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#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from typing import Sequence, Tuple, Union
import re
import numpy as np
import jax.numpy as jnp
from .constants import proteinseq_toks
RawMSA = Sequence[Tuple[str, str]]
class FastaBatchedDataset(object):
def __init__(self, sequence_labels, sequence_strs):
self.sequence_labels = list(sequence_labels)
self.sequence_strs = list(sequence_strs)
@classmethod
def from_file(cls, fasta_file):
sequence_labels, sequence_strs = [], []
cur_seq_label = None
buf = []
def _flush_current_seq():
nonlocal cur_seq_label, buf
if cur_seq_label is None:
return
sequence_labels.append(cur_seq_label)
sequence_strs.append("".join(buf))
cur_seq_label = None
buf = []
with open(fasta_file, "r") as infile:
for line_idx, line in enumerate(infile):
if line.startswith(">"): # label line
_flush_current_seq()
line = line[1:].strip()
if len(line) > 0:
cur_seq_label = line
else:
cur_seq_label = f"seqnum{line_idx:09d}"
else: # sequence line
buf.append(line.strip())
_flush_current_seq()
assert len(set(sequence_labels)) == len(sequence_labels), "Found duplicate sequence labels"
return cls(sequence_labels, sequence_strs)
def __len__(self):
return len(self.sequence_labels)
def __getitem__(self, idx):
return self.sequence_labels[idx], self.sequence_strs[idx]
def get_batch_indices(self, toks_per_batch, extra_toks_per_seq=0):
sizes = [(len(s), i) for i, s in enumerate(self.sequence_strs)]
sizes.sort()
batches = []
buf = []
max_len = 0
def _flush_current_buf():
nonlocal max_len, buf
if len(buf) == 0:
return
batches.append(buf)
buf = []
max_len = 0
for sz, i in sizes:
sz += extra_toks_per_seq
if max(sz, max_len) * (len(buf) + 1) > toks_per_batch:
_flush_current_buf()
max_len = max(max_len, sz)
buf.append(i)
_flush_current_buf()
return batches
class Alphabet(object):
def __init__(
self,
standard_toks: Sequence[str],
prepend_toks: Sequence[str] = ("<null_0>", "<pad>", "<eos>", "<unk>"),
append_toks: Sequence[str] = ("<cls>", "<mask>", "<sep>"),
prepend_bos: bool = True,
append_eos: bool = False,
use_msa: bool = False,
):
self.standard_toks = list(standard_toks)
self.prepend_toks = list(prepend_toks)
self.append_toks = list(append_toks)
self.prepend_bos = prepend_bos
self.append_eos = append_eos
self.use_msa = use_msa
self.all_toks = list(self.prepend_toks)
self.all_toks.extend(self.standard_toks)
for i in range((8 - (len(self.all_toks) % 8)) % 8):
self.all_toks.append(f"<null_{i + 1}>")
self.all_toks.extend(self.append_toks)
self.tok_to_idx = {tok: i for i, tok in enumerate(self.all_toks)}
self.unk_idx = self.tok_to_idx["<unk>"]
self.padding_idx = self.get_idx("<pad>")
self.cls_idx = self.get_idx("<cls>")
self.mask_idx = self.get_idx("<mask>")
self.eos_idx = self.get_idx("<eos>")
def __len__(self):
return len(self.all_toks)
def get_idx(self, tok):
return self.tok_to_idx.get(tok, self.unk_idx)
def get_tok(self, ind):
return self.all_toks[ind]
def to_dict(self):
return {"toks": self.toks}
def get_batch_converter(self):
if self.use_msa:
return MSABatchConverter(self)
else:
return BatchConverter(self)
@classmethod
def from_dict(cls, d, **kwargs):
return cls(standard_toks=d["toks"], **kwargs)
@classmethod
def from_architecture(cls, name: str) -> "Alphabet":
if name in ("ESM-1", "protein_bert_base"):
standard_toks = proteinseq_toks["toks"]
prepend_toks: Tuple[str, ...] = ("<null_0>", "<pad>", "<eos>", "<unk>")
append_toks: Tuple[str, ...] = ("<cls>", "<mask>", "<sep>")
prepend_bos = True
append_eos = False
use_msa = False
elif name in ("ESM-1b", "roberta_large"):
standard_toks = proteinseq_toks["toks"]
prepend_toks = ("<cls>", "<pad>", "<eos>", "<unk>")
append_toks = ("<mask>",)
prepend_bos = True
append_eos = True
use_msa = False
elif name in ("MSA Transformer", "msa_transformer"):
standard_toks = proteinseq_toks["toks"]
prepend_toks = ("<cls>", "<pad>", "<eos>", "<unk>")
append_toks = ("<mask>",)
prepend_bos = True
append_eos = False
use_msa = True
else:
raise ValueError("Unknown architecture selected")
return cls(standard_toks, prepend_toks, append_toks, prepend_bos, append_eos, use_msa)
class BatchConverter(object):
"""Callable to convert an unprocessed (labels + strings) batch to a
processed (labels + tensor) batch.
