generated/googleapis/lib/language/v1.dart

// This is a generated file (see the discoveryapis_generator project).

library googleapis.language.v1;

import 'dart:core' as core;
import 'dart:async' as async;
import 'dart:convert' as convert;

import 'package:_discoveryapis_commons/_discoveryapis_commons.dart' as commons;
import 'package:http/http.dart' as http;

export 'package:_discoveryapis_commons/_discoveryapis_commons.dart'
    show ApiRequestError, DetailedApiRequestError;

const core.String USER_AGENT = 'dart-api-client language/v1';

/// Provides natural language understanding technologies to developers. Examples
/// include sentiment analysis, entity recognition, entity sentiment analysis,
/// and text annotations.
class LanguageApi {
  /// Apply machine learning models to reveal the structure and meaning of text
  static const CloudLanguageScope =
      "https://www.googleapis.com/auth/cloud-language";

  /// View and manage your data across Google Cloud Platform services
  static const CloudPlatformScope =
      "https://www.googleapis.com/auth/cloud-platform";

  final commons.ApiRequester _requester;

  DocumentsResourceApi get documents => new DocumentsResourceApi(_requester);

  LanguageApi(http.Client client,
      {core.String rootUrl: "https://language.googleapis.com/",
      core.String servicePath: ""})
      : _requester =
            new commons.ApiRequester(client, rootUrl, servicePath, USER_AGENT);
}

class DocumentsResourceApi {
  final commons.ApiRequester _requester;

  DocumentsResourceApi(commons.ApiRequester client) : _requester = client;

  /// Finds named entities (currently proper names and common nouns) in the text
  /// along with entity types, salience, mentions for each entity, and
  /// other properties.
  ///
  /// [request] - The metadata request object.
  ///
  /// Request parameters:
  ///
  /// [$fields] - Selector specifying which fields to include in a partial
  /// response.
  ///
  /// Completes with a [AnalyzeEntitiesResponse].
  ///
  /// Completes with a [commons.ApiRequestError] if the API endpoint returned an
  /// error.
  ///
  /// If the used [http.Client] completes with an error when making a REST call,
  /// this method will complete with the same error.
  async.Future<AnalyzeEntitiesResponse> analyzeEntities(
      AnalyzeEntitiesRequest request,
      {core.String $fields}) {
    var _url = null;
    var _queryParams = new core.Map<core.String, core.List<core.String>>();
    var _uploadMedia = null;
    var _uploadOptions = null;
    var _downloadOptions = commons.DownloadOptions.Metadata;
    var _body = null;

    if (request != null) {
      _body = convert.JSON.encode((request).toJson());
    }
    if ($fields != null) {
      _queryParams["fields"] = [$fields];
    }

    _url = 'v1/documents:analyzeEntities';

    var _response = _requester.request(_url, "POST",
        body: _body,
        queryParams: _queryParams,
        uploadOptions: _uploadOptions,
        uploadMedia: _uploadMedia,
        downloadOptions: _downloadOptions);
    return _response.then((data) => new AnalyzeEntitiesResponse.fromJson(data));
  }

  /// Finds entities, similar to AnalyzeEntities in the text and analyzes
  /// sentiment associated with each entity and its mentions.
  ///
  /// [request] - The metadata request object.
  ///
  /// Request parameters:
  ///
  /// [$fields] - Selector specifying which fields to include in a partial
  /// response.
  ///
  /// Completes with a [AnalyzeEntitySentimentResponse].
  ///
  /// Completes with a [commons.ApiRequestError] if the API endpoint returned an
  /// error.
  ///
  /// If the used [http.Client] completes with an error when making a REST call,
  /// this method will complete with the same error.
  async.Future<AnalyzeEntitySentimentResponse> analyzeEntitySentiment(
      AnalyzeEntitySentimentRequest request,
      {core.String $fields}) {
    var _url = null;
    var _queryParams = new core.Map<core.String, core.List<core.String>>();
    var _uploadMedia = null;
    var _uploadOptions = null;
    var _downloadOptions = commons.DownloadOptions.Metadata;
    var _body = null;

    if (request != null) {
      _body = convert.JSON.encode((request).toJson());
    }
    if ($fields != null) {
      _queryParams["fields"] = [$fields];
    }

    _url = 'v1/documents:analyzeEntitySentiment';

    var _response = _requester.request(_url, "POST",
        body: _body,
        queryParams: _queryParams,
        uploadOptions: _uploadOptions,
        uploadMedia: _uploadMedia,
        downloadOptions: _downloadOptions);
    return _response
        .then((data) => new AnalyzeEntitySentimentResponse.fromJson(data));
  }

  /// Analyzes the sentiment of the provided text.
  ///
  /// [request] - The metadata request object.
  ///
  /// Request parameters:
  ///
  /// [$fields] - Selector specifying which fields to include in a partial
  /// response.
  ///
  /// Completes with a [AnalyzeSentimentResponse].
  ///
  /// Completes with a [commons.ApiRequestError] if the API endpoint returned an
  /// error.
  ///
  /// If the used [http.Client] completes with an error when making a REST call,
  /// this method will complete with the same error.
  async.Future<AnalyzeSentimentResponse> analyzeSentiment(
      AnalyzeSentimentRequest request,
      {core.String $fields}) {
    var _url = null;
    var _queryParams = new core.Map<core.String, core.List<core.String>>();
    var _uploadMedia = null;
    var _uploadOptions = null;
    var _downloadOptions = commons.DownloadOptions.Metadata;
    var _body = null;

    if (request != null) {
      _body = convert.JSON.encode((request).toJson());
    }
    if ($fields != null) {
      _queryParams["fields"] = [$fields];
    }

    _url = 'v1/documents:analyzeSentiment';

    var _response = _requester.request(_url, "POST",
        body: _body,
        queryParams: _queryParams,
        uploadOptions: _uploadOptions,
        uploadMedia: _uploadMedia,
        downloadOptions: _downloadOptions);
    return _response
        .then((data) => new AnalyzeSentimentResponse.fromJson(data));
  }

  /// Analyzes the syntax of the text and provides sentence boundaries and
  /// tokenization along with part of speech tags, dependency trees, and other
  /// properties.
  ///
  /// [request] - The metadata request object.
  ///
  /// Request parameters:
  ///
  /// [$fields] - Selector specifying which fields to include in a partial
  /// response.
  ///
  /// Completes with a [AnalyzeSyntaxResponse].
  ///
  /// Completes with a [commons.ApiRequestError] if the API endpoint returned an
  /// error.
  ///
  /// If the used [http.Client] completes with an error when making a REST call,
  /// this method will complete with the same error.
  async.Future<AnalyzeSyntaxResponse> analyzeSyntax(
      AnalyzeSyntaxRequest request,
      {core.String $fields}) {
    var _url = null;
    var _queryParams = new core.Map<core.String, core.List<core.String>>();
    var _uploadMedia = null;
    var _uploadOptions = null;
    var _downloadOptions = commons.DownloadOptions.Metadata;
    var _body = null;

    if (request != null) {
      _body = convert.JSON.encode((request).toJson());
    }
    if ($fields != null) {
      _queryParams["fields"] = [$fields];
    }

