError("Invalid boolean value: {}".format(attr)) + + if data_type == "str": + return self.deserialize_unicode(attr) + return eval(data_type)(attr) # nosec # pylint: disable=eval-used + + @staticmethod + def deserialize_unicode(data): + """Preserve unicode objects in Python 2, otherwise return data + as a string. + + :param str data: response string to be deserialized. + :return: Deserialized string. + :rtype: str or unicode + """ + # We might be here because we have an enum modeled as string, + # and we try to deserialize a partial dict with enum inside + if isinstance(data, Enum): + return data + + # Consider this is real string + try: + if isinstance(data, unicode): # type: ignore + return data + except NameError: + return str(data) + return str(data) + + @staticmethod + def deserialize_enum(data, enum_obj): + """Deserialize string into enum object. + + If the string is not a valid enum value it will be returned as-is + and a warning will be logged. + + :param str data: Response string to be deserialized. If this value is + None or invalid it will be returned as-is. + :param Enum enum_obj: Enum object to deserialize to. + :return: Deserialized enum object. + :rtype: Enum + """ + if isinstance(data, enum_obj) or data is None: + return data + if isinstance(data, Enum): + data = data.value + if isinstance(data, int): + # Workaround. We might consider remove it in the future. + try: + return list(enum_obj.__members__.values())[data] + except IndexError as exc: + error = "{!r} is not a valid index for enum {!r}" + raise DeserializationError(error.format(data, enum_obj)) from exc + try: + return enum_obj(str(data)) + except ValueError: + for enum_value in enum_obj: + if enum_value.value.lower() == str(data).lower(): + return enum_value + # We don't fail anymore for unknown value, we deserialize as a string + _LOGGER.warning("Deserializer is not able to find %s as valid enum in %s", data, enum_obj) + return Deserializer.deserialize_unicode(data) + + @staticmethod + def deserialize_bytearray(attr): + """Deserialize string into bytearray. + + :param str attr: response string to be deserialized. + :return: Deserialized bytearray + :rtype: bytearray + :raises TypeError: if string format invalid. + """ + if isinstance(attr, ET.Element): + attr = attr.text + return bytearray(b64decode(attr)) # type: ignore + + @staticmethod + def deserialize_base64(attr): + """Deserialize base64 encoded string into string. + + :param str attr: response string to be deserialized. + :return: Deserialized base64 string + :rtype: bytearray + :raises TypeError: if string format invalid. + """ + if isinstance(attr, ET.Element): + attr = attr.text + padding = "=" * (3 - (len(attr) + 3) % 4) # type: ignore + attr = attr + padding # type: ignore + encoded = attr.replace("-", "+").replace("_", "/") + return b64decode(encoded) + + @staticmethod + def deserialize_decimal(attr): + """Deserialize string into Decimal object. + + :param str attr: response string to be deserialized. + :return: Deserialized decimal + :raises DeserializationError: if string format invalid. + :rtype: decimal + """ + if isinstance(attr, ET.Element): + attr = attr.text + try: + return decimal.Decimal(str(attr)) # type: ignore + except decimal.DecimalException as err: + msg = "Invalid decimal {}".format(attr) + raise DeserializationError(msg) from err + + @staticmethod + def deserialize_long(attr): + """Deserialize string into long (Py2) or int (Py3). + + :param str attr: response string to be deserialized. + :return: Deserialized int + :rtype: long or int + :raises ValueError: if string format invalid. + """ + if isinstance(attr, ET.Element): + attr = attr.text + return _long_type(attr) # type: ignore + + @staticmethod + def deserialize_duration(attr): + """Deserialize ISO-8601 formatted string into TimeDelta object. + + :param str attr: response string to be deserialized. + :return: Deserialized duration + :rtype: TimeDelta + :raises DeserializationError: if string format invalid. + """ + if isinstance(attr, ET.Element): + attr = attr.text + try: + duration = isodate.parse_duration(attr) + except (ValueError, OverflowError, AttributeError) as err: + msg = "Cannot deserialize duration object." + raise DeserializationError(msg) from err + return duration + + @staticmethod + def deserialize_date(attr): + """Deserialize ISO-8601 formatted string into Date object. + + :param str attr: response string to be deserialized. + :return: Deserialized date + :rtype: Date + :raises DeserializationError: if string format invalid. + """ + if isinstance(attr, ET.Element): + attr = attr.text + if re.search(r"[^\W\d_]", attr, re.I + re.U): # type: ignore + raise DeserializationError("Date must have only digits and -. Received: %s" % attr) + # This must NOT use defaultmonth/defaultday. Using None ensure this raises an exception. + return isodate.parse_date(attr, defaultmonth=0, defaultday=0) + + @staticmethod + def deserialize_time(attr): + """Deserialize ISO-8601 formatted string into time object. + + :param str attr: response string to be deserialized. + :return: Deserialized time + :rtype: datetime.time + :raises DeserializationError: if string format invalid. + """ + if isinstance(attr, ET.Element): + attr = attr.text + if re.search(r"[^\W\d_]", attr, re.I + re.U): # type: ignore + raise DeserializationError("Date must have only digits and -. Received: %s" % attr) + return isodate.parse_time(attr) + + @staticmethod + def deserialize_rfc(attr): + """Deserialize RFC-1123 formatted string into Datetime object. + + :param str attr: response string to be deserialized. + :return: Deserialized RFC datetime + :rtype: Datetime + :raises DeserializationError: if string format invalid. + """ + if isinstance(attr, ET.Element): + attr = attr.text + try: + parsed_date = email.utils.parsedate_tz(attr) # type: ignore + date_obj = datetime.datetime( + *parsed_date[:6], tzinfo=datetime.timezone(datetime.timedelta(minutes=(parsed_date[9] or 0) / 60)) + ) + if not date_obj.tzinfo: + date_obj = date_obj.astimezone(tz=TZ_UTC) + except ValueError as err: + msg = "Cannot deserialize to rfc datetime object." + raise DeserializationError(msg) from err + return date_obj + + @staticmethod + def deserialize_iso(attr): + """Deserialize ISO-8601 formatted string into Datetime object. + + :param str attr: response string to be deserialized. + :return: Deserialized ISO datetime + :rtype: Datetime + :raises DeserializationError: if string format invalid. + """ + if isinstance(attr, ET.Element): + attr = attr.text + try: + attr = attr.upper() # type: ignore + match = Deserializer.valid_date.match(attr) + if not match: + raise ValueError("Invalid datetime string: " + attr) + + check_decimal = attr.split(".") + if len(check_decimal) > 1: + decimal_str = "" + for digit in check_decimal[1]: + if digit.isdigit(): + decimal_str += digit + else: + break + if len(decimal_str) > 6: + attr = attr.replace(decimal_str, decimal_str[0:6]) + + date_obj = isodate.parse_datetime(attr) + test_utc = date_obj.utctimetuple() + if test_utc.tm_year > 9999 or test_utc.tm_year < 1: + raise OverflowError("Hit max or min date") + except (ValueError, OverflowError, AttributeError) as err: + msg = "Cannot deserialize datetime object." + raise DeserializationError(msg) from err + return date_obj + + @staticmethod + def deserialize_unix(attr): + """Serialize Datetime object into IntTime format. + This is represented as seconds. + + :param int attr: Object to be serialized. + :return: Deserialized datetime + :rtype: Datetime + :raises DeserializationError: if format invalid + """ + if isinstance(attr, ET.Element): + attr = int(attr.text) # type: ignore + try: + attr = int(attr) + date_obj = datetime.datetime.fromtimestamp(attr, TZ_UTC) + except ValueError as err: + msg = "Cannot deserialize to unix datetime object." + raise DeserializationError(msg) from err + return date_obj diff --git a/sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/textanalytics/_utils/utils.py b/sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/textanalytics/_utils/utils.py new file mode 100644 index 000000000000..35c9c836f85f --- /dev/null +++ b/sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/textanalytics/_utils/utils.py @@ -0,0 +1,25 @@ +# -------------------------------------------------------------------------- +# 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. +# -------------------------------------------------------------------------- + +from abc import ABC +from typing import Generic, TYPE_CHECKING, TypeVar + +if TYPE_CHECKING: + from .serialization import Deserializer, Serializer + + +TClient = TypeVar("TClient") +TConfig = TypeVar("TConfig") + + +class ClientMixinABC(ABC, Generic[TClient, TConfig]): + """DO NOT use this class. It is for internal typing use only.""" + + _client: TClient + _config: TConfig + _serialize: "Serializer" + _deserialize: "Deserializer" diff --git a/sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/textanalytics/_version.py b/sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/textanalytics/_version.py new file mode 100644 index 000000000000..be71c81bd282 --- /dev/null +++ b/sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/textanalytics/_version.py @@ -0,0 +1,9 @@ +# 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. +# -------------------------------------------------------------------------- + +VERSION = "1.0.0b1" diff --git a/sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/textanalytics/aio/__init__.py b/sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/textanalytics/aio/__init__.py new file mode 100644 index 000000000000..ec7b738c9f3c --- /dev/null +++ b/sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/textanalytics/aio/__init__.py @@ -0,0 +1,29 @@ +# 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=wrong-import-position + +from typing import TYPE_CHECKING + +if TYPE_CHECKING: + from ._patch import * # pylint: disable=unused-wildcard-import + +from ._client import TextClient # type: ignore + +try: + from ._patch import __all__ as _patch_all + from ._patch import * +except ImportError: + _patch_all = [] +from ._patch import patch_sdk as _patch_sdk + +__all__ = [ + "TextClient", +] +__all__.extend([p for p in _patch_all if p not in __all__]) # pyright: ignore + +_patch_sdk() diff --git a/sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/textanalytics/aio/_client.py b/sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/textanalytics/aio/_client.py new file mode 100644 index 000000000000..e659bf3fdcfd --- /dev/null +++ b/sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/textanalytics/aio/_client.py @@ -0,0 +1,115 @@ +# pylint: disable=line-too-long,useless-suppression +# 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. +# -------------------------------------------------------------------------- + +from copy import deepcopy +from typing import Any, Awaitable, TYPE_CHECKING, Union +from typing_extensions import Self + +from azure.core import AsyncPipelineClient +from azure.core.credentials import AzureKeyCredential +from azure.core.pipeline import policies +from azure.core.rest import AsyncHttpResponse, HttpRequest + +from .._utils.serialization import Deserializer, Serializer +from ._configuration import TextClientConfiguration +from ._operations import _TextClientOperationsMixin + +if TYPE_CHECKING: + from azure.core.credentials_async import AsyncTokenCredential + + +class TextClient(_TextClientOperationsMixin): + """The language service API is a suite of natural language processing (NLP) skills 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, language + detection and question answering. Further documentation can be found in https://learn.microsoft.com/azure/cognitive-services/language-service/overview + https://learn.microsoft.com/azure/cognitive-services/language-service/overview>`_.0. + + :param endpoint: Supported Cognitive Services endpoint (e.g., + https://.api.cognitiveservices.azure.com). Required. + :type endpoint: str + :param credential: Credential used to authenticate requests to the service. Is either a key + credential type or a token credential type. Required. + :type credential: ~azure.core.credentials.AzureKeyCredential or + ~azure.core.credentials_async.AsyncTokenCredential + :keyword api_version: The API version to use for this operation. Default value is + "2025-05-15-preview". Note that overriding this default value may result in unsupported + behavior. + :paramtype api_version: str + :keyword int polling_interval: Default waiting time between two polls for LRO operations if no + Retry-After header is present. + """ + + def __init__( + self, endpoint: str, credential: Union[AzureKeyCredential, "AsyncTokenCredential"], **kwargs: Any + ) -> None: + _endpoint = "{Endpoint}/language" + self._config = TextClientConfiguration(endpoint=endpoint, credential=credential, **kwargs) + + _policies = kwargs.pop("policies", None) + if _policies is None: + _policies = [ + policies.RequestIdPolicy(**kwargs), + self._config.headers_policy, + self._config.user_agent_policy, + self._config.proxy_policy, + policies.ContentDecodePolicy(**kwargs), + self._config.redirect_policy, + self._config.retry_policy, + self._config.authentication_policy, + self._config.custom_hook_policy, + self._config.logging_policy, + policies.DistributedTracingPolicy(**kwargs), + policies.SensitiveHeaderCleanupPolicy(**kwargs) if self._config.redirect_policy else None, + self._config.http_logging_policy, + ] + self._client: AsyncPipelineClient = AsyncPipelineClient(base_url=_endpoint, policies=_policies, **kwargs) + + self._serialize = Serializer() + self._deserialize = Deserializer() + self._serialize.client_side_validation = False + + def send_request( + self, request: HttpRequest, *, stream: bool = False, **kwargs: Any + ) -> Awaitable[AsyncHttpResponse]: + """Runs the network request through the client's chained policies. + + >>> from azure.core.rest import HttpRequest + >>> request = HttpRequest("GET", "https://www.example.org/") + + >>> response = await client.send_request(request) + + + For more information on this code flow, see https://aka.ms/azsdk/dpcodegen/python/send_request + + :param request: The network request you want to make. Required. + :type request: ~azure.core.rest.HttpRequest + :keyword bool stream: Whether the response payload will be streamed. Defaults to False. + :return: The response of your network call. Does not do error handling on your response. + :rtype: ~azure.core.rest.AsyncHttpResponse + """ + + request_copy = deepcopy(request) + path_format_arguments = { + "Endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str"), + } + + request_copy.url = self._client.format_url(request_copy.url, **path_format_arguments) + return self._client.send_request(request_copy, stream=stream, **kwargs) # type: ignore + + async def close(self) -> None: + await self._client.close() + + async def __aenter__(self) -> Self: + await self._client.__aenter__() + return self + + async def __aexit__(self, *exc_details: Any) -> None: + await self._client.__aexit__(*exc_details) diff --git a/sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/textanalytics/aio/_configuration.py b/sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/textanalytics/aio/_configuration.py new file mode 100644 index 000000000000..35634bd1dacf --- /dev/null +++ b/sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/textanalytics/aio/_configuration.py @@ -0,0 +1,75 @@ +# 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. +# -------------------------------------------------------------------------- + +from typing import Any, TYPE_CHECKING, Union + +from azure.core.credentials import AzureKeyCredential +from azure.core.pipeline import policies + +from .._version import VERSION + +if TYPE_CHECKING: + from azure.core.credentials_async import AsyncTokenCredential + + +class TextClientConfiguration: # pylint: disable=too-many-instance-attributes + """Configuration for TextClient. + + Note that all parameters used to create this instance are saved as instance + attributes. + + :param endpoint: Supported Cognitive Services endpoint (e.g., + https://.api.cognitiveservices.azure.com). Required. + :type endpoint: str + :param credential: Credential used to authenticate requests to the service. Is either a key + credential type or a token credential type. Required. + :type credential: ~azure.core.credentials.AzureKeyCredential or + ~azure.core.credentials_async.AsyncTokenCredential + :keyword api_version: The API version to use for this operation. Default value is + "2025-05-15-preview". Note that overriding this default value may result in unsupported + behavior. + :paramtype api_version: str + """ + + def __init__( + self, endpoint: str, credential: Union[AzureKeyCredential, "AsyncTokenCredential"], **kwargs: Any + ) -> None: + api_version: str = kwargs.pop("api_version", "2025-05-15-preview") + + if endpoint is None: + raise ValueError("Parameter 'endpoint' must not be None.") + if credential is None: + raise ValueError("Parameter 'credential' must not be None.") + + self.endpoint = endpoint + self.credential = credential + self.api_version = api_version + self.credential_scopes = kwargs.pop("credential_scopes", ["https://cognitiveservices.azure.com/.default"]) + kwargs.setdefault("sdk_moniker", "ai-textanalytics/{}".format(VERSION)) + self.polling_interval = kwargs.get("polling_interval", 30) + self._configure(**kwargs) + + def _infer_policy(self, **kwargs): + if isinstance(self.credential, AzureKeyCredential): + return policies.AzureKeyCredentialPolicy(self.credential, "Ocp-Apim-Subscription-Key", **kwargs) + if hasattr(self.credential, "get_token"): + return policies.AsyncBearerTokenCredentialPolicy(self.credential, *self.credential_scopes, **kwargs) + raise TypeError(f"Unsupported credential: {self.credential}") + + def _configure(self, **kwargs: Any) -> None: + self.user_agent_policy = kwargs.get("user_agent_policy") or policies.UserAgentPolicy(**kwargs) + self.headers_policy = kwargs.get("headers_policy") or policies.HeadersPolicy(**kwargs) + self.proxy_policy = kwargs.get("proxy_policy") or policies.ProxyPolicy(**kwargs) + self.logging_policy = kwargs.get("logging_policy") or policies.NetworkTraceLoggingPolicy(**kwargs) + self.http_logging_policy = kwargs.get("http_logging_policy") or policies.HttpLoggingPolicy(**kwargs) + self.custom_hook_policy = kwargs.get("custom_hook_policy") or policies.CustomHookPolicy(**kwargs) + self.redirect_policy = kwargs.get("redirect_policy") or policies.AsyncRedirectPolicy(**kwargs) + self.retry_policy = kwargs.get("retry_policy") or policies.AsyncRetryPolicy(**kwargs) + self.authentication_policy = kwargs.get("authentication_policy") + if self.credential and not self.authentication_policy: + self.authentication_policy = self._infer_policy(**kwargs) diff --git a/sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/textanalytics/aio/_operations/__init__.py b/sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/textanalytics/aio/_operations/__init__.py new file mode 100644 index 000000000000..46ed8f84233c --- /dev/null +++ b/sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/textanalytics/aio/_operations/__init__.py @@ -0,0 +1,23 @@ +# 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=wrong-import-position + +from typing import TYPE_CHECKING + +if TYPE_CHECKING: + from ._patch import * # pylint: disable=unused-wildcard-import + +from ._operations import _TextClientOperationsMixin # type: ignore # pylint: disable=unused-import + +from ._patch import __all__ as _patch_all +from ._patch import * +from ._patch import patch_sdk as _patch_sdk + +__all__ = [] +__all__.extend([p for p in _patch_all if p not in __all__]) # pyright: ignore +_patch_sdk() diff --git a/sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/textanalytics/aio/_operations/_operations.py b/sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/textanalytics/aio/_operations/_operations.py new file mode 100644 index 000000000000..267b7f8f26b7 --- /dev/null +++ b/sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/textanalytics/aio/_operations/_operations.py @@ -0,0 +1,618 @@ +# pylint: disable=line-too-long,useless-suppression +# 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. +# -------------------------------------------------------------------------- +from collections.abc import MutableMapping +from io import IOBase +import json +from typing import Any, AsyncIterator, Callable, Dict, IO, List, Optional, TypeVar, Union, cast, overload + +from azure.core import AsyncPipelineClient +from azure.core.exceptions import ( + ClientAuthenticationError, + HttpResponseError, + ResourceExistsError, + ResourceNotFoundError, + ResourceNotModifiedError, + StreamClosedError, + StreamConsumedError, + map_error, +) +from azure.core.pipeline import PipelineResponse +from azure.core.polling import AsyncLROPoller, AsyncNoPolling, AsyncPollingMethod +from azure.core.polling.async_base_polling import AsyncLROBasePolling +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 ... import models as _models +from ..._operations._operations import ( + build_text_analyze_text_cancel_job_request, + build_text_analyze_text_job_status_request, + build_text_analyze_text_request, + build_text_analyze_text_submit_job_request, +) +from ..._utils.model_base import SdkJSONEncoder, _deserialize, _failsafe_deserialize +from ..._utils.utils import ClientMixinABC +from .._configuration import TextClientConfiguration + +JSON = MutableMapping[str, Any] +_Unset: Any = object() +T = TypeVar("T") +ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]] + + +class _TextClientOperationsMixin( + ClientMixinABC[AsyncPipelineClient[HttpRequest, AsyncHttpResponse], TextClientConfiguration] +): + + @overload + async def analyze_text( + self, + body: _models.AnalyzeTextTask, + *, + show_stats: Optional[bool] = None, + content_type: str = "application/json", + **kwargs: Any + ) -> _models.AnalyzeTextTaskResult: + """Request text analysis over a collection of documents. + + :param body: The input documents to analyze. Required. + :type body: ~azure.ai.textanalytics.models.AnalyzeTextTask + :keyword show_stats: (Optional) if set to true, response will contain request and document + level statistics. Default value is None. + :paramtype show_stats: bool + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: AnalyzeTextTaskResult. The AnalyzeTextTaskResult is compatible with MutableMapping + :rtype: ~azure.ai.textanalytics.models.AnalyzeTextTaskResult + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def analyze_text( + self, body: JSON, *, show_stats: Optional[bool] = None, content_type: str = "application/json", **kwargs: Any + ) -> _models.AnalyzeTextTaskResult: + """Request text analysis over a collection of documents. + + :param body: The input documents to analyze. Required. + :type body: JSON + :keyword show_stats: (Optional) if set to true, response will contain request and document + level statistics. Default value is None. + :paramtype show_stats: bool + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: AnalyzeTextTaskResult. The AnalyzeTextTaskResult is compatible with MutableMapping + :rtype: ~azure.ai.textanalytics.models.AnalyzeTextTaskResult + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def analyze_text( + self, + body: IO[bytes], + *, + show_stats: Optional[bool] = None, + content_type: str = "application/json", + **kwargs: Any + ) -> _models.AnalyzeTextTaskResult: + """Request text analysis over a collection of documents. + + :param body: The input documents to analyze. Required. + :type body: IO[bytes] + :keyword show_stats: (Optional) if set to true, response will contain request and document + level statistics. Default value is None. + :paramtype show_stats: bool + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: AnalyzeTextTaskResult. The AnalyzeTextTaskResult is compatible with MutableMapping + :rtype: ~azure.ai.textanalytics.models.AnalyzeTextTaskResult + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace_async + async def analyze_text( + self, body: Union[_models.AnalyzeTextTask, JSON, IO[bytes]], *, show_stats: Optional[bool] = None, **kwargs: Any + ) -> _models.AnalyzeTextTaskResult: + """Request text analysis over a collection of documents. + + :param body: The input documents to analyze. Is one of the following types: AnalyzeTextTask, + JSON, IO[bytes] Required. + :type body: ~azure.ai.textanalytics.models.AnalyzeTextTask or JSON or IO[bytes] + :keyword show_stats: (Optional) if set to true, response will contain request and document + level statistics. Default value is None. + :paramtype show_stats: bool + :return: AnalyzeTextTaskResult. The AnalyzeTextTaskResult is compatible with MutableMapping + :rtype: ~azure.ai.textanalytics.models.AnalyzeTextTaskResult + :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 = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[_models.AnalyzeTextTaskResult] = kwargs.pop("cls", None) + + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_text_analyze_text_request( + show_stats=show_stats, + content_type=content_type, + api_version=self._config.api_version, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "Endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _failsafe_deserialize(_models.ErrorResponse, response) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.AnalyzeTextTaskResult, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def analyze_text_job_status( + self, + job_id: str, + *, + show_stats: Optional[bool] = None, + top: Optional[int] = None, + skip: Optional[int] = None, + **kwargs: Any + ) -> _models.AnalyzeTextJobState: + """Get analysis status and results. + + Get the status of an analysis job. A job can consist of one or more tasks. After all tasks + succeed, the job transitions to the succeeded state and results are available for each task. + + :param job_id: job ID. Required. + :type job_id: str + :keyword show_stats: (Optional) if set to true, response will contain request and document + level statistics. Default value is None. + :paramtype show_stats: bool + :keyword top: The maximum number of resources to return from the collection. Default value is + None. + :paramtype top: int + :keyword skip: An offset into the collection of the first resource to be returned. Default + value is None. + :paramtype skip: int + :return: AnalyzeTextJobState. The AnalyzeTextJobState is compatible with MutableMapping + :rtype: ~azure.ai.textanalytics.models.AnalyzeTextJobState + :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 = kwargs.pop("params", {}) or {} + + cls: ClsType[_models.AnalyzeTextJobState] = kwargs.pop("cls", None) + + _request = build_text_analyze_text_job_status_request( + job_id=job_id, + show_stats=show_stats, + top=top, + skip=skip, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "Endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = kwargs.pop("stream", False) + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [200]: + if _stream: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _failsafe_deserialize(_models.ErrorResponse, response) + raise HttpResponseError(response=response, model=error) + + if _stream: + deserialized = response.iter_bytes() + else: + deserialized = _deserialize(_models.AnalyzeTextJobState, response.json()) + + if cls: + return cls(pipeline_response, deserialized, {}) # type: ignore + + return deserialized # type: ignore + + async def _analyze_text_submit_job_initial( + self, + body: Union[JSON, IO[bytes]] = _Unset, + *, + analysis_input: _models.MultiLanguageAnalysisInput = _Unset, + tasks: List[_models.AnalyzeTextLROTask] = _Unset, + display_name: Optional[str] = None, + default_language: Optional[str] = None, + cancel_after: Optional[float] = None, + **kwargs: Any + ) -> AsyncIterator[bytes]: + error_map: MutableMapping = { + 401: ClientAuthenticationError, + 404: ResourceNotFoundError, + 409: ResourceExistsError, + 304: ResourceNotModifiedError, + } + error_map.update(kwargs.pop("error_map", {}) or {}) + + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[AsyncIterator[bytes]] = kwargs.pop("cls", None) + + if body is _Unset: + if analysis_input is _Unset: + raise TypeError("missing required argument: analysis_input") + if tasks is _Unset: + raise TypeError("missing required argument: tasks") + body = { + "analysisInput": analysis_input, + "cancelAfter": cancel_after, + "defaultLanguage": default_language, + "displayName": display_name, + "tasks": tasks, + } + body = {k: v for k, v in body.items() if v is not None} + content_type = content_type or "application/json" + _content = None + if isinstance(body, (IOBase, bytes)): + _content = body + else: + _content = json.dumps(body, cls=SdkJSONEncoder, exclude_readonly=True) # type: ignore + + _request = build_text_analyze_text_submit_job_request( + content_type=content_type, + api_version=self._config.api_version, + content=_content, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "Endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = True + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [202]: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _failsafe_deserialize(_models.ErrorResponse, response) + raise HttpResponseError(response=response, model=error) + + response_headers = {} + response_headers["Operation-Location"] = self._deserialize("str", response.headers.get("Operation-Location")) + + deserialized = response.iter_bytes() + + if cls: + return cls(pipeline_response, deserialized, response_headers) # type: ignore + + return deserialized # type: ignore + + @overload + async def begin_analyze_text_submit_job( + self, + *, + analysis_input: _models.MultiLanguageAnalysisInput, + tasks: List[_models.AnalyzeTextLROTask], + content_type: str = "application/json", + display_name: Optional[str] = None, + default_language: Optional[str] = None, + cancel_after: Optional[float] = None, + **kwargs: Any + ) -> AsyncLROPoller[None]: + """Submit a collection of text documents for analysis. Specify one or more unique tasks to be + executed as a long-running operation. + + :keyword analysis_input: Contains the input to be analyzed. Required. + :paramtype analysis_input: ~azure.ai.textanalytics.models.MultiLanguageAnalysisInput + :keyword tasks: List of tasks to be performed as part of the LRO. Required. + :paramtype tasks: list[~azure.ai.textanalytics.models.AnalyzeTextLROTask] + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :keyword display_name: Name for the task. Default value is None. + :paramtype display_name: str + :keyword default_language: Default language to use for records requesting automatic language + detection. Default value is None. + :paramtype default_language: str + :keyword cancel_after: Optional duration in seconds after which the job will be canceled if not + completed. Default value is None. + :paramtype cancel_after: float + :return: An instance of AsyncLROPoller that returns None + :rtype: ~azure.core.polling.AsyncLROPoller[None] + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def begin_analyze_text_submit_job( + self, body: JSON, *, content_type: str = "application/json", **kwargs: Any + ) -> AsyncLROPoller[None]: + """Submit a collection of text documents for analysis. Specify one or more unique tasks to be + executed as a long-running operation. + + :param body: Required. + :type body: JSON + :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. + Default value is "application/json". + :paramtype content_type: str + :return: An instance of AsyncLROPoller that returns None + :rtype: ~azure.core.polling.AsyncLROPoller[None] + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @overload + async def begin_analyze_text_submit_job( + self, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any + ) -> AsyncLROPoller[None]: + """Submit a collection of text documents for analysis. Specify one or more unique tasks to be + executed as a long-running operation. + + :param body: Required. + :type body: IO[bytes] + :keyword content_type: Body Parameter content-type. Content type parameter for binary body. + Default value is "application/json". + :paramtype content_type: str + :return: An instance of AsyncLROPoller that returns None + :rtype: ~azure.core.polling.AsyncLROPoller[None] + :raises ~azure.core.exceptions.HttpResponseError: + """ + + @distributed_trace_async + async def begin_analyze_text_submit_job( + self, + body: Union[JSON, IO[bytes]] = _Unset, + *, + analysis_input: _models.MultiLanguageAnalysisInput = _Unset, + tasks: List[_models.AnalyzeTextLROTask] = _Unset, + display_name: Optional[str] = None, + default_language: Optional[str] = None, + cancel_after: Optional[float] = None, + **kwargs: Any + ) -> AsyncLROPoller[None]: + """Submit a collection of text documents for analysis. Specify one or more unique tasks to be + executed as a long-running operation. + + :param body: Is either a JSON type or a IO[bytes] type. Required. + :type body: JSON or IO[bytes] + :keyword analysis_input: Contains the input to be analyzed. Required. + :paramtype analysis_input: ~azure.ai.textanalytics.models.MultiLanguageAnalysisInput + :keyword tasks: List of tasks to be performed as part of the LRO. Required. + :paramtype tasks: list[~azure.ai.textanalytics.models.AnalyzeTextLROTask] + :keyword display_name: Name for the task. Default value is None. + :paramtype display_name: str + :keyword default_language: Default language to use for records requesting automatic language + detection. Default value is None. + :paramtype default_language: str + :keyword cancel_after: Optional duration in seconds after which the job will be canceled if not + completed. Default value is None. + :paramtype cancel_after: float + :return: An instance of AsyncLROPoller that returns None + :rtype: ~azure.core.polling.AsyncLROPoller[None] + :raises ~azure.core.exceptions.HttpResponseError: + """ + _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {}) + _params = kwargs.pop("params", {}) or {} + + content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None)) + cls: ClsType[None] = kwargs.pop("cls", None) + polling: Union[bool, AsyncPollingMethod] = kwargs.pop("polling", True) + lro_delay = kwargs.pop("polling_interval", self._config.polling_interval) + cont_token: Optional[str] = kwargs.pop("continuation_token", None) + if cont_token is None: + raw_result = await self._analyze_text_submit_job_initial( + body=body, + analysis_input=analysis_input, + tasks=tasks, + display_name=display_name, + default_language=default_language, + cancel_after=cancel_after, + content_type=content_type, + cls=lambda x, y, z: x, + headers=_headers, + params=_params, + **kwargs + ) + await raw_result.http_response.read() # type: ignore + kwargs.pop("error_map", None) + + def get_long_running_output(pipeline_response): # pylint: disable=inconsistent-return-statements + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + path_format_arguments = { + "Endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str"), + } + + if polling is True: + polling_method: AsyncPollingMethod = cast( + AsyncPollingMethod, + AsyncLROBasePolling(lro_delay, path_format_arguments=path_format_arguments, **kwargs), + ) + elif polling is False: + polling_method = cast(AsyncPollingMethod, AsyncNoPolling()) + else: + polling_method = polling + if cont_token: + return AsyncLROPoller[None].from_continuation_token( + polling_method=polling_method, + continuation_token=cont_token, + client=self._client, + deserialization_callback=get_long_running_output, + ) + return AsyncLROPoller[None](self._client, raw_result, get_long_running_output, polling_method) # type: ignore + + async def _analyze_text_cancel_job_initial(self, job_id: str, **kwargs: Any) -> AsyncIterator[bytes]: + 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 = kwargs.pop("params", {}) or {} + + cls: ClsType[AsyncIterator[bytes]] = kwargs.pop("cls", None) + + _request = build_text_analyze_text_cancel_job_request( + job_id=job_id, + api_version=self._config.api_version, + headers=_headers, + params=_params, + ) + path_format_arguments = { + "Endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str"), + } + _request.url = self._client.format_url(_request.url, **path_format_arguments) + + _stream = True + pipeline_response: PipelineResponse = await self._client._pipeline.run( # type: ignore # pylint: disable=protected-access + _request, stream=_stream, **kwargs + ) + + response = pipeline_response.http_response + + if response.status_code not in [202]: + try: + await response.read() # Load the body in memory and close the socket + except (StreamConsumedError, StreamClosedError): + pass + map_error(status_code=response.status_code, response=response, error_map=error_map) + error = _failsafe_deserialize(_models.ErrorResponse, response) + raise HttpResponseError(response=response, model=error) + + response_headers = {} + response_headers["Operation-Location"] = self._deserialize("str", response.headers.get("Operation-Location")) + + deserialized = response.iter_bytes() + + if cls: + return cls(pipeline_response, deserialized, response_headers) # type: ignore + + return deserialized # type: ignore + + @distributed_trace_async + async def begin_analyze_text_cancel_job(self, job_id: str, **kwargs: Any) -> AsyncLROPoller[None]: + """Cancel a long-running Text Analysis job. + + Cancel a long-running Text Analysis job. + + :param job_id: The job ID to cancel. Required. + :type job_id: str + :return: An instance of AsyncLROPoller that returns None + :rtype: ~azure.core.polling.AsyncLROPoller[None] + :raises ~azure.core.exceptions.HttpResponseError: + """ + _headers = kwargs.pop("headers", {}) or {} + _params = kwargs.pop("params", {}) or {} + + cls: ClsType[None] = kwargs.pop("cls", None) + polling: Union[bool, AsyncPollingMethod] = kwargs.pop("polling", True) + lro_delay = kwargs.pop("polling_interval", self._config.polling_interval) + cont_token: Optional[str] = kwargs.pop("continuation_token", None) + if cont_token is None: + raw_result = await self._analyze_text_cancel_job_initial( + job_id=job_id, cls=lambda x, y, z: x, headers=_headers, params=_params, **kwargs + ) + await raw_result.http_response.read() # type: ignore + kwargs.pop("error_map", None) + + def get_long_running_output(pipeline_response): # pylint: disable=inconsistent-return-statements + if cls: + return cls(pipeline_response, None, {}) # type: ignore + + path_format_arguments = { + "Endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str"), + } + + if polling is True: + polling_method: AsyncPollingMethod = cast( + AsyncPollingMethod, + AsyncLROBasePolling(lro_delay, path_format_arguments=path_format_arguments, **kwargs), + ) + elif polling is False: + polling_method = cast(AsyncPollingMethod, AsyncNoPolling()) + else: + polling_method = polling + if cont_token: + return AsyncLROPoller[None].from_continuation_token( + polling_method=polling_method, + continuation_token=cont_token, + client=self._client, + deserialization_callback=get_long_running_output, + ) + return AsyncLROPoller[None](self._client, raw_result, get_long_running_output, polling_method) # type: ignore diff --git a/sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/textanalytics/aio/_operations/_patch.py b/sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/textanalytics/aio/_operations/_patch.py new file mode 100644 index 000000000000..8bcb627aa475 --- /dev/null +++ b/sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/textanalytics/aio/_operations/_patch.py @@ -0,0 +1,21 @@ +# 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. +# -------------------------------------------------------------------------- +"""Customize generated code here. + +Follow our quickstart for examples: https://aka.ms/azsdk/python/dpcodegen/python/customize +""" +from typing import List + +__all__: List[str] = [] # Add all objects you want publicly available to users at this package level + + +def patch_sdk(): + """Do not remove from this file. + + `patch_sdk` is a last resort escape hatch that allows you to do customizations + you can't accomplish using the techniques described in + https://aka.ms/azsdk/python/dpcodegen/python/customize + """ diff --git a/sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/textanalytics/aio/_patch.py b/sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/textanalytics/aio/_patch.py new file mode 100644 index 000000000000..8bcb627aa475 --- /dev/null +++ b/sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/textanalytics/aio/_patch.py @@ -0,0 +1,21 @@ +# 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. +# -------------------------------------------------------------------------- +"""Customize generated code here. + +Follow our quickstart for examples: https://aka.ms/azsdk/python/dpcodegen/python/customize +""" +from typing import List + +__all__: List[str] = [] # Add all objects you want publicly available to users at this package level + + +def patch_sdk(): + """Do not remove from this file. + + `patch_sdk` is a last resort escape hatch that allows you to do customizations + you can't accomplish using the techniques described in + https://aka.ms/azsdk/python/dpcodegen/python/customize + """ diff --git a/sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/textanalytics/models/__init__.py b/sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/textanalytics/models/__init__.py new file mode 100644 index 000000000000..8e2a12ae589e --- /dev/null +++ b/sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/textanalytics/models/__init__.py @@ -0,0 +1,402 @@ +# 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=wrong-import-position + +from typing import TYPE_CHECKING + +if TYPE_CHECKING: + from ._patch import * # pylint: disable=unused-wildcard-import + + +from ._models import ( # type: ignore + AbstractiveSummarizationLROResult, + AbstractiveSummarizationLROTask, + AbstractiveSummarizationResult, + AbstractiveSummarizationTaskParameters, + AbstractiveSummary, + AbstractiveSummaryDocumentResultWithDetectedLanguage, + AgeMetadata, + AllowOverlapEntityPolicyType, + AnalyzeTextEntityLinkingInput, + AnalyzeTextEntityRecognitionInput, + AnalyzeTextJobState, + AnalyzeTextKeyPhraseExtractionInput, + AnalyzeTextLROResult, + AnalyzeTextLROTask, + AnalyzeTextLanguageDetectionInput, + AnalyzeTextPiiEntitiesRecognitionInput, + AnalyzeTextSentimentAnalysisInput, + AnalyzeTextTask, + AnalyzeTextTaskResult, + AreaMetadata, + BaseEntityOverlapPolicy, + BaseMetadata, + BaseRedactionPolicy, + CharacterMaskPolicyType, + ClassificationDocumentResultWithDetectedLanguage, + ClassificationResult, + CurrencyMetadata, + CustomEntitiesLROTask, + CustomEntitiesResult, + CustomEntitiesTaskParameters, + CustomEntityRecognitionLROResult, + CustomLabelClassificationResult, + CustomMultiLabelClassificationLROResult, + CustomMultiLabelClassificationLROTask, + CustomMultiLabelClassificationTaskParameters, + CustomSingleLabelClassificationLROResult, + CustomSingleLabelClassificationLROTask, + CustomSingleLabelClassificationTaskParameters, + DateMetadata, + DateTimeMetadata, + DateValue, + DetectedLanguage, + DocumentError, + DocumentStatistics, + DocumentWarning, + EntitiesDocumentResultWithDetectedLanguage, + EntitiesDocumentResultWithMetadata, + EntitiesDocumentResultWithMetadataDetectedLanguage, + EntitiesLROTask, + EntitiesResult, + EntitiesTaskParameters, + EntitiesTaskResult, + EntitiesWithMetadataAutoResult, + Entity, + EntityInferenceOptions, + EntityLinkingLROResult, + EntityLinkingLROTask, + EntityLinkingResult, + EntityLinkingResultWithDetectedLanguage, + EntityLinkingTaskParameters, + EntityLinkingTaskResult, + EntityMaskPolicyType, + EntityRecognitionLROResult, + EntitySynonym, + EntitySynonyms, + EntityTag, + EntityWithMetadata, + Error, + ErrorResponse, + ExtractedSummaryDocumentResultWithDetectedLanguage, + ExtractedSummarySentence, + ExtractiveSummarizationLROResult, + ExtractiveSummarizationLROTask, + ExtractiveSummarizationResult, + ExtractiveSummarizationTaskParameters, + FhirBundle, + HealthcareAssertion, + HealthcareEntitiesDocumentResultWithDocumentDetectedLanguage, + HealthcareEntity, + HealthcareEntityLink, + HealthcareLROResult, + HealthcareLROTask, + HealthcareRelation, + HealthcareRelationEntity, + HealthcareResult, + HealthcareTaskParameters, + InformationMetadata, + InnerErrorModel, + KeyPhraseExtractionLROResult, + KeyPhraseLROTask, + KeyPhraseResult, + KeyPhraseTaskParameters, + KeyPhraseTaskResult, + KeyPhrasesDocumentResultWithDetectedLanguage, + LanguageDetectionAnalysisInput, + LanguageDetectionDocumentResult, + LanguageDetectionResult, + LanguageDetectionTaskParameters, + LanguageDetectionTaskResult, + LanguageInput, + LengthMetadata, + LinkedEntity, + Match, + MatchLongestEntityPolicyType, + MultiLanguageAnalysisInput, + MultiLanguageInput, + NoMaskPolicyType, + NumberMetadata, + NumericRangeMetadata, + OrdinalMetadata, + PiiEntityRecognitionLROResult, + PiiEntityWithTags, + PiiLROTask, + PiiResult, + PiiResultWithDetectedLanguage, + PiiTaskParameters, + PiiTaskResult, + RequestStatistics, + SentenceAssessment, + SentenceSentiment, + SentenceTarget, + SentimentAnalysisLROTask, + SentimentAnalysisTaskParameters, + SentimentConfidenceScores, + SentimentDocumentResultWithDetectedLanguage, + SentimentLROResult, + SentimentResponse, + SentimentTaskResult, + SpeedMetadata, + SummaryContext, + TargetConfidenceScoreLabel, + TargetRelation, + Tasks, + TemperatureMetadata, + TemporalSetMetadata, + TemporalSpanMetadata, + TemporalSpanValues, + TimeMetadata, + ValueExclusionPolicy, + VolumeMetadata, + WeightMetadata, +) + +from ._enums import ( # type: ignore + AgeUnit, + AnalyzeTextLROResultsKind, + AnalyzeTextLROTaskKind, + AnalyzeTextTaskKind, + AnalyzeTextTaskResultsKind, + AreaUnit, + Association, + Certainty, + Conditionality, + DocumentSentimentValue, + EntityCategory, + ErrorCode, + ExtractiveSummarizationSortingCriteria, + FhirVersion, + HealthcareDocumentType, + HealthcareEntityCategory, + InformationUnit, + InnerErrorCode, + LengthUnit, + MetadataKind, + NumberKind, + PiiCategoriesExclude, + PiiCategory, + PiiDomain, + PolicyKind, + RangeInclusivity, + RangeKind, + RedactionCharacter, + RedactionPolicyKind, + RelationType, + RelativeTo, + ScriptCode, + ScriptKind, + SentenceSentimentValue, + SpeedUnit, + State, + StringIndexType, + SummaryLengthBucket, + TargetRelationType, + TemperatureUnit, + TemporalModifier, + Temporality, + TokenSentimentValue, + VolumeUnit, + WarningCodeValue, + WeightUnit, +) +from ._patch import __all__ as _patch_all +from ._patch import * +from ._patch import patch_sdk as _patch_sdk + +__all__ = [ + "AbstractiveSummarizationLROResult", + "AbstractiveSummarizationLROTask", + "AbstractiveSummarizationResult", + "AbstractiveSummarizationTaskParameters", + "AbstractiveSummary", + "AbstractiveSummaryDocumentResultWithDetectedLanguage", + "AgeMetadata", + "AllowOverlapEntityPolicyType", + "AnalyzeTextEntityLinkingInput", + "AnalyzeTextEntityRecognitionInput", + "AnalyzeTextJobState", + "AnalyzeTextKeyPhraseExtractionInput", + "AnalyzeTextLROResult", + "AnalyzeTextLROTask", + "AnalyzeTextLanguageDetectionInput", + "AnalyzeTextPiiEntitiesRecognitionInput", + "AnalyzeTextSentimentAnalysisInput", + "AnalyzeTextTask", + "AnalyzeTextTaskResult", + "AreaMetadata", + "BaseEntityOverlapPolicy", + "BaseMetadata", + "BaseRedactionPolicy", + "CharacterMaskPolicyType", + "ClassificationDocumentResultWithDetectedLanguage", + "ClassificationResult", + "CurrencyMetadata", + "CustomEntitiesLROTask", + "CustomEntitiesResult", + "CustomEntitiesTaskParameters", + "CustomEntityRecognitionLROResult", + "CustomLabelClassificationResult", + "CustomMultiLabelClassificationLROResult", + "CustomMultiLabelClassificationLROTask", + "CustomMultiLabelClassificationTaskParameters", + "CustomSingleLabelClassificationLROResult", + "CustomSingleLabelClassificationLROTask", + "CustomSingleLabelClassificationTaskParameters", + "DateMetadata", + "DateTimeMetadata", + "DateValue", + "DetectedLanguage", + "DocumentError", + "DocumentStatistics", + "DocumentWarning", + "EntitiesDocumentResultWithDetectedLanguage", + "EntitiesDocumentResultWithMetadata", + "EntitiesDocumentResultWithMetadataDetectedLanguage", + "EntitiesLROTask", + "EntitiesResult", + "EntitiesTaskParameters", + "EntitiesTaskResult", + "EntitiesWithMetadataAutoResult", + "Entity", + "EntityInferenceOptions", + "EntityLinkingLROResult", + "EntityLinkingLROTask", + "EntityLinkingResult", + "EntityLinkingResultWithDetectedLanguage", + "EntityLinkingTaskParameters", + "EntityLinkingTaskResult", + "EntityMaskPolicyType", + "EntityRecognitionLROResult", + "EntitySynonym", + "EntitySynonyms", + "EntityTag", + "EntityWithMetadata", + "Error", + "ErrorResponse", + "ExtractedSummaryDocumentResultWithDetectedLanguage", + "ExtractedSummarySentence", + "ExtractiveSummarizationLROResult", + "ExtractiveSummarizationLROTask", + "ExtractiveSummarizationResult", + "ExtractiveSummarizationTaskParameters", + "FhirBundle", + "HealthcareAssertion", + "HealthcareEntitiesDocumentResultWithDocumentDetectedLanguage", + "HealthcareEntity", + "HealthcareEntityLink", + "HealthcareLROResult", + "HealthcareLROTask", + "HealthcareRelation", + "HealthcareRelationEntity", + "HealthcareResult", + "HealthcareTaskParameters", + "InformationMetadata", + "InnerErrorModel", + "KeyPhraseExtractionLROResult", + "KeyPhraseLROTask", + "KeyPhraseResult", + "KeyPhraseTaskParameters", + "KeyPhraseTaskResult", + "KeyPhrasesDocumentResultWithDetectedLanguage", + "LanguageDetectionAnalysisInput", + "LanguageDetectionDocumentResult", + "LanguageDetectionResult", + "LanguageDetectionTaskParameters", + "LanguageDetectionTaskResult", + "LanguageInput", + "LengthMetadata", + "LinkedEntity", + "Match", + "MatchLongestEntityPolicyType", + "MultiLanguageAnalysisInput", + "MultiLanguageInput", + "NoMaskPolicyType", + "NumberMetadata", + "NumericRangeMetadata", + "OrdinalMetadata", + "PiiEntityRecognitionLROResult", + "PiiEntityWithTags", + "PiiLROTask", + "PiiResult", + "PiiResultWithDetectedLanguage", + "PiiTaskParameters", + "PiiTaskResult", + "RequestStatistics", + "SentenceAssessment", + "SentenceSentiment", + "SentenceTarget", + "SentimentAnalysisLROTask", + "SentimentAnalysisTaskParameters", + "SentimentConfidenceScores", + "SentimentDocumentResultWithDetectedLanguage", + "SentimentLROResult", + "SentimentResponse", + "SentimentTaskResult", + "SpeedMetadata", + "SummaryContext", + "TargetConfidenceScoreLabel", + "TargetRelation", + "Tasks", + "TemperatureMetadata", + "TemporalSetMetadata", + "TemporalSpanMetadata", + "TemporalSpanValues", + "TimeMetadata", + "ValueExclusionPolicy", + "VolumeMetadata", + "WeightMetadata", + "AgeUnit", + "AnalyzeTextLROResultsKind", + "AnalyzeTextLROTaskKind", + "AnalyzeTextTaskKind", + "AnalyzeTextTaskResultsKind", + "AreaUnit", + "Association", + "Certainty", + "Conditionality", + "DocumentSentimentValue", + "EntityCategory", + "ErrorCode", + "ExtractiveSummarizationSortingCriteria", + "FhirVersion", + "HealthcareDocumentType", + "HealthcareEntityCategory", + "InformationUnit", + "InnerErrorCode", + "LengthUnit", + "MetadataKind", + "NumberKind", + "PiiCategoriesExclude", + "PiiCategory", + "PiiDomain", + "PolicyKind", + "RangeInclusivity", + "RangeKind", + "RedactionCharacter", + "RedactionPolicyKind", + "RelationType", + "RelativeTo", + "ScriptCode", + "ScriptKind", + "SentenceSentimentValue", + "SpeedUnit", + "State", + "StringIndexType", + "SummaryLengthBucket", + "TargetRelationType", + "TemperatureUnit", + "TemporalModifier", + "Temporality", + "TokenSentimentValue", + "VolumeUnit", + "WarningCodeValue", + "WeightUnit", +] +__all__.extend([p