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from typing import (
Union,
TYPE_CHECKING,
TypeVar,
Sequence,
Optional,
List,
Dict,
Generator,
Iterable,
Tuple,
ForwardRef,
)
if TYPE_CHECKING:
import scipy.sparse
import tensorflow
import torch
import numpy as np
from PIL.Image import Image as PILImage
from .. import Document
ArrayType = TypeVar(
'ArrayType',
np.ndarray,
scipy.sparse.spmatrix,
tensorflow.SparseTensor,
tensorflow.Tensor,
torch.Tensor,
Sequence[float],
)
DocumentContentType = Union[bytes, str, ArrayType]
ProtoValueType = Optional[Union[str, bool, float]]
StructValueType = Union[
ProtoValueType, List[ProtoValueType], Dict[str, ProtoValueType]
]
DocumentArraySourceType = Union[
Sequence[Document], Document, Generator[Document], Iterable[Document]
]
T = TypeVar('T')
AnyDNN = TypeVar(
'AnyDNN'
) #: The type of any implementation of a Deep Neural Network object
DocumentArraySingletonIndexType = Union[int, str]
DocumentArrayMultipleIndexType = Union[
slice, Sequence[int], Sequence[str], Sequence[bool], Ellipsis
]
DocumentArraySingleAttributeType = Tuple[
Union[DocumentArraySingletonIndexType, DocumentArrayMultipleIndexType], str
]
DocumentArrayMultipleAttributeType = Tuple[
Union[DocumentArraySingletonIndexType, DocumentArrayMultipleIndexType],
Sequence[str],
]
DocumentArrayIndexType = Union[
DocumentArraySingletonIndexType,
DocumentArrayMultipleIndexType,
DocumentArraySingleAttributeType,
DocumentArrayMultipleAttributeType,
]
Image = TypeVar(
'Image',
str,
ForwardRef('np.ndarray'),
ForwardRef('PILImage'),
)
Text = TypeVar('Text', bound=str)
Audio = TypeVar('Audio', str, ForwardRef('np.ndarray'))
Video = TypeVar('Video', str, ForwardRef('np.ndarray'))
Mesh = TypeVar('Mesh', str, ForwardRef('np.ndarray'))
Tabular = TypeVar('Tabular', bound=str)
Blob = TypeVar('Blob', str, bytes)
JSON = TypeVar('JSON', str, dict)