    _url = 'v1/documents:analyzeSyntax';

    var _response = _requester.request(_url, "POST",
        body: _body,
        queryParams: _queryParams,
        uploadOptions: _uploadOptions,
        uploadMedia: _uploadMedia,
        downloadOptions: _downloadOptions);
    return _response.then((data) => new AnalyzeSyntaxResponse.fromJson(data));
  }

  /// A convenience method that provides all the features that analyzeSentiment,
  /// analyzeEntities, and analyzeSyntax provide in one call.
  ///
  /// [request] - The metadata request object.
  ///
  /// Request parameters:
  ///
  /// [$fields] - Selector specifying which fields to include in a partial
  /// response.
  ///
  /// Completes with a [AnnotateTextResponse].
  ///
  /// Completes with a [commons.ApiRequestError] if the API endpoint returned an
  /// error.
  ///
  /// If the used [http.Client] completes with an error when making a REST call,
  /// this method will complete with the same error.
  async.Future<AnnotateTextResponse> annotateText(AnnotateTextRequest request,
      {core.String $fields}) {
    var _url = null;
    var _queryParams = new core.Map<core.String, core.List<core.String>>();
    var _uploadMedia = null;
    var _uploadOptions = null;
    var _downloadOptions = commons.DownloadOptions.Metadata;
    var _body = null;

    if (request != null) {
      _body = convert.JSON.encode((request).toJson());
    }
    if ($fields != null) {
      _queryParams["fields"] = [$fields];
    }

    _url = 'v1/documents:annotateText';

    var _response = _requester.request(_url, "POST",
        body: _body,
        queryParams: _queryParams,
        uploadOptions: _uploadOptions,
        uploadMedia: _uploadMedia,
        downloadOptions: _downloadOptions);
    return _response.then((data) => new AnnotateTextResponse.fromJson(data));
  }

  /// Classifies a document into categories.
  ///
  /// [request] - The metadata request object.
  ///
  /// Request parameters:
  ///
  /// [$fields] - Selector specifying which fields to include in a partial
  /// response.
  ///
  /// Completes with a [ClassifyTextResponse].
  ///
  /// Completes with a [commons.ApiRequestError] if the API endpoint returned an
  /// error.
  ///
  /// If the used [http.Client] completes with an error when making a REST call,
  /// this method will complete with the same error.
  async.Future<ClassifyTextResponse> classifyText(ClassifyTextRequest request,
      {core.String $fields}) {
    var _url = null;
    var _queryParams = new core.Map<core.String, core.List<core.String>>();
    var _uploadMedia = null;
    var _uploadOptions = null;
    var _downloadOptions = commons.DownloadOptions.Metadata;
    var _body = null;

    if (request != null) {
      _body = convert.JSON.encode((request).toJson());
    }
    if ($fields != null) {
      _queryParams["fields"] = [$fields];
    }

    _url = 'v1/documents:classifyText';

    var _response = _requester.request(_url, "POST",
        body: _body,
        queryParams: _queryParams,
        uploadOptions: _uploadOptions,
        uploadMedia: _uploadMedia,
        downloadOptions: _downloadOptions);
    return _response.then((data) => new ClassifyTextResponse.fromJson(data));
  }
}

/// The entity analysis request message.
class AnalyzeEntitiesRequest {
  /// Input document.
  Document document;

  /// The encoding type used by the API to calculate offsets.
  /// Possible string values are:
  /// - "NONE" : If `EncodingType` is not specified, encoding-dependent
  /// information (such as
  /// `begin_offset`) will be set at `-1`.
  /// - "UTF8" : Encoding-dependent information (such as `begin_offset`) is
  /// calculated based
  /// on the UTF-8 encoding of the input. C++ and Go are examples of languages
  /// that use this encoding natively.
  /// - "UTF16" : Encoding-dependent information (such as `begin_offset`) is
  /// calculated based
  /// on the UTF-16 encoding of the input. Java and Javascript are examples of
  /// languages that use this encoding natively.
  /// - "UTF32" : Encoding-dependent information (such as `begin_offset`) is
  /// calculated based
  /// on the UTF-32 encoding of the input. Python is an example of a language
  /// that uses this encoding natively.
  core.String encodingType;

  AnalyzeEntitiesRequest();

  AnalyzeEntitiesRequest.fromJson(core.Map _json) {
    if (_json.containsKey("document")) {
      document = new Document.fromJson(_json["document"]);
    }
    if (_json.containsKey("encodingType")) {
      encodingType = _json["encodingType"];
    }
  }

  core.Map<core.String, core.Object> toJson() {
    final core.Map<core.String, core.Object> _json =
        new core.Map<core.String, core.Object>();
    if (document != null) {
      _json["document"] = (document).toJson();
    }
    if (encodingType != null) {
      _json["encodingType"] = encodingType;
    }
    return _json;
  }
}

/// The entity analysis response message.
class AnalyzeEntitiesResponse {
  /// The recognized entities in the input document.
  core.List<Entity> entities;

  /// The language of the text, which will be the same as the language specified
  /// in the request or, if not specified, the automatically-detected language.
  /// See Document.language field for more details.
  core.String language;

  AnalyzeEntitiesResponse();

  AnalyzeEntitiesResponse.fromJson(core.Map _json) {
    if (_json.containsKey("entities")) {
      entities = _json["entities"]
          .map<Entity>((value) => new Entity.fromJson(value))
          .toList();
    }
    if (_json.containsKey("language")) {
      language = _json["language"];
    }
  }

  core.Map<core.String, core.Object> toJson() {
    final core.Map<core.String, core.Object> _json =
        new core.Map<core.String, core.Object>();
    if (entities != null) {
      _json["entities"] = entities.map((value) => (value).toJson()).toList();
    }
    if (language != null) {
      _json["language"] = language;
    }
    return _json;
  }
}

/// The entity-level sentiment analysis request message.
class AnalyzeEntitySentimentRequest {
  /// Input document.
  Document document;

  /// The encoding type used by the API to calculate offsets.
  /// Possible string values are:
  /// - "NONE" : If `EncodingType` is not specified, encoding-dependent
  /// information (such as
  /// `begin_offset`) will be set at `-1`.
  /// - "UTF8" : Encoding-dependent information (such as `begin_offset`) is
  /// calculated based
  /// on the UTF-8 encoding of the input. C++ and Go are examples of languages
  /// that use this encoding natively.
  /// - "UTF16" : Encoding-dependent information (such as `begin_offset`) is
  /// calculated based
  /// on the UTF-16 encoding of the input. Java and Javascript are examples of
  /// languages that use this encoding natively.
  /// - "UTF32" : Encoding-dependent information (such as `begin_offset`) is
  /// calculated based
  /// on the UTF-32 encoding of the input. Python is an example of a language
  /// that uses this encoding natively.
  core.String encodingType;

  AnalyzeEntitySentimentRequest();

