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import os
from collections import defaultdict
from contextlib import nullcontext
from dataclasses import is_dataclass
from io import BytesIO
from typing import (
Any,
ContextManager,
Generator,
Iterable,
Protocol,
Sequence,
TypeVar,
runtime_checkable,
)
from warnings import warn
import huggingface_hub
import numpy as np
import torch
import zstd
from .esmfold2_constants_esm3 import CHAIN_BREAK_STR
from .esmfold2_utils_types import FunctionAnnotation
MAX_SUPPORTED_DISTANCE = 1e6
TSequence = TypeVar("TSequence", bound=Sequence)
@runtime_checkable
class Concatable(Protocol):
@classmethod
def concat(cls, objs: list[Concatable]) -> Concatable: ...
def slice_python_object_as_numpy(
obj: TSequence, idx: int | list[int] | slice | np.ndarray
) -> TSequence:
"""
Slice a python object (like a list, string, or tuple) as if it was a numpy object.
Example:
>>> obj = "ABCDE"
>>> slice_python_object_as_numpy(obj, [1, 3, 4])
"BDE"
>>> obj = [1, 2, 3, 4, 5]
>>> slice_python_object_as_numpy(obj, np.arange(5) < 3)
[1, 2, 3]
"""
if np.isscalar(idx):
idx = [int(idx)] # type: ignore
if isinstance(idx, np.ndarray) and idx.dtype == bool:
sliced_obj = [obj[i] for i in np.where(idx)[0]]
elif isinstance(idx, slice):
sliced_obj = obj[idx]
else:
sliced_obj = [obj[i] for i in idx] # type: ignore
match obj, sliced_obj:
case str(), list():
sliced_obj = "".join(sliced_obj)
case _:
sliced_obj = obj.__class__(sliced_obj) # type: ignore
return sliced_obj # type: ignore
def slice_any_object(
obj: TSequence, idx: int | list[int] | slice | np.ndarray
) -> TSequence:
"""
Slice a arbitrary object (like a list, string, or tuple) as if it was a numpy object. Similar to `slice_python_object_as_numpy`, but detects if it's a numpy array or Tensor and uses the existing slice method if so.
If the object is a dataclass, it will simply apply the index to the object, under the assumption that the object has correcty implemented numpy indexing.
Example:
>>> obj = "ABCDE"
>>> slice_any_object(obj, [1, 3, 4])
"BDE"
>>> obj = np.array([1, 2, 3, 4, 5])
>>> slice_any_object(obj, np.arange(5) < 3)
np.array([1, 2, 3])
>>> obj = ProteinChain.from_rcsb("1a3a", "A")
>>> slice_any_object(obj, np.arange(len(obj)) < 10)
# ProteinChain w/ length 10
"""
if isinstance(obj, (np.ndarray, torch.Tensor)):
return obj[idx] # type: ignore
elif is_dataclass(obj):
# if passing a dataclass, assume it implements a custom slice
return obj[idx] # type: ignore
else:
return slice_python_object_as_numpy(obj, idx)
def rbf(values, v_min, v_max, n_bins=16):
"""
Returns RBF encodings in a new dimension at the end.
