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305 lines
13 KiB
Python
305 lines
13 KiB
Python
# Copyright 2022 The JAX Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# https://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import abc
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from collections.abc import Callable, Sequence
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from types import ModuleType
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from typing import Any, Protocol, Union, runtime_checkable
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import numpy as np
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from jax._src.sharding import Sharding
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from jax._src.partition_spec import PartitionSpec
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# TODO(jakevdp) de-duplicate this with the DTypeLike definition in typing.py.
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# We redefine these here to prevent circular imports.
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@runtime_checkable
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class SupportsDType(Protocol):
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@property
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def dtype(self) -> np.dtype: ...
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DTypeLike = Union[str, type[Any], np.dtype, SupportsDType]
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Axis = Union[int, Sequence[int], None]
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Shard = Any
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# TODO: alias this to xla_client.Traceback
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Device = Any
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Traceback = Any
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# TODO(jakevdp): fix import cycles and import this from jax._src.lax.
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PrecisionLike = Any
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class Array(abc.ABC):
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aval: Any
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@property
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def dtype(self) -> np.dtype: ...
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@property
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def ndim(self) -> int: ...
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@property
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def size(self) -> int: ...
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@property
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def itemsize(self) -> int: ...
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@property
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def shape(self) -> tuple[int, ...]: ...
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def __init__(self, shape, dtype=None, buffer=None, offset=0, strides=None,
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order=None):
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raise TypeError("jax.numpy.ndarray() should not be instantiated explicitly."
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" Use jax.numpy.array, or jax.numpy.zeros instead.")
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def __array_namespace__(self, *, api_version: None | str = ...) -> ModuleType: ...
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def __getitem__(self, key) -> Array: ...
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def __setitem__(self, key, value) -> None: ...
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def __len__(self) -> int: ...
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def __iter__(self) -> Any: ...
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def __reversed__(self) -> Any: ...
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def __round__(self, ndigits=None) -> Array: ...
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# Comparisons
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# these return bool for object, so ignore override errors.
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def __lt__(self, other) -> Array: ...
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def __le__(self, other) -> Array: ...
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def __eq__(self, other) -> Array: ... # type: ignore[override]
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def __ne__(self, other) -> Array: ... # type: ignore[override]
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def __gt__(self, other) -> Array: ...
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def __ge__(self, other) -> Array: ...
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# Unary arithmetic
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def __neg__(self) -> Array: ...
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def __pos__(self) -> Array: ...
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def __abs__(self) -> Array: ...
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def __invert__(self) -> Array: ...
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# Binary arithmetic
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def __add__(self, other) -> Array: ...
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def __sub__(self, other) -> Array: ...
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def __mul__(self, other) -> Array: ...
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def __matmul__(self, other) -> Array: ...
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def __truediv__(self, other) -> Array: ...
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def __floordiv__(self, other) -> Array: ...
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def __mod__(self, other) -> Array: ...
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def __divmod__(self, other) -> tuple[Array, Array]: ...
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def __pow__(self, other) -> Array: ...
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def __lshift__(self, other) -> Array: ...
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def __rshift__(self, other) -> Array: ...
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def __and__(self, other) -> Array: ...
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def __xor__(self, other) -> Array: ...
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def __or__(self, other) -> Array: ...
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def __radd__(self, other) -> Array: ...
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def __rsub__(self, other) -> Array: ...
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def __rmul__(self, other) -> Array: ...
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def __rmatmul__(self, other) -> Array: ...
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def __rtruediv__(self, other) -> Array: ...
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def __rfloordiv__(self, other) -> Array: ...
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def __rmod__(self, other) -> Array: ...
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def __rdivmod__(self, other) -> Array: ...
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def __rpow__(self, other) -> Array: ...
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def __rlshift__(self, other) -> Array: ...
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def __rrshift__(self, other) -> Array: ...
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def __rand__(self, other) -> Array: ...
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def __rxor__(self, other) -> Array: ...
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def __ror__(self, other) -> Array: ...
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def __bool__(self) -> bool: ...
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def __complex__(self) -> complex: ...
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def __int__(self) -> int: ...
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def __float__(self) -> float: ...
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def __index__(self) -> int: ...
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def __buffer__(self, flags: int) -> memoryview: ...
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def __release_buffer__(self, view: memoryview) -> None: ...
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# np.ndarray methods:
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def all(self, axis: Axis = None, out: None = None,
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keepdims: bool = False, *, where: ArrayLike | None = None) -> Array: ...
