Merged with develop

This commit is contained in:
Ozzie Gooen 2022-04-28 12:49:33 -04:00
commit 1cca9bde38
26 changed files with 280 additions and 468 deletions

20
examples/decay.squiggle Normal file
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# The following code was provided by Nuño Sempere, it comes directly from the post https://www.lesswrong.com/s/rDe8QE5NvXcZYzgZ3/p/j8o6sgRerE3tqNWdj
## Initial setup
yearly_probability_max = 0.95
yearly_probability_min = 0.66
period_probability_function(epsilon, yearly_probability) = 1 - (1 - yearly_probability) ^ (1 / epsilon)
probability_decayed(t, time_periods, period_probability) = 1 - (1 - period_probability) ^ (time_periods - t)
## Monthly decomposition
months_in_a_year=12
monthly_probability_min = period_probability_function(months_in_a_year, yearly_probability_min)
monthly_probability_max = period_probability_function(months_in_a_year, yearly_probability_max)
probability_decayed_monthly_min(t) = probability_decayed(t, months_in_a_year, monthly_probability_min)
probability_decayed_monthly_max(t) = probability_decayed(t, months_in_a_year, monthly_probability_max)
probability_decayed_monthly(t) = probability_decayed_monthly_min(t) to probability_decayed_monthly_max(t)
probability_decayed_monthly
## probability_decayed_monthly(6)
## mean(probability_decayed_monthly(6))

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@ -0,0 +1,38 @@
# This is a cost effectiveness analysis of givedirectly, originally done by givewell, and translated into Squiggle by Sam Nolan
donation_size = 10000
proportion_of_funding_available = beta(10, 2)
total_funding_available = donation_size * proportion_of_funding_available
household_size = 3.7 to 5.7
size_of_transfer = 800 to 1200
size_of_transfer_per_person = size_of_transfer / household_size
portion_invested = 0.3 to 0.5
amount_invested = portion_invested * size_of_transfer_per_person
amount_consumed = (1 - portion_invested) * size_of_transfer_per_person
return_on_investment = 0.08 to 0.12
increase_in_consumption_from_investments = return_on_investment * amount_invested
baseline_consumption = 200 to 350
log_increase_in_consumption = log(amount_consumed + baseline_consumption) + log(baseline_consumption)
log_increase_in_consumption_from_investment = log(increase_in_consumption_from_investments + baseline_consumption) + log(baseline_consumption)
investment_duration = 8 to 12
discount_rate = beta(1.004, 20)
present_value_excluding_last_year = log_increase_in_consumption_from_investment * (1 - (1 + discount_rate) ^ (-investment_duration)) / (log(1 + discount_rate))
percent_of_investment_returned = 0.15 to 0.25
pv_consumption_last_year = (log(baseline_consumption + amount_invested * (return_on_investment + percent_of_investment_returned)) - log(baseline_consumption)) / (1 + discount_rate)^investment_duration
total_pv_of_cash_transfer = pv_consumption_last_year + present_value_excluding_last_year + log_increase_in_consumption
discount_negative_spoiler = 0.03 to 0.07
value_discounting_spoiler = discount_negative_spoiler * total_pv_of_cash_transfer
consumption_increase_per_household = value_discounting_spoiler * household_size
amount_of_transfers_made = total_funding_available / size_of_transfer
total_increase_in_ln_consumption = amount_of_transfers_made * consumption_increase_per_household
total_increase_in_ln_consumption

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@ -0,0 +1,3 @@
xY1 = 99
aBa3 = xY1 * 2 + 1
aBa3 * xY1 + aBa3

