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v@CAname '%U' is not definedexactlyMissing type objectmodule compiled against API version 0x%x but this version of numpy is 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 .SeedSequence__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.__%.4s__ returned non-%.4s (type %.200s)%s() got multiple values for keyword argument '%U'ndarraydoes not matchnumpy.random._mt19937.MT19937._legacy_seedingflexiblenumpy.core._multiarray_umath%.200s() keywords must be stringscomplexfloating_mt19937numpy.random._mt19937.MT19937.__reduce_cython__multiple bases have vtable conflict: '%.200s' and '%.200s'integerloader__package__numpy.random._mt19937<stringsource>at most%.200s() takes %.8s %zd positional argument%.1s (%zd given)floatingan integer is required_legacy_seedingjumped_ARRAY_API is NULL pointer%s.%s size changed, may indicate binary incompatibility. Expected %zd from C header, got %zd from PyObjectsSeedlessSequence_ARRAY_APIFATAL: module compiled as unknown endian4294967295__file__parentsubmodule_search_locations_mt19937.pyxnumpy.random._mt19937.MT19937NULL result without error in PyObject_Callnumpy.random._mt19937.MT19937.__init____loader__flatiterinthasattr(): attribute name must be stringnumpy.random._mt19937.MT19937.jumpednumpy.random._mt19937.MT19937.state.__get__number%s (%s:%d)cython_runtime%.200s.%.200s size changed, may indicate binary incompatibility. Expected %zd from C header, got %zd from PyObject__path__invalid vtable found for imported typecharacterufuncmodule compiled against ABI version 0x%x but this version of numpy is 0x%xBitGeneratornumpy.random._mt19937.MT19937.state.__set__Unable to initialize pickling for %.200snumpy/random/_mt19937.cpython-312.so.p/numpy/random/_mt19937.pyx.corigininit numpy.random._mt19937__init__.cython-30.pxdcompile time Python version %d.%d of module '%.100s' %s runtime version %d.%dcan't convert negative value to uint32_tnumpy.random._mt19937.MT19937.__setstate_cython__instance exception may not have a separate value_ARRAY_API is not PyCapsule object%.200s.%.200s is not a type objectcalling %R should have returned an instance of BaseException, not %Rname
    MT19937(seed=None)

    Container for the Mersenne Twister 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.

    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
    -----
    ``MT19937`` provides a capsule containing function pointers that produce
    doubles, and unsigned 32 and 64- bit integers [1]_. These are not
    directly consumable in Python and must be consumed by a ``Generator``
    or similar object that supports low-level access.

    The Python stdlib module "random" also contains a Mersenne Twister
    pseudo-random number generator.

    **State and Seeding**

    The ``MT19937`` state vector consists of a 624-element array of
    32-bit unsigned integers plus a single integer value between 0 and 624
    that indexes the current position within the main array.

    The input seed is processed by `SeedSequence` to fill the whole state. The
    first element is reset such that only its most significant bit is set.

    **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, MT19937, SeedSequence
    >>> sg = SeedSequence(1234)
    >>> rg = [Generator(MT19937(s)) for s in sg.spawn(10)]

    Another method is to use `MT19937.jumped` which advances the state as-if
    :math:`2^{128}` random numbers have been generated ([1]_, [2]_). This
    allows the original sequence to be split so that distinct segments can be
    used in each worker process. All generators should be chained to ensure
    that the segments come from the same sequence.

    >>> from numpy.random import Generator, MT19937, SeedSequence
    >>> sg = SeedSequence(1234)
    >>> bit_generator = MT19937(sg)
    >>> rg = []
    >>> for _ in range(10):
    ...    rg.append(Generator(bit_generator))
    ...    # Chain the BitGenerators
    ...    bit_generator = bit_generator.jumped()

    **Compatibility Guarantee**

    ``MT19937`` makes a guarantee that a fixed seed will always produce
    the same random integer stream.

    References
    ----------
    .. [1] Hiroshi Haramoto, Makoto Matsumoto, and Pierre L'Ecuyer, "A Fast
        Jump Ahead Algorithm for Linear Recurrences in a Polynomial Space",
        Sequences and Their Applications - SETA, 290--298, 2008.
    .. [2] Hiroshi Haramoto, Makoto Matsumoto, Takuji Nishimura, François
        Panneton, Pierre L'Ecuyer, "Efficient Jump Ahead for F2-Linear
        Random Number Generators", INFORMS JOURNAL ON COMPUTING, Vol. 20,
        No. 3, Summer 2008, pp. 385-390.

    at leastvalue too large to convert to uint32_tunsignedintegernumpy.import_arraybuiltinsextension 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 typetypevalue too large to convert to intbroadcastFATAL: module compiled as little endian, but detected different endianness at runtimeModule '_mt19937' has already been imported. Re-initialisation is not supported.numpy.random.bit_generator
        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 typeInterpreter change detected - this module can only be loaded into one interpreter per process.__builtins__%s() got an unexpected keyword argument '%U' while calling a Python objectraise: exception class must be a subclass of BaseException__del__Cannot convert %.200s to %.200snumpy.random._mt19937.MT19937.jump_inplacesignedintegerinexactgenericCImportErrorMT19937Seed array must be 1-dSeed must be between 0 and 2**32 - 1Seed must be non-emptyTypeErrorValueError*?__all__anyasarrayastypebit_generatorcasting__class__cline_in_tracebackdisableenable__enter____exit__formatgcgenerate_stateget__getstate____import__index_initializingint64isenabledjumpskey__main____name__npnumpy.core.multiarray failed to importnumpy.core.umath failed to importoperatororderpos__pyx_state__pyx_vtable__range__reduce____reduce_ex__safeseedself._bitgen cannot be converted to a Python object for pickling__setstate____spec__squeezestate is not a legacy MT19937 statestate must be a dictstate must be for a {0} PRNG__test__uint32unsafezeros
        _legacy_seeding(seed)

        Seed the generator in a backward compatible way. For modern
        applications, creating a new instance is preferable. Calling this
        overrides self._seed_seq

        Parameters
        ----------
        seed : {None, int, array_like}
            Random seed initializing the pseudo-random number generator.
            Can be an integer in [0, 2**32-1], array of integers in
            [0, 2**32-1], a `SeedSequence, or ``None``. If `seed`
            is ``None``, then fresh, unpredictable entropy will be pulled from
            the OS.

        Raises
        ------
        ValueError
            If seed values are out of range for the PRNG.
        
        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 : MT19937
            New instance of generator jumped iter times

        Notes
        -----
        The jump step is computed using a modified version of Matsumoto's
        implementation of Horner's method. The step polynomial is precomputed
        to perform 2**128 steps. The jumped state has been verified to match
        the state produced using Matsumoto's original code.

        References
        ----------
        .. [1] Matsumoto, M, Generating multiple disjoint streams of
           pseudorandom number sequences.  Accessed on: May 6, 2020.
           http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/JUMP/
        .. [2] Hiroshi Haramoto, Makoto Matsumoto, Takuji Nishimura, François
           Panneton, Pierre L'Ecuyer, "Efficient Jump Ahead for F2-Linear
           Random Number Generators", INFORMS JOURNAL ON COMPUTING, Vol. 20,
           No. 3, Summer 2008, pp. 385-390.
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