// Copyright 2020 Google LLC
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//     https://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
//

include "utils/flatbuffers/flatbuffers.fbs";
include "utils/container/bit-vector.fbs";
include "annotator/experimental/experimental.fbs";
include "utils/grammar/rules.fbs";
include "utils/tokenizer.fbs";
include "annotator/entity-data.fbs";
include "utils/normalization.fbs";
include "utils/zlib/buffer.fbs";
include "utils/resources.fbs";
include "utils/intents/intent-config.fbs";
include "utils/codepoint-range.fbs";
include "annotator/grammar/dates/dates.fbs";

file_identifier "TC2 ";

// The possible model modes, represents a bit field.
namespace libtextclassifier3;
enum ModeFlag : int {
  NONE = 0,
  ANNOTATION = 1,
  CLASSIFICATION = 2,
  ANNOTATION_AND_CLASSIFICATION = 3,
  SELECTION = 4,
  ANNOTATION_AND_SELECTION = 5,
  CLASSIFICATION_AND_SELECTION = 6,
  ALL = 7,
}

// Enum for specifying the annotation usecase.
namespace libtextclassifier3;
enum AnnotationUsecase : int {
  // Results are optimized for Smart{Select,Share,Linkify}.
  ANNOTATION_USECASE_SMART = 0,
  // Smart{Select,Share,Linkify}

  // Results are optimized for using TextClassifier as an infrastructure that
  // annotates as much as possible.
  ANNOTATION_USECASE_RAW = 1,
}

namespace libtextclassifier3;
enum DatetimeExtractorType : int {
  UNKNOWN_DATETIME_EXTRACTOR_TYPE = 0,
  AM = 1,
  PM = 2,
  JANUARY = 3,
  FEBRUARY = 4,
  MARCH = 5,
  APRIL = 6,
  MAY = 7,
  JUNE = 8,
  JULY = 9,
  AUGUST = 10,
  SEPTEMBER = 11,
  OCTOBER = 12,
  NOVEMBER = 13,
  DECEMBER = 14,
  NEXT = 15,
  NEXT_OR_SAME = 16,
  LAST = 17,
  NOW = 18,
  TOMORROW = 19,
  YESTERDAY = 20,
  PAST = 21,
  FUTURE = 22,
  DAY = 23,
  WEEK = 24,
  MONTH = 25,
  YEAR = 26,
  MONDAY = 27,
  TUESDAY = 28,
  WEDNESDAY = 29,
  THURSDAY = 30,
  FRIDAY = 31,
  SATURDAY = 32,
  SUNDAY = 33,
  DAYS = 34,
  WEEKS = 35,
  MONTHS = 36,

  // TODO(zilka): Make the following 3 values singular for consistency.
  HOURS = 37,

  MINUTES = 38,
  SECONDS = 39,
  YEARS = 40,
  DIGITS = 41,
  SIGNEDDIGITS = 42,
  ZERO = 43,
  ONE = 44,
  TWO = 45,
  THREE = 46,
  FOUR = 47,
  FIVE = 48,
  SIX = 49,
  SEVEN = 50,
  EIGHT = 51,
  NINE = 52,
  TEN = 53,
  ELEVEN = 54,
  TWELVE = 55,
  THIRTEEN = 56,
  FOURTEEN = 57,
  FIFTEEN = 58,
  SIXTEEN = 59,
  SEVENTEEN = 60,
  EIGHTEEN = 61,
  NINETEEN = 62,
  TWENTY = 63,
  THIRTY = 64,
  FORTY = 65,
  FIFTY = 66,
  SIXTY = 67,
  SEVENTY = 68,
  EIGHTY = 69,
  NINETY = 70,
  HUNDRED = 71,
  THOUSAND = 72,
  NOON = 73,
  MIDNIGHT = 74,
}

namespace libtextclassifier3;
enum DatetimeGroupType : int {
  GROUP_UNKNOWN = 0,
  GROUP_UNUSED = 1,
  GROUP_YEAR = 2,
  GROUP_MONTH = 3,
  GROUP_DAY = 4,
  GROUP_HOUR = 5,
  GROUP_MINUTE = 6,
  GROUP_SECOND = 7,
  GROUP_AMPM = 8,
  GROUP_RELATIONDISTANCE = 9,
  GROUP_RELATION = 10,
  GROUP_RELATIONTYPE = 11,

  // Dummy groups serve just as an inflator of the selection. E.g. we might want
  // to select more text than was contained in an envelope of all extractor
  // spans.
  GROUP_DUMMY1 = 12,