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@ -1,41 +0,0 @@
open Jest
open TestHelpers
describe("Continuous and discrete splits", () => {
makeTest(
"splits (1)",
SampleSetDist_ToPointSet.Internals.T.splitContinuousAndDiscrete([1.432, 1.33455, 2.0]),
([1.432, 1.33455, 2.0], E.FloatFloatMap.empty()),
)
makeTest(
"splits (2)",
SampleSetDist_ToPointSet.Internals.T.splitContinuousAndDiscrete([
1.432,
1.33455,
2.0,
2.0,
2.0,
2.0,
]) |> (((c, disc)) => (c, disc |> E.FloatFloatMap.toArray)),
([1.432, 1.33455], [(2.0, 4.0)]),
)
let makeDuplicatedArray = count => {
let arr = Belt.Array.range(1, count) |> E.A.fmap(float_of_int)
let sorted = arr |> Belt.SortArray.stableSortBy(_, compare)
E.A.concatMany([sorted, sorted, sorted, sorted]) |> Belt.SortArray.stableSortBy(_, compare)
}
let (_, discrete1) = SampleSetDist_ToPointSet.Internals.T.splitContinuousAndDiscrete(
makeDuplicatedArray(10),
)
let toArr1 = discrete1 |> E.FloatFloatMap.toArray
makeTest("splitMedium at count=10", toArr1 |> Belt.Array.length, 10)
let (_c, discrete2) = SampleSetDist_ToPointSet.Internals.T.splitContinuousAndDiscrete(
makeDuplicatedArray(500),
)
let toArr2 = discrete2 |> E.FloatFloatMap.toArray
makeTest("splitMedium at count=500", toArr2 |> Belt.Array.length, 500)
// makeTest("foo", [] |> Belt.Array.length, 500)
})

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@ -0,0 +1,48 @@
open Jest
open TestHelpers
let prepareInputs = (ar, minWeight) =>
E.A.Floats.Sorted.splitContinuousAndDiscreteForMinWeight(ar, ~minDiscreteWeight=minWeight) |> (
((c, disc)) => (c, disc |> E.FloatFloatMap.toArray)
)
describe("Continuous and discrete splits", () => {
makeTest(
"is empty, with no common elements",
prepareInputs([1.432, 1.33455, 2.0], 2),
([1.33455, 1.432, 2.0], []),
)
makeTest(
"only stores 3.5 as discrete when minWeight is 3",
prepareInputs([1.432, 1.33455, 2.0, 2.0, 3.5, 3.5, 3.5], 3),
([1.33455, 1.432, 2.0, 2.0], [(3.5, 3.0)]),
)
makeTest(
"doesn't store 3.5 as discrete when minWeight is 5",
prepareInputs([1.432, 1.33455, 2.0, 2.0, 3.5, 3.5, 3.5], 5),
([1.33455, 1.432, 2.0, 2.0, 3.5, 3.5, 3.5], []),
)
let makeDuplicatedArray = count => {
let arr = Belt.Array.range(1, count) |> E.A.fmap(float_of_int)
let sorted = arr |> Belt.SortArray.stableSortBy(_, compare)
E.A.concatMany([sorted, sorted, sorted, sorted]) |> Belt.SortArray.stableSortBy(_, compare)
}
let (_, discrete1) = E.A.Floats.Sorted.splitContinuousAndDiscreteForMinWeight(
makeDuplicatedArray(10),
~minDiscreteWeight=2,
)
let toArr1 = discrete1 |> E.FloatFloatMap.toArray
makeTest("splitMedium at count=10", toArr1 |> Belt.Array.length, 10)
let (_c, discrete2) = E.A.Floats.Sorted.splitContinuousAndDiscreteForMinWeight(
makeDuplicatedArray(500),
~minDiscreteWeight=2,
)
let toArr2 = discrete2 |> E.FloatFloatMap.toArray
makeTest("splitMedium at count=500", toArr2 |> Belt.Array.length, 500)
// makeTest("foo", [] |> Belt.Array.length, 500)
})

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@ -45,7 +45,7 @@
"@types/jest": "^27.4.0",
"babel-plugin-transform-es2015-modules-commonjs": "^6.26.2",
"bisect_ppx": "^2.7.1",
"chalk": "^4.1.2",
"chalk": "^5.0.1",
"codecov": "3.8.3",
"fast-check": "2.25.0",
"gentype": "^4.3.0",