for p in _patch_all if p not in __all__]) # pyright: ignore +_patch_sdk() diff --git a/sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/textanalytics/models/_enums.py b/sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/textanalytics/models/_enums.py new file mode 100644 index 000000000000..69193c93a210 --- /dev/null +++ b/sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/textanalytics/models/_enums.py @@ -0,0 +1,1979 @@ +# pylint: disable=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. +# -------------------------------------------------------------------------- + +from enum import Enum +from azure.core import CaseInsensitiveEnumMeta + + +class AgeUnit(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """The Age Unit of measurement.""" + + UNSPECIFIED = "Unspecified" + """Unspecified time period""" + YEAR = "Year" + """Time period of a year""" + MONTH = "Month" + """Time period of a month""" + WEEK = "Week" + """Time period of a week""" + DAY = "Day" + """Time period of a day""" + + +class AnalyzeTextLROResultsKind(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """The kind of the response object returned by the analyze-text long running task.""" + + SENTIMENT_ANALYSIS_LRO_RESULTS = "SentimentAnalysisLROResults" + """Sentiment analysis LRO results""" + ENTITY_RECOGNITION_LRO_RESULTS = "EntityRecognitionLROResults" + """Entity recognition LRO results""" + PII_ENTITY_RECOGNITION_LRO_RESULTS = "PiiEntityRecognitionLROResults" + """PII entity recognition LRO results""" + KEY_PHRASE_EXTRACTION_LRO_RESULTS = "KeyPhraseExtractionLROResults" + """Key phrase extraction LRO results""" + ENTITY_LINKING_LRO_RESULTS = "EntityLinkingLROResults" + """Entity linking LRO results""" + HEALTHCARE_LRO_RESULTS = "HealthcareLROResults" + """Healthcare LRO results""" + CUSTOM_ENTITY_RECOGNITION_LRO_RESULTS = "CustomEntityRecognitionLROResults" + """Custom entity recognition LRO results""" + CUSTOM_SINGLE_LABEL_CLASSIFICATION_LRO_RESULTS = "CustomSingleLabelClassificationLROResults" + """Custom single label classification LRO results""" + CUSTOM_MULTI_LABEL_CLASSIFICATION_LRO_RESULTS = "CustomMultiLabelClassificationLROResults" + """Custom multi label classification LRO results""" + EXTRACTIVE_SUMMARIZATION_LRO_RESULTS = "ExtractiveSummarizationLROResults" + """Extractive summarization LRO results""" + ABSTRACTIVE_SUMMARIZATION_LRO_RESULTS = "AbstractiveSummarizationLROResults" + """Abstractive summarization LRO results""" + + +class AnalyzeTextLROTaskKind(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """The kind of the long running analyze text tasks supported.""" + + SENTIMENT_ANALYSIS = "SentimentAnalysis" + """Sentiment analysis task""" + ENTITY_RECOGNITION = "EntityRecognition" + """Entity recognition task""" + PII_ENTITY_RECOGNITION = "PiiEntityRecognition" + """PII entity recognition task""" + KEY_PHRASE_EXTRACTION = "KeyPhraseExtraction" + """Key phrase extraction task""" + ENTITY_LINKING = "EntityLinking" + """Entity linking task""" + HEALTHCARE = "Healthcare" + """Healthcare task""" + CUSTOM_ENTITY_RECOGNITION = "CustomEntityRecognition" + """Custom entity recognition task""" + CUSTOM_SINGLE_LABEL_CLASSIFICATION = "CustomSingleLabelClassification" + """Custom single label classification task""" + CUSTOM_MULTI_LABEL_CLASSIFICATION = "CustomMultiLabelClassification" + """Custom multi label classification task""" + EXTRACTIVE_SUMMARIZATION = "ExtractiveSummarization" + """Extractive summarization task""" + ABSTRACTIVE_SUMMARIZATION = "AbstractiveSummarization" + """Abstractive summarization task""" + + +class AnalyzeTextTaskKind(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """The kind of the analyze-text tasks supported.""" + + SENTIMENT_ANALYSIS = "SentimentAnalysis" + """Sentiment analysis task""" + ENTITY_RECOGNITION = "EntityRecognition" + """Entity recognition task""" + PII_ENTITY_RECOGNITION = "PiiEntityRecognition" + """PII entity recognition task""" + KEY_PHRASE_EXTRACTION = "KeyPhraseExtraction" + """Key phrase extraction task""" + LANGUAGE_DETECTION = "LanguageDetection" + """Language detection task""" + ENTITY_LINKING = "EntityLinking" + """Entity linking task""" + + +class AnalyzeTextTaskResultsKind(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """The kind of the response object returned by the analyze-text task.""" + + SENTIMENT_ANALYSIS_RESULTS = "SentimentAnalysisResults" + """Sentiment analysis results""" + ENTITY_RECOGNITION_RESULTS = "EntityRecognitionResults" + """Entity recognition results""" + PII_ENTITY_RECOGNITION_RESULTS = "PiiEntityRecognitionResults" + """PII entity recognition results""" + KEY_PHRASE_EXTRACTION_RESULTS = "KeyPhraseExtractionResults" + """Key phrase extraction results""" + LANGUAGE_DETECTION_RESULTS = "LanguageDetectionResults" + """Language detection results""" + ENTITY_LINKING_RESULTS = "EntityLinkingResults" + """Entity linking results""" + + +class AreaUnit(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """The area unit of measurement.""" + + UNSPECIFIED = "Unspecified" + """Unspecified area unit""" + SQUARE_KILOMETER = "SquareKilometer" + """Area unit in square kilometers""" + SQUARE_HECTOMETER = "SquareHectometer" + """Area unit in square hectometers""" + SQUARE_DECAMETER = "SquareDecameter" + """Area unit in square decameters""" + SQUARE_DECIMETER = "SquareDecimeter" + """Area unit in square decimeters""" + SQUARE_METER = "SquareMeter" + """Area unit in square meters""" + SQUARE_CENTIMETER = "SquareCentimeter" + """Area unit in square centimeters""" + SQUARE_MILLIMETER = "SquareMillimeter" + """Area unit in square millimeters""" + SQUARE_INCH = "SquareInch" + """Area unit in square inches""" + SQUARE_FOOT = "SquareFoot" + """Area unit in square feet""" + SQUARE_MILE = "SquareMile" + """Area unit in square miles""" + SQUARE_YARD = "SquareYard" + """Area unit in square yards""" + ACRE = "Acre" + """Area unit in acres""" + + +class Association(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Describes if the entity is the subject of the text or if it describes someone else.""" + + SUBJECT = "subject" + """Subject association""" + OTHER = "other" + """Other association""" + + +class Certainty(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Describes the entities certainty and polarity.""" + + POSITIVE = "positive" + """Positive certainty""" + POSITIVE_POSSIBLE = "positivePossible" + """Possibly positive certainty""" + NEUTRAL_POSSIBLE = "neutralPossible" + """Possibly neutral certainty""" + NEGATIVE_POSSIBLE = "negativePossible" + """Possibly negative certainty""" + NEGATIVE = "negative" + """Negative certainty""" + + +class Conditionality(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Describes any conditionality on the entity.""" + + HYPOTHETICAL = "hypothetical" + """Hypothetical conditionality""" + CONDITIONAL = "conditional" + """Conditional conditionality""" + + +class DocumentSentimentValue(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Predicted sentiment for document (Negative, Neutral, Positive, or Mixed).""" + + POSITIVE = "positive" + """Positive statement""" + NEUTRAL = "neutral" + """Neutral statement""" + NEGATIVE = "negative" + """Negative statement""" + MIXED = "mixed" + """Mixed statement""" + + +class EntityCategory(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Contains all the entity categories detected by entity recognition.""" + + ADDRESS = "Address" + """Specific street-level mentions of locations: house/building numbers, streets, avenues, + highways, intersections referenced by name.""" + NUMERIC = "Numeric" + """Numeric values, including digits and number words.""" + AGE = "Age" + """Age-related values.""" + CURRENCY = "Currency" + """Currency-related values.""" + NUMBER = "Number" + """Numbers without a unit""" + NUMBER_RANGE = "NumberRange" + """Range of Numbers""" + PERCENTAGE = "Percentage" + """Percentage-related values.""" + ORDINAL = "Ordinal" + """Ordinal numbers.""" + TEMPERATURE = "Temperature" + """Temperature-related values.""" + DIMENSION = "Dimension" + """Dimension of measurements""" + LENGTH = "Length" + """Length of an object.""" + WEIGHT = "Weight" + """Weight of an object.""" + HEIGHT = "Height" + """Height of an object.""" + SPEED = "Speed" + """Speed of an object.""" + AREA = "Area" + """Area of an object.""" + VOLUME = "Volume" + """Volume of an object.""" + INFORMATION = "Information" + """Unit of measure for digital information.""" + TEMPORAL = "Temporal" + """Items relating to time.""" + DATE = "Date" + """Calendar dates.""" + TIME = "Time" + """Times of day.""" + DATE_TIME = "DateTime" + """Calendar dates with time.""" + DATE_RANGE = "DateRange" + """Range of dates.""" + TIME_RANGE = "TimeRange" + """Range of times.""" + DATE_TIME_RANGE = "DateTimeRange" + """Range of date and time.""" + DURATION = "Duration" + """Duration of time.""" + SET_TEMPORAL = "SetTemporal" + """Set of time-related values.""" + EVENT = "Event" + """Social, sports, business, political, educational, natural, historical, criminal, violent, + legal, military events with a timed period.""" + SPORTS_EVENT = "SportsEvent" + """Sports event-related values.""" + CULTURAL_EVENT = "CulturalEvent" + """Cultural event-related values.""" + NATURAL_EVENT = "NaturalEvent" + """Natural event-related values.""" + LOCATION = "Location" + """Particular point or place in physical space.""" + GPE = "GPE" + """Cities, countries/regions, states.""" + CITY = "City" + """City-related values.""" + STATE = "State" + """State-related values.""" + COUNTRY_REGION = "CountryRegion" + """Country or region-related values.""" + CONTINENT = "Continent" + """Continent-related values.""" + STRUCTURAL = "Structural" + """Manmade structures.""" + AIRPORT = "Airport" + """Airports.""" + GEOLOGICAL = "Geological" + """Geographic and natural features such as rivers, oceans, and deserts.""" + ORGANIZATION = "Organization" + """Corporations, agencies, and other groups of people defined by some established organizational + structure. These labels can include companies, political parties/movements, musical bands, + sport clubs, government bodies, and public organizations. Nationalities or religions are not + ORGANIZATION.""" + ORGANIZATION_MEDICAL = "OrganizationMedical" + """Medical companies and groups.""" + ORGANIZATION_STOCK_EXCHANGE = "OrganizationStockExchange" + """Stock exchange groups.""" + ORGANIZATION_SPORTS = "OrganizationSports" + """Sports-related organizations.""" + PERSON = "Person" + """First, last, and middle names, names of fictional characters, and aliases. Titles, such as + 'Mr.' or 'President', are not considered part of the named entity.""" + PERSON_TYPE = "PersonType" + """Human roles classified by a group membership.""" + EMAIL = "Email" + """Email addresses.""" + URL = "URL" + """URLs to websites.""" + IP = "IP" + """network IP addresses.""" + PHONE_NUMBER = "PhoneNumber" + """Phone numbers (US and EU phone numbers only).""" + PRODUCT = "Product" + """Single or group of commercial, consumable objects, electronics, vehicles, food groups.""" + COMPUTING_PRODUCT = "ComputingProduct" + """Computing products.""" + SKILL = "Skill" + """A capability, skill, or expertise.""" + + +class ErrorCode(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Human-readable error code.""" + + INVALID_REQUEST = "InvalidRequest" + """Invalid request error""" + INVALID_ARGUMENT = "InvalidArgument" + """Invalid argument error""" + UNAUTHORIZED = "Unauthorized" + """Unauthorized access error""" + FORBIDDEN = "Forbidden" + """Forbidden access error""" + NOT_FOUND = "NotFound" + """Not found error""" + PROJECT_NOT_FOUND = "ProjectNotFound" + """Project not found error""" + OPERATION_NOT_FOUND = "OperationNotFound" + """Operation not found error""" + AZURE_COGNITIVE_SEARCH_NOT_FOUND = "AzureCognitiveSearchNotFound" + """Azure Cognitive Search not found error""" + AZURE_COGNITIVE_SEARCH_INDEX_NOT_FOUND = "AzureCognitiveSearchIndexNotFound" + """Azure Cognitive Search index not found error""" + TOO_MANY_REQUESTS = "TooManyRequests" + """Too many requests error""" + AZURE_COGNITIVE_SEARCH_THROTTLING = "AzureCognitiveSearchThrottling" + """Azure Cognitive Search throttling error""" + AZURE_COGNITIVE_SEARCH_INDEX_LIMIT_REACHED = "AzureCognitiveSearchIndexLimitReached" + """Azure Cognitive Search index limit reached error""" + INTERNAL_SERVER_ERROR = "InternalServerError" + """Internal server error""" + SERVICE_UNAVAILABLE = "ServiceUnavailable" + """Service unavailable error""" + TIMEOUT = "Timeout" + """Timeout error""" + QUOTA_EXCEEDED = "QuotaExceeded" + """Quota exceeded error""" + CONFLICT = "Conflict" + """Conflict error""" + WARNING = "Warning" + """Warning error""" + + +class ExtractiveSummarizationSortingCriteria(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """The sorting criteria to use for the results of Extractive Summarization.""" + + OFFSET = "Offset" + """Indicates that results should be sorted in order of appearance in the text.""" + RANK = "Rank" + """Indicates that results should be sorted in order of importance (i.e. rank score) according to + the model.""" + + +class FhirVersion(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """The FHIR Spec version.""" + + ENUM_4_0_1 = "4.0.1" + """Version 4.0.1""" + + +class HealthcareDocumentType(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Document type.""" + + NONE = "None" + """None document type""" + CLINICAL_TRIAL = "ClinicalTrial" + """Clinical trial document type""" + DISCHARGE_SUMMARY = "DischargeSummary" + """Discharge summary document type""" + PROGRESS_NOTE = "ProgressNote" + """Progress note document type""" + HISTORY_AND_PHYSICAL = "HistoryAndPhysical" + """History and physical document type""" + CONSULT = "Consult" + """Consult document type""" + IMAGING = "Imaging" + """Imaging document type""" + PATHOLOGY = "Pathology" + """Pathology document type""" + PROCEDURE_NOTE = "ProcedureNote" + """Procedure note document type""" + + +class HealthcareEntityCategory(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Healthcare Entity Category.""" + + BODY_STRUCTURE = "BodyStructure" + """Body structure""" + AGE = "Age" + """Age""" + GENDER = "Gender" + """Gender""" + EXAMINATION_NAME = "ExaminationName" + """Examination name""" + DATE = "Date" + """Date""" + DIRECTION = "Direction" + """Direction""" + FREQUENCY = "Frequency" + """Frequency""" + MEASUREMENT_VALUE = "MeasurementValue" + """Measurement value""" + MEASUREMENT_UNIT = "MeasurementUnit" + """Measurement unit""" + RELATIONAL_OPERATOR = "RelationalOperator" + """Relational operator""" + TIME = "Time" + """Time""" + GENE_OR_PROTEIN = "GeneOrProtein" + """Gene or protein""" + VARIANT = "Variant" + """Variant""" + ADMINISTRATIVE_EVENT = "AdministrativeEvent" + """Administrative event""" + CARE_ENVIRONMENT = "CareEnvironment" + """Care environment""" + HEALTHCARE_PROFESSION = "HealthcareProfession" + """Healthcare profession""" + DIAGNOSIS = "Diagnosis" + """Diagnosis""" + SYMPTOM_OR_SIGN = "SymptomOrSign" + """Symptom or sign""" + CONDITION_QUALIFIER = "ConditionQualifier" + """Condition qualifier""" + MEDICATION_CLASS = "MedicationClass" + """Medication class""" + MEDICATION_NAME = "MedicationName" + """Medication name""" + DOSAGE = "Dosage" + """Dosage""" + MEDICATION_FORM = "MedicationForm" + """Medication form""" + MEDICATION_ROUTE = "MedicationRoute" + """Medication route""" + FAMILY_RELATION = "FamilyRelation" + """Family relation""" + TREATMENT_NAME = "TreatmentName" + """Treatment name""" + ETHNICITY = "Ethnicity" + """Ethnicity""" + COURSE = "Course" + """Course""" + EXPRESSION = "Expression" + """Expression""" + MUTATION_TYPE = "MutationType" + """Mutation type""" + CONDITION_SCALE = "ConditionScale" + """Condition scale""" + ALLERGEN = "Allergen" + """Allergen""" + EMPLOYMENT = "Employment" + """Employment""" + LIVING_STATUS = "LivingStatus" + """Living status""" + SUBSTANCE_USE = "SubstanceUse" + """Substance use""" + SUBSTANCE_USE_AMOUNT = "SubstanceUseAmount" + """Substance use amount""" + + +class InformationUnit(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """The information (data) Unit of measurement.""" + + UNSPECIFIED = "Unspecified" + """Unspecified data size unit""" + BIT = "Bit" + """Data size unit in bits""" + KILOBIT = "Kilobit" + """Data size unit in kilobits""" + MEGABIT = "Megabit" + """Data size unit in megabits""" + GIGABIT = "Gigabit" + """Data size unit in gigabits""" + TERABIT = "Terabit" + """Data size unit in terabits""" + PETABIT = "Petabit" + """Data size unit in petabits""" + BYTE = "Byte" + """Data size unit in bytes""" + KILOBYTE = "Kilobyte" + """Data size unit in kilobytes""" + MEGABYTE = "Megabyte" + """Data size unit in megabytes""" + GIGABYTE = "Gigabyte" + """Data size unit in gigabytes""" + TERABYTE = "Terabyte" + """Data size unit in terabytes""" + PETABYTE = "Petabyte" + """Data size unit in petabytes""" + + +class InnerErrorCode(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Human-readable error code.""" + + INVALID_REQUEST = "InvalidRequest" + """Invalid request error""" + INVALID_PARAMETER_VALUE = "InvalidParameterValue" + """Invalid parameter value error""" + KNOWLEDGE_BASE_NOT_FOUND = "KnowledgeBaseNotFound" + """Knowledge base not found error""" + AZURE_COGNITIVE_SEARCH_NOT_FOUND = "AzureCognitiveSearchNotFound" + """Azure Cognitive Search not found error""" + AZURE_COGNITIVE_SEARCH_THROTTLING = "AzureCognitiveSearchThrottling" + """Azure Cognitive Search throttling error""" + EXTRACTION_FAILURE = "ExtractionFailure" + """Extraction failure error""" + INVALID_REQUEST_BODY_FORMAT = "InvalidRequestBodyFormat" + """Invalid request body format error""" + EMPTY_REQUEST = "EmptyRequest" + """Empty request error""" + MISSING_INPUT_DOCUMENTS = "MissingInputDocuments" + """Missing input documents error""" + INVALID_DOCUMENT = "InvalidDocument" + """Invalid document error""" + MODEL_VERSION_INCORRECT = "ModelVersionIncorrect" + """Model version incorrect error""" + INVALID_DOCUMENT_BATCH = "InvalidDocumentBatch" + """Invalid document batch error""" + UNSUPPORTED_LANGUAGE_CODE = "UnsupportedLanguageCode" + """Unsupported language code error""" + INVALID_COUNTRY_HINT = "InvalidCountryHint" + """Invalid country hint error""" + + +class LengthUnit(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """The length unit of measurement.""" + + UNSPECIFIED = "Unspecified" + """Unspecified length unit.""" + KILOMETER = "Kilometer" + """Length unit in kilometers.""" + HECTOMETER = "Hectometer" + """Length unit in hectometers.""" + DECAMETER = "Decameter" + """Length unit in decameters.""" + METER = "Meter" + """Length unit in meters.""" + DECIMETER = "Decimeter" + """Length unit in decimeters.""" + CENTIMETER = "Centimeter" + """Length unit in centimeters.""" + MILLIMETER = "Millimeter" + """Length unit in millimeters.""" + MICROMETER = "Micrometer" + """Length unit in micrometers.""" + NANOMETER = "Nanometer" + """Length unit in nanometers.""" + PICOMETER = "Picometer" + """Length unit in picometers.""" + MILE = "Mile" + """Length unit in miles.""" + YARD = "Yard" + """Length unit in yards.""" + INCH = "Inch" + """Length unit in inches.""" + FOOT = "Foot" + """Length unit in feet.""" + LIGHT_YEAR = "LightYear" + """Length unit in light years.""" + POINT = "Point" + """Length unit in points.""" + + +class MetadataKind(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """The entity Metadata object kind.""" + + DATE_METADATA = "DateMetadata" + """Metadata for date-related values.""" + DATE_TIME_METADATA = "DateTimeMetadata" + """Metadata for date and time-related values.""" + TIME_METADATA = "TimeMetadata" + """Metadata for time-related values.""" + TEMPORAL_SET_METADATA = "TemporalSetMetadata" + """Metadata for set of time-related values.""" + NUMBER_METADATA = "NumberMetadata" + """Metadata for numeric values.""" + ORDINAL_METADATA = "OrdinalMetadata" + """Metadata for ordinal numbers.""" + SPEED_METADATA = "SpeedMetadata" + """Metadata for speed-related values.""" + WEIGHT_METADATA = "WeightMetadata" + """Metadata for weight-related values.""" + LENGTH_METADATA = "LengthMetadata" + """Metadata for length-related values.""" + VOLUME_METADATA = "VolumeMetadata" + """Metadata for volume-related values.""" + AREA_METADATA = "AreaMetadata" + """Metadata for area-related values.""" + AGE_METADATA = "AgeMetadata" + """Metadata for age-related values.""" + INFORMATION_METADATA = "InformationMetadata" + """Metadata for information-related values.""" + TEMPERATURE_METADATA = "TemperatureMetadata" + """Metadata for temperature-related values.""" + CURRENCY_METADATA = "CurrencyMetadata" + """Metadata for currency-related values.""" + NUMERIC_RANGE_METADATA = "NumericRangeMetadata" + """Metadata for numeric range values.""" + TEMPORAL_SPAN_METADATA = "TemporalSpanMetadata" + """Metadata for temporal span values.""" + + +class NumberKind(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """The type of the extracted number entity.""" + + INTEGER = "Integer" + """Integer number""" + DECIMAL = "Decimal" + """Decimal number""" + POWER = "Power" + """Power number""" + FRACTION = "Fraction" + """Fraction number""" + PERCENT = "Percent" + """Percent number""" + UNSPECIFIED = "Unspecified" + """Unspecified number kind""" + + +class PiiCategoriesExclude(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """(Optional) describes the PII categories to return.""" + + ABA_ROUTING_NUMBER = "ABARoutingNumber" + """ABA Routing number""" + AR_NATIONAL_IDENTITY_NUMBER = "ARNationalIdentityNumber" + """AR National Identity Number""" + AU_BANK_ACCOUNT_NUMBER = "AUBankAccountNumber" + """AT Bank Account Number""" + AU_DRIVERS_LICENSE_NUMBER = "AUDriversLicenseNumber" + """AU Driver's License Number""" + AU_MEDICAL_ACCOUNT_NUMBER = "AUMedicalAccountNumber" + """AU Medical Account Number""" + AU_PASSPORT_NUMBER = "AUPassportNumber" + """AU Passport Number""" + AU_TAX_FILE_NUMBER = "AUTaxFileNumber" + """AU Tax File Number""" + AU_BUSINESS_NUMBER = "AUBusinessNumber" + """AU Business Number""" + AU_COMPANY_NUMBER = "AUCompanyNumber" + """AU Company Number""" + AT_IDENTITY_CARD = "ATIdentityCard" + """AT Identity Card""" + AT_TAX_IDENTIFICATION_NUMBER = "ATTaxIdentificationNumber" + """AT Tax Identification Number""" + AT_VALUE_ADDED_TAX_NUMBER = "ATValueAddedTaxNumber" + """AT Value Added Tax Number""" + AZURE_DOCUMENT_DB_AUTH_KEY = "AzureDocumentDBAuthKey" + """Azure Document DB Auth Key""" + AZURE_IAAS_DATABASE_CONNECTION_AND_SQL_STRING = "AzureIAASDatabaseConnectionAndSQLString" + """Azure IAAS Database Connection And SQL String""" + AZURE_IO_T_CONNECTION_STRING = "AzureIoTConnectionString" + """Azure IoT Connection String""" + AZURE_PUBLISH_SETTING_PASSWORD = "AzurePublishSettingPassword" + """Azure Publish Setting Password""" + AZURE_REDIS_CACHE_STRING = "AzureRedisCacheString" + """Azure Redis Cache String""" + AZURE_SAS = "AzureSAS" + """Azure SAS""" + AZURE_SERVICE_BUS_STRING = "AzureServiceBusString" + """Azure Service Bus String""" + AZURE_STORAGE_ACCOUNT_KEY = "AzureStorageAccountKey" + """Azure Storage Account Key""" + AZURE_STORAGE_ACCOUNT_GENERIC = "AzureStorageAccountGeneric" + """Azure Storage Account Generic""" + BE_NATIONAL_NUMBER = "BENationalNumber" + """BE National Number""" + BE_NATIONAL_NUMBER_V2 = "BENationalNumberV2" + """BE National Number V2""" + BE_VALUE_ADDED_TAX_NUMBER = "BEValueAddedTaxNumber" + """BE Value Added Tax Number""" + BRCPF_NUMBER = "BRCPFNumber" + """BR CPF Number""" + BR_LEGAL_ENTITY_NUMBER = "BRLegalEntityNumber" + """BR Legal Entity Number""" + BR_NATIONAL_IDRG = "BRNationalIDRG" + """BR National ID RG""" + BG_UNIFORM_CIVIL_NUMBER = "BGUniformCivilNumber" + """BG Uniform Civil Number""" + CA_BANK_ACCOUNT_NUMBER = "CABankAccountNumber" + """CA Bank Account Number""" + CA_DRIVERS_LICENSE_NUMBER = "CADriversLicenseNumber" + """CA Driver's License Number""" + CA_HEALTH_SERVICE_NUMBER = "CAHealthServiceNumber" + """CA Health Service Number""" + CA_PASSPORT_NUMBER = "CAPassportNumber" + """CA Passport Number""" + CA_PERSONAL_HEALTH_IDENTIFICATION = "CAPersonalHealthIdentification" + """CA Personal Health Identification""" + CA_SOCIAL_INSURANCE_NUMBER = "CASocialInsuranceNumber" + """CA Social Insurance Number""" + CL_IDENTITY_CARD_NUMBER = "CLIdentityCardNumber" + """CL Identity Card Number""" + CN_RESIDENT_IDENTITY_CARD_NUMBER = "CNResidentIdentityCardNumber" + """CN Resident Identity Card Number""" + CREDIT_CARD_NUMBER = "CreditCardNumber" + """Credit Card Number""" + HR_IDENTITY_CARD_NUMBER = "HRIdentityCardNumber" + """HR Identity Card Number""" + HR_NATIONAL_ID_NUMBER = "HRNationalIDNumber" + """HR National ID Number""" + HR_PERSONAL_IDENTIFICATION_NUMBER = "HRPersonalIdentificationNumber" + """HR Personal Identification Number""" + HR_PERSONAL_IDENTIFICATION_OIB_NUMBER_V2 = "HRPersonalIdentificationOIBNumberV2" + """HR Personal Identification OIB Number V2""" + CY_IDENTITY_CARD = "CYIdentityCard" + """CY Identity Card""" + CY_TAX_IDENTIFICATION_NUMBER = "CYTaxIdentificationNumber" + """CY Tax Identification Number""" + CZ_PERSONAL_IDENTITY_NUMBER = "CZPersonalIdentityNumber" + """CZ Personal Identity Number""" + CZ_PERSONAL_IDENTITY_V2 = "CZPersonalIdentityV2" + """CZ Personal Identity V2""" + DK_PERSONAL_IDENTIFICATION_NUMBER = "DKPersonalIdentificationNumber" + """DK Personal Identification Number""" + DK_PERSONAL_IDENTIFICATION_V2 = "DKPersonalIdentificationV2" + """DK Personal Identification V2""" + DRUG_ENFORCEMENT_AGENCY_NUMBER = "DrugEnforcementAgencyNumber" + """Drug Enforcement Agency Number""" + EE_PERSONAL_IDENTIFICATION_CODE = "EEPersonalIdentificationCode" + """EE Personal Identification Code""" + EU_DEBIT_CARD_NUMBER = "EUDebitCardNumber" + """EU Debit Card Number""" + EU_DRIVERS_LICENSE_NUMBER = "EUDriversLicenseNumber" + """EU Driver's License Number""" + EUGPS_COORDINATES = "EUGPSCoordinates" + """EU GPS Coordinates""" + EU_NATIONAL_IDENTIFICATION_NUMBER = "EUNationalIdentificationNumber" + """EU National Identification Number""" + EU_PASSPORT_NUMBER = "EUPassportNumber" + """EU Passport Number""" + EU_SOCIAL_SECURITY_NUMBER = "EUSocialSecurityNumber" + """EU Social Security Number""" + EU_TAX_IDENTIFICATION_NUMBER = "EUTaxIdentificationNumber" + """EU Tax Identification Number""" + FI_EUROPEAN_HEALTH_NUMBER = "FIEuropeanHealthNumber" + """FI European Health Number""" + FI_NATIONAL_ID = "FINationalID" + """FI National ID""" + FI_NATIONAL_IDV2 = "FINationalIDV2" + """FI National ID V2""" + FI_PASSPORT_NUMBER = "FIPassportNumber" + """FI Passport Number""" + FR_DRIVERS_LICENSE_NUMBER = "FRDriversLicenseNumber" + """FR Driver's License Number""" + FR_HEALTH_INSURANCE_NUMBER = "FRHealthInsuranceNumber" + """FR Health Insurance Number""" + FR_NATIONAL_ID = "FRNationalID" + """FR National ID""" + FR_PASSPORT_NUMBER = "FRPassportNumber" + """FR Passport Number""" + FR_SOCIAL_SECURITY_NUMBER = "FRSocialSecurityNumber" + """FR Social Security Number""" + FR_TAX_IDENTIFICATION_NUMBER = "FRTaxIdentificationNumber" + """FR Tax Identification Number""" + FR_VALUE_ADDED_TAX_NUMBER = "FRValueAddedTaxNumber" + """FR Value Added Tax Number""" + DE_DRIVERS_LICENSE_NUMBER = "DEDriversLicenseNumber" + """DE Driver's License Number""" + DE_PASSPORT_NUMBER = "DEPassportNumber" + """DE Passport Number""" + DE_IDENTITY_CARD_NUMBER = "DEIdentityCardNumber" + """DE Identity Card Number""" + DE_TAX_IDENTIFICATION_NUMBER = "DETaxIdentificationNumber" + """DE Tax Identification Number""" + DE_VALUE_ADDED_NUMBER = "DEValueAddedNumber" + """DE Value Added Number""" + GR_NATIONAL_ID_CARD = "GRNationalIDCard" + """GR National ID Card""" + GR_NATIONAL_IDV2 = "GRNationalIDV2" + """GR National ID V2""" + GR_TAX_IDENTIFICATION_NUMBER = "GRTaxIdentificationNumber" + """GR Tax Identification Number""" + HK_IDENTITY_CARD_NUMBER = "HKIdentityCardNumber" + """HK Identity Card Number""" + HU_VALUE_ADDED_NUMBER = "HUValueAddedNumber" + """HU Value Added Number""" + HU_PERSONAL_IDENTIFICATION_NUMBER = "HUPersonalIdentificationNumber" + """HU Personal Identification Number""" + HU_TAX_IDENTIFICATION_NUMBER = "HUTaxIdentificationNumber" + """HU Tax Identification Number""" + IN_PERMANENT_ACCOUNT = "INPermanentAccount" + """IN Permanent Account""" + IN_UNIQUE_IDENTIFICATION_NUMBER = "INUniqueIdentificationNumber" + """IN Unique Identification Number""" + ID_IDENTITY_CARD_NUMBER = "IDIdentityCardNumber" + """ID Identity Card Number""" + INTERNATIONAL_BANKING_ACCOUNT_NUMBER = "InternationalBankingAccountNumber" + """International Banking Account Number""" + IE_PERSONAL_PUBLIC_SERVICE_NUMBER = "IEPersonalPublicServiceNumber" + """IE Personal Public Service Number""" + IE_PERSONAL_PUBLIC_SERVICE_NUMBER_V2 = "IEPersonalPublicServiceNumberV2" + """IE Personal Public Service Number V2""" + IL_BANK_ACCOUNT_NUMBER = "ILBankAccountNumber" + """IL Bank Account Number""" + IL_NATIONAL_ID = "ILNationalID" + """IL National ID""" + IT_DRIVERS_LICENSE_NUMBER = "ITDriversLicenseNumber" + """IT Driver's License Number""" + IT_FISCAL_CODE = "ITFiscalCode" + """IT Fiscal Code""" + IT_VALUE_ADDED_TAX_NUMBER = "ITValueAddedTaxNumber" + """IT Value Added Tax Number""" + JP_BANK_ACCOUNT_NUMBER = "JPBankAccountNumber" + """JP Bank Account Number""" + JP_DRIVERS_LICENSE_NUMBER = "JPDriversLicenseNumber" + """JP Driver's License Number""" + JP_PASSPORT_NUMBER = "JPPassportNumber" + """JP Passport Number""" + JP_RESIDENT_REGISTRATION_NUMBER = "JPResidentRegistrationNumber" + """JP Resident Registration Number""" + JP_SOCIAL_INSURANCE_NUMBER = "JPSocialInsuranceNumber" + """JP Social Insurance Number""" + JP_MY_NUMBER_CORPORATE = "JPMyNumberCorporate" + """JP My Number Corporate""" + JP_MY_NUMBER_PERSONAL = "JPMyNumberPersonal" + """JP My Number Personal""" + JP_RESIDENCE_CARD_NUMBER = "JPResidenceCardNumber" + """JP Residence Card Number""" + LV_PERSONAL_CODE = "LVPersonalCode" + """LV Personal Code""" + LT_PERSONAL_CODE = "LTPersonalCode" + """LT Personal Code""" + LU_NATIONAL_IDENTIFICATION_NUMBER_NATURAL = "LUNationalIdentificationNumberNatural" + """LU National Identification Number Natural""" + LU_NATIONAL_IDENTIFICATION_NUMBER_NON_NATURAL = "LUNationalIdentificationNumberNonNatural" + """LU National Identification Number Non Natural""" + MY_IDENTITY_CARD_NUMBER = "MYIdentityCardNumber" + """MY Identity Card Number""" + MT_IDENTITY_CARD_NUMBER = "MTIdentityCardNumber" + """MT Identity Card Number""" + MT_TAX_ID_NUMBER = "MTTaxIDNumber" + """MT Tax ID Number""" + NL_CITIZENS_SERVICE_NUMBER = "NLCitizensServiceNumber" + """NL Citizens Service Number""" + NL_CITIZENS_SERVICE_NUMBER_V2 = "NLCitizensServiceNumberV2" + """NL Citizens Service Number V2""" + NL_TAX_IDENTIFICATION_NUMBER = "NLTaxIdentificationNumber" + """NL Tax Identification Number""" + NL_VALUE_ADDED_TAX_NUMBER = "NLValueAddedTaxNumber" + """NL Value Added Tax Number""" + NZ_BANK_ACCOUNT_NUMBER = "NZBankAccountNumber" + """NZ Bank Account Number""" + NZ_DRIVERS_LICENSE_NUMBER = "NZDriversLicenseNumber" + """NZ Driver's License Number""" + NZ_INLAND_REVENUE_NUMBER = "NZInlandRevenueNumber" + """NZ Inland Revenue Number""" + NZ_MINISTRY_OF_HEALTH_NUMBER = "NZMinistryOfHealthNumber" + """NZ Ministry Of Health Number""" + NZ_SOCIAL_WELFARE_NUMBER = "NZSocialWelfareNumber" + """NZ Social Welfare Number""" + NO_IDENTITY_NUMBER = "NOIdentityNumber" + """NO Identity Number""" + PH_UNIFIED_MULTI_PURPOSE_ID_NUMBER = "PHUnifiedMultiPurposeIDNumber" + """PH Unified Multi Purpose ID Number""" + PL_IDENTITY_CARD = "PLIdentityCard" + """PL Identity Card""" + PL_NATIONAL_ID = "PLNationalID" + """PL National ID""" + PL_NATIONAL_IDV2 = "PLNationalIDV2" + """PL National ID V2""" + PL_PASSPORT_NUMBER = "PLPassportNumber" + """PL Passport Number""" + PL_TAX_IDENTIFICATION_NUMBER = "PLTaxIdentificationNumber" + """PL Tax Identification Number""" + PLREGON_NUMBER = "PLREGONNumber" + """PL REGON Number""" + PT_CITIZEN_CARD_NUMBER = "PTCitizenCardNumber" + """PT Citizen Card Number""" + PT_CITIZEN_CARD_NUMBER_V2 = "PTCitizenCardNumberV2" + """PT Citizen Card Number V2""" + PT_TAX_IDENTIFICATION_NUMBER = "PTTaxIdentificationNumber" + """PT Tax Identification Number""" + RO_PERSONAL_NUMERICAL_CODE = "ROPersonalNumericalCode" + """RO Personal Numerical Code""" + RU_PASSPORT_NUMBER_DOMESTIC = "RUPassportNumberDomestic" + """RU Passport Number Domestic""" + RU_PASSPORT_NUMBER_INTERNATIONAL = "RUPassportNumberInternational" + """RU Passport Number International""" + SA_NATIONAL_ID = "SANationalID" + """SA National ID""" + SG_NATIONAL_REGISTRATION_IDENTITY_CARD_NUMBER = "SGNationalRegistrationIdentityCardNumber" + """SG National Registration Identity Card Number""" + SK_PERSONAL_NUMBER = "SKPersonalNumber" + """SK Personal Number""" + SI_TAX_IDENTIFICATION_NUMBER = "SITaxIdentificationNumber" + """SI Tax Identification Number""" + SI_UNIQUE_MASTER_CITIZEN_NUMBER = "SIUniqueMasterCitizenNumber" + """SI Unique Master Citizen Number""" + ZA_IDENTIFICATION_NUMBER = "ZAIdentificationNumber" + """ZA Identification Number""" + KR_RESIDENT_REGISTRATION_NUMBER = "KRResidentRegistrationNumber" + """KR Resident Registration Number""" + ESDNI = "ESDNI" + """ES DNI""" + ES_SOCIAL_SECURITY_NUMBER = "ESSocialSecurityNumber" + """ES Social Security Number""" + ES_TAX_IDENTIFICATION_NUMBER = "ESTaxIdentificationNumber" + """ES Tax Identification Number""" + SQL_SERVER_CONNECTION_STRING = "SQLServerConnectionString" + """SQL Server Connection String""" + SE_NATIONAL_ID = "SENationalID" + """SE National ID""" + SE_NATIONAL_IDV2 = "SENationalIDV2" + """SE National ID V2""" + SE_PASSPORT_NUMBER = "SEPassportNumber" + """SE Passport Number""" + SE_TAX_IDENTIFICATION_NUMBER = "SETaxIdentificationNumber" + """SE Tax Identification Number""" + SWIFT_CODE = "SWIFTCode" + """SWIFT Code""" + CH_SOCIAL_SECURITY_NUMBER = "CHSocialSecurityNumber" + """CH Social Security Number""" + TW_NATIONAL_ID = "TWNationalID" + """TW National ID""" + TW_PASSPORT_NUMBER = "TWPassportNumber" + """TW Passport Number""" + TW_RESIDENT_CERTIFICATE = "TWResidentCertificate" + """TW Resident Certificate""" + TH_POPULATION_IDENTIFICATION_CODE = "THPopulationIdentificationCode" + """TH Population Identification Code""" + TR_NATIONAL_IDENTIFICATION_NUMBER = "TRNationalIdentificationNumber" + """TR National Identification Number""" + UK_DRIVERS_LICENSE_NUMBER = "UKDriversLicenseNumber" + """UK Driver's License Number""" + UK_ELECTORAL_ROLL_NUMBER = "UKElectoralRollNumber" + """UK Electoral Roll Number""" + UK_NATIONAL_HEALTH_NUMBER = "UKNationalHealthNumber" + """UK National Health Number""" + UK_NATIONAL_INSURANCE_NUMBER = "UKNationalInsuranceNumber" + """UK National Insurance Number""" + UK_UNIQUE_TAXPAYER_NUMBER = "UKUniqueTaxpayerNumber" + """UK Unique Taxpayer Number""" + USUK_PASSPORT_NUMBER = "USUKPassportNumber" + """US UK Passport Number""" + US_BANK_ACCOUNT_NUMBER = "USBankAccountNumber" + """US Bank Account Number""" + US_DRIVERS_LICENSE_NUMBER = "USDriversLicenseNumber" + """US Driver's License Number""" + US_INDIVIDUAL_TAXPAYER_IDENTIFICATION = "USIndividualTaxpayerIdentification" + """US Individual Taxpayer Identification""" + US_SOCIAL_SECURITY_NUMBER = "USSocialSecurityNumber" + """US Social Security Number""" + UA_PASSPORT_NUMBER_DOMESTIC = "UAPassportNumberDomestic" + """UA Passport Number Domestic""" + UA_PASSPORT_NUMBER_INTERNATIONAL = "UAPassportNumberInternational" + """UA Passport Number International""" + ORGANIZATION = "Organization" + """Organization""" + EMAIL = "Email" + """Email""" + URL = "URL" + """URL""" + AGE = "Age" + """Age""" + PHONE_NUMBER = "PhoneNumber" + """Phone Number""" + IP_ADDRESS = "IPAddress" + """IP Address""" + DATE = "Date" + """Date""" + PERSON = "Person" + """Person""" + ADDRESS = "Address" + """Address""" + DATE_OF_BIRTH = "DateOfBirth" + """Date Of Birth""" + BANK_ACCOUNT_NUMBER = "BankAccountNumber" + """Bank Account Number""" + PASSPORT_NUMBER = "PassportNumber" + """Passport Number""" + DRIVERS_LICENSE_NUMBER = "DriversLicenseNumber" + """Drivers License Number""" + NEIGHBORHOOD = "Neighborhood" + """Neighborhood""" + SORT_CODE = "SortCode" + """Sort Code. 6-digit number used in the UK to identify a specific bank and branch where a bank + account is held""" + PIN = "PIN" + """PIN""" + VIN = "VIN" + """VIN""" + LICENSE_PLATE = "LicensePlate" + """License Plate""" + + +class PiiCategory(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """(Optional) describes the PII categories to return.""" + + ABA_ROUTING_NUMBER = "ABARoutingNumber" + """ABA Routing number""" + AR_NATIONAL_IDENTITY_NUMBER = "ARNationalIdentityNumber" + """AR National Identity Number""" + AU_BANK_ACCOUNT_NUMBER = "AUBankAccountNumber" + """AT Bank Account Number""" + AU_DRIVERS_LICENSE_NUMBER = "AUDriversLicenseNumber" + """AU Driver's License Number""" + AU_MEDICAL_ACCOUNT_NUMBER = "AUMedicalAccountNumber" + """AU Medical Account Number""" + AU_PASSPORT_NUMBER = "AUPassportNumber" + """AU Passport Number""" + AU_TAX_FILE_NUMBER = "AUTaxFileNumber" + """AU Tax File Number""" + AU_BUSINESS_NUMBER = "AUBusinessNumber" + """AU Business Number""" + AU_COMPANY_NUMBER = "AUCompanyNumber" + """AU Company Number""" + AT_IDENTITY_CARD = "ATIdentityCard" + """AT Identity Card""" + AT_TAX_IDENTIFICATION_NUMBER = "ATTaxIdentificationNumber" + """AT Tax Identification Number""" + AT_VALUE_ADDED_TAX_NUMBER = "ATValueAddedTaxNumber" + """AT Value Added Tax Number""" + AZURE_DOCUMENT_DB_AUTH_KEY = "AzureDocumentDBAuthKey" + """Azure Document DB Auth Key""" + AZURE_IAAS_DATABASE_CONNECTION_AND_SQL_STRING = "AzureIAASDatabaseConnectionAndSQLString" + """Azure IAAS Database Connection And SQL String""" + AZURE_IO_T_CONNECTION_STRING = "AzureIoTConnectionString" + """Azure IoT Connection String""" + AZURE_PUBLISH_SETTING_PASSWORD = "AzurePublishSettingPassword" + """Azure Publish Setting Password""" + AZURE_REDIS_CACHE_STRING = "AzureRedisCacheString" + """Azure Redis Cache String""" + AZURE_SAS = "AzureSAS" + """Azure SAS""" + AZURE_SERVICE_BUS_STRING = "AzureServiceBusString" + """Azure Service Bus String""" + AZURE_STORAGE_ACCOUNT_KEY = "AzureStorageAccountKey" + """Azure Storage Account Key""" + AZURE_STORAGE_ACCOUNT_GENERIC = "AzureStorageAccountGeneric" + """Azure Storage Account Generic""" + BE_NATIONAL_NUMBER = "BENationalNumber" + """BE National Number""" + BE_NATIONAL_NUMBER_V2 = "BENationalNumberV2" + """BE National Number V2""" + BE_VALUE_ADDED_TAX_NUMBER = "BEValueAddedTaxNumber" + """BE Value Added Tax Number""" + BRCPF_NUMBER = "BRCPFNumber" + """BR CPF Number""" + BR_LEGAL_ENTITY_NUMBER = "BRLegalEntityNumber" + """BR Legal Entity Number""" + BR_NATIONAL_IDRG = "BRNationalIDRG" + """BR National ID RG""" + BG_UNIFORM_CIVIL_NUMBER = "BGUniformCivilNumber" + """BG Uniform Civil Number""" + CA_BANK_ACCOUNT_NUMBER = "CABankAccountNumber" + """CA Bank Account Number""" + CA_DRIVERS_LICENSE_NUMBER = "CADriversLicenseNumber" + """CA Driver's License Number""" + CA_HEALTH_SERVICE_NUMBER = "CAHealthServiceNumber" + """CA Health Service Number""" + CA_PASSPORT_NUMBER = "CAPassportNumber" + """CA Passport Number""" + CA_PERSONAL_HEALTH_IDENTIFICATION = "CAPersonalHealthIdentification" + """CA Personal Health Identification""" + CA_SOCIAL_INSURANCE_NUMBER = "CASocialInsuranceNumber" + """CA Social Insurance Number""" + CL_IDENTITY_CARD_NUMBER = "CLIdentityCardNumber" + """CL Identity Card Number""" + CN_RESIDENT_IDENTITY_CARD_NUMBER = "CNResidentIdentityCardNumber" + """CN Resident Identity Card Number""" + CREDIT_CARD_NUMBER = "CreditCardNumber" + """Credit Card Number""" + HR_IDENTITY_CARD_NUMBER = "HRIdentityCardNumber" + """HR Identity Card Number""" + HR_NATIONAL_ID_NUMBER = "HRNationalIDNumber" + """HR National ID Number""" + HR_PERSONAL_IDENTIFICATION_NUMBER = "HRPersonalIdentificationNumber" + """HR Personal Identification Number""" + HR_PERSONAL_IDENTIFICATION_OIB_NUMBER_V2 = "HRPersonalIdentificationOIBNumberV2" + """HR Personal Identification OIB Number V2""" + CY_IDENTITY_CARD = "CYIdentityCard" + """CY Identity Card""" + CY_TAX_IDENTIFICATION_NUMBER = "CYTaxIdentificationNumber" + """CY Tax Identification Number""" + CZ_PERSONAL_IDENTITY_NUMBER = "CZPersonalIdentityNumber" + """CZ Personal Identity Number""" + CZ_PERSONAL_IDENTITY_V2 = "CZPersonalIdentityV2" + """CZ Personal Identity V2""" + DK_PERSONAL_IDENTIFICATION_NUMBER = "DKPersonalIdentificationNumber" + """DK Personal Identification Number""" + DK_PERSONAL_IDENTIFICATION_V2 = "DKPersonalIdentificationV2" + """DK Personal Identification V2""" + DRUG_ENFORCEMENT_AGENCY_NUMBER = "DrugEnforcementAgencyNumber" + """Drug Enforcement Agency Number""" + EE_PERSONAL_IDENTIFICATION_CODE = "EEPersonalIdentificationCode" + """EE Personal Identification Code""" + EU_DEBIT_CARD_NUMBER = "EUDebitCardNumber" + """EU Debit Card Number""" + EU_DRIVERS_LICENSE_NUMBER = "EUDriversLicenseNumber" + """EU Driver's License Number""" + EUGPS_COORDINATES = "EUGPSCoordinates" + """EU GPS Coordinates""" + EU_NATIONAL_IDENTIFICATION_NUMBER = "EUNationalIdentificationNumber" + """EU National Identification Number""" + EU_PASSPORT_NUMBER = "EUPassportNumber" + """EU Passport Number""" + EU_SOCIAL_SECURITY_NUMBER = "EUSocialSecurityNumber" + """EU Social Security Number""" + EU_TAX_IDENTIFICATION_NUMBER = "EUTaxIdentificationNumber" + """EU Tax Identification Number""" + FI_EUROPEAN_HEALTH_NUMBER = "FIEuropeanHealthNumber" + """FI European Health Number""" + FI_NATIONAL_ID = "FINationalID" + """FI National ID""" + FI_NATIONAL_IDV2 = "FINationalIDV2" + """FI National ID V2""" + FI_PASSPORT_NUMBER = "FIPassportNumber" + """FI Passport Number""" + FR_DRIVERS_LICENSE_NUMBER = "FRDriversLicenseNumber" + """FR Driver's License Number""" + FR_HEALTH_INSURANCE_NUMBER = "FRHealthInsuranceNumber" + """FR Health Insurance Number""" + FR_NATIONAL_ID = "FRNationalID" + """FR National ID""" + FR_PASSPORT_NUMBER = "FRPassportNumber" + """FR Passport Number""" + FR_SOCIAL_SECURITY_NUMBER = "FRSocialSecurityNumber" + """FR Social Security Number""" + FR_TAX_IDENTIFICATION_NUMBER = "FRTaxIdentificationNumber" + """FR Tax Identification Number""" + FR_VALUE_ADDED_TAX_NUMBER = "FRValueAddedTaxNumber" + """FR Value Added Tax Number""" + DE_DRIVERS_LICENSE_NUMBER = "DEDriversLicenseNumber" + """DE Driver's License Number""" + DE_PASSPORT_NUMBER = "DEPassportNumber" + """DE Passport Number""" + DE_IDENTITY_CARD_NUMBER = "DEIdentityCardNumber" + """DE Identity Card Number""" + DE_TAX_IDENTIFICATION_NUMBER = "DETaxIdentificationNumber" + """DE Tax Identification Number""" + DE_VALUE_ADDED_NUMBER = "DEValueAddedNumber" + """DE Value Added Number""" + GR_NATIONAL_ID_CARD = "GRNationalIDCard" + """GR National ID Card""" + GR_NATIONAL_IDV2 = "GRNationalIDV2" + """GR National ID V2""" + GR_TAX_IDENTIFICATION_NUMBER = "GRTaxIdentificationNumber" + """GR Tax Identification Number""" + HK_IDENTITY_CARD_NUMBER = "HKIdentityCardNumber" + """HK Identity Card Number""" + HU_VALUE_ADDED_NUMBER = "HUValueAddedNumber" + """HU Value Added Number""" + HU_PERSONAL_IDENTIFICATION_NUMBER = "HUPersonalIdentificationNumber" + """HU Personal Identification Number""" + HU_TAX_IDENTIFICATION_NUMBER = "HUTaxIdentificationNumber" + """HU Tax Identification Number""" + IN_PERMANENT_ACCOUNT = "INPermanentAccount" + """IN Permanent Account""" + IN_UNIQUE_IDENTIFICATION_NUMBER = "INUniqueIdentificationNumber" + """IN Unique Identification Number""" + ID_IDENTITY_CARD_NUMBER = "IDIdentityCardNumber" + """ID Identity Card Number""" + INTERNATIONAL_BANKING_ACCOUNT_NUMBER = "InternationalBankingAccountNumber" + """International Banking Account Number""" + IE_PERSONAL_PUBLIC_SERVICE_NUMBER = "IEPersonalPublicServiceNumber" + """IE Personal Public Service Number""" + IE_PERSONAL_PUBLIC_SERVICE_NUMBER_V2 = "IEPersonalPublicServiceNumberV2" + """IE Personal Public Service Number V2""" + IL_BANK_ACCOUNT_NUMBER = "ILBankAccountNumber" + """IL Bank Account Number""" + IL_NATIONAL_ID = "ILNationalID" + """IL National ID""" + IT_DRIVERS_LICENSE_NUMBER = "ITDriversLicenseNumber" + """IT Driver's License Number""" + IT_FISCAL_CODE = "ITFiscalCode" + """IT Fiscal Code""" + IT_VALUE_ADDED_TAX_NUMBER = "ITValueAddedTaxNumber" + """IT Value Added Tax Number""" + JP_BANK_ACCOUNT_NUMBER = "JPBankAccountNumber" + """JP Bank Account Number""" + JP_DRIVERS_LICENSE_NUMBER = "JPDriversLicenseNumber" + """JP Driver's License Number""" + JP_PASSPORT_NUMBER = "JPPassportNumber" + """JP Passport Number""" + JP_RESIDENT_REGISTRATION_NUMBER = "JPResidentRegistrationNumber" + """JP Resident Registration Number""" + JP_SOCIAL_INSURANCE_NUMBER = "JPSocialInsuranceNumber" + """JP Social Insurance Number""" + JP_MY_NUMBER_CORPORATE = "JPMyNumberCorporate" + """JP My Number Corporate""" + JP_MY_NUMBER_PERSONAL = "JPMyNumberPersonal" + """JP My Number Personal""" + JP_RESIDENCE_CARD_NUMBER = "JPResidenceCardNumber" + """JP Residence Card Number""" + LV_PERSONAL_CODE = "LVPersonalCode" + """LV Personal Code""" + LT_PERSONAL_CODE = "LTPersonalCode" + """LT Personal Code""" + LU_NATIONAL_IDENTIFICATION_NUMBER_NATURAL = "LUNationalIdentificationNumberNatural" + """LU National Identification Number Natural""" + LU_NATIONAL_IDENTIFICATION_NUMBER_NON_NATURAL = "LUNationalIdentificationNumberNonNatural" + """LU National Identification Number Non Natural""" + MY_IDENTITY_CARD_NUMBER = "MYIdentityCardNumber" + """MY Identity Card Number""" + MT_IDENTITY_CARD_NUMBER = "MTIdentityCardNumber" + """MT Identity Card Number""" + MT_TAX_ID_NUMBER = "MTTaxIDNumber" + """MT Tax ID Number""" + NL_CITIZENS_SERVICE_NUMBER = "NLCitizensServiceNumber" + """NL Citizens Service Number""" + NL_CITIZENS_SERVICE_NUMBER_V2 = "NLCitizensServiceNumberV2" + """NL Citizens Service Number V2""" + NL_TAX_IDENTIFICATION_NUMBER = "NLTaxIdentificationNumber" + """NL Tax Identification Number""" + NL_VALUE_ADDED_TAX_NUMBER = "NLValueAddedTaxNumber" + """NL Value Added Tax Number""" + NZ_BANK_ACCOUNT_NUMBER = "NZBankAccountNumber" + """NZ Bank Account Number""" + NZ_DRIVERS_LICENSE_NUMBER = "NZDriversLicenseNumber" + """NZ Driver's License Number""" + NZ_INLAND_REVENUE_NUMBER = "NZInlandRevenueNumber" + """NZ Inland Revenue Number""" + NZ_MINISTRY_OF_HEALTH_NUMBER = "NZMinistryOfHealthNumber" + """NZ Ministry Of Health Number""" + NZ_SOCIAL_WELFARE_NUMBER = "NZSocialWelfareNumber" + """NZ Social Welfare Number""" + NO_IDENTITY_NUMBER = "NOIdentityNumber" + """NO Identity Number""" + PH_UNIFIED_MULTI_PURPOSE_ID_NUMBER = "PHUnifiedMultiPurposeIDNumber" + """PH Unified Multi Purpose ID Number""" + PL_IDENTITY_CARD = "PLIdentityCard" + """PL Identity Card""" + PL_NATIONAL_ID = "PLNationalID" + """PL National ID""" + PL_NATIONAL_IDV2 = "PLNationalIDV2" + """PL National ID V2""" + PL_PASSPORT_NUMBER = "PLPassportNumber" + """PL Passport Number""" + PL_TAX_IDENTIFICATION_NUMBER = "PLTaxIdentificationNumber" + """PL Tax Identification Number""" + PLREGON_NUMBER = "PLREGONNumber" + """PL REGON Number""" + PT_CITIZEN_CARD_NUMBER = "PTCitizenCardNumber" + """PT Citizen Card Number""" + PT_CITIZEN_CARD_NUMBER_V2 = "PTCitizenCardNumberV2" + """PT Citizen Card Number V2""" + PT_TAX_IDENTIFICATION_NUMBER = "PTTaxIdentificationNumber" + """PT Tax Identification Number""" + RO_PERSONAL_NUMERICAL_CODE = "ROPersonalNumericalCode" + """RO Personal Numerical Code""" + RU_PASSPORT_NUMBER_DOMESTIC = "RUPassportNumberDomestic" + """RU Passport Number Domestic""" + RU_PASSPORT_NUMBER_INTERNATIONAL = "RUPassportNumberInternational" + """RU Passport Number International""" + SA_NATIONAL_ID = "SANationalID" + """SA National ID""" + SG_NATIONAL_REGISTRATION_IDENTITY_CARD_NUMBER = "SGNationalRegistrationIdentityCardNumber" + """SG National Registration Identity Card Number""" + SK_PERSONAL_NUMBER = "SKPersonalNumber" + """SK Personal Number""" + SI_TAX_IDENTIFICATION_NUMBER = "SITaxIdentificationNumber" + """SI Tax Identification Number""" + SI_UNIQUE_MASTER_CITIZEN_NUMBER = "SIUniqueMasterCitizenNumber" + """SI