Use repeat_n in Vec::extend_with

[DaniPopes]

Simplify Vec::extend_with to use extend(repeat_n(...)). This delegates to extend_trusted which essentially does the same thing but using a single loop; the "don't clone the last element" logic is handled inside of the repeat_n iterator.

This generates slightly better code by removing the manual store of the last element: https://godbolt.org/z/TG6GPY9Eh

[rustbot]

r? @Mark-Simulacrum

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They will have a look at your PR within the next two weeks and either review your PR or reassign to another reviewer.

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Why was this reviewer chosen?

The reviewer was selected based on:

• Owners of files modified in this PR: libs
• libs expanded to 7 candidates
• Random selection from Mark-Simulacrum, jhpratt, nia-e

[scottmcm]

r? scottmcm
@bors try @rust-timer queue

[rust-bors]

☀️ Try build successful (CI)
Build commit: 4ac3534 (4ac3534e21548421df6f6113981f3b65a1477e1a, parent: 0164cc139285d5452053bcc5a83da046a501ed61)

[rust-timer]

Finished benchmarking commit (4ac3534): comparison URL.

Overall result: ❌✅ regressions and improvements - please read:

Benchmarking means the PR may be perf-sensitive. It's automatically marked not fit for rolling up. Overriding is possible but disadvised: it risks changing compiler perf.

Next, please: If you can, justify the regressions found in this try perf run in writing along with @rustbot label: +perf-regression-triaged. If not, fix the regressions and do another perf run. Neutral or positive results will clear the label automatically.

@bors rollup=never
@rustbot label: -S-waiting-on-perf +perf-regression

Instruction count

Our most reliable metric. Used to determine the overall result above. However, even this metric can be noisy.

	mean	range	count
Regressions ❌ (primary)	0.6%	[0.1%, 19.4%]	81
Regressions ❌ (secondary)	0.3%	[0.2%, 0.7%]	39
Improvements ✅ (primary)	-1.7%	[-1.7%, -1.7%]	1
Improvements ✅ (secondary)	-0.6%	[-0.7%, -0.6%]	6
All ❌✅ (primary)	0.5%	[-1.7%, 19.4%]	82

Max RSS (memory usage)

Results (primary 0.1%, secondary -0.3%)

A less reliable metric. May be of interest, but not used to determine the overall result above.

	mean	range	count
Regressions ❌ (primary)	3.0%	[2.4%, 3.5%]	2
Regressions ❌ (secondary)	0.6%	[0.5%, 0.7%]	2
Improvements ✅ (primary)	-2.8%	[-3.8%, -1.9%]	2
Improvements ✅ (secondary)	-2.2%	[-2.2%, -2.2%]	1
All ❌✅ (primary)	0.1%	[-3.8%, 3.5%]	4

Cycles

Results (primary 12.6%, secondary -1.4%)

A less reliable metric. May be of interest, but not used to determine the overall result above.

	mean	range	count
Regressions ❌ (primary)	12.6%	[6.5%, 18.6%]	2
Regressions ❌ (secondary)	-	-	0
Improvements ✅ (primary)	-	-	0
Improvements ✅ (secondary)	-1.4%	[-2.3%, -0.6%]	2
All ❌✅ (primary)	12.6%	[6.5%, 18.6%]	2

Binary size

Results (primary 0.7%)

A less reliable metric. May be of interest, but not used to determine the overall result above.

	mean	range	count
Regressions ❌ (primary)	0.7%	[0.1%, 1.8%]	33
Regressions ❌ (secondary)	-	-	0
Improvements ✅ (primary)	-0.0%	[-0.0%, -0.0%]	3
Improvements ✅ (secondary)	-	-	0
All ❌✅ (primary)	0.7%	[-0.0%, 1.8%]	36

Bootstrap: 482.686s -> 483.185s (0.10%)
Artifact size: 390.94 MiB -> 390.90 MiB (-0.01%)