@tensorflow/tfjs-core
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Hardware-accelerated JavaScript library for machine intelligence
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JavaScript
/**
* @license
* Copyright 2017 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
import * as tf from '../index';
import { backend } from '../index';
import { ALL_ENVS, describeWithFlags } from '../jasmine_util';
import { expectArraysClose } from '../test_util';
describeWithFlags('stridedSlice', ALL_ENVS, () => {
it('with ellipsisMask=1', async () => {
const t = tf.tensor2d([
[1, 2, 3, 4, 5],
[2, 3, 4, 5, 6],
[3, 4, 5, 6, 7],
[4, 5, 6, 7, 8],
[5, 6, 7, 8, 9],
[6, 7, 8, 9, 10],
[7, 8, 9, 10, 11],
[8, 8, 9, 10, 11],
[9, 8, 9, 10, 11],
[10, 8, 9, 10, 11],
]);
const begin = [0, 4];
const end = [0, 5];
const strides = [1, 1];
const beginMask = 0;
const endMask = 0;
const ellipsisMask = 1;
const output = t.stridedSlice(begin, end, strides, beginMask, endMask, ellipsisMask);
expect(output.shape).toEqual([10, 1]);
expectArraysClose(await output.data(), [5, 6, 7, 8, 9, 10, 11, 11, 11, 11]);
});
it('with ellipsisMask=1, begin / end masks and start / end normalization', async () => {
const t = tf.randomNormal([1, 6, 2006, 4]);
const output = tf.stridedSlice(t, [0, 0, 0], [0, 2004, 0], [1, 1, 1], 6, 4, 1);
expect(output.shape).toEqual([1, 6, 2004, 4]);
});
it('with ellipsisMask=1 and start / end normalization', async () => {
const t = tf.tensor3d([
[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]], [[5, 5, 5], [6, 6, 6]]
]);
const begin = [1, 0];
const end = [2, 1];
const strides = [1, 1];
const beginMask = 0;
const endMask = 0;
const ellipsisMask = 1;
const output = tf.stridedSlice(t, begin, end, strides, beginMask, endMask, ellipsisMask);
expect(output.shape).toEqual([3, 2, 1]);
expectArraysClose(await output.data(), [1, 2, 3, 4, 5, 6]);
});
it('with ellipsisMask=2', async () => {
const t = tf.tensor3d([
[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]], [[5, 5, 5], [6, 6, 6]]
]);
const begin = [1, 0, 0];
const end = [2, 1, 3];
const strides = [1, 1, 1];
const beginMask = 0;
const endMask = 0;
const ellipsisMask = 2;
const output = tf.stridedSlice(t, begin, end, strides, beginMask, endMask, ellipsisMask);
expect(output.shape).toEqual([1, 2, 3]);
expectArraysClose(await output.data(), [3, 3, 3, 4, 4, 4]);
});
it('with ellipsisMask=2 and start / end normalization', async () => {
const t = tf.tensor4d([
[[[1, 1], [1, 1], [1, 1]], [[2, 2], [2, 2], [2, 2]]],
[[[3, 3], [3, 3], [3, 3]], [[4, 4], [4, 4], [4, 4]]],
[[[5, 5], [5, 5], [5, 5]], [[6, 6], [6, 6], [6, 6]]]
]);
const begin = [1, 0, 0];
const end = [2, 1, 1];
const strides = [1, 1, 1];
const beginMask = 0;
const endMask = 0;
const ellipsisMask = 2;
const output = tf.stridedSlice(t, begin, end, strides, beginMask, endMask, ellipsisMask);
expect(output.shape).toEqual([1, 2, 3, 1]);
expectArraysClose(await output.data(), [3, 3, 3, 4, 4, 4]);
});
it('both ellipsis mask and newAxisMask are set', async () => {
const tensor = tf.tensor1d([0, 1, 2, 3]);
const result = tf.stridedSlice(tensor, [0], [3], [2], 0, 0, 1, 1);
expectArraysClose(await result.data(), [0, 1, 2, 3]);
});
it('both ellipsis mask and shrinkAxisMask are set', async () => {
const tensor = tf.tensor1d([0, 1, 2, 3]);
const result = tf.stridedSlice(tensor, [0], [3], [2], 0, 0, 1, 0, 1);
expectArraysClose(await result.data(), [0, 1, 2, 3]);
});
it('stridedSlice with first axis being new', async () => {
// Python slice code: t[tf.newaxis,0:3]
const t = tf.tensor1d([0, 1, 2, 3]);
const begin = [0, 0];
const end = [1, 3];
const strides = [1, 2];
const beginMask = 0;
const endMask = 0;
const ellipsisMask = 0;
const newAxisMask = 1;
const output = tf.stridedSlice(t, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask);
expect(output.shape).toEqual([1, 2]);
expectArraysClose(await output.data(), [0, 2]);
});
it('strided slice with several new axes', async () => {
// Python slice code: t[1:2,tf.newaxis,0:3,tf.newaxis,2:5]
const t = tf.zeros([2, 3, 4, 5]);
const begin = [1, 0, 0, 0, 2];
const end = [2, 1, 3, 1, 5];
const strides = [1, 1, 1, 1, 1];
const beginMask = 0;
const endMask = 0;
const ellipsisMask = 0;
const newAxisMask = 0b1010;
const output = tf.stridedSlice(t, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask);
expect(output.shape).toEqual([1, 1, 3, 1, 2, 5]);
expectArraysClose(await output.data(), new Array(30).fill(0));
});
it('strided slice with new axes and shrink axes', () => {
// Python slice code: t[1:2,tf.newaxis,1,tf.newaxis,2,2:5]
const t = tf.zeros([2, 3, 4, 5]);
const begin = [1, 0, 1, 0, 2, 2];
