gh-150638: Improve performance of json.loads and json.load for numeric data

[eendebakpt]

_match_number_unicode() (the C accelerator behind json.loads) previously allocated a PyBytes object for every number, copied the digits into it, and then called the generic PyLong_FromString / PyFloat_FromString parsers.
This PR parses the common cases directly from the already-scanned text.

Benchmark	main	this PR	speedup
json.loads, number-heavy document (script below)	3.05 ms	2.38 ms	1.28×
json.load, same document via file object	3.17 ms	2.48 ms	1.28×
pyperformance bm_json_loads	25.2 µs	23.9 µs	1.05×

The standard bm_json_loads document is string/dict-dominated, so it gains
less.

Benchmark script

"""Benchmark json.loads() and json.load() on a number-heavy document.

The document is generated deterministically at import time (no external
files) and resembles a typical telemetry/API payload: a list of records
mixing integers, 19-digit timestamps, negative integers, floats, short
strings, booleans and small integer arrays.

json.load(fp) is json.loads(fp.read()); here fp is an in-memory io.StringIO
(rewound each call) so the same document is parsed without disk noise.

Inline data size: ~304 KiB (2000 records).
"""
import io
import json
import pyperf


def build_document(n=2000):
    return [
        {
            "id": i,
            "timestamp": 1_700_000_000_000_000_000 + i * 1_000,  # 19-digit int
            "value": i * 1.5 - 1000.0,                           # float
            "delta": -i,                                         # negative int
            "label": "item-%d" % i,                              # short string
            "ok": i % 2 == 0,                                    # bool
            "samples": [i, -i, i * 2, i * 3, i * 5],             # int array
        }
        for i in range(n)
    ]


JSON_DATA = json.dumps(build_document())
STREAM = io.StringIO(JSON_DATA)


def load_from_stream():
    STREAM.seek(0)
    return json.load(STREAM)


if __name__ == "__main__":
    runner = pyperf.Runner()
    runner.metadata["description"] = "json.loads()/json.load() on a number-heavy document"
    runner.bench_func("json_loads", json.loads, JSON_DATA)
    runner.bench_func("json_load", load_from_stream)

• Issue: Improve performance of json.loads and json.load for numeric data #150638

[markshannon]

Looks good, but the wide scope of numstr makes it a bit hard to reason about refleaks.

Can you remove the early declaration of numstr and define it where you need it.

[markshannon]

numstr is only defined on lines 1068 and 1108.
You could declare it only in those places, and decref it in the same blocks.