  AnalyzeEntitySentimentRequest.fromJson(core.Map _json) {
    if (_json.containsKey("document")) {
      document = new Document.fromJson(_json["document"]);
    }
    if (_json.containsKey("encodingType")) {
      encodingType = _json["encodingType"];
    }
  }

  core.Map<core.String, core.Object> toJson() {
    final core.Map<core.String, core.Object> _json =
        new core.Map<core.String, core.Object>();
    if (document != null) {
      _json["document"] = (document).toJson();
    }
    if (encodingType != null) {
      _json["encodingType"] = encodingType;
    }
    return _json;
  }
}

/// The entity-level sentiment analysis response message.
class AnalyzeEntitySentimentResponse {
  /// The recognized entities in the input document with associated sentiments.
  core.List<Entity> entities;

  /// The language of the text, which will be the same as the language specified
  /// in the request or, if not specified, the automatically-detected language.
  /// See Document.language field for more details.
  core.String language;

  AnalyzeEntitySentimentResponse();

  AnalyzeEntitySentimentResponse.fromJson(core.Map _json) {
    if (_json.containsKey("entities")) {
      entities = _json["entities"]
          .map<Entity>((value) => new Entity.fromJson(value))
          .toList();
    }
    if (_json.containsKey("language")) {
      language = _json["language"];
    }
  }

  core.Map<core.String, core.Object> toJson() {
    final core.Map<core.String, core.Object> _json =
        new core.Map<core.String, core.Object>();
    if (entities != null) {
      _json["entities"] = entities.map((value) => (value).toJson()).toList();
    }
    if (language != null) {
      _json["language"] = language;
    }
    return _json;
  }
}

/// The sentiment analysis request message.
class AnalyzeSentimentRequest {
  /// Input document.
  Document document;

  /// The encoding type used by the API to calculate sentence offsets.
  /// Possible string values are:
  /// - "NONE" : If `EncodingType` is not specified, encoding-dependent
  /// information (such as
  /// `begin_offset`) will be set at `-1`.
  /// - "UTF8" : Encoding-dependent information (such as `begin_offset`) is
  /// calculated based
  /// on the UTF-8 encoding of the input. C++ and Go are examples of languages
  /// that use this encoding natively.
  /// - "UTF16" : Encoding-dependent information (such as `begin_offset`) is
  /// calculated based
  /// on the UTF-16 encoding of the input. Java and Javascript are examples of
  /// languages that use this encoding natively.
  /// - "UTF32" : Encoding-dependent information (such as `begin_offset`) is
  /// calculated based
  /// on the UTF-32 encoding of the input. Python is an example of a language
  /// that uses this encoding natively.
  core.String encodingType;

  AnalyzeSentimentRequest();

  AnalyzeSentimentRequest.fromJson(core.Map _json) {
    if (_json.containsKey("document")) {
      document = new Document.fromJson(_json["document"]);
    }
    if (_json.containsKey("encodingType")) {
      encodingType = _json["encodingType"];
    }
  }

  core.Map<core.String, core.Object> toJson() {
    final core.Map<core.String, core.Object> _json =
        new core.Map<core.String, core.Object>();
    if (document != null) {
      _json["document"] = (document).toJson();
    }
    if (encodingType != null) {
      _json["encodingType"] = encodingType;
    }
    return _json;
  }
}

/// The sentiment analysis response message.
class AnalyzeSentimentResponse {
  /// The overall sentiment of the input document.
  Sentiment documentSentiment;

  /// The language of the text, which will be the same as the language specified
  /// in the request or, if not specified, the automatically-detected language.
  /// See Document.language field for more details.
  core.String language;

  /// The sentiment for all the sentences in the document.
  core.List<Sentence> sentences;

  AnalyzeSentimentResponse();

  AnalyzeSentimentResponse.fromJson(core.Map _json) {
    if (_json.containsKey("documentSentiment")) {
      documentSentiment = new Sentiment.fromJson(_json["documentSentiment"]);
    }
    if (_json.containsKey("language")) {
      language = _json["language"];
    }
    if (_json.containsKey("sentences")) {
      sentences = _json["sentences"]
          .map<Sentence>((value) => new Sentence.fromJson(value))
          .toList();
    }
  }

  core.Map<core.String, core.Object> toJson() {
    final core.Map<core.String, core.Object> _json =
        new core.Map<core.String, core.Object>();
    if (documentSentiment != null) {
      _json["documentSentiment"] = (documentSentiment).toJson();
    }
    if (language != null) {
      _json["language"] = language;
    }
    if (sentences != null) {
      _json["sentences"] = sentences.map((value) => (value).toJson()).toList();
    }
    return _json;
  }
}

/// The syntax analysis request message.
class AnalyzeSyntaxRequest {
  /// Input document.
  Document document;

  /// The encoding type used by the API to calculate offsets.
  /// Possible string values are:
  /// - "NONE" : If `EncodingType` is not specified, encoding-dependent
  /// information (such as
  /// `begin_offset`) will be set at `-1`.
  /// - "UTF8" : Encoding-dependent information (such as `begin_offset`) is
  /// calculated based
  /// on the UTF-8 encoding of the input. C++ and Go are examples of languages
  /// that use this encoding natively.
  /// - "UTF16" : Encoding-dependent information (such as `begin_offset`) is
  /// calculated based
  /// on the UTF-16 encoding of the input. Java and Javascript are examples of
  /// languages that use this encoding natively.
  /// - "UTF32" : Encoding-dependent information (such as `begin_offset`) is
  /// calculated based
  /// on the UTF-32 encoding of the input. Python is an example of a language
  /// that uses this encoding natively.
  core.String encodingType;

  AnalyzeSyntaxRequest();

  AnalyzeSyntaxRequest.fromJson(core.Map _json) {
    if (_json.containsKey("document")) {
      document = new Document.fromJson(_json["document"]);
    }
    if (_json.containsKey("encodingType")) {
      encodingType = _json["encodingType"];
    }
  }

  core.Map<core.String, core.Object> toJson() {
    final core.Map<core.String, core.Object> _json =
        new core.Map<core.String, core.Object>();
    if (document != null) {
      _json["document"] = (document).toJson();
    }
    if (encodingType != null) {
      _json["encodingType"] = encodingType;
    }
    return _json;
  }
}

/// The syntax analysis response message.
class AnalyzeSyntaxResponse {
  /// The language of the text, which will be the same as the language specified
  /// in the request or, if not specified, the automatically-detected language.
  /// See Document.language field for more details.
  core.String language;

  /// Sentences in the input document.
  core.List<Sentence> sentences;

  /// Tokens, along with their syntactic information, in the input document.
  core.List<Token> tokens;

  AnalyzeSyntaxResponse();

  AnalyzeSyntaxResponse.fromJson(core.Map _json) {
    if (_json.containsKey("language")) {
      language = _json["language"];
    }
    if (_json.containsKey("sentences")) {
      sentences = _json["sentences"]
          .map<Sentence>((value) => new Sentence.fromJson(value))
          .toList();
    }
    if (_json.containsKey("tokens")) {
      tokens = _json["tokens"]
          .map<Token>((value) => new Token.fromJson(value))
          .toList();
    }
  }

  core.Map<core.String, core.Object> toJson() {
    final core.Map<core.String, core.Object> _json =
        new core.Map<core.String, core.Object>();
    if (language != null) {
      _json["language"] = language;
    }
    if (sentences != null) {
      _json["sentences"] = sentences.map((value) => (value).toJson()).toList();
    }
    if (tokens != null) {
      _json["tokens"] = tokens.map((value) => (value).toJson()).toList();
    }
    return _json;
  }
}

