carmaWrap.py

 
from __future__ import annotations
import carmaWrap
import typing
import numpy
_Shape = typing.Tuple[int, ...]
 
__all__ = [
    "MA_GENEPIN",
    "MA_MALLOC",
    "MA_PAGELOCK",
    "MA_PORTABLE",
    "MA_WRICOMB",
    "MA_ZEROCPY",
    "MemAlloc",
    "clock",
    "context",
    "device",
    "deviceSync",
    "host_obj_double",
    "host_obj_double_complex",
    "host_obj_float",
    "host_obj_float_complex",
    "host_obj_int",
    "obj_double",
    "obj_double_complex",
    "obj_float",
    "obj_float_complex",
    "obj_int",
    "obj_uint",
    "obj_uint16",
    "potri_double",
    "potri_float",
    "potri_int",
    "potri_uint",
    "potri_uint16",
    "sparse_obj_double",
    "sparse_obj_float",
    "syevd_double",
    "syevd_double_complex",
    "syevd_float",
    "syevd_float_complex",
    "syevd_int",
    "syevd_uint",
    "syevd_uint16",
    "timer"
]
 
 
class MemAlloc():
    """
    Members:
 
      MA_MALLOC
 
      MA_PAGELOCK
 
      MA_ZEROCPY
 
      MA_PORTABLE
 
      MA_WRICOMB
 
      MA_GENEPIN
    """
    def __eq__(self, other: object) -> bool: ...
    def __getstate__(self) -> int: ...
    def __hash__(self) -> int: ...
    def __index__(self) -> int: ...
    def __init__(self, value: int) -> None: ...
    def __int__(self) -> int: ...
    def __ne__(self, other: object) -> bool: ...
    def __repr__(self) -> str: ...
    def __setstate__(self, state: int) -> None: ...
    @property
    def name(self) -> str:
        """
        :type: str
        """
    @property
    def value(self) -> int:
        """
        :type: int
        """
    MA_GENEPIN: carmaWrap.MemAlloc # value = <MemAlloc.MA_GENEPIN: 5>
    MA_MALLOC: carmaWrap.MemAlloc # value = <MemAlloc.MA_MALLOC: 0>
    MA_PAGELOCK: carmaWrap.MemAlloc # value = <MemAlloc.MA_PAGELOCK: 1>
    MA_PORTABLE: carmaWrap.MemAlloc # value = <MemAlloc.MA_PORTABLE: 3>
    MA_WRICOMB: carmaWrap.MemAlloc # value = <MemAlloc.MA_WRICOMB: 4>
    MA_ZEROCPY: carmaWrap.MemAlloc # value = <MemAlloc.MA_ZEROCPY: 2>
    __members__: dict # value = {'MA_MALLOC': <MemAlloc.MA_MALLOC: 0>, 'MA_PAGELOCK': <MemAlloc.MA_PAGELOCK: 1>, 'MA_ZEROCPY': <MemAlloc.MA_ZEROCPY: 2>, 'MA_PORTABLE': <MemAlloc.MA_PORTABLE: 3>, 'MA_WRICOMB': <MemAlloc.MA_WRICOMB: 4>, 'MA_GENEPIN': <MemAlloc.MA_GENEPIN: 5>}
    pass
class clock():
    def __init__(self, context: context, i: int) -> None: 
        """
        Create a CarmaClock object which provides timing based on GPU clock count
 
        Args:
            context: (CarmaContext): carma context
 
            i: (int): time buffer size
          
        """
    def tic(self) -> None: ...
    def toc(self) -> None: ...
    @property
    def cc(self) -> int:
        """
        :type: int
        """
    @property
    def gpu_freq(self) -> float:
        """
        :type: float
        """
    @property
    def time_buffer(self) -> obj_double:
        """
        :type: obj_double
        """
    pass
class context():
    def activate_tensor_cores(self, flag: bool) -> None: 
        """
        Set the cublas math mode using tensor cores or not
        """
    def get_device(self, arg0: int) -> device: ...
    @staticmethod
    def get_instance() -> context: ...
    @staticmethod
    def get_instance_1gpu(arg0: int) -> context: ...
    @staticmethod
    def get_instance_ngpu(arg0: int, arg1: numpy.ndarray[numpy.int32]) -> context: ...
    def set_active_device(self, arg0: int) -> int: ...
    def set_active_device_force(self, arg0: int) -> int: ...
    @property
    def activeRealDevice(self) -> int:
        """
        :type: int
        """
    @property
    def active_device(self) -> int:
        """
        :type: int
        """
    @property
    def cudaRuntimeGetVersion(self) -> int:
        """
        :type: int
        """
    @property
    def driverVersion(self) -> int:
        """
        :type: int
        """
    @property
    def ndevice(self) -> int:
        """
        :type: int
        """
    @property
    def ndevices(self) -> int:
        """
        :type: int
        """
    pass
class device():
    @property
    def compute_perf(self) -> float:
        """
        :type: float
        """
    @property
    def cores_per_sm(self) -> float:
        """
        :type: float
        """
    @property
    def id(self) -> cudaDeviceProp:
        """
        :type: cudaDeviceProp
        """
    @property
    def name(self) -> str:
        """
        :type: str
        """
    @property
    def total_mem(self) -> int:
        """
        :type: int
        """
    pass
class host_obj_double():
    @typing.overload
    def __init__(self, h_data: numpy.ndarray[numpy.float64], malloc_type: MemAlloc = MemAlloc.MA_MALLOC) -> None: 
        """
        TODO
 
        TODO
        """
    @typing.overload
    def __init__(self, d_data: host_obj_double) -> None: ...
    @property
    def nbElem(self) -> int:
        """
        TODO
 
        :type: int
        """
    @property
    def shape(self) -> numpy.ndarray[numpy.int64]:
        """
        TODO
 
        :type: numpy.ndarray[numpy.int64]
        """
    pass
class host_obj_double_complex():
    @typing.overload
    def __init__(self, h_data: numpy.ndarray[complex128], malloc_type: MemAlloc = MemAlloc.MA_MALLOC) -> None: 
        """
        TODO
 
        TODO
        """
    @typing.overload
    def __init__(self, d_data: host_obj_double_complex) -> None: ...
    @property
    def nbElem(self) -> int:
        """
        TODO
 
        :type: int
        """
    @property
    def shape(self) -> numpy.ndarray[numpy.int64]:
        """
        TODO
 
        :type: numpy.ndarray[numpy.int64]
        """
    pass
class host_obj_float():
    @typing.overload
    def __init__(self, h_data: numpy.ndarray[numpy.float32], malloc_type: MemAlloc = MemAlloc.MA_MALLOC) -> None: 
        """
        TODO
 
        TODO
        """
    @typing.overload
    def __init__(self, d_data: host_obj_float) -> None: ...
    @property
    def nbElem(self) -> int:
        """
        TODO
 
        :type: int
        """
    @property
    def shape(self) -> numpy.ndarray[numpy.int64]:
        """
        TODO
 
