diff --git a/README.md b/README.md
index 7b6334f6..28ccf3c7 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,16 @@
# Human Library
-## 3D Face Detection, Face Embedding & Recognition, Body Pose Tracking, Hand & Finger Tracking, Iris Analysis, Age & Gender & Emotion Prediction & Gesture Recognition
+### 3D Face Detection, Face Embedding & Recognition,
+### Body Pose Tracking, Hand & Finger Tracking,
+### Iris Analysis, Age & Gender & Emotion Prediction
+### & Gesture Recognition
+
+
+
+Native JavaScript module using TensorFlow/JS Machine Learning library
+Compatible with *Browser*, *WebWorker* and *NodeJS* execution on both Windows and Linux
+- Browser/WebWorker: Compatible with *CPU*, *WebGL*, *WASM* and *WebGPU* backends
+- NodeJS: Compatible with software *tfjs-node* and CUDA accelerated backends *tfjs-node-gpu*
@@ -39,28 +49,17 @@
-Compatible with *Browser*, *WebWorker* and *NodeJS* execution on both Windows and Linux
-- Browser/WebWorker: Compatible with *CPU*, *WebGL*, *WASM* and *WebGPU* backends
-- NodeJS: Compatible with software *tfjs-node* and CUDA accelerated backends *tfjs-node-gpu*
-- (and maybe with React-Native as it doesn't use any DOM objects)
-
-
-
*See [issues](https://github.com/vladmandic/human/issues?q=) and [discussions](https://github.com/vladmandic/human/discussions) for list of known limitations and planned enhancements*
*Suggestions are welcome!*
-
-
-
+
## Options ##
-
-
-
+
## Examples
@@ -75,4 +74,3 @@ Compatible with *Browser*, *WebWorker* and *NodeJS* execution on both Windows an
**Using webcam:**
-
diff --git a/demo/browser.js b/demo/browser.js
index 24fd3477..f77a1b82 100644
--- a/demo/browser.js
+++ b/demo/browser.js
@@ -1,4 +1,9 @@
-import Human from '../dist/human.esm.js';
+// eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars
+// import * as tf from '@tensorflow/tfjs';
+// import Human from '../dist/human.esm-nobundle.js';
+
+import Human from '../dist/human.esm.js'; // equivalent of @vladmandic/human
+
import draw from './draw.js';
import Menu from './menu.js';
import GLBench from './gl-bench.js';
diff --git a/dist/demo-browser-index.js b/dist/demo-browser-index.js
index bb4a6112..afa9332f 100644
--- a/dist/demo-browser-index.js
+++ b/dist/demo-browser-index.js
@@ -5,38 +5,31 @@
author: '
*/
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Got strides ${n} and dilations '${i}'`);let o=s,l=!1;s.rank===3&&(l=!0,o=q(s,[1,s.shape[0],s.shape[1],s.shape[2]])),F(o.rank===4,()=>`Error in avgPool: x must be rank 4 but got rank ${o.rank}.`),a!=null&&F(Ut(r),()=>`Error in avgPool: pad must be an integer when using, dimRoundingMode ${a} but got pad ${r}.`);let c={x:o},u={filterSize:t,strides:n,pad:r,dimRoundingMode:a},h=D.runKernel(Ja,c,u);return h=ye(h,s.dtype),l?q(h,[h.shape[1],h.shape[2],h.shape[3]]):h}var Nu=O({avgPool_:uN});function cN(e,t,n,r,a,s="NDHWC"){let i=R(e,"x","avgPool3d","float32"),o=i,l=!1;i.rank===4&&(l=!0,o=q(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]])),F(o.rank===5,()=>`Error in avgPool3d: x must be rank 5 but got rank ${o.rank}.`),F(s==="NDHWC",()=>`Error in avgPool3d: Only NDHWC is currently supported, but got dataFormat of ${s}`),a!=null&&F(Ut(r),()=>`Error in avgPool3d: pad must be an integer when using, dimRoundingMode ${a} but got pad ${r}.`);let c={x:o},u={filterSize:t,strides:n,pad:r,dimRoundingMode:a,dataFormat:s},h=D.runKernel(au,c,u);return h=ye(h,o.dtype),l?q(h,[h.shape[1],h.shape[2],h.shape[3],h.shape[4]]):h}var Vf=O({avgPool3d_:cN});function hN(e,t=0){F(e.length>=1,()=>"Pass at least one tensor to concat");let n=Qu(e,"tensors","concat","string_or_numeric");if(n[0].dtype==="complex64"&&n.forEach(s=>{if(s.dtype!=="complex64")throw new Error(`Cannot concatenate complex64 tensors with a tensor