/// The request message for the text annotation API, which can perform multiple
/// analysis types (sentiment, entities, and syntax) in one call.
class AnnotateTextRequest {
  /// Input document.
  Document document;

  /// The encoding type used by the API to calculate offsets.
  /// Possible string values are:
  /// - "NONE" : If `EncodingType` is not specified, encoding-dependent
  /// information (such as
  /// `begin_offset`) will be set at `-1`.
  /// - "UTF8" : Encoding-dependent information (such as `begin_offset`) is
  /// calculated based
  /// on the UTF-8 encoding of the input. C++ and Go are examples of languages
  /// that use this encoding natively.
  /// - "UTF16" : Encoding-dependent information (such as `begin_offset`) is
  /// calculated based
  /// on the UTF-16 encoding of the input. Java and Javascript are examples of
  /// languages that use this encoding natively.
  /// - "UTF32" : Encoding-dependent information (such as `begin_offset`) is
  /// calculated based
  /// on the UTF-32 encoding of the input. Python is an example of a language
  /// that uses this encoding natively.
  core.String encodingType;

  /// The enabled features.
  Features features;

  AnnotateTextRequest();

  AnnotateTextRequest.fromJson(core.Map _json) {
    if (_json.containsKey("document")) {
      document = new Document.fromJson(_json["document"]);
    }
    if (_json.containsKey("encodingType")) {
      encodingType = _json["encodingType"];
    }
    if (_json.containsKey("features")) {
      features = new Features.fromJson(_json["features"]);
    }
  }

  core.Map<core.String, core.Object> toJson() {
    final core.Map<core.String, core.Object> _json =
        new core.Map<core.String, core.Object>();
    if (document != null) {
      _json["document"] = (document).toJson();
    }
    if (encodingType != null) {
      _json["encodingType"] = encodingType;
    }
    if (features != null) {
      _json["features"] = (features).toJson();
    }
    return _json;
  }
}

/// The text annotations response message.
class AnnotateTextResponse {
  /// Categories identified in the input document.
  core.List<ClassificationCategory> categories;

  /// The overall sentiment for the document. Populated if the user enables
  /// AnnotateTextRequest.Features.extract_document_sentiment.
  Sentiment documentSentiment;

  /// Entities, along with their semantic information, in the input document.
  /// Populated if the user enables
  /// AnnotateTextRequest.Features.extract_entities.
  core.List<Entity> entities;

  /// The language of the text, which will be the same as the language specified
  /// in the request or, if not specified, the automatically-detected language.
  /// See Document.language field for more details.
  core.String language;

  /// Sentences in the input document. Populated if the user enables
  /// AnnotateTextRequest.Features.extract_syntax.
  core.List<Sentence> sentences;

  /// Tokens, along with their syntactic information, in the input document.
  /// Populated if the user enables
  /// AnnotateTextRequest.Features.extract_syntax.
  core.List<Token> tokens;

  AnnotateTextResponse();

  AnnotateTextResponse.fromJson(core.Map _json) {
    if (_json.containsKey("categories")) {
      categories = _json["categories"]
          .map<ClassificationCategory>(
              (value) => new ClassificationCategory.fromJson(value))
          .toList();
    }
    if (_json.containsKey("documentSentiment")) {
      documentSentiment = new Sentiment.fromJson(_json["documentSentiment"]);
    }
    if (_json.containsKey("entities")) {
      entities = _json["entities"]
          .map<Entity>((value) => new Entity.fromJson(value))
          .toList();
    }
    if (_json.containsKey("language")) {
      language = _json["language"];
    }
    if (_json.containsKey("sentences")) {
      sentences = _json["sentences"]
          .map<Sentence>((value) => new Sentence.fromJson(value))
          .toList();
    }
    if (_json.containsKey("tokens")) {
      tokens = _json["tokens"]
          .map<Token>((value) => new Token.fromJson(value))
          .toList();
    }
  }

  core.Map<core.String, core.Object> toJson() {
    final core.Map<core.String, core.Object> _json =
        new core.Map<core.String, core.Object>();
    if (categories != null) {
      _json["categories"] =
          categories.map((value) => (value).toJson()).toList();
    }
    if (documentSentiment != null) {
      _json["documentSentiment"] = (documentSentiment).toJson();
    }
    if (entities != null) {
      _json["entities"] = entities.map((value) => (value).toJson()).toList();
    }
    if (language != null) {
      _json["language"] = language;
    }
    if (sentences != null) {
      _json["sentences"] = sentences.map((value) => (value).toJson()).toList();
    }
    if (tokens != null) {
      _json["tokens"] = tokens.map((value) => (value).toJson()).toList();
    }
    return _json;
  }
}

/// Represents a category returned from the text classifier.
class ClassificationCategory {
  /// The classifier's confidence of the category. Number represents how certain
  /// the classifier is that this category represents the given text.
  core.double confidence;

  /// The name of the category representing the document.
  core.String name;

  ClassificationCategory();

  ClassificationCategory.fromJson(core.Map _json) {
    if (_json.containsKey("confidence")) {
      confidence = _json["confidence"];
    }
    if (_json.containsKey("name")) {
      name = _json["name"];
    }
  }

  core.Map<core.String, core.Object> toJson() {
    final core.Map<core.String, core.Object> _json =
        new core.Map<core.String, core.Object>();
    if (confidence != null) {
      _json["confidence"] = confidence;
    }
    if (name != null) {
      _json["name"] = name;
    }
    return _json;
  }
}

/// The document classification request message.
class ClassifyTextRequest {
  /// Input document.
  Document document;

  ClassifyTextRequest();

  ClassifyTextRequest.fromJson(core.Map _json) {
    if (_json.containsKey("document")) {
      document = new Document.fromJson(_json["document"]);
    }
  }

  core.Map<core.String, core.Object> toJson() {
    final core.Map<core.String, core.Object> _json =
        new core.Map<core.String, core.Object>();
    if (document != null) {
      _json["document"] = (document).toJson();
    }
    return _json;
  }
}

/// The document classification response message.
class ClassifyTextResponse {
  /// Categories representing the input document.
  core.List<ClassificationCategory> categories;

  ClassifyTextResponse();

  ClassifyTextResponse.fromJson(core.Map _json) {
    if (_json.containsKey("categories")) {
      categories = _json["categories"]
          .map<ClassificationCategory>(
              (value) => new ClassificationCategory.fromJson(value))
          .toList();
    }
  }

  core.Map<core.String, core.Object> toJson() {
    final core.Map<core.String, core.Object> _json =
        new core.Map<core.String, core.Object>();
    if (categories != null) {
      _json["categories"] =
          categories.map((value) => (value).toJson()).toList();
    }
    return _json;
  }
}

/// Represents dependency parse tree information for a token. (For more
/// information on dependency labels, see
/// http://www.aclweb.org/anthology/P13-2017
class DependencyEdge {
  /// Represents the head of this token in the dependency tree.
  /// This is the index of the token which has an arc going to this token.
  /// The index is the position of the token in the array of tokens returned
  /// by the API method. If this token is a root token, then the
  /// `head_token_index` is its own index.
  core.int headTokenIndex;