"""
rbf_centers = torch.linspace(
v_min, v_max, n_bins, device=values.device, dtype=values.dtype
)
rbf_centers = rbf_centers.view([1] * len(values.shape) + [-1])
rbf_std = (v_max - v_min) / n_bins
z = (values.unsqueeze(-1) - rbf_centers) / rbf_std
return torch.exp(-(z**2))
def batched_gather(data, inds, dim=0, no_batch_dims=0):
ranges = []
for i, s in enumerate(data.shape[:no_batch_dims]):
r = torch.arange(s)
r = r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape) - i - 1))))
ranges.append(r)
remaining_dims = [slice(None) for _ in range(len(data.shape) - no_batch_dims)]
remaining_dims[dim - no_batch_dims if dim >= 0 else dim] = inds
ranges.extend(remaining_dims)
return data[ranges]
def node_gather(s: torch.Tensor, edges: torch.Tensor) -> torch.Tensor:
return batched_gather(s.unsqueeze(-3), edges, -2, no_batch_dims=len(s.shape) - 1)
def knn_graph(
coords: torch.Tensor,
coord_mask: torch.Tensor,
padding_mask: torch.Tensor,
sequence_id: torch.Tensor,
*,
no_knn: int,
):
L = coords.shape[-2]
num_by_dist = min(no_knn, L)
device = coords.device
coords = coords.nan_to_num()
coord_mask = ~(coord_mask[..., None, :] & coord_mask[..., :, None])
padding_pairwise_mask = padding_mask[..., None, :] | padding_mask[..., :, None]
if sequence_id is not None:
padding_pairwise_mask |= torch.unsqueeze(sequence_id, 1) != torch.unsqueeze(
sequence_id, 2
)
dists = (coords.unsqueeze(-2) - coords.unsqueeze(-3)).norm(dim=-1)
arange = torch.arange(L, device=device)
seq_dists = (arange.unsqueeze(-1) - arange.unsqueeze(-2)).abs()
# We only support up to a certain distance, above that, we use sequence distance
# instead. This is so that when a large portion of the structure is masked out,
# the edges are built according to sequence distance.
max_dist = MAX_SUPPORTED_DISTANCE
if not (dists[~coord_mask] < max_dist).all():
raise ValueError(
f"Coordinate pairwise distances exceed max supported distance ({max_dist}). "
)
struct_then_seq_dist = (
seq_dists.to(dists.dtype)
.mul(1e2)
.add(max_dist)
.where(coord_mask, dists)
.masked_fill(padding_pairwise_mask, torch.inf)
)
dists, edges = struct_then_seq_dist.sort(dim=-1, descending=False)
# This is a L x L tensor, where we index by rows first,
# and columns are the edges we should pick.
chosen_edges = edges[..., :num_by_dist]
chosen_mask = dists[..., :num_by_dist].isfinite()
return chosen_edges, chosen_mask
def stack_variable_length_tensors(
sequences: Sequence[torch.Tensor],
constant_value: int | float = 0,
dtype: torch.dtype | None = None,
) -> torch.Tensor:
"""Automatically stack tensors together, padding variable lengths with the
value in constant_value. Handles an arbitrary number of dimensions.
Examples:
>>> tensor1, tensor2 = torch.ones([2]), torch.ones([5])
>>> stack_variable_length_tensors(tensor1, tensor2)
tensor of shape [2, 5]. First row is [1, 1, 0, 0, 0]. Second row is all ones.
>>> tensor1, tensor2 = torch.ones([2, 4]), torch.ones([5, 3])
>>> stack_variable_length_tensors(tensor1, tensor2)
tensor of shape [2, 5, 4]
"""
batch_size = len(sequences)
shape = [batch_size] + np.max([seq.shape for seq in sequences], 0).tolist()
if dtype is None:
dtype = sequences[0].dtype
device = sequences[0].device
array = torch.full(shape, constant_value, dtype=dtype, device=device)
for arr, seq in zip(array, sequences):
arrslice = tuple(slice(dim) for dim in seq.shape)
arr[arrslice] = seq
return array
def binpack(
tensor: torch.Tensor, sequence_id: torch.Tensor | None, pad_value: int | float
):
"""
Args:
tensor (Tensor): [B, L, ...]
Returns:
Tensor: [B_binpacked, L_binpacked, ...]
"""
if sequence_id is None:
return tensor
num_sequences = sequence_id.max(dim=-1).values + 1
dims = sequence_id.shape + tensor.shape[2:]
output_tensor = torch.full(
dims, fill_value=pad_value, dtype=tensor.dtype, device=tensor.device
)
idx = 0
for batch_idx, (batch_seqid, batch_num_sequences) in enumerate(
zip(sequence_id, num_sequences)
):
for seqid in range(batch_num_sequences):
mask = batch_seqid == seqid
output_tensor[batch_idx, mask] = tensor[idx, : mask.sum()]
idx += 1
return output_tensor
def unbinpack(
tensor: torch.Tensor, sequence_id: torch.Tensor | None, pad_value: int | float
):
"""
Args:
tensor (Tensor): [B, L, ...]