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def any(self, axis: Axis = None, out: None = None,
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keepdims: bool = False, *, where: ArrayLike | None = None) -> Array: ...
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def argmax(self, axis: int | None = None, out: None = None,
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keepdims: bool | None = None) -> Array: ...
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def argmin(self, axis: int | None = None, out: None = None,
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keepdims: bool | None = None) -> Array: ...
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def argpartition(self, kth: int, axis: int = -1) -> Array: ...
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def argsort(self, axis: int | None = -1, *, kind: None = None, order: None = None,
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stable: bool = True, descending: bool = False) -> Array: ...
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def astype(self, dtype: DTypeLike | None = None, copy: bool = False,
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device: Device | Sharding | None = None) -> Array: ...
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def choose(self, choices: Sequence[ArrayLike], out: None = None, mode: str = 'raise') -> Array: ...
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def clip(self, min: ArrayLike | None = None, max: ArrayLike | None = None) -> Array: ...
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def compress(self, condition: ArrayLike,
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axis: int | None = None, *, out: None = None,
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size: int | None = None, fill_value: ArrayLike = 0) -> Array: ...
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def conj(self) -> Array: ...
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def conjugate(self) -> Array: ...
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def copy(self) -> Array: ...
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def cumprod(self, axis: Axis = None, dtype: DTypeLike | None = None,
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out: None = None) -> Array: ...
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def cumsum(self, axis: Axis = None, dtype: DTypeLike | None = None,
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out: None = None) -> Array: ...
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def diagonal(self, offset: int = 0, axis1: int = 0, axis2: int = 1) -> Array: ...
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def dot(self, b: ArrayLike, *, precision: PrecisionLike = None,
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preferred_element_type: DTypeLike | None = None) -> Array: ...
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def flatten(self, order: str = "C") -> Array: ...
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@property
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def imag(self) -> Array: ...
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def item(self, *args: int) -> Any: ...
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def max(self, axis: Axis = None, out: None = None,
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keepdims: bool = False, initial: ArrayLike | None = None,
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where: ArrayLike | None = None) -> Array: ...
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def mean(self, axis: Axis = None, dtype: DTypeLike | None = None,
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out: None = None, keepdims: bool = False, *,
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where: ArrayLike | None = None) -> Array: ...
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def min(self, axis: Axis = None, out: None = None,
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keepdims: bool = False, initial: ArrayLike | None = None,
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where: ArrayLike | None = None) -> Array: ...
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@property
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def nbytes(self) -> int: ...
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def nonzero(self, *, fill_value: None | ArrayLike | tuple[ArrayLike, ...] = None,
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size: int | None = None) -> tuple[Array, ...]: ...
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def prod(self, axis: Axis = None, dtype: DTypeLike | None = None,
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out: None = None, keepdims: bool = False,
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initial: ArrayLike | None = None, where: ArrayLike | None = None,
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promote_integers: bool = True) -> Array: ...
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def ptp(self, axis: Axis = None, out: None = None,
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keepdims: bool = False) -> Array: ...
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def ravel(self, order: str = 'C') -> Array: ...
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@property
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def real(self) -> Array: ...
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def repeat(self, repeats: ArrayLike, axis: int | None = None, *,
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total_repeat_length: int | None = None) -> Array: ...
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def reshape(self, *args: Any, order: str = "C") -> Array: ...
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def round(self, decimals: int = 0, out: None = None) -> Array: ...
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def searchsorted(self, v: ArrayLike, side: str = 'left',
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sorter: ArrayLike | None = None, *, method: str = 'scan') -> Array: ...
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def sort(self, axis: int | None = -1, *, kind: None = None,
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order: None = None, stable: bool = True, descending: bool = False) -> Array: ...
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def squeeze(self, axis: Axis = None) -> Array: ...
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def std(self, axis: Axis = None, dtype: DTypeLike | None = None,
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out: None = None, ddof: int = 0, keepdims: bool = False, *,
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where: ArrayLike | None = None, correction: int | float | None = None) -> Array: ...
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def sum(self, axis: Axis = None, dtype: DTypeLike | None = None,
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out: None = None, keepdims: bool = False, initial: ArrayLike | None = None,
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where: ArrayLike | None = None, promote_integers: bool = True) -> Array: ...
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def swapaxes(self, axis1: int, axis2: int) -> Array: ...
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def take(self, indices: ArrayLike, axis: int | None = None, out: None = None,
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mode: str | None = None, unique_indices: bool = False, indices_are_sorted: bool = False,
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fill_value: StaticScalar | None = None) -> Array: ...
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def tobytes(self, order: str = 'C') -> bytes: ...
