%% WARNING: DO NOT EDIT, AUTO-GENERATED CODE! %% See https://github.com/aws-beam/aws-codegen for more details. %% @doc Welcome to the Amazon Web Services Clean Rooms ML API Reference. %% %% Amazon Web Services Clean Rooms ML provides a privacy-enhancing method for %% two parties to identify similar users in their data without the need to %% share their data with each other. The first party brings the training data %% to Clean Rooms so that they can create and configure an audience model %% (lookalike model) and associate it with a collaboration. The second party %% then brings their seed data to Clean Rooms and generates an audience %% (lookalike segment) that resembles the training data. %% %% To learn more about Amazon Web Services Clean Rooms ML concepts, %% procedures, and best practices, see the Clean Rooms User Guide: %% https://docs.aws.amazon.com/clean-rooms/latest/userguide/machine-learning.html. %% %% To learn more about SQL commands, functions, and conditions supported in %% Clean Rooms, see the Clean Rooms SQL Reference: %% https://docs.aws.amazon.com/clean-rooms/latest/sql-reference/sql-reference.html. -module(aws_cleanroomsml). -export([create_audience_model/2, create_audience_model/3, create_configured_audience_model/2, create_configured_audience_model/3, create_training_dataset/2, create_training_dataset/3, delete_audience_generation_job/3, delete_audience_generation_job/4, delete_audience_model/3, delete_audience_model/4, delete_configured_audience_model/3, delete_configured_audience_model/4, delete_configured_audience_model_policy/3, delete_configured_audience_model_policy/4, delete_training_dataset/3, delete_training_dataset/4, get_audience_generation_job/2, get_audience_generation_job/4, get_audience_generation_job/5, get_audience_model/2, get_audience_model/4, get_audience_model/5, get_configured_audience_model/2, get_configured_audience_model/4, get_configured_audience_model/5, get_configured_audience_model_policy/2, get_configured_audience_model_policy/4, get_configured_audience_model_policy/5, get_training_dataset/2, get_training_dataset/4, get_training_dataset/5, list_audience_export_jobs/1, list_audience_export_jobs/3, list_audience_export_jobs/4, list_audience_generation_jobs/1, list_audience_generation_jobs/3, list_audience_generation_jobs/4, list_audience_models/1, list_audience_models/3, list_audience_models/4, list_configured_audience_models/1, list_configured_audience_models/3, list_configured_audience_models/4, list_tags_for_resource/2, list_tags_for_resource/4, list_tags_for_resource/5, list_training_datasets/1, list_training_datasets/3, list_training_datasets/4, put_configured_audience_model_policy/3, put_configured_audience_model_policy/4, start_audience_export_job/2, start_audience_export_job/3, start_audience_generation_job/2, start_audience_generation_job/3, tag_resource/3, tag_resource/4, untag_resource/3, untag_resource/4, update_configured_audience_model/3, update_configured_audience_model/4]). -include_lib("hackney/include/hackney_lib.hrl"). %%==================================================================== %% API %%==================================================================== %% @doc Defines the information necessary to create an audience model. %% %% An audience model is a machine learning model that Clean Rooms ML trains %% to measure similarity between users. Clean Rooms ML manages training and %% storing the audience model. The audience model can be used in multiple %% calls to the `StartAudienceGenerationJob' API. create_audience_model(Client, Input) -> create_audience_model(Client, Input, []). create_audience_model(Client, Input0, Options0) -> Method = post, Path = ["/audience-model"], SuccessStatusCode = 200, Options = [{send_body_as_binary, false}, {receive_body_as_binary, false}, {append_sha256_content_hash, false} | Options0], Headers = [], Input1 = Input0, CustomHeaders = [], Input2 = Input1, Query_ = [], Input = Input2, request(Client, Method, Path, Query_, CustomHeaders ++ Headers, Input, Options, SuccessStatusCode). %% @doc Defines the information necessary to create a configured audience %% model. create_configured_audience_model(Client, Input) -> create_configured_audience_model(Client, Input, []). create_configured_audience_model(Client, Input0, Options0) -> Method = post, Path = ["/configured-audience-model"], SuccessStatusCode = 200, Options = [{send_body_as_binary, false}, {receive_body_as_binary, false}, {append_sha256_content_hash, false} | Options0], Headers = [], Input1 = Input0, CustomHeaders = [], Input2 = Input1, Query_ = [], Input = Input2, request(Client, Method, Path, Query_, CustomHeaders ++ Headers, Input, Options, SuccessStatusCode). %% @doc Defines the information necessary to create a training dataset, or %% seed audience. %% %% In Clean Rooms ML, the `TrainingDataset' is metadata that points to a %% Glue table, which is read only during `AudienceModel' creation. create_training_dataset(Client, Input) -> create_training_dataset(Client, Input, []). create_training_dataset(Client, Input0, Options0) -> Method = post, Path = ["/training-dataset"], SuccessStatusCode = 200, Options = [{send_body_as_binary, false}, {receive_body_as_binary, false}, {append_sha256_content_hash, false} | Options0], Headers = [], Input1 = Input0, CustomHeaders = [], Input2 = Input1, Query_ = [], Input = Input2, request(Client, Method, Path, Query_, CustomHeaders ++ Headers, Input, Options, SuccessStatusCode). %% @doc Deletes the specified audience generation job, and removes all data %% associated with the job. delete_audience_generation_job(Client, AudienceGenerationJobArn, Input) -> delete_audience_generation_job(Client, AudienceGenerationJobArn, Input, []). delete_audience_generation_job(Client, AudienceGenerationJobArn, Input0, Options0) -> Method = delete, Path = ["/audience-generation-job/", aws_util:encode_uri(AudienceGenerationJobArn), ""], SuccessStatusCode = 200, Options = [{send_body_as_binary, false}, {receive_body_as_binary, false}, {append_sha256_content_hash, false} | Options0], Headers = [], Input1 = Input0, CustomHeaders = [], Input2 = Input1, Query_ = [], Input = Input2, request(Client, Method, Path, Query_, CustomHeaders ++ Headers, Input, Options, SuccessStatusCode). %% @doc Specifies an audience model that you want to delete. %% %% You can't delete an audience model if there are any configured %% audience models that depend on the audience model. delete_audience_model(Client, AudienceModelArn, Input) -> delete_audience_model(Client, AudienceModelArn, Input, []). delete_audience_model(Client, AudienceModelArn, Input0, Options0) -> Method = delete, Path = ["/audience-model/", aws_util:encode_uri(AudienceModelArn), ""], SuccessStatusCode = 200, Options = [{send_body_as_binary, false}, {receive_body_as_binary, false}, {append_sha256_content_hash, false} | Options0], Headers = [], Input1 = Input0, CustomHeaders = [], Input2 = Input1, Query_ = [], Input = Input2, request(Client, Method, Path, Query_, CustomHeaders ++ Headers, Input, Options, SuccessStatusCode). %% @doc Deletes the specified configured audience model. %% %% You can't delete a configured