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import torch
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from ...util import default, instantiate_from_config
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class EDMSampling:
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def __init__(self, p_mean=-1.2, p_std=1.2):
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self.p_mean = p_mean
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self.p_std = p_std
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def __call__(self, n_samples, rand=None):
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log_sigma = self.p_mean + self.p_std * default(rand, torch.randn((n_samples,)))
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return log_sigma.exp()
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class DiscreteSampling:
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def __init__(self, discretization_config, num_idx, do_append_zero=False, flip=True, idx_range=None):
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self.num_idx = num_idx
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self.sigmas = instantiate_from_config(discretization_config)(
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num_idx, do_append_zero=do_append_zero, flip=flip
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)
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self.idx_range = idx_range
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def idx_to_sigma(self, idx):
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return self.sigmas[idx]
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def __call__(self, n_samples, rand=None):
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if self.idx_range is None:
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idx = default(
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rand,
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torch.randint(0, self.num_idx, (n_samples,)),
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)
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else:
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idx = default(
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rand,
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torch.randint(self.idx_range[0], self.idx_range[1], (n_samples,)),
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)
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return self.idx_to_sigma(idx)
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