  GROUP_DUMMY2 = 13,
  GROUP_ABSOLUTETIME = 14,
}

// Options for the model that predicts text selection.
namespace libtextclassifier3;
table SelectionModelOptions {
  // If true, before the selection is returned, the unpaired brackets contained
  // in the predicted selection are stripped from the both selection ends.
  // The bracket codepoints are defined in the Unicode standard:
  // http://www.unicode.org/Public/UNIDATA/BidiBrackets.txt
  strip_unpaired_brackets:bool = true;

  // Number of hypothetical click positions on either side of the actual click
  // to consider in order to enforce symmetry.
  symmetry_context_size:int;

  // Number of examples to bundle in one batch for inference.
  batch_size:int = 1024;

  // Whether to always classify a suggested selection or only on demand.
  always_classify_suggested_selection:bool = false;
}

// Options for the model that classifies a text selection.
namespace libtextclassifier3;
table ClassificationModelOptions {
  // Limits for phone numbers.
  phone_min_num_digits:int = 7;

  phone_max_num_digits:int = 15;

  // Limits for addresses.
  address_min_num_tokens:int;

  // Maximum number of tokens to attempt a classification (-1 is unlimited).
  max_num_tokens:int = -1;
}

// Options for post-checks, checksums and verification to apply on a match.
namespace libtextclassifier3;
table VerificationOptions {
  verify_luhn_checksum:bool = false;

  // Lua verifier to use.
  // Index of the lua verifier in the model.
  lua_verifier:int = -1;
}

// Behaviour of rule capturing groups.
// This specifies how the text and span of a capturing group, in a regular
// expression or from a capturing match in a grammar rule, should be handled.
namespace libtextclassifier3;
table CapturingGroup {
  // If true, the span of the capturing group will be used to
  // extend the selection.
  extend_selection:bool = true;

  // If set, the text of the capturing group will be used to set a field in
  // the classfication result entity data.
  entity_field_path:FlatbufferFieldPath;

  // If set, the flatbuffer entity data will be merged with the
  // classification result entity data.
  serialized_entity_data:string;

  // If set, normalization to apply before text is used in entity data.
  normalization_options:NormalizationOptions;

  entity_data:EntityData;
}

// List of regular expression matchers to check.
namespace libtextclassifier3.RegexModel_;
table Pattern {
  // The name of the collection of a match.
  collection_name:string;

  // The pattern to check.
  pattern:string;

  // The modes for which to apply the patterns.
  enabled_modes:ModeFlag = ALL;

  // The final score to assign to the results of this pattern.
  target_classification_score:float = 1;

  // Priority score used for conflict resolution with the other models.
  priority_score:float = 0;

  // If true, will use an approximate matching implementation implemented
  // using Find() instead of the true Match(). This approximate matching will
  // use the first Find() result and then check that it spans the whole input.
  use_approximate_matching:bool = false;

  compressed_pattern:CompressedBuffer;

  // Verification to apply on a match.
  verification_options:VerificationOptions;

  capturing_group:[CapturingGroup];

  // Entity data to set for a match.
  serialized_entity_data:string;

  entity_data:EntityData;
}

namespace libtextclassifier3;
table RegexModel {
  patterns:[RegexModel_.Pattern];

  // If true, will compile the regexes only on first use.
  lazy_regex_compilation:bool = true;

  // Lua scripts for match verification.
  // The verifier can access:
  // * `context`: The context as a string.
  // * `match`: The groups of the regex match as an array, each group gives
  // * `begin`: span start
  // * `end`: span end
  // * `text`: the text
  // The verifier is expected to return a boolean, indicating whether the
  // verification succeeded or not.
  lua_verifier:[string];
}

// List of regex patterns.
namespace libtextclassifier3.DatetimeModelPattern_;
table Regex {
  pattern:string;

  // The ith entry specifies the type of the ith capturing group.
  // This is used to decide how the matched content has to be parsed.
  groups:[DatetimeGroupType];

  compressed_pattern:CompressedBuffer;
}

namespace libtextclassifier3;
table DatetimeModelPattern {
  regexes:[DatetimeModelPattern_.Regex];

  // List of locale indices in DatetimeModel that represent the locales that
  // these patterns should be used for. If empty, can be used for all locales.
  locales:[int];

  // The final score to assign to the results of this pattern.
  target_classification_score:float = 1;

  // Priority score used for conflict resolution with the other models.
  priority_score:float = 0;