        :type: numpy.ndarray[numpy.int64]
        """
    pass
class host_obj_float_complex():
    @typing.overload
    def __init__(self, h_data: numpy.ndarray[numpy.complex64], malloc_type: MemAlloc = MemAlloc.MA_MALLOC) -> None: 
        """
        TODO
 
        TODO
        """
    @typing.overload
    def __init__(self, d_data: host_obj_float_complex) -> None: ...
    @property
    def nbElem(self) -> int:
        """
        TODO
 
        :type: int
        """
    @property
    def shape(self) -> numpy.ndarray[numpy.int64]:
        """
        TODO
 
        :type: numpy.ndarray[numpy.int64]
        """
    pass
class host_obj_int():
    @typing.overload
    def __init__(self, h_data: numpy.ndarray[numpy.int32], malloc_type: MemAlloc = MemAlloc.MA_MALLOC) -> None: 
        """
        TODO
 
        TODO
        """
    @typing.overload
    def __init__(self, d_data: host_obj_int) -> None: ...
    @property
    def nbElem(self) -> int:
        """
        TODO
 
        :type: int
        """
    @property
    def shape(self) -> numpy.ndarray[numpy.int64]:
        """
        TODO
 
        :type: numpy.ndarray[numpy.int64]
        """
    pass
class obj_double():
    @typing.overload
    def __init__(self, context: context, h_data: numpy.ndarray[numpy.float64]) -> None: 
        """
        TODO
 
        TODO
        """
    @typing.overload
    def __init__(self, context: context, d_data: obj_double) -> None: ...
    def __repr__(self) -> str: ...
    @typing.overload
    def add_stream(self) -> int: 
        """
        TODO
 
        TODO
        """
    @typing.overload
    def add_stream(self, np: int) -> int: ...
    def aimax(self, incx: int = 1) -> int: 
        """
        TODO
        """
    def aimin(self, incx: int = 1) -> int: 
        """
        TODO
        """
    def asum(self, incx: int = 1) -> float: 
        """
        TODO
        """
    def axpy(self, alpha: float, source: obj_double, incx: int = 1, incy: int = 1, offset: int = 0) -> None: 
        """
        TODO
        """
    def clip(self, data_min: float, data_max: float) -> None: 
        """
        TODO
        """
    def copy(self, arg0: obj_double, arg1: int, arg2: int) -> None: 
        """
        TODO
        """
    def copy_from(self, data: obj_double, nb_elem: int = -1) -> None: 
        """
        TODO
        """
    def copy_into(self, dest: obj_double, nb_elem: int = -1) -> None: 
        """
        TODO
        """
    @typing.overload
    def del_stream(self) -> int: 
        """
        TODO
 
        TODO
        """
    @typing.overload
    def del_stream(self, np: int) -> int: ...
    def destroy_prng(self) -> int: ...
    def destroy_prng_host(self) -> int: ...
    def device2host(self, data: numpy.ndarray[numpy.float64]) -> None: 
        """
        TODO
        """
    def dgmm(self, vectX: obj_double, alpha: float = 1, side: str = 'r', matC: obj_double = None, incx: int = 1) -> obj_double: 
        """
        this method performs one of the matrix‐marix operations matC = diag(vectX)*matA if side='l'
        """
    def dot(self, source: obj_double, incx: int = 1, incy: int = 1) -> float: 
        """
        TODO
        """
    def fft(self, dest: obj_double = None, direction: int = 1) -> None: ...
    def geam(self, matB: obj_double, opA: str = 'N', opB: str = 'N', alpha: float = 1, matC: obj_double = None, beta: float = 0) -> obj_double: 
        """
        this method performs the symmetric rank- k update
        """
    def gemm(self, matB: obj_double, op_a: str = 'N', op_b: str = 'N', alpha: float = 1, matC: obj_double = None, beta: float = 0) -> obj_double: 
        """
        this method performs one of the matrix‐marix operations matC = alpha * op_a(matA) * op_b(matB) + beta * matC
        """
    def gemv(self, vectx: obj_double, alpha: float = 1, op: str = 'N', vecty: obj_double = None, beta: float = 0) -> obj_double: 
        """
        this method performs one of the matrix‐vector operations vecty = alpha * op(mat) * vectx + beta * vecty
        """
    def ger(self, vecty: obj_double, mat: obj_double = None, alpha: float = 1) -> obj_double: 
        """
        this method performs the symmetric rank 1 operation A = alpha * x * y T + A
        """
    def host2device(self, data: numpy.ndarray[numpy.float64]) -> None: 
        """
        TODO
        """
    @typing.overload
    def init_prng(self) -> int: ...
    @typing.overload
    def init_prng(self, arg0: int) -> int: ...
    def init_prng_host(self, arg0: int) -> int: ...
    def init_reduceCub(self) -> None: 
        """
        TODO
        """
    def is_rng_init(self) -> bool: ...
    def magma_gemv(self, vectx: obj_double, alpha: float = 1, op: str = 'N', vecty: obj_double = None, beta: float = 0) -> obj_double: 
        """
        this method performs one of the matrix‐vector operations vecty = alpha * op(mat) * vectx + beta * vecty
        """
    def nrm2(self, incx: int = 1) -> float: 
        """
        TODO
        """
    @typing.overload
    def prng(self, arg0: float, arg1: str, arg2: float, arg3: float) -> int: ...
    @typing.overload
    def prng(self, arg0: float, arg1: str, arg2: float) -> int: ...
    @typing.overload
    def prng(self, arg0: str, arg1: float, arg2: float) -> int: ...
    @typing.overload
    def prng(self, arg0: str, arg1: float) -> int: ...
    @typing.overload
    def prng(self, arg0: str) -> int: ...
    @typing.overload
    def prng_host(self, arg0: str) -> int: ...
    @typing.overload
    def prng_host(self, arg0: str, arg1: float) -> int: ...
    @typing.overload
    def prng_host(self, arg0: str, arg1: float, arg2: float) -> int: ...
    def prng_montagn(self, arg0: float) -> int: ...
    def random(self, seed: int = 1234, j: str = 'U') -> None: ...
    def random_host(self, seed: int = 1234, j: str = 'U') -> None: ...
    def reduceCub(self) -> None: 
        """
        TODO
        """
    def reset(self) -> int: 
        """
        TODO
        """
    def rot(self, arg0: obj_double, arg1: int, arg2: int, arg3: float, arg4: float) -> None: 
        """
        TODO
        """
    def scale(self, scale: float, incx: int = 1) -> None: 
        """
        TODO
        """
    def sum(self) -> float: 
        """
        TODO
        """
    def swap(self, source: obj_double, incx: int = 1, incy: int = 1) -> None: 
        """
        TODO
        """
    def swap_ptr(self, ptr: obj_double) -> None: 
        """
        TODO
        """
    def symm(self, matB: obj_double, alpha: float = 1, matC: obj_double = None, beta: float = 0, side: str = 'l', uplo: str = 'u') -> obj_double: 
        """
        this method performs one of the matrix‐marix operations matC = alpha * matA * matB + beta * C
        """
    def symv(self, vectx: obj_double, alpha: float = 1, uplo: str = 'l', vecty: obj_double = None, beta: float = 0) -> obj_double: 
        """
        this method performs one of the matrix‐vector operations vecty = alpha * mat * vectx + beta * vecty
        """
    def syrk(self, fill: str = 'U', op: str = 'N', alpha: float = 1, matC: obj_double = None, beta: float = 0) -> obj_double: 
        """
        this method performs the symmetric rank- k update
        """
    def syrkx(self, matB: obj_double, fill: str = 'U', op: str = 'N', alpha: float = 1, matC: obj_double = None, beta: float = 0) -> obj_double: 
        """
        this method performs the symmetric rank- k update
        """
    def to_cupy(self) -> object: 
        """
        TODO
        """
    def transpose(self, source: obj_double) -> int: 
        """
        TODO
        """
    def wait_all_streams(self) -> int: 
        """
        TODO
        """
    def wait_stream(self, steam: int) -> int: 
        """
        TODO
        """
    @property
    def context(self) -> context:
        """
        TODO
 