Unique Master Citizen Number""" + ZA_IDENTIFICATION_NUMBER = "ZAIdentificationNumber" + """ZA Identification Number""" + KR_RESIDENT_REGISTRATION_NUMBER = "KRResidentRegistrationNumber" + """KR Resident Registration Number""" + ESDNI = "ESDNI" + """ES DNI""" + ES_SOCIAL_SECURITY_NUMBER = "ESSocialSecurityNumber" + """ES Social Security Number""" + ES_TAX_IDENTIFICATION_NUMBER = "ESTaxIdentificationNumber" + """ES Tax Identification Number""" + SQL_SERVER_CONNECTION_STRING = "SQLServerConnectionString" + """SQL Server Connection String""" + SE_NATIONAL_ID = "SENationalID" + """SE National ID""" + SE_NATIONAL_IDV2 = "SENationalIDV2" + """SE National ID V2""" + SE_PASSPORT_NUMBER = "SEPassportNumber" + """SE Passport Number""" + SE_TAX_IDENTIFICATION_NUMBER = "SETaxIdentificationNumber" + """SE Tax Identification Number""" + SWIFT_CODE = "SWIFTCode" + """SWIFT Code""" + CH_SOCIAL_SECURITY_NUMBER = "CHSocialSecurityNumber" + """CH Social Security Number""" + TW_NATIONAL_ID = "TWNationalID" + """TW National ID""" + TW_PASSPORT_NUMBER = "TWPassportNumber" + """TW Passport Number""" + TW_RESIDENT_CERTIFICATE = "TWResidentCertificate" + """TW Resident Certificate""" + TH_POPULATION_IDENTIFICATION_CODE = "THPopulationIdentificationCode" + """TH Population Identification Code""" + TR_NATIONAL_IDENTIFICATION_NUMBER = "TRNationalIdentificationNumber" + """TR National Identification Number""" + UK_DRIVERS_LICENSE_NUMBER = "UKDriversLicenseNumber" + """UK Driver's License Number""" + UK_ELECTORAL_ROLL_NUMBER = "UKElectoralRollNumber" + """UK Electoral Roll Number""" + UK_NATIONAL_HEALTH_NUMBER = "UKNationalHealthNumber" + """UK National Health Number""" + UK_NATIONAL_INSURANCE_NUMBER = "UKNationalInsuranceNumber" + """UK National Insurance Number""" + UK_UNIQUE_TAXPAYER_NUMBER = "UKUniqueTaxpayerNumber" + """UK Unique Taxpayer Number""" + USUK_PASSPORT_NUMBER = "USUKPassportNumber" + """US UK Passport Number""" + US_BANK_ACCOUNT_NUMBER = "USBankAccountNumber" + """US Bank Account Number""" + US_DRIVERS_LICENSE_NUMBER = "USDriversLicenseNumber" + """US Driver's License Number""" + US_INDIVIDUAL_TAXPAYER_IDENTIFICATION = "USIndividualTaxpayerIdentification" + """US Individual Taxpayer Identification""" + US_SOCIAL_SECURITY_NUMBER = "USSocialSecurityNumber" + """US Social Security Number""" + UA_PASSPORT_NUMBER_DOMESTIC = "UAPassportNumberDomestic" + """UA Passport Number Domestic""" + UA_PASSPORT_NUMBER_INTERNATIONAL = "UAPassportNumberInternational" + """UA Passport Number International""" + ORGANIZATION = "Organization" + """Organization""" + EMAIL = "Email" + """Email""" + URL = "URL" + """URL""" + AGE = "Age" + """Age""" + PHONE_NUMBER = "PhoneNumber" + """Phone Number""" + IP_ADDRESS = "IPAddress" + """IP Address""" + DATE = "Date" + """Date""" + PERSON = "Person" + """Person""" + ADDRESS = "Address" + """Address""" + DATE_OF_BIRTH = "DateOfBirth" + """Date Of Birth""" + BANK_ACCOUNT_NUMBER = "BankAccountNumber" + """Bank Account Number""" + PASSPORT_NUMBER = "PassportNumber" + """Passport Number""" + DRIVERS_LICENSE_NUMBER = "DriversLicenseNumber" + """Drivers License Number""" + NEIGHBORHOOD = "Neighborhood" + """Neighborhood""" + SORT_CODE = "SortCode" + """Sort Code. 6-digit number used in the UK to identify a specific bank and branch where a bank + account is held""" + PIN = "PIN" + """PIN""" + VIN = "VIN" + """VIN""" + LICENSE_PLATE = "LicensePlate" + """License Plate""" + ALL = "All" + """All PII categories.""" + DEFAULT = "Default" + """Default PII categories for the language.""" + + +class PiiDomain(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """PII domains.""" + + PHI = "phi" + """Indicates that entities in the Personal Health Information domain should be redacted.""" + NONE = "none" + """Indicates that no domain is specified.""" + + +class PolicyKind(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Kinds of overlap policies supported.""" + + MATCH_LONGEST = "matchLongest" + """Represents MatchLongestEntityPolicyType""" + ALLOW_OVERLAP = "allowOverlap" + """Represents AllowOverlapEntityPolicyType""" + + +class RangeInclusivity(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """The range inclusiveness of this property property.""" + + NONE_INCLUSIVE = "NoneInclusive" + """No inclusivity""" + LEFT_INCLUSIVE = "LeftInclusive" + """Left side inclusive""" + RIGHT_INCLUSIVE = "RightInclusive" + """Right side inclusive""" + LEFT_RIGHT_INCLUSIVE = "LeftRightInclusive" + """Both sides inclusive""" + + +class RangeKind(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """The kind of the number range entity.""" + + NUMBER = "Number" + """Number range""" + SPEED = "Speed" + """Speed range""" + WEIGHT = "Weight" + """Weight range""" + LENGTH = "Length" + """Length range""" + VOLUME = "Volume" + """Volume range""" + AREA = "Area" + """Area range""" + AGE = "Age" + """Age range""" + INFORMATION = "Information" + """Information range""" + TEMPERATURE = "Temperature" + """Temperature range""" + CURRENCY = "Currency" + """Currency range""" + + +class RedactionCharacter(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Optional parameter to use a Custom Character to be used for redaction in PII responses. Default + character will be * as before. We allow specific ascii characters for redaction. + """ + + EXCLAMATION_POINT = "!" + """Exclamation point character""" + NUMBER_SIGN = "#" + """Number sign character""" + DOLLAR = "$" + """Dollar sign character""" + PER_CENT = "%" + """Percent sign character""" + AMPERSAND = "&" + """Ampersand character""" + ASTERISK = "*" + """Asterisk character""" + PLUS = "+" + """Plus sign character""" + MINUS = "-" + """Minus sign character""" + EQUALS = "=" + """Equals sign character""" + QUESTION_MARK = "?" + """Question mark character""" + AT_SIGN = "@" + """At sign character""" + CARET = "^" + """Caret character""" + UNDERSCORE = "_" + """Underscore character""" + TILDE = "~" + """Tilde character""" + + +class RedactionPolicyKind(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Kinds of redaction policies supported.""" + + NO_MASK = "noMask" + """Do not redact detected entities.""" + CHARACTER_MASK = "characterMask" + """React detected entities with redaction character.""" + ENTITY_MASK = "entityMask" + """Redact detected entities with entity type.""" + + +class RelationType(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Type of relation. Examples include: ``DosageOfMedication`` or 'FrequencyOfMedication', etc.""" + + ABBREVIATION = "Abbreviation" + """Abbreviation""" + DIRECTION_OF_BODY_STRUCTURE = "DirectionOfBodyStructure" + """Direction of body structure""" + DIRECTION_OF_CONDITION = "DirectionOfCondition" + """Direction of condition""" + DIRECTION_OF_EXAMINATION = "DirectionOfExamination" + """Direction of examination""" + DIRECTION_OF_TREATMENT = "DirectionOfTreatment" + """Direction of treatment""" + DOSAGE_OF_MEDICATION = "DosageOfMedication" + """Dosage of medication""" + FORM_OF_MEDICATION = "FormOfMedication" + """Form of medication""" + FREQUENCY_OF_MEDICATION = "FrequencyOfMedication" + """Frequency of medication""" + FREQUENCY_OF_TREATMENT = "FrequencyOfTreatment" + """Frequency of treatment""" + QUALIFIER_OF_CONDITION = "QualifierOfCondition" + """Qualifier of condition""" + RELATION_OF_EXAMINATION = "RelationOfExamination" + """Relation of examination""" + ROUTE_OF_MEDICATION = "RouteOfMedication" + """Route of medication""" + TIME_OF_CONDITION = "TimeOfCondition" + """Time of condition""" + TIME_OF_EVENT = "TimeOfEvent" + """Time of event""" + TIME_OF_EXAMINATION = "TimeOfExamination" + """Time of examination""" + TIME_OF_MEDICATION = "TimeOfMedication" + """Time of medication""" + TIME_OF_TREATMENT = "TimeOfTreatment" + """Time of treatment""" + UNIT_OF_CONDITION = "UnitOfCondition" + """Unit of condition""" + UNIT_OF_EXAMINATION = "UnitOfExamination" + """Unit of examination""" + VALUE_OF_CONDITION = "ValueOfCondition" + """Value of condition""" + VALUE_OF_EXAMINATION = "ValueOfExamination" + """Value of examination""" + BODY_SITE_OF_CONDITION = "BodySiteOfCondition" + """Body site of condition""" + BODY_SITE_OF_TREATMENT = "BodySiteOfTreatment" + """Body site of treatment""" + COURSE_OF_CONDITION = "CourseOfCondition" + """Course of condition""" + COURSE_OF_EXAMINATION = "CourseOfExamination" + """Course of examination""" + COURSE_OF_MEDICATION = "CourseOfMedication" + """Course of medication""" + COURSE_OF_TREATMENT = "CourseOfTreatment" + """Course of treatment""" + EXAMINATION_FINDS_CONDITION = "ExaminationFindsCondition" + """Examination finds condition""" + EXPRESSION_OF_GENE = "ExpressionOfGene" + """Expression of gene""" + EXPRESSION_OF_VARIANT = "ExpressionOfVariant" + """Expression of variant""" + FREQUENCY_OF_CONDITION = "FrequencyOfCondition" + """Frequency of condition""" + MUTATION_TYPE_OF_GENE = "MutationTypeOfGene" + """Mutation type of gene""" + MUTATION_TYPE_OF_VARIANT = "MutationTypeOfVariant" + """Mutation type of variant""" + SCALE_OF_CONDITION = "ScaleOfCondition" + """Scale of condition""" + VARIANT_OF_GENE = "VariantOfGene" + """Variant of gene""" + + +class RelativeTo(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """The reference point that the ordinal number denotes.""" + + CURRENT = "Current" + """Current state or position""" + END = "End" + """End state or position""" + START = "Start" + """Start state or position""" + + +class ScriptCode(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Identifies the script of the input document. Maps to the ISO 15924 standard script code.""" + + ARAB = "Arab" + """Script code for the Arabic script.""" + ARMN = "Armn" + """Script code for the Armenian script.""" + BENG = "Beng" + """Script code for the Bangla script.""" + CANS = "Cans" + """Script code for the UnifiedCanadianAboriginalSyllabics script.""" + CYRL = "Cyrl" + """Script code for the Cyrillic script.""" + DEVA = "Deva" + """Script code for the Devanagari script.""" + ETHI = "Ethi" + """Script code for the Ethiopic script.""" + GEOR = "Geor" + """Script code for the Georgian script.""" + GREK = "Grek" + """Script code for the Greek script.""" + GUJR = "Gujr" + """Script code for the Gujarati script.""" + GURU = "Guru" + """Script code for the Gurmukhi script.""" + HANG = "Hang" + """Script code for the Hangul script.""" + HANI = "Hani" + """Script code for the HanLiteral script.""" + HANS = "Hans" + """Script code for the HanSimplified script.""" + HANT = "Hant" + """Script code for the HanTraditional script.""" + HEBR = "Hebr" + """Script code for the Hebrew script.""" + JPAN = "Jpan" + """Script code for the Japanese script.""" + KHMR = "Khmr" + """Script code for the Khmer script.""" + KNDA = "Knda" + """Script code for the Kannada script.""" + LAOO = "Laoo" + """Script code for the Lao script.""" + LATN = "Latn" + """Script code for the Latin script.""" + MLYM = "Mlym" + """Script code for the Malayalam script.""" + MONG = "Mong" + """Script code for the Mongolian script.""" + MTEI = "Mtei" + """Script code for the Meitei script.""" + MYMR = "Mymr" + """Script code for the Myanmar script.""" + OLCK = "Olck" + """Script code for the Santali script.""" + ORYA = "Orya" + """Script code for the Odia script.""" + SINH = "Sinh" + """Script code for the Sinhala script.""" + SHRD = "Shrd" + """Script code for the Sharada script.""" + TAML = "Taml" + """Script code for the Tamil script.""" + TELU = "Telu" + """Script code for the Telugu script.""" + THAA = "Thaa" + """Script code for the Thaana script.""" + THAI = "Thai" + """Script code for the Thai script.""" + TIBT = "Tibt" + """Script code for the Tibetan script.""" + + +class ScriptKind(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Identifies the script of the input document. Maps to the ISO 15924 standard formal name.""" + + ARABIC = "Arabic" + """Script name for the Arabic script.""" + ARMENIAN = "Armenian" + """Script name for the Armenian script.""" + BANGLA = "Bangla" + """Script name for the Bangla script.""" + UNIFIED_CANADIAN_ABORIGINAL_SYLLABICS = "UnifiedCanadianAboriginalSyllabics" + """Script name for the UnifiedCanadianAboriginalSyllabics script.""" + CYRILLIC = "Cyrillic" + """Script name for the Cyrillic script.""" + DEVANAGARI = "Devanagari" + """Script name for the Devanagari script.""" + ETHIOPIC = "Ethiopic" + """Script name for the Ethiopic script.""" + GEORGIAN = "Georgian" + """Script name for the Georgian script.""" + GREEK = "Greek" + """Script name for the Greek script.""" + GUJARATI = "Gujarati" + """Script name for the Gujarati script.""" + GURMUKHI = "Gurmukhi" + """Script name for the Gurmukhi script.""" + HANGUL = "Hangul" + """Script name for the Hangul script.""" + HAN_LITERAL = "HanLiteral" + """Script name for the HanLiteral script.""" + HAN_SIMPLIFIED = "HanSimplified" + """Script name for the HanSimplified script.""" + HAN_TRADITIONAL = "HanTraditional" + """Script name for the HanTraditional script.""" + HEBREW = "Hebrew" + """Script name for the Hebrew script.""" + JAPANESE = "Japanese" + """Script name for the Japanese script.""" + KHMER = "Khmer" + """Script name for the Khmer script.""" + KANNADA = "Kannada" + """Script name for the Kannada script.""" + LAO = "Lao" + """Script name for the Lao script.""" + LATIN = "Latin" + """Script name for the Latin script.""" + MALAYALAM = "Malayalam" + """Script name for the Malayalam script.""" + MEITEI = "Meitei" + """Script name for the Meitei script.""" + MONGOLIAN = "Mongolian" + """Script name for the Mongolian script.""" + MYANMAR = "Myanmar" + """Script name for the Myanmar script.""" + ODIA = "Odia" + """Script name for the Odia script.""" + SANTALI = "Santali" + """Script name for the Santali script.""" + SHARADA = "Sharada" + """Script name for the Sharada script.""" + SINHALA = "Sinhala" + """Script name for the Sinhala script.""" + TAMIL = "Tamil" + """Script name for the Tamil script.""" + TELUGU = "Telugu" + """Script name for the Telugu script.""" + THAANA = "Thaana" + """Script name for the Thaana script.""" + THAI = "Thai" + """Script name for the Thai script.""" + TIBETAN = "Tibetan" + """Script name for the Tibetan script.""" + + +class SentenceSentimentValue(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """The predicted Sentiment for the sentence.""" + + POSITIVE = "positive" + """Positive sentiment""" + NEUTRAL = "neutral" + """Neutral sentiment""" + NEGATIVE = "negative" + """Negative sentiment""" + + +class SpeedUnit(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """The speed Unit of measurement.""" + + UNSPECIFIED = "Unspecified" + """Unspecified speed unit.""" + METERS_PER_SECOND = "MetersPerSecond" + """Speed unit in meters per second.""" + KILOMETERS_PER_HOUR = "KilometersPerHour" + """Speed unit in kilometers per hour.""" + KILOMETERS_PER_MINUTE = "KilometersPerMinute" + """Speed unit in kilometers per minute.""" + KILOMETERS_PER_SECOND = "KilometersPerSecond" + """Speed unit in kilometers per second.""" + MILES_PER_HOUR = "MilesPerHour" + """Speed unit in miles per hour.""" + KNOTS = "Knots" + """Speed unit in knots.""" + FEET_PER_SECOND = "FeetPerSecond" + """Speed unit in feet per second.""" + FEET_PER_MINUTE = "FeetPerMinute" + """Speed unit in feet per minute.""" + YARDS_PER_MINUTE = "YardsPerMinute" + """Speed unit in yards per minute.""" + YARDS_PER_SECOND = "YardsPerSecond" + """Speed unit in yards per second.""" + METERS_PER_MILLISECOND = "MetersPerMillisecond" + """Speed unit in meters per millisecond.""" + CENTIMETERS_PER_MILLISECOND = "CentimetersPerMillisecond" + """Speed unit in centimeters per millisecond.""" + KILOMETERS_PER_MILLISECOND = "KilometersPerMillisecond" + """Speed unit in Kilometers per millisecond.""" + + +class State(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """The status of the task at the mentioned last update time.""" + + NOT_STARTED = "notStarted" + """Not started status""" + RUNNING = "running" + """Running status""" + SUCCEEDED = "succeeded" + """Succeeded status""" + PARTIALLY_COMPLETED = "partiallyCompleted" + """Partially completed status""" + FAILED = "failed" + """Failed status""" + CANCELLED = "cancelled" + """Cancelled status""" + CANCELLING = "cancelling" + """Cancelling status""" + + +class StringIndexType(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Specifies the method used to interpret string offsets. Defaults to Text Elements (Graphemes) + according to Unicode v8.0.0. For additional information see + `https://aka.ms/text-analytics-offsets `_. + """ + + TEXT_ELEMENTS_V8 = "TextElements_v8" + """Returned offset and length values will correspond to TextElements (Graphemes and Grapheme + clusters) confirming to the Unicode 8.0.0 standard. Use this option if your application is + written in .Net Framework or .Net Core and you will be using StringInfo.""" + UNICODE_CODE_POINT = "UnicodeCodePoint" + """Returned offset and length values will correspond to Unicode code points. Use this option if + your application is written in a language that support Unicode, for example Python.""" + UTF16_CODE_UNIT = "Utf16CodeUnit" + """Returned offset and length values will correspond to UTF-16 code units. Use this option if your + application is written in a language that support Unicode, for example Java, JavaScript.""" + + +class SummaryLengthBucket(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Enum that defines the length of the output summaries.""" + + SHORT = "short" + """Instructs model to generate shorter length summaries.""" + MEDIUM = "medium" + """Instructs model to generate medium length summaries.""" + LONG = "long" + """Instructs model to generate longer length summaries.""" + + +class TargetRelationType(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """The type related to the target.""" + + ASSESSMENT = "assessment" + """Assessment relation.""" + TARGET = "target" + """Target relation.""" + + +class TemperatureUnit(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """The temperature Unit of measurement.""" + + UNSPECIFIED = "Unspecified" + """Unspecified temperature unit""" + FAHRENHEIT = "Fahrenheit" + """Temperature unit in Fahrenheit""" + KELVIN = "Kelvin" + """Temperature unit in Kelvin""" + RANKINE = "Rankine" + """Temperature unit in Rankine""" + CELSIUS = "Celsius" + """Temperature unit in Celsius""" + + +class Temporality(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Describes temporal information regarding the entity.""" + + CURRENT = "current" + """Current temporality""" + PAST = "past" + """Past temporality""" + FUTURE = "future" + """Future temporality""" + + +class TemporalModifier(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """An optional modifier of a date/time instance.""" + + AFTER_APPROX = "AfterApprox" + """After an approximate time""" + BEFORE = "Before" + """Before a specific time""" + BEFORE_START = "BeforeStart" + """Before the start of a time period""" + APPROX = "Approx" + """Approximately at a specific time""" + REFERENCE_UNDEFINED = "ReferenceUndefined" + """Reference to an undefined time""" + SINCE_END = "SinceEnd" + """Since the end of a time period""" + AFTER_MID = "AfterMid" + """After the middle of a time period""" + START = "Start" + """At the start of a time period""" + AFTER = "After" + """After a specific time""" + BEFORE_END = "BeforeEnd" + """Before the end of a time period""" + UNTIL = "Until" + """Until a specific time""" + END = "End" + """At the end of a time period""" + LESS = "Less" + """Less than a specific time""" + SINCE = "Since" + """Since a specific time""" + AFTER_START = "AfterStart" + """After the start of a time period""" + BEFORE_APPROX = "BeforeApprox" + """Before an approximate time""" + MID = "Mid" + """In the middle of a time period""" + MORE = "More" + """More than a specific time""" + + +class TokenSentimentValue(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """The predicted Sentiment for the sentence.""" + + POSITIVE = "positive" + """Positive sentiment""" + MIXED = "mixed" + """Mixed sentiment""" + NEGATIVE = "negative" + """Negative sentiment""" + + +class VolumeUnit(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """The Volume Unit of measurement.""" + + UNSPECIFIED = "Unspecified" + """Unspecified volume unit.""" + CUBIC_METER = "CubicMeter" + """Volume unit in cubic meters.""" + CUBIC_CENTIMETER = "CubicCentimeter" + """Volume unit in cubic centimeters.""" + CUBIC_MILLIMETER = "CubicMillimeter" + """Volume unit in cubic millimeters.""" + HECTOLITER = "Hectoliter" + """Volume unit in hectoliters.""" + DECALITER = "Decaliter" + """Volume unit in decaliters.""" + LITER = "Liter" + """Volume unit in liters.""" + CENTILITER = "Centiliter" + """Volume unit in centiliters.""" + MILLILITER = "Milliliter" + """Volume unit in milliliters.""" + CUBIC_YARD = "CubicYard" + """Volume unit in cubic yards.""" + CUBIC_INCH = "CubicInch" + """Volume unit in cubic inches.""" + CUBIC_FOOT = "CubicFoot" + """Volume unit in cubic feet.""" + CUBIC_MILE = "CubicMile" + """Volume unit in cubic miles.""" + FLUID_OUNCE = "FluidOunce" + """Volume unit in fluid ounces.""" + TEASPOON = "Teaspoon" + """Volume unit in teaspoons.""" + TABLESPOON = "Tablespoon" + """Volume unit in tablespoons.""" + PINT = "Pint" + """Volume unit in pints.""" + QUART = "Quart" + """Volume unit in quarts.""" + CUP = "Cup" + """Volume unit in cups.""" + GILL = "Gill" + """Volume unit in gills.""" + PINCH = "Pinch" + """Volume unit in pinches.""" + FLUID_DRAM = "FluidDram" + """Volume unit in fluid drams.""" + BARREL = "Barrel" + """Volume unit in barrels.""" + MINIM = "Minim" + """Volume unit in minims.""" + CORD = "Cord" + """Volume unit in cords.""" + PECK = "Peck" + """Volume unit in pecks.""" + BUSHEL = "Bushel" + """Volume unit in bushels.""" + HOGSHEAD = "Hogshead" + """Volume unit in hogsheads.""" + + +class WarningCodeValue(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """Defines the list of the warning codes.""" + + LONG_WORDS_IN_DOCUMENT = "LongWordsInDocument" + """Long words in document warning""" + DOCUMENT_TRUNCATED = "DocumentTruncated" + """Document truncated warning""" + + +class WeightUnit(str, Enum, metaclass=CaseInsensitiveEnumMeta): + """The weight Unit of measurement.""" + + UNSPECIFIED = "Unspecified" + """Unspecified weight unit""" + KILOGRAM = "Kilogram" + """Weight unit in kilograms""" + GRAM = "Gram" + """Weight unit in grams""" + MILLIGRAM = "Milligram" + """Weight unit in milligrams""" + GALLON = "Gallon" + """Volume unit in gallons""" + METRIC_TON = "MetricTon" + """Weight unit in metric tons""" + TON = "Ton" + """Weight unit in tons""" + POUND = "Pound" + """Weight unit in pounds""" + OUNCE = "Ounce" + """Weight unit in ounces""" + GRAIN = "Grain" + """Weight unit in grains""" + PENNY_WEIGHT = "PennyWeight" + """Weight unit in pennyweights""" + LONG_TON_BRITISH = "LongTonBritish" + """Weight unit in long tons (British)""" + SHORT_TON_US = "ShortTonUS" + """Weight unit in short tons (US)""" + SHORT_HUNDRED_WEIGHT_US = "ShortHundredWeightUS" + """Weight unit in short hundredweights (US)""" + STONE = "Stone" + """Weight unit in stones""" + DRAM = "Dram" + """Weight unit in drams""" diff --git a/sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/textanalytics/models/_models.py b/sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/textanalytics/models/_models.py new file mode 100644 index 000000000000..1de9d3e2b73d --- /dev/null +++ b/sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/textanalytics/models/_models.py @@ -0,0 +1,6624 @@ +# 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=useless-super-delegation + +import datetime +from typing import Any, Dict, List, Literal, Mapping, Optional, TYPE_CHECKING, Union, overload + +from .._utils.model_base import Model as _Model, rest_discriminator, rest_field +from ._enums import ( + AnalyzeTextLROResultsKind, + AnalyzeTextLROTaskKind, + AnalyzeTextTaskKind, + AnalyzeTextTaskResultsKind, + MetadataKind, + PolicyKind, + RedactionPolicyKind, +) + +if TYPE_CHECKING: + from .. import models as _models + + +class AnalyzeTextLROResult(_Model): + """Contains the AnalyzeText long running operation result object. + + You probably want to use the sub-classes and not this class directly. Known sub-classes are: + AbstractiveSummarizationLROResult, CustomEntityRecognitionLROResult, + CustomMultiLabelClassificationLROResult, CustomSingleLabelClassificationLROResult, + EntityLinkingLROResult, EntityRecognitionLROResult, ExtractiveSummarizationLROResult, + HealthcareLROResult, KeyPhraseExtractionLROResult, PiiEntityRecognitionLROResult, + SentimentLROResult + + :ivar last_update_date_time: The last updated time in UTC for the task. Required. + :vartype last_update_date_time: ~datetime.datetime + :ivar status: The status of the task at the mentioned last update time. Required. Known values + are: "notStarted", "running", "succeeded", "partiallyCompleted", "failed", "cancelled", and + "cancelling". + :vartype status: str or ~azure.ai.textanalytics.models.State + :ivar task_name: task name. + :vartype task_name: str + :ivar kind: Kind of the task. Required. Known values are: "SentimentAnalysisLROResults", + "EntityRecognitionLROResults", "PiiEntityRecognitionLROResults", + "KeyPhraseExtractionLROResults", "EntityLinkingLROResults", "HealthcareLROResults", + "CustomEntityRecognitionLROResults", "CustomSingleLabelClassificationLROResults", + "CustomMultiLabelClassificationLROResults", "ExtractiveSummarizationLROResults", and + "AbstractiveSummarizationLROResults". + :vartype kind: str or ~azure.ai.textanalytics.models.AnalyzeTextLROResultsKind + """ + + __mapping__: Dict[str, _Model] = {} + last_update_date_time: datetime.datetime = rest_field( + name="lastUpdateDateTime", visibility=["read", "create", "update", "delete", "query"], format="rfc3339" + ) + """The last updated time in UTC for the task. Required.""" + status: Union[str, "_models.State"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The status of the task at the mentioned last update time. Required. Known values are: + \"notStarted\", \"running\", \"succeeded\", \"partiallyCompleted\", \"failed\", \"cancelled\", + and \"cancelling\".""" + task_name: Optional[str] = rest_field(name="taskName", visibility=["read", "create", "update", "delete", "query"]) + """task name.""" + kind: str = rest_discriminator(name="kind", visibility=["read", "create", "update", "delete", "query"]) + """Kind of the task. Required. Known values are: \"SentimentAnalysisLROResults\", + \"EntityRecognitionLROResults\", \"PiiEntityRecognitionLROResults\", + \"KeyPhraseExtractionLROResults\", \"EntityLinkingLROResults\", \"HealthcareLROResults\", + \"CustomEntityRecognitionLROResults\", \"CustomSingleLabelClassificationLROResults\", + \"CustomMultiLabelClassificationLROResults\", \"ExtractiveSummarizationLROResults\", and + \"AbstractiveSummarizationLROResults\".""" + + @overload + def __init__( + self, + *, + last_update_date_time: datetime.datetime, + status: Union[str, "_models.State"], + kind: str, + task_name: Optional[str] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class AbstractiveSummarizationLROResult(AnalyzeTextLROResult, discriminator="AbstractiveSummarizationLROResults"): + """An object representing the results for an Abstractive Summarization task. + + :ivar last_update_date_time: The last updated time in UTC for the task. Required. + :vartype last_update_date_time: ~datetime.datetime + :ivar status: The status of the task at the mentioned last update time. Required. Known values + are: "notStarted", "running", "succeeded", "partiallyCompleted", "failed", "cancelled", and + "cancelling". + :vartype status: str or ~azure.ai.textanalytics.models.State + :ivar task_name: task name. + :vartype task_name: str + :ivar kind: Kind of the task. Required. Abstractive summarization LRO results + :vartype kind: str or ~azure.ai.textanalytics.models.ABSTRACTIVE_SUMMARIZATION_LRO_RESULTS + :ivar results: Results of the task. Required. + :vartype results: ~azure.ai.textanalytics.models.AbstractiveSummarizationResult + """ + + kind: Literal[AnalyzeTextLROResultsKind.ABSTRACTIVE_SUMMARIZATION_LRO_RESULTS] = rest_discriminator(name="kind", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """Kind of the task. Required. Abstractive summarization LRO results""" + results: "_models.AbstractiveSummarizationResult" = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Results of the task. Required.""" + + @overload + def __init__( + self, + *, + last_update_date_time: datetime.datetime, + status: Union[str, "_models.State"], + results: "_models.AbstractiveSummarizationResult", + task_name: Optional[str] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, kind=AnalyzeTextLROResultsKind.ABSTRACTIVE_SUMMARIZATION_LRO_RESULTS, **kwargs) + + +class AnalyzeTextLROTask(_Model): + """The long running task to be performed by the service on the input documents. + + You probably want to use the sub-classes and not this class directly. Known sub-classes are: + AbstractiveSummarizationLROTask, CustomEntitiesLROTask, CustomMultiLabelClassificationLROTask, + CustomSingleLabelClassificationLROTask, EntityLinkingLROTask, EntitiesLROTask, + ExtractiveSummarizationLROTask, HealthcareLROTask, KeyPhraseLROTask, PiiLROTask, + SentimentAnalysisLROTask + + :ivar task_name: task name. + :vartype task_name: str + :ivar kind: The kind of task to perform. Required. Known values are: "SentimentAnalysis", + "EntityRecognition", "PiiEntityRecognition", "KeyPhraseExtraction", "EntityLinking", + "Healthcare", "CustomEntityRecognition", "CustomSingleLabelClassification", + "CustomMultiLabelClassification", "ExtractiveSummarization", and "AbstractiveSummarization". + :vartype kind: str or ~azure.ai.textanalytics.models.AnalyzeTextLROTaskKind + """ + + __mapping__: Dict[str, _Model] = {} + task_name: Optional[str] = rest_field(name="taskName", visibility=["read", "create", "update", "delete", "query"]) + """task name.""" + kind: str = rest_discriminator(name="kind", visibility=["read", "create", "update", "delete", "query"]) + """The kind of task to perform. Required. Known values are: \"SentimentAnalysis\", + \"EntityRecognition\", \"PiiEntityRecognition\", \"KeyPhraseExtraction\", \"EntityLinking\", + \"Healthcare\", \"CustomEntityRecognition\", \"CustomSingleLabelClassification\", + \"CustomMultiLabelClassification\", \"ExtractiveSummarization\", and + \"AbstractiveSummarization\".""" + + @overload + def __init__( + self, + *, + kind: str, + task_name: Optional[str] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class AbstractiveSummarizationLROTask(AnalyzeTextLROTask, discriminator="AbstractiveSummarization"): + """An object representing the task definition for an Abstractive Summarization task. + + :ivar task_name: task name. + :vartype task_name: str + :ivar kind: The Abstractive Summarization kind of the long running task. Required. Abstractive + summarization task + :vartype kind: str or ~azure.ai.textanalytics.models.ABSTRACTIVE_SUMMARIZATION + :ivar parameters: Parameters for the Abstractive Summarization task. + :vartype parameters: ~azure.ai.textanalytics.models.AbstractiveSummarizationTaskParameters + """ + + kind: Literal[AnalyzeTextLROTaskKind.ABSTRACTIVE_SUMMARIZATION] = rest_discriminator(name="kind", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """The Abstractive Summarization kind of the long running task. Required. Abstractive + summarization task""" + parameters: Optional["_models.AbstractiveSummarizationTaskParameters"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Parameters for the Abstractive Summarization task.""" + + @overload + def __init__( + self, + *, + task_name: Optional[str] = None, + parameters: Optional["_models.AbstractiveSummarizationTaskParameters"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, kind=AnalyzeTextLROTaskKind.ABSTRACTIVE_SUMMARIZATION, **kwargs) + + +class AbstractiveSummarizationResult(_Model): + """An object representing the pre-built Abstractive Summarization results of each document. + + :ivar errors: Errors by document id. Required. + :vartype errors: list[~azure.ai.textanalytics.models.DocumentError] + :ivar statistics: if showStats=true was specified in the request this field will contain + information about the request payload. + :vartype statistics: ~azure.ai.textanalytics.models.RequestStatistics + :ivar model_version: This field indicates which model is used for scoring. Required. + :vartype model_version: str + :ivar documents: Response by document. Required. + :vartype documents: + list[~azure.ai.textanalytics.models.AbstractiveSummaryDocumentResultWithDetectedLanguage] + """ + + errors: List["_models.DocumentError"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Errors by document id. Required.""" + statistics: Optional["_models.RequestStatistics"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """if showStats=true was specified in the request this field will contain information about the + request payload.""" + model_version: str = rest_field(name="modelVersion", visibility=["read", "create", "update", "delete", "query"]) + """This field indicates which model is used for scoring. Required.""" + documents: List["_models.AbstractiveSummaryDocumentResultWithDetectedLanguage"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Response by document. Required.""" + + @overload + def __init__( + self, + *, + errors: List["_models.DocumentError"], + model_version: str, + documents: List["_models.AbstractiveSummaryDocumentResultWithDetectedLanguage"], + statistics: Optional["_models.RequestStatistics"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class AbstractiveSummarizationTaskParameters(_Model): + """Supported parameters for the pre-built Abstractive Summarization task. + + :ivar logging_opt_out: logging opt out. + :vartype logging_opt_out: bool + :ivar model_version: model version. + :vartype model_version: str + :ivar sentence_count: Controls the approximate number of sentences in the output summaries. + :vartype sentence_count: int + :ivar string_index_type: String index type. Known values are: "TextElements_v8", + "UnicodeCodePoint", and "Utf16CodeUnit". + :vartype string_index_type: str or ~azure.ai.textanalytics.models.StringIndexType + :ivar summary_length: (NOTE: Recommended to use summaryLength over sentenceCount) Controls the + approximate length of the output summaries. Known values are: "short", "medium", and "long". + :vartype summary_length: str or ~azure.ai.textanalytics.models.SummaryLengthBucket + :ivar instruction: (Optional) If provided, the query will be used to generate the summary. + :vartype instruction: str + """ + + logging_opt_out: Optional[bool] = rest_field( + name="loggingOptOut", visibility=["read", "create", "update", "delete", "query"] + ) + """logging opt out.""" + model_version: Optional[str] = rest_field( + name="modelVersion", visibility=["read", "create", "update", "delete", "query"] + ) + """model version.""" + sentence_count: Optional[int] = rest_field( + name="sentenceCount", visibility=["read", "create", "update", "delete", "query"] + ) + """Controls the approximate number of sentences in the output summaries.""" + string_index_type: Optional[Union[str, "_models.StringIndexType"]] = rest_field( + name="stringIndexType", visibility=["read", "create", "update", "delete", "query"] + ) + """String index type. Known values are: \"TextElements_v8\", \"UnicodeCodePoint\", and + \"Utf16CodeUnit\".""" + summary_length: Optional[Union[str, "_models.SummaryLengthBucket"]] = rest_field( + name="summaryLength", visibility=["read", "create", "update", "delete", "query"] + ) + """(NOTE: Recommended to use summaryLength over sentenceCount) Controls the approximate length of + the output summaries. Known values are: \"short\", \"medium\", and \"long\".""" + instruction: Optional[str] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """(Optional) If provided, the query will be used to generate the summary.""" + + @overload + def __init__( + self, + *, + logging_opt_out: Optional[bool] = None, + model_version: Optional[str] = None, + sentence_count: Optional[int] = None, + string_index_type: Optional[Union[str, "_models.StringIndexType"]] = None, + summary_length: Optional[Union[str, "_models.SummaryLengthBucket"]] = None, + instruction: Optional[str] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class AbstractiveSummary(_Model): + """An object representing a single summary with context for given document. + + :ivar text: The text of the summary. Required. + :vartype text: str + :ivar contexts: The context list of the summary. + :vartype contexts: list[~azure.ai.textanalytics.models.SummaryContext] + """ + + text: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The text of the summary. Required.""" + contexts: Optional[List["_models.SummaryContext"]] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """The context list of the summary.""" + + @overload + def __init__( + self, + *, + text: str, + contexts: Optional[List["_models.SummaryContext"]] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class AbstractiveSummaryDocumentResultWithDetectedLanguage(_Model): # pylint: disable=name-too-long + """An object representing the Abstractive Summarization result of a single document with detected + language. + + :ivar id: Unique, non-empty document identifier. Required. + :vartype id: str + :ivar warnings: Warnings encountered while processing document. Required. + :vartype warnings: list[~azure.ai.textanalytics.models.DocumentWarning] + :ivar statistics: if showStats=true was specified in the request this field will contain + information about the document payload. + :vartype statistics: ~azure.ai.textanalytics.models.DocumentStatistics + :ivar summaries: A list of abstractive summaries. Required. + :vartype summaries: list[~azure.ai.textanalytics.models.AbstractiveSummary] + :ivar detected_language: If 'language' is set to 'auto' for the document in the request this + field will contain a 2 letter ISO 639-1 representation of the language detected for this + document. + :vartype detected_language: ~azure.ai.textanalytics.models.DetectedLanguage + """ + + id: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Unique, non-empty document identifier. Required.""" + warnings: List["_models.DocumentWarning"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Warnings encountered while processing document. Required.""" + statistics: Optional["_models.DocumentStatistics"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """if showStats=true was specified in the request this field will contain information about the + document payload.""" + summaries: List["_models.AbstractiveSummary"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """A list of abstractive summaries. Required.""" + detected_language: Optional["_models.DetectedLanguage"] = rest_field( + name="detectedLanguage", visibility=["read", "create", "update", "delete", "query"] + ) + """If 'language' is set to 'auto' for the document in the request this field will contain a 2 + letter ISO 639-1 representation of the language detected for this document.""" + + @overload + def __init__( + self, + *, + id: str, # pylint: disable=redefined-builtin + warnings: List["_models.DocumentWarning"], + summaries: List["_models.AbstractiveSummary"], + statistics: Optional["_models.DocumentStatistics"] = None, + detected_language: Optional["_models.DetectedLanguage"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class BaseMetadata(_Model): + """The abstract base class for entity Metadata. + + You probably want to use the sub-classes and not this class directly. Known sub-classes are: + AgeMetadata, AreaMetadata, CurrencyMetadata, DateMetadata, DateTimeMetadata, + InformationMetadata, LengthMetadata, NumberMetadata, NumericRangeMetadata, OrdinalMetadata, + SpeedMetadata, TemperatureMetadata, TemporalSetMetadata, TemporalSpanMetadata, TimeMetadata, + VolumeMetadata, WeightMetadata + + :ivar metadata_kind: The entity Metadata object kind. Required. Known values are: + "DateMetadata", "DateTimeMetadata", "TimeMetadata", "TemporalSetMetadata", "NumberMetadata", + "OrdinalMetadata", "SpeedMetadata", "WeightMetadata", "LengthMetadata", "VolumeMetadata", + "AreaMetadata", "AgeMetadata", "InformationMetadata", "TemperatureMetadata", + "CurrencyMetadata", "NumericRangeMetadata", and "TemporalSpanMetadata". + :vartype metadata_kind: str or ~azure.ai.textanalytics.models.MetadataKind + """ + + __mapping__: Dict[str, _Model] = {} + metadata_kind: str = rest_discriminator( + name="metadataKind", visibility=["read", "create", "update", "delete", "query"] + ) + """The entity Metadata object kind. Required. Known values are: \"DateMetadata\", + \"DateTimeMetadata\", \"TimeMetadata\", \"TemporalSetMetadata\", \"NumberMetadata\", + \"OrdinalMetadata\", \"SpeedMetadata\", \"WeightMetadata\", \"LengthMetadata\", + \"VolumeMetadata\", \"AreaMetadata\", \"AgeMetadata\", \"InformationMetadata\", + \"TemperatureMetadata\", \"CurrencyMetadata\", \"NumericRangeMetadata\", and + \"TemporalSpanMetadata\".""" + + @overload + def __init__( + self, + *, + metadata_kind: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class AgeMetadata(BaseMetadata, discriminator="AgeMetadata"): + """Represents the Age entity Metadata model. + + :ivar value: The numeric value that the extracted text denotes. Required. + :vartype value: float + :ivar metadata_kind: Kind of the metadata. Required. Metadata for age-related values. + :vartype metadata_kind: str or ~azure.ai.textanalytics.models.AGE_METADATA + :ivar unit: Unit of measure for age. Required. Known values are: "Unspecified", "Year", + "Month", "Week", and "Day". + :vartype unit: str or ~azure.ai.textanalytics.models.AgeUnit + """ + + value: float = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The numeric value that the extracted text denotes. Required.""" + metadata_kind: Literal[MetadataKind.AGE_METADATA] = rest_discriminator(name="metadataKind", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """Kind of the metadata. Required. Metadata for age-related values.""" + unit: Union[str, "_models.AgeUnit"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Unit of measure for age. Required. Known values are: \"Unspecified\", \"Year\", \"Month\", + \"Week\", and \"Day\".""" + + @overload + def __init__( + self, + *, + value: float, + unit: Union[str, "_models.AgeUnit"], + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, metadata_kind=MetadataKind.AGE_METADATA, **kwargs) + + +class BaseEntityOverlapPolicy(_Model): + """The abstract base class for entity OverlapPolicy. + + You probably want to use the sub-classes and not this class directly. Known sub-classes are: + AllowOverlapEntityPolicyType, MatchLongestEntityPolicyType + + :ivar policy_kind: The entity OverlapPolicy object kind. Required. Known values are: + "matchLongest" and "allowOverlap". + :vartype policy_kind: str or ~azure.ai.textanalytics.models.PolicyKind + """ + + __mapping__: Dict[str, _Model] = {} + policy_kind: str = rest_discriminator(name="policyKind", visibility=["read", "create", "update", "delete", "query"]) + """The entity OverlapPolicy object kind. Required. Known values are: \"matchLongest\" and + \"allowOverlap\".""" + + @overload + def __init__( + self, + *, + policy_kind: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class AllowOverlapEntityPolicyType(BaseEntityOverlapPolicy, discriminator="allowOverlap"): + """Represents the allow overlap policy. Will apply no post processing logic for the entities. + Whatever the model predicts is what will be returned to the user. This allows the user to get a + full view of every single model's possible values and apply their own custom logic on entity + selection. + + :ivar policy_kind: The entity OverlapPolicy object kind. Required. Represents + AllowOverlapEntityPolicyType + :vartype policy_kind: str or ~azure.ai.textanalytics.models.ALLOW_OVERLAP + """ + + policy_kind: Literal[PolicyKind.ALLOW_OVERLAP] = rest_discriminator(name="policyKind", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """The entity OverlapPolicy object kind. Required. Represents AllowOverlapEntityPolicyType""" + + @overload + def __init__( + self, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, policy_kind=PolicyKind.ALLOW_OVERLAP, **kwargs) + + +class AnalyzeTextTask(_Model): + """Collection of documents to analyze and a single task to execute. + + You probably want to use the sub-classes and not this class directly. Known sub-classes are: + AnalyzeTextEntityLinkingInput, AnalyzeTextEntityRecognitionInput, + AnalyzeTextKeyPhraseExtractionInput, AnalyzeTextLanguageDetectionInput, + AnalyzeTextPiiEntitiesRecognitionInput, AnalyzeTextSentimentAnalysisInput + + :ivar kind: The kind of task to perform. Required. Known values are: "SentimentAnalysis", + "EntityRecognition", "PiiEntityRecognition", "KeyPhraseExtraction", "LanguageDetection", and + "EntityLinking". + :vartype kind: str or ~azure.ai.textanalytics.models.AnalyzeTextTaskKind + """ + + __mapping__: Dict[str, _Model] = {} + kind: str = rest_discriminator(name="kind", visibility=["read", "create", "update", "delete", "query"]) + """The kind of task to perform. Required. Known values are: \"SentimentAnalysis\", + \"EntityRecognition\", \"PiiEntityRecognition\", \"KeyPhraseExtraction\", + \"LanguageDetection\", and \"EntityLinking\".""" + + @overload + def __init__( + self, + *, + kind: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class AnalyzeTextEntityLinkingInput(AnalyzeTextTask, discriminator="EntityLinking"): + """Contains the analyze text Entity linking input. + + :ivar kind: Kind for Entity linking input. Required. Entity linking task + :vartype kind: str or ~azure.ai.textanalytics.models.ENTITY_LINKING + :ivar analysis_input: Contains the analysis input to be handled by the service. + :vartype analysis_input: ~azure.ai.textanalytics.models.MultiLanguageAnalysisInput + :ivar parameters: Task parameters. + :vartype parameters: ~azure.ai.textanalytics.models.EntityLinkingTaskParameters + """ + + kind: Literal[AnalyzeTextTaskKind.ENTITY_LINKING] = rest_discriminator(name="kind", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """Kind for Entity linking input. Required. Entity linking task""" + analysis_input: Optional["_models.MultiLanguageAnalysisInput"] = rest_field( + name="analysisInput", visibility=["read", "create", "update", "delete", "query"] + ) + """Contains the analysis input to be handled by the service.""" + parameters: Optional["_models.EntityLinkingTaskParameters"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Task parameters.""" + + @overload + def __init__( + self, + *, + analysis_input: Optional["_models.MultiLanguageAnalysisInput"] = None, + parameters: Optional["_models.EntityLinkingTaskParameters"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, kind=AnalyzeTextTaskKind.ENTITY_LINKING, **kwargs) + + +class AnalyzeTextEntityRecognitionInput(AnalyzeTextTask, discriminator="EntityRecognition"): + """The entity recognition analyze text input task request. + + :ivar kind: The kind of task. Required. Entity recognition task + :vartype kind: str or ~azure.ai.textanalytics.models.ENTITY_RECOGNITION + :ivar analysis_input: The input to be analyzed. + :vartype analysis_input: ~azure.ai.textanalytics.models.MultiLanguageAnalysisInput + :ivar parameters: Task parameters. + :vartype parameters: ~azure.ai.textanalytics.models.EntitiesTaskParameters + """ + + kind: Literal[AnalyzeTextTaskKind.ENTITY_RECOGNITION] = rest_discriminator(name="kind", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """The kind of task. Required. Entity recognition task""" + analysis_input: Optional["_models.MultiLanguageAnalysisInput"] = rest_field( + name="analysisInput", visibility=["read", "create", "update", "delete", "query"] + ) + """The input to be analyzed.""" + parameters: Optional["_models.EntitiesTaskParameters"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Task parameters.""" + + @overload + def __init__( + self, + *, + analysis_input: Optional["_models.MultiLanguageAnalysisInput"] = None, + parameters: Optional["_models.EntitiesTaskParameters"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, kind=AnalyzeTextTaskKind.ENTITY_RECOGNITION, **kwargs) + + +class AnalyzeTextJobState(_Model): + """The object containing the analyze job LRO job state. + + :ivar display_name: display name. + :vartype display_name: str + :ivar created_date_time: Date and time job created. Required. + :vartype created_date_time: ~datetime.datetime + :ivar expiration_date_time: Date and time job expires. + :vartype expiration_date_time: ~datetime.datetime + :ivar job_id: job ID. Required. + :vartype job_id: str + :ivar last_updated_date_time: last updated date and time. Required. + :vartype last_updated_date_time: ~datetime.datetime + :ivar status: status. Required. Known values are: "notStarted", "running", "succeeded", + "partiallyCompleted", "failed", "cancelled", and "cancelling". + :vartype status: str or ~azure.ai.textanalytics.models.State + :ivar errors: errors. + :vartype errors: list[~azure.ai.textanalytics.models.Error] + :ivar next_link: next link. + :vartype next_link: str + :ivar tasks: List of tasks. Required. + :vartype tasks: ~azure.ai.textanalytics.models.Tasks + :ivar statistics: if showStats=true was specified in the request this field will contain + information about the request payload. + :vartype statistics: ~azure.ai.textanalytics.models.RequestStatistics + """ + + display_name: Optional[str] = rest_field( + name="displayName", visibility=["read", "create", "update", "delete", "query"] + ) + """display name.""" + created_date_time: datetime.datetime = rest_field( + name="createdDateTime", visibility=["read", "create", "update", "delete", "query"], format="rfc3339" + ) + """Date