| 1 | /**
|
| 2 | * @license
|
| 3 | * Copyright 2017 Google LLC. All Rights Reserved.
|
| 4 | * Licensed under the Apache License, Version 2.0 (the "License");
|
| 5 | * you may not use this file except in compliance with the License.
|
| 6 | * You may obtain a copy of the License at
|
| 7 | *
|
| 8 | * http://www.apache.org/licenses/LICENSE-2.0
|
| 9 | *
|
| 10 | * Unless required by applicable law or agreed to in writing, software
|
| 11 | * distributed under the License is distributed on an "AS IS" BASIS,
|
| 12 | * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
| 13 | * See the License for the specific language governing permissions and
|
| 14 | * limitations under the License.
|
| 15 | * =============================================================================
|
| 16 | */
|
| 17 | import * as tf from '../index';
|
| 18 | import { ALL_ENVS, describeWithFlags } from '../jasmine_util';
|
| 19 | import { expectArraysClose } from '../test_util';
|
| 20 | describeWithFlags('batchNorm4D', ALL_ENVS, () => {
|
| 21 | it('simple batchnorm4D, no offset or scale, 2x1x1x2', async () => {
|
| 22 | const xT = tf.tensor4d([2, 4, 9, 23], [2, 1, 1, 2]);
|
| 23 | const meanT = tf.tensor1d([1, 2]);
|
| 24 | const varianceT = tf.tensor1d([2, 3]);
|
| 25 | const varianceEpsilon = .001;
|
| 26 | const result = tf.batchNorm4d(xT, meanT, varianceT, undefined, undefined, varianceEpsilon);
|
| 27 | const x = await xT.array();
|
| 28 | const mean = await meanT.array();
|
| 29 | const variance = await varianceT.array();
|
| 30 | expectArraysClose(await result.data(), [
|
| 31 | (x[0][0][0][0] - mean[0]) * 1 / Math.sqrt(variance[0] + varianceEpsilon),
|
| 32 | (x[0][0][0][1] - mean[1]) * 1 / Math.sqrt(variance[1] + varianceEpsilon),
|
| 33 | (x[1][0][0][0] - mean[0]) * 1 / Math.sqrt(variance[0] + varianceEpsilon),
|
| 34 | (x[1][0][0][1] - mean[1]) * 1 / Math.sqrt(variance[1] + varianceEpsilon)
|
| 35 | ]);
|
| 36 | });
|
| 37 | it('simple batchnorm4D, no offset, 2x1x1x2', async () => {
|
| 38 | const xT = tf.tensor4d([2, 4, 9, 23], [2, 1, 1, 2]);
|
| 39 | const meanT = tf.tensor1d([1, 2]);
|
| 40 | const varianceT = tf.tensor1d([2, 3]);
|
| 41 | const scaleT = tf.tensor1d([4, 5]);
|
| 42 | const varianceEpsilon = .001;
|
| 43 | const result = tf.batchNorm4d(xT, meanT, varianceT, undefined, scaleT, varianceEpsilon);
|
| 44 | const x = await xT.buffer();
|
| 45 | const mean = await meanT.buffer();
|
| 46 | const variance = await varianceT.buffer();
|
| 47 | const scale = await scaleT.buffer();
|
| 48 | expectArraysClose(await result.data(), [
|
| 49 | (x.get(0, 0, 0, 0) - mean.get(0)) * scale.get(0) /
|
| 50 | Math.sqrt(variance.get(0) + varianceEpsilon),
|
| 51 | (x.get(0, 0, 0, 1) - mean.get(1)) * scale.get(1) /
|
| 52 | Math.sqrt(variance.get(1) + varianceEpsilon),
|
| 53 | (x.get(1, 0, 0, 0) - mean.get(0)) * scale.get(0) /
|
| 54 | Math.sqrt(variance.get(0) + varianceEpsilon),
|
| 55 | (x.get(1, 0, 0, 1) - mean.get(1)) * scale.get(1) /
|
| 56 | Math.sqrt(variance.get(1) + varianceEpsilon)
|
| 57 | ]);
|
| 58 | });
|
| 59 | it('simple batchnorm4D, no scale, 2x1x1x2', async () => {
|
| 60 | const xT = tf.tensor4d([2, 4, 9, 23], [2, 1, 1, 2]);
|
| 61 | const meanT = tf.tensor1d([1, 2]);
|
| 62 | const varianceT = tf.tensor1d([2, 3]);
|
| 63 | const offsetT = tf.tensor1d([4, 5]);
|
| 64 | const varianceEpsilon = .001;
|
| 65 | const result = tf.batchNorm4d(xT, meanT, varianceT, offsetT, undefined, varianceEpsilon);
|
| 66 | const x = await xT.buffer();
|
| 67 | const mean = await meanT.buffer();
|
| 68 | const variance = await varianceT.buffer();
|
| 69 | const offset = await offsetT.buffer();
|
| 70 | expectArraysClose(await result.data(), [
|
| 71 | offset.get(0) +
|
| 72 | (x.get(0, 0, 0, 0) - mean.get(0)) * 1 /
|
| 73 | Math.sqrt(variance.get(0) + varianceEpsilon),
|
| 74 | offset.get(1) +
|
| 75 | (x.get(0, 0, 0, 1) - mean.get(1)) * 1 /
|
| 76 | Math.sqrt(variance.get(1) + varianceEpsilon),
|
| 77 | offset.get(0) +
|
| 78 | (x.get(1, 0, 0, 0) - mean.get(0)) * 1 /
|
| 79 | Math.sqrt(variance.get(0) + varianceEpsilon),
|
| 80 | offset.get(1) +
|
| 81 | (x.get(1, 0, 0, 1) - mean.get(1)) * 1 /
|
| 82 | Math.sqrt(variance.get(1) + varianceEpsilon)
|
| 83 | ]);
|
| 84 | });
|
| 85 | it('simple batchnorm4D, 2x1x1x2', async () => {
|
| 86 | const xT = tf.tensor4d([2, 4, 9, 23], [2, 1, 1, 2]);
|
| 87 | const meanT = tf.tensor1d([1, 2]);
|
| 88 | const varianceT = tf.tensor1d([2, 3]);
|
| 89 | const offsetT = tf.tensor1d([3, 4]);
|
| 90 | const scaleT = tf.tensor1d([4, 5]);
|
| 91 | const varianceEpsilon = .001;
|
| 92 | const result = tf.batchNorm4d(xT, meanT, varianceT, offsetT, scaleT, varianceEpsilon);
|
| 93 | const x = await xT.buffer();
|
| 94 | const mean = await meanT.buffer();
|
| 95 | const variance = await varianceT.buffer();
|
| 96 | const scale = await scaleT.buffer();
|
| 97 | const offset = await offsetT.buffer();
|
| 98 | expectArraysClose(await result.data(), [
|
| 99 | offset.get(0) +
|
| 100 | (x.get(0, 0, 0, 0) - mean.get(0)) * scale.get(0) /
|
| 101 | Math.sqrt(variance.get(0) + varianceEpsilon),
|
| 102 | offset.get(1) +
|
| 103 | (x.get(0, 0, 0, 1) - mean.get(1)) * scale.get(1) /
|
| 104 | Math.sqrt(variance.get(1) + varianceEpsilon),
|
| 105 | offset.get(0) +
|
| 106 | (x.get(1, 0, 0, 0) - mean.get(0)) * scale.get(0) /
|
| 107 | Math.sqrt(variance.get(0) + varianceEpsilon),
|
| 108 | offset.get(1) +
|
| 109 | (x.get(1, 0, 0, 1) - mean.get(1)) * scale.get(1) /
|
| 110 | Math.sqrt(variance.get(1) + varianceEpsilon)
|
| 111 | ]);
|
| 112 | });
|
| 113 | it('accepts a tensor-like object', async () => {
|
| 114 | const x = [[[[2, 4]]], [[[9, 23]]]]; // 2x1x1x2
|
| 115 | const mean = [1, 2];
|
| 116 | const variance = [2, 3];
|
| 117 | const offset = [3, 4];
|
| 118 | const scale = [4, 5];
|
| 119 | const varianceEpsilon = .001;
|
| 120 | const result = tf.batchNorm4d(x, mean, variance, offset, scale, varianceEpsilon);
|
| 121 | expectArraysClose(await result.data(), [
|
| 122 | offset[0] +
|
| 123 | (x[0][0][0][0] - mean[0]) * scale[0] /
|
| 124 | Math.sqrt(variance[0] + varianceEpsilon),
|
| 125 | offset[1] +
|
| 126 | (x[0][0][0][1] - mean[1]) * scale[1] /
|
| 127 | Math.sqrt(variance[1] + varianceEpsilon),
|
| 128 | offset[0] +
|
| 129 | (x[1][0][0][0] - mean[0]) * scale[0] /
|
| 130 | Math.sqrt(variance[0] + varianceEpsilon),
|
| 131 | offset[1] +
|
| 132 | (x[1][0][0][1] - mean[1]) * scale[1] /
|
| 133 | Math.sqrt(variance[1] + varianceEpsilon)
|
| 134 | ]);
|
| 135 | });
|
| 136 | it('simple batchnorm4D gradients, 2x1x1x2', async () => {
|
| 137 | const x = tf.tensor4d([2, 4, 9, 23], [2, 1, 1, 2]);
|
| 138 | const mean = tf.tensor1d([1, 2]);
|
| 139 | const variance = tf.tensor1d([2, 3]);
|
| 140 | const offset = tf.tensor1d([3, 4]);
|
| 141 | const scale = tf.tensor1d([2, 5]);
|
| 142 | const varianceEpsilon = .001;
|
| 143 | const dy = tf.tensor4d([-1, -1, -1, -1], [2, 1, 1, 2]);
|
| 144 | const gradX = tf.grad((x) => tf.batchNorm4d(x, mean, variance, offset, scale, varianceEpsilon))(x, dy);
|
| 145 | expectArraysClose(await gradX.data(), [-1.414, -2.887, -1.414, -2.887]);
|
| 146 | expect(gradX.shape).toEqual([2, 1, 1, 2]);
|
| 147 | const gradMean = tf.grad((mean) => tf.batchNorm4d(x, mean, variance, offset, scale, varianceEpsilon))(mean, dy);
|
| 148 | expectArraysClose(await gradMean.data(), [2.828, 5.773]);
|
| 149 | expect(gradMean.shape).toEqual([2]);
|
| 150 | const gradVariance = tf.grad((variance) => tf.batchNorm4d(x, mean, variance, offset, scale, varianceEpsilon))(variance, dy);
|
| 151 | expectArraysClose(await gradVariance.data(), [3.180, 11.060]);
|
| 152 | expect(gradVariance.shape).toEqual([2]);
|
| 153 | const gradOffset = tf.grad((offset) => tf.batchNorm4d(x, mean, variance, offset, scale, varianceEpsilon))(offset, dy);
|
| 154 | expectArraysClose(await gradOffset.data(), await dy.sum([0, 1, 2]).data());
|
| 155 | expect(gradOffset.shape).toEqual([2]);
|
| 156 | const gradScale = tf.grad((scale) => tf.batchNorm4d(x, mean, variance, offset, scale, varianceEpsilon))(scale, dy);
|
| 157 | expectArraysClose(await gradScale.data(), [-6.362, -13.277]);
|
| 158 | expect(gradScale.shape).toEqual([2]);
|
| 159 | });
|
| 160 | it('batchnorm4D gradients, same shapes in x, mean and variance', async () => {
|
| 161 | const x = tf.tensor4d([10, 20, 30, 40], [2, 1, 1, 2]);
|
| 162 | const mean = tf.tensor4d([0, 5, 10, 15], [2, 1, 1, 2]);
|
| 163 | const variance = tf.tensor4d([2, 4, 6, 8], [2, 1, 1, 2]);
|
| 164 | const scale = tf.tensor4d([2, 5, 2, 5], [2, 1, 1, 2]);
|
| 165 | const offset = tf.tensor4d([0, 0, 0, 0], [2, 1, 1, 2]);
|
| 166 | const varianceEpsilon = .001;
|
| 167 | const dy = tf.tensor4d([-1, -1, -1, -1], [2, 1, 1, 2]);
|
| 168 | const gradX = tf.grad((x) => tf.batchNorm4d(x, mean, variance, offset, scale, varianceEpsilon))(x, dy);
|
| 169 | expectArraysClose(await gradX.data(), [-1.414, -2.500, -0.816, -1.768]);
|
| 170 | expect(gradX.shape).toEqual([2, 1, 1, 2]);
|
| 171 | const gradMean = tf.grad((mean) => tf.batchNorm4d(x, mean, variance, offset, scale, varianceEpsilon))(mean, dy);
|
| 172 | expectArraysClose(await gradMean.data(), [1.414, 2.500, 0.816, 1.768]);
|
| 173 | expect(gradMean.shape).toEqual([2, 1, 1, 2]);
|
| 174 | const gradVariance = tf.grad((variance) => tf.batchNorm4d(x, mean, variance, offset, scale, varianceEpsilon))(variance, dy);
|
| 175 | expectArraysClose(await gradVariance.data(), [3.533, 4.686, 1.360, 2.762]);
|
| 176 | expect(gradVariance.shape).toEqual([2, 1, 1, 2]);
|
| 177 | const gradOffset = tf.grad((offset) => tf.batchNorm4d(x, mean, variance, offset, scale, varianceEpsilon))(offset, dy);
|
| 178 | expectArraysClose(await gradOffset.data(), await dy.data());
|
| 179 | expect(gradOffset.shape).toEqual([2, 1, 1, 2]);
|
| 180 | const gradScale = tf.grad((scale) => tf.batchNorm4d(x, mean, variance, offset, scale, varianceEpsilon))(scale, dy);
|
| 181 | expectArraysClose(await gradScale.data(), [-7.069, -7.499, -8.164, -8.838]);
|
| 182 | expect(gradScale.shape).toEqual([2, 1, 1, 2]);
|
| 183 | });
|
| 184 | });
|
| 185 | describeWithFlags('batchNorm3D', ALL_ENVS, () => {
|
| 186 | it('simple batchnorm3D, no offset or scale, 2x1x2', async () => {
|
| 187 | const xT = tf.tensor3d([2, 4, 9, 23], [2, 1, 2]);
|
| 188 | const meanT = tf.tensor1d([1, 2]);
|
| 189 | const varianceT = tf.tensor1d([2, 3]);
|
| 190 | const varianceEpsilon = .001;
|
| 191 | const result = tf.batchNorm3d(xT, meanT, varianceT, undefined, undefined, varianceEpsilon);
|
| 192 | const x = await xT.buffer();
|
| 193 | const mean = await meanT.buffer();
|
| 194 | const variance = await varianceT.buffer();
|
| 195 | expectArraysClose(await result.data(), [
|
| 196 | (x.get(0, 0, 0) - mean.get(0)) * 1 /
|
| 197 | Math.sqrt(variance.get(0) + varianceEpsilon),
|
| 198 | (x.get(0, 0, 1) - mean.get(1)) * 1 /
|
| 199 | Math.sqrt(variance.get(1) + varianceEpsilon),
|
| 200 | (x.get(1, 0, 0) - mean.get(0)) * 1 /
|
| 201 | Math.sqrt(variance.get(0) + varianceEpsilon),
|
| 202 | (x.get(1, 0, 1) - mean.get(1)) * 1 /
|
| 203 | Math.sqrt(variance.get(1) + varianceEpsilon)
|
| 204 | ]);
|
| 205 | });
|
| 206 | it('simple batchnorm3D, no offset, 2x1x2', async () => {
|
| 207 | const xT = tf.tensor3d([2, 4, 9, 23], [2, 1, 2]);
|
| 208 | const meanT = tf.tensor1d([1, 2]);
|
| 209 | const varianceT = tf.tensor1d([2, 3]);
|
| 210 | const scaleT = tf.tensor1d([4, 5]);
|
| 211 | const varianceEpsilon = .001;
|
| 212 | const result = tf.batchNorm3d(xT, meanT, varianceT, undefined, scaleT, varianceEpsilon);
|
| 213 | const x = await xT.buffer();
|
| 214 | const mean = await meanT.buffer();
|
| 215 | const variance = await varianceT.buffer();
|
| 216 | const scale = await scaleT.buffer();
|
| 217 | expectArraysClose(await result.data(), [
|
| 218 | (x.get(0, 0, 0) - mean.get(0)) * scale.get(0) /
|
| 219 | Math.sqrt(variance.get(0) + varianceEpsilon),
|
| 220 | (x.get(0, 0, 1) - mean.get(1)) * scale.get(1) /
|
| 221 | Math.sqrt(variance.get(1) + varianceEpsilon),
|
| 222 | (x.get(1, 0, 0) - mean.get(0)) * scale.get(0) /
|
| 223 | Math.sqrt(variance.get(0) + varianceEpsilon),
|
| 224 | (x.get(1, 0, 1) - mean.get(1)) * scale.get(1) /
|
| 225 | Math.sqrt(variance.get(1) + varianceEpsilon)
|
| 226 | ]);
|
| 227 | });
|
| 228 | it('simple batchnorm3D, no scale, 2x1x2', async () => {
|
| 229 | const xT = tf.tensor3d([2, 4, 9, 23], [2, 1, 2]);
|
| 230 | const meanT = tf.tensor1d([1, 2]);
|
| 231 | const varianceT = tf.tensor1d([2, 3]);
|
| 232 | const offsetT = tf.tensor1d([4, 5]);
|
| 233 | const varianceEpsilon = .001;
|
| 234 | const result = tf.batchNorm3d(xT, meanT, varianceT, offsetT, undefined, varianceEpsilon);
|
| 235 | const x = await xT.buffer();
|
| 236 | const mean = await meanT.buffer();
|
| 237 | const variance = await varianceT.buffer();
|
| 238 | const offset = await offsetT.buffer();
|
| 239 | expectArraysClose(await result.data(), [
|
| 240 | offset.get(0) +
|
| 241 | (x.get(0, 0, 0) - mean.get(0)) * 1 /
|
| 242 | Math.sqrt(variance.get(0) + varianceEpsilon),
|
| 243 | offset.get(1) +
|
| 244 | (x.get(0, 0, 1) - mean.get(1)) * 1 /
|
| 245 | Math.sqrt(variance.get(1) + varianceEpsilon),
|
| 246 | offset.get(0) +
|
| 247 | (x.get(1, 0, 0) - mean.get(0)) * 1 /
|
| 248 | Math.sqrt(variance.get(0) + varianceEpsilon),
|
| 249 | offset.get(1) +
|
| 250 | (x.get(1, 0, 1) - mean.get(1)) * 1 /
|
| 251 | Math.sqrt(variance.get(1) + varianceEpsilon)
|
| 252 | ]);
|
| 253 | });
|
| 254 | it('simple batchnorm3D, 2x1x2', async () => {
|
| 255 | const xT = tf.tensor3d([2, 4, 9, 23], [2, 1, 2]);
|
| 256 | const meanT = tf.tensor1d([1, 2]);
|
| 257 | const varianceT = tf.tensor1d([2, 3]);
|
| 258 | const offsetT = tf.tensor1d([3, 4]);
|
| 259 | const scaleT = tf.tensor1d([4, 5]);
|
| 260 | const varianceEpsilon = .001;
|
| 261 | const result = tf.batchNorm3d(xT, meanT, varianceT, offsetT, scaleT, varianceEpsilon);
|
| 262 | const x = await xT.buffer();
|
| 263 | const mean = await meanT.buffer();
|
| 264 | const variance = await varianceT.buffer();
|
| 265 | const offset = await offsetT.buffer();
|
| 266 | const scale = await scaleT.buffer();
|
| 267 | expectArraysClose(await result.data(), [
|
| 268 | offset.get(0) +
|
| 269 | (x.get(0, 0, 0) - mean.get(0)) * scale.get(0) /
|
| 270 | Math.sqrt(variance.get(0) + varianceEpsilon),
|
| 271 | offset.get(1) +
|
| 272 | (x.get(0, 0, 1) - mean.get(1)) * scale.get(1) /
|
| 273 | Math.sqrt(variance.get(1) + varianceEpsilon),
|
| 274 | offset.get(0) +
|
| 275 | (x.get(1, 0, 0) - mean.get(0)) * scale.get(0) /
|
| 276 | Math.sqrt(variance.get(0) + varianceEpsilon),
|
| 277 | offset.get(1) +
|
| 278 | (x.get(1, 0, 1) - mean.get(1)) * scale.get(1) /
|
| 279 | Math.sqrt(variance.get(1) + varianceEpsilon)
|
| 280 | ]);
|
| 281 | });
|
| 282 | it('accepts a tensor-like object', async () => {
|
| 283 | const x = [[[2, 4]], [[9, 23]]]; // 2x1x2
|
| 284 | const mean = [1, 2];
|
| 285 | const variance = [2, 3];
|
| 286 | const offset = [3, 4];
|
| 287 | const scale = [4, 5];
|
| 288 | const varianceEpsilon = .001;
|
| 289 | const result = tf.batchNorm3d(x, mean, variance, offset, scale, varianceEpsilon);
|
| 290 | expectArraysClose(await result.data(), [
|
| 291 | offset[0] +
|
| 292 | (x[0][0][0] - mean[0]) * scale[0] /
|
| 293 | Math.sqrt(variance[0] + varianceEpsilon),
|
| 294 | offset[1] +
|
| 295 | (x[0][0][1] - mean[1]) * scale[1] /
|
| 296 | Math.sqrt(variance[1] + varianceEpsilon),
|
| 297 | offset[0] +
|
| 298 | (x[1][0][0] - mean[0]) * scale[0] /
|
| 299 | Math.sqrt(variance[0] + varianceEpsilon),
|
| 300 | offset[1] +
|
| 301 | (x[1][0][1] - mean[1]) * scale[1] /
|
