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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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