/* ----------------------------------------------------------------------- *//**
 *
 * Licensed to the Apache Software Foundation (ASF) under one
 * or more contributor license agreements.  See the NOTICE file
 * distributed with this work for additional information
 * regarding copyright ownership.  The ASF licenses this file
 * to you 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
 *
 *   http://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.
 *
 *
 * @file madlib_keras_model_selection.sql_in
 *
 * @brief SQL functions for model hopper distributed training
 * @date August 2019
 *
 *
 *//* ----------------------------------------------------------------------- */

m4_include(`SQLCommon.m4')


CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.madlib_keras_fit_multiple_model(
    source_table            VARCHAR,
    model_output_table      VARCHAR,
    model_selection_table   VARCHAR,
    num_iterations          INTEGER,
    gpus_per_host           INTEGER,
    validation_table        VARCHAR
) RETURNS VOID AS $$
    PythonFunctionBodyOnly(`deep_learning', `madlib_keras_fit_multiple_model')
    with AOControl(False):
        fit_obj = madlib_keras_fit_multiple_model.FitMultipleModel(**globals())
$$ LANGUAGE plpythonu VOLATILE
m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA', `');

CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.madlib_keras_fit_multiple_model(
    source_table            VARCHAR,
    model_output_table      VARCHAR,
    model_selection_table   VARCHAR,
    num_iterations          INTEGER,
    gpus_per_host           INTEGER
) RETURNS VOID AS $$
    SELECT MADLIB_SCHEMA.madlib_keras_fit_multiple_model($1, $2, $3, $4, $5, NULL);
$$ LANGUAGE sql VOLATILE
m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA');

CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.fit_transition_multiple_model(
    state                      BYTEA,
    dependent_var              BYTEA,
    independent_var            BYTEA,
    dependent_var_shape        INTEGER[],
    independent_var_shape      INTEGER[],
    model_architecture         TEXT,
    compile_params             TEXT,
    fit_params                 TEXT,
    current_seg_id             INTEGER,
    seg_ids                    INTEGER[],
    images_per_seg             INTEGER[],
    gpus_per_host              INTEGER,
    segments_per_host          INTEGER,
    prev_serialized_weights    BYTEA,
    is_final_iteration         BOOLEAN
) RETURNS BYTEA AS $$
PythonFunctionBodyOnlyNoSchema(`deep_learning', `madlib_keras')
    return madlib_keras.fit_transition(is_multiple_model = True, **globals())
$$ LANGUAGE plpythonu
m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');

DROP AGGREGATE IF EXISTS MADLIB_SCHEMA.fit_step_multiple_model(
    BYTEA,
    BYTEA,
    TEXT,
    TEXT,
    TEXT,
    TEXT,
    TEXT,
    INTEGER,
    INTEGER[],
    INTEGER[],
    INTEGER,
    INTEGER,
    BYTEA,
    BOOLEAN);
CREATE AGGREGATE MADLIB_SCHEMA.fit_step_multiple_model(
    /* dep_var */                BYTEA,
    /* ind_var */                BYTEA,
    /* dep_var_shape */          INTEGER[],
    /* ind_var_shape */          INTEGER[],
    /* model_architecture */     TEXT,
    /* compile_params */         TEXT,
    /* fit_params */             TEXT,
    /* current_seg_id */         INTEGER,
    /* seg_ids*/                 INTEGER[],
    /* images_per_seg*/          INTEGER[],
    /* gpus_per_host  */         INTEGER,
    /* segments_per_host  */     INTEGER,
    /* serialized_weights */     BYTEA,
    /* is_final_iteration */     BOOLEAN
)(
    STYPE=BYTEA,
    SFUNC=MADLIB_SCHEMA.fit_transition_multiple_model
);