        :type: context
        """
    @property
    def d_ptr(self) -> int:
        """
        TODO
 
        :type: int
        """
    @property
    def device(self) -> int:
        """
        TODO
 
        :type: int
        """
    @property
    def nbElem(self) -> int:
        """
        TODO
 
        :type: int
        """
    @property
    def nb_streams(self) -> int:
        """
        TODO
 
        :type: int
        """
    @property
    def o_data(self) -> float:
        """
        TODO
 
        :type: float
        """
    @property
    def shape(self) -> numpy.ndarray[numpy.int64]:
        """
        TODO
 
        :type: numpy.ndarray[numpy.int64]
        """
    pass
class obj_double_complex():
    @typing.overload
    def __init__(self, context: context, h_data: numpy.ndarray[complex128]) -> None: 
        """
        TODO
 
        TODO
        """
    @typing.overload
    def __init__(self, context: context, d_data: obj_double_complex) -> None: ...
    def __repr__(self) -> str: ...
    @typing.overload
    def add_stream(self) -> int: 
        """
        TODO
 
        TODO
        """
    @typing.overload
    def add_stream(self, np: int) -> int: ...
    def aimax(self, incx: int = 1) -> int: 
        """
        TODO
        """
    def aimin(self, incx: int = 1) -> int: 
        """
        TODO
        """
    def copy(self, arg0: obj_double_complex, arg1: int, arg2: int) -> None: 
        """
        TODO
        """
    def copy_from(self, data: obj_double_complex, nb_elem: int = -1) -> None: 
        """
        TODO
        """
    def copy_into(self, dest: obj_double_complex, nb_elem: int = -1) -> None: 
        """
        TODO
        """
    @typing.overload
    def del_stream(self) -> int: 
        """
        TODO
 
        TODO
        """
    @typing.overload
    def del_stream(self, np: int) -> int: ...
    def destroy_prng(self) -> int: ...
    def device2host(self, data: numpy.ndarray[complex128]) -> None: 
        """
        TODO
        """
    def fft(self, dest: obj_double_complex = None, direction: int = 1) -> None: ...
    def host2device(self, data: numpy.ndarray[complex128]) -> None: 
        """
        TODO
        """
    @typing.overload
    def init_prng(self) -> int: ...
    @typing.overload
    def init_prng(self, arg0: int) -> int: ...
    def init_reduceCub(self) -> None: 
        """
        TODO
        """
    def is_rng_init(self) -> bool: ...
    @typing.overload
    def prng(self, arg0: str, arg1: float, arg2: float) -> int: ...
    @typing.overload
    def prng(self, arg0: str, arg1: float) -> int: ...
    @typing.overload
    def prng(self, arg0: str) -> int: ...
    def random(self, seed: int = 1234, j: str = 'U') -> None: ...
    def random_host(self, seed: int = 1234, j: str = 'U') -> None: ...
    def reduceCub(self) -> None: 
        """
        TODO
        """
    def reset(self) -> int: 
        """
        TODO
        """
    def swap(self, source: obj_double_complex, incx: int = 1, incy: int = 1) -> None: 
        """
        TODO
        """
    def swap_ptr(self, ptr: obj_double_complex) -> None: 
        """
        TODO
        """
    def transpose(self, source: obj_double_complex) -> int: 
        """
        TODO
        """
    def wait_all_streams(self) -> int: 
        """
        TODO
        """
    def wait_stream(self, steam: int) -> int: 
        """
        TODO
        """
    @property
    def context(self) -> context:
        """
        TODO
 
        :type: context
        """
    @property
    def device(self) -> int:
        """
        TODO
 
        :type: int
        """
    @property
    def nbElem(self) -> int:
        """
        TODO
 
        :type: int
        """
    @property
    def nb_streams(self) -> int:
        """
        TODO
 
        :type: int
        """
    @property
    def shape(self) -> numpy.ndarray[numpy.int64]:
        """
        TODO
 
        :type: numpy.ndarray[numpy.int64]
        """
    pass
class obj_float():
    @typing.overload
    def __init__(self, context: context, h_data: numpy.ndarray[numpy.float32]) -> None: 
        """
        TODO
 
        TODO
        """
    @typing.overload
    def __init__(self, context: context, d_data: obj_float) -> None: ...
    def __repr__(self) -> str: ...
    @typing.overload
    def add_stream(self) -> int: 
        """
        TODO
 
        TODO
        """
    @typing.overload
    def add_stream(self, np: int) -> int: ...
    def aimax(self, incx: int = 1) -> int: 
        """
        TODO
        """
    def aimin(self, incx: int = 1) -> int: 
        """
        TODO
        """
    def asum(self, incx: int = 1) -> float: 
        """
        TODO
        """
    def axpy(self, alpha: float, source: obj_float, incx: int = 1, incy: int = 1, offset: int = 0) -> None: 
        """
        TODO
        """
    def clip(self, data_min: float, data_max: float) -> None: 
        """
        TODO
        """
    def copy(self, arg0: obj_float, arg1: int, arg2: int) -> None: 
        """
        TODO
        """
    def copy_from(self, data: obj_float, nb_elem: int = -1) -> None: 
        """
        TODO
        """
    def copy_into(self, dest: obj_float, nb_elem: int = -1) -> None: 
        """
        TODO
        """
    @typing.overload
    def del_stream(self) -> int: 
        """
        TODO
 
