from typing import Any, List, Literal, Union, overload, Sequence

from jax._src.typing import (
    Array, ArrayLike, DTypeLike
)
from jax._src.core import AxisName
from jax._src.cudnn.scaled_matmul_stablehlo import BlockScaleConfig
from jax._src.lax.lax import DotDimensionNumbers

from jax.nn import initializers as initializers

_Axis = Union[None, int, Sequence[int]]


def celu(x: ArrayLike, alpha: ArrayLike = ...) -> Array: ...
def dot_product_attention(
    query: ArrayLike,
    key: ArrayLike,
    value: ArrayLike,
    bias: ArrayLike | None = ...,
    mask: ArrayLike | None = ...,
    *,
    scale: float | None = ...,
    is_causal: bool = ...,
    query_seq_lengths: ArrayLike | None = ...,
    key_value_seq_lengths: ArrayLike | None = ...,
    local_window_size: int | tuple[int, int] | None = ...,
    implementation: Literal['xla', 'cudnn'] | None = ...) -> Array: ...
def elu(x: ArrayLike, alpha: ArrayLike = ...) -> Array: ...
def gelu(x: ArrayLike, approximate: bool = ...) -> Array: ...
def get_scaled_dot_general_config(mode: Literal['nvfp4', 'mxfp8'],
                                  global_scale: Array | None = ...) -> BlockScaleConfig: ...
def glu(x: ArrayLike, axis: int = ...) -> Array: ...
def hard_sigmoid(x: ArrayLike) -> Array: ...
def hard_silu(x: ArrayLike) -> Array: ...
def hard_swish(x: ArrayLike) -> Array: ...
def hard_tanh(x: ArrayLike) -> Array: ...
def identity(x: ArrayLike) -> Array: ...
def leaky_relu(x: ArrayLike, negative_slope: ArrayLike = ...) -> Array: ...
def log_sigmoid(x: ArrayLike) -> Array: ...
def log_softmax(x: ArrayLike,
                axis: int | tuple[int, ...] | None = ...,
                where: ArrayLike | None = ...) -> Array: ...
@overload
def logsumexp(a: ArrayLike, axis: _Axis = ..., b: ArrayLike | None = ...,
              keepdims: bool = ..., return_sign: Literal[False] = ..., where: ArrayLike | None = ...) -> Array: ...

@overload
def logsumexp(a: ArrayLike, axis: _Axis = ..., b: ArrayLike | None = ...,
              keepdims: bool = ..., *, return_sign: Literal[True], where: ArrayLike | None = ...) -> tuple[Array, Array]: ...

@overload
def logsumexp(a: ArrayLike, axis: _Axis = ..., b: ArrayLike | None = ...,
              keepdims: bool = ..., return_sign: bool = ..., where: ArrayLike | None = ...) -> Array | tuple[Array, Array]: ...
def mish(x: ArrayLike) -> Array: ...
def one_hot(x: Any, num_classes: int, *,
            dtype: Any = ..., axis: int | AxisName = ...) -> Array: ...
def relu(x: ArrayLike) -> Array: ...
def relu6(x: ArrayLike) -> Array: ...
def scaled_dot_general(
    lhs: ArrayLike, rhs: ArrayLike,
    dimension_numbers: DotDimensionNumbers,
    preferred_element_type: DTypeLike = ...,
    configs: List[BlockScaleConfig] | None = ...,
    implementation: Literal['cudnn'] | None = ...,
  ) -> Array: ...
def scaled_matmul(
    lhs: Array,
    rhs: Array,
    lhs_scales: Array,
    rhs_scales: Array,
    preferred_element_type: DTypeLike = ...,
) -> Array: ...
def selu(x: ArrayLike) -> Array: ...
def sigmoid(x: ArrayLike) -> Array: ...
def silu(x: ArrayLike) -> Array: ...
def soft_sign(x: ArrayLike) -> Array: ...
def softmax(x: ArrayLike,
            axis: int | tuple[int, ...] | None = ...,
            where: ArrayLike | None = ...) -> Array: ...
def softplus(x: ArrayLike) -> Array: ...
def sparse_plus(x: ArrayLike) -> Array: ...
def sparse_sigmoid(x: ArrayLike) -> Array: ...
def squareplus(x: ArrayLike, b: ArrayLike = ...) -> Array: ...
def standardize(x: ArrayLike,
                axis: int | tuple[int, ...] | None = ...,
                mean: ArrayLike | None = ...,
                variance: ArrayLike | None = ...,
                epsilon: ArrayLike = ...,
                where: ArrayLike | None = ...) -> Array: ...
def swish(x: ArrayLike) -> Array: ...
def tanh(x: ArrayLike, /) -> Array: ...