  /// The parse label for the token.
  /// Possible string values are:
  /// - "UNKNOWN" : Unknown
  /// - "ABBREV" : Abbreviation modifier
  /// - "ACOMP" : Adjectival complement
  /// - "ADVCL" : Adverbial clause modifier
  /// - "ADVMOD" : Adverbial modifier
  /// - "AMOD" : Adjectival modifier of an NP
  /// - "APPOS" : Appositional modifier of an NP
  /// - "ATTR" : Attribute dependent of a copular verb
  /// - "AUX" : Auxiliary (non-main) verb
  /// - "AUXPASS" : Passive auxiliary
  /// - "CC" : Coordinating conjunction
  /// - "CCOMP" : Clausal complement of a verb or adjective
  /// - "CONJ" : Conjunct
  /// - "CSUBJ" : Clausal subject
  /// - "CSUBJPASS" : Clausal passive subject
  /// - "DEP" : Dependency (unable to determine)
  /// - "DET" : Determiner
  /// - "DISCOURSE" : Discourse
  /// - "DOBJ" : Direct object
  /// - "EXPL" : Expletive
  /// - "GOESWITH" : Goes with (part of a word in a text not well edited)
  /// - "IOBJ" : Indirect object
  /// - "MARK" : Marker (word introducing a subordinate clause)
  /// - "MWE" : Multi-word expression
  /// - "MWV" : Multi-word verbal expression
  /// - "NEG" : Negation modifier
  /// - "NN" : Noun compound modifier
  /// - "NPADVMOD" : Noun phrase used as an adverbial modifier
  /// - "NSUBJ" : Nominal subject
  /// - "NSUBJPASS" : Passive nominal subject
  /// - "NUM" : Numeric modifier of a noun
  /// - "NUMBER" : Element of compound number
  /// - "P" : Punctuation mark
  /// - "PARATAXIS" : Parataxis relation
  /// - "PARTMOD" : Participial modifier
  /// - "PCOMP" : The complement of a preposition is a clause
  /// - "POBJ" : Object of a preposition
  /// - "POSS" : Possession modifier
  /// - "POSTNEG" : Postverbal negative particle
  /// - "PRECOMP" : Predicate complement
  /// - "PRECONJ" : Preconjunt
  /// - "PREDET" : Predeterminer
  /// - "PREF" : Prefix
  /// - "PREP" : Prepositional modifier
  /// - "PRONL" : The relationship between a verb and verbal morpheme
  /// - "PRT" : Particle
  /// - "PS" : Associative or possessive marker
  /// - "QUANTMOD" : Quantifier phrase modifier
  /// - "RCMOD" : Relative clause modifier
  /// - "RCMODREL" : Complementizer in relative clause
  /// - "RDROP" : Ellipsis without a preceding predicate
  /// - "REF" : Referent
  /// - "REMNANT" : Remnant
  /// - "REPARANDUM" : Reparandum
  /// - "ROOT" : Root
  /// - "SNUM" : Suffix specifying a unit of number
  /// - "SUFF" : Suffix
  /// - "TMOD" : Temporal modifier
  /// - "TOPIC" : Topic marker
  /// - "VMOD" : Clause headed by an infinite form of the verb that modifies a
  /// noun
  /// - "VOCATIVE" : Vocative
  /// - "XCOMP" : Open clausal complement
  /// - "SUFFIX" : Name suffix
  /// - "TITLE" : Name title
  /// - "ADVPHMOD" : Adverbial phrase modifier
  /// - "AUXCAUS" : Causative auxiliary
  /// - "AUXVV" : Helper auxiliary
  /// - "DTMOD" : Rentaishi (Prenominal modifier)
  /// - "FOREIGN" : Foreign words
  /// - "KW" : Keyword
  /// - "LIST" : List for chains of comparable items
  /// - "NOMC" : Nominalized clause
  /// - "NOMCSUBJ" : Nominalized clausal subject
  /// - "NOMCSUBJPASS" : Nominalized clausal passive
  /// - "NUMC" : Compound of numeric modifier
  /// - "COP" : Copula
  /// - "DISLOCATED" : Dislocated relation (for fronted/topicalized elements)
  /// - "ASP" : Aspect marker
  /// - "GMOD" : Genitive modifier
  /// - "GOBJ" : Genitive object
  /// - "INFMOD" : Infinitival modifier
  /// - "MES" : Measure
  /// - "NCOMP" : Nominal complement of a noun
  core.String label;

  DependencyEdge();

  DependencyEdge.fromJson(core.Map _json) {
    if (_json.containsKey("headTokenIndex")) {
      headTokenIndex = _json["headTokenIndex"];
    }
    if (_json.containsKey("label")) {
      label = _json["label"];
    }
  }

  core.Map<core.String, core.Object> toJson() {
    final core.Map<core.String, core.Object> _json =
        new core.Map<core.String, core.Object>();
    if (headTokenIndex != null) {
      _json["headTokenIndex"] = headTokenIndex;
    }
    if (label != null) {
      _json["label"] = label;
    }
    return _json;
  }
}

/// ################################################################ #
///
/// Represents the input to API methods.
class Document {
  /// The content of the input in string format.
  core.String content;

  /// The Google Cloud Storage URI where the file content is located.
  /// This URI must be of the form: gs://bucket_name/object_name. For more
  /// details, see https://cloud.google.com/storage/docs/reference-uris.
  /// NOTE: Cloud Storage object versioning is not supported.
  core.String gcsContentUri;

  /// The language of the document (if not specified, the language is
  /// automatically detected). Both ISO and BCP-47 language codes are
  /// accepted.<br>
  /// [Language Support](/natural-language/docs/languages)
  /// lists currently supported languages for each API method.
  /// If the language (either specified by the caller or automatically detected)
  /// is not supported by the called API method, an `INVALID_ARGUMENT` error
  /// is returned.
  core.String language;

  /// Required. If the type is not set or is `TYPE_UNSPECIFIED`,
  /// returns an `INVALID_ARGUMENT` error.
  /// Possible string values are:
  /// - "TYPE_UNSPECIFIED" : The content type is not specified.
  /// - "PLAIN_TEXT" : Plain text
  /// - "HTML" : HTML
  core.String type;

  Document();

  Document.fromJson(core.Map _json) {
    if (_json.containsKey("content")) {
      content = _json["content"];
    }
    if (_json.containsKey("gcsContentUri")) {
      gcsContentUri = _json["gcsContentUri"];
    }
    if (_json.containsKey("language")) {
      language = _json["language"];
    }
    if (_json.containsKey("type")) {
      type = _json["type"];
    }
  }

  core.Map<core.String, core.Object> toJson() {
    final core.Map<core.String, core.Object> _json =
        new core.Map<core.String, core.Object>();
    if (content != null) {
      _json["content"] = content;
    }
    if (gcsContentUri != null) {
      _json["gcsContentUri"] = gcsContentUri;
    }
    if (language != null) {
      _json["language"] = language;
    }
    if (type != null) {
      _json["type"] = type;
    }
    return _json;
  }
}