        TODO
        """
    @typing.overload
    def del_stream(self, np: int) -> int: ...
    def destroy_prng(self) -> int: ...
    def destroy_prng_host(self) -> int: ...
    def device2host(self, data: numpy.ndarray[numpy.float32]) -> None: 
        """
        TODO
        """
    def dgmm(self, vectX: obj_float, alpha: float = 1, side: str = 'r', matC: obj_float = None, incx: int = 1) -> obj_float: 
        """
        this method performs one of the matrix‐marix operations matC = diag(vectX)*matA if side='l'
        """
    def dot(self, source: obj_float, incx: int = 1, incy: int = 1) -> float: 
        """
        TODO
        """
    def fft(self, dest: obj_float = None, direction: int = 1) -> None: ...
    def geam(self, matB: obj_float, opA: str = 'N', opB: str = 'N', alpha: float = 1, matC: obj_float = None, beta: float = 0) -> obj_float: 
        """
        this method performs the symmetric rank- k update
        """
    def gemm(self, matB: obj_float, op_a: str = 'N', op_b: str = 'N', alpha: float = 1, matC: obj_float = None, beta: float = 0) -> obj_float: 
        """
        this method performs one of the matrix‐marix operations matC = alpha * op_a(matA) * op_b(matB) + beta * matC
        """
    def gemv(self, vectx: obj_float, alpha: float = 1, op: str = 'N', vecty: obj_float = None, beta: float = 0) -> obj_float: 
        """
        this method performs one of the matrix‐vector operations vecty = alpha * op(mat) * vectx + beta * vecty
        """
    def ger(self, vecty: obj_float, mat: obj_float = None, alpha: float = 1) -> obj_float: 
        """
        this method performs the symmetric rank 1 operation A = alpha * x * y T + A
        """
    def host2device(self, data: numpy.ndarray[numpy.float32]) -> None: 
        """
        TODO
        """
    @typing.overload
    def init_prng(self) -> int: ...
    @typing.overload
    def init_prng(self, arg0: int) -> int: ...
    def init_prng_host(self, arg0: int) -> int: ...
    def init_reduceCub(self) -> None: 
        """
        TODO
        """
    def is_rng_init(self) -> bool: ...
    def magma_gemv(self, vectx: obj_float, alpha: float = 1, op: str = 'N', vecty: obj_float = None, beta: float = 0) -> obj_float: 
        """
        this method performs one of the matrix‐vector operations vecty = alpha * op(mat) * vectx + beta * vecty
        """
    def nrm2(self, incx: int = 1) -> float: 
        """
        TODO
        """
    @typing.overload
    def prng(self, arg0: float, arg1: str, arg2: float, arg3: float) -> int: ...
    @typing.overload
    def prng(self, arg0: float, arg1: str, arg2: float) -> int: ...
    @typing.overload
    def prng(self, arg0: str, arg1: float, arg2: float) -> int: ...
    @typing.overload
    def prng(self, arg0: str, arg1: float) -> int: ...
    @typing.overload
    def prng(self, arg0: str) -> int: ...
    @typing.overload
    def prng_host(self, arg0: str) -> int: ...
    @typing.overload
    def prng_host(self, arg0: str, arg1: float) -> int: ...
    @typing.overload
    def prng_host(self, arg0: str, arg1: float, arg2: float) -> int: ...
    def prng_montagn(self, arg0: float) -> int: ...
    def random(self, seed: int = 1234, j: str = 'U') -> None: ...
    def random_host(self, seed: int = 1234, j: str = 'U') -> None: ...
    def reduceCub(self) -> None: 
        """
        TODO
        """
    def reset(self) -> int: 
        """
        TODO
        """
    def rot(self, arg0: obj_float, arg1: int, arg2: int, arg3: float, arg4: float) -> None: 
        """
        TODO
        """
    def scale(self, scale: float, incx: int = 1) -> None: 
        """
        TODO
        """
    def sum(self) -> float: 
        """
        TODO
        """
    def swap(self, source: obj_float, incx: int = 1, incy: int = 1) -> None: 
        """
        TODO
        """
    def swap_ptr(self, ptr: obj_float) -> None: 
        """
        TODO
        """
    def symm(self, matB: obj_float, alpha: float = 1, matC: obj_float = None, beta: float = 0, side: str = 'l', uplo: str = 'u') -> obj_float: 
        """
        this method performs one of the matrix‐marix operations matC = alpha * matA * matB + beta * C
        """
    def symv(self, vectx: obj_float, alpha: float = 1, uplo: str = 'l', vecty: obj_float = None, beta: float = 0) -> obj_float: 
        """
        this method performs one of the matrix‐vector operations vecty = alpha * mat * vectx + beta * vecty
        """
    def syrk(self, fill: str = 'U', op: str = 'N', alpha: float = 1, matC: obj_float = None, beta: float = 0) -> obj_float: 
        """
        this method performs the symmetric rank- k update
        """
    def syrkx(self, matB: obj_float, fill: str = 'U', op: str = 'N', alpha: float = 1, matC: obj_float = None, beta: float = 0) -> obj_float: 
        """
        this method performs the symmetric rank- k update
        """
    def to_cupy(self) -> object: 
        """
        TODO
        """
    def transpose(self, source: obj_float) -> int: 
        """
        TODO
        """
    def wait_all_streams(self) -> int: 
        """
        TODO
        """
    def wait_stream(self, steam: int) -> int: 
        """
        TODO
        """
    @property
    def context(self) -> context:
        """
        TODO
 
        :type: context
        """
    @property
    def d_ptr(self) -> int:
        """
        TODO
 
        :type: int
        """
    @property
    def device(self) -> int:
        """
        TODO
 
        :type: int
        """
    @property
    def nbElem(self) -> int:
        """
        TODO
 
        :type: int
        """
    @property
    def nb_streams(self) -> int:
        """
        TODO
 
        :type: int
        """
    @property
    def o_data(self) -> float:
        """
        TODO
 
        :type: float
        """
    @property
    def shape(self) -> numpy.ndarray[numpy.int64]:
        """
        TODO
 
        :type: numpy.ndarray[numpy.int64]
        """
    pass
class obj_float_complex():
    @typing.overload
    def __init__(self, context: context, h_data: numpy.ndarray[numpy.complex64]) -> None: 
        """
        TODO
 
        TODO
        """
    @typing.overload
    def __init__(self, context: context, d_data: obj_float_complex) -> None: ...
    def __repr__(self) -> str: ...
    @typing.overload
    def add_stream(self) -> int: 
        """
        TODO
 
        TODO
        """
    @typing.overload
    def add_stream(self, np: int) -> int: ...
    def aimax(self, incx: int = 1) -> int: 
        """
        TODO
        """
    def aimin(self, incx: int = 1) -> int: 
        """
        TODO
        """
    def copy(self, arg0: obj_float_complex, arg1: int, arg2: int) -> None: 
        """
        TODO
        """
    def copy_from(self, data: obj_float_complex, nb_elem: int = -1) -> None: 
        """
        TODO
        """
    def copy_into(self, dest: obj_float_complex, nb_elem: int = -1) -> None: 
        """
        TODO
        """
    @typing.overload
    def del_stream(self) -> int: 
        """
        TODO
 
