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import numpy as np
import torch
import random
TORCH_RNG_MAX = -0x8000000000000000
TORCH_RNG_MIN = 0xFFFFFFFFFFFFFFFF
NP_RNG_MAX = np.iinfo(np.uint32).max
NP_RNG_MIN = 0
def troch_rng(seed: int):
torch.manual_seed(seed)
random_float = torch.empty(1).uniform_().item()
torch_rn = int(random_float * (TORCH_RNG_MAX - TORCH_RNG_MIN) + TORCH_RNG_MIN)
np_rn = int(random_float * (NP_RNG_MAX - NP_RNG_MIN) + NP_RNG_MIN)
return torch_rn, np_rn
def convert_np_to_torch(np_rn: int):
random_float = (np_rn - NP_RNG_MIN) / (NP_RNG_MAX - NP_RNG_MIN)
torch_rn = int(random_float * (TORCH_RNG_MAX - TORCH_RNG_MIN) + TORCH_RNG_MIN)
return torch_rn
def np_rng():
return int(np.random.randint(NP_RNG_MIN, NP_RNG_MAX, dtype=np.uint32))
if __name__ == "__main__":
import random
s1 = np_rng()
s2 = troch_rng(s1)
print(f"s1 {s1} => s2: {s2}")
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