| 302 | Math.sqrt(variance[1] + varianceEpsilon)
|
| 303 | ]);
|
| 304 | });
|
| 305 | it('batchnorm3D, x,mean,var,offset,scale are all 3D', async () => {
|
| 306 | const shape = [2, 1, 2];
|
| 307 | const xT = tf.tensor3d([2, 4, 9, 23], shape);
|
| 308 | const meanT = tf.tensor3d([1, 2, 3, 4], shape);
|
| 309 | const varianceT = tf.tensor3d([2, 3, 4, 5], shape);
|
| 310 | const offsetT = tf.tensor3d([3, 4, 5, 6], shape);
|
| 311 | const scaleT = tf.tensor3d([4, 5, 6, 7], shape);
|
| 312 | const varianceEpsilon = .001;
|
| 313 | const result = tf.batchNorm3d(xT, meanT, varianceT, offsetT, scaleT, varianceEpsilon);
|
| 314 | const x = await xT.buffer();
|
| 315 | const mean = await meanT.buffer();
|
| 316 | const variance = await varianceT.buffer();
|
| 317 | const offset = await offsetT.buffer();
|
| 318 | const scale = await scaleT.buffer();
|
| 319 | expectArraysClose(await result.data(), [
|
| 320 | offset.get(0, 0, 0) +
|
| 321 | (x.get(0, 0, 0) - mean.get(0, 0, 0)) * scale.get(0, 0, 0) /
|
| 322 | Math.sqrt(variance.get(0, 0, 0) + varianceEpsilon),
|
| 323 | offset.get(0, 0, 1) +
|
| 324 | (x.get(0, 0, 1) - mean.get(0, 0, 1)) * scale.get(0, 0, 1) /
|
| 325 | Math.sqrt(variance.get(0, 0, 1) + varianceEpsilon),
|
| 326 | offset.get(1, 0, 0) +
|
| 327 | (x.get(1, 0, 0) - mean.get(1, 0, 0)) * scale.get(1, 0, 0) /
|
| 328 | Math.sqrt(variance.get(1, 0, 0) + varianceEpsilon),
|
| 329 | offset.get(1, 0, 1) +
|
| 330 | (x.get(1, 0, 1) - mean.get(1, 0, 1)) * scale.get(1, 0, 1) /
|
| 331 | Math.sqrt(variance.get(1, 0, 1) + varianceEpsilon)
|
| 332 | ]);
|
| 333 | });
|
| 334 | it('simple batchnorm3D gradients, 2x1x2', async () => {
|
| 335 | const x = tf.tensor3d([2, 4, 9, 23], [2, 1, 2]);
|
| 336 | const mean = tf.tensor1d([1, 2]);
|
| 337 | const variance = tf.tensor1d([2, 3]);
|
| 338 | const offset = tf.tensor1d([3, 4]);
|
| 339 | const scale = tf.tensor1d([2, 5]);
|
| 340 | const varianceEpsilon = .001;
|
| 341 | const dy = tf.tensor3d([1, 1, 1, 1], [2, 1, 2]);
|
| 342 | const gradX = tf.grad((x) => tf.batchNorm3d(x, mean, variance, offset, scale, varianceEpsilon))(x, dy);
|
| 343 | expectArraysClose(await gradX.data(), [1.414, 2.887, 1.414, 2.887]);
|
| 344 | expect(gradX.shape).toEqual([2, 1, 2]);
|
| 345 | const gradMean = tf.grad((mean) => tf.batchNorm3d(x, mean, variance, offset, scale, varianceEpsilon))(mean, dy);
|
| 346 | expectArraysClose(await gradMean.data(), [-2.828, -5.773]);
|
| 347 | expect(gradMean.shape).toEqual([2]);
|
| 348 | const gradVariance = tf.grad((variance) => tf.batchNorm3d(x, mean, variance, offset, scale, varianceEpsilon))(variance, dy);
|
| 349 | expectArraysClose(await gradVariance.data(), [-3.180, -11.060]);
|
| 350 | expect(gradVariance.shape).toEqual([2]);
|
| 351 | const gradOffset = tf.grad((offset) => tf.batchNorm3d(x, mean, variance, offset, scale, varianceEpsilon))(offset, dy);
|
| 352 | expectArraysClose(await gradOffset.data(), [2, 2]);
|
| 353 | expect(gradOffset.shape).toEqual([2]);
|
| 354 | const gradScale = tf.grad((scale) => tf.batchNorm3d(x, mean, variance, offset, scale, varianceEpsilon))(scale, dy);
|
| 355 | expectArraysClose(await gradScale.data(), [6.362, 13.277]);
|
| 356 | expect(gradScale.shape).toEqual([2]);
|
| 357 | });
|
| 358 | it('batchnorm3D gradients, same shapes in x, mean and variance', async () => {
|
| 359 | const x = tf.tensor3d([10, 20, 30, 40], [2, 1, 2]);
|
| 360 | const mean = tf.tensor3d([0, 5, 10, 15], [2, 1, 2]);
|
| 361 | const variance = tf.tensor3d([2, 4, 6, 8], [2, 1, 2]);
|
| 362 | const scale = tf.tensor3d([2, 5, 2, 5], [2, 1, 2]);
|
| 363 | const offset = tf.tensor3d([0, 0, 0, 0], [2, 1, 2]);
|
| 364 | const varianceEpsilon = .001;
|
| 365 | const dy = tf.tensor3d([1, 1, 1, 1], [2, 1, 2]);
|
| 366 | const gradX = tf.grad((x) => tf.batchNorm3d(x, mean, variance, offset, scale, varianceEpsilon))(x, dy);
|
| 367 | expectArraysClose(await gradX.data(), [1.414, 2.500, 0.816, 1.768]);
|
| 368 | expect(gradX.shape).toEqual([2, 1, 2]);
|
| 369 | const gradMean = tf.grad((mean) => tf.batchNorm3d(x, mean, variance, offset, scale, varianceEpsilon))(mean, dy);
|
| 370 | expectArraysClose(await gradMean.data(), [-1.414, -2.500, -0.816, -1.768]);
|
| 371 | expect(gradMean.shape).toEqual([2, 1, 2]);
|
| 372 | const gradVariance = tf.grad((variance) => tf.batchNorm3d(x, mean, variance, offset, scale, varianceEpsilon))(variance, dy);
|
| 373 | expectArraysClose(await gradVariance.data(), [-3.533, -4.686, -1.360, -2.762]);
|
| 374 | expect(gradVariance.shape).toEqual([2, 1, 2]);
|
| 375 | const gradOffset = tf.grad((offset) => tf.batchNorm3d(x, mean, variance, offset, scale, varianceEpsilon))(offset, dy);
|
| 376 | expectArraysClose(await gradOffset.data(), [1, 1, 1, 1]);
|
| 377 | expect(gradOffset.shape).toEqual([2, 1, 2]);
|
| 378 | const gradScale = tf.grad((scale) => tf.batchNorm3d(x, mean, variance, offset, scale, varianceEpsilon))(scale, dy);
|
| 379 | expectArraysClose(await gradScale.data(), [7.069, 7.499, 8.164, 8.838]);
|
| 380 | expect(gradScale.shape).toEqual([2, 1, 2]);
|
| 381 | });
|
| 382 | it('batchnorm matches tensorflow, 2x3x3', async () => {
|
| 383 | const x = tf.tensor3d([
|
| 384 | 0.49955603, 0.04158615, -1.09440524, 2.03854165, -0.61578344,
|
| 385 | 2.87533573, 1.18105987, 0.807462, 1.87888837, 2.26563962, -0.37040935,
|
| 386 | 1.35848753, -0.75347094, 0.15683117, 0.91925946, 0.34121279,
|
| 387 | 0.92717143, 1.89683965
|
| 388 | ], [2, 3, 3]);
|
| 389 | const mean = tf.tensor1d([0.39745062, -0.48062894, 0.4847822]);
|
| 390 | const variance = tf.tensor1d([0.32375343, 0.67117643, 1.08334653]);
|
| 391 | const offset = tf.tensor1d([0.69398749, -1.29056387, 0.9429723]);
|
| 392 | const scale = tf.tensor1d([-0.5607271, 0.9878457, 0.25181573]);
|
| 393 | const varianceEpsilon = .001;
|
| 394 | const result = tf.batchNorm3d(x, mean, variance, offset, scale, varianceEpsilon);
|
| 395 | expectArraysClose(await result.data(), [
|
| 396 | 0.59352049, -0.66135202, 0.5610874, -0.92077015, -1.45341019, 1.52106473,
|
| 397 | -0.07704776, 0.26144429, 1.28010017, -1.14422404, -1.15776136, 1.15425493,
|
| 398 | 1.82644104, -0.52249442, 1.04803919, 0.74932291, 0.40568101, 1.2844412
|
| 399 | ]);
|
| 400 | });
|
| 401 | });
|
| 402 | describeWithFlags('batchNorm2D', ALL_ENVS, () => {
|
| 403 | it('simple batchnorm2D, no offset or scale, 2x2', async () => {
|
| 404 | const xT = tf.tensor2d([2, 4, 9, 23], [2, 2]);
|
| 405 | const meanT = tf.tensor1d([1, 2]);
|
| 406 | const varianceT = tf.tensor1d([2, 3]);
|
| 407 | const varianceEpsilon = .001;
|
| 408 | const result = tf.batchNorm2d(xT, meanT, varianceT, undefined, undefined, varianceEpsilon);
|
| 409 | const x = await xT.buffer();
|
| 410 | const mean = await meanT.buffer();
|
| 411 | const variance = await varianceT.buffer();
|
| 412 | expectArraysClose(await result.data(), [
|
| 413 | (x.get(0, 0) - mean.get(0)) * 1 /
|
| 414 | Math.sqrt(variance.get(0) + varianceEpsilon),
|
| 415 | (x.get(0, 1) - mean.get(1)) * 1 /
|
| 416 | Math.sqrt(variance.get(1) + varianceEpsilon),
|
| 417 | (x.get(1, 0) - mean.get(0)) * 1 /
|
| 418 | Math.sqrt(variance.get(0) + varianceEpsilon),
|
| 419 | (x.get(1, 1) - mean.get(1)) * 1 /
|
| 420 | Math.sqrt(variance.get(1) + varianceEpsilon)
|
| 421 | ]);
|
| 422 | });
|
| 423 | it('simple batchnorm2D, no offset, 2x2', async () => {
|
| 424 | const xT = tf.tensor2d([2, 4, 9, 23], [2, 2]);
|
| 425 | const meanT = tf.tensor1d([1, 2]);
|
| 426 | const varianceT = tf.tensor1d([2, 3]);
|
| 427 | const scaleT = tf.tensor1d([4, 5]);
|
| 428 | const varianceEpsilon = .001;
|
| 429 | const result = tf.batchNorm2d(xT, meanT, varianceT, undefined, scaleT, varianceEpsilon);
|
| 430 | const x = await xT.buffer();
|
| 431 | const mean = await meanT.buffer();
|
| 432 | const variance = await varianceT.buffer();
|
| 433 | const scale = await scaleT.buffer();
|
| 434 | expectArraysClose(await result.data(), [
|
| 435 | (x.get(0, 0) - mean.get(0)) * scale.get(0) /
|
| 436 | Math.sqrt(variance.get(0) + varianceEpsilon),
|
| 437 | (x.get(0, 1) - mean.get(1)) * scale.get(1) /
|
| 438 | Math.sqrt(variance.get(1) + varianceEpsilon),
|
| 439 | (x.get(1, 0) - mean.get(0)) * scale.get(0) /
|
| 440 | Math.sqrt(variance.get(0) + varianceEpsilon),
|
| 441 | (x.get(1, 1) - mean.get(1)) * scale.get(1) /
|
| 442 | Math.sqrt(variance.get(1) + varianceEpsilon)
|
| 443 | ]);
|
| 444 | });
|
| 445 | it('simple batchnorm2D, no scale, 2x2', async () => {
|
| 446 | const xT = tf.tensor2d([2, 4, 9, 23], [2, 2]);
|
| 447 | const meanT = tf.tensor1d([1, 2]);
|
| 448 | const varianceT = tf.tensor1d([2, 3]);
|
| 449 | const offsetT = tf.tensor1d([4, 5]);
|
| 450 | const varianceEpsilon = .001;
|
| 451 | const result = tf.batchNorm2d(xT, meanT, varianceT, offsetT, undefined, varianceEpsilon);
|
| 452 | const offset = await offsetT.array();
|
| 453 | const mean = await meanT.array();
|
| 454 | const variance = await varianceT.array();
|
| 455 | const x = await xT.array();
|
| 456 | expectArraysClose(await result.data(), [
|
| 457 | offset[0] +
|
| 458 | (x[0][0] - mean[0]) * 1 / Math.sqrt(variance[0] + varianceEpsilon),
|
| 459 | offset[1] +
|
| 460 | (x[0][1] - mean[1]) * 1 / Math.sqrt(variance[1] + varianceEpsilon),
|
| 461 | offset[0] +
|
| 462 | (x[1][0] - mean[0]) * 1 / Math.sqrt(variance[0] + varianceEpsilon),
|
| 463 | offset[1] +
|
| 464 | (x[1][1] - mean[1]) * 1 / Math.sqrt(variance[1] + varianceEpsilon)
|
| 465 | ]);
|
| 466 | });
|
| 467 | it('simple batchnorm2D, 2x2', async () => {
|
| 468 | const xT = tf.tensor2d([2, 4, 9, 23], [2, 2]);
|
| 469 | const meanT = tf.tensor1d([1, 2]);
|
| 470 | const varianceT = tf.tensor1d([2, 3]);
|
| 471 | const offsetT = tf.tensor1d([3, 4]);
|
| 472 | const scaleT = tf.tensor1d([4, 5]);
|
| 473 | const varianceEpsilon = .001;
|
| 474 | const result = tf.batchNorm2d(xT, meanT, varianceT, offsetT, scaleT, varianceEpsilon);
|
| 475 | const offset = await offsetT.array();
|
| 476 | const mean = await meanT.array();
|
| 477 | const variance = await varianceT.array();
|
| 478 | const scale = await scaleT.array();
|
| 479 | const x = await xT.array();
|
| 480 | expectArraysClose(await result.data(), [
|
| 481 | offset[0] +
|
| 482 | (x[0][0] - mean[0]) * scale[0] /
|
| 483 | Math.sqrt(variance[0] + varianceEpsilon),
|
| 484 | offset[1] +
|
| 485 | (x[0][1] - mean[1]) * scale[1] /
|
| 486 | Math.sqrt(variance[1] + varianceEpsilon),
|
| 487 | offset[0] +
|
| 488 | (x[1][0] - mean[0]) * scale[0] /
|
| 489 | Math.sqrt(variance[0] + varianceEpsilon),
|
| 490 | offset[1] +
|
| 491 | (x[1][1] - mean[1]) * scale[1] /
|
| 492 | Math.sqrt(variance[1] + varianceEpsilon)
|
| 493 | ]);
|
| 494 | });
|
| 495 | it('simple batchnorm2D gradients, 2x2', async () => {
|
| 496 | const x = tf.tensor2d([2, 4, 9, 23], [2, 2]);
|
| 497 | const mean = tf.tensor1d([1, 2]);
|
| 498 | const variance = tf.tensor1d([2, 3]);
|
| 499 | const offset = tf.tensor1d([3, 4]);
|
| 500 | const scale = tf.tensor1d([2, 5]);
|
| 501 | const varianceEpsilon = .001;
|
| 502 | const dy = tf.tensor2d([1, 1, 1, 1], [2, 2]);
|
| 503 | const [gradX, gradMean, gradVariance, gradOffset, gradScale] = tf.grads((x, mean, variance, offset, scale) => tf.batchNorm2d(x, mean, variance, offset, scale, varianceEpsilon))([x, mean, variance, offset, scale], dy);
|
| 504 | expectArraysClose(await gradX.data(), [1.414, 2.887, 1.414, 2.887]);
|
| 505 | expect(gradX.shape).toEqual([2, 2]);
|
| 506 | expectArraysClose(await gradMean.data(), [-2.828, -5.773]);
|
| 507 | expect(gradMean.shape).toEqual([2]);
|
| 508 | expectArraysClose(await gradVariance.data(), [-3.180, -11.060]);
|
| 509 | expect(gradVariance.shape).toEqual([2]);
|
| 510 | expectArraysClose(await gradOffset.data(), [2, 2]);
|
| 511 | expect(gradOffset.shape).toEqual([2]);
|
| 512 | expectArraysClose(await gradScale.data(), [6.362, 13.277]);
|
| 513 | expect(gradScale.shape).toEqual([2]);
|
| 514 | });
|
| 515 | it('gradient with clones batchnorm2D', async () => {
|
| 516 | const x = tf.tensor2d([2, 4, 9, 23], [2, 2]);
|
| 517 | const mean = tf.tensor1d([1, 2]);
|
| 518 | const variance = tf.tensor1d([2, 3]);
|
| 519 | const offset = tf.tensor1d([3, 4]);
|
| 520 | const scale = tf.tensor1d([2, 5]);
|
| 521 | const varianceEpsilon = .001;
|
| 522 | const dy = tf.tensor2d([1, 1, 1, 1], [2, 2]);
|
| 523 | const [gradX, gradMean, gradVariance, gradOffset, gradScale] = tf.grads((x, mean, variance, offset, scale) => tf.batchNorm2d(x.clone(), mean.clone(), variance.clone(), offset.clone(), scale.clone(), varianceEpsilon)
|
| 524 | .clone())([x, mean, variance, offset, scale], dy);
|
| 525 | expectArraysClose(await gradX.data(), [1.414, 2.887, 1.414, 2.887]);
|
| 526 | expect(gradX.shape).toEqual([2, 2]);
|
| 527 | expectArraysClose(await gradMean.data(), [-2.828, -5.773]);
|
| 528 | expect(gradMean.shape).toEqual([2]);
|
| 529 | expectArraysClose(await gradVariance.data(), [-3.180, -11.060]);
|
| 530 | expect(gradVariance.shape).toEqual([2]);
|
| 531 | expectArraysClose(await gradOffset.data(), [2, 2]);
|
| 532 | expect(gradOffset.shape).toEqual([2]);
|
| 533 | expectArraysClose(await gradScale.data(), [6.362, 13.277]);
|
| 534 | expect(gradScale.shape).toEqual([2]);
|
| 535 | });
|
| 536 | it('batchnorm2D gradients, same shapes in x, mean and variance', async () => {
|
| 537 | const x = tf.tensor2d([10, 20, 30, 40], [2, 2]);
|
| 538 | const mean = tf.tensor2d([0, 5, 10, 15], [2, 2]);
|
| 539 | const variance = tf.tensor2d([2, 4, 6, 8], [2, 2]);
|
| 540 | const scale = tf.tensor2d([2, 5, 2, 5], [2, 2]);
|
| 541 | const offset = tf.tensor2d([0, 0, 0, 0], [2, 2]);
|
| 542 | const varianceEpsilon = .001;
|
| 543 | const dy = tf.tensor2d([1, 1, 1, 1], [2, 2]);
|
| 544 | const gradX = tf.grad((x) => tf.batchNorm2d(x, mean, variance, offset, scale, varianceEpsilon))(x, dy);
|
| 545 | expectArraysClose(await gradX.data(), [1.414, 2.500, 0.816, 1.768]);
|
| 546 | expect(gradX.shape).toEqual([2, 2]);
|
| 547 | const gradMean = tf.grad((mean) => tf.batchNorm2d(x, mean, variance, offset, scale, varianceEpsilon))(mean, dy);
|
| 548 | expectArraysClose(await gradMean.data(), [-1.414, -2.500, -0.816, -1.768]);
|
| 549 | expect(gradMean.shape).toEqual([2, 2]);
|
| 550 | const gradVariance = tf.grad((variance) => tf.batchNorm2d(x, mean, variance, offset, scale, varianceEpsilon))(variance, dy);
|
| 551 | expectArraysClose(await gradVariance.data(), [-3.533, -4.686, -1.360, -2.762]);
|
| 552 | expect(gradVariance.shape).toEqual([2, 2]);
|
| 553 | const gradOffset = tf.grad((offset) => tf.batchNorm2d(x, mean, variance, offset, scale, varianceEpsilon))(offset, dy);
|
| 554 | expectArraysClose(await gradOffset.data(), [1, 1, 1, 1]);
|
| 555 | expect(gradOffset.shape).toEqual([2, 2]);
|
| 556 | const gradScale = tf.grad((scale) => tf.batchNorm2d(x, mean, variance, offset, scale, varianceEpsilon))(scale, dy);
|
| 557 | expectArraysClose(await gradScale.data(), [7.069, 7.499, 8.164, 8.838]);
|
| 558 | expect(gradScale.shape).toEqual([2, 2]);
|
| 559 | });
|
| 560 | it('gradient with clones', () => {
|
| 561 | const x = tf.zeros([2, 2]);
|
| 562 | const mean = tf.zeros([2, 2]);
|
| 563 | const variance = tf.zeros([2, 2]);
|
| 564 | const scale = tf.zeros([2, 2]);
|
| 565 | const offset = tf.zeros([2, 2]);
|
| 566 | const varianceEpsilon = .001;
|
| 567 | const gradF = tf.grads((x, mean, variance, offset, scale) => tf.batchNorm2d(x.clone(), mean.clone(), variance.clone(), offset.clone(), scale.clone(), varianceEpsilon)
|
| 568 | .clone());
|
| 569 | const [gradX, gradMean, gradVariance, gradOffset, gradScale] = gradF([x, mean, variance, offset, scale]);
|
| 570 | expect(gradX.shape).toEqual(x.shape);
|