        TODO
        """
    @typing.overload
    def del_stream(self, np: int) -> int: ...
    def destroy_prng(self) -> int: ...
    def device2host(self, data: numpy.ndarray[numpy.complex64]) -> None: 
        """
        TODO
        """
    def fft(self, dest: obj_float_complex = None, direction: int = 1) -> None: ...
    def host2device(self, data: numpy.ndarray[numpy.complex64]) -> None: 
        """
        TODO
        """
    @typing.overload
    def init_prng(self) -> int: ...
    @typing.overload
    def init_prng(self, arg0: int) -> int: ...
    def init_reduceCub(self) -> None: 
        """
        TODO
        """
    def is_rng_init(self) -> bool: ...
    @typing.overload
    def prng(self, arg0: str, arg1: float, arg2: float) -> int: ...
    @typing.overload
    def prng(self, arg0: str, arg1: float) -> int: ...
    @typing.overload
    def prng(self, arg0: str) -> int: ...
    def random(self, seed: int = 1234, j: str = 'U') -> None: ...
    def random_host(self, seed: int = 1234, j: str = 'U') -> None: ...
    def reduceCub(self) -> None: 
        """
        TODO
        """
    def reset(self) -> int: 
        """
        TODO
        """
    def swap(self, source: obj_float_complex, incx: int = 1, incy: int = 1) -> None: 
        """
        TODO
        """
    def swap_ptr(self, ptr: obj_float_complex) -> None: 
        """
        TODO
        """
    def transpose(self, source: obj_float_complex) -> int: 
        """
        TODO
        """
    def wait_all_streams(self) -> int: 
        """
        TODO
        """
    def wait_stream(self, steam: int) -> int: 
        """
        TODO
        """
    @property
    def context(self) -> context:
        """
        TODO
 
        :type: context
        """
    @property
    def device(self) -> int:
        """
        TODO
 
        :type: int
        """
    @property
    def nbElem(self) -> int:
        """
        TODO
 
        :type: int
        """
    @property
    def nb_streams(self) -> int:
        """
        TODO
 
        :type: int
        """
    @property
    def shape(self) -> numpy.ndarray[numpy.int64]:
        """
        TODO
 
        :type: numpy.ndarray[numpy.int64]
        """
    pass
class obj_int():
    @typing.overload
    def __init__(self, context: context, h_data: numpy.ndarray[numpy.int32]) -> None: 
        """
        TODO
 
        TODO
        """
    @typing.overload
    def __init__(self, context: context, d_data: obj_int) -> None: ...
    def __repr__(self) -> str: ...
    @typing.overload
    def add_stream(self) -> int: 
        """
        TODO
 
        TODO
        """
    @typing.overload
    def add_stream(self, np: int) -> int: ...
    def aimax(self, incx: int = 1) -> int: 
        """
        TODO
        """
    def aimin(self, incx: int = 1) -> int: 
        """
        TODO
        """
    def asum(self, incx: int = 1) -> int: 
        """
        TODO
        """
    def axpy(self, alpha: int, source: obj_int, incx: int = 1, incy: int = 1, offset: int = 0) -> None: 
        """
        TODO
        """
    def clip(self, data_min: int, data_max: int) -> None: 
        """
        TODO
        """
    def copy(self, arg0: obj_int, arg1: int, arg2: int) -> None: 
        """
        TODO
        """
    def copy_from(self, data: obj_int, nb_elem: int = -1) -> None: 
        """
        TODO
        """
    def copy_into(self, dest: obj_int, nb_elem: int = -1) -> None: 
        """
        TODO
        """
    @typing.overload
    def del_stream(self) -> int: 
        """
        TODO
 
        TODO
        """
    @typing.overload
    def del_stream(self, np: int) -> int: ...
    def destroy_prng(self) -> int: ...
    def destroy_prng_host(self) -> int: ...
    def device2host(self, data: numpy.ndarray[numpy.int32]) -> None: 
        """
        TODO
        """
    def dgmm(self, vectX: obj_int, alpha: int = 1, side: str = 'r', matC: obj_int = None, incx: int = 1) -> obj_int: 
        """
        this method performs one of the matrix‐marix operations matC = diag(vectX)*matA if side='l'
        """
    def dot(self, source: obj_int, incx: int = 1, incy: int = 1) -> int: 
        """
        TODO
        """
    def fft(self, dest: obj_int = None, direction: int = 1) -> None: ...
    def geam(self, matB: obj_int, opA: str = 'N', opB: str = 'N', alpha: int = 1, matC: obj_int = None, beta: int = 0) -> obj_int: 
        """
        this method performs the symmetric rank- k update
        """
    def gemm(self, matB: obj_int, op_a: str = 'N', op_b: str = 'N', alpha: int = 1, matC: obj_int = None, beta: int = 0) -> obj_int: 
        """
        this method performs one of the matrix‐marix operations matC = alpha * op_a(matA) * op_b(matB) + beta * matC
        """
    def gemv(self, vectx: obj_int, alpha: int = 1, op: str = 'N', vecty: obj_int = None, beta: int = 0) -> obj_int: 
        """
        this method performs one of the matrix‐vector operations vecty = alpha * op(mat) * vectx + beta * vecty
        """
    def ger(self, vecty: obj_int, mat: obj_int = None, alpha: int = 1) -> obj_int: 
        """
        this method performs the symmetric rank 1 operation A = alpha * x * y T + A
        """
    def host2device(self, data: numpy.ndarray[numpy.int32]) -> None: 
        """
        TODO
        """
    @typing.overload
    def init_prng(self) -> int: ...
    @typing.overload
    def init_prng(self, arg0: int) -> int: ...
    def init_prng_host(self, arg0: int) -> int: ...
    def init_reduceCub(self) -> None: 
        """
        TODO
        """
    def is_rng_init(self) -> bool: ...
    def magma_gemv(self, vectx: obj_int, alpha: int = 1, op: str = 'N', vecty: obj_int = None, beta: int = 0) -> obj_int: 
        """
        this method performs one of the matrix‐vector operations vecty = alpha * op(mat) * vectx + beta * vecty
        """
    def nrm2(self, incx: int = 1) -> int: 
        """
        TODO
        """
    @typing.overload
    def prng(self, arg0: int, arg1: str, arg2: float, arg3: float) -> int: ...
    @typing.overload
    def prng(self, arg0: int, arg1: str, arg2: float) -> int: ...
    @typing.overload
    def prng(self, arg0: str, arg1: float, arg2: float) -> int: ...
    @typing.overload
    def prng(self, arg0: str, arg1: float) -> int: ...
    @typing.overload
    def prng(self, arg0: str) -> int: ...
    @typing.overload
    def prng_host(self, arg0: str) -> int: ...
    @typing.overload
    def prng_host(self, arg0: str, arg1: int) -> int: ...
    @typing.overload
    def prng_host(self, arg0: str, arg1: int, arg2: int) -> int: ...
    def prng_montagn(self, arg0: float) -> int: ...
    def random(self, seed: int = 1234, j: str = 'U') -> None: ...
    def random_host(self, seed: int = 1234, j: str = 'U') -> None: ...
    def reduceCub(self) -> None: 
        """
        TODO
        """
    def reset(self) -> int: 
        """
        TODO
        """
    def rot(self, arg0: obj_int, arg1: int, arg2: int, arg3: int, arg4: int) -> None: 
        """
        TODO
        """
    def scale(self, scale: int, incx: int = 1) -> None: 
        """
        TODO
        """
    def sum(self) -> int: 
        """
        TODO
        """
    def swap(self, source: obj_int, incx: int = 1, incy: int = 1) -> None: 
        """
        TODO
        """
    def swap_ptr(self, ptr: obj_int) -> None: 
        """
        TODO
        """
    def symm(self, matB: obj_int, alpha: int = 1, matC: obj_int = None, beta: int = 0, side: str = 'l', uplo: str = 'u') -> obj_int: 
        """
        this method performs one of the matrix‐marix operations matC = alpha * matA * matB + beta * C
        """
    def symv(self, vectx: obj_int, alpha: int = 1, uplo: str = 'l', vecty: obj_int = None, beta: int = 0) -> obj_int: 
        """
        this method performs one of the matrix‐vector operations vecty = alpha * mat * vectx + beta * vecty
        """
    def syrk(self, fill: str = 'U', op: str = 'N', alpha: int = 1, matC: obj_int = None, beta: int = 0) -> obj_int: 
        """
        this method performs the symmetric rank- k update
        """
    def syrkx(self, matB: obj_int, fill: str = 'U', op: str = 'N', alpha: int = 1, matC: obj_int = None, beta: int = 0) -> obj_int: 
        """
        this method performs the symmetric rank- k update
        """
    def to_cupy(self) -> object: 
        """
        TODO
        """
    def transpose(self, source: obj_int) -> int: 
        """
        TODO
        """
    def wait_all_streams(self) -> int: 
        """
        TODO
        """
    def wait_stream(self, steam: int) -> int: 
        """
        TODO
        """
    @property
    def context(self) -> context:
        """
        TODO
 