const end = [2, 1, 2, 1, 3, 5];
const strides = [1, 1, 1, 1, 1, 1];
const beginMask = 0;
const endMask = 0;
const ellipsisMask = 0;
const newAxisMask = 0b1010;
const shrinkAxisMask = 0b10100;
const output = tf.stridedSlice(t, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask);
expect(output.shape).toEqual([1, 1, 1, 3]);
});
it('stridedSlice should support 1d tensor', async () => {
const tensor = tf.tensor1d([0, 1, 2, 3]);
const output = tf.stridedSlice(tensor, [0], [3], [2]);
expect(output.shape).toEqual([2]);
expectArraysClose(await output.data(), [0, 2]);
});
it('stridedSlice should support 1d tensor', async () => {
const tensor = tf.tensor1d([0, 1, 2, 3]);
const output = tf.stridedSlice(tensor, [0], [3], [2]);
expect(output.shape).toEqual([2]);
expectArraysClose(await output.data(), [0, 2]);
});
it('stridedSlice with 1d tensor should be used by tensor directly', async () => {
const t = tf.tensor1d([0, 1, 2, 3]);
const output = t.stridedSlice([0], [3], [2]);
expect(output.shape).toEqual([2]);
expectArraysClose(await output.data(), [0, 2]);
});
it('stridedSlice should support 1d tensor empty result', async () => {
const tensor = tf.tensor1d([0, 1, 2, 3]);
const output = tf.stridedSlice(tensor, [10], [3], [2]);
expect(output.shape).toEqual([0]);
expectArraysClose(await output.data(), []);
});
it('stridedSlice should support 1d tensor negative begin', async () => {
const tensor = tf.tensor1d([0, 1, 2, 3]);
const output = tf.stridedSlice(tensor, [-3], [3], [1]);
expect(output.shape).toEqual([2]);
expectArraysClose(await output.data(), [1, 2]);
});
it('stridedSlice should support 1d tensor out of range begin', async () => {
const tensor = tf.tensor1d([0, 1, 2, 3]);
const output = tf.stridedSlice(tensor, [-5], [3], [1]);
expect(output.shape).toEqual([3]);
expectArraysClose(await output.data(), [0, 1, 2]);
});
it('stridedSlice should support 1d tensor negative end', async () => {
const tensor = tf.tensor1d([0, 1, 2, 3]);
const output = tf.stridedSlice(tensor, [1], [-2], [1]);
expect(output.shape).toEqual([1]);
expectArraysClose(await output.data(), [1]);
});
it('stridedSlice should support 1d tensor out of range end', async () => {
const tensor = tf.tensor1d([0, 1, 2, 3]);
const output = tf.stridedSlice(tensor, [-3], [5], [1]);
expect(output.shape).toEqual([3]);
expectArraysClose(await output.data(), [1, 2, 3]);
});
it('stridedSlice should support 1d tensor begin mask', async () => {
const tensor = tf.tensor1d([0, 1, 2, 3]);
const output = tf.stridedSlice(tensor, [1], [3], [1], 1);
expect(output.shape).toEqual([3]);
expectArraysClose(await output.data(), [0, 1, 2]);
});
it('stridedSlice should support 1d tensor nagtive begin and stride', async () => {
const tensor = tf.tensor1d([0, 1, 2, 3]);
const output = tf.stridedSlice(tensor, [-2], [-3], [-1]);
expect(output.shape).toEqual([1]);
expectArraysClose(await output.data(), [2]);
});
it('stridedSlice should support 1d tensor' +
' out of range begin and negative stride', async () => {
const tensor = tf.tensor1d([0, 1, 2, 3]);
const output = tf.stridedSlice(tensor, [5], [-2], [-1]);
expect(output.shape).toEqual([1]);
expectArraysClose(await output.data(), [3]);
});
it('stridedSlice should support 1d tensor nagtive end and stride', async () => {
const tensor = tf.tensor1d([0, 1, 2, 3]);
const output = tf.stridedSlice(tensor, [2], [-4], [-1]);
expect(output.shape).toEqual([2]);
expectArraysClose(await output.data(), [2, 1]);
});
it('stridedSlice should support 1d tensor' +
' out of range end and negative stride', async () => {
const tensor = tf.tensor1d([0, 1, 2, 3]);
const output = tf.stridedSlice(tensor, [-3], [-5], [-1]);
expect(output.shape).toEqual([2]);
expectArraysClose(await output.data(), [1, 0]);
});
it('stridedSlice should support 1d tensor end mask', async () => {
const tensor = tf.tensor1d([0, 1, 2, 3]);
const output = tf.stridedSlice(tensor, [1], [3], [1], 0, 1);
expect(output.shape).toEqual([3]);
expectArraysClose(await output.data(), [1, 2, 3]);
});
it('stridedSlice should support 1d tensor shrink axis mask', async () => {
const tensor = tf.tensor1d([0, 1, 2, 3]);
const output = tf.stridedSlice(tensor, [1], [3], [1], 0, 0, 0, 0, 1);
expect(output.shape).toEqual([]);
expectArraysClose(await output.data(), [1]);
});
it('stridedSlice should support 1d tensor negative stride', async () => {
const tensor = tf.tensor1d([0, 1, 2, 3]);
const output = tf.stridedSlice(tensor, [-1], [-4], [-1]);
expect(output.shape).toEqual([3]);
expectArraysClose(await output.data(), [3, 2, 1]);
});
it('stridedSlice should support 1d tensor even length stride', async () => {
const tensor = tf.tensor1d([0, 1, 2, 3]);