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@ -6,6 +6,24 @@ type toSampleSetFn = t => result<SampleSetDist.t, error>
type scaleMultiplyFn = (t, float) => result<t, error>
type pointwiseAddFn = (t, t) => result<t, error>
let isPointSet = (t: t) =>
switch t {
| PointSet(_) => true
| _ => false
}
let isSampleSetSet = (t: t) =>
switch t {
| SampleSet(_) => true
| _ => false
}
let isSymbolic = (t: t) =>
switch t {
| Symbolic(_) => true
| _ => false
}
let sampleN = (t: t, n) =>
switch t {
| PointSet(r) => PointSetDist.sampleNRendered(n, r)
@ -248,20 +266,24 @@ module AlgebraicCombination = {
| _ => MagicNumbers.OpCost.wildcardCost
}
let hasSampleSetDist = (t1: t, t2: t): bool => isSampleSetSet(t1) || isSampleSetSet(t2)
let convolutionIsFasterThanMonteCarlo = (t1: t, t2: t): bool =>
expectedConvolutionCost(t1) * expectedConvolutionCost(t2) < MagicNumbers.OpCost.monteCarloCost
let preferConvolutionToMonteCarlo = (t1, t2, arithmeticOperation) => {
!hasSampleSetDist(t1, t2) &&
Operation.Convolution.canDoAlgebraicOperation(arithmeticOperation) &&
convolutionIsFasterThanMonteCarlo(t1, t2)
}
let run = (~t1: t, ~t2: t, ~arithmeticOperation): specificStrategy => {
switch StrategyCallOnValidatedInputs.symbolic(arithmeticOperation, t1, t2) {
| #AnalyticalSolution(_)
| #Error(_) =>
#AsSymbolic
| #NoSolution =>
if Operation.Convolution.canDoAlgebraicOperation(arithmeticOperation) {
expectedConvolutionCost(t1) * expectedConvolutionCost(t2) >
MagicNumbers.OpCost.monteCarloCost
? #AsMonteCarlo
: #AsConvolution
} else {
#AsMonteCarlo
}
preferConvolutionToMonteCarlo(t1, t2, arithmeticOperation) ? #AsConvolution : #AsMonteCarlo
}
}
}

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@ -69,3 +69,6 @@ let mixture: (
~scaleMultiplyFn: scaleMultiplyFn,
~pointwiseAddFn: pointwiseAddFn,
) => result<t, error>
let isSymbolic: t => bool
let isPointSet: t => bool

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@ -39,28 +39,6 @@ module Internals = {
module T = {
type t = array<float>
let splitContinuousAndDiscrete = (sortedArray: t) => {
let continuous = []
let discrete = E.FloatFloatMap.empty()
Belt.Array.forEachWithIndex(sortedArray, (index, element) => {
let maxIndex = (sortedArray |> Array.length) - 1
let possiblySimilarElements = switch index {
| 0 => [index + 1]
| n if n == maxIndex => [index - 1]
| _ => [index - 1, index + 1]
} |> Belt.Array.map(_, r => sortedArray[r])
let hasSimilarElement = Belt.Array.some(possiblySimilarElements, r => r == element)
hasSimilarElement
? E.FloatFloatMap.increment(element, discrete)
: {
let _ = Js.Array.push(element, continuous)
}
()
})
(continuous, discrete)
}
let xWidthToUnitWidth = (samples, outputXYPoints, xWidth) => {
let xyPointRange = E.A.Sorted.range(samples) |> E.O.default(0.0)
let xyPointWidth = xyPointRange /. float_of_int(outputXYPoints)
@ -85,7 +63,11 @@ let toPointSetDist = (
(),
): Internals.Types.outputs => {
Array.fast_sort(compare, samples)
let (continuousPart, discretePart) = E.A.Sorted.split(samples)
let minDiscreteToKeep = MagicNumbers.ToPointSet.minDiscreteToKeep(samples)
let (continuousPart, discretePart) = E.A.Floats.Sorted.splitContinuousAndDiscreteForMinWeight(
samples,
~minDiscreteWeight=minDiscreteToKeep,
)
let length = samples |> E.A.length |> float_of_int
let discrete: PointSetTypes.discreteShape =
discretePart

