"use strict";
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/**
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* @license
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* Copyright 2018 Google Inc. All Rights Reserved.
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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* =============================================================================
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*/
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var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
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return new (P || (P = Promise))(function (resolve, reject) {
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function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
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function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
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function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }
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step((generator = generator.apply(thisArg, _arguments || [])).next());
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});
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};
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var __generator = (this && this.__generator) || function (thisArg, body) {
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var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
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return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
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function verb(n) { return function (v) { return step([n, v]); }; }
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function step(op) {
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if (f) throw new TypeError("Generator is already executing.");
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while (_) try {
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if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
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if (y = 0, t) op = [op[0] & 2, t.value];
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switch (op[0]) {
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case 0: case 1: t = op; break;
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case 4: _.label++; return { value: op[1], done: false };
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case 5: _.label++; y = op[1]; op = [0]; continue;
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case 7: op = _.ops.pop(); _.trys.pop(); continue;
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default:
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if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
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if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
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if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
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if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
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if (t[2]) _.ops.pop();
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_.trys.pop(); continue;
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}
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op = body.call(thisArg, _);
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} catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
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if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
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}
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};
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Object.defineProperty(exports, "__esModule", { value: true });
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var tensor_util_env_1 = require("../tensor_util_env");
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var util = require("../util");
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var logical_ops_1 = require("./logical_ops");
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var segment_ops_1 = require("./segment_ops");
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/**
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* Apply boolean mask to tensor.
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*
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* ```js
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* const tensor = tf.tensor2d([1, 2, 3, 4, 5, 6], [3, 2]);
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* const mask = tf.tensor1d([1, 0, 1], 'bool');
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* const result = await tf.booleanMaskAsync(tensor, mask);
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* result.print();
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* ```
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*
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* @param tensor N-D tensor.
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* @param mask K-D boolean tensor, K <= N and K must be known statically.
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* @param axis A 0-D int Tensor representing the axis in tensor to mask from.
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* By default, axis is 0 which will mask from the first dimension.
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* Otherwise K + axis <= N.
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*/
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/** @doc {heading: 'Tensors', subheading: 'Slicing and Joining'} */
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function booleanMaskAsync_(tensor, mask, axis) {
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return __awaiter(this, void 0, void 0, function () {
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var $tensor, $mask, axisFrom, maskDim, tensorShape, leadingSize, i, targetTensorShape, reshapedTensor, reshapedMask, positivePositions, indices, res;
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return __generator(this, function (_a) {
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switch (_a.label) {
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case 0:
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$tensor = tensor_util_env_1.convertToTensor(tensor, 'tensor', 'boolMask');
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$mask = tensor_util_env_1.convertToTensor(mask, 'mask', 'boolMask', 'bool');
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axisFrom = axis == null ? 0 : axis;
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maskDim = $mask.rank;
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tensorShape = $tensor.shape;
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util.assert(maskDim > 0, function () { return 'mask cannot be scalar'; });
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util.assertShapesMatch(tensorShape.slice(axisFrom, axisFrom + maskDim), $mask.shape, "mask's shape must match the first K dimensions of tensor's shape,");
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leadingSize = 1;
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for (i = axisFrom; i < axisFrom + maskDim; i++) {
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leadingSize *= tensorShape[i];
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}
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targetTensorShape = tensorShape.slice(0, axisFrom)
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.concat([leadingSize], tensorShape.slice(axisFrom + maskDim));
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reshapedTensor = $tensor.reshape(targetTensorShape);
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reshapedMask = $mask.reshape([-1]);
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return [4 /*yield*/, logical_ops_1.whereAsync(reshapedMask)];
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case 1:
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positivePositions = _a.sent();
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indices = positivePositions.squeeze([1]);
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res = segment_ops_1.gather(reshapedTensor, indices, axisFrom);
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// Ensure no memory leak.
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if (tensor !== $tensor) {
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$tensor.dispose();
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}
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if (mask !== $mask) {
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$mask.dispose();
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}
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indices.dispose();
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reshapedTensor.dispose();
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reshapedMask.dispose();
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positivePositions.dispose();
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return [2 /*return*/, res];
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}
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});
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});
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}
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exports.booleanMaskAsync = booleanMaskAsync_;
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//# sourceMappingURL=boolean_mask.js.map
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