const output = tf.stridedSlice(tensor, [0], [2], [2]);
expect(output.shape).toEqual([1]);
expectArraysClose(await output.data(), [0]);
});
it('stridedSlice should support 1d tensor odd length stride', async () => {
const tensor = tf.tensor1d([0, 1, 2, 3]);
const output = tf.stridedSlice(tensor, [0], [3], [2]);
expect(output.shape).toEqual([2]);
expectArraysClose(await output.data(), [0, 2]);
});
it('stridedSlice should support 2d tensor identity', async () => {
const tensor = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
const output = tf.stridedSlice(tensor, [0, 0], [2, 3], [1, 1]);
expect(output.shape).toEqual([2, 3]);
expectArraysClose(await output.data(), [1, 2, 3, 4, 5, 6]);
});
it('stridedSlice should support 2d tensor', async () => {
const tensor = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
const output = tf.stridedSlice(tensor, [1, 0], [2, 2], [1, 1]);
expect(output.shape).toEqual([1, 2]);
expectArraysClose(await output.data(), [4, 5]);
});
it('stridedSlice should support 2d tensor strides', async () => {
const tensor = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
const output = tf.stridedSlice(tensor, [0, 0], [2, 3], [2, 2]);
expect(output.shape).toEqual([1, 2]);
expectArraysClose(await output.data(), [1, 3]);
});
it('stridedSlice with 2d tensor should be used by tensor directly', async () => {
const t = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
const output = t.stridedSlice([1, 0], [2, 2], [1, 1]);
expect(output.shape).toEqual([1, 2]);
expectArraysClose(await output.data(), [4, 5]);
});
it('stridedSlice should support 2d tensor negative strides', async () => {
const tensor = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
const output = tf.stridedSlice(tensor, [1, -1], [2, -4], [2, -1]);
expect(output.shape).toEqual([1, 3]);
expectArraysClose(await output.data(), [6, 5, 4]);
});
it('stridedSlice should support 2d tensor begin mask', async () => {
const tensor = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
const output = tf.stridedSlice(tensor, [1, 0], [2, 2], [1, 1], 1);
expect(output.shape).toEqual([2, 2]);
expectArraysClose(await output.data(), [1, 2, 4, 5]);
});
it('stridedSlice should support 2d tensor shrink mask', async () => {
const tensor = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
const output = tf.stridedSlice(tensor, [1, 0], [2, 2], [1, 1], 0, 0, 0, 0, 1);
expect(output.shape).toEqual([2]);
expectArraysClose(await output.data(), [4, 5]);
});
it('stridedSlice should support 2d tensor end mask', async () => {
const tensor = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
const output = tf.stridedSlice(tensor, [1, 0], [2, 2], [1, 1], 0, 2);
expect(output.shape).toEqual([1, 3]);
expectArraysClose(await output.data(), [4, 5, 6]);
});
it('stridedSlice should support 2d tensor' +
' negative strides and begin mask', async () => {
const tensor = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
const output = tf.stridedSlice(tensor, [1, -2], [2, -4], [1, -1], 2);
expect(output.shape).toEqual([1, 3]);
expectArraysClose(await output.data(), [6, 5, 4]);
});
it('stridedSlice should support 2d tensor' +
' negative strides and end mask', async () => {
const tensor = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
const output = tf.stridedSlice(tensor, [1, -2], [2, -3], [1, -1], 0, 2);
expect(output.shape).toEqual([1, 2]);
expectArraysClose(await output.data(), [5, 4]);
});
it('stridedSlice should support 3d tensor identity', async () => {
const tensor = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 3, 2]);
const output = tf.stridedSlice(tensor, [0, 0, 0], [2, 3, 2], [1, 1, 1]);
expect(output.shape).toEqual([2, 3, 2]);
expectArraysClose(await output.data(), [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]);
});
it('stridedSlice should support 3d tensor negative stride', async () => {
const tensor = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 3, 2]);
const output = tf.stridedSlice(tensor, [-1, -1, -1], [-3, -4, -3], [-1, -1, -1]);
expect(output.shape).toEqual([2, 3, 2]);
expectArraysClose(await output.data(), [12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1]);
});
it('stridedSlice should support 3d tensor strided 2', async () => {
const tensor = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 3, 2]);
const output = tf.stridedSlice(tensor, [0, 0, 0], [2, 3, 2], [2, 2, 2]);
expect(output.shape).toEqual([1, 2, 1]);
expectArraysClose(await output.data(), [1, 5]);
});
it('stridedSlice should support 3d tensor shrink mask', async () => {
const tensor = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 3, 2]);