| 571 | expect(gradMean.shape).toEqual(mean.shape);
|
| 572 | expect(gradVariance.shape).toEqual(variance.shape);
|
| 573 | expect(gradOffset.shape).toEqual(offset.shape);
|
| 574 | expect(gradScale.shape).toEqual(scale.shape);
|
| 575 | });
|
| 576 | it('batchnorm2D matches tensorflow, 3x3', async () => {
|
| 577 | const x = tf.tensor2d([
|
| 578 | 0.3136892, 0.92389025, 0.594782, 0.05021042, 0.67545404, 0.93910035,
|
| 579 | 0.13277993, 0.96474269, 0.88608916
|
| 580 | ], [3, 3]);
|
| 581 | const mean = tf.tensor1d([0.19526312, 0.74857256, 0.45166398]);
|
| 582 | const variance = tf.tensor1d([0.22963001, 0.61521992, 0.46623685]);
|
| 583 | const offset = tf.tensor1d([0.43098484, 0.77712237, 0.47916298]);
|
| 584 | const scale = tf.tensor1d([0.62186907, 0.85673736, 0.19201061]);
|
| 585 | const varianceEpsilon = .001;
|
| 586 | const result = tf.batchNorm2d(x, mean, variance, offset, scale, varianceEpsilon);
|
| 587 | expectArraysClose(await result.data(), [
|
| 588 | 0.58433646, 0.96846228, 0.51936529, 0.24315402, 0.69732157, 0.61608542,
|
| 589 | 0.35007446, 1.01304821, 0.60119441
|
| 590 | ]);
|
| 591 | });
|
| 592 | it('throws when passed x as a non-tensor', () => {
|
| 593 | const mean = tf.tensor1d([1, 2]);
|
| 594 | const variance = tf.tensor1d([2, 3]);
|
| 595 | expect(() => tf.batchNorm({}, mean, variance))
|
| 596 | .toThrowError(/Argument 'x' passed to 'batchNorm' must be a Tensor/);
|
| 597 | });
|
| 598 | it('throws when passed mean as a non-tensor', () => {
|
| 599 | const x = tf.tensor4d([2, 4, 9, 23], [2, 1, 1, 2]);
|
| 600 | const variance = tf.tensor1d([2, 3]);
|
| 601 | expect(() => tf.batchNorm(x, {}, variance))
|
| 602 | .toThrowError(/Argument 'mean' passed to 'batchNorm' must be a Tensor/);
|
| 603 | });
|
| 604 | it('throws when passed variance as a non-tensor', () => {
|
| 605 | const x = tf.tensor4d([2, 4, 9, 23], [2, 1, 1, 2]);
|
| 606 | const mean = tf.tensor1d([1, 2]);
|
| 607 | const e = /Argument 'variance' passed to 'batchNorm' must be a Tensor/;
|
| 608 | expect(() => tf.batchNorm(x, mean, {})).toThrowError(e);
|
| 609 | });
|
| 610 | it('throws when passed scale as a non-tensor', () => {
|
| 611 | const x = tf.tensor4d([2, 4, 9, 23], [2, 1, 1, 2]);
|
| 612 | const mean = tf.tensor1d([1, 2]);
|
| 613 | const variance = tf.tensor1d([2, 3]);
|
| 614 | const epsilon = .001;
|
| 615 | expect(() => tf.batchNorm(x, mean, variance, epsilon, {}))
|
| 616 | .toThrowError(/Argument 'scale' passed to 'batchNorm' must be a Tensor/);
|
| 617 | });
|
| 618 | it('throws when passed offset as a non-tensor', () => {
|
| 619 | const x = tf.tensor4d([2, 4, 9, 23], [2, 1, 1, 2]);
|
| 620 | const mean = tf.tensor1d([1, 2]);
|
| 621 | const variance = tf.tensor1d([2, 3]);
|
| 622 | const epsilon = .001;
|
| 623 | const scale = tf.tensor1d([0.62186907, 0.85673736, 0.19201061]);
|
| 624 | const e = /Argument 'offset' passed to 'batchNorm' must be a Tensor/;
|
| 625 | expect(() => tf.batchNorm(x, mean, variance, {}, scale, epsilon))
|
| 626 | .toThrowError(e);
|
| 627 | });
|
| 628 | it('accepts a tensor-like object', async () => {
|
| 629 | const x = [[2, 4], [9, 23]];
|
| 630 | const mean = [1, 2];
|
| 631 | const variance = [2, 3];
|
| 632 | const offset = [3, 4];
|
| 633 | const scale = [4, 5];
|
| 634 | const varianceEpsilon = .001;
|
| 635 | const result = tf.batchNorm2d(x, mean, variance, offset, scale, varianceEpsilon);
|
| 636 | expectArraysClose(await result.data(), [
|
| 637 | offset[0] +
|
| 638 | (x[0][0] - mean[0]) * scale[0] /
|
| 639 | Math.sqrt(variance[0] + varianceEpsilon),
|
| 640 | offset[1] +
|
| 641 | (x[0][1] - mean[1]) * scale[1] /
|
| 642 | Math.sqrt(variance[1] + varianceEpsilon),
|
| 643 | offset[0] +
|
| 644 | (x[1][0] - mean[0]) * scale[0] /
|
| 645 | Math.sqrt(variance[0] + varianceEpsilon),
|
| 646 | offset[1] +
|
| 647 | (x[1][1] - mean[1]) * scale[1] /
|
| 648 | Math.sqrt(variance[1] + varianceEpsilon)
|
| 649 | ]);
|
| 650 | });
|
| 651 | it('throws error when x is a string tensor', () => {
|
| 652 | const mean = [1, 2];
|
| 653 | const variance = [2, 3];
|
| 654 | const offset = [3, 4];
|
| 655 | const scale = [4, 5];
|
| 656 | const varianceEpsilon = .001;
|
| 657 | const f = () => tf.batchNorm2d([['a', 'b'], ['c', 'd']], mean, variance, offset, scale, varianceEpsilon);
|
| 658 | expect(f).toThrowError(/Argument 'x' passed to 'batchNorm' must be numeric/);
|
| 659 | });
|
| 660 | it('throws error when mean is a string tensor', () => {
|
| 661 | const x = [[2, 4], [9, 23]];
|
| 662 | const variance = [2, 3];
|
| 663 | const offset = [3, 4];
|
| 664 | const scale = [4, 5];
|
| 665 | const varianceEpsilon = .001;
|
| 666 | const f = () => tf.batchNorm2d(x, ['a', 'b'], variance, offset, scale, varianceEpsilon);
|
| 667 | expect(f).toThrowError(/Argument 'mean' passed to 'batchNorm' must be numeric/);
|
| 668 | });
|
| 669 | it('throws error when variance is a string tensor', () => {
|
| 670 | const x = [[2, 4], [9, 23]];
|
| 671 | const mean = [1, 2];
|
| 672 | const offset = [3, 4];
|
| 673 | const scale = [4, 5];
|
| 674 | const varianceEpsilon = .001;
|
| 675 | const f = () => tf.batchNorm2d(x, mean, ['a', 'b'], offset, scale, varianceEpsilon);
|
| 676 | expect(f).toThrowError(/'variance' passed to 'batchNorm' must be numeric/);
|
| 677 | });
|
| 678 | it('throws error when scale is a string tensor', () => {
|
| 679 | const x = [[2, 4], [9, 23]];
|
| 680 | const mean = [1, 2];
|
| 681 | const variance = [2, 3];
|
| 682 | const offset = [3, 4];
|
| 683 | const varianceEpsilon = .001;
|
| 684 | const f = () => tf.batchNorm2d(x, mean, variance, offset, ['a', 'b'], varianceEpsilon);
|
| 685 | expect(f).toThrowError(/'scale' passed to 'batchNorm' must be numeric/);
|
| 686 | });
|
| 687 | it('throws error when offset is a string tensor', () => {
|
| 688 | const x = [[2, 4], [9, 23]];
|
| 689 | const mean = [1, 2];
|
| 690 | const variance = [2, 3];
|
| 691 | const scale = [4, 5];
|
| 692 | const varianceEpsilon = .001;
|
| 693 | const f = () => tf.batchNorm2d(x, mean, variance, ['a', 'b'], scale, varianceEpsilon);
|
| 694 | expect(f).toThrowError(/'offset' passed to 'batchNorm' must be numeric/);
|
| 695 | });
|
| 696 | });
|
| 697 | //# sourceMappingURL=data:application/json;base64,{"version":3,"file":"batchnorm_test.js","sourceRoot":"","sources":["../../../../../../tfjs-core/src/ops/batchnorm_test.ts"],"names":[],"mappings":"AAAA;;;;;;;;;;;;;;;GAeG;AAEH,OAAO,KAAK,EAAE,MAAM,UAAU,CAAC;AAC/B,OAAO,EAAC,QAAQ,EAAE,iBAAiB,EAAC,MAAM,iBAAiB,CAAC;AAC5D,OAAO,EAAC,iBAAiB,EAAC,MAAM,cAAc,CAAC;AAE/C,iBAAiB,CAAC,aAAa,EAAE,QAAQ,EAAE,GAAG,EAAE;IAC9C,EAAE,CAAC,iDAAiD,EAAE,KAAK,IAAI,EAAE;QAC/D,MAAM,EAAE,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACpD,MAAM,KAAK,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAClC,MAAM,SAAS,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACtC,MAAM,eAAe,GAAG,IAAI,CAAC;QAE7B,MAAM,MAAM,GAAG,EAAE,CAAC,WAAW,CACzB,EAAE,EAAE,KAAK,EAAE,SAAS,EAAE,SAAS,EAAE,SAAS,EAAE,eAAe,CAAC,CAAC;QAEjE,MAAM,CAAC,GAAG,MAAM,EAAE,CAAC,KAAK,EAAE,CAAC;QAC3B,MAAM,IAAI,GAAG,MAAM,KAAK,CAAC,KAAK,EAAE,CAAC;QACjC,MAAM,QAAQ,GAAG,MAAM,SAAS,CAAC,KAAK,EAAE,CAAC;QACzC,iBAAiB,CAAC,MAAM,MAAM,CAAC,IAAI,EAAE,EAAE;YACrC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,GAAG,IAAI,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,GAAG,IAAI,CAAC,IAAI,CAAC,QAAQ,CAAC,CAAC,CAAC,GAAG,eAAe,CAAC;YACxE,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,GAAG,IAAI,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,GAAG,IAAI,CAAC,IAAI,CAAC,QAAQ,CAAC,CAAC,CAAC,GAAG,eAAe,CAAC;YACxE,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,GAAG,IAAI,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,GAAG,IAAI,CAAC,IAAI,CAAC,QAAQ,CAAC,CAAC,CAAC,GAAG,eAAe,CAAC;YACxE,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,GAAG,IAAI,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,GAAG,IAAI,CAAC,IAAI,CAAC,QAAQ,CAAC,CAAC,CAAC,GAAG,eAAe,CAAC;SACzE,CAAC,CAAC;IACL,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,wCAAwC,EAAE,KAAK,IAAI,EAAE;QACtD,MAAM,EAAE,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACpD,MAAM,KAAK,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAClC,MAAM,SAAS,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACtC,MAAM,MAAM,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACnC,MAAM,eAAe,GAAG,IAAI,CAAC;QAE7B,MAAM,MAAM,GAAG,EAAE,CAAC,WAAW,CACzB,EAAE,EAAE,KAAK,EAAE,SAAS,EAAE,SAAS,EAAE,MAAM,EAAE,eAAe,CAAC,CAAC;QAC9D,MAAM,CAAC,GAAG,MAAM,EAAE,CAAC,MAAM,EAAE,CAAC;QAC5B,MAAM,IAAI,GAAG,MAAM,KAAK,CAAC,MAAM,EAAE,CAAC;QAClC,MAAM,QAAQ,GAAG,MAAM,SAAS,CAAC,MAAM,EAAE,CAAC;QAC1C,MAAM,KAAK,GAAG,MAAM,MAAM,CAAC,MAAM,EAAE,CAAC;QAEpC,iBAAiB,CAAC,MAAM,MAAM,CAAC,IAAI,EAAE,EAAE;YACrC,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,GAAG,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,GAAG,KAAK,CAAC,GAAG,CAAC,CAAC,CAAC;gBAC5C,IAAI,CAAC,IAAI,CAAC,QAAQ,CAAC,GAAG,CAAC,CAAC,CAAC,GAAG,eAAe,CAAC;YAChD,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,GAAG,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,GAAG,KAAK,CAAC,GAAG,CAAC,CAAC,CAAC;gBAC5C,IAAI,CAAC,IAAI,CAAC,QAAQ,CAAC,GAAG,CAAC,CAAC,CAAC,GAAG,eAAe,CAAC;YAChD,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,GAAG,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,GAAG,KAAK,CAAC,GAAG,CAAC,CAAC,CAAC;gBAC5C,IAAI,CAAC,IAAI,CAAC,QAAQ,CAAC,GAAG,CAAC,CAAC,CAAC,GAAG,eAAe,CAAC;YAChD,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,GAAG,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,GAAG,KAAK,CAAC,GAAG,CAAC,CAAC,CAAC;gBAC5C,IAAI,CAAC,IAAI,CAAC,QAAQ,CAAC,GAAG,CAAC,CAAC,CAAC,GAAG,eAAe,CAAC;SACjD,CAAC,CAAC;IACL,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,uCAAuC,EAAE,KAAK,IAAI,EAAE;QACrD,MAAM,EAAE,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACpD,MAAM,KAAK,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAClC,MAAM,SAAS,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACtC,MAAM,OAAO,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAEpC,MAAM,eAAe,GAAG,IAAI,CAAC;QAE7B,MAAM,MAAM,GAAG,EAAE,CAAC,WAAW,CACzB,EAAE,EAAE,KAAK,EAAE,SAAS,EAAE,OAAO,EAAE,SAAS,EAAE,eAAe,CAAC,CAAC;QAC/D,MAAM,CAAC,GAAG,MAAM,EAAE,CAAC,MAAM,EAAE,CAAC;QAC5B,MAAM,IAAI,GAAG,MAAM,KAAK,CAAC,MAAM,EAAE,CAAC;QAClC,MAAM,QAAQ,GAAG,MAAM,SAAS,CAAC,MAAM,EAAE,CAAC;QAC1C,MAAM,MAAM,GAAG,MAAM,OAAO,CAAC,MAAM,EAAE,CAAC;QAEtC,iBAAiB,CAAC,MAAM,MAAM,CAAC,IAAI,EAAE,EAAE;YACrC,MAAM,CAAC,GAAG,CAAC,CAAC,CAAC;gBACT,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,GAAG,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC;oBACjC,IAAI,CAAC,IAAI,CAAC,QAAQ,CAAC,GAAG,CAAC,CAAC,CAAC,GAAG,eAAe,CAAC;YACpD,MAAM,CAAC,GAAG,CAAC,CAAC,CAAC;gBACT,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,GAAG,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC;oBACjC,IAAI,CAAC,IAAI,CAAC,QAAQ,CAAC,GAAG,CAAC,CAAC,CAAC,GAAG,eAAe,CAAC;YACpD,MAAM,CAAC,GAAG,CAAC,CAAC,CAAC;gBACT,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,GAAG,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC;oBACjC,IAAI,CAAC,IAAI,CAAC,QAAQ,CAAC,GAAG,CAAC,CAAC,CAAC,GAAG,eAAe,CAAC;YACpD,MAAM,CAAC,GAAG,CAAC,CAAC,CAAC;gBACT,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,GAAG,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC;oBACjC,IAAI,CAAC,IAAI,CAAC,QAAQ,CAAC,GAAG,CAAC,CAAC,CAAC,GAAG,eAAe,CAAC;SACrD,CAAC,CAAC;IACL,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,6BAA6B,EAAE,KAAK,IAAI,EAAE;QAC3C,MAAM,EAAE,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACpD,MAAM,KAAK,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAClC,MAAM,SAAS,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACtC,MAAM,OAAO,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACpC,MAAM,MAAM,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAEnC,MAAM,eAAe,GAAG,IAAI,CAAC;QAE7B,MAAM,MAAM,GACR,EAAE,CAAC,WAAW,CAAC,EAAE,EAAE,KAAK,EAAE,SAAS,EAAE,OAAO,EAAE,MAAM,EAAE,eAAe,CAAC,CAAC;QAC3E,MAAM,CAAC,GAAG,MAAM,EAAE,CAAC,MAAM,EAAE,CAAC;QAC5B,MAAM,IAAI,GAAG,MAAM,KAAK,CAAC,MAAM,EAAE,CAAC;QAClC,MAAM,QAAQ,GAAG,MAAM,SAAS,CAAC,MAAM,EAAE,CAAC;QAC1C,MAAM,KAAK,GAAG,MAAM,MAAM,CAAC,MAAM,EAAE,CAAC;QACpC,MAAM,MAAM,GAAG,MAAM,OAAO,CAAC,MAAM,EAAE,CAAC;QAEtC,iBAAiB,CAAC,MAAM,MAAM,CAAC,IAAI,EAAE,EAAE;YACrC,MAAM,CAAC,GAAG,CAAC,CAAC,CAAC;gBACT,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,GAAG,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,GAAG,KAAK,CAAC,GAAG,CAAC,CAAC,CAAC;oBAC5C,IAAI,CAAC,IAAI,CAAC,QAAQ,CAAC,GAAG,CAAC,CAAC,CAAC,GAAG,eAAe,CAAC;YACpD,MAAM,CAAC,GAAG,CAAC,CAAC,CAAC;gBACT,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,GAAG,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,GAAG,KAAK,CAAC,GAAG,CAAC,CAAC,CAAC;oBAC5C,IAAI,CAAC,IAAI,CAAC,QAAQ,CAAC,GAAG,CAAC,CAAC,CAAC,GAAG,eAAe,CAAC;YACpD,MAAM,CAAC,GAAG,CAAC,CAAC,CAAC;gBACT,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,GAAG,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,GAAG,KAAK,CAAC,GAAG,CAAC,CAAC,CAAC;oBAC5C,IAAI,CAAC,IAAI,CAAC,QAAQ,CAAC,GAAG,CAAC,CAAC,CAAC,GAAG,eAAe,CAAC;YACpD,MAAM,CAAC,GAAG,CAAC,CAAC,CAAC;gBACT,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,GAAG,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,GAAG,KAAK,CAAC,GAAG,CAAC,CAAC,CAAC;oBAC5C,IAAI,CAAC,IAAI,CAAC,QAAQ,CAAC,GAAG,CAAC,CAAC,CAAC,GAAG,eAAe,CAAC;SACrD,CAAC,CAAC;IACL,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,8BAA8B,EAAE,KAAK,IAAI,EAAE;QAC5C,MAAM,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC,CAAE,UAAU;QAChD,MAAM,IAAI,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC;QACpB,MAAM,QAAQ,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC;QACxB,MAAM,MAAM,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC;QACtB,MAAM,KAAK,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC;QAErB,MAAM,eAAe,GAAG,IAAI,CAAC;QAE7B,MAAM,MAAM,GACR,EAAE,CAAC,WAAW,CAAC,CAAC,EAAE,IAAI,EAAE,QAAQ,EAAE,MAAM,EAAE,KAAK,EAAE,eAAe,CAAC,CAAC;QAEtE,iBAAiB,CAAC,MAAM,MAAM,CAAC,IAAI,EAAE,EAAE;YACrC,MAAM,CAAC,CAAC,CAAC;gBACL,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,GAAG,IAAI,CAAC,CAAC,CAAC,CAAC,GAAG,KAAK,CAAC,CAAC,CAAC;oBAChC,IAAI,CAAC,IAAI,CAAC,QAAQ,CAAC,CAAC,CAAC,GAAG,eAAe,CAAC;YAChD,MAAM,CAAC,CAAC,CAAC;gBACL,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,GAAG,IAAI,CAAC,CAAC,CAAC,CAAC,GAAG,KAAK,CAAC,CAAC,CAAC;oBAChC,IAAI,CAAC,IAAI,CAAC,QAAQ,CAAC,CAAC,CAAC,GAAG,eAAe,CAAC;YAChD,MAAM,CAAC,CAAC,CAAC;gBACL,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,GAAG,IAAI,CAAC,CAAC,CAAC,CAAC,GAAG,KAAK,CAAC,CAAC,CAAC;oBAChC,IAAI,CAAC,IAAI,CAAC,QAAQ,CAAC,CAAC,CAAC,GAAG,eAAe,CAAC;YAChD,MAAM,CAAC,CAAC,CAAC;gBACL,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,GAAG,IAAI,CAAC,CAAC,CAAC,CAAC,GAAG,KAAK,CAAC,CAAC,CAAC;oBAChC,IAAI,CAAC,IAAI,CAAC,QAAQ,CAAC,CAAC,CAAC,GAAG,eAAe,CAAC;SACjD,CAAC,CAAC;IACL,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,uCAAuC,EAAE,KAAK,IAAI,EAAE;QACrD,MAAM,CAAC,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACnD,MAAM,IAAI,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACjC,MAAM,QAAQ,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACrC,MAAM,MAAM,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACnC,MAAM,KAAK,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAElC,MAAM,eAAe,GAAG,IAAI,CAAC;QAE7B,MAAM,EAAE,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACvD,MAAM,KAAK,GAAG,EAAE,CAAC,IAAI,CACjB,CAAC,CAAc,EAAE,EAAE,CAAC,EAAE,CAAC,WAAW,CAC9B,CAAC,EAAE,IAAI,EAAE,QAAQ,EAAE,MAAM,EAAE,KAAK,EAAE,eAAe,CAAC,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,CAAC;QACnE,iBAAiB,CAAC,MAAM,KAAK,CAAC,IAAI,EAAE,EAAE,CAAC,CAAC,KAAK,EAAE,CAAC,KAAK,EAAE,CAAC,KAAK,EAAE,CAAC,KAAK,CAAC,CAAC,CAAC;QACxE,MAAM,CAAC,KAAK,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAC1C,MAAM,QAAQ,GAAG,EAAE,CAAC,IAAI,CACpB,CAAC,IAAiB,EAAE,EAAE,CAAC,EAAE,CAAC,WAAW,CACjC,CAAC,EAAE,IAAI,EAAE,QAAQ,EAAE,MAAM,EAAE,KAAK,EAAE,eAAe,CAAC,CAAC,CAAC,IAAI,EAAE,EAAE,CAAC,CAAC;QACtE,iBAAiB,CAAC,MAAM,QAAQ,CAAC,IAAI,EAAE,EAAE,CAAC,KAAK,EAAE,KAAK,CAAC,CAAC,CAAC;QACzD,MAAM,CAAC,QAAQ,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;QACpC,MAAM,YAAY,GAAG,EAAE,CAAC,IAAI,CACxB,CAAC,QAAqB,EAAE,EAAE,CAAC,EAAE,CAAC,WAAW,CACrC,CAAC,EAAE,IAAI,EAAE,QAAQ,EAAE,MAAM,EAAE,KAAK,EAAE,eAAe,CAAC,CAAC,CAAC,QAAQ,EAAE,EAAE,CAAC,CAAC;QAC1E,iBAAiB,CAAC,MAAM,YAAY,CAAC,IAAI,EAAE,EAAE,CAAC,KAAK,EAAE,MAAM,CAAC,CAAC,CAAC;QAC9D,MAAM,CAAC,YAAY,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;QACxC,MAAM,UAAU,GAAG,EAAE,CAAC,IAAI,CACtB,CAAC,MAAmB,EAAE,EAAE,CAAC,EAAE,CAAC,WAAW,CACnC,CAAC,EAAE,IAAI,EAAE,QAAQ,EAAE,MAAM,EAAE,KAAK,EAAE,eAAe,CAAC,CAAC,CAAC,MAAM,EAAE,EAAE,CAAC,CAAC;QACxE,iBAAiB,CAAC,MAAM,UAAU,CAAC,IAAI,EAAE,EAAE,MAAM,EAAE,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,IAAI,EAAE,CAAC,CAAC;QAC3E,MAAM,CAAC,UAAU,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;QACtC,MAAM,SAAS,GAAG,EAAE,CAAC,IAAI,CACrB,CAAC,KAAkB,EAAE,EAAE,CAAC,EAAE,CAAC,WAAW,CAClC,CAAC,EAAE,IAAI,EAAE,QAAQ,EAAE,MAAM,EAAE,KAAK,EAAE,eAAe,CAAC,CAAC,CAAC,KAAK,EAAE,EAAE,CAAC,CAAC;QACvE,iBAAiB,CAAC,MAAM,SAAS,CAAC,IAAI,EAAE,EAAE,CAAC,CAAC,KAAK,EAAE,CAAC,MAAM,CAAC,CAAC,CAAC;QAC7D,MAAM,CAAC,SAAS,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;IACvC,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,4DAA4D,EAAE,KAAK,IAAI,EAAE;QAC1E,MAAM,CAAC,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACtD,MAAM,IAAI,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,EAAE,EAAE,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACvD,MAAM,QAAQ,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACzD,MAAM,KAAK,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACtD,MAAM,MAAM,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAEvD,MAAM,eAAe,GAAG,IAAI,CAAC;QAE7B,MAAM,EAAE,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACvD,MAAM,KAAK,GAAG,EAAE,CAAC,IAAI,CACjB,CAAC,CAAc,EAAE,EAAE,CAAC,EAAE,CAAC,WAAW,CAC9B,CAAC,EAAE,IAAI,EAAE,QAAQ,EAAE,MAAM,EAAE,KAAK,EAAE,eAAe,CAAC,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,CAAC;QACnE,iBAAiB,CAAC,MAAM,KAAK,CAAC,IAAI,EAAE,EAAE,CAAC,CAAC,KAAK,EAAE,CAAC,KAAK,EAAE,CAAC,KAAK,EAAE,CAAC,KAAK,CAAC,CAAC,CAAC;QACxE,MAAM,CAAC,KAAK,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAC1C,MAAM,QAAQ,GAAG,EAAE,CAAC,IAAI,CACpB,CAAC,IAAiB,EAAE,EAAE,CAAC,EAAE,CAAC,WAAW,CACjC,CAAC,EAAE,IAAI,EAAE,QAAQ,EAAE,MAAM,EAAE,KAAK,EAAE,eAAe,CAAC,CAAC,CAAC,IAAI,EAAE,EAAE,CAAC,CAAC;QACtE,iBAAiB,CAAC,MAAM,QAAQ,CAAC,IAAI,EAAE,EAAE,CAAC,KAAK,EAAE,KAAK,EAAE,KAAK,EAAE,KAAK,CAAC,CAAC,CAAC;QACvE,MAAM,CAAC,QAAQ,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAC7C,MAAM,YAAY,GAAG,EAAE,CAAC,IAAI,CACxB,CAAC,QAAqB,EAAE,EAAE,CAAC,EAAE,CAAC,WAAW,CACrC,CAAC,EAAE,IAAI,EAAE,QAAQ,EAAE,MAAM,EAAE,KAAK,EAAE,eAAe,CAAC,CAAC,CAAC,QAAQ,EAAE,EAAE,CAAC,CAAC;QAC1E,iBAAiB,CAAC,MAAM,YAAY,CAAC,IAAI,EAAE,EAAE,CAAC,KAAK,EAAE,KAAK,EAAE,KAAK,EAAE,KAAK,CAAC,CAAC,CAAC;QAC3E,MAAM,CAAC,YAAY,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACjD,MAAM,UAAU,GAAG,EAAE,CAAC,IAAI,CACtB,CAAC,MAAmB,EAAE,EAAE,CAAC,EAAE,CAAC,WAAW,CACnC,CAAC,EAAE,IAAI,EAAE,QAAQ,EAAE,MAAM,EAAE,KAAK,EAAE,eAAe,CAAC,CAAC,CAAC,MAAM,EAAE,EAAE,CAAC,CAAC;QACxE,iBAAiB,CAAC,MAAM,UAAU,CAAC,IAAI,EAAE,EAAE,MAAM,EAAE,CAAC,IAAI,EAAE,CAAC,CAAC;QAC5D,MAAM,CAAC,UAAU,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAC/C,MAAM,SAAS,GAAG,EAAE,CAAC,IAAI,CACrB,CAAC,KAAkB,EAAE,EAAE,CAAC,EAAE,CAAC,WAAW,CAClC,CAAC,EAAE,IAAI,EAAE,QAAQ,EAAE,MAAM,EAAE,KAAK,EAAE,eAAe,CAAC,CAAC,CAAC,KAAK,EAAE,EAAE,CAAC,CAAC;QACvE,iBAAiB,CAAC,MAAM,SAAS,CAAC,IAAI,EAAE,EAAE,CAAC,CAAC,KAAK,EAAE,CAAC,KAAK,EAAE,CAAC,KAAK,EAAE,CAAC,KAAK,CAAC,CAAC,CAAC;QAC5E,MAAM,CAAC,SAAS,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;IAChD,CAAC,CAAC,CAAC;AACL,CAAC,CAAC,CAAC;AAEH,iBAAiB,CAAC,aAAa,EAAE,QAAQ,EAAE,GAAG,EAAE;IAC9C,EAAE,CAAC,+CAA+C,EAAE,KAAK,IAAI,EAAE;QAC7D,MAAM,EAAE,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACjD,MAAM,KAAK,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAClC,MAAM,SAAS,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACtC,MAAM,eAAe,GAAG,IAAI,CAAC;QAE7B,MAAM,MAAM,GAAG,EAAE,CAAC,WAAW,CACzB,EAAE,EAAE,KAAK,EAAE,SAAS,EAAE,SAAS,EAAE,SAAS,EAAE,eAAe,CAAC,CAAC;QACjE,MAAM,CAAC,GAAG,MAAM,EAAE,CAAC,MAAM,EAAE,CAAC;QAC5B,MAAM,IAAI,GAAG,MAAM,KAAK,CAAC,MAAM,EAAE,CAAC;QAClC,MAAM,QAAQ,GAAG,MAAM,SAAS,CAAC,MAAM,EAAE,CAAC;QAC1C,iBAAiB,CAAC,MAAM,MAAM,CAAC,IAAI,EAAE,EAAE;YACrC,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,GAAG,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC;gBAC9B,IAAI,CAAC,IAAI,CAAC,QAAQ,CAAC,GAAG,CAAC,CAAC,CAAC,GAAG,eAAe,CAAC;YAChD,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,GAAG,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC;gBAC9B,IAAI,CAAC,IAAI,CAAC,QAAQ,CAAC,GAAG,CAAC,CAAC,CAAC,GAAG,eAAe,CAAC;YAChD,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,GAAG,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC;gBAC9B,IAAI,CAAC,IAAI,CAAC,QAAQ,CAAC,GAAG,CAAC,CAAC,CAAC,GAAG,eAAe,CAAC;YAChD,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,GAAG,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC;gBAC9B,IAAI,CAAC,IAAI,CAAC,QAAQ,CAAC,GAAG,CAAC,CAAC,CAAC,GAAG,eAAe,CAAC;SACjD,CAAC,CAAC;IACL,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,sCAAsC,EAAE,KAAK,IAAI,EAAE;QACpD,MAAM,EAAE,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACjD,MAAM,KAAK,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAClC,MAAM,SAAS,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACtC,MAAM,MAAM,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACnC,MAAM,eAAe,GAAG,IAAI,CAAC;QAE7B,MAAM,MAAM,GAAG,EAAE,CAAC,WAAW,CACzB,EAAE,EAAE,KAAK,EAAE,SAAS,EAAE,SAAS,EAAE,MAAM,EAAE,eAAe,CAAC,CAAC;QAE9D,MAAM,CAAC,GAAG,MAAM,EAAE,CAAC,MAAM,EAAE,CAAC;QAC5B,MAAM,IAAI,GAAG,MAAM,KAAK,CAAC,MAAM,EAAE,CAAC;QAClC,MAAM,QAAQ,GAAG,MAAM,SAAS,CAAC,MAAM,EAAE,CAAC;QAC1C,MAAM,KAAK,GAAG,MAAM,MAAM,CAAC,MAAM,EAAE,CAAC;QACpC,iBAAiB,CAAC,MAAM,MAAM,CAAC,IAAI,EAAE,EAAE;YACrC,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,GAAG,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,GAAG,KAAK,CAAC,GAAG,CAAC,CAAC,CAAC;gBACzC,IAAI,CAAC,IAAI,CAAC,QAAQ,CAAC,GAAG,CAAC,CAAC,CAAC,GAAG,eAAe,CAAC;YAChD,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,GAAG,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,GAAG,KAAK,CAAC,GAAG,CAAC,CAAC,CAAC;gBACzC,IAAI,CAAC,IAAI,CAAC,QAAQ,CAAC,GAAG,CAAC,CAAC,CAAC,GAAG,eAAe,CAAC;YAChD,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,GAAG,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,GAAG,KAAK,CAAC,GAAG,CAAC,CAAC,CAAC;gBACzC,IAAI,CAAC,IAAI,CAAC,QAAQ,CAAC,GAAG,CAAC,CAAC,CAAC,GAAG,eAAe,CAAC;YAChD,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,GAAG,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,GAAG,KAAK,CAAC,GAAG,CAAC,CAAC,CAAC;gBACzC,IAAI,CAAC,IAAI,CAAC,QAAQ,CAAC,GAAG,CAAC,CAAC,CAAC,GAAG,eAAe,CAAC;SACjD,CAAC,CAAC;IACL,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,qCAAqC,EAAE,KAAK,IAAI,EAAE;QACnD,MAAM,EAAE,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACjD,MAAM,KAAK,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAClC,MAAM,SAAS,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACtC,MAAM,OAAO,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAEpC,MAAM,eAAe,GAAG,IAAI,CAAC;QAE7B,MAAM,MAAM,GAAG,EAAE,CAAC,WAAW,CACzB,EAAE,EAAE,KAAK,EAAE,SAAS,EAAE,OAAO,EAAE,SAAS,EAAE,eAAe,CAAC,CAAC;QAE/D,MAAM,CAAC,GAAG,MAAM,EAAE,CAAC,MAAM,EAAE,CAAC;QAC5B,MAAM,IAAI,GAAG,MAAM,KAAK,CAAC,MAAM,EAAE,CAAC;QAClC,MAAM,QAAQ,GAAG,MAAM,SAAS,CAAC,MAAM,EAAE,CAAC;QAC1C,MAAM,MAAM,GAAG,MAAM,OAAO,CAAC,MAAM,EAAE,CAAC;QACtC,iBAAiB,CAAC,MAAM,MAAM,CAAC,IAAI,EAAE,EAAE;YACrC,MAAM,CAAC,GAAG,CAAC,CAAC,CAAC;gBACT,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,GAAG,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC;oBAC9B,IAAI,CAAC,IAAI,CAAC,QAAQ,CAAC,GAAG,CAAC,CAAC,CAAC,GAAG,eAAe,CAAC;YACpD,MAAM,CAAC,GAAG,CAAC,CAAC,CAAC;gBACT,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,GAAG,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC;oBAC9B,IAAI,CAAC,IAAI,CAAC,QAAQ,CAAC,GAAG,CAAC,CAAC,CAAC,GAAG,eAAe,CAAC;YACpD,MAAM,CAAC,GAAG,CAAC,CAAC,CAAC;gBACT,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,GAAG,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC;oBAC9B,IAAI,CAAC,IAAI,CAAC,QAAQ,CAAC,GAAG,CAAC,CAAC,CAAC,GAAG,eAAe,CAAC;YACpD,MAAM,CAAC,GAAG,CAAC,CAAC,CAAC;gBACT,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,GAAG,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC;oBAC9B,IAAI,CAAC,IAAI,CAAC,QAAQ,CAAC,GAAG,CAAC,CAAC,CAAC,GAAG,eAAe,CAAC;SACrD,CAAC,CAAC;IACL,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,2BAA2B,EAAE,KAAK,IAAI,EAAE;QACzC,MAAM,EAAE,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACjD,MAAM,KAAK,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAClC,MAAM,SAAS,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACtC,MAAM,OAAO,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACpC,MAAM,MAAM,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAEnC,MAAM,eAAe,GAAG,IAAI,CAAC;QAE7B,MAAM,MAAM,GACR,EAAE,CAAC,WAAW,CAAC,EAAE,EAAE,KAAK,EAAE,SAAS,EAAE,OAAO,EAAE,MAAM,EAAE,eAAe,CAAC,CAAC;QAC3E,MAAM,CAAC,GAAG,MAAM,EAAE,CAAC,MAAM,EAAE,CAAC;QAC5B,MAAM,IAAI,GAAG,MAAM,KAAK,CAAC,MAAM,EAAE,CAAC;QAClC,MAAM,QAAQ,GAAG,MAAM,SAAS,CAAC,MAAM,EAAE,CAAC;QAC1C,MAAM,MAAM,GAAG,MAAM,OAAO,CAAC,MAAM,EAAE,CAAC;QACtC,MAAM,KAAK,GAAG,MAAM,MAAM,CAAC,MAAM,EAAE,CAAC;QAEpC,iBAAiB,CAAC,MAAM,MAAM,CAAC,IAAI,EAAE,EAAE;YACrC,MAAM,CAAC,GAAG,CAAC,CAAC,CAAC;gBACT,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,GAAG,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,GAAG,KAAK,CAAC,GAAG,CAAC,CAAC,CAAC;oBACzC,IAAI,CAAC,IAAI,CAAC,QAAQ,CAAC,GAAG,CAAC,CAAC,CAAC,GAAG,eAAe,CAAC;YACpD,MAAM,CAAC,GAAG,CAAC,CAAC,CAAC;gBACT,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,GAAG,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,GAAG,KAAK,CAAC,GAAG,CAAC,CAAC,CAAC;oBACzC,IAAI,CAAC,IAAI,CAAC,QAAQ,CAAC,GAAG,CAAC,CAAC,CAAC,GAAG,eAAe,CAAC;YACpD,MAAM,CAAC,GAAG,CAAC,CAAC,CAAC;gBACT,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,GAAG,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,GAAG,KAAK,CAAC,GAAG,CAAC,CAAC,CAAC;oBACzC,IAAI,CAAC,IAAI,CAAC,QAAQ,CAAC,GAAG,CAAC,CAAC,CAAC,GAAG,eAAe,CAAC;YACpD,MAAM,CAAC,GAAG,CAAC,CAAC,CAAC;gBACT,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,GAAG,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,GAAG,KAAK,CAAC,GAAG,CAAC,CAAC,CAAC;oBACzC,IAAI,CAAC,IAAI,CAAC,QAAQ,CAAC,GAAG,CAAC,CAAC,CAAC,GAAG,eAAe,CAAC;SACrD,CAAC,CAAC;IACL,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,8BAA8B,EAAE,KAAK,IAAI,EAAE;QAC5C,MAAM,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,CAAC,CAAC,CAAC,CAAE,QAAQ;QAC1C,MAAM,IAAI,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC;QACpB,MAAM,QAAQ,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC;QACxB,MAAM,MAAM,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC;QACtB,MAAM,KAAK,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC;QAErB,MAAM,eAAe,GAAG,IAAI,CAAC;QAE7B,MAAM,MAAM,GACR,EAAE,CAAC,WAAW,CAAC,CAAC,EAAE,IAAI,EAAE,QAAQ,EAAE,MAAM,EAAE,KAAK,EAAE,eAAe,CAAC,CAAC;QAEtE,iBAAiB,CAAC,MAAM,MAAM,CAAC,IAAI,EAAE,EAAE;YACrC,MAAM,CAAC,CAAC,CAAC;gBACL,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,GAAG,IAAI,CAAC,CAAC,CAAC,CAAC,GAAG,KAAK,CAAC,CAAC,CAAC;oBAC7B,IAAI,CAAC,IAAI,CAAC,QAAQ,CAAC,CAAC,CAAC,GAAG,eAAe,CAAC;YAChD,MAAM,CAAC,CAAC,CAAC;gBACL,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,GAAG,IAAI,CAAC,CAAC,CAAC,CAAC,GAAG,KAAK,CAAC,CAAC,CAAC;oBAC7B,IAAI,CAAC,IAAI,CAAC,QAAQ,CAAC,CAAC,CAAC,GAAG,eAAe,CAAC;YAChD,MAAM,CAAC,CAAC,CAAC;gBACL,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,GAAG,IAAI,CAAC,CAAC,CAAC,CAAC,GAAG,KAAK,CAAC,CAAC,CAAC;oBAC7B,IAAI,CAAC,IAAI,CAAC,QAAQ,CAAC,CAAC,CAAC,GAAG,eAAe,CAAC;YAChD,MAAM,CAAC,CAAC,CAAC;gBACL,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,GAAG,IAAI,CAAC,CAAC,CAAC,CAAC,GAAG,KAAK,CAAC,CAAC,CAAC;oBAC7B,IAAI,CAAC,IAAI,CAAC,QAAQ,CAAC,CAAC,CAAC,GAAG,eAAe,CAAC;SACjD,CAAC,CAAC;IACL,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,iDAAiD,EAAE,KAAK,IAAI,EAAE;QAC/D,MAAM,KAAK,GAA6B,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC;QAClD,MAAM,EAAE,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,EAAE,CAAC,EAAE,KAAK,CAAC,CAAC;QAC7C,MAAM,KAAK,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,KAAK,CAAC,CAAC;QAC/C,MAAM,SAAS,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,KAAK,CAAC,CAAC;QACnD,MAAM,OAAO,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,KAAK,CAAC,CAAC;QACjD,MAAM,MAAM,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,KAAK,CAAC,CAAC;QAEhD,MAAM,eAAe,GAAG,IAAI,CAAC;QAE7B,MAAM,MAAM,GACR,EAAE,CAAC,WAAW,CAAC,EAAE,EAAE,KAAK,EAAE,SAAS,EAAE,OAAO,EAAE,MAAM,EAAE,eAAe,CAAC,CAAC;QAE3E,MAAM,CAAC,GAAG,MAAM,EAAE,CAAC,MAAM,EAAE,CAAC;QAC5B,MAAM,IAAI,GAAG,MAAM,KAAK,CAAC,MAAM,EAAE,CAAC;QAClC,MAAM,QAAQ,GAAG,MAAM,SAAS,CAAC,MAAM,EAAE,CAAC;QAC1C,MAAM,MAAM,GAAG,MAAM,OAAO,CAAC,MAAM,EAAE,CAAC;QACtC,MAAM,KAAK,GAAG,MAAM,MAAM,CAAC,MAAM,EAAE,CAAC;QACpC,iBAAiB,CAAC,MAAM,MAAM,CAAC,IAAI,EAAE,EAAE;YACrC,MAAM,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC;gBACf,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,GAAG,IAAI,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,GAAG,KAAK,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC;oBACrD,IAAI,CAAC,IAAI,CAAC,QAAQ,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,GAAG,eAAe,CAAC;YAC1D,MAAM,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC;gBACf,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,GAAG,IAAI,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,GAAG,KAAK,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC;oBACrD,IAAI,CAAC,IAAI,CAAC,QAAQ,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,GAAG,eAAe,CAAC;YAC1D,MAAM,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC;gBACf,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,GAAG,IAAI,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,GAAG,KAAK,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC;oBACrD,IAAI,CAAC,IAAI,CAAC,QAAQ,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,GAAG,eAAe,CAAC;YAC1D,MAAM,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC;gBACf,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,GAAG,IAAI,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,GAAG,KAAK,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC;oBACrD,IAAI,CAAC,IAAI,CAAC,QAAQ,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,GAAG,eAAe,CAAC;SAC3D,CAAC,CAAC;IACL,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,qCAAqC,EAAE,KAAK,IAAI,EAAE;QACnD,MAAM,CAAC,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAChD,MAAM,IAAI,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACjC,MAAM,QAAQ,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACrC,MAAM,MAAM,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACnC,MAAM,KAAK,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAElC,MAAM,eAAe,GAAG,IAAI,CAAC;QAE7B,MAAM,EAAE,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAChD,MAAM,KAAK,GAAG,EAAE,CAAC,IAAI,CACjB,CAAC,CAAc,EAAE,EAAE,CAAC,EAAE,CAAC,WAAW,CAC9B,CAAC,EAAE,IAAI,EAAE,QAAQ,EAAE,MAAM,EAAE,KAAK,EAAE,eAAe,CAAC,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,CAAC;QACnE,iBAAiB,CAAC,MAAM,KAAK,CAAC,IAAI,EAAE,EAAE,CAAC,KAAK,EAAE,KAAK,EAAE,KAAK,EAAE,KAAK,CAAC,CAAC,CAAC;QACpE,MAAM,CAAC,KAAK,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACvC,MAAM,QAAQ,GAAG,EAAE,CAAC,IAAI,CACpB,CAAC,IAAiB,EAAE,EAAE,CAAC,EAAE,CAAC,WAAW,CACjC,CAAC,EAAE,IAAI,EAAE,QAAQ,EAAE,MAAM,EAAE,KAAK,EAAE,eAAe,CAAC,CAAC,CAAC,IAAI,EAAE,EAAE,CAAC,CAAC