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2025-06-12 7b72ac13a83764a662159d4a49b7fffb90476ecb

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/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * 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
 *
 * 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.
 * =============================================================================
 */
import { backend_util, util } from '@tensorflow/tfjs-core';
export function concatImpl(inputs, outShape, dtype, simplyConcat) {
    const outVals = util.getArrayFromDType(dtype, util.sizeFromShape(outShape));
    if (simplyConcat && dtype !== 'string') {
        // Use built-in TypedArray.set() method for speed.
        let offset = 0;
        inputs.forEach(input => {
            const size = util.sizeFromShape(input.shape);
            outVals.set(input.vals, offset);
            offset += size;
        });
    }
    else {
        let colOffset = 0;
        inputs.forEach(input => {
            const decodedData = dtype === 'string' ?
                backend_util.fromUint8ToStringArray(input.vals) :
                input.vals;
            let tIdx = 0;
            for (let row = 0; row < input.shape[0]; ++row) {
                const resIdx = row * outShape[1] + colOffset;
                for (let col = 0; col < input.shape[1]; ++col) {
                    outVals[resIdx + col] = decodedData[tIdx++];
                }
            }
            colOffset += input.shape[1];
        });
    }
    return outVals;
}
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