/// Represents a phrase in the text that is a known entity, such as
/// a person, an organization, or location. The API associates information, such
/// as salience and mentions, with entities.
class Entity {
  /// The mentions of this entity in the input document. The API currently
  /// supports proper noun mentions.
  core.List<EntityMention> mentions;

  /// Metadata associated with the entity.
  ///
  /// Currently, Wikipedia URLs and Knowledge Graph MIDs are provided, if
  /// available. The associated keys are "wikipedia_url" and "mid",
  /// respectively.
  core.Map<core.String, core.String> metadata;

  /// The representative name for the entity.
  core.String name;

  /// The salience score associated with the entity in the [0, 1.0] range.
  ///
  /// The salience score for an entity provides information about the
  /// importance or centrality of that entity to the entire document text.
  /// Scores closer to 0 are less salient, while scores closer to 1.0 are highly
  /// salient.
  core.double salience;

  /// For calls to AnalyzeEntitySentiment or if
  /// AnnotateTextRequest.Features.extract_entity_sentiment is set to
  /// true, this field will contain the aggregate sentiment expressed for this
  /// entity in the provided document.
  Sentiment sentiment;

  /// The entity type.
  /// Possible string values are:
  /// - "UNKNOWN" : Unknown
  /// - "PERSON" : Person
  /// - "LOCATION" : Location
  /// - "ORGANIZATION" : Organization
  /// - "EVENT" : Event
  /// - "WORK_OF_ART" : Work of art
  /// - "CONSUMER_GOOD" : Consumer goods
  /// - "OTHER" : Other types
  core.String type;

  Entity();

  Entity.fromJson(core.Map _json) {
    if (_json.containsKey("mentions")) {
      mentions = _json["mentions"]
          .map<EntityMention>((value) => new EntityMention.fromJson(value))
          .toList();
    }
    if (_json.containsKey("metadata")) {
      metadata = _json["metadata"];
    }
    if (_json.containsKey("name")) {
      name = _json["name"];
    }
    if (_json.containsKey("salience")) {
      salience = _json["salience"];
    }
    if (_json.containsKey("sentiment")) {
      sentiment = new Sentiment.fromJson(_json["sentiment"]);
    }
    if (_json.containsKey("type")) {
      type = _json["type"];
    }
  }

  core.Map<core.String, core.Object> toJson() {
    final core.Map<core.String, core.Object> _json =
        new core.Map<core.String, core.Object>();
    if (mentions != null) {
      _json["mentions"] = mentions.map((value) => (value).toJson()).toList();
    }
    if (metadata != null) {
      _json["metadata"] = metadata;
    }
    if (name != null) {
      _json["name"] = name;
    }
    if (salience != null) {
      _json["salience"] = salience;
    }
    if (sentiment != null) {
      _json["sentiment"] = (sentiment).toJson();
    }
    if (type != null) {
      _json["type"] = type;
    }
    return _json;
  }
}

/// Represents a mention for an entity in the text. Currently, proper noun
/// mentions are supported.
class EntityMention {
  /// For calls to AnalyzeEntitySentiment or if
  /// AnnotateTextRequest.Features.extract_entity_sentiment is set to
  /// true, this field will contain the sentiment expressed for this mention of
  /// the entity in the provided document.
  Sentiment sentiment;

  /// The mention text.
  TextSpan text;

  /// The type of the entity mention.
  /// Possible string values are:
  /// - "TYPE_UNKNOWN" : Unknown
  /// - "PROPER" : Proper name
  /// - "COMMON" : Common noun (or noun compound)
  core.String type;

  EntityMention();

  EntityMention.fromJson(core.Map _json) {
    if (_json.containsKey("sentiment")) {
      sentiment = new Sentiment.fromJson(_json["sentiment"]);
    }
    if (_json.containsKey("text")) {
      text = new TextSpan.fromJson(_json["text"]);
    }
    if (_json.containsKey("type")) {
      type = _json["type"];
    }
  }

  core.Map<core.String, core.Object> toJson() {
    final core.Map<core.String, core.Object> _json =
        new core.Map<core.String, core.Object>();
    if (sentiment != null) {
      _json["sentiment"] = (sentiment).toJson();
    }
    if (text != null) {
      _json["text"] = (text).toJson();
    }
    if (type != null) {
      _json["type"] = type;
    }
    return _json;
  }
}

/// All available features for sentiment, syntax, and semantic analysis.
/// Setting each one to true will enable that specific analysis for the input.
class Features {
  /// Classify the full document into categories.
  core.bool classifyText;

  /// Extract document-level sentiment.
  core.bool extractDocumentSentiment;

  /// Extract entities.
  core.bool extractEntities;

  /// Extract entities and their associated sentiment.
  core.bool extractEntitySentiment;

  /// Extract syntax information.
  core.bool extractSyntax;

  Features();

  Features.fromJson(core.Map _json) {
    if (_json.containsKey("classifyText")) {
      classifyText = _json["classifyText"];
    }
    if (_json.containsKey("extractDocumentSentiment")) {
      extractDocumentSentiment = _json["extractDocumentSentiment"];
    }
    if (_json.containsKey("extractEntities")) {
      extractEntities = _json["extractEntities"];
    }
    if (_json.containsKey("extractEntitySentiment")) {
      extractEntitySentiment = _json["extractEntitySentiment"];
    }
    if (_json.containsKey("extractSyntax")) {
      extractSyntax = _json["extractSyntax"];
    }
  }

  core.Map<core.String, core.Object> toJson() {
    final core.Map<core.String, core.Object> _json =
        new core.Map<core.String, core.Object>();
    if (classifyText != null) {
      _json["classifyText"] = classifyText;
    }
    if (extractDocumentSentiment != null) {
      _json["extractDocumentSentiment"] = extractDocumentSentiment;
    }
    if (extractEntities != null) {
      _json["extractEntities"] = extractEntities;
    }
    if (extractEntitySentiment != null) {
      _json["extractEntitySentiment"] = extractEntitySentiment;
    }
    if (extractSyntax != null) {
      _json["extractSyntax"] = extractSyntax;
    }
    return _json;
  }
}

/// Represents part of speech information for a token. Parts of speech
/// are as defined in
/// http://www.lrec-conf.org/proceedings/lrec2012/pdf/274_Paper.pdf
class PartOfSpeech {
  /// The grammatical aspect.
  /// Possible string values are:
  /// - "ASPECT_UNKNOWN" : Aspect is not applicable in the analyzed language or
  /// is not predicted.
  /// - "PERFECTIVE" : Perfective
  /// - "IMPERFECTIVE" : Imperfective
  /// - "PROGRESSIVE" : Progressive
  core.String aspect;