        :type: context
        """
    @property
    def d_ptr(self) -> int:
        """
        TODO
 
        :type: int
        """
    @property
    def device(self) -> int:
        """
        TODO
 
        :type: int
        """
    @property
    def nbElem(self) -> int:
        """
        TODO
 
        :type: int
        """
    @property
    def nb_streams(self) -> int:
        """
        TODO
 
        :type: int
        """
    @property
    def o_data(self) -> int:
        """
        TODO
 
        :type: int
        """
    @property
    def shape(self) -> numpy.ndarray[numpy.int64]:
        """
        TODO
 
        :type: numpy.ndarray[numpy.int64]
        """
    pass
class obj_uint():
    @typing.overload
    def __init__(self, context: context, h_data: numpy.ndarray[numpy.uint32]) -> None: 
        """
        TODO
 
        TODO
        """
    @typing.overload
    def __init__(self, context: context, d_data: obj_uint) -> None: ...
    def __repr__(self) -> str: ...
    @typing.overload
    def add_stream(self) -> int: 
        """
        TODO
 
        TODO
        """
    @typing.overload
    def add_stream(self, np: int) -> int: ...
    def aimax(self, incx: int = 1) -> int: 
        """
        TODO
        """
    def aimin(self, incx: int = 1) -> int: 
        """
        TODO
        """
    def asum(self, incx: int = 1) -> int: 
        """
        TODO
        """
    def axpy(self, alpha: int, source: obj_uint, incx: int = 1, incy: int = 1, offset: int = 0) -> None: 
        """
        TODO
        """
    def clip(self, data_min: int, data_max: int) -> None: 
        """
        TODO
        """
    def copy(self, arg0: obj_uint, arg1: int, arg2: int) -> None: 
        """
        TODO
        """
    def copy_from(self, data: obj_uint, nb_elem: int = -1) -> None: 
        """
        TODO
        """
    def copy_into(self, dest: obj_uint, nb_elem: int = -1) -> None: 
        """
        TODO
        """
    @typing.overload
    def del_stream(self) -> int: 
        """
        TODO
 
        TODO
        """
    @typing.overload
    def del_stream(self, np: int) -> int: ...
    def destroy_prng(self) -> int: ...
    def destroy_prng_host(self) -> int: ...
    def device2host(self, data: numpy.ndarray[numpy.uint32]) -> None: 
        """
        TODO
        """
    def dgmm(self, vectX: obj_uint, alpha: int = 1, side: str = 'r', matC: obj_uint = None, incx: int = 1) -> obj_uint: 
        """
        this method performs one of the matrix‐marix operations matC = diag(vectX)*matA if side='l'
        """
    def dot(self, source: obj_uint, incx: int = 1, incy: int = 1) -> int: 
        """
        TODO
        """
    def fft(self, dest: obj_uint = None, direction: int = 1) -> None: ...
    def geam(self, matB: obj_uint, opA: str = 'N', opB: str = 'N', alpha: int = 1, matC: obj_uint = None, beta: int = 0) -> obj_uint: 
        """
        this method performs the symmetric rank- k update
        """
    def gemm(self, matB: obj_uint, op_a: str = 'N', op_b: str = 'N', alpha: int = 1, matC: obj_uint = None, beta: int = 0) -> obj_uint: 
        """
        this method performs one of the matrix‐marix operations matC = alpha * op_a(matA) * op_b(matB) + beta * matC
        """
    def gemv(self, vectx: obj_uint, alpha: int = 1, op: str = 'N', vecty: obj_uint = None, beta: int = 0) -> obj_uint: 
        """
        this method performs one of the matrix‐vector operations vecty = alpha * op(mat) * vectx + beta * vecty
        """
    def ger(self, vecty: obj_uint, mat: obj_uint = None, alpha: int = 1) -> obj_uint: 
        """
        this method performs the symmetric rank 1 operation A = alpha * x * y T + A
        """
    def host2device(self, data: numpy.ndarray[numpy.uint32]) -> None: 
        """
        TODO
        """
    @typing.overload
    def init_prng(self) -> int: ...
    @typing.overload
    def init_prng(self, arg0: int) -> int: ...
    def init_prng_host(self, arg0: int) -> int: ...
    def init_reduceCub(self) -> None: 
        """
        TODO
        """
    def is_rng_init(self) -> bool: ...
    def magma_gemv(self, vectx: obj_uint, alpha: int = 1, op: str = 'N', vecty: obj_uint = None, beta: int = 0) -> obj_uint: 
        """
        this method performs one of the matrix‐vector operations vecty = alpha * op(mat) * vectx + beta * vecty
        """
    def nrm2(self, incx: int = 1) -> int: 
        """
        TODO
        """
    @typing.overload
    def prng(self, arg0: int, arg1: str, arg2: float, arg3: float) -> int: ...
    @typing.overload
    def prng(self, arg0: int, arg1: str, arg2: float) -> int: ...
    @typing.overload
    def prng(self, arg0: str, arg1: float, arg2: float) -> int: ...
    @typing.overload
    def prng(self, arg0: str, arg1: float) -> int: ...
    @typing.overload
    def prng(self, arg0: str) -> int: ...
    @typing.overload
    def prng_host(self, arg0: str) -> int: ...
    @typing.overload
    def prng_host(self, arg0: str, arg1: int) -> int: ...
    @typing.overload
    def prng_host(self, arg0: str, arg1: int, arg2: int) -> int: ...
    def prng_montagn(self, arg0: float) -> int: ...
    def random(self, seed: int = 1234, j: str = 'U') -> None: ...
    def random_host(self, seed: int = 1234, j: str = 'U') -> None: ...
    def reduceCub(self) -> None: 
        """
        TODO
        """
    def reset(self) -> int: 
        """
        TODO
        """
    def rot(self, arg0: obj_uint, arg1: int, arg2: int, arg3: int, arg4: int) -> None: 
        """
        TODO
        """
    def scale(self, scale: int, incx: int = 1) -> None: 
        """
        TODO
        """
    def sum(self) -> int: 
        """
        TODO
        """
    def swap(self, source: obj_uint, incx: int = 1, incy: int = 1) -> None: 
        """
        TODO
        """
    def swap_ptr(self, ptr: obj_uint) -> None: 
        """
        TODO
        """
    def symm(self, matB: obj_uint, alpha: int = 1, matC: obj_uint = None, beta: int = 0, side: str = 'l', uplo: str = 'u') -> obj_uint: 
        """
        this method performs one of the matrix‐marix operations matC = alpha * matA * matB + beta * C
        """
    def symv(self, vectx: obj_uint, alpha: int = 1, uplo: str = 'l', vecty: obj_uint = None, beta: int = 0) -> obj_uint: 
        """
        this method performs one of the matrix‐vector operations vecty = alpha * mat * vectx + beta * vecty
        """
    def syrk(self, fill: str = 'U', op: str = 'N', alpha: int = 1, matC: obj_uint = None, beta: int = 0) -> obj_uint: 
        """
        this method performs the symmetric rank- k update
        """
    def syrkx(self, matB: obj_uint, fill: str = 'U', op: str = 'N', alpha: int = 1, matC: obj_uint = None, beta: int = 0) -> obj_uint: 
        """
        this method performs the symmetric rank- k update
        """
    def to_cupy(self) -> object: 
        """
        TODO
        """
    def transpose(self, source: obj_uint) -> int: 
        """
        TODO
        """
    def wait_all_streams(self) -> int: 
        """
        TODO
        """
    def wait_stream(self, steam: int) -> int: 
        """
        TODO
        """
    @property
    def context(self) -> context:
        """
        TODO
 
