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#,wÖ!µT¬Pðf½ðȣm¥ÍâyìU¼[~ޚ©+ìv})hݺP=pÊgœžÐ\¨=¡UOà‚íÍæÇChímìíÂvsvš߂ë{ú¶¾o\Ì=ÍÖ.—ƒù'Ù\ö%ç“úÃÑa担ò#_Ì×~×ïÿj^~®ó÷Gõ¬–=Îk—w‚ù£•Œg'yïÇk“ú£æ›çoNjãõññ¤¾p±ð,§ƒ"¼6™»{öbôõÎ=¯¿
        jumped(jumps=1)

        Returns a new bit generator with the state jumped

        The state of the returned bit generator is jumped as-if
        (2**128) * jumps random numbers have been generated.

        Parameters
        ----------
        jumps : integer, positive
            Number of times to jump the state of the bit generator returned

        Returns
        -------
        bit_generator : Philox
            New instance of generator jumped iter times
        
        advance(delta)

        Advance the underlying RNG as-if delta draws have occurred.

        Parameters
        ----------
        delta : integer, positive
            Number of draws to advance the RNG. Must be less than the
            size state variable in the underlying RNG.

        Returns
        -------
        self : Philox
            RNG advanced delta steps

        Notes
        -----
        Advancing a RNG updates the underlying RNG state as-if a given
        number of calls to the underlying RNG have been made. In general
        there is not a one-to-one relationship between the number output
        random values from a particular distribution and the number of
        draws from the core RNG.  This occurs for two reasons:

        * The random values are simulated using a rejection-based method
          and so, on average, more than one value from the underlying
          RNG is required to generate an single draw.
        * The number of bits required to generate a simulated value
          differs from the number of bits generated by the underlying
          RNG.  For example, two 16-bit integer values can be simulated
          from a single draw of a 32-bit RNG.

        Advancing the RNG state resets any pre-computed random numbers.
        This is required to ensure exact reproducibility.
         <exactlyMissing type objectnumpy.random._philox.Philox.advancename '%U' is not definedModule '_philox' has already been imported. Re-initialisation is not supported._cython_3_2_4._common_types_metatype__setstate_cython__numpy.random._philox.Philox.jumpeddtypeSeedSequence__name__ must be set to a string object%s() got multiple values for keyword argument '%U'__int__ returned non-int (type %.200s).  The ability to return an instance of a strict subclass of int is deprecated, and may be removed in a future version of Python.numpy.random._philox.Philox.__init__ndarraydoes not matchShared Cython type %.200s has the wrong size, try recompiling%.200s() %sflexiblenumpy.core._multiarray_umath80000000000000000000000000Failed to import '%.20s.decompress' - cannot initialise module strings. String compression was configured with the C macro 'CYTHON_COMPRESS_STRINGS=%d'.%.200s() keywords must be stringsnumpy.random._philox.Philox.state.__get__complexfloatingzlib__qualname__multiple bases have vtable conflict: '%.200s' and '%.200s'integerloader__package__numpy/random/_philox.pyx<stringsource>__vectorcalloffset__func_doc__dict__at most%.200s() takes %.8s %zd positional argument%.1s (%zd given)floatingvariablemodule was compiled against NumPy C-API version 0x%x (NumPy 1.20) but the running NumPy has C-API version 0x%x. Check the section C-API incompatibility at the Troubleshooting ImportError section at https://numpy.org/devdocs/user/troubleshooting-importerror.html#c-api-incompatibility for indications on how to solve this problem.jumpednumpy.random._philox.Philox.state.__set__an integer is requirednumpy._core._multiarray_umath_ARRAY_API is NULL pointerstatecan't convert negative value to uint64_tadvancesSeedlessSequence_ARRAY_APIFATAL: module compiled as unknown endiantakes no arguments__weaklistoffset__func_closureneeds an argument_philox__file__parentsubmodule_search_locations%.200s() %s (%zd given)numpy.random._philox.Philox.__reduce_cython__functionfunc_dict__reduce_cython__NULL result without error in PyObject_Call__loader___cython_3_2_4.cython_function_or_methodfunc_name__globals__C %.8s %.200s.%.200s has wrong signature (expected %.500s, got %.500s)__name__flatiter__defaults__changes to cyfunction.__kwdefaults__ will not currently affect the values used in function callsnumbercython_runtimetakes no keyword arguments%.200s.%.200s size changed, may indicate binary incompatibility. Expected %zd from C header, got %zd from PyObject__init____path__invalid vtable found for imported typecharacterufuncmodule compiled against ABI version 0x%x but this version of numpy is 0x%xShared Cython type %.200s is not a type object__code____kwdefaults__ must be set to a dict objectBitGeneratorUnable to initialize pickling for %.200sorigincompile time Python version %d.%d of module '%.100s' %s runtime version %d.%d../numpy/__init__.cython-30.pxd__int__ returned non-int (type %.200s)can't convert negative value to uint32_t_is_coroutineinstance exception may not have a separate valuenumpy_ARRAY_API is not PyCapsule objectnumpy.random._philox_cython_3_2_4func_globalsfunc_code%.200s.%.200s is not a type objectcalling %R should have returned an instance of BaseException, not %Rnametakes exactly one argument__reduce____closure____qualname__ must be set to a string objectat leastvalue too large to convert to uint32_tunsignedinteger%.200s does not export expected C %.8s %.200snumpy.import_arraybuiltinsfunc_defaults__defaults__ must be set to a tuple objectextension type '%.200s' has no __dict__ slot, but base type '%.200s' has: either add 'cdef dict __dict__' to the extension type or add '__slots__ = [...]' to the base typetypedecompressunbound method %.200S() needs an argument__annotations__ must be set to a dict objectnumpy.random._philox.Philox.__setstate_cython__value too large to convert to intbroadcastFATAL: module compiled as little endian, but detected different endianness at runtime__doc__changes to cyfunction.__defaults__ will not currently affect the values used in function callsnumpy.random.bit_generatornumpy.random._philox.Philox.jump_inplacenumpy.random._philox.Philox
        Get or set the PRNG state

