# 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=useless-super-delegation import datetime from typing import Any, Dict, List, Literal, Mapping, Optional, TYPE_CHECKING, Union, overload from .. import _model_base from .._model_base import rest_discriminator, rest_field from ._enums import OperationKind if TYPE_CHECKING: from .. import models as _models class AddressValue(_model_base.Model): """Address field value. :ivar house_number: House or building number. :vartype house_number: str :ivar po_box: Post office box number. :vartype po_box: str :ivar road: Street name. :vartype road: str :ivar city: Name of city, town, village, etc. :vartype city: str :ivar state: First-level administrative division. :vartype state: str :ivar postal_code: Postal code used for mail sorting. :vartype postal_code: str :ivar country_region: Country/region. :vartype country_region: str :ivar street_address: Street-level address, excluding city, state, countryRegion, and postalCode. :vartype street_address: str :ivar unit: Apartment or office number. :vartype unit: str :ivar city_district: Districts or boroughs within a city, such as Brooklyn in New York City or City of Westminster in London. :vartype city_district: str :ivar state_district: Second-level administrative division used in certain locales. :vartype state_district: str :ivar suburb: Unofficial neighborhood name, like Chinatown. :vartype suburb: str :ivar house: Build name, such as World Trade Center. :vartype house: str :ivar level: Floor number, such as 3F. :vartype level: str """ house_number: Optional[str] = rest_field(name="houseNumber") """House or building number.""" po_box: Optional[str] = rest_field(name="poBox") """Post office box number.""" road: Optional[str] = rest_field() """Street name.""" city: Optional[str] = rest_field() """Name of city, town, village, etc.""" state: Optional[str] = rest_field() """First-level administrative division.""" postal_code: Optional[str] = rest_field(name="postalCode") """Postal code used for mail sorting.""" country_region: Optional[str] = rest_field(name="countryRegion") """Country/region.""" street_address: Optional[str] = rest_field(name="streetAddress") """Street-level address, excluding city, state, countryRegion, and postalCode.""" unit: Optional[str] = rest_field() """Apartment or office number.""" city_district: Optional[str] = rest_field(name="cityDistrict") """Districts or boroughs within a city, such as Brooklyn in New York City or City of Westminster in London.""" state_district: Optional[str] = rest_field(name="stateDistrict") """Second-level administrative division used in certain locales.""" suburb: Optional[str] = rest_field() """Unofficial neighborhood name, like Chinatown.""" house: Optional[str] = rest_field() """Build name, such as World Trade Center.""" level: Optional[str] = rest_field() """Floor number, such as 3F.""" @overload def __init__( self, *, house_number: Optional[str] = None, po_box: Optional[str] = None, road: Optional[str] = None, city: Optional[str] = None, state: Optional[str] = None, postal_code: Optional[str] = None, country_region: Optional[str] = None, street_address: Optional[str] = None, unit: Optional[str] = None, city_district: Optional[str] = None, state_district: Optional[str] = None, suburb: Optional[str] = None, house: Optional[str] = None, level: 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 AnalyzeBatchDocumentsRequest(_model_base.Model): """Batch document analysis parameters. All required parameters must be populated in order to send to server. :ivar azure_blob_source: Azure Blob Storage location containing the batch documents. Either azureBlobSource or azureBlobFileListSource must be specified. :vartype azure_blob_source: ~azure.ai.documentintelligence.models.AzureBlobContentSource :ivar azure_blob_file_list_source: Azure Blob Storage file list specifying the batch documents. Either azureBlobSource or azureBlobFileListSource must be specified. :vartype azure_blob_file_list_source: ~azure.ai.documentintelligence.models.AzureBlobFileListContentSource :ivar result_container_url: Azure Blob Storage container URL where analyze result files will be stored. Required. :vartype result_container_url: str :ivar result_prefix: Blob name prefix of result files. :vartype result_prefix: str :ivar overwrite_existing: Overwrite existing analyze result files?. :vartype overwrite_existing: bool """ azure_blob_source: Optional["_models.AzureBlobContentSource"] = rest_field(name="azureBlobSource") """Azure Blob Storage location containing the batch documents. Either azureBlobSource or azureBlobFileListSource must be specified.""" azure_blob_file_list_source: Optional["_models.AzureBlobFileListContentSource"] = rest_field( name="azureBlobFileListSource" ) """Azure Blob Storage file list specifying the batch documents. Either azureBlobSource or azureBlobFileListSource must be specified.""" result_container_url: str = rest_field(name="resultContainerUrl") """Azure Blob Storage container URL where analyze result files will be stored. Required.""" result_prefix: Optional[str] = rest_field(name="resultPrefix") """Blob name prefix of result files.""" overwrite_existing: Optional[bool] = rest_field(name="overwriteExisting") """Overwrite existing analyze result files?.""" @overload def __init__( self, *, result_container_url: str, azure_blob_source: Optional["_models.AzureBlobContentSource"] = None, azure_blob_file_list_source: Optional["_models.AzureBlobFileListContentSource"] = None, result_prefix: Optional[str] = None, overwrite_existing: 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 AnalyzeBatchOperation(_model_base.Model): """Status and result of the analyze batch operation. :ivar result_id: Analyze batch operation result ID. :vartype result_id: str :ivar status: Operation status. notStarted, running, succeeded, or failed. Required. Known values are: "notStarted", "running", "failed", "succeeded", "canceled", and "skipped". :vartype status: str or ~azure.ai.documentintelligence.models.DocumentIntelligenceOperationStatus :ivar created_date_time: Date and time (UTC) when the operation was submitted. Required. :vartype created_date_time: ~datetime.datetime :ivar last_updated_date_time: Date and time (UTC) when the status was last updated. Required. :vartype last_updated_date_time: ~datetime.datetime :ivar percent_completed: Operation progress (0-100). :vartype percent_completed: int :ivar error: Encountered error during batch document analysis. :vartype error: ~azure.ai.documentintelligence.models.DocumentIntelligenceError :ivar result: Batch document analysis result. :vartype result: ~azure.ai.documentintelligence.models.AnalyzeBatchResult """ result_id: Optional[str] = rest_field(name="resultId") """Analyze batch operation result ID.""" status: Union[str, "_models.DocumentIntelligenceOperationStatus"] = rest_field() """Operation status. notStarted, running, succeeded, or failed. Required. Known values are: \"notStarted\", \"running\", \"failed\", \"succeeded\", \"canceled\", and \"skipped\".""" created_date_time: datetime.datetime = rest_field(name="createdDateTime", format="rfc3339") """Date and time (UTC) when the operation was submitted. Required.""" last_updated_date_time: datetime.datetime = rest_field(name="lastUpdatedDateTime", format="rfc3339") """Date and time (UTC) when the status was last updated. Required.""" percent_completed: Optional[int] = rest_field(name="percentCompleted") """Operation progress (0-100).""" error: Optional["_models.DocumentIntelligenceError"] = rest_field() """Encountered error during batch document analysis.""" result: Optional["_models.AnalyzeBatchResult"] = rest_field() """Batch document analysis result.""" @overload def __init__( self, *, status: Union[str, "_models.DocumentIntelligenceOperationStatus"], created_date_time: datetime.datetime, last_updated_date_time: datetime.datetime, result_id: Optional[str] = None, percent_completed: Optional[int] = None, error: Optional["_models.DocumentIntelligenceError"] = None, result: Optional["_models.AnalyzeBatchResult"] = 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 AnalyzeBatchOperationDetail(_model_base.Model): """Operation detail for a document in a batch analysis. :ivar status: Analyze status. succeeded, failed, or skipped. Required. Known values are: "notStarted", "running", "failed", "succeeded", "canceled", and "skipped". :vartype status: str or ~azure.ai.documentintelligence.models.DocumentIntelligenceOperationStatus :ivar source_url: URL of the source document. Required. :vartype source_url: str :ivar result_url: URL of the analyze result JSON. :vartype result_url: str :ivar error: Encountered error. :vartype error: ~azure.ai.documentintelligence.models.DocumentIntelligenceError """ status: Union[str, "_models.DocumentIntelligenceOperationStatus"] = rest_field() """Analyze status. succeeded, failed, or skipped. Required. Known values are: \"notStarted\", \"running\", \"failed\", \"succeeded\", \"canceled\", and \"skipped\".""" source_url: str = rest_field(name="sourceUrl") """URL of the source document. Required.""" result_url: Optional[str] = rest_field(name="resultUrl") """URL of the analyze result JSON.""" error: Optional["_models.DocumentIntelligenceError"] = rest_field() """Encountered error.""" @overload def __init__( self, *, status: Union[str, "_models.DocumentIntelligenceOperationStatus"], source_url: str, result_url: Optional[str] = None, error: Optional["_models.DocumentIntelligenceError"] = 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 AnalyzeBatchResult(_model_base.Model): """Batch document analysis result. :ivar succeeded_count: Number of documents that completed with status succeeded. Required. :vartype succeeded_count: int :ivar failed_count: Number of documents that completed with status failed. Required. :vartype failed_count: int :ivar skipped_count: Number of documents that completed with status skipped. Required. :vartype skipped_count: int :ivar details: Operation detail for each document in the batch. :vartype details: list[~azure.ai.documentintelligence.models.AnalyzeBatchOperationDetail] """ succeeded_count: int = rest_field(name="succeededCount") """Number of documents that completed with status succeeded. Required.""" failed_count: int = rest_field(name="failedCount") """Number of documents that completed with status failed. Required.""" skipped_count: int = rest_field(name="skippedCount") """Number of documents that completed with status skipped. Required.""" details: Optional[List["_models.AnalyzeBatchOperationDetail"]] = rest_field() """Operation detail for each document in the batch.""" @overload def __init__( self, *, succeeded_count: int, failed_count: int, skipped_count: int, details: Optional[List["_models.AnalyzeBatchOperationDetail"]] = 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 AnalyzedDocument(_model_base.Model): """An object describing the location and semantic content of a document. :ivar doc_type: Document type. Required. :vartype doc_type: str :ivar bounding_regions: Bounding regions covering the document. :vartype bounding_regions: list[~azure.ai.documentintelligence.models.BoundingRegion] :ivar spans: Location of the document in the reading order concatenated content. Required. :vartype spans: list[~azure.ai.documentintelligence.models.DocumentSpan] :ivar fields: Dictionary of named field values. :vartype fields: dict[str, ~azure.ai.documentintelligence.models.DocumentField] :ivar confidence: Confidence of correctly extracting the document. Required. :vartype confidence: float """ doc_type: str = rest_field(name="docType") """Document type. Required.""" bounding_regions: Optional[List["_models.BoundingRegion"]] = rest_field(name="boundingRegions") """Bounding regions covering the document.""" spans: List["_models.DocumentSpan"] = rest_field() """Location of the document in the reading order concatenated content. Required.""" fields: Optional[Dict[str, "_models.DocumentField"]] = rest_field() """Dictionary of named field values.""" confidence: float = rest_field() """Confidence of correctly extracting the document. Required.""" @overload def __init__( self, *, doc_type: str, spans: List["_models.DocumentSpan"], confidence: float, bounding_regions: Optional[List["_models.BoundingRegion"]] = None, fields: Optional[Dict[str, "_models.DocumentField"]] = 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 AnalyzeDocumentRequest(_model_base.Model): """Document analysis parameters. :ivar url_source: Document URL to analyze. Either urlSource or base64Source must be specified. :vartype url_source: str :ivar bytes_source: Base64 encoding of the document to analyze. Either urlSource or base64Source must be specified. :vartype bytes_source: bytes """ url_source: Optional[str] = rest_field(name="urlSource") """Document URL to analyze. Either urlSource or base64Source must be specified.""" bytes_source: Optional[bytes] = rest_field(name="base64Source", format="base64") """Base64 encoding of the document to analyze. Either urlSource or base64Source must be specified.""" @overload def __init__( self, *, url_source: Optional[str] = None, bytes_source: Optional[bytes] = 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 AnalyzeResult(_model_base.Model): """Document analysis result. :ivar api_version: API version used