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import torch as t |
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def split_batch(obj, n_samples, split_size): |
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n_passes = (n_samples + split_size - 1) // split_size |
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if isinstance(obj, t.Tensor): |
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return t.split(obj, split_size, dim=0) |
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elif isinstance(obj, list): |
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return list(zip(*[t.split(item, split_size, dim=0) for item in obj])) |
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elif obj is None: |
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return [None] * n_passes |
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else: |
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raise TypeError('Unknown input type') |
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def get_starts(total_length, n_ctx, hop_length): |
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starts = [] |
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for start in range(0, total_length - n_ctx + hop_length, hop_length): |
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if start + n_ctx >= total_length: |
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start = total_length - n_ctx |
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starts.append(start) |
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return starts |
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