+ with dtype ${s.dtype}. `)}),n.length===1)return Nr(n[0]);let r=n,a={axis:t};return D.runKernel(qi,r,a)}var ct=O({concat_:hN});function dN(e){let t={x:R(e,"x","sigmoid")};return D.runKernel(Ms,t)}var Yn=O({sigmoid_:dN});function pN(e,t,n){let r=R(e,"x","slice","string_or_numeric");if(r.rank===0)throw new Error("Slicing scalar is not possible");let a={x:r},s={begin:t,size:n};return D.runKernel(To,a,s)}var Me=O({slice_:pN});function fN(e){let t={x:R(e,"x","tanh")};return D.runKernel(Ls,t)}var Vo=O({tanh_:fN});function mN(e,t,n,r,a,s){let i=R(e,"forgetBias","basicLSTMCell"),o=R(t,"lstmKernel","basicLSTMCell"),l=R(n,"lstmBias","basicLSTMCell"),c=R(r,"data","basicLSTMCell"),u=R(a,"c","basicLSTMCell"),h=R(s,"h","basicLSTMCell"),d=ct([c,h],1),p=qe(d,o),f=oe(p,l),m=f.shape[0],A=f.shape[1]/4,y=[m,A],g=Me(f,[0,0],y),b=Me(f,[0,A],y),x=Me(f,[0,A*2],y),w=Me(f,[0,A*3],y),_=oe(L(Yn(g),Vo(b)),L(u,Yn(oe(i,x)))),N=L(Vo(_),Yn(w));return[_,N]}var u8=O({basicLSTMCell_:mN});function AN(e,t,n){let r=R(e,"x","batchToSpaceND"),a=t.reduce((o,l)=>o*l);F(r.rank>=1+t.length,()=>`input rank is ${r.rank} but should be > than blockShape.length ${t.length}`),F(n.length===t.length,()=>`crops.length is ${n.length} but should be equal to blockShape.length ${t.length}`),F(r.shape[0]%a==0,()=>`input tensor batch is ${r.shape[0]} but is not divisible by the product of the elements of blockShape ${t.join(" * ")} === ${a}`);let s={x:r},i={blockShape:t,crops:n};return D.runKernel(su,s,i)}var Su=O({batchToSpaceND_:AN});function yN(e){let t;return e.rank===0||e.rank===1?t=q(e,[1,1,1,e.size]):e.rank===2?t=q(e,[1,1,e.shape[0],e.shape[1]]):e.rank===3?t=q(e,[1,e.shape[0],e.shape[1],e.shape[2]]):t=e,t}function gN(e,t,n,r,a,s){s==null&&(s=.001);let i=R(e,"x","batchNorm"),o=R(t,"mean","batchNorm"),l=R(n,"variance","batchNorm"),c;a!=null&&(c=R(a,"scale","batchNorm"));let u;r!=null&&(u=R(r,"offset","batchNorm")),F(o.rank===l.rank,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),F(u==null||o.rank===u.rank,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),F(c==null||o.rank===c.rank,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let h={x:yN(i),scale:c,offset:u,mean:o,variance:l},d={varianceEpsilon:s},p=D.runKernel(cs,h,d);return q(p,i.shape)}var Hs=O({batchNorm_:gN});function xN(e,t,n,r,a,s){let i=R(e,"x","batchNorm"),o=R(t,"mean","batchNorm"),l=R(n,"variance","batchNorm"),c;a!=null&&(c=R(a,"scale","batchNorm"));let u;return r!=null&&(u=R(r,"offset","batchNorm")),F(i.rank===2,()=>`Error in batchNorm2D: x must be rank 2 but got rank ${i.rank}.`),F(o.rank===2||o.rank===1,()=>`Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${o.rank}.`),F(l.rank===2||l.rank===1,()=>`Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${l.rank}.`),c!=null&&F(c.rank===2||c.rank===1,()=>`Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${c.rank}.`),u!=null&&F(u.rank===2||u.rank===1,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${u.rank}.`),Hs(i,o,l,u,c,s)}var X2=O({batchNorm2d_:xN});function wN(e,t,n,r,a,s){let i=R(e,"x","batchNorm"),o=R(t,"mean","batchNorm"),l=R(n,"variance","batchNorm"),c;a!=null&&(c=R(a,"scale","batchNorm"));let u;return r!=null&&(u=R(r,"offset","batchNorm")),F(i.