  /// The grammatical case.
  /// Possible string values are:
  /// - "CASE_UNKNOWN" : Case is not applicable in the analyzed language or is
  /// not predicted.
  /// - "ACCUSATIVE" : Accusative
  /// - "ADVERBIAL" : Adverbial
  /// - "COMPLEMENTIVE" : Complementive
  /// - "DATIVE" : Dative
  /// - "GENITIVE" : Genitive
  /// - "INSTRUMENTAL" : Instrumental
  /// - "LOCATIVE" : Locative
  /// - "NOMINATIVE" : Nominative
  /// - "OBLIQUE" : Oblique
  /// - "PARTITIVE" : Partitive
  /// - "PREPOSITIONAL" : Prepositional
  /// - "REFLEXIVE_CASE" : Reflexive
  /// - "RELATIVE_CASE" : Relative
  /// - "VOCATIVE" : Vocative
  core.String case_;

  /// The grammatical form.
  /// Possible string values are:
  /// - "FORM_UNKNOWN" : Form is not applicable in the analyzed language or is
  /// not predicted.
  /// - "ADNOMIAL" : Adnomial
  /// - "AUXILIARY" : Auxiliary
  /// - "COMPLEMENTIZER" : Complementizer
  /// - "FINAL_ENDING" : Final ending
  /// - "GERUND" : Gerund
  /// - "REALIS" : Realis
  /// - "IRREALIS" : Irrealis
  /// - "SHORT" : Short form
  /// - "LONG" : Long form
  /// - "ORDER" : Order form
  /// - "SPECIFIC" : Specific form
  core.String form;

  /// The grammatical gender.
  /// Possible string values are:
  /// - "GENDER_UNKNOWN" : Gender is not applicable in the analyzed language or
  /// is not predicted.
  /// - "FEMININE" : Feminine
  /// - "MASCULINE" : Masculine
  /// - "NEUTER" : Neuter
  core.String gender;

  /// The grammatical mood.
  /// Possible string values are:
  /// - "MOOD_UNKNOWN" : Mood is not applicable in the analyzed language or is
  /// not predicted.
  /// - "CONDITIONAL_MOOD" : Conditional
  /// - "IMPERATIVE" : Imperative
  /// - "INDICATIVE" : Indicative
  /// - "INTERROGATIVE" : Interrogative
  /// - "JUSSIVE" : Jussive
  /// - "SUBJUNCTIVE" : Subjunctive
  core.String mood;

  /// The grammatical number.
  /// Possible string values are:
  /// - "NUMBER_UNKNOWN" : Number is not applicable in the analyzed language or
  /// is not predicted.
  /// - "SINGULAR" : Singular
  /// - "PLURAL" : Plural
  /// - "DUAL" : Dual
  core.String number;

  /// The grammatical person.
  /// Possible string values are:
  /// - "PERSON_UNKNOWN" : Person is not applicable in the analyzed language or
  /// is not predicted.
  /// - "FIRST" : First
  /// - "SECOND" : Second
  /// - "THIRD" : Third
  /// - "REFLEXIVE_PERSON" : Reflexive
  core.String person;

  /// The grammatical properness.
  /// Possible string values are:
  /// - "PROPER_UNKNOWN" : Proper is not applicable in the analyzed language or
  /// is not predicted.
  /// - "PROPER" : Proper
  /// - "NOT_PROPER" : Not proper
  core.String proper;

  /// The grammatical reciprocity.
  /// Possible string values are:
  /// - "RECIPROCITY_UNKNOWN" : Reciprocity is not applicable in the analyzed
  /// language or is not
  /// predicted.
  /// - "RECIPROCAL" : Reciprocal
  /// - "NON_RECIPROCAL" : Non-reciprocal
  core.String reciprocity;

  /// The part of speech tag.
  /// Possible string values are:
  /// - "UNKNOWN" : Unknown
  /// - "ADJ" : Adjective
  /// - "ADP" : Adposition (preposition and postposition)
  /// - "ADV" : Adverb
  /// - "CONJ" : Conjunction
  /// - "DET" : Determiner
  /// - "NOUN" : Noun (common and proper)
  /// - "NUM" : Cardinal number
  /// - "PRON" : Pronoun
  /// - "PRT" : Particle or other function word
  /// - "PUNCT" : Punctuation
  /// - "VERB" : Verb (all tenses and modes)
  /// - "X" : Other: foreign words, typos, abbreviations
  /// - "AFFIX" : Affix
  core.String tag;

  /// The grammatical tense.
  /// Possible string values are:
  /// - "TENSE_UNKNOWN" : Tense is not applicable in the analyzed language or is
  /// not predicted.
  /// - "CONDITIONAL_TENSE" : Conditional
  /// - "FUTURE" : Future
  /// - "PAST" : Past
  /// - "PRESENT" : Present
  /// - "IMPERFECT" : Imperfect
  /// - "PLUPERFECT" : Pluperfect
  core.String tense;

  /// The grammatical voice.
  /// Possible string values are:
  /// - "VOICE_UNKNOWN" : Voice is not applicable in the analyzed language or is
  /// not predicted.
  /// - "ACTIVE" : Active
  /// - "CAUSATIVE" : Causative
  /// - "PASSIVE" : Passive
  core.String voice;

  PartOfSpeech();

  PartOfSpeech.fromJson(core.Map _json) {
    if (_json.containsKey("aspect")) {
      aspect = _json["aspect"];
    }
    if (_json.containsKey("case")) {
      case_ = _json["case"];
    }
    if (_json.containsKey("form")) {
      form = _json["form"];
    }
    if (_json.containsKey("gender")) {
      gender = _json["gender"];
    }
    if (_json.containsKey("mood")) {
      mood = _json["mood"];
    }
    if (_json.containsKey("number")) {
      number = _json["number"];
    }
    if (_json.containsKey("person")) {
      person = _json["person"];
    }
    if (_json.containsKey("proper")) {
      proper = _json["proper"];
    }
    if (_json.containsKey("reciprocity")) {
      reciprocity = _json["reciprocity"];
    }
    if (_json.containsKey("tag")) {
      tag = _json["tag"];
    }
    if (_json.containsKey("tense")) {
      tense = _json["tense"];
    }
    if (_json.containsKey("voice")) {
      voice = _json["voice"];
    }
  }

  core.Map<core.String, core.Object> toJson() {
    final core.Map<core.String, core.Object> _json =
        new core.Map<core.String, core.Object>();
    if (aspect != null) {
      _json["aspect"] = aspect;
    }
    if (case_ != null) {
      _json["case"] = case_;
    }
    if (form != null) {
      _json["form"] = form;
    }
    if (gender != null) {
      _json["gender"] = gender;
    }
    if (mood != null) {
      _json["mood"] = mood;
    }
    if (number != null) {
      _json["number"] = number;
    }
    if (person != null) {
      _json["person"] = person;
    }
    if (proper != null) {
      _json["proper"] = proper;
    }
    if (reciprocity != null) {
      _json["reciprocity"] = reciprocity;
    }
    if (tag != null) {
      _json["tag"] = tag;
    }
    if (tense != null) {
      _json["tense"] = tense;
    }
    if (voice != null) {
      _json["voice"] = voice;
    }
    return _json;
  }
}

/// Represents a sentence in the input document.
class Sentence {
  /// For calls to AnalyzeSentiment or if
  /// AnnotateTextRequest.Features.extract_document_sentiment is set to
  /// true, this field will contain the sentiment for the sentence.
  Sentiment sentiment;

  /// The sentence text.
  TextSpan text;