Returns:
Tensor: [B_unbinpacked, L_unbinpack, ...]
"""
if sequence_id is None:
return tensor
unpacked_tensors = []
num_sequences = sequence_id.max(dim=-1).values + 1
for batch_idx, (batch_seqid, batch_num_sequences) in enumerate(
zip(sequence_id, num_sequences)
):
for seqid in range(batch_num_sequences):
mask = batch_seqid == seqid
unpacked = tensor[batch_idx, mask]
unpacked_tensors.append(unpacked)
return stack_variable_length_tensors(unpacked_tensors, pad_value)
def fp32_autocast_context(device_type: str) -> ContextManager[Any]: # type: ignore
"""
Returns an autocast context manager that disables downcasting by AMP.
Args:
device_type: The device type ('cpu' or 'cuda')
Returns:
An autocast context manager with the specified behavior.
"""
if device_type == "cpu":
return torch.amp.autocast(device_type, enabled=False) # type: ignore
elif device_type == "mps":
# For MPS, just return a no-op context manager (nullcontext) since MPS does not support autocast.
return nullcontext()
elif device_type == "cuda":
return torch.amp.autocast(device_type, dtype=torch.float32) # type: ignore
else:
raise ValueError(f"Unsupported device type: {device_type}")
def merge_ranges(ranges: list[range], merge_gap_max: int | None = None) -> list[range]:
"""Merge overlapping ranges into sorted, non-overlapping segments.
Args:
ranges: collection of ranges to merge.
merge_gap_max: optionally merge neighboring ranges that are separated by a gap
no larger than this size.
Returns:
non-overlapping ranges merged from the inputs, sorted by position.
"""
ranges = sorted(ranges, key=lambda r: r.start)
merge_gap_max = merge_gap_max if merge_gap_max is not None else 0
assert merge_gap_max >= 0, f"Invalid merge_gap_max: {merge_gap_max}"
merged = []
for r in ranges:
if not merged:
merged.append(r)
else:
last = merged[-1]
if last.stop + merge_gap_max >= r.start:
merged[-1] = range(last.start, max(last.stop, r.stop))
else:
merged.append(r)
return merged
def merge_annotations(
annotations: list[FunctionAnnotation], merge_gap_max: int | None = None
) -> list[FunctionAnnotation]:
"""Merges annotations into non-overlapping segments.
Args:
annotations: annotations to merge.
merge_gap_max: optionally merge neighboring ranges that are separated by a gap
no larger than this size.
Returns:
non-overlapping annotations with gaps merged.
"""
grouped: dict[str, list[range]] = defaultdict(list)
for a in annotations:
# +1 since FunctionAnnotation.end is inlcusive.
grouped[a.label].append(range(a.start, a.end + 1))
merged = []
for label, ranges in grouped.items():
merged_ranges = merge_ranges(ranges, merge_gap_max=merge_gap_max)
for range_ in merged_ranges:
annotation = FunctionAnnotation(
label=label,
start=range_.start,
end=range_.stop - 1, # convert range.stop exclusive -> inclusive.
