"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
/**
 * Validate sparseToDense inputs.
 *
 * @param sparseIndices A 0-D, 1-D, or 2-D Tensor of type int32.
 * sparseIndices[i] contains the complete index where sparseValues[i] will be
 * placed.
 * @param sparseValues A 0-D or 1-D Tensor. Values
 * corresponding to each row of sparseIndices, or a scalar value to be used for
 * all sparse indices.
 * @param outputShape number[]. Shape of the dense output tensor.
 * @param validateIndices boolean. indice validation is not supported, error
 * will be thrown if it is set.
 */
function validateInput(sparseIndices, sparseValues, outputShape, defaultValues) {
    if (sparseIndices.dtype !== 'int32') {
        throw new Error('tf.sparseToDense() expects the indices to be int32 type,' +
            (" but the dtype was " + sparseIndices.dtype + "."));
    }
    if (sparseIndices.rank > 2) {
        throw new Error('sparseIndices should be a scalar, vector, or matrix,' +
            (" but got shape " + sparseIndices.shape + "."));
    }
    var numElems = sparseIndices.rank > 0 ? sparseIndices.shape[0] : 1;
    var numDims = sparseIndices.rank > 1 ? sparseIndices.shape[1] : 1;
    if (outputShape.length !== numDims) {
        throw new Error('outputShape has incorrect number of elements:,' +
            (" " + outputShape.length + ", should be: " + numDims + "."));
    }
    var numValues = sparseValues.size;
    if (!(sparseValues.rank === 0 ||
        sparseValues.rank === 1 && numValues === numElems)) {
        throw new Error('sparseValues has incorrect shape ' +
            (sparseValues.shape + ", should be [] or [" + numElems + "]"));
    }
    if (sparseValues.dtype !== defaultValues.dtype) {
        throw new Error('sparseValues.dtype must match defaultValues.dtype');
    }
}
exports.validateInput = validateInput;
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