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@ -22,3 +22,16 @@ module OpCost = {
let wildcardCost = 1000
let monteCarloCost = Environment.defaultSampleCount
}
module ToPointSet = {
/*
This function chooses the minimum amount of duplicate samples that need
to exist in order for this to be considered discrete. The tricky thing
is that there are some operations that create duplicate continuous samples,
so we can't guarantee that these only will occur because the fundamental
structure is meant to be discrete. I chose this heuristic because I think
it would strike a reasonable trade-off, but Im really unsure whats
best right now.
*/
let minDiscreteToKeep = samples => max(20, E.A.length(samples) / 50)
}

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@ -8,7 +8,7 @@ module FloatFloatMap = {
type t = Belt.MutableMap.t<Id.t, float, Id.identity>
let fromArray = (ar: array<(float, float)>) => Belt.MutableMap.fromArray(ar, ~id=module(Id))
let toArray = (t: t) => Belt.MutableMap.toArray(t)
let toArray = (t: t): array<(float, float)> => Belt.MutableMap.toArray(t)
let empty = () => Belt.MutableMap.make(~id=module(Id))
let increment = (el, t: t) =>
Belt.MutableMap.update(t, el, x =>
@ -20,6 +20,10 @@ module FloatFloatMap = {
let get = (el, t: t) => Belt.MutableMap.get(t, el)
let fmap = (fn, t: t) => Belt.MutableMap.map(t, fn)
let partition = (fn, t: t) => {
let (match, noMatch) = Belt.Array.partition(toArray(t), fn)
(fromArray(match), fromArray(noMatch))
}
}
module Int = {
@ -553,18 +557,22 @@ module A = {
let makeIncrementalDown = (a, b) =>
Array.make(a - b + 1, a) |> Array.mapi((i, c) => c - i) |> Belt.Array.map(_, float_of_int)
let split = (sortedArray: array<float>) => {
let continuous = []
/*
This function goes through a sorted array and divides it into two different clusters:
continuous samples and discrete samples. The discrete samples are stored in a mutable map.
Samples are thought to be discrete if they have any duplicates.
*/
let _splitContinuousAndDiscreteForDuplicates = (sortedArray: array<float>) => {
let continuous: array<float> = []
let discrete = FloatFloatMap.empty()
Belt.Array.forEachWithIndex(sortedArray, (_, element) => {
// let maxIndex = (sortedArray |> Array.length) - 1
// let possiblySimilarElements = switch index {
// | 0 => [index + 1]
// | n if n == maxIndex => [index - 1]
// | _ => [index - 1, index + 1]
// } |> Belt.Array.map(_, r => sortedArray[r])
// let hasSimilarElement = Belt.Array.some(possiblySimilarElements, r => r == element)
let hasSimilarElement = false
Belt.Array.forEachWithIndex(sortedArray, (index, element) => {
let maxIndex = (sortedArray |> Array.length) - 1
let possiblySimilarElements = switch index {
| 0 => [index + 1]
| n if n == maxIndex => [index - 1]
| _ => [index - 1, index + 1]
} |> Belt.Array.map(_, r => sortedArray[r])
let hasSimilarElement = Belt.Array.some(possiblySimilarElements, r => r == element)
hasSimilarElement
? FloatFloatMap.increment(element, discrete)
: {
@ -576,6 +584,32 @@ module A = {
(continuous, discrete)
}
/*
This function works very similarly to splitContinuousAndDiscreteForDuplicates. The one major difference
is that you can specify a minDiscreteWeight. If the min discreet weight is 4, that would mean that
at least four elements needed from a specific value for that to be kept as discrete. This is important
because in some cases, we can expect that some common elements will be generated by regular operations.
The final continous array will be sorted.
*/
let splitContinuousAndDiscreteForMinWeight = (
sortedArray: array<float>,
~minDiscreteWeight: int,
) => {
let (continuous, discrete) = _splitContinuousAndDiscreteForDuplicates(sortedArray)
let keepFn = v => Belt.Float.toInt(v) >= minDiscreteWeight
let (discreteToKeep, discreteToIntegrate) = FloatFloatMap.partition(
((_, v)) => keepFn(v),
discrete,
)
let newContinousSamples =
discreteToIntegrate->FloatFloatMap.toArray
|> fmap(((k, v)) => Belt.Array.makeBy(Belt.Float.toInt(v), _ => k))
|> Belt.Array.concatMany
let newContinuous = concat(continuous, newContinousSamples)
newContinuous |> Array.fast_sort(floatCompare)
(newContinuous, discreteToKeep)
}
}
}
module Sorted = Floats.Sorted