)
merged.append(annotation)
return merged
def replace_inf(data):
if data is None:
return None
array = np.asarray(data, dtype=np.float32)
array = np.where(np.isinf(array), 1000, array)
return array.tolist()
def maybe_tensor(x, convert_none_to_nan: bool = False) -> torch.Tensor | None:
if x is None:
return None
if isinstance(x, torch.Tensor):
return x
if isinstance(x, list) and all(isinstance(t, torch.Tensor) for t in x):
return torch.stack(x)
if convert_none_to_nan:
x = np.asarray(x, dtype=np.float32)
x = np.where(x is None, np.nan, x)
return torch.tensor(x)
def maybe_list(x, convert_nan_to_none: bool = False) -> list | None:
if x is None:
return None
if not convert_nan_to_none:
return x.tolist()
# Handle both torch.tensor and np.ndarray input.
if isinstance(x, torch.Tensor):
nan_mask = torch.isnan(x).cpu().numpy()
np_arr = x.cpu().numpy().astype(object)
elif isinstance(x, np.ndarray):
nan_mask = np.isnan(x)
np_arr = x.astype(object)
else:
raise TypeError("maybe_list can only work with torch.tensor or np.ndarray.")
np_arr[nan_mask] = None
return np_arr.tolist()
def huggingfacehub_login():
"""Authenticates with the Hugging Face Hub using the HF_TOKEN environment
variable, else by prompting the user"""
token = os.environ.get("HF_TOKEN")
huggingface_hub.login(token=token)
def get_chainbreak_boundaries_from_sequence(sequence: Sequence[str]) -> np.ndarray:
chain_boundaries = [0]
for i, aa in enumerate(sequence):
if aa == CHAIN_BREAK_STR:
if i == (len(sequence) - 1):
raise ValueError(
"Encountered chain break token at end of sequence, this is unexpected."
)
if i == (len(sequence) - 2):
warn(
"Encountered chain break token at penultimate position, this is unexpected."
)
chain_boundaries.append(i)
chain_boundaries.append(i + 1)
chain_boundaries.append(len(sequence))
assert len(chain_boundaries) % 2 == 0
chain_boundaries = np.array(chain_boundaries).reshape(-1, 2)
return chain_boundaries
def deserialize_tensors(b: bytes) -> Any:
buf = BytesIO(zstd.ZSTD_uncompress(b))
d = torch.load(buf, map_location="cpu", weights_only=False)
return d
def join_lists(
lists: Sequence[Sequence[Any]], separator: Sequence[Any] | None = None
) -> list[Any]:
"""Joins multiple lists with separator element. Like str.join but for lists.
Example: [[1, 2], [3], [4]], separator=[0] -> [1, 2, 0, 3, 0, 4]
Args:
lists: Lists of elements to chain
separator: separators to intsert between chained output.
Returns:
Joined lists.
"""
if not lists:
return []
joined = []
joined.extend(lists[0])
for l in lists[1:]:
if separator:
joined.extend(separator)
joined.extend(l)
return joined
def iterate_with_intermediate(
lists: Iterable, intermediate
) -> Generator[Any, None, None]:
"""
Iterate over the iterable, yielding the intermediate value between
every element of the intermediate. Useful for joining objects with
separator tokens.
"""
it = iter(lists)
yield next(it)
for l in it:
yield intermediate
yield l
def concat_objects(objs: Sequence[Any], separator: Any | None = None):
"""
Concat objects with each other using a separator token.
Supports:
- Concatable (objects that implement `concat` classmethod)
- strings
- lists
- numpy arrays
- torch Tensors
Example:
>>> foo = "abc"
>>> bar = "def"
>>> concat_objects([foo, bar], "|")
"abc|def"
"""
match objs[0]:
case Concatable():
return objs[0].__class__.concat(objs) # type: ignore
case str():
assert isinstance(
separator, str
), "Trying to join strings but separator is not a string"
return separator.join(objs)
case list():
if separator is not None:
return join_lists(objs, [separator])
else:
return join_lists(objs)
case np.ndarray():
if separator is not None:
return np.concatenate(
list(iterate_with_intermediate(objs, np.array([separator])))
)
else:
return np.concatenate(objs)
case torch.Tensor():
if separator is not None:
return torch.cat(
list(iterate_with_intermediate(objs, torch.tensor([separator])))
)
else:
return torch.cat(objs) # type: ignore
case _:
raise TypeError(type(objs[0]))
|