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