to produce this result. Required. :vartype api_version: str :ivar model_id: Document model ID used to produce this result. Required. :vartype model_id: str :ivar string_index_type: Method used to compute string offset and length. Required. Known values are: "textElements", "unicodeCodePoint", and "utf16CodeUnit". :vartype string_index_type: str or ~azure.ai.documentintelligence.models.StringIndexType :ivar content_format: Format of the analyze result top-level content. Known values are: "text" and "markdown". :vartype content_format: str or ~azure.ai.documentintelligence.models.DocumentContentFormat :ivar content: Concatenate string representation of all textual and visual elements in reading order. Required. :vartype content: str :ivar pages: Analyzed pages. Required. :vartype pages: list[~azure.ai.documentintelligence.models.DocumentPage] :ivar paragraphs: Extracted paragraphs. :vartype paragraphs: list[~azure.ai.documentintelligence.models.DocumentParagraph] :ivar tables: Extracted tables. :vartype tables: list[~azure.ai.documentintelligence.models.DocumentTable] :ivar figures: Extracted figures. :vartype figures: list[~azure.ai.documentintelligence.models.DocumentFigure] :ivar sections: Extracted sections. :vartype sections: list[~azure.ai.documentintelligence.models.DocumentSection] :ivar key_value_pairs: Extracted key-value pairs. :vartype key_value_pairs: list[~azure.ai.documentintelligence.models.DocumentKeyValuePair] :ivar styles: Extracted font styles. :vartype styles: list[~azure.ai.documentintelligence.models.DocumentStyle] :ivar languages: Detected languages. :vartype languages: list[~azure.ai.documentintelligence.models.DocumentLanguage] :ivar documents: Extracted documents. :vartype documents: list[~azure.ai.documentintelligence.models.AnalyzedDocument] :ivar warnings: List of warnings encountered. :vartype warnings: list[~azure.ai.documentintelligence.models.DocumentIntelligenceWarning] """ api_version: str = rest_field(name="apiVersion") """API version used to produce this result. Required.""" model_id: str = rest_field(name="modelId") """Document model ID used to produce this result. Required.""" string_index_type: Union[str, "_models.StringIndexType"] = rest_field(name="stringIndexType") """Method used to compute string offset and length. Required. Known values are: \"textElements\", \"unicodeCodePoint\", and \"utf16CodeUnit\".""" content_format: Optional[Union[str, "_models.DocumentContentFormat"]] = rest_field(name="contentFormat") """Format of the analyze result top-level content. Known values are: \"text\" and \"markdown\".""" content: str = rest_field() """Concatenate string representation of all textual and visual elements in reading order. Required.""" pages: List["_models.DocumentPage"] = rest_field() """Analyzed pages. Required.""" paragraphs: Optional[List["_models.DocumentParagraph"]] = rest_field() """Extracted paragraphs.""" tables: Optional[List["_models.DocumentTable"]] = rest_field() """Extracted tables.""" figures: Optional[List["_models.DocumentFigure"]] = rest_field() """Extracted figures.""" sections: Optional[List["_models.DocumentSection"]] = rest_field() """Extracted sections.""" key_value_pairs: Optional[List["_models.DocumentKeyValuePair"]] = rest_field(name="keyValuePairs") """Extracted key-value pairs.""" styles: Optional[List["_models.DocumentStyle"]] = rest_field() """Extracted font styles.""" languages: Optional[List["_models.DocumentLanguage"]] = rest_field() """Detected languages.""" documents: Optional[List["_models.AnalyzedDocument"]] = rest_field() """Extracted documents.""" warnings: Optional[List["_models.DocumentIntelligenceWarning"]] = rest_field() """List of warnings encountered.""" @overload def __init__( self, *, api_version: str, model_id: str, string_index_type: Union[str, "_models.StringIndexType"], content: str, pages: List["_models.DocumentPage"], content_format: Optional[Union[str, "_models.DocumentContentFormat"]] = None, paragraphs: Optional[List["_models.DocumentParagraph"]] = None, tables: Optional[List["_models.DocumentTable"]] = None, figures: Optional[List["_models.DocumentFigure"]] = None, sections: Optional[List["_models.DocumentSection"]] = None, key_value_pairs: Optional[List["_models.DocumentKeyValuePair"]] = None, styles: Optional[List["_models.DocumentStyle"]] = None, languages: Optional[List["_models.DocumentLanguage"]] = None, documents: Optional[List["_models.AnalyzedDocument"]] = None, warnings: Optional[List["_models.DocumentIntelligenceWarning"]] = 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 AuthorizeClassifierCopyRequest(_model_base.Model): """Request body to authorize document classifier copy. All required parameters must be populated in order to send to server. :ivar classifier_id: Unique document classifier name. Required. :vartype classifier_id: str :ivar description: Document classifier description. :vartype description: str :ivar tags: List of key-value tag attributes associated with the document classifier. :vartype tags: dict[str, str] """ classifier_id: str = rest_field(name="classifierId") """Unique document classifier name. Required.""" description: Optional[str] = rest_field() """Document classifier description.""" tags: Optional[Dict[str, str]] = rest_field() """List of key-value tag attributes associated with the document classifier.""" @overload def __init__( self, *, classifier_id: str, description: Optional[str] = None, tags: Optional[Dict[str, 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 AuthorizeCopyRequest(_model_base.Model): """Request body to authorize document model copy. All required parameters must be populated in order to send to server. :ivar model_id: Unique document model name. Required. :vartype model_id: str :ivar description: Document model description. :vartype description: str :ivar tags: List of key-value tag attributes associated with the document model. :vartype tags: dict[str, str] """ model_id: str = rest_field(name="modelId") """Unique document model name. Required.""" description: Optional[str] = rest_field() """Document model description.""" tags: Optional[Dict[str, str]] = rest_field() """List of key-value tag attributes associated with the document model.""" @overload def __init__( self, *, model_id: str, description: Optional[str] = None, tags: Optional[Dict[str, 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 AzureBlobContentSource(_model_base.Model): """Azure Blob Storage content. :ivar container_url: Azure Blob Storage container URL. Required. :vartype container_url: str :ivar prefix: Blob name prefix. :vartype prefix: str """ container_url: str = rest_field(name="containerUrl") """Azure Blob Storage container URL. Required.""" prefix: Optional[str] = rest_field() """Blob name prefix.""" @overload def __init__( self, *, container_url: str, prefix: 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 AzureBlobFileListContentSource(_model_base.Model): """File list in Azure Blob Storage. :ivar container_url: Azure Blob Storage container URL. Required. :vartype container_url: str :ivar file_list: Path to a JSONL file within the container specifying a subset of documents. Required. :vartype file_list: str """ container_url: str = rest_field(name="containerUrl") """Azure Blob Storage container URL. Required.""" file_list: str = rest_field(name="fileList") """Path to a JSONL file within the container specifying a subset of documents. Required.""" @overload def __init__( self, *, container_url: str, file_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 BoundingRegion(_model_base.Model): """Bounding polygon on a specific page of the input. :ivar page_number: 1-based page number of page containing the bounding region. Required. :vartype page_number: int :ivar polygon: Bounding polygon on the page, or the entire page if not specified. Coordinates specified relative to the top-left of the page. The numbers represent the x, y values of the polygon vertices, clockwise from the left (-180 degrees inclusive) relative to the element orientation. Required. :vartype polygon: list[float] """ page_number: int = rest_field(name="pageNumber") """1-based page number of page containing the bounding region. Required.""" polygon: List[float] = rest_field() """Bounding polygon on the page, or the entire page if not specified. Coordinates specified relative to the top-left of the page. The numbers represent the x, y values of the polygon vertices, clockwise from the left (-180 degrees inclusive) relative to the element orientation. Required.""" @overload def __init__( self, *, page_number: int, polygon: List[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 BuildDocumentClassifierRequest(_model_base.Model): """Request body to build a new custom document classifier. All required parameters must be populated in order to send to server. :ivar classifier_id: Unique document classifier name. Required. :vartype classifier_id: str :ivar description: Document classifier description. :vartype description: str :ivar base_classifier_id: Base classifierId on top of which to train the classifier. :vartype base_classifier_id: str :ivar doc_types: List of document types to classify against. Required. :vartype doc_types: dict[str, ~azure.ai.documentintelligence.models.ClassifierDocumentTypeDetails] :ivar allow_overwrite: Allow overwriting an existing classifier with the same name. :vartype allow_overwrite: bool """ classifier_id: str = rest_field(name="classifierId") """Unique document classifier name. Required.""" description: Optional[str] = rest_field() """Document classifier description.""" base_classifier_id: Optional[str] = rest_field(name="baseClassifierId") """Base classifierId on top of which to train the classifier.""" doc_types: Dict[str, "_models.ClassifierDocumentTypeDetails"] = rest_field(name="docTypes") """List of document types to classify against. Required.""" allow_overwrite: Optional[bool] = rest_field(name="allowOverwrite") """Allow overwriting an existing classifier with the same name.""" @overload def __init__( self, *, classifier_id: str, doc_types: Dict[str, "_models.ClassifierDocumentTypeDetails"], description: Optional[str] = None, base_classifier_id: Optional[str] = None, allow_overwrite: 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 BuildDocumentModelRequest(_model_base.Model): """Request body to build a new custom document model. All required parameters must be populated in order to send to server. :ivar model_id: Unique document model name. Required. :vartype model_id: str :ivar description: Document model description. :vartype description: str :ivar build_mode: Custom document model build mode. Required. Known values are: "template" and "neural". :vartype build_mode: str or ~azure.ai.documentintelligence.models.DocumentBuildMode :ivar azure_blob_source: Azure Blob Storage location containing the training data. Either azureBlobSource or azureBlobFileListSource must be specified. :vartype azure_blob_source: ~azure.ai.documentintelligence.models.AzureBlobContentSource :ivar azure_blob_file_list_source: Azure Blob Storage file list specifying the training data. Either azureBlobSource or azureBlobFileListSource must be specified. :vartype azure_blob_file_list_source: ~azure.ai.documentintelligence.models.AzureBlobFileListContentSource :ivar tags: List of key-value tag attributes associated with the document model. :vartype tags: dict[str, str] :ivar max_training_hours: Max number of V100-equivalent GPU hours to use for model training. Default=0.5. :vartype max_training_hours: float :ivar allow_overwrite: Allow overwriting an existing model with the same name. :vartype allow_overwrite: bool """ model_id: str = rest_field(name="modelId") """Unique document model name. Required.""" description: Optional[str] = rest_field() """Document model description.""" build_mode: Union[str, "_models.DocumentBuildMode"] = rest_field(name="buildMode") """Custom document model build mode. Required. Known values are: \"template\" and \"neural\".""" azure_blob_source: Optional["_models.AzureBlobContentSource"] = rest_field(name="azureBlobSource") """Azure Blob Storage location containing the training data. Either azureBlobSource or azureBlobFileListSource must be specified.""" azure_blob_file_list_source: Optional["_models.AzureBlobFileListContentSource"] = rest_field( name="azureBlobFileListSource" ) """Azure Blob Storage file list specifying the training data. Either azureBlobSource or azureBlobFileListSource must be specified.""" tags: Optional[Dict[str, str]] = rest_field() """List of key-value tag attributes associated with the document model.""" max_training_hours: Optional[float] = rest_field(name="maxTrainingHours") """Max number of V100-equivalent GPU hours to use for model training. Default=0.5.""" allow_overwrite: Optional[bool] = rest_field(name="allowOverwrite") """Allow overwriting an existing model with the same name.""" @overload def __init__( self, *, model_id: str, build_mode: Union[str, "_models.DocumentBuildMode"], description: Optional[str] = None, azure_blob_source: Optional["_models.AzureBlobContentSource"] = None, azure_blob_file_list_source: Optional["_models.AzureBlobFileListContentSource"] = None, tags: Optional[Dict[str, str]] = None, max_training_hours: Optional[float] = None, allow_overwrite: 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 