const output = tf.stridedSlice(tensor, [0, 0, 0], [2, 3, 2], [1, 1, 1], 0, 0, 0, 0, 1);
expect(output.shape).toEqual([3, 2]);
expectArraysClose(await output.data(), [1, 2, 3, 4, 5, 6]);
});
it('stridedSlice should support 3d with smaller length of begin array', async () => {
const tensor = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 3, 1, 2]);
const output = tf.stridedSlice(tensor, [1, 0], [2, 3], [1, 1], 0, 0, 0, 0, 0);
expect(output.shape).toEqual([1, 3, 1, 2]);
expectArraysClose(await output.data(), [7, 8, 9, 10, 11, 12]);
});
it('stridedSlice should support 3d with smaller length of end array', async () => {
const tensor = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 3, 1, 2]);
const output = tf.stridedSlice(tensor, [1, 0], [2, 3], [1, 1], 0, 0, 0, 0, 0);
expect(output.shape).toEqual([1, 3, 1, 2]);
expectArraysClose(await output.data(), [7, 8, 9, 10, 11, 12]);
});
it('stridedSlice should throw when passed a non-tensor', () => {
expect(() => tf.stridedSlice({}, [0], [0], [1]))
.toThrowError(/Argument 'x' passed to 'stridedSlice' must be a Tensor/);
});
it('stridedSlice should handle negative end with ellipsisMask', () => {
const a = tf.ones([1, 240, 1, 10]);
const output = tf.stridedSlice(a, [0, 0, 0], [0, -1, 0], [1, 1, 1], 3, 1, 4);
expect(output.shape).toEqual([1, 239, 1, 10]);
});
it('stridedSlice should handle negative begin with ellipsis_mask', () => {
const a = tf.ones([1, 36, 17, 3]);
const output = tf.stridedSlice(a, [0, -1], [0, 0], [1, 1], 0, 2, 1, 0, 0);
expect(output.shape).toEqual([1, 36, 17, 1]);
});
it('accepts a tensor-like object', async () => {
const tensor = [0, 1, 2, 3];
const output = tf.stridedSlice(tensor, [0], [3], [2]);
expect(output.shape).toEqual([2]);
expectArraysClose(await output.data(), [0, 2]);
});
it('accepts int32 tensor', async () => {
if (backend() && backend().floatPrecision() === 32) {
// TODO: Use skip() instead when it is implemented
const tensor = tf.tensor2d([1, 2, 3, 4, 12345678, 6], [2, 3], 'int32');
const output = tf.stridedSlice(tensor, [1, 0], [2, 2], [1, 1]);
expect(output.shape).toEqual([1, 2]);
expect(output.dtype).toEqual('int32');
expectArraysClose(await output.data(), [4, 12345678]);
}
});
it('ensure no memory leak', async () => {
const numTensorsBefore = tf.memory().numTensors;
const numDataIdBefore = tf.engine().backend.numDataIds();
const tensor = tf.tensor1d([0, 1, 2, 3]);
const output = tf.stridedSlice(tensor, [0], [3], [2]);
expect(output.shape).toEqual([2]);
expectArraysClose(await output.data(), [0, 2]);
tensor.dispose();
output.dispose();
const numTensorsAfter = tf.memory().numTensors;
const numDataIdAfter = tf.engine().backend.numDataIds();
expect(numTensorsAfter).toBe(numTensorsBefore);
expect(numDataIdAfter).toBe(numDataIdBefore);
});
});
//# 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xHQUFhLENBQUMsQ0FBQyxFQUFFLENBQUMsRUFBRSxDQUFDLEVBQUUsQ0FBQyxFQUFFLENBQUMsQ0FBQyxDQUFDO1FBQzFDLE1BQU0sU0FBUyxHQUFHLENBQUMsQ0FBQztRQUNwQixNQUFNLE9BQU8sR0FBRyxDQUFDLENBQUM7UUFDbEIsTUFBTSxZQUFZLEdBQUcsQ0FBQyxDQUFDO1FBQ3ZCLE1BQU0sV0FBVyxHQUFHLE1BQU0sQ0FBQztRQUMzQixNQUFNLE1BQU0sR0FBRyxFQUFFLENBQUMsWUFBWSxDQUMxQixDQUFDLEVBQUUsS0FBSyxFQUFFLEdBQUcsRUFBRSxPQUFPLEVBQUUsU0FBUyxFQUFFLE9BQU8sRUFBRSxZQUFZLEVBQUUsV0FBVyxDQUFDLENBQUM7UUFDM0UsTUFBTSxDQUFDLE1BQU0sQ0FBQyxLQUFLLENBQUMsQ0FBQyxPQUFPLENBQUMsQ0FBQyxDQUFDLEVBQUUsQ0FBQyxFQUFFLENBQUMsRUFBRSxDQUFDLEVBQUUsQ0FBQyxFQUFFLENBQUMsQ0FBQyxDQUFDLENBQUM7UUFDakQsaUJBQWlCLENBQUMsTUFBTSxNQUFNLENBQUMsSUFBSSxFQUFFLEVBQUUsSUFBSSxLQUFLLENBQUMsRUFBRSxDQUFDLENBQUMsSUFBSSxDQUFDLENBQUMsQ0FBQyxDQUFDLENBQUM7SUFDaEUsQ0FBQyxDQUFDLENBQUM7SUFFSCxFQUFFLENBQUMsNkNBQTZDLEVBQUUsR0FBRyxFQUFFO1FBQ3JELDBEQUEwRDtRQUMxRCxNQUFNLENBQUMsR0FBRyxFQUFFLENBQUMsS0FBSyxDQUFDLENBQUMsQ0FBQyxFQUFFLENBQUMsRUFBRSxDQUFDLEVBQUUsQ0FBQyxDQUFDLENBQUMsQ0FBQztRQUNqQyxNQUFNLEtBQUssR0FBRyxDQUFDLENBQUMsRUFBRSxDQUFDLEVBQUUsQ0FBQyxFQUFFLENBQUMsRUFBRSxDQUFDLEVBQUUsQ0FBQyxDQUFDLENBQUM7UUFDakMsTUFBTSxHQUFHLEdBQUcsQ0FBQyxDQUFDLEVBQUUsQ0FBQyxFQUFFLENBQUMsRUFBRSxDQUFDLEVBQUUsQ0FBQyxFQUFFLENBQUMsQ0FBQyxDQUFDO1FBQy9CLE1BQU0sT0FBTyxHQUFhLENBQUMsQ0FBQyxFQUFFLENBQUMsRUFBRSxDQUFDLEVBQUUsQ0FBQyxFQUFFLENBQUMsRUFBRSxDQUFDLENBQUMsQ0FBQztRQUM3QyxNQUFNLFNBQVMsR0FBRyxDQUFDLENBQUM7UUFDcEIsTUFBTSxPQUFPLEdBQUcsQ0FBQyxDQUFDO1FBQ2xCLE1BQU0sWUFBWSxHQUFHLENBQUMsQ0FBQztRQUN2QixNQUFNLFdBQVcsR0FBRyxNQUFNLENBQUM7UUFDM0IsTUFBTSxjQUFjLEdBQUcsT0FBTyxDQUFDO1FBQy9CLE1BQU0sTUFBTSxHQUFHLEVBQUUsQ0FBQyxZQUFZLENBQzFCLENBQUMsRUFBRSxLQUFLLEVBQUUsR0FBRyxFQUFFLE9BQU8sRUFBRSxTQUFTLEVBQUUsT0FBTyxFQUFFLFlBQVksRUFBRSxXQUFXLEVBQ3JFLGNBQWMsQ0FBQyxDQUFDO1FBQ3BCLE1BQU0sQ0FBQyxNQUFNLENBQUMsS0FBSyxDQUFDLENBQUMsT0FBTyxDQUFDLENBQUMsQ0FBQyxFQUFFLENBQUMsRUFBRSxDQUFDLEVBQUUsQ0FBQyxDQUFDLENBQUMsQ0FBQztJQUM3QyxDQUFDLENBQUMsQ0FBQztJQUVILEVBQUUsQ0FBQyx1Q0FBdUMsRUFBRSxLQUFLLElBQUksRUFBRTtRQUNyRCxNQUFNLE1BQU0sR0FBRyxFQUFFLENBQUMsUUFBUSxDQUFDLENBQUMsQ0FBQyxFQUFFLENBQUMsRUFBRSxDQUFDLEVBQUUsQ0FBQyxDQUFDLENBQUMsQ0FBQztRQUN6QyxNQUFNLE1BQU0sR0FBRyxFQUFFLENBQUMsWUFBWSxDQUFDLE1BQU0sRUFBRSxDQUFDLENBQUMsQ0FBQyxFQUFFLENBQUMsQ0FBQyxDQUFDLEVBQUUsQ0FBQyxDQUFDLENBQUMsQ0FBQyxDQUFDO1FBQ3RELE1BQU0sQ0FBQyxNQUFNLENBQUMsS0FBSyxDQUFDLENBQUMsT0FBTyxDQUFDLENBQUMsQ0FBQyxDQUFDLENBQUMsQ0FBQztRQUNsQyxpQkFBaUIsQ0FBQyxNQUFNLE1BQU0sQ0FBQyxJQUFJLEVBQUUsRUFBRSxDQUFDLENBQUMsRUFBRSxDQUFDLENBQUMsQ0FBQyxDQUFDO0lBQ2pELENBQUMsQ0FBQyxDQUFDO0lBRUgsRUFBRSxDQUFDLHVDQUF1QyxFQUFFLEtBQUssSUFBSSxFQUFFO1FBQ3JELE1BQU0sTUFBTSxHQUFHLEVBQUUsQ0FBQyxRQUFRLENBQUMsQ0FBQyxDQUFDLEVBQUUsQ0FBQyxFQUFFLENBQUMsRUFBRSxDQUFDLENBQUMsQ0FBQyxDQUFDO1FBQ3pDLE1BQU0sTUFBTSxHQUFHLEVBQUUsQ0FBQyxZQUFZLENBQUMsTUFBTSxFQUFFLENBQUMsQ0FBQyxDQUFDLEVBQUUsQ0FBQyxDQUFDLENBQUMsRUFBRSxDQUFDLENBQUMsQ0FBQyxDQUFDLENBQUM7UUFDdEQsTUFBTSxDQUFDLE1BQU0sQ0FBQyxLQUFLLENBQUMsQ0FBQyxPQUFPLENBQUMsQ0FBQyxDQUFDLENBQUMsQ0FBQyxDQUFDO1FBQ2xDLGlCQUFpQixDQUFDLE1BQU0sTUFBTSxDQUFDLElBQUksRUFBRSxFQUFFLENBQUMsQ0FBQyxFQUFFLENBQUMsQ0FBQyxDQUFDLENBQUM7SUFDakQsQ0FBQyxDQUFDLENBQUM7SUFFSCxFQUFFLENBQUMsK0RBQStELEVBQy9ELEtBQUssSUFBSSxFQUFFO1FBQ1QsTUFBTSxDQUFDLEdBQUcsRUFBRSxDQUFDLFFBQVEsQ0FBQyxDQUFDLENBQUMsRUFBRSxDQUFDLEVBQUUsQ0FBQyxFQUFFLENBQUMsQ0FBQyxDQUFDLENBQUM7UUFDcEMsTUFBTSxNQUFNLEdBQUcsQ0FBQyxDQUFDLFlBQVksQ0FBQyxDQUFDLENBQUMsQ0FBQyxFQUFFLENBQUMsQ0FBQyxDQUFDLEVBQUUsQ0FBQyxDQUFDLENBQUMsQ0FBQyxDQUFDO1FBQzdDLE1BQU0sQ0FBQyxNQUFNLENBQUMsS0FBSyxDQUFDLENBQUMsT0FBTyxDQUFDLENBQUMsQ0FBQyxDQUFDLENBQUMsQ0FBQztRQUNsQyxpQkFBaUIsQ0FBQyxNQUFNLE1BQU0sQ0FBQyxJQUFJLEVBQUUsRUFBRSxDQUFDLENBQUMsRUFBRSxDQUFDLENBQUMsQ0FBQyxDQUFDO0lBQ2pELENBQUMsQ0FBQyxDQUFDO0lBRU4sRUFBRSxDQUFDLG9EQUFvRCxFQUFFLEtBQUssSUFBSSxFQUFFO1FBQ2xFLE1BQU0sTUFBTSxHQUFHLEVBQUUsQ0FBQyxRQUFRLENBQUMsQ0FBQyxDQUFDLEVBQUUsQ0FBQyxFQUFFLENBQUMsRUFBRSxDQUFDLENBQUMsQ0FBQyxDQUFDO1FBQ3pDLE1BQU0sTUFBTSxHQUFHLEVBQUUsQ0FBQyxZQUFZLENBQUMsTUFBTSxFQUFFLENBQUMsRUFBRSxDQUFDLEVBQUUsQ0FBQyxDQUFDLENBQUMsRUFBRSxDQUFDLENBQUMsQ0FBQyxDQUFDLENBQUM7UUFDdkQsTUFBTSxDQUFDLE1BQU0sQ0FBQyxLQUFLLENBQUMsQ0FBQyxPQUFPLENBQUMsQ0FBQyxDQUFDLENBQUMsQ0FBQyxDQUFDO1FBQ2xDLGlCQUFpQixDQUFDLE1BQU0sTUFBTSxDQUFDLElBQUksRUFBRSxFQUFFLEVBQUUsQ0FBQyxDQUFDO0lBQzdDLENBQUMsQ0FBQyxDQUFDO0lBRUgsRUFBRSxDQUFDLHNEQUFzRCxFQUFFLEtBQUssSUFBSSxFQUFFO1FBQ3BFLE1BQU0sTUFBTSxHQUFHLEVBQUUsQ0FBQyxRQUFRLENBQUMsQ0FBQyxDQUFDLEVBQUUsQ0FBQyxFQUFFLENBQUMsRUFBRSxDQUFDLENBQUMsQ0FBQyxDQUFDO1FBQ3pDLE1BQU0sTUFBTSxHQUFHLEVBQUUsQ0FBQyxZQUFZLENBQUMsTUFBTSxFQUFFLENBQUMsQ0FBQyxDQUFDLENBQUMsRUFBRSxDQUFDLENBQUMsQ0FBQyxFQUFFLENBQUMsQ0FBQyxDQUFDLENBQUMsQ0FBQztRQUN2RCxNQUFNLENBQUMsTUFBTSxDQUFDLEtBQUssQ0FBQyxDQUFDLE9BQU8sQ0FBQyxDQUFDLENBQUMsQ0FBQyxDQUFDLENBQUM7UUFDbEMsaUJBQWlCLENBQUMsTUFBTSxNQUFNLENBQUMsSUFBSSxFQUFFLEVBQUUsQ0FBQyxDQUFDLEVBQUUsQ0FBQyxDQUFDLENBQUMsQ0FBQztJQUNqRCxDQUFDLENBQUMsQ0FBQztJQUVILEVBQUUsQ0FBQywwREFBMEQsRUFBRSxLQUFLLElBQUksRUFBRTtRQUN4RSxNQUFNLE1BQU0sR0FBRyxFQUFFLENBQUMsUUFBUSxDQUFDLENBQUMsQ0FBQyxFQUFFLENBQUMsRUFBRSxDQUFDLEVBQUUsQ0FBQyxDQUFDLENBQUMsQ0FBQztRQUN6QyxNQUFNLE1BQU0sR0FBRyxFQUFFLENBQUMsWUFBWSxDQUFDLE1BQU0sRUFBRSxDQUFDLENBQUMsQ0FBQyxDQUFDLEVBQUUsQ0FBQyxDQUFDLENBQUMsRUFBRSxDQUFDLENBQUMsQ0FBQyxDQUFDLENBQUM7UUFDdkQsTUFBTSxDQUFDLE1BQU0sQ0FBQyxLQUFLLENBQUMsQ0FBQyxPQUFPLENBQUMsQ0FBQyxDQUFDLENBQUMsQ0FBQyxDQUFDO1FBQ2xDLGlCQUFpQixDQUFDLE1BQU0sTUFBTSxDQUFDLElBQUksRUFBRSxFQUFFLENBQUMsQ0FBQyxFQUFFLENBQUMsRUFBRSxDQUFDLENBQUMsQ0FBQyxDQUFDO0lBQ3BELENBQUMsQ0FBQyxDQUFDO0lBRUgsRUFBRSxDQUFDLG9EQUFvRCxFQUFFLEtBQUssSUFBSSxFQUFFO1FBQ2xFLE1BQU0sTUFBTSxHQUFHLEVBQUUsQ0FBQyxRQUFRLENBQUMsQ0FBQyxDQUFDLEVBQUUsQ0FBQyxFQUFFLENBQUMsRUFBRSxDQUFDLENBQUMsQ0FBQyxDQUFDO1FBQ3pDLE1BQU0sTUFBTSxHQUFHLEVBQUUsQ0FBQyxZQUFZLENBQUMsTUFBTSxFQUFFLENBQUMsQ0FBQyxDQUFDLEVBQUUsQ0FBQyxDQUFDLENBQUMsQ0FBQyxFQUFFLENBQUMsQ0FBQyxDQUFDLENBQUMsQ0FBQztRQUN2RCxNQUFNLENBQUMsTUFBTSxDQUFDLEtBQUssQ0FBQyxDQUFDLE9BQU8sQ0FBQyxDQUFDLENBQUMsQ0FBQyxDQUFDLENBQUM7UUFDbEMsaUJBQWlCLENBQUMsTUFBTSxNQUFNLENBQUMsSUFBSSxFQUFFLEVBQUUsQ0FBQyxDQUFDLENBQUMsQ0FBQyxDQUFDO0lBQzlDLENBQUMsQ0FBQyxDQUFDO0lBRUgsRUFBRSxDQUFDLHdEQUF3RCxFQUFFLEtBQUssSUFBSSxFQUFFO1FBQ3RFLE1BQU0sTUFBTSxHQUFHLEVBQUUsQ0FBQyxRQUFRLENBQUMsQ0FBQyxDQUFDLEVBQUUsQ0FBQyxFQUFFLENBQUMsRUFBRSxDQUFDLENBQUMsQ0FBQyxDQUFDO1FBQ3pDLE1BQU0sTUFBTSxHQUFHLEVBQUUsQ0FBQyxZQUFZLENBQUMsTUFBTSxFQUFFLENBQUMsQ0FBQyxDQUFDLENBQUMsRUFBRSxDQUFDLENBQUMsQ0FBQyxFQUFFLENBQUMsQ0FBQyxDQUFDLENBQUMsQ0FBQztRQUN2RCxNQUFNLENBQUMsTUFBTSxDQUFDLEtBQUssQ0FBQyxDQUFDLE9BQU8sQ0FBQyxDQUFDLENBQUMsQ0FBQyxDQUFDLENBQUM7UUFDbEMsaUJBQWlCLENBQUMsTUFBTSxNQUFNLENBQUMsSUFBSSxFQUFFLEVBQUUsQ0FBQyxDQUFDLEVBQUUsQ0FBQyxFQUFFLENBQUMsQ0