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65 lines
No EOL
2.1 KiB
Python
65 lines
No EOL
2.1 KiB
Python
"""
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/* Copyright (c) 2023 Amazon
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Written by Jan Buethe */
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/*
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Redistribution and use in source and binary forms, with or without
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modification, are permitted provided that the following conditions
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are met:
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- Redistributions of source code must retain the above copyright
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notice, this list of conditions and the following disclaimer.
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- Redistributions in binary form must reproduce the above copyright
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notice, this list of conditions and the following disclaimer in the
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documentation and/or other materials provided with the distribution.
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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
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LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
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A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER
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OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
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EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
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PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
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PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
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LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
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NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
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SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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*/
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"""
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import torch
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def find(a, v):
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try:
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idx = a.index(v)
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except:
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idx = -1
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return idx
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def interleave_tensors(tensors, dim=-2):
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""" interleave list of tensors along sequence dimension """
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x = torch.cat([x.unsqueeze(dim) for x in tensors], dim=dim)
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x = torch.flatten(x, dim - 1, dim)
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return x
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def _interleave(x, pcm_levels=256):
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repeats = pcm_levels // (2*x.size(-1))
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x = x.unsqueeze(-1)
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p = torch.flatten(torch.repeat_interleave(torch.cat((x, 1 - x), dim=-1), repeats, dim=-1), -2)
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return p
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def get_pdf_from_tree(x):
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pcm_levels = x.size(-1)
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p = _interleave(x[..., 1:2])
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n = 4
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while n <= pcm_levels:
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p = p * _interleave(x[..., n//2:n])
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n *= 2
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return p |