and time job created. Required.""" + expiration_date_time: Optional[datetime.datetime] = rest_field( + name="expirationDateTime", visibility=["read", "create", "update", "delete", "query"], format="rfc3339" + ) + """Date and time job expires.""" + job_id: str = rest_field(name="jobId", visibility=["read"]) + """job ID. Required.""" + last_updated_date_time: datetime.datetime = rest_field( + name="lastUpdatedDateTime", visibility=["read", "create", "update", "delete", "query"], format="rfc3339" + ) + """last updated date and time. Required.""" + status: Union[str, "_models.State"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """status. Required. Known values are: \"notStarted\", \"running\", \"succeeded\", + \"partiallyCompleted\", \"failed\", \"cancelled\", and \"cancelling\".""" + errors: Optional[List["_models.Error"]] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """errors.""" + next_link: Optional[str] = rest_field(name="nextLink", visibility=["read", "create", "update", "delete", "query"]) + """next link.""" + tasks: "_models.Tasks" = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """List of tasks. Required.""" + statistics: Optional["_models.RequestStatistics"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """if showStats=true was specified in the request this field will contain information about the + request payload.""" + + @overload + def __init__( + self, + *, + created_date_time: datetime.datetime, + last_updated_date_time: datetime.datetime, + status: Union[str, "_models.State"], + tasks: "_models.Tasks", + display_name: Optional[str] = None, + expiration_date_time: Optional[datetime.datetime] = None, + errors: Optional[List["_models.Error"]] = None, + next_link: Optional[str] = None, + statistics: Optional["_models.RequestStatistics"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class AnalyzeTextKeyPhraseExtractionInput(AnalyzeTextTask, discriminator="KeyPhraseExtraction"): + """Contains the analyze text KeyPhraseExtraction task input. + + :ivar kind: Kind of the task. Required. Key phrase extraction task + :vartype kind: str or ~azure.ai.textanalytics.models.KEY_PHRASE_EXTRACTION + :ivar analysis_input: Contains the input documents. + :vartype analysis_input: ~azure.ai.textanalytics.models.MultiLanguageAnalysisInput + :ivar parameters: Key phrase extraction task parameters. + :vartype parameters: ~azure.ai.textanalytics.models.KeyPhraseTaskParameters + """ + + kind: Literal[AnalyzeTextTaskKind.KEY_PHRASE_EXTRACTION] = rest_discriminator(name="kind", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """Kind of the task. Required. Key phrase extraction task""" + analysis_input: Optional["_models.MultiLanguageAnalysisInput"] = rest_field( + name="analysisInput", visibility=["read", "create", "update", "delete", "query"] + ) + """Contains the input documents.""" + parameters: Optional["_models.KeyPhraseTaskParameters"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Key phrase extraction task parameters.""" + + @overload + def __init__( + self, + *, + analysis_input: Optional["_models.MultiLanguageAnalysisInput"] = None, + parameters: Optional["_models.KeyPhraseTaskParameters"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, kind=AnalyzeTextTaskKind.KEY_PHRASE_EXTRACTION, **kwargs) + + +class AnalyzeTextLanguageDetectionInput(AnalyzeTextTask, discriminator="LanguageDetection"): + """Contains the language detection document analysis task input. + + :ivar kind: Kind of the task. Required. Language detection task + :vartype kind: str or ~azure.ai.textanalytics.models.LANGUAGE_DETECTION + :ivar analysis_input: Documents to be analyzed. + :vartype analysis_input: ~azure.ai.textanalytics.models.LanguageDetectionAnalysisInput + :ivar parameters: task parameters. + :vartype parameters: ~azure.ai.textanalytics.models.LanguageDetectionTaskParameters + """ + + kind: Literal[AnalyzeTextTaskKind.LANGUAGE_DETECTION] = rest_discriminator(name="kind", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """Kind of the task. Required. Language detection task""" + analysis_input: Optional["_models.LanguageDetectionAnalysisInput"] = rest_field( + name="analysisInput", visibility=["read", "create", "update", "delete", "query"] + ) + """Documents to be analyzed.""" + parameters: Optional["_models.LanguageDetectionTaskParameters"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """task parameters.""" + + @overload + def __init__( + self, + *, + analysis_input: Optional["_models.LanguageDetectionAnalysisInput"] = None, + parameters: Optional["_models.LanguageDetectionTaskParameters"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, kind=AnalyzeTextTaskKind.LANGUAGE_DETECTION, **kwargs) + + +class AnalyzeTextPiiEntitiesRecognitionInput(AnalyzeTextTask, discriminator="PiiEntityRecognition"): + """Contains the analyze text PIIEntityRecognition task input. + + :ivar kind: Kind of the task. Required. PII entity recognition task + :vartype kind: str or ~azure.ai.textanalytics.models.PII_ENTITY_RECOGNITION + :ivar analysis_input: Contains the input documents. + :vartype analysis_input: ~azure.ai.textanalytics.models.MultiLanguageAnalysisInput + :ivar parameters: Pii task parameters. + :vartype parameters: ~azure.ai.textanalytics.models.PiiTaskParameters + """ + + kind: Literal[AnalyzeTextTaskKind.PII_ENTITY_RECOGNITION] = rest_discriminator(name="kind", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """Kind of the task. Required. PII entity recognition task""" + analysis_input: Optional["_models.MultiLanguageAnalysisInput"] = rest_field( + name="analysisInput", visibility=["read", "create", "update", "delete", "query"] + ) + """Contains the input documents.""" + parameters: Optional["_models.PiiTaskParameters"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Pii task parameters.""" + + @overload + def __init__( + self, + *, + analysis_input: Optional["_models.MultiLanguageAnalysisInput"] = None, + parameters: Optional["_models.PiiTaskParameters"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, kind=AnalyzeTextTaskKind.PII_ENTITY_RECOGNITION, **kwargs) + + +class AnalyzeTextSentimentAnalysisInput(AnalyzeTextTask, discriminator="SentimentAnalysis"): + """Contains the analyze text SentimentAnalysis task input. + + :ivar kind: Kind of the task. Required. Sentiment analysis task + :vartype kind: str or ~azure.ai.textanalytics.models.SENTIMENT_ANALYSIS + :ivar analysis_input: Contains the input documents. + :vartype analysis_input: ~azure.ai.textanalytics.models.MultiLanguageAnalysisInput + :ivar parameters: Sentiment Analysis task parameters. + :vartype parameters: ~azure.ai.textanalytics.models.SentimentAnalysisTaskParameters + """ + + kind: Literal[AnalyzeTextTaskKind.SENTIMENT_ANALYSIS] = rest_discriminator(name="kind", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """Kind of the task. Required. Sentiment analysis task""" + analysis_input: Optional["_models.MultiLanguageAnalysisInput"] = rest_field( + name="analysisInput", visibility=["read", "create", "update", "delete", "query"] + ) + """Contains the input documents.""" + parameters: Optional["_models.SentimentAnalysisTaskParameters"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Sentiment Analysis task parameters.""" + + @overload + def __init__( + self, + *, + analysis_input: Optional["_models.MultiLanguageAnalysisInput"] = None, + parameters: Optional["_models.SentimentAnalysisTaskParameters"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, kind=AnalyzeTextTaskKind.SENTIMENT_ANALYSIS, **kwargs) + + +class AnalyzeTextTaskResult(_Model): + """The result object for the analyze task. + + You probably want to use the sub-classes and not this class directly. Known sub-classes are: + EntityLinkingTaskResult, EntitiesTaskResult, KeyPhraseTaskResult, LanguageDetectionTaskResult, + PiiTaskResult, SentimentTaskResult + + :ivar kind: The kind of task result. Required. Known values are: "SentimentAnalysisResults", + "EntityRecognitionResults", "PiiEntityRecognitionResults", "KeyPhraseExtractionResults", + "LanguageDetectionResults", and "EntityLinkingResults". + :vartype kind: str or ~azure.ai.textanalytics.models.AnalyzeTextTaskResultsKind + """ + + __mapping__: Dict[str, _Model] = {} + kind: str = rest_discriminator(name="kind", visibility=["read", "create", "update", "delete", "query"]) + """The kind of task result. Required. Known values are: \"SentimentAnalysisResults\", + \"EntityRecognitionResults\", \"PiiEntityRecognitionResults\", \"KeyPhraseExtractionResults\", + \"LanguageDetectionResults\", and \"EntityLinkingResults\".""" + + @overload + def __init__( + self, + *, + kind: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class AreaMetadata(BaseMetadata, discriminator="AreaMetadata"): + """Represents the Area entity Metadata model. + + :ivar value: The numeric value that the extracted text denotes. Required. + :vartype value: float + :ivar metadata_kind: Kind of the metadata. Required. Metadata for area-related values. + :vartype metadata_kind: str or ~azure.ai.textanalytics.models.AREA_METADATA + :ivar unit: Unit of measure for area. Required. Known values are: "Unspecified", + "SquareKilometer", "SquareHectometer", "SquareDecameter", "SquareDecimeter", "SquareMeter", + "SquareCentimeter", "SquareMillimeter", "SquareInch", "SquareFoot", "SquareMile", "SquareYard", + and "Acre". + :vartype unit: str or ~azure.ai.textanalytics.models.AreaUnit + """ + + value: float = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The numeric value that the extracted text denotes. Required.""" + metadata_kind: Literal[MetadataKind.AREA_METADATA] = rest_discriminator(name="metadataKind", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """Kind of the metadata. Required. Metadata for area-related values.""" + unit: Union[str, "_models.AreaUnit"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Unit of measure for area. Required. Known values are: \"Unspecified\", \"SquareKilometer\", + \"SquareHectometer\", \"SquareDecameter\", \"SquareDecimeter\", \"SquareMeter\", + \"SquareCentimeter\", \"SquareMillimeter\", \"SquareInch\", \"SquareFoot\", \"SquareMile\", + \"SquareYard\", and \"Acre\".""" + + @overload + def __init__( + self, + *, + value: float, + unit: Union[str, "_models.AreaUnit"], + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, metadata_kind=MetadataKind.AREA_METADATA, **kwargs) + + +class BaseRedactionPolicy(_Model): + """The abstract base class for RedactionPolicy. + + You probably want to use the sub-classes and not this class directly. Known sub-classes are: + CharacterMaskPolicyType, EntityMaskPolicyType, NoMaskPolicyType + + :ivar policy_kind: The entity RedactionPolicy object kind. Required. Known values are: + "noMask", "characterMask", and "entityMask". + :vartype policy_kind: str or ~azure.ai.textanalytics.models.RedactionPolicyKind + """ + + __mapping__: Dict[str, _Model] = {} + policy_kind: str = rest_discriminator(name="policyKind", visibility=["read", "create", "update", "delete", "query"]) + """The entity RedactionPolicy object kind. Required. Known values are: \"noMask\", + \"characterMask\", and \"entityMask\".""" + + @overload + def __init__( + self, + *, + policy_kind: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class CharacterMaskPolicyType(BaseRedactionPolicy, discriminator="characterMask"): + """Represents the policy of redacting with a redaction character. + + :ivar policy_kind: The entity RedactionPolicy object kind. Required. React detected entities + with redaction character. + :vartype policy_kind: str or ~azure.ai.textanalytics.models.CHARACTER_MASK + :ivar redaction_character: Optional parameter to use a Custom Character to be used for + redaction in PII responses. Default character will bce * as before. We allow specific ascii + characters for redaction. Known values are: "!", "#", "$", "%", "&", "*", "+", "-", "=", "?", + "@", "^", "_", and "~". + :vartype redaction_character: str or ~azure.ai.textanalytics.models.RedactionCharacter + """ + + policy_kind: Literal[RedactionPolicyKind.CHARACTER_MASK] = rest_discriminator(name="policyKind", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """The entity RedactionPolicy object kind. Required. React detected entities with redaction + character.""" + redaction_character: Optional[Union[str, "_models.RedactionCharacter"]] = rest_field( + name="redactionCharacter", visibility=["read", "create", "update", "delete", "query"] + ) + """Optional parameter to use a Custom Character to be used for redaction in PII responses. Default + character will bce * as before. We allow specific ascii characters for redaction. Known values + are: \"!\", \"#\", \"$\", \"%\", \"&\", \"*\", \"+\", \"-\", \"=\", \"?\", \"@\", \"^\", \"_\", + and \"~\".""" + + @overload + def __init__( + self, + *, + redaction_character: Optional[Union[str, "_models.RedactionCharacter"]] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, policy_kind=RedactionPolicyKind.CHARACTER_MASK, **kwargs) + + +class ClassificationDocumentResultWithDetectedLanguage(_Model): # pylint: disable=name-too-long + """Contains the classification doc result for the task with detected language. + + :ivar id: Unique, non-empty document identifier. Required. + :vartype id: str + :ivar warnings: Warnings encountered while processing document. Required. + :vartype warnings: list[~azure.ai.textanalytics.models.DocumentWarning] + :ivar statistics: if showStats=true was specified in the request this field will contain + information about the document payload. + :vartype statistics: ~azure.ai.textanalytics.models.DocumentStatistics + :ivar class_property: Contains the classification doc results for all docs. Required. + :vartype class_property: list[~azure.ai.textanalytics.models.ClassificationResult] + :ivar detected_language: If 'language' is set to 'auto' for the document in the request this + field will contain a 2 letter ISO 639-1 representation of the language detected for this + document. + :vartype detected_language: ~azure.ai.textanalytics.models.DetectedLanguage + """ + + id: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Unique, non-empty document identifier. Required.""" + warnings: List["_models.DocumentWarning"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Warnings encountered while processing document. Required.""" + statistics: Optional["_models.DocumentStatistics"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """if showStats=true was specified in the request this field will contain information about the + document payload.""" + class_property: List["_models.ClassificationResult"] = rest_field( + name="class", visibility=["read", "create", "update", "delete", "query"] + ) + """Contains the classification doc results for all docs. Required.""" + detected_language: Optional["_models.DetectedLanguage"] = rest_field( + name="detectedLanguage", visibility=["read", "create", "update", "delete", "query"] + ) + """If 'language' is set to 'auto' for the document in the request this field will contain a 2 + letter ISO 639-1 representation of the language detected for this document.""" + + @overload + def __init__( + self, + *, + id: str, # pylint: disable=redefined-builtin + warnings: List["_models.DocumentWarning"], + class_property: List["_models.ClassificationResult"], + statistics: Optional["_models.DocumentStatistics"] = None, + detected_language: Optional["_models.DetectedLanguage"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class ClassificationResult(_Model): + """Contains the classification result. + + :ivar category: Classification type. Required. + :vartype category: str + :ivar confidence_score: Confidence score between 0 and 1 of the recognized class. Required. + :vartype confidence_score: float + """ + + category: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Classification type. Required.""" + confidence_score: float = rest_field( + name="confidenceScore", visibility=["read", "create", "update", "delete", "query"] + ) + """Confidence score between 0 and 1 of the recognized class. Required.""" + + @overload + def __init__( + self, + *, + category: str, + confidence_score: float, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class CurrencyMetadata(BaseMetadata, discriminator="CurrencyMetadata"): + """Represents the Currency ) entity Metadata model. + + :ivar value: The numeric value that the extracted text denotes. Required. + :vartype value: float + :ivar metadata_kind: Kind of the metadata. Required. Metadata for currency-related values. + :vartype metadata_kind: str or ~azure.ai.textanalytics.models.CURRENCY_METADATA + :ivar unit: Currency unit. Required. + :vartype unit: str + :ivar iso4217: The alphabetic code based on another ISO standard, ISO 3166, which lists the + codes for country names. The first two letters of the ISO 4217 three-letter code are the same + as the code for the country name, and, where possible, the third letter corresponds to the + first letter of the currency name. + :vartype iso4217: str + """ + + value: float = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The numeric value that the extracted text denotes. Required.""" + metadata_kind: Literal[MetadataKind.CURRENCY_METADATA] = rest_discriminator(name="metadataKind", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """Kind of the metadata. Required. Metadata for currency-related values.""" + unit: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Currency unit. Required.""" + iso4217: Optional[str] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The alphabetic code based on another ISO standard, ISO 3166, which lists the codes for country + names. The first two letters of the ISO 4217 three-letter code are the same as the code for the + country name, and, where possible, the third letter corresponds to the first letter of the + currency name.""" + + @overload + def __init__( + self, + *, + value: float, + unit: str, + iso4217: Optional[str] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, metadata_kind=MetadataKind.CURRENCY_METADATA, **kwargs) + + +class CustomEntitiesLROTask(AnalyzeTextLROTask, discriminator="CustomEntityRecognition"): + """Contains the custom text LRO task. + + :ivar task_name: task name. + :vartype task_name: str + :ivar kind: Kind of the task. Required. Custom entity recognition task + :vartype kind: str or ~azure.ai.textanalytics.models.CUSTOM_ENTITY_RECOGNITION + :ivar parameters: task parameters. + :vartype parameters: ~azure.ai.textanalytics.models.CustomEntitiesTaskParameters + """ + + kind: Literal[AnalyzeTextLROTaskKind.CUSTOM_ENTITY_RECOGNITION] = rest_discriminator(name="kind", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """Kind of the task. Required. Custom entity recognition task""" + parameters: Optional["_models.CustomEntitiesTaskParameters"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """task parameters.""" + + @overload + def __init__( + self, + *, + task_name: Optional[str] = None, + parameters: Optional["_models.CustomEntitiesTaskParameters"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, kind=AnalyzeTextLROTaskKind.CUSTOM_ENTITY_RECOGNITION, **kwargs) + + +class CustomEntitiesResult(_Model): + """Contains the list of detected custom entities result for the documents. + + :ivar errors: Errors by document id. Required. + :vartype errors: list[~azure.ai.textanalytics.models.DocumentError] + :ivar statistics: if showStats=true was specified in the request this field will contain + information about the request payload. + :vartype statistics: ~azure.ai.textanalytics.models.RequestStatistics + :ivar project_name: This field indicates the project name for the model. Required. + :vartype project_name: str + :ivar deployment_name: This field indicates the deployment name for the model. Required. + :vartype deployment_name: str + :ivar documents: Enumeration of the document results. Required. + :vartype documents: + list[~azure.ai.textanalytics.models.EntitiesDocumentResultWithDetectedLanguage] + """ + + errors: List["_models.DocumentError"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Errors by document id. Required.""" + statistics: Optional["_models.RequestStatistics"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """if showStats=true was specified in the request this field will contain information about the + request payload.""" + project_name: str = rest_field(name="projectName", visibility=["read", "create", "update", "delete", "query"]) + """This field indicates the project name for the model. Required.""" + deployment_name: str = rest_field(name="deploymentName", visibility=["read", "create", "update", "delete", "query"]) + """This field indicates the deployment name for the model. Required.""" + documents: List["_models.EntitiesDocumentResultWithDetectedLanguage"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Enumeration of the document results. Required.""" + + @overload + def __init__( + self, + *, + errors: List["_models.DocumentError"], + project_name: str, + deployment_name: str, + documents: List["_models.EntitiesDocumentResultWithDetectedLanguage"], + statistics: Optional["_models.RequestStatistics"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class CustomEntitiesTaskParameters(_Model): + """Supported parameters for a Custom Entities task. + + :ivar logging_opt_out: logging opt out. + :vartype logging_opt_out: bool + :ivar project_name: This field indicates the project name for the model. Required. + :vartype project_name: str + :ivar deployment_name: This field indicates the deployment name for the model. Required. + :vartype deployment_name: str + :ivar string_index_type: Optional parameter to provide the string index type used to interpret + string offsets. Defaults to TextElements (Graphemes). Known values are: "TextElements_v8", + "UnicodeCodePoint", and "Utf16CodeUnit". + :vartype string_index_type: str or ~azure.ai.textanalytics.models.StringIndexType + """ + + logging_opt_out: Optional[bool] = rest_field( + name="loggingOptOut", visibility=["read", "create", "update", "delete", "query"] + ) + """logging opt out.""" + project_name: str = rest_field(name="projectName", visibility=["read", "create", "update", "delete", "query"]) + """This field indicates the project name for the model. Required.""" + deployment_name: str = rest_field(name="deploymentName", visibility=["read", "create", "update", "delete", "query"]) + """This field indicates the deployment name for the model. Required.""" + string_index_type: Optional[Union[str, "_models.StringIndexType"]] = rest_field( + name="stringIndexType", visibility=["read", "create", "update", "delete", "query"] + ) + """Optional parameter to provide the string index type used to interpret string offsets. Defaults + to TextElements (Graphemes). Known values are: \"TextElements_v8\", \"UnicodeCodePoint\", and + \"Utf16CodeUnit\".""" + + @overload + def __init__( + self, + *, + project_name: str, + deployment_name: str, + logging_opt_out: Optional[bool] = None, + string_index_type: Optional[Union[str, "_models.StringIndexType"]] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class CustomEntityRecognitionLROResult(AnalyzeTextLROResult, discriminator="CustomEntityRecognitionLROResults"): + """Contains the custom entity recognition job result. + + :ivar last_update_date_time: The last updated time in UTC for the task. Required. + :vartype last_update_date_time: ~datetime.datetime + :ivar status: The status of the task at the mentioned last update time. Required. Known values + are: "notStarted", "running", "succeeded", "partiallyCompleted", "failed", "cancelled", and + "cancelling". + :vartype status: str or ~azure.ai.textanalytics.models.State + :ivar task_name: task name. + :vartype task_name: str + :ivar kind: Kind of the task. Required. Custom entity recognition LRO results + :vartype kind: str or ~azure.ai.textanalytics.models.CUSTOM_ENTITY_RECOGNITION_LRO_RESULTS + :ivar results: List of results. Required. + :vartype results: ~azure.ai.textanalytics.models.CustomEntitiesResult + """ + + kind: Literal[AnalyzeTextLROResultsKind.CUSTOM_ENTITY_RECOGNITION_LRO_RESULTS] = rest_discriminator(name="kind", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """Kind of the task. Required. Custom entity recognition LRO results""" + results: "_models.CustomEntitiesResult" = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """List of results. Required.""" + + @overload + def __init__( + self, + *, + last_update_date_time: datetime.datetime, + status: Union[str, "_models.State"], + results: "_models.CustomEntitiesResult", + task_name: Optional[str] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, kind=AnalyzeTextLROResultsKind.CUSTOM_ENTITY_RECOGNITION_LRO_RESULTS, **kwargs) + + +class CustomLabelClassificationResult(_Model): + """Contains the custom label classification results. + + :ivar errors: Errors by document id. Required. + :vartype errors: list[~azure.ai.textanalytics.models.DocumentError] + :ivar statistics: if showStats=true was specified in the request this field will contain + information about the request payload. + :vartype statistics: ~azure.ai.textanalytics.models.RequestStatistics + :ivar project_name: This field indicates the project name for the model. Required. + :vartype project_name: str + :ivar deployment_name: This field indicates the deployment name for the model. Required. + :vartype deployment_name: str + :ivar documents: Response by document. Required. + :vartype documents: + list[~azure.ai.textanalytics.models.ClassificationDocumentResultWithDetectedLanguage] + """ + + errors: List["_models.DocumentError"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Errors by document id. Required.""" + statistics: Optional["_models.RequestStatistics"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """if showStats=true was specified in the request this field will contain information about the + request payload.""" + project_name: str = rest_field(name="projectName", visibility=["read", "create", "update", "delete", "query"]) + """This field indicates the project name for the model. Required.""" + deployment_name: str = rest_field(name="deploymentName", visibility=["read", "create", "update", "delete", "query"]) + """This field indicates the deployment name for the model. Required.""" + documents: List["_models.ClassificationDocumentResultWithDetectedLanguage"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Response by document. Required.""" + + @overload + def __init__( + self, + *, + errors: List["_models.DocumentError"], + project_name: str, + deployment_name: str, + documents: List["_models.ClassificationDocumentResultWithDetectedLanguage"], + statistics: Optional["_models.RequestStatistics"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class CustomMultiLabelClassificationLROResult( + AnalyzeTextLROResult, discriminator="CustomMultiLabelClassificationLROResults" +): + """Contains the custom multi label classification job result. + + :ivar last_update_date_time: The last updated time in UTC for the task. Required. + :vartype last_update_date_time: ~datetime.datetime + :ivar status: The status of the task at the mentioned last update time. Required. Known values + are: "notStarted", "running", "succeeded", "partiallyCompleted", "failed", "cancelled", and + "cancelling". + :vartype status: str or ~azure.ai.textanalytics.models.State + :ivar task_name: task name. + :vartype task_name: str + :ivar kind: Kind of the task. Required. Custom multi label classification LRO results + :vartype kind: str or + ~azure.ai.textanalytics.models.CUSTOM_MULTI_LABEL_CLASSIFICATION_LRO_RESULTS + :ivar results: List of results. Required. + :vartype results: ~azure.ai.textanalytics.models.CustomLabelClassificationResult + """ + + kind: Literal[AnalyzeTextLROResultsKind.CUSTOM_MULTI_LABEL_CLASSIFICATION_LRO_RESULTS] = rest_discriminator(name="kind", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """Kind of the task. Required. Custom multi label classification LRO results""" + results: "_models.CustomLabelClassificationResult" = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """List of results. Required.""" + + @overload + def __init__( + self, + *, + last_update_date_time: datetime.datetime, + status: Union[str, "_models.State"], + results: "_models.CustomLabelClassificationResult", + task_name: Optional[str] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, kind=AnalyzeTextLROResultsKind.CUSTOM_MULTI_LABEL_CLASSIFICATION_LRO_RESULTS, **kwargs) + + +class CustomMultiLabelClassificationLROTask(AnalyzeTextLROTask, discriminator="CustomMultiLabelClassification"): + """Use custom models to classify text into multi label taxonomy. + + :ivar task_name: task name. + :vartype task_name: str + :ivar kind: Kind of the task. Required. Custom multi label classification task + :vartype kind: str or ~azure.ai.textanalytics.models.CUSTOM_MULTI_LABEL_CLASSIFICATION + :ivar parameters: Task parameters. + :vartype parameters: + ~azure.ai.textanalytics.models.CustomMultiLabelClassificationTaskParameters + """ + + kind: Literal[AnalyzeTextLROTaskKind.CUSTOM_MULTI_LABEL_CLASSIFICATION] = rest_discriminator(name="kind", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """Kind of the task. Required. Custom multi label classification task""" + parameters: Optional["_models.CustomMultiLabelClassificationTaskParameters"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Task parameters.""" + + @overload + def __init__( + self, + *, + task_name: Optional[str] = None, + parameters: Optional["_models.CustomMultiLabelClassificationTaskParameters"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, kind=AnalyzeTextLROTaskKind.CUSTOM_MULTI_LABEL_CLASSIFICATION, **kwargs) + + +class CustomMultiLabelClassificationTaskParameters(_Model): # pylint: disable=name-too-long + """Supported parameters for a Custom Multi Classification task. + + :ivar logging_opt_out: logging opt out. + :vartype logging_opt_out: bool + :ivar project_name: This field indicates the project name for the model. Required. + :vartype project_name: str + :ivar deployment_name: This field indicates the deployment name for the model. Required. + :vartype deployment_name: str + """ + + logging_opt_out: Optional[bool] = rest_field( + name="loggingOptOut", visibility=["read", "create", "update", "delete", "query"] + ) + """logging opt out.""" + project_name: str = rest_field(name="projectName", visibility=["read", "create", "update", "delete", "query"]) + """This field indicates the project name for the model. Required.""" + deployment_name: str = rest_field(name="deploymentName", visibility=["read", "create", "update", "delete", "query"]) + """This field indicates the deployment name for the model. Required.""" + + @overload + def __init__( + self, + *, + project_name: str, + deployment_name: str, + logging_opt_out: Optional[bool] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class CustomSingleLabelClassificationLROResult( + AnalyzeTextLROResult, discriminator="CustomSingleLabelClassificationLROResults" +): + """Contains the custom single label classification job result. + + :ivar last_update_date_time: The last updated time in UTC for the task. Required. + :vartype last_update_date_time: ~datetime.datetime + :ivar status: The status of the task at the mentioned last update time. Required. Known values + are: "notStarted", "running", "succeeded", "partiallyCompleted", "failed", "cancelled", and + "cancelling". + :vartype status: str or ~azure.ai.textanalytics.models.State + :ivar task_name: task name. + :vartype task_name: str + :ivar kind: Kind of the task. Required. Custom single label classification LRO results + :vartype kind: str or + ~azure.ai.textanalytics.models.CUSTOM_SINGLE_LABEL_CLASSIFICATION_LRO_RESULTS + :ivar results: List of results. Required. + :vartype results: ~azure.ai.textanalytics.models.CustomLabelClassificationResult + """ + + kind: Literal[AnalyzeTextLROResultsKind.CUSTOM_SINGLE_LABEL_CLASSIFICATION_LRO_RESULTS] = rest_discriminator(name="kind", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """Kind of the task. Required. Custom single label classification LRO results""" + results: "_models.CustomLabelClassificationResult" = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """List of results. Required.""" + + @overload + def __init__( + self, + *, + last_update_date_time: datetime.datetime, + status: Union[str, "_models.State"], + results: "_models.CustomLabelClassificationResult", + task_name: Optional[str] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, kind=AnalyzeTextLROResultsKind.CUSTOM_SINGLE_LABEL_CLASSIFICATION_LRO_RESULTS, **kwargs) + + +class CustomSingleLabelClassificationLROTask(AnalyzeTextLROTask, discriminator="CustomSingleLabelClassification"): + """Use custom models to classify text into single label taxonomy. + + :ivar task_name: task name. + :vartype task_name: str + :ivar kind: Kind of the task. Required. Custom single label classification task + :vartype kind: str or ~azure.ai.textanalytics.models.CUSTOM_SINGLE_LABEL_CLASSIFICATION + :ivar parameters: Task parameters. + :vartype parameters: + ~azure.ai.textanalytics.models.CustomSingleLabelClassificationTaskParameters + """ + + kind: Literal[AnalyzeTextLROTaskKind.CUSTOM_SINGLE_LABEL_CLASSIFICATION] = rest_discriminator(name="kind", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """Kind of the task. Required. Custom single label classification task""" + parameters: Optional["_models.CustomSingleLabelClassificationTaskParameters"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Task parameters.""" + + @overload + def __init__( + self, + *, + task_name: Optional[str] = None, + parameters: Optional["_models.CustomSingleLabelClassificationTaskParameters"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, kind=AnalyzeTextLROTaskKind.CUSTOM_SINGLE_LABEL_CLASSIFICATION, **kwargs) + + +class CustomSingleLabelClassificationTaskParameters(_Model): # pylint: disable=name-too-long + """Supported parameters for a Custom Single Classification task. + + :ivar logging_opt_out: logging opt out. + :vartype logging_opt_out: bool + :ivar project_name: This field indicates the project name for the model. Required. + :vartype project_name: str + :ivar deployment_name: This field indicates the deployment name for the model. Required. + :vartype deployment_name: str + """ + + logging_opt_out: Optional[bool] = rest_field( + name="loggingOptOut", visibility=["read", "create", "update", "delete", "query"] + ) + """logging opt out.""" + project_name: str = rest_field(name="projectName", visibility=["read", "create", "update", "delete", "query"]) + """This field indicates the project name for the model. Required.""" + deployment_name: str = rest_field(name="deploymentName", visibility=["read", "create", "update", "delete", "query"]) + """This field indicates the deployment name for the model. Required.""" + + @overload + def __init__( + self, + *, + project_name: str, + deployment_name: str, + logging_opt_out: Optional[bool] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class DateMetadata(BaseMetadata, discriminator="DateMetadata"): + """A Metadata for date entity instances. + + :ivar date_values: List of date values. + :vartype date_values: list[~azure.ai.textanalytics.models.DateValue] + :ivar metadata_kind: Kind of the metadata. Required. Metadata for date-related values. + :vartype metadata_kind: str or ~azure.ai.textanalytics.models.DATE_METADATA + """ + + date_values: Optional[List["_models.DateValue"]] = rest_field( + name="dateValues", visibility=["read", "create", "update", "delete", "query"] + ) + """List of date values.""" + metadata_kind: Literal[MetadataKind.DATE_METADATA] = rest_discriminator(name="metadataKind", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """Kind of the metadata. Required. Metadata for date-related values.""" + + @overload + def __init__( + self, + *, + date_values: Optional[List["_models.DateValue"]] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, metadata_kind=MetadataKind.DATE_METADATA, **kwargs) + + +class DateTimeMetadata(BaseMetadata, discriminator="DateTimeMetadata"): + """A Metadata for datetime entity instances. + + :ivar date_values: List of date values. + :vartype date_values: list[~azure.ai.textanalytics.models.DateValue] + :ivar metadata_kind: Kind of the metadata. Required. Metadata for date and time-related values. + :vartype metadata_kind: str or ~azure.ai.textanalytics.models.DATE_TIME_METADATA + """ + + date_values: Optional[List["_models.DateValue"]] = rest_field( + name="dateValues", visibility=["read", "create", "update", "delete", "query"] + ) + """List of date values.""" + metadata_kind: Literal[MetadataKind.DATE_TIME_METADATA] = rest_discriminator(name="metadataKind", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """Kind of the metadata. Required. Metadata for date and time-related values.""" + + @overload + def __init__( + self, + *, + date_values: Optional[List["_models.DateValue"]] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, metadata_kind=MetadataKind.DATE_TIME_METADATA, **kwargs) + + +class DateValue(_Model): + """Represents the date value. + + :ivar timex: An extended ISO 8601 date/time representation as described in + (`https://github.com/Microsoft/Recognizers-Text/blob/master/Patterns/English/English-DateTime.yaml + `_). + Required. + :vartype timex: str + :ivar value: The actual time that the extracted text denote. Required. + :vartype value: str + :ivar modifier: Modifier for datetime to indicate point of reference like before, after etc. + Known values are: "AfterApprox", "Before", "BeforeStart", "Approx", "ReferenceUndefined", + "SinceEnd", "AfterMid", "Start", "After", "BeforeEnd", "Until", "End", "Less", "Since", + "AfterStart", "BeforeApprox", "Mid", and "More". + :vartype modifier: str or ~azure.ai.textanalytics.models.TemporalModifier + """ + + timex: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """An extended ISO 8601 date/time representation as described in + (`https://github.com/Microsoft/Recognizers-Text/blob/master/Patterns/English/English-DateTime.yaml + `_). + Required.""" + value: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The actual time that the extracted text denote. Required.""" + modifier: Optional[Union[str, "_models.TemporalModifier"]] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Modifier for datetime to indicate point of reference like before, after etc. Known values are: + \"AfterApprox\", \"Before\", \"BeforeStart\", \"Approx\", \"ReferenceUndefined\", \"SinceEnd\", + \"AfterMid\", \"Start\", \"After\", \"BeforeEnd\", \"Until\", \"End\", \"Less\", \"Since\", + \"AfterStart\", \"BeforeApprox\", \"Mid\", and \"More\".""" + + @overload + def __init__( + self, + *, + timex: str, + value: str, + modifier: Optional[Union[str, "_models.TemporalModifier"]] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class DetectedLanguage(_Model): + """Contains the details of the detected language for the text. + + :ivar name: Long name of a detected language (e.g. English, French). Required. + :vartype name: str + :ivar iso6391_name: A two letter representation of the detected language according to the ISO + 639-1 standard (e.g. en, fr). Required. + :vartype iso6391_name: str + :ivar confidence_score: A confidence score between 0 and 1. Scores close to 1 indicate 100% + certainty that the identified language is true. Required. + :vartype confidence_score: float + :ivar script_name: Identifies the script name of the input document according to the ISO 15924 + standard. Known values are: "Arabic", "Armenian", "Bangla", + "UnifiedCanadianAboriginalSyllabics", "Cyrillic", "Devanagari", "Ethiopic", "Georgian", + "Greek", "Gujarati", "Gurmukhi", "Hangul", "HanLiteral", "HanSimplified", "HanTraditional", + "Hebrew", "Japanese", "Khmer", "Kannada", "Lao", "Latin", "Malayalam", "Meitei", "Mongolian", + "Myanmar", "Odia", "Santali", "Sharada", "Sinhala", "Tamil", "Telugu", "Thaana", "Thai", and + "Tibetan". + :vartype script_name: str or ~azure.ai.textanalytics.models.ScriptKind + :ivar script_iso15924_code: Identifies the script code of the input document according to the + ISO 15924 standard. Known values are: "Arab", "Armn", "Beng", "Cans", "Cyrl", "Deva", "Ethi", + "Geor", "Grek", "Gujr", "Guru", "Hang", "Hani", "Hans", "Hant", "Hebr", "Jpan", "Khmr", "Knda", + "Laoo", "Latn", "Mlym", "Mong", "Mtei", "Mymr", "Olck", "Orya", "Sinh", "Shrd", "Taml", "Telu", + "Thaa", "Thai", and "Tibt". + :vartype script_iso15924_code: str or ~azure.ai.textanalytics.models.ScriptCode + """ + + name: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Long name of a detected language (e.g. English, French). Required.""" + iso6391_name: str = rest_field(name="iso6391Name", visibility=["read", "create", "update", "delete", "query"]) + """A two letter representation of the detected language according to the ISO 639-1 standard (e.g. + en, fr). Required.""" + confidence_score: float = rest_field( + name="confidenceScore", visibility=["read", "create", "update", "delete", "query"] + ) + """A confidence score between 0 and 1. Scores close to 1 indicate 100% certainty that the + identified language is true. Required.""" + script_name: Optional[Union[str, "_models.ScriptKind"]] = rest_field( + name="scriptName", visibility=["read", "create", "update", "delete", "query"] + ) + """Identifies the script name of the input document according to the ISO 15924 standard. Known + values are: \"Arabic\", \"Armenian\", \"Bangla\", \"UnifiedCanadianAboriginalSyllabics\", + \"Cyrillic\", \"Devanagari\", \"Ethiopic\", \"Georgian\", \"Greek\", \"Gujarati\", + \"Gurmukhi\", \"Hangul\", \"HanLiteral\", \"HanSimplified\", \"HanTraditional\", \"Hebrew\", + \"Japanese\", \"Khmer\", \"Kannada\", \"Lao\", \"Latin\", \"Malayalam\", \"Meitei\", + \"Mongolian\", \"Myanmar\", \"Odia\", \"Santali\", \"Sharada\", \"Sinhala\", \"Tamil\", + \"Telugu\", \"Thaana\", \"Thai\", and \"Tibetan\".""" + script_iso15924_code: Optional[Union[str, "_models.ScriptCode"]] = rest_field( + name="scriptIso15924Code", visibility=["read", "create", "update", "delete", "query"] + ) + """Identifies the script code of the input document according to the ISO 15924 standard. Known + values are: \"Arab\", \"Armn\", \"Beng\", \"Cans\", \"Cyrl\", \"Deva\", \"Ethi\", \"Geor\", + \"Grek\", \"Gujr\", \"Guru\", \"Hang\", \"Hani\", \"Hans\", \"Hant\", \"Hebr\", \"Jpan\", + \"Khmr\", \"Knda\", \"Laoo\", \"Latn\", \"Mlym\", \"Mong\", \"Mtei\", \"Mymr\", \"Olck\", + \"Orya\", \"Sinh\", \"Shrd\", \"Taml\", \"Telu\", \"Thaa\", \"Thai\", and \"Tibt\".""" + + @overload + def __init__( + self, + *, + name: str, + iso6391_name: str, + confidence_score: float, + script_name: Optional[Union[str, "_models.ScriptKind"]] = None, + script_iso15924_code: Optional[Union[str, "_models.ScriptCode"]] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class DocumentError(_Model): + """Contains details of errors encountered during a job execution. + + :ivar id: The ID of the input document. Required. + :vartype id: str + :ivar error: Error encountered. Required. + :vartype error: ~azure.ai.textanalytics.models.Error + """ + + id: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The ID of the input document. Required.""" + error: "_models.Error" = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Error encountered. Required.""" + + @overload + def __init__( + self, + *, + id: str, # pylint: disable=redefined-builtin + error: "_models.Error", + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class DocumentStatistics(_Model): + """if showStats=true was specified in the request this field will contain information about the + document payload. + + :ivar characters_count: Number of text elements recognized in the document. Required. + :vartype characters_count: int + :ivar transactions_count: Number of transactions for the document. Required. + :vartype transactions_count: int + """ + + characters_count: int = rest_field( + name="charactersCount", visibility=["read", "create", "update", "delete", "query"] + ) + """Number of text elements recognized in the document. Required.""" + transactions_count: int = rest_field( + name="transactionsCount", visibility=["read", "create", "update", "delete", "query"] + ) + """Number of transactions for the document. Required.""" + + @overload + def __init__( + self, + *, + characters_count: int, + transactions_count: int, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class DocumentWarning(_Model): + """Contains the warnings object with warnings encountered for the processed document. + + :ivar code: Warning code. Required. Known values are: "LongWordsInDocument" and + "DocumentTruncated". + :vartype code: str or ~azure.ai.textanalytics.models.WarningCodeValue + :ivar message: Warning message. Required. + :vartype message: str + :ivar target_ref: A JSON pointer reference indicating the target object. + :vartype target_ref: str + """ + + code: Union[str, "_models.WarningCodeValue"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Warning code. Required. Known values are: \"LongWordsInDocument\" and \"DocumentTruncated\".""" + message: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Warning message. Required.""" + target_ref: Optional[str] = rest_field(name="targetRef", visibility=["read", "create", "update", "delete", "query"]) + """A JSON pointer reference indicating the target object.""" + + @overload + def __init__( + self, + *, + code: Union[str, "_models.WarningCodeValue"], + message: str, + target_ref: Optional[str] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class EntitiesDocumentResultWithDetectedLanguage(_Model): # pylint: disable=name-too-long + """Contains the entity recognition task result for the document with detected language. + + :ivar id: Unique, non-empty document identifier. Required. + :vartype id: str + :ivar warnings: Warnings encountered while processing document. Required. + :vartype warnings: list[~azure.ai.textanalytics.models.DocumentWarning] + :ivar statistics: if showStats=true was specified in the request this field will contain + information about the document payload. + :vartype statistics: ~azure.ai.textanalytics.models.DocumentStatistics + :ivar entities: Recognized entities in the document. Required. + :vartype entities: list[~azure.ai.textanalytics.models.Entity] + :ivar detected_language: If 'language' is set to 'auto' for the document in the request this + field will contain a 2 letter ISO 639-1 representation of the language detected for this + document. + :vartype detected_language: ~azure.ai.textanalytics.models.DetectedLanguage + """ + + id: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Unique, non-empty document identifier. Required.""" + warnings: List["_models.DocumentWarning"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Warnings encountered while processing document. Required.""" + statistics: Optional["_models.DocumentStatistics"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """if showStats=true was specified in the request this field will contain information about the + document payload.""" + entities: List["_models.Entity"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Recognized entities in the document. Required.""" + detected_language: Optional["_models.DetectedLanguage"] = rest_field( + name="detectedLanguage", visibility=["read", "create", "update", "delete", "query"] + ) + """If 'language' is set to 'auto' for the document in the request this field will contain a 2 + letter ISO 639-1 representation of the language detected for this document.""" + + @overload + def __init__( + self, + *, + id: str, # pylint: disable=redefined-builtin + warnings: List["_models.DocumentWarning"], + entities: List["_models.Entity"], + statistics: Optional["_models.DocumentStatistics"] = None, + detected_language: Optional["_models.DetectedLanguage"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class EntitiesDocumentResultWithMetadata(_Model): + """Entity documents result with metadata. + + :ivar id: Unique, non-empty document identifier. Required. + :vartype id: str + :ivar warnings: Warnings encountered while processing document. Required. + :vartype warnings: list[~azure.ai.textanalytics.models.DocumentWarning] + :ivar statistics: if showStats=true was specified in the request this field will contain + information about the document payload. + :vartype statistics: ~azure.ai.textanalytics.models.DocumentStatistics + :ivar entities: Recognized entities in the document. Required. + :vartype entities: list[~azure.ai.textanalytics.models.EntityWithMetadata] + """ + + id: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Unique, non-empty document identifier. Required.""" + warnings: List["_models.DocumentWarning"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Warnings encountered while processing document. Required.""" + statistics: Optional["_models.DocumentStatistics"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """if showStats=true was specified in the request this field will contain information about the + document payload.""" + entities: List["_models.EntityWithMetadata"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Recognized entities in the document. Required.""" + + @overload + def __init__( + self, + *, + id: str, # pylint: disable=redefined-builtin + warnings: List["_models.DocumentWarning"], + entities: List["_models.EntityWithMetadata"], + statistics: Optional["_models.DocumentStatistics"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class EntitiesDocumentResultWithMetadataDetectedLanguage(_Model): # pylint: disable=name-too-long + """Contains the entity recognition task result for