audience model if there are any %% lookalike models that use the configured audience model. If you delete a %% configured audience model, it will be removed from any collaborations that %% it is associated to. delete_configured_audience_model(Client, ConfiguredAudienceModelArn, Input) -> delete_configured_audience_model(Client, ConfiguredAudienceModelArn, Input, []). delete_configured_audience_model(Client, ConfiguredAudienceModelArn, Input0, Options0) -> Method = delete, Path = ["/configured-audience-model/", aws_util:encode_uri(ConfiguredAudienceModelArn), ""], SuccessStatusCode = 200, Options = [{send_body_as_binary, false}, {receive_body_as_binary, false}, {append_sha256_content_hash, false} | Options0], Headers = [], Input1 = Input0, CustomHeaders = [], Input2 = Input1, Query_ = [], Input = Input2, request(Client, Method, Path, Query_, CustomHeaders ++ Headers, Input, Options, SuccessStatusCode). %% @doc Deletes the specified configured audience model policy. delete_configured_audience_model_policy(Client, ConfiguredAudienceModelArn, Input) -> delete_configured_audience_model_policy(Client, ConfiguredAudienceModelArn, Input, []). delete_configured_audience_model_policy(Client, ConfiguredAudienceModelArn, Input0, Options0) -> Method = delete, Path = ["/configured-audience-model/", aws_util:encode_uri(ConfiguredAudienceModelArn), "/policy"], SuccessStatusCode = 200, Options = [{send_body_as_binary, false}, {receive_body_as_binary, false}, {append_sha256_content_hash, false} | Options0], Headers = [], Input1 = Input0, CustomHeaders = [], Input2 = Input1, Query_ = [], Input = Input2, request(Client, Method, Path, Query_, CustomHeaders ++ Headers, Input, Options, SuccessStatusCode). %% @doc Specifies a training dataset that you want to delete. %% %% You can't delete a training dataset if there are any audience models %% that depend on the training dataset. In Clean Rooms ML, the %% `TrainingDataset' is metadata that points to a Glue table, which is %% read only during `AudienceModel' creation. This action deletes the %% metadata. delete_training_dataset(Client, TrainingDatasetArn, Input) -> delete_training_dataset(Client, TrainingDatasetArn, Input, []). delete_training_dataset(Client, TrainingDatasetArn, Input0, Options0) -> Method = delete, Path = ["/training-dataset/", aws_util:encode_uri(TrainingDatasetArn), ""], SuccessStatusCode = 200, Options = [{send_body_as_binary, false}, {receive_body_as_binary, false}, {append_sha256_content_hash, false} | Options0], Headers = [], Input1 = Input0, CustomHeaders = [], Input2 = Input1, Query_ = [], Input = Input2, request(Client, Method, Path, Query_, CustomHeaders ++ Headers, Input, Options, SuccessStatusCode). %% @doc Returns information about an audience generation job. get_audience_generation_job(Client, AudienceGenerationJobArn) when is_map(Client) -> get_audience_generation_job(Client, AudienceGenerationJobArn, #{}, #{}). get_audience_generation_job(Client, AudienceGenerationJobArn, QueryMap, HeadersMap) when is_map(Client), is_map(QueryMap), is_map(HeadersMap) -> get_audience_generation_job(Client, AudienceGenerationJobArn, QueryMap, HeadersMap, []). get_audience_generation_job(Client, AudienceGenerationJobArn, QueryMap, HeadersMap, Options0) when is_map(Client), is_map(QueryMap), is_map(HeadersMap), is_list(Options0) -> Path = ["/audience-generation-job/", aws_util:encode_uri(AudienceGenerationJobArn), ""], SuccessStatusCode = 200, Options = [{send_body_as_binary, false}, {receive_body_as_binary, false} | Options0], Headers = [], Query_ = [], request(Client, get, Path, Query_, Headers, undefined, Options, SuccessStatusCode). %% @doc Returns information about an audience model get_audience_model(Client, AudienceModelArn) when is_map(Client) -> get_audience_model(Client, AudienceModelArn, #{}, #{}). get_audience_model(Client, AudienceModelArn, QueryMap, HeadersMap) when is_map(Client), is_map(QueryMap), is_map(HeadersMap) -> get_audience_model(Client, AudienceModelArn, QueryMap, HeadersMap, []). get_audience_model(Client, AudienceModelArn, QueryMap, HeadersMap, Options0) when is_map(Client), is_map(QueryMap), is_map(HeadersMap), is_list(Options0) -> Path = ["/audience-model/", aws_util:encode_uri(AudienceModelArn), ""], SuccessStatusCode = 200, Options = [{send_body_as_binary, false}, {receive_body_as_binary, false} | Options0], Headers = [], Query_ = [], request(Client, get, Path, Query_, Headers, undefined, Options, SuccessStatusCode). %% @doc Returns information about a specified configured audience model. get_configured_audience_model(Client, ConfiguredAudienceModelArn) when is_map(Client) -> get_configured_audience_model(Client, ConfiguredAudienceModelArn, #{}, #{}). get_configured_audience_model(Client, ConfiguredAudienceModelArn, QueryMap, HeadersMap) when is_map(Client), is_map(QueryMap), is_map(HeadersMap) -> get_configured_audience_model(Client, ConfiguredAudienceModelArn, QueryMap, HeadersMap, []). get_configured_audience_model(Client, ConfiguredAudienceModelArn, QueryMap, HeadersMap, Options0) when is_map(Client), is_map(QueryMap), is_map(HeadersMap), is_list(Options0) -> Path = ["/configured-audience-model/", aws_util:encode_uri(ConfiguredAudienceModelArn), ""], SuccessStatusCode = 200, Options = [{send_body_as_binary, false}, {receive_body_as_binary, false} | Options0], Headers = [], Query_ = [], request(Client, get, Path, Query_, Headers, undefined, Options, SuccessStatusCode). %% @doc Returns information about a configured audience model policy. get_configured_audience_model_policy(Client, ConfiguredAudienceModelArn) when is_map(Client) -> get_configured_audience_model_policy(Client, ConfiguredAudienceModelArn, #{}, #{}). get_configured_audience_model_policy(Client, ConfiguredAudienceModelArn, QueryMap, HeadersMap) when is_map(Client), is_map(QueryMap), is_map(HeadersMap) -> get_configured_audience_model_policy(Client, ConfiguredAudienceModelArn, QueryMap, HeadersMap, []). get_configured_audience_model_policy(Client, ConfiguredAudienceModelArn, QueryMap, HeadersMap, Options0) when is_map(Client), is_map(QueryMap), is_map(HeadersMap), is_list(Options0) -> Path = ["/configured-audience-model/", aws_util:encode_uri(ConfiguredAudienceModelArn), "/policy"], SuccessStatusCode = 200, Options = [{send_body_as_binary, false}, {receive_body_as_binary, false} | Options0], Headers = [], Query_ = [], request(Client, get, Path, Query_, Headers, undefined, Options, SuccessStatusCode). %% @doc Returns information about a training dataset. get_training_dataset(Client, TrainingDatasetArn) when is_map(Client) -> get_training_dataset(Client, TrainingDatasetArn, #{}, #{}). get_training_dataset(Client, TrainingDatasetArn, QueryMap, HeadersMap) when is_map(Client), is_map(QueryMap), is_map(HeadersMap) -> get_training_dataset(Client, TrainingDatasetArn, QueryMap, HeadersMap, []). get_training_dataset(Client, TrainingDatasetArn, QueryMap, HeadersMap, Options0) when is_map(Client), is_map(QueryMap), is_map(HeadersMap), is_list(Options0) -> Path = ["/training-dataset/", aws_util:encode_uri(TrainingDatasetArn), ""], SuccessStatusCode = 200, Options = [{send_body_as_binary, false}, {receive_body_as_binary, false} | Options0], Headers = [], Query_ = [], request(Client, get, Path, Query_, Headers, undefined, Options, SuccessStatusCode). %% @doc Returns a list of the audience export jobs. list_audience_export_jobs(Client) when is_map(Client) -> list_audience_export_jobs(Client, #{}, #{}). list_audience_export_jobs(Client, QueryMap, HeadersMap) when is_map(Client), is_map(QueryMap), is_map(HeadersMap) -> list_audience_export_jobs(Client, QueryMap, HeadersMap, []). list_audience_export_jobs(Client, QueryMap, HeadersMap, Options0) when is_map(Client), is_map(QueryMap), is_map(HeadersMap), is_list(Options0) -> Path = ["/audience-export-job"], SuccessStatusCode = 200, Options = [{send_body_as_binary, false}, {receive_body_as_binary, false} | Options0], Headers = [], Query0_ = [ {<<"audienceGenerationJobArn">>, maps:get(<<"audienceGenerationJobArn">>, QueryMap, undefined)}, {<<"maxResults">>, maps:get(<<"maxResults">>, QueryMap, undefined)}, {<<"nextToken">>, maps:get(<<"nextToken">>, QueryMap, undefined)} ], Query_ = [H || {_, V} = H <- Query0_, V =/= undefined], request(Client, get, Path, Query_, Headers, undefined, Options, SuccessStatusCode). %% @doc Returns a list of audience generation jobs. list_audience_generation_jobs(Client) when is_map(Client) -> list_audience_generation_jobs(Client, #{}, #{}). list_audience_generation_jobs(Client, QueryMap, HeadersMap) when is_map(Client), is_map(QueryMap), is_map(HeadersMap) -> list_audience_generation_jobs(Client, QueryMap, HeadersMap, []). list_audience_generation_jobs(Client, QueryMap, HeadersMap, Options0) when is_map(Client), is_map(QueryMap), is_map(HeadersMap), is_list(Options0) -> Path = ["/audience-generation-job"], SuccessStatusCode = 200, Options = [{send_body_as_binary, false}, {receive_body_as_binary, false} | Options0], Headers = [], Query0_ = [ {<<"collaborationId">>, maps:get(<<"collaborationId">>, QueryMap, undefined)}, {<<"configuredAudienceModelArn">>, maps:get(<<"configuredAudienceModelArn">>, QueryMap, undefined)}, {<<"maxResults">>, maps:get(<<"maxResults">>, QueryMap, undefined)}, {<<"nextToken">>, maps:get(<<"nextToken">>, QueryMap, undefined)} ], Query_ = [H || {_, V} = H <- Query0_, V =/= undefined], request(Client, get, Path, Query_, Headers, undefined, Options, SuccessStatusCode). %% @doc Returns a list of audience models. list_audience_models(Client) when is_map(Client) -> list_audience_models(Client, #{}, #{}). list_audience_models(Client, QueryMap, HeadersMap) when is_map(Client), is_map(QueryMap), is_map(HeadersMap) -> list_audience_models(Client, QueryMap, HeadersMap, []). list_audience_models(Client, QueryMap, HeadersMap, Options0) when is_map(Client), is_map(QueryMap), is_map(HeadersMap), is_list(Options0) -> Path = ["/audience-model"], SuccessStatusCode = 200, Options = [{send_body_as_binary, false}, {receive_body_as_binary, false} | Options0], Headers = [], Query0_ = [ {<<"maxResults">>, maps:get(<<"maxResults">>, QueryMap, undefined)}, {<<"nextToken">>, maps:get(<<"nextToken">>, QueryMap, undefined)} ], Query_ = [H || {_, V} = H <- Query0_, V =/= undefined], request(Client, get, Path, Query_, Headers, undefined, Options, SuccessStatusCode). %% @doc Returns a list of the configured audience models. list_configured_audience_models(Client) when is_map(Client) -> list_configured_audience_models(Client, #{}, #{}). list_configured_audience_models(Client, QueryMap, HeadersMap) when is_map(Client), is_map(QueryMap), is_map(HeadersMap) -> list_configured_audience_models(Client, QueryMap, HeadersMap, []). list_configured_audience_models(Client, QueryMap, HeadersMap, Options0) when