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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 { TensorBuffer, util } from '@tensorflow/tfjs-core';
export function uniqueImpl(values, axis, shape, dtype) {
    // Normalize and validate axis.
    const $axis = util.parseAxisParam(axis, shape)[0];
    // Calculate the new shape that is suitable for extracting data along the
    // given axis.
    //
    // The rank is 3.
    // The size of the 1st dimension is the size of all the axes < the given axis.
    // The size of the 2nd dimension is the same as the size of the given axis.
    // The size of the 3rd dimension is the size of all the axes > the given axis.
    //
    // For example, for a 4D tensor with shape=[2, 3, 5, 4] and axis=2, the
    // newShape would be: [2*3, 5, 4].
    //
    // Note that this is not the final output shape. This will be the shape for an
    // intermediate TensorBuffer (see inputBuffer below) to allow us to extract
    // values along the given axis. To demonstrate how it works, consider the
    // following example:
    //
    // Input: a 3D tensor, with shape [1, 2, 3]
    // [
    //   [
    //      [1,2,3],
    //      [4,5,6]
    //   ]
    // ]
    // Axis: 2 (the last axis).
    // Along axis 2, we expect to extract 3 tensors: [1,4], [2,5], [3,6].
    //
    // For this example, newShape would be: [2, 3, 1], where 2 is calculated from
    // 1*2. The re-shaped data would look like:
    //
    // [
    //   [
    //     [1], [2], [3]
    //   ],
    //   [
    //     [4], [5], [6]
    //   ]
    // ]
    //
    // Then, we can construct a 3-level nested loop by the following dimension
    // order to extract the values along the axis (dimension1):
    // i: dimension1       // 0,1,2 (newShape[1])
    //   m: dimension0     // 0,1   (newShape[0])
    //     n: dimension2   // 0     (newShape[2])
    //
    //                       m, i, n
    //                      ---------
    // Iteration 0: data at [0, 0, 0] => "1"
    // Iteration 1: data at [1, 0, 0] => "4"
    // We got [1,4].
    // Iteration 2: data at [0, 1, 0] => "2"
    // Iteration 3: data at [1, 1, 0] => "5"
    // We got [2,5].
    // Iteration 4: data at [0, 2, 0] => "3"
    // Iteration 5: data at [1, 2, 0] => "6"
    // We got [3,6].
    const newShape = [1, shape[0], 1];
    for (let i = 0; i < $axis; i++) {
        newShape[0] *= shape[i];
    }
    newShape[1] = shape[$axis];
    for (let i = $axis + 1; i < shape.length; i++) {
        newShape[2] *= shape[i];
    }
    // A map from unique elements (their string representations) to their values
    // in "indices" (below).
    const uniqueElements = new Map();
    // The indices of each unique element in the original tensor along the given
    // axis. It is 1D and has the same size as the given axis.
    const indices = new Int32Array(shape[$axis]);
    // Create a buffer so we can easily extract value at a given location.
    const inputBuffer = new TensorBuffer(newShape, dtype, values);
    // The indices along the given axis that have unique elements. This is a
    // de-duped version of "indices" above.
    const uniqueIndices = [];
    const is1DTensor = newShape[0] === 1 && newShape[2] === 1;
    for (let i = 0; i < shape[$axis]; i++) {
        // Extract values along the axis.
        let element;
        if (is1DTensor) {
            // Fast path for 1D tensor input.
            element = values[i].toString();
        }
        else {
            const axisValues = [];
            for (let m = 0; m < newShape[0]; m++) {
                for (let n = 0; n < newShape[2]; n++) {
                    axisValues.push(inputBuffer.get(m, i, n));
                }
            }
            element = axisValues.join(',');
        }
        // Dedup and update various indices.
        const existingIndex = uniqueElements.get(element);
        if (existingIndex != null) {
            indices[i] = existingIndex;
        }
        else {
            const uniqueIndex = uniqueElements.size;
            uniqueElements.set(element, uniqueIndex);
            indices[i] = uniqueIndex;
            uniqueIndices.push(i);
        }
    }
    // Now we know where each of the unique elements are located along the axis
    // (uniqueIndices). Extract them from input buffer and store them in the
    // output buffer.
    const outputTmpShape = newShape.slice();
    outputTmpShape[1] = uniqueElements.size;
    const outputBuffer = new TensorBuffer(outputTmpShape, dtype);
    uniqueIndices.forEach((uniqueElementIndex, i) => {
        for (let m = 0; m < newShape[0]; m++) {
            for (let n = 0; n < newShape[2]; n++) {
                outputBuffer.set(inputBuffer.get(m, uniqueElementIndex, n), m, i, n);
            }
        }
    });
    // The output shape can be calculated from the input shape with the size of
    // the given axis replaced by the number of unique elements along that axis.
