Api-roll 43: 2016-12-13

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';
+
+/**
+ * Google Cloud Natural Language API provides natural language understanding
+ * technologies to developers. Examples include sentiment analysis, entity
+ * recognition, and text annotations.
+ */
+class LanguageApi {
+  /** 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 finds proper names) in the text,
+   * entity types, salience, mentions for each entity, and other properties.
+   *
+   * [request] - The metadata request object.
+   *
+   * Request parameters:
+   *
+   * 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) {
+    var _url = null;
+    var _queryParams = new core.Map();
+    var _uploadMedia = null;
+    var _uploadOptions = null;
+    var _downloadOptions = commons.DownloadOptions.Metadata;
+    var _body = null;
+
+    if (request != null) {
+      _body = convert.JSON.encode((request).toJson());
+    }
+
+    _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));
+  }
+
+  /**
+   * Analyzes the sentiment of the provided text.
+   *
+   * [request] - The metadata request object.
+   *
+   * Request parameters:
+   *
+   * 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) {
+    var _url = null;
+    var _queryParams = new core.Map();
+    var _uploadMedia = null;
+    var _uploadOptions = null;
+    var _downloadOptions = commons.DownloadOptions.Metadata;
+    var _body = null;
+
+    if (request != null) {
+      _body = convert.JSON.encode((request).toJson());
+    }
+
+    _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:
+   *
+   * 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) {
+    var _url = null;
+    var _queryParams = new core.Map();
+    var _uploadMedia = null;
+    var _uploadOptions = null;
+    var _downloadOptions = commons.DownloadOptions.Metadata;
+    var _body = null;
+
+    if (request != null) {
+      _body = convert.JSON.encode((request).toJson());
+    }
+
+    _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:
+   *
+   * 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) {
+    var _url = null;
+    var _queryParams = new core.Map();
+    var _uploadMedia = null;
+    var _uploadOptions = null;
+    var _downloadOptions = commons.DownloadOptions.Metadata;
+    var _body = null;
+
+    if (request != null) {
+      _body = convert.JSON.encode((request).toJson());
+    }
+
+    _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));
+  }
+
+}
+
+
+
+/** 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 toJson() {
+    var _json = new core.Map();
+    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((value) => new Entity.fromJson(value)).toList();
+    }
+    if (_json.containsKey("language")) {
+      language = _json["language"];
+    }
+  }
+
+  core.Map toJson() {
+    var _json = new core.Map();
+    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. Currently, `analyzeSentiment` only supports English text
+   * (Document.language="EN").
+   */
+  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 toJson() {
+    var _json = new core.Map();
+    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((value) => new Sentence.fromJson(value)).toList();
+    }
+  }
+
+  core.Map toJson() {
+    var _json = new core.Map();
+    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 toJson() {
+    var _json = new core.Map();
+    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((value) => new Sentence.fromJson(value)).toList();
+    }
+    if (_json.containsKey("tokens")) {
+      tokens = _json["tokens"].map((value) => new Token.fromJson(value)).toList();
+    }
+  }
+
+  core.Map toJson() {
+    var _json = new core.Map();
+    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 toJson() {
+    var _json = new core.Map();
+    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 {
+  /**
+   * 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("documentSentiment")) {
+      documentSentiment = new Sentiment.fromJson(_json["documentSentiment"]);
+    }
+    if (_json.containsKey("entities")) {
+      entities = _json["entities"].map((value) => new Entity.fromJson(value)).toList();
+    }
+    if (_json.containsKey("language")) {
+      language = _json["language"];
+    }
+    if (_json.containsKey("sentences")) {
+      sentences = _json["sentences"].map((value) => new Sentence.fromJson(value)).toList();
+    }
+    if (_json.containsKey("tokens")) {
+      tokens = _json["tokens"].map((value) => new Token.fromJson(value)).toList();
+    }
+  }
+
+  core.Map toJson() {
+    var _json = new core.Map();
+    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 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)
+   */
+  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 toJson() {
+    var _json = new core.Map();
+    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>
+   * **Current Language Restrictions:**
+   *
+   *  * Only English, Spanish, and Japanese textual content are supported.
+   * 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 toJson() {
+    var _json = new core.Map();
+    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;
+  /**
+   * 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((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("type")) {
+      type = _json["type"];
+    }
+  }
+
+  core.Map toJson() {
+    var _json = new core.Map();
+    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 (type != null) {
+      _json["type"] = type;
+    }
+    return _json;
+  }
+}
+
+/**
+ * Represents a mention for an entity in the text. Currently, proper noun
+ * mentions are supported.
+ */
+class EntityMention {
+  /** 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("text")) {
+      text = new TextSpan.fromJson(_json["text"]);
+    }
+    if (_json.containsKey("type")) {
+      type = _json["type"];
+    }
+  }
+
+  core.Map toJson() {
+    var _json = new core.Map();
+    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 {
+  /** Extract document-level sentiment. */
+  core.bool extractDocumentSentiment;
+  /** Extract entities. */
+  core.bool extractEntities;
+  /** Extract syntax information. */
+  core.bool extractSyntax;
+
+  Features();
+
+  Features.fromJson(core.Map _json) {
+    if (_json.containsKey("extractDocumentSentiment")) {
+      extractDocumentSentiment = _json["extractDocumentSentiment"];
+    }
+    if (_json.containsKey("extractEntities")) {
+      extractEntities = _json["extractEntities"];
+    }
+    if (_json.containsKey("extractSyntax")) {
+      extractSyntax = _json["extractSyntax"];
+    }
+  }
+
+  core.Map toJson() {
+    var _json = new core.Map();
+    if (extractDocumentSentiment != null) {
+      _json["extractDocumentSentiment"] = extractDocumentSentiment;
+    }
+    if (extractEntities != null) {
+      _json["extractEntities"] = extractEntities;
+    }
+    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 toJson() {
+    var _json = new core.Map();
+    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 toJson() {
+    var _json = new core.Map();
+    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 toJson() {
+    var _json = new core.Map();
+    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` which 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 purpose.
+ *
+ * - 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 will be 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 toJson() {
+    var _json = new core.Map();
+    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 toJson() {
+    var _json = new core.Map();
+    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 toJson() {
+    var _json = new core.Map();
+    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;
+  }
+}