the document with metadata and detected + language. + + :ivar id: Unique, non-empty document identifier. Required. + :vartype id: str + :ivar warnings: Warnings encountered while processing document. Required. + :vartype warnings: list[~azure.ai.textanalytics.models.DocumentWarning] + :ivar statistics: if showStats=true was specified in the request this field will contain + information about the document payload. + :vartype statistics: ~azure.ai.textanalytics.models.DocumentStatistics + :ivar entities: Recognized entities in the document. Required. + :vartype entities: list[~azure.ai.textanalytics.models.EntityWithMetadata] + :ivar detected_language: If 'language' is set to 'auto' for the document in the request this + field will contain a 2 letter ISO 639-1 representation of the language detected for this + document. + :vartype detected_language: ~azure.ai.textanalytics.models.DetectedLanguage + """ + + id: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Unique, non-empty document identifier. Required.""" + warnings: List["_models.DocumentWarning"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Warnings encountered while processing document. Required.""" + statistics: Optional["_models.DocumentStatistics"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """if showStats=true was specified in the request this field will contain information about the + document payload.""" + entities: List["_models.EntityWithMetadata"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Recognized entities in the document. Required.""" + detected_language: Optional["_models.DetectedLanguage"] = rest_field( + name="detectedLanguage", visibility=["read", "create", "update", "delete", "query"] + ) + """If 'language' is set to 'auto' for the document in the request this field will contain a 2 + letter ISO 639-1 representation of the language detected for this document.""" + + @overload + def __init__( + self, + *, + id: str, # pylint: disable=redefined-builtin + warnings: List["_models.DocumentWarning"], + entities: List["_models.EntityWithMetadata"], + statistics: Optional["_models.DocumentStatistics"] = None, + detected_language: Optional["_models.DetectedLanguage"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class EntitiesLROTask(AnalyzeTextLROTask, discriminator="EntityRecognition"): + """An object representing the task definition for an Entities Recognition task. + + :ivar task_name: task name. + :vartype task_name: str + :ivar kind: The kind of task. Required. Entity recognition task + :vartype kind: str or ~azure.ai.textanalytics.models.ENTITY_RECOGNITION + :ivar parameters: Task parameters. + :vartype parameters: ~azure.ai.textanalytics.models.EntitiesTaskParameters + """ + + kind: Literal[AnalyzeTextLROTaskKind.ENTITY_RECOGNITION] = rest_discriminator(name="kind", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """The kind of task. Required. Entity recognition task""" + parameters: Optional["_models.EntitiesTaskParameters"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Task parameters.""" + + @overload + def __init__( + self, + *, + task_name: Optional[str] = None, + parameters: Optional["_models.EntitiesTaskParameters"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, kind=AnalyzeTextLROTaskKind.ENTITY_RECOGNITION, **kwargs) + + +class EntitiesResult(_Model): + """Contains the entity recognition task result. + + :ivar errors: Errors by document id. Required. + :vartype errors: list[~azure.ai.textanalytics.models.DocumentError] + :ivar statistics: if showStats=true was specified in the request this field will contain + information about the request payload. + :vartype statistics: ~azure.ai.textanalytics.models.RequestStatistics + :ivar model_version: This field indicates which model is used for scoring. Required. + :vartype model_version: str + :ivar documents: Response by document. Required. + :vartype documents: list[~azure.ai.textanalytics.models.EntitiesDocumentResultWithMetadata] + """ + + errors: List["_models.DocumentError"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Errors by document id. Required.""" + statistics: Optional["_models.RequestStatistics"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """if showStats=true was specified in the request this field will contain information about the + request payload.""" + model_version: str = rest_field(name="modelVersion", visibility=["read", "create", "update", "delete", "query"]) + """This field indicates which model is used for scoring. Required.""" + documents: List["_models.EntitiesDocumentResultWithMetadata"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Response by document. Required.""" + + @overload + def __init__( + self, + *, + errors: List["_models.DocumentError"], + model_version: str, + documents: List["_models.EntitiesDocumentResultWithMetadata"], + statistics: Optional["_models.RequestStatistics"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class EntitiesTaskParameters(_Model): + """Supported parameters for an Entity Recognition task. + + :ivar logging_opt_out: logging opt out. + :vartype logging_opt_out: bool + :ivar model_version: model version. + :vartype model_version: str + :ivar string_index_type: (Optional) parameter to provide the string index type used to + interpret string offsets. Defaults to TextElements (Graphemes). Known values are: + "TextElements_v8", "UnicodeCodePoint", and "Utf16CodeUnit". + :vartype string_index_type: str or ~azure.ai.textanalytics.models.StringIndexType + :ivar inclusion_list: (Optional) request parameter that limits the output to the requested + entity types included in this list. We will apply inclusionList before exclusionList. + :vartype inclusion_list: list[str or ~azure.ai.textanalytics.models.EntityCategory] + :ivar exclusion_list: (Optional) request parameter that filters out any entities that are + included the excludeList. When a user specifies an excludeList, they cannot get a prediction + returned with an entity in that list. We will apply inclusionList before exclusionList. + :vartype exclusion_list: list[str or ~azure.ai.textanalytics.models.EntityCategory] + :ivar overlap_policy: (Optional) describes the type of overlap policy to apply to the ner + output. + :vartype overlap_policy: ~azure.ai.textanalytics.models.BaseEntityOverlapPolicy + :ivar inference_options: (Optional) request parameter that allows the user to provide settings + for running the inference. + :vartype inference_options: ~azure.ai.textanalytics.models.EntityInferenceOptions + """ + + logging_opt_out: Optional[bool] = rest_field( + name="loggingOptOut", visibility=["read", "create", "update", "delete", "query"] + ) + """logging opt out.""" + model_version: Optional[str] = rest_field( + name="modelVersion", visibility=["read", "create", "update", "delete", "query"] + ) + """model version.""" + string_index_type: Optional[Union[str, "_models.StringIndexType"]] = rest_field( + name="stringIndexType", visibility=["read", "create", "update", "delete", "query"] + ) + """(Optional) parameter to provide the string index type used to interpret string offsets. + Defaults to TextElements (Graphemes). Known values are: \"TextElements_v8\", + \"UnicodeCodePoint\", and \"Utf16CodeUnit\".""" + inclusion_list: Optional[List[Union[str, "_models.EntityCategory"]]] = rest_field( + name="inclusionList", visibility=["read", "create", "update", "delete", "query"] + ) + """(Optional) request parameter that limits the output to the requested entity types included in + this list. We will apply inclusionList before exclusionList.""" + exclusion_list: Optional[List[Union[str, "_models.EntityCategory"]]] = rest_field( + name="exclusionList", visibility=["read", "create", "update", "delete", "query"] + ) + """(Optional) request parameter that filters out any entities that are included the excludeList. + When a user specifies an excludeList, they cannot get a prediction returned with an entity in + that list. We will apply inclusionList before exclusionList.""" + overlap_policy: Optional["_models.BaseEntityOverlapPolicy"] = rest_field( + name="overlapPolicy", visibility=["read", "create", "update", "delete", "query"] + ) + """(Optional) describes the type of overlap policy to apply to the ner output.""" + inference_options: Optional["_models.EntityInferenceOptions"] = rest_field( + name="inferenceOptions", visibility=["read", "create", "update", "delete", "query"] + ) + """(Optional) request parameter that allows the user to provide settings for running the + inference.""" + + @overload + def __init__( + self, + *, + logging_opt_out: Optional[bool] = None, + model_version: Optional[str] = None, + string_index_type: Optional[Union[str, "_models.StringIndexType"]] = None, + inclusion_list: Optional[List[Union[str, "_models.EntityCategory"]]] = None, + exclusion_list: Optional[List[Union[str, "_models.EntityCategory"]]] = None, + overlap_policy: Optional["_models.BaseEntityOverlapPolicy"] = None, + inference_options: Optional["_models.EntityInferenceOptions"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class EntitiesTaskResult(AnalyzeTextTaskResult, discriminator="EntityRecognitionResults"): + """Contains the entity task. + + :ivar kind: kind of the task. Required. Entity recognition results + :vartype kind: str or ~azure.ai.textanalytics.models.ENTITY_RECOGNITION_RESULTS + :ivar results: Results for entity recognition. Required. + :vartype results: ~azure.ai.textanalytics.models.EntitiesWithMetadataAutoResult + """ + + kind: Literal[AnalyzeTextTaskResultsKind.ENTITY_RECOGNITION_RESULTS] = rest_discriminator(name="kind", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """kind of the task. Required. Entity recognition results""" + results: "_models.EntitiesWithMetadataAutoResult" = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Results for entity recognition. Required.""" + + @overload + def __init__( + self, + *, + results: "_models.EntitiesWithMetadataAutoResult", + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, kind=AnalyzeTextTaskResultsKind.ENTITY_RECOGNITION_RESULTS, **kwargs) + + +class EntitiesWithMetadataAutoResult(_Model): + """Contains the entity recognition task result. + + :ivar errors: Errors by document id. Required. + :vartype errors: list[~azure.ai.textanalytics.models.DocumentError] + :ivar statistics: if showStats=true was specified in the request this field will contain + information about the request payload. + :vartype statistics: ~azure.ai.textanalytics.models.RequestStatistics + :ivar model_version: This field indicates which model is used for scoring. Required. + :vartype model_version: str + :ivar documents: Response by document. Required. + :vartype documents: + list[~azure.ai.textanalytics.models.EntitiesDocumentResultWithMetadataDetectedLanguage] + """ + + errors: List["_models.DocumentError"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Errors by document id. Required.""" + statistics: Optional["_models.RequestStatistics"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """if showStats=true was specified in the request this field will contain information about the + request payload.""" + model_version: str = rest_field(name="modelVersion", visibility=["read", "create", "update", "delete", "query"]) + """This field indicates which model is used for scoring. Required.""" + documents: List["_models.EntitiesDocumentResultWithMetadataDetectedLanguage"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Response by document. Required.""" + + @overload + def __init__( + self, + *, + errors: List["_models.DocumentError"], + model_version: str, + documents: List["_models.EntitiesDocumentResultWithMetadataDetectedLanguage"], + statistics: Optional["_models.RequestStatistics"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class Entity(_Model): + """Defines the detected entity object containing the entity category and entity text detected, + etc. + + :ivar text: Entity text as appears in the request. Required. + :vartype text: str + :ivar category: Entity type. Required. + :vartype category: str + :ivar subcategory: (Optional) Entity sub type. + :vartype subcategory: str + :ivar offset: Start position for the entity text. Use of different 'stringIndexType' values can + affect the offset returned. Required. + :vartype offset: int + :ivar length: Length for the entity text. Use of different 'stringIndexType' values can affect + the length returned. Required. + :vartype length: int + :ivar confidence_score: Confidence score between 0 and 1 of the extracted entity. Required. + :vartype confidence_score: float + """ + + text: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Entity text as appears in the request. Required.""" + category: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Entity type. Required.""" + subcategory: Optional[str] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """(Optional) Entity sub type.""" + offset: int = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Start position for the entity text. Use of different 'stringIndexType' values can affect the + offset returned. Required.""" + length: int = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Length for the entity text. Use of different 'stringIndexType' values can affect the length + returned. Required.""" + confidence_score: float = rest_field( + name="confidenceScore", visibility=["read", "create", "update", "delete", "query"] + ) + """Confidence score between 0 and 1 of the extracted entity. Required.""" + + @overload + def __init__( + self, + *, + text: str, + category: str, + offset: int, + length: int, + confidence_score: float, + subcategory: Optional[str] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class EntityInferenceOptions(_Model): + """The class that houses the inference options allowed for named entity recognition. + + :ivar exclude_normalized_values: Option to include/exclude the detected entity values to be + normalized and included in the metadata. The numeric and temporal entity types support value + normalization. + :vartype exclude_normalized_values: bool + """ + + exclude_normalized_values: Optional[bool] = rest_field( + name="excludeNormalizedValues", visibility=["read", "create", "update", "delete", "query"] + ) + """Option to include/exclude the detected entity values to be normalized and included in the + metadata. The numeric and temporal entity types support value normalization.""" + + @overload + def __init__( + self, + *, + exclude_normalized_values: Optional[bool] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class EntityLinkingLROResult(AnalyzeTextLROResult, discriminator="EntityLinkingLROResults"): + """Contains the analyze text Entity linking task LRO result. + + :ivar last_update_date_time: The last updated time in UTC for the task. Required. + :vartype last_update_date_time: ~datetime.datetime + :ivar status: The status of the task at the mentioned last update time. Required. Known values + are: "notStarted", "running", "succeeded", "partiallyCompleted", "failed", "cancelled", and + "cancelling". + :vartype status: str or ~azure.ai.textanalytics.models.State + :ivar task_name: task name. + :vartype task_name: str + :ivar kind: Kind of the task. Required. Entity linking LRO results + :vartype kind: str or ~azure.ai.textanalytics.models.ENTITY_LINKING_LRO_RESULTS + :ivar results: Entity linking result. Required. + :vartype results: ~azure.ai.textanalytics.models.EntityLinkingResult + """ + + kind: Literal[AnalyzeTextLROResultsKind.ENTITY_LINKING_LRO_RESULTS] = rest_discriminator(name="kind", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """Kind of the task. Required. Entity linking LRO results""" + results: "_models.EntityLinkingResult" = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Entity linking result. Required.""" + + @overload + def __init__( + self, + *, + last_update_date_time: datetime.datetime, + status: Union[str, "_models.State"], + results: "_models.EntityLinkingResult", + task_name: Optional[str] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, kind=AnalyzeTextLROResultsKind.ENTITY_LINKING_LRO_RESULTS, **kwargs) + + +class EntityLinkingLROTask(AnalyzeTextLROTask, discriminator="EntityLinking"): + """Contains the analyze text Entity linking LRO task. + + :ivar task_name: task name. + :vartype task_name: str + :ivar kind: Kind of task result. Required. Entity linking task + :vartype kind: str or ~azure.ai.textanalytics.models.ENTITY_LINKING + :ivar parameters: Task parameters. + :vartype parameters: ~azure.ai.textanalytics.models.EntityLinkingTaskParameters + """ + + kind: Literal[AnalyzeTextLROTaskKind.ENTITY_LINKING] = rest_discriminator(name="kind", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """Kind of task result. Required. Entity linking task""" + parameters: Optional["_models.EntityLinkingTaskParameters"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Task parameters.""" + + @overload + def __init__( + self, + *, + task_name: Optional[str] = None, + parameters: Optional["_models.EntityLinkingTaskParameters"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, kind=AnalyzeTextLROTaskKind.ENTITY_LINKING, **kwargs) + + +class EntityLinkingResult(_Model): + """Entity linking result. + + :ivar errors: Errors by document id. Required. + :vartype errors: list[~azure.ai.textanalytics.models.DocumentError] + :ivar statistics: if showStats=true was specified in the request this field will contain + information about the request payload. + :vartype statistics: ~azure.ai.textanalytics.models.RequestStatistics + :ivar model_version: This field indicates which model is used for scoring. Required. + :vartype model_version: str + :ivar documents: Response by document. Required. + :vartype documents: + list[~azure.ai.textanalytics.models.EntityLinkingResultWithDetectedLanguage] + """ + + errors: List["_models.DocumentError"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Errors by document id. Required.""" + statistics: Optional["_models.RequestStatistics"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """if showStats=true was specified in the request this field will contain information about the + request payload.""" + model_version: str = rest_field(name="modelVersion", visibility=["read", "create", "update", "delete", "query"]) + """This field indicates which model is used for scoring. Required.""" + documents: List["_models.EntityLinkingResultWithDetectedLanguage"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Response by document. Required.""" + + @overload + def __init__( + self, + *, + errors: List["_models.DocumentError"], + model_version: str, + documents: List["_models.EntityLinkingResultWithDetectedLanguage"], + statistics: Optional["_models.RequestStatistics"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class EntityLinkingResultWithDetectedLanguage(_Model): + """Entity linking document result with auto language detection. + + :ivar id: Unique, non-empty document identifier. Required. + :vartype id: str + :ivar warnings: Warnings encountered while processing document. Required. + :vartype warnings: list[~azure.ai.textanalytics.models.DocumentWarning] + :ivar statistics: if showStats=true was specified in the request this field will contain + information about the document payload. + :vartype statistics: ~azure.ai.textanalytics.models.DocumentStatistics + :ivar entities: Recognized well known entities in the document. Required. + :vartype entities: list[~azure.ai.textanalytics.models.LinkedEntity] + :ivar detected_language: If 'language' is set to 'auto' for the document in the request this + field will contain a 2 letter ISO 639-1 representation of the language detected for this + document. + :vartype detected_language: ~azure.ai.textanalytics.models.DetectedLanguage + """ + + id: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Unique, non-empty document identifier. Required.""" + warnings: List["_models.DocumentWarning"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Warnings encountered while processing document. Required.""" + statistics: Optional["_models.DocumentStatistics"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """if showStats=true was specified in the request this field will contain information about the + document payload.""" + entities: List["_models.LinkedEntity"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Recognized well known entities in the document. Required.""" + detected_language: Optional["_models.DetectedLanguage"] = rest_field( + name="detectedLanguage", visibility=["read", "create", "update", "delete", "query"] + ) + """If 'language' is set to 'auto' for the document in the request this field will contain a 2 + letter ISO 639-1 representation of the language detected for this document.""" + + @overload + def __init__( + self, + *, + id: str, # pylint: disable=redefined-builtin + warnings: List["_models.DocumentWarning"], + entities: List["_models.LinkedEntity"], + statistics: Optional["_models.DocumentStatistics"] = None, + detected_language: Optional["_models.DetectedLanguage"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class EntityLinkingTaskParameters(_Model): + """Supported parameters for an Entity Linking task. + + :ivar logging_opt_out: logging opt out. + :vartype logging_opt_out: bool + :ivar model_version: model version. + :vartype model_version: str + :ivar string_index_type: Optional parameter to provide the string index type used to interpret + string offsets. Defaults to TextElements (Graphemes). Known values are: "TextElements_v8", + "UnicodeCodePoint", and "Utf16CodeUnit". + :vartype string_index_type: str or ~azure.ai.textanalytics.models.StringIndexType + """ + + logging_opt_out: Optional[bool] = rest_field( + name="loggingOptOut", visibility=["read", "create", "update", "delete", "query"] + ) + """logging opt out.""" + model_version: Optional[str] = rest_field( + name="modelVersion", visibility=["read", "create", "update", "delete", "query"] + ) + """model version.""" + string_index_type: Optional[Union[str, "_models.StringIndexType"]] = rest_field( + name="stringIndexType", visibility=["read", "create", "update", "delete", "query"] + ) + """Optional parameter to provide the string index type used to interpret string offsets. Defaults + to TextElements (Graphemes). Known values are: \"TextElements_v8\", \"UnicodeCodePoint\", and + \"Utf16CodeUnit\".""" + + @overload + def __init__( + self, + *, + logging_opt_out: Optional[bool] = None, + model_version: Optional[str] = None, + string_index_type: Optional[Union[str, "_models.StringIndexType"]] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class EntityLinkingTaskResult(AnalyzeTextTaskResult, discriminator="EntityLinkingResults"): + """Contains the analyze text Entity linking task result. + + :ivar kind: Kind of task result. Required. Entity linking results + :vartype kind: str or ~azure.ai.textanalytics.models.ENTITY_LINKING_RESULTS + :ivar results: Entity linking result. Required. + :vartype results: ~azure.ai.textanalytics.models.EntityLinkingResult + """ + + kind: Literal[AnalyzeTextTaskResultsKind.ENTITY_LINKING_RESULTS] = rest_discriminator(name="kind", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """Kind of task result. Required. Entity linking results""" + results: "_models.EntityLinkingResult" = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Entity linking result. Required.""" + + @overload + def __init__( + self, + *, + results: "_models.EntityLinkingResult", + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, kind=AnalyzeTextTaskResultsKind.ENTITY_LINKING_RESULTS, **kwargs) + + +class EntityMaskPolicyType(BaseRedactionPolicy, discriminator="entityMask"): + """Represents the policy of redacting PII with the entity type. + + :ivar policy_kind: The entity OverlapPolicy object kind. Required. Redact detected entities + with entity type. + :vartype policy_kind: str or ~azure.ai.textanalytics.models.ENTITY_MASK + """ + + policy_kind: Literal[RedactionPolicyKind.ENTITY_MASK] = rest_discriminator(name="policyKind", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """The entity OverlapPolicy object kind. Required. Redact detected entities with entity type.""" + + @overload + def __init__( + self, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, policy_kind=RedactionPolicyKind.ENTITY_MASK, **kwargs) + + +class EntityRecognitionLROResult(AnalyzeTextLROResult, discriminator="EntityRecognitionLROResults"): + """Contains the entity recognition job task result. + + :ivar last_update_date_time: The last updated time in UTC for the task. Required. + :vartype last_update_date_time: ~datetime.datetime + :ivar status: The status of the task at the mentioned last update time. Required. Known values + are: "notStarted", "running", "succeeded", "partiallyCompleted", "failed", "cancelled", and + "cancelling". + :vartype status: str or ~azure.ai.textanalytics.models.State + :ivar task_name: task name. + :vartype task_name: str + :ivar kind: Kind of the task. Required. Entity recognition LRO results + :vartype kind: str or ~azure.ai.textanalytics.models.ENTITY_RECOGNITION_LRO_RESULTS + :ivar results: Results for the task. Required. + :vartype results: ~azure.ai.textanalytics.models.EntitiesResult + """ + + kind: Literal[AnalyzeTextLROResultsKind.ENTITY_RECOGNITION_LRO_RESULTS] = rest_discriminator(name="kind", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """Kind of the task. Required. Entity recognition LRO results""" + results: "_models.EntitiesResult" = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Results for the task. Required.""" + + @overload + def __init__( + self, + *, + last_update_date_time: datetime.datetime, + status: Union[str, "_models.State"], + results: "_models.EntitiesResult", + task_name: Optional[str] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, kind=AnalyzeTextLROResultsKind.ENTITY_RECOGNITION_LRO_RESULTS, **kwargs) + + +class EntitySynonym(_Model): + """The entity synonyms used to enhance pii entity detection. + + :ivar synonym: The synonym to be used for context. Required. + :vartype synonym: str + :ivar language: The 2 letter ISO 639-1 language the synonym. + :vartype language: str + """ + + synonym: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The synonym to be used for context. Required.""" + language: Optional[str] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The 2 letter ISO 639-1 language the synonym.""" + + @overload + def __init__( + self, + *, + synonym: str, + language: Optional[str] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class EntitySynonyms(_Model): + """Object that allows the user to provide synonyms for context words that to enhance pii entity + detection. + + :ivar entity_type: The entity name. Required. Known values are: "Address", "Numeric", "Age", + "Currency", "Number", "NumberRange", "Percentage", "Ordinal", "Temperature", "Dimension", + "Length", "Weight", "Height", "Speed", "Area", "Volume", "Information", "Temporal", "Date", + "Time", "DateTime", "DateRange", "TimeRange", "DateTimeRange", "Duration", "SetTemporal", + "Event", "SportsEvent", "CulturalEvent", "NaturalEvent", "Location", "GPE", "City", "State", + "CountryRegion", "Continent", "Structural", "Airport", "Geological", "Organization", + "OrganizationMedical", "OrganizationStockExchange", "OrganizationSports", "Person", + "PersonType", "Email", "URL", "IP", "PhoneNumber", "Product", "ComputingProduct", and "Skill". + :vartype entity_type: str or ~azure.ai.textanalytics.models.EntityCategory + :ivar synonyms: The entity synonyms. Required. + :vartype synonyms: list[~azure.ai.textanalytics.models.EntitySynonym] + """ + + entity_type: Union[str, "_models.EntityCategory"] = rest_field( + name="entityType", visibility=["read", "create", "update", "delete", "query"] + ) + """The entity name. Required. Known values are: \"Address\", \"Numeric\", \"Age\", \"Currency\", + \"Number\", \"NumberRange\", \"Percentage\", \"Ordinal\", \"Temperature\", \"Dimension\", + \"Length\", \"Weight\", \"Height\", \"Speed\", \"Area\", \"Volume\", \"Information\", + \"Temporal\", \"Date\", \"Time\", \"DateTime\", \"DateRange\", \"TimeRange\", + \"DateTimeRange\", \"Duration\", \"SetTemporal\", \"Event\", \"SportsEvent\", + \"CulturalEvent\", \"NaturalEvent\", \"Location\", \"GPE\", \"City\", \"State\", + \"CountryRegion\", \"Continent\", \"Structural\", \"Airport\", \"Geological\", + \"Organization\", \"OrganizationMedical\", \"OrganizationStockExchange\", + \"OrganizationSports\", \"Person\", \"PersonType\", \"Email\", \"URL\", \"IP\", + \"PhoneNumber\", \"Product\", \"ComputingProduct\", and \"Skill\".""" + synonyms: List["_models.EntitySynonym"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The entity synonyms. Required.""" + + @overload + def __init__( + self, + *, + entity_type: Union[str, "_models.EntityCategory"], + synonyms: List["_models.EntitySynonym"], + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class EntityTag(_Model): + """Entity tag object which contains the name of the tags abd any associated confidence score. + Entity Tags are used to express some similarities/affinity between entities. + + :ivar name: Name of the tag. Entity Tag names will be unique globally. Required. + :vartype name: str + :ivar confidence_score: Detection score between 0 and 1 of the extracted entity. + :vartype confidence_score: float + """ + + name: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Name of the tag. Entity Tag names will be unique globally. Required.""" + confidence_score: Optional[float] = rest_field( + name="confidenceScore", visibility=["read", "create", "update", "delete", "query"] + ) + """Detection score between 0 and 1 of the extracted entity.""" + + @overload + def __init__( + self, + *, + name: str, + confidence_score: Optional[float] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class EntityWithMetadata(_Model): + """Entity object with tags and metadata. + + :ivar text: Entity text as appears in the request. Required. + :vartype text: str + :ivar category: Entity type. Required. + :vartype category: str + :ivar subcategory: (Optional) Entity sub type. + :vartype subcategory: str + :ivar offset: Start position for the entity text. Use of different 'stringIndexType' values can + affect the offset returned. Required. + :vartype offset: int + :ivar length: Length for the entity text. Use of different 'stringIndexType' values can affect + the length returned. Required. + :vartype length: int + :ivar confidence_score: Confidence score between 0 and 1 of the extracted entity. Required. + :vartype confidence_score: float + :ivar type: An entity type is the lowest (or finest) granularity at which the entity has been + detected. The type maps to the specific metadata attributes associated with the entity + detected. + :vartype type: str + :ivar tags: List of entity tags. Tags are to express some similarities/affinity between + entities. + :vartype tags: list[~azure.ai.textanalytics.models.EntityTag] + :ivar metadata: The entity metadata object. + :vartype metadata: ~azure.ai.textanalytics.models.BaseMetadata + """ + + text: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Entity text as appears in the request. Required.""" + category: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Entity type. Required.""" + subcategory: Optional[str] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """(Optional) Entity sub type.""" + offset: int = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Start position for the entity text. Use of different 'stringIndexType' values can affect the + offset returned. Required.""" + length: int = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Length for the entity text. Use of different 'stringIndexType' values can affect the length + returned. Required.""" + confidence_score: float = rest_field( + name="confidenceScore", visibility=["read", "create", "update", "delete", "query"] + ) + """Confidence score between 0 and 1 of the extracted entity. Required.""" + type: Optional[str] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """An entity type is the lowest (or finest) granularity at which the entity has been detected. The + type maps to the specific metadata attributes associated with the entity detected.""" + tags: Optional[List["_models.EntityTag"]] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """List of entity tags. Tags are to express some similarities/affinity between entities.""" + metadata: Optional["_models.BaseMetadata"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The entity metadata object.""" + + @overload + def __init__( + self, + *, + text: str, + category: str, + offset: int, + length: int, + confidence_score: float, + subcategory: Optional[str] = None, + type: Optional[str] = None, + tags: Optional[List["_models.EntityTag"]] = None, + metadata: Optional["_models.BaseMetadata"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class Error(_Model): + """The error response object returned when the service encounters some errors during processing + the request. + + :ivar code: One of a server-defined set of error codes. Required. Known values are: + "InvalidRequest", "InvalidArgument", "Unauthorized", "Forbidden", "NotFound", + "ProjectNotFound", "OperationNotFound", "AzureCognitiveSearchNotFound", + "AzureCognitiveSearchIndexNotFound", "TooManyRequests", "AzureCognitiveSearchThrottling", + "AzureCognitiveSearchIndexLimitReached", "InternalServerError", "ServiceUnavailable", + "Timeout", "QuotaExceeded", "Conflict", and "Warning". + :vartype code: str or ~azure.ai.textanalytics.models.ErrorCode + :ivar message: A human-readable representation of the error. Required. + :vartype message: str + :ivar target: The target of the error. + :vartype target: str + :ivar details: An array of details about specific errors that led to this reported error. + :vartype details: list[~azure.ai.textanalytics.models.Error] + :ivar innererror: An object containing more specific information than the current object about + the error. + :vartype innererror: ~azure.ai.textanalytics.models.InnerErrorModel + """ + + code: Union[str, "_models.ErrorCode"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """One of a server-defined set of error codes. Required. Known values are: \"InvalidRequest\", + \"InvalidArgument\", \"Unauthorized\", \"Forbidden\", \"NotFound\", \"ProjectNotFound\", + \"OperationNotFound\", \"AzureCognitiveSearchNotFound\", \"AzureCognitiveSearchIndexNotFound\", + \"TooManyRequests\", \"AzureCognitiveSearchThrottling\", + \"AzureCognitiveSearchIndexLimitReached\", \"InternalServerError\", \"ServiceUnavailable\", + \"Timeout\", \"QuotaExceeded\", \"Conflict\", and \"Warning\".""" + message: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """A human-readable representation of the error. Required.""" + target: Optional[str] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The target of the error.""" + details: Optional[List["_models.Error"]] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """An array of details about specific errors that led to this reported error.""" + innererror: Optional["_models.InnerErrorModel"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """An object containing more specific information than the current object about the error.""" + + @overload + def __init__( + self, + *, + code: Union[str, "_models.ErrorCode"], + message: str, + target: Optional[str] = None, + details: Optional[List["_models.Error"]] = None, + innererror: Optional["_models.InnerErrorModel"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class ErrorResponse(_Model): + """Error response. + + :ivar error: The error object. Required. + :vartype error: ~azure.ai.textanalytics.models.Error + """ + + error: "_models.Error" = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The error object. Required.""" + + @overload + def __init__( + self, + *, + error: "_models.Error", + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class ExtractedSummaryDocumentResultWithDetectedLanguage(_Model): # pylint: disable=name-too-long + """A ranked list of sentences representing the extracted summary. + + :ivar id: Unique, non-empty document identifier. Required. + :vartype id: str + :ivar warnings: Warnings encountered while processing document. Required. + :vartype warnings: list[~azure.ai.textanalytics.models.DocumentWarning] + :ivar statistics: if showStats=true was specified in the request this field will contain + information about the document payload. + :vartype statistics: ~azure.ai.textanalytics.models.DocumentStatistics + :ivar sentences: Specifies the the extracted sentences from the input document. Required. + :vartype sentences: list[~azure.ai.textanalytics.models.ExtractedSummarySentence] + :ivar detected_language: If 'language' is set to 'auto' for the document in the request this + field will contain a 2 letter ISO 639-1 representation of the language detected for this + document. + :vartype detected_language: ~azure.ai.textanalytics.models.DetectedLanguage + """ + + id: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Unique, non-empty document identifier. Required.""" + warnings: List["_models.DocumentWarning"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Warnings encountered while processing document. Required.""" + statistics: Optional["_models.DocumentStatistics"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """if showStats=true was specified in the request this field will contain information about the + document payload.""" + sentences: List["_models.ExtractedSummarySentence"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Specifies the the extracted sentences from the input document. Required.""" + detected_language: Optional["_models.DetectedLanguage"] = rest_field( + name="detectedLanguage", visibility=["read", "create", "update", "delete", "query"] + ) + """If 'language' is set to 'auto' for the document in the request this field will contain a 2 + letter ISO 639-1 representation of the language detected for this document.""" + + @overload + def __init__( + self, + *, + id: str, # pylint: disable=redefined-builtin + warnings: List["_models.DocumentWarning"], + sentences: List["_models.ExtractedSummarySentence"], + statistics: Optional["_models.DocumentStatistics"] = None, + detected_language: Optional["_models.DetectedLanguage"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class ExtractedSummarySentence(_Model): + """Represents an extracted sentences from the input document. + + :ivar text: The extracted sentence text. Required. + :vartype text: str + :ivar rank_score: A double value representing the relevance of the sentence within the summary. + Higher values indicate higher importance. Required. + :vartype rank_score: float + :ivar offset: The sentence offset from the start of the document, based on the value of the + parameter StringIndexType. Required. + :vartype offset: int + :ivar length: The length of the sentence. Required. + :vartype length: int + """ + + text: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The extracted sentence text. Required.""" + rank_score: float = rest_field(name="rankScore", visibility=["read", "create", "update", "delete", "query"]) + """A double value representing the relevance of the sentence within the summary. Higher values + indicate higher importance. Required.""" + offset: int = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The sentence offset from the start of the document, based on the value of the parameter + StringIndexType. Required.""" + length: int = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The length of the sentence. Required.""" + + @overload + def __init__( + self, + *, + text: str, + rank_score: float, + offset: int, + length: int, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class ExtractiveSummarizationLROResult(AnalyzeTextLROResult, discriminator="ExtractiveSummarizationLROResults"): + """An object representing the results for an Extractive Summarization task. + + :ivar last_update_date_time: The last updated time in UTC for the task. Required. + :vartype last_update_date_time: ~datetime.datetime + :ivar status: The status of the task at the mentioned last update time. Required. Known values + are: "notStarted", "running", "succeeded", "partiallyCompleted", "failed", "cancelled", and + "cancelling". + :vartype status: str or ~azure.ai.textanalytics.models.State + :ivar task_name: task name. + :vartype task_name: str + :ivar kind: Kind of the task. Required. Extractive summarization LRO results + :vartype kind: str or ~azure.ai.textanalytics.models.EXTRACTIVE_SUMMARIZATION_LRO_RESULTS + :ivar results: Results of the task. Required. + :vartype results: ~azure.ai.textanalytics.models.ExtractiveSummarizationResult + """ + + kind: Literal[AnalyzeTextLROResultsKind.EXTRACTIVE_SUMMARIZATION_LRO_RESULTS] = rest_discriminator(name="kind", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """Kind of the task. Required. Extractive summarization LRO results""" + results: "_models.ExtractiveSummarizationResult" = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Results of the task. Required.""" + + @overload + def __init__( + self, + *, + last_update_date_time: datetime.datetime, + status: Union[str, "_models.State"], + results: "_models.ExtractiveSummarizationResult", + task_name: Optional[str] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, kind=AnalyzeTextLROResultsKind.EXTRACTIVE_SUMMARIZATION_LRO_RESULTS, **kwargs) + + +class ExtractiveSummarizationLROTask(AnalyzeTextLROTask, discriminator="ExtractiveSummarization"): + """An object representing the task definition for an Extractive Summarization task. + + :ivar task_name: task name. + :vartype task_name: str + :ivar kind: The Extractive Summarization kind of the long running task. Required. Extractive + summarization task + :vartype kind: str or ~azure.ai.textanalytics.models.EXTRACTIVE_SUMMARIZATION + :ivar parameters: Parameters for the Extractive Summarization task. + :vartype parameters: ~azure.ai.textanalytics.models.ExtractiveSummarizationTaskParameters + """ + + kind: Literal[AnalyzeTextLROTaskKind.EXTRACTIVE_SUMMARIZATION] = rest_discriminator(name="kind", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """The Extractive Summarization kind of the long running task. Required. Extractive summarization + task""" + parameters: Optional["_models.ExtractiveSummarizationTaskParameters"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Parameters for the Extractive Summarization task.""" + + @overload + def __init__( + self, + *, + task_name: Optional[str] = None, + parameters: Optional["_models.ExtractiveSummarizationTaskParameters"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, kind=AnalyzeTextLROTaskKind.EXTRACTIVE_SUMMARIZATION, **kwargs) + + +class ExtractiveSummarizationResult(_Model): + """An object representing the pre-built Extractive Summarization results of each document. + + :ivar errors: Errors by document id. Required. + :vartype errors: list[~azure.ai.textanalytics.models.DocumentError] + :ivar statistics: if showStats=true was specified in the request this field will contain + information about the request payload. + :vartype statistics: ~azure.ai.textanalytics.models.RequestStatistics + :ivar model_version: This field indicates which model is used for scoring. Required. + :vartype model_version: str + :ivar documents: Response by document. Required. + :vartype documents: + list[~azure.ai.textanalytics.models.ExtractedSummaryDocumentResultWithDetectedLanguage] + """ + + errors: List["_models.DocumentError"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Errors by document id. Required.""" + statistics: Optional["_models.RequestStatistics"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """if showStats=true was specified in the request this field will contain information about the + request payload.""" + model_version: str = rest_field(name="modelVersion", visibility=["read", "create", "update", "delete", "query"]) + """This field indicates which model is used for scoring. Required.""" + documents: List["_models.ExtractedSummaryDocumentResultWithDetectedLanguage"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Response by document. Required.""" + + @overload + def __init__( + self, + *, + errors: List["_models.DocumentError"], + model_version: str, + documents: List["_models.ExtractedSummaryDocumentResultWithDetectedLanguage"], + statistics: Optional["_models.RequestStatistics"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class ExtractiveSummarizationTaskParameters(_Model): + """Supported parameters for an Extractive Summarization task. + + :ivar logging_opt_out: logging opt out. + :vartype logging_opt_out: bool + :ivar model_version: model version. + :vartype model_version: str + :ivar sentence_count: Specifies the number of sentences in the extracted summary. + :vartype sentence_count: int + :ivar sort_by: Specifies how to sort the extracted summaries. Known values are: "Offset" and + "Rank". + :vartype sort_by: str or ~azure.ai.textanalytics.models.ExtractiveSummarizationSortingCriteria + :ivar string_index_type: Specifies the method used to interpret string offsets. Known values + are: "TextElements_v8", "UnicodeCodePoint", and "Utf16CodeUnit". + :vartype string_index_type: str or ~azure.ai.textanalytics.models.StringIndexType + :ivar query: (Optional) If provided, the query will be used to extract most relevant sentences + from the document. + :vartype query: str + """ + + logging_opt_out: Optional[bool] = rest_field( + name="loggingOptOut", visibility=["read", "create", "update", "delete", "query"] + ) + """logging opt out.""" + model_version: Optional[str] = rest_field( + name="modelVersion", visibility=["read", "create", "update", "delete", "query"] + ) + """model version.""" + sentence_count: Optional[int] = rest_field( + name="sentenceCount", visibility=["read", "create", "update", "delete", "query"] + ) + """Specifies the number of sentences in the extracted summary.""" + sort_by: Optional[Union[str, "_models.ExtractiveSummarizationSortingCriteria"]] = rest_field( + name="sortBy", visibility=["read", "create", "update", "delete", "query"] + ) + """Specifies how to sort the extracted summaries. Known values are: \"Offset\" and \"Rank\".""" + string_index_type: Optional[Union[str, "_models.StringIndexType"]] = rest_field( + name="stringIndexType", visibility=["read", "create", "update", "delete", "query"] + ) + """Specifies the method used to interpret string offsets. Known values are: \"TextElements_v8\", + \"UnicodeCodePoint\", and \"Utf16CodeUnit\".""" + query: Optional[str] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """(Optional) If provided, the query will be used to extract most relevant sentences from the + document.""" + + @overload + def __init__( + self, + *, + logging_opt_out: Optional[bool] = None, + model_version: Optional[str] = None, + sentence_count: Optional[int] = None, + sort_by: Optional[Union[str, "_models.ExtractiveSummarizationSortingCriteria"]] = None, + string_index_type: Optional[Union[str, "_models.StringIndexType"]] = None, + query: Optional[str] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class FhirBundle(_Model): + """JSON bundle containing a FHIR compatible object for consumption in other Healthcare tools. For + additional information see `https://www.hl7.org/fhir/overview.html + `_. + + """ + + +class HealthcareAssertion(_Model): + """Assertion of the entity. + + :ivar conditionality: Describes any conditionality on the entity. Known values are: + "hypothetical" and "conditional". + :vartype conditionality: str or ~azure.ai.textanalytics.models.Conditionality + :ivar certainty: Describes the entities certainty and polarity. Known values are: "positive", + "positivePossible", "neutralPossible", "negativePossible", and "negative". + :vartype certainty: str or ~azure.ai.textanalytics.models.Certainty + :ivar association: Describes if the entity is the subject of the text or if it describes + someone else. Known values are: "subject" and "other". + :vartype association: str or ~azure.ai.textanalytics.models.Association + :ivar temporality: Describes temporal information regarding the entity. Known values are: + "current", "past", and "future". + :vartype temporality: str or ~azure.ai.textanalytics.models.Temporality + """ + + conditionality: Optional[Union[str, "_models.Conditionality"]] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Describes any conditionality on the entity. Known values are: \"hypothetical\" and + \"conditional\".""" + certainty: Optional[Union[str, "_models.Certainty"]] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Describes the entities certainty and polarity. Known values are: \"positive\", + \"positivePossible\", \"neutralPossible\", \"negativePossible\", and \"negative\".""" + association: Optional[Union[str, "_models.Association"]] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Describes if the entity is the subject of the text or if it describes someone else. Known + values are: \"subject\" and \"other\".""" + temporality: Optional[Union[str, "_models.Temporality"]] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Describes temporal information regarding the entity. Known values are: \"current\", \"past\", + and \"future\".""" + + @overload + def __init__( + self, + *, + conditionality: Optional[Union[str, "_models.Conditionality"]] = None, + certainty: Optional[Union[str, "_models.Certainty"]] = None, + association: Optional[Union[str, "_models.Association"]] = None, + temporality: Optional[Union[str, "_models.Temporality"]] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class HealthcareEntitiesDocumentResultWithDocumentDetectedLanguage(_Model): # pylint: disable=name-too-long + """Result object for the processed Healthcare document with detected language. + + :ivar id: Unique, non-empty document identifier. Required. + :vartype id: str + :ivar warnings: Warnings encountered while processing document. Required. + :vartype warnings: list[~azure.ai.textanalytics.models.DocumentWarning] + :ivar statistics: if showStats=true was specified in the request this field will contain + information about the document payload. + :vartype statistics: ~azure.ai.textanalytics.models.DocumentStatistics + :ivar entities: Healthcare entities. Required. + :vartype entities: list[~azure.ai.textanalytics.models.HealthcareEntity] + :ivar relations: Healthcare entity relations. Required. + :vartype relations: list[~azure.ai.textanalytics.models.HealthcareRelation] + :ivar fhir_bundle: JSON bundle containing a FHIR compatible object for consumption in other + Healthcare tools. For additional information see `https://www.hl7.org/fhir/overview.html + `_. + :vartype fhir_bundle: ~azure.ai.textanalytics.models.FhirBundle + :ivar detected_language: If 'language' is set to 'auto' for the document in the request this + field will contain a 2 letter ISO 639-1 representation of the language detected for this + document. + :vartype detected_language: ~azure.ai.textanalytics.models.DetectedLanguage + """ + + id: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Unique, non-empty document identifier. Required.""" + warnings: List["_models.DocumentWarning"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Warnings encountered while processing document. Required.""" + statistics: Optional["_models.DocumentStatistics"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """if showStats=true was specified in the request this field will contain information about the + document payload.""" + entities: List["_models.HealthcareEntity"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Healthcare entities. Required.""" + relations: List["_models.HealthcareRelation"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Healthcare entity relations. Required.""" + fhir_bundle: Optional["_models.FhirBundle"] = rest_field( + name="fhirBundle", visibility=["read", "create", "update", "delete", "query"] + ) + """JSON bundle containing a FHIR compatible object for consumption in other Healthcare tools. For + additional information see `https://www.hl7.org/fhir/overview.html + `_.""" + detected_language: Optional["_models.DetectedLanguage"] = rest_field( + name="detectedLanguage", visibility=["read", "create", "update", "delete", "query"] + ) + """If 'language' is set to 'auto' for the document in the request this field will contain a 2 + letter ISO 639-1 representation of the language detected for this document.""" + + @overload + def __init__( + self, + *, + id: str, # pylint: disable=redefined-builtin + warnings: List["_models.DocumentWarning"], + entities: List["_models.HealthcareEntity"], + relations: List["_models.HealthcareRelation"], + statistics: Optional["_models.DocumentStatistics"] = None, + fhir_bundle: Optional["_models.FhirBundle"] = None, + detected_language: Optional["_models.DetectedLanguage"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class HealthcareEntity(_Model): + """Healthcare entity extracted from the document. + + :ivar text: Entity text as appears in the request. Required. + :vartype text: str + :ivar category: Healthcare Entity Category. Required. Known values are: "BodyStructure", "Age", + "Gender", "ExaminationName", "Date", "Direction", "Frequency", "MeasurementValue", + "MeasurementUnit", "RelationalOperator", "Time", "GeneOrProtein", "Variant", + "AdministrativeEvent", "CareEnvironment", "HealthcareProfession", "Diagnosis", "SymptomOrSign", + "ConditionQualifier", "MedicationClass", "MedicationName", "Dosage", "MedicationForm", + "MedicationRoute", "FamilyRelation", "TreatmentName", "Ethnicity", "Course", "Expression", + "MutationType", "ConditionScale", "Allergen", "Employment", "LivingStatus", "SubstanceUse", and + "SubstanceUseAmount". + :vartype category: str or ~azure.ai.textanalytics.models.HealthcareEntityCategory + :ivar subcategory: (Optional) Entity sub type. + :vartype subcategory: str + :ivar offset: Start position for the entity text. Use of different 'stringIndexType' values can + affect the offset returned. Required. + :vartype offset: int + :ivar length: Length for the entity text. Use of different 'stringIndexType' values can affect + the length returned. Required. + :vartype length: int + :ivar confidence_score: Confidence score between 0 and 1 of the extracted entity. Required. + :vartype confidence_score: float + :ivar assertion: Assertion of the entity. + :vartype assertion: ~azure.ai.textanalytics.models.HealthcareAssertion + :ivar name: Preferred name for the entity. Example: 'histologically' would have a 'name' of + 'histologic'. + :vartype name: str + :ivar links: Entity references in known data sources. + :vartype links: list[~azure.ai.textanalytics.models.HealthcareEntityLink] + """ + + text: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Entity text as appears in the request. Required.""" + category: Union[str, "_models.HealthcareEntityCategory"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Healthcare Entity Category. Required. Known values are: \"BodyStructure\", \"Age\", \"Gender\", + \"ExaminationName\", \"Date\", \"Direction\", \"Frequency\", \"MeasurementValue\", + \"MeasurementUnit\", \"RelationalOperator\", \"Time\", \"GeneOrProtein\", \"Variant\", + \"AdministrativeEvent\", \"CareEnvironment\", \"HealthcareProfession\", \"Diagnosis\", + \"SymptomOrSign\", \"ConditionQualifier\", \"MedicationClass\", \"MedicationName\", \"Dosage\", + \"MedicationForm\", \"MedicationRoute\", \"FamilyRelation\", \"TreatmentName\", \"Ethnicity\", + \"Course\", \"Expression\", \"MutationType\", \"ConditionScale\", \"Allergen\", \"Employment\", + \"LivingStatus\", \"SubstanceUse\", and \"SubstanceUseAmount\".""" + subcategory: Optional[str] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """(Optional) Entity sub type.""" + offset: int = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Start position for the entity text. Use of different 'stringIndexType' values can affect the + offset returned. Required.""" + length: int = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Length for the entity text. Use of different 'stringIndexType' values can affect the length + returned. Required.""" + confidence_score: float = rest_field( + name="confidenceScore", visibility=["read", "create", "update", "delete", "query"] + ) + """Confidence score between 0 and 1 of the extracted entity. Required.""" + assertion: Optional["_models.HealthcareAssertion"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Assertion of the entity.""" + name: Optional[str] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Preferred name for the entity. Example: 'histologically' would have a 'name' of 'histologic'.""" + links: Optional[List["_models.HealthcareEntityLink"]] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Entity references in known data sources.""" + + @overload + def __init__( + self, + *, + text: str, + category: Union[str, "_models.HealthcareEntityCategory"], + offset: int, + length: int, + confidence_score: float, + subcategory: Optional[str] = None, + assertion: Optional["_models.HealthcareAssertion"] = None, + name: Optional[str] = None, + links: Optional[List["_models.HealthcareEntityLink"]] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class HealthcareEntityLink(_Model): + """Reference to an entity in known data sources. + + :ivar data_source: Entity Catalog. Examples include: UMLS, CHV, MSH, etc. Required. + :vartype data_source: str + :ivar id: Entity id in the given source catalog. Required. + :vartype id: str + """ + + data_source: str = rest_field(name="dataSource", visibility=["read", "create", "update", "delete", "query"]) + """Entity Catalog. Examples include: UMLS, CHV, MSH, etc. Required.""" + id: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Entity id in the given source catalog. Required.""" + + @overload + def __init__( + self, + *, + data_source: str, + id: str, # pylint: disable=redefined-builtin + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class HealthcareLROResult(AnalyzeTextLROResult, discriminator="HealthcareLROResults"): + """Healthcare Analyze Text long tunning operation result object. + + :ivar last_update_date_time: The last updated time in UTC for the task. Required. + :vartype last_update_date_time: ~datetime.datetime + :ivar status: The status of the task at the mentioned last update time. Required. Known values + are: "notStarted", "running", "succeeded", "partiallyCompleted", "failed", "cancelled", and + "cancelling". + :vartype status: str or ~azure.ai.textanalytics.models.State + :ivar task_name: task name. + :vartype task_name: str + :ivar kind: Kind of the task. Required. Healthcare LRO results + :vartype kind: str or ~azure.ai.textanalytics.models.HEALTHCARE_LRO_RESULTS + :ivar results: Results of the task. Required. + :vartype results: ~azure.ai.textanalytics.models.HealthcareResult + """ + + kind: Literal[AnalyzeTextLROResultsKind.HEALTHCARE_LRO_RESULTS] = rest_discriminator(name="kind", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """Kind of the task. Required. Healthcare LRO results""" + results: "_models.HealthcareResult" = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Results of the task. Required.""" + + @overload + def __init__( + self, + *, + last_update_date_time: datetime.datetime, + status: Union[str, "_models.State"], + results: "_models.HealthcareResult", + task_name: Optional[str] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, kind=AnalyzeTextLROResultsKind.HEALTHCARE_LRO_RESULTS, **kwargs) + + +class HealthcareLROTask(AnalyzeTextLROTask, discriminator="Healthcare"): + """The long running task to be performed by the service on the Healthcare input documents. + + :ivar task_name: task name. + :vartype task_name: str + :ivar kind: Healthcare kind of the long running task. Required. Healthcare task + :vartype kind: str or ~azure.ai.textanalytics.models.HEALTHCARE + :ivar parameters: Parameters for the Healthcare task. + :vartype parameters: ~azure.ai.textanalytics.models.HealthcareTaskParameters + """ + + kind: Literal[AnalyzeTextLROTaskKind.HEALTHCARE] = rest_discriminator(name="kind", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """Healthcare kind of the long running task. Required. Healthcare task""" + parameters: Optional["_models.HealthcareTaskParameters"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Parameters for the Healthcare task.""" + + @overload + def __init__( + self, + *, + task_name: Optional[str] = None, + parameters: Optional["_models.HealthcareTaskParameters"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, kind=AnalyzeTextLROTaskKind.HEALTHCARE, **kwargs) + + +class HealthcareRelation(_Model): + """Every relation is an entity graph of a certain relationType, where all entities are connected + and have specific roles within the relation context. + + :ivar relation_type: Type of relation. Examples include: ``DosageOfMedication`` or + 'FrequencyOfMedication', etc. Required. Known values are: "Abbreviation", + "DirectionOfBodyStructure", "DirectionOfCondition", "DirectionOfExamination", + "DirectionOfTreatment", "DosageOfMedication", "FormOfMedication", "FrequencyOfMedication", + "FrequencyOfTreatment", "QualifierOfCondition", "RelationOfExamination", "RouteOfMedication", + "TimeOfCondition", "TimeOfEvent", "TimeOfExamination", "TimeOfMedication", "TimeOfTreatment", + "UnitOfCondition", "UnitOfExamination", "ValueOfCondition", "ValueOfExamination", + "BodySiteOfCondition", "BodySiteOfTreatment", "CourseOfCondition", "CourseOfExamination", + "CourseOfMedication", "CourseOfTreatment", "ExaminationFindsCondition", "ExpressionOfGene", + "ExpressionOfVariant", "FrequencyOfCondition", "MutationTypeOfGene", "MutationTypeOfVariant", + "ScaleOfCondition", and "VariantOfGene". + :vartype relation_type: str or ~azure.ai.textanalytics.models.RelationType + :ivar entities: The entities in the relation. Required. + :vartype entities: list[~azure.ai.textanalytics.models.HealthcareRelationEntity] + :ivar confidence_score: Confidence score between 0 and 1 of the extracted relation. + :vartype confidence_score: float + """ + + relation_type: Union[str, "_models.RelationType"] = rest_field( + name="relationType", visibility=["read", "create", "update", "delete", "query"] + ) + """Type of relation. Examples include: ``DosageOfMedication`` or 'FrequencyOfMedication', etc. + Required. Known values are: \"Abbreviation\", \"DirectionOfBodyStructure\", + \"DirectionOfCondition\", \"DirectionOfExamination\", \"DirectionOfTreatment\", + \"DosageOfMedication\", \"FormOfMedication\", \"FrequencyOfMedication\", + \"FrequencyOfTreatment\", \"QualifierOfCondition\", \"RelationOfExamination\", + \"RouteOfMedication\", \"TimeOfCondition\", \"TimeOfEvent\", \"TimeOfExamination\", + \"TimeOfMedication\", \"TimeOfTreatment\", \"UnitOfCondition\", \"UnitOfExamination\", + \"ValueOfCondition\", \"ValueOfExamination\", \"BodySiteOfCondition\", \"BodySiteOfTreatment\", + \"CourseOfCondition\", \"CourseOfExamination\", \"CourseOfMedication\", \"CourseOfTreatment\", + \"ExaminationFindsCondition\", \"ExpressionOfGene\", \"ExpressionOfVariant\", + \"FrequencyOfCondition\", \"MutationTypeOfGene\", \"MutationTypeOfVariant\", + \"ScaleOfCondition\", and \"VariantOfGene\".""" + entities: List["_models.HealthcareRelationEntity"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """The entities in the relation. Required.""" + confidence_score: Optional[float] = rest_field( + name="confidenceScore", visibility=["read", "create", "update", "delete", "query"] + ) + """Confidence score between 0 and 1 of the extracted relation.""" + + @overload + def __init__( + self, + *, + relation_type: Union[str, "_models.RelationType"], + entities: List["_models.HealthcareRelationEntity"], + confidence_score: Optional[float] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class HealthcareRelationEntity(_Model): + """Entity in the relation. + + :ivar ref: Reference link object, using a JSON pointer RFC 6901 (URI Fragment Identifier + Representation), pointing to the entity . Required. + :vartype ref: str + :ivar role: Role of entity in the relationship. For example: 'CD20-positive diffuse large + B-cell lymphoma' has the following entities with their roles in parenthesis: CD20 + (GeneOrProtein), Positive (Expression), diffuse large B-cell lymphoma (Diagnosis). Required. + :vartype role: str + """ + + ref: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Reference link object, using a JSON pointer RFC 6901 (URI Fragment Identifier Representation), + pointing to the entity . Required.""" + role: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Role of entity in the relationship. For example: 'CD20-positive diffuse large B-cell lymphoma' + has the following entities with their roles in parenthesis: CD20 (GeneOrProtein), Positive + (Expression), diffuse large B-cell lymphoma (Diagnosis). Required.""" + + @overload + def __init__( + self, + *, + ref: str, + role: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class HealthcareResult(_Model): + """Result object for the processed Healthcare task. + + :ivar errors: Errors by document id. Required. + :vartype errors: list[~azure.ai.textanalytics.models.DocumentError] + :ivar statistics: if showStats=true was specified in the request this field will contain + information about the request payload. + :vartype statistics: ~azure.ai.textanalytics.models.RequestStatistics + :ivar model_version: This field indicates which model is used for scoring. Required. + :vartype model_version: str + :ivar documents: List of result objects for the processed Healthcare documents. Required. + :vartype documents: + list[~azure.ai.textanalytics.models.HealthcareEntitiesDocumentResultWithDocumentDetectedLanguage] + """ + + errors: List["_models.DocumentError"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Errors by document id. Required.""" + statistics: Optional["_models.RequestStatistics"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """if showStats=true was specified in the request this field will contain information about the + request payload.""" + model_version: str = rest_field(name="modelVersion", visibility=["read", "create", "update", "delete", "query"]) + """This field indicates which model is used for scoring. Required.""" + documents: List["_models.HealthcareEntitiesDocumentResultWithDocumentDetectedLanguage"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """List of result objects for the processed Healthcare documents. Required.""" + + @overload + def __init__( + self, + *, + errors: List["_models.DocumentError"], + model_version: str, + documents: List["_models.HealthcareEntitiesDocumentResultWithDocumentDetectedLanguage"], + statistics: Optional["_models.RequestStatistics"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class HealthcareTaskParameters(_Model): + """Supported parameters for a Healthcare task. + + :ivar logging_opt_out: logging opt out. + :vartype logging_opt_out: bool + :ivar model_version: model version. + :vartype model_version: str + :ivar string_index_type: Specifies the method used to interpret string offsets. Known values + are: "TextElements_v8", "UnicodeCodePoint", and "Utf16CodeUnit". + :vartype string_index_type: str or ~azure.ai.textanalytics.models.StringIndexType + :ivar fhir_version: The FHIR Spec version that the result will use to format the fhirBundle. + For additional information see `https://www.hl7.org/fhir/overview.html + `_. "4.0.1" + :vartype fhir_version: str or ~azure.ai.textanalytics.models.FhirVersion + :ivar document_type: Document type that can be provided as input for Fhir Documents. Expect to + have fhirVersion provided when used. Behavior of using None enum is the same as not using the + documentType parameter. Known values are: "None", "ClinicalTrial", "DischargeSummary", + "ProgressNote", "HistoryAndPhysical", "Consult", "Imaging", "Pathology", and "ProcedureNote". + :vartype document_type: str or ~azure.ai.textanalytics.models.HealthcareDocumentType + """ + + logging_opt_out: Optional[bool] = rest_field( + name="loggingOptOut", visibility=["read", "create", "update", "delete", "query"] + ) + """logging opt out.""" + model_version: Optional[str] = rest_field( + name="modelVersion", visibility=["read", "create", "update", "delete", "query"] + ) + """model version.""" + string_index_type: Optional[Union[str, "_models.StringIndexType"]] = rest_field( + name="stringIndexType", visibility=["read", "create", "update", "delete", "query"] + ) + """Specifies the method used to interpret string offsets. Known values are: \"TextElements_v8\", + \"UnicodeCodePoint\", and \"Utf16CodeUnit\".""" + fhir_version: Optional[Union[str, "_models.FhirVersion"]] = rest_field( + name="fhirVersion", visibility=["read", "create", "update", "delete", "query"] + ) + """The FHIR Spec version that the result will use to format the fhirBundle. For additional + information see `https://www.hl7.org/fhir/overview.html + `_. \"4.0.1\"""" + document_type: Optional[Union[str, "_models.HealthcareDocumentType"]] = rest_field( + name="documentType", visibility=["read", "create", "update", "delete", "query"] + ) + """Document type that can be provided as input for Fhir Documents. Expect to have fhirVersion + provided when used. Behavior of using None enum is the same as not using the documentType + parameter. Known values are: \"None\", \"ClinicalTrial\", \"DischargeSummary\", + \"ProgressNote\", \"HistoryAndPhysical\", \"Consult\", \"Imaging\", \"Pathology\", and + \"ProcedureNote\".""" + + @overload + def __init__( + self, + *, + logging_opt_out: Optional[bool] = None, + model_version: Optional[str] = None, + string_index_type: Optional[Union[str, "_models.StringIndexType"]] = None, + fhir_version: Optional[Union[str, "_models.FhirVersion"]] = None, + document_type: Optional[Union[str, "_models.HealthcareDocumentType"]] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class InformationMetadata(BaseMetadata, discriminator="InformationMetadata"): + """Represents the Information (data) entity Metadata model. + + :ivar value: The numeric value that the extracted text denotes. Required. + :vartype value: float + :ivar metadata_kind: Kind of the metadata. Required. Metadata for information-related values. + :vartype metadata_kind: str or ~azure.ai.textanalytics.models.INFORMATION_METADATA + :ivar unit: Unit of measure for information. Required. Known values are: "Unspecified", "Bit", + "Kilobit", "Megabit", "Gigabit", "Terabit", "Petabit", "Byte", "Kilobyte", "Megabyte", + "Gigabyte", "Terabyte", and "Petabyte". + :vartype unit: str or ~azure.ai.textanalytics.models.InformationUnit + """ + + value: float = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The numeric value that the extracted text denotes. Required.""" + metadata_kind: Literal[MetadataKind.INFORMATION_METADATA] = rest_discriminator(name="metadataKind", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """Kind of the metadata. Required. Metadata for information-related values.""" + unit: Union[str, "_models.InformationUnit"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Unit of measure for information. Required. Known values are: \"Unspecified\", \"Bit\", + \"Kilobit\", \"Megabit\", \"Gigabit\", \"Terabit\", \"Petabit\", \"Byte\", \"Kilobyte\", + \"Megabyte\", \"Gigabyte\", \"Terabyte\", and \"Petabyte\".""" + + @overload + def __init__( + self, + *, + value: float, + unit: Union[str, "_models.InformationUnit"], + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, metadata_kind=MetadataKind.INFORMATION_METADATA, **kwargs) + + +class InnerErrorModel(_Model): + """An object containing more specific information about the error. As per Microsoft One API + guidelines - + `https://github.com/Microsoft/api-guidelines/blob/vNext/Guidelines.md#7102-error-condition-responses + `_. + + :ivar code: One of a server-defined set of error codes. Required. Known values are: + "InvalidRequest", "InvalidParameterValue", "KnowledgeBaseNotFound", + "AzureCognitiveSearchNotFound", "AzureCognitiveSearchThrottling", "ExtractionFailure", + "InvalidRequestBodyFormat", "EmptyRequest", "MissingInputDocuments", "InvalidDocument", + "ModelVersionIncorrect", "InvalidDocumentBatch", "UnsupportedLanguageCode", and + "InvalidCountryHint". + :vartype code: str or ~azure.ai.textanalytics.models.InnerErrorCode + :ivar message: Error message. Required. + :vartype message: str + :ivar details: Error details. + :vartype details: dict[str, str] + :ivar target: Error target. + :vartype target: str + :ivar innererror: An object containing more specific information than the current object about + the error. + :vartype innererror: ~azure.ai.textanalytics.models.InnerErrorModel + """ + + code: Union[str, "_models.InnerErrorCode"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """One of a server-defined set of error codes. Required. Known values are: \"InvalidRequest\", + \"InvalidParameterValue\", \"KnowledgeBaseNotFound\", \"AzureCognitiveSearchNotFound\", + \"AzureCognitiveSearchThrottling\", \"ExtractionFailure\", \"InvalidRequestBodyFormat\", + \"EmptyRequest\", \"MissingInputDocuments\", \"InvalidDocument\", \"ModelVersionIncorrect\", + \"InvalidDocumentBatch\", \"UnsupportedLanguageCode\", and \"InvalidCountryHint\".""" + message: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Error message. Required.""" + details: Optional[Dict[str, str]] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Error details.""" + target: Optional[str] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Error target.""" + innererror: Optional["_models.InnerErrorModel"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """An object containing more specific information than the current object about the error.""" + + @overload + def __init__( + self, + *, + code: Union[str, "_models.InnerErrorCode"], + message: str, + details: Optional[Dict[str, str]] = None, + target: Optional[str] = None, + innererror: Optional["_models.InnerErrorModel"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class KeyPhraseExtractionLROResult(AnalyzeTextLROResult, discriminator="KeyPhraseExtractionLROResults"): + """Contains the analyze text KeyPhraseExtraction LRO task. + + :ivar last_update_date_time: The last updated time in UTC for the task. Required. + :vartype last_update_date_time: ~datetime.datetime + :ivar status: The status of the task at the mentioned last update time. Required. Known values + are: "notStarted", "running", "succeeded", "partiallyCompleted", "failed", "cancelled", and + "cancelling". + :vartype status: str or ~azure.ai.textanalytics.models.State + :ivar task_name: task name. + :vartype task_name: str + :ivar kind: Kind of the task. Required. Key phrase extraction LRO results + :vartype kind: str or ~azure.ai.textanalytics.models.KEY_PHRASE_EXTRACTION_LRO_RESULTS + :ivar results: The list of Key phrase extraction results. Required. + :vartype results: ~azure.ai.textanalytics.models.KeyPhraseResult + """ + + kind: Literal[AnalyzeTextLROResultsKind.KEY_PHRASE_EXTRACTION_LRO_RESULTS] = rest_discriminator(name="kind", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """Kind of the task. Required. Key phrase extraction LRO results""" + results: "_models.KeyPhraseResult" = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The list of Key phrase extraction results. Required.""" + + @overload + def __init__( + self, + *, + last_update_date_time: datetime.datetime, + status: Union[str, "_models.State"], + results: "_models.KeyPhraseResult", + task_name: Optional[str] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, kind=AnalyzeTextLROResultsKind.KEY_PHRASE_EXTRACTION_LRO_RESULTS, **kwargs) + + +class KeyPhraseLROTask(AnalyzeTextLROTask, discriminator="KeyPhraseExtraction"): + """An object representing the task definition for a Key Phrase Extraction task. + + :ivar task_name: task name. + :vartype task_name: str + :ivar kind: Kind of the task. Required. Key phrase extraction task + :vartype kind: str or ~azure.ai.textanalytics.models.KEY_PHRASE_EXTRACTION + :ivar parameters: Key phrase extraction task parameters. + :vartype parameters: ~azure.ai.textanalytics.models.KeyPhraseTaskParameters + """ + + kind: Literal[AnalyzeTextLROTaskKind.KEY_PHRASE_EXTRACTION] = rest_discriminator(name="kind", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """Kind of the task. Required. Key phrase extraction task""" + parameters: Optional["_models.KeyPhraseTaskParameters"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Key phrase extraction task parameters.""" + + @overload + def __init__( + self, + *, + task_name: Optional[str] = None, + parameters: Optional["_models.KeyPhraseTaskParameters"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, kind=AnalyzeTextLROTaskKind.KEY_PHRASE_EXTRACTION, **kwargs) + + +class KeyPhraseResult(_Model): + """Contains the KeyPhraseResult. + + :ivar errors: Errors by document id. Required. + :vartype errors: list[~azure.ai.textanalytics.models.DocumentError] + :ivar statistics: if showStats=true was specified in the request this field will contain + information about the request payload. + :vartype statistics: ~azure.ai.textanalytics.models.RequestStatistics + :ivar model_version: This field indicates which model is used for scoring. Required. + :vartype model_version: str + :ivar documents: Response by document. Required. + :vartype documents: + list[~azure.ai.textanalytics.models.KeyPhrasesDocumentResultWithDetectedLanguage] + """ + + errors: List["_models.DocumentError"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Errors by document id. Required.""" + statistics: Optional["_models.RequestStatistics"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """if showStats=true was specified in the request this field will contain information about the + request payload.""" + model_version: str = rest_field(name="modelVersion", visibility=["read", "create", "update", "delete", "query"]) + """This field indicates which model is used for scoring. Required.""" + documents: List["_models.KeyPhrasesDocumentResultWithDetectedLanguage"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Response by document. Required.""" + + @overload + def __init__( + self, + *, + errors: List["_models.DocumentError"], + model_version: str, + documents: List["_models.KeyPhrasesDocumentResultWithDetectedLanguage"], + statistics: Optional["_models.RequestStatistics"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class KeyPhrasesDocumentResultWithDetectedLanguage(_Model): # pylint: disable=name-too-long + """A ranked list of sentences representing the extracted summary. + + :ivar id: Unique, non-empty document identifier. Required. + :vartype id: str + :ivar warnings: Warnings encountered while processing document. Required. + :vartype warnings: list[~azure.ai.textanalytics.models.DocumentWarning] + :ivar statistics: if showStats=true was specified in the request this field will contain + information about the document payload. + :vartype statistics: ~azure.ai.textanalytics.models.DocumentStatistics + :ivar key_phrases: A list of representative words or phrases. The number of key phrases + returned is proportional to the number of words in the input document. Required. + :vartype key_phrases: list[str] + :ivar detected_language: If 'language' is set to 'auto' for the document in the request this + field will contain a 2 letter ISO 639-1 representation of the language detected for this + document. + :vartype detected_language: ~azure.ai.textanalytics.models.DetectedLanguage + """ + + id: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Unique, non-empty document identifier. Required.""" + warnings: List["_models.DocumentWarning"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Warnings encountered while processing document. Required.""" + statistics: Optional["_models.DocumentStatistics"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """if showStats=true was specified in the request this field will contain information about the + document payload.""" + key_phrases: List[str] = rest_field(name="keyPhrases", visibility=["read", "create", "update", "delete", "query"]) + """A list of representative words or phrases. The number of key phrases returned is proportional + to the number of words in the input document. Required.""" + detected_language: Optional["_models.DetectedLanguage"] = rest_field( + name="detectedLanguage", visibility=["read", "create", "update", "delete", "query"] + ) + """If 'language' is set to 'auto' for the document in the request this field will contain a 2 + letter ISO 639-1 representation of the language detected for this document.""" + + @overload + def __init__( + self, + *, + id: str, # pylint: disable=redefined-builtin + warnings: List["_models.DocumentWarning"], + key_phrases: List[str], + statistics: Optional["_models.DocumentStatistics"] = None, + detected_language: Optional["_models.DetectedLanguage"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class KeyPhraseTaskParameters(_Model): + """Supported parameters for a Key Phrase Extraction task. + + :ivar logging_opt_out: logging opt out. + :vartype logging_opt_out: bool + :ivar model_version: model version. + :vartype model_version: str + """ + + logging_opt_out: Optional[bool] = rest_field( + name="loggingOptOut", visibility=["read", "create", "update", "delete", "query"] + ) + """logging opt out.""" + model_version: Optional[str] = rest_field( + name="modelVersion", visibility=["read", "create", "update", "delete", "query"] + ) + """model version.""" + + @overload + def __init__( + self, + *, + logging_opt_out: Optional[bool] = None, + model_version: Optional[str] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class KeyPhraseTaskResult(AnalyzeTextTaskResult, discriminator="KeyPhraseExtractionResults"): + """Contains the analyze text KeyPhraseExtraction task result. + + :ivar kind: Kind of the task results. Required. Key phrase extraction results + :vartype kind: str or ~azure.ai.textanalytics.models.KEY_PHRASE_EXTRACTION_RESULTS + :ivar results: The list of Key phrase extraction results. Required. + :vartype results: ~azure.ai.textanalytics.models.KeyPhraseResult + """ + + kind: Literal[AnalyzeTextTaskResultsKind.KEY_PHRASE_EXTRACTION_RESULTS] = rest_discriminator(name="kind", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """Kind of the task results. Required. Key phrase extraction results""" + results: "_models.KeyPhraseResult" = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The list of Key phrase extraction results. Required.""" + + @overload + def __init__( + self, + *, + results: "_models.KeyPhraseResult", + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, kind=AnalyzeTextTaskResultsKind.KEY_PHRASE_EXTRACTION_RESULTS, **kwargs) + + +class LanguageDetectionAnalysisInput(_Model): + """Contains the language detection document analysis input. + + :ivar documents: List of documents to be analyzed. + :vartype documents: list[~azure.ai.textanalytics.models.LanguageInput] + """ + + documents: Optional[List["_models.LanguageInput"]] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """List of documents to be analyzed.""" + + @overload + def __init__( + self, + *, + documents: Optional[List["_models.LanguageInput"]] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class LanguageDetectionDocumentResult(_Model): + """Contains the language detection for a document. + + :ivar id: Unique, non-empty document identifier. Required. + :vartype id: str + :ivar warnings: Warnings encountered while processing document. Required. + :vartype warnings: list[~azure.ai.textanalytics.models.DocumentWarning] + :ivar statistics: if showStats=true was specified in the request this field will contain + information about the document payload. + :vartype statistics: ~azure.ai.textanalytics.models.DocumentStatistics + :ivar detected_language: Detected Language. Required. + :vartype detected_language: ~azure.ai.textanalytics.models.DetectedLanguage + """ + + id: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Unique, non-empty document identifier. Required.""" + warnings: List["_models.DocumentWarning"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Warnings encountered while processing document. Required.""" + statistics: Optional["_models.DocumentStatistics"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """if showStats=true was specified in the request this field will contain information about the + document payload.""" + detected_language: "_models.DetectedLanguage" = rest_field( + name="detectedLanguage", visibility=["read", "create", "update", "delete", "query"] + ) + """Detected Language. Required.""" + + @overload + def __init__( + self, + *, + id: str, # pylint: disable=redefined-builtin + warnings: List["_models.DocumentWarning"], + detected_language: "_models.DetectedLanguage", + statistics: Optional["_models.DocumentStatistics"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class LanguageDetectionResult(_Model): + """Contains the language detection result for the request. + + :ivar errors: Errors by document id. Required. + :vartype errors: list[~azure.ai.textanalytics.models.DocumentError] + :ivar statistics: if showStats=true was specified in the request this field will contain + information about the request payload. + :vartype statistics: ~azure.ai.textanalytics.models.RequestStatistics + :ivar model_version: This field indicates which model is used for scoring. Required. + :vartype model_version: str + :ivar documents: Enumeration of language detection results for each input document. Required. + :vartype documents: list[~azure.ai.textanalytics.models.LanguageDetectionDocumentResult] + """ + + errors: List["_models.DocumentError"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Errors by document id. Required.""" + statistics: Optional["_models.RequestStatistics"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """if showStats=true was specified in the request this field will contain information about the + request payload.""" + model_version: str = rest_field(name="modelVersion", visibility=["read", "create", "update", "delete", "query"]) + """This field indicates which model is used for scoring. Required.""" + documents: List["_models.LanguageDetectionDocumentResult"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Enumeration of language detection results for each input document. Required.""" + + @overload + def __init__( + self, + *, + errors: List["_models.DocumentError"], + model_version: str, + documents: List["_models.LanguageDetectionDocumentResult"], + statistics: Optional["_models.RequestStatistics"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class LanguageDetectionTaskParameters(_Model): + """Supported parameters for a Language Detection task. + + :ivar logging_opt_out: logging opt out. + :vartype logging_opt_out: bool + :ivar model_version: model version. + :vartype model_version: str + """ + + logging_opt_out: Optional[bool] = rest_field( + name="loggingOptOut", visibility=["read", "create", "update", "delete", "query"] + ) + """logging opt out.""" + model_version: Optional[str] = rest_field( + name="modelVersion", visibility=["read", "create", "update", "delete", "query"] + ) + """model version.""" + + @overload + def __init__( + self, + *, + logging_opt_out: Optional[bool] = None, + model_version: Optional[str] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class LanguageDetectionTaskResult(AnalyzeTextTaskResult, discriminator="LanguageDetectionResults"): + """Contains the language detection task result for the request. + + :ivar kind: Kind of the task result. Required. Language detection results + :vartype kind: str or ~azure.ai.textanalytics.models.LANGUAGE_DETECTION_RESULTS + :ivar results: Contains the language detection results. Required. + :vartype results: ~azure.ai.textanalytics.models.LanguageDetectionResult + """ + + kind: Literal[AnalyzeTextTaskResultsKind.LANGUAGE_DETECTION_RESULTS] = rest_discriminator(name="kind", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """Kind of the task result. Required. Language detection results""" + results: "_models.LanguageDetectionResult" = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Contains the language detection results. Required.""" + + @overload + def __init__( + self, + *, + results: "_models.LanguageDetectionResult", + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, kind=AnalyzeTextTaskResultsKind.LANGUAGE_DETECTION_RESULTS, **kwargs) + + +class LanguageInput(_Model): + """Contains the language detection input. + + :ivar id: A unique, non-empty document identifier. Required. + :vartype id: str + :ivar text: The input text to process. Required. + :vartype text: str + :ivar country_hint: The country hint to help with language detection of the text. + :vartype country_hint: str + """ + + id: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """A unique, non-empty document identifier. Required.""" + text: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The input text to process. Required.""" + country_hint: Optional[str] = rest_field( + name="countryHint", visibility=["read", "create", "update", "delete", "query"] + ) + """The country hint to help with language detection of the text.""" + + @overload + def __init__( + self, + *, + id: str, # pylint: disable=redefined-builtin + text: str, + country_hint: Optional[str] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class LengthMetadata(BaseMetadata, discriminator="LengthMetadata"): + """Represents the Length entity Metadata model. + + :ivar value: The numeric value that the extracted text denotes. Required. + :vartype value: float + :ivar metadata_kind: Kind of the metadata. Required. Metadata for length-related values. + :vartype metadata_kind: str or ~azure.ai.textanalytics.models.LENGTH_METADATA + :ivar unit: Unit of measure for length. Required. Known values are: "Unspecified", "Kilometer", + "Hectometer", "Decameter", "Meter", "Decimeter", "Centimeter", "Millimeter", "Micrometer", + "Nanometer", "Picometer", "Mile", "Yard", "Inch", "Foot", "LightYear", and "Point". + :vartype unit: str or ~azure.ai.textanalytics.models.LengthUnit + """ + + value: float = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The numeric value that the extracted text denotes. Required.""" + metadata_kind: Literal[MetadataKind.LENGTH_METADATA] = rest_discriminator(name="metadataKind", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """Kind of the metadata. Required. Metadata for length-related values.""" + unit: Union[str, "_models.LengthUnit"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Unit of measure for length. Required. Known values are: \"Unspecified\", \"Kilometer\", + \"Hectometer\", \"Decameter\", \"Meter\", \"Decimeter\", \"Centimeter\", \"Millimeter\", + \"Micrometer\", \"Nanometer\", \"Picometer\", \"Mile\", \"Yard\", \"Inch\", \"Foot\", + \"LightYear\", and \"Point\".""" + + @overload + def __init__( + self, + *, + value: float, + unit: Union[str, "_models.LengthUnit"], + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, metadata_kind=MetadataKind.LENGTH_METADATA, **kwargs) + + +class LinkedEntity(_Model): + """The LinkedEntity object containing the detected entity with the associated sources/links. + + :ivar name: Entity Linking formal name. Required. + :vartype name: str + :ivar matches: List of instances this entity appears in the text. Required. + :vartype matches: list[~azure.ai.textanalytics.models.Match] + :ivar language: Language used in the data source. Required. + :vartype language: str + :ivar id: Unique identifier of the recognized entity from the data source. + :vartype id: str + :ivar url: URL for the entity's page from the data source. Required. + :vartype url: str + :ivar data_source: Data source used to extract entity linking, such as Wiki/Bing etc. Required. + :vartype data_source: str + :ivar bing_id: Bing Entity Search API unique identifier of the recognized entity. + :vartype bing_id: str + """ + + name: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Entity Linking formal name. Required.""" + matches: List["_models.Match"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """List of instances this entity appears in the text. Required.""" + language: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Language used in the data source. Required.""" + id: Optional[str] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Unique identifier of the recognized entity from the data source.""" + url: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """URL for the entity's page from the data source. Required.""" + data_source: str = rest_field(name="dataSource", visibility=["read", "create", "update", "delete", "query"]) + """Data source used to extract entity linking, such as Wiki/Bing etc. Required.""" + bing_id: Optional[str] = rest_field(name="bingId", visibility=["read", "create", "update", "delete", "query"]) + """Bing Entity Search API unique identifier of the recognized entity.""" + + @overload + def __init__( + self, + *, + name: str, + matches: List["_models.Match"], + language: str, + url: str, + data_source: str, + id: Optional[str] = None, # pylint: disable=redefined-builtin + bing_id: Optional[str] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class Match(_Model): + """The Match object containing the detected entity text with the offset and the length. + + :ivar confidence_score: If a well known item is recognized, a decimal number denoting the + confidence level between 0 and 1 will be returned. Required. + :vartype confidence_score: float + :ivar text: Entity text as appears in the request. Required. + :vartype text: str + :ivar offset: Start position for the entity match text. Required. + :vartype offset: int + :ivar length: Length for the entity match text. Required. + :vartype length: int + """ + + confidence_score: float = rest_field( + name="confidenceScore", visibility=["read", "create", "update", "delete", "query"] + ) + """If a well known item is recognized, a decimal number denoting the confidence level between 0 + and 1 will be returned. Required.""" + text: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Entity text as appears in the request. Required.""" + offset: int = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Start position for the entity match text. Required.""" + length: int = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Length for the entity match text. Required.""" + + @overload + def __init__( + self, + *, + confidence_score: float, + text: str, + offset: int, + length: int, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class MatchLongestEntityPolicyType(BaseEntityOverlapPolicy, discriminator="matchLongest"): + """Represents the Match longest overlap policy. No overlapping entities as far as it is possible. + 1. If there are overlapping entities, the longest one will be returned. 2. If the set of + characters predicted for 2 or more entities are exactly the same, select the entity that has + the higher confidence score.3. If the entity scores are identical, return all entities that are + still present after applying the previous rules. 