Why does numpy.random.Generator.choice provides different results (seeded) with given uniform distribution compared to default uniform distribution?

Simple test code:

pop = numpy.arange(20)
rng = numpy.random.default_rng(1)
rng.choice(pop,p=numpy.repeat(1/len(pop),len(pop))) # yields 10
rng = numpy.random.default_rng(1)
rng.choice(pop) # yields 9

The numpy documentation says:

The probabilities associated with each entry in a. If not given the sample assumes a uniform distribution over all entries in a.

I don't know of any other way to create a uniform distribution, but numpy.repeat(1/len(pop),len(pop)).

Is numpy using something else? Why?

If not, how does setting the distribution affects the seed?

Shouldn't the distribution and the seed be independent?

What am I missing here?