  // The modes for which to apply the patterns.
  enabled_modes:ModeFlag = ALL;

  // The annotation usecases for which to apply the patterns.
  // This is a flag field for values of AnnotationUsecase.
  enabled_annotation_usecases:uint = 4294967295;
}

namespace libtextclassifier3;
table DatetimeModelExtractor {
  extractor:DatetimeExtractorType;
  pattern:string;
  locales:[int];
  compressed_pattern:CompressedBuffer;
}

namespace libtextclassifier3;
table DatetimeModel {
  // List of BCP 47 locale strings representing all locales supported by the
  // model. The individual patterns refer back to them using an index.
  locales:[string];

  patterns:[DatetimeModelPattern];
  extractors:[DatetimeModelExtractor];

  // If true, will use the extractors for determining the match location as
  // opposed to using the location where the global pattern matched.
  use_extractors_for_locating:bool = true;

  // List of locale ids, rules of whose are always run, after the requested
  // ones.
  default_locales:[int];

  // If true, will generate the alternative interpretations for ambiguous
  // datetime expressions.
  generate_alternative_interpretations_when_ambiguous:bool = false;

  // If true, will compile the regexes only on first use.
  lazy_regex_compilation:bool = true;

  // If true, will give only future dates (when the day is not specified).
  prefer_future_for_unspecified_date:bool = false;
}

// Configuration for the tokenizer.
namespace libtextclassifier3;
table GrammarTokenizerOptions {
  tokenization_type:TokenizationType = ICU;

  // If true, white space tokens will be kept when using the icu tokenizer.
  icu_preserve_whitespace_tokens:bool = false;

  // Codepoint ranges that determine what role the different codepoints play
  // during tokenized. The ranges must not overlap.
  tokenization_codepoint_config:[TokenizationCodepointRange];

  // A set of codepoint ranges to use in the mixed tokenization mode to identify
  // stretches of tokens to re-tokenize using the internal tokenizer.
  internal_tokenizer_codepoint_ranges:[CodepointRange];

  // If true, tokens will be also split when the codepoint's script_id changes
  // as defined in TokenizationCodepointRange.
  tokenize_on_script_change:bool = false;
}

// Options for grammar date/datetime/date range annotations.
namespace libtextclassifier3.GrammarDatetimeModel_;
table AnnotationOptions {
  // If enabled, extract special day offset like today, yesterday, etc.
  enable_special_day_offset:bool = true;

  // If true, merge the adjacent day of week, time and date. e.g.
  // "20/2/2016 at 8pm" is extracted as a single instance instead of two
  // instance: "20/2/2016" and "8pm".
  merge_adjacent_components:bool = true;

  // List the extra id of requested dates.
  extra_requested_dates:[string];

  // If true, try to include preposition to the extracted annotation. e.g.
  // "at 6pm". if it's false, only 6pm is included. offline-actions has
  // special requirements to include preposition.
  include_preposition:bool = true;

  // If enabled, extract range in date annotator.
  // input: Monday, 5-6pm
  // If the flag is true, The extracted annotation only contains 1 range
  // instance which is from Monday 5pm to 6pm.
  // If the flag is false, The extracted annotation contains two date
  // instance: "Monday" and "6pm".
  enable_date_range:bool = true;
  reserved_6:int16 (deprecated);

  // If enabled, the rule priority score is used to set the priority score of
  // the annotation.
  // In case of false the annotation priority score is set from
  // GrammarDatetimeModel's priority_score
  use_rule_priority_score:bool = false;

  // If enabled, annotator will try to resolve the ambiguity by generating
  // possible alternative interpretations of the input text
  // e.g. '9:45' will be resolved to '9:45 AM' and '9:45 PM'.
  generate_alternative_interpretations_when_ambiguous:bool;

  // List of spans which grammar will ignore during the match e.g. if
  // “@” is in the allowed span list and input is “12 March @ 12PM” then “@”
  // will be ignored and 12 March @ 12PM will be translate to
  // {Day:12 Month: March Hour: 12 MERIDIAN: PM}.
  // This can also be achieved by adding additional rules e.g.
  // <Digit_Day> <Month> <Time>
  // <Digit_Day> <Month> @ <Time>
  // Though this is doable in the grammar but requires multiple rules, this
  // list enables the rule to represent multiple rules.
  ignored_spans:[string];
}

namespace libtextclassifier3;
table GrammarDatetimeModel {
  // List of BCP 47 locale strings representing all locales supported by the
  // model.
  locales:[string];