ClassifierCopyAuthorization(_model_base.Model): """Authorization to copy a document classifier to the specified target resource and classifierId. :ivar target_resource_id: ID of the target Azure resource where the document classifier should be copied to. Required. :vartype target_resource_id: str :ivar target_resource_region: Location of the target Azure resource where the document classifier should be copied to. Required. :vartype target_resource_region: str :ivar target_classifier_id: Identifier of the target document classifier. Required. :vartype target_classifier_id: str :ivar target_classifier_location: URL of the copied document classifier in the target account. Required. :vartype target_classifier_location: str :ivar access_token: Token used to authorize the request. Required. :vartype access_token: str :ivar expiration_date_time: Date/time when the access token expires. Required. :vartype expiration_date_time: ~datetime.datetime """ target_resource_id: str = rest_field(name="targetResourceId") """ID of the target Azure resource where the document classifier should be copied to. Required.""" target_resource_region: str = rest_field(name="targetResourceRegion") """Location of the target Azure resource where the document classifier should be copied to. Required.""" target_classifier_id: str = rest_field(name="targetClassifierId") """Identifier of the target document classifier. Required.""" target_classifier_location: str = rest_field(name="targetClassifierLocation") """URL of the copied document classifier in the target account. Required.""" access_token: str = rest_field(name="accessToken") """Token used to authorize the request. Required.""" expiration_date_time: datetime.datetime = rest_field(name="expirationDateTime", format="rfc3339") """Date/time when the access token expires. Required.""" @overload def __init__( self, *, target_resource_id: str, target_resource_region: str, target_classifier_id: str, target_classifier_location: str, access_token: str, expiration_date_time: datetime.datetime, ) -> 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 ClassifierDocumentTypeDetails(_model_base.Model): """Classifier document type info. :ivar source_kind: Type of training data source. Known values are: "url", "base64", "azureBlob", and "azureBlobFileList". :vartype source_kind: str or ~azure.ai.documentintelligence.models.ContentSourceKind :ivar azure_blob_source: Azure Blob Storage location containing the training data for a classifier document type. Either azureBlobSource or azureBlobFileListSource must be specified. :vartype azure_blob_source: ~azure.ai.documentintelligence.models.AzureBlobContentSource :ivar azure_blob_file_list_source: Azure Blob Storage file list specifying the training data for a classifier document type. Either azureBlobSource or azureBlobFileListSource must be specified. :vartype azure_blob_file_list_source: ~azure.ai.documentintelligence.models.AzureBlobFileListContentSource """ source_kind: Optional[Union[str, "_models.ContentSourceKind"]] = rest_field(name="sourceKind") """Type of training data source. Known values are: \"url\", \"base64\", \"azureBlob\", and \"azureBlobFileList\".""" azure_blob_source: Optional["_models.AzureBlobContentSource"] = rest_field(name="azureBlobSource") """Azure Blob Storage location containing the training data for a classifier document type. Either azureBlobSource or azureBlobFileListSource must be specified.""" azure_blob_file_list_source: Optional["_models.AzureBlobFileListContentSource"] = rest_field( name="azureBlobFileListSource" ) """Azure Blob Storage file list specifying the training data for a classifier document type. Either azureBlobSource or azureBlobFileListSource must be specified.""" @overload def __init__( self, *, source_kind: Optional[Union[str, "_models.ContentSourceKind"]] = None, azure_blob_source: Optional["_models.AzureBlobContentSource"] = None, azure_blob_file_list_source: Optional["_models.AzureBlobFileListContentSource"] = 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 ClassifyDocumentRequest(_model_base.Model): """Document classification parameters. :ivar url_source: Document URL to classify. Either urlSource or base64Source must be specified. :vartype url_source: str :ivar bytes_source: Base64 encoding of the document to classify. Either urlSource or base64Source must be specified. :vartype bytes_source: bytes """ url_source: Optional[str] = rest_field(name="urlSource") """Document URL to classify. Either urlSource or base64Source must be specified.""" bytes_source: Optional[bytes] = rest_field(name="base64Source", format="base64") """Base64 encoding of the document to classify. Either urlSource or base64Source must be specified.""" @overload def __init__( self, *, url_source: Optional[str] = None, bytes_source: Optional[bytes] = 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 ComposeDocumentModelRequest(_model_base.Model): """Request body to create a composed document model from component document models. All required parameters must be populated in order to send to server. :ivar model_id: Unique document model name. Required. :vartype model_id: str :ivar description: Document model description. :vartype description: str :ivar classifier_id: Custom classifier to split and classify the input file. Required. :vartype classifier_id: str :ivar split: File splitting behavior. Known values are: "auto", "none", and "perPage". :vartype split: str or ~azure.ai.documentintelligence.models.SplitMode :ivar doc_types: Dictionary mapping supported docTypes to the corresponding document models. Required. :vartype doc_types: dict[str, ~azure.ai.documentintelligence.models.DocumentTypeDetails] :ivar tags: List of key-value tag attributes associated with the document model. :vartype tags: dict[str, str] """ model_id: str = rest_field(name="modelId") """Unique document model name. Required.""" description: Optional[str] = rest_field() """Document model description.""" classifier_id: str = rest_field(name="classifierId") """Custom classifier to split and classify the input file. Required.""" split: Optional[Union[str, "_models.SplitMode"]] = rest_field() """File splitting behavior. Known values are: \"auto\", \"none\", and \"perPage\".""" doc_types: Dict[str, "_models.DocumentTypeDetails"] = rest_field(name="docTypes") """Dictionary mapping supported docTypes to the corresponding document models. Required.""" tags: Optional[Dict[str, str]] = rest_field() """List of key-value tag attributes associated with the document model.""" @overload def __init__( self, *, model_id: str, classifier_id: str, doc_types: Dict[str, "_models.DocumentTypeDetails"], description: Optional[str] = None, split: Optional[Union[str, "_models.SplitMode"]] = None, tags: Optional[Dict[str, 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 CurrencyValue(_model_base.Model): """Currency field value. :ivar amount: Currency amount. Required. :vartype amount: float :ivar currency_symbol: Currency symbol label, if any. :vartype currency_symbol: str :ivar currency_code: Resolved currency code (ISO 4217), if any. :vartype currency_code: str """ amount: float = rest_field() """Currency amount. Required.""" currency_symbol: Optional[str] = rest_field(name="currencySymbol") """Currency symbol label, if any.""" currency_code: Optional[str] = rest_field(name="currencyCode") """Resolved currency code (ISO 4217), if any.""" @overload def __init__( self, *, amount: float, currency_symbol: Optional[str] = None, currency_code: 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 CustomDocumentModelsDetails(_model_base.Model): """Details regarding custom document models. :ivar count: Number of custom document models in the current resource. Required. :vartype count: int :ivar limit: Maximum number of custom document models supported in the current resource. Required. :vartype limit: int """ count: int = rest_field() """Number of custom document models in the current resource. Required.""" limit: int = rest_field() """Maximum number of custom document models supported in the current resource. Required.""" @overload def __init__( self, *, count: int, limit: 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 DocumentBarcode(_model_base.Model): """A barcode object. :ivar kind: Barcode kind. Required. Known values are: "QRCode", "PDF417", "UPCA", "UPCE", "Code39", "Code128", "EAN8", "EAN13", "DataBar", "Code93", "Codabar", "DataBarExpanded", "ITF", "MicroQRCode", "Aztec", "DataMatrix", and "MaxiCode". :vartype kind: str or ~azure.ai.documentintelligence.models.DocumentBarcodeKind :ivar value: Barcode value. Required. :vartype value: str :ivar polygon: Bounding polygon of the barcode, with coordinates specified relative to the top-left of the page. The numbers represent the x, y values of the polygon vertices, clockwise from the left (-180 degrees inclusive) relative to the element orientation. :vartype polygon: list[float] :ivar span: Location of the barcode in the reading order concatenated content. Required. :vartype span: ~azure.ai.documentintelligence.models.DocumentSpan :ivar confidence: Confidence of correctly extracting the barcode. Required. :vartype confidence: float """ kind: Union[str, "_models.DocumentBarcodeKind"] = rest_field() """Barcode kind. Required. Known values are: \"QRCode\", \"PDF417\", \"UPCA\", \"UPCE\", \"Code39\", \"Code128\", \"EAN8\", \"EAN13\", \"DataBar\", \"Code93\", \"Codabar\", \"DataBarExpanded\", \"ITF\", \"MicroQRCode\", \"Aztec\", \"DataMatrix\", and \"MaxiCode\".""" value: str = rest_field() """Barcode value. Required.""" polygon: Optional[List[float]] = rest_field() """Bounding polygon of the barcode, with coordinates specified relative to the top-left of the page. The numbers represent the x, y values of the polygon vertices, clockwise from the left (-180 degrees inclusive) relative to the element orientation.""" span: "_models.DocumentSpan" = rest_field() """Location of the barcode in the reading order concatenated content. Required.""" confidence: float = rest_field() """Confidence of correctly extracting the barcode. Required.""" @overload def __init__( self, *, kind: Union[str, "_models.DocumentBarcodeKind"], value: str, span: "_models.DocumentSpan", confidence: float, polygon: Optional[List[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 DocumentCaption(_model_base.Model): """A caption object describing a table or figure. :ivar content: Content of the caption. Required. :vartype content: str :ivar bounding_regions: Bounding regions covering the caption. :vartype bounding_regions: list[~azure.ai.documentintelligence.models.BoundingRegion] :ivar spans: Location of the caption in the reading order concatenated content. Required. :vartype spans: list[~azure.ai.documentintelligence.models.DocumentSpan] :ivar elements: Child elements of the caption. :vartype elements: list[str] """ content: str = rest_field() """Content of the caption. Required.""" bounding_regions: Optional[List["_models.BoundingRegion"]] = rest_field(name="boundingRegions") """Bounding regions covering the caption.""" spans: List["_models.DocumentSpan"] = rest_field() """Location of the caption in the reading order concatenated content. Required.""" elements: Optional[List[str]] = rest_field() """Child elements of the caption.""" @overload def __init__( self, *, content: str, spans: List["_models.DocumentSpan"], bounding_regions: Optional[List["_models.BoundingRegion"]] = None, elements: Optional[List[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 DocumentIntelligenceOperationDetails(_model_base.Model): """Operation info. You probably want to use the sub-classes and not this class directly. Known sub-classes are: DocumentClassifierBuildOperationDetails, DocumentClassifierCopyToOperationDetails, DocumentModelBuildOperationDetails, DocumentModelComposeOperationDetails, DocumentModelCopyToOperationDetails :ivar operation_id: Operation ID. Required. :vartype operation_id: str :ivar status: Operation status. notStarted, running, completed, or failed. Required. Known values are: "notStarted", "running", "failed", "succeeded", "canceled", and "skipped". :vartype status: str or ~azure.ai.documentintelligence.models.DocumentIntelligenceOperationStatus :ivar percent_completed: Operation progress (0-100). :vartype percent_completed: int :ivar created_date_time: Date and time (UTC) when the operation was created. Required. :vartype created_date_time: ~datetime.datetime :ivar last_updated_date_time: Date and time (UTC) when the status was last updated. Required. :vartype last_updated_date_time: ~datetime.datetime :ivar kind: Type of operation. Required. Known values are: "documentModelBuild", "documentModelCompose", "documentModelCopyTo", "documentClassifierCopyTo", and "documentClassifierBuild". :vartype kind: str or ~azure.ai.documentintelligence.models.OperationKind :ivar resource_location: URL of the resource targeted by this operation. Required. :vartype resource_location: str :ivar api_version: API version used to create this operation. :vartype api_version: str :ivar tags: List of key-value tag attributes associated with the document model. :vartype tags: dict[str, str] :ivar error: Encountered error. :vartype error: ~azure.ai.documentintelligence.models.DocumentIntelligenceError """ __mapping__: