[issue46020] Optimize long_pow for the common case

[Raymond Hettinger]

Raymond Hettinger <raymond.hettinger at gmail.com> added the comment:

The situation for floats is also disappointing:

$ python3.11 -m timeit -s 'x=1.1' 'x ** 2'
5000000 loops, best of 5: 60.8 nsec per loop
$ python3.11 -m timeit -s 'x=1.1' 'x ** 2.0'
5000000 loops, best of 5: 51.5 nsec per loop
$ python3.11 -m timeit -s 'x=1.1' 'x * x'
20000000 loops, best of 5: 17.7 nsec per loop

Complex numbers are more balanced.  Surprisingly, some of the complex cases are faster than their float counterparts:

$ python3.11 -m timeit -s 'x=1.1+2.2j' 'x ** 2'
5000000 loops, best of 5: 42.4 nsec per loop
$ python3.11 -m timeit -s 'x=1.1+2.2j' 'x ** 2.0'
5000000 loops, best of 5: 43.3 nsec per loop
$ python3.11 -m timeit -s 'x=1.1+2.2j' 'x ** 2.0j'
2000000 loops, best of 5: 107 nsec per loop
$ python3.11 -m timeit -s 'x=1.1+2.2j' 'x * x'
10000000 loops, best of 5: 30.6 nsec per loop

Decimal still shows a large difference:

$ python3.11 -m timeit -s 'from decimal import Decimal' -s 'x=Decimal("1.1")' 'x ** 2'
1000000 loops, best of 5: 206 nsec per loop
$ python3.11 -m timeit -s 'from decimal import Decimal' -s 'x=Decimal("1.1")' 'x ** Decimal(2)'
1000000 loops, best of 5: 281 nsec per loop
$ python3.11 -m timeit -s 'from decimal import Decimal' -s 'x=Decimal("1.1")' 'x * x'
5000000 loops, best of 5: 60.9 nsec per loop

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<https://bugs.python.org/issue46020>
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