This patch implements the `RandomGenerator`, a new input generator that
enables conformance testing for functions with large input spaces (e.g.,
double-precision math functions).
**Architectural Refactoring**
To support different generation strategies in a clean and extensible
way, the existing `ExhaustiveGenerator` was refactored into a new class
hierarchy:
* A new abstract base class, `RangeBasedGenerator`, was introduced using
the Curiously Recurring Template Pattern (CRTP). It contains the common
logic for generators that operate on a sequence of ranges.
* `ExhaustiveGenerator` now inherits from this base class, simplifying
its implementation.
**New Components**
* The new `RandomGenerator` class also inherits from
`RangeBasedGenerator`. It implements a strategy that randomly samples a
specified number of points from the total input space.
* Random number generation is handled by a new, self-contained
`RandomState` class (a `xorshift64*` PRNG seeded with `splitmix64`) to
ensure deterministic and reproducible random streams for testing.
**Example Usage**
As a first use case and demonstration of this new capability, this patch
also adds the first double-precision conformance test for the `log`
function. This test uses the new `RandomGenerator` to validate the
implementations from the `llvm-libm`, `cuda-math`, and `hip-math`
providers.
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