Dict[str, _model_base.Model] = {} operation_id: str = rest_field(name="operationId", visibility=["read", "create"]) """Operation ID. Required.""" status: Union[str, "_models.DocumentIntelligenceOperationStatus"] = rest_field() """Operation status. notStarted, running, completed, or failed. Required. Known values are: \"notStarted\", \"running\", \"failed\", \"succeeded\", \"canceled\", and \"skipped\".""" percent_completed: Optional[int] = rest_field(name="percentCompleted") """Operation progress (0-100).""" created_date_time: datetime.datetime = rest_field(name="createdDateTime", format="rfc3339") """Date and time (UTC) when the operation was created. Required.""" last_updated_date_time: datetime.datetime = rest_field(name="lastUpdatedDateTime", format="rfc3339") """Date and time (UTC) when the status was last updated. Required.""" kind: str = rest_discriminator(name="kind") """Type of operation. Required. Known values are: \"documentModelBuild\", \"documentModelCompose\", \"documentModelCopyTo\", \"documentClassifierCopyTo\", and \"documentClassifierBuild\".""" resource_location: str = rest_field(name="resourceLocation") """URL of the resource targeted by this operation. Required.""" api_version: Optional[str] = rest_field(name="apiVersion") """API version used to create this operation.""" tags: Optional[Dict[str, str]] = rest_field() """List of key-value tag attributes associated with the document model.""" error: Optional["_models.DocumentIntelligenceError"] = rest_field() """Encountered error.""" @overload def __init__( self, *, operation_id: str, status: Union[str, "_models.DocumentIntelligenceOperationStatus"], created_date_time: datetime.datetime, last_updated_date_time: datetime.datetime, kind: str, resource_location: str, percent_completed: Optional[int] = None, api_version: Optional[str] = None, tags: Optional[Dict[str, str]] = None, error: Optional["_models.DocumentIntelligenceError"] = 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 DocumentClassifierBuildOperationDetails( DocumentIntelligenceOperationDetails, discriminator="documentClassifierBuild" ): """Get Operation response object. :ivar operation_id: Operation ID. Required. :vartype operation_id: str :ivar status: Operation status. notStarted, running, completed, or failed. Required. Known values are: "notStarted", "running", "failed", "succeeded", "canceled", and "skipped". :vartype status: str or ~azure.ai.documentintelligence.models.DocumentIntelligenceOperationStatus :ivar percent_completed: Operation progress (0-100). :vartype percent_completed: int :ivar created_date_time: Date and time (UTC) when the operation was created. Required. :vartype created_date_time: ~datetime.datetime :ivar last_updated_date_time: Date and time (UTC) when the status was last updated. Required. :vartype last_updated_date_time: ~datetime.datetime :ivar resource_location: URL of the resource targeted by this operation. Required. :vartype resource_location: str :ivar api_version: API version used to create this operation. :vartype api_version: str :ivar tags: List of key-value tag attributes associated with the document model. :vartype tags: dict[str, str] :ivar error: Encountered error. :vartype error: ~azure.ai.documentintelligence.models.DocumentIntelligenceError :ivar result: Operation result upon success. :vartype result: ~azure.ai.documentintelligence.models.DocumentClassifierDetails :ivar kind: Type of operation. Required. Build a new custom classifier model. :vartype kind: str or ~azure.ai.documentintelligence.models.DOCUMENT_CLASSIFIER_BUILD """ result: Optional["_models.DocumentClassifierDetails"] = rest_field() """Operation result upon success.""" kind: Literal[OperationKind.DOCUMENT_CLASSIFIER_BUILD] = rest_discriminator(name="kind") # type: ignore """Type of operation. Required. Build a new custom classifier model.""" @overload def __init__( self, *, operation_id: str, status: Union[str, "_models.DocumentIntelligenceOperationStatus"], created_date_time: datetime.datetime, last_updated_date_time: datetime.datetime, resource_location: str, percent_completed: Optional[int] = None, api_version: Optional[str] = None, tags: Optional[Dict[str, str]] = None, error: Optional["_models.DocumentIntelligenceError"] = None, result: Optional["_models.DocumentClassifierDetails"] = 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=OperationKind.DOCUMENT_CLASSIFIER_BUILD, **kwargs) class DocumentClassifierCopyToOperationDetails( DocumentIntelligenceOperationDetails, discriminator="documentClassifierCopyTo" ): """Get Operation response object. :ivar operation_id: Operation ID. Required. :vartype operation_id: str :ivar status: Operation status. notStarted, running, completed, or failed. Required. Known values are: "notStarted", "running", "failed", "succeeded", "canceled", and "skipped". :vartype status: str or ~azure.ai.documentintelligence.models.DocumentIntelligenceOperationStatus :ivar percent_completed: Operation progress (0-100). :vartype percent_completed: int :ivar created_date_time: Date and time (UTC) when the operation was created. Required. :vartype created_date_time: ~datetime.datetime :ivar last_updated_date_time: Date and time (UTC) when the status was last updated. Required. :vartype last_updated_date_time: ~datetime.datetime :ivar resource_location: URL of the resource targeted by this operation. Required. :vartype resource_location: str :ivar api_version: API version used to create this operation. :vartype api_version: str :ivar tags: List of key-value tag attributes associated with the document model. :vartype tags: dict[str, str] :ivar error: Encountered error. :vartype error: ~azure.ai.documentintelligence.models.DocumentIntelligenceError :ivar result: Operation result upon success. :vartype result: ~azure.ai.documentintelligence.models.DocumentClassifierDetails :ivar kind: Type of operation. Required. Copy an existing document classifier to potentially a different resource, region, or subscription. :vartype kind: str or ~azure.ai.documentintelligence.models.DOCUMENT_CLASSIFIER_COPY_TO """ result: Optional["_models.DocumentClassifierDetails"] = rest_field() """Operation result upon success.""" kind: Literal[OperationKind.DOCUMENT_CLASSIFIER_COPY_TO] = rest_discriminator(name="kind") # type: ignore """Type of operation. Required. Copy an existing document classifier to potentially a different resource, region, or subscription.""" @overload def __init__( self, *, operation_id: str, status: Union[str, "_models.DocumentIntelligenceOperationStatus"], created_date_time: datetime.datetime, last_updated_date_time: datetime.datetime, resource_location: str, percent_completed: Optional[int] = None, api_version: Optional[str] = None, tags: Optional[Dict[str, str]] = None, error: Optional["_models.DocumentIntelligenceError"] = None, result: Optional["_models.DocumentClassifierDetails"] = 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=OperationKind.DOCUMENT_CLASSIFIER_COPY_TO, **kwargs) class DocumentClassifierDetails(_model_base.Model): """Document classifier info. Readonly variables are only populated by the server, and will be ignored when sending a request. :ivar classifier_id: Unique document classifier name. Required. :vartype classifier_id: str :ivar description: Document classifier description. :vartype description: str :ivar created_date_time: Date and time (UTC) when the document classifier was created. Required. :vartype created_date_time: ~datetime.datetime :ivar expiration_date_time: Date and time (UTC) when the document classifier will expire. :vartype expiration_date_time: ~datetime.datetime :ivar modified_date_time: Date and time (UTC) when the document model was last modified. :vartype modified_date_time: ~datetime.datetime :ivar api_version: API version used to create this document classifier. Required. :vartype api_version: str :ivar base_classifier_id: Base classifierId on top of which the classifier was trained. :vartype base_classifier_id: str :ivar doc_types: List of document types to classify against. Required. :vartype doc_types: dict[str, ~azure.ai.documentintelligence.models.ClassifierDocumentTypeDetails] :ivar warnings: List of warnings encountered while building the classifier. :vartype warnings: list[~azure.ai.documentintelligence.models.DocumentIntelligenceWarning] """ classifier_id: str = rest_field(name="classifierId", visibility=["read", "create"]) """Unique document classifier name. Required.""" description: Optional[str] = rest_field() """Document classifier description.""" created_date_time: datetime.datetime = rest_field(name="createdDateTime", format="rfc3339") """Date and time (UTC) when the document classifier was created. Required.""" expiration_date_time: Optional[datetime.datetime] = rest_field(name="expirationDateTime", format="rfc3339") """Date and time (UTC) when the document classifier will expire.""" modified_date_time: Optional[datetime.datetime] = rest_field( name="modifiedDateTime", visibility=["read"], format="rfc3339" ) """Date and time (UTC) when the document model was last modified.""" api_version: str = rest_field(name="apiVersion") """API version used to create this document classifier. Required.""" base_classifier_id: Optional[str] = rest_field(name="baseClassifierId") """Base classifierId on top of which the classifier was trained.""" doc_types: Dict[str, "_models.ClassifierDocumentTypeDetails"] = rest_field(name="docTypes") """List of document types to classify against. Required.""" warnings: Optional[List["_models.DocumentIntelligenceWarning"]] = rest_field() """List of warnings encountered while building the classifier.""" @overload def __init__( self, *, classifier_id: str, created_date_time: datetime.datetime, api_version: str, doc_types: Dict[str, "_models.ClassifierDocumentTypeDetails"], description: Optional[str] = None, expiration_date_time: Optional[datetime.datetime] = None, base_classifier_id: Optional[str] = None, warnings: Optional[List["_models.DocumentIntelligenceWarning"]] = 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 DocumentField(_model_base.Model): """An object representing the content and location of a field value. :ivar type: Data type of the field value. Required. Known values are: "string", "date", "time", "phoneNumber", "number", "integer", "selectionMark", "countryRegion", "signature", "array", "object", "currency", "address", "boolean", and "selectionGroup". :vartype type: str or ~azure.ai.documentintelligence.models.DocumentFieldType :ivar value_string: String value. :vartype value_string: str :ivar value_date: Date value in YYYY-MM-DD format (ISO 8601). :vartype value_date: ~datetime.date :ivar value_time: Time value in hh:mm:ss format (ISO 8601). :vartype value_time: ~datetime.time :ivar value_phone_number: Phone number value in E.164 format (ex. +19876543210). :vartype value_phone_number: str :ivar value_number: Floating point value. :vartype value_number: float :ivar value_integer: Integer value. :vartype value_integer: int :ivar value_selection_mark: Selection mark value. Known values are: "selected" and "unselected". :vartype value_selection_mark: str or ~azure.ai.documentintelligence.models.DocumentSelectionMarkState :ivar value_signature: Presence of signature. Known values are: "signed" and "unsigned". :vartype value_signature: str or ~azure.ai.documentintelligence.models.DocumentSignatureType :ivar value_country_region: 3-letter country code value (ISO 3166-1 alpha-3). :vartype value_country_region: str :ivar value_array: Array of field values. :vartype value_array: list[~azure.ai.documentintelligence.models.DocumentField] :ivar value_object: Dictionary of named field values. :vartype value_object: dict[str, ~azure.ai.documentintelligence.models.DocumentField] :ivar value_currency: Currency value. :vartype value_currency: ~azure.ai.documentintelligence.models.CurrencyValue :ivar value_address: Address value. :vartype value_address: ~azure.ai.documentintelligence.models.AddressValue :ivar value_boolean: Boolean value. :vartype value_boolean: bool :ivar value_selection_group: Selection group value. :vartype value_selection_group: list[str] :ivar content: Field content. :vartype content: str :ivar bounding_regions: Bounding regions covering the field. :vartype bounding_regions: list[~azure.ai.documentintelligence.models.BoundingRegion] :ivar spans: Location of the field in the reading order concatenated content. :vartype spans: list[~azure.ai.documentintelligence.models.DocumentSpan] :ivar confidence: Confidence of correctly extracting the field. :vartype confidence: float """ type: Union[str, "_models.DocumentFieldType"] = rest_field() """Data type of the field value. Required. Known values are: \"string\", \"date\", \"time\", \"phoneNumber\", \"number\", \"integer\", \"selectionMark\", \"countryRegion\", \"signature\", \"array\", \"object\", \"currency\", \"address\", \"boolean\", and \"selectionGroup\".""" value_string: Optional[str] = rest_field(name="valueString") """String value.""" value_date: Optional[datetime.date] = rest_field(name="valueDate") """Date value in YYYY-MM-DD format (ISO 8601).""" value_time: Optional[datetime.time] = rest_field(name="valueTime") """Time value in hh:mm:ss format (ISO 8601).""" value_phone_number: Optional[str] = rest_field(name="valuePhoneNumber") """Phone number value in E.164 format (ex. +19876543210).""" value_number: Optional[float] = rest_field(name="valueNumber") """Floating point value.""" value_integer: Optional[int] = rest_field(name="valueInteger") """Integer value.""" value_selection_mark: Optional[Union[str, "_models.DocumentSelectionMarkState"]] = rest_field( name="valueSelectionMark" ) """Selection mark value. Known values are: \"selected\" and \"unselected\".""" value_signature: Optional[Union[str, "_models.DocumentSignatureType"]] = rest_field(name="valueSignature") """Presence of signature. Known values are: \"signed\" and \"unsigned\".""" value_country_region: Optional[str] = rest_field(name="valueCountryRegion") """3-letter country code value (ISO 3166-1 alpha-3).""" value_array: Optional[List["_models.DocumentField"]] = rest_field(name="valueArray") """Array of field values.""" value_object: Optional[Dict[str, "_models.DocumentField"]] = rest_field(name="valueObject") """Dictionary of named field values.""" value_currency: Optional["_models.CurrencyValue"] = rest_field(name="valueCurrency") """Currency value.""" value_address: Optional["_models.AddressValue"] = rest_field(name="valueAddress") """Address value.""" value_boolean: Optional[bool] = rest_field(name="valueBoolean") """Boolean value.""" value_selection_group: Optional[List[str]] = rest_field(name="valueSelectionGroup") """Selection group value.""" content: Optional[str] = rest_field() """Field content.""" bounding_regions: Optional[List["_models.BoundingRegion"]] = rest_field(name="boundingRegions") """Bounding regions covering the field.""" spans: Optional[List["_models.DocumentSpan"]] = rest_field() """Location of the field in the reading order concatenated content.""" confidence: Optional[float] = rest_field() """Confidence of correctly extracting the field.""" @overload def __init__( self, *, type: Union[str, "_models.DocumentFieldType"], value_string: Optional[str] = None, value_date: Optional[datetime.date] = None, value_time: Optional[datetime.time] = None, value_phone_number: Optional[str] = None, value_number: Optional[float] = None, value_integer: Optional[int] = None, value_selection_mark: Optional[Union[str, "_models.DocumentSelectionMarkState"]] = None, value_signature: Optional[Union[str, "_models.DocumentSignatureType"]] = None, value_country_region: Optional[str] = None, value_array: Optional[List["_models.DocumentField"]] = None, value_object: Optional[Dict[str, "_models.DocumentField"]] = None, value_currency: Optional["_models.CurrencyValue"] = None, value_address: Optional["_models.AddressValue"] = None, value_boolean: Optional[bool] = None, value_selection_group: Optional[List[str]] = None, content: Optional[str] = None, bounding_regions: Optional[List["_models.BoundingRegion"]] = None, spans: Optional[List["_models.DocumentSpan"]] = None, confidence: 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 DocumentFieldSchema(_model_base.Model): """Description of the field semantic schema using a JSON Schema style syntax. :ivar type: Semantic data type of the field value. Required. Known values are: "string", "date", "time", "phoneNumber", "number", "integer", "selectionMark", "countryRegion", "signature", "array", "object", "currency", "address", "boolean", and "selectionGroup". :vartype type: str or ~azure.ai.documentintelligence.models.DocumentFieldType :ivar description: Field description. :vartype description: str :ivar example: Example field content. :vartype example: str :ivar items_schema: Field type schema of each array element. :vartype items_schema: ~azure.ai.documentintelligence.models.DocumentFieldSchema :ivar properties: Named sub-fields of the object field. :vartype properties: dict[str, ~azure.ai.documentintelligence.models.DocumentFieldSchema] """ type: Union[str, "_models.DocumentFieldType"] = rest_field() """Semantic data type of the field value. Required. Known values are: \"string\", \"date\", \"time\", \"phoneNumber\", \"number\", \"integer\", \"selectionMark\", \"countryRegion\", \"signature\", \"array\", \"object\", \"currency\", \"address\", \"boolean\", and \"selectionGroup\".""" description: Optional[str] = rest_field() """Field description.""" example: Optional[str] = rest_field() """Example field content.""" items_schema: Optional["_models.DocumentFieldSchema"] = rest_field(name="items") """Field type schema of each array element.""" properties: Optional[Dict[str, "_models.DocumentFieldSchema"]] = rest_field() """Named sub-fields of the object field.""" @overload def __init__( self, *, type: Union[str, "_models.DocumentFieldType"], description: Optional[str] = None, example: Optional[str] = None, items_schema: Optional["_models.DocumentFieldSchema"] = None, properties: Optional[Dict[str, "_models.DocumentFieldSchema"]] = 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 DocumentFigure(_model_base.Model): """An object representing a figure in the document. :ivar bounding_regions: Bounding regions covering the figure. :vartype bounding_regions: list[~azure.ai.documentintelligence.models.BoundingRegion] :ivar spans: Location of the figure in the reading order concatenated content. Required. :vartype spans: list[~azure.ai.documentintelligence.models.DocumentSpan] :ivar elements: Child elements of the figure, excluding any caption or footnotes. :vartype elements: list[str] :ivar caption: Caption associated with the figure. :vartype caption: ~azure.ai.documentintelligence.models.DocumentCaption :ivar footnotes: List of footnotes associated with the figure. :vartype footnotes: list[~azure.ai.documentintelligence.models.DocumentFootnote] :ivar id: Figure ID. :vartype id: str """ bounding_regions: Optional[List["_models.BoundingRegion"]] = rest_field(name="boundingRegions") """Bounding regions covering the figure.""" spans: List["_models.DocumentSpan"] = rest_field() """Location of the figure in the reading order concatenated content. Required.""" elements: Optional[List[str]] = rest_field() """Child elements of the figure, excluding any caption or footnotes.""" caption: Optional["_models.DocumentCaption"] = rest_field() """Caption associated with the figure.""" footnotes: Optional[List["_models.DocumentFootnote"]] = rest_field() """List of footnotes associated with the figure.""" id: Optional[str] = rest_field() """Figure ID.""" @overload def __init__( self, *, spans: List["_models.DocumentSpan"], bounding_regions: Optional[List["_models.BoundingRegion"]] = None, elements: Optional[List[str]] = None, caption: Optional["_models.DocumentCaption"] = None, footnotes: Optional[List["_models.DocumentFootnote"]] = None, id: Optional[str] = None, # 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 DocumentFootnote(_model_base.Model): """A footnote object describing a table or figure. :ivar content: Content of the footnote. Required. :vartype content: str :ivar bounding_regions: Bounding regions covering the footnote. :vartype bounding_regions: list[~azure.ai.documentintelligence.models.BoundingRegion] :ivar spans: Location of the footnote in the reading order concatenated content. Required. :vartype spans: list[~azure.ai.documentintelligence.models.DocumentSpan] :ivar elements: Child elements of the footnote. :vartype elements: list[str] """ content: str = rest_field() """Content of the footnote. Required.""" bounding_regions: Optional[List["_models.BoundingRegion"]] = rest_field(name="boundingRegions") """Bounding regions covering the footnote.""" spans: List["_models.DocumentSpan"] = rest_field() """Location of the footnote in the reading order concatenated content. Required.""" elements: Optional[List[str]] = rest_field() """Child elements of the footnote.""" @overload def __init__( self, *, content: str, spans: List["_models.DocumentSpan"], bounding_regions: Optional[List["_models.BoundingRegion"]] = None, elements: Optional[List[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 DocumentFormula(_model_base.Model): """A formula object. :ivar kind: Formula kind. Required. Known values are: "inline" and "display". :vartype kind: str or ~azure.ai.documentintelligence.models.DocumentFormulaKind :ivar value: LaTex expression describing the formula. Required. :vartype value: str :ivar polygon: Bounding polygon of the formula, with coordinates specified relative to the top-left of the page. The numbers represent the x, y values of the polygon vertices, clockwise from the left (-180 degrees inclusive) relative to the element orientation. :vartype polygon: list[float] :ivar span: Location of the formula in the reading order concatenated content. Required. :vartype span: ~azure.ai.documentintelligence.models.DocumentSpan :ivar confidence: Confidence of correctly extracting the formula. Required. :vartype confidence: float """ kind: Union[str, "_models.DocumentFormulaKind"] = rest_field() """Formula kind. Required. Known values are: \"inline\" and \"display\".""" value: str = rest_field() """LaTex expression describing the formula. Required.""" polygon: Optional[List[float]] = rest_field() """Bounding polygon of the formula, with coordinates specified relative to the top-left of the page. The numbers represent the x, y values of the polygon vertices, clockwise from the left (-180 degrees inclusive) relative to the element orientation.""" span: "_models.DocumentSpan" = rest_field() """Location of the formula in the reading order concatenated content. Required.""" confidence: float = rest_field() """Confidence of correctly extracting the formula. Required.""" @overload def __init__( self, *, kind: Union[str, "_models.DocumentFormulaKind"], value: str, span: "_models.DocumentSpan", confidence: float, polygon: Optional[List[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 DocumentIntelligenceError(_model_base.Model): """The error object. :ivar code: One of a server-defined set of error codes. Required. :vartype code: str :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.documentintelligence.models.DocumentIntelligenceError] :ivar innererror: An object containing more specific information than the current object about the error. :vartype innererror: ~azure.ai.documentintelligence.models.DocumentIntelligenceInnerError """ code: str = rest_field() """One of a server-defined set of error codes. Required.""" message: str = rest_field() """A human-readable representation of the error. Required.""" target: Optional[str] = rest_field() """The target of the error.""" details: Optional[List["_models.DocumentIntelligenceError"]] = rest_field() """An array of details about specific errors that led to this reported error.""" innererror: Optional["_models.DocumentIntelligenceInnerError"] = rest_field() """An object containing more specific information than the current object about the error.""" @overload def __init__( self, *, code: str, message: str, target: Optional[str] = None, details: Optional[List["_models.DocumentIntelligenceError"]] = None, innererror: Optional["_models.DocumentIntelligenceInnerError"] = 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 DocumentIntelligenceErrorResponse(_model_base.Model): """Error response object. All required parameters must be populated in order to send to server. :ivar error: Error info. Required. :vartype error: ~azure.ai.documentintelligence.models.DocumentIntelligenceError """ error: "_models.DocumentIntelligenceError" = rest_field() """Error info. Required.""" @overload def __init__( self, *, error: "_models.DocumentIntelligenceError", ) -> 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 DocumentIntelligenceInnerError(_model_base.Model): """An object containing more specific information about the error. :ivar code: One of a server-defined set of error codes. :vartype code: str :ivar message: A human-readable representation of the error. :vartype message: str :ivar innererror: Inner error. :vartype innererror: ~azure.ai.documentintelligence.models.DocumentIntelligenceInnerError """ code: Optional[str] = rest_field() """One of a server-defined set of error codes.""" message: Optional[str] = rest_field() """A human-readable representation of the error.""" innererror: Optional["_models.DocumentIntelligenceInnerError"] = rest_field() """Inner error.""" @overload def __init__( self, *, code: Optional[str] = None, message: Optional[str] = None, innererror: Optional["_models.DocumentIntelligenceInnerError"] = 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 DocumentIntelligenceResourceDetails(_model_base.Model): """General information regarding the current resource. :ivar custom_document_models: Details regarding custom document models. Required. :vartype custom_document_models: ~azure.ai.documentintelligence.models.CustomDocumentModelsDetails """ custom_document_models: "_models.CustomDocumentModelsDetails" = rest_field(name="customDocumentModels") """Details regarding custom document models. Required.""" @overload def __init__( self, *, custom_document_models: "_models.CustomDocumentModelsDetails", ) -> 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 DocumentIntelligenceWarning(_model_base.Model): """The error object. :ivar code: One of a server-defined set of warning codes. Required. :vartype code: str :ivar message: A human-readable representation of the warning. Required. :vartype message: str :ivar target: The target of the error. :vartype target: str """ code: str = rest_field() """One of a server-defined set of warning