        :type: context
        """
    @property
    def d_ptr(self) -> int:
        """
        TODO
 
        :type: int
        """
    @property
    def device(self) -> int:
        """
        TODO
 
        :type: int
        """
    @property
    def nbElem(self) -> int:
        """
        TODO
 
        :type: int
        """
    @property
    def nb_streams(self) -> int:
        """
        TODO
 
        :type: int
        """
    @property
    def o_data(self) -> int:
        """
        TODO
 
        :type: int
        """
    @property
    def shape(self) -> numpy.ndarray[numpy.int64]:
        """
        TODO
 
        :type: numpy.ndarray[numpy.int64]
        """
    pass
class obj_uint16():
    @typing.overload
    def __init__(self, context: context, h_data: numpy.ndarray[numpy.uint16]) -> None: 
        """
        TODO
 
        TODO
        """
    @typing.overload
    def __init__(self, context: context, d_data: obj_uint16) -> None: ...
    def __repr__(self) -> str: ...
    @typing.overload
    def add_stream(self) -> int: 
        """
        TODO
 
        TODO
        """
    @typing.overload
    def add_stream(self, np: int) -> int: ...
    def aimax(self, incx: int = 1) -> int: 
        """
        TODO
        """
    def aimin(self, incx: int = 1) -> int: 
        """
        TODO
        """
    def asum(self, incx: int = 1) -> int: 
        """
        TODO
        """
    def axpy(self, alpha: int, source: obj_uint16, incx: int = 1, incy: int = 1, offset: int = 0) -> None: 
        """
        TODO
        """
    def clip(self, data_min: int, data_max: int) -> None: 
        """
        TODO
        """
    def copy(self, arg0: obj_uint16, arg1: int, arg2: int) -> None: 
        """
        TODO
        """
    def copy_from(self, data: obj_uint16, nb_elem: int = -1) -> None: 
        """
        TODO
        """
    def copy_into(self, dest: obj_uint16, nb_elem: int = -1) -> None: 
        """
        TODO
        """
    @typing.overload
    def del_stream(self) -> int: 
        """
        TODO
 
