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[chore] update dependencies (#4188)

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Update dependencies:
- github.com/gin-gonic/gin v1.10.0 -> v1.10.1
- github.com/gin-contrib/sessions v1.10.3 -> v1.10.4
- github.com/jackc/pgx/v5 v5.7.4 -> v5.7.5
- github.com/minio/minio-go/v7 v7.0.91 -> v7.0.92
- github.com/pquerna/otp v1.4.0 -> v1.5.0
- github.com/tdewolff/minify/v2 v2.23.5 -> v2.23.8
- github.com/yuin/goldmark v1.7.11 -> v1.7.12
- go.opentelemetry.io/otel{,/*} v1.35.0 -> v1.36.0
- modernc.org/sqlite v1.37.0 -> v1.37.1

Reviewed-on: https://codeberg.org/superseriousbusiness/gotosocial/pulls/4188
Reviewed-by: Daenney <daenney@noreply.codeberg.org>
Co-authored-by: kim <grufwub@gmail.com>
Co-committed-by: kim <grufwub@gmail.com>
This commit is contained in:
kim 2025-05-22 16:27:55 +02:00 • committed by kim
commit b6ff55662e
214 changed files with 44839 additions and 32023 deletions
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36
vendor/go.opentelemetry.io/otel/sdk/metric/exemplar/fixed_size_reservoir.go generated vendored
View file

@ -6,7 +6,7 @@ package exemplar // import "go.opentelemetry.io/otel/sdk/metric/exemplar"
import (
"context"
"math"
"math/rand"
"math/rand/v2"
"time"
"go.opentelemetry.io/otel/attribute"
@ -44,18 +44,11 @@ type FixedSizeReservoir struct {
// w is the largest random number in a distribution that is used to compute
// the next next.
w float64
// rng is used to make sampling decisions.
//
// Do not use crypto/rand. There is no reason for the decrease in performance
// given this is not a security sensitive decision.
rng *rand.Rand
}
func newFixedSizeReservoir(s *storage) *FixedSizeReservoir {
r := &FixedSizeReservoir{
storage: s,
rng: rand.New(rand.NewSource(time.Now().UnixNano())),
}
r.reset()
return r
@ -64,26 +57,15 @@ func newFixedSizeReservoir(s *storage) *FixedSizeReservoir {
// randomFloat64 returns, as a float64, a uniform pseudo-random number in the
// open interval (0.0,1.0).
func (r *FixedSizeReservoir) randomFloat64() float64 {
// TODO: This does not return a uniform number. rng.Float64 returns a
// uniformly random int in [0,2^53) that is divided by 2^53. Meaning it
// returns multiples of 2^-53, and not all floating point numbers between 0
// and 1 (i.e. for values less than 2^-4 the 4 last bits of the significand
// are always going to be 0).
// TODO: Use an algorithm that avoids rejection sampling. For example:
//
// An alternative algorithm should be considered that will actually return
// a uniform number in the interval (0,1). For example, since the default
// rand source provides a uniform distribution for Int63, this can be
// converted following the prototypical code of Mersenne Twister 64 (Takuji
// Nishimura and Makoto Matsumoto:
// http://www.math.sci.hiroshima-u.ac.jp/m-mat/MT/VERSIONS/C-LANG/mt19937-64.c)
//
// (float64(rng.Int63()>>11) + 0.5) * (1.0 / 4503599627370496.0)
//
// There are likely many other methods to explore here as well.
f := r.rng.Float64()
// const precision = 1 << 53 // 2^53
// // Generate an integer in [1, 2^53 - 1]
// v := rand.Uint64() % (precision - 1) + 1
// return float64(v) / float64(precision)
f := rand.Float64()
for f == 0 {
f = r.rng.Float64()
f = rand.Float64()
}
return f
}
@ -146,7 +128,7 @@ func (r *FixedSizeReservoir) Offer(ctx context.Context, t time.Time, n Value, a
} else {
if r.count == r.next {
// Overwrite a random existing measurement with the one offered.
idx := int(r.rng.Int63n(int64(cap(r.store))))
idx := int(rand.Int64N(int64(cap(r.store))))
r.store[idx] = newMeasurement(ctx, t, n, a)
r.advance()
}