codes. Required.""" message: str = rest_field() """A human-readable representation of the warning. Required.""" target: Optional[str] = rest_field() """The target of the error.""" @overload def __init__( self, *, code: str, message: str, target: 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 DocumentKeyValueElement(_model_base.Model): """An object representing the field key or value in a key-value pair. :ivar content: Concatenated content of the key-value element in reading order. Required. :vartype content: str :ivar bounding_regions: Bounding regions covering the key-value element. :vartype bounding_regions: list[~azure.ai.documentintelligence.models.BoundingRegion] :ivar spans: Location of the key-value element in the reading order concatenated content. Required. :vartype spans: list[~azure.ai.documentintelligence.models.DocumentSpan] """ content: str = rest_field() """Concatenated content of the key-value element in reading order. Required.""" bounding_regions: Optional[List["_models.BoundingRegion"]] = rest_field(name="boundingRegions") """Bounding regions covering the key-value element.""" spans: List["_models.DocumentSpan"] = rest_field() """Location of the key-value element in the reading order concatenated content. Required.""" @overload def __init__( self, *, content: str, spans: List["_models.DocumentSpan"], bounding_regions: Optional[List["_models.BoundingRegion"]] = 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 DocumentKeyValuePair(_model_base.Model): """An object representing a form field with distinct field label (key) and field value (may be empty). :ivar key: Field label of the key-value pair. Required. :vartype key: ~azure.ai.documentintelligence.models.DocumentKeyValueElement :ivar value: Field value of the key-value pair. :vartype value: ~azure.ai.documentintelligence.models.DocumentKeyValueElement :ivar confidence: Confidence of correctly extracting the key-value pair. Required. :vartype confidence: float """ key: "_models.DocumentKeyValueElement" = rest_field() """Field label of the key-value pair. Required.""" value: Optional["_models.DocumentKeyValueElement"] = rest_field() """Field value of the key-value pair.""" confidence: float = rest_field() """Confidence of correctly extracting the key-value pair. Required.""" @overload def __init__( self, *, key: "_models.DocumentKeyValueElement", confidence: float, value: Optional["_models.DocumentKeyValueElement"] = 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 DocumentLanguage(_model_base.Model): """An object representing the detected language for a given text span. :ivar locale: Detected language. Value may an ISO 639-1 language code (ex. "en", "fr") or BCP 47 language tag (ex. "zh-Hans"). Required. :vartype locale: str :ivar spans: Location of the text elements in the concatenated content the language applies to. Required. :vartype spans: list[~azure.ai.documentintelligence.models.DocumentSpan] :ivar confidence: Confidence of correctly identifying the language. Required. :vartype confidence: float """ locale: str = rest_field() """Detected language. Value may an ISO 639-1 language code (ex. \"en\", \"fr\") or BCP 47 language tag (ex. \"zh-Hans\"). Required.""" spans: List["_models.DocumentSpan"] = rest_field() """Location of the text elements in the concatenated content the language applies to. Required.""" confidence: float = rest_field() """Confidence of correctly identifying the language. Required.""" @overload def __init__( self, *, locale: str, spans: List["_models.DocumentSpan"], confidence: 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 DocumentLine(_model_base.Model): """A content line object consisting of an adjacent sequence of content elements, such as words and selection marks. :ivar content: Concatenated content of the contained elements in reading order. Required. :vartype content: str :ivar polygon: Bounding polygon of the line, with coordinates specified relative to the top-left of the page. The numbers represent the x, y values of the polygon vertices, clockwise from the left (-180 degrees inclusive) relative to the element orientation. :vartype polygon: list[float] :ivar spans: Location of the line in the reading order concatenated content. Required. :vartype spans: list[~azure.ai.documentintelligence.models.DocumentSpan] """ content: str = rest_field() """Concatenated content of the contained elements in reading order. Required.""" polygon: Optional[List[float]] = rest_field() """Bounding polygon of the line, with coordinates specified relative to the top-left of the page. The numbers represent the x, y values of the polygon vertices, clockwise from the left (-180 degrees inclusive) relative to the element orientation.""" spans: List["_models.DocumentSpan"] = rest_field() """Location of the line in the reading order concatenated content. Required.""" @overload def __init__( self, *, content: str, spans: List["_models.DocumentSpan"], polygon: Optional[List[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 DocumentModelBuildOperationDetails(DocumentIntelligenceOperationDetails, discriminator="documentModelBuild"): """Get Operation response object. :ivar operation_id: Operation ID. Required. :vartype operation_id: str :ivar status: Operation status. notStarted, running, completed, or failed. Required. Known values are: "notStarted", "running", "failed", "succeeded", "canceled", and "skipped". :vartype status: str or ~azure.ai.documentintelligence.models.DocumentIntelligenceOperationStatus :ivar percent_completed: Operation progress (0-100). :vartype percent_completed: int :ivar created_date_time: Date and time (UTC) when the operation was created. Required. :vartype created_date_time: ~datetime.datetime :ivar last_updated_date_time: Date and time (UTC) when the status was last updated. Required. :vartype last_updated_date_time: ~datetime.datetime :ivar resource_location: URL of the resource targeted by this operation. Required. :vartype resource_location: str :ivar api_version: API version used to create this operation. :vartype api_version: str :ivar tags: List of key-value tag attributes associated with the document model. :vartype tags: dict[str, str] :ivar error: Encountered error. :vartype error: ~azure.ai.documentintelligence.models.DocumentIntelligenceError :ivar result: Operation result upon success. :vartype result: ~azure.ai.documentintelligence.models.DocumentModelDetails :ivar kind: Type of operation. Required. Build a new custom document model. :vartype kind: str or ~azure.ai.documentintelligence.models.DOCUMENT_MODEL_BUILD """ result: Optional["_models.DocumentModelDetails"] = rest_field() """Operation result upon success.""" kind: Literal[OperationKind.DOCUMENT_MODEL_BUILD] = rest_discriminator(name="kind") # type: ignore """Type of operation. Required. Build a new custom document model.""" @overload def __init__( self, *, operation_id: str, status: Union[str, "_models.DocumentIntelligenceOperationStatus"], created_date_time: datetime.datetime, last_updated_date_time: datetime.datetime, resource_location: str, percent_completed: Optional[int] = None, api_version: Optional[str] = None, tags: Optional[Dict[str, str]] = None, error: Optional["_models.DocumentIntelligenceError"] = None, result: Optional["_models.DocumentModelDetails"] = 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=OperationKind.DOCUMENT_MODEL_BUILD, **kwargs) class DocumentModelComposeOperationDetails(DocumentIntelligenceOperationDetails, discriminator="documentModelCompose"): """Get Operation response object. :ivar operation_id: Operation ID. Required. :vartype operation_id: str :ivar status: Operation status. notStarted, running, completed, or failed. Required. Known values are: "notStarted", "running", "failed", "succeeded", "canceled", and "skipped". :vartype status: str or ~azure.ai.documentintelligence.models.DocumentIntelligenceOperationStatus :ivar percent_completed: Operation progress (0-100). :vartype percent_completed: int :ivar created_date_time: Date and time (UTC) when the operation was created. Required. :vartype created_date_time: ~datetime.datetime :ivar last_updated_date_time: Date and time (UTC) when the status was last updated. Required. :vartype last_updated_date_time: ~datetime.datetime :ivar resource_location: URL of the resource targeted by this operation. Required. :vartype resource_location: str :ivar api_version: API version used to create this operation. :vartype api_version: str :ivar tags: List of key-value tag attributes associated with the document model. :vartype tags: dict[str, str] :ivar error: Encountered error. :vartype error: ~azure.ai.documentintelligence.models.DocumentIntelligenceError :ivar result: Operation result upon success. :vartype result: ~azure.ai.documentintelligence.models.DocumentModelDetails :ivar kind: Type of operation. Required. Compose a new custom document model from existing models. :vartype kind: str or ~azure.ai.documentintelligence.models.DOCUMENT_MODEL_COMPOSE """ result: Optional["_models.DocumentModelDetails"] = rest_field() """Operation result upon success.""" kind: Literal[OperationKind.DOCUMENT_MODEL_COMPOSE] = rest_discriminator(name="kind") # type: ignore """Type of operation. Required. Compose a new custom document model from existing models.""" @overload def __init__( self, *, operation_id: str, status: Union[str, "_models.DocumentIntelligenceOperationStatus"], created_date_time: datetime.datetime, last_updated_date_time: datetime.datetime, resource_location: str, percent_completed: Optional[int] = None, api_version: Optional[str] = None, tags: Optional[Dict[str, str]] = None, error: Optional["_models.DocumentIntelligenceError"] = None, result: Optional["_models.DocumentModelDetails"] = 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=OperationKind.DOCUMENT_MODEL_COMPOSE, **kwargs) class DocumentModelCopyToOperationDetails(DocumentIntelligenceOperationDetails, discriminator="documentModelCopyTo"): """Get Operation response object. :ivar operation_id: Operation ID. Required. :vartype operation_id: str :ivar status: Operation status. notStarted, running, completed, or failed. Required. Known values are: "notStarted", "running", "failed", "succeeded", "canceled", and "skipped". :vartype status: str or ~azure.ai.documentintelligence.models.DocumentIntelligenceOperationStatus :ivar percent_completed: Operation progress (0-100). :vartype percent_completed: int :ivar created_date_time: Date and time (UTC) when the operation was created. Required. :vartype created_date_time: ~datetime.datetime :ivar last_updated_date_time: Date and time (UTC) when the status was last updated. Required. :vartype last_updated_date_time: ~datetime.datetime :ivar resource_location: URL of the resource targeted by this operation. Required. :vartype resource_location: str :ivar api_version: API version used to create this operation. :vartype api_version: str :ivar tags: List of key-value tag attributes associated with the document model. :vartype tags: dict[str, str] :ivar error: Encountered error. :vartype error: ~azure.ai.documentintelligence.models.DocumentIntelligenceError :ivar result: Operation result upon success. :vartype result: ~azure.ai.documentintelligence.models.DocumentModelDetails :ivar kind: Type of operation. Required. Copy an existing document model to potentially a different resource, region, or subscription. :vartype kind: str or ~azure.ai.documentintelligence.models.DOCUMENT_MODEL_COPY_TO """ result: Optional["_models.DocumentModelDetails"] = rest_field() """Operation result upon success.""" kind: Literal[OperationKind.DOCUMENT_MODEL_COPY_TO] = rest_discriminator(name="kind") # type: ignore """Type of operation. Required. Copy an existing document model to potentially a different resource, region, or subscription.""" @overload def __init__( self, *, operation_id: str, status: Union[str, "_models.DocumentIntelligenceOperationStatus"], created_date_time: datetime.datetime, last_updated_date_time: datetime.datetime, resource_location: str, percent_completed: Optional[int] = None, api_version: Optional[str] = None, tags: Optional[Dict[str, str]] = None, error: Optional["_models.DocumentIntelligenceError"] = None, result: Optional["_models.DocumentModelDetails"] = 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=OperationKind.DOCUMENT_MODEL_COPY_TO, **kwargs) class DocumentModelDetails(_model_base.Model): """Document model info. Readonly variables are only populated by the server, and will be ignored when sending a request. :ivar model_id: Unique document model name. Required. :vartype model_id: str :ivar description: Document model description. :vartype description: str :ivar created_date_time: Date and time (UTC) when the document model was created. Required. :vartype created_date_time: ~datetime.datetime :ivar expiration_date_time: Date and time (UTC) when the document model will expire. :vartype expiration_date_time: ~datetime.datetime :ivar modified_date_time: Date and time (UTC) when the document model was last modified. :vartype modified_date_time: ~datetime.datetime :ivar api_version: API version used to create this document model. :vartype api_version: str :ivar tags: List of key-value tag attributes associated with the document model. :vartype tags: dict[str, str] :ivar build_mode: Custom document model build mode. Known