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from typing import Any, Dict, Iterable, Iterator, Optional, TypeVar
from .pytorch import IterableDataset
from .utils import PipelineStage
T = TypeVar('T')
Sample = Dict[str, Any]
class MockDataset(IterableDataset):
sample: Any
length: int
def __init__(self, sample: Any, length: int) -> None: ...
def __iter__(self) -> Iterator[Any]: ...
class repeatedly(IterableDataset, PipelineStage):
source: Any
length: Optional[int]
nbatches: Optional[int]
nepochs: Optional[int]
def __init__(self, source: Any, nepochs: Optional[int] = None,
nbatches: Optional[int] = None, length: Optional[int] = None) -> None: ...
def invoke(self, source: Iterable[T]) -> Iterator[T]: ...
class with_epoch(IterableDataset):
length: int
source: Optional[Iterator[Any]]
def __init__(self, dataset: Any, length: int) -> None: ...
def __getstate__(self) -> Dict[str, Any]: ...
def invoke(self, dataset: Iterable[T]) -> Iterator[T]: ...
class with_length(IterableDataset, PipelineStage):
dataset: Any
length: int
def __init__(self, dataset: Any, length: int) -> None: ...
def invoke(self, dataset: Iterable[T]) -> Iterator[T]: ...
def __len__(self) -> int: ...
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