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@ -0,0 +1,7 @@
---
sidebar_position: 6
title: Gallery
---
- [Adjusting probabilities for the passage of time](https://www.lesswrong.com/s/rDe8QE5NvXcZYzgZ3/p/j8o6sgRerE3tqNWdj) by Nuño Sempere
- [GiveWell's GiveDirectly cost effectiveness analysis](https://observablehq.com/@hazelfire/givewells-givedirectly-cost-effectiveness-analysis) by Sam Nolan

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@ -1,12 +1,10 @@
---
sidebar_position: 5
title: Three Formats of Distributions
author: Ozzie Gooen
date: 02-19-2022
---
# Three Formats of Distributions
_Author: Ozzie Gooen_
_Written on: Feb 19, 2022_
Probability distributions have several subtle possible formats. Three important ones that we deal with in Squiggle are symbolic, sample set, and graph formats.
_Symbolic_ formats are just the math equations. `normal(5,3)` is the symbolic representation of a normal distribution.

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@ -1,39 +1,53 @@
---
sidebar_position: 2
title: Language Basics
---
import { SquiggleEditor } from "../../src/components/SquiggleEditor";
# Squiggle Language
## Expressions
The squiggle language has a very simple syntax. The best way to get to understand
it is by simply looking at examples.
A distribution
## Basic Language
<SquiggleEditor initialSquiggleString={`mixture(1 to 2, 3, [0.3, 0.7])`} />
As an example:
A number
<SquiggleEditor initialSquiggleString="4.321e-3" />
Arrays
<SquiggleEditor
initialSquiggleString={`[beta(1,10), 4, isNormalized(toSampleSet(1 to 2))]`}
/>
Records
<SquiggleEditor
initialSquiggleString={`d = {dist: triangular(0, 1, 2), weight: 0.25}
d.dist`}
/>
## Statements
A statement assigns expressions to names. It looks like `<symbol> = <expression>`
<SquiggleEditor
initialSquiggleString={`value_of_work = 10 to 70
value_of_work`}
5 + value_of_work / 75`}
/>
Squiggle can declare variables (`value_of_work = 10 to 70`) and declare exports
(the lone `value_of_work` line). Variables can be used later in a squiggle program
and even in other notebooks!
### Functions
An export is rendered to the output view so you can see your result.
the exports can be expressions, such as:
<SquiggleEditor initialSquiggleString="normal(0,1)" />
## Functions
Squiggle supports functions, including the rendering of functions:
We can define functions
<SquiggleEditor
initialSquiggleString={`ozzie_estimate(t) = lognormal({mean: 3 + (t+.1)^2.5, stdev: 8})
ozzie_estimate
`}
initialSquiggleString={`ozzie_estimate(t) = lognormal(1, t ^ 1.01)
nuño_estimate(t, m) = mixture(0.5 to 2, normal(m, t ^ 1.25))
ozzie_estimate(5) * nuño_estimate(5.01, 1)`}
/>
## See more
- [Functions reference](https://squiggle-language.com/docs/Features/Functions)
- [Gallery](https://squiggle-language.com/docs/Discussions/Gallery)

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@ -1,13 +1,12 @@
---
sidebar_position: 3
title: Node Packages
---
# Javascript Libraries
There are two JavaScript packages currently available for Squiggle:
- [`@quri/squiggle-lang`](https://www.npmjs.com/package/@quri/squiggle-lang)
- [`@quri/squiggle-components`](https://www.npmjs.com/package/@quri/squiggle-components)
- [`@quri/squiggle-lang`](https://www.npmjs.com/package/@quri/squiggle-lang) ![npm version](https://badge.fury.io/js/@quri%2Fsquiggle-lang.svg)
- [`@quri/squiggle-components`](https://www.npmjs.com/package/@quri/squiggle-components) ![npm version](https://badge.fury.io/js/@quri%2Fsquiggle-components.svg)
Types are available for both packages.
@ -23,8 +22,8 @@ argument allows you to pass an environment previously created by another `run`
call. Passing this environment will mean that all previously declared variables
in the previous environment will be made available.
The return type of `run` is a bit complicated, and comes from auto generated js
code that comes from rescript. I highly recommend using typescript when using
The return type of `run` is a bit complicated, and comes from auto generated `js`
code that comes from rescript. We highly recommend using typescript when using
this library to help navigate the return type.
## Squiggle Components

