keras/keras_core/random/seed_generator.py

import random as python_random

from keras_core.api_export import keras_core_export


@keras_core_export("keras_core.random.SeedGenerator")
class SeedGenerator:
    def __init__(self, seed):
        from keras_core.backend import Variable

        if seed is None:
            seed = make_default_seed()
        if not isinstance(seed, int):
            raise ValueError(
                "Argument `seed` must be an integer. " f"Received: seed={seed}"
            )

        def seed_initializer(*args, **kwargs):
            return [seed, 0]

        self.state = Variable(
            seed_initializer, shape=(2,), dtype="uint32", trainable=False
        )


def make_default_seed():
    return python_random.randint(1, int(1e9))


def draw_seed(seed):
    from keras_core.backend import convert_to_tensor

    if isinstance(seed, SeedGenerator):
        # Use * 1 to create a copy
        new_seed_value = seed.state.value * 1
        seed.state.assign(
            seed.state + convert_to_tensor([0, 1], dtype="uint32")
        )
        return new_seed_value
    elif isinstance(seed, int):
        return convert_to_tensor([seed, 0], dtype="uint32")
    elif seed is None:
        return convert_to_tensor([make_default_seed(), 0], dtype="uint32")
    raise ValueError(
        "Argument `seed` must be either an integer "
        "or an instance of `SeedGenerator`. "
        f"Received: seed={seed} (of type {type(seed)})"
    )