  // If true, will give only future dates (when the day is not specified).
  prefer_future_for_unspecified_date:bool = false;

  // Grammar specific tokenizer options.
  grammar_tokenizer_options:GrammarTokenizerOptions;

  // The modes for which to apply the grammars.
  enabled_modes:ModeFlag = ALL;

  // The datetime grammar rules.
  datetime_rules:dates.DatetimeRules;

  // The final score to assign to the results of grammar model
  target_classification_score:float = 1;

  // The priority score used for conflict resolution with the other models.
  priority_score:float = 0;

  // Options for grammar annotations.
  annotation_options:GrammarDatetimeModel_.AnnotationOptions;
}

namespace libtextclassifier3.DatetimeModelLibrary_;
table Item {
  key:string;
  value:DatetimeModel;
}

// A set of named DateTime models.
namespace libtextclassifier3;
table DatetimeModelLibrary {
  models:[DatetimeModelLibrary_.Item];
}

// Classification result to instantiate for a rule match.
namespace libtextclassifier3.GrammarModel_;
table RuleClassificationResult {
  // The name of the collection.
  collection_name:string;

  // The score.
  target_classification_score:float = 1;

  // The priority score used for conflict resolution with the other models.
  priority_score:float = 0;

  // Behaviour of capturing matches.
  capturing_group:[CapturingGroup];

  // Entity data to set for a match.
  serialized_entity_data:string;

  // Enabled modes.
  enabled_modes:ModeFlag = ALL;

  entity_data:EntityData;
}

// Configuration for grammar based annotators.
namespace libtextclassifier3;
table GrammarModel {
  // The grammar rules.
  rules:grammar.RulesSet;

  rule_classification_result:[GrammarModel_.RuleClassificationResult];

  // Number of tokens in the context to use for classification and text
  // selection suggestion.
  // A value -1 uses the full context.
  context_left_num_tokens:int;

  context_right_num_tokens:int;

  // Grammar specific tokenizer options.
  tokenizer_options:GrammarTokenizerOptions;
}

namespace libtextclassifier3.MoneyParsingOptions_;
table QuantitiesNameToExponentEntry {
  key:string (key);
  value:int;
}

namespace libtextclassifier3;
table MoneyParsingOptions {
  // Separators (codepoints) marking decimal or thousand in the money amount.
  separators:[int];

  // Mapping between a quantity string (e.g. "million") and the power of 10
  // it multiplies the amount with (e.g. 6 in case of "million").
  // NOTE: The entries need to be sorted by key since we use LookupByKey.
  quantities_name_to_exponent:[MoneyParsingOptions_.QuantitiesNameToExponentEntry];
}

namespace libtextclassifier3.ModelTriggeringOptions_;
table CollectionToPriorityEntry {
  key:string (key);
  value:float;
}

// Options controlling the output of the Tensorflow Lite models.
namespace libtextclassifier3;
table ModelTriggeringOptions {
  // Lower bound threshold for filtering annotation model outputs.
  min_annotate_confidence:float = 0;

  // The modes for which to enable the models.
  enabled_modes:ModeFlag = ALL;

  // Comma-separated list of locales (BCP 47 tags) that dictionary
  // classification supports.
  dictionary_locales:string;

  // Comma-separated list of locales (BCP 47 tags) that the model supports, that
  // are used to prevent  triggering on input in unsupported languages. If
  // empty, the model will trigger on all inputs.
  locales:string;

  // Priority score assigned to the "other" class from ML model.
  other_collection_priority_score:float = -1000;

  // Priority score assigned to knowledge engine annotations.
  knowledge_priority_score:float = 0;
  reserved_7:int16 (deprecated);

  // Apply a factor to the priority score for entities that are added to this
  // map. Key: collection type e.g. "address", "phone"..., Value: float number.
  // NOTE: The entries here need to be sorted since we use LookupByKey.
  collection_to_priority:[ModelTriggeringOptions_.CollectionToPriorityEntry];
}

// Options controlling the output of the classifier.
namespace libtextclassifier3;
table OutputOptions {
  // Lists of collection names that will be filtered out at the output:
  // - For annotation, the spans of given collection are simply dropped.
  // - For classification, the result is mapped to the class "other".
  // - For selection, the spans of given class are returned as
  // single-selection.
  filtered_collections_annotation:[string];

  filtered_collections_classification:[string];
  filtered_collections_selection:[string];
}

namespace libtextclassifier3.Model_;
table EmbeddingPruningMask {
  // If true, use pruning mask. In this case, we use mask
  // pruning_mask to determine the mapping of hashed-charactergrams.
  enabled:bool;