;QACtE,iBAAiB,CAAC,MAAM,QAAQ,CAAC,IAAI,EAAE,EAAE,CAAC,CAAC,KAAK,EAAE,CAAC,KAAK,CAAC,CAAC,CAAC;QAC3D,MAAM,CAAC,QAAQ,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;QACpC,MAAM,YAAY,GAAG,EAAE,CAAC,IAAI,CACxB,CAAC,QAAqB,EAAE,EAAE,CAAC,EAAE,CAAC,WAAW,CACrC,CAAC,EAAE,IAAI,EAAE,QAAQ,EAAE,MAAM,EAAE,KAAK,EAAE,eAAe,CAAC,CAAC,CAAC,QAAQ,EAAE,EAAE,CAAC,CAAC;QAC1E,iBAAiB,CAAC,MAAM,YAAY,CAAC,IAAI,EAAE,EAAE,CAAC,CAAC,KAAK,EAAE,CAAC,MAAM,CAAC,CAAC,CAAC;QAChE,MAAM,CAAC,YAAY,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;QACxC,MAAM,UAAU,GAAG,EAAE,CAAC,IAAI,CACtB,CAAC,MAAmB,EAAE,EAAE,CAAC,EAAE,CAAC,WAAW,CACnC,CAAC,EAAE,IAAI,EAAE,QAAQ,EAAE,MAAM,EAAE,KAAK,EAAE,eAAe,CAAC,CAAC,CAAC,MAAM,EAAE,EAAE,CAAC,CAAC;QACxE,iBAAiB,CAAC,MAAM,UAAU,CAAC,IAAI,EAAE,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACnD,MAAM,CAAC,UAAU,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;QACtC,MAAM,SAAS,GAAG,EAAE,CAAC,IAAI,CACrB,CAAC,KAAkB,EAAE,EAAE,CAAC,EAAE,CAAC,WAAW,CAClC,CAAC,EAAE,IAAI,EAAE,QAAQ,EAAE,MAAM,EAAE,KAAK,EAAE,eAAe,CAAC,CAAC,CAAC,KAAK,EAAE,EAAE,CAAC,CAAC;QACvE,iBAAiB,CAAC,MAAM,SAAS,CAAC,IAAI,EAAE,EAAE,CAAC,KAAK,EAAE,MAAM,CAAC,CAAC,CAAC;QAC3D,MAAM,CAAC,SAAS,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;IACvC,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,4DAA4D,EAAE,KAAK,IAAI,EAAE;QAC1E,MAAM,CAAC,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACnD,MAAM,IAAI,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,EAAE,EAAE,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACpD,MAAM,QAAQ,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACtD,MAAM,KAAK,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACnD,MAAM,MAAM,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAEpD,MAAM,eAAe,GAAG,IAAI,CAAC;QAE7B,MAAM,EAAE,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAChD,MAAM,KAAK,GAAG,EAAE,CAAC,IAAI,CACjB,CAAC,CAAc,EAAE,EAAE,CAAC,EAAE,CAAC,WAAW,CAC9B,CAAC,EAAE,IAAI,EAAE,QAAQ,EAAE,MAAM,EAAE,KAAK,EAAE,eAAe,CAAC,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,CAAC;QACnE,iBAAiB,CAAC,MAAM,KAAK,CAAC,IAAI,EAAE,EAAE,CAAC,KAAK,EAAE,KAAK,EAAE,KAAK,EAAE,KAAK,CAAC,CAAC,CAAC;QACpE,MAAM,CAAC,KAAK,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACvC,MAAM,QAAQ,GAAG,EAAE,CAAC,IAAI,CACpB,CAAC,IAAiB,EAAE,EAAE,CAAC,EAAE,CAAC,WAAW,CACjC,CAAC,EAAE,IAAI,EAAE,QAAQ,EAAE,MAAM,EAAE,KAAK,EAAE,eAAe,CAAC,CAAC,CAAC,IAAI,EAAE,EAAE,CAAC,CAAC;QACtE,iBAAiB,CAAC,MAAM,QAAQ,CAAC,IAAI,EAAE,EAAE,CAAC,CAAC,KAAK,EAAE,CAAC,KAAK,EAAE,CAAC,KAAK,EAAE,CAAC,KAAK,CAAC,CAAC,CAAC;QAC3E,MAAM,CAAC,QAAQ,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAC1C,MAAM,YAAY,GAAG,EAAE,CAAC,IAAI,CACxB,CAAC,QAAqB,EAAE,EAAE,CAAC,EAAE,CAAC,WAAW,CACrC,CAAC,EAAE,IAAI,EAAE,QAAQ,EAAE,MAAM,EAAE,KAAK,EAAE,eAAe,CAAC,CAAC,CAAC,QAAQ,EAAE,EAAE,CAAC,CAAC;QAC1E,iBAAiB,CACb,MAAM,YAAY,CAAC,IAAI,EAAE,EAAE,CAAC,CAAC,KAAK,EAAE,CAAC,KAAK,EAAE,CAAC,KAAK,EAAE,CAAC,KAAK,CAAC,CAAC,CAAC;QACjE,MAAM,CAAC,YAAY,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAC9C,MAAM,UAAU,GAAG,EAAE,CAAC,IAAI,CACtB,CAAC,MAAmB,EAAE,EAAE,CAAC,EAAE,CAAC,WAAW,CACnC,CAAC,EAAE,IAAI,EAAE,QAAQ,EAAE,MAAM,EAAE,KAAK,EAAE,eAAe,CAAC,CAAC,CAAC,MAAM,EAAE,EAAE,CAAC,CAAC;QACxE,iBAAiB,CAAC,MAAM,UAAU,CAAC,IAAI,EAAE,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACzD,MAAM,CAAC,UAAU,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAC5C,MAAM,SAAS,GAAG,EAAE,CAAC,IAAI,CACrB,CAAC,KAAkB,EAAE,EAAE,CAAC,EAAE,CAAC,WAAW,CAClC,CAAC,EAAE,IAAI,EAAE,QAAQ,EAAE,MAAM,EAAE,KAAK,EAAE,eAAe,CAAC,CAAC,CAAC,KAAK,EAAE,EAAE,CAAC,CAAC;QACvE,iBAAiB,CAAC,MAAM,SAAS,CAAC,IAAI,EAAE,EAAE,CAAC,KAAK,EAAE,KAAK,EAAE,KAAK,EAAE,KAAK,CAAC,CAAC,CAAC;QACxE,MAAM,CAAC,SAAS,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;IAC7C,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,qCAAqC,EAAE,KAAK,IAAI,EAAE;QACnD,MAAM,CAAC,GAAG,EAAE,CAAC,QAAQ,CACjB;YACE,UAAU,EAAE,UAAU,EAAE,CAAC,UAAU,EAAE,UAAU,EAAE,CAAC,UAAU;YAC5D,UAAU,EAAE,UAAU,EAAE,QAAQ,EAAE,UAAU,EAAE,UAAU,EAAE,CAAC,UAAU;YACrE,UAAU,EAAE,CAAC,UAAU,EAAE,UAAU,EAAE,UAAU,EAAE,UAAU;YAC3D,UAAU,EAAE,UAAU;SACvB,EACD,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACf,MAAM,IAAI,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,UAAU,EAAE,CAAC,UAAU,EAAE,SAAS,CAAC,CAAC,CAAC;QAC/D,MAAM,QAAQ,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,UAAU,EAAE,UAAU,EAAE,UAAU,CAAC,CAAC,CAAC;QACnE,MAAM,MAAM,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,UAAU,EAAE,CAAC,UAAU,EAAE,SAAS,CAAC,CAAC,CAAC;QACjE,MAAM,KAAK,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,SAAS,EAAE,SAAS,EAAE,UAAU,CAAC,CAAC,CAAC;QAC/D,MAAM,eAAe,GAAG,IAAI,CAAC;QAE7B,MAAM,MAAM,GACR,EAAE,CAAC,WAAW,CAAC,CAAC,EAAE,IAAI,EAAE,QAAQ,EAAE,MAAM,EAAE,KAAK,EAAE,eAAe,CAAC,CAAC;QAEtE,iBAAiB,CAAC,MAAM,MAAM,CAAC,IAAI,EAAE,EAAE;YACrC,UAAU,EAAE,CAAC,UAAU,EAAE,SAAS,EAAE,CAAC,UAAU,EAAE,CAAC,UAAU,EAAE,UAAU;YACxE,CAAC,UAAU,EAAE,UAAU,EAAE,UAAU,EAAE,CAAC,UAAU,EAAE,CAAC,UAAU,EAAE,UAAU;YACzE,UAAU,EAAE,CAAC,UAAU,EAAE,UAAU,EAAE,UAAU,EAAE,UAAU,EAAE,SAAS;SACvE,CAAC,CAAC;IACL,CAAC,CAAC,CAAC;AACL,CAAC,CAAC,CAAC;AAEH,iBAAiB,CAAC,aAAa,EAAE,QAAQ,EAAE,GAAG,EAAE;IAC9C,EAAE,CAAC,6CAA6C,EAAE,KAAK,IAAI,EAAE;QAC3D,MAAM,EAAE,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAC9C,MAAM,KAAK,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAClC,MAAM,SAAS,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACtC,MAAM,eAAe,GAAG,IAAI,CAAC;QAE7B,MAAM,MAAM,GAAG,EAAE,CAAC,WAAW,CACzB,EAAE,EAAE,KAAK,EAAE,SAAS,EAAE,SAAS,EAAE,SAAS,EAAE,eAAe,CAAC,CAAC;QAEjE,MAAM,CAAC,GAAG,MAAM,EAAE,CAAC,MAAM,EAAE,CAAC;QAC5B,MAAM,IAAI,GAAG,MAAM,KAAK,CAAC,MAAM,EAAE,CAAC;QAClC,MAAM,QAAQ,GAAG,MAAM,SAAS,CAAC,MAAM,EAAE,CAAC;QAC1C,iBAAiB,CAAC,MAAM,MAAM,CAAC,IAAI,EAAE,EAAE;YACrC,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,GAAG,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC;gBAC3B,IAAI,CAAC,IAAI,CAAC,QAAQ,CAAC,GAAG,CAAC,CAAC,CAAC,GAAG,eAAe,CAAC;YAChD,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,GAAG,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC;gBAC3B,IAAI,CAAC,IAAI,CAAC,QAAQ,CAAC,GAAG,CAAC,CAAC,CAAC,GAAG,eAAe,CAAC;YAChD,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,GAAG,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC;gBAC3B,IAAI,CAAC,IAAI,CAAC,QAAQ,CAAC,GAAG,CAAC,CAAC,CAAC,GAAG,eAAe,CAAC;YAChD,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,GAAG,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC;gBAC3B,IAAI,CAAC,IAAI,CAAC,QAAQ,CAAC,GAAG,CAAC,CAAC,CAAC,GAAG,eAAe,CAAC;SACjD,CAAC,CAAC;IACL,CAAC,CAAC,CAAC;IACH,EAAE,CAAC,oCAAoC,EAAE,KAAK,IAAI,EAAE;QAClD,MAAM,EAAE,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAC9C,MAAM,KAAK,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAClC,MAAM,SAAS,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACtC,MAAM,MAAM,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACnC,MAAM,eAAe,GAAG,IAAI,CAAC;QAE7B,MAAM,MAAM,GAAG,EAAE,CAAC,WAAW,CACzB,EAAE,EAAE,KAAK,EAAE,SAAS,EAAE,SAAS,EAAE,MAAM,EAAE,eAAe,CAAC,CAAC;QAE9D,MAAM,CAAC,GAAG,MAAM,EAAE,CAAC,MAAM,EAAE,CAAC;QAC5B,MAAM,IAAI,GAAG,MAAM,KAAK,CAAC,MAAM,EAAE,CAAC;QAClC,MAAM,QAAQ,GAAG,MAAM,SAAS,CAAC,MAAM,EAAE,CAAC;QAC1C,MAAM,KAAK,GAAG,MAAM,MAAM,CAAC,MAAM,EAAE,CAAC;QACpC,iBAAiB,CAAC,MAAM,MAAM,CAAC,IAAI,EAAE,EAAE;YACrC,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,GAAG,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,GAAG,KAAK,CAAC,GAAG,CAAC,CAAC,CAAC;gBACtC,IAAI,CAAC,IAAI,CAAC,QAAQ,CAAC,GAAG,CAAC,CAAC,CAAC,GAAG,eAAe,CAAC;YAChD,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,GAAG,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,GAAG,KAAK,CAAC,GAAG,CAAC,CAAC,CAAC;gBACtC,IAAI,CAAC,IAAI,CAAC,QAAQ,CAAC,GAAG,CAAC,CAAC,CAAC,GAAG,eAAe,CAAC;YAChD,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,GAAG,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,GAAG,KAAK,CAAC,GAAG,CAAC,CAAC,CAAC;gBACtC,IAAI,CAAC,IAAI,CAAC,QAAQ,CAAC,GAAG,CAAC,CAAC,CAAC,GAAG,eAAe,CAAC;YAChD,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,GAAG,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,GAAG,KAAK,CAAC,GAAG,CAAC,CAAC,CAAC;gBACtC,IAAI,CAAC,IAAI,CAAC,QAAQ,CAAC,GAAG,CAAC,CAAC,CAAC,GAAG,eAAe,CAAC;SACjD,CAAC,CAAC;IACL,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,mCAAmC,EAAE,KAAK,IAAI,EAAE;QACjD,MAAM,EAAE,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAC9C,MAAM,KAAK,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAClC,MAAM,SAAS,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACtC,MAAM,OAAO,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAEpC,MAAM,eAAe,GAAG,IAAI,CAAC;QAE7B,MAAM,MAAM,GAAG,EAAE,CAAC,WAAW,CACzB,EAAE,EAAE,KAAK,EAAE,SAAS,EAAE,OAAO,EAAE,SAAS,EAAE,eAAe,CAAC,CAAC;QAE/D,MAAM,MAAM,GAAG,MAAM,OAAO,CAAC,KAAK,EAAE,CAAC;QACrC,MAAM,IAAI,GAAG,MAAM,KAAK,CAAC,KAAK,EAAE,CAAC;QACjC,MAAM,QAAQ,GAAG,MAAM,SAAS,CAAC,KAAK,EAAE,CAAC;QACzC,MAAM,CAAC,GAAG,MAAM,EAAE,CAAC,KAAK,EAAE,CAAC;QAE3B,iBAAiB,CAAC,MAAM,MAAM,CAAC,IAAI,EAAE,EAAE;YACrC,MAAM,CAAC,CAAC,CAAC;gBACL,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,GAAG,IAAI,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,GAAG,IAAI,CAAC,IAAI,CAAC,QAAQ,CAAC,CAAC,CAAC,GAAG,eAAe,CAAC;YACtE,MAAM,CAAC,CAAC,CAAC;gBACL,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,GAAG,IAAI,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,GAAG,IAAI,CAAC,IAAI,CAAC,QAAQ,CAAC,CAAC,CAAC,GAAG,eAAe,CAAC;YACtE,MAAM,CAAC,CAAC,CAAC;gBACL,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,GAAG,IAAI,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,GAAG,IAAI,CAAC,IAAI,CAAC,QAAQ,CAAC,CAAC,CAAC,GAAG,eAAe,CAAC;YACtE,MAAM,CAAC,CAAC,CAAC;gBACL,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,GAAG,IAAI,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,GAAG,IAAI,CAAC,IAAI,CAAC,QAAQ,CAAC,CAAC,CAAC,GAAG,eAAe,CAAC;SACvE,CAAC,CAAC;IACL,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,yBAAyB,EAAE,KAAK,IAAI,EAAE;QACvC,MAAM,EAAE,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAC9C,MAAM,KAAK,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAClC,MAAM,SAAS,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACtC,MAAM,OAAO,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACpC,MAAM,MAAM,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAEnC,MAAM,eAAe,GAAG,IAAI,CAAC;QAE7B,MAAM,MAAM,GACR,EAAE,CAAC,WAAW,CAAC,EAAE,EAAE,KAAK,EAAE,SAAS,EAAE,OAAO,EAAE,MAAM,EAAE,eAAe,CAAC,CAAC;QAE3E,MAAM,MAAM,GAAG,MAAM,OAAO,CAAC,KAAK,EAAE,CAAC;QACrC,MAAM,IAAI,GAAG,MAAM,KAAK,CAAC,KAAK,EAAE,CAAC;QACjC,MAAM,QAAQ,GAAG,MAAM,SAAS,CAAC,KAAK,EAAE,CAAC;QACzC,MAAM,KAAK,GAAG,MAAM,MAAM,CAAC,KAAK,EAAE,CAAC;QACnC,MAAM,CAAC,GAAG,MAAM,EAAE,CAAC,KAAK,EAAE,CAAC;QAE3B,iBAAiB,CAAC,MAAM,MAAM,CAAC,IAAI,EAAE,EAAE;YACrC,MAAM,CAAC,CAAC,CAAC;gBACL,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,GAAG,IAAI,CAAC,CAAC,CAAC,CAAC,GAAG,KAAK,CAAC,CAAC,CAAC;oBAC1B,IAAI,CAAC,IAAI,CAAC,QAAQ,CAAC,CAAC,CAAC,GAAG,eAAe,CAAC;YAChD,MAAM,CAAC,CAAC,CAAC;gBACL,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,GAAG,IAAI,CAAC,CAAC,CAAC,CAAC,GAAG,KAAK,CAAC,CAAC,CAAC;oBAC1B,IAAI,CAAC,IAAI,CAAC,QAAQ,CAAC,CAAC,CAAC,GAAG,eAAe,CAAC;YAChD,MAAM,CAAC,CAAC,CAAC;gBACL,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,GAAG,IAAI,CAAC,CAAC,CAAC,CAAC,GAAG,KAAK,CAAC,CAAC,CAAC;oBAC1B,IAAI,CAAC,IAAI,CAAC,QAAQ,CAAC,CAAC,CAAC,GAAG,eAAe,CAAC;YAChD,MAAM,CAAC,CAAC,CAAC;gBACL,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,GAAG,IAAI,CAAC,CAAC,CAAC,CAAC,GAAG,KAAK,CAAC,CAAC,CAAC;oBAC1B,IAAI,CAAC,IAAI,CAAC,QAAQ,CAAC,CAAC,CAAC,GAAG,eAAe,CAAC;SACjD,CAAC,CAAC;IACL,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,mCAAmC,EAAE,KAAK,IAAI,EAAE;QACjD,MAAM,CAAC,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAC7C,MAAM,IAAI,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACjC,MAAM,QAAQ,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACrC,MAAM,MAAM,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACnC,MAAM,KAAK,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAElC,MAAM,eAAe,GAAG,IAAI,CAAC;QAE7B,MAAM,EAAE,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAC7C,MAAM,CAAC,KAAK,EAAE,QAAQ,EAAE,YAAY,EAAE,UAAU,EAAE,SAAS,CAAC,GAAG,EAAE,CAAC,KAAK,CACnE,CAAC,CAAc,EAAE,IAAiB,EAAE,QAAqB,EACxD,MAAmB,EAAE,KAAkB,EAAE,EAAE,CACxC,EAAE,CAAC,WAAW,CAAC,CAAC,EAAE,IAAI,EAAE,QAAQ,EAAE,MAAM,EAAE,KAAK,EAAE,eAAe,CAAC,CAAC,CACtE,CAAC,CAAC,EAAE,IAAI,EAAE,QAAQ,EAAE,MAAM,EAAE,KAAK,CAAC,EAAE,EAAE,CAAC,CAAC;QAC5C,iBAAiB,CAAC,MAAM,KAAK,CAAC,IAAI,EAAE,EAAE,CAAC,KAAK,EAAE,KAAK,EAAE,KAAK,EAAE,KAAK,CAAC,CAAC,CAAC;QACpE,MAAM,CAAC,KAAK,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACpC,iBAAiB,CAAC,MAAM,QAAQ,CAAC,IAAI,EAAE,EAAE,CAAC,CAAC,KAAK,EAAE,CAAC,KAAK,CAAC,CAAC,CAAC;QAC3D,MAAM,CAAC,QAAQ,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;QACpC,iBAAiB,CAAC,MAAM,YAAY,CAAC,IAAI,EAAE,EAAE,CAAC,CAAC,KAAK,EAAE,CAAC,MAAM,CAAC,CAAC,CAAC;QAChE,MAAM,CAAC,YAAY,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;QACxC,iBAAiB,CAAC,MAAM,UAAU,CAAC,IAAI,EAAE,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACnD,MAAM,CAAC,UAAU,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;QACtC,iBAAiB,CAAC,MAAM,SAAS,CAAC,IAAI,EAAE,EAAE,CAAC,KAAK,EAAE,MAAM,CAAC,CAAC,CAAC;QAC3D,MAAM,CAAC,SAAS,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;IACvC,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,kCAAkC,EAAE,KAAK,IAAI,EAAE;QAChD,MAAM,CAAC,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAC7C,MAAM,IAAI,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACjC,MAAM,QAAQ,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACrC,MAAM,MAAM,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACnC,MAAM,KAAK,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAElC,MAAM,eAAe,GAAG,IAAI,CAAC;QAE7B,MAAM,EAAE,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAC7C,MAAM,CAAC,KAAK,EAAE,QAAQ,EAAE,YAAY,EAAE,UAAU,EAAE,SAAS,CAAC,GAAG,EAAE,CAAC,KAAK,CACnE,CAAC,CAAc,EAAE,IAAiB,EAAE,QAAqB,EACxD,MAAmB,EAAE,KAAkB,EAAE,EAAE,CACxC,EAAE,CAAC,WAAW,CACR,CAAC,CAAC,KAAK,EAAE,EAAE,IAAI,CAAC,KAAK,EAAE,EAAE,QAAQ,CAAC,KAAK,EAAE,EAAE,MAAM,CAAC,KAAK,EAAE,EACzD,KAAK,CAAC,KAAK,EAAE,EAAE,eAAe,CAAC;aAChC,KAAK,EAAE,CAAC,CAAC,CAAC,CAAC,EAAE,IAAI,EAAE,QAAQ,EAAE,MAAM,EAAE,KAAK,CAAC,EAAE,EAAE,CAAC,CAAC;QAC9D,iBAAiB,CAAC,MAAM,KAAK,CAAC,IAAI,EAAE,EAAE,CAAC,KAAK,EAAE,KAAK,EAAE,KAAK,EAAE,KAAK,CAAC,CAAC,CAAC;QACpE,MAAM,CAAC,KAAK,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACpC,iBAAiB,CAAC,MAAM,QAAQ,CAAC,IAAI,EAAE,EAAE,CAAC,CAAC,KAAK,EAAE,CAAC,KAAK,CAAC,CAAC,CAAC;QAC3D,MAAM,CAAC,QAAQ,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;QACpC,iBAAiB,CAAC,MAAM,YAAY,CAAC,IAAI,EAAE,EAAE,CAAC,CAAC,KAAK,EAAE,CAAC,MAAM,CAAC,CAAC,CAAC;QAChE,MAAM,CAAC,YAAY,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;QACxC,iBAAiB,CAAC,MAAM,UAAU,CAAC,IAAI,EAAE,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACnD,MAAM,CAAC,UAAU,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;QACtC,iBAAiB,CAAC,MAAM,SAAS,CAAC,IAAI,EAAE,EAAE,CAAC,KAAK,EAAE,MAAM,CAAC,CAAC,CAAC;QAC3D,MAAM,CAAC,SAAS,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;IACvC,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,4DAA4D,EAAE,KAAK,IAAI,EAAE;QAC1E,MAAM,CAAC,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAChD,MAAM,IAAI,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,EAAE,EAAE,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACjD,MAAM,QAAQ,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACnD,MAAM,KAAK,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAChD,MAAM,MAAM,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAEjD,MAAM,eAAe,GAAG,IAAI,CAAC;QAE7B,MAAM,EAAE,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAC7C,MAAM,KAAK,GAAG,EAAE,CAAC,IAAI,CACjB,CAAC,CAAc,EAAE,EAAE,CAAC,EAAE,CAAC,WAAW,CAC9B,CAAC,EAAE,IAAI,EAAE,QAAQ,EAAE,MAAM,EAAE,KAAK,EAAE,eAAe,CAAC,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,CAAC;QACnE,iBAAiB,CAAC,MAAM,KAAK,CAAC,IAAI,EAAE,EAAE,CAAC,KAAK,EAAE,KAAK,EAAE,KAAK,EAAE,KAAK,CAAC,CAAC,CAAC;QACpE,MAAM,CAAC,KAAK,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACpC,MAAM,QAAQ,GAAG,EAAE,CAAC,IAAI,CACpB,CAAC,IAAiB,EAAE,EAAE,CAAC,EAAE,CAAC,WAAW,CACjC,CAAC,EAAE,IAAI,EAAE,QAAQ,EAAE,MAAM,EAAE,KAAK,EAAE,eAAe,CAAC,CAAC,CAAC,IAAI,EAAE,EAAE,CAAC,CAAC;QACtE,iBAAiB,CAAC,MAAM,QAAQ,CAAC,IAAI,EAAE,EAAE,CAAC,CAAC,KAAK,EAAE,CAAC,KAAK,EAAE,CAAC,KAAK,EAAE,CAAC,KAAK,CAAC,CAAC,CAAC;QAC3E,MAAM,CAAC,QAAQ,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACvC,MAAM,YAAY,GAAG,EAAE,CAAC,IAAI,CACxB,CAAC,QAAqB,EAAE,EAAE,CAAC,EAAE,CAAC,WAAW,CACrC,CAAC,EAAE,IAAI,EAAE,QAAQ,EAAE,MAAM,EAAE,KAAK,EAAE,eAAe,CAAC,CAAC,CAAC,QAAQ,EAAE,EAAE,CAAC,CAAC;QAC1E,iBAAiB,CACb,MAAM,YAAY,CAAC,IAAI,EAAE,EAAE,CAAC,CAAC,KAAK,EAAE,CAAC,KAAK,EAAE,CAAC,KAAK,EAAE,CAAC,KAAK,CAAC,CAAC,CAAC;QACjE,MAAM,CAAC,YAAY,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAC3C,MAAM,UAAU,GAAG,EAAE,CAAC,IAAI,CACtB,CAAC,MAAmB,EAAE,EAAE,CAAC,EAAE,CAAC,WAAW,CACnC,CAAC,EAAE,IAAI,EAAE,QAAQ,EAAE,MAAM,EAAE,KAAK,EAAE,eAAe,CAAC,CAAC,CAAC,MAAM,EAAE,EAAE,CAAC,CAAC;QACxE,iBAAiB,CAAC,MAAM,UAAU,CAAC,IAAI,EAAE,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACzD,MAAM,CAAC,UAAU,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACzC,MAAM,SAAS,GAAG,EAAE,CAAC,IAAI,CACrB,CAAC,KAAkB,EAAE,EAAE,CAAC,EAAE,CAAC,WAAW,CAClC,CAAC,EAAE,IAAI,EAAE,QAAQ,EAAE,MAAM,EAAE,KAAK,EAAE,eAAe,CAAC,CAAC,CAAC,KAAK,EAAE,EAAE,CAAC,CAAC;QACvE,iBAAiB,CAAC,MAAM,SAAS,CAAC,IAAI,EAAE,EAAE,CAAC,KAAK,EAAE,KAAK,EAAE,KAAK,EAAE,KAAK,CAAC,CAAC,CAAC;QACxE,MAAM,CAAC,SAAS,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;IAC1C,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,sBAAsB,EAAE,GAAG,EAAE;QAC9B,MAAM,CAAC,GAAG,EAAE,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAC3B,MAAM,IAAI,GAAG,EAAE,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAC9B,MAAM,QAAQ,GAAG,EAAE,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAClC,MAAM,KAAK,GAAG,EAAE,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAC/B,MAAM,MAAM,GAAG,EAAE,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAEhC,MAAM,eAAe,GAAG,IAAI,CAAC;QAE7B,MAAM,KAAK,GAAG,EAAE,CAAC,KAAK,CAClB,CAAC,CAAc,EAAE,IAAiB,EAAE,QAAqB,EACxD,MAAmB,EAAE,KAAkB,EAAE,EAAE,CACxC,EAAE,CAAC,WAAW,CACR,CAAC,CAAC,KAAK,EAAE,EAAE,IAAI,CAAC,KAAK,EAAE,EAAE,QAAQ,CAAC,KAAK,EAAE,EAAE,MAAM,CAAC,KAAK,EAAE,EACzD,KAAK,CAAC,KAAK,EAAE,EAAE,eAAe,CAAC;aAChC,KAAK,EAAE,CAAC,CAAC;QACtB,MAAM,CAAC,KAAK,EAAE,QAAQ,EAAE,YAAY,EAAE,UAAU,EAAE,SAAS,CAAC,GACxD,KAAK,CAAC,CAAC,CAAC,EAAE,IAAI,EAAE,QAAQ,EAAE,MAAM,EAAE,KAAK,CAAC,CAAC,CAAC;QAC9C,MAAM,CAAC,KAAK,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,KAAK,CAAC,CAAC;QACrC,MAAM,CAAC,QAAQ,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,IAAI,CAAC,KAAK,CAAC,CAAC;QAC3C,MAAM,CAAC,YAAY,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,QAAQ,CAAC,KAAK,CAAC,CAAC;QACnD,MAAM,CAAC,UAAU,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,MAAM,CAAC,KAAK,CAAC,CAAC;QAC/C,MAAM,CAAC,SAAS,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,KAAK,CAAC,KAAK,CAAC,CAAC;IAC/C,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,qCAAqC,EAAE,KAAK,IAAI,EAAE;QACnD,MAAM,CAAC,GAAG,EAAE,CAAC,QAAQ,CACjB;YACE,SAAS,EAAE,UAAU,EAAE,QAAQ,EAAE,UAAU,EAAE,UAAU,EAAE,UAAU;YACnE,UAAU,EAAE,UAAU,EAAE,UAAU;SACnC,EACD,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACZ,MAAM,IAAI,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,UAAU,EAAE,UAAU,EAAE,UAAU,CAAC,CAAC,CAAC;QAC/D,MAAM,QAAQ,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,UAAU,EAAE,UAAU,EAAE,UAAU,CAAC,CAAC,CAAC;QACnE,MAAM,MAAM,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,UAAU,EAAE,UAAU,EAAE,UAAU,CAAC,CAAC,CAAC;QACjE,MAAM,KAAK,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,UAAU,EAAE,UAAU,EAAE,UAAU,CAAC,CAAC,CAAC;QAChE,MAAM,eAAe,GAAG,IAAI,CAAC;QAE7B,MAAM,MAAM,GACR,EAAE,CAAC,WAAW,CAAC,CAAC,EAAE,IAAI,EAAE,QAAQ,EAAE,MAAM,EAAE,KAAK,EAAE,eAAe,CAAC,CAAC;QAEtE,iBAAiB,CAAC,MAAM,MAAM,CAAC,IAAI,EAAE,EAAE;YACrC,UAAU,EAAE,UAAU,EAAE,UAAU,EAAE,UAAU,EAAE,UAAU,EAAE,UAAU;YACtE,UAAU,EAAE,UAAU,EAAE,UAAU;SACnC,CAAC,CAAC;IACL,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,sCAAsC,EAAE,GAAG,EAAE;QAC9C,MAAM,IAAI,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACjC,MAAM,QAAQ,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAErC,MAAM,CAAC,GAAG,EAAE,CAAC,EAAE,CAAC,SAAS,CAAC,EAAe,EAAE,IAAI,EAAE,QAAQ,CAAC,CAAC;aACtD,YAAY,CAAC,qDAAqD,CAAC,CAAC;IAC3E,CAAC,CAAC,CAAC;IACH,EAAE,CAAC,yCAAyC,EAAE,GAAG,EAAE;QACjD,MAAM,CAAC,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACnD,MAAM,QAAQ,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAErC,MAAM,CAAC,GAAG,EAAE,CAAC,EAAE,CAAC,SAAS,CAAC,CAAC,EAAE,EAAe,EAAE,QAAQ,CAAC,CAAC;aACnD,YAAY,CAAC,wDAAwD,CAAC,CAAC;IAC9E,CAAC,CAAC,CAAC;IACH,EAAE,CAAC,6CAA6C,EAAE,GAAG,EAAE;QACrD,MAAM,CAAC,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACnD,MAAM,IAAI,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAEjC,MAAM,CAAC,GAAG,4DAA4D,CAAC;QACvE,MAAM,CAAC,GAAG,EAAE,CAAC,EAAE,CAAC,SAAS,CAAC,CAAC,EAAE,IAAI,EAAE,EAAe,CAAC,CAAC,CAAC,YAAY,CAAC,CAAC,CAAC,CAAC;IACvE,CAAC,CAAC,CAAC;IACH,EAAE,CAAC,0CAA0C,EAAE,GAAG,EAAE;QAClD,MAAM,CAAC,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACnD,MAAM,IAAI,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACjC,MAAM,QAAQ,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACrC,MAAM,OAAO,GAAG,IAAI,CAAC;QAErB,MAAM,CAAC,GAAG,EAAE,CAAC,EAAE,CAAC,SAAS,CAAC,CAAC,EAAE,IAAI,EAAE,QAAQ,EAAE,OAAO,EAAE,EAAe,CAAC,CAAC;aAClE,YAAY,CACT,yDAAyD,CAAC,CAAC;IACrE,CAAC,CAAC,CAAC;IACH,EAAE,CAAC,2CAA2C,EAAE,GAAG,EAAE;QACnD,MAAM,CAAC,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACnD,MAAM,IAAI,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACjC,MAAM,QAAQ,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACrC,MAAM,OAAO,GAAG,IAAI,CAAC;QACrB,MAAM,KAAK,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,UAAU,EAAE,UAAU,EAAE,UAAU,CAAC,CAAC,CAAC;QAEhE,MAAM,CAAC,GAAG,0DAA0D,CAAC;QACrE,MAAM,CACF,GAAG,EAAE,CAAC,EAAE,CAAC,SAAS,CAAC,CAAC,EAAE,IAAI,EAAE,QAAQ,EAAE,EAAe,EAAE,KAAK,EAAE,OAAO,CAAC,CAAC;aACtE,YAAY,CAAC,CAAC,CAAC,CAAC;IACvB,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,8BAA8B,EAAE,KAAK,IAAI,EAAE;QAC5C,MAAM,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,EAAE,CAAC,CAAC,CAAC;QAC5B,MAAM,IAAI,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC;QACpB,MAAM,QAAQ,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC;QACxB,MAAM,MAAM,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC;QACtB,MAAM,KAAK,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC;QAErB,MAAM,eAAe,GAAG,IAAI,CAAC;QAE7B,MAAM,MAAM,GACR,EAAE,CAAC,WAAW,CAAC,CAAC,EAAE,IAAI,EAAE,QAAQ,EAAE,MAAM,EAAE,KAAK,EAAE,eAAe,CAAC,CAAC;QAEtE,iBAAiB,CAAC,MAAM,MAAM,CAAC,IAAI,EAAE,EAAE;YACrC,MAAM,CAAC,CAAC,CAAC;gBACL,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,GAAG,IAAI,CAAC,CAAC,CAAC,CAAC,GAAG,KAAK,CAAC,CAAC,CAAC;oBAC1B,IAAI,CAAC,IAAI,CAAC,QAAQ,CAAC,CAAC,CAAC,GAAG,eAAe,CAAC;YAChD,MAAM,CAAC,CAAC,CAAC;gBACL,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,GAAG,IAAI,CAAC,CAAC,CAAC,CAAC,GAAG,KAAK,CAAC,CAAC,CAAC;oBAC1B,IAAI,CAAC,IAAI,CAAC,QAAQ,CAAC,CAAC,CAAC,GAAG,eAAe,CAAC;YAChD,MAAM,CAAC,CAAC,CAAC;gBACL,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,GAAG,IAAI,CAAC,CAAC,CAAC,CAAC,GAAG,KAAK,CAAC,CAAC,CAAC;oBAC1B,IAAI,CAAC,IAAI,CAAC,QAAQ,CAAC,CAAC,CAAC,GAAG,eAAe,CAAC;YAChD,MAAM,CAAC,CAAC,CAAC;gBACL,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,GAAG,IAAI,CAAC,CAAC,CAAC,CAAC,GAAG,KAAK,CAAC,CAAC,CAAC;oBAC1B,IAAI,CAAC,IAAI,CAAC,QAAQ,CAAC,CAAC,CAAC,GAAG,eAAe,CAAC;SACjD,CAAC,CAAC;IACL,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,wCAAwC,EAAE,GAAG,EAAE;QAChD,MAAM,IAAI,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC;QACpB,MAAM,QAAQ,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC;QACxB,MAAM,MAAM,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC;QACtB,MAAM,KAAK,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC;QAErB,MAAM,eAAe,GAAG,IAAI,CAAC;QAE7B,MAAM,CAAC,GAAG,GAAG,EAAE,CAAC,EAAE,CAAC,WAAW,CAC1B,CAAC,CAAC,GAAG,EAAE,GAAG,CAAC,EAAE,CAAC,GAAG,EAAE,GAAG,CAAC,CAAC,EAAE,IAAI,EAAE,QAAQ,EAAE,MAAM,EAAE,KAAK,EACvD,eAAe,CAAC,CAAC;QACrB,MAAM,CAAC,CAAC,CAAC,CAAC,YAAY,CAClB,oDAAoD,CAAC,CAAC;IAC5D,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,2CAA2C,EAAE,GAAG,EAAE;QACnD,MAAM,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,EAAE,CAAC,CAAC,CAAC;QAC5B,MAAM,QAAQ,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC;QACxB,MAAM,MAAM,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC;QACtB,MAAM,KAAK,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC;QAErB,MAAM,eAAe,GAAG,IAAI,CAAC;QAE7B,MAAM,CAAC,GAAG,GAAG,EAAE,CACX,EAAE,CAAC,WAAW,CAAC,CAAC,EAAE,CAAC,GAAG,EAAE,GAAG,CAAC,EAAE,QAAQ,EAAE,MAAM,EAAE,KAAK,EAAE,eAAe,CAAC,CAAC;QAC5E,MAAM,CAAC,CAAC,CAAC,CAAC,YAAY,CAClB,uDAAuD,CAAC,CAAC;IAC/D,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,+CAA+C,EAAE,GAAG,EAAE;QACvD,MAAM,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,EAAE,CAAC,CAAC,CAAC;QAC5B,MAAM,IAAI,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC;QACpB,MAAM,MAAM,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC;QACtB,MAAM,KAAK,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC;QAErB,MAAM,eAAe,GAAG,IAAI,CAAC;QAE7B,MAAM,CAAC,GAAG,GAAG,EAAE,CACX,EAAE,CAAC,WAAW,CAAC,CAAC,EAAE,IAAI,EAAE,CAAC,GAAG,EAAE,GAAG,CAAC,EAAE,MAAM,EAAE,KAAK,EAAE,eAAe,CAAC,CAAC;QACxE,MAAM,CAAC,CAAC,CAAC,CAAC,YAAY,CAAC,kDAAkD,CAAC,CAAC;IAC7E,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,4CAA4C,EAAE,GAAG,EAAE;QACpD,MAAM,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,EAAE,CAAC,CAAC,CAAC;QAC5B,MAAM,IAAI,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC;QACpB,MAAM,QAAQ,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC;QACxB,MAAM,MAAM,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC;QAEtB,MAAM,eAAe,GAAG,IAAI,CAAC;QAE7B,MAAM,CAAC,GAAG,GAAG,EAAE,CACX,EAAE,CAAC,WAAW,CAAC,CAAC,EAAE,IAAI,EAAE,QAAQ,EAAE,MAAM,EAAE,CAAC,GAAG,EAAE,GAAG,CAAC,EAAE,eAAe,CAAC,CAAC;QAC3E,MAAM,CAAC,CAAC,CAAC,CAAC,YAAY,CAAC,+CAA+C,CAAC,CAAC;IAC1E,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,6CAA6C,EAAE,GAAG,EAAE;QACrD,MAAM,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,EAAE,CAAC,CAAC,CAAC;QAC5B,MAAM,IAAI,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC;QACpB,MAAM,QAAQ,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC;QACxB,MAAM,KAAK,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC;QAErB,MAAM,eAAe,GAAG,IAAI,CAAC;QAE7B,MAAM,CAAC,GAAG,GAAG,EAAE,CACX,EAAE,CAAC,WAAW,CAAC,CAAC,EAAE,IAAI,EAAE,QAAQ,EAAE,CAAC,GAAG,EAAE,GAAG,CAAC,EAAE,KAAK,EAAE,eAAe,CAAC,CAAC;QAC1E,MAAM,CAAC,CAAC,CAAC,CAAC,YAAY,CAAC,gDAAgD,CAAC,CAAC;IAC3E,CAAC,CAAC,CAAC;AACL,CAAC,CAAC,CAAC","sourcesContent":["/**\n * @license\n * Copyright 2017 Google LLC. 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 * as tf from '../index';\nimport {ALL_ENVS, describeWithFlags} from '../jasmine_util';\nimport {expectArraysClose} from '../test_util';\n\ndescribeWithFlags('batchNorm4D', ALL_ENVS, () => {\n  it('simple batchnorm4D, no offset or scale, 2x1x1x2', async () => {\n    const xT = tf.tensor4d([2, 4, 9, 23], [2, 1, 1, 2]);\n    const meanT = tf.tensor1d([1, 2]);\n    const varianceT = tf.tensor1d([2, 3]);\n    const varianceEpsilon = .001;\n\n    const result = tf.batchNorm4d(\n        xT, meanT, varianceT, undefined, undefined, varianceEpsilon);\n\n    const x = await xT.array();\n    const mean = await meanT.array();\n    const variance = await varianceT.array();\n    expectArraysClose(await result.data(), [\n      (x[0][0][0][0] - mean[0]) * 1 / Math.sqrt(variance[0] + varianceEpsilon),\n      (x[0][0][0][1] - mean[1]) * 1 / Math.sqrt(variance[1] + varianceEpsilon),\n      (x[1][0][0][0] - mean[0]) * 1 / Math.sqrt(variance[0] + varianceEpsilon),\n      (x[1][0][0][1] - mean[1]) * 1 / Math.sqrt(variance[1] + varianceEpsilon)\n    ]);\n  });\n\n  it('simple batchnorm4D, no offset, 2x1x1x2', async () => {\n    const xT = tf.tensor4d([2, 4, 9, 23], [2, 1, 1, 2]);\n    const meanT = tf.tensor1d([1, 2]);\n    const varianceT = tf.tensor1d([2, 3]);\n    const scaleT = tf.tensor1d([4, 5]);\n    const varianceEpsilon = .001;\n\n    const result = tf.batchNorm4d(\n        xT, meanT, varianceT, undefined, scaleT, varianceEpsilon);\n    const x = await xT.buffer();\n    const mean = await meanT.buffer();\n    const variance = await varianceT.buffer();\n    const scale = await scaleT.buffer();\n\n    expectArraysClose(await result.data(), [\n      (x.get(0, 0, 0, 0) - mean.get(0)) * scale.get(0) /\n          Math.sqrt(variance.get(0) + varianceEpsilon),\n      (x.get(0, 0, 0, 1) - mean.get(1)) * scale.get(1) /\n          Math.sqrt(variance.get(1) + varianceEpsilon),\n      (x.get(1, 0, 0, 0) - mean.get(0)) * scale.get(0) /\n          Math.sqrt(variance.get(0) + varianceEpsilon),\n      (x.get(1, 0, 0, 1) - mean.get(1)) * scale.get(1) /\n          Math.sqrt(variance.get(1) + varianceEpsilon)\n    ]);\n  });\n\n  it('simple batchnorm4D, no scale, 2x1x1x2', async () => {\n    const xT = tf.tensor4d([2, 4, 9, 23], [2, 1, 1, 2]);\n    const meanT = tf.tensor1d([1, 2]);\n    const varianceT = tf.tensor1d([2, 3]);\n    const offsetT = tf.tensor1d([4, 5]);\n\n    const varianceEpsilon = .001;\n\n    const result = tf.batchNorm4d(\n        xT, meanT, varianceT, offsetT, undefined, varianceEpsilon);\n    const x = await xT.buffer();\n    const mean = await meanT.buffer();\n    const variance = await varianceT.buffer();\n    const offset = await offsetT.buffer();\n\n    expectArraysClose(await result.data(), [\n      offset.get(0) +\n          (x.get(0, 0, 0, 0) - mean.get(0)) * 1 /\n              Math.sqrt(variance.get(0) + varianceEpsilon),\n      offset.get(1) +\n          (x.get(0, 0, 0, 1) - mean.get(1)) * 1 /\n              Math.sqrt(variance.get(1) + varianceEpsilon),\n      offset.get(0) +\n          (x.get(1, 0, 0, 0) - mean.get(0)) * 1 /\n              Math.sqrt(variance.get(0) + varianceEpsilon),\n      offset.get(1) +\n          (x.get(1, 0, 0, 1) - mean.get(1)) * 1 /\n              Math.sqrt(variance.get(1) + varianceEpsilon)\n    ]);\n  });\n\n  it('simple batchnorm4D, 2x1x1x2', async () => {\n    const xT = tf.tensor4d([2, 4, 9, 23], [2, 1, 1, 2]);\n    const meanT = tf.tensor1d([1, 2]);\n    const varianceT = tf.tensor1d([2, 3]);\n    const offsetT = tf.tensor1d([3, 4]);\n    const scaleT = tf.tensor1d([4, 5]);\n\n    const varianceEpsilon = .001;\n\n    const result =\n        tf.batchNorm4d(xT, meanT, varianceT, offsetT, scaleT, varianceEpsilon);\n    const x = await xT.buffer();\n    const mean = await meanT.buffer();\n    const variance = await varianceT.buffer();\n    const scale = await scaleT.buffer();\n    const offset = await offsetT.buffer();\n\n    expectArraysClose(await result.data(), [\n      offset.get(0) +\n          (x.get(0, 0, 0, 0) - mean.get(0)) * scale.get(0) /\n              Math.sqrt(variance.get(0) + varianceEpsilon),\n      offset.get(1) +\n          (x.get(0, 0, 0, 1) - mean.get(1)) * scale.get(1) /\n              Math.sqrt(variance.get(1) + varianceEpsilon),\n      offset.get(0) +\n          (x.get(1, 0, 0, 0) - mean.get(0)) * scale.get(0) /\n              Math.sqrt(variance.get(0) + varianceEpsilon),\n      offset.get(1) +\n          (x.get(1, 0, 0, 1) - mean.get(1)) * scale.get(1) /\n              Math.sqrt(variance.get(1) + varianceEpsilon)\n    ]);\n  });\n\n  it('accepts a tensor-like object', async () => {\n    const x = [[[[2, 4]]], [[[9, 23]]]];  // 2x1x1x2\n    const mean = [1, 2];\n    const variance = [2, 3];\n    const offset = [3, 4];\n    const scale = [4, 5];\n\n    const varianceEpsilon = .001;\n\n    const result =\n        tf.batchNorm4d(x, mean, variance, offset, scale, varianceEpsilon);\n\n    expectArraysClose(await result.data(), [\n      offset[0] +\n          (x[0][0][0][0] - mean[0]) * scale[0] /\n              Math.sqrt(variance[0] + varianceEpsilon),\n      offset[1] +\n          (x[0][0][0][1] - mean[1]) * scale[1] /\n              Math.sqrt(variance[1] + varianceEpsilon),\n      offset[0] +\n          (x[1][0][0][0] - mean[0]) * scale[0] /\n              Math.sqrt(variance[0] + varianceEpsilon),\n      offset[1] +\n          (x[1][0][0][1] - mean[1]) * scale[1] /\n              Math.sqrt(variance[1] + varianceEpsilon)\n    ]);\n  });\n\n  it('simple batchnorm4D gradients, 2x1x1x2', async () => {\n    const x = tf.tensor4d([2, 4, 9, 23], [2, 1, 1, 2]);\n    const mean = tf.tensor1d([1, 2]);\n    const variance = tf.tensor1d([2, 3]);\n    const offset = tf.tensor1d([3, 4]);\n    const scale = tf.tensor1d([2, 5]);\n\n    const varianceEpsilon = .001;\n\n    const dy = tf.tensor4d([-1, -1, -1, -1], [2, 1, 1, 2]);\n    const gradX = tf.grad(\n        (x: tf.Tensor4D) => tf.batchNorm4d(\n            x, mean, variance, offset, scale, varianceEpsilon))(x, dy);\n    expectArraysClose(await gradX.data(), [-1.414, -2.887, -1.414, -2.887]);\n    expect(gradX.shape).toEqual([2, 1, 1, 2]);\n    const gradMean = tf.grad(\n        (mean: tf.Tensor1D) => tf.batchNorm4d(\n            x, mean, variance, offset, scale, varianceEpsilon))(mean, dy);\n    expectArraysClose(await gradMean.data(), [2.828, 5.773]);\n    expect(gradMean.shape).toEqual([2]);\n    const gradVariance = tf.grad(\n        (variance: tf.Tensor1D) => tf.batchNorm4d(\n            x, mean, variance, offset, scale, varianceEpsilon))(variance, dy);\n    expectArraysClose(await gradVariance.data(), [3.180, 11.060]);\n    expect(gradVariance.shape).toEqual([2]);\n    const gradOffset = tf.grad(\n        (offset: tf.Tensor1D) => tf.batchNorm4d(\n            x, mean, variance, offset, scale, varianceEpsilon))(offset, dy);\n    expectArraysClose(await gradOffset.data(), await dy.sum([0, 1, 2]).data());\n    expect(gradOffset.shape).toEqual([2]);\n    const gradScale = tf.grad(\n        (scale: tf.Tensor1D) => tf.batchNorm4d(\n            x, mean, variance, offset, scale, varianceEpsilon))(scale, dy);\n    expectArraysClose(await gradScale.data(), [-6.362, -13.277]);\n    expect(gradScale.shape).toEqual([2]);\n  });\n\n  it('batchnorm4D gradients, same shapes in x, mean and variance', async () => {\n    const x = tf.tensor4d([10, 20, 30, 40], [2, 1, 1, 2]);\n    const mean = tf.tensor4d([0, 5, 10, 15], [2, 1, 1, 2]);\n    const variance = tf.tensor4d([2, 4, 6, 8], [2, 1, 1, 2]);\n    const scale = tf.tensor4d([2, 5, 2, 5], [2, 1, 1, 2]);\n    const offset = tf.tensor4d([0, 0, 0, 0], [2, 1, 1, 2]);\n\n    const varianceEpsilon = .001;\n\n    const dy = tf.tensor4d([-1, -1, -1, -1], [2, 1, 1, 2]);\n    const gradX = tf.grad(\n        (x: tf.Tensor4D) => tf.batchNorm4d(\n            x, mean, variance, offset, scale, varianceEpsilon))(x, dy);\n    expectArraysClose(await gradX.data(), [-1.414, -2.500, -0.816, -1.768]);\n    expect(gradX.shape).toEqual([2, 1, 1, 2]);\n    const gradMean = tf.grad(\n        (mean: tf.Tensor4D) => tf.batchNorm4d(\n            x, mean, variance, offset, scale, varianceEpsilon))(mean, dy);\n    expectArraysClose(await gradMean.data(), [1.414, 2.500, 0.816, 1.768]);\n    expect(gradMean.shape).toEqual([2, 1, 1, 2]);\n    const gradVariance = tf.grad(\n        (variance: tf.Tensor4D) => tf.batchNorm4d(\n            x, mean, variance, offset, scale, varianceEpsilon))(variance, dy);\n    expectArraysClose(await gradVariance.data(), [3.533, 4.686, 1.360, 2.762]);\n    expect(gradVariance.shape).toEqual([2, 1, 1, 2]);\n    const gradOffset = tf.grad(\n        (offset: tf.Tensor4D) => tf.batchNorm4d(\n            x, mean, variance, offset, scale, varianceEpsilon))(offset, dy);\n    expectArraysClose(await gradOffset.data(), await dy.data());\n    expect(gradOffset.shape).toEqual([2, 1, 1, 2]);\n    const gradScale = tf.grad(\n        (scale: tf.Tensor4D) => tf.batchNorm4d(\n            x, mean, variance, offset, scale, varianceEpsilon))(scale, dy);\n    expectArraysClose(await gradScale.data(), [-7.069, -7.499, -8.164, -8.838]);\n    expect(gradScale.shape).toEqual([2, 1, 1, 2]);\n  });\n});\n\ndescribeWithFlags('batchNorm3D', ALL_ENVS, () => {\n  it('simple batchnorm3D, no offset or scale, 2x1x2', async () => {\n    const xT = tf.tensor3d([2, 4, 9, 23], [2, 1, 2]);\n    const meanT = tf.tensor1d([1, 2]);\n    const varianceT = tf.tensor1d([2, 3]);\n    const varianceEpsilon = .001;\n\n    const result = tf.batchNorm3d(\n        xT, meanT, varianceT, undefined, undefined, varianceEpsilon);\n    const x = await xT.buffer();\n    const mean = await meanT.buffer();\n    const variance = await varianceT.buffer();\n    expectArraysClose(await result.data(), [\n      (x.get(0, 0, 0) - mean.get(0)) * 1 /\n          Math.sqrt(variance.get(0) + varianceEpsilon),\n      (x.get(0, 0, 1) - mean.get(1)) * 1 /\n          Math.sqrt(variance.get(1) + varianceEpsilon),\n      (x.get(1, 0, 0) - mean.get(0)) * 1 /\n          Math.sqrt(variance.get(0) + varianceEpsilon),\n      (x.get(1, 0, 1) - mean.get(1)) * 1 /\n          Math.sqrt(variance.get(1) + varianceEpsilon)\n    ]);\n  });\n\n  it('simple batchnorm3D, no offset, 2x1x2', async () => {\n    const xT = tf.tensor3d([2, 4, 9, 23], [2, 1, 2]);\n    const meanT = tf.tensor1d([1, 2]);\n    const varianceT = tf.tensor1d([2, 3]);\n    const scaleT = tf.tensor1d([4, 5]);\n    const varianceEpsilon = .001;\n\n    const result = tf.batchNorm3d(\n        xT, meanT, varianceT, undefined, scaleT, varianceEpsilon);\n\n    const x = await xT.buffer();\n    const mean = await meanT.buffer();\n    const variance = await varianceT.buffer();\n    const scale = await scaleT.buffer();\n    expectArraysClose(await result.data(), [\n      (x.get(0, 0, 0) - mean.get(0)) * scale.get(0) /\n          Math.sqrt(variance.get(0) + varianceEpsilon),\n      (x.get(0, 0, 1) - mean.get(1)) * scale.get(1) /\n          Math.sqrt(variance.get(1) + varianceEpsilon),\n      (x.get(1, 0, 0) - mean.get(0)) * scale.get(0) /\n          Math.sqrt(variance.get(0) + varianceEpsilon),\n      (x.get(1, 0, 1) - mean.get(1)) * scale.get(1) /\n          Math.sqrt(variance.get(1) + varianceEpsilon)\n    ]);\n  });\n\n  it('simple batchnorm3D, no scale, 2x1x2', async () => {\n    const xT = tf.tensor3d([2, 4, 9, 23], [2, 1, 2]);\n    const meanT = tf.tensor1d([1, 2]);\n    const varianceT = tf.tensor1d([2, 3]);\n    const offsetT = tf.tensor1d([4, 5]);\n\n    const varianceEpsilon = .001;\n\n    const result = tf.batchNorm3d(\n        xT, meanT, varianceT, offsetT, undefined, varianceEpsilon);\n\n    const x = await xT.buffer();\n    const mean = await meanT.buffer();\n    const variance = await varianceT.buffer();\n    const offset = await offsetT.buffer();\n    expectArraysClose(await result.data(), [\n      offset.get(0) +\n          (x.get(0, 0, 0) - mean.get(0)) * 1 /\n              Math.sqrt(variance.get(0) + varianceEpsilon),\n      offset.get(1) +\n          (x.get(0, 0, 1) - mean.get(1)) * 1 /\n              Math.sqrt(variance.get(1) + varianceEpsilon),\n      offset.get(0) +\n          (x.get(1, 0, 0) - mean.get(0)) * 1 /\n              Math.sqrt(variance.get(0) + varianceEpsilon),\n      offset.get(1) +\n          (x.get(1, 0, 1) - mean.get(1)) * 1 /\n              Math.sqrt(variance.get(1) + varianceEpsilon)\n    ]);\n  });\n\n  it('simple batchnorm3D, 2x1x2', async () => {\n    const xT = tf.tensor3d([2, 4, 9, 23], [2, 1, 2]);\n    const meanT = tf.tensor1d([1, 2]);\n    const varianceT = tf.tensor1d([2, 3]);\n    const offsetT = tf.tensor1d([3, 4]);\n    const scaleT = tf.tensor1d([4, 5]);\n\n    const varianceEpsilon = .001;\n\n    const result =\n        tf.batchNorm3d(xT, meanT, varianceT, offsetT, scaleT, varianceEpsilon);\n    const x = await xT.buffer();\n    const mean = await meanT.buffer();\n    const variance = await varianceT.buffer();\n    const offset = await offsetT.buffer();\n    const scale = await scaleT.buffer();\n\n    expectArraysClose(await result.data(), [\n      offset.get(0) +\n          (x.get(0, 0, 0) - mean.get(0)) * scale.get(0) /\n              Math.sqrt(variance.get(0) + varianceEpsilon),\n      offset.get(1) +\n          (x.get(0, 0, 1) - mean.get(1)) * scale.get(1) /\n              Math.sqrt(variance.get(1) + varianceEpsilon),\n      offset.get(0) +\n          (x.get(1, 0, 0) - mean.get(0)) * scale.get(0) /\n              Math.sqrt(variance.get(0) + varianceEpsilon),\n      offset.get(1) +\n          (x.get(1, 0, 1) - mean.get(1)) * scale.get(1) /\n              Math.sqrt(variance.get(1) + varianceEpsilon)\n    ]);\n  });\n\n  it('accepts a tensor-like object', async () => {\n    const x = [[[2, 4]], [[9, 23]]];  // 2x1x2\n    const mean = [1, 2];\n    const variance = [2, 3];\n    const offset = [3, 4];\n    const scale = [4, 5];\n\n    const varianceEpsilon = .001;\n\n    const result =\n        tf.batchNorm3d(x, mean, variance, offset, scale, varianceEpsilon);\n\n    expectArraysClose(await result.data(), [\n      offset[0] +\n          (x[0][0][0] - mean[0]) * scale[0] /\n              Math.sqrt(variance[0] + varianceEpsilon),\n      offset[1] +\n          (x[0][0][1] - mean[1]) * scale[1] /\n              Math.sqrt(variance[1] + varianceEpsilon),\n      offset[0] +\n          (x[1][0][0] - mean[0]) * scale[0] /\n              Math.sqrt(variance[0] + varianceEpsilon),\n      offset[1] +\n          (x[1][0][1] - mean[1]) * scale[1] /\n              Math.sqrt(variance[1] + varianceEpsilon)\n    ]);\n  });\n\n  it('batchnorm3D, x,mean,var,offset,scale are all 3D', async () => {\n    const shape: [number, number, number] = [2, 1, 2];\n    const xT = tf.tensor3d([2, 4, 9, 23], shape);\n    const meanT = tf.tensor3d([1, 2, 3, 4], shape);\n    const varianceT = tf.tensor3d([2, 3, 4, 5], shape);\n    const offsetT = tf.tensor3d([3, 4, 5, 6], shape);\n    const scaleT = tf.tensor3d([4, 5, 6, 7], shape);\n\n    const varianceEpsilon = .001;\n\n    const result =\n        tf.batchNorm3d(xT, meanT, varianceT, offsetT, scaleT, varianceEpsilon);\n\n    const x = await xT.buffer();\n    const mean = await meanT.buffer();\n    const variance = await varianceT.buffer();\n    const offset = await offsetT.buffer();\n    const scale = await scaleT.buffer();\n    expectArraysClose(await result.data(), [\n      offset.get(0, 0, 0) +\n          (x.get(0, 0, 0) - mean.get(0, 0, 0)) * scale.get(0, 0, 0) /\n              Math.sqrt(variance.get(0, 0, 0) + varianceEpsilon),\n      offset.get(0, 0, 1) +\n          (x.get(0, 0, 1) - mean.get(0, 0, 1)) * scale.get(0, 0, 1) /\n              Math.sqrt(variance.get(0, 0, 1) + varianceEpsilon),\n      offset.get(1, 0, 0) +\n          (x.get(1, 0, 0) - mean.get(1, 0, 0)) * scale.get(1, 0, 0) /\n              Math.sqrt(variance.get(1, 0, 0) + varianceEpsilon),\n      offset.get(1, 0, 1) +\n          (x.get(1, 0, 1) - mean.get(1, 0, 1)) * scale.get(1, 0, 1) /\n              Math.sqrt(variance.get(1, 0, 1) + varianceEpsilon)\n    ]);\n  });\n\n  it('simple batchnorm3D gradients, 2x1x2', async () => {\n    const x = tf.tensor3d([2, 4, 9, 23], [2, 1, 2]);\n    const mean = tf.tensor1d([1, 2]);\n    const variance = tf.tensor1d([2, 3]);\n    const offset = tf.tensor1d([3, 4]);\n    const scale = tf.tensor1d([2, 5]);\n\n    const varianceEpsilon = .001;\n\n    const dy = tf.tensor3d([1, 1, 1, 1], [2, 1, 2]);\n    const gradX = tf.grad(\n        (x: tf.Tensor3D) => tf.batchNorm3d(\n            x, mean, variance, offset, scale, varianceEpsilon))(x, dy);\n    expectArraysClose(await gradX.data(), [1.414, 2.887, 1.414, 2.887]);\n    expect(gradX.shape).toEqual([2, 1, 2]);\n    const gradMean = tf.grad(\n        (mean: tf.Tensor1D) => tf.batchNorm3d(\n            x, mean, variance, offset, scale, varianceEpsilon))(mean, dy);\n    expectArraysClose(await gradMean.data(), [-2.828, -5.773]);\n    expect(gradMean.shape).toEqual([2]);\n    const gradVariance = tf.grad(\n        (variance: tf.Tensor1D) => tf.batchNorm3d(\n            x, mean, variance, offset, scale, varianceEpsilon))(variance, dy);\n    expectArraysClose(await gradVariance.data(), [-3.180, -11.060]);\n    expect(gradVariance.shape).toEqual([2]);\n    const gradOffset = tf.grad(\n        (offset: tf.Tensor1D) => tf.batchNorm3d(\n            x, mean, variance, offset, scale, varianceEpsilon))(offset, dy);\n    expectArraysClose(await gradOffset.data(), [2, 2]);\n    expect(gradOffset.shape).toEqual([2]);\n    const gradScale = tf.grad(\n        (scale: tf.Tensor1D) => tf.batchNorm3d(\n            x, mean, variance, offset, scale, varianceEpsilon))(scale, dy);\n    expectArraysClose(await gradScale.data(), [6.362, 13.277]);\n    expect(gradScale.shape).toEqual([2]);\n  });\n\n  it('batchnorm3D gradients, same shapes in x, mean and variance', async () => {\n    const x = tf.tensor3d([10, 20, 30, 40], [2, 1, 2]);\n    const mean = tf.tensor3d([0, 5, 10, 15], [2, 1, 2]);\n    const variance = tf.tensor3d([2, 4, 6, 8], [2, 1, 2]);\n    const scale = tf.tensor3d([2, 5, 2, 5], [2, 1, 2]);\n    const offset = tf.tensor3d([0, 0, 0, 0], [2, 1, 2]);\n\n    const varianceEpsilon = .001;\n\n    const dy = tf.tensor3d([1, 1, 1, 1], [2, 1, 2]);\n    const gradX = tf.grad(\n        (x: tf.Tensor3D) => tf.batchNorm3d(\n            x, mean, variance, offset, scale, varianceEpsilon))(x, dy);\n    expectArraysClose(await gradX.data(), [1.414, 2.500, 0.816, 1.768]);\n    expect(gradX.shape).toEqual([2, 1, 2]);\n    const gradMean = tf.grad(\n        (mean: tf.Tensor3D) => tf.batchNorm3d(\n            x, mean, variance, offset, scale, varianceEpsilon))(mean, dy);\n    expectArraysClose(await gradMean.data(), [-1.414, -2.500, -0.816, -1.768]);\n    expect(gradMean.shape).toEqual([2, 1, 2]);\n    const gradVariance = tf.grad(\n        (variance: tf.Tensor3D) => tf.batchNorm3d(\n            x, mean, variance, offset, scale, varianceEpsilon))(variance, dy);\n    expectArraysClose(\n        await gradVariance.data(), [-3.533, -4.686, -1.360, -2.762]);\n    expect(gradVariance.shape).toEqual([2, 1, 2]);\n    const gradOffset = tf.grad(\n        (offset: tf.Tensor3D) => tf.batchNorm3d(\n            x, mean, variance, offset, scale, varianceEpsilon))(offset, dy);\n    expectArraysClose(await gradOffset.data(), [1, 1, 1, 1]);\n    expect(gradOffset.shape).toEqual([2, 1, 2]);\n    const gradScale = tf.grad(\n        (scale: tf.Tensor3D) => tf.batchNorm3d(\n            x, mean, variance, offset, scale, varianceEpsilon))(scale, dy);\n    expectArraysClose(await gradScale.data(), [7.069, 7.499, 8.164, 8.838]);\n    expect(gradScale.shape).toEqual([2, 1, 2]);\n  });\n\n  it('batchnorm matches tensorflow, 2x3x3', async () => {\n    const x = tf.tensor3d(\n        [\n          0.49955603, 0.04158615, -1.09440524, 2.03854165, -0.61578344,\n          2.87533573, 1.18105987, 0.807462, 1.87888837, 2.26563962, -0.37040935,\n          1.35848753, -0.75347094, 0.15683117, 0.91925946, 0.34121279,\n          0.92717143, 1.89683965\n        ],\n        [2, 3, 3]);\n    const mean = tf.tensor1d([0.39745062, -0.48062894, 0.4847822]);\n    const variance = tf.tensor1d([0.32375343, 0.67117643, 1.08334653]);\n    const offset = tf.tensor1d([0.69398749, -1.29056387, 0.9429723]);\n    const scale = tf.tensor1d([-0.5607271, 0.9878457, 0.25181573]);\n    const varianceEpsilon = .001;\n\n    const result =\n        tf.batchNorm3d(x, mean, variance, offset, scale, varianceEpsilon);\n\n    