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def tolist(self) -> list[Any]: ...
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def trace(self, offset: int | ArrayLike = 0, axis1: int = 0, axis2: int = 1,
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dtype: DTypeLike | None = None, out: None = None) -> Array: ...
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def transpose(self, *args: Any) -> Array: ...
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@property
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def T(self) -> Array: ...
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@property
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def mT(self) -> Array: ...
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def var(self, axis: Axis = None, dtype: DTypeLike | None = None,
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out: None = None, ddof: int = 0, keepdims: bool = False, *,
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where: ArrayLike | None = None, correction: int | float | None = None) -> Array: ...
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def view(self, dtype: DTypeLike | None = None, type: None = None) -> Array: ...
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# Even though we don't always support the NumPy array protocol, e.g., for
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# tracer types, for type checking purposes we must declare support so we
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# implement the NumPy ArrayLike protocol.
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def __array__(self, dtype: np.dtype | None = ...,
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copy: bool | None = ...) -> np.ndarray: ...
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def __dlpack__(self) -> Any: ...
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# JAX extensions
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@property
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def at(self) -> _IndexUpdateHelper: ...
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@property
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def weak_type(self) -> bool: ...
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# Methods defined on ArrayImpl, but not on Tracers
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def addressable_data(self, index: int) -> Array: ...
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def block_until_ready(self) -> Array: ...
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def copy_to_host_async(self) -> None: ...
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def delete(self) -> None: ...
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def devices(self) -> set[Device]: ...
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@property
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def sharding(self) -> Sharding: ...
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@property
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def committed(self) -> bool: ...
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@property
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def device(self) -> Device | Sharding: ...
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@property
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def addressable_shards(self) -> Sequence[Shard]: ...
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@property
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def global_shards(self) -> Sequence[Shard]: ...
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def is_deleted(self) -> bool: ...
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@property
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def is_fully_addressable(self) -> bool: ...
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@property
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def is_fully_replicated(self) -> bool: ...
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def on_device_size_in_bytes(self) -> int: ...
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@property
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def traceback(self) -> Traceback: ...
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def unsafe_buffer_pointer(self) -> int: ...
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def to_device(self, device: Device | Sharding, *,
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stream: int | Any | None = ...) -> Array: ...
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StaticScalar = Union[
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np.bool_, np.number, # NumPy scalar types
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bool, int, float, complex, # Python scalar types
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]
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ArrayLike = Union[
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Array, # JAX array type
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np.ndarray, # NumPy array type
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StaticScalar, # valid scalars
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]
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# TODO: restructure to avoid re-defining this here?
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# from jax._src.numpy.lax_numpy import _IndexUpdateHelper
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class _IndexUpdateHelper:
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def __getitem__(self, index: Any) -> _IndexUpdateRef: ...
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class _IndexUpdateRef:
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def get(self, indices_are_sorted: bool = False, unique_indices: bool = False,
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mode: str | None = None, fill_value: StaticScalar | None = None,
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out_spec: Sharding | PartitionSpec | None = None) -> Array: ...
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def set(self, values: Any,
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indices_are_sorted: bool = False, unique_indices: bool = False,
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mode: str | None = None, fill_value: StaticScalar | None = None) -> Array: ...
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def add(self, values: Any, indices_are_sorted: bool = False,
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unique_indices: bool = False, mode: str | None = None) -> Array: ...
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def subtract(self, values: Any, *, indices_are_sorted: bool = False,
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unique_indices: bool = False, mode: str | None = None) -> Array: ...
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def mul(self, values: Any, indices_are_sorted: bool = False,
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unique_indices: bool = False, mode: str | None = None) -> Array: ...
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def multiply(self, values: Any, indices_are_sorted: bool = False,
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unique_indices: bool = False, mode: str | None = None) -> Array: ...
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def divide(self, values: Any, indices_are_sorted: bool = False,
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unique_indices: bool = False, mode: str | None = None) -> Array: ...
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def power(self, values: Any, indices_are_sorted: bool = False,
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unique_indices: bool = False, mode: str | None = None) -> Array: ...
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def min(self, values: Any, indices_are_sorted: bool = False,
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unique_indices: bool = False, mode: str | None = None) -> Array: ...
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def max(self, values: Any, indices_are_sorted: bool = False,
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unique_indices: bool = False, mode: str | None = None) -> Array: ...
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def apply(self, func: Callable[[ArrayLike], ArrayLike], indices_are_sorted: bool = False,
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unique_indices: bool = False, mode: str | None = None) -> Array: ...
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