  // Packing of the binary pruning mask into uint64 values.
  pruning_mask:[ulong] (force_align: 16);

  // Number of buckets before pruning.
  full_num_buckets:int;

  // Index of row of compressed embedding matrix to which all pruned buckets
  // are mapped.
  pruned_row_bucket_id:int;
}

namespace libtextclassifier3.Model_;
table ConflictResolutionOptions {
  // If true, will prioritize the longest annotation during conflict
  // resolution.
  prioritize_longest_annotation:bool = false;

  // If true, the annotator will perform conflict resolution between the
  // different sub-annotators also in the RAW mode. If false, no conflict
  // resolution will be performed in RAW mode.
  do_conflict_resolution_in_raw_mode:bool = true;
}

namespace libtextclassifier3;
table Model {
  // Comma-separated list of locales supported by the model as BCP 47 tags.
  locales:string;

  version:int;

  // A name for the model that can be used for e.g. logging.
  name:string;

  selection_feature_options:FeatureProcessorOptions;
  classification_feature_options:FeatureProcessorOptions;

  // Tensorflow Lite models.
  selection_model:[ubyte] (force_align: 16);

  classification_model:[ubyte] (force_align: 16);
  embedding_model:[ubyte] (force_align: 16);

  // Options for the different models.
  selection_options:SelectionModelOptions;

  classification_options:ClassificationModelOptions;
  regex_model:RegexModel;
  datetime_model:DatetimeModel;

  // Options controlling the output of the models.
  triggering_options:ModelTriggeringOptions;

  // Global switch that controls if SuggestSelection(), ClassifyText() and
  // Annotate() will run. If a mode is disabled it returns empty/no-op results.
  enabled_modes:ModeFlag = ALL;

  // If true, will snap the selections that consist only of whitespaces to the
  // containing suggested span. Otherwise, no suggestion is proposed, since the
  // selections are not part of any token.
  snap_whitespace_selections:bool = true;

  // Global configuration for the output of SuggestSelection(), ClassifyText()
  // and Annotate().
  output_options:OutputOptions;

  // Configures how Intents should be generated on Android.
  android_intent_options:AndroidIntentFactoryOptions;

  intent_options:IntentFactoryModel;

  // Model resources.
  resources:ResourcePool;

  // Schema data for handling entity data.
  entity_data_schema:[ubyte];

  number_annotator_options:NumberAnnotatorOptions;
  duration_annotator_options:DurationAnnotatorOptions;

  // Comma-separated list of locales (BCP 47 tags) that the model supports, that
  // are used to prevent  triggering on input in unsupported languages. If
  // empty, the model will trigger on all inputs.
  triggering_locales:string;

  embedding_pruning_mask:Model_.EmbeddingPruningMask;
  grammar_datetime_model:GrammarDatetimeModel;
  contact_annotator_options:ContactAnnotatorOptions;
  money_parsing_options:MoneyParsingOptions;
  translate_annotator_options:TranslateAnnotatorOptions;
  grammar_model:GrammarModel;
  conflict_resolution_options:Model_.ConflictResolutionOptions;
  experimental_model:ExperimentalModel;
  pod_ner_model:PodNerModel;
  vocab_model:VocabModel;
}

// Method for selecting the center token.
namespace libtextclassifier3.FeatureProcessorOptions_;
enum CenterTokenSelectionMethod : int {
  DEFAULT_CENTER_TOKEN_METHOD = 0,
  // Invalid option.

  // Use click indices to determine the center token.
  CENTER_TOKEN_FROM_CLICK = 1,

  // Use selection indices to get a token range, and select the middle of it
  // as the center token.
  CENTER_TOKEN_MIDDLE_OF_SELECTION = 2,
}

// Bounds-sensitive feature extraction configuration.
namespace libtextclassifier3.FeatureProcessorOptions_;
table BoundsSensitiveFeatures {
  // Enables the extraction of bounds-sensitive features, instead of the click
  // context features.
  enabled:bool;

  // The numbers of tokens to extract in specific locations relative to the
  // bounds.
  // Immediately before the span.
  num_tokens_before:int;

  // Inside the span, aligned with the beginning.
  num_tokens_inside_left:int;

  // Inside the span, aligned with the end.
  num_tokens_inside_right:int;

  // Immediately after the span.
  num_tokens_after:int;