"""
def __init__(self, alphabet):
self.alphabet = alphabet
def __call__(self, raw_batch: Sequence[Tuple[str, str]], return_j=True):
# RoBERTa uses an eos token, while ESM-1 does not.
batch_size = len(raw_batch)
max_len = max(len(seq_str) for _, seq_str in raw_batch)
tokens_np = np.ones(
[
batch_size,
max_len + int(self.alphabet.prepend_bos) + int(self.alphabet.append_eos)
],
dtype=np.int64
) * self.alphabet.padding_idx
labels = []
strs = []
for i, (label, seq_str) in enumerate(raw_batch):
labels.append(label)
strs.append(seq_str)
if self.alphabet.prepend_bos:
tokens_np[i, 0] = self.alphabet.cls_idx
seq = np.array([self.alphabet.get_idx(s) for s in seq_str], dtype=np.int64)
tokens_np[
i,
int(self.alphabet.prepend_bos): len(seq_str) + int(self.alphabet.prepend_bos),
] = seq
if self.alphabet.append_eos:
tokens_np[i, len(seq_str) + int(self.alphabet.prepend_bos)] = self.alphabet.eos_idx
if return_j:
tokens = jnp.array(tokens_np)
else:
tokens = tokens_np
return labels, strs, tokens
class MSABatchConverter(BatchConverter):
def __call__(self, inputs: Union[Sequence[RawMSA], RawMSA], return_j=True):
if isinstance(inputs[0][0], str):
# Input is a single MSA
raw_batch: Sequence[RawMSA] = [inputs] # type: ignore
else:
raw_batch = inputs # type: ignore
batch_size = len(raw_batch)
max_alignments = max(len(msa) for msa in raw_batch)
max_seqlen = max(len(msa[0][1]) for msa in raw_batch)
tokens_np = np.ones(
[
batch_size,
max_alignments,
max_seqlen + int(self.alphabet.prepend_bos) + int(self.alphabet.append_eos),
],
dtype=np.int64,
) * self.alphabet.padding_idx
labels = []
strs = []
for i, msa in enumerate(raw_batch):
msa_seqlens = set(len(seq) for _, seq in msa)
if not len(msa_seqlens) == 1:
raise RuntimeError(
"Received unaligned sequences for input to MSA, all sequence "
"lengths must be equal."
)
msa_labels, msa_strs, msa_tokens = super().__call__(msa, return_j=False)
labels.append(msa_labels)
strs.append(msa_strs)
tokens_np[i, :msa_tokens.shape[0], :msa_tokens.shape[1]] = msa_tokens
if return_j:
tokens = jnp.array(tokens_np)
else:
tokens = tokens_np
return labels, strs, tokens
def read_fasta(
path,
keep_gaps=True,
keep_insertions=True,
to_upper=False,
):
with open(path, "r") as f:
for result in read_alignment_lines(
f, keep_gaps=keep_gaps, keep_insertions=keep_insertions, to_upper=to_upper
):
yield result
def read_alignment_lines(
lines,
keep_gaps=True,
keep_insertions=True,
to_upper=False,
):
seq = desc = None
def parse(s):
if not keep_gaps:
s = re.sub("-", "", s)
if not keep_insertions:
s = re.sub("[a-z]", "", s)
return s.upper() if to_upper else s
for line in lines:
# Line may be empty if seq % file_line_width == 0
if len(line) > 0 and line[0] == ">":
if seq is not None:
yield desc, parse(seq)
desc = line.strip()
seq = ""
else:
assert isinstance(seq, str)
seq += line.strip()
assert isinstance(seq, str) and isinstance(desc, str)
yield desc, parse(seq)
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