|
|
import sys |
|
|
from typing import ( |
|
|
Any, |
|
|
Callable, |
|
|
Iterator, |
|
|
List, |
|
|
Optional, |
|
|
Tuple, |
|
|
TypeVar, |
|
|
Union, |
|
|
overload, |
|
|
) |
|
|
|
|
|
import numpy as np |
|
|
|
|
|
T = TypeVar('T') |
|
|
|
|
|
def glob_with_braces(pattern: str) -> List[str]: ... |
|
|
def fnmatch_with_braces(filename: str, pattern: str) -> bool: ... |
|
|
def make_seed(*args: Any) -> int: ... |
|
|
def is_iterable(obj: Any) -> bool: ... |
|
|
|
|
|
class PipelineStage: |
|
|
def invoke(self, *args: Any, **kw: Any) -> Any: ... |
|
|
def run(self, *args: Any, **kw: Any) -> Any: ... |
|
|
|
|
|
def identity(x: T) -> T: ... |
|
|
def safe_eval(s: str, expr: str = "{}") -> Any: ... |
|
|
def lookup_sym(sym: str, modules: List[str]) -> Optional[Any]: ... |
|
|
def repeatedly0(loader: Iterator[T], nepochs: int = sys.maxsize, nbatches: int = sys.maxsize) -> Iterator[T]: ... |
|
|
def guess_batchsize(batch: Union[Tuple, List]) -> int: ... |
|
|
|
|
|
def repeatedly( |
|
|
source: Iterator[T], |
|
|
nepochs: Optional[int] = None, |
|
|
nbatches: Optional[int] = None, |
|
|
nsamples: Optional[int] = None, |
|
|
batchsize: Callable[..., int] = guess_batchsize, |
|
|
) -> Iterator[T]: ... |
|
|
|
|
|
def pytorch_worker_info(group: Any = None) -> Tuple[int, int, int, int]: ... |
|
|
def pytorch_worker_seed(group: Any = None) -> int: ... |
|
|
|
|
|
@overload |
|
|
def deprecated(func: Callable[..., Any]) -> Callable[..., Any]: ... |
|
|
@overload |
|
|
def deprecated(arg: Optional[str] = None) -> Callable[[Callable[..., Any]], Callable[..., Any]]: ... |
|
|
|
|
|
class ObsoleteException(Exception): ... |
|
|
|
|
|
def obsolete(func: Optional[Callable[..., Any]] = None, *, reason: Optional[str] = None) -> Union[Callable[..., Any], Callable[[Callable[..., Any]], Callable[..., Any]]]: ... |
|
|
|
|
|
def compute_sample_weights(n_w_pairs: List[Tuple[float, float]]) -> np.ndarray: ... |
|
|
|