  // If true, also extracts the tokens of the entire span and adds up their
  // features forming one "token" to include in the extracted features.
  include_inside_bag:bool;

  // If true, includes the selection length (in the number of tokens) as a
  // feature.
  include_inside_length:bool;

  // If true, for selection, single token spans are not run through the model
  // and their score is assumed to be zero.
  score_single_token_spans_as_zero:bool;
}

namespace libtextclassifier3;
table FeatureProcessorOptions {
  // Number of buckets used for hashing charactergrams.
  num_buckets:int = -1;

  // Size of the embedding.
  embedding_size:int = -1;

  // Number of bits for quantization for embeddings.
  embedding_quantization_bits:int = 8;

  // Context size defines the number of words to the left and to the right of
  // the selected word to be used as context. For example, if context size is
  // N, then we take N words to the left and N words to the right of the
  // selected word as its context.
  context_size:int = -1;

  // Maximum number of words of the context to select in total.
  max_selection_span:int = -1;

  // Orders of charactergrams to extract. E.g., 2 means character bigrams, 3
  // character trigrams etc.
  chargram_orders:[int];

  // Maximum length of a word, in codepoints.
  max_word_length:int = 20;

  // If true, will use the unicode-aware functionality for extracting features.
  unicode_aware_features:bool = false;

  // Whether to extract the token case feature.
  extract_case_feature:bool = false;

  // Whether to extract the selection mask feature.
  extract_selection_mask_feature:bool = false;

  // List of regexps to run over each token. For each regexp, if there is a
  // match, a dense feature of 1.0 is emitted. Otherwise -1.0 is used.
  regexp_feature:[string];

  // Whether to remap all digits to a single number.
  remap_digits:bool = false;

  // Whether to lower-case each token before generating hashgrams.
  lowercase_tokens:bool;

  // If true, the selection classifier output will contain only the selections
  // that are feasible (e.g., those that are shorter than max_selection_span),
  // if false, the output will be a complete cross-product of possible
  // selections to the left and possible selections to the right, including the
  // infeasible ones.
  // NOTE: Exists mainly for compatibility with older models that were trained
  // with the non-reduced output space.
  selection_reduced_output_space:bool = true;

  // Collection names.
  collections:[string];

  // An index of collection in collections to be used if a collection name can't
  // be mapped to an id.
  default_collection:int = -1;

  // If true, will split the input by lines, and only use the line that contains
  // the clicked token.
  only_use_line_with_click:bool = false;

  // If true, will split tokens that contain the selection boundary, at the
  // position of the boundary.
  // E.g. "foo{bar}@google.com" -> "foo", "bar", "@google.com"
  split_tokens_on_selection_boundaries:bool = false;

  // Codepoint ranges that determine how different codepoints are tokenized.
  // The ranges must not overlap.
  tokenization_codepoint_config:[TokenizationCodepointRange];

  center_token_selection_method:FeatureProcessorOptions_.CenterTokenSelectionMethod;

  // If true, span boundaries will be snapped to containing tokens and not
  // required to exactly match token boundaries.
  snap_label_span_boundaries_to_containing_tokens:bool;

  // A set of codepoint ranges supported by the model.
  supported_codepoint_ranges:[CodepointRange];

  // A set of codepoint ranges to use in the mixed tokenization mode to identify
  // stretches of tokens to re-tokenize using the internal tokenizer.
  internal_tokenizer_codepoint_ranges:[CodepointRange];

  // Minimum ratio of supported codepoints in the input context. If the ratio
  // is lower than this, the feature computation will fail.
  min_supported_codepoint_ratio:float = 0;

  // Used for versioning the format of features the model expects.
  // - feature_version == 0:
  // For each token the features consist of:
  // - chargram embeddings
  // - dense features
  // Chargram embeddings for tokens are concatenated first together,
  // and at the end, the dense features for the tokens are concatenated
  // to it. So the resulting feature vector has two regions.
  feature_version:int = 0;

  tokenization_type:TokenizationType = INTERNAL_TOKENIZER;
  icu_preserve_whitespace_tokens:bool = false;

  // List of codepoints that will be stripped from beginning and end of
  // predicted spans.
  ignored_span_boundary_codepoints:[int];

  bounds_sensitive_features:FeatureProcessorOptions_.BoundsSensitiveFeatures;