    const outputShape = shape.slice();
    outputShape[$axis] = outputTmpShape[1];
    return {
        outputValues: outputBuffer.values,
        outputShape,
        indices,
    };
}
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All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {BackendValues, DataType, TensorBuffer, TypedArray, util} from '@tensorflow/tfjs-core';\n\nexport function uniqueImpl(\n    values: BackendValues, axis: number, shape: number[], dtype: DataType): {\n  outputValues: BackendValues,\n  outputShape: number[],\n  indices: BackendValues\n} {\n  // Normalize and validate axis.\n  const $axis = util.parseAxisParam(axis, shape)[0];\n\n  // Calculate the new shape that is suitable for extracting data along the\n  // given axis.\n  //\n  // The rank is 3.\n  // The size of the 1st dimension is the size of all the axes < the given axis.\n  // The size of the 2nd dimension is the same as the size of the given axis.\n  // The size of the 3rd dimension is the size of all the axes > the given axis.\n  //\n  // For example, for a 4D tensor with shape=[2, 3, 5, 4] and axis=2, the\n  // newShape would be: [2*3, 5, 4].\n  //\n  // Note that this is not the final output shape. This will be the shape for an\n  // intermediate TensorBuffer (see inputBuffer below) to allow us to extract\n  // values along the given axis. To demonstrate how it works, consider the\n  // following example:\n  //\n  // Input: a 3D tensor, with shape [1, 2, 3]\n  // [\n  //   [\n  //      [1,2,3],\n  //      [4,5,6]\n  //   ]\n  // ]\n  // Axis: 2 (the last axis).\n  // Along axis 2, we expect to extract 3 tensors: [1,4], [2,5], [3,6].\n  //\n  // For this example, newShape would be: [2, 3, 1], where 2 is calculated from\n  // 1*2. The re-shaped data would look like:\n  //\n  // [\n  //   [\n  //     [1], [2], [3]\n  //   ],\n  //   [\n  //     [4], [5], [6]\n  //   ]\n  // ]\n  //\n  // Then, we can construct a 3-level nested loop by the following dimension\n  // order to extract the values along the axis (dimension1):\n  // i: dimension1       // 0,1,2 (newShape[1])\n  //   m: dimension0     // 0,1   (newShape[0])\n  //     n: dimension2   // 0     (newShape[2])\n  //\n  //                       m, i, n\n  //                      ---------\n  // Iteration 0: data at [0, 0, 0] => \"1\"\n  // Iteration 1: data at [1, 0, 0] => \"4\"\n  // We got [1,4].\n  // Iteration 2: data at [0, 1, 0] => \"2\"\n  // Iteration 3: data at [1, 1, 0] => \"5\"\n  // We got [2,5].\n  // Iteration 4: data at [0, 2, 0] => \"3\"\n  // Iteration 5: data at [1, 2, 0] => \"6\"\n  // We got [3,6].\n  const newShape = [1, shape[0], 1];\n  for (let i = 0; i < $axis; i++) {\n    newShape[0] *= shape[i];\n  }\n  newShape[1] = shape[$axis];\n  for (let i = $axis + 1; i < shape.length; i++) {\n    newShape[2] *= shape[i];\n  }\n\n  // A map from unique elements (their string representations) to their values\n  // in \"indices\" (below).\n  const uniqueElements = new Map<string, number>();\n  // The indices of each unique element in the original tensor along the given\n  // axis. It is 1D and has the same size as the given axis.\n  const indices = new Int32Array(shape[$axis]);\n  // Create a buffer so we can easily extract value at a given location.\n  const inputBuffer = new TensorBuffer(newShape, dtype, values as TypedArray);\n  // The indices along the given axis that have unique elements. This is a\n  // de-duped version of \"indices\" above.\n  const uniqueIndices: number[] = [];\n  const is1DTensor = newShape[0] === 1 && newShape[2] === 1;\n  for (let i = 0; i < shape[$axis]; i++) {\n    // Extract values along the axis.\n    let element: string;\n    if (is1DTensor) {\n      // Fast path for 1D tensor input.\n      element = values[i].toString();\n    } else {\n      const axisValues = [];\n      for (let m = 0; m < newShape[0]; m++) {\n        for (let n = 0; n < newShape[2]; n++) {\n          axisValues.push(inputBuffer.get(m, i, n));\n        }\n      }\n      element = axisValues.join(',');\n    }\n\n    // Dedup and update various indices.\n    const existingIndex = uniqueElements.get(element);\n    if (existingIndex != null) {\n      indices[i] = existingIndex;\n    } else {\n      const uniqueIndex = uniqueElements.size;\n      uniqueElements.set(element, uniqueIndex);\n      indices[i] = uniqueIndex;\n      uniqueIndices.push(i);\n    }\n  }\n\n  // Now we know where each of the unique elements are located along the axis\n  // (uniqueIndices). Extract them from input buffer and store them in the\n  // output buffer.\n  const outputTmpShape = newShape.slice();\n  outputTmpShape[1] = uniqueElements.size;\n  const outputBuffer = new TensorBuffer(outputTmpShape, dtype);\n  uniqueIndices.forEach((uniqueElementIndex, i) => {\n    for (let m = 0; m < newShape[0]; m++) {\n      for (let n = 0; n < newShape[2]; n++) {\n        outputBuffer.set(inputBuffer.get(m, uniqueElementIndex, n), m, i, n);\n      }\n    }\n  });\n\n  // The output shape can be calculated from the input shape with the size of\n  // the given axis replaced by the number of unique elements along that axis.\n  const outputShape = shape.slice();\n  outputShape[$axis] = outputTmpShape[1];\n\n  return {\n    outputValues: outputBuffer.values as BackendValues,\n    outputShape,\n    indices,\n  };\n}\n"]}