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${i.rank}.`),F(o.rank===3||o.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${o.rank}.`),F(l.rank===3||l.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${l.rank}.`),c!=null&&F(c.rank===3||c.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${c.rank}.`),u!=null&&F(u.rank===3||u.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${u.rank}.`),Hs(i,o,l,u,c,s)}var K2=O({batchNorm3d_:wN});function _N(e,t,n,r,a,s){let i=R(e,"x","batchNorm"),o=R(t,"mean","batchNorm"),l=R(n,"variance","batchNorm"),c;a!=null&&(c=R(a,"scale","batchNorm"));let u;return r!=null&&(u=R(r,"offset","batchNorm")),F(i.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${i.rank}.`),F(o.rank===4||o.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${o.rank}.`),F(l.rank===4||l.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${l.rank}.`),c!=null&&F(c.rank===4||c.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${c.rank}.`),u!=null&&F(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${u.rank}.`),Hs(i,o,l,u,c,s)}var Z2=O({batchNorm4d_:_N});function bN(e,t,n){let r=R(e,"x","bincount"),a=R(t,"weights","bincount");F(r.dtype==="int32",()=>`Error in bincount: input dtype must be int32, but got ${r.dtype}`),F(n>=0,()=>`size must be non-negative, but got ${n}.`),F(a.size===r.size||a.size===0,()=>`Error in bincount: weights must have the same size as input or0-length, but got input shape: ${r.shape}, weights shape: ${a.shape}.`);let s={x:r,weights:a},i={size:n};return D.runKernel(wh,s,i)}var J2=O({bincount_:bN});function vN(e,t){let n=R(e,"broadcastTo","x"),r=n.shape;if(t.some(l=>!(l>0)||l%1!=0))throw new Error(`broadcastTo(): Invalid broadcast shape [${t}].`);if(t.lengthn.rank){let l=n.shape.slice();for(;l.length=0;l--)if(a[l]===t[l])s[l]=1;else if(n.shape[l]!==1)throw new Error(`broadcastTo(): [${r}] cannot be broadcast to [${t}].`);if(s.map((l,c)=>l>1?c:-1).filter(l=>l>=0).length===0)return Nr(n);let i={x:n},o={reps:s};return D.runKernel(Aa,i,o)}var Tu=O({broadcastTo_:vN});function kN(e){let t={x:R(e,"x","ceil")};return D.runKernel(es,t)}var Uf=O({ceil_:kN});function IN(e,t,n){let r=R(e,"x","clipByValue");F(t<=n,()=>`Error in clip: min (${t}) must be less than or equal to max (${n}).`);let a={x:r},s={clipValueMin:t,clipValueMax:n};return D.runKernel(ma,a,s)}var pn=O({clipByValue_:IN});function NN(e){return ct(e,0)}var Y2=O({concat1d_:NN});function SN(e,t){return ct(e,t)}var ed=O({concat2d_:SN});function TN(e,t){return ct(e,t)}var Q2=O({concat3d_:TN});function EN(e,t){return ct(e,t)}var e0=O({concat4d_:EN});function CN(e,t,n,r,a="NHWC",s=[1,1],i){let o=R(e,"x","conv2d"),l=R(t,"filter","conv2d"),c=o,u=!1;o.rank===3&&(u=!0,c=q(o,[1,o.shape[0],o.shape[1],o.shape[2]])),F(c.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${c.rank}.`),F(l.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${l.rank}.`),i!=null&&F(Ut(r),()=>`Error in conv2d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${r}.`);let h=a==="NHWC"?c.shape[3]:c.shape[1];F(h===l.shape[2],()=>`Error in conv2d: depth of input (${h}) must match input depth for filter ${l.shape[2]}.`),F(zr(n,s),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${n} and dilations '${s}'`);let d={x:c,filter:l},p={strides:n,pad:r,dataFormat:a,dilations:s,dimRoundingMode:i},f=D.runKernel(ts,d,p);return u?q(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var Kr=O({conv2d_:CN});function RN(e,t,n,r,a="NWC",s=1,i){let o=R(e,"x","conv1d"),l=R(t,"filter","conv1d"),c=o,u=!1;o.rank===2&&(u=!0,c=q(o,[1,o.shape[0],o.shape[1]])),F(c.rank===3,()=>`Error in conv1d: input must be rank 3, but got rank ${c.rank}.