  // List of allowed charactergrams. The extracted charactergrams are filtered
  // using this list, and charactergrams that are not present are interpreted as
  // out-of-vocabulary.
  // If no allowed_chargrams are specified, all charactergrams are allowed.
  // The field is typed as bytes type to allow non-UTF8 chargrams.
  allowed_chargrams:[string];

  // If true, tokens will be also split when the codepoint's script_id changes
  // as defined in TokenizationCodepointRange.
  tokenize_on_script_change:bool = false;

  // If true, the pipe character '|' will be used as a newline character when
  // splitting lines.
  use_pipe_character_for_newline:bool = true;
}

namespace libtextclassifier3;
table NumberAnnotatorOptions {
  // If true, number and percentage annotations will be produced.
  enabled:bool = false;

  // Score to assign to the annotated numbers and percentages in the annotator.
  score:float = 1;

  // Number priority score used for conflict resolution with the other models.
  priority_score:float = 0;

  // The modes in which to enable number and percentage annotations.
  enabled_modes:ModeFlag = ALL;

  // The annotation usecases for which to produce number annotations.
  // This is a flag field for values of AnnotationUsecase.
  enabled_annotation_usecases:uint = 4294967295;

  // [Deprecated] A list of codepoints that can form a prefix of a valid number.
  allowed_prefix_codepoints:[int];

  // [Deprecated] A list of codepoints that can form a suffix of a valid number.
  allowed_suffix_codepoints:[int];

  // [Deprecated] List of codepoints that will be stripped from beginning of
  // predicted spans.
  ignored_prefix_span_boundary_codepoints:[int];

  // [Deprecated] List of codepoints that will be stripped from end of predicted
  // spans.
  ignored_suffix_span_boundary_codepoints:[int];

  // [Deprecated] If true, percent annotations will be produced.
  enable_percentage:bool = false;

  // Zero separated and ordered list of suffixes that mark a percent.
  percentage_pieces_string:string;

  // [Deprecated] List of suffixes offsets in the percent_pieces_string string.
  percentage_pieces_offsets:[int];

  // Priority score for the percentage annotation.
  percentage_priority_score:float = 1;

  // Float number priority score used for conflict resolution with the other
  // models.
  float_number_priority_score:float = 0;

  // The maximum number of digits an annotated number can have. Requirement:
  // the value should be less or equal to 20.
  max_number_of_digits:int = 20;

  // The annotation usecases for which to produce percentage annotations.
  // This is a flag field for values of AnnotationUsecase.
  percentage_annotation_usecases:uint = 2;
}

// DurationAnnotator is so far tailored for English and Japanese only.
namespace libtextclassifier3;
table DurationAnnotatorOptions {
  // If true, duration annotations will be produced.
  enabled:bool = false;

  // Score to assign to the annotated durations from the annotator.
  score:float = 1;

  // Priority score used for conflict resolution with the other models.
  priority_score:float = 0;

  // The modes in which to enable duration annotations.
  enabled_modes:ModeFlag = ALL;

  // The annotation usecases for which to produce duration annotations.
  enabled_annotation_usecases:uint = 4294967295;

  // Durations typically look like XX hours and XX minutes etc... The list of
  // strings below enumerate variants of "hours", "minutes", etc. in these
  // expressions. These are verbatim strings that are matched against tokens in
  // the input.
  week_expressions:[string];

  day_expressions:[string];
  hour_expressions:[string];
  minute_expressions:[string];
  second_expressions:[string];

  // List of expressions that doesn't break a duration expression (can become
  // a part of it) but has not semantic meaning.
  filler_expressions:[string];

  // List of expressions that mean half of a unit of duration (e.g. "half an
  // hour").
  half_expressions:[string];

  // Set of condepoints that can split the Annotator tokens to sub-tokens for
  // sub-token matching.
  sub_token_separator_codepoints:[int];

  // If this is true, unit must be associated with quantity. For example, a
  // phrase "minute" is not parsed as one minute duration if this is true.
  require_quantity:bool;

  // If this is true, dangling quantity is included in the annotation. For
  // example, "10 minutes 20" is interpreted as 10 minutes and 20 seconds.
  enable_dangling_quantity_interpretation:bool = true;
}

namespace libtextclassifier3;
table ContactAnnotatorOptions {
  // Supported for English genitives only so far.
  enable_declension:bool;

  // For each language there is a customized list of supported declensions.
  language:string;
}

namespace libtextclassifier3.TranslateAnnotatorOptions_;
enum Algorithm : int {
  DEFAULT_ALGORITHM = 0,
  BACKOFF = 1,
}