        Returns
        -------
        state : dict
            Dictionary containing the information required to describe the
            state of the PRNG
        base class '%.200s' is not a heap typenumpy.random._commonInterpreter change detected - this module can only be loaded into one interpreter per process.__builtins____module__<cyfunction %U at %p>__kwdefaults__
    Philox(seed=None, counter=None, key=None)

    Container for the Philox (4x64) pseudo-random number generator.

    Parameters
    ----------
    seed : {None, int, array_like[ints], SeedSequence}, optional
        A seed to initialize the `BitGenerator`. If None, then fresh,
        unpredictable entropy will be pulled from the OS. If an ``int`` or
        ``array_like[ints]`` is passed, then it will be passed to
        `SeedSequence` to derive the initial `BitGenerator` state. One may also
        pass in a `SeedSequence` instance.
    counter : {None, int, array_like}, optional
        Counter to use in the Philox state. Can be either
        a Python int (long in 2.x) in [0, 2**256) or a 4-element uint64 array.
        If not provided, the RNG is initialized at 0.
    key : {None, int, array_like}, optional
        Key to use in the Philox state.  Unlike ``seed``, the value in key is
        directly set. Can be either a Python int in [0, 2**128) or a 2-element
        uint64 array. `key` and ``seed`` cannot both be used.

    Attributes
    ----------
    lock: threading.Lock
        Lock instance that is shared so that the same bit git generator can
        be used in multiple Generators without corrupting the state. Code that
        generates values from a bit generator should hold the bit generator's
        lock.

    Notes
    -----
    Philox is a 64-bit PRNG that uses a counter-based design based on weaker
    (and faster) versions of cryptographic functions [1]_. Instances using
    different values of the key produce independent sequences.  Philox has a
    period of :math:`2^{256} - 1` and supports arbitrary advancing and jumping
    the sequence in increments of :math:`2^{128}`. These features allow
    multiple non-overlapping sequences to be generated.

    `Philox` provides a capsule containing function pointers that produce
    doubles, and unsigned 32 and 64- bit integers. These are not
    directly consumable in Python and must be consumed by a `Generator`
    or similar object that supports low-level access.

    **State and Seeding**

    The `Philox` state vector consists of a 256-bit value encoded as
    a 4-element uint64 array and a 128-bit value encoded as a 2-element uint64
    array. The former is a counter which is incremented by 1 for every 4 64-bit
    randoms produced. The second is a key which determined the sequence
    produced. Using different keys produces independent sequences.

    The input ``seed`` is processed by `SeedSequence` to generate the key. The
    counter is set to 0.

    Alternately, one can omit the ``seed`` parameter and set the ``key`` and
    ``counter`` directly.

    **Parallel Features**

    The preferred way to use a BitGenerator in parallel applications is to use
    the `SeedSequence.spawn` method to obtain entropy values, and to use these
    to generate new BitGenerators:

    >>> from numpy.random import Generator, Philox, SeedSequence
    >>> sg = SeedSequence(1234)
    >>> rg = [Generator(Philox(s)) for s in sg.spawn(10)]

    `Philox` can be used in parallel applications by calling the :meth:`jumped`
    method to advance the state as-if :math:`2^{128}` random numbers have
    been generated. Alternatively, :meth:`advance` can be used to advance the
    counter for any positive step in [0, 2**256). When using :meth:`jumped`, all
    generators should be chained to ensure that the segments come from the same
    sequence.

    >>> from numpy.random import Generator, Philox
    >>> bit_generator = Philox(1234)
    >>> rg = []
    >>> for _ in range(10):
    ...    rg.append(Generator(bit_generator))
    ...    bit_generator = bit_generator.jumped()

    Alternatively, `Philox` can be used in parallel applications by using
    a sequence of distinct keys where each instance uses different key.

    >>> key = 2**96 + 2**33 + 2**17 + 2**9
    >>> rg = [Generator(Philox(key=key+i)) for i in range(10)]

    **Compatibility Guarantee**

    `Philox` makes a guarantee that a fixed ``seed`` will always produce
    the same random integer stream.

    Examples
    --------
    >>> from numpy.random import Generator, Philox
    >>> rg = Generator(Philox(1234))
    >>> rg.standard_normal()
    0.123  # random

    References
    ----------
    .. [1] John K. Salmon, Mark A. Moraes, Ron O. Dror, and David E. Shaw,
           "Parallel Random Numbers: As Easy as 1, 2, 3," Proceedings of
           the International Conference for High Performance Computing,
           Networking, Storage and Analysis (SC11), New York, NY: ACM, 2011.
    %s() got an unexpected keyword argument '%U'raise: exception class must be a subclass of BaseException while calling a Python object__del__init numpy.random._philoxBad call flags for CyFunctionCannot convert %.200s to %.200ssignedintegerinexactkeywords must be strings__annotations__generic__pyx_capi__
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