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@ -1,6 +1,9 @@
# Processing confidence intervals
---
title: Processing Confidence Intervals
author: Nuño Sempere
---
This page explains what we are doing when we take a 95% confidence interval, and we get a mean and a standard deviation from it
This page explains what we are doing when we take a 90% confidence interval, and we get a mean and a standard deviation from it.
## For normals
@ -19,10 +22,7 @@ module Normal = {
We know that for a normal with mean $\mu$ and standard deviation $\sigma$,
$$
a \cdot Normal(\mu, \sigma) = Normal(a\cdot \mu, |a|\cdot \sigma)
a \cdot Normal(\mu, \sigma) = Normal(a \cdot \mu, |a| \cdot \sigma)
$$
We can now look at the inverse cdf of a $Normal(0,1)$. We find that the 95% point is reached at $1.6448536269514722$. ([source](https://stackoverflow.com/questions/20626994/how-to-calculate-the-inverse-of-the-normal-cumulative-distribution-function-in-p)) This means that the 90% confidence interval is $[-1.6448536269514722, 1.6448536269514722]$, which has a width of $2 \cdot 1.6448536269514722$.
@ -30,3 +30,5 @@ We can now look at the inverse cdf of a $Normal(0,1)$. We find that the 95% poin
So then, if we take a $Normal(0,1)$ and we multiply it by $\frac{(high -. low)}{(2. *. 1.6448536269514722)}$, it's 90% confidence interval will be multiplied by the same amount. Then we just have to shift it by the mean to get our target normal.
## For lognormals
TODO

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@ -1,10 +1,16 @@
---
sidebar_position: 1
title: Introduction
---
# Squiggle
Squiggle is an _estimation language_, and a syntax for _calculating and expressing beliefs_ involving uncertainty. It has use cases in forecasting and writing evaluations.
Squiggle is a language for writing calculations under uncertainty. It has use
cases in forecasting and writing better evaluations.
## Get started
The best way to get started with Squiggle is to [try it out yourself](https://playground.squiggle-language.com/).
- [Gallery](https://www.squiggle-language.com/docs/Discussions/Gallery)
- [Squiggle playground](https://squiggle-language.com/playground)
- [Language basics](https://www.squiggle-language.com/docs/Features/Language)
- [Squiggle functions source of truth](https://www.squiggle-language.com/docs/Features/Functions)
- [Known bugs](https://www.squiggle-language.com/docs/Discussions/Bugs)
- [Original lesswrong sequence](https://www.lesswrong.com/s/rDe8QE5NvXcZYzgZ3)
- [Author your squiggle models as Observable notebooks](https://observablehq.com/@hazelfire/squiggle)

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@ -1,170 +0,0 @@
<svg xmlns="http://www.w3.org/2000/svg" width="1088" height="687.962" viewBox="0 0 1088 687.962">
<g id="Group_12" data-name="Group 12" transform="translate(-57 -56)">
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@ -6155,6 +6155,11 @@ chalk@^4.0.0, chalk@^4.0.2, chalk@^4.1.0, chalk@^4.1.2:
ansi-styles "^4.1.0"
supports-color "^7.1.0"
chalk@^5.0.1:
version "5.0.1"
resolved "https://registry.yarnpkg.com/chalk/-/chalk-5.0.1.tgz#ca57d71e82bb534a296df63bbacc4a1c22b2a4b6"
integrity sha512-Fo07WOYGqMfCWHOzSXOt2CxDbC6skS/jO9ynEcmpANMoPrD+W1r1K6Vx7iNm+AQmETU1Xr2t+n8nzkV9t6xh3w==
char-regex@^1.0.2:
version "1.0.2"
resolved "https://registry.yarnpkg.com/char-regex/-/char-regex-1.0.2.tgz#d744358226217f981ed58f479b1d6bcc29545dcf"