expectArraysClose(await result.data(), [\n      0.59352049, -0.66135202, 0.5610874, -0.92077015, -1.45341019, 1.52106473,\n      -0.07704776, 0.26144429, 1.28010017, -1.14422404, -1.15776136, 1.15425493,\n      1.82644104, -0.52249442, 1.04803919, 0.74932291, 0.40568101, 1.2844412\n    ]);\n  });\n});\n\ndescribeWithFlags('batchNorm2D', ALL_ENVS, () => {\n  it('simple batchnorm2D, no offset or scale, 2x2', async () => {\n    const xT = tf.tensor2d([2, 4, 9, 23], [2, 2]);\n    const meanT = tf.tensor1d([1, 2]);\n    const varianceT = tf.tensor1d([2, 3]);\n    const varianceEpsilon = .001;\n\n    const result = tf.batchNorm2d(\n        xT, meanT, varianceT, undefined, undefined, varianceEpsilon);\n\n    const x = await xT.buffer();\n    const mean = await meanT.buffer();\n    const variance = await varianceT.buffer();\n    expectArraysClose(await result.data(), [\n      (x.get(0, 0) - mean.get(0)) * 1 /\n          Math.sqrt(variance.get(0) + varianceEpsilon),\n      (x.get(0, 1) - mean.get(1)) * 1 /\n          Math.sqrt(variance.get(1) + varianceEpsilon),\n      (x.get(1, 0) - mean.get(0)) * 1 /\n          Math.sqrt(variance.get(0) + varianceEpsilon),\n      (x.get(1, 1) - mean.get(1)) * 1 /\n          Math.sqrt(variance.get(1) + varianceEpsilon)\n    ]);\n  });\n  it('simple batchnorm2D, no offset, 2x2', async () => {\n    const xT = tf.tensor2d([2, 4, 9, 23], [2, 2]);\n    const meanT = tf.tensor1d([1, 2]);\n    const varianceT = tf.tensor1d([2, 3]);\n    const scaleT = tf.tensor1d([4, 5]);\n    const varianceEpsilon = .001;\n\n    const result = tf.batchNorm2d(\n        xT, meanT, varianceT, undefined, scaleT, varianceEpsilon);\n\n    const x = await xT.buffer();\n    const mean = await meanT.buffer();\n    const variance = await varianceT.buffer();\n    const scale = await scaleT.buffer();\n    expectArraysClose(await result.data(), [\n      (x.get(0, 0) - mean.get(0)) * scale.get(0) /\n          Math.sqrt(variance.get(0) + varianceEpsilon),\n      (x.get(0, 1) - mean.get(1)) * scale.get(1) /\n          Math.sqrt(variance.get(1) + varianceEpsilon),\n      (x.get(1, 0) - mean.get(0)) * scale.get(0) /\n          Math.sqrt(variance.get(0) + varianceEpsilon),\n      (x.get(1, 1) - mean.get(1)) * scale.get(1) /\n          Math.sqrt(variance.get(1) + varianceEpsilon)\n    ]);\n  });\n\n  it('simple batchnorm2D, no scale, 2x2', async () => {\n    const xT = tf.tensor2d([2, 4, 9, 23], [2, 2]);\n    const meanT = tf.tensor1d([1, 2]);\n    const varianceT = tf.tensor1d([2, 3]);\n    const offsetT = tf.tensor1d([4, 5]);\n\n    const varianceEpsilon = .001;\n\n    const result = tf.batchNorm2d(\n        xT, meanT, varianceT, offsetT, undefined, varianceEpsilon);\n\n    const offset = await offsetT.array();\n    const mean = await meanT.array();\n    const variance = await varianceT.array();\n    const x = await xT.array();\n\n    expectArraysClose(await result.data(), [\n      offset[0] +\n          (x[0][0] - mean[0]) * 1 / Math.sqrt(variance[0] + varianceEpsilon),\n      offset[1] +\n          (x[0][1] - mean[1]) * 1 / Math.sqrt(variance[1] + varianceEpsilon),\n      offset[0] +\n          (x[1][0] - mean[0]) * 1 / Math.sqrt(variance[0] + varianceEpsilon),\n      offset[1] +\n          (x[1][1] - mean[1]) * 1 / Math.sqrt(variance[1] + varianceEpsilon)\n    ]);\n  });\n\n  it('simple batchnorm2D, 2x2', async () => {\n    const xT = tf.tensor2d([2, 4, 9, 23], [2, 2]);\n    const meanT = tf.tensor1d([1, 2]);\n    const varianceT = tf.tensor1d([2, 3]);\n    const offsetT = tf.tensor1d([3, 4]);\n    const scaleT = tf.tensor1d([4, 5]);\n\n    const varianceEpsilon = .001;\n\n    const result =\n        tf.batchNorm2d(xT, meanT, varianceT, offsetT, scaleT, varianceEpsilon);\n\n    const offset = await offsetT.array();\n    const mean = await meanT.array();\n    const variance = await varianceT.array();\n    const scale = await scaleT.array();\n    const x = await xT.array();\n\n    expectArraysClose(await result.data(), [\n      offset[0] +\n          (x[0][0] - mean[0]) * scale[0] /\n              Math.sqrt(variance[0] + varianceEpsilon),\n      offset[1] +\n          (x[0][1] - mean[1]) * scale[1] /\n              Math.sqrt(variance[1] + varianceEpsilon),\n      offset[0] +\n          (x[1][0] - mean[0]) * scale[0] /\n              Math.sqrt(variance[0] + varianceEpsilon),\n      offset[1] +\n          (x[1][1] - mean[1]) * scale[1] /\n              Math.sqrt(variance[1] + varianceEpsilon)\n    ]);\n  });\n\n  it('simple batchnorm2D gradients, 2x2', async () => {\n    const x = tf.tensor2d([2, 4, 9, 23], [2, 2]);\n    const mean = tf.tensor1d([1, 2]);\n    const variance = tf.tensor1d([2, 3]);\n    const offset = tf.tensor1d([3, 4]);\n    const scale = tf.tensor1d([2, 5]);\n\n    const varianceEpsilon = .001;\n\n    const dy = tf.tensor2d([1, 1, 1, 1], [2, 2]);\n    const [gradX, gradMean, gradVariance, gradOffset, gradScale] = tf.grads(\n        (x: tf.Tensor2D, mean: tf.Tensor1D, variance: tf.Tensor1D,\n         offset: tf.Tensor1D, scale: tf.Tensor1D) =>\n            tf.batchNorm2d(x, mean, variance, offset, scale, varianceEpsilon))(\n        [x, mean, variance, offset, scale], dy);\n    expectArraysClose(await gradX.data(), [1.414, 2.887, 1.414, 2.887]);\n    expect(gradX.shape).toEqual([2, 2]);\n    expectArraysClose(await gradMean.data(), [-2.828, -5.773]);\n    expect(gradMean.shape).toEqual([2]);\n    expectArraysClose(await gradVariance.data(), [-3.180, -11.060]);\n    expect(gradVariance.shape).toEqual([2]);\n    expectArraysClose(await gradOffset.data(), [2, 2]);\n    expect(gradOffset.shape).toEqual([2]);\n    expectArraysClose(await gradScale.data(), [6.362, 13.277]);\n    expect(gradScale.shape).toEqual([2]);\n  });\n\n  it('gradient with clones batchnorm2D', async () => {\n    const x = tf.tensor2d([2, 4, 9, 23], [2, 2]);\n    const mean = tf.tensor1d([1, 2]);\n    const variance = tf.tensor1d([2, 3]);\n    const offset = tf.tensor1d([3, 4]);\n    const scale = tf.tensor1d([2, 5]);\n\n    const varianceEpsilon = .001;\n\n    const dy = tf.tensor2d([1, 1, 1, 1], [2, 2]);\n    const [gradX, gradMean, gradVariance, gradOffset, gradScale] = tf.grads(\n        (x: tf.Tensor2D, mean: tf.Tensor1D, variance: tf.Tensor1D,\n         offset: tf.Tensor1D, scale: tf.Tensor1D) =>\n            tf.batchNorm2d(\n                  x.clone(), mean.clone(), variance.clone(), offset.clone(),\n                  scale.clone(), varianceEpsilon)\n                .clone())([x, mean, variance, offset, scale], dy);\n    expectArraysClose(await gradX.data(), [1.414, 2.887, 1.414, 2.887]);\n    expect(gradX.shape).toEqual([2, 2]);\n    expectArraysClose(await gradMean.data(), [-2.828, -5.773]);\n    expect(gradMean.shape).toEqual([2]);\n    expectArraysClose(await gradVariance.data(), [-3.180, -11.060]);\n    expect(gradVariance.shape).toEqual([2]);\n    expectArraysClose(await gradOffset.data(), [2, 2]);\n    expect(gradOffset.shape).toEqual([2]);\n    expectArraysClose(await gradScale.data(), [6.362, 13.277]);\n    expect(gradScale.shape).toEqual([2]);\n  });\n\n  it('batchnorm2D gradients, same shapes in x, mean and variance', async () => {\n    const x = tf.tensor2d([10, 20, 30, 40], [2, 2]);\n    const mean = tf.tensor2d([0, 5, 10, 15], [2, 2]);\n    const variance = tf.tensor2d([2, 4, 6, 8], [2, 2]);\n    const scale = tf.tensor2d([2, 5, 2, 5], [2, 2]);\n    const offset = tf.tensor2d([0, 0, 0, 0], [2, 2]);\n\n    const varianceEpsilon = .001;\n\n    const dy = tf.tensor2d([1, 1, 1, 1], [2, 2]);\n    const gradX = tf.grad(\n        (x: tf.Tensor2D) => tf.batchNorm2d(\n            x, mean, variance, offset, scale, varianceEpsilon))(x, dy);\n    expectArraysClose(await gradX.data(), [1.414, 2.500, 0.816, 1.768]);\n    expect(gradX.shape).toEqual([2, 2]);\n    const gradMean = tf.grad(\n        (mean: tf.Tensor2D) => tf.batchNorm2d(\n            x, mean, variance, offset, scale, varianceEpsilon))(mean, dy);\n    expectArraysClose(await gradMean.data(), [-1.414, -2.500, -0.816, -1.768]);\n    expect(gradMean.shape).toEqual([2, 2]);\n    const gradVariance = tf.grad(\n        (variance: tf.Tensor2D) => tf.batchNorm2d(\n            x, mean, variance, offset, scale, varianceEpsilon))(variance, dy);\n    expectArraysClose(\n        await gradVariance.data(), [-3.533, -4.686, -1.360, -2.762]);\n    expect(gradVariance.shape).toEqual([2, 2]);\n    const gradOffset = tf.grad(\n        (offset: tf.Tensor2D) => tf.batchNorm2d(\n            x, mean, variance, offset, scale, varianceEpsilon))(offset, dy);\n    expectArraysClose(await gradOffset.data(), [1, 1, 1, 1]);\n    expect(gradOffset.shape).toEqual([2, 2]);\n    const gradScale = tf.grad(\n        (scale: tf.Tensor2D) => tf.batchNorm2d(\n            x, mean, variance, offset, scale, varianceEpsilon))(scale, dy);\n    expectArraysClose(await gradScale.data(), [7.069, 7.499, 8.164, 8.838]);\n    expect(gradScale.shape).toEqual([2, 2]);\n  });\n\n  it('gradient with clones', () => {\n    const x = tf.zeros([2, 2]);\n    const mean = tf.zeros([2, 2]);\n    const variance = tf.zeros([2, 2]);\n    const scale = tf.zeros([2, 2]);\n    const offset = tf.zeros([2, 2]);\n\n    const varianceEpsilon = .001;\n\n    const gradF = tf.grads(\n        (x: tf.Tensor2D, mean: tf.Tensor2D, variance: tf.Tensor2D,\n         offset: tf.Tensor2D, scale: tf.Tensor2D) =>\n            tf.batchNorm2d(\n                  x.clone(), mean.clone(), variance.clone(), offset.clone(),\n                  scale.clone(), varianceEpsilon)\n                .clone());\n    const [gradX, gradMean, gradVariance, gradOffset, gradScale] =\n        gradF([x, mean, variance, offset, scale]);\n    expect(gradX.shape).toEqual(x.shape);\n    expect(gradMean.shape).toEqual(mean.shape);\n    expect(gradVariance.shape).toEqual(variance.shape);\n    expect(gradOffset.shape).toEqual(offset.shape);\n    expect(gradScale.shape).toEqual(scale.shape);\n  });\n\n  it('batchnorm2D matches tensorflow, 3x3', async () => {\n    const x = tf.tensor2d(\n        [\n          0.3136892, 0.92389025, 0.594782, 0.05021042, 0.67545404, 0.93910035,\n          0.13277993, 0.96474269, 0.88608916\n        ],\n        [3, 3]);\n    const mean = tf.tensor1d([0.19526312, 0.74857256, 0.45166398]);\n    const variance = tf.tensor1d([0.22963001, 0.61521992, 0.46623685]);\n    const offset = tf.tensor1d([0.43098484, 0.77712237, 0.47916298]);\n    const scale = tf.tensor1d([0.62186907, 0.85673736, 0.19201061]);\n    const varianceEpsilon = .001;\n\n    const result =\n        tf.batchNorm2d(x, mean, variance, offset, scale, varianceEpsilon);\n\n    expectArraysClose(await result.data(), [\n      0.58433646, 0.96846228, 0.51936529, 0.24315402, 0.69732157, 0.61608542,\n      0.35007446, 1.01304821, 0.60119441\n    ]);\n  });\n\n  it('throws when passed x as a non-tensor', () => {\n    const mean = tf.tensor1d([1, 2]);\n    const variance = tf.tensor1d([2, 3]);\n\n    expect(() => tf.batchNorm({} as tf.Tensor, mean, variance))\n        .toThrowError(/Argument 'x' passed to 'batchNorm' must be a Tensor/);\n  });\n  it('throws when passed mean as a non-tensor', () => {\n    const x = tf.tensor4d([2, 4, 9, 23], [2, 1, 1, 2]);\n    const variance = tf.tensor1d([2, 3]);\n\n    expect(() => tf.batchNorm(x, {} as tf.Tensor, variance))\n        .toThrowError(/Argument 'mean' passed to 'batchNorm' must be a Tensor/);\n  });\n  it('throws when passed variance as a non-tensor', () => {\n    const x = tf.tensor4d([2, 4, 9, 23], [2, 1, 1, 2]);\n    const mean = tf.tensor1d([1, 2]);\n\n    const e = /Argument 'variance' passed to 'batchNorm' must be a Tensor/;\n    expect(() => tf.batchNorm(x, mean, {} as tf.Tensor)).toThrowError(e);\n  });\n  it('throws when passed scale as a non-tensor', () => {\n    const x = tf.tensor4d([2, 4, 9, 23], [2, 1, 1, 2]);\n    const mean = tf.tensor1d([1, 2]);\n    const variance = tf.tensor1d([2, 3]);\n    const epsilon = .001;\n\n    expect(() => tf.batchNorm(x, mean, variance, epsilon, {} as tf.Tensor))\n        .toThrowError(\n            /Argument 'scale' passed to 'batchNorm' must be a Tensor/);\n  });\n  it('throws when passed offset as a non-tensor', () => {\n    const x = tf.tensor4d([2, 4, 9, 23], [2, 1, 1, 2]);\n    const mean = tf.tensor1d([1, 2]);\n    const variance = tf.tensor1d([2, 3]);\n    const epsilon = .001;\n    const scale = tf.tensor1d([0.62186907, 0.85673736, 0.19201061]);\n\n    const e = /Argument 'offset' passed to 'batchNorm' must be a Tensor/;\n    expect(\n        () => tf.batchNorm(x, mean, variance, {} as tf.Tensor, scale, epsilon))\n        .toThrowError(e);\n  });\n\n  it('accepts a tensor-like object', async () => {\n    const x = [[2, 4], [9, 23]];\n    const mean = [1, 2];\n    const variance = [2, 3];\n    const offset = [3, 4];\n    const scale = [4, 5];\n\n    const varianceEpsilon = .001;\n\n    const result =\n        tf.batchNorm2d(x, mean, variance, offset, scale, varianceEpsilon);\n\n    expectArraysClose(await result.data(), [\n      offset[0] +\n          (x[0][0] - mean[0]) * scale[0] /\n              Math.sqrt(variance[0] + varianceEpsilon),\n      offset[1] +\n          (x[0][1] - mean[1]) * scale[1] /\n              Math.sqrt(variance[1] + varianceEpsilon),\n      offset[0] +\n          (x[1][0] - mean[0]) * scale[0] /\n              Math.sqrt(variance[0] + varianceEpsilon),\n      offset[1] +\n          (x[1][1] - mean[1]) * scale[1] /\n              Math.sqrt(variance[1] + varianceEpsilon)\n    ]);\n  });\n\n  it('throws error when x is a string tensor', () => {\n    const mean = [1, 2];\n    const variance = [2, 3];\n    const offset = [3, 4];\n    const scale = [4, 5];\n\n    const varianceEpsilon = .001;\n\n    const f = () => tf.batchNorm2d(\n        [['a', 'b'], ['c', 'd']], mean, variance, offset, scale,\n        varianceEpsilon);\n    expect(f).toThrowError(\n        /Argument 'x' passed to 'batchNorm' must be numeric/);\n  });\n\n  it('throws error when mean is a string tensor', () => {\n    const x = [[2, 4], [9, 23]];\n    const variance = [2, 3];\n    const offset = [3, 4];\n    const scale = [4, 5];\n\n    const varianceEpsilon = .001;\n\n    const f = () =>\n        tf.batchNorm2d(x, ['a', 'b'], variance, offset, scale, varianceEpsilon);\n    expect(f).toThrowError(\n        /Argument 'mean' passed to 'batchNorm' must be numeric/);\n  });\n\n  it('throws error when variance is a string tensor', () => {\n    const x = [[2, 4], [9, 23]];\n    const mean = [1, 2];\n    const offset = [3, 4];\n    const scale = [4, 5];\n\n    const varianceEpsilon = .001;\n\n    const f = () =>\n        tf.batchNorm2d(x, mean, ['a', 'b'], offset, scale, varianceEpsilon);\n    expect(f).toThrowError(/'variance' passed to 'batchNorm' must be numeric/);\n  });\n\n  it('throws error when scale is a string tensor', () => {\n    const x = [[2, 4], [9, 23]];\n    const mean = [1, 2];\n    const variance = [2, 3];\n    const offset = [3, 4];\n\n    const varianceEpsilon = .001;\n\n    const f = () =>\n        tf.batchNorm2d(x, mean, variance, offset, ['a', 'b'], varianceEpsilon);\n    expect(f).toThrowError(/'scale' passed to 'batchNorm' must be numeric/);\n  });\n\n  it('throws error when offset is a string tensor', () => {\n    const x = [[2, 4], [9, 23]];\n    const mean = [1, 2];\n    const variance = [2, 3];\n    const scale = [4, 5];\n\n    const varianceEpsilon = .001;\n\n    const f = () =>\n        tf.batchNorm2d(x, mean, variance, ['a', 'b'], scale, varianceEpsilon);\n    expect(f).toThrowError(/'offset' passed to 'batchNorm' must be numeric/);\n  });\n});\n"]} |
| \ | No newline at end of file |