values are: "template" and "neural". :vartype build_mode: str or ~azure.ai.documentintelligence.models.DocumentBuildMode :ivar azure_blob_source: Azure Blob Storage location containing the training data. Either azureBlobSource or azureBlobFileListSource must be specified. :vartype azure_blob_source: ~azure.ai.documentintelligence.models.AzureBlobContentSource :ivar azure_blob_file_list_source: Azure Blob Storage file list specifying the training data. Either azureBlobSource or azureBlobFileListSource must be specified. :vartype azure_blob_file_list_source: ~azure.ai.documentintelligence.models.AzureBlobFileListContentSource :ivar classifier_id: For composed models, the custom classifier to split and classify the input file. :vartype classifier_id: str :ivar split: For composed models, the file splitting behavior. Known values are: "auto", "none", and "perPage". :vartype split: str or ~azure.ai.documentintelligence.models.SplitMode :ivar doc_types: Supported document types. :vartype doc_types: dict[str, ~azure.ai.documentintelligence.models.DocumentTypeDetails] :ivar warnings: List of warnings encountered while building the model. :vartype warnings: list[~azure.ai.documentintelligence.models.DocumentIntelligenceWarning] :ivar training_hours: Number of V100-equivalent GPU hours consumed for model training. :vartype training_hours: float """ model_id: str = rest_field(name="modelId", visibility=["read", "create"]) """Unique document model name. Required.""" description: Optional[str] = rest_field() """Document model description.""" created_date_time: datetime.datetime = rest_field(name="createdDateTime", visibility=["read"], format="rfc3339") """Date and time (UTC) when the document model was created. Required.""" expiration_date_time: Optional[datetime.datetime] = rest_field( name="expirationDateTime", visibility=["read"], format="rfc3339" ) """Date and time (UTC) when the document model will expire.""" modified_date_time: Optional[datetime.datetime] = rest_field( name="modifiedDateTime", visibility=["read"], format="rfc3339" ) """Date and time (UTC) when the document model was last modified.""" api_version: Optional[str] = rest_field(name="apiVersion", visibility=["read"]) """API version used to create this document model.""" tags: Optional[Dict[str, str]] = rest_field() """List of key-value tag attributes associated with the document model.""" build_mode: Optional[Union[str, "_models.DocumentBuildMode"]] = rest_field(name="buildMode", visibility=["read"]) """Custom document model build mode. Known values are: \"template\" and \"neural\".""" azure_blob_source: Optional["_models.AzureBlobContentSource"] = rest_field( name="azureBlobSource", visibility=["read"] ) """Azure Blob Storage location containing the training data. Either azureBlobSource or azureBlobFileListSource must be specified.""" azure_blob_file_list_source: Optional["_models.AzureBlobFileListContentSource"] = rest_field( name="azureBlobFileListSource", visibility=["read"] ) """Azure Blob Storage file list specifying the training data. Either azureBlobSource or azureBlobFileListSource must be specified.""" classifier_id: Optional[str] = rest_field(name="classifierId") """For composed models, the custom classifier to split and classify the input file.""" split: Optional[Union[str, "_models.SplitMode"]] = rest_field() """For composed models, the file splitting behavior. Known values are: \"auto\", \"none\", and \"perPage\".""" doc_types: Optional[Dict[str, "_models.DocumentTypeDetails"]] = rest_field(name="docTypes", visibility=["read"]) """Supported document types.""" warnings: Optional[List["_models.DocumentIntelligenceWarning"]] = rest_field(visibility=["read"]) """List of warnings encountered while building the model.""" training_hours: Optional[float] = rest_field(name="trainingHours", visibility=["read"]) """Number of V100-equivalent GPU hours consumed for model training.""" @overload def __init__( self, *, model_id: str, description: Optional[str] = None, tags: Optional[Dict[str, str]] = None, classifier_id: Optional[str] = None, split: Optional[Union[str, "_models.SplitMode"]] = 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 DocumentPage(_model_base.Model): """Content and layout elements extracted from a page from the input. :ivar page_number: 1-based page number in the input document. Required. :vartype page_number: int :ivar angle: The general orientation of the content in clockwise direction, measured in degrees between (-180, 180]. :vartype angle: float :ivar width: The width of the image/PDF in pixels/inches, respectively. :vartype width: float :ivar height: The height of the image/PDF in pixels/inches, respectively. :vartype height: float :ivar unit: The unit used by the width, height, and polygon properties. For images, the unit is "pixel". For PDF, the unit is "inch". Known values are: "pixel" and "inch". :vartype unit: str or ~azure.ai.documentintelligence.models.LengthUnit :ivar spans: Location of the page in the reading order concatenated content. Required. :vartype spans: list[~azure.ai.documentintelligence.models.DocumentSpan] :ivar words: Extracted words from the page. :vartype words: list[~azure.ai.documentintelligence.models.DocumentWord] :ivar selection_marks: Extracted selection marks from the page. :vartype selection_marks: list[~azure.ai.documentintelligence.models.DocumentSelectionMark] :ivar lines: Extracted lines from the page, potentially containing both textual and visual elements. :vartype lines: list[~azure.ai.documentintelligence.models.DocumentLine] :ivar barcodes: Extracted barcodes from the page. :vartype barcodes: list[~azure.ai.documentintelligence.models.DocumentBarcode] :ivar formulas: Extracted formulas from the page. :vartype formulas: list[~azure.ai.documentintelligence.models.DocumentFormula] """ page_number: int = rest_field(name="pageNumber") """1-based page number in the input document. Required.""" angle: Optional[float] = rest_field() """The general orientation of the content in clockwise direction, measured in degrees between (-180, 180].""" width: Optional[float] = rest_field() """The width of the image/PDF in pixels/inches, respectively.""" height: Optional[float] = rest_field() """The height of the image/PDF in pixels/inches, respectively.""" unit: Optional[Union[str, "_models.LengthUnit"]] = rest_field() """The unit used by the width, height, and polygon properties. For images, the unit is \"pixel\". For PDF, the unit is \"inch\". Known values are: \"pixel\" and \"inch\".""" spans: List["_models.DocumentSpan"] = rest_field() """Location of the page in the reading order concatenated content. Required.""" words: Optional[List["_models.DocumentWord"]] = rest_field() """Extracted words from the page.""" selection_marks: Optional[List["_models.DocumentSelectionMark"]] = rest_field(name="selectionMarks") """Extracted selection marks from the page.""" lines: Optional[List["_models.DocumentLine"]] = rest_field() """Extracted lines from the page, potentially containing both textual and visual elements.""" barcodes: Optional[List["_models.DocumentBarcode"]] = rest_field() """Extracted barcodes from the page.""" formulas: Optional[List["_models.DocumentFormula"]] = rest_field() """Extracted formulas from the page.""" @overload def __init__( self, *, page_number: int, spans: List["_models.DocumentSpan"], angle: Optional[float] = None, width: Optional[float] = None, height: Optional[float] = None, unit: Optional[Union[str, "_models.LengthUnit"]] = None, words: Optional[List["_models.DocumentWord"]] = None, selection_marks: Optional[List["_models.DocumentSelectionMark"]] = None, lines: Optional[List["_models.DocumentLine"]] = None, barcodes: Optional[List["_models.DocumentBarcode"]] = None, formulas: Optional[List["_models.DocumentFormula"]] = 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 DocumentParagraph(_model_base.Model): """A paragraph object consisting with contiguous lines generally with common alignment and spacing. :ivar role: Semantic role of the paragraph. Known values are: "pageHeader", "pageFooter", "pageNumber", "title", "sectionHeading", "footnote", and "formulaBlock". :vartype role: str or ~azure.ai.documentintelligence.models.ParagraphRole :ivar content: Concatenated content of the paragraph in reading order. Required. :vartype content: str :ivar bounding_regions: Bounding regions covering the paragraph. :vartype bounding_regions: list[~azure.ai.documentintelligence.models.BoundingRegion] :ivar spans: Location of the paragraph in the reading order concatenated content. Required. :vartype spans: list[~azure.ai.documentintelligence.models.DocumentSpan] """ role: Optional[Union[str, "_models.ParagraphRole"]] = rest_field() """Semantic role of the paragraph. Known values are: \"pageHeader\", \"pageFooter\", \"pageNumber\", \"title\", \"sectionHeading\", \"footnote\", and \"formulaBlock\".""" content: str = rest_field() """Concatenated content of the paragraph in reading order. Required.""" bounding_regions: Optional[List["_models.BoundingRegion"]] = rest_field(name="boundingRegions") """Bounding regions covering the paragraph.""" spans: List["_models.DocumentSpan"] = rest_field() """Location of the paragraph in the reading order concatenated content. Required.""" @overload def __init__( self, *, content: str, spans: List["_models.DocumentSpan"], role: Optional[Union[str, "_models.ParagraphRole"]] = None, bounding_regions: Optional[List["_models.BoundingRegion"]] = 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 DocumentSection(_model_base.Model): """An object representing a section in the document. :ivar spans: Location of the section in the reading order concatenated content. Required. :vartype spans: list[~azure.ai.documentintelligence.models.DocumentSpan] :ivar elements: Child elements of the section. :vartype elements: list[str] """ spans: List["_models.DocumentSpan"] = rest_field() """Location of the section in the reading order concatenated content. Required.""" elements: Optional[List[str]] = rest_field() """Child elements of the section.""" @overload def __init__( self, *, spans: List["_models.DocumentSpan"], elements: Optional[List[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 DocumentSelectionMark(_model_base.Model): """A selection mark object representing check boxes, radio buttons, and other elements indicating a selection. :ivar state: State of the selection mark. Required. Known values are: "selected" and "unselected". :vartype state: str or ~azure.ai.documentintelligence.models.DocumentSelectionMarkState :ivar polygon: Bounding polygon of the selection mark, with coordinates specified relative to the top-left of the page. The numbers represent the x, y values of the polygon vertices, clockwise from the left (-180 degrees inclusive) relative to the element orientation. :vartype polygon: list[float] :ivar span: Location of the selection mark in the reading order concatenated content. Required. :vartype span: ~azure.ai.documentintelligence.models.DocumentSpan :ivar confidence: Confidence of correctly extracting the selection mark. Required. :vartype confidence: float """ state: Union[str, "_models.DocumentSelectionMarkState"] = rest_field() """State of the selection mark. Required. Known values are: \"selected\" and \"unselected\".""" polygon: Optional[List[float]] = rest_field() """Bounding polygon of the selection mark, with coordinates specified relative to the top-left of the page. The numbers represent the x, y values of the polygon vertices, clockwise from the left (-180 degrees inclusive) relative to the element orientation.""" span: "_models.DocumentSpan" = rest_field() """Location of the selection mark in the reading order concatenated content. Required.""" confidence: float = rest_field() """Confidence of correctly extracting the selection mark. Required.""" @overload def __init__( self, *, state: Union[str, "_models.DocumentSelectionMarkState"], span: "_models.DocumentSpan", confidence: float, polygon: Optional[List[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 DocumentSpan(_model_base.Model): """Contiguous region of the concatenated content property, specified as an offset and length. :ivar offset: Zero-based index of the content represented by the span. Required. :vartype offset: int :ivar length: Number of characters in the content represented by the span. Required. :vartype length: int """ offset: int = rest_field() """Zero-based index of the content represented by the span. Required.""" length: int = rest_field() """Number of characters in the content represented by the span. 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 DocumentStyle(_model_base.Model): """An object representing observed text styles. :ivar is_handwritten: Is content handwritten?. :vartype is_handwritten: bool :ivar similar_font_family: Visually most similar font from among the set of supported font families, with fallback fonts following CSS convention (ex. 