FBQyxDQUFDLENBQUM7SUFDcEQsQ0FBQyxDQUFDLENBQUM7SUFFSCxFQUFFLENBQUMsa0RBQWtELEVBQUUsS0FBSyxJQUFJLEVBQUU7UUFDaEUsTUFBTSxNQUFNLEdBQUcsRUFBRSxDQUFDLFFBQVEsQ0FBQyxDQUFDLENBQUMsRUFBRSxDQUFDLEVBQUUsQ0FBQyxFQUFFLENBQUMsQ0FBQyxDQUFDLENBQUM7UUFDekMsTUFBTSxNQUFNLEdBQUcsRUFBRSxDQUFDLFlBQVksQ0FBQyxNQUFNLEVBQUUsQ0FBQyxDQUFDLENBQUMsRUFBRSxDQUFDLENBQUMsQ0FBQyxFQUFFLENBQUMsQ0FBQyxDQUFDLEVBQUUsQ0FBQyxDQUFDLENBQUM7UUFDekQsTUFBTSxDQUFDLE1BQU0sQ0FBQyxLQUFLLENBQUMsQ0FBQyxPQUFPLENBQUMsQ0FBQyxDQUFDLENBQUMsQ0FBQyxDQUFDO1FBQ2xDLGlCQUFpQixDQUFDLE1BQU0sTUFBTSxDQUFDLElBQUksRUFBRSxFQUFFLENBQUMsQ0FBQyxFQUFFLENBQUMsRUFBRSxDQUFDLENBQUMsQ0FBQyxDQUFDO0lBQ3BELENBQUMsQ0FBQyxDQUFDO0lBRUgsRUFBRSxDQUFDLGdFQUFnRSxFQUNoRSxLQUFLLElBQUksRUFBRTtRQUNULE1BQU0sTUFBTSxHQUFHLEVBQUUsQ0FBQyxRQUFRLENBQUMsQ0FBQyxDQUFDLEVBQUUsQ0FBQyxFQUFFLENBQUMsRUFBRSxDQUFDLENBQUMsQ0FBQyxDQUFDO1FBQ3pDLE1BQU0sTUFBTSxHQUFHLEVBQUUsQ0FBQyxZQUFZLENBQUMsTUFBTSxFQUFFLENBQUMsQ0FBQyxDQUFDLENBQUMsRUFBRSxDQUFDLENBQUMsQ0FBQyxDQUFDLEVBQUUsQ0FBQyxDQUFDLENBQUMsQ0FBQyxDQUFDLENBQUM7UUFDekQsTUFBTSxDQUFDLE1BQU0sQ0FBQyxLQUFLLENBQUMsQ0FBQyxPQUFPLENBQUMsQ0FBQyxDQUFDLENBQUMsQ0FBQyxDQUFDO1FBQ2xDLGlCQUFpQixDQUFDLE1BQU0sTUFBTSxDQUFDLElBQUksRUFBRSxFQUFFLENBQUMsQ0FBQyxDQUFDLENBQUMsQ0FBQztJQUM5QyxDQUFDLENBQUMsQ0FBQztJQUVOLEVBQUUsQ0FBQyx1Q0FBdUM7UUFDbkMseUNBQXlDLEVBQzdDLEtBQUssSUFBSSxFQUFFO1FBQ1QsTUFBTSxNQUFNLEdBQUcsRUFBRSxDQUFDLFFBQVEsQ0FBQyxDQUFDLENBQUMsRUFBRSxDQUFDLEVBQUUsQ0FBQyxFQUFFLENBQUMsQ0FBQyxDQUFDLENBQUM7UUFDekMsTUFBTSxNQUFNLEdBQUcsRUFBRSxDQUFDLFlBQVksQ0FBQyxNQUFNLEVBQUUsQ0FBQyxDQUFDLENBQUMsRUFBRSxDQUFDLENBQUMsQ0FBQyxDQUFDLEVBQUUsQ0FBQyxDQUFDLENBQUMsQ0FBQyxDQUFDLENBQUM7UUFDeEQsTUFBTSxDQUFDLE1BQU0sQ0FBQyxLQUFLLENBQUMsQ0FBQyxPQUFPLENBQUMsQ0FBQyxDQUFDLENBQUMsQ0FBQyxDQUFDO1FBQ2xDLGlCQUFpQixDQUFDLE1BQU0sTUFBTSxDQUFDLElBQUksRUFBRSxFQUFFLENBQUMsQ0FBQyxDQUFDLENBQUMsQ0FBQztJQUM5QyxDQUFDLENBQUMsQ0FBQztJQUVOLEVBQUUsQ0FBQyw4REFBOEQsRUFDOUQsS0FBSyxJQUFJLEVBQUU7UUFDVCxNQUFNLE1BQU0sR0FBRyxFQUFFLENBQUMsUUFBUSxDQUFDLENBQUMsQ0FBQyxFQUFFLENBQUMsRUFBRSxDQUFDLEVBQUUsQ0FBQyxDQUFDLENBQUMsQ0FBQztRQUN6QyxNQUFNLE1BQU0sR0FBRyxFQUFFLENBQUMsWUFBWSxDQUFDLE1BQU0sRUFBRSxDQUFDLENBQUMsQ0FBQyxFQUFFLENBQUMsQ0FBQyxDQUFDLENBQUMsRUFBRSxDQUFDLENBQUMsQ0FBQyxDQUFDLENBQUMsQ0FBQztRQUN4RCxNQUFNLENBQUMsTUFBTSxDQUFDLEtBQUssQ0FBQyxDQUFDLE9BQU8sQ0FBQyxDQUFDLENBQUMsQ0FBQyxDQUFDLENBQUM7UUFDbEMsaUJBQWlCLENBQUMsTUFBTSxNQUFNLENBQUMsSUFBSSxFQUFFLEVBQUUsQ0FBQyxDQUFDLEVBQUUsQ0FBQyxDQUFDLENBQUMsQ0FBQztJQUNqRCxDQUFDLENBQUMsQ0FBQztJQUVOLEVBQUUsQ0FBQyx1Q0FBdUM7UUFDbkMsdUNBQXVDLEVBQzNDLEtBQUssSUFBSSxFQUFFO1FBQ1QsTUFBTSxNQUFNLEdBQUcsRUFBRSxDQUFDLFFBQVEsQ0FBQyxDQUFDLENBQUMsRUFBRSxDQUFDLEVBQUUsQ0FBQyxFQUFFLENBQUMsQ0FBQyxDQUFDLENBQUM7UUFDekMsTUFBTSxNQUFNLEdBQUcsRUFBRSxDQUFDLFlBQVksQ0FBQyxNQUFNLEVBQUUsQ0FBQyxDQUFDLENBQUMsQ0FBQyxFQUFFLENBQUMsQ0FBQyxDQUFDLENBQUMsRUFBRSxDQUFDLENBQUMsQ0FBQyxDQUFDLENBQUMsQ0FBQztRQUN6RCxNQUFNLENBQUMsTUFBTSxDQUFDLEtBQUssQ0FBQyxDQUFDLE9BQU8sQ0FBQyxDQUFDLENBQUMsQ0FBQyxDQUFDLENBQUM7UUFDbEMsaUJBQWlCLENBQUMsTUFBTSxNQUFNLENBQUMsSUFBSSxFQUFFLEVBQUUsQ0FBQyxDQUFDLEVBQUUsQ0FBQyxDQUFDLENBQUMsQ0FBQztJQUNqRCxDQUFDLENBQUMsQ0FBQztJQUVOLEVBQUUsQ0FBQyxnREFBZ0QsRUFBRSxLQUFLLElBQUksRUFBRTtRQUM5RCxNQUFNLE1BQU0sR0FBRyxFQUFFLENBQUMsUUFBUSxDQUFDLENBQUMsQ0FBQyxFQUFFLENBQUMsRUFBRSxDQUFDLEVBQUUsQ0FBQyxDQUFDLENBQUMsQ0FBQztRQUN6QyxNQUFNLE1BQU0sR0FBRyxFQUFFLENBQUMsWUFBWSxDQUFDLE1BQU0sRUFBRSxDQUFDLENBQUMsQ0FBQyxFQUFFLENBQUMsQ0FBQyxDQUFDLEVBQUUsQ0FBQyxDQUFDLENBQUMsRUFBRSxDQUFDLEVBQUUsQ0FBQyxDQUFDLENBQUM7UUFDNUQsTUFBTSxDQUFDLE1BQU0sQ0FBQyxLQUFLLENBQUMsQ0FBQyxPQUFPLENBQUMsQ0FBQyxDQUFDLENBQUMsQ0FBQyxDQUFDO1FBQ2xDLGlCQUFpQixDQUFDLE1BQU0sTUFBTSxDQUFDLElBQUksRUFBRSxFQUFFLENBQUMsQ0FBQyxFQUFFLENBQUMsRUFBRSxDQUFDLENBQUMsQ0FBQyxDQUFDO0lBQ3BELENBQUMsQ0FBQyxDQUFDO0lBRUgsRUFBRSxDQUFDLHdEQUF3RCxFQUFFLEtBQUssSUFBSSxFQUFFO1FBQ3RFLE1BQU0sTUFBTSxHQUFHLEVBQUUsQ0FBQyxRQUFRLENBQUMsQ0FBQyxDQUFDLEVBQUUsQ0FBQyxFQUFFLENBQUMsRUFBRSxDQUFDLENBQUMsQ0FBQyxDQUFDO1FBQ3pDLE1BQU0sTUFBTSxHQUFHLEVBQUUsQ0FBQyxZQUFZLENBQUMsTUFBTSxFQUFFLENBQUMsQ0FBQyxDQUFDLEVBQUUsQ0FBQyxDQUFDLENBQUMsRUFBRSxDQUFDLENBQUMsQ0FBQyxFQUFFLENBQUMsRUFBRSxDQUFDLEVBQUUsQ0FBQyxFQUFFLENBQUMsRUFBRSxDQUFDLENBQUMsQ0FBQztRQUNyRSxNQUFNLENBQUMsTUFBTSxDQUFDLEtBQUssQ0FBQyxDQUFDLE9BQU8sQ0FBQyxFQUFFLENBQUMsQ0FBQztRQUNqQyxpQkFBaUIsQ0FBQyxNQUFNLE1BQU0sQ0FBQyxJQUFJLEVBQUUsRUFBRSxDQUFDLENBQUMsQ0FBQyxDQUFDLENBQUM7SUFDOUMsQ0FBQyxDQUFDLENBQUM7SUFFSCxFQUFFLENBQUMsdURBQXVELEVBQUUsS0FBSyxJQUFJLEVBQUU7UUFDckUsTUFBTSxNQUFNLEdBQUcsRUFBRSxDQUFDLFFBQVEsQ0FBQyxDQUFDLENBQUMsRUFBRSxDQUFDLEVBQUUsQ0FBQyxFQUFFLENBQUMsQ0FBQyxDQUFDLENBQUM7UUFDekMsTUFBTSxNQUFNLEdBQUcsRUFBRSxDQUFDLFlBQVksQ0FBQyxNQUFNLEVBQUUsQ0FBQyxDQUFDLENBQUMsQ0FBQyxFQUFFLENBQUMsQ0FBQyxDQUFDLENBQUMsRUFBRSxDQUFDLENBQUMsQ0FBQyxDQUFDLENBQUMsQ0FBQztRQUN6RCxNQUFNLENBQUMsTUFBTSxDQUFDLEtBQUssQ0FBQyxDQUFDLE9BQU8sQ0FBQyxDQUFDLENBQUMsQ0FBQyxDQUFDLENBQUM7UUFDbEMsaUJBQWlCLENBQUMsTUFBTSxNQUFNLENBQUMsSUFBSSxFQUFFLEVBQUUsQ0FBQyxDQUFDLEVBQUUsQ0FBQyxFQUFFLENBQUMsQ0FBQyxDQUFDLENBQUM7SUFDcEQsQ0FBQyxDQUFDLENBQUM7SUFFSCxFQUFFLENBQUMsMERBQTBELEVBQUUsS0FBSyxJQUFJLEVBQUU7UUFDeEUsTUFBTSxNQUFNLEdBQUcsRUFBRSxDQUFDLFFBQVEsQ0FBQyxDQUFDLENBQUMsRUFBRSxDQUFDLEVBQUUsQ0FBQyxFQUFFLENBQUMsQ0FBQyxDQUFDLENBQUM7UUFDekMsTUFBTSxNQUFNLEdBQUcsRUFBRSxDQUFDLFlBQVksQ0FBQyxNQUFNLEVBQUUsQ0FBQyxDQUFDLENBQUMsRUFBRSxDQUFDLENBQUMsQ0FBQyxFQUFFLENBQUMsQ0FBQyxDQUFDLENBQUMsQ0FBQztRQUN0RCxNQUFNLENBQUMsTUFBTSxDQUFDLEtBQUssQ0FBQyxDQUFDLE9BQU8sQ0FBQyxDQUFDLENBQUMsQ0FBQyxDQUFDLENBQUM7UUFDbEMsaUJBQWlCLENBQUMsTUFBTSxNQUFNLENBQUMsSUFBSSxFQUFFLEVBQUUsQ0FBQyxDQUFDLENBQUMsQ0FBQyxDQUFDO0lBQzlDLENBQUMsQ0FBQyxDQUFDO0lBRUgsRUFBRSxDQUFDLHlEQUF5RCxFQUFFLEtBQUssSUFBSSxFQUFFO1FBQ3ZFLE1BQU0sTUFBTSxHQUFHLEVBQUUsQ0FBQyxRQUFRLENBQUMsQ0FBQyxDQUFDLEVBQUUsQ0FBQyxFQUFFLENBQUMsRUFBRSxDQUFDLENBQUMsQ0FBQyxDQUFDO1FBQ3pDLE1BQU0sTUFBTSxHQUFHLEVBQUUsQ0FBQyxZQUFZLENBQUMsTUFBTSxFQUFFLENBQUMsQ0FBQyxDQUFDLEVBQUUsQ0FBQyxDQUFDLENBQUMsRUFBRSxDQUFDLENBQUMsQ0FBQyxDQUFDLENBQUM7UUFDdEQsTUFBTSxDQUFDLE1BQU0sQ0FBQyxLQUFLLENBQUMsQ0FBQyxPQUFPLENBQUMsQ0FBQyxDQUFDLENBQUMsQ0FBQyxDQUFDO1FBQ2xDLGlCQUFpQixDQUFDLE1BQU0sTUFBTSxDQUFDLElBQUksRUFBRSxFQUFFLENBQUMsQ0FBQyxFQUFFLENBQUMsQ0FBQyxDQUFDLENBQUM7SUFDakQsQ0FBQyxDQUFDLENBQUM7SUFFSCxFQUFFLENBQUMsZ0RBQWdELEVBQUUsS0FBSyxJQUFJLEVBQUU7UUFDOUQsTUFBTSxNQUFNLEdBQUcsRUFBRSxDQUFDLFFBQVEsQ0FBQyxDQUFDLENBQUMsRUFBRSxDQUFDLEVBQUUsQ0FBQyxFQUFFLENBQUMsRUFBRSxDQUFDLEVBQUUsQ0FBQyxDQUFDLEVBQUUsQ0FBQyxDQUFDLEVBQUUsQ0FBQyxDQUFDLENBQUMsQ0FBQztRQUN2RCxNQUFNLE1BQU0sR0FBRyxFQUFFLENBQUMsWUFBWSxDQUFDLE1BQU0sRUFBRSxDQUFDLENBQUMsRUFBRSxDQUFDLENBQUMsRUFBRSxDQUFDLENBQUMsRUFBRSxDQUFDLENBQUMsRUFBRSxDQUFDLENBQUMsRUFBRSxDQUFDLENBQUMsQ0FBQyxDQUFDO1FBQy9ELE1BQU0sQ0FBQyxNQUFNLENBQUMsS0FBSyxDQUFDLENBQUMsT0FBTyxDQUFDLENBQUMsQ0FBQyxFQUFFLENBQUMsQ0FBQyxDQUFDLENBQUM7UUFDckMsaUJBQWlCLENBQUMsTUFBTSxNQUFNLENBQUMsSUFBSSxFQUFFLEVBQUUsQ0FBQyxDQUFDLEVBQUUsQ0FBQyxFQUFFLENBQUMsRUFBRSxDQUFDLEVBQUUsQ0FBQyxFQUFFLENBQUMsQ0FBQyxDQUFDLENBQUM7SUFDN0QsQ0FBQyxDQUFDLENBQUM7SUFFSCxFQUFFLENBQUMsdUNBQXVDLEVBQUUsS0FBSyxJQUFJLEVBQUU7UUFDckQsTUFBTSxNQUFNLEdBQUcsRUFBRSxDQUFDLFFBQVEsQ0FBQyxDQUFDLENBQUMsRUFBRSxDQUFDLEVBQUUsQ0FBQyxFQUFFLENBQUMsRUFBRSxDQUFDLEVBQUUsQ0FBQyxDQUFDLEVBQUUsQ0FBQyxDQUFDLEVBQUUsQ0FBQyxDQUFDLENBQUMsQ0FBQztRQUN2RCxNQUFNLE1BQU0sR0FBRyxFQUFFLENBQUMsWUFBWSxDQUFDLE1BQU0sRUFBRSxDQUFDLENBQUMsRUFBRSxDQUFDLENBQUMsRUFBRSxDQUFDLENBQUMsRUFBRSxDQUFDLENBQUMsRUFBRSxDQUFDLENBQUMsRUFBRSxDQUFDLENBQUMsQ0FBQyxDQUFDO1FBQy9ELE1BQU0sQ0FBQyxNQUFNLENBQUMsS0FBSyxDQUFDLENBQUMsT0FBTyxDQUFDLENBQUMsQ0FBQyxFQUFFLENBQUMsQ0FBQyxDQUFDLENBQUM7UUFDckMsaUJBQWlCLENBQUMsTUFBTSxNQUFNLENBQUMsSUFBSSxFQUFFLEVBQUUsQ0FBQyxDQUFDLEVBQUUsQ0FBQyxDQUFDLENBQUMsQ0FBQztJQUNqRCxDQUFDLENBQUMsQ0FBQztJQUVILEVBQUUsQ0FBQywrQ0FBK0MsRUFBRSxLQUFLLElBQUksRUFBRTtRQUM3RCxNQUFNLE1BQU0sR0FBRyxFQUFFLENBQUMsUUFBUSxDQUFDLENBQUMsQ0FBQyxFQUFFLENBQUMsRUFBRSxDQUFDLEVBQUUsQ0FBQyxFQUFFLENBQUMsRUFBRSxDQUFDLENBQUMsRUFBRSxDQUFDLENBQUMsRUFBRSxDQUFDLENBQUMsQ0FBQyxDQUFDO1FBQ3ZELE1BQU0sTUFBTSxHQUFHLEVBQUUsQ0FBQyxZQUFZLENBQUMsTUFBTSxFQUFFLENBQUMsQ0FBQyxFQUFFLENBQUMsQ0FBQyxFQUFFLENBQUMsQ0FBQyxFQUFFLENBQUMsQ0FBQyxFQUFFLENBQUMsQ0FBQyxFQUFFLENBQUMsQ0FBQyxDQUFDLENBQUM7UUFDL0QsTUFBTSxDQUFDLE1BQU0sQ0FBQyxLQUFLLENBQUMsQ0FBQyxPQUFPLENBQUMsQ0FBQyxDQUFDLEVBQUUsQ0FBQyxDQUFDLENBQUMsQ0FBQztRQUNyQyxpQkFBaUIsQ0FBQyxNQUFNLE1BQU0sQ0FBQyxJQUFJLEVBQUUsRUFBRSxDQUFDLENBQUMsRUFBRSxDQUFDLENBQUMsQ0FBQyxDQUFDO0lBQ2pELENBQUMsQ0FBQyxDQUFDO0lBRUgsRUFBRSxDQUFDLCtEQUErRCxFQUMvRCxLQUFLLElBQUksRUFBRTtRQUNULE1BQU0sQ0FBQyxHQUFHLEVBQUUsQ0FBQyxRQUFRLENBQUMsQ0FBQyxDQUFDLEVBQUUsQ0FBQyxFQUFFLENBQUMsRUFBRSxDQUFDLEVBQUUsQ0FBQyxFQUFFLENBQUMsQ0FBQyxFQUFFLENBQUMsQ0FBQyxFQUFFLENBQUMsQ0FBQyxDQUFDLENBQUM7UUFDbEQsTUFBTSxNQUFNLEdBQUcsQ0FBQyxDQUFDLFlBQVksQ0FBQyxDQUFDLENBQUMsRUFBRSxDQUFDLENBQUMsRUFBRSxDQUFDLENBQUMsRUFBRSxDQUFDLENBQUMsRUFBRSxDQUFDLENBQUMsRUFBRSxDQUFDLENBQUMsQ0FBQyxDQUFDO1FBQ3RELE1BQU0sQ0FBQyxNQUFNLENBQUMsS0FBSyxDQUFDLENBQUMsT0FBTyxDQUFDLENBQUMsQ0FBQyxFQUFFLENBQUMsQ0FBQyxDQUFDLENBQUM7UUFDckMsaUJBQWlCLENBQUMsTUFBTSxNQUFNLENBQUMsSUFBSSxFQUFFLEVBQUUsQ0FBQyxDQUFDLEVBQUUsQ0FBQyxDQUFDLENBQUMsQ0FBQztJQUNqRCxDQUFDLENBQUMsQ0FBQztJQUVOLEVBQUUsQ0FBQyx3REFBd0QsRUFBRSxLQUFLLElBQUksRUFBRTtRQUN0RSxNQUFNLE1BQU0sR0FBRyxFQUFFLENBQUMsUUFBUSxDQUFDLENBQUMsQ0FBQyxFQUFFLENBQUMsRUFBRSxDQUFDLEVBQUUsQ0FBQyxFQUFFLENBQUMsRUFBRSxDQUFDLENBQUMsRUFBRSxDQUFDLENBQUMsRUFBRSxDQUFDLENBQUMsQ0FBQyxDQUFDO1FBQ3ZELE1BQU0sTUFBTSxHQUFHLEVBQUUsQ0FBQyxZQUFZLENBQUMsTUFBTSxFQUFFLENBQUMsQ0FBQyxFQUFFLENBQUMsQ0FBQyxDQUFDLEVBQUUsQ0FBQyxDQUFDLEVBQUUsQ0FBQyxDQUFDLENBQUMsRUFBRSxDQUFDLENBQUMsRUFBRSxDQUFDLENBQUMsQ0FBQyxDQUFDLENBQUM7UUFDbEUsTUFBTSxDQUFDLE1BQU0sQ0FBQyxLQUFLLENBQUMsQ0FBQyxPQUFPLENBQUMsQ0FBQyxDQUFDLEVBQUUsQ0FBQyxDQUFDLENBQUMsQ0FBQztRQUNyQyxpQkFBaUIsQ0FBQyxNQUFNLE1BQU0sQ0FBQyxJQUFJLEVBQUUsRUFBRSxDQUFDLENBQUMsRUFBRSxDQUFDLEVBQUUsQ0FBQyxDQUFDLENBQUMsQ0FBQztJQUNwRCxDQUFDLENBQUMsQ0FBQztJQUVILEVBQUUsQ0FBQyxrREFBa0QsRUFBRSxLQUFLLElBQUksRUFBRTtRQUNoRSxNQUFNLE1BQU0sR0FBRyxFQUFFLENBQUMsUUFBUSxDQUFDLENBQUMsQ0FBQyxFQUFFLENBQUMsRUFBRSxDQUFDLEVBQUUsQ0FBQyxFQUFFLENBQUMsRUFBRSxDQUFDLENBQUMsRUFBRSxDQUFDLENBQUMsRUFBRSxDQUFDLENBQUMsQ0FBQyxDQUFDO1FBQ3ZELE1BQU0sTUFBTSxHQUFHLEVBQUUsQ0FBQyxZQUFZLENBQUMsTUFBTSxFQUFFLENBQUMsQ0FBQyxFQUFFLENBQUMsQ0FBQyxFQUFFLENBQUMsQ0FBQyxFQUFFLENBQUMsQ0FBQyxFQUFFLENBQUMsQ0FBQyxFQUFFLENBQUMsQ0FBQyxFQUFFLENBQUMsQ0FBQyxDQUFDO1FBQ2xFLE1BQU0sQ0FBQyxNQUFNLENBQUMsS0FBSyxDQUFDLENBQUMsT0FBTyxDQUFDLENBQUMsQ0FBQyxFQUFFLENBQUMsQ0FBQyxDQUFDLENBQUM7UUFDckMsaUJBQWlCLENBQUMsTUFBTSxNQUFNLENBQUMsSUFBSSxFQUFFLEVBQUUsQ0FBQyxDQUFDLEVBQUUsQ0FBQyxFQUFFLENBQUMsRUFBRSxDQUFDLENBQUMsQ0FBQyxDQUFDO0lBQ3ZELENBQUMsQ0FBQyxDQUFDO0lBRUgsRUFBRSxDQUFDLG1EQUFtRCxFQUFFLEtBQUssSUFBSSxFQUFFO1FBQ2pFLE1BQU0sTUFBTSxHQUFHLEVBQUUsQ0FBQyxRQUFRLENBQUMsQ0FBQyxDQUFDLEVBQUUsQ0FBQyxFQUFFLENBQUMsRUFBRSxDQUFDLEVBQUUsQ0FBQyxFQUFFLENBQUMsQ0FBQyxFQUFFLENBQUMsQ0FBQyxFQUFFLENBQUMsQ0FBQyxDQUFDLENBQUM7UUFDdkQsTUFBTSxNQUFNLEdBQ1IsRUFBRSxDQUFDLFlBQVksQ0FBQyxNQUFNLEVBQUUsQ0FBQyxDQUFDLEVBQUUsQ0FBQyxDQUFDLEVBQUUsQ0FBQyxDQUFDLEVBQUUsQ0FBQyxDQUFDLEVBQUUsQ0FBQyxDQUFDLEVBQUUsQ0FBQyxDQUFDLEVBQUUsQ0FBQyxFQUFFLENBQUMsRUFBRSxDQUFDLEVBQUUsQ0FBQyxFQUFFLENBQUMsQ0FBQyxDQUFDO1FBQ25FLE1BQU0sQ0FBQyxNQUFNLENBQUMsS0FBSyxDQUFDLENBQUMsT0FBTyxDQUFDLENBQUMsQ0FBQyxDQUFDLENBQUMsQ0FBQztRQUNsQyxpQkFBaUIsQ0FBQyxNQUFNLE1BQU0sQ0FBQyxJQUFJLEVBQUUsRUFBRSxDQUFDLENBQUMsRUFBRSxDQUFDLENBQUMsQ0FBQyxDQUFDO0lBQ2pELENBQUMsQ0FBQyxDQUFDO0lBRUgsRUFBRSxDQUFDLGdEQUFnRCxFQUFFLEtBQUssSUFBSSxFQUFFO1FBQzlELE1BQU0sTUFBTSxHQUFHLEVBQUUsQ0FBQyxRQUFRLENBQUMsQ0FBQyxDQUFDLEVBQUUsQ0FBQyxFQUFFLENBQUMsRUFBRSxDQUFDLEVBQUUsQ0FBQyxFQUFFLENBQUMsQ0FBQyxFQUFFLENBQUMsQ0FBQyxFQUFFLENBQUMsQ0FBQyxDQUFDLENBQUM7UUFDdkQsTUFBTSxNQUFNLEdBQUcsRUFBRSxDQUFDLFlBQVksQ0FBQyxNQUFNLEVBQUUsQ0FBQyxDQUFDLEVBQUUsQ0FBQyxDQUFDLEVBQUUsQ0FBQyxDQUFDLEVBQUUsQ0FBQyxDQUFDLEVBQUUsQ0FBQyxDQUFDLEVBQUUsQ0FBQyxDQUFDLEVBQUUsQ0FBQyxFQUFFLENBQUMsQ0FBQyxDQUFDO1FBQ3JFLE1BQU0sQ0FBQyxNQUFNLENBQUMsS0FBSyxDQUFDLENBQUMsT0FBTyxDQUFDLENBQUMsQ0FBQyxFQUFFLENBQUMsQ0FBQyxDQUFDLENBQUM7UUFDckMsaUJBQWlCLENBQUMsTUFBTSxNQUFNLENBQUMsSUFBSSxFQUFFLEVBQUUsQ0FBQyxDQUFDLEVBQUUsQ0FBQyxFQUFFLENBQUMsQ0FBQyxDQUFDLENBQUM7SUFDcEQsQ0FBQyxDQUFDLENBQUM7SUFFSCxFQUFFLENBQUMsdUNBQXVDO1FBQ25DLGtDQUFrQyxFQUN0QyxLQUFLLElBQUksRUFBRTtRQUNULE1BQU0sTUFBTSxHQUFHLEVBQUUsQ0FBQyxRQUFRLENBQUMsQ0FBQyxDQUFDLEVBQUUsQ0FBQyxFQUFFLENBQUMsRUFBRSxDQUFDLEVBQUUsQ0FBQyxFQUFFLENBQUMsQ0FBQyxFQUFFLENBQUMsQ0FBQyxFQUFFLENBQUMsQ0FBQyxDQUFDLENBQUM7UUFDdkQsTUFBTSxNQUFNLEdBQUcsRUFBRSxDQUFDLFlBQVksQ0FBQyxNQUFNLEVBQUUsQ0FBQyxDQUFDLEVBQUUsQ0FBQyxDQUFDLENBQUMsRUFBRSxDQUFDLENBQUMsRUFBRSxDQUFDLENBQUMsQ0FBQyxFQUFFLENBQUMsQ0FBQyxFQUFFLENBQUMsQ0FBQyxDQUFDLEVBQUUsQ0FBQyxDQUFDLENBQUM7UUFDckUsTUFBTSxDQUFDLE1BQU0sQ0FBQyxLQUFLLENBQUMsQ0FBQyxPQUFPLENBQUMsQ0FBQyxDQUFDLEVBQUUsQ0FBQyxDQUFDLENBQUMsQ0FBQztRQUNyQyxpQkFBaUIsQ0FBQyxNQUFNLE1BQU0sQ0FBQyxJQUFJLEVBQUUsRUFBRSxDQUFDLENBQUMsRUFBRSxDQUFDLEVBQUUsQ0FBQyxDQUFDLENBQUMsQ0FBQztJQUNwRCxDQUFDLENBQUMsQ0FBQztJQUVOLEVBQUUsQ0FBQyx1Q0FBdUM7UUFDbkMsZ0NBQWdDLEVBQ3BDLEtBQUssSUFBSSxFQUFFO1FBQ1QsTUFBTSxNQUFNLEdBQUcsRUFBRSxDQUFDLFFBQVEsQ0FBQyxDQUFDLENBQUMsRUFBRSxDQUFDLEVBQUUsQ0FBQyxFQUFFLENBQUMsRUFBRSxDQUFDLEVBQUUsQ0FBQyxDQUFDLEVBQUUsQ0FBQyxDQUFDLEVBQUUsQ0FBQyxDQUFDLENBQUMsQ0FBQztRQUN2RCxNQUFNLE1BQU0sR0FBRyxFQUFFLENBQUMsWUFBWSxDQUFDLE1BQU0sRUFBRSxDQUFDLENBQUMsRUFBRSxDQUFDLENBQUMsQ0FBQyxFQUFFLENBQUMsQ0FBQyxFQUFFLENBQUMsQ0FBQyxDQUFDLEVBQUUsQ0FBQyxDQUFDLEVBQUUsQ0FBQyxDQUFDLENBQUMsRUFBRSxDQUFDLEVBQUUsQ0FBQyxDQUFDLENBQUM7UUFDeEUsTUFBTSxDQUFDLE1BQU0sQ0FBQyxLQUFLLENBQUMsQ0FBQyxPQUFPLENBQUMsQ0FBQyxDQUFDLEVBQUUsQ0FBQyxDQUFDLENBQUMsQ0FBQztRQUNyQyxpQkFBaUIsQ0FBQyxNQUFNLE1BQU0sQ0FBQyxJQUFJLEVBQUUsRUFBRSxDQUFDLENBQUMsRUFBRSxDQUFDLENBQUMsQ0FBQyxDQUFDO0lBQ2pELENBQUMsQ0FBQyxDQUFDO0lBRU4sRUFBRSxDQUFDLGdEQUFnRCxFQUFFLEtBQUssSUFBSSxFQUFFO1FBQzlELE1BQU0sTUFBTSxHQUNSLEVBQUUsQ0FBQyxRQUFRLENBQUMsQ0FBQyxDQUFDLEVBQUUsQ0FBQyxFQUFFLENBQUMsRUFBRSxDQUFDLEVBQUUsQ0FBQyxFQUFFLENBQUMsRUFBRSxDQUFDLEVBQUUsQ0FBQyxFQUFFLENBQUMsRUFBRSxFQUFFLEVBQUUsRUFBRSxFQUFFLEVBQUUsQ0FBQyxFQUFFLENBQUMsQ0FBQyxFQUFFLENBQUMsRUFBRSxDQUFDLENBQUMsQ0FBQyxDQUFDO1FBQ3BFLE1BQU0sTUFBTSxHQUFHLEVBQUUsQ0FBQyxZQUFZLENBQUMsTUFBTSxFQUFFLENBQUMsQ0FBQyxFQUFFLENBQUMsRUFBRSxDQUFDLENBQUMsRUFBRSxDQUFDLENBQUMsRUFBRSxDQUFDLEVBQUUsQ0FBQyxDQUFDLEVBQUUsQ0FBQyxDQUFDLEVBQU