3. If there is partial overlap (as in Hello + Text Analytics) follow the above steps starting from 1. + + :ivar policy_kind: The entity OverlapPolicy object kind. Required. Represents + MatchLongestEntityPolicyType + :vartype policy_kind: str or ~azure.ai.textanalytics.models.MATCH_LONGEST + """ + + policy_kind: Literal[PolicyKind.MATCH_LONGEST] = rest_discriminator(name="policyKind", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """The entity OverlapPolicy object kind. Required. Represents MatchLongestEntityPolicyType""" + + @overload + def __init__( + self, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, policy_kind=PolicyKind.MATCH_LONGEST, **kwargs) + + +class MultiLanguageAnalysisInput(_Model): + """Collection of input documents to be analyzed by the service. + + :ivar documents: The input documents to be analyzed. + :vartype documents: list[~azure.ai.textanalytics.models.MultiLanguageInput] + """ + + documents: Optional[List["_models.MultiLanguageInput"]] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """The input documents to be analyzed.""" + + @overload + def __init__( + self, + *, + documents: Optional[List["_models.MultiLanguageInput"]] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class MultiLanguageInput(_Model): + """Contains an input document to be analyzed by the service. + + :ivar id: A unique, non-empty document identifier. Required. + :vartype id: str + :ivar text: The input text to process. Required. + :vartype text: str + :ivar language: (Optional) This is the 2 letter ISO 639-1 representation of a language. For + example, use \\"en\\" for English; \\"es\\" for Spanish etc. If not set, use \\"en\\" for + English as default. (Following only applies to 2023-04-15-preview and above) For Auto Language + Detection, use \\"auto\\". If not set, use \\"en\\" for English as default. + :vartype language: str + """ + + id: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """A unique, non-empty document identifier. Required.""" + text: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The input text to process. Required.""" + language: Optional[str] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """(Optional) This is the 2 letter ISO 639-1 representation of a language. For example, use + \\"en\\" for English; \\"es\\" for Spanish etc. If not set, use \\"en\\" for English as + default. (Following only applies to 2023-04-15-preview and above) For Auto Language Detection, + use \\"auto\\". If not set, use \\"en\\" for English as default.""" + + @overload + def __init__( + self, + *, + id: str, # pylint: disable=redefined-builtin + text: str, + language: Optional[str] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class NoMaskPolicyType(BaseRedactionPolicy, discriminator="noMask"): + """Represents the policy of not redacting found PII. + + :ivar policy_kind: The entity RedactionPolicy object kind. Required. Do not redact detected + entities. + :vartype policy_kind: str or ~azure.ai.textanalytics.models.NO_MASK + """ + + policy_kind: Literal[RedactionPolicyKind.NO_MASK] = rest_discriminator(name="policyKind", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """The entity RedactionPolicy object kind. Required. Do not redact detected entities.""" + + @overload + def __init__( + self, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, policy_kind=RedactionPolicyKind.NO_MASK, **kwargs) + + +class NumberMetadata(BaseMetadata, discriminator="NumberMetadata"): + """A metadata for numeric entity instances. + + :ivar metadata_kind: Kind of the metadata. Required. Metadata for numeric values. + :vartype metadata_kind: str or ~azure.ai.textanalytics.models.NUMBER_METADATA + :ivar number_kind: Kind of the number type. Required. Known values are: "Integer", "Decimal", + "Power", "Fraction", "Percent", and "Unspecified". + :vartype number_kind: str or ~azure.ai.textanalytics.models.NumberKind + :ivar value: A numeric representation of what the extracted text denotes. Required. + :vartype value: float + """ + + metadata_kind: Literal[MetadataKind.NUMBER_METADATA] = rest_discriminator(name="metadataKind", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """Kind of the metadata. Required. Metadata for numeric values.""" + number_kind: Union[str, "_models.NumberKind"] = rest_field( + name="numberKind", visibility=["read", "create", "update", "delete", "query"] + ) + """Kind of the number type. Required. Known values are: \"Integer\", \"Decimal\", \"Power\", + \"Fraction\", \"Percent\", and \"Unspecified\".""" + value: float = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """A numeric representation of what the extracted text denotes. Required.""" + + @overload + def __init__( + self, + *, + number_kind: Union[str, "_models.NumberKind"], + value: float, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, metadata_kind=MetadataKind.NUMBER_METADATA, **kwargs) + + +class NumericRangeMetadata(BaseMetadata, discriminator="NumericRangeMetadata"): + """represents the Metadata of numeric intervals. + + :ivar metadata_kind: Kind of the metadata. Required. Metadata for numeric range values. + :vartype metadata_kind: str or ~azure.ai.textanalytics.models.NUMERIC_RANGE_METADATA + :ivar range_kind: Kind of numeric ranges supported - like Number, Speed, etc. Required. Known + values are: "Number", "Speed", "Weight", "Length", "Volume", "Area", "Age", "Information", + "Temperature", and "Currency". + :vartype range_kind: str or ~azure.ai.textanalytics.models.RangeKind + :ivar minimum: The beginning value of the interval. Required. + :vartype minimum: float + :ivar maximum: The ending value of the interval. Required. + :vartype maximum: float + :ivar range_inclusivity: The inclusiveness of this range. Known values are: "NoneInclusive", + "LeftInclusive", "RightInclusive", and "LeftRightInclusive". + :vartype range_inclusivity: str or ~azure.ai.textanalytics.models.RangeInclusivity + """ + + metadata_kind: Literal[MetadataKind.NUMERIC_RANGE_METADATA] = rest_discriminator(name="metadataKind", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """Kind of the metadata. Required. Metadata for numeric range values.""" + range_kind: Union[str, "_models.RangeKind"] = rest_field( + name="rangeKind", visibility=["read", "create", "update", "delete", "query"] + ) + """Kind of numeric ranges supported - like Number, Speed, etc. Required. Known values are: + \"Number\", \"Speed\", \"Weight\", \"Length\", \"Volume\", \"Area\", \"Age\", \"Information\", + \"Temperature\", and \"Currency\".""" + minimum: float = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The beginning value of the interval. Required.""" + maximum: float = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The ending value of the interval. Required.""" + range_inclusivity: Optional[Union[str, "_models.RangeInclusivity"]] = rest_field( + name="rangeInclusivity", visibility=["read", "create", "update", "delete", "query"] + ) + """The inclusiveness of this range. Known values are: \"NoneInclusive\", \"LeftInclusive\", + \"RightInclusive\", and \"LeftRightInclusive\".""" + + @overload + def __init__( + self, + *, + range_kind: Union[str, "_models.RangeKind"], + minimum: float, + maximum: float, + range_inclusivity: Optional[Union[str, "_models.RangeInclusivity"]] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, metadata_kind=MetadataKind.NUMERIC_RANGE_METADATA, **kwargs) + + +class OrdinalMetadata(BaseMetadata, discriminator="OrdinalMetadata"): + """A metadata for numeric entity instances. + + :ivar metadata_kind: Kind of the metadata. Required. Metadata for ordinal numbers. + :vartype metadata_kind: str or ~azure.ai.textanalytics.models.ORDINAL_METADATA + :ivar offset: The offset with respect to the reference (e.g., offset = -1 indicates the second + to last). Required. + :vartype offset: str + :ivar relative_to: The reference point that the ordinal number denotes. Required. Known values + are: "Current", "End", and "Start". + :vartype relative_to: str or ~azure.ai.textanalytics.models.RelativeTo + :ivar value: A simple arithmetic expression that the ordinal denotes. Required. + :vartype value: str + """ + + metadata_kind: Literal[MetadataKind.ORDINAL_METADATA] = rest_discriminator(name="metadataKind", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """Kind of the metadata. Required. Metadata for ordinal numbers.""" + offset: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The offset with respect to the reference (e.g., offset = -1 indicates the second to last). + Required.""" + relative_to: Union[str, "_models.RelativeTo"] = rest_field( + name="relativeTo", visibility=["read", "create", "update", "delete", "query"] + ) + """The reference point that the ordinal number denotes. Required. Known values are: \"Current\", + \"End\", and \"Start\".""" + value: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """A simple arithmetic expression that the ordinal denotes. Required.""" + + @overload + def __init__( + self, + *, + offset: str, + relative_to: Union[str, "_models.RelativeTo"], + value: str, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, metadata_kind=MetadataKind.ORDINAL_METADATA, **kwargs) + + +class PiiEntityRecognitionLROResult(AnalyzeTextLROResult, discriminator="PiiEntityRecognitionLROResults"): + """Contains the PII LRO results. + + :ivar last_update_date_time: The last updated time in UTC for the task. Required. + :vartype last_update_date_time: ~datetime.datetime + :ivar status: The status of the task at the mentioned last update time. Required. Known values + are: "notStarted", "running", "succeeded", "partiallyCompleted", "failed", "cancelled", and + "cancelling". + :vartype status: str or ~azure.ai.textanalytics.models.State + :ivar task_name: task name. + :vartype task_name: str + :ivar kind: The kind of the task. Required. PII entity recognition LRO results + :vartype kind: str or ~azure.ai.textanalytics.models.PII_ENTITY_RECOGNITION_LRO_RESULTS + :ivar results: The list of pii results. Required. + :vartype results: ~azure.ai.textanalytics.models.PiiResult + """ + + kind: Literal[AnalyzeTextLROResultsKind.PII_ENTITY_RECOGNITION_LRO_RESULTS] = rest_discriminator(name="kind", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """The kind of the task. Required. PII entity recognition LRO results""" + results: "_models.PiiResult" = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The list of pii results. Required.""" + + @overload + def __init__( + self, + *, + last_update_date_time: datetime.datetime, + status: Union[str, "_models.State"], + results: "_models.PiiResult", + task_name: Optional[str] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, kind=AnalyzeTextLROResultsKind.PII_ENTITY_RECOGNITION_LRO_RESULTS, **kwargs) + + +class PiiEntityWithTags(_Model): + """Entity object with tags. + + :ivar text: Entity text as appears in the request. Required. + :vartype text: str + :ivar category: Entity type. Required. + :vartype category: str + :ivar subcategory: (Optional) Entity sub type. + :vartype subcategory: str + :ivar offset: Start position for the entity text. Use of different 'stringIndexType' values can + affect the offset returned. Required. + :vartype offset: int + :ivar length: Length for the entity text. Use of different 'stringIndexType' values can affect + the length returned. Required. + :vartype length: int + :ivar confidence_score: Confidence score between 0 and 1 of the extracted entity. Required. + :vartype confidence_score: float + :ivar type: An entity type is the lowest (or finest) granularity at which the entity has been + detected. The type maps to the specific metadata attributes associated with the entity + detected. + :vartype type: str + :ivar tags: List of entity tags. Tags are to express some similarities/affinity between + entities. + :vartype tags: list[~azure.ai.textanalytics.models.EntityTag] + :ivar mask: Optional field which will be returned only when using the redaction policy kind + “MaskWithEntityType”. This field will contain the exact mask text used to mask the PII entity + in the original text. + :vartype mask: str + :ivar mask_offset: Start position of masked text in the redacted text when using the redaction + policy kind “MaskWithEntityType”. + :vartype mask_offset: int + :ivar mask_length: The length of the masked text. Will be present when using the redaction + policy kind “MaskWithEntityType”. + :vartype mask_length: int + """ + + text: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Entity text as appears in the request. Required.""" + category: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Entity type. Required.""" + subcategory: Optional[str] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """(Optional) Entity sub type.""" + offset: int = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Start position for the entity text. Use of different 'stringIndexType' values can affect the + offset returned. Required.""" + length: int = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Length for the entity text. Use of different 'stringIndexType' values can affect the length + returned. Required.""" + confidence_score: float = rest_field( + name="confidenceScore", visibility=["read", "create", "update", "delete", "query"] + ) + """Confidence score between 0 and 1 of the extracted entity. Required.""" + type: Optional[str] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """An entity type is the lowest (or finest) granularity at which the entity has been detected. The + type maps to the specific metadata attributes associated with the entity detected.""" + tags: Optional[List["_models.EntityTag"]] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """List of entity tags. Tags are to express some similarities/affinity between entities.""" + mask: Optional[str] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Optional field which will be returned only when using the redaction policy kind + “MaskWithEntityType”. This field will contain the exact mask text used to mask the PII entity + in the original text.""" + mask_offset: Optional[int] = rest_field( + name="maskOffset", visibility=["read", "create", "update", "delete", "query"] + ) + """Start position of masked text in the redacted text when using the redaction policy kind + “MaskWithEntityType”.""" + mask_length: Optional[int] = rest_field( + name="maskLength", visibility=["read", "create", "update", "delete", "query"] + ) + """The length of the masked text. Will be present when using the redaction policy kind + “MaskWithEntityType”.""" + + @overload + def __init__( + self, + *, + text: str, + category: str, + offset: int, + length: int, + confidence_score: float, + subcategory: Optional[str] = None, + type: Optional[str] = None, + tags: Optional[List["_models.EntityTag"]] = None, + mask: Optional[str] = None, + mask_offset: Optional[int] = None, + mask_length: Optional[int] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class PiiLROTask(AnalyzeTextLROTask, discriminator="PiiEntityRecognition"): + """Contains the analyze text PIIEntityRecognition LRO task. + + :ivar task_name: task name. + :vartype task_name: str + :ivar kind: Kind of the task. Required. PII entity recognition task + :vartype kind: str or ~azure.ai.textanalytics.models.PII_ENTITY_RECOGNITION + :ivar parameters: Pii task parameters. + :vartype parameters: ~azure.ai.textanalytics.models.PiiTaskParameters + """ + + kind: Literal[AnalyzeTextLROTaskKind.PII_ENTITY_RECOGNITION] = rest_discriminator(name="kind", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """Kind of the task. Required. PII entity recognition task""" + parameters: Optional["_models.PiiTaskParameters"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Pii task parameters.""" + + @overload + def __init__( + self, + *, + task_name: Optional[str] = None, + parameters: Optional["_models.PiiTaskParameters"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, kind=AnalyzeTextLROTaskKind.PII_ENTITY_RECOGNITION, **kwargs) + + +class PiiResult(_Model): + """Contains the PiiResult. + + :ivar errors: Errors by document id. Required. + :vartype errors: list[~azure.ai.textanalytics.models.DocumentError] + :ivar statistics: if showStats=true was specified in the request this field will contain + information about the request payload. + :vartype statistics: ~azure.ai.textanalytics.models.RequestStatistics + :ivar model_version: This field indicates which model is used for scoring. Required. + :vartype model_version: str + :ivar documents: Response by document. Required. + :vartype documents: list[~azure.ai.textanalytics.models.PiiResultWithDetectedLanguage] + """ + + errors: List["_models.DocumentError"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Errors by document id. Required.""" + statistics: Optional["_models.RequestStatistics"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """if showStats=true was specified in the request this field will contain information about the + request payload.""" + model_version: str = rest_field(name="modelVersion", visibility=["read", "create", "update", "delete", "query"]) + """This field indicates which model is used for scoring. Required.""" + documents: List["_models.PiiResultWithDetectedLanguage"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Response by document. Required.""" + + @overload + def __init__( + self, + *, + errors: List["_models.DocumentError"], + model_version: str, + documents: List["_models.PiiResultWithDetectedLanguage"], + statistics: Optional["_models.RequestStatistics"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class PiiResultWithDetectedLanguage(_Model): + """Contains the PII results with detected language. + + :ivar id: Unique, non-empty document identifier. Required. + :vartype id: str + :ivar warnings: Warnings encountered while processing document. Required. + :vartype warnings: list[~azure.ai.textanalytics.models.DocumentWarning] + :ivar statistics: if showStats=true was specified in the request this field will contain + information about the document payload. + :vartype statistics: ~azure.ai.textanalytics.models.DocumentStatistics + :ivar redacted_text: Returns redacted text. Required. + :vartype redacted_text: str + :ivar entities: Recognized entities in the document. Required. + :vartype entities: list[~azure.ai.textanalytics.models.PiiEntityWithTags] + :ivar detected_language: If 'language' is set to 'auto' for the document in the request this + field will contain a 2 letter ISO 639-1 representation of the language detected for this + document. + :vartype detected_language: ~azure.ai.textanalytics.models.DetectedLanguage + """ + + id: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Unique, non-empty document identifier. Required.""" + warnings: List["_models.DocumentWarning"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Warnings encountered while processing document. Required.""" + statistics: Optional["_models.DocumentStatistics"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """if showStats=true was specified in the request this field will contain information about the + document payload.""" + redacted_text: str = rest_field(name="redactedText", visibility=["read", "create", "update", "delete", "query"]) + """Returns redacted text. Required.""" + entities: List["_models.PiiEntityWithTags"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Recognized entities in the document. Required.""" + detected_language: Optional["_models.DetectedLanguage"] = rest_field( + name="detectedLanguage", visibility=["read", "create", "update", "delete", "query"] + ) + """If 'language' is set to 'auto' for the document in the request this field will contain a 2 + letter ISO 639-1 representation of the language detected for this document.""" + + @overload + def __init__( + self, + *, + id: str, # pylint: disable=redefined-builtin + warnings: List["_models.DocumentWarning"], + redacted_text: str, + entities: List["_models.PiiEntityWithTags"], + statistics: Optional["_models.DocumentStatistics"] = None, + detected_language: Optional["_models.DetectedLanguage"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class PiiTaskParameters(_Model): + """Supported parameters for a PII Entities Recognition task. + + :ivar logging_opt_out: logging opt out. + :vartype logging_opt_out: bool + :ivar model_version: model version. + :vartype model_version: str + :ivar domain: Domain for PII task. Known values are: "phi" and "none". + :vartype domain: str or ~azure.ai.textanalytics.models.PiiDomain + :ivar pii_categories: Enumeration of PII categories to be returned in the response. + :vartype pii_categories: list[str or ~azure.ai.textanalytics.models.PiiCategory] + :ivar string_index_type: StringIndexType to be used for analysis. Known values are: + "TextElements_v8", "UnicodeCodePoint", and "Utf16CodeUnit". + :vartype string_index_type: str or ~azure.ai.textanalytics.models.StringIndexType + :ivar exclude_pii_categories: Enumeration of PII categories to be excluded in the response. + :vartype exclude_pii_categories: list[str or + ~azure.ai.textanalytics.models.PiiCategoriesExclude] + :ivar redaction_policy: RedactionPolicy to be used on the input. + :vartype redaction_policy: ~azure.ai.textanalytics.models.BaseRedactionPolicy + :ivar value_exclusion_policy: Policy for specific words and terms that should be excluded from + detection by the PII detection service. + :vartype value_exclusion_policy: ~azure.ai.textanalytics.models.ValueExclusionPolicy + :ivar entity_synonyms: (Optional) request parameter that allows the user to provide synonyms + for context words that to enhance pii entity detection. + :vartype entity_synonyms: list[~azure.ai.textanalytics.models.EntitySynonyms] + """ + + logging_opt_out: Optional[bool] = rest_field( + name="loggingOptOut", visibility=["read", "create", "update", "delete", "query"] + ) + """logging opt out.""" + model_version: Optional[str] = rest_field( + name="modelVersion", visibility=["read", "create", "update", "delete", "query"] + ) + """model version.""" + domain: Optional[Union[str, "_models.PiiDomain"]] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Domain for PII task. Known values are: \"phi\" and \"none\".""" + pii_categories: Optional[List[Union[str, "_models.PiiCategory"]]] = rest_field( + name="piiCategories", visibility=["read", "create", "update", "delete", "query"] + ) + """Enumeration of PII categories to be returned in the response.""" + string_index_type: Optional[Union[str, "_models.StringIndexType"]] = rest_field( + name="stringIndexType", visibility=["read", "create", "update", "delete", "query"] + ) + """StringIndexType to be used for analysis. Known values are: \"TextElements_v8\", + \"UnicodeCodePoint\", and \"Utf16CodeUnit\".""" + exclude_pii_categories: Optional[List[Union[str, "_models.PiiCategoriesExclude"]]] = rest_field( + name="excludePiiCategories", visibility=["read", "create", "update", "delete", "query"] + ) + """Enumeration of PII categories to be excluded in the response.""" + redaction_policy: Optional["_models.BaseRedactionPolicy"] = rest_field( + name="redactionPolicy", visibility=["read", "create", "update", "delete", "query"] + ) + """RedactionPolicy to be used on the input.""" + value_exclusion_policy: Optional["_models.ValueExclusionPolicy"] = rest_field( + name="valueExclusionPolicy", visibility=["read", "create", "update", "delete", "query"] + ) + """Policy for specific words and terms that should be excluded from detection by the PII detection + service.""" + entity_synonyms: Optional[List["_models.EntitySynonyms"]] = rest_field( + name="entitySynonyms", visibility=["read", "create", "update", "delete", "query"] + ) + """(Optional) request parameter that allows the user to provide synonyms for context words that to + enhance pii entity detection.""" + + @overload + def __init__( + self, + *, + logging_opt_out: Optional[bool] = None, + model_version: Optional[str] = None, + domain: Optional[Union[str, "_models.PiiDomain"]] = None, + pii_categories: Optional[List[Union[str, "_models.PiiCategory"]]] = None, + string_index_type: Optional[Union[str, "_models.StringIndexType"]] = None, + exclude_pii_categories: Optional[List[Union[str, "_models.PiiCategoriesExclude"]]] = None, + redaction_policy: Optional["_models.BaseRedactionPolicy"] = None, + value_exclusion_policy: Optional["_models.ValueExclusionPolicy"] = None, + entity_synonyms: Optional[List["_models.EntitySynonyms"]] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class PiiTaskResult(AnalyzeTextTaskResult, discriminator="PiiEntityRecognitionResults"): + """Contains the analyze text PIIEntityRecognition LRO task. + + :ivar kind: The kind of the task. Required. PII entity recognition results + :vartype kind: str or ~azure.ai.textanalytics.models.PII_ENTITY_RECOGNITION_RESULTS + :ivar results: The list of pii results. Required. + :vartype results: ~azure.ai.textanalytics.models.PiiResult + """ + + kind: Literal[AnalyzeTextTaskResultsKind.PII_ENTITY_RECOGNITION_RESULTS] = rest_discriminator(name="kind", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """The kind of the task. Required. PII entity recognition results""" + results: "_models.PiiResult" = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The list of pii results. Required.""" + + @overload + def __init__( + self, + *, + results: "_models.PiiResult", + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, kind=AnalyzeTextTaskResultsKind.PII_ENTITY_RECOGNITION_RESULTS, **kwargs) + + +class RequestStatistics(_Model): + """if showStats=true was specified in the request this field will contain information about the + request payload. + + :ivar documents_count: Number of documents submitted in the request. Required. + :vartype documents_count: int + :ivar valid_documents_count: Number of valid documents. This excludes empty, over-size limit or + non-supported languages documents. Required. + :vartype valid_documents_count: int + :ivar erroneous_documents_count: Number of invalid documents. This includes empty, over-size + limit or non-supported languages documents. Required. + :vartype erroneous_documents_count: int + :ivar transactions_count: Number of transactions for the request. Required. + :vartype transactions_count: int + """ + + documents_count: int = rest_field(name="documentsCount", visibility=["read", "create", "update", "delete", "query"]) + """Number of documents submitted in the request. Required.""" + valid_documents_count: int = rest_field( + name="validDocumentsCount", visibility=["read", "create", "update", "delete", "query"] + ) + """Number of valid documents. This excludes empty, over-size limit or non-supported languages + documents. Required.""" + erroneous_documents_count: int = rest_field( + name="erroneousDocumentsCount", visibility=["read", "create", "update", "delete", "query"] + ) + """Number of invalid documents. This includes empty, over-size limit or non-supported languages + documents. Required.""" + transactions_count: int = rest_field( + name="transactionsCount", visibility=["read", "create", "update", "delete", "query"] + ) + """Number of transactions for the request. Required.""" + + @overload + def __init__( + self, + *, + documents_count: int, + valid_documents_count: int, + erroneous_documents_count: int, + transactions_count: int, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class SentenceAssessment(_Model): + """Represents a sentence assessment and the assessments or target objects related to it. + + :ivar sentiment: The sentiment of the sentence. Required. Known values are: "positive", + "mixed", and "negative". + :vartype sentiment: str or ~azure.ai.textanalytics.models.TokenSentimentValue + :ivar confidence_scores: Represents the confidence scores across all sentiment classes: + positive and negative. Required. + :vartype confidence_scores: ~azure.ai.textanalytics.models.TargetConfidenceScoreLabel + :ivar offset: The target offset from the start of the sentence. Required. + :vartype offset: int + :ivar length: The length of the target. Required. + :vartype length: int + :ivar text: The target text detected. Required. + :vartype text: str + :ivar is_negated: The indicator representing if the assessment is negated. Required. + :vartype is_negated: bool + """ + + sentiment: Union[str, "_models.TokenSentimentValue"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """The sentiment of the sentence. Required. Known values are: \"positive\", \"mixed\", and + \"negative\".""" + confidence_scores: "_models.TargetConfidenceScoreLabel" = rest_field( + name="confidenceScores", visibility=["read", "create", "update", "delete", "query"] + ) + """Represents the confidence scores across all sentiment classes: positive and negative. Required.""" + offset: int = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The target offset from the start of the sentence. Required.""" + length: int = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The length of the target. Required.""" + text: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The target text detected. Required.""" + is_negated: bool = rest_field(name="isNegated", visibility=["read", "create", "update", "delete", "query"]) + """The indicator representing if the assessment is negated. Required.""" + + @overload + def __init__( + self, + *, + sentiment: Union[str, "_models.TokenSentimentValue"], + confidence_scores: "_models.TargetConfidenceScoreLabel", + offset: int, + length: int, + text: str, + is_negated: bool, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class SentenceSentiment(_Model): + """A document's sentence sentiment. + + :ivar text: The sentence text. Required. + :vartype text: str + :ivar sentiment: The predicted Sentiment for the sentence. Required. Known values are: + "positive", "neutral", and "negative". + :vartype sentiment: str or ~azure.ai.textanalytics.models.SentenceSentimentValue + :ivar confidence_scores: The sentiment confidence score between 0 and 1 for the sentence for + all classes. Required. + :vartype confidence_scores: ~azure.ai.textanalytics.models.SentimentConfidenceScores + :ivar offset: The target offset from the start of the sentence. Required. + :vartype offset: int + :ivar length: The length of the target. Required. + :vartype length: int + :ivar targets: The array of sentence targets for the sentence. + :vartype targets: list[~azure.ai.textanalytics.models.SentenceTarget] + :ivar assessments: The array of assessments for the sentence. + :vartype assessments: list[~azure.ai.textanalytics.models.SentenceAssessment] + """ + + text: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The sentence text. Required.""" + sentiment: Union[str, "_models.SentenceSentimentValue"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """The predicted Sentiment for the sentence. Required. Known values are: \"positive\", + \"neutral\", and \"negative\".""" + confidence_scores: "_models.SentimentConfidenceScores" = rest_field( + name="confidenceScores", visibility=["read", "create", "update", "delete", "query"] + ) + """The sentiment confidence score between 0 and 1 for the sentence for all classes. Required.""" + offset: int = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The target offset from the start of the sentence. Required.""" + length: int = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The length of the target. Required.""" + targets: Optional[List["_models.SentenceTarget"]] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """The array of sentence targets for the sentence.""" + assessments: Optional[List["_models.SentenceAssessment"]] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """The array of assessments for the sentence.""" + + @overload + def __init__( + self, + *, + text: str, + sentiment: Union[str, "_models.SentenceSentimentValue"], + confidence_scores: "_models.SentimentConfidenceScores", + offset: int, + length: int, + targets: Optional[List["_models.SentenceTarget"]] = None, + assessments: Optional[List["_models.SentenceAssessment"]] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class SentenceTarget(_Model): + """Represents a sentence target and the assessments or target objects related to it. + + :ivar sentiment: The sentiment of the sentence. Required. Known values are: "positive", + "mixed", and "negative". + :vartype sentiment: str or ~azure.ai.textanalytics.models.TokenSentimentValue + :ivar confidence_scores: Represents the confidence scores across all sentiment classes: + positive and negative. Required. + :vartype confidence_scores: ~azure.ai.textanalytics.models.TargetConfidenceScoreLabel + :ivar offset: The target offset from the start of the sentence. Required. + :vartype offset: int + :ivar length: The length of the target. Required. + :vartype length: int + :ivar text: The target text detected. Required. + :vartype text: str + :ivar relations: The array of either assessment or target objects which is related to the + target. Required. + :vartype relations: list[~azure.ai.textanalytics.models.TargetRelation] + """ + + sentiment: Union[str, "_models.TokenSentimentValue"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """The sentiment of the sentence. Required. Known values are: \"positive\", \"mixed\", and + \"negative\".""" + confidence_scores: "_models.TargetConfidenceScoreLabel" = rest_field( + name="confidenceScores", visibility=["read", "create", "update", "delete", "query"] + ) + """Represents the confidence scores across all sentiment classes: positive and negative. Required.""" + offset: int = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The target offset from the start of the sentence. Required.""" + length: int = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The length of the target. Required.""" + text: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The target text detected. Required.""" + relations: List["_models.TargetRelation"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The array of either assessment or target objects which is related to the target. Required.""" + + @overload + def __init__( + self, + *, + sentiment: Union[str, "_models.TokenSentimentValue"], + confidence_scores: "_models.TargetConfidenceScoreLabel", + offset: int, + length: int, + text: str, + relations: List["_models.TargetRelation"], + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class SentimentAnalysisLROTask(AnalyzeTextLROTask, discriminator="SentimentAnalysis"): + """An object representing the task definition for a Sentiment Analysis task. + + :ivar task_name: task name. + :vartype task_name: str + :ivar kind: The Sentiment Analysis kind of the long running task. Required. Sentiment analysis + task + :vartype kind: str or ~azure.ai.textanalytics.models.SENTIMENT_ANALYSIS + :ivar parameters: Parameters for the Sentiment Analysis task. + :vartype parameters: ~azure.ai.textanalytics.models.SentimentAnalysisTaskParameters + """ + + kind: Literal[AnalyzeTextLROTaskKind.SENTIMENT_ANALYSIS] = rest_discriminator(name="kind", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """The Sentiment Analysis kind of the long running task. Required. Sentiment analysis task""" + parameters: Optional["_models.SentimentAnalysisTaskParameters"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Parameters for the Sentiment Analysis task.""" + + @overload + def __init__( + self, + *, + task_name: Optional[str] = None, + parameters: Optional["_models.SentimentAnalysisTaskParameters"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, kind=AnalyzeTextLROTaskKind.SENTIMENT_ANALYSIS, **kwargs) + + +class SentimentAnalysisTaskParameters(_Model): + """Supported parameters for a Sentiment Analysis task. + + :ivar logging_opt_out: logging opt out. + :vartype logging_opt_out: bool + :ivar model_version: model version. + :vartype model_version: str + :ivar opinion_mining: Whether to use opinion mining in the request or not. + :vartype opinion_mining: bool + :ivar string_index_type: Specifies the method used to interpret string offsets. Known values + are: "TextElements_v8", "UnicodeCodePoint", and "Utf16CodeUnit". + :vartype string_index_type: str or ~azure.ai.textanalytics.models.StringIndexType + """ + + logging_opt_out: Optional[bool] = rest_field( + name="loggingOptOut", visibility=["read", "create", "update", "delete", "query"] + ) + """logging opt out.""" + model_version: Optional[str] = rest_field( + name="modelVersion", visibility=["read", "create", "update", "delete", "query"] + ) + """model version.""" + opinion_mining: Optional[bool] = rest_field( + name="opinionMining", visibility=["read", "create", "update", "delete", "query"] + ) + """Whether to use opinion mining in the request or not.""" + string_index_type: Optional[Union[str, "_models.StringIndexType"]] = rest_field( + name="stringIndexType", visibility=["read", "create", "update", "delete", "query"] + ) + """Specifies the method used to interpret string offsets. Known values are: \"TextElements_v8\", + \"UnicodeCodePoint\", and \"Utf16CodeUnit\".""" + + @overload + def __init__( + self, + *, + logging_opt_out: Optional[bool] = None, + model_version: Optional[str] = None, + opinion_mining: Optional[bool] = None, + string_index_type: Optional[Union[str, "_models.StringIndexType"]] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class SentimentConfidenceScores(_Model): + """Represents the confidence scores between 0 and 1 across all sentiment classes: positive, + neutral, negative. + + :ivar positive: Confidence score for positive sentiment. Required. + :vartype positive: float + :ivar neutral: Confidence score for neutral sentiment. Required. + :vartype neutral: float + :ivar negative: Confidence score for negative sentiment. Required. + :vartype negative: float + """ + + positive: float = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Confidence score for positive sentiment. Required.""" + neutral: float = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Confidence score for neutral sentiment. Required.""" + negative: float = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Confidence score for negative sentiment. Required.""" + + @overload + def __init__( + self, + *, + positive: float, + neutral: float, + negative: float, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class SentimentDocumentResultWithDetectedLanguage(_Model): # pylint: disable=name-too-long + """Sentiment analysis per document. + + :ivar id: Unique, non-empty document identifier. Required. + :vartype id: str + :ivar warnings: Warnings encountered while processing document. Required. + :vartype warnings: list[~azure.ai.textanalytics.models.DocumentWarning] + :ivar statistics: if showStats=true was specified in the request this field will contain + information about the document payload. + :vartype statistics: ~azure.ai.textanalytics.models.DocumentStatistics + :ivar sentiment: Predicted sentiment for document (Negative, Neutral, Positive, or Mixed). + Required. Known values are: "positive", "neutral", "negative", and "mixed". + :vartype sentiment: str or ~azure.ai.textanalytics.models.DocumentSentimentValue + :ivar confidence_scores: The sentiment confidence score between 0 and 1 for the sentence for + all classes. Required. + :vartype confidence_scores: ~azure.ai.textanalytics.models.SentimentConfidenceScores + :ivar sentences: The document's sentences sentiment. Required. + :vartype sentences: list[~azure.ai.textanalytics.models.SentenceSentiment] + :ivar detected_language: If 'language' is set to 'auto' for the document in the request this + field will contain a 2 letter ISO 639-1 representation of the language detected for this + document. + :vartype detected_language: ~azure.ai.textanalytics.models.DetectedLanguage + """ + + id: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Unique, non-empty document identifier. Required.""" + warnings: List["_models.DocumentWarning"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Warnings encountered while processing document. Required.""" + statistics: Optional["_models.DocumentStatistics"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """if showStats=true was specified in the request this field will contain information about the + document payload.""" + sentiment: Union[str, "_models.DocumentSentimentValue"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Predicted sentiment for document (Negative, Neutral, Positive, or Mixed). Required. Known + values are: \"positive\", \"neutral\", \"negative\", and \"mixed\".""" + confidence_scores: "_models.SentimentConfidenceScores" = rest_field( + name="confidenceScores", visibility=["read", "create", "update", "delete", "query"] + ) + """The sentiment confidence score between 0 and 1 for the sentence for all classes. Required.""" + sentences: List["_models.SentenceSentiment"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """The document's sentences sentiment. Required.""" + detected_language: Optional["_models.DetectedLanguage"] = rest_field( + name="detectedLanguage", visibility=["read", "create", "update", "delete", "query"] + ) + """If 'language' is set to 'auto' for the document in the request this field will contain a 2 + letter ISO 639-1 representation of the language detected for this document.""" + + @overload + def __init__( + self, + *, + id: str, # pylint: disable=redefined-builtin + warnings: List["_models.DocumentWarning"], + sentiment: Union[str, "_models.DocumentSentimentValue"], + confidence_scores: "_models.SentimentConfidenceScores", + sentences: List["_models.SentenceSentiment"], + statistics: Optional["_models.DocumentStatistics"] = None, + detected_language: Optional["_models.DetectedLanguage"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class SentimentLROResult(AnalyzeTextLROResult, discriminator="SentimentAnalysisLROResults"): + """Contains the Sentiment Analysis LRO results. + + :ivar last_update_date_time: The last updated time in UTC for the task. Required. + :vartype last_update_date_time: ~datetime.datetime + :ivar status: The status of the task at the mentioned last update time. Required. Known values + are: "notStarted", "running", "succeeded", "partiallyCompleted", "failed", "cancelled", and + "cancelling". + :vartype status: str or ~azure.ai.textanalytics.models.State + :ivar task_name: task name. + :vartype task_name: str + :ivar kind: Kind of the task. Required. Sentiment analysis LRO results + :vartype kind: str or ~azure.ai.textanalytics.models.SENTIMENT_ANALYSIS_LRO_RESULTS + :ivar results: The sentiment analysis results. Required. + :vartype results: ~azure.ai.textanalytics.models.SentimentResponse + """ + + kind: Literal[AnalyzeTextLROResultsKind.SENTIMENT_ANALYSIS_LRO_RESULTS] = rest_discriminator(name="kind", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """Kind of the task. Required. Sentiment analysis LRO results""" + results: "_models.SentimentResponse" = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The sentiment analysis results. Required.""" + + @overload + def __init__( + self, + *, + last_update_date_time: datetime.datetime, + status: Union[str, "_models.State"], + results: "_models.SentimentResponse", + task_name: Optional[str] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, kind=AnalyzeTextLROResultsKind.SENTIMENT_ANALYSIS_LRO_RESULTS, **kwargs) + + +class SentimentResponse(_Model): + """Sentiment analysis results for the input documents. + + :ivar errors: Errors by document id. Required. + :vartype errors: list[~azure.ai.textanalytics.models.DocumentError] + :ivar statistics: if showStats=true was specified in the request this field will contain + information about the request payload. + :vartype statistics: ~azure.ai.textanalytics.models.RequestStatistics + :ivar model_version: This field indicates which model is used for scoring. Required. + :vartype model_version: str + :ivar documents: The sentiment analysis results for each document in the input. Required. + :vartype documents: + list[~azure.ai.textanalytics.models.SentimentDocumentResultWithDetectedLanguage] + """ + + errors: List["_models.DocumentError"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Errors by document id. Required.""" + statistics: Optional["_models.RequestStatistics"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """if showStats=true was specified in the request this field will contain information about the + request payload.""" + model_version: str = rest_field(name="modelVersion", visibility=["read", "create", "update", "delete", "query"]) + """This field indicates which model is used for scoring. Required.""" + documents: List["_models.SentimentDocumentResultWithDetectedLanguage"] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """The sentiment analysis results for each document in the input. Required.""" + + @overload + def __init__( + self, + *, + errors: List["_models.DocumentError"], + model_version: str, + documents: List["_models.SentimentDocumentResultWithDetectedLanguage"], + statistics: Optional["_models.RequestStatistics"] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class SentimentTaskResult(AnalyzeTextTaskResult, discriminator="SentimentAnalysisResults"): + """Contains the analyze text SentimentAnalysis LRO task result. + + :ivar kind: Kind of the task. Required. Sentiment analysis results + :vartype kind: str or ~azure.ai.textanalytics.models.SENTIMENT_ANALYSIS_RESULTS + :ivar results: The sentiment analysis results. Required. + :vartype results: ~azure.ai.textanalytics.models.SentimentResponse + """ + + kind: Literal[AnalyzeTextTaskResultsKind.SENTIMENT_ANALYSIS_RESULTS] = rest_discriminator(name="kind", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """Kind of the task. Required. Sentiment analysis results""" + results: "_models.SentimentResponse" = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The sentiment analysis results. Required.""" + + @overload + def __init__( + self, + *, + results: "_models.SentimentResponse", + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, kind=AnalyzeTextTaskResultsKind.SENTIMENT_ANALYSIS_RESULTS, **kwargs) + + +class SpeedMetadata(BaseMetadata, discriminator="SpeedMetadata"): + """Represents the Speed entity Metadata model. + + :ivar value: The numeric value that the extracted text denotes. Required. + :vartype value: float + :ivar metadata_kind: Kind of the metadata. Required. Metadata for speed-related values. + :vartype metadata_kind: str or ~azure.ai.textanalytics.models.SPEED_METADATA + :ivar unit: Unit of measure for speed. Required. Known values are: "Unspecified", + "MetersPerSecond", "KilometersPerHour", "KilometersPerMinute", "KilometersPerSecond", + "MilesPerHour", "Knots", "FeetPerSecond", "FeetPerMinute", "YardsPerMinute", "YardsPerSecond", + "MetersPerMillisecond", "CentimetersPerMillisecond", and "KilometersPerMillisecond". + :vartype unit: str or ~azure.ai.textanalytics.models.SpeedUnit + """ + + value: float = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The numeric value that the extracted text denotes. Required.""" + metadata_kind: Literal[MetadataKind.SPEED_METADATA] = rest_discriminator(name="metadataKind", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """Kind of the metadata. Required. Metadata for speed-related values.""" + unit: Union[str, "_models.SpeedUnit"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Unit of measure for speed. Required. Known values are: \"Unspecified\", \"MetersPerSecond\", + \"KilometersPerHour\", \"KilometersPerMinute\", \"KilometersPerSecond\", \"MilesPerHour\", + \"Knots\", \"FeetPerSecond\", \"FeetPerMinute\", \"YardsPerMinute\", \"YardsPerSecond\", + \"MetersPerMillisecond\", \"CentimetersPerMillisecond\", and \"KilometersPerMillisecond\".""" + + @overload + def __init__( + self, + *, + value: float, + unit: Union[str, "_models.SpeedUnit"], + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, metadata_kind=MetadataKind.SPEED_METADATA, **kwargs) + + +class SummaryContext(_Model): + """The context of the summary. + + :ivar offset: Start position for the context. Use of different 'stringIndexType' values can + affect the offset returned. Required. + :vartype offset: int + :ivar length: The length of the context. Use of different 'stringIndexType' values can affect + the length returned. Required. + :vartype length: int + """ + + offset: int = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Start position for the context. Use of different 'stringIndexType' values can affect the offset + returned. Required.""" + length: int = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The length of the context. Use of different 'stringIndexType' values can affect the length + returned. Required.""" + + @overload + def __init__( + self, + *, + offset: int, + length: int, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class TargetConfidenceScoreLabel(_Model): + """Represents the confidence scores across all sentiment classes: positive and negative. + + :ivar positive: Confidence score for positive sentiment. Required. + :vartype positive: float + :ivar negative: Confidence score for negative sentiment. Required. + :vartype negative: float + """ + + positive: float = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Confidence score for positive sentiment. Required.""" + negative: float = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Confidence score for negative sentiment. Required.""" + + @overload + def __init__( + self, + *, + positive: float, + negative: float, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class TargetRelation(_Model): + """Represents the relation between assessments and/or targets. + + :ivar ref: The JSON pointer indicating the linked object. Required. + :vartype ref: str + :ivar relation_type: The type related to the target. Required. Known values are: "assessment" + and "target". + :vartype relation_type: str or ~azure.ai.textanalytics.models.TargetRelationType + """ + + ref: str = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The JSON pointer indicating the linked object. Required.""" + relation_type: Union[str, "_models.TargetRelationType"] = rest_field( + name="relationType", visibility=["read", "create", "update", "delete", "query"] + ) + """The type related to the target. Required. Known values are: \"assessment\" and \"target\".""" + + @overload + def __init__( + self, + *, + ref: str, + relation_type: Union[str, "_models.TargetRelationType"], + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class Tasks(_Model): + """Container for the tasks status for the LRO job. + + :ivar completed: Count of completed tasks. Required. + :vartype completed: int + :ivar failed: Count of failed tasks. Required. + :vartype failed: int + :ivar in_progress: Count of inprogress tasks. Required. + :vartype in_progress: int + :ivar total: Count of total tasks. Required. + :vartype total: int + :ivar items_property: Enumerable of Analyze text job results. + :vartype items_property: list[~azure.ai.textanalytics.models.AnalyzeTextLROResult] + """ + + completed: int = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Count of completed tasks. Required.""" + failed: int = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Count of failed tasks. Required.""" + in_progress: int = rest_field(name="inProgress", visibility=["read", "create", "update", "delete", "query"]) + """Count of inprogress tasks. Required.""" + total: int = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Count of total tasks. Required.""" + items_property: Optional[List["_models.AnalyzeTextLROResult"]] = rest_field( + name="items", visibility=["read", "create", "update", "delete", "query"] + ) + """Enumerable of Analyze text job results.""" + + @overload + def __init__( + self, + *, + completed: int, + failed: int, + in_progress: int, + total: int, + items_property: Optional[List["_models.AnalyzeTextLROResult"]] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class TemperatureMetadata(BaseMetadata, discriminator="TemperatureMetadata"): + """Represents the Information entity Metadata model. + + :ivar value: The numeric value that the extracted text denotes. Required. + :vartype value: float + :ivar metadata_kind: Kind of the metadata. Required. Metadata for temperature-related values. + :vartype metadata_kind: str or ~azure.ai.textanalytics.models.TEMPERATURE_METADATA + :ivar unit: Unit of measure for temperature. Required. Known values are: "Unspecified", + "Fahrenheit", "Kelvin", "Rankine", and "Celsius". + :vartype unit: str or ~azure.ai.textanalytics.models.TemperatureUnit + """ + + value: float = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The numeric value that the extracted text denotes. Required.""" + metadata_kind: Literal[MetadataKind.TEMPERATURE_METADATA] = rest_discriminator(name="metadataKind", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """Kind of the metadata. Required. Metadata for temperature-related values.""" + unit: Union[str, "_models.TemperatureUnit"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Unit of measure for temperature. Required. Known values are: \"Unspecified\", \"Fahrenheit\", + \"Kelvin\", \"Rankine\", and \"Celsius\".""" + + @overload + def __init__( + self, + *, + value: float, + unit: Union[str, "_models.TemperatureUnit"], + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, metadata_kind=MetadataKind.TEMPERATURE_METADATA, **kwargs) + + +class TemporalSetMetadata(BaseMetadata, discriminator="TemporalSetMetadata"): + """A Metadata for temporal set entity instances. + + :ivar date_values: List of date values. + :vartype date_values: list[~azure.ai.textanalytics.models.DateValue] + :ivar metadata_kind: Kind of the metadata. Required. Metadata for set of time-related values. + :vartype metadata_kind: str or ~azure.ai.textanalytics.models.TEMPORAL_SET_METADATA + """ + + date_values: Optional[List["_models.DateValue"]] = rest_field( + name="dateValues", visibility=["read", "create", "update", "delete", "query"] + ) + """List of date values.""" + metadata_kind: Literal[MetadataKind.TEMPORAL_SET_METADATA] = rest_discriminator(name="metadataKind", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """Kind