  Sentence();

  Sentence.fromJson(core.Map _json) {
    if (_json.containsKey("sentiment")) {
      sentiment = new Sentiment.fromJson(_json["sentiment"]);
    }
    if (_json.containsKey("text")) {
      text = new TextSpan.fromJson(_json["text"]);
    }
  }

  core.Map<core.String, core.Object> toJson() {
    final core.Map<core.String, core.Object> _json =
        new core.Map<core.String, core.Object>();
    if (sentiment != null) {
      _json["sentiment"] = (sentiment).toJson();
    }
    if (text != null) {
      _json["text"] = (text).toJson();
    }
    return _json;
  }
}

/// Represents the feeling associated with the entire text or entities in
/// the text.
class Sentiment {
  /// A non-negative number in the [0, +inf) range, which represents
  /// the absolute magnitude of sentiment regardless of score (positive or
  /// negative).
  core.double magnitude;

  /// Sentiment score between -1.0 (negative sentiment) and 1.0
  /// (positive sentiment).
  core.double score;

  Sentiment();

  Sentiment.fromJson(core.Map _json) {
    if (_json.containsKey("magnitude")) {
      magnitude = _json["magnitude"];
    }
    if (_json.containsKey("score")) {
      score = _json["score"];
    }
  }

  core.Map<core.String, core.Object> toJson() {
    final core.Map<core.String, core.Object> _json =
        new core.Map<core.String, core.Object>();
    if (magnitude != null) {
      _json["magnitude"] = magnitude;
    }
    if (score != null) {
      _json["score"] = score;
    }
    return _json;
  }
}

/// The `Status` type defines a logical error model that is suitable for
/// different
/// programming environments, including REST APIs and RPC APIs. It is used by
/// [gRPC](https://github.com/grpc). The error model is designed to be:
///
/// - Simple to use and understand for most users
/// - Flexible enough to meet unexpected needs
///
/// # Overview
///
/// The `Status` message contains three pieces of data: error code, error
/// message,
/// and error details. The error code should be an enum value of
/// google.rpc.Code, but it may accept additional error codes if needed.  The
/// error message should be a developer-facing English message that helps
/// developers *understand* and *resolve* the error. If a localized user-facing
/// error message is needed, put the localized message in the error details or
/// localize it in the client. The optional error details may contain arbitrary
/// information about the error. There is a predefined set of error detail types
/// in the package `google.rpc` that can be used for common error conditions.
///
/// # Language mapping
///
/// The `Status` message is the logical representation of the error model, but
/// it
/// is not necessarily the actual wire format. When the `Status` message is
/// exposed in different client libraries and different wire protocols, it can
/// be
/// mapped differently. For example, it will likely be mapped to some exceptions
/// in Java, but more likely mapped to some error codes in C.
///
/// # Other uses
///
/// The error model and the `Status` message can be used in a variety of
/// environments, either with or without APIs, to provide a
/// consistent developer experience across different environments.
///
/// Example uses of this error model include:
///
/// - Partial errors. If a service needs to return partial errors to the client,
/// it may embed the `Status` in the normal response to indicate the partial
///     errors.
///
/// - Workflow errors. A typical workflow has multiple steps. Each step may
///     have a `Status` message for error reporting.
///
/// - Batch operations. If a client uses batch request and batch response, the
///     `Status` message should be used directly inside batch response, one for
///     each error sub-response.
///
/// - Asynchronous operations. If an API call embeds asynchronous operation
///     results in its response, the status of those operations should be
///     represented directly using the `Status` message.
///
/// - Logging. If some API errors are stored in logs, the message `Status` could
/// be used directly after any stripping needed for security/privacy reasons.
class Status {
  /// The status code, which should be an enum value of google.rpc.Code.
  core.int code;

  /// A list of messages that carry the error details.  There is a common set of
  /// message types for APIs to use.
  ///
  /// The values for Object must be JSON objects. It can consist of `num`,
  /// `String`, `bool` and `null` as well as `Map` and `List` values.
  core.List<core.Map<core.String, core.Object>> details;

  /// A developer-facing error message, which should be in English. Any
  /// user-facing error message should be localized and sent in the
  /// google.rpc.Status.details field, or localized by the client.
  core.String message;

  Status();

  Status.fromJson(core.Map _json) {
    if (_json.containsKey("code")) {
      code = _json["code"];
    }
    if (_json.containsKey("details")) {
      details = _json["details"];
    }
    if (_json.containsKey("message")) {
      message = _json["message"];
    }
  }

  core.Map<core.String, core.Object> toJson() {
    final core.Map<core.String, core.Object> _json =
        new core.Map<core.String, core.Object>();
    if (code != null) {
      _json["code"] = code;
    }
    if (details != null) {
      _json["details"] = details;
    }
    if (message != null) {
      _json["message"] = message;
    }
    return _json;
  }
}

/// Represents an output piece of text.
class TextSpan {
  /// The API calculates the beginning offset of the content in the original
  /// document according to the EncodingType specified in the API request.
  core.int beginOffset;

  /// The content of the output text.
  core.String content;

  TextSpan();

  TextSpan.fromJson(core.Map _json) {
    if (_json.containsKey("beginOffset")) {
      beginOffset = _json["beginOffset"];
    }
    if (_json.containsKey("content")) {
      content = _json["content"];
    }
  }

  core.Map<core.String, core.Object> toJson() {
    final core.Map<core.String, core.Object> _json =
        new core.Map<core.String, core.Object>();
    if (beginOffset != null) {
      _json["beginOffset"] = beginOffset;
    }
    if (content != null) {
      _json["content"] = content;
    }
    return _json;
  }
}

/// Represents the smallest syntactic building block of the text.
class Token {
  /// Dependency tree parse for this token.
  DependencyEdge dependencyEdge;

  /// [Lemma](https://en.wikipedia.org/wiki/Lemma_%28morphology%29) of the
  /// token.
  core.String lemma;

  /// Parts of speech tag for this token.
  PartOfSpeech partOfSpeech;

  /// The token text.
  TextSpan text;

  Token();

  Token.fromJson(core.Map _json) {
    if (_json.containsKey("dependencyEdge")) {
      dependencyEdge = new DependencyEdge.fromJson(_json["dependencyEdge"]);
    }
    if (_json.containsKey("lemma")) {
      lemma = _json["lemma"];
    }
    if (_json.containsKey("partOfSpeech")) {
      partOfSpeech = new PartOfSpeech.fromJson(_json["partOfSpeech"]);
    }
    if (_json.containsKey("text")) {
      text = new TextSpan.fromJson(_json["text"]);
    }
  }

  core.Map<core.String, core.Object> toJson() {
    final core.Map<core.String, core.Object> _json =
        new core.Map<core.String, core.Object>();
    if (dependencyEdge != null) {
      _json["dependencyEdge"] = (dependencyEdge).toJson();
    }
    if (lemma != null) {
      _json["lemma"] = lemma;
    }
    if (partOfSpeech != null) {
      _json["partOfSpeech"] = (partOfSpeech).toJson();
    }
    if (text != null) {
      _json["text"] = (text).toJson();
    }
    return _json;
  }
}