`),F(l.rank===3,()=>`Error in conv1d: filter must be rank 3, but got rank ${l.rank}.`),i!=null&&F(Ut(r),()=>`Error in conv1d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${r}.`),F(c.shape[2]===l.shape[1],()=>`Error in conv1d: depth of input (${c.shape[2]}) must match input depth for filter ${l.shape[1]}.`),F(zr(n,s),()=>`Error in conv1D: Either stride or dilation must be 1. Got stride ${n} and dilation '${s}'`),F(a==="NWC",()=>`Error in conv1d: got dataFormat of ${a} but only NWC is currently supported.`);let h=q(l,[1,l.shape[0],l.shape[1],l.shape[2]]),d=q(c,[c.shape[0],1,c.shape[1],c.shape[2]]),p=Kr(d,h,[1,n],r,"NHWC",[1,s],i);return u?q(p,[p.shape[2],p.shape[3]]):q(p,[p.shape[0],p.shape[2],p.shape[3]])}var td=O({conv1d_:RN});function FN(e,t,n,r,a,s="NHWC",i){F(e.length===t.rank,()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);let o=e,l=t,c=!1;t.rank===3&&(c=!0,l=q(t,[1,t.shape[0],t.shape[1],t.shape[2]]),o=[1,e[0],e[1],e[2]]),F(o.length===4,()=>`Error in conv2dDerInput: inShape must be length 4, but got length ${o.length}.`),F(l.rank===4,()=>`Error in conv2dDerInput: dy must be rank 4, but got rank ${l.rank}`),F(n.rank===4,()=>`Error in conv2dDerInput: filter must be rank 4, but got rank ${n.rank}`);let u=s==="NHWC"?o[3]:o[1],h=s==="NHWC"?l.shape[3]:l.shape[1];F(u===n.shape[2],()=>`Error in conv2dDerInput: depth of input (${u}) must match input depth for filter ${n.shape[2]}.`),F(h===n.shape[3],()=>`Error in conv2dDerInput: depth of output (${h}) must match output depth for filter ${n.shape[3]}.`),i!=null&&F(Ut(a),()=>`Error in conv2dDerInput: pad must be an integer when using, dimRoundingMode ${i} but got pad ${a}.`);let d={dy:l,filter:n},p={strides:r,pad:a,dataFormat:s,dimRoundingMode:i,inputShape:o},f=D.runKernel(ns,d,p);return c?q(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var Jm=O({conv2DBackpropInput_:FN});function MN(e,t,n,r,a,s){let i=R(e,"x","conv2dTranspose"),o=R(t,"filter","conv2dTranspose");return Jm(n,i,o,r,a,"NHWC",s)}var nd=O({conv2dTranspose_:MN});function $N(e,t,n,r,a="NDHWC",s=[1,1,1]){let i=R(e,"x","conv3d"),o=R(t,"filter","conv3d"),l=i,c=!1;i.rank===4&&(c=!0,l=q(i,[1,i.shape[0],i.shape[1],i.shape[2],i.shape[3]])),F(l.rank===5,()=>`Error in conv3d: input must be rank 5, but got rank ${l.rank}.`),F(o.rank===5,()=>`Error in conv3d: filter must be rank 5, but got rank ${o.rank}.`),F(l.shape[4]===o.shape[3],()=>`Error in conv3d: depth of input (${l.shape[4]}) must match input depth for filter ${o.shape[3]}.`),F(zr(n,s),()=>`Error in conv3D: Either strides or dilations must be 1. Got strides ${n} and dilations '${s}'`),F(a==="NDHWC",()=>`Error in conv3d: got dataFormat of ${a} but only NDHWC is currently supported.`);let u={x:l,filter:o},h={strides:n,pad:r,dataFormat:a,dilations:s},d=D.runKernel(ou,u,h);return c?q(d,[d.shape[1],d.shape[2],d.shape[3],d.shape[4]]):d}var Hf=O({conv3d_:$N});function DN(e,t,n,r,a){F(e.length===t.rank,()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);let s=e,i=t,o=!1;t.rank===4&&(o=!0,i=q(t,[1,t.shape[0],t.shape[1],t.shape[2],t.shape[3]]),s=[1,e[0],e[1],e[2],e[3]]);let l=s[4],c=i.shape[4];F(s.length===5,()=>`Error in conv3dDerInput: inShape must be length 5, but got length ${s.length}.