        TODO
        """
    @typing.overload
    def del_stream(self, np: int) -> int: ...
    def destroy_prng(self) -> int: ...
    def destroy_prng_host(self) -> int: ...
    def device2host(self, data: numpy.ndarray[numpy.uint16]) -> None: 
        """
        TODO
        """
    def dgmm(self, vectX: obj_uint16, alpha: int = 1, side: str = 'r', matC: obj_uint16 = None, incx: int = 1) -> obj_uint16: 
        """
        this method performs one of the matrix‐marix operations matC = diag(vectX)*matA if side='l'
        """
    def dot(self, source: obj_uint16, incx: int = 1, incy: int = 1) -> int: 
        """
        TODO
        """
    def fft(self, dest: obj_uint16 = None, direction: int = 1) -> None: ...
    def geam(self, matB: obj_uint16, opA: str = 'N', opB: str = 'N', alpha: int = 1, matC: obj_uint16 = None, beta: int = 0) -> obj_uint16: 
        """
        this method performs the symmetric rank- k update
        """
    def gemm(self, matB: obj_uint16, op_a: str = 'N', op_b: str = 'N', alpha: int = 1, matC: obj_uint16 = None, beta: int = 0) -> obj_uint16: 
        """
        this method performs one of the matrix‐marix operations matC = alpha * op_a(matA) * op_b(matB) + beta * matC
        """
    def gemv(self, vectx: obj_uint16, alpha: int = 1, op: str = 'N', vecty: obj_uint16 = None, beta: int = 0) -> obj_uint16: 
        """
        this method performs one of the matrix‐vector operations vecty = alpha * op(mat) * vectx + beta * vecty
        """
    def ger(self, vecty: obj_uint16, mat: obj_uint16 = None, alpha: int = 1) -> obj_uint16: 
        """
        this method performs the symmetric rank 1 operation A = alpha * x * y T + A
        """
    def host2device(self, data: numpy.ndarray[numpy.uint16]) -> None: 
        """
        TODO
        """
    @typing.overload
    def init_prng(self) -> int: ...
    @typing.overload
    def init_prng(self, arg0: int) -> int: ...
    def init_prng_host(self, arg0: int) -> int: ...
    def init_reduceCub(self) -> None: 
        """
        TODO
        """
    def is_rng_init(self) -> bool: ...
    def magma_gemv(self, vectx: obj_uint16, alpha: int = 1, op: str = 'N', vecty: obj_uint16 = None, beta: int = 0) -> obj_uint16: 
        """
        this method performs one of the matrix‐vector operations vecty = alpha * op(mat) * vectx + beta * vecty
        """
    def nrm2(self, incx: int = 1) -> int: 
        """
        TODO
        """
    @typing.overload
    def prng(self, arg0: int, arg1: str, arg2: float, arg3: float) -> int: ...
    @typing.overload
    def prng(self, arg0: int, arg1: str, arg2: float) -> int: ...
    @typing.overload
    def prng(self, arg0: str, arg1: float, arg2: float) -> int: ...
    @typing.overload
    def prng(self, arg0: str, arg1: float) -> int: ...
    @typing.overload
    def prng(self, arg0: str) -> int: ...
    @typing.overload
    def prng_host(self, arg0: str) -> int: ...
    @typing.overload
    def prng_host(self, arg0: str, arg1: int) -> int: ...
    @typing.overload
    def prng_host(self, arg0: str, arg1: int, arg2: int) -> int: ...
    def prng_montagn(self, arg0: float) -> int: ...
    def random(self, seed: int = 1234, j: str = 'U') -> None: ...
    def random_host(self, seed: int = 1234, j: str = 'U') -> None: ...
    def reduceCub(self) -> None: 
        """
        TODO
        """
    def reset(self) -> int: 
        """
        TODO
        """
    def rot(self, arg0: obj_uint16, arg1: int, arg2: int, arg3: int, arg4: int) -> None: 
        """
        TODO
        """
    def scale(self, scale: int, incx: int = 1) -> None: 
        """
        TODO
        """
    def sum(self) -> int: 
        """
        TODO
        """
    def swap(self, source: obj_uint16, incx: int = 1, incy: int = 1) -> None: 
        """
        TODO
        """
    def swap_ptr(self, ptr: obj_uint16) -> None: 
        """
        TODO
        """
    def symm(self, matB: obj_uint16, alpha: int = 1, matC: obj_uint16 = None, beta: int = 0, side: str = 'l', uplo: str = 'u') -> obj_uint16: 
        """
        this method performs one of the matrix‐marix operations matC = alpha * matA * matB + beta * C
        """
    def symv(self, vectx: obj_uint16, alpha: int = 1, uplo: str = 'l', vecty: obj_uint16 = None, beta: int = 0) -> obj_uint16: 
        """
        this method performs one of the matrix‐vector operations vecty = alpha * mat * vectx + beta * vecty
        """
    def syrk(self, fill: str = 'U', op: str = 'N', alpha: int = 1, matC: obj_uint16 = None, beta: int = 0) -> obj_uint16: 
        """
        this method performs the symmetric rank- k update
        """
    def syrkx(self, matB: obj_uint16, fill: str = 'U', op: str = 'N', alpha: int = 1, matC: obj_uint16 = None, beta: int = 0) -> obj_uint16: 
        """
        this method performs the symmetric rank- k update
        """
    def to_cupy(self) -> object: 
        """
        TODO
        """
    def transpose(self, source: obj_uint16) -> int: 
        """
        TODO
        """
    def wait_all_streams(self) -> int: 
        """
        TODO
        """
    def wait_stream(self, steam: int) -> int: 
        """
        TODO
        """
    @property
    def context(self) -> context:
        """
        TODO
 
        :type: context
        """
    @property
    def d_ptr(self) -> int:
        """
        TODO
 
        :type: int
        """
    @property
    def device(self) -> int:
        """
        TODO
 
        :type: int
        """
    @property
    def nbElem(self) -> int:
        """
        TODO
 
        :type: int
        """
    @property
    def nb_streams(self) -> int:
        """
        TODO
 
        :type: int
        """
    @property
    def o_data(self) -> int:
        """
        TODO
 
        :type: int
        """
    @property
    def shape(self) -> numpy.ndarray[numpy.int64]:
        """
        TODO
 
        :type: numpy.ndarray[numpy.int64]
        """
    pass
class sparse_obj_double():
    def get_csr(self) -> object: ...
    pass
class sparse_obj_float():
    def get_csr(self) -> object: ...
    pass
class timer():
    def __init__(self) -> None: ...
    def reset(self) -> None: ...
    def set_stream(self, arg0: device) -> None: ...
    def start(self) -> None: ...
    def stop(self) -> None: ...
    @property
    def total_time(self) -> float:
        """
        :type: float
        """
    pass
def deviceSync(arg0: str, arg1: int) -> None:
    pass
def potri_double(d_A: obj_double, d_res: obj_double = None) -> None:
    pass
def potri_float(d_A: obj_float, d_res: obj_float = None) -> None:
    pass
def potri_int(d_A: obj_int, d_res: obj_int = None) -> None:
    pass
def potri_uint(d_A: obj_uint, d_res: obj_uint = None) -> None:
    pass
def potri_uint16(d_A: obj_uint16, d_res: obj_uint16 = None) -> None:
    pass
def syevd_double(d_mat_a: obj_double, eigenvals: obj_double, d_U: obj_double = None, computeU: bool = True) -> None:
    pass
def syevd_double_complex(d_mat_a: obj_double_complex, eigenvals: obj_double_complex, d_U: obj_double_complex = None, computeU: bool = True) -> None:
    pass
def syevd_float(d_mat_a: obj_float, eigenvals: obj_float, d_U: obj_float = None, computeU: bool = True) -> None:
    pass
def syevd_float_complex(d_mat_a: obj_float_complex, eigenvals: obj_float_complex, d_U: obj_float_complex = None, computeU: bool = True) -> None:
    pass
def syevd_int(d_mat_a: obj_int, eigenvals: obj_int, d_U: obj_int = None, computeU: bool = True) -> None:
    pass
def syevd_uint(d_mat_a: obj_uint, eigenvals: obj_uint, d_U: obj_uint = None, computeU: bool = True) -> None:
    pass
def syevd_uint16(d_mat_a: obj_uint16, eigenvals: obj_uint16, d_U: obj_uint16 = None, computeU: bool = True) -> None:
    pass
MA_GENEPIN: carmaWrap.MemAlloc # value = <MemAlloc.MA_GENEPIN: 5>
MA_MALLOC: carmaWrap.MemAlloc # value = <MemAlloc.MA_MALLOC: 0>
MA_PAGELOCK: carmaWrap.MemAlloc # value = <MemAlloc.MA_PAGELOCK: 1>
MA_PORTABLE: carmaWrap.MemAlloc # value = <MemAlloc.MA_PORTABLE: 3>
MA_WRICOMB: carmaWrap.MemAlloc # value = <MemAlloc.MA_WRICOMB: 4>
MA_ZEROCPY: carmaWrap.MemAlloc # value = <MemAlloc.MA_ZEROCPY: 2>
__version__ = '5.4.4'