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èLûÿÿHƒûÿuàA½À	o~ADžÀ	L‰çL‰îè“þÿÿHƒÄL‰ç[]A\A]é1ÿÿóúHƒìHƒÄà while calling a Python objectNULL result without error in PyObject_Call__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)%.200s.%.200s is not a type object%.200s.%.200s size changed, may indicate binary incompatibility. Expected %zd from C header, got %zd from PyObject%s.%s size changed, may indicate binary incompatibility. Expected %zd from C header, got %zd from PyObject%.200s does not export expected C variable %.200sC variable %.200s.%.200s has wrong signature (expected %.500s, got %.500s)Interpreter change detected - this module can only be loaded into one interpreter per process.calling %R should have returned an instance of BaseException, not %Rraise: exception class must be a subclass of BaseException%s() got multiple values for keyword argument '%U'%.200s() keywords must be strings%s() got an unexpected keyword argument '%U'Cannot convert %.200s to %.200scan't convert negative value to uint32_tvalue too large to convert to uint32_tvalue too large to convert to intnumpy.random.mt19937.MT19937.__setstate_cython__numpy.random.mt19937.MT19937.__reduce_cython__Module 'mt19937' has already been imported. Re-initialisation is not supported.compiletime version %s of module '%.100s' does not match runtime version %sUnable to initialize pickling for %sinvalid vtable found for imported type_ARRAY_API is not PyCapsule objectmodule compiled against ABI version 0x%x but this version of numpy is 0x%xmodule compiled against API version 0x%x but this version of numpy is 0x%xFATAL: module compiled as unknown endianFATAL: module compiled as little endian, but detected different endianness at runtimenumpy.random.mt19937.MT19937.jump_inplacenumpy.random.mt19937.MT19937.state.__get__%.200s() takes %.8s %zd positional argument%.1s (%zd given)numpy.random.mt19937.MT19937.jumpednumpy.random.mt19937.MT19937.__init__hasattr(): attribute name must be stringnumpy.random.mt19937.MT19937._legacy_seedingnumpy.random.mt19937.MT19937.state.__set__
    MT19937(seed=None)

    Container for the Mersenne Twister pseudo-random number generator.

    Parameters
    ----------
    seed : {None, int, array_like[ints], ISeedSequence}, 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 an implementor of the `ISeedSequence` interface like
        `SeedSequence`.

    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 and 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.

    
        Get or set the PRNG state

        Returns
        -------
        state : dict
            Dictionary containing the information required to describe the
            state of the PRNG
        __pyx_capi__name__loader__loader__file__origin__package__parent__path__submodule_search_locationsMissing type objectname '%U' is not definedintan integer is requirednumpy/random/mt19937.c%s (%s:%d)stringsource%d.%d%smt19937.pyxbuiltinscython_runtime__builtins__4294967295__init__.pxdnumpy.random.bit_generatorBitGeneratortype.pxdnumpydtypeflatiterbroadcastndarrayufuncbit_generator.pxdSeedSequenceSeedlessSequencenumpy.random.commondoubleLEGACY_POISSON_LAM_MAXuint64_tMAXSIZEnumpy.core._multiarray_umath_ARRAY_API_ARRAY_API not found_ARRAY_API is NULL pointernumpy.import_arrayinit numpy.random.mt19937at leastat mostjumped__init____del__numpy.random.mt19937.MT19937state_legacy_seeding__reduce_cython____setstate_cython__ðÿÿÿÿÐÿÿØÿÿàÿÿ´ÿÿÜÿÿ\ÿÿlÿÿ|ÿÿh)ÿÿø)ÿÿ)ÿÿ¸)ÿÿà)ÿÿ>+ÿÿ3+ÿÿ+ÿÿn+ÿÿR+ÿÿ3,ÿÿ%,ÿÿù*ÿÿ^,ÿÿJ,ÿÿ¤;ÿÿL;ÿÿ<8ÿÿ<;ÿÿ,8ÿÿ¬Bÿÿ BÿÿˆBÿÿÚBÿÿ¿Bÿÿ#DÿÿDÿÿeBÿÿRDÿÿ<DÿÿP¿ÿÿþ¾ÿÿϾÿÿ¨¿ÿÿ„¿ÿÿ‚ËÿÿzËÿÿ^ËÿÿCËÿÿÕËÿÿFormat string allocated too short.state is not a legacy MT19937 stateself._bitgen cannot be converted to a Python object for picklingnumpy.core.umath failed to importndarray is not Fortran contiguousNon-native byte order not supportedFormat string allocated too short, see comment in numpy.pxdunknown dtype code in numpy.pxd (%d)numpy.core.multiarray failed to importSeed must be between 0 and 2**32 - 1ndarray is not C contiguousstate must be for a {0} PRNGSeed must be non-emptySeed array must be 1-dstate must be a 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        jumped(jumps=1)

        Returns a new bit generator with the state jumped

        The state of the returned big 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
        
        _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.
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