`),F(i.rank===5,()=>`Error in conv3dDerInput: dy must be rank 5, but got rank ${i.rank}`),F(n.rank===5,()=>`Error in conv3dDerInput: filter must be rank 5, but got rank ${n.rank}`),F(l===n.shape[3],()=>`Error in conv3dDerInput: depth of input (${l}) must match input depth for filter ${n.shape[3]}.`),F(c===n.shape[4],()=>`Error in conv3dDerInput: depth of output (${c}) must match output depth for filter ${n.shape[4]}.`);let u={dy:i,filter:n},h={pad:a,strides:r,inputShape:s},d=D.runKernel(kh,u,h);return o?q(d,[d.shape[1],d.shape[2],d.shape[3],d.shape[4]]):d}var H5=O({conv3DBackpropInput_:DN});function ON(e,t,n,r,a){let s=R(e,"x","conv3dTranspose"),i=R(t,"filter","conv3dTranspose");return H5(n,s,i,r,a)}var c8=O({conv3dTranspose_:ON});function zN(e){let t={x:R(e,"x","cos")};return D.runKernel(rs,t)}var Eu=O({cos_:zN});function PN(e){let t={x:R(e,"x","cosh")};return D.runKernel(Xi,t)}var rd=O({cosh_:PN});function LN(e,t=0,n=!1,r=!1){let a={x:R(e,"x","cumsum")},s={axis:t,exclusive:n,reverse:r};return D.runKernel(as,a,s)}var ad=O({cumsum_:LN});function WN(e,t,n,r=!1){let a=R(e,"x","denseBincount"),s=R(t,"weights","denseBincount");F(a.dtype==="int32",()=>`Error in denseBincount: input dtype must be int32, but got ${a.dtype}`),F(a.rank<=2,()=>`Error in denseBincount: input must be at most rank 2, but got rank ${a.rank}.`),F(n>=0,()=>`size must be non-negative, but got ${n}.`),F(s.size===a.size||s.size===0,()=>`Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${a.shape}, weights shape: ${s.shape}.`);let i={x:a,weights:s},o={size:n,binaryOutput:r};return D.runKernel(Ih,i,o)}var t0=O({denseBincount_:WN});function BN(e,t,n="NHWC"){let r=R(e,"x","depthToSpace"),a=n==="NHWC"?r.shape[1]:r.shape[2],s=n==="NHWC"?r.shape[2]:r.shape[3],i=n==="NHWC"?r.shape[3]:r.shape[1];F(a*t>=0,()=>`Negative dimension size caused by overflow when multiplying
${a} and ${t} for depthToSpace with input shape
- ${r.shape}`),M(s*t>=0,()=>`Negative dimension size caused by overflow when multiplying
+ ${r.shape}`),F(s*t>=0,()=>`Negative dimension size caused by overflow when multiplying
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Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};Ed.className="Adamax";Ta(Ed);var Vu=class extends Qr{constructor(e){super();this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let r=Array.isArray(e)?e[n].tensor:e[t];if(r==null)return;let a=z.registeredVariables[t];U(()=>{let s=oe(W(this.c,r),a);a.assign(s)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Bt(Se(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does 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Expected ${i}, but was ${t.shape[0]}`),F(0<=s&&s<=1,()=>`softNmsSigma must be in [0, 1], but was '${s}'`),{maxOutputSize:n,iouThreshold:r,scoreThreshold:a,softNmsSigma:s}}function EE(e,t,n,r=.5,a=Number.NEGATIVE_INFINITY){let s=R(e,"boxes","nonMaxSuppression"),i=R(t,"scores","nonMaxSuppression"),o=ul(s,i,n,r,a);n=o.maxOutputSize,r=o.iouThreshold,a=o.scoreThreshold;let l={maxOutputSize:n,iouThreshold:r,scoreThreshold:a};return D.runKernel(yo,{boxes:s,scores:i},l)}var CE=O({nonMaxSuppression_:EE});function FE(e,t,n){let r=RE(e,t,n),a=r<0?-(r+1):r;e.splice(a,0,t)}function RE(e,t,n){return $E(e,t,n||ME)}function ME(e,t){return e>t?1:e>>1);let o=n(t,e[s]);o>0?r=s+1:(a=s,i=!o)}return i?r:-r-1}function ex(e,t,n,r,a){return rA(e,t,n,r,a,0)}function tx(e,t,n,r,a,s){return rA(e,t,n,r,a,0,!1,s,!0)}function nx(e,t,n,r,a,s){return rA(e,t,n,r,a,s,!0)}function rA(e,t,n,r,a,s,i=!1,o=!1,l=!1){let c=[];for(let A=0;A