of the metadata. Required. Metadata for set of time-related values.""" + + @overload + def __init__( + self, + *, + date_values: Optional[List["_models.DateValue"]] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, metadata_kind=MetadataKind.TEMPORAL_SET_METADATA, **kwargs) + + +class TemporalSpanMetadata(BaseMetadata, discriminator="TemporalSpanMetadata"): + """represents the Metadata of a date and/or time span. + + :ivar metadata_kind: Kind of the metadata. Required. Metadata for temporal span values. + :vartype metadata_kind: str or ~azure.ai.textanalytics.models.TEMPORAL_SPAN_METADATA + :ivar span_values: List of temporal spans detected. + :vartype span_values: list[~azure.ai.textanalytics.models.TemporalSpanValues] + """ + + metadata_kind: Literal[MetadataKind.TEMPORAL_SPAN_METADATA] = rest_discriminator(name="metadataKind", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """Kind of the metadata. Required. Metadata for temporal span values.""" + span_values: Optional[List["_models.TemporalSpanValues"]] = rest_field( + name="spanValues", visibility=["read", "create", "update", "delete", "query"] + ) + """List of temporal spans detected.""" + + @overload + def __init__( + self, + *, + span_values: Optional[List["_models.TemporalSpanValues"]] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, metadata_kind=MetadataKind.TEMPORAL_SPAN_METADATA, **kwargs) + + +class TemporalSpanValues(_Model): + """Temporal span object. + + :ivar begin: Start value for the span. + :vartype begin: str + :ivar end: End value for the span. + :vartype end: str + :ivar duration: An optional duration value formatted based on the ISO 8601 + (`https://en.wikipedia.org/wiki/ISO_8601#Durations + `_). + :vartype duration: str + :ivar modifier: Modifier for datetime to indicate point of reference like before, after etc. + Known values are: "AfterApprox", "Before", "BeforeStart", "Approx", "ReferenceUndefined", + "SinceEnd", "AfterMid", "Start", "After", "BeforeEnd", "Until", "End", "Less", "Since", + "AfterStart", "BeforeApprox", "Mid", and "More". + :vartype modifier: str or ~azure.ai.textanalytics.models.TemporalModifier + :ivar timex: An optional triplet containing the beginning, the end, and the duration all stated + as ISO 8601 formatted strings. + :vartype timex: str + """ + + begin: Optional[str] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Start value for the span.""" + end: Optional[str] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """End value for the span.""" + duration: Optional[str] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """An optional duration value formatted based on the ISO 8601 + (`https://en.wikipedia.org/wiki/ISO_8601#Durations + `_).""" + modifier: Optional[Union[str, "_models.TemporalModifier"]] = rest_field( + visibility=["read", "create", "update", "delete", "query"] + ) + """Modifier for datetime to indicate point of reference like before, after etc. Known values are: + \"AfterApprox\", \"Before\", \"BeforeStart\", \"Approx\", \"ReferenceUndefined\", \"SinceEnd\", + \"AfterMid\", \"Start\", \"After\", \"BeforeEnd\", \"Until\", \"End\", \"Less\", \"Since\", + \"AfterStart\", \"BeforeApprox\", \"Mid\", and \"More\".""" + timex: Optional[str] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """An optional triplet containing the beginning, the end, and the duration all stated as ISO 8601 + formatted strings.""" + + @overload + def __init__( + self, + *, + begin: Optional[str] = None, + end: Optional[str] = None, + duration: Optional[str] = None, + modifier: Optional[Union[str, "_models.TemporalModifier"]] = None, + timex: Optional[str] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class TimeMetadata(BaseMetadata, discriminator="TimeMetadata"): + """A Metadata for time entity instances. + + :ivar date_values: List of date values. + :vartype date_values: list[~azure.ai.textanalytics.models.DateValue] + :ivar metadata_kind: Kind of the metadata. Required. Metadata for time-related values. + :vartype metadata_kind: str or ~azure.ai.textanalytics.models.TIME_METADATA + """ + + date_values: Optional[List["_models.DateValue"]] = rest_field( + name="dateValues", visibility=["read", "create", "update", "delete", "query"] + ) + """List of date values.""" + metadata_kind: Literal[MetadataKind.TIME_METADATA] = rest_discriminator(name="metadataKind", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """Kind of the metadata. Required. Metadata for time-related values.""" + + @overload + def __init__( + self, + *, + date_values: Optional[List["_models.DateValue"]] = None, + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, metadata_kind=MetadataKind.TIME_METADATA, **kwargs) + + +class ValueExclusionPolicy(_Model): + """Policy for specific words and terms that should be excluded from detection by the PII detection + service. + + :ivar case_sensitive: Option to make the values excluded values case sensitive. Required. + :vartype case_sensitive: bool + :ivar excluded_values: List of words and terms that should be excluded from detection by the + PII detection service. Required. + :vartype excluded_values: list[str] + """ + + case_sensitive: bool = rest_field(name="caseSensitive", visibility=["read", "create", "update", "delete", "query"]) + """Option to make the values excluded values case sensitive. Required.""" + excluded_values: List[str] = rest_field( + name="excludedValues", visibility=["read", "create", "update", "delete", "query"] + ) + """List of words and terms that should be excluded from detection by the PII detection service. + Required.""" + + @overload + def __init__( + self, + *, + case_sensitive: bool, + excluded_values: List[str], + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + + +class VolumeMetadata(BaseMetadata, discriminator="VolumeMetadata"): + """Represents the Volume entity Metadata model. + + :ivar value: The numeric value that the extracted text denotes. Required. + :vartype value: float + :ivar metadata_kind: Kind of the metadata. Required. Metadata for volume-related values. + :vartype metadata_kind: str or ~azure.ai.textanalytics.models.VOLUME_METADATA + :ivar unit: Unit of measure for volume. Required. Known values are: "Unspecified", + "CubicMeter", "CubicCentimeter", "CubicMillimeter", "Hectoliter", "Decaliter", "Liter", + "Centiliter", "Milliliter", "CubicYard", "CubicInch", "CubicFoot", "CubicMile", "FluidOunce", + "Teaspoon", "Tablespoon", "Pint", "Quart", "Cup", "Gill", "Pinch", "FluidDram", "Barrel", + "Minim", "Cord", "Peck", "Bushel", and "Hogshead". + :vartype unit: str or ~azure.ai.textanalytics.models.VolumeUnit + """ + + value: float = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The numeric value that the extracted text denotes. Required.""" + metadata_kind: Literal[MetadataKind.VOLUME_METADATA] = rest_discriminator(name="metadataKind", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """Kind of the metadata. Required. Metadata for volume-related values.""" + unit: Union[str, "_models.VolumeUnit"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Unit of measure for volume. Required. Known values are: \"Unspecified\", \"CubicMeter\", + \"CubicCentimeter\", \"CubicMillimeter\", \"Hectoliter\", \"Decaliter\", \"Liter\", + \"Centiliter\", \"Milliliter\", \"CubicYard\", \"CubicInch\", \"CubicFoot\", \"CubicMile\", + \"FluidOunce\", \"Teaspoon\", \"Tablespoon\", \"Pint\", \"Quart\", \"Cup\", \"Gill\", + \"Pinch\", \"FluidDram\", \"Barrel\", \"Minim\", \"Cord\", \"Peck\", \"Bushel\", and + \"Hogshead\".""" + + @overload + def __init__( + self, + *, + value: float, + unit: Union[str, "_models.VolumeUnit"], + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, metadata_kind=MetadataKind.VOLUME_METADATA, **kwargs) + + +class WeightMetadata(BaseMetadata, discriminator="WeightMetadata"): + """Represents the Weight ) entity Metadata model. + + :ivar value: The numeric value that the extracted text denotes. Required. + :vartype value: float + :ivar metadata_kind: Kind of the metadata. Required. Metadata for weight-related values. + :vartype metadata_kind: str or ~azure.ai.textanalytics.models.WEIGHT_METADATA + :ivar unit: Unit of measure for weight. Required. Known values are: "Unspecified", "Kilogram", + "Gram", "Milligram", "Gallon", "MetricTon", "Ton", "Pound", "Ounce", "Grain", "PennyWeight", + "LongTonBritish", "ShortTonUS", "ShortHundredWeightUS", "Stone", and "Dram". + :vartype unit: str or ~azure.ai.textanalytics.models.WeightUnit + """ + + value: float = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """The numeric value that the extracted text denotes. Required.""" + metadata_kind: Literal[MetadataKind.WEIGHT_METADATA] = rest_discriminator(name="metadataKind", visibility=["read", "create", "update", "delete", "query"]) # type: ignore + """Kind of the metadata. Required. Metadata for weight-related values.""" + unit: Union[str, "_models.WeightUnit"] = rest_field(visibility=["read", "create", "update", "delete", "query"]) + """Unit of measure for weight. Required. Known values are: \"Unspecified\", \"Kilogram\", + \"Gram\", \"Milligram\", \"Gallon\", \"MetricTon\", \"Ton\", \"Pound\", \"Ounce\", \"Grain\", + \"PennyWeight\", \"LongTonBritish\", \"ShortTonUS\", \"ShortHundredWeightUS\", \"Stone\", and + \"Dram\".""" + + @overload + def __init__( + self, + *, + value: float, + unit: Union[str, "_models.WeightUnit"], + ) -> None: ... + + @overload + def __init__(self, mapping: Mapping[str, Any]) -> None: + """ + :param mapping: raw JSON to initialize the model. + :type mapping: Mapping[str, Any] + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, metadata_kind=MetadataKind.WEIGHT_METADATA, **kwargs) diff --git a/sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/textanalytics/models/_patch.py b/sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/textanalytics/models/_patch.py new file mode 100644 index 000000000000..8bcb627aa475 --- /dev/null +++ b/sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/textanalytics/models/_patch.py @@ -0,0 +1,21 @@ +# 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. +# -------------------------------------------------------------------------- +"""Customize generated code here. + +Follow our quickstart for examples: https://aka.ms/azsdk/python/dpcodegen/python/customize +""" +from typing import List + +__all__: List[str] = [] # Add all objects you want publicly available to users at this package level + + +def patch_sdk(): + """Do not remove from this file. + + `patch_sdk` is a last resort escape hatch that allows you to do customizations + you can't accomplish using the techniques described in + https://aka.ms/azsdk/python/dpcodegen/python/customize + """ diff --git a/sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/textanalytics/py.typed b/sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/textanalytics/py.typed new file mode 100644 index 000000000000..e5aff4f83af8 --- /dev/null +++ b/sdk/cognitivelanguage/azure-ai-textanalytics/azure/ai/textanalytics/py.typed @@ -0,0 +1 @@ +# Marker file for PEP 561. \ No newline at end of file diff --git a/sdk/cognitivelanguage/azure-ai-textanalytics/dev_requirements.txt b/sdk/cognitivelanguage/azure-ai-textanalytics/dev_requirements.txt new file mode 100644 index 000000000000..105486471444 --- /dev/null +++ b/sdk/cognitivelanguage/azure-ai-textanalytics/dev_requirements.txt @@ -0,0 +1,3 @@ +-e ../../../tools/azure-sdk-tools +../../core/azure-core +aiohttp \ No newline at end of file diff --git a/sdk/cognitivelanguage/azure-ai-textanalytics/generated_samples/successful_abstractive_summarization_summary_length_prompt_task_result.py b/sdk/cognitivelanguage/azure-ai-textanalytics/generated_samples/successful_abstractive_summarization_summary_length_prompt_task_result.py new file mode 100644 index 000000000000..ce3f6bc48364 --- /dev/null +++ b/sdk/cognitivelanguage/azure-ai-textanalytics/generated_samples/successful_abstractive_summarization_summary_length_prompt_task_result.py @@ -0,0 +1,33 @@ +# 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. +# -------------------------------------------------------------------------- + +from azure.ai.textanalytics import TextClient + +""" +# PREREQUISITES + pip install azure-ai-textanalytics +# USAGE + python successful_abstractive_summarization_summary_length_prompt_task_result.py +""" + + +def main(): + client = TextClient( + endpoint="{Endpoint}", + credential="CREDENTIAL", + ) + + response = client.analyze_text_job_status( + job_id="c0f2a446-05d9-48fc-ba8f-3ef4af8d0b18", + ) + print(response) + + +# x-ms-original-file: 2025-05-15-preview/SuccessfulAbstractiveSummarizationSummaryLengthPromptTaskResult.json +if __name__ == "__main__": + main() diff --git a/sdk/cognitivelanguage/azure-ai-textanalytics/generated_samples/successful_abstractive_summarization_task_result.py b/sdk/cognitivelanguage/azure-ai-textanalytics/generated_samples/successful_abstractive_summarization_task_result.py new file mode 100644 index 000000000000..350ff605a9cb --- /dev/null +++ b/sdk/cognitivelanguage/azure-ai-textanalytics/generated_samples/successful_abstractive_summarization_task_result.py @@ -0,0 +1,33 @@ +# 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. +# -------------------------------------------------------------------------- + +from azure.ai.textanalytics import TextClient + +""" +# PREREQUISITES + pip install azure-ai-textanalytics +# USAGE + python successful_abstractive_summarization_task_result.py +""" + + +def main(): + client = TextClient( + endpoint="{Endpoint}", + credential="CREDENTIAL", + ) + + response = client.analyze_text_job_status( + job_id="c0f2a446-05d9-48fc-ba8f-3ef4af8d0b18", + ) + print(response) + + +# x-ms-original-file: 2025-05-15-preview/SuccessfulAbstractiveSummarizationTaskResult.json +if __name__ == "__main__": + main() diff --git a/sdk/cognitivelanguage/azure-ai-textanalytics/generated_samples/successful_analyze_text_jobs_cancel_request.py b/sdk/cognitivelanguage/azure-ai-textanalytics/generated_samples/successful_analyze_text_jobs_cancel_request.py new file mode 100644 index 000000000000..0d757eeed35e --- /dev/null +++ b/sdk/cognitivelanguage/azure-ai-textanalytics/generated_samples/successful_analyze_text_jobs_cancel_request.py @@ -0,0 +1,32 @@ +# 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. +# -------------------------------------------------------------------------- + +from azure.ai.textanalytics import TextClient + +""" +# PREREQUISITES + pip install azure-ai-textanalytics +# USAGE + python successful_analyze_text_jobs_cancel_request.py +""" + + +def main(): + client = TextClient( + endpoint="{Endpoint}", + credential="CREDENTIAL", + ) + + client.begin_analyze_text_cancel_job( + job_id="c0f2a446-05d9-48fc-ba8f-3ef4af8d0b18", + ).result() + + +# x-ms-original-file: 2025-05-15-preview/SuccessfulAnalyzeTextJobsCancelRequest.json +if __name__ == "__main__": + main() diff --git a/sdk/cognitivelanguage/azure-ai-textanalytics/generated_samples/successful_analyze_text_jobs_multiple_task_status_request.py b/sdk/cognitivelanguage/azure-ai-textanalytics/generated_samples/successful_analyze_text_jobs_multiple_task_status_request.py new file mode 100644 index 000000000000..5b1892875a86 --- /dev/null +++ b/sdk/cognitivelanguage/azure-ai-textanalytics/generated_samples/successful_analyze_text_jobs_multiple_task_status_request.py @@ -0,0 +1,33 @@ +# 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. +# -------------------------------------------------------------------------- + +from azure.ai.textanalytics import TextClient + +""" +# PREREQUISITES + pip install azure-ai-textanalytics +# USAGE + python successful_analyze_text_jobs_multiple_task_status_request.py +""" + + +def main(): + client = TextClient( + endpoint="{Endpoint}", + credential="CREDENTIAL", + ) + + response = client.analyze_text_job_status( + job_id="c0f2a446-05d9-48fc-ba8f-3ef4af8d0b18", + ) + print(response) + + +# x-ms-original-file: 2025-05-15-preview/SuccessfulAnalyzeTextJobsMultipleTaskStatusRequest.json +if __name__ == "__main__": + main() diff --git a/sdk/cognitivelanguage/azure-ai-textanalytics/generated_samples/successful_entity_linking_request.py b/sdk/cognitivelanguage/azure-ai-textanalytics/generated_samples/successful_entity_linking_request.py new file mode 100644 index 000000000000..1de93c6c4dde --- /dev/null +++ b/sdk/cognitivelanguage/azure-ai-textanalytics/generated_samples/successful_entity_linking_request.py @@ -0,0 +1,42 @@ +# 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. +# -------------------------------------------------------------------------- + +from azure.ai.textanalytics import TextClient + +""" +# PREREQUISITES + pip install azure-ai-textanalytics +# USAGE + python successful_entity_linking_request.py +""" + + +def main(): + client = TextClient( + endpoint="{Endpoint}", + credential="CREDENTIAL", + ) + + response = client.analyze_text( + body={ + "analysisInput": { + "documents": [ + {"id": "1", "language": "en", "text": "Microsoft was founded by Bill Gates and Paul Allen."}, + {"id": "2", "language": "en", "text": "Pike place market is my favorite Seattle attraction."}, + ] + }, + "kind": "EntityLinking", + "parameters": {"modelVersion": "latest"}, + }, + ) + print(response) + + +# x-ms-original-file: 2025-05-15-preview/SuccessfulEntityLinkingRequest.json +if __name__ == "__main__": + main() diff --git a/sdk/cognitivelanguage/azure-ai-textanalytics/generated_samples/successful_entity_recognition_exclusion_request.py b/sdk/cognitivelanguage/azure-ai-textanalytics/generated_samples/successful_entity_recognition_exclusion_request.py new file mode 100644 index 000000000000..66c3d466d0c3 --- /dev/null +++ b/sdk/cognitivelanguage/azure-ai-textanalytics/generated_samples/successful_entity_recognition_exclusion_request.py @@ -0,0 +1,51 @@ +# pylint: disable=line-too-long,useless-suppression +# 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. +# -------------------------------------------------------------------------- + +from azure.ai.textanalytics import TextClient + +""" +# PREREQUISITES + pip install azure-ai-textanalytics +# USAGE + python successful_entity_recognition_exclusion_request.py +""" + + +def main(): + client = TextClient( + endpoint="{Endpoint}", + credential="CREDENTIAL", + ) + + response = client.analyze_text( + body={ + "analysisInput": { + "documents": [ + {"id": "2", "language": "en", "text": "When I was 5 years old I had $90.00 dollars to my name."}, + { + "id": "3", + "language": "en", + "text": "When we flew from LAX it seemed like we were moving at 10 meters per second. I was lucky to see Amsterdam, Effile Tower, and the Nile.", + }, + ] + }, + "kind": "EntityRecognition", + "parameters": { + "exclusionList": ["Numeric"], + "modelVersion": "latest", + "overlapPolicy": {"policyKind": "allowOverlap"}, + }, + }, + ) + print(response) + + +# x-ms-original-file: 2025-05-15-preview/SuccessfulEntityRecognitionExclusionRequest.json +if __name__ == "__main__": + main() diff --git a/sdk/cognitivelanguage/azure-ai-textanalytics/generated_samples/successful_entity_recognition_inclusion_request.py b/sdk/cognitivelanguage/azure-ai-textanalytics/generated_samples/successful_entity_recognition_inclusion_request.py new file mode 100644 index 000000000000..889dc45ee21e --- /dev/null +++ b/sdk/cognitivelanguage/azure-ai-textanalytics/generated_samples/successful_entity_recognition_inclusion_request.py @@ -0,0 +1,47 @@ +# pylint: disable=line-too-long,useless-suppression +# 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. +# -------------------------------------------------------------------------- + +from azure.ai.textanalytics import TextClient + +""" +# PREREQUISITES + pip install azure-ai-textanalytics +# USAGE + python successful_entity_recognition_inclusion_request.py +""" + + +def main(): + client = TextClient( + endpoint="{Endpoint}", + credential="CREDENTIAL", + ) + + response = client.analyze_text( + body={ + "analysisInput": { + "documents": [ + {"id": "2", "language": "en", "text": "When I was 5 years old I had $90.00 dollars to my name."}, + { + "id": "3", + "language": "en", + "text": "When we flew from LAX it seemed like we were moving at 10 meters per second. I was lucky to see Amsterdam, Effile Tower, and the Nile.", + }, + ] + }, + "kind": "EntityRecognition", + "parameters": {"inclusionList": ["Location"], "modelVersion": "latest"}, + }, + ) + print(response) + + +# x-ms-original-file: 2025-05-15-preview/SuccessfulEntityRecognitionInclusionRequest.json +if __name__ == "__main__": + main() diff --git a/sdk/cognitivelanguage/azure-ai-textanalytics/generated_samples/successful_entity_recognition_inference_options_request.py b/sdk/cognitivelanguage/azure-ai-textanalytics/generated_samples/successful_entity_recognition_inference_options_request.py new file mode 100644 index 000000000000..a6fcc303f84c --- /dev/null +++ b/sdk/cognitivelanguage/azure-ai-textanalytics/generated_samples/successful_entity_recognition_inference_options_request.py @@ -0,0 +1,41 @@ +# 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. +# -------------------------------------------------------------------------- + +from azure.ai.textanalytics import TextClient + +""" +# PREREQUISITES + pip install azure-ai-textanalytics +# USAGE + python successful_entity_recognition_inference_options_request.py +""" + + +def main(): + client = TextClient( + endpoint="{Endpoint}", + credential="CREDENTIAL", + ) + + response = client.analyze_text( + body={ + "analysisInput": { + "documents": [ + {"id": "1", "language": "en", "text": "When I was 5 years old I had $90.00 dollars to my name."} + ] + }, + "kind": "EntityRecognition", + "parameters": {"inferenceOptions": {"excludeNormalizedValues": True}, "modelVersion": "latest"}, + }, + ) + print(response) + + +# x-ms-original-file: 2025-05-15-preview/SuccessfulEntityRecognitionInferenceOptionsRequest.json +if __name__ == "__main__": + main() diff --git a/sdk/cognitivelanguage/azure-ai-textanalytics/generated_samples/successful_entity_recognition_overlap_policy.py b/sdk/cognitivelanguage/azure-ai-textanalytics/generated_samples/successful_entity_recognition_overlap_policy.py new file mode 100644 index 000000000000..f055392b0a8e --- /dev/null +++ b/sdk/cognitivelanguage/azure-ai-textanalytics/generated_samples/successful_entity_recognition_overlap_policy.py @@ -0,0 +1,45 @@ +# 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. +# -------------------------------------------------------------------------- + +from azure.ai.textanalytics import TextClient + +""" +# PREREQUISITES + pip install azure-ai-textanalytics +# USAGE + python successful_entity_recognition_overlap_policy.py +""" + + +def main(): + client = TextClient( + endpoint="{Endpoint}", + credential="CREDENTIAL", + ) + + response = client.analyze_text( + body={ + "analysisInput": { + "documents": [ + { + "id": "4", + "language": "en", + "text": "25th April Meeting was an intresting one. At least we gont to experience the WorldCup", + } + ] + }, + "kind": "EntityRecognition", + "parameters": {"modelVersion": "latest", "overlapPolicy": {"policyKind": "matchLongest"}}, + }, + ) + print(response) + + +# x-ms-original-file: 2025-05-15-preview/SuccessfulEntityRecognitionOverlapPolicy.json +if __name__ == "__main__": + main() diff --git a/sdk/cognitivelanguage/azure-ai-textanalytics/generated_samples/successful_entity_recognition_request.py b/sdk/cognitivelanguage/azure-ai-textanalytics/generated_samples/successful_entity_recognition_request.py new file mode 100644 index 000000000000..8097c6dadb11 --- /dev/null +++ b/sdk/cognitivelanguage/azure-ai-textanalytics/generated_samples/successful_entity_recognition_request.py @@ -0,0 +1,53 @@ +# pylint: disable=line-too-long,useless-suppression +# 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. +# -------------------------------------------------------------------------- + +from azure.ai.textanalytics import TextClient + +""" +# PREREQUISITES + pip install azure-ai-textanalytics +# USAGE + python successful_entity_recognition_request.py +""" + + +def main(): + client = TextClient( + endpoint="{Endpoint}", + credential="CREDENTIAL", + ) + + response = client.analyze_text( + body={ + "analysisInput": { + "documents": [ + {"id": "2", "language": "en", "text": "When I was 5 years old I had $90.00 dollars to my name."}, + { + "id": "3", + "language": "en", + "text": "When we flew from LAX it seemed like we were moving at 10 meters per second. I was lucky to see Amsterdam, Effile Tower, and the Nile.", + }, + { + "id": "4", + "language": "en", + "text": "25th April Meeting was an intresting one. At least we gont to experience the WorldCup", + }, + {"id": "5", "language": "en", "text": "My IP is 127.12.1.1 and my phone number is 5555555555"}, + ] + }, + "kind": "EntityRecognition", + "parameters": {"modelVersion": "latest", "overlapPolicy": {"policyKind": "allowOverlap"}}, + }, + ) + print(response) + + +# x-ms-original-file: 2025-05-15-preview/SuccessfulEntityRecognitionRequest.json +if __name__ == "__main__": + main() diff --git a/sdk/cognitivelanguage/azure-ai-textanalytics/generated_samples/successful_healthcare_document_type_task_status_request.py b/sdk/cognitivelanguage/azure-ai-textanalytics/generated_samples/successful_healthcare_document_type_task_status_request.py new file mode 100644 index 000000000000..a4e11450c862 --- /dev/null +++ b/sdk/cognitivelanguage/azure-ai-textanalytics/generated_samples/successful_healthcare_document_type_task_status_request.py @@ -0,0 +1,33 @@ +# 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. +# -------------------------------------------------------------------------- + +from azure.ai.textanalytics import TextClient + +""" +# PREREQUISITES + pip install azure-ai-textanalytics +# USAGE + python successful_healthcare_document_type_task_status_request.py +""" + + +def main(): + client = TextClient( + endpoint="{Endpoint}", + credential="CREDENTIAL", + ) + + response = client.analyze_text_job_status( + job_id="15e4a46b-62e2-4386-8d36-9c2a92bb45dd", + ) + print(response) + + +# x-ms-original-file: 2025-05-15-preview/SuccessfulHealthcareDocumentTypeTaskStatusRequest.json +if __name__ == "__main__": + main() diff --git a/sdk/cognitivelanguage/azure-ai-textanalytics/generated_samples/successful_healthcare_task_status_request.py b/sdk/cognitivelanguage/azure-ai-textanalytics/generated_samples/successful_healthcare_task_status_request.py new file mode 100644 index 000000000000..6302ba601156 --- /dev/null +++ b/sdk/cognitivelanguage/azure-ai-textanalytics/generated_samples/successful_healthcare_task_status_request.py @@ -0,0 +1,33 @@ +# 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. +# -------------------------------------------------------------------------- + +from azure.ai.textanalytics import TextClient + +""" +# PREREQUISITES + pip install azure-ai-textanalytics +# USAGE + python successful_healthcare_task_status_request.py +""" + + +def main(): + client = TextClient( + endpoint="{Endpoint}", + credential="CREDENTIAL", + ) + + response = client.analyze_text_job_status( + job_id="1780194a-e9c1-4298-b0d4-fdc59ba818a0", + ) + print(response) + + +# x-ms-original-file: 2025-05-15-preview/SuccessfulHealthcareTaskStatusRequest.json +if __name__ == "__main__": + main() diff --git a/sdk/cognitivelanguage/azure-ai-textanalytics/generated_samples/successful_key_phrase_extraction_request.py b/sdk/cognitivelanguage/azure-ai-textanalytics/generated_samples/successful_key_phrase_extraction_request.py new file mode 100644 index 000000000000..ad6f8b590110 --- /dev/null +++ b/sdk/cognitivelanguage/azure-ai-textanalytics/generated_samples/successful_key_phrase_extraction_request.py @@ -0,0 +1,43 @@ +# 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. +# -------------------------------------------------------------------------- + +from azure.ai.textanalytics import TextClient + +""" +# PREREQUISITES + pip install azure-ai-textanalytics +# USAGE + python successful_key_phrase_extraction_request.py +""" + + +def main(): + client = TextClient( + endpoint="{Endpoint}", + credential="CREDENTIAL", + ) + + response = client.analyze_text( + body={ + "analysisInput": { + "documents": [ + {"id": "1", "language": "en", "text": "Microsoft was founded by Bill Gates and Paul Allen."}, + {"id": "2", "language": "en", "text": "Text Analytics is one of the Azure Cognitive Services."}, + {"id": "3", "language": "en", "text": "My cat might need to see a veterinarian."}, + ] + }, + "kind": "KeyPhraseExtraction", + "parameters": {"modelVersion": "latest"}, + }, + ) + print(response) + + +# x-ms-original-file: 2025-05-15-preview/SuccessfulKeyPhraseExtractionRequest.json +if __name__ == "__main__": + main() diff --git a/sdk/cognitivelanguage/azure-ai-textanalytics/generated_samples/successful_language_detection_request.py b/sdk/cognitivelanguage/azure-ai-textanalytics/generated_samples/successful_language_detection_request.py new file mode 100644 index 000000000000..22789a457239 --- /dev/null +++ b/sdk/cognitivelanguage/azure-ai-textanalytics/generated_samples/successful_language_detection_request.py @@ -0,0 +1,44 @@ +# 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. +# -------------------------------------------------------------------------- + +from azure.ai.textanalytics import TextClient + +""" +# PREREQUISITES + pip install azure-ai-textanalytics +# USAGE + python successful_language_detection_request.py +""" + + +def main(): + client = TextClient( + endpoint="{Endpoint}", + credential="CREDENTIAL", + ) + + response = client.analyze_text( + body={ + "analysisInput": { + "documents": [ + {"id": "1", "text": "Hello world"}, + {"id": "2", "text": "Bonjour tout le monde"}, + {"id": "3", "text": "Hola mundo"}, + {"id": "4", "text": "Tumhara naam kya hai?"}, + ] + }, + "kind": "LanguageDetection", + "parameters": {"modelVersion": "latest"}, + }, + ) + print(response) + + +# x-ms-original-file: 2025-05-15-preview/SuccessfulLanguageDetectionRequest.json +if __name__ == "__main__": + main() diff --git a/sdk/cognitivelanguage/azure-ai-textanalytics/generated_samples/successful_pii_entity_recognition_exclusion_request.py b/sdk/cognitivelanguage/azure-ai-textanalytics/generated_samples/successful_pii_entity_recognition_exclusion_request.py new file mode 100644 index 000000000000..31a008811716 --- /dev/null +++ b/sdk/cognitivelanguage/azure-ai-textanalytics/generated_samples/successful_pii_entity_recognition_exclusion_request.py @@ -0,0 +1,48 @@ +# pylint: disable=line-too-long,useless-suppression +# 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. +# -------------------------------------------------------------------------- + +from azure.ai.textanalytics import TextClient + +""" +# PREREQUISITES + pip install azure-ai-textanalytics +# USAGE + python successful_pii_entity_recognition_exclusion_request.py +""" + + +def main(): + client = TextClient( + endpoint="{Endpoint}", + credential="CREDENTIAL", + ) + + response = client.analyze_text( + body={ + "analysisInput": { + "documents": [ + {"id": "1", "language": "en", "text": "My SSN is 859-98-0987"}, + { + "id": "2", + "language": "en", + "text": "Your ABA number - 111000025 - is the first 9 digits in the lower left hand corner of your personal check.", + }, + {"id": "3", "language": "en", "text": "Is 998.214.865-68 your Brazilian CPF number?"}, + ] + }, + "kind": "PiiEntityRecognition", + "parameters": {"excludePiiCategories": ["USSocialSecurityNumber"], "modelVersion": "latest"}, + }, + ) + print(response) + + +# x-ms-original-file: 2025-05-15-preview/SuccessfulPiiEntityRecognitionExclusionRequest.json +if __name__ == "__main__": + main() diff --git a/sdk/cognitivelanguage/azure-ai-textanalytics/generated_samples/successful_pii_entity_recognition_masked_entities.py b/sdk/cognitivelanguage/azure-ai-textanalytics/generated_samples/successful_pii_entity_recognition_masked_entities.py new file mode 100644 index 000000000000..ee6244d55a32 --- /dev/null +++ b/sdk/cognitivelanguage/azure-ai-textanalytics/generated_samples/successful_pii_entity_recognition_masked_entities.py @@ -0,0 +1,41 @@ +# 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. +# -------------------------------------------------------------------------- + +from azure.ai.textanalytics import TextClient + +""" +# PREREQUISITES + pip install azure-ai-textanalytics +# USAGE + python successful_pii_entity_recognition_masked_entities.py +""" + + +def main(): + client = TextClient( + endpoint="{Endpoint}", + credential="CREDENTIAL", + ) + + response = client.analyze_text( + body={ + "analysisInput": { + "documents": [ + {"id": "1", "language": "en", "text": "My name is John Doe My phone number is 424 878 9192"} + ] + }, + "kind": "PiiEntityRecognition", + "parameters": {"modelVersion": "latest", "redactionPolicy": {"policyKind": "entityMask"}}, + }, + ) + print(response) + + +# x-ms-original-file: 2025-05-15-preview/SuccessfulPiiEntityRecognitionMaskedEntities.json +if __name__ == "__main__": + main() diff --git a/sdk/cognitivelanguage/azure-ai-textanalytics/generated_samples/successful_pii_entity_recognition_redaction_policy_request.py b/sdk/cognitivelanguage/azure-ai-textanalytics/generated_samples/successful_pii_entity_recognition_redaction_policy_request.py new file mode 100644 index 000000000000..2b69b0d127cd --- /dev/null +++ b/sdk/cognitivelanguage/azure-ai-textanalytics/generated_samples/successful_pii_entity_recognition_redaction_policy_request.py @@ -0,0 +1,51 @@ +# pylint: disable=line-too-long,useless-suppression +# 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. +# -------------------------------------------------------------------------- + +from azure.ai.textanalytics import TextClient + +""" +# PREREQUISITES + pip install azure-ai-textanalytics +# USAGE + python successful_pii_entity_recognition_redaction_policy_request.py +""" + + +def main(): + client = TextClient( + endpoint="{Endpoint}", + credential="CREDENTIAL", + ) + + response = client.analyze_text( + body={ + "analysisInput": { + "documents": [ + {"id": "1", "language": "en", "text": "My SSN is 859-98-0987"}, + { + "id": "2", + "language": "en", + "text": "Your ABA number - 111000025 - is the first 9 digits in the lower left hand corner of your personal check.", + }, + {"id": "3", "language": "en", "text": "Is 998.214.865-68 your Brazilian CPF number?"}, + ] + }, + "kind": "PiiEntityRecognition", + "parameters": { + "modelVersion": "latest", + "redactionPolicy": {"policyKind": "characterMask", "redactionCharacter": "-"}, + }, + }, + ) + print(response) + + +# x-ms-original-file: 2025-05-15-preview/SuccessfulPiiEntityRecognitionRedactionPolicyRequest.json +if __name__ == "__main__": + main() diff --git a/sdk/cognitivelanguage/azure-ai-textanalytics/generated_samples/successful_pii_entity_recognition_request.py b/sdk/cognitivelanguage/azure-ai-textanalytics/generated_samples/successful_pii_entity_recognition_request.py new file mode 100644 index 000000000000..4a6555e5a66e --- /dev/null +++ b/sdk/cognitivelanguage/azure-ai-textanalytics/generated_samples/successful_pii_entity_recognition_request.py @@ -0,0 +1,48 @@ +# pylint: disable=line-too-long,useless-suppression +# 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. +# -------------------------------------------------------------------------- + +from azure.ai.textanalytics import TextClient + +""" +# PREREQUISITES + pip install azure-ai-textanalytics +# USAGE + python successful_pii_entity_recognition_request.py +""" + + +def main(): + client = TextClient( + endpoint="{Endpoint}", + credential="CREDENTIAL", + ) + + response = client.analyze_text( + body={ + "analysisInput": { + "documents": [ + {"id": "1", "language": "en", "text": "My SSN is 859-98-0987"}, + { + "id": "2", + "language": "en", + "text": "Your ABA number - 111000025 - is the first 9 digits in the lower left hand corner of your personal check.", + }, + {"id": "3", "language": "en", "text": "Is 998.214.865-68 your Brazilian CPF number?"}, + ] + }, + "kind": "PiiEntityRecognition", + "parameters": {"modelVersion": "latest"}, + }, + ) + print(response) + + +# x-ms-original-file: 2025-05-15-preview/SuccessfulPiiEntityRecognitionRequest.json +if __name__ == "__main__": + main() diff --git a/sdk/cognitivelanguage/azure-ai-textanalytics/generated_samples/successful_sentiment_analysis_request.py b/sdk/cognitivelanguage/azure-ai-textanalytics/generated_samples/successful_sentiment_analysis_request.py new file mode 100644 index 000000000000..42dec349d322 --- /dev/null +++ b/sdk/cognitivelanguage/azure-ai-textanalytics/generated_samples/successful_sentiment_analysis_request.py @@ -0,0 +1,46 @@ +# pylint: disable=line-too-long,useless-suppression +# 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. +# -------------------------------------------------------------------------- + +from azure.ai.textanalytics import TextClient + +""" +# PREREQUISITES + pip install azure-ai-textanalytics +# USAGE + python successful_sentiment_analysis_request.py +""" + + +def main(): + client = TextClient( + endpoint="{Endpoint}", + credential="CREDENTIAL", + ) + + response = client.analyze_text( + body={ + "analysisInput": { + "documents": [ + { + "id": "1", + "language": "en", + "text": "Great atmosphere. Close to plenty of restaurants, hotels, and transit! Staff are friendly and helpful.", + } + ] + }, + "kind": "SentimentAnalysis", + "parameters": {"modelVersion": "latest"}, + }, + ) + print(response) + + +# x-ms-original-file: 2025-05-15-preview/SuccessfulSentimentAnalysisRequest.json +if __name__ == "__main__": + main() diff --git a/sdk/cognitivelanguage/azure-ai-textanalytics/generated_tests/conftest.py b/sdk/cognitivelanguage/azure-ai-textanalytics/generated_tests/conftest.py new file mode 100644 index 000000000000..09a9ff0054bd --- /dev/null +++ b/sdk/cognitivelanguage/azure-ai-textanalytics/generated_tests/conftest.py @@ -0,0 +1,35 @@ +# 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. +# -------------------------------------------------------------------------- +import os +import pytest +from dotenv import load_dotenv +from devtools_testutils import ( + test_proxy, + add_general_regex_sanitizer, + add_body_key_sanitizer, + add_header_regex_sanitizer, +) + +load_dotenv() + + +# For security, please avoid record sensitive identity information in recordings +@pytest.fixture(scope="session", autouse=True) +def add_sanitizers(test_proxy): + text_subscription_id = os.environ.get("TEXT_SUBSCRIPTION_ID", "00000000-0000-0000-0000-000000000000") + text_tenant_id = os.environ.get("TEXT_TENANT_ID", "00000000-0000-0000-0000-000000000000") + text_client_id = os.environ.get("TEXT_CLIENT_ID", "00000000-0000-0000-0000-000000000000") + text_client_secret = os.environ.get("TEXT_CLIENT_SECRET", "00000000-0000-0000-0000-000000000000") + add_general_regex_sanitizer(regex=text_subscription_id, value="00000000-0000-0000-0000-000000000000") + add_general_regex_sanitizer(regex=text_tenant_id, value="00000000-0000-0000-0000-000000000000") + add_general_regex_sanitizer(regex=text_client_id, value="00000000-0000-0000-0000-000000000000") + add_general_regex_sanitizer(regex=text_client_secret, value="00000000-0000-0000-0000-000000000000") + + add_header_regex_sanitizer(key="Set-Cookie", value="[set-cookie;]") + add_header_regex_sanitizer(key="Cookie", value="cookie;") + add_body_key_sanitizer(json_path="$..access_token", value="access_token") diff --git a/sdk/cognitivelanguage/azure-ai-textanalytics/generated_tests/test_text.py b/sdk/cognitivelanguage/azure-ai-textanalytics/generated_tests/test_text.py new file mode 100644 index 000000000000..d88a9f16299f --- /dev/null +++ b/sdk/cognitivelanguage/azure-ai-textanalytics/generated_tests/test_text.py @@ -0,0 +1,69 @@ +# 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. +# -------------------------------------------------------------------------- +import pytest +from devtools_testutils import recorded_by_proxy +from testpreparer import TextClientTestBase, TextPreparer + + +@pytest.mark.skip("you may need to update the auto-generated test case before run it") +class TestText(TextClientTestBase): + @TextPreparer() + @recorded_by_proxy + def test_analyze_text(self, text_endpoint): + client = self.create_client(endpoint=text_endpoint) + response = client.analyze_text( + body={ + "kind": "EntityLinking", + "analysisInput": {"documents": [{"id": "str", "text": "str", "language": "str"}]}, + "parameters": {"loggingOptOut": bool, "modelVersion": "str", "stringIndexType": "str"}, + }, + ) + + # please add some check logic here by yourself + # ... + + @TextPreparer() + @recorded_by_proxy + def test_analyze_text_job_status(self, text_endpoint): + client = self.create_client(endpoint=text_endpoint) + response = client.analyze_text_job_status( + job_id="str", + ) + + # please add some check logic here by yourself + # ... + + @TextPreparer() + @recorded_by_proxy + def test_begin_analyze_text_submit_job(self, text_endpoint): + client = self.create_client(endpoint=text_endpoint) + response = client.begin_analyze_text_submit_job( + body={ + "analysisInput": {"documents": [{"id": "str", "text": "str", "language": "str"}]}, + "tasks": ["analyze_text_lro_task"], + "cancelAfter": 0.0, + "defaultLanguage": "str", + "displayName": "str", + }, + analysis_input={"documents": [{"id": "str", "text": "str", "language": "str"}]}, + tasks=["analyze_text_lro_task"], + ).result() # call '.result()' to poll until service return final result + + # please add some check logic here by yourself + # ... + + @TextPreparer() + @recorded_by_proxy + def test_begin_analyze_text_cancel_job(self, text_endpoint): + client = self.create_client(endpoint=text_endpoint) + response = client.begin_analyze_text_cancel_job( + job_id="str", + ).result() # call '.result()' to poll until service return final result + + # please add some check logic here by yourself + # ... diff --git a/sdk/cognitivelanguage/azure-ai-textanalytics/generated_tests/test_text_async.py b/sdk/cognitivelanguage/azure-ai-textanalytics/generated_tests/test_text_async.py new file mode 100644 index 000000000000..ad8c21e68626 --- /dev/null +++ b/sdk/cognitivelanguage/azure-ai-textanalytics/generated_tests/test_text_async.py @@ -0,0 +1,74 @@ +# 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. +# -------------------------------------------------------------------------- +import pytest +from devtools_testutils.aio import recorded_by_proxy_async +from testpreparer import TextPreparer +from testpreparer_async import TextClientTestBaseAsync + + +@pytest.mark.skip("you may need to update the auto-generated test case before run it") +class TestTextAsync(TextClientTestBaseAsync): + @TextPreparer() + @recorded_by_proxy_async + async def test_analyze_text(self, text_endpoint): + client = self.create_async_client(endpoint=text_endpoint) + response = await client.analyze_text( + body={ + "kind": "EntityLinking", + "analysisInput": {"documents": [{"id": "str", "text": "str", "language": "str"}]}, + "parameters": {"loggingOptOut": bool, "modelVersion": "str", "stringIndexType": "str"}, + }, + ) + + # please add some check logic here by yourself + # ... + + @TextPreparer() + @recorded_by_proxy_async + async def test_analyze_text_job_status(self, text_endpoint): + client = self.create_async_client(endpoint=text_endpoint) + response = await client.analyze_text_job_status( + job_id="str", + ) + + # please add some check logic here by yourself + # ... + + @TextPreparer() + @recorded_by_proxy_async + async def test_begin_analyze_text_submit_job(self, text_endpoint): + client = self.create_async_client(endpoint=text_endpoint) + response = await ( + await client.begin_analyze_text_submit_job( + body={ + "analysisInput": {"documents": [{"id": "str", "text": "str", "language": "str"}]}, + "tasks": ["analyze_text_lro_task"], + "cancelAfter": 0.0, + "defaultLanguage": "str", + "displayName": "str", + }, + analysis_input={"documents": [{"id": "str", "text": "str", "language": "str"}]}, + tasks=["analyze_text_lro_task"], + ) + ).result() # call '.result()' to poll until service return final result + + # please add some check logic here by yourself + # ... + + @TextPreparer() + @recorded_by_proxy_async + async def test_begin_analyze_text_cancel_job(self, text_endpoint): + client = self.create_async_client(endpoint=text_endpoint) + response = await ( + await client.begin_analyze_text_cancel_job( + job_id="str", + ) + ).result() # call '.result()' to poll until service return final result + + # please add some check logic here by yourself + # ... diff --git a/sdk/cognitivelanguage/azure-ai-textanalytics/generated_tests/testpreparer.py b/sdk/cognitivelanguage/azure-ai-textanalytics/generated_tests/testpreparer.py new file mode 100644 index 000000000000..751d995535cc --- /dev/null +++ b/sdk/cognitivelanguage/azure-ai-textanalytics/generated_tests/testpreparer.py @@ -0,0 +1,24 @@ +# 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. +# -------------------------------------------------------------------------- +from azure.ai.textanalytics import TextClient +from devtools_testutils import AzureRecordedTestCase, PowerShellPreparer +import functools + + +class TextClientTestBase(AzureRecordedTestCase): + + def create_client(self, endpoint): + credential = self.get_credential(TextClient) + return self.create_client_from_credential( + TextClient, + credential=credential, + endpoint=endpoint, + ) + + +TextPreparer = functools.partial(PowerShellPreparer, "text", text_endpoint="https://fake_text_endpoint.com") diff --git a/sdk/cognitivelanguage/azure-ai-textanalytics/generated_tests/testpreparer_async.py b/sdk/cognitivelanguage/azure-ai-textanalytics/generated_tests/testpreparer_async.py new file mode 100644 index 000000000000..2c13c3b39304 --- /dev/null +++ b/sdk/cognitivelanguage/azure-ai-textanalytics/generated_tests/testpreparer_async.py @@ -0,0 +1,20 @@ +# 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. +# -------------------------------------------------------------------------- +from azure.ai.textanalytics.aio import TextClient +from devtools_testutils import AzureRecordedTestCase + + +class TextClientTestBaseAsync(AzureRecordedTestCase): + + def create_async_client(self, endpoint): + credential = self.get_credential(TextClient, is_async=True) + return self.create_client_from_credential( + TextClient, + credential=credential, + endpoint=endpoint, + ) diff --git a/sdk/cognitivelanguage/azure-ai-textanalytics/pyproject.toml b/sdk/cognitivelanguage/azure-ai-textanalytics/pyproject.toml new file mode 100644 index 000000000000..e7687fdae93b --- /dev/null +++ b/sdk/cognitivelanguage/azure-ai-textanalytics/pyproject.toml @@ -0,0 +1,2 @@ +[packaging] +auto_update = false \ No newline at end of file diff --git a/sdk/cognitivelanguage/azure-ai-textanalytics/setup.py b/sdk/cognitivelanguage/azure-ai-textanalytics/setup.py new file mode 100644 index 000000000000..76097a1d9028 --- /dev/null +++ b/sdk/cognitivelanguage/azure-ai-textanalytics/setup.py @@ -0,0 +1,72 @@ +# 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. +# -------------------------------------------------------------------------- + + +import os +import re +from setuptools import setup, find_packages + + +PACKAGE_NAME = "azure-ai-textanalytics" +PACKAGE_PPRINT_NAME = "Azure Ai Textanalytics" +PACKAGE_NAMESPACE = "azure.ai.textanalytics" + +# a.b.c => a/b/c +package_folder_path = PACKAGE_NAMESPACE.replace(".", "/") + +# Version extraction inspired from 'requests' +with open(os.path.join(package_folder_path, "_version.py"), "r") as fd: + version = re.search(r'^VERSION\s*=\s*[\'"]([^\'"]*)[\'"]', fd.read(), re.MULTILINE).group(1) + +if not version: + raise RuntimeError("Cannot find version information") + + +setup( + name=PACKAGE_NAME, + version=version, + description="Microsoft Corporation {} Client Library for Python".format(PACKAGE_PPRINT_NAME), + long_description=open("README.md", "r").read(), + long_description_content_type="text/markdown", + license="MIT License", + author="Microsoft Corporation", + author_email="azpysdkhelp@microsoft.com", + url="https://github.com/Azure/azure-sdk-for-python/tree/main/sdk", + keywords="azure, azure sdk", + classifiers=[ + "Development Status :: 4 - Beta", + "Programming Language :: Python", + "Programming Language :: Python :: 3 :: Only", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.9", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3.13", + "License :: OSI Approved :: MIT License", + ], + zip_safe=False, + packages=find_packages( + exclude=[ + "tests", + # Exclude packages that will be covered by PEP420 or nspkg + "azure", + "azure.ai", + ] + ), + include_package_data=True, + package_data={ + "azure.ai.textanalytics": ["py.typed"], + }, + install_requires=[ + "isodate>=0.6.1", + "azure-core>=1.35.0", + "typing-extensions>=4.6.0", + ], + python_requires=">=3.9", +) diff --git a/sdk/cognitivelanguage/azure-ai-textanalytics/tsp-location.yaml b/sdk/cognitivelanguage/azure-ai-textanalytics/tsp-location.yaml new file mode 100644 index 000000000000..b95dd2b834c5 --- /dev/null +++ b/sdk/cognitivelanguage/azure-ai-textanalytics/tsp-location.yaml @@ -0,0 +1,4 @@ +directory: specification/cognitiveservices/Language.AnalyzeText +commit: 438119579eac021346cedd524909ab7552ee9a95 +repo: Azure/azure-rest-api-specs +additionalDirectories: diff --git a/sdk/cognitivelanguage/ci.yml b/sdk/cognitivelanguage/ci.yml index 10f8866342d6..b758b9b6992a 100644 --- a/sdk/cognitivelanguage/ci.yml +++ b/sdk/cognitivelanguage/ci.yml @@ -35,3 +35,5 @@ extends: safeName: azureailanguagequestionanswering - name: azure-ai-language-conversations safeName: azureailanguageconversations + - name: azure-ai-textanalytics + safeName: azureaitextanalytics \ No newline at end of file