// Backoff is the algorithm shipped with Android Q.
namespace libtextclassifier3.TranslateAnnotatorOptions_;
table BackoffOptions {
  // The minimum size of text to prefer for detection (in codepoints).
  min_text_size:int = 20;

  // For reducing the score when text is less than the preferred size.
  penalize_ratio:float = 1;

  // Original detection score to surrounding text detection score ratios.
  subject_text_score_ratio:float = 0.4;
}

namespace libtextclassifier3;
table TranslateAnnotatorOptions {
  enabled:bool = false;

  // Score to assign to the classification results.
  score:float = 1;

  // Priority score used for conflict resolution with the other models.
  priority_score:float;

  algorithm:TranslateAnnotatorOptions_.Algorithm;
  backoff_options:TranslateAnnotatorOptions_.BackoffOptions;
}

namespace libtextclassifier3.PodNerModel_;
table Collection {
  // Collection's name (e.g., "location", "person").
  name:string;

  // Priority scores used for conflict resolution with the other annotators
  // when the annotation is made over a single/multi token text.
  single_token_priority_score:float;

  multi_token_priority_score:float;
}

namespace libtextclassifier3.PodNerModel_.Label_;
enum BoiseType : int {
  NONE = 0,
  BEGIN = 1,
  O = 2,
  // No label.

  INTERMEDIATE = 3,
  SINGLE = 4,
  END = 5,
}

namespace libtextclassifier3.PodNerModel_.Label_;
enum MentionType : int {
  UNDEFINED = 0,
  NAM = 1,
  NOM = 2,
}

namespace libtextclassifier3.PodNerModel_;
table Label {
  boise_type:Label_.BoiseType;
  mention_type:Label_.MentionType;
  collection_id:int;
  // points to the collections array above.
}

namespace libtextclassifier3;
table PodNerModel {
  tflite_model:[ubyte];
  word_piece_vocab:[ubyte];
  lowercase_input:bool = true;

  // Index of mention_logits tensor in the output of the tflite model. Can
  // be found in the textproto output after model is converted to tflite.
  logits_index_in_output_tensor:int = 0;

  // Whether to append a period at the end of an input that doesn't already
  // end in punctuation.
  append_final_period:bool = false;

  // Priority score used for conflict resolution with the other models. Used
  // only if collections_array is empty.
  priority_score:float = 0;

  // Maximum number of wordpieces supported by the model.
  max_num_wordpieces:int = 128;

  // In case of long text (number of wordpieces greater than the max) we use
  // sliding window approach, this determines the number of overlapping
  // wordpieces between two consecutive windows. This overlap enables context
  // for each word NER annotates.
  sliding_window_num_wordpieces_overlap:int = 20;
  reserved_9:int16 (deprecated);

  // The possible labels the ner model can output. If empty the default labels
  // will be used.
  labels:[PodNerModel_.Label];

  // If the ratio of unknown wordpieces in the input text is greater than this
  // maximum, the text won't be annotated.
  max_ratio_unknown_wordpieces:float = 0.1;

  // Possible collections for labeled entities.
  collections:[PodNerModel_.Collection];

  // Minimum word-length and wordpieces-length required for the text to be
  // annotated.
  min_number_of_tokens:int = 1;

  min_number_of_wordpieces:int = 1;
}

namespace libtextclassifier3;
table VocabModel {
  // A trie that stores a list of vocabs that triggers "Define". A id is
  // returned when looking up a vocab from the trie and the id can be used
  // to access more information about that vocab. The marisa trie library
  // requires 8-byte alignment because the first thing in a marisa trie is a
  // 64-bit integer.
  vocab_trie:[ubyte] (force_align: 8);

  // A bit vector that tells if the vocab should trigger "Define" for users of
  // beginner proficiency only. To look up the bit vector, use the id returned
  // by the trie.
  beginner_level:BitVectorData;

  // A sorted list of indices of vocabs that should not trigger "Define" if
  // its leading character is in upper case. The indices are those returned by
  // trie. You may perform binary search to look up an index.
  do_not_trigger_in_upper_case:BitVectorData;

  // Comma-separated list of locales (BCP 47 tags) that the model supports, that
  // are used to prevent  triggering on input in unsupported languages. If
  // empty, the model will trigger on all inputs.
  triggering_locales:string;

  // The final score to assign to the results of the vocab model
  target_classification_score:float = 1;

  // Priority score used for conflict resolution with the other models.
  priority_score:float = 0;
}

root_type libtextclassifier3.Model;