is_map(Client), is_map(QueryMap), is_map(HeadersMap), is_list(Options0) -> Path = ["/configured-audience-model"], SuccessStatusCode = 200, Options = [{send_body_as_binary, false}, {receive_body_as_binary, false} | Options0], Headers = [], Query0_ = [ {<<"maxResults">>, maps:get(<<"maxResults">>, QueryMap, undefined)}, {<<"nextToken">>, maps:get(<<"nextToken">>, QueryMap, undefined)} ], Query_ = [H || {_, V} = H <- Query0_, V =/= undefined], request(Client, get, Path, Query_, Headers, undefined, Options, SuccessStatusCode). %% @doc Returns a list of tags for a provided resource. list_tags_for_resource(Client, ResourceArn) when is_map(Client) -> list_tags_for_resource(Client, ResourceArn, #{}, #{}). list_tags_for_resource(Client, ResourceArn, QueryMap, HeadersMap) when is_map(Client), is_map(QueryMap), is_map(HeadersMap) -> list_tags_for_resource(Client, ResourceArn, QueryMap, HeadersMap, []). list_tags_for_resource(Client, ResourceArn, QueryMap, HeadersMap, Options0) when is_map(Client), is_map(QueryMap), is_map(HeadersMap), is_list(Options0) -> Path = ["/tags/", aws_util:encode_uri(ResourceArn), ""], SuccessStatusCode = 200, Options = [{send_body_as_binary, false}, {receive_body_as_binary, false} | Options0], Headers = [], Query_ = [], request(Client, get, Path, Query_, Headers, undefined, Options, SuccessStatusCode). %% @doc Returns a list of training datasets. list_training_datasets(Client) when is_map(Client) -> list_training_datasets(Client, #{}, #{}). list_training_datasets(Client, QueryMap, HeadersMap) when is_map(Client), is_map(QueryMap), is_map(HeadersMap) -> list_training_datasets(Client, QueryMap, HeadersMap, []). list_training_datasets(Client, QueryMap, HeadersMap, Options0) when is_map(Client), is_map(QueryMap), is_map(HeadersMap), is_list(Options0) -> Path = ["/training-dataset"], SuccessStatusCode = 200, Options = [{send_body_as_binary, false}, {receive_body_as_binary, false} | Options0], Headers = [], Query0_ = [ {<<"maxResults">>, maps:get(<<"maxResults">>, QueryMap, undefined)}, {<<"nextToken">>, maps:get(<<"nextToken">>, QueryMap, undefined)} ], Query_ = [H || {_, V} = H <- Query0_, V =/= undefined], request(Client, get, Path, Query_, Headers, undefined, Options, SuccessStatusCode). %% @doc Create or update the resource policy for a configured audience model. put_configured_audience_model_policy(Client, ConfiguredAudienceModelArn, Input) -> put_configured_audience_model_policy(Client, ConfiguredAudienceModelArn, Input, []). put_configured_audience_model_policy(Client, ConfiguredAudienceModelArn, Input0, Options0) -> Method = put, Path = ["/configured-audience-model/", aws_util:encode_uri(ConfiguredAudienceModelArn), "/policy"], SuccessStatusCode = 200, Options = [{send_body_as_binary, false}, {receive_body_as_binary, false}, {append_sha256_content_hash, false} | Options0], Headers = [], Input1 = Input0, CustomHeaders = [], Input2 = Input1, Query_ = [], Input = Input2, request(Client, Method, Path, Query_, CustomHeaders ++ Headers, Input, Options, SuccessStatusCode). %% @doc Export an audience of a specified size after you have generated an %% audience. start_audience_export_job(Client, Input) -> start_audience_export_job(Client, Input, []). start_audience_export_job(Client, Input0, Options0) -> Method = post, Path = ["/audience-export-job"], SuccessStatusCode = 200, Options = [{send_body_as_binary, false}, {receive_body_as_binary, false}, {append_sha256_content_hash, false} | Options0], Headers = [], Input1 = Input0, CustomHeaders = [], Input2 = Input1, Query_ = [], Input = Input2, request(Client, Method, Path, Query_, CustomHeaders ++ Headers, Input, Options, SuccessStatusCode). %% @doc Information necessary to start the audience generation job. start_audience_generation_job(Client, Input) -> start_audience_generation_job(Client, Input, []). start_audience_generation_job(Client, Input0, Options0) -> Method = post, Path = ["/audience-generation-job"], SuccessStatusCode = 200, Options = [{send_body_as_binary, false}, {receive_body_as_binary, false}, {append_sha256_content_hash, false} | Options0], Headers = [], Input1 = Input0, CustomHeaders = [], Input2 = Input1, Query_ = [], Input = Input2, request(Client, Method, Path, Query_, CustomHeaders ++ Headers, Input, Options, SuccessStatusCode). %% @doc Adds metadata tags to a specified resource. tag_resource(Client, ResourceArn, Input) -> tag_resource(Client, ResourceArn, Input, []). tag_resource(Client, ResourceArn, Input0, Options0) -> Method = post, Path = ["/tags/", aws_util:encode_uri(ResourceArn), ""], SuccessStatusCode = 200, Options = [{send_body_as_binary, false}, {receive_body_as_binary, false}, {append_sha256_content_hash, false} | Options0], Headers = [], Input1 = Input0, CustomHeaders = [], Input2 = Input1, Query_ = [], Input = Input2, request(Client, Method, Path, Query_, CustomHeaders ++ Headers, Input, Options, SuccessStatusCode). %% @doc Removes metadata tags from a specified resource. untag_resource(Client, ResourceArn, Input) -> untag_resource(Client, ResourceArn, Input, []). untag_resource(Client, ResourceArn, Input0, Options0) -> Method = delete, Path = ["/tags/", aws_util:encode_uri(ResourceArn), ""], SuccessStatusCode = 200, Options = [{send_body_as_binary, false}, {receive_body_as_binary, false}, {append_sha256_content_hash, false} | Options0], Headers = [], Input1 = Input0, CustomHeaders = [], Input2 = Input1, QueryMapping = [ {<<"tagKeys">>, <<"tagKeys">>} ], {Query_, Input} = aws_request:build_headers(QueryMapping, Input2), request(Client, Method, Path, Query_, CustomHeaders ++ Headers, Input, Options, SuccessStatusCode). %% @doc Provides the information necessary to update a configured audience %% model. %% %% Updates that impact audience generation jobs take effect when a new job %% starts, but do not impact currently running jobs. update_configured_audience_model(Client, ConfiguredAudienceModelArn, Input) -> update_configured_audience_model(Client, ConfiguredAudienceModelArn, Input, []). update_configured_audience_model(Client, ConfiguredAudienceModelArn, Input0, Options0) -> Method = patch, Path = ["/configured-audience-model/", aws_util:encode_uri(ConfiguredAudienceModelArn), ""], SuccessStatusCode = 200, Options = [{send_body_as_binary, false}, {receive_body_as_binary, false}, {append_sha256_content_hash, false} | Options0], Headers = [], Input1 = Input0, CustomHeaders = [], Input2 = Input1, Query_ = [], Input = Input2, request(Client, Method, Path, Query_, CustomHeaders ++ Headers, Input, Options, SuccessStatusCode). %%==================================================================== %% Internal functions %%==================================================================== -spec request(aws_client:aws_client(), atom(), iolist(), list(), list(), map() | undefined, list(), pos_integer() | undefined) -> {ok, {integer(), list()}} | {ok, Result, {integer(), list(), hackney:client()}} | {error, Error, {integer(), list(), hackney:client()}} | {error, term()} when Result :: map(), Error :: map(). request(Client, Method, Path, Query, Headers0, Input, Options, SuccessStatusCode) -> RequestFun = fun() -> do_request(Client, Method, Path, Query, Headers0, Input, Options, SuccessStatusCode) end, aws_request:request(RequestFun, Options). do_request(Client, Method, Path, Query, Headers0, Input, Options, SuccessStatusCode) -> Client1 = Client#{service => <<"cleanrooms-ml">>}, Host = build_host(<<"cleanrooms-ml">>, Client1), URL0 = build_url(Host, Path, Client1), URL = aws_request:add_query(URL0, Query), AdditionalHeaders1 = [ {<<"Host">>, Host} , {<<"Content-Type">>, <<"application/x-amz-json-1.1">>} ], Payload = case proplists:get_value(send_body_as_binary, Options) of true -> maps:get(<<"Body">>, Input, <<"">>); false -> encode_payload(Input) end, AdditionalHeaders = case proplists:get_value(append_sha256_content_hash, Options, false) of true -> add_checksum_hash_header(AdditionalHeaders1, Payload); false -> AdditionalHeaders1 end, Headers1 = aws_request:add_headers(AdditionalHeaders, Headers0), MethodBin = aws_request:method_to_binary(Method), SignedHeaders = aws_request:sign_request(Client1, MethodBin, URL, Headers1, Payload), Response = hackney:request(Method, URL, SignedHeaders, Payload, Options), DecodeBody = not proplists:get_value(receive_body_as_binary, Options), handle_response(Response, SuccessStatusCode, DecodeBody). add_checksum_hash_header(Headers, Body) -> [ {<<"X-Amz-CheckSum-SHA256">>, base64:encode(crypto:hash(sha256, Body))} | Headers ]. handle_response({ok, StatusCode, ResponseHeaders}, SuccessStatusCode, _DecodeBody) when StatusCode =:= 200; StatusCode =:= 202; StatusCode =:= 204; StatusCode =:= 206; StatusCode =:= SuccessStatusCode -> {ok, {StatusCode, ResponseHeaders}}; handle_response({ok, StatusCode, ResponseHeaders}, _, _DecodeBody) -> {error, {StatusCode, ResponseHeaders}}; handle_response({ok, StatusCode, ResponseHeaders, Client}, SuccessStatusCode, DecodeBody) when StatusCode =:= 200; StatusCode =:= 202; StatusCode =:= 204; StatusCode =:= 206; StatusCode =:= SuccessStatusCode -> case hackney:body(Client) of {ok, <<>>} when StatusCode =:= 200; StatusCode =:= SuccessStatusCode -> {ok, #{}, {StatusCode, ResponseHeaders, Client}}; {ok, Body} -> Result = case DecodeBody of true -> try jsx:decode(Body) catch Error:Reason:Stack -> erlang:raise(error, {body_decode_failed, Error, Reason, StatusCode, Body}, Stack) end; false -> #{<<"Body">> => Body} end, {ok, Result, {StatusCode, ResponseHeaders, Client}} end; handle_response({ok, StatusCode, _ResponseHeaders, _Client}, _, _DecodeBody) when StatusCode =:= 503 -> %% Retriable error if retries are enabled {error, service_unavailable}; handle_response({ok, StatusCode, ResponseHeaders, Client}, _, _DecodeBody) -> {ok, Body} = hackney:body(Client), try DecodedError = jsx:decode(Body), {error, DecodedError, {StatusCode, ResponseHeaders, Client}} catch Error:Reason:Stack -> erlang:raise(error, {body_decode_failed, Error, Reason, StatusCode, Body}, Stack) end; handle_response({error, Reason}, _, _DecodeBody) -> {error, Reason}. build_host(_EndpointPrefix, #{region := <<"local">>, endpoint := Endpoint}) -> Endpoint; build_host(_EndpointPrefix, #{region := <<"local">>}) -> <<"localhost">>; build_host(EndpointPrefix, #{region := Region, endpoint := Endpoint}) -> aws_util:binary_join([EndpointPrefix, Region, Endpoint], <<".">>). build_url(Host, Path0, Client) -> Proto = aws_client:proto(Client), Path = erlang:iolist_to_binary(Path0), Port = aws_client:port(Client), aws_util:binary_join([Proto, <<"://">>, Host, <<":">>, Port, Path], <<"">>). -spec encode_payload(undefined | map()) -> binary(). encode_payload(undefined) -> <<>>; encode_payload(Input) -> jsx:encode(Input).