'Arial, sans-serif'). :vartype similar_font_family: str :ivar font_style: Font style. Known values are: "normal" and "italic". :vartype font_style: str or ~azure.ai.documentintelligence.models.DocumentFontStyle :ivar font_weight: Font weight. Known values are: "normal" and "bold". :vartype font_weight: str or ~azure.ai.documentintelligence.models.DocumentFontWeight :ivar color: Foreground color in #rrggbb hexadecimal format. :vartype color: str :ivar background_color: Background color in #rrggbb hexadecimal format.. :vartype background_color: str :ivar spans: Location of the text elements in the concatenated content the style applies to. Required. :vartype spans: list[~azure.ai.documentintelligence.models.DocumentSpan] :ivar confidence: Confidence of correctly identifying the style. Required. :vartype confidence: float """ is_handwritten: Optional[bool] = rest_field(name="isHandwritten") """Is content handwritten?.""" similar_font_family: Optional[str] = rest_field(name="similarFontFamily") """Visually most similar font from among the set of supported font families, with fallback fonts following CSS convention (ex. 'Arial, sans-serif').""" font_style: Optional[Union[str, "_models.DocumentFontStyle"]] = rest_field(name="fontStyle") """Font style. Known values are: \"normal\" and \"italic\".""" font_weight: Optional[Union[str, "_models.DocumentFontWeight"]] = rest_field(name="fontWeight") """Font weight. Known values are: \"normal\" and \"bold\".""" color: Optional[str] = rest_field() """Foreground color in #rrggbb hexadecimal format.""" background_color: Optional[str] = rest_field(name="backgroundColor") """Background color in #rrggbb hexadecimal format..""" spans: List["_models.DocumentSpan"] = rest_field() """Location of the text elements in the concatenated content the style applies to. Required.""" confidence: float = rest_field() """Confidence of correctly identifying the style. Required.""" @overload def __init__( self, *, spans: List["_models.DocumentSpan"], confidence: float, is_handwritten: Optional[bool] = None, similar_font_family: Optional[str] = None, font_style: Optional[Union[str, "_models.DocumentFontStyle"]] = None, font_weight: Optional[Union[str, "_models.DocumentFontWeight"]] = None, color: Optional[str] = None, background_color: 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 DocumentTable(_model_base.Model): """A table object consisting table cells arranged in a rectangular layout. :ivar row_count: Number of rows in the table. Required. :vartype row_count: int :ivar column_count: Number of columns in the table. Required. :vartype column_count: int :ivar cells: Cells contained within the table. Required. :vartype cells: list[~azure.ai.documentintelligence.models.DocumentTableCell] :ivar bounding_regions: Bounding regions covering the table. :vartype bounding_regions: list[~azure.ai.documentintelligence.models.BoundingRegion] :ivar spans: Location of the table in the reading order concatenated content. Required. :vartype spans: list[~azure.ai.documentintelligence.models.DocumentSpan] :ivar caption: Caption associated with the table. :vartype caption: ~azure.ai.documentintelligence.models.DocumentCaption :ivar footnotes: List of footnotes associated with the table. :vartype footnotes: list[~azure.ai.documentintelligence.models.DocumentFootnote] """ row_count: int = rest_field(name="rowCount") """Number of rows in the table. Required.""" column_count: int = rest_field(name="columnCount") """Number of columns in the table. Required.""" cells: List["_models.DocumentTableCell"] = rest_field() """Cells contained within the table. Required.""" bounding_regions: Optional[List["_models.BoundingRegion"]] = rest_field(name="boundingRegions") """Bounding regions covering the table.""" spans: List["_models.DocumentSpan"] = rest_field() """Location of the table in the reading order concatenated content. Required.""" caption: Optional["_models.DocumentCaption"] = rest_field() """Caption associated with the table.""" footnotes: Optional[List["_models.DocumentFootnote"]] = rest_field() """List of footnotes associated with the table.""" @overload def __init__( self, *, row_count: int, column_count: int, cells: List["_models.DocumentTableCell"], spans: List["_models.DocumentSpan"], bounding_regions: Optional[List["_models.BoundingRegion"]] = None, caption: Optional["_models.DocumentCaption"] = None, footnotes: Optional[List["_models.DocumentFootnote"]] = 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 DocumentTableCell(_model_base.Model): """An object representing the location and content of a table cell. :ivar kind: Table cell kind. Known values are: "content", "rowHeader", "columnHeader", "stubHead", and "description". :vartype kind: str or ~azure.ai.documentintelligence.models.DocumentTableCellKind :ivar row_index: Row index of the cell. Required. :vartype row_index: int :ivar column_index: Column index of the cell. Required. :vartype column_index: int :ivar row_span: Number of rows spanned by this cell. :vartype row_span: int :ivar column_span: Number of columns spanned by this cell. :vartype column_span: int :ivar content: Concatenated content of the table cell in reading order. Required. :vartype content: str :ivar bounding_regions: Bounding regions covering the table cell. :vartype bounding_regions: list[~azure.ai.documentintelligence.models.BoundingRegion] :ivar spans: Location of the table cell in the reading order concatenated content. Required. :vartype spans: list[~azure.ai.documentintelligence.models.DocumentSpan] :ivar elements: Child elements of the table cell. :vartype elements: list[str] """ kind: Optional[Union[str, "_models.DocumentTableCellKind"]] = rest_field() """Table cell kind. Known values are: \"content\", \"rowHeader\", \"columnHeader\", \"stubHead\", and \"description\".""" row_index: int = rest_field(name="rowIndex") """Row index of the cell. Required.""" column_index: int = rest_field(name="columnIndex") """Column index of the cell. Required.""" row_span: Optional[int] = rest_field(name="rowSpan") """Number of rows spanned by this cell.""" column_span: Optional[int] = rest_field(name="columnSpan") """Number of columns spanned by this cell.""" content: str = rest_field() """Concatenated content of the table cell in reading order. Required.""" bounding_regions: Optional[List["_models.BoundingRegion"]] = rest_field(name="boundingRegions") """Bounding regions covering the table cell.""" spans: List["_models.DocumentSpan"] = rest_field() """Location of the table cell in the reading order concatenated content. Required.""" elements: Optional[List[str]] = rest_field() """Child elements of the table cell.""" @overload def __init__( self, *, row_index: int, column_index: int, content: str, spans: List["_models.DocumentSpan"], kind: Optional[Union[str, "_models.DocumentTableCellKind"]] = None, row_span: Optional[int] = None, column_span: Optional[int] = None, bounding_regions: Optional[List["_models.BoundingRegion"]] = None, elements: Optional[List[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 DocumentTypeDetails(_model_base.Model): """Document type info. :ivar description: Document model description. :vartype description: str :ivar build_mode: Custom document model build mode. Known values are: "template" and "neural". :vartype build_mode: str or ~azure.ai.documentintelligence.models.DocumentBuildMode :ivar field_schema: Description of the document semantic schema using a JSON Schema style syntax. :vartype field_schema: dict[str, ~azure.ai.documentintelligence.models.DocumentFieldSchema] :ivar field_confidence: Estimated confidence for each field. :vartype field_confidence: dict[str, float] :ivar model_id: Document model to use for analyzing documents with specified type. :vartype model_id: str :ivar confidence_threshold: Only perform analysis if docType confidence is above threshold. :vartype confidence_threshold: float :ivar features: List of optional analysis features. :vartype features: list[str or ~azure.ai.documentintelligence.models.DocumentAnalysisFeature] :ivar query_fields: List of additional fields to extract. Ex. "NumberOfGuests,StoreNumber". :vartype query_fields: list[str] :ivar max_documents_to_analyze: Maximum number of documents of specified type to analyze. Default=all. :vartype max_documents_to_analyze: int """ description: Optional[str] = rest_field() """Document model description.""" build_mode: Optional[Union[str, "_models.DocumentBuildMode"]] = rest_field(name="buildMode") """Custom document model build mode. Known values are: \"template\" and \"neural\".""" field_schema: Optional[Dict[str, "_models.DocumentFieldSchema"]] = rest_field(name="fieldSchema") """Description of the document semantic schema using a JSON Schema style syntax.""" field_confidence: Optional[Dict[str, float]] = rest_field(name="fieldConfidence") """Estimated confidence for each field.""" model_id: Optional[str] = rest_field(name="modelId") """Document model to use for analyzing documents with specified type.""" confidence_threshold: Optional[float] = rest_field(name="confidenceThreshold") """Only perform analysis if docType confidence is above threshold.""" features: Optional[List[Union[str, "_models.DocumentAnalysisFeature"]]] = rest_field() """List of optional analysis features.""" query_fields: Optional[List[str]] = rest_field(name="queryFields") """List of additional fields to extract. Ex. \"NumberOfGuests,StoreNumber\".""" max_documents_to_analyze: Optional[int] = rest_field(name="maxDocumentsToAnalyze") """Maximum number of documents of specified type to analyze. Default=all.""" @overload def __init__( self, *, description: Optional[str] = None, build_mode: Optional[Union[str, "_models.DocumentBuildMode"]] = None, field_schema: Optional[Dict[str, "_models.DocumentFieldSchema"]] = None, field_confidence: Optional[Dict[str, float]] = None, model_id: Optional[str] = None, confidence_threshold: Optional[float] = None, features: Optional[List[Union[str, "_models.DocumentAnalysisFeature"]]] = None, query_fields: Optional[List[str]] = None, max_documents_to_analyze: 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 DocumentWord(_model_base.Model): """A word object consisting of a contiguous sequence of characters. For non-space delimited languages, such as Chinese, Japanese, and Korean, each character is represented as its own word. :ivar content: Text content of the word. Required. :vartype content: str :ivar polygon: Bounding polygon of the word, with coordinates specified relative to the top-left of the page. The numbers represent the x, y values of the polygon vertices, clockwise from the left (-180 degrees inclusive) relative to the element orientation. :vartype polygon: list[float] :ivar span: Location of the word in the reading order concatenated content. Required. :vartype span: ~azure.ai.documentintelligence.models.DocumentSpan :ivar confidence: Confidence of correctly extracting the word. Required. :vartype confidence: float """ content: str = rest_field() """Text content of the word. Required.""" polygon: Optional[List[float]] = rest_field() """Bounding polygon of the word, with coordinates specified relative to the top-left of the page. The numbers represent the x, y values of the polygon vertices, clockwise from the left (-180 degrees inclusive) relative to the element orientation.""" span: "_models.DocumentSpan" = rest_field() """Location of the word in the reading order concatenated content. Required.""" confidence: float = rest_field() """Confidence of correctly extracting the word. Required.""" @overload def __init__( self, *, content: str, span: "_models.DocumentSpan", confidence: float, polygon: Optional[List[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 ModelCopyAuthorization(_model_base.Model): """Authorization to copy a document model to the specified target resource and modelId. :ivar target_resource_id: ID of the target Azure resource where the document model should be copied to. Required. :vartype target_resource_id: str :ivar target_resource_region: Location of the target Azure resource where the document model should be copied to. Required. :vartype target_resource_region: str :ivar target_model_id: Identifier of the target document model. Required. :vartype target_model_id: str :ivar target_model_location: URL of the copied document model in the target account. Required. :vartype target_model_location: str :ivar access_token: Token used to authorize the request. Required. :vartype access_token: str :ivar expiration_date_time: Date/time when the access token expires. Required. :vartype expiration_date_time: ~datetime.datetime """ target_resource_id: str = rest_field(name="targetResourceId") """ID of the target Azure resource where the document model should be copied to. Required.""" target_resource_region: str = rest_field(name="targetResourceRegion") """Location of the target Azure resource where the document model should be copied to. Required.""" target_model_id: str = rest_field(name="targetModelId") """Identifier of the target document model. Required.""" target_model_location: str = rest_field(name="targetModelLocation") """URL of the copied document model in the target account. Required.""" access_token: str = rest_field(name="accessToken") """Token used to authorize the request. Required.""" expiration_date_time: datetime.datetime = rest_field(name="expirationDateTime", format="rfc3339") """Date/time when the access token expires. Required.""" @overload def __init__( self, *, target_resource_id: str, target_resource_region: str, target_model_id: str, target_model_location: str, access_token: str, expiration_date_time: datetime.datetime, ) -> 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)