/*
 *  ******************************************************************************
 *  *
 *  *
 *  * This program and the accompanying materials are made available under the
 *  * terms of the Apache License, Version 2.0 which is available at
 *  * https://www.apache.org/licenses/LICENSE-2.0.
 *  *
 *  * See the NOTICE file distributed with this work for additional
 *  * information regarding copyright ownership.
 *  * 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.
 *  *
 *  * SPDX-License-Identifier: Apache-2.0
 *  *****************************************************************************
 */

//
//  @author raver119@gmail.com
//

#ifndef LIBND4J_HEADERS_RANDOM_H
#define LIBND4J_HEADERS_RANDOM_H

#include <ops/declarable/headers/common.h>

namespace sd {
    namespace ops {
        #if NOT_EXCLUDED(OP_set_seed)
        DECLARE_CUSTOM_OP(set_seed, -2, 1, false, 0, -2);
        #endif

        #if NOT_EXCLUDED(OP_get_seed)
        DECLARE_CUSTOM_OP(get_seed, -2, 1, false, 0, 0);
        #endif

        /*
         * random_uniform distribution for types int32,int64, float16, float and double
         * by default dtype is float32
         *
         * input:
         *    0 - shape of output (1D int tensor)
         *    1 - min val (0D of output type) - optional (0 as default)
         *    2 - max val (0D of output type) - optional (inf as default)
         *
         * output:
         *    0 - uniformly distributed values of given type (between min and max)
         */
        #if NOT_EXCLUDED(OP_randomuniform)
        DECLARE_CUSTOM_OP(randomuniform, 1, 1, false, 0, 0);
        #endif
        /*
         * multinomial (categorical) random generator draws samples from a multinomial distribution
         *
         * Input array:
         *    0 - 2D ndarray with unnormalized log-probabilities with shape [batch_size (N), num_classes (K)]
         *    1 - array with one int value of samples number, number of independent samples to draw for each experiment 1,N.
         * Int arguments:
         *    0 - optional argument, corresponds to dimension with batch_size
         *    1 - optional argument, integer type to use for the output. Default int64.
         *
         * Output array:
         *    0 - 2D ndarray with the drawn samples of shape [batch_size, num_samples]
         */
        #if NOT_EXCLUDED(OP_random_multinomial)
        DECLARE_CUSTOM_OP(random_multinomial, 2, 1, false, 0, 0);
        #endif

        #if NOT_EXCLUDED(OP_random_normal)
        DECLARE_CUSTOM_OP(random_normal, 1, 1, true, 2, 0);
        #endif

        #if NOT_EXCLUDED(OP_random_bernoulli)
        DECLARE_CUSTOM_OP(random_bernoulli, 1, 1, true, 0, 1);
        #endif

        #if NOT_EXCLUDED(OP_random_exponential)
        DECLARE_CUSTOM_OP(random_exponential, 1, 1, true, 1, 0);
        #endif

        #if NOT_EXCLUDED(OP_random_crop)
        DECLARE_CUSTOM_OP(random_crop, 2, 1, false, 0, 0);
        #endif

        /**
         * random_gamma op.
         */
        #if NOT_EXCLUDED(OP_random_gamma)
        DECLARE_CUSTOM_OP(random_gamma, 2, 1, false, 0, 0);
        #endif

        /**
         * random_poisson op.
         */
        #if NOT_EXCLUDED(OP_random_poisson)
        DECLARE_CUSTOM_OP(random_poisson, 2, 1, false, 0, 0);
        #endif

    }
}

#endif